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1. Epigenomic alterations define lethal CIMP-positive ependymomas of infancy

Author

Mack, SC, Witt, H, Piro, RM, Gu, L, Zuyderduyn, S, Stütz, AM, Wang, X, Gallo, M, Garzia, L, Zayne, K, Zhang, X, Ramaswamy, V, Jäger, N, Jones, DTW, Sill, M, Pugh, TJ, Ryzhova, M, Wani, KM, Shih, DJH, Head, R, Remke, M, Bailey, SD, Zichner, T, Faria, CC, Barszczyk, M, Stark, S, Seker-Cin, H, Hutter, S, Johann, P, Bender, S, Hovestadt, V, Tzaridis, T, Dubuc, AM, Northcott, PA, Peacock, J, Bertrand, KC, Agnihotri, S, Cavalli, FMG, Clarke, I, Nethery-Brokx, K, Creasy, CL, Verma, SK, Koster, J, Wu, X, Yao, Y, Milde, T, Sin-Chan, P, Zuccaro, J, Lau, L, Pereira, S, Castelo-Branco, P, Hirst, M, Marra, MA, Roberts, SS, Fults, D, Massimi, L, Cho, YJ, Van Meter, T, Grajkowska, W, Lach, B, Kulozik, AE, von Deimling, A, Witt, O, Scherer, SW, Fan, X, Muraszko, KM, Kool, M, Pomeroy, SL, Gupta, N, Phillips, J, Huang, A, Tabori, U, Hawkins, C, Malkin, D, Kongkham, PN, Weiss, WA, Jabado, N, Rutka, JT, Bouffet, E, Korbel, JO, Lupien, M, Aldape, KD, Bader, GD, Eils, R, Lichter, P, Dirks, PB, Pfister, SM, Korshunov, A, and Taylor, MD

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Genetics, Pediatric, Human Genome, Rare Diseases, Animals, Brain Neoplasms, CpG Islands, DNA Methylation, Embryonic Stem Cells, Ependymoma, Epigenesis, Genetic, Epigenomics, Female, Gene Expression Regulation, Neoplastic, Gene Silencing, Histones, Humans, Infant, Mice, Mice, Inbred NOD, Mice, SCID, Mutation, Phenotype, Polycomb Repressive Complex 2, Prognosis, Rhombencephalon, Xenograft Model Antitumor Assays, General Science & Technology
Abstract

Ependymomas are common childhood brain tumours that occur throughout the nervous system, but are most common in the paediatric hindbrain. Current standard therapy comprises surgery and radiation, but not cytotoxic chemotherapy as it does not further increase survival. Whole-genome and whole-exome sequencing of 47 hindbrain ependymomas reveals an extremely low mutation rate, and zero significant recurrent somatic single nucleotide variants. Although devoid of recurrent single nucleotide variants and focal copy number aberrations, poor-prognosis hindbrain ependymomas exhibit a CpG island methylator phenotype. Transcriptional silencing driven by CpG methylation converges exclusively on targets of the Polycomb repressive complex 2 which represses expression of differentiation genes through trimethylation of H3K27. CpG island methylator phenotype-positive hindbrain ependymomas are responsive to clinical drugs that target either DNA or H3K27 methylation both in vitro and in vivo. We conclude that epigenetic modifiers are the first rational therapeutic candidates for this deadly malignancy, which is epigenetically deregulated but genetically bland.

Published
2014

2. MicroRNA dysregulation in B-cell non-Hodgkin lymphoma

Author

Lim EL and Marra MA

Subjects
Diseases of the blood and blood-forming organs, RC633-647.5
Abstract

Emilia L Lim,1 Marco A Marra1,21Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, BC, Canada; 2Department of Medical Genetics, University of British Columbia, Vancouver, BC, CanadaAbstract: B-cell non-Hodgkin lymphomas (NHLs) are lymphoproliferative disorders that can arise at different stages of B-cell development. Even though molecular classification of NHL has allowed for more accurate recognition of distinct aggressive lymphoma subtypes, many patients still fail to respond to standard therapy. As such, there is a need to identify biomarkers and therapeutic targets that can lead to more specific treatments for each NHL patient's disease. MicroRNAs (miRNAs) are small, 17–25 nt RNA molecules that regulate gene expression at the posttranscriptional level. miRNA expression and function is often coordinately dysregulated in NHL, and consequently results in each NHL disease type harboring a distinct miRNA expression signature. miRNA dysregulation may be a consequence of several mechanisms, ranging from dysregulation of the DNA sequences encoding the miRNA to transcriptional regulation of miRNA loci, to dysregulation of the miRNA biogenesis pathway or dysregulation of messenger RNA (mRNA) targets. This coordinated dysregulation of miRNA expression systematically results in the activation of several oncogenic pathways, and consequently the reprogramming of B-cell NHL transcriptomes. The widespread dysregulation of miRNAs suggests that miRNAs may be used as a diagnostic and prognostic tool, and also as actionable drug targets. In this review, we summarize the miRNA profiles of the most common B-cell NHLs, discuss the causes and consequences of miRNA dysregulation, and consider the prospects of miRNA-based biomarkers and therapeutic targets in NHL.Keywords: miRNA, non-Hodgkin lymphoma, dysregulation, therapy

Published
2013

4. The genetic basis and cell of origin of mixed phenotype acute leukaemia.

Author

Alexander, TB, Gu, Z, Iacobucci, I, Dickerson, K, Choi, JK, Xu, B, Payne-Turner, D, Yoshihara, H, Loh, ML, Horan, J, Buldini, B, Basso, G, Elitzur, S, de Haas, V, Zwaan, CM, Yeoh, A, Reinhardt, D, Tomizawa, D, Kiyokawa, N, Lammens, T, De Moerloose, B, Catchpoole, D, Hori, H, Moorman, A, Moore, AS, Hrusak, O, Meshinchi, S, Orgel, E, Devidas, M, Borowitz, M, Wood, B, Heerema, NA, Carrol, A, Yang, Y-L, Smith, MA, Davidsen, TM, Hermida, LC, Gesuwan, P, Marra, MA, Ma, Y, Mungall, AJ, Moore, RA, Jones, SJM, Valentine, M, Janke, LJ, Rubnitz, JE, Pui, C-H, Ding, L, Liu, Y, Zhang, J, Nichols, KE, Downing, JR, Cao, X, Shi, L, Pounds, S, Newman, S, Pei, D, Guidry Auvil, JM, Gerhard, DS, Hunger, SP, Inaba, H, Mullighan, CG, Alexander, TB, Gu, Z, Iacobucci, I, Dickerson, K, Choi, JK, Xu, B, Payne-Turner, D, Yoshihara, H, Loh, ML, Horan, J, Buldini, B, Basso, G, Elitzur, S, de Haas, V, Zwaan, CM, Yeoh, A, Reinhardt, D, Tomizawa, D, Kiyokawa, N, Lammens, T, De Moerloose, B, Catchpoole, D, Hori, H, Moorman, A, Moore, AS, Hrusak, O, Meshinchi, S, Orgel, E, Devidas, M, Borowitz, M, Wood, B, Heerema, NA, Carrol, A, Yang, Y-L, Smith, MA, Davidsen, TM, Hermida, LC, Gesuwan, P, Marra, MA, Ma, Y, Mungall, AJ, Moore, RA, Jones, SJM, Valentine, M, Janke, LJ, Rubnitz, JE, Pui, C-H, Ding, L, Liu, Y, Zhang, J, Nichols, KE, Downing, JR, Cao, X, Shi, L, Pounds, S, Newman, S, Pei, D, Guidry Auvil, JM, Gerhard, DS, Hunger, SP, Inaba, H, and Mullighan, CG

Abstract

Mixed phenotype acute leukaemia (MPAL) is a high-risk subtype of leukaemia with myeloid and lymphoid features, limited genetic characterization, and a lack of consensus regarding appropriate therapy. Here we show that the two principal subtypes of MPAL, T/myeloid (T/M) and B/myeloid (B/M), are genetically distinct. Rearrangement of ZNF384 is common in B/M MPAL, and biallelic WT1 alterations are common in T/M MPAL, which shares genomic features with early T-cell precursor acute lymphoblastic leukaemia. We show that the intratumoral immunophenotypic heterogeneity characteristic of MPAL is independent of somatic genetic variation, that founding lesions arise in primitive haematopoietic progenitors, and that individual phenotypic subpopulations can reconstitute the immunophenotypic diversity in vivo. These findings indicate that the cell of origin and founding lesions, rather than an accumulation of distinct genomic alterations, prime tumour cells for lineage promiscuity. Moreover, these findings position MPAL in the spectrum of immature leukaemias and provide a genetically informed framework for future clinical trials of potential treatments for MPAL.

Published
2018

5. Comprehensive and Integrated Genomic Characterization of Adult Soft Tissue Sarcomas

Author

Lazar, AJ, McLellan, MD, Bailey, MH, Miller, CA, Appelbaum, EL, Cordes, MG, Fronick, CC, Fulton, LA, Fulton, RS, Mardis, ER, Schmidt, HK, Wong, W, Wilson, RK, Yellapantula, V, Radenbaugh, AJ, Hoadley, KA, Hayes, DN, Parker, JS, Wilkerson, MD, Auman, JT, Balu, S, Bodenheimer, T, Hoyle, AP, Jefferys, SR, Jones, CD, Lehmann, K-V, Meng, S, Mieczkowski, PA, Mose, LE, Perou, CM, Roach, J, Senbabaoglu, Y, Shi, Y, Simons, JV, Skelly, T, Soloway, MG, Tan, D, Veluvolu, U, Davis, IJ, Hepperla, AJ, Brohl, AS, Kasaian, K, Mungall, K, Sadeghi, S, Barthel, FP, Verhaak, R, Hu, X, Chibon, F, Cherniack, AD, Shih, J, Beroukhim, R, Meyerson, M, Cibulskis, C, Gabriel, SB, Saksena, G, Schumacher, SE, Gao, Q, Wyczalkowski, M, Bowlby, R, Robertson, AG, Ally, A, Balasundaram, M, Brooks, D, Carlsen, R, Chuah, E, Dhalla, N, Holt, RA, Jones, SJM, Lee, D, Li, I, Ma, Y, Marra, MA, Mayo, M, Moore, RA, Mungall, AJ, Schein, JE, Sipahimalani, P, Tam, A, Thiessen, N, Wong, T, Danilova, L, Cope, L, Baylin, SB, Bootwalla, MS, Lai, PH, Laird, PW, Maglinte, DT, Van Den Berg, DJ, Weisenberger, DJ, Wrangle, J, Drill, E, Shen, R, Iype, L, Reynolds, SM, Shmulevich, I, Yau, C, Armenia, J, Liu, EM, Benz, C, Pastore, A, Sanchez-Vega, F, Schultz, N, Akbani, R, Hegde, AM, Liu, W, Lu, Y, Mills, GB, Weinstein, JN, Roszik, J, Anur, P, Spellman, P, Abeshouse, A, Chen, H-W, Gao, J, Heins, Z, Kundra, R, Larsson, E, Ochoa, A, Sander, C, Socci, N, Zhang, H, Noble, MS, Heiman, DI, Kim, J, Chin, L, Getz, G, Cho, J, Defreitas, T, Frazer, S, Gehlenborg, N, Lawrence, MS, Lin, P, Meier, S, Voet, D, Byers, L, Diao, L, Gay, CM, Wang, J, Newton, Y, Cooper, LAD, Gutman, DA, Lee, S, Nalisnik, M, Bowen, J, Gastier-Foster, JM, Gerken, M, Helsel, C, Hobensack, S, Leraas, KM, Lichtenberg, TM, Ramirez, NC, Wise, L, Zmuda, E, Anderson, ML, Castro, P, Ittmann, M, Gordienko, E, Paklina, O, Setdikova, G, Raut, CP, Karlan, BY, Lester, J, Belyaev, D, Fulidou, V, Potapova, O, Voronina, O, Demetri, GD, Ramalingam, SS, Behera, M, Delman, K, Owonikoko, TK, Sica, GL, Boyd, J, Magliocco, A, Salner, A, Bennett, J, Iacocca, M, Swanson, P, Dottino, P, Kalir, T, Pereira, E, Akeredolu, T, Crain, D, Curley, E, Gardner, J, Mallery, D, Morris, S, Paulauskis, J, Penny, R, Shelton, C, Shelton, T, Thompson, E, Hoon, DB, Parfitt, J, Birrer, M, Karseladze, A, Mariamidze, A, Dao, F, Levine, DA, Olvera, N, Maki, RG, Bartlett, J, Eschbacher, J, Dubina, M, Mozgovoy, E, Fedosenko, K, Manikhas, G, Sekhon, H, Ramirez, N, Ingram, DR, Torres, KE, DiSaia, P, Godwin, AK, Godwin, EM, Kuo, H, Madan, R, Reilly, C, Adebamowo, C, Adebamowo, SN, Bocklage, T, Higgins, K, Martinez, C, Boice, L, Grilley-Olson, JE, Huang, M, Perou, AH, Thorne, LB, Rathmell, WK, Gutmann, DH, Singer, S, Chudamani, S, Liu, J, Lolla, L, Naresh, R, Pihl, T, Sun, Q, Wan, Y, Wu, Y, Felau, I, Zenklusen, JC, Demchok, JA, Ferguson, ML, Hutter, CM, Sofia, HJ, Tarnuzzer, R, Wang, Z, Yang, L, Zhang, JJ, Demicco, EG, Doyle, LA, Hornick, JL, Rubin, BP, de Rijn, MV, Baker, L, Riedel, RF, Ding, L, Ladanyi, M, Novak, JE, Van Tine, BA, Davis, LE, Grilley-Olsen, JE, Pollock, RE, Jones, KB, Martignetti, JA, Tong, P, and Network, CGAR

Subjects
0301 basic medicine, Leiomyosarcoma, Adult, Epigenomics, DNA Copy Number Variations, Genomics, Biology, General Biochemistry, Genetics and Molecular Biology, Undifferentiated Pleomorphic Sarcoma, Article, 03 medical and health sciences, Young Adult, 0302 clinical medicine, medicine, Cluster Analysis, Humans, ATRX, Aged, Comparative genomics, Aged, 80 and over, Genome, Human, Sarcoma, Middle Aged, medicine.disease, Synovial sarcoma, 030104 developmental biology, 030220 oncology & carcinogenesis, Immunology, DNA methylation, Mutation, Cancer research, Genome-Wide Association Study
Abstract

Summary Sarcomas are a broad family of mesenchymal malignancies exhibiting remarkable histologic diversity. We describe the multi-platform molecular landscape of 206 adult soft tissue sarcomas representing 6 major types. Along with novel insights into the biology of individual sarcoma types, we report three overarching findings: (1) unlike most epithelial malignancies, these sarcomas (excepting synovial sarcoma) are characterized predominantly by copy-number changes, with low mutational loads and only a few genes ( TP53 , ATRX , RB1 ) highly recurrently mutated across sarcoma types; (2) within sarcoma types, genomic and regulomic diversity of driver pathways defines molecular subtypes associated with patient outcome; and (3) the immune microenvironment, inferred from DNA methylation and mRNA profiles, associates with outcome and may inform clinical trials of immune checkpoint inhibitors. Overall, this large-scale analysis reveals previously unappreciated sarcoma-type-specific changes in copy number, methylation, RNA, and protein, providing insights into refining sarcoma therapy and relationships to other cancer types.

Published
2017
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8. Integrated genomic characterization of oesophageal carcinoma

Author

Kim, J, Bowlby, R, Mungall, AJ, Robertson, AG, Odze, RD, Cherniack, AD, Shih, J, Pedamallu, CS, Cibulskis, C, Dunford, A, Meier, SR, Raphael, BJ, Wu, H-T, Wong, AM, Willis, JE, Bass, AJ, Derks, S, Garman, K, McCall, SJ, Wiznerowicz, M, Pantazi, A, Parfenov, M, Thorsson, V, Shmulevich, I, Dhankani, V, Miller, M, Sakai, R, Wang, K, Schultz, N, Shen, R, Arora, A, Weinhold, N, Sanchez-Vega, F, Kelsen, DP, Zhang, J, Felau, I, Demchok, J, Rabkin, CS, Camargo, MC, Zenklusen, JC, Bowen, J, Leraas, K, Lichtenberg, TM, Curtis, C, Seoane, JA, Ojesina, AI, Beer, DG, Gulley, ML, Pennathur, A, Luketich, JD, Zhou, Z, Weisenberger, DJ, Akbani, R, Lee, J-S, Liu, W, Mills, GB, Zhang, W, Reid, BJ, Hinoue, T, Laird, PW, Shen, H, Piazuelo, MB, Schneider, BG, McLellan, M, Taylor-Weiner, A, Lawrence, M, Cibulskis, K, Stewart, C, Getz, G, Lander, E, Gabriel, SB, Ding, L, McLellan, MD, Miller, CA, Appelbaum, EL, Cordes, MG, Fronick, CC, Fulton, LA, Mardis, ER, Wilson, RK, Schmidt, HK, Fulton, RS, Ally, A, Balasundaram, M, Carlsen, R, Chuah, E, Dhalla, N, Holt, RA, Jones, SJM, Kasaian, K, Brooks, D, Li, HI, Ma, Y, Marra, MA, Mayo, M, Moore, RA, Mungall, KL, Schein, JE, Sipahimalani, P, Tam, A, Thiessen, N, Wong, T, Beroukhim, R, Bullman, S, Murray, BA, Saksena, G, Schumacher, SE, Gabriel, S, Meyerson, M, Hadjipanayis, A, Kucherlapati, R, Ren, X, Park, PJ, Lee, S, Kucherlapati, M, Yang, L, Baylin, SB, Hoadley, KA, Bootwalla, MS, Lai, PH, Van den Berg, DJ, Berrios, M, Holbrook, A, Hwang, J-E, Jang, H-J, Weinstein, JN, Lu, Y, Sohn, BH, Mills, G, Seth, S, Protopopov, A, Bristow, CA, Mahadeshwar, HS, Tang, J, Song, X, Cho, J, Defrietas, T, Frazer, S, Gehlenborg, N, Heiman, DI, Lawrence, MS, Lin, P, Noble, MS, Doug, V, Zhang, H, Polak, P, Chin, L, Bernard, B, Iype, L, Reynolds, SM, Abeshouse, A, Armenia, J, Kundra, R, Ladanyi, M, Kjong-Van, L, Gao, J, Sander, C, Chakravarty, D, Radenbaugh, A, Hegde, A, Penny, R, Crain, D, Gardner, J, Curley, E, Mallery, D, Morris, S, Paulauskis, J, Shelton, T, Shelton, C, Frick, J, Gastier-Foster, JM, Gerken, M, Leraas, KM, Ramirez, NC, Wise, L, Zmuda, E, Tarvin, K, Saller, C, Park, YS, Button, M, Carvalho, AL, Reis, RM, Matsushita, MM, Lucchesi, F, de Oliveira, AT, Le, X, Paklina, O, Setdikova, G, Lee, J-H, Bennett, J, Iacocca, M, Huelsenbeck-Dill, L, Potapova, CO, Voronina, O, Liu, O, Fulidou, V, Cates, C, Sharp, A, Behera, M, Force, S, Khuri, F, Owonikoko, T, Pickens, A, Ramalingam, S, Sica, G, Dinjens, W, van Nistelrooij, A, Wijnhoven, B, Sandusky, G, Stepa, S, Juhl, IH, Zornig, C, Kwon, SY, Kelsen, D, Kim, GHK, Bartlett, J, Parfitt, J, Chetty, R, Darling, G, Knox, J, Wong, R, El-Zimaity, H, Liu, G, Boussioutas, A, Park, DY, Kemp, R, Carlotti, CG, da Cunha Tirapelli, DP, Saggioro, FP, Sankarankutty, AK, Noushmehr, H, dos Santos, JS, Trevisan, FA, Eschbacher, J, Dubina, M, Mozgovoy, E, Carey, F, Chalmers, S, Forgie, I, Godwin, A, Reilly, C, Madan, R, Naima, Z, Ferrer-Torres, D, Rathmell, WK, Dhir, R, Luketich, J, Ajani, JA, Janjigian, Y, Tang, L, Cheong, J-H, Chudamani, S, Liu, J, Lolla, L, Naresh, R, Pihl, T, Sun, Q, Wan, Y, Wu, Y, Demchok, JA, Ferguson, ML, Shaw, KRM, sheth, M, Tarnuzzer, R, Wang, Z, Hutter, CM, Sofia, HJ, Kim, J, Bowlby, R, Mungall, AJ, Robertson, AG, Odze, RD, Cherniack, AD, Shih, J, Pedamallu, CS, Cibulskis, C, Dunford, A, Meier, SR, Raphael, BJ, Wu, H-T, Wong, AM, Willis, JE, Bass, AJ, Derks, S, Garman, K, McCall, SJ, Wiznerowicz, M, Pantazi, A, Parfenov, M, Thorsson, V, Shmulevich, I, Dhankani, V, Miller, M, Sakai, R, Wang, K, Schultz, N, Shen, R, Arora, A, Weinhold, N, Sanchez-Vega, F, Kelsen, DP, Zhang, J, Felau, I, Demchok, J, Rabkin, CS, Camargo, MC, Zenklusen, JC, Bowen, J, Leraas, K, Lichtenberg, TM, Curtis, C, Seoane, JA, Ojesina, AI, Beer, DG, Gulley, ML, Pennathur, A, Luketich, JD, Zhou, Z, Weisenberger, DJ, Akbani, R, Lee, J-S, Liu, W, Mills, GB, Zhang, W, Reid, BJ, Hinoue, T, Laird, PW, Shen, H, Piazuelo, MB, Schneider, BG, McLellan, M, Taylor-Weiner, A, Lawrence, M, Cibulskis, K, Stewart, C, Getz, G, Lander, E, Gabriel, SB, Ding, L, McLellan, MD, Miller, CA, Appelbaum, EL, Cordes, MG, Fronick, CC, Fulton, LA, Mardis, ER, Wilson, RK, Schmidt, HK, Fulton, RS, Ally, A, Balasundaram, M, Carlsen, R, Chuah, E, Dhalla, N, Holt, RA, Jones, SJM, Kasaian, K, Brooks, D, Li, HI, Ma, Y, Marra, MA, Mayo, M, Moore, RA, Mungall, KL, Schein, JE, Sipahimalani, P, Tam, A, Thiessen, N, Wong, T, Beroukhim, R, Bullman, S, Murray, BA, Saksena, G, Schumacher, SE, Gabriel, S, Meyerson, M, Hadjipanayis, A, Kucherlapati, R, Ren, X, Park, PJ, Lee, S, Kucherlapati, M, Yang, L, Baylin, SB, Hoadley, KA, Bootwalla, MS, Lai, PH, Van den Berg, DJ, Berrios, M, Holbrook, A, Hwang, J-E, Jang, H-J, Weinstein, JN, Lu, Y, Sohn, BH, Mills, G, Seth, S, Protopopov, A, Bristow, CA, Mahadeshwar, HS, Tang, J, Song, X, Cho, J, Defrietas, T, Frazer, S, Gehlenborg, N, Heiman, DI, Lawrence, MS, Lin, P, Noble, MS, Doug, V, Zhang, H, Polak, P, Chin, L, Bernard, B, Iype, L, Reynolds, SM, Abeshouse, A, Armenia, J, Kundra, R, Ladanyi, M, Kjong-Van, L, Gao, J, Sander, C, Chakravarty, D, Radenbaugh, A, Hegde, A, Penny, R, Crain, D, Gardner, J, Curley, E, Mallery, D, Morris, S, Paulauskis, J, Shelton, T, Shelton, C, Frick, J, Gastier-Foster, JM, Gerken, M, Leraas, KM, Ramirez, NC, Wise, L, Zmuda, E, Tarvin, K, Saller, C, Park, YS, Button, M, Carvalho, AL, Reis, RM, Matsushita, MM, Lucchesi, F, de Oliveira, AT, Le, X, Paklina, O, Setdikova, G, Lee, J-H, Bennett, J, Iacocca, M, Huelsenbeck-Dill, L, Potapova, CO, Voronina, O, Liu, O, Fulidou, V, Cates, C, Sharp, A, Behera, M, Force, S, Khuri, F, Owonikoko, T, Pickens, A, Ramalingam, S, Sica, G, Dinjens, W, van Nistelrooij, A, Wijnhoven, B, Sandusky, G, Stepa, S, Juhl, IH, Zornig, C, Kwon, SY, Kelsen, D, Kim, GHK, Bartlett, J, Parfitt, J, Chetty, R, Darling, G, Knox, J, Wong, R, El-Zimaity, H, Liu, G, Boussioutas, A, Park, DY, Kemp, R, Carlotti, CG, da Cunha Tirapelli, DP, Saggioro, FP, Sankarankutty, AK, Noushmehr, H, dos Santos, JS, Trevisan, FA, Eschbacher, J, Dubina, M, Mozgovoy, E, Carey, F, Chalmers, S, Forgie, I, Godwin, A, Reilly, C, Madan, R, Naima, Z, Ferrer-Torres, D, Rathmell, WK, Dhir, R, Luketich, J, Ajani, JA, Janjigian, Y, Tang, L, Cheong, J-H, Chudamani, S, Liu, J, Lolla, L, Naresh, R, Pihl, T, Sun, Q, Wan, Y, Wu, Y, Demchok, JA, Ferguson, ML, Shaw, KRM, sheth, M, Tarnuzzer, R, Wang, Z, Hutter, CM, and Sofia, HJ

Abstract

Oesophageal cancers are prominent worldwide; however, there are few targeted therapies and survival rates for these cancers remain dismal. Here we performed a comprehensive molecular analysis of 164 carcinomas of the oesophagus derived from Western and Eastern populations. Beyond known histopathological and epidemiologic distinctions, molecular features differentiated oesophageal squamous cell carcinomas from oesophageal adenocarcinomas. Oesophageal squamous cell carcinomas resembled squamous carcinomas of other organs more than they did oesophageal adenocarcinomas. Our analyses identified three molecular subclasses of oesophageal squamous cell carcinomas, but none showed evidence for an aetiological role of human papillomavirus. Squamous cell carcinomas showed frequent genomic amplifications of CCND1 and SOX2 and/or TP63, whereas ERBB2, VEGFA and GATA4 and GATA6 were more commonly amplified in adenocarcinomas. Oesophageal adenocarcinomas strongly resembled the chromosomally unstable variant of gastric adenocarcinoma, suggesting that these cancers could be considered a single disease entity. However, some molecular features, including DNA hypermethylation, occurred disproportionally in oesophageal adenocarcinomas. These data provide a framework to facilitate more rational categorization of these tumours and a foundation for new therapies.

Published
2017

9. Initial sequencing and analysis of the human genome

Author

Lander, ES, Linton, LM, Birren, B, Nusbaum, C, Zody, MC, Baldwin, J, Devon, K, Dewar, K, Doyle, M, FitzHugh, W, Funke, R, Gage, D, Harris, K, Heaford, A, Howland, J, Kann, L, Lehoczky, J, LeVine, R, McEwan, P, McKernan, K, Meldrim, J, Mesirov, JP, Miranda, C, Morris, W, Naylor, J, Raymond, C, Rosetti, M, Santos, R, Sheridan, A, Sougnez, C, Stange-Thomann, N, Stojanovic, N, Subramanian, A, Wyman, D, Rogers, J, Sulston, J, Ainscough, R, Beck, S, Bentley, D, Burton, J, Clee, C, Carter, N, Coulson, A, Deadman, R, Deloukas, P, Dunham, A, Dunham, I, Durbin, R, French, L, Grafham, D, Gregory, S, Hubbard, T, Humphray, S, Hunt, A, Jones, M, Lloyd, C, McMurray, A, Matthews, L, Mercer, S, Milne, S, Mullikin, JC, Mungall, A, Plumb, R, Ross, M, Shownkeen, R, Sims, S, Waterston, RH, Wilson, RK, Hillier, LW, McPherson, JD, Marra, MA, Mardis, ER, Fulton, LA, Chinwalla, AT, Pepin, KH, Gish, WR, Chissoe, SL, Wendl, MC, Delehaunty, KD, Miner, TL, Delehaunty, A, Kramer, JB, Cook, LL, Fulton, RS, Johnson, DL, Minx, PJ, Clifton, SW, Hawkins, T, Branscomb, E, Predki, P, Richardson, P, Wenning, S, Slezak, T, Doggett, N, Cheng, JF, Olsen, A, Lucas, S, Elkin, C, Uberbacher, E, Frazier, M, Gibbs, RA, Muzny, DM, Scherer, SE, Bouck, JB, Sodergren, EJ, Worley, KC, Rives, CM, Gorrell, JH, Metzker, ML, Naylor, SL, Kucherlapati, RS, Nelson, DL, Weinstock, GM, Sakaki, Y, Fujiyama, A, Hattori, M, Yada, T, Toyoda, A, Itoh, T, Kawagoe, C, Watanabe, H, Totoki, Y, Taylor, T, Weissenbach, J, Heilig, R, Saurin, W, Artiguenave, F, Brottier, P, Bruls, T, Pelletier, E, Robert, C, Wincker, P, Smith, DR, Doucette-Stamm, L, Rubenfield, M, Weinstock, K, Lee, HM, Dubois, J, Rosenthal, A, Platzer, M, Nyakatura, G, Taudien, S, Rump, A, Yang, H, Yu, J, Wang, J, Huang, G, Gu, J, Hood, L, Rowen, L, Madan, A, Qin, S, Davis, RW, Federspiel, NA, Abola, AP, Proctor, MJ, Myers, RM, Schmutz, J, Dickson, M, Grimwood, J, Cox, DR, Olson, MV, Kaul, R, Shimizu, N, Kawasaki, K, Minoshima, S, Evans, GA, Athanasiou, M, Schultz, R, Roe, BA, Chen, F, Pan, H, Ramser, J, Lehrach, H, Reinhardt, R, McCombie, WR, de la Bastide, M, Dedhia, N, Blöcker, H, Hornischer, K, Nordsiek, G, Agarwala, R, Aravind, L, Bailey, JA, Bateman, A, Batzoglou, S, Birney, E, Bork, P, Brown, DG, Burge, CB, Cerutti, L, Chen, HC, Church, D, Clamp, M, Copley, RR, Doerks, T, Eddy, SR, Eichler, EE, Furey, TS, Galagan, J, Gilbert, JG, Harmon, C, Hayashizaki, Y, Haussler, D, Hermjakob, H, Hokamp, K, Jang, W, Johnson, LS, Jones, TA, Kasif, S, Kaspryzk, A, Kennedy, S, Kent, WJ, Kitts, P, Koonin, EV, Korf, I, Kulp, D, Lancet, D, Lowe, TM, McLysaght, A, Mikkelsen, T, Moran, JV, Mulder, N, Pollara, VJ, Ponting, CP, Schuler, G, Schultz, J, Slater, G, Smit, AF, Stupka, E, Szustakowski, J, Thierry-Mieg, D, Thierry-Mieg, J, Wagner, L, Wallis, J, Wheeler, R, Williams, A, Wolf, YI, Wolfe, KH, Yang, SP, Yeh, RF, Collins, F, Guyer, MS, Peterson, J, Felsenfeld, A, Wetterstrand, KA, Patrinos, A, Morgan, MJ, de Jong, P, Catanese, JJ, Osoegawa, K, Shizuya, H, Choi, S, Chen, YJ, and Szustakowki, J

Subjects
Genetics, Cancer genome sequencing, Chimpanzee genome project, Multidisciplinary, Cancer Genome Project, Gene density, DNA sequencing theory, Hybrid genome assembly, Computational biology, Biology, Genome, Personal genomics
Abstract

The human genome holds an extraordinary trove of information about human development, physiology, medicine and evolution. Here we report the results of an international collaboration to produce and make freely available a draft sequence of the human genome. We also present an initial analysis of the data, describing some of the insights that can be gleaned from the sequence.

Published
2016

10. Comprehensive genomic characterization of head and neck squamous cell carcinomas

Author

Lawrence, MS, Sougnez, C, Lichtenstein, L, Cibulskis, K, Lander, E, Gabriel, SB, Getz, G, Ally, A, Balasundaram, M, Birol, I, Bowlby, R, Brooks, D, Butterfield, YSN, Carlsen, R, Cheng, D, Chu, A, Dhalla, N, Guin, R, Holt, RA, Jones, SJM, Lee, D, Li, HI, Marra, MA, Mayo, M, Moore, RA, Mungall, AJ, Robertson, AG, Schein, JE, Sipahimalani, P, Tam, A, Thiessen, N, Wong, T, Protopopov, A, Santoso, N, Lee, S, Parfenov, M, Zhang, J, Mahadeshwar, HS, Tang, J, Ren, X, Seth, S, Haseley, P, Zeng, D, Yang, L, Xu, AW, Song, X, Pantazi, A, Bristow, CA, Hadjipanayis, A, Seidman, J, Chin, L, Park, PJ, Kucherlapati, R, Akbani, R, Casasent, T, Liu, W, Lu, Y, Mills, G, Motter, T, Weinstein, J, Diao, L, Wang, J, Hong Fan, Y, Liu, J, Wang, K, Auman, JT, Balu, S, Bodenheimer, T, Buda, E, Hayes, DN, Hoadley, KA, Hoyle, AP, Jefferys, SR, Jones, CD, Kimes, PK, Liu, Y, Marron, JS, Meng, S, Mieczkowski, PA, Mose, LE, Parker, JS, Perou, CM, Prins, JF, Roach, J, Shi, Y, Simons, JV, Singh, D, Soloway, MG, Tan, D, Veluvolu, U, Walter, V, Waring, S, Wilkerson, MD, and Wu, J

Abstract

© 2015 Macmillan Publishers Limited. All rights reserved. The Cancer Genome Atlas profiled 279 head and neck squamous cell carcinomas (HNSCCs) to provide a comprehensive landscape of somatic genomic alterations. Here we show that human-papillomavirus-associated tumours are dominated by helical domain mutations of the oncogene PIK3CA, novel alterations involving loss of TRAF3, and amplification of the cell cycle gene E2F1. Smoking-related HNSCCs demonstrate near universal loss-of-function TP53 mutations and CDKN2A inactivation with frequent copy number alterations including amplification of 3q26/28 and 11q13/22. A subgroup of oral cavity tumours with favourable clinical outcomes displayed infrequent copy number alterations in conjunction with activating mutations of HRAS or PIK3CA, coupled with inactivating mutations of CASP8, NOTCH1 and TP53. Other distinct subgroups contained loss-of-function alterations of the chromatin modifier NSD1, WNT pathway genes AJUBA and FAT1, and activation of oxidative stress factor NFE2L2, mainly in laryngeal tumours. Therapeutic candidate alterations were identified in most HNSCCs.

Published
2015

11. Functional DNA methylation differences between tissues, cell types, and across individuals discovered using the M&M algorithm

Author

Zhang, B, Zhou, Y, Lin, N, Lowdon, RF, Hong, C, Nagarajan, RP, Cheng, JB, Li, D, Stevens, M, Lee, HJ, Xing, X, Zhou, J, Sundaram, V, Elliott, G, Gu, J, Shi, T, Gascard, P, Sigaroudinia, M, Tlsty, TD, Kadlecek, T, Weiss, A, O'Geen, H, Farnham, PJ, Maire, CL, Ligon, KL, Madden, PAF, Tam, A, Moore, R, Hirst, M, Marra, MA, Costello, JF, and Wang, T

Subjects
Data Interpretation, Genome, Bioinformatics, genetic processes, Human Genome, High-Throughput Nucleotide Sequencing, DNA, Statistical, DNA Methylation, Biological Sciences, Medical and Health Sciences, Organ Specificity, Genetics, Humans, 2.1 Biological and endogenous factors, natural sciences, Generic health relevance, Aetiology, Sequence Analysis, Algorithms, Human, Biotechnology
Abstract

DNA methylation plays key roles in diverse biological processes such as X chromosome inactivation, transposable element repression, genomic imprinting, and tissue-specific gene expression. Sequencing-based DNA methylation profiling provides an unprecedented opportunity to map and compare complete DNA methylomes. This includes one of the most widely applied technologies for measuring DNA methylation: methylated DNA immunoprecipitation followed by sequencing (MeDIP-seq), coupled with a complementary method, methylation-sensitive restriction enzyme sequencing (MRE-seq). A computational approach that integrates data from these two different but complementary assays and predicts methylation differences between samples has been unavailable. Here, we present a novel integrative statistical framework M&M (for integration of MeDIP-seq and MRE-seq) that dynamically scales, normalizes, and combines MeDIPseq and MRE-seq data to detect differentially methylated regions. Using sample-matched whole-genome bisulfite sequencing (WGBS) as a gold standard, we demonstrate superior accuracy and reproducibility of M&M compared to existing analytical methods for MeDIP-seq data alone. M&M leverages the complementary nature of MeDIP-seq and MREseq data to allow rapid comparative analysis between whole methylomes at a fraction of the cost of WGBS. Comprehensive analysis of nineteen human DNA methylomes with M&M reveals distinct DNA methylation patterns among different tissue types, cell types, and individuals, potentially underscoring divergent epigenetic regulation at different scales of phenotypic diversity. We find that differential DNA methylation at enhancer elements, with concurrent changes in histone modifications and transcription factor binding, is common at the cell, tissue, and individual levels, whereas promoter methylation is more prominent in reinforcing fundamental tissue identities. © 2013, Published by Cold Spring Harbor Laboratory Press.

Published
2013

12. Integrated genomic characterization of endometrial carcinoma

Author

Getz, G, Gabriel, SB, Cibulskis, K, Lander, E, Sivachenko, A, Sougnez, C, Lawrence, M, Kandoth, C, Dooling, D, Fulton, R, Fulton, L, Kalicki-Veizer, J, McLellan, MD, O'Laughlin, M, Schmidt, H, Wilson, RK, Ye, K, Li, D, Ally, A, Balasundaram, M, Birol, I, Butterfield, YSN, Carlsen, R, Carter, C, Chu, A, Chuah, E, Chun, HJE, Dhalla, N, Guin, R, Hirst, C, Holt, RA, Jones, SJM, Lee, D, Li, HI, Marra, MA, Mayo, M, Moore, RA, Mungall, AJ, Plettner, P, Schein, JE, Sipahimalani, P, Tam, A, Varhol, RJ, Gordon Robertson, A, Cherniack, AD, Pashtan, I, Saksena, G, Onofrio, RC, Schumacher, SE, Tabak, B, Carter, SL, Hernandez, B, Gentry, J, Salvesen, HB, Ardlie, K, Winckler, W, Beroukhim, R, Meyerson, M, Hadjipanayis, A, Lee, S, Mahadeshwar, HS, Park, P, Protopopov, A, Ren, X, Seth, S, Song, X, Tang, J, Xi, R, Yang, L, Dong, Z, Kucherlapati, R, Chin, L, Zhang, J, Todd Auman, J, Balu, S, Bodenheimer, T, Buda, E, Neil Hayes, D, Hoyle, AP, Jefferys, SR, Jones, CD, Meng, S, Mieczkowski, PA, Mose, LE, Parker, JS, and Perou, CM

Subjects
endocrine system diseases
Abstract

We performed an integrated genomic, transcriptomic and proteomic characterization of 373 endometrial carcinomas using array-and sequencing-based technologies. Uterine serous tumours and ∼25% of high-grade endometrioid tumours had extensive copy number alterations, few DNA methylation changes, low oestrogen receptor/progesterone receptor levels, and frequent TP53 mutations. Most endometrioid tumours had few copy number alterations or TP53 mutations, but frequent mutations in PTEN, CTNNB1, PIK3CA, ARID1A and KRAS and novel mutations in the SWI/SNF chromatin remodelling complex gene ARID5B. A subset of endometrioid tumours that we identified had a markedly increased transversion mutation frequency and newly identified hotspot mutations in POLE. Our results classified endometrial cancers into four categories: POLE ultramutated, microsatellite instability hypermutated, copy-number low, and copy-number high. Uterine serous carcinomas share genomic features with ovarian serous and basal-like breast carcinomas. We demonstrated that the genomic features of endometrial carcinomas permit a reclassification that may affect post-surgical adjuvant treatment for women with aggressive tumours. © 2013 Macmillan Publishers Limited. All rights reserved.

Published
2013

13. Comprehensive molecular characterization of gastric adenocarcinoma

Author

Bass, AJ, Thorsson, V, Shmulevich, I, Reynolds, SM, Miller, M, Bernard, B, Hinoue, T, Laird, PW, Curtis, C, Shen, H, Weisenberger, DJ, Schultz, N, Shen, R, Weinhold, N, Keiser, DP, Bowlby, R, Sipahimalani, P, Cherniack, AD, Getz, G, Liu, Y, Noble, MS, Pedamallu, C, Sougnez, C, Taylor-Weiner, A, Akbani, R, Lee, J-S, Liu, W, Mills, GB, Yang, D, Zhang, W, Pantazi, A, Parfenov, M, Gulley, M, Piazuelo, MB, Schneider, BG, Kim, J, Boussioutas, A, Sheth, M, Demchok, JA, Rabkin, CS, Willis, JE, Ng, S, Garman, K, Beer, DG, Pennathur, A, Raphael, BJ, Wu, H-T, Odze, R, Kim, HK, Bowen, J, Leraas, KM, Lichtenberg, TM, Weaver, L, McLellan, M, Wiznerowicz, M, Sakai, R, Lawrence, MS, Cibulskis, K, Lichtenstein, L, Fisher, S, Gabriel, SB, Lander, ES, Ding, L, Niu, B, Ally, A, Balasundaram, M, Birol, I, Brooks, D, Butterfield, YSN, Carlsen, R, Chu, A, Chu, J, Chuah, E, Chun, H-JE, Clarke, A, Dhalla, N, Guin, R, Holt, RA, Jones, SJM, Kasaian, K, Lee, D, Li, HA, Lim, E, Ma, Y, Marra, MA, Mayo, M, Moore, RA, Mungall, AJ, Mungall, KL, Nip, KM, Robertson, AG, Schein, JE, Tam, A, Thiessen, N, Beroukhim, R, Carter, SL, Cho, J, DiCara, D, Frazer, S, Gehlenborg, N, Heiman, DI, Jung, J, Lin, P, Meyerson, M, Ojesina, AI, Pedamallu, CS, Saksena, G, Schumacher, SE, Stojanov, P, Tabak, B, Voet, D, Rosenberg, M, Zack, TI, Zhang, H, Zou, L, Protopopov, A, Santoso, N, Lee, S, Zhang, J, Mahadeshwar, HS, Tang, J, Ren, X, Seth, S, Yang, L, Xu, AW, Song, X, Xi, R, Bristow, CA, Hadjipanayis, A, Seidman, J, Chin, L, Park, PJ, Kucherlapati, R, Ling, S, Rao, A, Weinstein, JN, Kim, S-B, Lu, Y, Mills, G, Bootwalla, MS, Lai, PH, Triche, T, Van Den Berg, DJ, Baylin, SB, Herman, JG, Murray, BA, Askoy, BA, Ciriello, G, Dresdner, G, Gao, J, Gross, B, Jacobsen, A, Lee, W, Ramirez, R, Sander, C, Senbabaoglu, Y, Sinha, R, Sumer, SO, Sun, Y, Iype, L, Kramer, RW, Kreisberg, R, Rovira, H, Tasman, N, Haussler, D, Stuart, JM, Verhaak, RGW, Leiserson, MDM, Taylor, BS, Black, AD, Carney, JA, Gastier-Foster, JM, Helsel, C, McAllister, C, Ramirez, NC, Tabler, TR, Wise, L, Zmuda, E, Penny, R, Crain, D, Gardner, J, Lau, K, Curely, E, Mallery, D, Morris, S, Paulauskis, J, Shelton, T, Shelton, C, Sherman, M, Benz, C, Lee, J-H, Fedosenko, K, Manikhas, G, Voronina, O, Belyaev, D, Dolzhansky, O, Rathmell, WK, Brzezinski, J, Ibbs, M, Korski, K, Kycler, W, Lazniak, R, Leporowska, E, Mackiewicz, A, Murawa, D, Murawa, P, Spychala, A, Suchorska, WM, Tatka, H, Teresiak, M, Abdel-Misih, R, Bennett, J, Brown, J, Iacocca, M, Rabeno, B, Kwon, S-Y, Kemkes, A, Curley, E, Alexopoulou, I, Engel, J, Bartlett, J, Albert, M, Park, D-Y, Dhir, R, Luketich, J, Landreneau, R, Janjigian, YY, Kelsen, DP, Cho, E, Ladanyi, M, Tang, L, McCall, SJ, Park, YS, Cheong, J-H, Ajani, J, Camargo, MC, Alonso, S, Ayala, B, Jensen, MA, Pihl, T, Raman, R, Walton, J, Wan, Y, Eley, G, Shaw, KRM, Tarnuzzer, R, Wang, Z, Zenklusen, JC, Davidsen, T, Hutter, CM, Sofia, HJ, Burton, R, Chudamani, S, Liu, J, Bass, AJ, Thorsson, V, Shmulevich, I, Reynolds, SM, Miller, M, Bernard, B, Hinoue, T, Laird, PW, Curtis, C, Shen, H, Weisenberger, DJ, Schultz, N, Shen, R, Weinhold, N, Keiser, DP, Bowlby, R, Sipahimalani, P, Cherniack, AD, Getz, G, Liu, Y, Noble, MS, Pedamallu, C, Sougnez, C, Taylor-Weiner, A, Akbani, R, Lee, J-S, Liu, W, Mills, GB, Yang, D, Zhang, W, Pantazi, A, Parfenov, M, Gulley, M, Piazuelo, MB, Schneider, BG, Kim, J, Boussioutas, A, Sheth, M, Demchok, JA, Rabkin, CS, Willis, JE, Ng, S, Garman, K, Beer, DG, Pennathur, A, Raphael, BJ, Wu, H-T, Odze, R, Kim, HK, Bowen, J, Leraas, KM, Lichtenberg, TM, Weaver, L, McLellan, M, Wiznerowicz, M, Sakai, R, Lawrence, MS, Cibulskis, K, Lichtenstein, L, Fisher, S, Gabriel, SB, Lander, ES, Ding, L, Niu, B, Ally, A, Balasundaram, M, Birol, I, Brooks, D, Butterfield, YSN, Carlsen, R, Chu, A, Chu, J, Chuah, E, Chun, H-JE, Clarke, A, Dhalla, N, Guin, R, Holt, RA, Jones, SJM, Kasaian, K, Lee, D, Li, HA, Lim, E, Ma, Y, Marra, MA, Mayo, M, Moore, RA, Mungall, AJ, Mungall, KL, Nip, KM, Robertson, AG, Schein, JE, Tam, A, Thiessen, N, Beroukhim, R, Carter, SL, Cho, J, DiCara, D, Frazer, S, Gehlenborg, N, Heiman, DI, Jung, J, Lin, P, Meyerson, M, Ojesina, AI, Pedamallu, CS, Saksena, G, Schumacher, SE, Stojanov, P, Tabak, B, Voet, D, Rosenberg, M, Zack, TI, Zhang, H, Zou, L, Protopopov, A, Santoso, N, Lee, S, Zhang, J, Mahadeshwar, HS, Tang, J, Ren, X, Seth, S, Yang, L, Xu, AW, Song, X, Xi, R, Bristow, CA, Hadjipanayis, A, Seidman, J, Chin, L, Park, PJ, Kucherlapati, R, Ling, S, Rao, A, Weinstein, JN, Kim, S-B, Lu, Y, Mills, G, Bootwalla, MS, Lai, PH, Triche, T, Van Den Berg, DJ, Baylin, SB, Herman, JG, Murray, BA, Askoy, BA, Ciriello, G, Dresdner, G, Gao, J, Gross, B, Jacobsen, A, Lee, W, Ramirez, R, Sander, C, Senbabaoglu, Y, Sinha, R, Sumer, SO, Sun, Y, Iype, L, Kramer, RW, Kreisberg, R, Rovira, H, Tasman, N, Haussler, D, Stuart, JM, Verhaak, RGW, Leiserson, MDM, Taylor, BS, Black, AD, Carney, JA, Gastier-Foster, JM, Helsel, C, McAllister, C, Ramirez, NC, Tabler, TR, Wise, L, Zmuda, E, Penny, R, Crain, D, Gardner, J, Lau, K, Curely, E, Mallery, D, Morris, S, Paulauskis, J, Shelton, T, Shelton, C, Sherman, M, Benz, C, Lee, J-H, Fedosenko, K, Manikhas, G, Voronina, O, Belyaev, D, Dolzhansky, O, Rathmell, WK, Brzezinski, J, Ibbs, M, Korski, K, Kycler, W, Lazniak, R, Leporowska, E, Mackiewicz, A, Murawa, D, Murawa, P, Spychala, A, Suchorska, WM, Tatka, H, Teresiak, M, Abdel-Misih, R, Bennett, J, Brown, J, Iacocca, M, Rabeno, B, Kwon, S-Y, Kemkes, A, Curley, E, Alexopoulou, I, Engel, J, Bartlett, J, Albert, M, Park, D-Y, Dhir, R, Luketich, J, Landreneau, R, Janjigian, YY, Kelsen, DP, Cho, E, Ladanyi, M, Tang, L, McCall, SJ, Park, YS, Cheong, J-H, Ajani, J, Camargo, MC, Alonso, S, Ayala, B, Jensen, MA, Pihl, T, Raman, R, Walton, J, Wan, Y, Eley, G, Shaw, KRM, Tarnuzzer, R, Wang, Z, Zenklusen, JC, Davidsen, T, Hutter, CM, Sofia, HJ, Burton, R, Chudamani, S, and Liu, J

Abstract

Gastric cancer is a leading cause of cancer deaths, but analysis of its molecular and clinical characteristics has been complicated by histological and aetiological heterogeneity. Here we describe a comprehensive molecular evaluation of 295 primary gastric adenocarcinomas as part of The Cancer Genome Atlas (TCGA) project. We propose a molecular classification dividing gastric cancer into four subtypes: tumours positive for Epstein-Barr virus, which display recurrent PIK3CA mutations, extreme DNA hypermethylation, and amplification of JAK2, CD274 (also known as PD-L1) and PDCD1LG2 (also known as PD-L2); microsatellite unstable tumours, which show elevated mutation rates, including mutations of genes encoding targetable oncogenic signalling proteins; genomically stable tumours, which are enriched for the diffuse histological variant and mutations of RHOA or fusions involving RHO-family GTPase-activating proteins; and tumours with chromosomal instability, which show marked aneuploidy and focal amplification of receptor tyrosine kinases. Identification of these subtypes provides a roadmap for patient stratification and trials of targeted therapies.

Published
2014

14. Hippo Signaling Influences HNF4A and FOXA2 Enhancer Switching during Hepatocyte Differentiation

Author

Alder, O, Cullum, R, Lee, S, Kan, AC, Wei, W, Yi, Y, Garside, VC, Bilenky, M, Griffith, M, Morrissy, AS, Robertson, GA, Thiessen, N, Zhao, Y, Chen, Q, Pan, D, Jones, SJM, Marra, MA, Hoodless, PA, Alder, O, Cullum, R, Lee, S, Kan, AC, Wei, W, Yi, Y, Garside, VC, Bilenky, M, Griffith, M, Morrissy, AS, Robertson, GA, Thiessen, N, Zhao, Y, Chen, Q, Pan, D, Jones, SJM, Marra, MA, and Hoodless, PA

Abstract

Cell fate acquisition is heavily influenced by direct interactions between master regulators and tissue-specific enhancers. However, it remains unclear how lineage-specifying transcription factors, which are often expressed in both progenitor and mature cell populations, influence cell differentiation. Using in vivo mouse liver development as a model, we identified thousands of enhancers that are bound by the master regulators HNF4A and FOXA2 in a differentiation-dependent manner, subject to chromatin remodeling, and associated with differentially expressed target genes. Enhancers exclusively occupied in the embryo were found to be responsive to developmentally regulated TEAD2 and coactivator YAP1. Our data suggest that Hippo signaling may affect hepatocyte differentiation by influencing HNF4A and FOXA2 interactions with temporal enhancers. In summary, transcription factor-enhancer interactions are not only tissue specific but also differentiation dependent, which is an important consideration for researchers studying cancer biology or mammalian development and/or using transformed cell lines.

Published
2014

15. Comparison of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic modifications

Author

Harris, R, Wang, T, Coarfa, C, Nagarajan, R, Hong, C, Downey, S, Johnson, B, Fouse, S, Delaney, A, Zhao, Y, Olshen, A, Ballinger, T, Zhou, X, Forsberg, K, Gu, J, Echipare, L, O'Geen, H, Lister, R, Pelizzola, M, Xi, Y, Epstein, C, Bernstein, B, Hawkins, R, Ren, B, Chung, W, Gu, H, Bock, C, Gnirke, A, Zhang, M, Haussler, D, Ecker, J, Li, W, Farnham, P, Waterland, R, Meissner, A, Marra, M, Hirst, M, Milosavljevic, A, Costello, J, Harris RA, Wang T, Coarfa C, Nagarajan RP, Hong C, Downey SL, Johnson BE, Fouse SD, Delaney A, Zhao Y, Olshen A, Ballinger T, Zhou X, Forsberg KJ, Gu J, Echipare L, O'Geen H, Lister R, Pelizzola M, Xi Y, Epstein CB, Bernstein BE, Hawkins RD, Ren B, Chung WY, Gu H, Bock C, Gnirke A, Zhang MQ, Haussler D, Ecker JR, Li W, Farnham PJ, Waterland RA, Meissner A, Marra MA, Hirst M, Milosavljevic A, Costello JF, Harris, R, Wang, T, Coarfa, C, Nagarajan, R, Hong, C, Downey, S, Johnson, B, Fouse, S, Delaney, A, Zhao, Y, Olshen, A, Ballinger, T, Zhou, X, Forsberg, K, Gu, J, Echipare, L, O'Geen, H, Lister, R, Pelizzola, M, Xi, Y, Epstein, C, Bernstein, B, Hawkins, R, Ren, B, Chung, W, Gu, H, Bock, C, Gnirke, A, Zhang, M, Haussler, D, Ecker, J, Li, W, Farnham, P, Waterland, R, Meissner, A, Marra, M, Hirst, M, Milosavljevic, A, Costello, J, Harris RA, Wang T, Coarfa C, Nagarajan RP, Hong C, Downey SL, Johnson BE, Fouse SD, Delaney A, Zhao Y, Olshen A, Ballinger T, Zhou X, Forsberg KJ, Gu J, Echipare L, O'Geen H, Lister R, Pelizzola M, Xi Y, Epstein CB, Bernstein BE, Hawkins RD, Ren B, Chung WY, Gu H, Bock C, Gnirke A, Zhang MQ, Haussler D, Ecker JR, Li W, Farnham PJ, Waterland RA, Meissner A, Marra MA, Hirst M, Milosavljevic A, and Costello JF

Abstract

Analysis of DNA methylation patterns relies increasingly on sequencing-based profiling methods. The four most frequently used sequencing-based technologies are the bisulfite-based methods MethylC-seq and reduced representation bisulfite sequencing (RRBS), and the enrichment-based techniques methylated DNA immunoprecipitation sequencing (MeDIP-seq) and methylated DNA binding domain sequencing (MBD-seq). We applied all four methods to biological replicates of human embryonic stem cells to assess their genome-wide CpG coverage, resolution, cost, concordance and the influence of CpG density and genomic context. The methylation levels assessed by the two bisulfite methods were concordant (their difference did not exceed a given threshold) for 82% for CpGs and 99% of the non-CpG cytosines. Using binary methylation calls, the two enrichment methods were 99% concordant and regions assessed by all four methods were 97% concordant. We combined MeDIP-seq with methylation-sensitive restriction enzyme (MRE-seq) sequencing for comprehensive methylome coverage at lower cost. This, along with RNA-seq and ChIP-seq of the ES cells enabled us to detect regions with allele-specific epigenetic states, identifying most known imprinted regions and new loci with monoallelic epigenetic marks and monoallelic expression.

Published
2010

17. NTRK2 Fusion Driven Pediatric Glioblastoma: Identification of key molecular drivers by personalized oncology

Author

Levine, Shen, Y, Mungall, K, Serrano, J, Snuderl, M, Pleasance, E, Jones, SJM, Laskin, J, Marra, MA, Rassekh, R, Deyell, R, Yip, S, Cheng, S, and Dunham, C

Abstract

We describe the case of an 11-month-old girl with a rare cerebellar glioblastoma driven by a NACC2-NTRK2(Nucleus Accumbens Associated Protein 2-Neurotrophic Receptor Tyrosine Kinase 2) fusion. Initial workup of our case demonstrated homozygous CDKN2Adeletion, but immunohistochemistry for other driver mutations, including IDH1 R132H, BRAF V600E, and H3F3A K27M were negative, and ATRX was retained. Tissue was subsequently submitted for personalized oncogenomic analysis, including whole genome and whole transcriptome sequencing, which demonstrated an activating NTRK2fusion, as well as high PD-L1 expression, which was subsequently confirmed by immunohistochemistry. Furthermore, H3and IDHdemonstrated wildtype status. These findings suggested the possibility of treatment with either NTRK- or immune checkpoint- inhibitors through active clinical trials. Ultimately, the family pursued standard treatment that involved Head Start III chemotherapy and proton radiotherapy. Notably, at most recent follow upapproximately two years from initial diagnosis, the patient is in disease remission and thriving, suggesting favorable biology despite histologic malignancy. This case illustrates the value of personalized oncogenomics, as the molecular profiling revealed two actionable changes that would not have been apparent through routine diagnostics. NTRKfusions are known oncogenic drivers in a range of cancer types, but this is the first report of a NACC2-NTRK2fusion in a glioblastoma.LEARNING OBJECTIVESThis presentation will enable the learner to:1.Explore the current molecular landscape of pediatric high grade gliomas2.Recognize the value of personalized oncogenomic analysis, particularly in rare and/or aggressive tumors3.Discuss the current status of NTRK inhibitor clinical trials

Published
2019
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19. Subgroup-specific structural variation across 1,000 medulloblastoma genomes

Author

Jenny Q. Qian, Darell D. Bigner, Miklós Garami, Shaun D. Jackman, Wiesława Grajkowska, Nalin Gupta, Johan M. Kros, Poul H. Sorensen, Anna Kenney, Stéphanie Reynaud, Byung Kyu Cho, Ian F. Pollack, Marcel Kool, Steven C. Clifford, Kyu-Chang Wang, Inanc Birol, Tzvi Aviv, Hendrick Witt, Gemma Hoad, Martine F. Roussel, Christine Haberler, Pim J. French, Betty Luu, Cynthia Hawkins, Claudia C. Faria, Richard A. Moore, Karin M. Muraszko, Yuan Yao, Nanne K. Kloosterhof, Rameen Beroukhim, Leos Kren, Erna M.C. Michiels, Jan O. Korbel, Paul A. Northcott, Stefan M. Pfister, Marc Remke, Nina Thiessen, Jennifer A. Chan, Adam M. Fontebasso, Maryam Fouladi, Shin Jung, Richard G. Ellenbogen, Richard Corbett, László Bognár, Yoon Jae Cho, Massimo Zollo, Robert J. Wechsler-Reya, Steven E. Schumacher, Xing Fan, Michael J. Levy, Wolfram Scheurlen, Young Shin Ra, Adrian M. Stütz, William A. Weiss, Simon Bailey, Rajeev Vibhakar, Giuseppe Cinalli, Toshihiro Kumabe, Marco A. Marra, Christian R. Marshall, Eric Bouffet, Luca Massimi, Scott L. Pomeroy, Sarah S. Pernet-Fattet, Andrew J. Mungall, James T. Rutka, G. Yancey Gillespie, Charles G. Eberhart, Peter Hauser, Andy Chu, Jüri Reimand, Xiaochong Wu, Adi Rolider, Xin Wang, Stephen W. Scherer, Reid C. Thompson, Ka Ming Nip, Anne Jouvet, Timothy E. Van Meter, Robert A. Holt, Anthony Raymond, Livia Garzia, Teiji Tominaga, Erwin G. Van Meir, John Peacock, Michael D. Taylor, Achille Iolascon, Roger E. McLendon, Andrey Korshunov, Stephen C. Mack, Nada Jabado, Readman Chiu, Africa Fernandez-L, Eric Chuah, Richard Varhol, Hideo Nakamura, Samer K. Elbabaa, Uri Tabori, Peter B. Dirks, Gary D. Bader, Linda M. Liau, François Doz, Allan Lo, Janet C. Lindsey, Adrian M. Dubuc, Michelle Fèvre-Montange, David T.W. Jones, Carlos Gilberto Carlotti, Ali G. Saad, Steffen Albrecht, Michael K. Cooper, Karen Mungall, Daisuke Kawauchi, A. Sorana Morrissy, Boleslaw Lach, Karel Zitterbart, Joshua B. Rubin, Matthew Meyerson, Florence M.G. Cavalli, Yisu Li, Shenandoah Robinson, Marta Perek-Polnik, Olivier Delattre, David Malkin, Almos Klekner, James M. Olson, Steven J.M. Jones, Thomas Zichner, David W. Ellison, Seung-Ki Kim, Vijay Ramaswamy, Anath C. Lionel, David Shih, Jeffrey R. Leonard, Concezio Di Rocco, Pulmonary Medicine, Pediatrics, Neurology, Pathology, Northcott, Pa, Shih, Dj, Peacock, J, Garzia, L, Morrissy, A, Zichner, T, Stütz, Am, Korshunov, A, Reimand, J, Schumacher, Se, Beroukhim, R, Ellison, Dw, Marshall, Cr, Lionel, Ac, Mack, S, Dubuc, A, Yao, Y, Ramaswamy, V, Luu, B, Rolider, A, Cavalli, Fm, Wang, X, Remke, M, Wu, X, Chiu, Ry, Chu, A, Chuah, E, Corbett, Rd, Hoad, Gr, Jackman, Sd, Li, Y, Lo, A, Mungall, Kl, Nip, Km, Qian, Jq, Raymond, Ag, Thiessen, Nt, Varhol, Rj, Birol, I, Moore, Ra, Mungall, Aj, Holt, R, Kawauchi, D, Roussel, Mf, Kool, M, Jones, Dt, Witt, H, Fernandez L., A, Kenney, Am, Wechsler Reya, Rj, Dirks, P, Aviv, T, Grajkowska, Wa, Perek Polnik, M, Haberler, Cc, Delattre, O, Reynaud, S, Doz, Ff, Pernet Fattet, S, Cho, Bk, Kim, Sk, Wang, Kc, Scheurlen, W, Eberhart, Cg, Fèvre Montange, M, Jouvet, A, Pollack, If, Fan, X, Muraszko, Km, Gillespie, Gy, Di Rocco, C, Massimi, L, Michiels, Em, Kloosterhof, Nk, French, Pj, Kros, Jm, Olson, Jm, Ellenbogen, Rg, Zitterbart, K, Kren, L, Thompson, Rc, Cooper, Mk, Lach, B, Mclendon, Re, Bigner, Dd, Fontebasso, A, Albrecht, S, Jabado, N, Lindsey, Jc, Bailey, S, Gupta, N, Weiss, Wa, Bognár, L, Klekner, A, Van Meter, Te, Kumabe, T, Tominaga, T, Elbabaa, Sk, Leonard, Jr, Rubin, Jb, Liau, Lm, Van Meir, Eg, Fouladi, M, Nakamura, H, Cinalli, G, Garami, M, Hauser, P, Saad, Ag, Iolascon, Achille, Jung, S, Carlotti, Cg, Vibhakar, R, Ra, Y, Robinson, S, Zollo, Massimo, Faria, Cc, Chan, Ja, Levy, Ml, Sorensen, Ph, Meyerson, M, Pomeroy, Sl, Cho, Yj, Bader, Gd, Tabori, U, Hawkins, Ce, Bouffet, E, Scherer, Sw, Rutka, Jt, Malkin, D, Clifford, Sc, Jones, Sj, Korbel, Jo, Pfister, Sm, Marra, Ma, and Taylor, M. D.

Subjects
DNA Copy Number Variations, Oncogene Proteins, Fusion, medicine.medical_treatment, Genes, myc, Nerve Tissue Proteins, Biology, Bioinformatics, medulloblastoma, Article, Translocation, Genetic, Targeted therapy, Structural variation, 03 medical and health sciences, 0302 clinical medicine, Transforming Growth Factor beta, Gene Duplication, Gene duplication, medicine, Humans, Hedgehog Proteins, Cerebellar Neoplasms, Child, 030304 developmental biology, Medulloblastoma, 0303 health sciences, Multidisciplinary, Chromothripsis, PROTEÍNAS DE TRANSPORTE (GENÉTICA), Genome, Human, NF-kappa B, Cancer, Proteins, Genomics, medicine.disease, Human genetics, 3. Good health, PVT1, 030220 oncology & carcinogenesis, Genomic Structural Variation, RNA, Long Noncoding, Carrier Proteins, Signal Transduction
Abstract

Medulloblastoma, the most common malignant paediatric brain tumour, is currently treated with nonspecific cytotoxic therapies including surgery, whole-brain radiation, and aggressive chemotherapy. As medulloblastoma exhibits marked intertumoural heterogeneity, with at least four distinct molecular variants, previous attempts to identify targets for therapy have been underpowered because of small samples sizes. Here we report somatic copy number aberrations (SCNAs) in 1,087 unique medulloblastomas. SCNAs are common in medulloblastoma, and are predominantly subgroup-enriched. The most common region of focal copy number gain is a tandem duplication of SNCAIP, a gene associated with Parkinson's disease, which is exquisitely restricted to Group 4 alpha. Recurrent translocations of PVT1, including PVT1-MYC and PVT1-NDRG1, that arise through chromothripsis are restricted to Group 3. Numerous targetable SCNAs, including recurrent events targeting TGF-beta signalling in Group 3, and NF-kappa B signalling in Group 4, suggest future avenues for rational, targeted therapy.

Published
2012

20. The Genome of the Sea Urchin Strongylocentrotus purpuratus

Author

Amro Hamdoun, Virginia Brockton, Huyen Dinh, Qiang Tu, Richard O. Hynes, Maria Ina Arnone, Wratko Hlavina, L. Courtney Smith, Mariano A. Loza, David R. Burgess, Matthew P. Hoffman, Florian Raible, Qiu Autumn Yuan, Geoffrey Okwuonu, Mark Y. Tong, Jennifer Hume, Donna Maglott, Manisha Goel, Olivier Fedrigo, Manuel L. Gonzalez-Garay, Celina E. Juliano, Judith Hernandez, Gary M. Wessel, William F. Marzluff, Audrey J. Majeske, Christian Gache, Louise Duloquin, Xingzhi Song, François Lapraz, Fowler J, Alexandre Souvorov, Jared V. Goldstone, Georgia Panopoulou, Sandra Hines, Kyle M. Judkins, Clay Davis, Christine G. Elsik, Paul Kitts, Mariano Loza-Coll, Greg Wray, Taku Hibino, Eric Röttinger, Allison M. Churcher, Annamaria Locascio, Arcady Mushegian, Masashi Kinukawa, Anna Reade, Katherine M. Buckley, I. R. Gibbons, Bert Gold, Aleksandar Milosavljevic, David Epel, Victor D. Vacquier, Ling Ling Pu, Vincenzo Cavalieri, Erin L. Allgood, Lan Zhang, Lynne V. Nazareth, Constantin N. Flytzanis, Ian Bosdet, Yi-Hsien Su, Zeev Pancer, Matthew L. Rowe, Robert C. Angerer, David R. McClay, William H. Klein, Rachel F. Gray, Julian L. Wong, Shunsuke Yaguchi, Robert Bellé, Aaron J. Mackey, Herath Jayantha Gunaratne, Karl Frederik Bergeron, Bruce P. Brandhorst, Greg Murray, Avis H. Cohen, Stephanie Bell, Kristin Tessmar-Raible, Ian K. Townley, Bertrand Cosson, Thomas D. Glenn, Jongmin Nam, Cynthia A. Bradham, Michael Dean, Joseph Chacko, Anthony J. Robertson, Margherita Branno, Valeria Matranga, K. James Durbin, Esther Miranda, Lili Chen, Eran Elhaik, Robert D. Burke, Rita A. Wright, Paola Oliveri, Sandra L. Lee, Gary W. Moy, Alexander E Primus, Shawn S. McCafferty, Cristina Calestani, David A. Garfield, Erica Sodergren, Karen Wilson, Joel Smith, Marco A. Marra, Cynthia Messier, Julia Morales, Kim D. Pruitt, Rachel Thorn, Rachel Gill, John S. Taylor, Mark E. Hahn, Victor Sapojnikov, Meredith Howard-Ashby, Lynne M. Angerer, Maurice R. Elphick, Kathy R. Foltz, Anne Marie Genevière, Justin T. Reese, Blanca E. Galindo, Kim C. Worley, Andrew Leone, Glen Humphrey, Kevin Berney, Olga Ermolaeva, George Miner, David P. Terwilliger, Elly Suk Hen Chow, Lora Lewis, Dan Graur, C. Titus Brown, Gerard Manning, Kevin J. Peterson, Angela Jolivet, Michele K. Anderson, Francesca Rizzo, Ekaterina Voronina, Thierry Lepage, Giorgio Matassi, Antonio Fernandez-Guerra, Mamoru Nomura, Charles A. Whittaker, James R.R. Whittle, James A. Coffman, George M. Weinstock, Mohammed M. Idris, Ashlan M. Musante, Sebastian D. Fugmann, Katherine D. Walton, Sorin Istrail, Shu-Yu Wu, Cerrissa Hamilton, Jonah Cool, Jacqueline E. Schein, Stacey M. Curry, Athula Wikramanayke, Seth Carbonneau, Blair J. Rossetti, Christopher E. Killian, Melissa J. Landrum, Amanda P. Rawson, Jenifer C. Croce, Ryan C. Range, Rahul Satija, John J. Stegeman, Yufeng Shen, Cavit Agca, Terry Gaasterland, Rocky Cheung, Takae Kiyama, Nikki Adams, Jonathan P. Rast, Robert Piotr Olinski, Andrew Cree, Mark Scally, Shuguang Liang, David A. Parker, Rebecca Thomason, Gretchen E. Hofmann, Michelle M. Roux, Ronghui Xu, Robert A. Obar, Enrique Arboleda, Odile Mulner-Lorillon, Shannon Dugan-Rocha, David J. Bottjer, Gabriele Amore, Manoj P. Samanta, Waraporn Tongprasit, Véronique Duboc, La Ronda Jackson, Fred H. Wilt, Viktor Stolc, Anna T. Neill, Michael Raisch, Pei Yun Lee, Jia L. Song, Margaret Morgan, Brian T. Livingston, Sofia Hussain, Zheng Wei, Bryan J. Cole, Tonya F. Severson, Victor V. Solovyev, Finn Hallböök, Donna M. Muzny, Christine A. Byrum, Albert J. Poustka, Xiuqian Mu, Andrew R. Jackson, Shin Heesun, Euan R. Brown, Nansheng Chen, Patrick Cormier, Ralph Haygood, Pedro Martinez, R. Andrew Cameron, D. Wang, Wendy S. Beane, Eric H. Davidson, Christie Kovar, Hemant Kelkar, Charles A. Ettensohn, Sham V. Nair, Robert L. Morris, Stefan C. Materna, Michael C. Thorndyke, Richard A. Gibbs, Dan O Mellott, Department of Physiology and Biophysics, Stony Brook University [The State University of New York] ( SBU ), Astronomy Unit ( AU ), Queen Mary University of London ( QMUL ), Urban and Industrial Air Quality Group, CSIRO Energy Technology, Commonwealth Scientific and Industrial Research Organisation Energy Technology ( CSIRO Energy Technology ), Commonwealth Scientific and Industrial Research Organisation, Center for Polymer Studies ( CPS ), Boston University [Boston] ( BU ), Physics Department [Boston] ( BU-Physics ), Max Planck Institute for Psycholinguistics, Max-Planck-Institut, Department of Biology [Norton], Wheaton College [Norton], Mathematical Institute [Oxford] ( MI ), University of Oxford [Oxford], Centre for the Analysis of Time Series ( CATS ), London School of Economics and Political Science ( LSE ), Thomas Jefferson National Accelerator Facility ( Jefferson Lab ), Thomas Jefferson National Accelerator Facility, Laboratoire d'Energétique et de Mécanique Théorique Appliquée ( LEMTA ), Université de Lorraine ( UL ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire Evolution, Génomes et Spéciation ( LEGS ), Centre National de la Recherche Scientifique ( CNRS ), Department of Geology, University of Illinois at Urbana-Champaign [Urbana], Department of Electrical and Computer Engineering [Portland] ( ECE ), Portland State University [Portland] ( PSU ), Saint-Gobain Crystals [USA], SAINT-GOBAIN, Institute for Animal Health ( IAH ), Biotechnology and Biological Sciences Research Council, Center for Agricultural Resources Research, Chinese Academy of Sciences [Changchun Branch] ( CAS ), Ipsen Inc. [Milford] ( Ipsen ), IPSEN, Department of Physics [Berkeley], University of California [Berkeley], Institute for Climate and Atmospheric Science [Leeds] ( ICAS ), University of Leeds, Chung-Ang University ( CAU ), Chung-Ang University [Seoul], Antarctic Climate and Ecosystems Cooperative Research Center ( ACE-CRC ), Institute of Aerodynamics and Fluid Mechanics ( AER ), Technische Universität München [München] ( TUM ), Mer et santé ( MS ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Centre National de la Recherche Scientifique ( CNRS ), Imperial College London, Radio and Atmospheric Sciences Division, National Physical Laboratory [Teddington] ( NPL ), International Research Institute for Climate and Society ( IRI ), Earth Institute at Columbia University, Columbia University [New York]-Columbia University [New York], Soils Group, The Macaulay Institute, Department of Haematology, University of Cambridge [UK] ( CAM ), School of Biology and Biochemistry, Queen's University, Leslie Hill Institute for Plant Conservation ( PCU ), University of Cape Town, Institute for Microelectronics and Microsystems/ Istituto per la Microelettronica e Microsistemi ( IMM ), Consiglio Nazionale delle Ricerche ( CNR ), Laboratoire d'acoustique de l'université du Mans ( LAUM ), Le Mans Université ( UM ) -Centre National de la Recherche Scientifique ( CNRS ), Interactive Systems Labs ( ISL ), Carnegie Mellon University [Pittsburgh] ( CMU ), Dalian Institute of Chemical Physics ( DICP ), Architectures, Languages and Compilers to Harness the End of Moore Years ( ALCHEMY ), Laboratoire de Recherche en Informatique ( LRI ), Université Paris-Sud - Paris 11 ( UP11 ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -Université Paris-Sud - Paris 11 ( UP11 ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique ( Inria ), Clean Air Task Force ( CATF ), Clean Air Task Force, Space Physics Laboratory, Indian Space Research Organisation ( ISRO ), Centre d'études et de recherches appliquées à la gestion ( CERAG ), Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Centre National de la Recherche Scientifique ( CNRS ), Department of Microbiology and Immunology, College of Medicine and Health Sciences-Sultan Qaboos University, European Molecular Biology Laboratory [Heidelberg] ( EMBL ), Department of Biostatistics, University of Michigan [Ann Arbor], Department of Radiation Oncology [Michigan] ( Radonc ), Department of Physics and Astronomy [Leicester], University of Leicester, Informatique, Biologie Intégrative et Systèmes Complexes ( IBISC ), Université d'Évry-Val-d'Essonne ( UEVE ) -Centre National de la Recherche Scientifique ( CNRS ), Institut für Meteorologie und Klimaforschung ( IMK ), Karlsruher Institut für Technologie ( KIT ), Physics Department [UNB], University of New Brunswick ( UNB ), Laboratoire Parole et Langage ( LPL ), Centre National de la Recherche Scientifique ( CNRS ) -Aix Marseille Université ( AMU ), Institut des Sciences Chimiques de Rennes ( ISCR ), Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Ecole Nationale Supérieure de Chimie de Rennes-Institut National des Sciences Appliquées ( INSA ) -Centre National de la Recherche Scientifique ( CNRS ), Biogéosciences [Dijon] ( BGS ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS ), Bioprojet, Laboratoire de Matériaux à Porosité Contrôlée ( LMPC ), Université de Haute-Alsace (UHA) Mulhouse - Colmar ( Université de Haute-Alsace (UHA) ) -Ecole Nationale Supérieure de Chimie de Mulhouse-Centre National de la Recherche Scientifique ( CNRS ), School of Information Engineering [USTB] ( SIE ), University of Science and Technology Beijing [Beijing] ( USTB ), Laboratory for Atmospheric and Space Physics [Boulder] ( LASP ), University of Colorado Boulder [Boulder], Department of Applied Mathematics [Sheffield], University of Sheffield [Sheffield], School of Mathematics and Statistics [Sheffield] ( SoMaS ), Laboratoire de Mécanique de Lille - FRE 3723 ( LML ), Université de Lille, Sciences et Technologies-Ecole Centrale de Lille-Centre National de la Recherche Scientifique ( CNRS ), Computer Science Department [UCLA] ( CSD ), University of California at Los Angeles [Los Angeles] ( UCLA ), Développement et évolution ( DE ), Université Paris-Sud - Paris 11 ( UP11 ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire de Biologie du Développement de Villefranche sur mer ( LBDV ), Laboratoire Pierre Aigrain ( LPA ), Fédération de recherche du Département de physique de l'Ecole Normale Supérieure - ENS Paris ( FRDPENS ), Centre National de la Recherche Scientifique ( CNRS ) -École normale supérieure - Paris ( ENS Paris ) -Centre National de la Recherche Scientifique ( CNRS ) -École normale supérieure - Paris ( ENS Paris ) -Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Université Paris Diderot - Paris 7 ( UPD7 ) -Centre National de la Recherche Scientifique ( CNRS ), Department of Mathematics and Statistics [Mac Gill], McGill University, Departamento de Botánica [Comahue], Universidad nacional del Comahue, Bioénergétique Cellulaire et Pathologique ( BECP ), Université Joseph Fourier - Grenoble 1 ( UJF ) -Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ), Environnements et Paléoenvironnements OCéaniques ( EPOC ), Observatoire aquitain des sciences de l'univers ( OASU ), Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ) -École pratique des hautes études ( EPHE ) -Centre National de la Recherche Scientifique ( CNRS ), Institut Jacques Monod ( IJM ), Université Paris Diderot - Paris 7 ( UPD7 ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratori Nazionali del Sud ( LNS ), National Institute for Nuclear Physics ( INFN ), Departament de Matemàtiques [Barcelona], Universitat Autònoma de Barcelona [Barcelona] ( UAB ), Max-Planck-Institut für Kohlenforschung (coal research), Institute of Oceanology [CAS] ( IOCAS ), National Chiao Tung University ( NCTU ), Department of Hydrology and Water Resources ( HWR ), University of Arizona, Centre for Educational Technology, Environment Department [York], University of York [York, UK], State Key Laboratory of Nuclear Physics and Technology ( SKL-NPT ), Peking University [Beijing], Department of Physics and Astronomy [Iowa City], University of Iowa [Iowa], NASA Ames Research Center ( ARC ), Department of Materials, Digital Language & Knowledge Contents Research Association ( DICORA ), Hankuk University of Foreign Studies, Department of Physics [Coventry], University of Warwick [Coventry], Space Science and Technology Department [Didcot] ( RAL Space ), STFC Rutherford Appleton Laboratory ( RAL ), Science and Technology Facilities Council ( STFC ) -Science and Technology Facilities Council ( STFC ), Institut de biologie et chimie des protéines [Lyon] ( IBCP ), Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique ( CNRS ), H M Nautical Almanac Office [RAL] ( HMNAO ), Rutherford Appleton Laboratory, United Kingdom Met Office [Exeter], University College of London [London] ( UCL ), Department of Pathology and Laboratory Medicine [UCLA], University of California at Los Angeles [Los Angeles] ( UCLA ) -School of Medicine, School of Earth and Environmental Sciences [Seoul] ( SEES ), Seoul National University [Seoul], Department of Chemistry, Seoul Women's University, MicroMachines Centre ( MMC ), Nanyang Technological University [Singapour], Regroupement Québécois sur les Matériaux de Pointe ( RQMP ), École Polytechnique de Montréal ( EPM ) -Université de Sherbrooke [Sherbrooke]-McGill University-Université de Montréal-Fonds Québécois de Recherche sur la Nature et les Technologies ( FQRNT ), Département de Physique [Montréal], Université de Montréal, School of Earth and Environment [Leeds] ( SEE ), Centre for Ecology and Hydrology ( CEH ), Natural Environment Research Council ( NERC ), Norwegian Institute for Water Research ( NIVA ), Norwegian Institute for Water Research, Stony Brook University [SUNY] (SBU), State University of New York (SUNY)-State University of New York (SUNY), Astronomy Unit [London] (AU), Queen Mary University of London (QMUL), Commonwealth Scientific and Industrial Research Organisation Energy Technology (CSIRO Energy Technology), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Department of Biochemistry and Molecular Biology [Houston], The University of Texas Medical School at Houston, Mathematical Institute [Oxford] (MI), University of Oxford, Centre for the Analysis of Time Series (CATS), London School of Economics and Political Science (LSE), Thomas Jefferson National Accelerator Facility (Jefferson Lab), Laboratoire Énergies et Mécanique Théorique et Appliquée (LEMTA ), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Evolution, Génomes et Spéciation (LEGS), Centre National de la Recherche Scientifique (CNRS), University of Illinois System-University of Illinois System, Department of Electrical and Computer Engineering [Portland] (ECE), Portland State University [Portland] (PSU), Saint-Gobain, Institute for Animal Health (IAH), Biotechnology and Biological Sciences Research Council (BBSRC), Chinese Academy of Sciences [Changchun Branch] (CAS), Ipsen Inc. [Milford] (Ipsen), University of California [Berkeley] (UC Berkeley), University of California (UC)-University of California (UC), Institute for Climate and Atmospheric Science [Leeds] (ICAS), School of Earth and Environment [Leeds] (SEE), University of Leeds-University of Leeds, Chung-Ang University (CAU), Antarctic Climate and Ecosystems Cooperative Research Centre (ACE-CRC), Institute of Aerodynamics and Fluid Mechanics (AER), Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), Mer et santé (MS), Station biologique de Roscoff [Roscoff] (SBR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), National Physical Laboratory [Teddington] (NPL), International Research Institute for Climate and Society (IRI), Macaulay Institute, University of Cambridge [UK] (CAM), Queen's University [Kingston, Canada], Leslie Hill Institute for Plant Conservation (PCU), Istituto per la Microelettronica e Microsistemi [Catania] (IMM), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), Laboratoire d'Acoustique de l'Université du Mans (LAUM), Le Mans Université (UM)-Centre National de la Recherche Scientifique (CNRS), Interactive Systems Labs (ISL), Carnegie Mellon University [Pittsburgh] (CMU), Dalian Institute of Chemical Physics (DICP), Architectures, Languages and Compilers to Harness the End of Moore Years (ALCHEMY), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Clean Air Task Force (CATF), Indian Space Research Organisation (ISRO), Centre d'études et de recherches appliquées à la gestion (CERAG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Centre National de la Recherche Scientifique (CNRS), Sultan Qaboos University (SQU)-College of Medicine and Health Sciences [Baylor], Baylor University-Baylor University, European Molecular Biology Laboratory [Heidelberg] (EMBL), University of Michigan System-University of Michigan System, Department of Radiation Oncology [Michigan] (Radonc), Informatique, Biologie Intégrative et Systèmes Complexes (IBISC), Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), Institute for Meteorology and Climate Research (IMK), Karlsruhe Institute of Technology (KIT), University of New Brunswick (UNB), Laboratoire Parole et Langage (LPL), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Institut des Sciences Chimiques de Rennes (ISCR), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Ecole Nationale Supérieure de Chimie de Rennes (ENSCR)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Biogéosciences [UMR 6282] (BGS), Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Matériaux à Porosité Contrôlée (LMPC), Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS), School of Information Engineering [USTB] (SIE), University of Science and Technology Beijing [Beijing] (USTB), Laboratory for Atmospheric and Space Physics [Boulder] (LASP), University of Colorado [Boulder], School of Mathematics and Statistics [Sheffield] (SoMaS), Laboratoire de Mécanique de Lille - FRE 3723 (LML), Université de Lille, Sciences et Technologies-Centrale Lille-Centre National de la Recherche Scientifique (CNRS), Computer Science Department [UCLA] (CSD), University of California [Los Angeles] (UCLA), Développement et évolution (DE), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Biologie du Développement de Villefranche sur mer (LBDV), Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Pierre Aigrain (LPA), Fédération de recherche du Département de physique de l'Ecole Normale Supérieure - ENS Paris (FRDPENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Department of Mathematics and Statistics [Montréal], McGill University = Université McGill [Montréal, Canada], Departamento de Botánica [Bariloche], Centro Regional Universitario Bariloche [Bariloche] (CRUB), Universidad Nacional del Comahue [Neuquén] (UNCOMA)-Universidad Nacional del Comahue [Neuquén] (UNCOMA), Bioénergétique Cellulaire et Pathologique (BECP), Université Joseph Fourier - Grenoble 1 (UJF)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Environnements et Paléoenvironnements OCéaniques (EPOC), Observatoire aquitain des sciences de l'univers (OASU), Université Sciences et Technologies - Bordeaux 1 (UB)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Sciences et Technologies - Bordeaux 1 (UB)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Institut Jacques Monod (IJM (UMR_7592)), Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Laboratori Nazionali del Sud (LNS), Istituto Nazionale di Fisica Nucleare (INFN), Departament de Matemàtiques [Barcelona] (UAB), Universitat Autònoma de Barcelona (UAB), Max-Planck-Institut für Kohlenforschung (Coal Research), Max-Planck-Gesellschaft, CAS Institute of Oceanology (IOCAS), Chinese Academy of Sciences [Beijing] (CAS), National Chiao Tung University (NCTU), Department of Hydrology and Water Resources (HWR), State Key Laboratory of Nuclear Physics and Technology (SKL-NPT), University of Iowa [Iowa City], NASA Ames Research Center (ARC), Digital Language & Knowledge Contents Research Association (DICORA), Space Science and Technology Department [Didcot] (RAL Space), STFC Rutherford Appleton Laboratory (RAL), Science and Technology Facilities Council (STFC)-Science and Technology Facilities Council (STFC), Institut de biologie et chimie des protéines [Lyon] (IBCP), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), H M Nautical Almanac Office [RAL] (HMNAO), University College of London [London] (UCL), University of California (UC)-University of California (UC)-School of Medicine, School of Earth and Environmental Sciences [Seoul] (SEES), Seoul National University [Seoul] (SNU), MicroMachines Centre (MMC), Regroupement Québécois sur les Matériaux de Pointe (RQMP), École Polytechnique de Montréal (EPM)-Université de Sherbrooke (UdeS)-McGill University = Université McGill [Montréal, Canada]-Université de Montréal (UdeM)-Fonds Québécois de Recherche sur la Nature et les Technologies (FQRNT), Université de Montréal (UdeM), Centre for Ecology and Hydrology (CEH), Natural Environment Research Council (NERC), Norwegian Institute for Water Research (NIVA), SEA URCHIN GENOME SEQUENCING CONSORTIUM, SODERGREN E, WEINSTOCK GM, DAVIDSON EH, CAMERON RA, GIBBS RA, ANGERER RC, ANGERER LM, ARNONE MI, BURGESS DR, BURKE RD, COFFMAN JA, DEAN M, ELPHICK MR, ETTENSOHN CA, FOLTZ KR, HAMDOUN A, HYNES RO, KLEIN WH, MARZLUFF W, MCCLAY DR, MORRIS RL, MUSHEGIAN A, RAST JP, SMITH LC, THORNDYKE MC, VACQUIER VD, WESSEL GM, WRAY G, ZHANG L, ELSIK CG, ERMOLAEVA O, HLAVINA W, HOFMANN G, KITTS P, LANDRUM MJ, MACKEY AJ, MAGLOTT D, PANOPOULOU G, POUSTKA AJ, PRUITT K, SAPOJNIKOV V, SONG X, SOUVOROV A, SOLOVYEV V, WEI Z, WHITTAKER CA, WORLEY K, DURBIN KJ, SHEN Y, FEDRIGO O, GARFIELD D, HAYGOOD R, PRIMUS A, SATIJA R, SEVERSON T, GONZALEZ-GARAY ML, JACKSON AR, MILOSAVLJEVIC A, TONG M, KILLIAN CE, LIVINGSTON BT, WILT FH, ADAMS N, BELLE R, CARBONNEAU S, CHEUNG R, CORMIER P, COSSON B, CROCE J, FERNANDEZ-GUERRA A, GENEVIERE AM, GOEL M, KELKAR H, MORALES J, MULNER-LORILLON O, ROBERTSON AJ, GOLDSTONE JV, COLE B, EPEL D, GOLD B, HAHN ME, HOWARD-ASHBY M, SCALLY M, STEGEMAN JJ, ALLGOOD EL, COOL J, JUDKINS KM, MCCAFFERTY SS, MUSANTE AM, OBAR RA, RAWSON AP, ROSSETTI BJ, GIBBONS IR, HOFFMAN MP, LEONE A, ISTRAIL S, MATERNA SC, SAMANTA MP, STOLC V, TONGPRASIT W, TU Q, BERGERON KF, BRANDHORST BP, WHITTLE J, BERNEY K, BOTTJER DJ, CALESTANI C, PETERSON K, CHOW E, YUAN QA, ELHAIK E, GRAUR D, REESE JT, BOSDET I, HEESUN S, MARRA MA, SCHEIN J, ANDERSON MK, BROCKTON V, BUCKLEY KM, COHEN AH, FUGMANN SD, HIBINO T, LOZA-COLL M, MAJESKE AJ, MESSIER C, NAIR SV, PANCER Z, TERWILLIGER DP, AGCA C, ARBOLEDA E, CHEN N, CHURCHER AM, HALLBOOK F, HUMPHREY GW, IDRIS MM, KIYAMA T, LIANG S, MELLOTT D, MU X, MURRAY G, OLINSKI RP, RAIBLE F, ROWE M, TAYLOR JS, TESSMAR-RAIBLE K, WANG D, WILSON KH, YAGUCHI S, GAASTERLAND T, GALINDO BE, GUNARATNE HJ, JULIANO C, KINUKAWA M, MOY GW, NEILL AT, NOMURA M, RAISCH M, READE A, ROUX MM, SONG JL, SU YH, TOWNLEY IK, VORONINA E, WONG JL, AMORE G, BRANNO M, BROWN ER, CAVALIERI, V, DUBOC V, DULOQUIN L, FLYTZANIS C, GACHE C, LAPRAZ F, LEPAGE T, LOCASCIO A, MART, University of California-University of California, Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC), Consiglio Nazionale delle Ricerche (CNR), Centre National de la Recherche Scientifique (CNRS)-Le Mans Université (UM), Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Ecole Nationale Supérieure de Chimie de Rennes (ENSCR)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Biogéosciences [UMR 6282] [Dijon] (BGS), Centre National de la Recherche Scientifique (CNRS)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Ecole Nationale Supérieure de Chimie de Mulhouse-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Fédération de recherche du Département de physique de l'Ecole Normale Supérieure - ENS Paris (FRDPENS), Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-École pratique des hautes études (EPHE), University of California-University of California-School of Medicine, Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Ecole Nationale Supérieure de Chimie de Rennes (ENSCR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), Université de Lille, Sciences et Technologies-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille, Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Joseph Fourier - Grenoble 1 (UJF), University of Manchester Institute of Science and Technology (UMIST), Condensed Matter Physics and Materials Science Department, Brookhaven National Laboratory, Brookhaven National Laboratory [Upton, NY] (BNL), UT-Battelle, LLC-Stony Brook University [SUNY] (SBU), State University of New York (SUNY)-State University of New York (SUNY)-U.S. Department of Energy [Washington] (DOE)-UT-Battelle, LLC-Stony Brook University [SUNY] (SBU), State University of New York (SUNY)-State University of New York (SUNY)-U.S. Department of Energy [Washington] (DOE), Baylor College of Medicine (BCM), Baylor University, Laboratoire de Traitement de l'Information Medicale (LaTIM), Université européenne de Bretagne - European University of Brittany (UEB)-Université de Brest (UBO)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest), Laboratoire de Modélisation et Simulation Multi Echelle (MSME), Université Paris-Est Marne-la-Vallée (UPEM)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS), Duke University [Durham], Instituto Andaluz de Geofísica y Prevención de Desastres Sísmicos [Granada] (IAGPDS), Universidad de Granada (UGR), Laboratoire d'Ingénierie des Matériaux de Bretagne (LIMATB), Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université de Brest (UBO), University of New South Wales [Sydney] (UNSW), Celera Genomics (CRA), Celera Genomics, Paléobiodiversité et paléoenvironnements, Muséum national d'Histoire naturelle (MNHN)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Università degli Studi di Roma Tor Vergata [Roma], Unité de recherches forestières (BORDX PIERR UR ), Institut National de la Recherche Agronomique (INRA), Deptartment of Neuroscience, Uppsala University, State Key Laboratory of Palaeobiology and Stratigraphy, Nanjing Institute of Geology and Palaeontology (NIGPAS-CAS), Chinese Academy of Sciences [Nanjing Branch]-Chinese Academy of Sciences [Nanjing Branch], Institut Méditerranéen d'Ecologie et de Paléoécologie (IMEP), Université Paul Cézanne - Aix-Marseille 3-Université de Provence - Aix-Marseille 1-Avignon Université (AU)-Centre National de la Recherche Scientifique (CNRS), Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China, Université Paris Diderot - Paris 7 (UPD7), Department of Physical and Environmental Sciences [Toronto], University of Toronto at Scarborough, inconnu temporaire UPEMLV, Inconnu, Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Department of Atmospheric Sciences [Seattle], University of Washington [Seattle], National Institute of Advanced Industrial Science and Technology (AIST), Department of Pharmacy, Università degli studi di Genova = University of Genoa (UniGe), Interdisciplinary Arts and Sciences Department, St. Vincent's Hospital, Sydney, Laboratoire des Sciences de l'Environnement Marin (LEMAR) (LEMAR), Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Brest (UBO)-Institut Universitaire Européen de la Mer (IUEM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Department of Electrical Engineering (DEE-POSTECH), Pohang University of Science and Technology (POSTECH), Centre Suisse d'Electronique et de Microtechnique SA [Neuchatel] (CSEM), Centre Suisse d'Electronique et Microtechnique SA (CSEM), Human Genome Sequencing Center [Houston] (HGSC), Brookhaven National Laboratory, Meteorological Service of Canada, 4905 Dufferin Street, Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Mines-Télécom [Paris] (IMT), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Université Paris-Est Marne-la-Vallée (UPEM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Unité de Recherches Forestières, Department of Physical and Environmental Sciences, University of Toronto [Scarborough, Canada], National Institute for Nuclear Physics (INFN), University of Genoa (UNIGE), Institut de Recherche pour le Développement (IRD)-Institut Universitaire Européen de la Mer (IUEM), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université de Brest (UBO)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS), Universidad de Granada = University of Granada (UGR), Laboratoire d'Energétique et de Mécanique Théorique Appliquée (LEMTA ), Technische Universität München [München] (TUM), Queen's University [Kingston], Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Grenoble Alpes (UGA), Institut für Meteorologie und Klimaforschung (IMK), Karlsruher Institut für Technologie (KIT), Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU), Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Supérieure de Chimie de Rennes-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES), Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Ecole Centrale de Lille-Université de Lille, Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-École pratique des hautes études (EPHE)-Centre National de la Recherche Scientifique (CNRS), Universitat Autònoma de Barcelona [Barcelona] (UAB), École Polytechnique de Montréal (EPM)-Université de Sherbrooke [Sherbrooke]-Université de Montréal [Montréal]-McGill University-Fonds Québécois de Recherche sur la Nature et les Technologies (FQRNT), Université de Montréal [Montréal], U.S. Department of Energy [Washington] (DOE)-UT-Battelle, LLC-Stony Brook University [SUNY] (SBU), Université de Bretagne Sud (UBS)-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université de Brest (UBO)-Université de Brest (UBO), Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC), Université Paul Cézanne - Aix-Marseille 3-Centre National de la Recherche Scientifique (CNRS)-Avignon Université (AU)-Université de Provence - Aix-Marseille 1, Institut Universitaire Européen de la Mer (IUEM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Centre National de la Recherche Scientifique (CNRS)-Université de Brest (UBO), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Université de Lille, Sciences et Technologies-Ecole Centrale de Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), École normale supérieure - Paris (ENS Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Male, MESH: Signal Transduction, MESH: Sequence Analysis, DNA, MESH : Transcription Factors, MESH : Calcification, Physiologic, Genome, MESH : Proteins, 0302 clinical medicine, MESH : Embryonic Development, MESH: Gene Expression Regulation, Developmental, Innate, MESH: Embryonic Development, Developmental, Nervous System Physiological Phenomena, MESH: Animals, MESH: Proteins, [SDV.BDD]Life Sciences [q-bio]/Development Biology, Complement Activation, ComputingMilieux_MISCELLANEOUS, MESH: Evolution, Molecular, MESH : Strongylocentrotus purpuratus, Genetics, 0303 health sciences, MESH: Nervous System Physiological Phenomena, Multidisciplinary, biology, Medicine (all), MESH: Immunologic Factors, Gene Expression Regulation, Developmental, Genome project, MESH: Transcription Factors, MESH : Immunity, Innate, MESH : Complement Activation, MESH: Genes, Bacterial artificial chromosome (BAC)DeuterostomesStrongylocentrotus purpuratusVertebrate innovations, Echinoderm, MESH : Nervous System Physiological Phenomena, embryonic structures, MESH: Cell Adhesion Molecules, MESH : Genes, MESH: Immunity, Innate, Sequence Analysis, Signal Transduction, MESH: Computational Biology, Genome evolution, MESH: Complement Activation, Sequence analysis, Evolution, MESH: Strongylocentrotus purpuratus, MESH : Male, Embryonic Development, MESH : Immunologic Factors, Article, MESH: Calcification, Physiologic, Calcification, MESH : Cell Adhesion Molecules, Evolution, Molecular, 03 medical and health sciences, Calcification, Physiologic, Animals, Immunologic Factors, MESH: Genome, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, MESH : Evolution, Molecular, Physiologic, Gene, Strongylocentrotus purpuratus, [ SDV.BBM ] Life Sciences [q-bio]/Biochemistry, Molecular Biology, 030304 developmental biology, MESH : Signal Transduction, Bacterial artificial chromosome, Immunity, Molecular, Computational Biology, Proteins, Cell Adhesion Molecules, Genes, Immunity, Innate, Transcription Factors, Sequence Analysis, DNA, DNA, biology.organism_classification, MESH: Male, Gene Expression Regulation, MESH : Animals, MESH : Gene Expression Regulation, Developmental, MESH : Genome, 030217 neurology & neurosurgery, MESH : Computational Biology, MESH : Sequence Analysis, DNA
Abstract

We report the sequence and analysis of the 814-megabase genome of the sea urchin Strongylocentrotus purpuratus , a model for developmental and systems biology. The sequencing strategy combined whole-genome shotgun and bacterial artificial chromosome (BAC) sequences. This use of BAC clones, aided by a pooling strategy, overcame difficulties associated with high heterozygosity of the genome. The genome encodes about 23,300 genes, including many previously thought to be vertebrate innovations or known only outside the deuterostomes. This echinoderm genome provides an evolutionary outgroup for the chordates and yields insights into the evolution of deuterostomes.

Published
2006

21. Long-read sequencing of an advanced cancer cohort resolves rearrangements, unravels haplotypes, and reveals methylation landscapes.

Author

O'Neill K, Pleasance E, Fan J, Akbari V, Chang G, Dixon K, Csizmok V, MacLennan S, Porter V, Galbraith A, Grisdale CJ, Culibrk L, Dupuis JH, Corbett R, Hopkins J, Bowlby R, Pandoh P, Smailus DE, Cheng D, Wong T, Frey C, Shen Y, Lewis E, Paulin LF, Sedlazeck FJ, Nelson JMT, Chuah E, Mungall KL, Moore RA, Coope R, Mungall AJ, McConechy MK, Williamson LM, Schrader KA, Yip S, Marra MA, Laskin J, and Jones SJM

Abstract

The Long-Read Personalized OncoGenomics (POG) dataset comprises a cohort of 189 patient tumors and 41 matched normal samples sequenced using the Oxford Nanopore Technologies PromethION platform. This dataset from the POG program and the Marathon of Hope Cancer Centres Network includes DNA and RNA short-read sequence data, analytics, and clinical information. We show the potential of long-read sequencing for resolving complex cancer-related structural variants, viral integrations, and extrachromosomal circular DNA. Long-range phasing facilitates the discovery of allelically differentially methylated regions (aDMRs) and allele-specific expression, including recurrent aDMRs in the cancer genes RET and CDKN2A. Germline promoter methylation in MLH1 can be directly observed in Lynch syndrome. Promoter methylation in BRCA1 and RAD51C is a likely driver behind homologous recombination deficiency where no coding driver mutation was found. This dataset demonstrates applications for long-read sequencing in precision medicine and is available as a resource for developing analytical approaches using this technology., Competing Interests: Declaration of interests The following authors disclose relevant potential competing interests: K.O.N., V.P., L.F.P., K.D., J.L., and S.J.M.J. received travel funding from Oxford Nanopore Technologies to present at conferences in 2022 and 2023., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Published
2024
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22. Diagnostic and Therapeutic Implications of a FUS::TFCP2 Fusion and ALK Activation in a Metastatic Rhabdomyosarcoma.

Author

Csizmok V, Grisdale CJ, Williamson LM, Lim HJ, Lee L, Renouf DJ, Jones SJM, Marra MA, Laskin J, and Smrke A

Subjects
Humans, Male, Adult, DNA-Binding Proteins genetics, DNA-Binding Proteins metabolism, Anaplastic Lymphoma Kinase genetics, Rhabdomyosarcoma genetics, Rhabdomyosarcoma pathology, Rhabdomyosarcoma drug therapy, RNA-Binding Protein FUS genetics, Oncogene Proteins, Fusion genetics, Transcription Factors genetics
Abstract

The identification of gene fusions in rare sarcoma subtypes can have diagnostic, prognostic, and therapeutic impacts for advanced cancer patients. Here, we present a case of a 31-year-old male with a lytic lesion of the left mandible initially diagnosed as an osteosarcoma but found to have a TFCP2 fusion and ALK alteration, redefining the diagnosis and providing rationale for a novel treatment strategy. Histologically, the tumor displayed hypercellular, spindled to epithelioid neoplasm and nuclear pleomorphism, while immunohistochemistry showed diffuse SATB2 and focal desmin staining. Whole genome and transcriptome analysis revealed a FUS::TFCP2 fusion, the defining alteration of a rare molecularly characterized subtype of soft tissue sarcoma termed intraosseous rhabdomyosarcoma. An internal ALK deletion and extremely high ALK RNA expression were also identified, suggesting potential benefit of an ALK inhibitor. This patient displayed a rapid and dramatic clinical and radiographic response to an ALK inhibitor, alectinib. Unfortunately, the response was short-lived, likely due to the advanced stage and aggressiveness of the disease. This report describes genome and transcriptome characterization of an intraosseous rhabdomyosarcoma, few of which exist in the literature, as well as providing evidence that inhibition of ALK may be a rational treatment strategy for patients with this exceedingly rare soft tissue sarcoma subtype characterized by TFCP2 fusions and ALK activation., (© 2024 The Author(s). Genes, Chromosomes and Cancer published by Wiley Periodicals LLC.)

Published
2024
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23. Rapid microdissection of tissue sections via laser ablation.

Author

Coope RJ, Pleasance S, Pandoh P, Schlosser C, Corbett RD, and Marra MA

Subjects
Humans, Laser Therapy methods, Microdissection methods, Exome Sequencing, Time Factors, Laser Capture Microdissection methods
Abstract

We demonstrate a method for tissue microdissection using scanning laser ablation that is approximately two orders of magnitude faster than conventional laser capture microdissection. Our novel approach uses scanning laser optics and a slide coating under the tissue that can be excited by the laser to selectively eject regions of tissue for further processing. Tissue was dissected at 0.117 s/mm 2 without reduction in yield, sequencing insert size or base quality compared with undissected tissue. From eight cases, 58-416 mm 2 of tissue was obtained from one to four slides in 7-48 seconds total dissection time per case. These samples underwent exome sequencing and we found the variant allelic fraction increased in regions enriched for tumour as expected. This suggests that our ablation technique may be useful as a tool in both clinical and research labs., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Published
2024
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24. Whole genome and transcriptome integrated analyses guide clinical care of pediatric poor prognosis cancers.

Author

Deyell RJ, Shen Y, Titmuss E, Dixon K, Williamson LM, Pleasance E, Nelson JMT, Abbasi S, Krzywinski M, Armstrong L, Bonakdar M, Ch'ng C, Chuah E, Dunham C, Fok A, Jones M, Lee AF, Ma Y, Moore RA, Mungall AJ, Mungall KL, Rogers PC, Schrader KA, Virani A, Wee K, Young SS, Zhao Y, Jones SJM, Laskin J, Marra MA, and Rassekh SR

Subjects
Humans, Child, Female, Adolescent, Male, Child, Preschool, Prognosis, Infant, Transcriptome, Young Adult, Whole Genome Sequencing, Germ-Line Mutation, Mutation, Genome, Human genetics, Genetic Predisposition to Disease, Neoplasms genetics, Neoplasms therapy, Gene Expression Profiling methods, DNA Copy Number Variations
Abstract

The role for routine whole genome and transcriptome analysis (WGTA) for poor prognosis pediatric cancers remains undetermined. Here, we characterize somatic mutations, structural rearrangements, copy number variants, gene expression, immuno-profiles and germline cancer predisposition variants in children and adolescents with relapsed, refractory or poor prognosis malignancies who underwent somatic WGTA and matched germline sequencing. Seventy-nine participants with a median age at enrollment of 8.8 y (range 6 months to 21.2 y) are included. Germline pathogenic/likely pathogenic variants are identified in 12% of participants, of which 60% were not known prior. Therapeutically actionable variants are identified by targeted gene report and whole genome in 32% and 62% of participants, respectively, and increase to 96% after integrating transcriptome analyses. Thirty-two molecularly informed therapies are pursued in 28 participants with 54% achieving a clinical benefit rate; objective response or stable disease ≥6 months. Integrated WGTA identifies therapeutically actionable variants in almost all tumors and are directly translatable to clinical care of children with poor prognosis cancers., (© 2024. The Author(s).)

Published
2024
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25. Exploration of Germline Correlates and Risk of Immune-Related Adverse Events in Advanced Cancer Patients Treated with Immune Checkpoint Inhibitors.

Author

Titmuss E, Yu IS, Pleasance ED, Williamson LM, Mungall K, Mungall AJ, Renouf DJ, Moore R, Jones SJM, Marra MA, Laskin JJ, and Savage KJ

Subjects
Humans, Male, Female, Middle Aged, Aged, Germ-Line Mutation, Adult, Aged, 80 and over, Immune Checkpoint Inhibitors adverse effects, Immune Checkpoint Inhibitors therapeutic use, Neoplasms drug therapy
Abstract

Immune checkpoint inhibitors (ICIs) are increasingly used in the treatment of many tumor types, and durable responses can be observed in select populations. However, patients may exhibit significant immune-related adverse events (irAEs) that may lead to morbidity. There is limited information on whether the presence of specific germline mutations may highlight those at elevated risk of irAEs. We evaluated 117 patients with metastatic solid tumors or hematologic malignancies who underwent genomic analysis through the ongoing Personalized OncoGenomics (POG) program at BC Cancer and received an ICI during their treatment history. Charts were reviewed for irAEs. Whole genome sequencing of a fresh biopsy and matched normal specimens (blood) was performed at the time of POG enrollment. Notably, we found that MHC class I alleles in the HLA-B27 family, which have been previously associated with autoimmune conditions, were associated with grade 3 hepatitis and pneumonitis (q = 0.007) in patients treated with combination PD-1/PD-L1 and CTLA-4 inhibitors, and PD-1 inhibitors in combination with IDO-1 inhibitors. These data highlight that some patients may have a genetic predisposition to developing irAEs.

Published
2024
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26. Multisite clinical cross-validation and variant interpretation of a next generation sequencing panel for lymphoid cancer prognostication.

Author

Sabatini PJB, Bridgers J, Huang S, Downs G, Zhang T, Sheen C, Park N, Kridel R, Marra MA, Steidl C, Scott DW, and Karsan A

Abstract

Aims: Genomic sequencing of lymphomas is under-represented in routine clinical testing despite having prognostic and predictive value. Clinical implementation is challenging due to a lack of consensus on reportable targets and a paucity of reference samples. We organised a cross-validation study of a lymphoma-tailored next-generation sequencing panel between two College of American Pathologists (CAP)-accredited clinical laboratories to mitigate these challenges., Methods: A consensus for the genomic targets was discussed between the two institutes based on recurrence in diffuse large B-cell lymphoma, follicular lymphoma, mantle cell lymphoma, chronic lymphocytic leukaemia and T-cell lymphomas. Using the same genomic targets, each laboratory ordered libraries independently and a cross-validation study was designed to exchange samples (8 cell lines and 22 clinical samples) and their FASTQ files., Results: The sensitivity of the panel when comparing different library preparation and bioinformatic workflows was between 97% and 99% and specificity was 100% when a 5% limit of detection cut-off was applied. To evaluate how the current standards for variant classification of tumours apply to lymphomas, the Association for Molecular Pathology/American Society of Clinical Oncology/CAP and OncoKB classification systems were applied to the panel. The majority of variants were assigned a possibly actionable class or likely pathogenic due to more limited evidence in the literature., Conclusions: The cross-validation study highlights the benefits of sample and data exchange for clinical validation and provided a framework for reporting the findings in lymphoid malignancies., Competing Interests: Competing interests: RK: research funding from AbbVie and travel expenses from Eisai. DWS: Consulting: AbbVie, AstraZeneca, Incyte; Research funding: Janssen, Roche/Genentech; named inventor on patents describing using gene expression to subtype aggressive B-cell lymphomas, including one licensed to NanoString. CSt: Has performed consultancy for Bayer, and has received research funding from Epizyme and Trillium Therapeutics and is a named inventor on a patent filed by the National Cancer Institute 'Methods for determining lymphoma type'., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Published
2024
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27. A longitudinal single-cell atlas of treatment response in pediatric AML.

Author

Lambo S, Trinh DL, Ries RE, Jin D, Setiadi A, Ng M, Leblanc VG, Loken MR, Brodersen LE, Dai F, Pardo LM, Ma X, Vercauteren SM, Meshinchi S, and Marra MA

Subjects
Humans, Child, Prognosis, Recurrence, Leukemia, Myeloid, Acute drug therapy, Leukemia, Myeloid, Acute genetics
Abstract

Pediatric acute myeloid leukemia (pAML) is characterized by heterogeneous cellular composition, driver alterations and prognosis. Characterization of this heterogeneity and how it affects treatment response remains understudied in pediatric patients. We used single-cell RNA sequencing and single-cell ATAC sequencing to profile 28 patients representing different pAML subtypes at diagnosis, remission and relapse. At diagnosis, cellular composition differed between genetic subgroups. Upon relapse, cellular hierarchies transitioned toward a more primitive state regardless of subtype. Primitive cells in the relapsed tumor were distinct compared to cells at diagnosis, with under-representation of myeloid transcriptional programs and over-representation of other lineage programs. In some patients, this was accompanied by the appearance of a B-lymphoid-like hierarchy. Our data thus reveal the emergence of apparent subtype-specific plasticity upon treatment and inform on potentially targetable processes., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Published
2023
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28. Genomic structures and regulation patterns at HPV integration sites in cervical cancer.

Author

Porter VL, O'Neill K, MacLennan S, Corbett RD, Ng M, Culibrk L, Hamadeh Z, Iden M, Schmidt R, Tsaih SW, Chang G, Fan J, Nip KM, Akbari V, Chan SK, Hopkins J, Moore RA, Chuah E, Mungall KL, Mungall AJ, Birol I, Jones SJM, Rader JS, and Marra MA

Abstract

Human papillomavirus (HPV) integration has been implicated in transforming HPV infection into cancer, but its genomic consequences have been difficult to study using short-read technologies. To resolve the dysregulation associated with HPV integration, we performed long-read sequencing on 63 cervical cancer genomes. We identified six categories of integration events based on HPV-human genomic structures. Of all HPV integrants, defined as two HPV-human breakpoints bridged by an HPV sequence, 24% contained variable copies of HPV between the breakpoints, a phenomenon we termed heterologous integration. Analysis of DNA methylation within and in proximity to the HPV genome at individual integration events revealed relationships between methylation status of the integrant and its orientation and structure. Dysregulation of the human epigenome and neighboring gene expression in cis with the HPV-integrated allele was observed over megabase-ranges of the genome. By elucidating the structural, epigenetic, and allele-specific impacts of HPV integration, we provide insight into the role of integrated HPV in cervical cancer.

Published
2023
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29. Relapse Timing Is Associated With Distinct Evolutionary Dynamics in Diffuse Large B-Cell Lymphoma.

Author

Hilton LK, Ngu HS, Collinge B, Dreval K, Ben-Neriah S, Rushton CK, Wong JCH, Cruz M, Roth A, Boyle M, Meissner B, Slack GW, Farinha P, Craig JW, Gerrie AS, Freeman CL, Villa D, Rodrigo JA, Song K, Crump M, Shepherd L, Hay AE, Kuruvilla J, Savage KJ, Kridel R, Karsan A, Marra MA, Sehn LH, Steidl C, Morin RD, and Scott DW

Subjects
Humans, Neoplasm Recurrence, Local drug therapy, Chronic Disease, Transplantation, Autologous, Antineoplastic Combined Chemotherapy Protocols therapeutic use, Lymphoma, Large B-Cell, Diffuse therapy, Lymphoma, Large B-Cell, Diffuse drug therapy, Hematopoietic Stem Cell Transplantation
Abstract

Purpose: Diffuse large B-cell lymphoma (DLBCL) is cured in more than 60% of patients, but outcomes remain poor for patients experiencing disease progression or relapse (refractory or relapsed DLBCL [rrDLBCL]), particularly if these events occur early. Although previous studies examining cohorts of rrDLBCL have identified features that are enriched at relapse, few have directly compared serial biopsies to uncover biological and evolutionary dynamics driving rrDLBCL. Here, we sought to confirm the relationship between relapse timing and outcomes after second-line (immuno)chemotherapy and determine the evolutionary dynamics that underpin that relationship., Patients and Methods: Outcomes were examined in a population-based cohort of 221 patients with DLBCL who experienced progression/relapse after frontline treatment and were treated with second-line (immuno)chemotherapy with an intention-to-treat with autologous stem-cell transplantation (ASCT). Serial DLBCL biopsies from a partially overlapping cohort of 129 patients underwent molecular characterization, including whole-genome or whole-exome sequencing in 73 patients., Results: Outcomes to second-line therapy and ASCT are superior for late relapse (>2 years postdiagnosis) versus primary refractory (<9 months) or early relapse (9-24 months). Diagnostic and relapse biopsies were mostly concordant for cell-of-origin classification and genetics-based subgroup. Despite this concordance, the number of mutations exclusive to each biopsy increased with time since diagnosis, and late relapses shared few mutations with their diagnostic counterpart, demonstrating a branching evolution pattern. In patients with highly divergent tumors, many of the same genes acquired new mutations independently in each tumor, suggesting that the earliest mutations in a shared precursor cell constrain tumor evolution toward the same genetics-based subgroups at both diagnosis and relapse., Conclusion: These results suggest that late relapses commonly represent genetically distinct and chemotherapy-naïve disease and have implications for optimal patient management.

Published
2023
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30. Genetic subdivisions of follicular lymphoma defined by distinct coding and noncoding mutation patterns.

Author

Dreval K, Hilton LK, Cruz M, Shaalan H, Ben-Neriah S, Boyle M, Collinge B, Coyle KM, Duns G, Farinha P, Grande BM, Meissner B, Pararajalingam P, Rushton CK, Slack GW, Wong J, Mungall AJ, Marra MA, Connors JM, Steidl C, Scott DW, and Morin RD

Subjects
Humans, Mutation, Lymphoma, Follicular pathology, Lymphoma, Large B-Cell, Diffuse genetics, Lymphoma, Large B-Cell, Diffuse pathology
Abstract

Follicular lymphoma (FL) accounts for ∼20% of all new lymphoma cases. Increases in cytological grade are a feature of the clinical progression of this malignancy, and eventual histologic transformation (HT) to the aggressive diffuse large B-cell lymphoma (DLBCL) occurs in up to 15% of patients. Clinical or genetic features to predict the risk and timing of HT have not been described comprehensively. In this study, we analyzed whole-genome sequencing data from 423 patients to compare the protein coding and noncoding mutation landscapes of untransformed FL, transformed FL, and de novo DLBCL. This revealed 2 genetically distinct subgroups of FL, which we have named DLBCL-like (dFL) and constrained FL (cFL). Each subgroup has distinguishing mutational patterns, aberrant somatic hypermutation rates, and biological and clinical characteristics. We implemented a machine learning-derived classification approach to stratify patients with FL into cFL and dFL subgroups based on their genomic features. Using separate validation cohorts, we demonstrate that cFL status, whether assigned with this full classifier or a single-gene approximation, is associated with a reduced rate of HT. This implies distinct biological features of cFL that constrain its evolution, and we highlight the potential for this classification to predict HT from genetic features present at diagnosis., (© 2023 by The American Society of Hematology. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), permitting only noncommercial, nonderivative use with attribution. All other rights reserved.)

Published
2023
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31. Chemokine expression predicts T cell-inflammation and improved survival with checkpoint inhibition across solid cancers.

Author

Romero JM, Titmuss E, Wang Y, Vafiadis J, Pacis A, Jang GH, Zhang A, Golesworthy B, Lenko T, Williamson LM, Grünwald B, O'Kane GM, Jones SJM, Marra MA, Wilson JM, Gallinger S, Laskin J, and Zogopoulos G

Abstract

Immune checkpoint inhibitors (ICI) are highly effective in specific cancers where canonical markers of antitumor immunity are used for patient selection. Improved predictors of T cell-inflammation are needed to identify ICI-responsive tumor subsets in additional cancer types. We investigated associations of a 4-chemokine expression signature (c-Score: CCL4, CCL5, CXCL9, CXCL10) with metrics of antitumor immunity across tumor types. Across cancer entities from The Cancer Genome Atlas, subgroups of tumors displayed high expression of the c-Score (c-Score hi ) with increased expression of immune checkpoint (IC) genes and transcriptional hallmarks of the cancer-immunity cycle. There was an incomplete association of the c-Score with high tumor mutation burden (TMB), with only 15% of c-Score hi tumors displaying ≥10 mutations per megabase. In a heterogeneous pan-cancer cohort of 82 patients, with advanced and previously treated solid cancers, c-Score hi tumors had a longer median time to progression (103 versus 72 days, P = 0.012) and overall survival (382 versus 196 days, P = 0.038) following ICI therapy initiation, compared to patients with low c-Score expression. We also found c-Score stratification to outperform TMB assignment for overall survival prediction (HR = 0.42 [0.22-0.79], P = 0.008 versus HR = 0.60 [0.29-1.27], P = 0.18, respectively). Assessment of the c-Score using the TIDE and PredictIO databases, which include ICI treatment outcomes from 10 tumor types, provided further support for the c-Score as a predictive ICI therapeutic biomarker. In summary, the c-Score identifies patients with hallmarks of T cell-inflammation and potential response to ICI treatment across cancer types, which is missed by TMB assignment., (© 2023. Nature Publishing Group UK.)

Published
2023
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32. A high-throughput pipeline for DNA/RNA/small RNA purification from tissue samples for sequencing.

Author

Xu J, Pandoh PK, Corbett RD, Smailus D, Bowlby R, Brooks D, McDonald H, Haile S, Chahal S, Bilobram S, Mungall KL, Mungall AJ, Coope R, Moore RA, Zhao Y, Jones SJ, and Marra MA

Subjects
High-Throughput Nucleotide Sequencing methods, RNA genetics, DNA genetics
Abstract

High-throughput total nucleic acid (TNA) purification methods based on solid-phase reversible immobilization (SPRI) beads produce TNA suitable for both genomic and transcriptomic applications. Even so, small RNA species, including miRNA, bind weakly to SPRI beads under standard TNA purification conditions, necessitating a separate workflow using column-based methods that are difficult to automate. Here, an SPRI-based high-throughput TNA purification protocol that recovers DNA, RNA and small RNA, called GSC-modified RLT+ Aline bead-based protocol (GRAB-ALL), which incorporates modifications to enhance small RNA recovery is presented. GRAB-ALL was benchmarked against existing nucleic acid purification workflows and GRAB-ALL efficiently purifies TNA, including small RNA, for next-generation sequencing applications in a plate-based format suitable for automated high-throughput sample preparation.

Published
2023
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33. GRETTA: an R package for mapping in silico genetic interaction and essentiality networks.

Author

Takemon Y and Marra MA

Subjects
Humans, Mutation, Software
Abstract

Summary: Mapping genetic interactions and essentiality networks in human cell lines has been used to identify vulnerabilities of cells carrying specific genetic alterations and to associate novel functions to genes, respectively. In vitro and in vivo genetic screens to decipher these networks are resource-intensive, limiting the throughput of samples that can be analyzed. In this application note, we provide an R package we call Genetic inteRaction and EssenTiality neTwork mApper (GRETTA). GRETTA is an accessible tool for in silico genetic interaction screens and essentiality network analyses using publicly available data, requiring only basic R programming knowledge., Availability and Implementation: The R package, GRETTA, is licensed under GNU General Public License v3.0 and freely available at https://github.com/ytakemon/GRETTA and https://doi.org/10.5281/zenodo.6940757, with documentation and tutorial. A Singularity container is also available at https://cloud.sylabs.io/library/ytakemon/gretta/gretta., (© The Author(s) 2023. Published by Oxford University Press.)

Published
2023
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34. Oncogenic Viruses and the Epigenome: How Viruses Hijack Epigenetic Mechanisms to Drive Cancer.

Author

MacLennan SA and Marra MA

Subjects
Humans, Oncogenic Viruses genetics, Epigenome, Epigenesis, Genetic, DNA Methylation, Neoplasms pathology, Viruses
Abstract

Globally, viral infections substantially contribute to cancer development. Oncogenic viruses are taxonomically heterogeneous and drive cancers using diverse strategies, including epigenomic dysregulation. Here, we discuss how oncogenic viruses disrupt epigenetic homeostasis to drive cancer and focus on how virally mediated dysregulation of host and viral epigenomes impacts the hallmarks of cancer. To illustrate the relationship between epigenetics and viral life cycles, we describe how epigenetic changes facilitate the human papillomavirus (HPV) life cycle and how changes to this process can spur malignancy. We also highlight the clinical impact of virally mediated epigenetic changes on cancer diagnosis, prognosis, and treatment.

Published
2023
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35. Multi-Omic Analysis of CIC's Functional Networks Reveals Novel Interaction Partners and a Potential Role in Mitotic Fidelity.

Author

Takemon Y, LeBlanc VG, Song J, Chan SY, Lee SD, Trinh DL, Ahmad ST, Brothers WR, Corbett RD, Gagliardi A, Moradian A, Cairncross JG, Yip S, Aparicio SAJR, Chan JA, Hughes CS, Morin GB, Gorski SM, Chittaranjan S, and Marra MA

Abstract

CIC encodes a transcriptional repressor and MAPK signalling effector that is inactivated by loss-of-function mutations in several cancer types, consistent with a role as a tumour suppressor. Here, we used bioinformatic, genomic, and proteomic approaches to investigate CIC's interaction networks. We observed both previously identified and novel candidate interactions between CIC and SWI/SNF complex members, as well as novel interactions between CIC and cell cycle regulators and RNA processing factors. We found that CIC loss is associated with an increased frequency of mitotic defects in human cell lines and an in vivo mouse model and with dysregulated expression of mitotic regulators. We also observed aberrant splicing in CIC-deficient cell lines, predominantly at 3' and 5' untranslated regions of genes, including genes involved in MAPK signalling, DNA repair, and cell cycle regulation. Our study thus characterises the complexity of CIC's functional network and describes the effect of its loss on cell cycle regulation, mitotic integrity, and transcriptional splicing, thereby expanding our understanding of CIC's potential roles in cancer. In addition, our work exemplifies how multi-omic, network-based analyses can be used to uncover novel insights into the interconnected functions of pleiotropic genes/proteins across cellular contexts.

Published
2023
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36. Homologous recombination deficiency signatures in gastrointestinal and thoracic cancers correlate with platinum therapy duration.

Author

Tsang ES, Csizmok V, Williamson LM, Pleasance E, Topham JT, Karasinska JM, Titmuss E, Schrader I, Yip S, Tessier-Cloutier B, Mungall K, Ng T, Sun S, Lim HJ, Loree JM, Laskin J, Marra MA, Jones SJM, Schaeffer DF, and Renouf DJ

Abstract

There is emerging evidence about the predictive role of homologous recombination deficiency (HRD), but this is less defined in gastrointestinal (GI) and thoracic malignancies. We reviewed whole genome (WGS) and transcriptomic (RNA-Seq) data from advanced GI and thoracic cancers in the Personalized OncoGenomics trial (NCT02155621) to evaluate HRD scores and single base substitution (SBS)3, which is associated with BRCA1/2 mutations and potentially predictive of defective HRD. HRD scores were calculated by sum of loss of heterozygosity, telomeric allelic imbalance, and large-scale state transitions scores. Regression analyses examined the association between HRD and time to progression on platinum (TTPp). We included 223 patients with GI (n = 154) or thoracic (n = 69) malignancies. TTPp was associated with SBS3 (p < 0.01) but not HRD score in patients with GI malignancies, whereas neither was associated with TTPp in thoracic malignancies. Tumors with gBRCA1/2 mutations and a somatic second alteration exhibited high SBS3 and HRD scores, but these signatures were also present in several tumors with germline but no somatic second alterations, suggesting silencing of the wild-type allele or BRCA1/2 haploinsufficiency. Biallelic inactivation of an HR gene, including loss of XRCC2 and BARD1, was identified in BRCA1/2 wild-type HRD tumors and these patients had prolonged response to platinum. Thoracic cases with high HRD score were associated with high RECQL5 expression (p ≤ 0.025), indicating another potential mechanism of HRD. SBS3 was more strongly associated with TTPp in patients with GI malignancies and may be complementary to using HRD and BRCA status in identifying patients who benefit from platinum therapy., (© 2023. The Author(s).)

Published
2023
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37. Immune Activation following Irbesartan Treatment in a Colorectal Cancer Patient: A Case Study.

Author

Titmuss E, Milne K, Jones MR, Ng T, Topham JT, Brown SD, Schaeffer DF, Kalloger S, Wilson D, Corbett RD, Williamson LM, Mungall K, Mungall AJ, Holt RA, Nelson BH, Jones SJM, Laskin J, Lim HJ, and Marra MA

Subjects
Humans, Irbesartan therapeutic use, CD8-Positive T-Lymphocytes pathology, Antihypertensive Agents therapeutic use, Colorectal Neoplasms drug therapy, Colorectal Neoplasms genetics, Colorectal Neoplasms pathology
Abstract

Colorectal cancers are one of the most prevalent tumour types worldwide and, despite the emergence of targeted and biologic therapies, have among the highest mortality rates. The Personalized OncoGenomics (POG) program at BC Cancer performs whole genome and transcriptome analysis (WGTA) to identify specific alterations in an individual's cancer that may be most effectively targeted. Informed using WGTA, a patient with advanced mismatch repair-deficient colorectal cancer was treated with the antihypertensive drug irbesartan and experienced a profound and durable response. We describe the subsequent relapse of this patient and potential mechanisms of response using WGTA and multiplex immunohistochemistry (m-IHC) profiling of biopsies before and after treatment from the same metastatic site of the L3 spine. We did not observe marked differences in the genomic landscape before and after treatment. Analyses revealed an increase in immune signalling and infiltrating immune cells, particularly CD8+ T cells, in the relapsed tumour. These results indicate that the observed anti-tumour response to irbesartan may have been due to an activated immune response. Determining whether there may be other cancer contexts in which irbesartan may be similarly valuable will require additional studies.

Published
2023
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38. Relapse timing is associated with distinct evolutionary dynamics in DLBCL.

Author

Hilton LK, Ngu HS, Collinge B, Dreval K, Ben-Neriah S, Rushton CK, Wong JCH, Cruz M, Roth A, Boyle M, Meissner B, Slack GW, Farinha P, Craig JW, Gerrie AS, Freeman CL, Villa D, Crump M, Shepherd L, Hay AE, Kuruvilla J, Savage KJ, Kridel R, Karsan A, Marra MA, Sehn LH, Steidl C, Morin RD, and Scott DW

Abstract

Diffuse large B-cell lymphoma (DLBCL) is cured in over 60% of patients, but outcomes are poor for patients with relapsed or refractory disease (rrDLBCL). Here, we performed whole genome/exome sequencing (WGS/WES) on tumors from 73 serially-biopsied patients with rrDLBCL. Based on the observation that outcomes to salvage therapy/autologous stem cell transplantation are related to time-to-relapse, we stratified patients into groups according to relapse timing to explore the relationship to genetic divergence and sensitivity to salvage immunochemotherapy. The degree of mutational divergence increased with time between biopsies, yet tumor pairs were mostly concordant for cell-of-origin, oncogene rearrangement status and genetics-based subgroup. In patients with highly divergent tumors, several genes acquired exclusive mutations independently in each tumor, which, along with concordance of genetics-based subgroups, suggests that the earliest mutations in a shared precursor cell constrain tumor evolution. These results suggest that late relapses commonly represent genetically distinct and chemotherapy-naïve disease.

Published
2023
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39. Genetic subgroups inform on pathobiology in adult and pediatric Burkitt lymphoma.

Author

Thomas N, Dreval K, Gerhard DS, Hilton LK, Abramson JS, Ambinder RF, Barta S, Bartlett NL, Bethony J, Bhatia K, Bowen J, Bryan AC, Cesarman E, Casper C, Chadburn A, Cruz M, Dittmer DP, Dyer MA, Farinha P, Gastier-Foster JM, Gerrie AS, Grande BM, Greiner T, Griner NB, Gross TG, Harris NL, Irvin JD, Jaffe ES, Henry D, Huppi R, Leal FE, Lee MS, Martin JP, Martin MR, Mbulaiteye SM, Mitsuyasu R, Morris V, Mullighan CG, Mungall AJ, Mungall K, Mutyaba I, Nokta M, Namirembe C, Noy A, Ogwang MD, Omoding A, Orem J, Ott G, Petrello H, Pittaluga S, Phelan JD, Ramos JC, Ratner L, Reynolds SJ, Rubinstein PG, Sissolak G, Slack G, Soudi S, Swerdlow SH, Traverse-Glehen A, Wilson WH, Wong J, Yarchoan R, ZenKlusen JC, Marra MA, Staudt LM, Scott DW, and Morin RD

Subjects
Child, Humans, Adult, Herpesvirus 4, Human, Mutation, Burkitt Lymphoma pathology, Epstein-Barr Virus Infections, Lymphoma, Large B-Cell, Diffuse pathology
Abstract

Burkitt lymphoma (BL) accounts for most pediatric non-Hodgkin lymphomas, being less common but significantly more lethal when diagnosed in adults. Much of the knowledge of the genetics of BL thus far has originated from the study of pediatric BL (pBL), leaving its relationship to adult BL (aBL) and other adult lymphomas not fully explored. We sought to more thoroughly identify the somatic changes that underlie lymphomagenesis in aBL and any molecular features that associate with clinical disparities within and between pBL and aBL. Through comprehensive whole-genome sequencing of 230 BL and 295 diffuse large B-cell lymphoma (DLBCL) tumors, we identified additional significantly mutated genes, including more genetic features that associate with tumor Epstein-Barr virus status, and unraveled new distinct subgroupings within BL and DLBCL with 3 predominantly comprising BLs: DGG-BL (DDX3X, GNA13, and GNAI2), IC-BL (ID3 and CCND3), and Q53-BL (quiet TP53). Each BL subgroup is characterized by combinations of common driver and noncoding mutations caused by aberrant somatic hypermutation. The largest subgroups of BL cases, IC-BL and DGG-BL, are further characterized by distinct biological and gene expression differences. IC-BL and DGG-BL and their prototypical genetic features (ID3 and TP53) had significant associations with patient outcomes that were different among aBL and pBL cohorts. These findings highlight shared pathogenesis between aBL and pBL, and establish genetic subtypes within BL that serve to delineate tumors with distinct molecular features, providing a new framework for epidemiologic, diagnostic, and therapeutic strategies., (Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), permitting only noncommercial, nonderivative use with attribution. All other rights reserved.)

Published
2023
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40. Author Correction: Failure of human rhombic lip differentiation underlies medulloblastoma formation.

Author

Hendrikse LD, Haldipur P, Saulnier O, Millman J, Sjoboen AH, Erickson AW, Ong W, Gordon V, Coudière-Morrison L, Mercier AL, Shokouhian M, Suárez RA, Ly M, Borlase S, Scott DS, Vladoiu MC, Farooq H, Sirbu O, Nakashima T, Nambu S, Funakoshi Y, Bahcheli A, Diaz-Mejia JJ, Golser J, Bach K, Phuong-Bao T, Skowron P, Wang EY, Kumar SA, Balin P, Visvanathan A, Lee JJY, Ayoub R, Chen X, Chen X, Mungall KL, Luu B, Bérubé P, Wang YC, Pfister SM, Kim SK, Delattre O, Bourdeaut F, Doz F, Masliah-Planchon J, Grajkowska WA, Loukides J, Dirks P, Fèvre-Montange M, Jouvet A, French PJ, Kros JM, Zitterbart K, Bailey SD, Eberhart CG, Rao AAN, Giannini C, Olson JM, Garami M, Hauser P, Phillips JJ, Ra YS, de Torres C, Mora J, Li KKW, Ng HK, Poon WS, Pollack IF, López-Aguilar E, Gillespie GY, Van Meter TE, Shofuda T, Vibhakar R, Thompson RC, Cooper MK, Rubin JB, Kumabe T, Jung S, Lach B, Iolascon A, Ferrucci V, de Antonellis P, Zollo M, Cinalli G, Robinson S, Stearns DS, Van Meir EG, Porrati P, Finocchiaro G, Massimino M, Carlotti CG, Faria CC, Roussel MF, Boop F, Chan JA, Aldinger KA, Razavi F, Silvestri E, McLendon RE, Thompson EM, Ansari M, Garre ML, Chico F, Eguía P, Pérezpeña M, Morrissy AS, Cavalli FMG, Wu X, Daniels C, Rich JN, Jones SJM, Moore RA, Marra MA, Huang X, Reimand J, Sorensen PH, Wechsler-Reya RJ, Weiss WA, Pugh TJ, Garzia L, Kleinman CL, Stein LD, Jabado N, Malkin D, Ayrault O, Golden JA, Ellison DW, Doble B, Ramaswamy V, Werbowetski-Ogilvie TE, Suzuki H, Millen KJ, and Taylor MD

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2022
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41. Integrative analysis of KRAS wildtype metastatic pancreatic ductal adenocarcinoma reveals mutation and expression-based similarities to cholangiocarcinoma.

Author

Topham JT, Tsang ES, Karasinska JM, Metcalfe A, Ali H, Kalloger SE, Csizmok V, Williamson LM, Titmuss E, Nielsen K, Negri GL, Spencer Miko SE, Jang GH, Denroche RE, Wong HL, O'Kane GM, Moore RA, Mungall AJ, Loree JM, Notta F, Wilson JM, Bathe OF, Tang PA, Goodwin R, Morin GB, Knox JJ, Gallinger S, Laskin J, Marra MA, Jones SJM, Schaeffer DF, and Renouf DJ

Subjects
Bile Ducts, Intrahepatic, Humans, Mutation, Proto-Oncogene Proteins p21(ras) genetics, Transcription Factors genetics, Pancreatic Neoplasms, Adenocarcinoma pathology, Bile Duct Neoplasms genetics, Carcinoma, Pancreatic Ductal pathology, Cholangiocarcinoma genetics, Pancreatic Neoplasms pathology
Abstract

Oncogenic KRAS mutations are absent in approximately 10% of patients with metastatic pancreatic ductal adenocarcinoma (mPDAC) and may represent a subgroup of mPDAC with therapeutic options beyond standard-of-care cytotoxic chemotherapy. While distinct gene fusions have been implicated in KRAS wildtype mPDAC, information regarding other types of mutations remain limited, and gene expression patterns associated with KRAS wildtype mPDAC have not been reported. Here, we leverage sequencing data from the PanGen trial to perform comprehensive characterization of the molecular landscape of KRAS wildtype mPDAC and reveal increased frequency of chr1q amplification encompassing transcription factors PROX1 and NR5A2. By leveraging data from colorectal adenocarcinoma and cholangiocarcinoma samples, we highlight similarities between cholangiocarcinoma and KRAS wildtype mPDAC involving both mutation and expression-based signatures and validate these findings using an independent dataset. These data further establish KRAS wildtype mPDAC as a unique molecular entity, with therapeutic opportunities extending beyond gene fusion events., (© 2022. The Author(s).)

Published
2022
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42. Early-stage economic analysis of research-based comprehensive genomic sequencing for advanced cancer care.

Author

Weymann D, Laskin J, Jones SJM, Roscoe R, Lim HJ, Renouf DJ, Schrader KA, Sun S, Yip S, Marra MA, and Regier DA

Abstract

Genomic research is driving discovery for future population benefit. Limited evidence exists on immediate patient and health system impacts of research participation. This study uses real-world data and quasi-experimental matching to examine early-stage cost and health impacts of research-based genomic sequencing. British Columbia's Personalized OncoGenomics (POG) single-arm program applies whole genome and transcriptome analysis (WGTA) to characterize genomic landscapes in advanced cancers. Our cohort includes POG patients enrolled between 2014 and 2015 and 1:1 genetic algorithm-matched usual care controls. We undertake a cost consequence analysis and estimate 1-year effects of WGTA on patient management, patient survival, and health system costs reported in 2015 Canadian dollars. WGTA costs are imputed and forecast using system of equations modeling. We use Kaplan-Meier survival analysis to explore survival differences and inverse probability of censoring weighted linear regression to estimate mean 1-year survival times and costs. Non-parametric bootstrapping simulates sampling distributions and enables scenario analysis, revealing drivers of incremental costs, survival, and net monetary benefit for assumed willingness to pay thresholds. We identified 230 POG patients and 230 matched controls for cohort inclusion. The mean period cost of research-funded WGTA was $26,211 (SD: $14,191). Sequencing costs declined rapidly, with WGTA forecasts hitting $13,741 in 2021. The incremental healthcare system effect (non-research expenditures) was $5203 (95% CI: 75, 10,424) compared to usual care. No overall survival differences were observed, but outcome heterogeneity was present. POG patients receiving WGTA-informed treatment experienced incremental survival gains of 2.49 months (95% CI: 1.32, 3.64). Future cost consequences became favorable as WGTA cost drivers declined and WGTA-informed treatment rates improved to 60%. Our study demonstrates the ability of real-world data to support evaluations of only-in-research health technologies. We identify situations where precision oncology research initiatives may produce survival benefit at a cost that is within healthcare systems' willingness to pay. This economic evidence informs the early-stage healthcare impacts of precision oncology research., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Published
2022
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43. The Drivers, Mechanisms, and Consequences of Genome Instability in HPV-Driven Cancers.

Author

Porter VL and Marra MA

Abstract

Human papillomavirus (HPV) is the causative driver of cervical cancer and a contributing risk factor of head and neck cancer and several anogenital cancers. HPV's ability to induce genome instability contributes to its oncogenicity. HPV genes can induce genome instability in several ways, including modulating the cell cycle to favour proliferation, interacting with DNA damage repair pathways to bring high-fidelity repair pathways to viral episomes and away from the host genome, inducing DNA-damaging oxidative stress, and altering the length of telomeres. In addition, the presence of a chronic viral infection can lead to immune responses that also cause genome instability of the infected tissue. The HPV genome can become integrated into the host genome during HPV-induced tumorigenesis. Viral integration requires double-stranded breaks on the DNA; therefore, regions around the integration event are prone to structural alterations and themselves are targets of genome instability. In this review, we present the mechanisms by which HPV-dependent and -independent genome instability is initiated and maintained in HPV-driven cancers, both across the genome and at regions of HPV integration.

Published
2022
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44. TMBur: a distributable tumor mutation burden approach for whole genome sequencing.

Author

Titmuss E, Corbett RD, Davidson S, Abbasi S, Williamson LM, Pleasance ED, Shlien A, Renouf DJ, Jones SJM, Laskin J, and Marra MA

Subjects
Humans, Kaplan-Meier Estimate, Microsatellite Instability, Microsatellite Repeats genetics, Neoplasms metabolism, Neoplasms therapy, Patient Selection, Proportional Hazards Models, Reproducibility of Results, Biomarkers, Tumor genetics, Mutation, Neoplasms genetics, Whole Genome Sequencing methods
Abstract

Background: Tumor mutation burden (TMB) is a key characteristic used in a tumor-type agnostic context to inform the use of immune checkpoint inhibitors (ICI). Accurate and consistent measurement of TMB is crucial as it can significantly impact patient selection for therapy and clinical trials, with a threshold of 10 mutations/Mb commonly used as an inclusion criterion. Studies have shown that the most significant contributor to variability in mutation counts in whole genome sequence (WGS) data is differences in analysis methods, even more than differences in extraction or library construction methods. Therefore, tools for improving consistency in whole genome TMB estimation are of clinical importance., Methods: We developed a distributable TMB analysis suite, TMBur, to address the need for genomic TMB estimate consistency in projects that span jurisdictions. TMBur is implemented in Nextflow and performs all analysis steps to generate TMB estimates directly from fastq files, incorporating somatic variant calling with Manta, Strelka2, and Mutect2, and microsatellite instability profiling with MSISensor. These tools are provided in a Singularity container downloaded by the workflow at runtime, allowing the entire workflow to be run identically on most computing platforms. To test the reproducibility of TMBur TMB estimates, we performed replicate runs on WGS data derived from the COLO829 and COLO829BL cell lines at multiple research centres. The clinical value of derived TMB estimates was then evaluated using a cohort of 90 patients with advanced, metastatic cancer that received ICIs following WGS analysis. Patients were split into groups based on a threshold of 10/Mb, and time to progression from initiation of ICIs was examined using Kaplan-Meier and cox-proportional hazards analyses., Results: TMBur produced identical TMB estimates across replicates and at multiple analysis centres. The clinical utility of TMBur-derived TMB estimates were validated, with a genomic TMB ≥ 10/Mb demonstrating improved time to progression, even after correcting for differences in tumor type (HR = 0.39, p = 0.012)., Conclusions: TMBur, a shareable workflow, generates consistent whole genome derived TMB estimates predictive of response to ICIs across multiple analysis centres. Reproducible TMB estimates from this approach can improve collaboration and ensure equitable treatment and clinical trial access spanning jurisdictions., (© 2022. The Author(s).)

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2022
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45. Whole-genome and transcriptome analysis enhances precision cancer treatment options.

Author

Pleasance E, Bohm A, Williamson LM, Nelson JMT, Shen Y, Bonakdar M, Titmuss E, Csizmok V, Wee K, Hosseinzadeh S, Grisdale CJ, Reisle C, Taylor GA, Lewis E, Jones MR, Bleile D, Sadeghi S, Zhang W, Davies A, Pellegrini B, Wong T, Bowlby R, Chan SK, Mungall KL, Chuah E, Mungall AJ, Moore RA, Zhao Y, Deol B, Fisic A, Fok A, Regier DA, Weymann D, Schaeffer DF, Young S, Yip S, Schrader K, Levasseur N, Taylor SK, Feng X, Tinker A, Savage KJ, Chia S, Gelmon K, Sun S, Lim H, Renouf DJ, Jones SJM, Marra MA, and Laskin J

Subjects
Gene Expression Profiling, Genomics methods, Humans, Mutation, Precision Medicine methods, RNA, Transcriptome, Neoplasms drug therapy, Neoplasms genetics
Abstract

Background: Recent advances are enabling delivery of precision genomic medicine to cancer clinics. While the majority of approaches profile panels of selected genes or hotspot regions, comprehensive data provided by whole-genome and transcriptome sequencing and analysis (WGTA) present an opportunity to align a much larger proportion of patients to therapies., Patients and Methods: Samples from 570 patients with advanced or metastatic cancer of diverse types enrolled in the Personalized OncoGenomics (POG) program underwent WGTA. DNA-based data, including mutations, copy number and mutation signatures, were combined with RNA-based data, including gene expression and fusions, to generate comprehensive WGTA profiles. A multidisciplinary molecular tumour board used WGTA profiles to identify and prioritize clinically actionable alterations and inform therapy. Patient responses to WGTA-informed therapies were collected., Results: Clinically actionable targets were identified for 83% of patients, of which 37% of patients received WGTA-informed treatments. RNA expression data were particularly informative, contributing to 67% of WGTA-informed treatments; 25% of treatments were informed by RNA expression alone. Of a total 248 WGTA-informed treatments, 46% resulted in clinical benefit. RNA expression data were comparable to DNA-based mutation and copy number data in aligning to clinically beneficial treatments. Genome signatures also guided therapeutics including platinum, poly-ADP ribose polymerase inhibitors and immunotherapies. Patients accessed WGTA-informed treatments through clinical trials (19%), off-label use (35%) and as standard therapies (46%) including those which would not otherwise have been the next choice of therapy, demonstrating the utility of genomic information to direct use of chemotherapies as well as targeted therapies., Conclusions: Integrating RNA expression and genome data illuminated treatment options that resulted in 46% of treated patients experiencing positive clinical benefit, supporting the use of comprehensive WGTA profiling in clinical cancer care., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Published
2022
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46. Automated Library Construction and Analysis for High-Throughput Nanopore Sequencing of SARS-CoV-2.

Author

Coope RJN, Matic N, Pandoh PK, Corbett RD, Smailus DE, Pleasance S, Lowe CF, Ritchie G, Chorlton SD, Young M, Ally AA, Asano JK, Carlsen RE, Chahal SS, Zhao Y, Holmes DT, Romney MG, Jones SJM, and Marra MA

Subjects
Humans, Reproducibility of Results, SARS-CoV-2 genetics, COVID-19 diagnosis, COVID-19 epidemiology, Nanopore Sequencing, Nanopores
Abstract

Background: To support the implementation of high-throughput pipelines suitable for SARS-CoV-2 sequencing and analysis in a clinical laboratory, we developed an automated sample preparation and analysis workflow., Methods: We used the established ARTIC protocol with approximately 400 bp amplicons sequenced on Oxford Nanopore's MinION. Sequences were analyzed using Nextclade, assigning both a clade and quality score to each sample., Results: A total of 2179 samples on twenty-five 96-well plates were sequenced. Plates of purified RNA were processed within 12 h, sequencing required up to 24 h, and analysis of each pooled plate required 1 h. The use of samples with known threshold cycle (Ct) values enabled normalization, acted as a quality control check, and revealed a strong correlation between sample Ct values and successful analysis, with 85% of samples with Ct < 30 achieving a "good" Nextclade score. Less abundant samples responded to enrichment with the fraction of Ct > 30 samples achieving a "good" classification rising by 60% after addition of a post-ARTIC PCR normalization. Serial dilutions of 3 variant of concern samples, diluted from approximately Ct = 16 to approximately Ct = 50, demonstrated successful sequencing to Ct = 37. The sample set contained a median of 24 mutations per sample and a total of 1281 unique mutations with reduced sequence read coverage noted in some regions of some samples. A total of 10 separate strains were observed in the sample set, including 3 variants of concern prevalent in British Columbia in the spring of 2021., Conclusions: We demonstrated a robust automated sequencing pipeline that takes advantage of input Ct values to improve reliability., Competing Interests: Authors’ Disclosures or Potential Conflicts of Interest: Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts of interest: Employment or Leadership: None declared. Consultant or Advisory Role: None declared. Stock Ownership: S.D. Chorlton, PathoGene Inc., BugSeq Bioinformatics Inc. Honoraria: None declared. Research Funding: Researchers from the St. Paul’s Hospital Department of Pathology and Laboratory Medicine have received institutional support from the Public Health Agency of Canada’s COVID-19 Immunity Task Force Hot Spots Competitive Grant, 2021-HQ-000120 (M.G. Romney, N. Matic, G. Ritchie, C.F. Lowe, M. Young), Genome BC COVID-19 rapid response grant (M.G. Romney, N. Matic, G. Ritchie, C.F. Lowe, M. Young), Genome BC Rapid SARS-CoV-2 Vaccine Research Initiative in BC (M.G. Romney and C.F. Lowe), Providence Health Care Research Institute (N. Matic), and Canada Foundation for Innovation Exceptional Opportunities Fund–COVID-19 (C.F. Lowe). M.A. Marra, S. Jones, D.E. Smailus, R. Corbett, S. Pleasance, S. Chahal, R.E. Carlsen, R.J.N. Coope, P.K. Pandoh, J. Asano, A. Ally, Y. Zhao, and Canada’s Michael Smith Genome Sciences Centre, have also received institutional support from Genome BC (COV-088), Genome Canada (262SEQ), and the Canada Foundation for Innovation and BC Knowledge Development Fund (#40991). S.D. Chorlton has received funding from the Open Philanthropy Project. Expert Testimony: None declared. Patents: None declared. Other Remuneration: D. Holmes has received travel reimbursements from AACC and MSACL., (© American Association for Clinical Chemistry 2022. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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2022
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47. Failure of human rhombic lip differentiation underlies medulloblastoma formation.

Author

Hendrikse LD, Haldipur P, Saulnier O, Millman J, Sjoboen AH, Erickson AW, Ong W, Gordon V, Coudière-Morrison L, Mercier AL, Shokouhian M, Suárez RA, Ly M, Borlase S, Scott DS, Vladoiu MC, Farooq H, Sirbu O, Nakashima T, Nambu S, Funakoshi Y, Bahcheli A, Diaz-Mejia JJ, Golser J, Bach K, Phuong-Bao T, Skowron P, Wang EY, Kumar SA, Balin P, Visvanathan A, Lee JJY, Ayoub R, Chen X, Chen X, Mungall KL, Luu B, Bérubé P, Wang YC, Pfister SM, Kim SK, Delattre O, Bourdeaut F, Doz F, Masliah-Planchon J, Grajkowska WA, Loukides J, Dirks P, Fèvre-Montange M, Jouvet A, French PJ, Kros JM, Zitterbart K, Bailey SD, Eberhart CG, Rao AAN, Giannini C, Olson JM, Garami M, Hauser P, Phillips JJ, Ra YS, de Torres C, Mora J, Li KKW, Ng HK, Poon WS, Pollack IF, López-Aguilar E, Gillespie GY, Van Meter TE, Shofuda T, Vibhakar R, Thompson RC, Cooper MK, Rubin JB, Kumabe T, Jung S, Lach B, Iolascon A, Ferrucci V, de Antonellis P, Zollo M, Cinalli G, Robinson S, Stearns DS, Van Meir EG, Porrati P, Finocchiaro G, Massimino M, Carlotti CG, Faria CC, Roussel MF, Boop F, Chan JA, Aldinger KA, Razavi F, Silvestri E, McLendon RE, Thompson EM, Ansari M, Garre ML, Chico F, Eguía P, Pérezpeña M, Morrissy AS, Cavalli FMG, Wu X, Daniels C, Rich JN, Jones SJM, Moore RA, Marra MA, Huang X, Reimand J, Sorensen PH, Wechsler-Reya RJ, Weiss WA, Pugh TJ, Garzia L, Kleinman CL, Stein LD, Jabado N, Malkin D, Ayrault O, Golden JA, Ellison DW, Doble B, Ramaswamy V, Werbowetski-Ogilvie TE, Suzuki H, Millen KJ, and Taylor MD

Subjects
Cell Lineage, Cerebellum embryology, Cerebellum pathology, Core Binding Factor alpha Subunits genetics, Hedgehog Proteins metabolism, Histone Demethylases, Humans, Ki-67 Antigen metabolism, Muscle Proteins, Mutation, Otx Transcription Factors deficiency, Otx Transcription Factors genetics, Repressor Proteins, T-Box Domain Proteins metabolism, Transcription Factors, Cell Differentiation genetics, Cerebellar Neoplasms classification, Cerebellar Neoplasms genetics, Cerebellar Neoplasms pathology, Medulloblastoma classification, Medulloblastoma genetics, Medulloblastoma pathology, Metencephalon embryology, Metencephalon pathology
Abstract

Medulloblastoma (MB) comprises a group of heterogeneous paediatric embryonal neoplasms of the hindbrain with strong links to early development of the hindbrain 1-4 . Mutations that activate Sonic hedgehog signalling lead to Sonic hedgehog MB in the upper rhombic lip (RL) granule cell lineage 5-8 . By contrast, mutations that activate WNT signalling lead to WNT MB in the lower RL 9,10 . However, little is known about the more commonly occurring group 4 (G4) MB, which is thought to arise in the unipolar brush cell lineage 3,4 . Here we demonstrate that somatic mutations that cause G4 MB converge on the core binding factor alpha (CBFA) complex and mutually exclusive alterations that affect CBFA2T2, CBFA2T3, PRDM6, UTX and OTX2. CBFA2T2 is expressed early in the progenitor cells of the cerebellar RL subventricular zone in Homo sapiens, and G4 MB transcriptionally resembles these progenitors but are stalled in developmental time. Knockdown of OTX2 in model systems relieves this differentiation blockade, which allows MB cells to spontaneously proceed along normal developmental differentiation trajectories. The specific nature of the split human RL, which is destined to generate most of the neurons in the human brain, and its high level of susceptible EOMES + KI67 + unipolar brush cell progenitor cells probably predisposes our species to the development of G4 MB., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

Published
2022
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48. Genome-wide detection of imprinted differentially methylated regions using nanopore sequencing.

Author

Akbari V, Garant JM, O'Neill K, Pandoh P, Moore R, Marra MA, Hirst M, and Jones SJM

Subjects
Alleles, Animals, DNA Methylation, Female, Germ Cells, Mammals genetics, Mice, Pregnancy, Genomic Imprinting, Nanopore Sequencing
Abstract

Imprinting is a critical part of normal embryonic development in mammals, controlled by defined parent-of-origin (PofO) differentially methylated regions (DMRs) known as imprinting control regions. Direct nanopore sequencing of DNA provides a means to detect allelic methylation and to overcome the drawbacks of methylation array and short-read technologies. Here, we used publicly available nanopore sequencing data for 12 standard B-lymphocyte cell lines to acquire the genome-wide mapping of imprinted intervals in humans. Using the sequencing data, we were able to phase 95% of the human methylome and detect 94% of the previously well-characterized, imprinted DMRs. In addition, we found 42 novel imprinted DMRs (16 germline and 26 somatic), which were confirmed using whole-genome bisulfite sequencing (WGBS) data. Analysis of WGBS data in mouse ( Mus musculus ), rhesus monkey ( Macaca mulatta ), and chimpanzee ( Pan troglodytes ) suggested that 17 of these imprinted DMRs are conserved. Some of the novel imprinted intervals are within or close to imprinted genes without a known DMR. We also detected subtle parental methylation bias, spanning several kilobases at seven known imprinted clusters. At these blocks, hypermethylation occurs at the gene body of expressed allele(s) with mutually exclusive H3K36me3 and H3K27me3 allelic histone marks. These results expand upon our current knowledge of imprinting and the potential of nanopore sequencing to identify imprinting regions using only parent-offspring trios, as opposed to the large multi-generational pedigrees that have previously been required., Competing Interests: VA, JG, KO, PP, RM, MM, MH, SJ No competing interests declared, (© 2022, Akbari et al.)

Published
2022
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49. The impact of whole genome and transcriptome analysis (WGTA) on predictive biomarker discovery and diagnostic accuracy of advanced malignancies.

Author

Tessier-Cloutier B, Grewal JK, Jones MR, Pleasance E, Shen Y, Cai E, Dunham C, Hoang L, Horst B, Huntsman DG, Ionescu D, Karnezis AN, Lee AF, Lee CH, Lee TH, Twa DD, Mungall AJ, Mungall K, Naso JR, Ng T, Schaeffer DF, Sheffield BS, Skinnider B, Smith T, Williamson L, Zhong E, Regier DA, Laskin J, Marra MA, Gilks CB, Jones SJ, and Yip S

Subjects
Algorithms, Biomarkers, Tumor genetics, Gene Expression Profiling, Humans, Neoplasms diagnosis, Neoplasms drug therapy, Neoplasms genetics
Abstract

In this study, we evaluate the impact of whole genome and transcriptome analysis (WGTA) on predictive molecular profiling and histologic diagnosis in a cohort of advanced malignancies. WGTA was used to generate reports including molecular alterations and site/tissue of origin prediction. Two reviewers analyzed genomic reports, clinical history, and tumor pathology. We used National Comprehensive Cancer Network (NCCN) consensus guidelines, Food and Drug Administration (FDA) approvals, and provincially reimbursed treatments to define genomic biomarkers associated with approved targeted therapeutic options (TTOs). Tumor tissue/site of origin was reassessed for most cases using genomic analysis, including a machine learning algorithm (Supervised Cancer Origin Prediction Using Expression [SCOPE]) trained on The Cancer Genome Atlas data. WGTA was performed on 652 cases, including a range of primary tumor types/tumor sites and 15 malignant tumors of uncertain histogenesis (MTUH). At the time WGTA was performed, alterations associated with an approved TTO were identified in 39 (6%) cases; 3 of these were not identified through routine pathology workup. In seven (1%) cases, the pathology workup either failed, was not performed, or gave a different result from the WGTA. Approved TTOs identified by WGTA increased to 103 (16%) when applying 2021 guidelines. The histopathologic diagnosis was reviewed in 389 cases and agreed with the diagnostic consensus after WGTA in 94% of non-MTUH cases (n = 374). The remainder included situations where the morphologic diagnosis was changed based on WGTA and clinical data (0.5%), or where the WGTA was non-contributory (5%). The 15 MTUH were all diagnosed as specific tumor types by WGTA. Tumor board reviews including WGTA agreed with almost all initial predictive molecular profile and histopathologic diagnoses. WGTA was a powerful tool to assign site/tissue of origin in MTUH. Current efforts focus on improving therapeutic predictive power and decreasing cost to enhance use of WGTA data as a routine clinical test., (© 2022 The Authors. The Journal of Pathology: Clinical Research published by The Pathological Society of Great Britain and Ireland & John Wiley & Sons, Ltd.)

Published
2022
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50. The Neoantigen Landscape of the Coding and Noncoding Cancer Genome Space.

Author

Lau TTY, Sefid Dashti ZJ, Titmuss E, Pender A, Topham JT, Bridgers J, Loree JM, Feng X, Pleasance ED, Renouf DJ, Schrader KA, Sun S, Ho C, Marra MA, Laskin J, and Karsan A

Subjects
Biomarkers, Tumor, Humans, Immunotherapy, Mutation, Exome Sequencing, Antigens, Neoplasm genetics, Neoplasms genetics, Neoplasms therapy
Abstract

Tumor mutation burden (TMB) is a measure to predict patient responsiveness to immune checkpoint immunotherapy because with increased mutation frequency, the likelihood of a greater neoantigen burden is increased. Although neoantigen prediction tools exist, tumor neoantigen burden has not been adopted as a measure to predict immunotherapy response. With both measures, current guidelines are limited to the coding regions, but ectopic expression of sequences in the noncoding space may potentially be a source of neoantigens. A pan-cancer cohort of 574 advanced disease stage patients with whole genome and transcriptome sequencing was analyzed to report mutation burden and neoantigen counts within the coding and noncoding regions. The efficacy of tumor neoantigen burden, reported as tumor neoantigen count (TNC), including neoantigens derived from the expression of noncoding regions, compared with TMB as a predictor of response to immunotherapy for 80 patients who had received treatment, was evaluated. TMB was found to be the best predictor of response to immunotherapy, whereas expression-derived TNC from the noncoding regions did not improve prediction of response. Therefore, there is minimal benefit in extending the calculation of TNC to the noncoding space for the purposes of predicting response. However, it is likely that there is a wealth of neoantigens derived from the noncoding space that may impact patient outcomes and treatments., (Copyright © 2022 Association for Molecular Pathology and American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.)

Published
2022
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