Search

Your search keyword '"Kimes, Patrick K."' showing total 27 results
Start Over Author "Kimes, Patrick K."
27 results on '"Kimes, Patrick K."'

1. Whole-body CD8+ T cell visualization before and during cancer immunotherapy: a phase 1/2 trial

Author

Kist de Ruijter, Laura, van de Donk, Pim P., Hooiveld-Noeken, Jahlisa S., Giesen, Danique, Elias, Sjoerd G., Lub-de Hooge, Marjolijn N., Oosting, Sjoukje F., Jalving, Mathilde, Timens, Wim, Brouwers, Adrienne H., Kwee, Thomas C., Gietema, Jourik A., Fehrmann, Rudolf S. N., Fine, Bernard M., Sanabria Bohórquez, Sandra M., Yadav, Mahesh, Koeppen, Hartmut, Jing, Jing, Guelman, Sebastian, Lin, Mark T., Mamounas, Michael J., Eastham, Jeffrey Ryan, Kimes, Patrick K., Williams, Simon P., Ungewickell, Alexander, de Groot, Derk J. A., and de Vries, Elisabeth G. E.

Published
2022
Full Text
View/download PDF

3. The Integrated Genomic Landscape of Thymic Epithelial Tumors

Author

Radovich, Milan, Pickering, Curtis R, Felau, Ina, Ha, Gavin, Zhang, Hailei, Jo, Heejoon, Hoadley, Katherine A, Anur, Pavana, Zhang, Jiexin, McLellan, Mike, Bowlby, Reanne, Matthew, Thomas, Danilova, Ludmila, Hegde, Apurva M, Kim, Jaegil, Leiserson, Mark DM, Sethi, Geetika, Lu, Charles, Ryan, Michael, Su, Xiaoping, Cherniack, Andrew D, Robertson, Gordon, Akbani, Rehan, Spellman, Paul, Weinstein, John N, Hayes, D Neil, Raphael, Ben, Lichtenberg, Tara, Leraas, Kristen, Zenklusen, Jean Claude, Network, The Cancer Genome Atlas, Ally, Adrian, Appelbaum, Elizabeth L, Auman, J Todd, Balasundaram, Miruna, Balu, Saianand, Behera, Madhusmita, Beroukhim, Rameen, Berrios, Mario, Blandino, Giovanni, Bodenheimer, Tom, Bootwalla, Moiz S, Bowen, Jay, Brooks, Denise, Carcano, Flavio M, Carlsen, Rebecca, Carvalho, Andre L, Castro, Patricia, Chalabreysse, Lara, Chin, Lynda, Cho, Juok, Choe, Gina, Chuah, Eric, Chudamani, Sudha, Cibulskis, Carrie, Cope, Leslie, Cordes, Matthew G, Crain, Daniel, Curley, Erin, Defreitas, Timothy, Demchok, John A, Detterbeck, Frank, Dhalla, Noreen, Dienemann, Hendrik, Edenfield, W Jeff, Facciolo, Francesco, Ferguson, Martin L, Frazer, Scott, Fronick, Catrina C, Fulton, Lucinda A, Fulton, Robert S, Gabriel, Stacey B, Gardner, Johanna, Gastier-Foster, Julie M, Gehlenborg, Nils, Gerken, Mark, Getz, Gad, Heiman, David I, Hobensack, Shital, Holbrook, Andrea, Holt, Robert A, Hoyle, Alan P, Hutter, Carolyn M, Ittmann, Michael, Jefferys, Stuart R, Jones, Corbin D, Jones, Steven JM, Kasaian, Katayoon, Kimes, Patrick K, Lai, Phillip H, Laird, Peter W, Lawrence, Michael S, Lin, Pei, Liu, Jia, Lolla, Laxmi, Lu, Yiling, Ma, Yussanne, Maglinte, Dennis T, and Mallery, David

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Autoimmune Disease, Biotechnology, Human Genome, Cancer, Rare Diseases, 2.1 Biological and endogenous factors, Aetiology, Adolescent, Adult, Aged, Aged, 80 and over, Female, Genomics, Humans, Male, Middle Aged, Mutation, Neoplasms, Glandular and Epithelial, Thymoma, Thymus Neoplasms, Transcription Factors, TFII, Young Adult, Cancer Genome Atlas Network, TCGA, autoimmunity, genomics, myasthenia gravis, proteomics, thymic carcinoma, thymic epithelial tumors, thymoma, transcriptomics, Neurosciences, Oncology and Carcinogenesis, Oncology & Carcinogenesis, Biochemistry and cell biology, Oncology and carcinogenesis
Abstract

Thymic epithelial tumors (TETs) are one of the rarest adult malignancies. Among TETs, thymoma is the most predominant, characterized by a unique association with autoimmune diseases, followed by thymic carcinoma, which is less common but more clinically aggressive. Using multi-platform omics analyses on 117 TETs, we define four subtypes of these tumors defined by genomic hallmarks and an association with survival and World Health Organization histological subtype. We further demonstrate a marked prevalence of a thymoma-specific mutated oncogene, GTF2I, and explore its biological effects on multi-platform analysis. We further observe enrichment of mutations in HRAS, NRAS, and TP53. Last, we identify a molecular link between thymoma and the autoimmune disease myasthenia gravis, characterized by tumoral overexpression of muscle autoantigens, and increased aneuploidy.

Published
2018

4. Integrative Analysis Identifies Four Molecular and Clinical Subsets in Uveal Melanoma

Author

Robertson, A Gordon, Shih, Juliann, Yau, Christina, Gibb, Ewan A, Oba, Junna, Mungall, Karen L, Hess, Julian M, Uzunangelov, Vladislav, Walter, Vonn, Danilova, Ludmila, Lichtenberg, Tara M, Kucherlapati, Melanie, Kimes, Patrick K, Tang, Ming, Penson, Alexander, Babur, Ozgun, Akbani, Rehan, Bristow, Christopher A, Hoadley, Katherine A, Iype, Lisa, Chang, Matthew T, Network, TCGA Research, Abdel-Rahman, Mohamed H, Ally, Adrian, Auman, J Todd, Balasundaram, Miruna, Balu, Saianand, Benz, Christopher, Beroukhim, Rameen, Birol, Inanc, Bodenheimer, Tom, Bowen, Jay, Bowlby, Reanne, Brooks, Denise, Carlsen, Rebecca, Cebulla, Colleen M, Cherniack, Andrew D, Chin, Lynda, Cho, Juok, Chuah, Eric, Chudamani, Sudha, Cibulskis, Carrie, Cibulskis, Kristian, Cope, Leslie, Coupland, Sarah E, Defreitas, Timothy, Demchok, John A, Desjardins, Laurence, Dhalla, Noreen, Esmaeli, Bita, Felau, Ina, Ferguson, Martin L, Frazer, Scott, Gabriel, Stacey B, Gastier-Foster, Julie M, Gehlenborg, Nils, Gerken, Mark, Gershenwald, Jeffrey E, Getz, Gad, Griewank, Klaus G, Grimm, Elizabeth A, Hayes, D Neil, Hegde, Apurva M, Heiman, David I, Helsel, Carmen, Hobensack, Shital, Holt, Robert A, Hoyle, Alan P, Hu, Xin, Hutter, Carolyn M, Jager, Martine J, Jefferys, Stuart R, Jones, Corbin D, Jones, Steven JM, Kandoth, Cyriac, Kasaian, Katayoon, Kim, Jaegil, Kucherlapati, Raju, Lander, Eric, Lawrence, Michael S, Lazar, Alexander J, Lee, Semin, Leraas, Kristen M, Lin, Pei, Liu, Jia, Liu, Wenbin, Lolla, Laxmi, and Lu, Yiling

Subjects
Eye Disease and Disorders of Vision, Cancer, Human Genome, Rare Diseases, Genetics, Biomarkers, Tumor, DNA Copy Number Variations, DNA Methylation, Eukaryotic Initiation Factor-1, Gene Expression Regulation, Neoplastic, Humans, Melanoma, Monosomy, Mutation, Phosphoproteins, Prognosis, RNA Splicing Factors, Serine-Arginine Splicing Factors, Tumor Suppressor Proteins, Ubiquitin Thiolesterase, Uveal Neoplasms, TCGA Research Network, EIF1AX, GNA11, GNAQ, SF3B1, SRSF2, TCGA, molecular subtypes, monosomy 3, noncoding RNA, uveal melanoma, Neurosciences, Oncology and Carcinogenesis, Oncology & Carcinogenesis
Abstract

Comprehensive multiplatform analysis of 80 uveal melanomas (UM) identifies four molecularly distinct, clinically relevant subtypes: two associated with poor-prognosis monosomy 3 (M3) and two with better-prognosis disomy 3 (D3). We show that BAP1 loss follows M3 occurrence and correlates with a global DNA methylation state that is distinct from D3-UM. Poor-prognosis M3-UM divide into subsets with divergent genomic aberrations, transcriptional features, and clinical outcomes. We report change-of-function SRSF2 mutations. Within D3-UM, EIF1AX- and SRSF2/SF3B1-mutant tumors have distinct somatic copy number alterations and DNA methylation profiles, providing insight into the biology of these low- versus intermediate-risk clinical mutation subtypes.

Published
2017

5. Large-Margin Classification with Multiple Decision Rules

Author

Kimes, Patrick K., Hayes, D. Neil, Marron, J. S., and Liu, Yufeng

Subjects
Statistics - Machine Learning, Computer Science - Learning
Abstract

Binary classification is a common statistical learning problem in which a model is estimated on a set of covariates for some outcome indicating the membership of one of two classes. In the literature, there exists a distinction between hard and soft classification. In soft classification, the conditional class probability is modeled as a function of the covariates. In contrast, hard classification methods only target the optimal prediction boundary. While hard and soft classification methods have been studied extensively, not much work has been done to compare the actual tasks of hard and soft classification. In this paper we propose a spectrum of statistical learning problems which span the hard and soft classification tasks based on fitting multiple decision rules to the data. By doing so, we reveal a novel collection of learning tasks of increasing complexity. We study the problems using the framework of large-margin classifiers and a class of piecewise linear convex surrogates, for which we derive statistical properties and a corresponding sub-gradient descent algorithm. We conclude by applying our approach to simulation settings and a magnetic resonance imaging (MRI) dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study.

Published
2014

6. Statistical Significance for Hierarchical Clustering

Author

Kimes, Patrick K., Liu, Yufeng, Hayes, D. Neil, and Marron, J. S.

Subjects
Statistics - Methodology
Abstract

Cluster analysis has proved to be an invaluable tool for the exploratory and unsupervised analysis of high dimensional datasets. Among methods for clustering, hierarchical approaches have enjoyed substantial popularity in genomics and other fields for their ability to simultaneously uncover multiple layers of clustering structure. A critical and challenging question in cluster analysis is whether the identified clusters represent important underlying structure or are artifacts of natural sampling variation. Few approaches have been proposed for addressing this problem in the context of hierarchical clustering, for which the problem is further complicated by the natural tree structure of the partition, and the multiplicity of tests required to parse the layers of nested clusters. In this paper, we propose a Monte Carlo based approach for testing statistical significance in hierarchical clustering which addresses these issues. The approach is implemented as a sequential testing procedure guaranteeing control of the family-wise error rate. Theoretical justification is provided for our approach, and its power to detect true clustering structure is illustrated through several simulation studies and applications to two cancer gene expression datasets.

Published
2014

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

Author

Lawrence, Michael S, Sougnez, Carrie, Lichtenstein, Lee, Cibulskis, Kristian, Lander, Eric, Gabriel, Stacey B, Getz, Gad, Ally, Adrian, Balasundaram, Miruna, Birol, Inanc, Bowlby, Reanne, Brooks, Denise, Butterfield, Yaron SN, Carlsen, Rebecca, Cheng, Dean, Chu, Andy, Dhalla, Noreen, Guin, Ranabir, Holt, Robert A, Jones, Steven JM, Lee, Darlene, Li, Haiyan I, Marra, Marco A, Mayo, Michael, Moore, Richard A, Mungall, Andrew J, Gordon Robertson, A, Schein, Jacqueline E, Sipahimalani, Payal, Tam, Angela, Thiessen, Nina, Wong, Tina, Protopopov, Alexei, Santoso, Netty, Lee, Semin, Parfenov, Michael, Zhang, Jianhua, Mahadeshwar, Harshad S, Tang, Jiabin, Ren, Xiaojia, Seth, Sahil, Haseley, Psalm, Zeng, Dong, Yang, Lixing, Xu, Andrew W, Song, Xingzhi, Pantazi, Angeliki, Bristow, Christopher A, Hadjipanayis, Angela, Seidman, Jonathan, Chin, Lynda, Park, Peter J, Kucherlapati, Raju, Akbani, Rehan, Casasent, Tod, Liu, Wenbin, Lu, Yiling, Mills, Gordon, Motter, Thomas, Weinstein, John, Diao, Lixia, Wang, Jing, Hong Fan, You, Liu, Jinze, Wang, Kai, Todd Auman, J, Balu, Saianand, Bodenheimer, Thomas, Buda, Elizabeth, Neil Hayes, D, Hoadley, Katherine A, Hoyle, Alan P, Jefferys, Stuart R, Jones, Corbin D, Kimes, Patrick K, Liu, Yufeng, Marron, JS, Meng, Shaowu, Mieczkowski, Piotr A, Mose, Lisle E, Parker, Joel S, Perou, Charles M, Prins, Jan F, Roach, Jeffrey, Shi, Yan, Simons, Janae V, Singh, Darshan, Soloway, Matthew G, Tan, Donghui, Veluvolu, Umadevi, Walter, Vonn, Waring, Scot, Wilkerson, Matthew D, Wu, Junyuan, Zhao, Ni, Cherniack, Andrew D, Hammerman, Peter S, Tward, Aaron D, Sekhar Pedamallu, Chandra, and Saksena, Gordon

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Oncology and Carcinogenesis, Human Genome, Biotechnology, Dental/Oral and Craniofacial Disease, Cancer, 2.1 Biological and endogenous factors, Aetiology, Good Health and Well Being, Carcinoma, Squamous Cell, DNA Copy Number Variations, DNA, Neoplasm, Female, Gene Expression Regulation, Neoplastic, Genome, Human, Genomics, Head and Neck Neoplasms, Humans, Male, Molecular Targeted Therapy, Mutation, Oncogenes, RNA, Neoplasm, Signal Transduction, Squamous Cell Carcinoma of Head and Neck, Transcription Factors, Cancer Genome Atlas Network, General Science & Technology
Abstract

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

12. Discovery of antibodies and cognate surface targets for ovarian cancer by surface profiling.

Author

Schröfelbauer, Bärbel, Kimes, Patrick K., Hauke, Paige, Reid, Charlotte E., Shao, Kevin, Hill, Sarah J., Irizarry, Rafael, and Hahn, William C.

Subjects
*OVARIAN cancer, *IMMUNOGLOBULINS, *MASS spectrometry, *FLOW cytometry, *MEDICAL screening, *LABOR mobility
Abstract

Although antibodies targeting specific tumor-expressed antigens are the standard of care for some cancers, the identification of cancer-specific targets amenable to antibody binding has remained a bottleneck in development of new therapeutics. To overcome this challenge, we developed a high-throughput platform that allows for the unbiased, simultaneous discovery of antibodies and targets based on phenotypic binding profiles. Applying this platform to ovarian cancer, we identified a wide diversity of cancer targets including receptor tyrosine kinases, adhesion and migration proteins, proteases and proteins regulating angiogenesis in a single round of screening using genomics, flow cytometry, and mass spectrometry. In particular, we identified BCAM as a promising candidate for targeted therapy in high-grade serous ovarian cancers. More generally, this approach provides a rapid and flexible framework to identify cancer targets and antibodies. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

13. Abstract LB037: 89ZED88082A PET imaging to visualize CD8+ T cells in patients with cancer treated with immune checkpoint inhibitor

Author

de Ruijter, Laura Kist, primary, van de Donk, Pim P., additional, Hooiveld-Noeken, Jahlisa S., additional, Giesen, Danique, additional, Ungewickell, Alexander, additional, Fine, Bernard M., additional, Williams, Simon P., additional, Bohorquez, Sandra M. Sanabria, additional, Yadav, Mahesh, additional, Koeppen, Hartmut, additional, Jing, Jing, additional, Guelman, Sebastian, additional, Lin, Mark T., additional, Mamounas, Michael J., additional, Eastham, Jeffrey, additional, Kimes, Patrick K., additional, Glaudemans, Andor W., additional, Brouwers, Adrienne H., additional, Lub-de Hooge, Marjolijn N., additional, Gietema, Jourik A., additional, Schröder, Carolina P., additional, Timens, Wim, additional, Jalving, Mathilde, additional, Elias, Sjoerd, additional, Oosting, Sjoukje F., additional, de Groot, Derk J., additional, and de Vries, Elisabeth G., additional

Published
2021
Full Text
View/download PDF

14. Whole-body CD8+T cell visualization before and during cancer immunotherapy: a phase 1/2 trial

Author

Kist de Ruijter, Laura, van de Donk, Pim P., Hooiveld-Noeken, Jahlisa S., Giesen, Danique, Elias, Sjoerd G., Lub-de Hooge, Marjolijn N., Oosting, Sjoukje F., Jalving, Mathilde, Timens, Wim, Brouwers, Adrienne H., Kwee, Thomas C., Gietema, Jourik A., Fehrmann, Rudolf S. N., Fine, Bernard M., Sanabria Bohórquez, Sandra M., Yadav, Mahesh, Koeppen, Hartmut, Jing, Jing, Guelman, Sebastian, Lin, Mark T., Mamounas, Michael J., Eastham, Jeffrey Ryan, Kimes, Patrick K., Williams, Simon P., Ungewickell, Alexander, de Groot, Derk J. A., and de Vries, Elisabeth G. E.

Abstract

Immune checkpoint inhibitors (ICIs), by reinvigorating CD8+T cell mediated immunity, have revolutionized cancer therapy. Yet, the systemic CD8+T cell distribution, a potential biomarker of ICI response, remains poorly characterized. We assessed safety, imaging dose and timing, pharmacokinetics and immunogenicity of zirconium-89-labeled, CD8-specific, one-armed antibody positron emission tomography tracer 89ZED88082A in patients with solid tumors before and ~30 days after starting ICI therapy (NCT04029181). No tracer-related side effects occurred. Positron emission tomography imaging with 10 mg antibody revealed 89ZED88082A uptake in normal lymphoid tissues, and tumor lesions across the body varying within and between patients two days after tracer injection (n= 38, median patient maximum standard uptake value (SUVmax) 5.2, IQI 4.0–7.4). Higher SUVmaxwas associated with mismatch repair deficiency and longer overall survival. Uptake was higher in lesions with stromal/inflamed than desert immunophenotype. Tissue radioactivity was localized to areas with immunohistochemically confirmed CD8 expression. Re-imaging patients on treatment showed no change in average (geometric mean) tumor tracer uptake compared to baseline, but individual lesions showed diverse changes independent of tumor response. The imaging data suggest enormous heterogeneity in CD8+T cell distribution and pharmacodynamics within and between patients. In conclusion, 89ZED88082A can characterize the complex dynamics of CD8+T cells in the context of ICIs, and may inform immunotherapeutic treatments.

Published
2022
Full Text
View/download PDF

16. Comprehensive molecular profiling of lung adenocarcinoma

Author

Collisson, Eric A., Campbell, Joshua D., Brooks, Angela N., Berger, Alice H., Lee, William, Chmielecki, Juliann, Beer, David G., Cope, Leslie, Creighton, Chad J., Danilova, Ludmila, Ding, Li, Getz, Gad, Hammerman, Peter S., Neil Hayes, D., Hernandez, Bryan, Herman, James G., Heymach, John V., Jurisica, Igor, Kucherlapati, Raju, Kwiatkowski, David, Ladanyi, Marc, Robertson, Gordon, Schultz, Nikolaus, Shen, Ronglai, Sinha, Rileen, Sougnez, Carrie, Tsao, Ming-Sound, Travis, William D., Weinstein, John N., Wigle, Dennis A., Wilkerson, Matthew D., Chu, Andy, Cherniack, Andrew D., Hadjipanayis, Angela, Rosenberg, Mara, Weisenberger, Daniel J., Laird, Peter W., Radenbaugh, Amie, Ma, Singer, Stuart, Joshua M., Averett Byers, Lauren, Baylin, Stephen B., Govindan, Ramaswamy, Meyerson, Matthew, Rosenberg, Mara, Gabriel, Stacey B., Cibulskis, Kristian, Sougnez, Carrie, Kim, Jaegil, Stewart, Chip, Lichtenstein, Lee, Lander, Eric S., Lawrence, Michael S., Getz, Gad, Kandoth, Cyriac, Fulton, Robert, Fulton, Lucinda L., McLellan, Michael D., Wilson, Richard K., Ye, Kai, Fronick, Catrina C., Maher, Christopher A., Miller, Christopher A., Wendl, Michael C., Cabanski, Christopher, Ding, Li, Mardis, Elaine, Govindan, Ramaswamy, Creighton, Chad J., Wheeler, David, Balasundaram, Miruna, Butterfield, Yaron S. N., Carlsen, Rebecca, Chu, Andy, Chuah, Eric, Dhalla, Noreen, Guin, Ranabir, Hirst, Carrie, Lee, Darlene, Li, Haiyan I., Mayo, Michael, Moore, Richard A., Mungall, Andrew J., Schein, Jacqueline E., Sipahimalani, Payal, Tam, Angela, Varhol, Richard, Gordon Robertson, A., Wye, Natasja, Thiessen, Nina, Holt, Robert A., Jones, Steven J. M., Marra, Marco A., Campbell, Joshua D., Brooks, Angela N., Chmielecki, Juliann, Imielinski, Marcin, Onofrio, Robert C., Hodis, Eran, Zack, Travis, Sougnez, Carrie, Helman, Elena, Sekhar Pedamallu, Chandra, Mesirov, Jill, Cherniack, Andrew D., Saksena, Gordon, Schumacher, Steven E., Carter, Scott L., Hernandez, Bryan, Garraway, Levi, Beroukhim, Rameen, Gabriel, Stacey B., Getz, Gad, Meyerson, Matthew, Hadjipanayis, Angela, Lee, Semin, Mahadeshwar, Harshad S., Pantazi, Angeliki, Protopopov, Alexei, Ren, Xiaojia, Seth, Sahil, Song, Xingzhi, Tang, Jiabin, Yang, Lixing, Zhang, Jianhua, Chen, Peng-Chieh, Parfenov, Michael, Wei Xu, Andrew, Santoso, Netty, Chin, Lynda, Park, Peter J., Kucherlapati, Raju, Hoadley, Katherine A., Todd Auman, J., Meng, Shaowu, Shi, Yan, Buda, Elizabeth, Waring, Scot, Veluvolu, Umadevi, Tan, Donghui, Mieczkowski, Piotr A., Jones, Corbin D., Simons, Janae V., Soloway, Matthew G., Bodenheimer, Tom, Jefferys, Stuart R., Roach, Jeffrey, Hoyle, Alan P., Wu, Junyuan, Balu, Saianand, Singh, Darshan, Prins, Jan F., Marron, J. S., Parker, Joel S., Neil Hayes, D., Perou, Charles M., Liu, Jinze, Cope, Leslie, Danilova, Ludmila, Weisenberger, Daniel J., Maglinte, Dennis T., Lai, Philip H., Bootwalla, Moiz S., Van Den Berg, David J., Triche Jr, Timothy, Baylin, Stephen B., Laird, Peter W., Rosenberg, Mara, Chin, Lynda, Zhang, Jianhua, Cho, Juok, DiCara, Daniel, Heiman, David, Lin, Pei, Mallard, William, Voet, Douglas, Zhang, Hailei, Zou, Lihua, Noble, Michael S., Lawrence, Michael S., Saksena, Gordon, Gehlenborg, Nils, Thorvaldsdottir, Helga, Mesirov, Jill, Nazaire, Marc-Danie, Robinson, Jim, Getz, Gad, Lee, William, Arman Aksoy, B., Ciriello, Giovanni, Taylor, Barry S., Dresdner, Gideon, Gao, Jianjiong, Gross, Benjamin, Seshan, Venkatraman E., Ladanyi, Marc, Reva, Boris, Sinha, Rileen, Onur Sumer, S., Weinhold, Nils, Schultz, Nikolaus, Shen, Ronglai, Sander, Chris, Ng, Sam, Ma, Singer, Zhu, Jingchun, Radenbaugh, Amie, Stuart, Joshua M., Benz, Christopher C., Yau, Christina, Haussler, David, Spellman, Paul T., Wilkerson, Matthew D., Parker, Joel S., Hoadley, Katherine A., Kimes, Patrick K., Neil Hayes, D., Perou, Charles M., Broom, Bradley M., Wang, Jing, Lu, Yiling, Kwok Shing Ng, Patrick, Diao, Lixia, Averett Byers, Lauren, Liu, Wenbin, Heymach, John V., Amos, Christopher I., Weinstein, John N., Akbani, Rehan, Mills, Gordon B., Curley, Erin, Paulauskis, Joseph, Lau, Kevin, Morris, Scott, Shelton, Troy, Mallery, David, Gardner, Johanna, Penny, Robert, Saller, Charles, Tarvin, Katherine, Richards, William G., Cerfolio, Robert, Bryant, Ayesha, Raymond, Daniel P., Pennell, Nathan A., Farver, Carol, Czerwinski, Christine, Huelsenbeck-Dill, Lori, Iacocca, Mary, Petrelli, Nicholas, Rabeno, Brenda, Brown, Jennifer, Bauer, Thomas, Dolzhanskiy, Oleg, Potapova, Olga, Rotin, Daniil, Voronina, Olga, Nemirovich-Danchenko, Elena, Fedosenko, Konstantin V., Gal, Anthony, Behera, Madhusmita, Ramalingam, Suresh S., Sica, Gabriel, Flieder, Douglas, Boyd, Jeff, Weaver, JoEllen, Kohl, Bernard, Huy Quoc Thinh, Dang, Sandusky, George, Juhl, Hartmut, Duhig, Edwina, Illei, Peter, Gabrielson, Edward, Shin, James, Lee, Beverly, Rogers, Kristen, Trusty, Dante, Brock, Malcolm V., Williamson, Christina, Burks, Eric, Rieger-Christ, Kimberly, Holway, Antonia, Sullivan, Travis, Wigle, Dennis A., Asiedu, Michael K., Kosari, Farhad, Travis, William D., Rekhtman, Natasha, Zakowski, Maureen, Rusch, Valerie W., Zippile, Paul, Suh, James, Pass, Harvey, Goparaju, Chandra, Owusu-Sarpong, Yvonne, Bartlett, John M. S., Kodeeswaran, Sugy, Parfitt, Jeremy, Sekhon, Harmanjatinder, Albert, Monique, Eckman, John, Myers, Jerome B., Cheney, Richard, Morrison, Carl, Gaudioso, Carmelo, Borgia, Jeffrey A., Bonomi, Philip, Pool, Mark, Liptay, Michael J., Moiseenko, Fedor, Zaytseva, Irina, Dienemann, Hendrik, Meister, Michael, Schnabel, Philipp A., Muley, Thomas R., Peifer, Martin, Gomez-Fernandez, Carmen, Herbert, Lynn, Egea, Sophie, Huang, Mei, Thorne, Leigh B., Boice, Lori, Hill Salazar, Ashley, Funkhouser, William K., Kimryn Rathmell, W., Dhir, Rajiv, Yousem, Samuel A., Dacic, Sanja, Schneider, Frank, Siegfried, Jill M., Hajek, Richard, Watson, Mark A., McDonald, Sandra, Meyers, Bryan, Clarke, Belinda, Yang, Ian A., Fong, Kwun M., Hunter, Lindy, Windsor, Morgan, Bowman, Rayleen V., Peters, Solange, Letovanec, Igor, Khan, Khurram Z., Jensen, Mark A., Snyder, Eric E., Srinivasan, Deepak, Kahn, Ari B., Baboud, Julien, Pot, David A., Mills Shaw, Kenna R., Sheth, Margi, Davidsen, Tanja, Demchok, John A., Yang, Liming, Wang, Zhining, Tarnuzzer, Roy, Claude Zenklusen, Jean, Ozenberger, Bradley A., Sofia, Heidi J., Travis, William D., Cheney, Richard, Clarke, Belinda, Dacic, Sanja, Duhig, Edwina, Funkhouser, William K., Illei, Peter, Farver, Carol, Rekhtman, Natasha, Sica, Gabriel, Suh, James, and Tsao, Ming-Sound

Published
2014
Full Text
View/download PDF

23. Integrative Analysis Identifies Four Molecular and Clinical Subsets in Uveal Melanoma

Author

Robertson, A. Gordon, primary, Shih, Juliann, additional, Yau, Christina, additional, Gibb, Ewan A., additional, Oba, Junna, additional, Mungall, Karen L., additional, Hess, Julian M., additional, Uzunangelov, Vladislav, additional, Walter, Vonn, additional, Danilova, Ludmila, additional, Lichtenberg, Tara M., additional, Kucherlapati, Melanie, additional, Kimes, Patrick K., additional, Tang, Ming, additional, Penson, Alexander, additional, Babur, Ozgun, additional, Akbani, Rehan, additional, Bristow, Christopher A., additional, Hoadley, Katherine A., additional, Iype, Lisa, additional, Chang, Matthew T., additional, Cherniack, Andrew D., additional, Benz, Christopher, additional, Mills, Gordon B., additional, Verhaak, Roel G.W., additional, Griewank, Klaus G., additional, Felau, Ina, additional, Zenklusen, Jean C., additional, Gershenwald, Jeffrey E., additional, Schoenfield, Lynn, additional, Lazar, Alexander J., additional, Abdel-Rahman, Mohamed H., additional, Roman-Roman, Sergio, additional, Stern, Marc-Henri, additional, Cebulla, Colleen M., additional, Williams, Michelle D., additional, Jager, Martine J., additional, Coupland, Sarah E., additional, Esmaeli, Bita, additional, Kandoth, Cyriac, additional, and Woodman, Scott E., additional

Published
2018
Full Text
View/download PDF

24. Reproducible and replicable comparisons using SummarizedBenchmark.

Author

Kimes, Patrick K and Reyes, Alejandro

Subjects
*COMPUTATIONAL biology, *DATA analysis, *REPRODUCIBLE research, *GENE expression, *METADATA
Abstract

Summary Benchmark studies are widely used to compare and evaluate tools developed for answering various biological questions. Despite the popularity of these comparisons, the implementation is often ad hoc, with little consistency across studies. To address this problem, we developed SummarizedBenchmark, an R package and framework for organizing and structuring benchmark comparisons. SummarizedBenchmark defines a general grammar for benchmarking and allows for easier setup and execution of benchmark comparisons, while improving the reproducibility and replicability of such comparisons. We demonstrate the wide applicability of our framework using four examples from different applications. Availability and implementation SummarizedBenchmark is an R package available through Bioconductor (http://bioconductor.org/packages/SummarizedBenchmark). Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Published
2019
Full Text
View/download PDF

25. Large-margin classification with multiple decision rules.

Author

Kimes, Patrick K., Hayes, David Neil, Marron, J. S., and Liu, Yufeng

Subjects
*CONDITIONAL probability, *MACHINE learning, *SUPERVISED learning, *MAGNETIC resonance imaging, *CLASSIFIERS (Linguistics)
Abstract

Binary classification is a common statistical learning problem in which a model is estimated on a set of covariates for some outcome, indicating the membership of one of two classes. In the literature, there exists a distinction between hard and soft classification. In soft classification, the conditional class probability is modeled as a function of the covariates. In contrast, hard classification methods only target the optimal prediction boundary. While hard and soft classification methods have been studied extensively, not much work has been performed to compare the actual tasks of hard and soft classification. In this paper, we propose a spectrum of statistical learning problems that span the hard and soft classification tasks based on fitting multiple decision rules to the data. By doing so, we reveal a novel collection of learning tasks of increasing complexity. We study the problems using the framework of large-margin classifiers and a class of piecewise linear convex surrogates, for which we derive statistical properties and a corresponding sub-gradient descent algorithm. We conclude by applying our approach to simulation settings and a magnetic resonance imaging (MRI) dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. [ABSTRACT FROM AUTHOR]

Published
2016
Full Text
View/download PDF

26. (89)ZED88082A PET imaging to visualize CD8(+) T cells in patients with cancer treated with immune checkpoint inhibitor

Author

Ruijter, Laura Kist, Donk, Pim P., Hooiveld-Noeken, Jahlisa S., Giesen, Danique, Ungewickell, Alexander, Fine, Bernard M., Williams, Simon P., Bohorquez, Sandra M. Sanabria, Yadav, Mahesh, Koeppen, Hartmut, Jing, Jing, Guelman, Sebastian, Lin, Mark T., Mamounas, Michael J., Eastham, Jeffrey, Kimes, Patrick K., Glaudemans, Andor W., Brouwers, Adrienne H., Lub-De Hooge, Marjolijn N., Gietema, Jourik A., Schroder, Carolina P., Wim Timens, Jalving, Mathilde, Elias, Sjoerd, Oosting, Sjoukje F., Groot, Derk J., Vries, Elisabeth G., Translational Immunology Groningen (TRIGR), ​Basic and Translational Research and Imaging Methodology Development in Groningen (BRIDGE), Guided Treatment in Optimal Selected Cancer Patients (GUTS), Damage and Repair in Cancer Development and Cancer Treatment (DARE), Groningen Research Institute for Asthma and COPD (GRIAC), and Targeted Gynaecologic Oncology (TARGON)

27. SigFuge: single gene clustering of RNA-seq reveals differential isoform usage among cancer samples

Author

Liu, Yufeng, Maher, Christopher A., Wilkerson, Matthew D., Johnson, Amy R., Perou, Charles M., Marron, J. S., Kimes, Patrick K., Hayes, D. Neil, Cabanski, Christopher R., Makowski, Liza, and Zhao, Ni

Subjects
3. Good health
Abstract

High-throughput sequencing technologies, including RNA-seq, have made it possible to move beyond gene expression analysis to study transcriptional events including alternative splicing and gene fusions. Furthermore, recent studies in cancer have suggested the importance of identifying transcriptionally altered loci as biomarkers for improved prognosis and therapy. While many statistical methods have been proposed for identifying novel transcriptional events with RNA-seq, nearly all rely on contrasting known classes of samples, such as tumor and normal. Few tools exist for the unsupervised discovery of such events without class labels. In this paper, we present SigFuge for identifying genomic loci exhibiting differential transcription patterns across many RNA-seq samples. SigFuge combines clustering with hypothesis testing to identify genes exhibiting alternative splicing, or differences in isoform expression. We apply SigFuge to RNA-seq cohorts of 177 lung and 279 head and neck squamous cell carcinoma samples from the Cancer Genome Atlas, and identify several cases of differential isoform usage including CDKN2A, a tumor suppressor gene known to be inactivated in a majority of lung squamous cell tumors. By not restricting attention to known sample stratifications, SigFuge offers a novel approach to unsupervised screening of genetic loci across RNA-seq cohorts. SigFuge is available as an R package through Bioconductor.

Catalog

Books, media, physical & digital resources
See catalog results