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1. The artificial intelligence-based model ANORAK improves histopathological grading of lung adenocarcinoma

Author

Pan, Xiaoxi, AbdulJabbar, Khalid, Coelho-Lima, Jose, Grapa, Anca-Ioana, Zhang, Hanyun, Cheung, Alvin Ho Kwan, Baena, Juvenal, Karasaki, Takahiro, Wilson, Claire Rachel, Sereno, Marco, Veeriah, Selvaraju, Aitken, Sarah J., Hackshaw, Allan, Nicholson, Andrew G., Jamal-Hanjani, Mariam, Swanton, Charles, Yuan, Yinyin, Le Quesne, John, and Moore, David A.

Published
2024
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2. Cross-Stream Interactions: Segmentation of Lung Adenocarcinoma Growth Patterns

Author

Pan, Xiaoxi, Zhang, Hanyun, Grapa, Anca-Ioana, AbdulJabbar, Khalid, Raza, Shan E Ahmed, Cheung, Ho Kwan Alvin, Karasaki, Takahiro, Quesne, John Le, Moore, David A., Swanton, Charles, Yuan, Yinyin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Qin, Wenjian, editor, Zaki, Nazar, editor, Zhang, Fa, editor, Wu, Jia, editor, and Yang, Fan, editor

Published
2022
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5. Body composition and lung cancer-associated cachexia in TRACERx

Author

Al-Sawaf, Othman, Weiss, Jakob, Skrzypski, Marcin, Lam, Jie Min, Karasaki, Takahiro, Zambrana, Francisco, Kidd, Andrew C, Frankell, Alexander M, Watkins, Thomas BK, Martinez-Ruiz, Carlos, Puttick, Clare, Black, James RM, Huebner, Ariana, Al Bakir, Maise, Sokac, Mateo, Collins, Susie, Veeriah, Selvaraju, Magno, Neil, Naceur-Lombardelli, Cristina, Prymas, Paulina, Toncheva, Antonia, Ward, Sophia, Jayanth, Nick, Salgado, Roberto, Bridge, Christopher P, Christiani, David C, Mak, Raymond H, Bay, Camden, Rosenthal, Michael, Sattar, Naveed, Welsh, Paul, Liu, Ying, Perrimon, Norbert, Popuri, Karteek, Beg, Mirza Faisal, McGranahan, Nicholas, Hackshaw, Allan, Breen, Danna M, O'Rahilly, Stephen, Birkbak, Nicolai J, Aerts, Hugo JWL, Jamal-Hanjani, Mariam, Swanton, Charles, Lester, Jason F, Bajaj, Amrita, Nakas, Apostolos, Sodha-Ramdeen, Azmina, Ang, Keng, Tufail, Mohamad, Chowdhry, Mohammed Fiyaz, Scotland, Molly, Boyles, Rebecca, Rathinam, Sridhar, Wilson, Claire, Marrone, Domenic, Dulloo, Sean, Fennell, Dean A, Matharu, Gurdeep, Shaw, Jacqui A, Riley, Joan, Primrose, Lindsay, Boleti, Ekaterini, Cheyne, Heather, Khalil, Mohammed, Richardson, Shirley, Cruickshank, Tracey, Price, Gillian, Kerr, Keith M, Benafif, Sarah, Gilbert, Kayleigh, Naidu, Babu, Patel, Akshay J, Osman, Aya, Lacson, Christer, Langman, Gerald, Shackleford, Helen, Djearaman, Madava, Kadiri, Salma, Middleton, Gary, Leek, Angela, Hodgkinson, Jack Davies, Totten, Nicola, Montero, Angeles, Smith, Elaine, Fontaine, Eustace, Granato, Felice, Doran, Helen, Novasio, Juliette, Rammohan, Kendadai, Joseph, Leena, Bishop, Paul, Shah, Rajesh, Moss, Stuart, Joshi, Vijay, Crosbie, Philip, Gomes, Fabio, Brown, Kate, Carter, Mathew, Chaturvedi, Anshuman, Priest, Lynsey, Oliveira, Pedro, Lindsay, Colin R, Blackhall, Fiona H, Krebs, Matthew G, Summers, Yvonne, Clipson, Alexandra, Tugwood, Jonathan, Kerr, Alastair, Rothwell, Dominic G, Kilgour, Elaine, Dive, Caroline, Schwarz, Roland F, Kaufmann, Tom L, Wilson, Gareth A, Rosenthal, Rachel, Van Loo, Peter, Szallasi, Zoltan, Kisistok, Judit, Diossy, Miklos, Demeulemeester, Jonas, Bunkum, Abigail, Stewart, Aengus, Magness, Alastair, Rowan, Andrew, Karamani, Angeliki, Chain, Benny, Campbell, Brittany B, Castignani, Carla, Bailey, Chris, Abbosh, Christopher, Weeden, Clare E, Lee, Claudia, Richard, Corentin, Hiley, Crispin T, Moore, David A, Pearce, David R, Karagianni, Despoina, Biswas, Dhruva, Levi, Dina, Hoxha, Elena, Cadieux, Elizabeth Larose, Lim, Emilia L, Colliver, Emma, Nye, Emma, Gronroos, Eva, Galvez-Cancino, Felip, Athanasopoulou, Foteini, Gimeno-Valiente, Francisco, Kassiotis, George, Stavrou, Georgia, Mastrokalos, Gerasimos, Zhai, Haoran, Lowe, Helen L, Matos, Ignacio Garcia, Goldman, Jacki, Reading, James L, Herrero, Javier, Rane, Jayant K, Nicod, Jerome, Hartley, John A, Peggs, Karl S, Enfield, Katey SS, Selvaraju, Kayalvizhi, Thol, Kerstin, Litchfield, Kevin, Ng, Kevin W, Chen, Kezhong, Dijkstra, Krijn, Grigoriadis, Kristiana, Thakkar, Krupa, Ensell, Leah, Shah, Mansi, Duran, Marcos Vasquez, Litovchenko, Maria, Sunderland, Mariana Werner, Hill, Mark S, Dietzen, Michelle, Leung, Michelle, Escudero, Mickael, Angelova, Mihaela, Tanic, Miljana, Sivakumar, Monica, Kanu, Nnennaya, Chervova, Olga, Lucas, Olivia, Pich, Oriol, Hobson, Philip, Pawlik, Piotr, Stone, Richard Kevin, Bentham, Robert, Hynds, Robert E, Vendramin, Roberto, Saghafinia, Sadegh, Lopez, Saioa, Gamble, Samuel, Ung, Seng Kuong Anakin, Quezada, Sergio A, Vanloo, Sharon, Zaccaria, Simone, Hessey, Sonya, Boeing, Stefan, Beck, Stephan, Bola, Supreet Kaur, Denner, Tamara, Marafioti, Teresa, Mourikis, Thanos P, Spanswick, Victoria, Barbe, Vittorio, Lu, Wei-Ting, Hill, William, Liu, Wing Kin, Wu, Yin, Naito, Yutaka, Ramsden, Zoe, Veiga, Catarina, Royle, Gary, Collins-Fekete, Charles-Antoine, Fraioli, Francesco, Ashford, Paul, Clark, Tristan, Forster, Martin D, Lee, Siow Ming, Borg, Elaine, Falzon, Mary, Papadatos-Pastos, Dionysis, Wilson, James, Ahmad, Tanya, Procter, Alexander James, Ahmed, Asia, Taylor, Magali N, Nair, Arjun, Lawrence, David, Patrini, Davide, Navani, Neal, Thakrar, Ricky M, Janes, Sam M, Hoogenboom, Emilie Martinoni, Monk, Fleur, Holding, James W, Choudhary, Junaid, Bhakhri, Kunal, Scarci, Marco, Hayward, Martin, Panagiotopoulos, Nikolaos, Gorman, Pat, Khiroya, Reena, Stephens, Robert CM, Wong, Yien Ning Sophia, Bandula, Steve, Sharp, Abigail, Smith, Sean, Gower, Nicole, Dhanda, Harjot Kaur, Chan, Kitty, Pilotti, Camilla, Leslie, Rachel, Grapa, Anca, Zhang, Hanyun, AbdulJabbar, Khalid, Pan, Xiaoxi, Yuan, Yinyin, Chuter, David, MacKenzie, Mairead, Chee, Serena, Alzetani, Aiman, Cave, Judith, Scarlett, Lydia, Richards, Jennifer, Ingram, Papawadee, Austin, Silvia, Lim, Eric, De Sousa, Paulo, Jordan, Simon, Rice, Alexandra, Raubenheimer, Hilgardt, Bhayani, Harshil, Ambrose, Lyn, Devaraj, Anand, Chavan, Hema, Begum, Sofina, Buderi, Silviu, Kaniu, Daniel, Malima, Mpho, Booth, Sarah, Nicholson, Andrew G, Fernandes, Nadia, Shah, Pratibha, Proli, Chiara, Hewish, Madeleine, Danson, Sarah, Shackcloth, Michael J, Robinson, Lily, Russell, Peter, Blyth, Kevin G, Dick, Craig, Le Quesne, John, Kirk, Alan, Asif, Mo, Bilancia, Rocco, Kostoulas, Nikos, and Thomas, Mathew

Subjects
Male, Proteomics, Cachexia, Lung Neoplasms, Antigens, Neoplasm, Carcinoma, Non-Small-Cell Lung, Body Weight, Body Composition, Humans, Neoplasm Recurrence, Local, Muscle, Skeletal, Neoplasm Proteins
Abstract

Cancer-associated cachexia (CAC) is a major contributor to morbidity and mortality in individuals with non-small cell lung cancer. Key features of CAC include alterations in body composition and body weight. Here, we explore the association between body composition and body weight with survival and delineate potential biological processes and mediators that contribute to the development of CAC. Computed tomography-based body composition analysis of 651 individuals in the TRACERx (TRAcking non-small cell lung Cancer Evolution through therapy (Rx)) study suggested that individuals in the bottom 20th percentile of the distribution of skeletal muscle or adipose tissue area at the time of lung cancer diagnosis, had significantly shorter lung cancer-specific survival and overall survival. This finding was validated in 420 individuals in the independent Boston Lung Cancer Study. Individuals classified as having developed CAC according to one or more features at relapse encompassing loss of adipose or muscle tissue, or body mass index-adjusted weight loss were found to have distinct tumor genomic and transcriptomic profiles compared with individuals who did not develop such features. Primary non-small cell lung cancers from individuals who developed CAC were characterized by enrichment of inflammatory signaling and epithelial-mesenchymal transitional pathways, and differentially expressed genes upregulated in these tumors included cancer-testis antigen MAGEA6 and matrix metalloproteinases, such as ADAMTS3. In an exploratory proteomic analysis of circulating putative mediators of cachexia performed in a subset of 110 individuals from TRACERx, a significant association between circulating GDF15 and loss of body weight, skeletal muscle and adipose tissue was identified at relapse, supporting the potential therapeutic relevance of targeting GDF15 in the management of CAC. ispartof: NATURE MEDICINE vol:29 issue:4 ispartof: location:United States status: Published online

Published
2023

6. The evolution of non-small cell lung cancer metastases in TRACERx

Author

Al Bakir, Maise, Huebner, Ariana, Martinez-Ruiz, Carlos, Grigoriadis, Kristiana, Watkins, Thomas BK, Pich, Oriol, Moore, David A, Veeriah, Selvaraju, Ward, Sophia, Laycock, Joanne, Johnson, Diana, Rowan, Andrew, Razaq, Maryam, Akther, Mita, Naceur-Lombardelli, Cristina, Prymas, Paulina, Toncheva, Antonia, Hessey, Sonya, Dietzen, Michelle, Colliver, Emma, Frankell, Alexander, Bunkum, Abigail, Lim, Emilia L, Karasaki, Takahiro, Abbosh, Christopher, Hiley, Crispin T, Hill, Mark S, Cook, Daniel E, Wilson, Gareth A, Salgado, Roberto, Nye, Emma, Stone, Richard Kevin, Fennell, Dean A, Price, Gillian, Kerr, Keith M, Naidu, Babu, Middleton, Gary, Summers, Yvonne, Lindsay, Colin R, Blackhall, Fiona H, Cave, Judith, Blyth, Kevin G, Nair, Arjun, Ahmed, Asia, Taylor, Magali N, Procter, Alexander James, Falzon, Mary, Lawrence, David, Navani, Neal, Thakrar, Ricky M, Janes, Sam M, Papadatos-Pastos, Dionysis, Forster, Martin D, Lee, Siow Ming, Ahmad, Tanya, Quezada, Sergio, Peggs, Karl S, Van Loo, Peter, Dive, Caroline, Hackshaw, Allan, Birkbak, Nicolai J, Zaccaria, Simone, Jamal-Hanjani, Mariam, McGranahan, Nicholas, Swanton, Charles, Lester, Jason F, Bajaj, Amrita, Nakas, Apostolos, Sodha-Ramdeen, Azmina, Ang, Keng, Tufail, Mohamad, Chowdhry, Mohammed Fiyaz, Scotland, Molly, Boyles, Rebecca, Rathinam, Sridhar, Wilson, Claire, Marrone, Domenic, Dulloo, Sean, Matharu, Gurdeep, Shaw, Jacqui A, Riley, Joan, Primrose, Lindsay, Boleti, Ekaterini, Cheyne, Heather, Khalil, Mohammed, Richardson, Shirley, Cruickshank, Tracey, Benafif, Sarah, Gilbert, Kayleigh, Patel, Akshay J, Osman, Aya, Lacson, Christer, Langman, Gerald, Shackleford, Helen, Djearaman, Madava, Kadiri, Salma, Leek, Angela, Hodgkinson, Jack Davies, Totten, Nicola, Montero, Angeles, Smith, Elaine, Fontaine, Eustace, Granato, Felice, Doran, Helen, Novasio, Juliette, Rammohan, Kendadai, Joseph, Leena, Bishop, Paul, Shah, Rajesh, Moss, Stuart, Joshi, Vijay, Crosbie, Philip, Gomes, Fabio, Brown, Kate, Carter, Mathew, Chaturvedi, Anshuman, Priest, Lynsey, Oliveira, Pedro, Krebs, Matthew G, Clipson, Alexandra, Tugwood, Jonathan, Kerr, Alastair, Rothwell, Dominic G, Kilgour, Elaine, Aerts, Hugo JWL, Schwarz, Roland F, Kaufmann, Tom L, Rosenthal, Rachel, Szallasi, Zoltan, Kisistok, Judit, Sokac, Mateo, Diossy, Miklos, Demeulemeester, Jonas, Stewart, Aengus, Magness, Alastair, Karamani, Angeliki, Chain, Benny, Campbell, Brittany B, Castignani, Carla, Bailey, Chris, Puttick, Clare, Weeden, Clare E, Lee, Claudia, Richard, Corentin, Pearce, David R, Karagianni, Despoina, Biswas, Dhruva, Levi, Dina, Hoxha, Elena, Larose Cadieux, Elizabeth, Gronroos, Eva, Galvez-Cancino, Felip, Athanasopoulou, Foteini, Gimeno-Valiente, Francisco, Kassiotis, George, Stavrou, Georgia, Mastrokalos, Gerasimos, Zhai, Haoran, Lowe, Helen L, Matos, Ignacio, Goldman, Jacki, Reading, James L, Black, James RM, Herrero, Javier, Rane, Jayant K, Nicod, Jerome, Lam, Jie Min, Hartley, John A, Enfield, Katey SS, Selvaraju, Kayalvizhi, Thol, Kerstin, Litchfield, Kevin, Ng, Kevin W, Chen, Kezhong, Dijkstra, Krijn, Thakkar, Krupa, Ensell, Leah, Shah, Mansi, Vasquez, Marcos, Litovchenko, Maria, Werner Sunderland, Mariana, Leung, Michelle, Escudero, Mickael, Angelova, Mihaela, Tanic, Miljana, Sivakumar, Monica, Kanu, Nnennaya, Chervova, Olga, Lucas, Olivia, Al-Sawaf, Othman, Hobson, Philip, Pawlik, Piotr, Bentham, Robert, Hynds, Robert E, Vendramin, Roberto, Saghafinia, Sadegh, Lopez, Saioa, Gamble, Samuel, Ung, Seng Kuong Anakin, Vanloo, Sharon, Boeing, Stefan, Beck, Stephan, Bola, Supreet Kaur, Denner, Tamara, Marafioti, Teresa, Mourikis, Thanos P, Spanswick, Victoria, Barbe, Vittorio, Lu, Wei-Ting, Hill, William, Liu, Wing Kin, Wu, Yin, Naito, Yutaka, Ramsden, Zoe, Veiga, Catarina, Royle, Gary, Collins-Fekete, Charles-Antoine, Fraioli, Francesco, Ashford, Paul, Clark, Tristan, Borg, Elaine, Wilson, James, Patrini, Davide, Martinoni Hoogenboom, Emilie, Monk, Fleur, Holding, James W, Choudhary, Junaid, Bhakhri, Kunal, Scarci, Marco, Hayward, Martin, Panagiotopoulos, Nikolaos, Gorman, Pat, Khiroya, Reena, Stephens, Robert CM, Wong, Yien Ning Sophia, Bandula, Steve, Sharp, Abigail, Smith, Sean, Gower, Nicole, Dhanda, Harjot Kaur, Chan, Kitty, Pilotti, Camilla, Leslie, Rachel, Grapa, Anca, Zhang, Hanyun, AbdulJabbar, Khalid, Pan, Xiaoxi, Yuan, Yinyin, Chuter, David, MacKenzie, Mairead, Chee, Serena, Alzetani, Aiman, Scarlett, Lydia, Richards, Jennifer, Ingram, Papawadee, Austin, Silvia, Lim, Eric, De Sousa, Paulo, Jordan, Simon, Rice, Alexandra, Raubenheimer, Hilgardt, Bhayani, Harshil, Ambrose, Lyn, Devaraj, Anand, Chavan, Hema, Begum, Sofina, Buderi, Silviu, Kaniu, Daniel, Malima, Mpho, Booth, Sarah, Nicholson, Andrew G, Fernandes, Nadia, Shah, Pratibha, Proli, Chiara, Hewish, Madeleine, Danson, Sarah, Shackcloth, Michael J, Robinson, Lily, Russell, Peter, Dick, Craig, Le Quesne, John, Kirk, Alan, Asif, Mo, Bilancia, Rocco, Kostoulas, Nikos, and Thomas, Mathew

Subjects
Clonal Evolution, Cohort Studies, Evolution, Molecular, Lung Neoplasms, Carcinoma, Non-Small-Cell Lung, Disease Progression, Humans, Neoplasm Metastasis, Neoplasm Recurrence, Local, Clone Cells
Abstract

Metastatic disease is responsible for the majority of cancer-related deaths1. We report the longitudinal evolutionary analysis of 126 non-small cell lung cancer (NSCLC) tumours from 421 prospectively recruited patients in TRACERx who developed metastatic disease, compared with a control cohort of 144 non-metastatic tumours. In 25% of cases, metastases diverged early, before the last clonal sweep in the primary tumour, and early divergence was enriched for patients who were smokers at the time of initial diagnosis. Simulations suggested that early metastatic divergence more frequently occurred at smaller tumour diameters (less than 8 mm). Single-region primary tumour sampling resulted in 83% of late divergence cases being misclassified as early, highlighting the importance of extensive primary tumour sampling. Polyclonal dissemination, which was associated with extrathoracic disease recurrence, was found in 32% of cases. Primary lymph node disease contributed to metastatic relapse in less than 20% of cases, representing a hallmark of metastatic potential rather than a route to subsequent recurrences/disease progression. Metastasis-seeding subclones exhibited subclonal expansions within primary tumours, probably reflecting positive selection. Our findings highlight the importance of selection in metastatic clone evolution within untreated primary tumours, the distinction between monoclonal versus polyclonal seeding in dictating site of recurrence, the limitations of current radiological screening approaches for early diverging tumours and the need to develop strategies to target metastasis-seeding subclones before relapse. ispartof: NATURE vol:616 issue:7957 ispartof: location:England status: Published online

Published
2023

7. The evolution of lung cancer and impact of subclonal selection in TRACERx

Author

Frankell, Alexander M, Dietzen, Michelle, Al Bakir, Maise, Lim, Emilia L, Karasaki, Takahiro, Ward, Sophia, Veeriah, Selvaraju, Colliver, Emma, Huebner, Ariana, Bunkum, Abigail, Hill, Mark S, Grigoriadis, Kristiana, Moore, David A, Black, James RM, Liu, Wing Kin, Thol, Kerstin, Pich, Oriol, Watkins, Thomas BK, Naceur-Lombardelli, Cristina, Cook, Daniel E, Salgado, Roberto, Wilson, Gareth A, Bailey, Chris, Angelova, Mihaela, Bentham, Robert, Martinez-Ruiz, Carlos, Abbosh, Christopher, Nicholson, Andrew G, Le Quesne, John, Biswas, Dhruva, Rosenthal, Rachel, Puttick, Clare, Hessey, Sonya, Lee, Claudia, Prymas, Paulina, Toncheva, Antonia, Smith, Jon, Xing, Wei, Nicod, Jerome, Price, Gillian, Kerr, Keith M, Naidu, Babu, Middleton, Gary, Blyth, Kevin G, Fennell, Dean A, Forster, Martin D, Lee, Siow Ming, Falzon, Mary, Hewish, Madeleine, Shackcloth, Michael J, Lim, Eric, Benafif, Sarah, Russell, Peter, Boleti, Ekaterini, Krebs, Matthew G, Lester, Jason F, Papadatos-Pastos, Dionysis, Ahmad, Tanya, Thakrar, Ricky M, Lawrence, David, Navani, Neal, Janes, Sam M, Dive, Caroline, Blackhall, Fiona H, Summers, Yvonne, Cave, Judith, Marafioti, Teresa, Herrero, Javier, Quezada, Sergio A, Peggs, Karl S, Schwarz, Roland F, Van Loo, Peter, Miedema, Daniel M, Birkbak, Nicolai J, Hiley, Crispin T, Hackshaw, Allan, Zaccaria, Simone, Jamal-Hanjani, Mariam, McGranahan, Nicholas, Swanton, Charles, Bajaj, Amrita, Nakas, Apostolos, Sodha-Ramdeen, Azmina, Ang, Keng, Tufail, Mohamad, Chowdhry, Mohammed Fiyaz, Scotland, Molly, Boyles, Rebecca, Rathinam, Sridhar, Wilson, Claire, Marrone, Domenic, Dulloo, Sean, Matharu, Gurdeep, Shaw, Jacqui A, Riley, Joa, Primrose, Lindsay, Cheyne, Heather, Khalil, Mohammed, Richardson, Shirley, Cruickshank, Tracey, Gilbert, Kayleigh, Patel, Akshay J, Osman, Aya, Lacson, Christer, Langman, Gerald, Shackleford, Helen, Djearaman, Madava, Kadiri, Salma, Leek, Angela, Hodgkinson, Jack Davies, Totten, Nicola, Montero, Angeles, Smith, Elaine, Fontaine, Eustace, Granato, Felice, Doran, Helen, Novasio, Juliette, Rammohan, Kendadai, Joseph, Leena, Bishop, Paul, Shah, Rajesh, Moss, Stuart, Joshi, Vijay, Crosbie, Philip, Gomes, Fabio, Brown, Kate, Carter, Mathew, Chaturvedi, Anshuman, Priest, Lynsey, Oliveira, Pedro, Lindsay, Colin R, Clipson, Alexandra, Tugwood, Jonathan, Kerr, Alastair, Rothwell, Dominic G, Kilgour, Elaine, Aerts, Hugo JWL, Kaufmann, Tom L, Szallasi, Zoltan, Kisistok, Judit, Sokac, Mateo, Diossy, Miklos, Demeulemeester, Jonas, Stewart, Aengus, Magness, Alastair, Rowan, Andrew, Karamani, Angeliki, Chain, Benny, Campbell, Brittany B, Castignani, Carla, Weeden, Clare E, Richard, Corentin, Pearce, David R, Karagianni, Despoina, Levi, Dina, Hoxha, Elena, Larose Cadieux, Elizabeth, Nye, Emma, Gronroos, Eva, Galvez-Cancino, Felip, Athanasopoulou, Foteini, Gimeno-Valiente, Francisco, Kassiotis, George, Stavrou, Georgia, Mastrokalos, Gerasimos, Zhai, Haoran L, Lowe, Helen L, Matos, Ignacio, Goldman, Jacki, Reading, James L, Rane, Jayant K, Lam, Jie Min, Hartley, John A, Enfield, Katey SS, Selvaraju, Kayalvizhi, Litchfield, Kevin, Ng, Kevin W, Chen, Kezhong, Dijkstra, Krijn, Thakkar, Krupa, Ensell, Leah, Shah, Mansi, Vasquez, Marcos, Litovchenko, Maria, Werner Sunderland, Mariana, Leung, Michelle, Escudero, Mickael, Tanic, Miljana, Sivakumar, Monica, Kanu, Nnennaya, Chervova, Olga, Lucas, Olivia, Al-Sawaf, Othman, Hobson, Philip, Pawlik, Piotr, Stone, Richard Kevin, Hynds, Robert E, Vendramin, Roberto, Saghafinia, Sadegh, Lopez, Saioa, Gamble, Samuel, Ung, Seng Kuong Anakin, Vanloo, Sharon, Boeing, Stefan, Beck, Stephan, Bola, Supreet Kaur, Denner, Tamara, Mourikis, Thanos P, Spanswick, Victoria, Barbe, Vittorio, Lu, Wei-Ting, Hill, William, Wu, Yin, Naito, Yutaka, Ramsden, Zoe, Veiga, Catarina, Royle, Gary, Collins-Fekete, Charles-Antoine, Fraioli, Francesco, Ashford, Paul, Clark, Tristan, Borg, Elaine, Wilson, James, Procter, Alexander James, Ahmed, Asia, Taylor, Magali N, Nair, Arjun, Patrini, Davide, Martinoni Hoogenboom, Emilie, Monk, Fleur, Holding, James W, Choudhary, Junaid, Bhakhri, Kunal, Scarci, Marco, Hayward, Martin, Panagiotopoulos, Nikolaos, Gorman, Pat, Khiroya, Reena, Stephens, Robert CM, Wong, Yien Ning Sophia, Bandula, Steve, Sharp, Abigail, Smith, Sean, Gower, Nicole, Dhanda, Harjot Kaur, Chan, Kitty, Pilotti, Camilla, Leslie, Rachel, Grapa, Anca, Zhang, Hanyun, AbdulJabbar, Khalid, Pan, Xiaoxi, Yuan, Yinyin, Chuter, David, MacKenzie, Mairead, Chee, Serena, Alzetani, Aiman, Scarlett, Lydia, Richards, Jennifer, Ingram, Papawadee, Austin, Silvia, De Sousa, Paulo, Jordan, Simon, Rice, Alexandra, Raubenheimer, Hilgardt, Bhayani, Harshil, Ambrose, Lyn, Devaraj, Anand, Chavan, Hema, Begum, Sofina, Buderi, Silviu, Kaniu, Daniel, Malima, Mpho, Booth, Sarah, Fernandes, Nadia, Shah, Pratibha, Proli, Chiara, Danson, Sarah, Robinson, Lily, Dick, Craig, Kirk, Alan, Asif, Mo, Bilancia, Rocco, Kostoulas, Nikos, and Thomas, Mathew

Subjects
Lung Neoplasms, Treatment Outcome, DNA Copy Number Variations, Mutagenesis, Carcinoma, Non-Small-Cell Lung, Mutation, Smoking, Humans, Adenocarcinoma of Lung, Neoplasm Recurrence, Local, Phylogeny
Abstract

Lung cancer is the leading cause of cancer-associated mortality worldwide1. Here we analysed 1,644 tumour regions sampled at surgery or during follow-up from the first 421 patients with non-small cell lung cancer prospectively enrolled into the TRACERx study. This project aims to decipher lung cancer evolution and address the primary study endpoint: determining the relationship between intratumour heterogeneity and clinical outcome. In lung adenocarcinoma, mutations in 22 out of 40 common cancer genes were under significant subclonal selection, including classical tumour initiators such as TP53 and KRAS. We defined evolutionary dependencies between drivers, mutational processes and whole genome doubling (WGD) events. Despite patients having a history of smoking, 8% of lung adenocarcinomas lacked evidence of tobacco-induced mutagenesis. These tumours also had similar detection rates for EGFR mutations and for RET, ROS1, ALK and MET oncogenic isoforms compared with tumours in never-smokers, which suggests that they have a similar aetiology and pathogenesis. Large subclonal expansions were associated with positive subclonal selection. Patients with tumours harbouring recent subclonal expansions, on the terminus of a phylogenetic branch, had significantly shorter disease-free survival. Subclonal WGD was detected in 19% of tumours, and 10% of tumours harboured multiple subclonal WGDs in parallel. Subclonal, but not truncal, WGD was associated with shorter disease-free survival. Copy number heterogeneity was associated with extrathoracic relapse within 1 year after surgery. These data demonstrate the importance of clonal expansion, WGD and copy number instability in determining the timing and patterns of relapse in non-small cell lung cancer and provide a comprehensive clinical cancer evolutionary data resource. ispartof: NATURE vol:616 issue:7957 ispartof: location:England status: Published online

Published
2023

8. Evolutionary characterization of lung adenocarcinoma morphology in TRACERx

Author

Karasaki, Takahiro, Moore, David A, Veeriah, Selvaraju, Naceur-Lombardelli, Cristina, Toncheva, Antonia, Magno, Neil, Ward, Sophia, Al Bakir, Maise, Watkins, Thomas BK, Grigoriadis, Kristiana, Huebner, Ariana, Hill, Mark S, Frankell, Alexander M, Abbosh, Christopher, Puttick, Clare, Zhai, Haoran, Gimeno-Valiente, Francisco, Saghafinia, Sadegh, Kanu, Nnennaya, Dietzen, Michelle, Pich, Oriol, Lim, Emilia L, Martinez-Ruiz, Carlos, Black, James RM, Biswas, Dhruva, Campbell, Brittany B, Lee, Claudia, Colliver, Emma, Enfield, Katey SS, Hessey, Sonya, Hiley, Crispin T, Zaccaria, Simone, Litchfield, Kevin, Birkbak, Nicolai J, Cadieux, Elizabeth Larose, Demeulemeester, Jonas, Van Loo, Peter, Adusumilli, Prasad R, Tan, Kay See, Cheema, Waseem, Sanchez-Vega, Francisco, Jones, David R, Rekhtman, Natasha, Travis, William D, Hackshaw, Allan, Marafioti, Teresa, Salgado, Roberto, Le Quesne, John, Nicholson, Andrew G, McGranahan, Nicholas, Swanton, Charles, Jamal-Hanjani, Mariam, Lester, Jason F, Bajaj, Amrita, Nakas, Apostolos, Sodha-Ramdeen, Azmina, Ang, Keng, Tufail, Mohamad, Chowdhry, Mohammed Fiyaz, Scotland, Molly, Boyles, Rebecca, Rathinam, Sridhar, Wilson, Claire, Marrone, Domenic, Dulloo, Sean, Fennell, Dean A, Matharu, Gurdeep, Shaw, Jacqui A, Riley, Joan, Primrose, Lindsay, Boleti, Ekaterini, Cheyne, Heather, Khalil, Mohammed, Richardson, Shirley, Cruickshank, Tracey, Price, Gillian, Kerr, Keith M, Benafif, Sarah, Gilbert, Kayleigh, Naidu, Babu, Patel, Akshay J, Osman, Aya, Lacson, Christer, Langman, Gerald, Shackleford, Helen, Djearaman, Madava, Kadiri, Salma, Middleton, Gary, Leek, Angela, Hodgkinson, Jack Davies, Totten, Nicola, Montero, Angeles, Smith, Elaine, Fontaine, Eustace, Granato, Felice, Doran, Helen, Novasio, Juliette, Rammohan, Kendadai, Joseph, Leena, Bishop, Paul, Shah, Rajesh, Moss, Stuart, Joshi, Vijay, Crosbie, Philip, Gomes, Fabio, Brown, Kate, Carter, Mathew, Chaturvedi, Anshuman, Priest, Lynsey, Oliveira, Pedro, Lindsay, Colin R, Blackhall, Fiona H, Krebs, Matthew G, Summers, Yvonne, Clipson, Alexandra, Tugwood, Jonathan, Kerr, Alastair, Rothwell, Dominic G, Kilgour, Elaine, Dive, Caroline, Aerts, Hugo JWL, Schwarz, Roland F, Kaufmann, Tom L, Wilson, Gareth A, Rosenthal, Rachel, Szallasi, Zoltan, Kisistok, Judit, Sokac, Mateo, Diossy, Miklos, Bunkum, Abigail, Stewart, Aengus, Magness, Alastair, Rowan, Andrew, Karamani, Angeliki, Chain, Benny, Castignani, Carla, Bailey, Chris, Weeden, Clare E, Richard, Corentin, Pearce, David R, Karagianni, Despoina, Levi, Dina, Hoxha, Elena, Nye, Emma, Gronroos, Eva, Galvez-Cancino, Felip, Athanasopoulou, Foteini, Kassiotis, George, Stavrou, Georgia, Mastrokalos, Gerasimos, Lowe, Helen L, Matos, Ignacio Garcia, Goldman, Jacki, Reading, James L, Herrero, Javier, Rane, Jayant K, Nicod, Jerome, Lam, Jie Min, Hartley, John A, Peggs, Karl S, Selvaraju, Kayalvizhi, Thol, Kerstin, Ng, Kevin W, Chen, Kezhong, Dijkstra, Krijn, Thakkar, Krupa, Ensell, Leah, Shah, Mansi, Duran, Marcos Vasquez, Litovchenko, Maria, Sunderland, Mariana Werner, Leung, Michelle, Escudero, Mickael, Angelova, Mihaela, Tanic, Miljana, Sivakumar, Monica, Chervova, Olga, Lucas, Olivia, Al-Sawaf, Othman, Prymas, Paulina, Hobson, Philip, Pawlik, Piotr, Stone, Richard Kevin, Bentham, Robert, Hynds, Robert E, Vendramin, Roberto, Lopez, Saioa, Gamble, Samuel, Ung, Seng Kuong Anakin, Quezada, Sergio A, Vanloo, Sharon, Boeing, Stefan, Beck, Stephan, Bola, Supreet Kaur, Denner, Tamara, Mourikis, Thanos P, Spanswick, Victoria, Barbe, Vittorio, Lu, Wei-Ting, Hill, William, Liu, Wing Kin, Wu, Yin, Naito, Yutaka, Ramsden, Zoe, Veiga, Catarina, Royle, Gary, Collins-Fekete, Charles-Antoine, Fraioli, Francesco, Ashford, Paul, Clark, Tristan, Forster, Martin D, Lee, Siow Ming, Borg, Elaine, Falzon, Mary, Papadatos-Pastos, Dionysis, Wilson, James, Ahmad, Tanya, Procter, Alexander James, Ahmed, Asia, Taylor, Magali N, Nair, Arjun, Lawrence, David, Patrini, Davide, Navani, Neal, Thakrar, Ricky M, Janes, Sam M, Hoogenboom, Emilie Martinoni, Monk, Fleur, Holding, James W, Choudhary, Junaid, Bhakhri, Kunal, Scarci, Marco, Hayward, Martin, Panagiotopoulos, Nikolaos, Gorman, Pat, Khiroya, Reena, Stephens, Robert CM, Wong, Yien Ning Sophia, Bandula, Steve, Sharp, Abigail, Smith, Sean, Gower, Nicole, Dhanda, Harjot Kaur, Chan, Kitty, Pilotti, Camilla, Leslie, Rachel, Grapa, Anca, Zhang, Hanyun, AbdulJabbar, Khalid, Pan, Xiaoxi, Yuan, Yinyin, Chuter, David, MacKenzie, Mairead, Chee, Serena, Alzetani, Aiman, Cave, Judith, Scarlett, Lydia, Richards, Jennifer, Ingram, Papawadee, Austin, Silvia, Lim, Eric, De Sousa, Paulo, Jordan, Simon, Rice, Alexandra, Raubenheimer, Hilgardt, Bhayani, Harshil, Ambrose, Lyn, Devaraj, Anand, Chavan, Hema, Begum, Sofina, Buderi, Silviu, Kaniu, Daniel, Malima, Mpho, Booth, Sarah, Fernandes, Nadia, Shah, Pratibha, Proli, Chiara, Hewish, Madeleine, Danson, Sarah, Shackcloth, Michael J, Robinson, Lily, Russell, Peter, Blyth, Kevin G, Dick, Craig, Kirk, Alan, Asif, Mo, Bilancia, Rocco, Kostoulas, Nikos, and Thomas, Mathew

Subjects
TRACERx Consortium
Abstract

Lung adenocarcinomas (LUADs) display a broad histological spectrum from low-grade lepidic tumors through to mid-grade acinar and papillary and high-grade solid, cribriform and micropapillary tumors. How morphology reflects tumor evolution and disease progression is poorly understood. Whole-exome sequencing data generated from 805 primary tumor regions and 121 paired metastatic samples across 248 LUADs from the TRACERx 421 cohort, together with RNA-sequencing data from 463 primary tumor regions, were integrated with detailed whole-tumor and regional histopathological analysis. Tumors with predominantly high-grade patterns showed increased chromosomal complexity, with higher burden of loss of heterozygosity and subclonal somatic copy number alterations. Individual regions in predominantly high-grade pattern tumors exhibited higher proliferation and lower clonal diversity, potentially reflecting large recent subclonal expansions. Co-occurrence of truncal loss of chromosomes 3p and 3q was enriched in predominantly low-/mid-grade tumors, while purely undifferentiated solid-pattern tumors had a higher frequency of truncal arm or focal 3q gains and SMARCA4 gene alterations compared with mixed-pattern tumors with a solid component, suggesting distinct evolutionary trajectories. Clonal evolution analysis revealed that tumors tend to evolve toward higher-grade patterns. The presence of micropapillary pattern and 'tumor spread through air spaces' were associated with intrathoracic recurrence, in contrast to the presence of solid/cribriform patterns, necrosis and preoperative circulating tumor DNA detection, which were associated with extra-thoracic recurrence. These data provide insights into the relationship between LUAD morphology, the underlying evolutionary genomic landscape, and clinical and anatomical relapse risk. ispartof: NATURE MEDICINE vol:29 issue:4 ispartof: location:United States status: Published online

Published
2023

10. Abstract LB153: Clinical relevance of spatial intermixing of growth patterns and immune infiltration in lung adenocarcinoma-from TRACERx to LATTICe-A

Author

Grapa, Anca-Ioana, primary, Zhang, Hanyun, additional, Pan, Xiaoxi, additional, AbdulJabbar, Khalid, additional, Coelho-Lima, Jose, additional, Cheung, Ho Kwan Alvin, additional, Aitken, Sarah J., additional, Moore, David A., additional, Swanton, Charles, additional, Quesne, John Le, additional, and Yuan, Yinyin, additional

Published
2022
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13. Optimal Transport vs Many-to-many assignment for Graph Matching

Author

Grapa, Anca-Ioana, Blanc-Féraud, Laure, van Obberghen-Schilling, Ellen, Descombes, Xavier, Morphologie et Images (MORPHEME), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut de Biologie Valrose (IBV), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Signal, Images et Systèmes (Laboratoire I3S - SIS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA), Institut de Biologie Valrose (IBV), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), ANR-11-LABX-0028,SIGNALIFE,Réseau d'Innovation sur les Voies de Signalisation en Sciences de la Vie(2011), ANR-19-P3IA-0002,3IA@cote d'azur,3IA Côte d'Azur(2019), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS)-Signal, Images et Systèmes (Laboratoire I3S - SIS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Grapa, Anca-Ioana, Centres d'excellences - Réseau d'Innovation sur les Voies de Signalisation en Sciences de la Vie - - SIGNALIFE2011 - ANR-11-LABX-0028 - LABX - VALID, and 3IA Côte d'Azur - - 3IA@cote d'azur2019 - ANR-19-P3IA-0002 - P3IA - VALID

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, MathematicsofComputing_DISCRETEMATHEMATICS
Abstract

National audience; Graph matching for shape comparison or network analysis is a challenging issue in machine learning and computer vision. Gener-ally, this problem is formulated as an assignment task, where we seek the optimal matching between the vertices that minimizes the differencebetween the graphs. We compare a standard approach to perform graph matching, to a slightly-adapted version of regularized optimal transport,initially conceived to obtain the Gromov-Wassersein distance between structured objects (e.g. graphs) with probability masses associated to thenodes. We adapt the latter formulation to undirected and unlabeled graphs of different dimensions, by adding dummy vertices to cast the probleminto an assignment framework. The experiments are performed on randomly generated graphs onto which different spatial transformations areapplied. The results are compared with respect to the matching cost and execution time, showcasing the different limitations and/or advantagesof using these techniques for the comparison of graph networks.

Published
2019

14. Fibronectin Extra Domains tune cellular responses and confer topographically distinct features to fibril networks

Author

Efthymiou, Georgios, primary, Radwanska, Agata, additional, Grapa, Anca-Ioana, additional, Beghelli-de la Forest Divonne, Stéphanie, additional, Grall, Dominique, additional, Schaub, Sébastien, additional, Hattab, Maurice, additional, Pisano, Sabrina, additional, Poet, Mallorie, additional, Pisani, Didier F., additional, Counillon, Laurent, additional, Descombes, Xavier, additional, Blanc-Féraud, Laure, additional, and Van Obberghen-Schilling, Ellen, additional

Published
2021
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15. Characterization of fibronectin networks using graph-based representations of the fibers from 2D confocal images

Author

Grapa, Anca-Ioana, Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Morphologie et Images (MORPHEME), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut de Biologie Valrose (IBV), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS)-Signal, Images et Systèmes (Laboratoire I3S - SIS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Université Côte d'Azur, Xavier Descombes, and Ellen Van Obberghen-Schilling

Subjects
Appariement de graphes, Traitement d’images, Extracellular matrix, Statistical parametric maps, Cartes statistiques des parametres, Image processing, Matrice extracellulaire, Machine learning, Fibronectine, Apprentissage machine, Fibronectin, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Graph-matching, Cancer
Abstract

A major constituent of the Extracellular Matrix is a large protein called the Fibronectin (FN). Cellular FN is organized in fibrillar networks and can be assembled differently in the presence of two Extra Domains, EDA and EDB. Our objective was to develop numerical quantitative biomarkers to characterize the geometrical organization of the four FN variants (that differ by the inclusion/exclusion of EDA/EDB) from 2D confocal microscopy images, and to compare sane and cancerous tissues. First, we showed through two classification pipelines, based on curvelet features and deep learning framework, that the FN variants can be distinguished with a similar performance to that of a human annotator. We constructed a graph-based representation of the fibers, which were detected using Gabor filters. Graphspecific attributes were employed to classify the variants, proving that the graph representation embeds relevant information from the confocal images. Furthermore, we identified various techniques capable to differentiate the graphs, allowing us to compare the FN variants quantitatively and qualitatively. Performance analysis using toy graphs showed that the methods, which are based on graph matching and optimal transport, can meaningfully compare graphs. Using the graph-matching framework, we proposed different methodologies for defining the prototype graph, representative of a certain FN class. Additionally, the graph matching served as a tool to compute parameter deformation maps between the variants. These deformation maps were analyzed in a statistical framework showing whether or not the variation of the parameters can be explained by the variance within the same class.; La fibronectine (FN) cellulaire, composante majeure de la matrice extracellulaire, est organisée en réseaux fibrillaires de maniéré différente suivant les deux extra-domaines EDB et EDA. Notre objectif a été le développement de biomarqueurs quantitatifs pour caractériser l'organisation géométrique des quatre variants de FN à partir d'images de microscopie confocale 2D, puis de comparer les tissus sains et cancéreux. Premièrement, nous avons montré à travers deux pipelines de classification fondés sur les curvelets et sur l'apprentissage profond, que les variants peuvent être distingués avec une performance similaire à celle d'un annotateur humain. Nous avons ensuite construit une représentation des fibres (détectées avec des filtres Gabor) fondée sur des graphes. Les variantes ont été classés en utilisant des attributs spécifiques aux graphes, prouvant que ceux-ci intègrent des informations pertinentes dans les images confocales. De plus, nous avons identifié différentes techniques capables de différencier les graphes, afin de comparer les variants de FN quantitativement et qualitativement. Une analyse des performances sur des exemples simples a montré la capacité des méthodes fondées sur l'appariement de graphes et le transport optimal, de comparer les graphes. Nous avons ensuite proposé différentes méthodologies pour définir le graphe représentatif d'une certaine classe. De plus, l'appariement de graphes nous a permis de calculer des cartes de déformation des paramètres entre tissus sains et cancéreux. Ces cartes ont ensuite été analysées dans un cadre statistique montrant si la variation du paramètre peut être expliquée ou non par la variance au sein d'une même classe.

Published
2020

16. Caractérisation des réseaux de fibronectine représentés par des graphes de fibres à partir d'images de microscopie confocale 2D

Author

Grapa, Anca-Ioana, Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Morphologie et Images (MORPHEME), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut de Biologie Valrose (IBV), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS)-Signal, Images et Systèmes (Laboratoire I3S - SIS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Université Côte d'Azur, Xavier Descombes, Ellen Van Obberghen-Schilling, Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Signal, Images et Systèmes (Laboratoire I3S - SIS), and COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS)

Subjects
Appariement de graphes, Traitement d’images, Extracellular matrix, Statistical parametric maps, Cartes statistiques des parametres, Image processing, Matrice extracellulaire, Machine learning, Fibronectine, Apprentissage machine, Fibronectin, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Graph-matching, Cancer
Abstract

A major constituent of the Extracellular Matrix is a large protein called the Fibronectin (FN). Cellular FN is organized in fibrillar networks and can be assembled differently in the presence of two Extra Domains, EDA and EDB. Our objective was to develop numerical quantitative biomarkers to characterize the geometrical organization of the four FN variants (that differ by the inclusion/exclusion of EDA/EDB) from 2D confocal microscopy images, and to compare sane and cancerous tissues. First, we showed through two classification pipelines, based on curvelet features and deep learning framework, that the FN variants can be distinguished with a similar performance to that of a human annotator. We constructed a graph-based representation of the fibers, which were detected using Gabor filters. Graphspecific attributes were employed to classify the variants, proving that the graph representation embeds relevant information from the confocal images. Furthermore, we identified various techniques capable to differentiate the graphs, allowing us to compare the FN variants quantitatively and qualitatively. Performance analysis using toy graphs showed that the methods, which are based on graph matching and optimal transport, can meaningfully compare graphs. Using the graph-matching framework, we proposed different methodologies for defining the prototype graph, representative of a certain FN class. Additionally, the graph matching served as a tool to compute parameter deformation maps between the variants. These deformation maps were analyzed in a statistical framework showing whether or not the variation of the parameters can be explained by the variance within the same class.; La fibronectine (FN) cellulaire, composante majeure de la matrice extracellulaire, est organisée en réseaux fibrillaires de maniéré différente suivant les deux extra-domaines EDB et EDA. Notre objectif a été le développement de biomarqueurs quantitatifs pour caractériser l'organisation géométrique des quatre variants de FN à partir d'images de microscopie confocale 2D, puis de comparer les tissus sains et cancéreux. Premièrement, nous avons montré à travers deux pipelines de classification fondés sur les curvelets et sur l'apprentissage profond, que les variants peuvent être distingués avec une performance similaire à celle d'un annotateur humain. Nous avons ensuite construit une représentation des fibres (détectées avec des filtres Gabor) fondée sur des graphes. Les variantes ont été classés en utilisant des attributs spécifiques aux graphes, prouvant que ceux-ci intègrent des informations pertinentes dans les images confocales. De plus, nous avons identifié différentes techniques capables de différencier les graphes, afin de comparer les variants de FN quantitativement et qualitativement. Une analyse des performances sur des exemples simples a montré la capacité des méthodes fondées sur l'appariement de graphes et le transport optimal, de comparer les graphes. Nous avons ensuite proposé différentes méthodologies pour définir le graphe représentatif d'une certaine classe. De plus, l'appariement de graphes nous a permis de calculer des cartes de déformation des paramètres entre tissus sains et cancéreux. Ces cartes ont ensuite été analysées dans un cadre statistique montrant si la variation du paramètre peut être expliquée ou non par la variance au sein d'une même classe.

Published
2020

17. Classification of the fibronectin variants with curvelets

Author

Grapa, Anca-Ioana, primary, Meunier, Raphael, additional, Blanc-Feraud, Laure, additional, Efthymiou, Georgios, additional, Schaub, Sebastien, additional, Radwanska, Agata, additional, van Obberghen-Schilling, Ellen, additional, and Descombes, Xavier, additional

Published
2018
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