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1. Identifying a stable and generalizable factor structure of major depressive disorder across three large longitudinal cohorts

Author

Schilsky, Richard L., Allen, Jennifer, Anderson, MaryAnn, Anstrom, Kevin, Araujo, Lucus, Arges, Kristine, Ardalan, Kaveh, Baldwin, Bridget, Balu, Suresh, Bashir, Mustafa R., Bhapkar, Manju, Bigelow, Robert, Black, Tanya, Blanco, Rosalia, Bloomfield, Gerald, Borkar, Durga, Bouk, Leah, Boulware, Ebony, Brugnoni, Nikki, Campbell, Erin, Campbell, Paul, Carin, Larry, Cassella, Tammy Jo, Cates, Tina, Montgomery, Ranee Chatterjee, Christian, Victoria, Choong, John, Cohen-Wolkowiez, Michael, Cook, Elizabeth, Cousins, Scott, Crawford, Ashley, Datta, Nisha, Daubert, Melissa, Davis, James, Dirkes, Jillian, Doan, Isabelle, Dockery, Marie, Douglas, Pamela S., Duckworth, Shelly, Dunham, Ashley, Dunn, Gary, Ebersohl, Ryan, Eckstrand, Julie, Fang, Vivienne, Flora, April, Ford, Emily, Foster, Lucia, Fraulo, Elizabeth, French, John, Ginsburg, Geoffrey S., Green, Cindy, Greene, Latoya, Guptill, Jeffrey, Hamel, Donna, Hamill, Jennifer, Harrington, Chris, Harrison, Rob, Hedges, Lauren, Heidenfelder, Brooke, Hernandez, Adrian F., Heydary, Cindy, Hicks, Tim, Hight, Lina, Hopkins, Deborah, Huang, Erich S., Huh, Grace, Hurst, Jillian, Inman, Kelly, Janas, Gemini, Jaffee, Glenn, Johnson, Janace, Keaton, Tiffanie, Khouri, Michel, King, Daniel, Korzekwinski, Jennifer, Koweek, Lynne H., Kuo, Anthony, Kwee, Lydia, Landis, Dawn, Lipsky, Rachele, Lopez, Desiree, Lowry, Carolyn, Marcom, Kelly, Marsolo, Keith, McAdams, Paige, McCall, Shannon, McGarrah, Robert, McGugan, John, Mee, Dani, Mervin-Blake, Sabrena, Mettu, Prithu, Meyer, Mathias, Meyers, Justin, Miller, Calire N., Moen, Rebecca, Muhlbaier, Lawrence H., Murphy, Michael, Neely, Ben, Newby, L. Kristin, Nicoldson, Jayne, Nguyen, Hoang, Nguyen, Maggie, O'Brien, Lori, Onal, Sumru, O'Quinn, Jeremey, Page, David, Pagidipati, Neha J., Parikh, Kishan, Palmer, Sarah R., Patrick-Lake, Bray, Pattison, Brenda, Pencina, Michael, Peterson, Eric D., Piccini, Jon, Poole, Terry, Povsic, Tom, Provencher, Alicia, Rabineau, Dawn, Rich, Annette, Rimmer, Susan, Schwartz, Fides, Serafin, Angela, Shah, Nishant, Shah, Svati, Shields, Kelly, Shipes, Steven, Shrader, Peter, Stiber, Jon, Sutton, Lynn, Swamy, Geeta, Thomas, Betsy, Torres, Sandra, Tucci, Debara, Twisdale, Anthony, Walker, Brooke, Whitney, Susan A., Williamson, Robin, Wilverding, Lauren, Wong, Charlene A., Wruck, Lisa, Young, Ellen, Perlmutter, Jane, Krug, Sarah, Bowman-Zatzkin, S. Whitney, Assimes, Themistocles, Bajaj, Vikram, Cheong, Maxwell, Das, Millie, Desai, Manisha, Fan, Alice C., Fleischmann, Dominik, Gambhir, Sanjiv S., Gold, Garry, Haddad, Francois, Hong, David, Langlotz, Curtis, Liao, Yaping J., Lu, Rong, Mahaffey, Kenneth W., Maron, David, McCue, Rebecca, Munshi, Rajan, Rodriguez, Fatima, Shashidhar, Sumana, Sledge, George, Spielman, Susie, Spitler, Ryan, Swope, Sue, Williams, Donna, Pepine, Carl J, Lantos, John D, Pignone, Michael, Heagerty, Patrick, Beskow, Laura, Bernard, Gordon, Abad, Kelley, Angi, Giulia, Califf, Robert M., Deang, Lawrence, Huynh, Joy, Liu, Manway, Mao, Cherry, Magdaleno, Michael, Marks, William J., Jr., Mega, Jessica, Miller, David, Ong, Nicole, Patel, Darshita, Ridaura, Vanessa, Shore, Scarlet, Short, Sarah, Tran, Michelle, Vu, Veronica, Wong, Celeste, Green, Robert C., Hernandez, John, Benge, Jolene, Negrete, Gislia, Sierra, Gelsey, Schaack, Terry, Tseng, Vincent W.S., Tharp, Jordan A., Reiter, Jacob E., Ferrer, Weston, Hong, David S., Doraiswamy, P. Murali, and Nickels, Stefanie

Published
2024
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2. Deep Learning Predicts Hip Fracture using Confounding Patient and Healthcare Variables

Author

Badgeley, Marcus A., Zech, John R., Oakden-Rayner, Luke, Glicksberg, Benjamin S., Liu, Manway, Gale, William, McConnell, Michael V., Percha, Beth, Snyder, Thomas M., and Dudley, Joel T.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Hip fractures are a leading cause of death and disability among older adults. Hip fractures are also the most commonly missed diagnosis on pelvic radiographs. Computer-Aided Diagnosis (CAD) algorithms have shown promise for helping radiologists detect fractures, but the image features underpinning their predictions are notoriously difficult to understand. In this study, we trained deep learning models on 17,587 radiographs to classify fracture, five patient traits, and 14 hospital process variables. All 20 variables could be predicted from a radiograph (p < 0.05), with the best performances on scanner model (AUC=1.00), scanner brand (AUC=0.98), and whether the order was marked "priority" (AUC=0.79). Fracture was predicted moderately well from the image (AUC=0.78) and better when combining image features with patient data (AUC=0.86, p=2e-9) or patient data plus hospital process features (AUC=0.91, p=1e-21). The model performance on a test set with matched patient variables was significantly lower than a random test set (AUC=0.67, p=0.003); and when the test set was matched on patient and image acquisition variables, the model performed randomly (AUC=0.52, 95% CI 0.46-0.58), indicating that these variables were the main source of the model's predictive ability overall. We also used Naive Bayes to combine evidence from image models with patient and hospital data and found their inclusion improved performance, but that this approach was nevertheless inferior to directly modeling all variables. If CAD algorithms are inexplicably leveraging patient and process variables in their predictions, it is unclear how radiologists should interpret their predictions in the context of other known patient data. Further research is needed to illuminate deep learning decision processes so that computers and clinicians can effectively cooperate.

Published
2018

3. Confounding variables can degrade generalization performance of radiological deep learning models

Author

Zech, John R., Badgeley, Marcus A., Liu, Manway, Costa, Anthony B., Titano, Joseph J., and Oermann, Eric K.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Early results in using convolutional neural networks (CNNs) on x-rays to diagnose disease have been promising, but it has not yet been shown that models trained on x-rays from one hospital or one group of hospitals will work equally well at different hospitals. Before these tools are used for computer-aided diagnosis in real-world clinical settings, we must verify their ability to generalize across a variety of hospital systems. A cross-sectional design was used to train and evaluate pneumonia screening CNNs on 158,323 chest x-rays from NIH (n=112,120 from 30,805 patients), Mount Sinai (42,396 from 12,904 patients), and Indiana (n=3,807 from 3,683 patients). In 3 / 5 natural comparisons, performance on chest x-rays from outside hospitals was significantly lower than on held-out x-rays from the original hospital systems. CNNs were able to detect where an x-ray was acquired (hospital system, hospital department) with extremely high accuracy and calibrate predictions accordingly. The performance of CNNs in diagnosing diseases on x-rays may reflect not only their ability to identify disease-specific imaging findings on x-rays, but also their ability to exploit confounding information. Estimates of CNN performance based on test data from hospital systems used for model training may overstate their likely real-world performance.

Published
2018
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4. Next-generation characterization of the Cancer Cell Line Encyclopedia

Author

Ghandi, Mahmoud, Huang, Franklin W, Jané-Valbuena, Judit, Kryukov, Gregory V, Lo, Christopher C, McDonald, E Robert, Barretina, Jordi, Gelfand, Ellen T, Bielski, Craig M, Li, Haoxin, Hu, Kevin, Andreev-Drakhlin, Alexander Y, Kim, Jaegil, Hess, Julian M, Haas, Brian J, Aguet, François, Weir, Barbara A, Rothberg, Michael V, Paolella, Brenton R, Lawrence, Michael S, Akbani, Rehan, Lu, Yiling, Tiv, Hong L, Gokhale, Prafulla C, de Weck, Antoine, Mansour, Ali Amin, Oh, Coyin, Shih, Juliann, Hadi, Kevin, Rosen, Yanay, Bistline, Jonathan, Venkatesan, Kavitha, Reddy, Anupama, Sonkin, Dmitriy, Liu, Manway, Lehar, Joseph, Korn, Joshua M, Porter, Dale A, Jones, Michael D, Golji, Javad, Caponigro, Giordano, Taylor, Jordan E, Dunning, Caitlin M, Creech, Amanda L, Warren, Allison C, McFarland, James M, Zamanighomi, Mahdi, Kauffmann, Audrey, Stransky, Nicolas, Imielinski, Marcin, Maruvka, Yosef E, Cherniack, Andrew D, Tsherniak, Aviad, Vazquez, Francisca, Jaffe, Jacob D, Lane, Andrew A, Weinstock, David M, Johannessen, Cory M, Morrissey, Michael P, Stegmeier, Frank, Schlegel, Robert, Hahn, William C, Getz, Gad, Mills, Gordon B, Boehm, Jesse S, Golub, Todd R, Garraway, Levi A, and Sellers, William R

Subjects
Genetics, Human Genome, Lung, Cancer, Biotechnology, Lung Cancer, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Antineoplastic Agents, Biomarkers, Tumor, Cell Line, Tumor, DNA Methylation, Drug Resistance, Neoplasm, Ethnicity, Gene Editing, Histones, Humans, MicroRNAs, Molecular Targeted Therapy, Neoplasms, Protein Array Analysis, RNA Splicing, General Science & Technology
Abstract

Large panels of comprehensively characterized human cancer models, including the Cancer Cell Line Encyclopedia (CCLE), have provided a rigorous framework with which to study genetic variants, candidate targets, and small-molecule and biological therapeutics and to identify new marker-driven cancer dependencies. To improve our understanding of the molecular features that contribute to cancer phenotypes, including drug responses, here we have expanded the characterizations of cancer cell lines to include genetic, RNA splicing, DNA methylation, histone H3 modification, microRNA expression and reverse-phase protein array data for 1,072 cell lines from individuals of various lineages and ethnicities. Integration of these data with functional characterizations such as drug-sensitivity, short hairpin RNA knockdown and CRISPR-Cas9 knockout data reveals potential targets for cancer drugs and associated biomarkers. Together, this dataset and an accompanying public data portal provide a resource for the acceleration of cancer research using model cancer cell lines.

Published
2019

5. timsTOF HT Improves Protein Identification and Quantitative Reproducibility for Deep Unbiased Plasma Protein Biomarker Discovery

Author

Vitko, Dijana, primary, Chou, Wan-Fang, additional, Nouri Golmaei, Sara, additional, Lee, Joon-Yong, additional, Belthangady, Chinmay, additional, Blume, John, additional, Chan, Jessica K., additional, Flores-Campuzano, Guillermo, additional, Hu, Yuntao, additional, Liu, Manway, additional, Marispini, Mark A., additional, Mora, Megan G., additional, Ramaswamy, Saividya, additional, Ranjan, Purva, additional, Williams, Preston B., additional, Zawada, Robert J. X., additional, Ma, Philip, additional, and Wilcox, Bruce E., additional

Published
2024
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6. Multi-omics profiling with untargeted proteomics for blood-based early detection of lung cancer

Author

Koh, Brian, primary, Liu, Manway, additional, Almonte, Rebecca, additional, Ariad, Daniel, additional, Bundalian, Ghristine, additional, Chan, Jessica, additional, Choi, Jinlyung, additional, Chou, Wan-Fang, additional, Cuaresma, Rea, additional, Hoedt, Esthelle, additional, Hopper, Lexie, additional, Hu, Yuntao, additional, Jain, Anisha, additional, Khaledian, Ehdieh, additional, Khin, Thidar, additional, Kokate, Ajinkya, additional, Lee, Joon-Yong, additional, Leung, Stephanie, additional, Lin, Chi-Hung, additional, Marispini, Mark, additional, Malekpour, Hoda, additional, Mora, Megan, additional, Mudaliar, Nithya, additional, Golmaei, Sara Nouri, additional, Qian, Hao, additional, Ramaiah, Madhuvanthi, additional, Ramaswamy, Saividya, additional, Ranjan, Purva, additional, Shu, Guanhua, additional, Spiro, Peter, additional, Ta, Benjamin, additional, Vitko, Dijana, additional, Waiss, Jacob, additional, Yanagihara, Zachary, additional, Zawada, Robert, additional, Zeng, Jimmy Yi, additional, Zhang, Susan, additional, Yee, James, additional, Blume, John E., additional, Belthangady, Chinmay, additional, Wilcox, Bruce, additional, and Ma, Philip, additional

Published
2024
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7. AXL Mediates Resistance to PI3Kα Inhibition by Activating the EGFR/PKC/mTOR Axis in Head and Neck and Esophageal Squamous Cell Carcinomas

Author

Elkabets, Moshe, Pazarentzos, Evangelos, Juric, Dejan, Sheng, Qing, Pelossof, Raphael A, Brook, Samuel, Benzaken, Ana Oaknin, Rodon, Jordi, Morse, Natasha, Yan, Jenny Jiacheng, Liu, Manway, Das, Rita, Chen, Yan, Tam, Angela, Wang, Huiqin, Liang, Jinsheng, Gurski, Joseph M, Kerr, Darcy A, Rosell, Rafael, Teixidó, Cristina, Huang, Alan, Ghossein, Ronald A, Rosen, Neal, Bivona, Trever G, Scaltriti, Maurizio, and Baselga, José

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Rare Diseases, Cancer, Dental/Oral and Craniofacial Disease, Development of treatments and therapeutic interventions, 5.1 Pharmaceuticals, Animals, Antibodies, Monoclonal, Humanized, Carcinoma, Squamous Cell, Cell Line, Tumor, Cetuximab, Class I Phosphatidylinositol 3-Kinases, Drug Resistance, Neoplasm, Esophageal Neoplasms, Esophageal Squamous Cell Carcinoma, Head and Neck Neoplasms, Humans, Mice, Phosphatidylinositol 3-Kinases, Phosphoinositide-3 Kinase Inhibitors, Protein Kinase C, Protein Kinase Inhibitors, Proto-Oncogene Proteins, Receptor Protein-Tyrosine Kinases, Signal Transduction, TOR Serine-Threonine Kinases, Thiazoles, Xenograft Model Antitumor Assays, Axl Receptor Tyrosine Kinase, Neurosciences, Oncology & Carcinogenesis, Biochemistry and cell biology, Oncology and carcinogenesis
Abstract

Phosphoinositide-3-kinase (PI3K)-α inhibitors have shown clinical activity in squamous cell carcinomas (SCCs) of head and neck (H&N) bearing PIK3CA mutations or amplification. Studying models of therapeutic resistance, we have observed that SCC cells that become refractory to PI3Kα inhibition maintain PI3K-independent activation of the mammalian target of rapamycin (mTOR). This persistent mTOR activation is mediated by the tyrosine kinase receptor AXL. AXL is overexpressed in resistant tumors from both laboratory models and patients treated with the PI3Kα inhibitor BYL719. AXL dimerizes with and phosphorylates epidermal growth factor receptor (EGFR), resulting in activation of phospholipase Cγ (PLCγ)-protein kinase C (PKC), which, in turn, activates mTOR. Combined treatment with PI3Kα and either EGFR, AXL, or PKC inhibitors reverts this resistance.

Published
2015

8. Identifying a stable and generalizable factor structure of major depressive disorder across three large longitudinal cohorts

Author

Tseng, Vincent W.-S., primary, Tharp, Jordan A., additional, Reiter, Jacob E., additional, Ferrer, Weston, additional, Hong, David S., additional, Doraiswamy, P Murali, additional, Nickels, Stefanie, additional, Schilsky, Richard L., additional, Allen, Jennifer, additional, Anderson, MaryAnn, additional, Anstrom, Kevin, additional, Araujo, Lucus, additional, Arges, Kristine, additional, Ardalan, Kaveh, additional, Baldwin, Bridget, additional, Balu, Suresh, additional, Bashir, Mustafa R., additional, Bhapkar, Manju, additional, Bigelow, Robert, additional, Black, Tanya, additional, Blanco, Rosalia, additional, Bloomfield, Gerald, additional, Borkar, Durga, additional, Bouk, Leah, additional, Boulware, Ebony, additional, Brugnoni, Nikki, additional, Campbell, Erin, additional, Campbell, Paul, additional, Carin, Larry, additional, Cassella, Tammy Jo, additional, Cates, Tina, additional, Montgomery, Ranee Chatterjee, additional, Christian, Victoria, additional, Choong, John, additional, Cohen-Wolkowiez, Michael, additional, Cook, Elizabeth, additional, Cousins, Scott, additional, Crawford, Ashley, additional, Datta, Nisha, additional, Daubert, Melissa, additional, Davis, James, additional, Dirkes, Jillian, additional, Doan, Isabelle, additional, Dockery, Marie, additional, Doraiswamy, P. Murali, additional, Douglas, Pamela S., additional, Duckworth, Shelly, additional, Dunham, Ashley, additional, Dunn, Gary, additional, Ebersohl, Ryan, additional, Eckstrand, Julie, additional, Fang, Vivienne, additional, Flora, April, additional, Ford, Emily, additional, Foster, Lucia, additional, Fraulo, Elizabeth, additional, French, John, additional, Ginsburg, Geoffrey S., additional, Green, Cindy, additional, Greene, Latoya, additional, Guptill, Jeffrey, additional, Hamel, Donna, additional, Hamill, Jennifer, additional, Harrington, Chris, additional, Harrison, Rob, additional, Hedges, Lauren, additional, Heidenfelder, Brooke, additional, Hernandez, Adrian F., additional, Heydary, Cindy, additional, Hicks, Tim, additional, Hight, Lina, additional, Hopkins, Deborah, additional, Huang, Erich S., additional, Huh, Grace, additional, Hurst, Jillian, additional, Inman, Kelly, additional, Janas, Gemini, additional, Jaffee, Glenn, additional, Johnson, Janace, additional, Keaton, Tiffanie, additional, Khouri, Michel, additional, King, Daniel, additional, Korzekwinski, Jennifer, additional, Koweek, Lynne H., additional, Kuo, Anthony, additional, Kwee, Lydia, additional, Landis, Dawn, additional, Lipsky, Rachele, additional, Lopez, Desiree, additional, Lowry, Carolyn, additional, Marcom, Kelly, additional, Marsolo, Keith, additional, McAdams, Paige, additional, McCall, Shannon, additional, McGarrah, Robert, additional, McGugan, John, additional, Mee, Dani, additional, Mervin-Blake, Sabrena, additional, Mettu, Prithu, additional, Meyer, Mathias, additional, Meyers, Justin, additional, Miller, Calire N., additional, Moen, Rebecca, additional, Muhlbaier, Lawrence H., additional, Murphy, Michael, additional, Neely, Ben, additional, Newby, L. Kristin, additional, Nicoldson, Jayne, additional, Nguyen, Hoang, additional, Nguyen, Maggie, additional, O'Brien, Lori, additional, Onal, Sumru, additional, O'Quinn, Jeremey, additional, Page, David, additional, Pagidipati, Neha J., additional, Parikh, Kishan, additional, Palmer, Sarah R., additional, Patrick-Lake, Bray, additional, Pattison, Brenda, additional, Pencina, Michael, additional, Peterson, Eric D., additional, Piccini, Jon, additional, Poole, Terry, additional, Povsic, Tom, additional, Provencher, Alicia, additional, Rabineau, Dawn, additional, Rich, Annette, additional, Rimmer, Susan, additional, Schwartz, Fides, additional, Serafin, Angela, additional, Shah, Nishant, additional, Shah, Svati, additional, Shields, Kelly, additional, Shipes, Steven, additional, Shrader, Peter, additional, Stiber, Jon, additional, Sutton, Lynn, additional, Swamy, Geeta, additional, Thomas, Betsy, additional, Torres, Sandra, additional, Tucci, Debara, additional, Twisdale, Anthony, additional, Walker, Brooke, additional, Whitney, Susan A., additional, Williamson, Robin, additional, Wilverding, Lauren, additional, Wong, Charlene A., additional, Wruck, Lisa, additional, Young, Ellen, additional, Perlmutter, Jane, additional, Krug, Sarah, additional, Bowman-Zatzkin, S. Whitney, additional, Assimes, Themistocles, additional, Bajaj, Vikram, additional, Cheong, Maxwell, additional, Das, Millie, additional, Desai, Manisha, additional, Fan, Alice C., additional, Fleischmann, Dominik, additional, Gambhir, Sanjiv S., additional, Gold, Garry, additional, Haddad, Francois, additional, Hong, David, additional, Langlotz, Curtis, additional, Liao, Yaping J., additional, Lu, Rong, additional, Mahaffey, Kenneth W., additional, Maron, David, additional, McCue, Rebecca, additional, Munshi, Rajan, additional, Rodriguez, Fatima, additional, Shashidhar, Sumana, additional, Sledge, George, additional, Spielman, Susie, additional, Spitler, Ryan, additional, Swope, Sue, additional, Williams, Donna, additional, Pepine, Carl J, additional, Lantos, John D, additional, Pignone, Michael, additional, Heagerty, Patrick, additional, Beskow, Laura, additional, Bernard, Gordon, additional, Abad, Kelley, additional, Angi, Giulia, additional, Califf, Robert M., additional, Deang, Lawrence, additional, Huynh, Joy, additional, Liu, Manway, additional, Mao, Cherry, additional, Magdaleno, Michael, additional, Marks, William J., additional, Mega, Jessica, additional, Miller, David, additional, Ong, Nicole, additional, Patel, Darshita, additional, Ridaura, Vanessa, additional, Shore, Scarlet, additional, Short, Sarah, additional, Tran, Michelle, additional, Vu, Veronica, additional, Wong, Celeste, additional, Green, Robert C., additional, Hernandez, John, additional, Benge, Jolene, additional, Negrete, Gislia, additional, Sierra, Gelsey, additional, and Schaack, Terry, additional

Published
2023
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10. Abstract 6606: Biomarker discovery in non-small-cell lung cancer enabled by deep multi-omics profiling of proteins, metabolites, transcripts, and genes in blood

Author

Choi, Jinlyung, primary, Kokate, Ajinkya, additional, Khaledian, Ehdieh, additional, Liu, Manway, additional, Prasad, Preethi, additional, Blume, John, additional, Chan, Jessica, additional, Cuaresma, Rea, additional, Dai, Kevin, additional, Khadka, Manoj, additional, Khin, Thidar, additional, Kodama, Yuya, additional, Lee, Joon-Yong, additional, Malekpour, Hoda, additional, Mora, Megan, additional, Mudaliar, Nithya, additional, Golmaei, Sara Nouri, additional, Ramaiah, Madhuvanthi, additional, Ramaswamy, Saividya, additional, Spiro, Peter, additional, Vitko, Dijana, additional, Swaminathan, Kavya, additional, Yee, James, additional, Young, Brian, additional, Belthangady, Chinmay, additional, Wilcox, Bruce, additional, Koh, Brian, additional, and Ma, Philip, additional

Published
2023
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11. Abstract 6597: A multi-omics classifier achieves high sensitivity and specificity for pancreatic ductal adenocarcinoma in a case-control study of 146 subjects

Author

Blume, John, primary, Bundalian, Ghristine, additional, Chan, Jessica, additional, Chao-Shern, Connie, additional, Choi, Jinlyung, additional, Cuaresma, Rea, additional, Dai, Kevin, additional, Golmaei, Sara N., additional, Jang, Jun Heok, additional, Khadka, Manoj, additional, Khaledian, Ehdieh, additional, Khin, Thidar, additional, Kodama, Yuya, additional, Kokate, Ajinkya, additional, Lee, Joon-Yong, additional, Liu, Manway, additional, Malekpour, Hoda, additional, Mora, Megan, additional, Mudaliar, Nithya, additional, Prasad, Preethi, additional, Ramaiah, Madhuvanthi, additional, Ramaswamy, Saividya, additional, Spiro, Peter, additional, Swaminathan, Kavya, additional, Vitko, Dijana, additional, Yee, James, additional, Young, Brian, additional, Zhang, Susan, additional, Belthangady, Chinmay, additional, Wilcox, Bruce, additional, Koh, Brian, additional, and Ma, Philip, additional

Published
2023
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12. Supplementary Figure 3 from Loss of Tuberous Sclerosis Complex 2 (TSC2) Is Frequent in Hepatocellular Carcinoma and Predicts Response to mTORC1 Inhibitor Everolimus

Author

Huynh, Hung, primary, Hao, Huai-Xiang, primary, Chan, Stephen L., primary, Chen, David, primary, Ong, Richard, primary, Soo, Khee Chee, primary, Pochanard, Panisa, primary, Yang, David, primary, Ruddy, David, primary, Liu, Manway, primary, Derti, Adnan, primary, Balak, Marissa N., primary, Palmer, Michael R., primary, Wang, Yan, primary, Lee, Benjamin H., primary, Sellami, Dalila, primary, Zhu, Andrew X., primary, Schlegel, Robert, primary, and Huang, Alan, primary

Published
2023
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13. Supplementary Figure 2 from Characterization of the Novel and Specific PI3Kα Inhibitor NVP-BYL719 and Development of the Patient Stratification Strategy for Clinical Trials

Author

Fritsch, Christine, primary, Huang, Alan, primary, Chatenay-Rivauday, Christian, primary, Schnell, Christian, primary, Reddy, Anupama, primary, Liu, Manway, primary, Kauffmann, Audrey, primary, Guthy, Daniel, primary, Erdmann, Dirk, primary, De Pover, Alain, primary, Furet, Pascal, primary, Gao, Hui, primary, Ferretti, Stephane, primary, Wang, Youzhen, primary, Trappe, Joerg, primary, Brachmann, Saskia M., primary, Maira, Sauveur-Michel, primary, Wilson, Christopher, primary, Boehm, Markus, primary, Garcia-Echeverria, Carlos, primary, Chene, Patrick, primary, Wiesmann, Marion, primary, Cozens, Robert, primary, Lehar, Joseph, primary, Schlegel, Robert, primary, Caravatti, Giorgio, primary, Hofmann, Francesco, primary, and Sellers, William R., primary

Published
2023
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14. Supplementary Figure 3 from Characterization of the Novel and Specific PI3Kα Inhibitor NVP-BYL719 and Development of the Patient Stratification Strategy for Clinical Trials

Author

Fritsch, Christine, primary, Huang, Alan, primary, Chatenay-Rivauday, Christian, primary, Schnell, Christian, primary, Reddy, Anupama, primary, Liu, Manway, primary, Kauffmann, Audrey, primary, Guthy, Daniel, primary, Erdmann, Dirk, primary, De Pover, Alain, primary, Furet, Pascal, primary, Gao, Hui, primary, Ferretti, Stephane, primary, Wang, Youzhen, primary, Trappe, Joerg, primary, Brachmann, Saskia M., primary, Maira, Sauveur-Michel, primary, Wilson, Christopher, primary, Boehm, Markus, primary, Garcia-Echeverria, Carlos, primary, Chene, Patrick, primary, Wiesmann, Marion, primary, Cozens, Robert, primary, Lehar, Joseph, primary, Schlegel, Robert, primary, Caravatti, Giorgio, primary, Hofmann, Francesco, primary, and Sellers, William R., primary

Published
2023
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15. Supplementary Figure Legends, Supplementary Table Legends, and Supplementary Tables 1 through 7 from Characterization of the Novel and Specific PI3Kα Inhibitor NVP-BYL719 and Development of the Patient Stratification Strategy for Clinical Trials

Author

Fritsch, Christine, primary, Huang, Alan, primary, Chatenay-Rivauday, Christian, primary, Schnell, Christian, primary, Reddy, Anupama, primary, Liu, Manway, primary, Kauffmann, Audrey, primary, Guthy, Daniel, primary, Erdmann, Dirk, primary, De Pover, Alain, primary, Furet, Pascal, primary, Gao, Hui, primary, Ferretti, Stephane, primary, Wang, Youzhen, primary, Trappe, Joerg, primary, Brachmann, Saskia M., primary, Maira, Sauveur-Michel, primary, Wilson, Christopher, primary, Boehm, Markus, primary, Garcia-Echeverria, Carlos, primary, Chene, Patrick, primary, Wiesmann, Marion, primary, Cozens, Robert, primary, Lehar, Joseph, primary, Schlegel, Robert, primary, Caravatti, Giorgio, primary, Hofmann, Francesco, primary, and Sellers, William R., primary

Published
2023
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16. Supplementary Figure 4 from Characterization of the Novel and Specific PI3Kα Inhibitor NVP-BYL719 and Development of the Patient Stratification Strategy for Clinical Trials

Author

Fritsch, Christine, primary, Huang, Alan, primary, Chatenay-Rivauday, Christian, primary, Schnell, Christian, primary, Reddy, Anupama, primary, Liu, Manway, primary, Kauffmann, Audrey, primary, Guthy, Daniel, primary, Erdmann, Dirk, primary, De Pover, Alain, primary, Furet, Pascal, primary, Gao, Hui, primary, Ferretti, Stephane, primary, Wang, Youzhen, primary, Trappe, Joerg, primary, Brachmann, Saskia M., primary, Maira, Sauveur-Michel, primary, Wilson, Christopher, primary, Boehm, Markus, primary, Garcia-Echeverria, Carlos, primary, Chene, Patrick, primary, Wiesmann, Marion, primary, Cozens, Robert, primary, Lehar, Joseph, primary, Schlegel, Robert, primary, Caravatti, Giorgio, primary, Hofmann, Francesco, primary, and Sellers, William R., primary

Published
2023
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17. Data from The HSP90 Inhibitor NVP-AUY922 Potently Inhibits Non–Small Cell Lung Cancer Growth

Author

Garon, Edward B., primary, Finn, Richard S., primary, Hamidi, Habib, primary, Dering, Judy, primary, Pitts, Sharon, primary, Kamranpour, Naeimeh, primary, Desai, Amrita J., primary, Hosmer, Wylie, primary, Ide, Susan, primary, Avsar, Emin, primary, Jensen, Michael Rugaard, primary, Quadt, Cornelia, primary, Liu, Manway, primary, Dubinett, Steven M., primary, and Slamon, Dennis J., primary

Published
2023
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18. Supplementary Figure Legends, Tables 1 - 3 from Loss of Tuberous Sclerosis Complex 2 (TSC2) Is Frequent in Hepatocellular Carcinoma and Predicts Response to mTORC1 Inhibitor Everolimus

Author

Huynh, Hung, primary, Hao, Huai-Xiang, primary, Chan, Stephen L., primary, Chen, David, primary, Ong, Richard, primary, Soo, Khee Chee, primary, Pochanard, Panisa, primary, Yang, David, primary, Ruddy, David, primary, Liu, Manway, primary, Derti, Adnan, primary, Balak, Marissa N., primary, Palmer, Michael R., primary, Wang, Yan, primary, Lee, Benjamin H., primary, Sellami, Dalila, primary, Zhu, Andrew X., primary, Schlegel, Robert, primary, and Huang, Alan, primary

Published
2023
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19. Supplementary Figure 2 from The HSP90 Inhibitor NVP-AUY922 Potently Inhibits Non–Small Cell Lung Cancer Growth

Author

Garon, Edward B., primary, Finn, Richard S., primary, Hamidi, Habib, primary, Dering, Judy, primary, Pitts, Sharon, primary, Kamranpour, Naeimeh, primary, Desai, Amrita J., primary, Hosmer, Wylie, primary, Ide, Susan, primary, Avsar, Emin, primary, Jensen, Michael Rugaard, primary, Quadt, Cornelia, primary, Liu, Manway, primary, Dubinett, Steven M., primary, and Slamon, Dennis J., primary

Published
2023
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20. Supplementary Figure 1 from Loss of Tuberous Sclerosis Complex 2 (TSC2) Is Frequent in Hepatocellular Carcinoma and Predicts Response to mTORC1 Inhibitor Everolimus

Author

Huynh, Hung, primary, Hao, Huai-Xiang, primary, Chan, Stephen L., primary, Chen, David, primary, Ong, Richard, primary, Soo, Khee Chee, primary, Pochanard, Panisa, primary, Yang, David, primary, Ruddy, David, primary, Liu, Manway, primary, Derti, Adnan, primary, Balak, Marissa N., primary, Palmer, Michael R., primary, Wang, Yan, primary, Lee, Benjamin H., primary, Sellami, Dalila, primary, Zhu, Andrew X., primary, Schlegel, Robert, primary, and Huang, Alan, primary

Published
2023
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21. Supplementary Table 1 from The HSP90 Inhibitor NVP-AUY922 Potently Inhibits Non–Small Cell Lung Cancer Growth

Author

Garon, Edward B., primary, Finn, Richard S., primary, Hamidi, Habib, primary, Dering, Judy, primary, Pitts, Sharon, primary, Kamranpour, Naeimeh, primary, Desai, Amrita J., primary, Hosmer, Wylie, primary, Ide, Susan, primary, Avsar, Emin, primary, Jensen, Michael Rugaard, primary, Quadt, Cornelia, primary, Liu, Manway, primary, Dubinett, Steven M., primary, and Slamon, Dennis J., primary

Published
2023
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22. Supplementary Figure 4 from Loss of Tuberous Sclerosis Complex 2 (TSC2) Is Frequent in Hepatocellular Carcinoma and Predicts Response to mTORC1 Inhibitor Everolimus

Author

Huynh, Hung, primary, Hao, Huai-Xiang, primary, Chan, Stephen L., primary, Chen, David, primary, Ong, Richard, primary, Soo, Khee Chee, primary, Pochanard, Panisa, primary, Yang, David, primary, Ruddy, David, primary, Liu, Manway, primary, Derti, Adnan, primary, Balak, Marissa N., primary, Palmer, Michael R., primary, Wang, Yan, primary, Lee, Benjamin H., primary, Sellami, Dalila, primary, Zhu, Andrew X., primary, Schlegel, Robert, primary, and Huang, Alan, primary

Published
2023
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23. Supplementary Figure 2 from Loss of Tuberous Sclerosis Complex 2 (TSC2) Is Frequent in Hepatocellular Carcinoma and Predicts Response to mTORC1 Inhibitor Everolimus

Author

Huynh, Hung, primary, Hao, Huai-Xiang, primary, Chan, Stephen L., primary, Chen, David, primary, Ong, Richard, primary, Soo, Khee Chee, primary, Pochanard, Panisa, primary, Yang, David, primary, Ruddy, David, primary, Liu, Manway, primary, Derti, Adnan, primary, Balak, Marissa N., primary, Palmer, Michael R., primary, Wang, Yan, primary, Lee, Benjamin H., primary, Sellami, Dalila, primary, Zhu, Andrew X., primary, Schlegel, Robert, primary, and Huang, Alan, primary

Published
2023
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24. Data from Loss of Tuberous Sclerosis Complex 2 (TSC2) Is Frequent in Hepatocellular Carcinoma and Predicts Response to mTORC1 Inhibitor Everolimus

Author

Huynh, Hung, primary, Hao, Huai-Xiang, primary, Chan, Stephen L., primary, Chen, David, primary, Ong, Richard, primary, Soo, Khee Chee, primary, Pochanard, Panisa, primary, Yang, David, primary, Ruddy, David, primary, Liu, Manway, primary, Derti, Adnan, primary, Balak, Marissa N., primary, Palmer, Michael R., primary, Wang, Yan, primary, Lee, Benjamin H., primary, Sellami, Dalila, primary, Zhu, Andrew X., primary, Schlegel, Robert, primary, and Huang, Alan, primary

Published
2023
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25. Supplementary Table 4 from Loss of Tuberous Sclerosis Complex 2 (TSC2) Is Frequent in Hepatocellular Carcinoma and Predicts Response to mTORC1 Inhibitor Everolimus

Author

Huynh, Hung, primary, Hao, Huai-Xiang, primary, Chan, Stephen L., primary, Chen, David, primary, Ong, Richard, primary, Soo, Khee Chee, primary, Pochanard, Panisa, primary, Yang, David, primary, Ruddy, David, primary, Liu, Manway, primary, Derti, Adnan, primary, Balak, Marissa N., primary, Palmer, Michael R., primary, Wang, Yan, primary, Lee, Benjamin H., primary, Sellami, Dalila, primary, Zhu, Andrew X., primary, Schlegel, Robert, primary, and Huang, Alan, primary

Published
2023
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26. Supplementary Figure 1 from Characterization of the Novel and Specific PI3Kα Inhibitor NVP-BYL719 and Development of the Patient Stratification Strategy for Clinical Trials

Author

Fritsch, Christine, primary, Huang, Alan, primary, Chatenay-Rivauday, Christian, primary, Schnell, Christian, primary, Reddy, Anupama, primary, Liu, Manway, primary, Kauffmann, Audrey, primary, Guthy, Daniel, primary, Erdmann, Dirk, primary, De Pover, Alain, primary, Furet, Pascal, primary, Gao, Hui, primary, Ferretti, Stephane, primary, Wang, Youzhen, primary, Trappe, Joerg, primary, Brachmann, Saskia M., primary, Maira, Sauveur-Michel, primary, Wilson, Christopher, primary, Boehm, Markus, primary, Garcia-Echeverria, Carlos, primary, Chene, Patrick, primary, Wiesmann, Marion, primary, Cozens, Robert, primary, Lehar, Joseph, primary, Schlegel, Robert, primary, Caravatti, Giorgio, primary, Hofmann, Francesco, primary, and Sellers, William R., primary

Published
2023
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27. Supplementary Figure Legends from The HSP90 Inhibitor NVP-AUY922 Potently Inhibits Non–Small Cell Lung Cancer Growth

Author

Garon, Edward B., primary, Finn, Richard S., primary, Hamidi, Habib, primary, Dering, Judy, primary, Pitts, Sharon, primary, Kamranpour, Naeimeh, primary, Desai, Amrita J., primary, Hosmer, Wylie, primary, Ide, Susan, primary, Avsar, Emin, primary, Jensen, Michael Rugaard, primary, Quadt, Cornelia, primary, Liu, Manway, primary, Dubinett, Steven M., primary, and Slamon, Dennis J., primary

Published
2023
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28. Supplementary Figure 5 from Loss of Tuberous Sclerosis Complex 2 (TSC2) Is Frequent in Hepatocellular Carcinoma and Predicts Response to mTORC1 Inhibitor Everolimus

Author

Huynh, Hung, primary, Hao, Huai-Xiang, primary, Chan, Stephen L., primary, Chen, David, primary, Ong, Richard, primary, Soo, Khee Chee, primary, Pochanard, Panisa, primary, Yang, David, primary, Ruddy, David, primary, Liu, Manway, primary, Derti, Adnan, primary, Balak, Marissa N., primary, Palmer, Michael R., primary, Wang, Yan, primary, Lee, Benjamin H., primary, Sellami, Dalila, primary, Zhu, Andrew X., primary, Schlegel, Robert, primary, and Huang, Alan, primary

Published
2023
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29. Supplementary Figure 1 from The HSP90 Inhibitor NVP-AUY922 Potently Inhibits Non–Small Cell Lung Cancer Growth

Author

Garon, Edward B., primary, Finn, Richard S., primary, Hamidi, Habib, primary, Dering, Judy, primary, Pitts, Sharon, primary, Kamranpour, Naeimeh, primary, Desai, Amrita J., primary, Hosmer, Wylie, primary, Ide, Susan, primary, Avsar, Emin, primary, Jensen, Michael Rugaard, primary, Quadt, Cornelia, primary, Liu, Manway, primary, Dubinett, Steven M., primary, and Slamon, Dennis J., primary

Published
2023
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31. Quantifying neurologic disease using biosensor measurements in-clinic and in free-living settings in multiple sclerosis

Author

Chitnis, Tanuja, Glanz, Bonnie I., Gonzalez, Cindy, Healy, Brian C., Saraceno, Taylor J., Sattarnezhad, Neda, Diaz-Cruz, Camilo, Polgar-Turcsanyi, Mariann, Tummala, Subhash, Bakshi, Rohit, Bajaj, Vikram S., Ben-Shimol, David, Bikhchandani, Nikhil, Blocker, Alexander W., Burkart, Joshua, Cendrillon, Raphael, Cusack, Michael P., Demiralp, Emre, Jooste, Sarel Kobus, Kharbouch, Alaa, Lee, Amy A., Lehár, Joseph, Liu, Manway, Mahadevan, Swaminathan, Murphy, Mark, Norton, Linda C., Parlikar, Tushar A., Pathak, Anupam, Shoeb, Ali, Soderberg, Erin, Stephens, Philip, Stoertz, Aaron H., Thng, Florence, Tumkur, Kashyap, Wang, Hongsheng, Rhodes, Jane, Rudick, Richard A., Ransohoff, Richard M., Phillips, Glenn A., Bruzik, Effie, Marks, William J., Weiner, Howard L., and Snyder, Thomas M.

Published
2019
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33. Abstract A038: High-dimensional, multi-omics analyses of proteins, metabolites, transcripts, and genes enable biomarker discovery in early- and late-stage pancreatic cancer

Author

Khaledian, Ehdieh, primary, Prasad, Preethi, additional, Blume, John, additional, Bundalian, Ghristine, additional, Chao-Shern, Connie, additional, Choi, Jinlyung, additional, Cuaresma, Rea, additional, Deyarmin, Jared, additional, Jang, Jun Heok, additional, Khadka, Manoj, additional, Khin, Thidar, additional, Kodama, Yuya, additional, Kokate, Ajinkya, additional, Lee, Joon-Yong, additional, Liu, Manway, additional, Mudaliar, Nithya, additional, Ramaiah, Madhuvanthi, additional, Ramaswamy, Saividya, additional, Spiro, Peter, additional, Swaminathan, Kavya, additional, Williams, Preston, additional, Yang, Mi, additional, Yee, James, additional, Young, Brian, additional, Zawada, Robert, additional, Zhang, Susan, additional, Belthangady, Chinmay, additional, Wilcox, Bruce, additional, and Ma, Philip, additional

Published
2022
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34. CDK 4/6 Inhibitors Sensitize PIK3CA Mutant Breast Cancer to PI3K Inhibitors

Author

Vora, Sadhna R., Juric, Dejan, Kim, Nayoon, Mino-Kenudson, Mari, Huynh, Tiffany, Costa, Carlotta, Lockerman, Elizabeth L., Pollack, Sarah F., Liu, Manway, Li, Xiaoyan, Lehar, Joseph, Wiesmann, Marion, Wartmann, Markus, Chen, Yan, Cao, Z. Alexander, Pinzon-Ortiz, Maria, Kim, Sunkyu, Schlegel, Robert, Huang, Alan, and Engelman, Jeffrey A.

Published
2014
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35. Addendum: The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity

Author

Barretina, Jordi, Caponigro, Giordano, Stransky, Nicolas, Venkatesan, Kavitha, Margolin, Adam A., Kim, Sungjoon, Wilson, Christopher J., Lehár, Joseph, Kryukov, Gregory V., Sonkin, Dmitriy, Reddy, Anupama, Liu, Manway, Murray, Lauren, Berger, Michael F., Monahan, John E., Morais, Paula, Meltzer, Jodi, Korejwa, Adam, Jané-Valbuena, Judit, Mapa, Felipa A., Thibault, Joseph, Bric-Furlong, Eva, Raman, Pichai, Shipway, Aaron, Engels, Ingo H., Cheng, Jill, Yu, Guoying K., Yu, Jianjun, Aspesi, Jr, Peter, de Silva, Melanie, Jagtap, Kalpana, Jones, Michael D., Wang, Li, Hatton, Charles, Palescandolo, Emanuele, Gupta, Supriya, Mahan, Scott, Sougnez, Carrie, Onofrio, Robert C., Liefeld, Ted, MacConaill, Laura, Winckler, Wendy, Reich, Michael, Li, Nanxin, Mesirov, Jill P., Gabriel, Stacey B., Getz, Gad, Ardlie, Kristin, Chan, Vivien, Myer, Vic E., Weber, Barbara L., Porter, Jeff, Warmuth, Markus, Finan, Peter, Harris, Jennifer L., Meyerson, Matthew, Golub, Todd R., Morrissey, Michael P., Sellers, William R., Schlegel, Robert, and Garraway, Levi A.

Published
2019
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36. Abstract 3924: Deep, unbiased multi-omics approach for identification of pancreatic cancer biomarkers from blood

Author

Wilcox, Bruce, primary, Blume, John, additional, Swaminathan, Kavya, additional, Williams, Preston, additional, Khadka, Manoj, additional, Deyarmin, Jared, additional, Ramaswamy, Saividya, additional, Kodama, Yuya, additional, Young, Brian, additional, Belthangady, Chinmay, additional, Liu, Manway, additional, Yang, Mi, additional, and Ma, Philip, additional

Published
2022
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37. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study

Author

Zech, John R., Badgeley, Marcus A., Liu, Manway, Costa, Anthony B., Titano, Joseph J., and Oermann, Eric Karl

Subjects
Health screening -- Usage, Pneumonia -- Diagnosis -- Risk factors, Artificial neural networks -- Usage, Melanocyte stimulating hormone, Natural language processing, Chest x-rays, Health, Neural network, Biological sciences
Abstract

Background There is interest in using convolutional neural networks (CNNs) to analyze medical imaging to provide computer-aided diagnosis (CAD). Recent work has suggested that image classification CNNs may not generalize to new data as well as previously believed. We assessed how well CNNs generalized across three hospital systems for a simulated pneumonia screening task. Methods and findings A cross-sectional design with multiple model training cohorts was used to evaluate model generalizability to external sites using split-sample validation. A total of 158,323 chest radiographs were drawn from three institutions: National Institutes of Health Clinical Center (NIH; 112,120 from 30,805 patients), Mount Sinai Hospital (MSH; 42,396 from 12,904 patients), and Indiana University Network for Patient Care (IU; 3,807 from 3,683 patients). These patient populations had an age mean (SD) of 46.9 years (16.6), 63.2 years (16.5), and 49.6 years (17) with a female percentage of 43.5%, 44.8%, and 57.3%, respectively. We assessed individual models using the area under the receiver operating characteristic curve (AUC) for radiographic findings consistent with pneumonia and compared performance on different test sets with DeLong's test. The prevalence of pneumonia was high enough at MSH (34.2%) relative to NIH and IU (1.2% and 1.0%) that merely sorting by hospital system achieved an AUC of 0.861 (95% CI 0.855-0.866) on the joint MSH-NIH dataset. Models trained on data from either NIH or MSH had equivalent performance on IU (P values 0.580 and 0.273, respectively) and inferior performance on data from each other relative to an internal test set (i.e., new data from within the hospital system used for training data; P values both Conclusion Pneumonia-screening CNNs achieved better internal than external performance in 3 out of 5 natural comparisons. When models were trained on pooled data from sites with different pneumonia prevalence, they performed better on new pooled data from these sites but not on external data. CNNs robustly identified hospital system and department within a hospital, which can have large differences in disease burden and may confound predictions., Author(s): John R. Zech 1, Marcus A. Badgeley 2, Manway Liu 2, Anthony B. Costa 3, Joseph J. Titano 4, Eric Karl Oermann 3,* Introduction There is significant interest in [...]

Published
2018
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38. Pharmacogenomic agreement between two cancer cell line data sets

Author

Stransky, Nicolas, Ghandi, Mahmoud, Kryukov, Gregory V., Garraway, Levi A., Lehár, Joseph, Liu, Manway, Sonkin, Dmitriy, Kauffmann, Audrey, Venkatesan, Kavitha, Edelman, Elena J., Riester, Markus, Barretina, Jordi, Caponigro, Giordano, Schlegel, Robert, Sellers, William R., Stegmeier, Frank, Morrissey, Michael, Amzallag, Arnaud, Pruteanu-Malinici, Iulian, Haber, Daniel A., Ramaswamy, Sridhar, Benes, Cyril H., Menden, Michael P., Iorio, Francesco, Stratton, Michael R., McDermott, Ultan, Garnett, Mathew J., and Saez-Rodriguez, Julio

Published
2015
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39. Carboxyl-ester lipase maturity-onset diabetes of the young is associated with development of pancreatic cysts and upregulated MAPK signaling in secretin-stimulated duodenal fluid

Author

Raeder, Helge, McAllister, Fiona E., Tjora, Erling, Bhatt, Shweta, Haldorsen, Ingfrid, Hu, Jiang, Willems, Stefan M., Vesterhus, Mette, Ouaamari, Abdelfattah El, Liu, Manway, Raeder, Maria B., Immervoll, Heike, Hoem, Dag, Dimcevski, Georg, Njolstad, Pal R., Molven, Anders, Gygi, Steven P., and Kulkarni, Rohit N.

Subjects
Diabetes -- Genetic aspects -- Development and progression, Lipase -- Identification and classification -- Health aspects, Cellular signal transduction -- Research, Mitogen-activated protein kinases -- Properties, Health
Abstract

Carboxyl-ester lipase (CEL) maturity-onset diabetes of the young (MODY) is a monogenic form of diabetes and pancreatic exocrine dysfunction due to mutations in the CEL gene encoding CEL. The pathogenic mechanism for diabetes development is unknown. Since CEL is expressed mainly in pancreatic acinar cells, we asked whether we could find structural pancreatic changes in CEL-MODY subjects during the course of diabetes development. Furthermore, we hypothesized that the diseased pancreas releases proteins that are detectable in pancreatic fluid and potentially reflect activation or inactivation of disease-specific pathways. We therefore investigated nondiabetic and diabetic CEL-mutation carriers by pancreatic imaging studies and secretin-stimulated duodenal juice sampling. The secretin-stimulated duodenal juice was studied using cytokine assays, mass spectrometry (MS) proteomics, and multiplexed MS-based measurement of kinase activities. We identified multiple pancreatic cysts in all eight diabetic mutation carriers but not in any of the four nondiabetic mutation carriers or the six healthy controls. Furthermore, we identified upregulated mitogen-activated protein kinase (MAPK) target proteins and MAPK-driven cytokines and increased MAPK activity in the secretin-stimulated duodenal juice. These findings show that subjects with CEL-MODY develop multiple pancreatic cysts by the time they develop diabetes and that upregulated MAPK signaling in the pancreatic secretome may reflect the pathophysiological development of pancreatic cysts and diabetes. Diabetes 2014;63:259-269 | DOI: 10.2337/db13-1012, Studies of patients with maturity-onset diabetes of the young (MODY) have provided important insight into disease mechanisms of pancreatic β-cell failure and diabetes development and also contributed to novel treatment [...]

Published
2014
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40. Carboxyl-Ester Lipase Maturity-Onset Diabetes of the Young Is Associated With Development of Pancreatic Cysts and Upregulated MAPK Signaling in Secretin-Stimulated Duodenal Fluid

Author

Ræder, Helge, McAllister, Fiona E., Tjora, Erling, Bhatt, Shweta, Haldorsen, Ingfrid, Hu, Jiang, Willems, Stefan M., Vesterhus, Mette, El Ouaamari, Abdelfattah, Liu, Manway, Ræder, Maria B., Immervoll, Heike, Hoem, Dag, Dimcevski, Georg, Njølstad, Pål R., Molven, Anders, Gygi, Steven P., and Kulkarni, Rohit N.

Published
2014
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41. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity

Author

Barretina, Jordi, Caponigro, Giordano, Stransky, Nicolas, Venkatesan, Kavitha, Margolin, Adam A., Kim, Sungjoon, Wilson, Christopher J., Lehár, Joseph, Kryukov, Gregory V., Sonkin, Dmitriy, Reddy, Anupama, Liu, Manway, Murray, Lauren, Berger, Michael F., Monahan, John E., Morais, Paula, Meltzer, Jodi, Korejwa, Adam, Jané-Valbuena, Judit, Mapa, Felipa A., Thibault, Joseph, Bric-Furlong, Eva, Raman, Pichai, Shipway, Aaron, Engels, Ingo H., Cheng, Jill, Yu, Guoying K., Yu, Jianjun, Aspesi, Peter, de Silva, Melanie, Jagtap, Kalpana, Jones, Michael D., Wang, Li, Hatton, Charles, Palescandolo, Emanuele, Gupta, Supriya, Mahan, Scott, Sougnez, Carrie, Onofrio, Robert C., Liefeld, Ted, MacConaill, Laura, Winckler, Wendy, Reich, Michael, Li, Nanxin, Mesirov, Jill P., Gabriel, Stacey B., Getz, Gad, Ardlie, Kristin, Chan, Vivien, Myer, Vic E., Weber, Barbara L., Porter, Jeff, Warmuth, Markus, Finan, Peter, Harris, Jennifer L., Meyerson, Matthew, Golub, Todd R., Morrissey, Michael P., Sellers, William R., Schlegel, Robert, and Garraway, Levi A.

Published
2012
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44. Accelerated postnatal growth increases lipogenic gene expression adipocyte size in low-birth weight mice

Author

Isganaitis, Elvira, Jimenez-Chillaron, Jose, Woo, Melissa, Chow, Alice, DeCoste, Jennifer, Vokes, Martha, Liu, Manway, Kasif, Simon, Zavacki, Ann-Marie, Leshan, Rebecca L., Myers, Martin G., and Patti, Mary-Elizabeth

Subjects
Gene expression -- Research -- Genetic aspects -- Health aspects, Obesity in children -- Risk factors -- Genetic aspects -- Research -- Prevention, Birth weight, Low -- Health aspects -- Research, Type 2 diabetes -- Risk factors -- Prevention -- Genetic aspects -- Research, Health, Prevention, Research, Genetic aspects, Risk factors, Health aspects
Abstract

OBJECTIVE--To characterize the hormonal milieu and adipose gene expression in response to catch-up growth (CUG), a growth pattern associated with obesity and diabetes risk, in a mouse model of low birth weight (LBW). RESEARCH DESIGN AND METHODS--ICR mice were food restricted by 50% from gestational days 12.5-18.5, reducing offspring birth weight by 25%. During the suckling period, dams were either fed ad libitum, permitting CUG in offspring, or food restricted, preventing CUG. Offspring were killed at age 3 weeks, and gonadal fat was removed for RNA extraction, array analysis, RT-PCR, and evaluation of cell size and number. Serum insulin, thyroxine (T4), corticosterone, and adipokines were measured. RESULTS--At age 3 weeks, LBW mice with CUG (designated U-C) had body weight comparable with controls (designated C-C); weight was reduced by 49% in LBW mice without CUG (designated U-U). Adiposity was altered by postnatal nutrition, with gonadal fat increased by 50% in U-C and decreased by 58% in U-U mice (P < 0.05 vs. C-C mice). Adipose expression of the lipogenic genes Fasn, AccI, Lpin1, and Srebf1 was significantly increased in U-C compared with both C-C and U-U mice (P < 0.05). Mitochondrial DNA copy number was reduced by >50% in U-C versus U-U mice (P = 0.014). Although cell numbers did not differ, mean adipocyte diameter was increased in U-C and reduced in U-U mice (P < 0.01). CONCLUSIONS--CUG results in increased adipose tissue lipogenic gene expression and adipocyte diameter but not increased cellularity, suggesting that catch-up fat is primarily associated with lipogenesis rather than adipogenesis in this murine model., Low-birth weight (LBW) infants are at increased risk for hypertension, type 2 diabetes, and metabolic syndrome (1). Mechanisms remain ill defined but may involve epigenetic regulation of development and gene [...]

Published
2009

45. Addendum: The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity

Author

Barretina, Jordi, primary, Caponigro, Giordano, additional, Stransky, Nicolas, additional, Venkatesan, Kavitha, additional, Margolin, Adam A., additional, Kim, Sungjoon, additional, Wilson, Christopher J., additional, Lehár, Joseph, additional, Kryukov, Gregory V., additional, Sonkin, Dmitriy, additional, Reddy, Anupama, additional, Liu, Manway, additional, Murray, Lauren, additional, Berger, Michael F., additional, Monahan, John E., additional, Morais, Paula, additional, Meltzer, Jodi, additional, Korejwa, Adam, additional, Jané-Valbuena, Judit, additional, Mapa, Felipa A., additional, Thibault, Joseph, additional, Bric-Furlong, Eva, additional, Raman, Pichai, additional, Shipway, Aaron, additional, Engels, Ingo H., additional, Cheng, Jill, additional, Yu, Guoying K., additional, Yu, Jianjun, additional, Aspesi, Peter, additional, de Silva, Melanie, additional, Jagtap, Kalpana, additional, Jones, Michael D., additional, Wang, Li, additional, Hatton, Charles, additional, Palescandolo, Emanuele, additional, Gupta, Supriya, additional, Mahan, Scott, additional, Sougnez, Carrie, additional, Onofrio, Robert C., additional, Liefeld, Ted, additional, MacConaill, Laura, additional, Winckler, Wendy, additional, Reich, Michael, additional, Li, Nanxin, additional, Mesirov, Jill P., additional, Gabriel, Stacey B., additional, Getz, Gad, additional, Ardlie, Kristin, additional, Chan, Vivien, additional, Myer, Vic E., additional, Weber, Barbara L., additional, Porter, Jeff, additional, Warmuth, Markus, additional, Finan, Peter, additional, Harris, Jennifer L., additional, Meyerson, Matthew, additional, Golub, Todd R., additional, Morrissey, Michael P., additional, Sellers, William R., additional, Schlegel, Robert, additional, and Garraway, Levi A., additional

Published
2018
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47. Consistency of drug profiles and predictors in large-scale cancer cell line data

Author

Stransky, Nicolas, Ghandi, Mahmoud, Kryukov, Gregory V., Garraway, Levi A., Amzallag, Arnaud, Pruteanu-Malinici, Iulian, Haber, Daniel A., Ramaswamy, Sridhar, Benes, Cyril H., Lehár, Joseph, Liu, Manway, Sonkin, Dmitriy, Kauffmann, Audrey, Venkatesan, Kavitha, Edelman, Elena J., Riester, Markus, Barretina, Jordi, Caponigro, Giordano, Schlegel, Robert, Sellers, William, Stegmeier, Frank, Morrissey, Michael, Menden, Michael P., Iorio, Francesco, Stratton, Michael R., McDermott, Ultan, Saez-Rodriguez, Julio, and Garnett, Mathew J.

Subjects
Inhibitory Concentration 50, Databases, Factual, Pharmacogenetics, Cell Line, Tumor, Neoplasms, Datasets as Topic, Humans, Reproducibility of Results, Article
Abstract

Large cancer cell line collections broadly capture the genomic diversity of human cancers and provide valuable insight into anti-cancer drug response. Here we show substantial agreement and biological consilience between drug sensitivity measurements and their associated genomic predictors from two publicly available large-scale pharmacogenomics resources: The Cancer Cell Line Encyclopedia and the Genomics of Drug Sensitivity in Cancer databases.

Published
2015

48. Loss of Tuberous Sclerosis Complex 2 (TSC2) Is Frequent in Hepatocellular Carcinoma and Predicts Response to mTORC1 Inhibitor Everolimus

Author

Huynh, Hung, primary, Hao, Huai-Xiang, additional, Chan, Stephen L., additional, Chen, David, additional, Ong, Richard, additional, Soo, Khee Chee, additional, Pochanard, Panisa, additional, Yang, David, additional, Ruddy, David, additional, Liu, Manway, additional, Derti, Adnan, additional, Balak, Marissa N., additional, Palmer, Michael R., additional, Wang, Yan, additional, Lee, Benjamin H., additional, Sellami, Dalila, additional, Zhu, Andrew X., additional, Schlegel, Robert, additional, and Huang, Alan, additional

Published
2015
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50. CANDI: an R package and Shiny app for annotating radiographs and evaluating computer-aided diagnosis.

Author

Badgeley, Marcus A, Liu, Manway, Snyder, Thomas M, Glicksberg, Benjamin S, Shervey, Mark, Dudley, Joel T, Zech, John, Shameer, Khader, Lehar, Joseph, Oermann, Eric K, and McConnell, Michael V

Subjects
*COMPUTER-aided diagnosis, *MEDICAL radiography, *MACHINE learning, *DATA analysis, *BIOINFORMATICS
Abstract

Motivation Radiologists have used algorithms for Computer-Aided Diagnosis (CAD) for decades. These algorithms use machine learning with engineered features, and there have been mixed findings on whether they improve radiologists' interpretations. Deep learning offers superior performance but requires more training data and has not been evaluated in joint algorithm-radiologist decision systems. Results We developed the Computer-Aided Note and Diagnosis Interface (CANDI) for collaboratively annotating radiographs and evaluating how algorithms alter human interpretation. The annotation app collects classification, segmentation, and image captioning training data, and the evaluation app randomizes the availability of CAD tools to facilitate clinical trials on radiologist enhancement. Availability and implementation Demonstrations and source code are hosted at (https://candi.nextgenhealthcare.org), and (https://github.com/mbadge/candi), respectively, under GPL-3 license. Supplementary information Supplementary material is available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Published
2019
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