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1. Development and Validation of the Gene Expression Predictor of High-grade Serous Ovarian Carcinoma Molecular SubTYPE (PrOTYPE)

Author

Talhouk, Aline, George, Joshy, Wang, Chen, Budden, Timothy, Tan, Tuan Zea, Chiu, Derek S, Kommoss, Stefan, San Leong, Huei, Chen, Stephanie, Intermaggio, Maria P, Gilks, Blake, Nazeran, Tayyebeh M, Volchek, Mila, Elatre, Wafaa, Bentley, Rex C, Senz, Janine, Lum, Amy, Chow, Veronica, Sudderuddin, Hanwei, Mackenzie, Robertson, Leong, Samuel CY, Liu, Geyi, Johnson, Dustin, Chen, Billy, Group, AOCS, Alsop, Jennifer, Banerjee, Susana N, Behrens, Sabine, Bodelon, Clara, Brand, Alison H, Brinton, Louise, Carney, Michael E, Chiew, Yoke-Eng, Cushing-Haugen, Kara L, Cybulski, Cezary, Ennis, Darren, Fereday, Sian, Fortner, Renée T, García-Donas, Jesús, Gentry-Maharaj, Aleksandra, Glasspool, Rosalind, Goranova, Teodora, Greene, Casey S, Haluska, Paul, Harris, Holly R, Hendley, Joy, Hernandez, Brenda Y, Herpel, Esther, Jimenez-Linan, Mercedes, Karpinskyj, Chloe, Kaufmann, Scott H, Keeney, Gary L, Kennedy, Catherine J, Köbel, Martin, Koziak, Jennifer M, Larson, Melissa C, Lester, Jenny, Lewsley, Liz-Anne, Lissowska, Jolanta, Lubiński, Jan, Luk, Hugh, Macintyre, Geoff, Mahner, Sven, McNeish, Iain A, Menkiszak, Janusz, Nevins, Nikilyn, Osorio, Ana, Oszurek, Oleg, Palacios, José, Hinsley, Samantha, Pearce, Celeste L, Pike, Malcolm C, Piskorz, Anna M, Ray-Coquard, Isabelle, Rhenius, Valerie, Rodriguez-Antona, Cristina, Sharma, Raghwa, Sherman, Mark E, De Silva, Dilrini, Singh, Naveena, Sinn, Peter, Slamon, Dennis, Song, Honglin, Steed, Helen, Stronach, Euan A, Thompson, Pamela J, Tołoczko, Aleksandra, Trabert, Britton, Traficante, Nadia, Tseng, Chiu-Chen, Widschwendter, Martin, Wilkens, Lynne R, Winham, Stacey J, Winterhoff, Boris, Beeghly-Fadiel, Alicia, Benitez, Javier, Berchuck, Andrew, Brenton, James D, Brown, Robert, and Chang-Claude, Jenny

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Cancer Genomics, Orphan Drug, Genetics, Precision Medicine, Rare Diseases, Human Genome, Women's Health, Ovarian Cancer, Cancer, 4.2 Evaluation of markers and technologies, Good Health and Well Being, Aged, Algorithms, Cystadenoma, Serous, Female, Gene Expression Regulation, Neoplastic, Humans, Lymphocytes, Tumor-Infiltrating, Middle Aged, Neoplasm Grading, Neoplasm Proteins, Neoplasm, Residual, Ovarian Neoplasms, Transcriptome, Oncology & Carcinogenesis, Clinical sciences, Oncology and carcinogenesis
Abstract

PurposeGene expression-based molecular subtypes of high-grade serous tubo-ovarian cancer (HGSOC), demonstrated across multiple studies, may provide improved stratification for molecularly targeted trials. However, evaluation of clinical utility has been hindered by nonstandardized methods, which are not applicable in a clinical setting. We sought to generate a clinical grade minimal gene set assay for classification of individual tumor specimens into HGSOC subtypes and confirm previously reported subtype-associated features.Experimental designAdopting two independent approaches, we derived and internally validated algorithms for subtype prediction using published gene expression data from 1,650 tumors. We applied resulting models to NanoString data on 3,829 HGSOCs from the Ovarian Tumor Tissue Analysis consortium. We further developed, confirmed, and validated a reduced, minimal gene set predictor, with methods suitable for a single-patient setting.ResultsGene expression data were used to derive the predictor of high-grade serous ovarian carcinoma molecular subtype (PrOTYPE) assay. We established a de facto standard as a consensus of two parallel approaches. PrOTYPE subtypes are significantly associated with age, stage, residual disease, tumor-infiltrating lymphocytes, and outcome. The locked-down clinical grade PrOTYPE test includes a model with 55 genes that predicted gene expression subtype with >95% accuracy that was maintained in all analytic and biological validations.ConclusionsWe validated the PrOTYPE assay following the Institute of Medicine guidelines for the development of omics-based tests. This fully defined and locked-down clinical grade assay will enable trial design with molecular subtype stratification and allow for objective assessment of the predictive value of HGSOC molecular subtypes in precision medicine applications.See related commentary by McMullen et al., p. 5271.

Published
2020

2. Genomic Profiling of Childhood Tumor Patient-Derived Xenograft Models to Enable Rational Clinical Trial Design

Author

Rokita, Jo Lynne, Rathi, Komal S, Cardenas, Maria F, Upton, Kristen A, Jayaseelan, Joy, Cross, Katherine L, Pfeil, Jacob, Egolf, Laura E, Way, Gregory P, Farrel, Alvin, Kendsersky, Nathan M, Patel, Khushbu, Gaonkar, Krutika S, Modi, Apexa, Berko, Esther R, Lopez, Gonzalo, Vaksman, Zalman, Mayoh, Chelsea, Nance, Jonas, McCoy, Kristyn, Haber, Michelle, Evans, Kathryn, McCalmont, Hannah, Bendak, Katerina, Böhm, Julia W, Marshall, Glenn M, Tyrrell, Vanessa, Kalletla, Karthik, Braun, Frank K, Qi, Lin, Du, Yunchen, Zhang, Huiyuan, Lindsay, Holly B, Zhao, Sibo, Shu, Jack, Baxter, Patricia, Morton, Christopher, Kurmashev, Dias, Zheng, Siyuan, Chen, Yidong, Bowen, Jay, Bryan, Anthony C, Leraas, Kristen M, Coppens, Sara E, Doddapaneni, HarshaVardhan, Momin, Zeineen, Zhang, Wendong, Sacks, Gregory I, Hart, Lori S, Krytska, Kateryna, Mosse, Yael P, Gatto, Gregory J, Sanchez, Yolanda, Greene, Casey S, Diskin, Sharon J, Vaske, Olena Morozova, Haussler, David, Gastier-Foster, Julie M, Kolb, E Anders, Gorlick, Richard, Li, Xiao-Nan, Reynolds, C Patrick, Kurmasheva, Raushan T, Houghton, Peter J, Smith, Malcolm A, Lock, Richard B, Raman, Pichai, Wheeler, David A, and Maris, John M

Subjects
Biological Sciences, Pediatric, Rare Diseases, Genetics, Human Genome, Pediatric Cancer, Pediatric Research Initiative, Clinical Research, Orphan Drug, Biotechnology, Cancer, Development of treatments and therapeutic interventions, 5.1 Pharmaceuticals, 2.1 Biological and endogenous factors, Aetiology, Good Health and Well Being, Animals, Cell Line, Tumor, Central Nervous System Neoplasms, Child, Clinical Trials as Topic, Disease Models, Animal, Genomics, Humans, Mice, Mutation, Neuroblastoma, Neurofibromin 1, Osteosarcoma, Precursor Cell Lymphoblastic Leukemia-Lymphoma, Recurrence, Rhabdomyosarcoma, Sarcoma, Ewing, Tumor Suppressor Protein p53, Exome Sequencing, Wilms Tumor, Xenograft Model Antitumor Assays, classifier, copy number profiling, patient-derived xenograft, pediatric cancer, preclinical testing, relapse, transcriptome sequencing, whole-exome sequencing, Pediatric cancer, whole exome sequencing, Biochemistry and Cell Biology, Medical Physiology, Biological sciences
Abstract

Accelerating cures for children with cancer remains an immediate challenge as a result of extensive oncogenic heterogeneity between and within histologies, distinct molecular mechanisms evolving between diagnosis and relapsed disease, and limited therapeutic options. To systematically prioritize and rationally test novel agents in preclinical murine models, researchers within the Pediatric Preclinical Testing Consortium are continuously developing patient-derived xenografts (PDXs)-many of which are refractory to current standard-of-care treatments-from high-risk childhood cancers. Here, we genomically characterize 261 PDX models from 37 unique pediatric cancers; demonstrate faithful recapitulation of histologies and subtypes; and refine our understanding of relapsed disease. In addition, we use expression signatures to classify tumors for TP53 and NF1 pathway inactivation. We anticipate that these data will serve as a resource for pediatric oncology drug development and will guide rational clinical trial design for children with cancer.

Published
2019

3. Oncogenic Signaling Pathways in The Cancer Genome Atlas

Author

Sanchez-Vega, Francisco, Mina, Marco, Armenia, Joshua, Chatila, Walid K, Luna, Augustin, La, Konnor C, Dimitriadoy, Sofia, Liu, David L, Kantheti, Havish S, Saghafinia, Sadegh, Chakravarty, Debyani, Daian, Foysal, Gao, Qingsong, Bailey, Matthew H, Liang, Wen-Wei, Foltz, Steven M, Shmulevich, Ilya, Ding, Li, Heins, Zachary, Ochoa, Angelica, Gross, Benjamin, Gao, Jianjiong, Zhang, Hongxin, Kundra, Ritika, Kandoth, Cyriac, Bahceci, Istemi, Dervishi, Leonard, Dogrusoz, Ugur, Zhou, Wanding, Shen, Hui, Laird, Peter W, Way, Gregory P, Greene, Casey S, Liang, Han, Xiao, Yonghong, Wang, Chen, Iavarone, Antonio, Berger, Alice H, Bivona, Trever G, Lazar, Alexander J, Hammer, Gary D, Giordano, Thomas, Kwong, Lawrence N, McArthur, Grant, Huang, Chenfei, Tward, Aaron D, Frederick, Mitchell J, McCormick, Frank, Meyerson, Matthew, Network, The Cancer Genome Atlas Research, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Dhankani, Varsha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Liu, Yuexin, Miller, Michael, Reynolds, Sheila, Thorsson, Vesteinn, Zhang, Wei, Akbani, Rehan, Broom, Bradley M, Hegde, Apurva M, and Ju, Zhenlin

Subjects
Biochemistry and Cell Biology, Bioinformatics and Computational Biology, Biological Sciences, Biomedical and Clinical Sciences, Genetics, Oncology and Carcinogenesis, Cancer, Cancer Genomics, Human Genome, 2.1 Biological and endogenous factors, Good Health and Well Being, Databases, Genetic, Genes, Neoplasm, Humans, Neoplasms, Phosphatidylinositol 3-Kinases, Signal Transduction, Transforming Growth Factor beta, Tumor Suppressor Protein p53, Wnt Proteins, Cancer Genome Atlas Research Network, PanCanAtlas, TCGA, cancer genome atlas, cancer genomics, combination therapy, pan-cancer, precision oncology, signaling pathways, therapeutics, whole exome sequencing, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract

Genetic alterations in signaling pathways that control cell-cycle progression, apoptosis, and cell growth are common hallmarks of cancer, but the extent, mechanisms, and co-occurrence of alterations in these pathways differ between individual tumors and tumor types. Using mutations, copy-number changes, mRNA expression, gene fusions and DNA methylation in 9,125 tumors profiled by The Cancer Genome Atlas (TCGA), we analyzed the mechanisms and patterns of somatic alterations in ten canonical pathways: cell cycle, Hippo, Myc, Notch, Nrf2, PI-3-Kinase/Akt, RTK-RAS, TGFβ signaling, p53 and β-catenin/Wnt. We charted the detailed landscape of pathway alterations in 33 cancer types, stratified into 64 subtypes, and identified patterns of co-occurrence and mutual exclusivity. Eighty-nine percent of tumors had at least one driver alteration in these pathways, and 57% percent of tumors had at least one alteration potentially targetable by currently available drugs. Thirty percent of tumors had multiple targetable alterations, indicating opportunities for combination therapy.

Published
2018

4. Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas

Author

Knijnenburg, Theo A, Wang, Linghua, Zimmermann, Michael T, Chambwe, Nyasha, Gao, Galen F, Cherniack, Andrew D, Fan, Huihui, Shen, Hui, Way, Gregory P, Greene, Casey S, Liu, Yuexin, Akbani, Rehan, Feng, Bin, Donehower, Lawrence A, Miller, Chase, Shen, Yang, Karimi, Mostafa, Chen, Haoran, Kim, Pora, Jia, Peilin, Shinbrot, Eve, Zhang, Shaojun, Liu, Jianfang, Hu, Hai, Bailey, Matthew H, Yau, Christina, Wolf, Denise, Zhao, Zhongming, Weinstein, John N, Li, Lei, Ding, Li, Mills, Gordon B, Laird, Peter W, Wheeler, David A, Shmulevich, Ilya, Network, The Cancer Genome Atlas Research, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Dhankani, Varsha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Miller, Michael, Reynolds, Sheila, Thorsson, Vesteinn, Zhang, Wei, Broom, Bradley M, Hegde, Apurva M, Ju, Zhenlin, Kanchi, Rupa S, Korkut, Anil, Li, Jun, Liang, Han, Ling, Shiyun, Liu, Wenbin, Lu, Yiling, Ng, Kwok-Shing, Rao, Arvind, Ryan, Michael, Wang, Jing, and Zhang, Jiexin

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Rare Diseases, Ovarian Cancer, Cancer, Human Genome, Cancer Genomics, Orphan Drug, Women's Health, 2.1 Biological and endogenous factors, Cell Line, Tumor, DNA Damage, Gene Silencing, Genome, Human, Humans, Loss of Heterozygosity, Machine Learning, Mutation, Neoplasms, Recombinational DNA Repair, Tumor Suppressor Proteins, Cancer Genome Atlas Research Network, DNA damage footprints, DNA damage repair, The Cancer Genome Atlas PanCanAtlas project, epigenetic silencing, integrative statistical analysis, mutational signatures, protein structure analysis, somatic copy-number alterations, somatic mutations, Biochemistry and Cell Biology, Medical Physiology, Biological sciences
Abstract

DNA damage repair (DDR) pathways modulate cancer risk, progression, and therapeutic response. We systematically analyzed somatic alterations to provide a comprehensive view of DDR deficiency across 33 cancer types. Mutations with accompanying loss of heterozygosity were observed in over 1/3 of DDR genes, including TP53 and BRCA1/2. Other prevalent alterations included epigenetic silencing of the direct repair genes EXO5, MGMT, and ALKBH3 in ∼20% of samples. Homologous recombination deficiency (HRD) was present at varying frequency in many cancer types, most notably ovarian cancer. However, in contrast to ovarian cancer, HRD was associated with worse outcomes in several other cancers. Protein structure-based analyses allowed us to predict functional consequences of rare, recurrent DDR mutations. A new machine-learning-based classifier developed from gene expression data allowed us to identify alterations that phenocopy deleterious TP53 mutations. These frequent DDR gene alterations in many human cancers have functional consequences that may determine cancer progression and guide therapy.

Published
2018

5. Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas.

Author

Way, Gregory P, Sanchez-Vega, Francisco, La, Konnor, Armenia, Joshua, Chatila, Walid K, Luna, Augustin, Sander, Chris, Cherniack, Andrew D, Mina, Marco, Ciriello, Giovanni, Schultz, Nikolaus, Cancer Genome Atlas Research Network, Sanchez, Yolanda, and Greene, Casey S

Subjects
Cancer Genome Atlas Research Network, Cell Line, Tumor, Humans, Neoplasms, ras Proteins, Signal Transduction, Gene Expression Regulation, Neoplastic, Genome, Human, Machine Learning, Gene expression, HRAS, KRAS, NF1, NRAS, Ras, TCGA, drug sensitivity, machine learning, pan-cancer, Genetics, Rare Diseases, Pediatric Research Initiative, Human Genome, Cancer, Biochemistry and Cell Biology, Medical Physiology
Abstract

Precision oncology uses genomic evidence to match patients with treatment but often fails to identify all patients who may respond. The transcriptome of these "hidden responders" may reveal responsive molecular states. We describe and evaluate a machine-learning approach to classify aberrant pathway activity in tumors, which may aid in hidden responder identification. The algorithm integrates RNA-seq, copy number, and mutations from 33 different cancer types across The Cancer Genome Atlas (TCGA) PanCanAtlas project to predict aberrant molecular states in tumors. Applied to the Ras pathway, the method detects Ras activation across cancer types and identifies phenocopying variants. The model, trained on human tumors, can predict response to MEK inhibitors in wild-type Ras cell lines. We also present data that suggest that multiple hits in the Ras pathway confer increased Ras activity. The transcriptome is underused in precision oncology and, combined with machine learning, can aid in the identification of hidden responders.

Published
2018

6. Comprehensive Cross-Population Analysis of High-Grade Serous Ovarian Cancer Supports No More Than Three Subtypes.

Author

Way, Gregory P, Rudd, James, Wang, Chen, Hamidi, Habib, Fridley, Brooke L, Konecny, Gottfried E, Goode, Ellen L, Greene, Casey S, and Doherty, Jennifer A

Subjects
Humans, Cystadenocarcinoma, Serous, Ovarian Neoplasms, Cluster Analysis, Reproducibility of Results, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Databases, Genetic, Female, Genetic Association Studies, Neoplasm Grading, molecular subtypes, ovarian cancer, reproducibility, unsupervised clustering, Cystadenocarcinoma, Serous, Gene Expression Regulation, Neoplastic, Databases, Genetic, Genetics
Abstract

Four gene expression subtypes of high-grade serous ovarian cancer (HGSC) have been previously described. In these early studies, a fraction of samples that did not fit well into the four subtype classifications were excluded. Therefore, we sought to systematically determine the concordance of transcriptomic HGSC subtypes across populations without removing any samples. We created a bioinformatics pipeline to independently cluster the five largest mRNA expression datasets using k-means and nonnegative matrix factorization (NMF). We summarized differential expression patterns to compare clusters across studies. While previous studies reported four subtypes, our cross-population comparison does not support four. Because these results contrast with previous reports, we attempted to reproduce analyses performed in those studies. Our results suggest that early results favoring four subtypes may have been driven by the inclusion of serous borderline tumors. In summary, our analysis suggests that either two or three, but not four, gene expression subtypes are most consistent across datasets.

Published
2016

7. Transparency and reproducibility in artificial intelligence

Author

Haibe-Kains, Benjamin, Adam, George Alexandru, Hosny, Ahmed, Khodakarami, Farnoosh, Shraddha, Thakkar, Kusko, Rebecca, Sansone, Susanna-Assunta, Tong, Weida, Wolfinger, Russ D., Mason, Christopher E., Jones, Wendell, Dopazo, Joaquin, Furlanello, Cesare, Waldron, Levi, Wang, Bo, McIntosh, Chris, Goldenberg, Anna, Kundaje, Anshul, Greene, Casey S., Broderick, Tamara, Hoffman, Michael M., Leek, Jeffrey T., Korthauer, Keegan, Huber, Wolfgang, Brazma, Alvis, Pineau, Joelle, Tibshirani, Robert, Hastie, Trevor, Ioannidis, John P. A., Quackenbush, John, Aerts, Hugo J. W. L., RS: Carim - B06 Imaging, Beeldvorming, and MUMC+: DA BV Research (9)

Subjects
Reproducibility, Multidisciplinary, Computer science, Reproducibility of Results, Breast Neoplasms, Transparency (behavior), Data science, Article, United Kingdom, United States, Artificial Intelligence, AI, Humans, Female, Early Detection of Cancer, Algorithms, Mammography
Abstract

Breakthroughs in artificial intelligence (AI) hold enormous potential as it can automate complex tasks and go even beyond human performance. In their study, McKinney et al. showed the high potential of AI for breast cancer screening. However, the lack of methods’ details and algorithm code undermines its scientific value. Here, we identify obstacles hindering transparent and reproducible AI research as faced by McKinney et al., and provide solutions to these obstacles with implications for the broader field.

Published
2020
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8. International genome-wide meta-analysis identifies new primary biliary cirrhosis risk loci and targetable pathogenic pathways

Author

Cordell, Heather J, Han, Younghun, Mells, George F, Li, Yafang, Hirschfield, Gideon M, Greene, Casey S, Xie, Gang, Juran, Brian D, Zhu, Dakai, Qian, David C, Floyd, James AB, Morley, Katherine I, Prati, Daniele, Lleo, Ana, Cusi, Daniele, Canadian-US PBC Consortium, Italian PBC Genetics Study Group, UK-PBC Consortium, Gershwin, M Eric, Anderson, Carl A, Lazaridis, Konstantinos N, Invernizzi, Pietro, Seldin, Michael F, Sandford, Richard N, Amos, Christopher I, Siminovitch, Katherine A, Sandford, Richard [0000-0002-7437-0560], and Apollo - University of Cambridge Repository

Subjects
Liver Cirrhosis, Biliary, Humans, Genome-Wide Association Study
Abstract

Primary biliary cirrhosis (PBC) is a classical autoimmune liver disease for which effective immunomodulatory therapy is lacking. Here we perform meta-analyses of discovery data sets from genome-wide association studies of European subjects (n=2,764 cases and 10,475 controls) followed by validation genotyping in an independent cohort (n=3,716 cases and 4,261 controls). We discover and validate six previously unknown risk loci for PBC (Pcombined

Published
2020
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9. Correcting for experiment-specific variability in expression compendia can remove underlying signals

Author

Lee, Alexandra J., Park, YoSon, Doing, Georgia, Hogan, Deborah A., and Greene, Casey S.

Subjects
0303 health sciences, Correction method, Artificial neural network, Computer science, Noise (signal processing), business.industry, Research, AcademicSubjects/SCI02254, Health Informatics, Pattern recognition, Signal, Expression (mathematics), Computer Science Applications, 03 medical and health sciences, 0302 clinical medicine, 030220 oncology & carcinogenesis, Gene expression, AcademicSubjects/SCI00960, Humans, Artificial intelligence, Neural Networks, Computer, business, 030217 neurology & neurosurgery, Analysis method, 030304 developmental biology
Abstract

Motivation In the past two decades, scientists in different laboratories have assayed gene expression from millions of samples. These experiments can be combined into compendia and analyzed collectively to extract novel biological patterns. Technical variability, or "batch effects," may result from combining samples collected and processed at different times and in different settings. Such variability may distort our ability to extract true underlying biological patterns. As more integrative analysis methods arise and data collections get bigger, we must determine how technical variability affects our ability to detect desired patterns when many experiments are combined. Objective We sought to determine the extent to which an underlying signal was masked by technical variability by simulating compendia comprising data aggregated across multiple experiments. Method We developed a generative multi-layer neural network to simulate compendia of gene expression experiments from large-scale microbial and human datasets. We compared simulated compendia before and after introducing varying numbers of sources of undesired variability. Results The signal from a baseline compendium was obscured when the number of added sources of variability was small. Applying statistical correction methods rescued the underlying signal in these cases. However, as the number of sources of variability increased, it became easier to detect the original signal even without correction. In fact, statistical correction reduced our power to detect the underlying signal. Conclusion When combining a modest number of experiments, it is best to correct for experiment-specific noise. However, when many experiments are combined, statistical correction reduces our ability to extract underlying patterns.

Published
2020

10. International genome-wide meta-analysis identifies new primary biliary cirrhosis risk loci and targetable pathogenic pathways

Author

Cordell, Heather J., Younghun, Han, Mells, George F., Yafang, Li, Hirschfield, Gideon M., Greene, Casey S., Gang, Xie, Juran 7, Brian D., Dakai Zhu, 2, Qian 2, David C., Floyd, James A. B., Morley, Katherine I., Daniele Prati 11, Ana Lleo 12, Daniele, Cusi, Eric Gershwin 15, M., Anderson 8, Carl A., Lazaridis 7, Konstantinos N., Pietro, Invernizzi, Seldin 15, Michael F., Sandford, Richard N., Amos 2, Christopher I., Siminovitch, Katherine A., Schlicht 7, Erik M., Craig Lammert, 7, Atkinson 19, Elizabeth J., Chan 19, Landon L., Mariza de Andrade 19, Tobias Balschun 20, Mason 21, Andrew L., Myers 22, Robert P., Jinyi Zhang 23, Piotr Milkiewicz 24, Jia Qu 25, Odin 26, Joseph A., Luketic 27, Velimir A., Bacon 28, Bruce R., Bodenheimer Jr 29, Henry C., Valentina Liakina 30, Catherine Vincent 31, Cynthia Levy 32, Gregersen, Peter K., Almasio, 33 Piero L., Domenico Alvaro 35, Pietro Andreone 36, Angelo Andriulli 37, Cristina Barlassina 38, Pier Maria Battezzati 39, Antonio Benedetti 40, Francesca Bernuzzi 41, Ilaria Bianchi 41, Maria Consiglia Bragazzi 42, Maurizia Brunetto 43, Savino Bruno 41, Giovanni Casella 44, Barbara Coco 43, Agostino Colli 45, Massimo Colombo 46, Silvia Colombo 47, Carmela Cursaro 36, Lory Saveria Croce, ` 48, Andrea Crosignani 39, Maria Francesca Donato 46, Gianfranco Elia 49, Luca Fabris 50, Carlo Ferrari 49, Annarosa Floreani 51, Barbara Foglieni 11, Rosanna Fontana 37, Galli, Andrea, Roberta Lazzari 51, Fabio Macaluso 34, Federica Malinverno 46, Marra, Fabio, Marco Marzioni 40, Alberto Mattalia 54, Renzo Montanari 55, Lorenzo Morini 56, Filomena Morisco 57, Mousa Hani S., 41, Luigi Muratori 36, Paolo Muratori 36, Niro 37, Grazia A., Palmieri 58, Vincenzo O., Antonio Picciotto 59, Mauro Podda 41, Piero Portincasa 58, Vincenzo Ronca 41, Floriano Rosina 60, Sonia Rossi 41, Ilaria Sogno 41, Giancarlo Spinzi 61, Marta Spreafico 11, Mario, Strazzabosco, Sonia Tarallo 60, Tarocchi, Mirko, Claudio Tiribelli 48, Pierluigi Toniutto 64, Maria Vinci 65, Massimo, Zuin, Chin Lye Ch’ng, Mesbah Rahman 68, Tom Yapp 69, Richard Sturgess 70, Christopher Healey 71, Marek, Czajkowski, Anton, Gunasekera, Pranab Gyawali 77, Purushothaman Premchand 78, Kapil Kapur 79, Richard Marley 80, Graham Foster 80, Alan Watson 81, Aruna Dias 82, Javaid Subhani 83, Rory Harvey 84, Roger McCorry 85, David Ramanaden 86, Jaber Gasem 87, Richard Evans 88, Thiriloganathan Mathialahan 89, Christopher Shorrock 90, George Lipscomb 91, Paul Southern 92, Jeremy Tibble 93, David, Gorard, Altaf Palegwala 98, Susan, Jones, Marco Carbone 101, Mohamed Dawwas 101, Graeme Alexander 101, Sunil, Dolwani, Martin Prince 104, Matthew Foxton 105, David Elphick 106, Harriet Mitchison 107, Ian Gooding 108, Mazn Karmo 109, Sushma, Saksena, Mike, Mendall, Minesh, Patel, Roland, Ede, Andrew Austin 119, Joanna, Sayer, Lorraine Hankey 122, Christopher Hovell 122, Neil Fisher 123, Martyn, Carter, Konrad Koss 126, Andrzej, Piotrowicz, Charles, Grimley, David, Neal, Guan Lim 135, Sass, Levi, Aftab Ala 138, Andrea Broad 139, Athar Saeed 139, Gordon Wood 140, Jonathan, Brown, Mark, Wilkinson, Harriet Gordon 145, John Ramage 146, Jo Ridpath 147, Theodore, Ngatchu, Bob Grover 151, Syed Shaukat 152, Ray Shidrawi 153, George, Abouda, Faiz Ali 156, Ian Rees 157, Imroz Salam 158, Mark Narain 159, Ashley, Brown, Simon Taylor Robinson 162, Simon Williams 163, Leonie Grellier 164, Paul Banim 165, Debasish Das 166, Andrew Chilton 166, Michael Heneghan 167, Howard, Curtis, Markus Gess 170, Ian, Drake, Mark, Aldersley, Mervyn, Davies, Rebecca, Jones, Alastair McNair 175, Raj Srirajaskanthan 176, Maxton, Pitcher, Sambit Sen 179, George, Bird, Adrian Barnardo 182, Paul Kitchen 182, Kevin Yoong 183, Oza, Chirag, Nurani Sivaramakrishnan 186, George MacFaul 187, David Jones 188, Amir Shah 189, Chris Evans 190, Subrata Saha 191, Katharine, Pollock, Peter, Bramley, Ashis, Mukhopadhya, Andrew, Fraser, Peter, Mills, Christopher, Shallcross, Stewart, Campbell, Andrew, Bathgate, Alan Shepherd 213, John Dillon 214, Simon Rushbrook 215, Robert Przemioslo 216 Christopher Macdonald, Jane, Metcalf, Udi Shmueli 221, Andrew, Davis, Asifabbas, Naqvi, Tom, Lee, Ryder, Stephen D., Jane Collier 231, Howard, Klass, Mary, Ninkovic, Matthew Cramp 238, Nicholas Sharer 239, Richard Aspinall 240, Patrick Goggin 240, Deb, Ghosh, Andrew Douds 243, Barbara Hoeroldt 244, Jonathan Booth 245, Earl Williams 246, Hyder Hussaini 247, William Stableforth 247, Reuben Ayres 248, Douglas Thorburn 249, Eileen Marshall 249, Andrew Burroughs 249, Steven, Mann, Martin Lombard 252, Paul Richardson 252, Imran Patanwala 252, Julia Maltby 253, Matthew Brookes 254, Ray, Mathew, Samir Vyas 257, Saket Singhal 258, Dermot, Gleeson, Sharat, Misra, Jeff, Butterworth, Keith George 265, Tim, Harding, Andrew, Douglass, Simon Panter 270, Jeremy Shearman 271, Gary Bray 272, Graham, Butcher, Daniel Forton 275, John, Mclindon, Matthew Cowan 279, Gregory Whatley 280, Aditya, Mandal, Hemant, Gupta, Pradeep, Sanghi, Sanjiv Jain 283, Steve Pereira 284, Geeta Prasad 285, Gill Watts 285, Mark Wright 286, James Neuberger 287, Fiona Gordon 288, Esther Unitt 289, Allister, Grant, Toby, Delahooke, Andrew Higham 293, Alison Brind 294, Mark Cox 295, Subramaniam Ramakrishnan 296, Alistair, King, Carole Collins 300, Simon, Whalley, Andy Li 303, Jocelyn Fraser 304, Andrew Bell 305, Voi Shim Wong 306, Amit, Singhal, Ian, Gee, Yeng Ang 312, Rupert Ransford 313, James Gotto 314, Charles, Millson, Jane Bowles 318, Caradog, Thomas, Melanie Harrison 70, Roman Galaska 71, Jennie, Kendall, Jessica, Whiteman, Caroline, Lawlor, Catherine, Gray, Keith Elliott 79, Caroline Mulvaney Jones, Lucie, Hobson, Greta Van Duyvenvoorde 90, Alison Loftus 91, Katie Seward 92, Ruth, Penn, Jane Maiden 98, Rose Damant 98, Janeane Hails 101, Rebecca, Cloudsdale, Valeria Silvestre 105, Sue Glenn 106, Eleanor Dungca 107, Natalie Wheatley 108, Helen Doyle 109, Melanie, Kent, Caroline, Hamilton, Delyth, Braim, Helen Wooldridge 117, Rachel Abrahams 117, Alison Paton 119, Nicola, Lancaster, Andrew Gibbins 122, Karen Hogben 122, Phillipa, Desousa, Florin, Muscariu, Janine, Musselwhite, Alexandra McKay 132, LaiTing Tan 135, Carole Foale 138, Jacqueline Brighton 138, Kerry Flahive 140, Estelle, Nambela, Paula, Townshend, Chris, Ford, Sophie, Holder, Caroline, Palmer, James Featherstone 147, Mariam Nasseri 151, Joy Sadeghian 153, Bronwen, Williams, Carol Thomas 156, Sally Ann Rolls 156, Abigail Hynes 159, Claire Duggan 159, Sarah Jones 159, Mary, Crossey, Glynis Stansfield 163, Carolyn MacNicol 163, Joy Wilkins 164, Elva Wilhelmsen 165, Parizade Raymode 166, Hye Jeong Lee 167, Emma, Durant, Rebecca, Bishop, Noma, Ncube, Sherill, Tripoli, Rebecca, Casey, Caroline Cowley 182, Richard Miller 183, Kathryn Houghton 188, Samantha Ducker 188, Fiona Wright 189, Bridget Bird 191, Gwen Baxter 191, Janie Keggans 191, Maggie, Hughes, Emma Grieve 196, Karin Young 196, Williams 197, D., Kate Ocker 199, Frances, Hines, Kirsty, Martin, Caron, Innes, Talal Valliani 216, Helen, Fairlamb, Sarah, Thornthwaite, Anne, Eastick, Elizabeth Tanqueray 221, Jennifer Morrison 222, Becky Holbrook 222, Julie, Browning, Kirsten, Walker, Susan, Congreave, Juliette, Verheyden, Susan, Slininger, Lizzie Stafford 231, Denise O’Donnell 231, Mark Ainsworth 231, Susan Lord 118, Linda, Kent, Linda March 238, Christine Dickson 239, Diane Simpson 239, Beverley Longhurst 240, Maria Hayes 240, Ervin, Shpuza, Nikki, White, Sarah, Besley, Sallyanne Pearson 244, Alice Wright 245, Linda Jones 245, Emma Gunter 246, Hannah Dewhurst 246, Anna Fouracres 247, Liz Farrington 247, Lyn Graves 247, Suzie Marriott 248, Marina Leoni 249, David Tyrer 252, Kate Martin 252, Lola Dali kemmery 253, Victoria Lambourne 253, Marie Green 254, Dawn, Sirdefield, Kelly Amor 255, Julie Colley 258, Bal Shinder 258, Jayne Jones 260, Marisa Mills 260, Mandy, Carnahan, Natalie Taylor 265, Kerenza Boulton 265, Julie, Tregonning, Carly Brown 270, Gayle Clifford 270, Emily Archer 271, Maria, Hamilton, Janette Curtis 278, Tracey Shewan 279, Sue Walsh 280, Karen, Warner, Kimberley Netherton 283, Mcdonald Mupudzi 286, Bridget Gunson 287, Jane Gitahi 288, Denise Gocher 289, Sally, Batham, Hilary, Pateman, Senayon, Desmennu, Jill Conder 293, Darren Clement 294, Susan Gallagher 294, Jacky Orpe 256, PuiChing Chan 296, Lynn, Currie, Lynn, O’Donohoe, Metod Oblak 300, Lisa Morgan 302, Marie Quinn 303, Isobel Amey 304, Yolanda Baird 304, Donna Cotterill 305, Lourdes Cumlat 306, Louise Winter 312, Sandra Greer 312, Katie, Spurdle, Joanna, Allison, Simon, Dyer, Helen, Sweeting, Jean, Kordula, Cordell, H. J., Han, Y., Mells, G. F., Li, Y., Hirschfield, G. M., Greene, C. S., Xie, G., Juran, B. D., Zhu, D., Qian, D. C., Floyd, J. A. B., Morley, K. I., Prati, D., Lleo, A., Cusi, D., Gershwin, M. E., Anderson, C. A., Lazaridis, K. N., Invernizzi, P., Seldin, M. F., Sandford, R. N., Amos, C. I., Siminovitch, K. A., Schlicht, E. M., Lammert, C., Atkinson, E. J., Chan, L. L., De Andrade, M., Balschun, T., Mason, A. L., Myers, R. P., Zhang, J., Milkiewicz, P., Qu, J., Odin, J. A., Luketic, V. A., Bacon, B. R., Bodenheimer, H. C., Liakina, V., Vincent, C., Levy, C., Gregersen, P. K., Almasio, P. L., Alvaro, D., Andreone, P., Andriulli, A., Barlassina, C., Battezzati, P. M., Benedetti, A., Bernuzzi, F., Bianchi, I., Bragazzi, M. C., Brunetto, M., Bruno, S., Casella, G., Coco, B., Colli, A., Colombo, M., Colombo, S., Cursaro, C., Croce, L. S., Crosignani, A., Donato, M. F., Elia, G., Fabris, L., Ferrari, C., Floreani, A., Foglieni, B., Fontana, R., Galli, A., Lazzari, R., Macaluso, F., Malinverno, F., Marra, F., Marzioni, M., Mattalia, A., Montanari, R., Morini, L., Morisco, F., Mousa Hani, S., Muratori, L., Muratori, P., Niro, G. A., Palmieri, V. O., Picciotto, A., Podda, M., Portincasa, P., Ronca, V., Rosina, F., Rossi, S., Sogno, I., Spinzi, G., Spreafico, M., Strazzabosco, M., Tarallo, S., Tarocchi, M., Tiribelli, C., Toniutto, P., Vinci, M., Zuin, M., Ch'Ng, C. 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S., Singhal, A., Gee, I., Ang, Y., Ransford, R., Gotto, J., Millson, C., Bowles, J., Thomas, C., Harrison, M., Galaska, R., Kendall, J., Whiteman, J., Lawlor, C., Gray, C., Elliott, K., Mulvaney-Jones, C., Hobson, L., Van Duyvenvoorde, G., Loftus, A., Seward, K., Penn, R., Maiden, J., Damant, R., Hails, J., Cloudsdale, R., Silvestre, V., Glenn, S., Dungca, E., Wheatley, N., Doyle, H., Kent, M., Hamilton, C., Braim, D., Wooldridge, H., Abrahams, R., Paton, A., Lancaster, N., Gibbins, A., Hogben, K., Desousa, P., Muscariu, F., Musselwhite, J., Mckay, A., Tan, L., Foale, C., Brighton, J., Flahive, K., Nambela, E., Townshend, P., Ford, C., Holder, S., Palmer, C., Featherstone, J., Nasseri, M., Sadeghian, J., Williams, B., Rolls, S. -A., Hynes, A., Duggan, C., Crossey, M., Stansfield, G., Macnicol, C., Wilkins, J., Wilhelmsen, E., Raymode, P., Lee, H. -J., Durant, E., Bishop, R., Ncube, N., Tripoli, S., Casey, R., Cowley, C., Miller, R., Houghton, K., Ducker, S., Wright, F., Bird, B., Baxter, G., Keggans, J., Hughes, M., Grieve, E., Young, K., Williams, D., Ocker, K., Hines, F., Martin, K., Innes, C., Valliani, T., Fairlamb, H., Thornthwaite, S., Eastick, A., Tanqueray, E., Morrison, J., Holbrook, B., Browning, J., Walker, K., Congreave, S., Verheyden, J., Slininger, S., Stafford, L., O'Donnell, D., Ainsworth, M., Lord, S., Kent, L., March, L., Dickson, C., Simpson, D., Longhurst, B., Hayes, M., Shpuza, E., White, N., Besley, S., Pearson, S., Wright, A., Jones, L., Gunter, E., Dewhurst, H., Fouracres, A., Farrington, L., Graves, L., Marriott, S., Leoni, M., Tyrer, D., Dali-Kemmery, L., Lambourne, V., Green, M., Sirdefield, D., Amor, K., Colley, J., Shinder, B., Jones, J., Mills, M., Carnahan, M., Taylor, N., Boulton, K., Tregonning, J., Brown, C., Clifford, G., Archer, E., Hamilton, M., Curtis, J., Shewan, T., Walsh, S., Warner, K., Netherton, K., Mupudzi, M., Gunson, B., Gitahi, J., Gocher, D., Batham, S., Pateman, H., Desmennu, S., Conder, J., Clement, D., Gallagher, S., Orpe, J., Chan, P., Currie, L., O'Donohoe, L., Oblak, M., Morgan, L., Quinn, M., Amey, I., Baird, Y., Cotterill, D., Cumlat, L., Winter, L., Greer, S., Spurdle, K., Allison, J., Dyer, S., Sweeting, H., Kordula, J., Cordell, Heather J, Han, Younghun, Mells, George F., Li, Yafang, Hirschfield, Gideon M., Greene, Casey S., Xie, Gang, Juran, Brian D., Zhu, Dakai, Qian, David C., Floyd, James A. B., Morley, Katherine I., Prati, Daniele, Lleo, Ana, Cusi, Daniele, Gershwin, M. Eric, Anderson, Carl A., Lazaridis, Konstantinos N., Invernizzi, Pietro, Seldin, Michael F., Sandford, Richard N., Amos, Christopher I., Siminovitch, Katherine A., Schlicht, Erik M., Lammert, Craig, Atkinson, Elizabeth J., Chan, Landon L., De Andrade, Mariza, Balschun, Tobia, Mason, Andrew L., Myers, Robert P., Zhang, Jinyi, Milkiewicz, Piotr, Qu, Jia, Odin, Joseph A., Luketic, Velimir A., Bacon, Bruce R., Bodenheimer, Henry C., Liakina, Valentina, Vincent, Catherine, Levy, Cynthia, Gregersen, Peter K., Almasio, Piero L., Alvaro, Domenico, Andreone, Pietro, Andriulli, Angelo, Barlassina, Cristina, Battezzati, Pier Maria, Benedetti, Antonio, Bernuzzi, Francesca, Bianchi, Ilaria, Bragazzi, Maria Consiglia, Brunetto, Maurizia, Bruno, Savino, Casella, Giovanni, Coco, Barbara, Colli, Agostino, Colombo, Massimo, Colombo, Silvia, Cursaro, Carmela, Crocè, Lory Saveria, Crosignani, Andrea, Donato, Maria Francesca, Elia, Gianfranco, Fabris, Luca, Ferrari, Carlo, Floreani, Annarosa, Foglieni, 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David, Palegwala, Altaf, Jones, Susan, Carbone, Marco, Dawwas, Mohamed, Alexander, Graeme, Dolwani, Sunil, Prince, Martin, Foxton, Matthew, Elphick, David, Mitchison, Harriet, Gooding, Ian, Karmo, Mazn, Saksena, Sushma, Mendall, Mike, Patel, Minesh, Ede, Roland, Austin, Andrew, Sayer, Joanna, Hankey, Lorraine, Hovell, Christopher, Fisher, Neil, Carter, Martyn, Koss, Konrad, Piotrowicz, Andrzej, Grimley, Charle, Neal, David, Lim, Guan, Levi, Sa, Ala, Aftab, Broad, Andrea, Saeed, Athar, Wood, Gordon, Brown, Jonathan, Wilkinson, Mark, Gordon, Harriet, Ramage, John, Ridpath, Jo, Ngatchu, Theodore, Grover, Bob, Shaukat, Syed, Shidrawi, Ray, Abouda, George, Ali, Faiz, Rees, Ian, Salam, Imroz, Narain, Mark, Brown, Ashley, Taylor-Robinson, Simon, Williams, Simon, Grellier, Leonie, Banim, Paul, Das, Debasish, Chilton, Andrew, Heneghan, Michael, Curtis, Howard, Gess, Marku, Drake, Ian, Aldersley, Mark, Davies, Mervyn, Jones, Rebecca, Mcnair, Alastair, Srirajaskanthan, Raj, Pitcher, Maxton, Sen, Sambit, Bird, George, Barnardo, Adrian, Kitchen, Paul, Yoong, Kevin, Chirag, Oza, Sivaramakrishnan, Nurani, Macfaul, George, Jones, David, Shah, Amir, Evans, Chri, Saha, Subrata, Pollock, Katharine, Bramley, Peter, Mukhopadhya, Ashi, Fraser, Andrew, Mills, Peter, Shallcross, Christopher, Campbell, Stewart, Bathgate, Andrew, Shepherd, Alan, Dillon, John, Rushbrook, Simon, Przemioslo, Robert, Macdonald, Christopher, Metcalf, Jane, Shmueli, Udi, Davis, Andrew, Naqvi, Asifabba, Lee, Tom, Stephen, Dyder, Collier, Jane, Klass, Howard, Ninkovic, Mary, Cramp, Matthew, Sharer, Nichola, Aspinall, Richard, Goggin, Patrick, Ghosh, Deb, Douds, Andrew, Hoeroldt, Barbara, Booth, Jonathan, Williams, Earl, Hussaini, Hyder, Stableforth, William, Ayres, Reuben, Thorburn, Dougla, Marshall, Eileen, Burroughs, Andrew, Mann, Steven, Lombard, Martin, Richardson, Paul, Patanwala, Imran, Maltby, Julia, Brookes, Matthew, Mathew, Ray, Vyas, Samir, Singhal, Saket, Gleeson, Dermot, Misra, Sharat, Butterworth, Jeff, George, Keith, Harding, Tim, Douglass, Andrew, Panter, Simon, Shearman, Jeremy, Bray, Gary, Butcher, Graham, Forton, Daniel, Mclindon, John, Cowan, Matthew, Whatley, Gregory, Mandal, Aditya, Gupta, Hemant, Sanghi, Pradeep, Jain, Sanjiv, Pereira, Steve, Prasad, Geeta, Watts, Gill, Wright, Mark, Neuberger, Jame, Gordon, Fiona, Unitt, Esther, Grant, Allister, Delahooke, Toby, Higham, Andrew, Brind, Alison, Cox, Mark, Ramakrishnan, Subramaniam, King, Alistair, Collins, Carole, Whalley, Simon, Li, Andy, Fraser, Jocelyn, Bell, Andrew, Wong, Voi Shim, Singhal, Amit, Gee, Ian, Ang, Yeng, Ransford, Rupert, Gotto, Jame, Millson, Charle, Bowles, Jane, Thomas, Caradog, Harrison, Melanie, Galaska, Roman, Kendall, Jennie, Whiteman, Jessica, Lawlor, Caroline, Gray, Catherine, Elliott, Keith, Mulvaney-Jones, Caroline, Hobson, Lucie, Van Duyvenvoorde, Greta, Loftus, Alison, Seward, Katie, Penn, Ruth, Maiden, Jane, Damant, Rose, Hails, Janeane, Cloudsdale, Rebecca, Silvestre, Valeria, Glenn, Sue, Dungca, Eleanor, Wheatley, Natalie, Doyle, Helen, Kent, Melanie, Hamilton, Caroline, Braim, Delyth, Wooldridge, Helen, Abrahams, Rachel, Paton, Alison, Lancaster, Nicola, Gibbins, Andrew, Hogben, Karen, Desousa, Phillipa, Muscariu, Florin, Musselwhite, Janine, Mckay, Alexandra, Tan, Laiting, Foale, Carole, Brighton, Jacqueline, Flahive, Kerry, Nambela, Estelle, Townshend, Paula, Ford, Chri, Holder, Sophie, Palmer, Caroline, Featherstone, Jame, Nasseri, Mariam, Sadeghian, Joy, Williams, Bronwen, Thomas, Carol, Rolls, Sally-Ann, Hynes, Abigail, Duggan, Claire, Jones, Sarah, Crossey, Mary, Stansfield, Glyni, Macnicol, Carolyn, Wilkins, Joy, Wilhelmsen, Elva, Raymode, Parizade, Lee, Hye-Jeong, Durant, Emma, Bishop, Rebecca, Ncube, Noma, Tripoli, Sherill, Casey, Rebecca, Cowley, Caroline, Miller, Richard, Houghton, Kathryn, Ducker, Samantha, Wright, Fiona, Bird, Bridget, Baxter, Gwen, Keggans, Janie, Hughes, Maggie, Grieve, Emma, Young, Karin, Ocker, Kate, Hines, France, Martin, Kirsty, Innes, Caron, Valliani, Talal, Fairlamb, Helen, Thornthwaite, Sarah, Eastick, Anne, Tanqueray, Elizabeth, Morrison, Jennifer, Holbrook, Becky, Browning, Julie, Walker, Kirsten, Congreave, Susan, Verheyden, Juliette, Slininger, Susan, Stafford, Lizzie, O'Donnell, Denise, Ainsworth, Mark, Lord, Susan, Kent, Linda, March, Linda, Dickson, Christine, Simpson, Diane, Longhurst, Beverley, Hayes, Maria, Shpuza, Ervin, White, Nikki, Besley, Sarah, Pearson, Sallyanne, Wright, Alice, Jones, Linda, Gunter, Emma, Dewhurst, Hannah, Fouracres, Anna, Farrington, Liz, Graves, Lyn, Marriott, Suzie, Leoni, Marina, Tyrer, David, Martin, Kate, Dali-Kemmery, Lola, Lambourne, Victoria, Green, Marie, Sirdefield, Dawn, Amor, Kelly, Colley, Julie, Shinder, Bal, Jones, Jayne, Mills, Marisa, Carnahan, Mandy, Taylor, Natalie, Boulton, Kerenza, Tregonning, Julie, Brown, Carly, Clifford, Gayle, Archer, Emily, Hamilton, Maria, Curtis, Janette, Shewan, Tracey, Walsh, Sue, Warner, Karen, Netherton, Kimberley, Mupudzi, Mcdonald, Gunson, Bridget, Gitahi, Jane, Gocher, Denise, Batham, Sally, Pateman, Hilary, Desmennu, Senayon, Conder, Jill, Clement, Darren, Gallagher, Susan, Orpe, Jacky, Chan, Puiching, Currie, Lynn, O'Donohoe, Lynn, Oblak, Metod, Morgan, Lisa, Quinn, Marie, Amey, Isobel, Baird, Yolanda, Cotterill, Donna, Cumlat, Lourde, Winter, Louise, Greer, Sandra, Spurdle, Katie, Allison, Joanna, Dyer, Simon, Sweeting, Helen, Kordula, Jean, Heather J., Cordell, Younghun, Han, George F., Mell, Yafang, Li, Gideon M., Hirschfield, Casey S., Greene, Gang, Xie, Brian D., Juran, Dakai, Zhu, David C., Qian, James A. B., Floyd, Katherine I., Morley, Daniele, Prati, Ana, Lleo, Daniele, Cusi, Canadian US PBC, Consortium, Italian PBC Genetics Study, Group, UK PBC, Consortium, M., Eric Gershwin, Carl A., Anderson, Konstantinos N., Lazaridi, Pietro, Invernizzi, Michael F., Seldin, Richard N., Sandford, Christopher I., Amo, Katherine A., Siminovitch, Cordell H.J., Han Y., Mells G.F., Li Y., Hirschfield G.M., Greene C.S., Xie G., Juran B.D., Zhu D., Qian D.C., Floyd J.A.B., Morley K.I., Prati D., Lleo A., Cusi D., Gershwin M.E., Anderson C.A., Lazaridis K.N., Invernizzi P., Seldin M.F., Sandford R.N., Amos C.I., Siminovitch K.A., Schlicht E.M., Lammert C., Atkinson E.J., Chan L.L., De Andrade M., Balschun T., Mason A.L., Myers R.P., Zhang J., Milkiewicz P., Qu J., Odin J.A., Luketic V.A., Bacon B.R., Bodenheimer H.C., Liakina V., Vincent C., Levy C., Gregersen P.K., Almasio P.L., Alvaro D., Andreone P., Andriulli A., Barlassina C., Battezzati P.M., Benedetti A., Bernuzzi F., Bianchi I., Bragazzi M.C., Brunetto M., Bruno S., Casella G., Coco B., Colli A., Colombo M., Colombo S., Cursaro C., Croce L.S., Crosignani A., Donato M.F., Elia G., Fabris L., Ferrari C., Floreani A., Foglieni B., Fontana R., Galli A., Lazzari R., Macaluso F., Malinverno F., Marra F., Marzioni M., Mattalia A., Montanari R., Morini L., Morisco F., Mousa Hani S., Muratori L., Muratori P., Niro G.A., Palmieri V.O., Picciotto A., Podda M., Portincasa P., Ronca V., Rosina F., Rossi S., Sogno I., Spinzi G., Spreafico M., Strazzabosco M., Tarallo S., Tarocchi M., Tiribelli C., Toniutto P., Vinci M., Zuin M., Ch'Ng C.L., Rahman M., Yapp T., Sturgess R., Healey C., Czajkowski M., Gunasekera A., Gyawali P., Premchand P., Kapur K., Marley R., Foster G., Watson A., Dias A., Subhani J., Harvey R., McCorry R., Ramanaden D., Gasem J., Evans R., Mathialahan T., Shorrock C., Lipscomb G., Southern P., Tibble J., Gorard D., Palegwala A., Jones S., Carbone M., Dawwas M., Alexander G., Dolwani S., Prince M., Foxton M., Elphick D., Mitchison H., Gooding I., Karmo M., Saksena S., Mendall M., Patel M., Ede R., Austin A., Sayer J., Hankey L., Hovell C., Fisher N., Carter M., Koss K., Piotrowicz A., Grimley C., Neal D., Lim G., Levi S., Ala A., Broad A., Saeed A., Wood G., Brown J., Wilkinson M., Gordon H., Ramage J., Ridpath J., Ngatchu T., Grover B., Shaukat S., Shidrawi R., Abouda G., Ali F., Rees I., Salam I., Narain M., Brown A., Taylor-Robinson S., Williams S., Grellier L., Banim P., Das D., Chilton A., Heneghan M., Curtis H., Gess M., Drake I., Aldersley M., Davies M., Jones R., McNair A., Srirajaskanthan R., Pitcher M., Sen S., Bird G., Barnardo A., Kitchen P., Yoong K., Chirag O., Sivaramakrishnan N., MacFaul G., Jones D., Shah A., Evans C., Saha S., Pollock K., Bramley P., Mukhopadhya A., Fraser A., Mills P., Shallcross C., Campbell S., Bathgate A., Shepherd A., Dillon J., Rushbrook S., Przemioslo R., Macdonald C., Metcalf J., Shmueli U., Davis A., Naqvi A., Lee T., Stephen D., Collier J., Klass H., Ninkovic M., Cramp M., Sharer N., Aspinall R., Goggin P., Ghosh D., Douds A., Hoeroldt B., Booth J., Williams E., Hussaini H., Stableforth W., Ayres R., Thorburn D., Marshall E., Burroughs A., Mann S., Lombard M., Richardson P., Patanwala I., Maltby J., Brookes M., Mathew R., Vyas S., Singhal S., Gleeson D., Misra S., Butterworth J., George K., Harding T., Douglass A., Panter S., Shearman J., Bray G., Butcher G., Forton D., McLindon J., Cowan M., Whatley G., Mandal A., Gupta H., Sanghi P., Jain S., Pereira S., Prasad G., Watts G., Wright M., Neuberger J., Gordon F., Unitt E., Grant A., Delahooke T., Higham A., Brind A., Cox M., Ramakrishnan S., King A., Collins C., Whalley S., Li A., Fraser J., Bell A., Wong V.S., Singhal A., Gee I., Ang Y., Ransford R., Gotto J., Millson C., Bowles J., Thomas C., Harrison M., Galaska R., Kendall J., Whiteman J., Lawlor C., Gray C., Elliott K., Mulvaney-Jones C., Hobson L., Van Duyvenvoorde G., Loftus A., Seward K., Penn R., Maiden J., Damant R., Hails J., Cloudsdale R., Silvestre V., Glenn S., Dungca E., Wheatley N., Doyle H., Kent M., Hamilton C., Braim D., Wooldridge H., Abrahams R., Paton A., Lancaster N., Gibbins A., Hogben K., Desousa P., Muscariu F., Musselwhite J., McKay A., Tan L., Foale C., Brighton J., Flahive K., Nambela E., Townshend P., Ford C., Holder S., Palmer C., Featherstone J., Nasseri M., Sadeghian J., Williams B., Rolls S.-A., Hynes A., Duggan C., Crossey M., Stansfield G., MacNicol C., Wilkins J., Wilhelmsen E., Raymode P., Lee H.-J., Durant E., Bishop R., Ncube N., Tripoli S., Casey R., Cowley C., Miller R., Houghton K., Ducker S., Wright F., Bird B., Baxter G., Keggans J., Hughes M., Grieve E., Young K., Williams D., Ocker K., Hines F., Martin K., Innes C., Valliani T., Fairlamb H., Thornthwaite S., Eastick A., Tanqueray E., Morrison J., Holbrook B., Browning J., Walker K., Congreave S., Verheyden J., Slininger S., Stafford L., O'Donnell D., Ainsworth M., Lord S., Kent L., March L., Dickson C., Simpson D., Longhurst B., Hayes M., Shpuza E., White N., Besley S., Pearson S., Wright A., Jones L., Gunter E., Dewhurst H., Fouracres A., Farrington L., Graves L., Marriott S., Leoni M., Tyrer D., Dali-Kemmery L., Lambourne V., Green M., Sirdefield D., Amor K., Colley J., Shinder B., Jones J., Mills M., Carnahan M., Taylor N., Boulton K., Tregonning J., Brown C., Clifford G., Archer E., Hamilton M., Curtis J., Shewan T., Walsh S., Warner K., Netherton K., Mupudzi M., Gunson B., Gitahi J., Gocher D., Batham S., Pateman H., Desmennu S., Conder J., Clement D., Gallagher S., Orpe J., Chan P., Currie L., O'Donohoe L., Oblak M., Morgan L., Quinn M., Amey I., Baird Y., Cotterill D., Cumlat L., Winter L., Greer S., Spurdle K., Allison J., Dyer S., Sweeting H., Kordula J., Cordell, H, Han, Y, Mells, G, Li, Y, Hirschfield, G, Greene, C, Xie, G, Juran, B, Zhu, D, Qian, D, Floyd, J, Morley, K, Prati, D, Lleo, A, Cusi, D, Gershwin, M, Anderson, C, Lazaridis, K, Invernizzi, P, Seldin, M, Sandford, R, Amos, C, Siminovitch, K, Schlicht, E, Lammert, C, Atkinson, E, Chan, L, De Andrade, M, Balschun, T, Mason, A, Myers, R, Zhang, J, Milkiewicz, P, Qu, J, Odin, J, Luketic, V, Bacon, B, Bodenheimer, H, Liakina, V, Vincent, C, Levy, C, Gregersen, P, Almasio, P, Alvaro, D, Andreone, P, Andriulli, A, Barlassina, C, Battezzati, P, Benedetti, A, Bernuzzi, F, Bianchi, I, Bragazzi, M, Brunetto, M, Bruno, S, Casella, G, Coco, B, Colli, A, Colombo, M, Colombo, S, Cursaro, C, Crocè, L, Crosignani, A, Donato, M, Elia, G, Fabris, L, Ferrari, C, Floreani, A, Foglieni, B, Fontana, R, Galli, A, Lazzari, R, Macaluso, F, Malinverno, F, Marra, F, Marzioni, M, Mattalia, A, Montanari, R, Morini, L, Morisco, F, Mousa Hani, S, Muratori, L, Muratori, P, Niro, G, Palmieri, V, Picciotto, A, Podda, M, Portincasa, P, Ronca, V, Rosina, F, Rossi, S, Sogno, I, Spinzi, G, Spreafico, M, Strazzabosco, M, Tarallo, S, Tarocchi, M, Tiribelli, C, Toniutto, P, Vinci, M, Zuin, M, Ch'Ng, C, Rahman, M, Yapp, T, Sturgess, R, Healey, C, Czajkowski, M, Gunasekera, A, Gyawali, P, Premchand, P, Kapur, K, Marley, R, Foster, G, Watson, A, Dias, A, Subhani, J, Harvey, R, Mccorry, R, Ramanaden, D, Gasem, J, Evans, R, Mathialahan, T, Shorrock, C, Lipscomb, G, Southern, P, Tibble, J, Gorard, D, Palegwala, A, Jones, S, Carbone, M, Dawwas, M, Alexander, G, Dolwani, S, Prince, M, Foxton, M, Elphick, D, Mitchison, H, Gooding, I, Karmo, M, Saksena, S, Mendall, M, Patel, M, Ede, R, Austin, A, Sayer, J, Hankey, L, Hovell, C, Fisher, N, Carter, M, Koss, K, Piotrowicz, A, Grimley, C, Neal, D, Lim, G, Levi, S, Ala, A, Broad, A, Saeed, A, Wood, G, Brown, J, Wilkinson, M, Gordon, H, Ramage, J, Ridpath, J, Ngatchu, T, Grover, B, Shaukat, S, Shidrawi, R, Abouda, G, Ali, F, Rees, I, Salam, I, Narain, M, Brown, A, Taylor Robinson, S, Williams, S, Grellier, L, Banim, P, Das, D, Chilton, A, Heneghan, M, Curtis, H, Gess, M, Drake, I, Aldersley, M, Davies, M, Jones, R, Mcnair, A, Srirajaskanthan, R, Pitcher, M, Sen, S, Bird, G, Barnardo, A, Kitchen, P, Yoong, K, Chirag, O, Sivaramakrishnan, N, Macfaul, G, Jones, D, Shah, A, Evans, C, Saha, S, Pollock, K, Bramley, P, Mukhopadhya, A, Fraser, A, Mills, P, Shallcross, C, Campbell, S, Bathgate, A, Shepherd, A, Dillon, J, Rushbrook, S, Przemioslo, R, Macdonald, C, Metcalf, J, Shmueli, U, Davis, A, Naqvi, A, Lee, T, Stephen, D, Collier, J, Klass, H, Ninkovic, M, Cramp, M, Sharer, N, Aspinall, R, Goggin, P, Ghosh, D, Douds, A, Hoeroldt, B, Booth, J, Williams, E, Hussaini, H, Stableforth, W, Ayres, R, Thorburn, D, Marshall, E, Burroughs, A, Mann, S, Lombard, M, Richardson, P, Patanwala, I, Maltby, J, Brookes, M, Mathew, R, Vyas, S, Singhal, S, Gleeson, D, Misra, S, Butterworth, J, George, K, Harding, T, Douglass, A, Panter, S, Shearman, J, Bray, G, Butcher, G, Forton, D, Mclindon, J, Cowan, M, Whatley, G, Mandal, A, Gupta, H, Sanghi, P, Jain, S, Pereira, S, Prasad, G, Watts, G, Wright, M, Neuberger, J, Gordon, F, Unitt, E, Grant, A, Delahooke, T, Higham, A, Brind, A, Cox, M, Ramakrishnan, S, King, A, Collins, C, Whalley, S, Li, A, Fraser, J, Bell, A, Wong, V, Singhal, A, Gee, I, Ang, Y, Ransford, R, Gotto, J, Millson, C, Bowles, J, Thomas, C, Harrison, M, Galaska, R, Kendall, J, Whiteman, J, Lawlor, C, Gray, C, Elliott, K, Mulvaney Jones, C, Hobson, L, Van Duyvenvoorde, G, Loftus, A, Seward, K, Penn, R, Maiden, J, Damant, R, Hails, J, Cloudsdale, R, Silvestre, V, Glenn, S, Dungca, E, Wheatley, N, Doyle, H, Kent, M, Hamilton, C, Braim, D, Wooldridge, H, Abrahams, R, Paton, A, Lancaster, N, Gibbins, A, Hogben, K, Desousa, P, Muscariu, F, Musselwhite, J, Mckay, A, Tan, L, Foale, C, Brighton, J, Flahive, K, Nambela, E, Townshend, P, Ford, C, Holder, S, Palmer, C, Featherstone, J, Nasseri, M, Sadeghian, J, Williams, B, Rolls, S, Hynes, A, Duggan, C, Crossey, M, Stansfield, G, Macnicol, C, Wilkins, J, Wilhelmsen, E, Raymode, P, Lee, H, Durant, E, Bishop, R, Ncube, N, Tripoli, S, Casey, R, Cowley, C, Miller, R, Houghton, K, Ducker, S, Wright, F, Bird, B, Baxter, G, Keggans, J, Hughes, M, Grieve, E, Young, K, Williams, D, Ocker, K, Hines, F, Martin, K, Innes, C, Valliani, T, Fairlamb, H, Thornthwaite, S, Eastick, A, Tanqueray, E, Morrison, J, Holbrook, B, Browning, J, Walker, K, Congreave, S, Verheyden, J, Slininger, S, Stafford, L, O'Donnell, D, Ainsworth, M, Lord, S, Kent, L, March, L, Dickson, C, Simpson, D, Longhurst, B, Hayes, M, Shpuza, E, White, N, Besley, S, Pearson, S, Wright, A, Jones, L, Gunter, E, Dewhurst, H, Fouracres, A, Farrington, L, Graves, L, Marriott, S, Leoni, M, Tyrer, D, Dali Kemmery, L, Lambourne, V, Green, M, Sirdefield, D, Amor, K, Colley, J, Shinder, B, Jones, J, Mills, M, Carnahan, M, Taylor, N, Boulton, K, Tregonning, J, Brown, C, Clifford, G, Archer, E, Hamilton, M, Curtis, J, Shewan, T, Walsh, S, Warner, K, Netherton, K, Mupudzi, M, Gunson, B, Gitahi, J, Gocher, D, Batham, S, Pateman, H, Desmennu, S, Conder, J, Clement, D, Gallagher, S, Orpe, J, Chan, P, Currie, L, O'Donohoe, L, Oblak, M, Morgan, L, Quinn, M, Amey, I, Baird, Y, Cotterill, D, Cumlat, L, Winter, L, Greer, S, Spurdle, K, Allison, J, Dyer, S, Sweeting, H, and Kordula, J

Subjects
Liver Cirrhosis, Genetics and Molecular Biology (all), pathogenesi, risk assessment EMTREE medical terms: Article, genetic association, genotype, EMTREE drug terms: chemokine receptor CCR6, genetic risk, Biochemistry, meta-analysi, primary biliary cirrhosi, chemokine receptor CCR6 [EMTREE drug terms], single nucleotide polymorphism, genetic variability, Article [risk assessment EMTREE medical terms], Liver Cirrhosis, Biliary, pathogenesis, Biliary, Chemistry (all), STAT protein GEOBASE Subject Index: disease treatment, cohort analysis, genome wide meta analysis PBC, signal transduction, gene locus, cohort analysi, CBP, Article, Physics and Astronomy (all), macrophage inflammatory protein 3alpha, Humans, controlled study, human, interleukin 27, genome, Biochemistry, Genetics and Molecular Biology (all), meta analysi, interleukin 12p40, interleukin 12, Janus kinase, meta-analysis, pathogen, genetic predisposition, major clinical study, meta analysis, primary biliary cirrhosis, disease treatment [STAT protein GEOBASE Subject Index], Genome-Wide Association Study, gene locu
Abstract

Primary biliary cirrhosis (PBC) is a classical autoimmune liver disease for which effective immunomodulatory therapy is lacking. Here we perform meta-analyses of discovery data sets from genome-wide association studies of European subjects (n=2,764 cases and 10,475 controls) followed by validation genotyping in an independent cohort (n=3,716 cases and 4,261 controls). We discover and validate six previously unknown risk loci for PBC (Pcombined, Primary biliary cirrhosis is an autoimmune liver disease with poor therapeutic options. Here Cordell et al. a perform meta-analysis of European genome-wide association studies identifying six novel risk loci and a number of potential therapeutic pathways.

Published
2015
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11. Big Data Bioinformatics

Author

GREENE, CASEY S., TAN, JIE, UNG, MATTHEW, MOORE, JASON H., and CHENG, CHAO

Subjects
0301 basic medicine, Web server, Computer science, Test data generation, Big data, computer.software_genre, General Biochemistry, Genetics and Molecular Biology, Article, 03 medical and health sciences, Artificial Intelligence, Profiling (information science), Data Mining, Humans, Precision Medicine, Molecular Biology, business.industry, Gene Expression Profiling, R Programming Language, Computational Biology, Genomics, Sequence Analysis, DNA, Data science, High-Throughput Screening Assays, 030104 developmental biology, business, computer, Software
Abstract

Recent technological advances allow for high throughput profiling of biological systems in a cost-efficient manner. The low cost of data generation is leading us to the “big data” era. The availability of big data provides unprecedented opportunities but also raises new challenges for data mining and analysis. In this review, we introduce key concepts in the analysis of big data, including both “machine learning” algorithms as well as “unsupervised” and “supervised” examples of each. We note packages for the R programming language that are available to perform machine learning analyses. In addition to programming based solutions, we review webservers that allow users with limited or no programming background to perform these analyses on large data compendia.

Published
2016

12. Development and Validation of the Gene Expression Predictor of High-grade Serous Ovarian Carcinoma Molecular SubTYPE (PrOTYPE)

Author

Sven Mahner, Robertson Mackenzie, Aline Talhouk, Linda E. Kelemen, Gottfried E. Konecny, Jennifer Alsop, Rosalind Glasspool, Chiu-Chen Tseng, Joy Hendley, Dennis J. Slamon, Jennifer A. Doherty, Andrew Berchuck, Anna H. Wu, Anna M. Piskorz, Chen Wang, Cristina Rodríguez-Antona, D.G.H. de Silva, Valerie Rhenius, Peter A. Fasching, Stacey J. Winham, Gary L. Keeney, Teodora Goranova, Joshy George, Jan Lubinski, Michelle J. Henderson, Rex C. Bentley, Jenny Lester, Sabine Behrens, Joellen M. Schildkraut, Michael E. Carney, Timothy Budden, David G. Huntsman, Oleg Oszurek, Michael S. Anglesio, Jacek Gronwald, Ruby Yun-Ju Huang, Martin Köbel, Javier Benitez, Martin Widschwendter, Melissa C. Larson, Raghwa Sharma, Clara Bodelon, Usha Menon, Janusz Menkiszak, Blake Gilks, María Josefa Mosteiro García, Jesús García-Donas, Wafaa Elatre, Scott H. Kaufmann, Paul Haluska, Pamela J. Thompson, Boris Winterhoff, Susan J. Ramus, Louise A. Brinton, Simon A. Gayther, Mary Anne Rossing, Georgia Chenevix-Trench, Hugh Luk, Jolanta Lissowska, Marc T. Goodman, Billy Chen, Beth Y. Karlan, Naveena Singh, Sian Fereday, Mark E. Sherman, Ana Osorio, Lynne R. Wilkens, Maria P. Intermaggio, Brenda Y. Hernandez, Britton Trabert, Esther Herpel, Mercedes Jimenez-Linan, Janine Senz, Geyi Liu, Celeste Leigh Pearce, Samuel C Y Leong, Iain A. McNeish, Isabelle Ray-Coquard, Susana Banerjee, Malcolm C. Pike, Liz-Anne Lewsley, Helen Steed, Honglin Song, Samantha Hinsley, David D.L. Bowtell, James D. Brenton, Holly R. Harris, Tuan Zea Tan, Cezary Cybulski, Alicia Beeghly-Fadiel, A. Toloczko, Nikilyn Nevins, Robert S. Brown, Darren Ennis, Stephanie Chen, Euan A. Stronach, José Palacios, Sandra Orsulic, Anna deFazio, Geoff Macintyre, Kara L. Cushing-Haugen, Mila Volchek, Aleksandra Gentry-Maharaj, Jenny Chang-Claude, Ellen L. Goode, Paul D.P. Pharoah, Hanwei Sudderuddin, Stefan Kommoss, Derek S. Chiu, Huei San Leong, Peter Sinn, Catherine J. Kennedy, Chloe Karpinskyj, Alison Brand, Amy Lum, Veronica Chow, Nicolas Wentzensen, Tayyebeh M. Nazeran, Nadia Traficante, Dustin Johnson, Yoke-Eng Chiew, Casey S. Greene, Jennifer M Koziak, Renée T. Fortner, Imperial College Healthcare NHS Trust- BRC Funding, Cancer Research UK, Ovarian Cancer Action, Talhouk, Aline [0000-0001-7760-410X], George, Joshy [0000-0001-8510-8229], Wang, Chen [0000-0003-2638-3081], Tan, Tuan Zea [0000-0001-6624-1593], Behrens, Sabine [0000-0002-9714-104X], Bodelon, Clara [0000-0002-6578-2678], Brinton, Louise [0000-0003-3853-8562], Fortner, Renée T [0000-0002-1426-8505], García-Donas, Jesús [0000-0001-7731-3601], Gentry-Maharaj, Aleksandra [0000-0001-7270-9762], Glasspool, Rosalind [0000-0002-5000-1680], Greene, Casey S [0000-0001-8713-9213], Harris, Holly R [0000-0002-2572-6727], Kaufmann, Scott H [0000-0002-4900-7145], Kennedy, Catherine J [0000-0002-4465-5784], Köbel, Martin [0000-0002-6615-2037], Koziak, Jennifer M [0000-0001-5830-0397], Lissowska, Jolanta [0000-0003-2695-5799], McNeish, Iain A [0000-0002-9387-7586], Menkiszak, Janusz [0000-0001-8279-7196], Hinsley, Samantha [0000-0001-6903-4688], Pike, Malcolm C [0000-0003-4891-1199], Rodriguez-Antona, Cristina [0000-0001-8750-7338], Sinn, Peter [0000-0003-2836-6699], Trabert, Britton [0000-0002-1539-6090], Widschwendter, Martin [0000-0002-7778-8380], Winham, Stacey J [0000-0002-8492-9102], Brenton, James D [0000-0002-5738-6683], Brown, Robert [0000-0001-7960-5755], Chang-Claude, Jenny [0000-0001-8919-1971], deFazio, Anna [0000-0003-0057-4744], Fasching, Peter A [0000-0003-4885-8471], Kelemen, Linda E [0000-0003-4362-9784], Menon, Usha [0000-0003-3708-1732], Pharoah, Paul DP [0000-0001-8494-732X], Ramus, Susan J [0000-0003-0005-7798], Doherty, Jennifer A [0000-0002-1454-8187], Anglesio, Michael S [0000-0003-1639-5003], and Apollo - University of Cambridge Repository

Subjects
0301 basic medicine, Oncology, Cancer Research, medicine.medical_specialty, Neoplasm, Residual, Bevacizumab, 03 medical and health sciences, Ovarian tumor, Lymphocytes, Tumor-Infiltrating, 0302 clinical medicine, Ovarian carcinoma, Internal medicine, medicine, Humans, 1112 Oncology and Carcinogenesis, Oncology & Carcinogenesis, Stage (cooking), Aged, Ovarian Neoplasms, business.industry, Cystadenoma, Serous, Cancer, Middle Aged, Precision medicine, Omics, medicine.disease, Neoplasm Proteins, Gene Expression Regulation, Neoplastic, Serous fluid, 030104 developmental biology, 030220 oncology & carcinogenesis, Female, Neoplasm Grading, Transcriptome, business, Algorithms, medicine.drug
Abstract

Purpose: Gene expression–based molecular subtypes of high-grade serous tubo-ovarian cancer (HGSOC), demonstrated across multiple studies, may provide improved stratification for molecularly targeted trials. However, evaluation of clinical utility has been hindered by nonstandardized methods, which are not applicable in a clinical setting. We sought to generate a clinical grade minimal gene set assay for classification of individual tumor specimens into HGSOC subtypes and confirm previously reported subtype-associated features. Experimental Design: Adopting two independent approaches, we derived and internally validated algorithms for subtype prediction using published gene expression data from 1,650 tumors. We applied resulting models to NanoString data on 3,829 HGSOCs from the Ovarian Tumor Tissue Analysis consortium. We further developed, confirmed, and validated a reduced, minimal gene set predictor, with methods suitable for a single-patient setting. Results: Gene expression data were used to derive the predictor of high-grade serous ovarian carcinoma molecular subtype (PrOTYPE) assay. We established a de facto standard as a consensus of two parallel approaches. PrOTYPE subtypes are significantly associated with age, stage, residual disease, tumor-infiltrating lymphocytes, and outcome. The locked-down clinical grade PrOTYPE test includes a model with 55 genes that predicted gene expression subtype with >95% accuracy that was maintained in all analytic and biological validations. Conclusions: We validated the PrOTYPE assay following the Institute of Medicine guidelines for the development of omics-based tests. This fully defined and locked-down clinical grade assay will enable trial design with molecular subtype stratification and allow for objective assessment of the predictive value of HGSOC molecular subtypes in precision medicine applications. See related commentary by McMullen et al., p. 5271

Published
2020
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13. An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Author

Yuxiang Jiang, Tal Ronnen Oron, Wyatt T. Clark, Asma R. Bankapur, Daniel D’Andrea, Rosalba Lepore, Christopher S. Funk, Indika Kahanda, Karin M. Verspoor, Asa Ben-Hur, Da Chen Emily Koo, Duncan Penfold-Brown, Dennis Shasha, Noah Youngs, Richard Bonneau, Alexandra Lin, Sayed M. E. Sahraeian, Pier Luigi Martelli, Giuseppe Profiti, Rita Casadio, Renzhi Cao, Zhaolong Zhong, Jianlin Cheng, Adrian Altenhoff, Nives Skunca, Christophe Dessimoz, Tunca Dogan, Kai Hakala, Suwisa Kaewphan, Farrokh Mehryary, Tapio Salakoski, Filip Ginter, Hai Fang, Ben Smithers, Matt Oates, Julian Gough, Petri Törönen, Patrik Koskinen, Liisa Holm, Ching-Tai Chen, Wen-Lian Hsu, Kevin Bryson, Domenico Cozzetto, Federico Minneci, David T. Jones, Samuel Chapman, Dukka BKC, Ishita K. Khan, Daisuke Kihara, Dan Ofer, Nadav Rappoport, Amos Stern, Elena Cibrian-Uhalte, Paul Denny, Rebecca E. Foulger, Reija Hieta, Duncan Legge, Ruth C. Lovering, Michele Magrane, Anna N. Melidoni, Prudence Mutowo-Meullenet, Klemens Pichler, Aleksandra Shypitsyna, Biao Li, Pooya Zakeri, Sarah ElShal, Léon-Charles Tranchevent, Sayoni Das, Natalie L. Dawson, David Lee, Jonathan G. Lees, Ian Sillitoe, Prajwal Bhat, Tamás Nepusz, Alfonso E. Romero, Rajkumar Sasidharan, Haixuan Yang, Alberto Paccanaro, Jesse Gillis, Adriana E. Sedeño-Cortés, Paul Pavlidis, Shou Feng, Juan M. Cejuela, Tatyana Goldberg, Tobias Hamp, Lothar Richter, Asaf Salamov, Toni Gabaldon, Marina Marcet-Houben, Fran Supek, Qingtian Gong, Wei Ning, Yuanpeng Zhou, Weidong Tian, Marco Falda, Paolo Fontana, Enrico Lavezzo, Stefano Toppo, Carlo Ferrari, Manuel Giollo, Damiano Piovesan, Silvio C.E. Tosatto, Angela del Pozo, José M. Fernández, Paolo Maietta, Alfonso Valencia, Michael L. Tress, Alfredo Benso, Stefano Di Carlo, Gianfranco Politano, Alessandro Savino, Hafeez Ur Rehman, Matteo Re, Marco Mesiti, Giorgio Valentini, Joachim W. Bargsten, Aalt D. J. van Dijk, Branislava Gemovic, Sanja Glisic, Vladmir Perovic, Veljko Veljkovic, Nevena Veljkovic, Danillo C. Almeida-e-Silva, Ricardo Z. N. Vencio, Malvika Sharan, Jörg Vogel, Lakesh Kansakar, Shanshan Zhang, Slobodan Vucetic, Zheng Wang, Michael J. E. Sternberg, Mark N. Wass, Rachael P. Huntley, Maria J. Martin, Claire O’Donovan, Peter N. Robinson, Yves Moreau, Anna Tramontano, Patricia C. Babbitt, Steven E. Brenner, Michal Linial, Christine A. Orengo, Burkhard Rost, Casey S. Greene, Sean D. Mooney, Iddo Friedberg, Predrag Radivojac, Jiang, Yuxiang, Oron, Tal Ronnen, Clark, Wyatt T., Bankapur, Asma R., D’Andrea, Daniel, Lepore, Rosalba, Funk, Christopher S., Kahanda, Indika, Verspoor, Karin M., Ben-Hur, Asa, Koo, Da Chen Emily, Penfold-Brown, Duncan, Shasha, Denni, Youngs, Noah, Bonneau, Richard, Lin, Alexandra, Sahraeian, Sayed M. E., Martelli, Pier Luigi, Profiti, Giuseppe, Casadio, Rita, Cao, Renzhi, Zhong, Zhaolong, Cheng, Jianlin, Altenhoff, Adrian, Skunca, Nive, Dessimoz, Christophe, Dogan, Tunca, Hakala, Kai, Kaewphan, Suwisa, Mehryary, Farrokh, Salakoski, Tapio, Ginter, Filip, Fang, Hai, Smithers, Ben, Oates, Matt, Gough, Julian, Törönen, Petri, Koskinen, Patrik, Holm, Liisa, Chen, Ching-Tai, Hsu, Wen-Lian, Bryson, Kevin, Cozzetto, Domenico, Minneci, Federico, Jones, David T., Chapman, Samuel, Bkc, Dukka, Khan, Ishita K., Kihara, Daisuke, Ofer, Dan, Rappoport, Nadav, Stern, Amo, Cibrian-Uhalte, Elena, Denny, Paul, Foulger, Rebecca E., Hieta, Reija, Legge, Duncan, Lovering, Ruth C., Magrane, Michele, Melidoni, Anna N., Mutowo-Meullenet, Prudence, Pichler, Klemen, Shypitsyna, Aleksandra, Li, Biao, Zakeri, Pooya, Elshal, Sarah, Tranchevent, Léon-Charle, Das, Sayoni, Dawson, Natalie L., Lee, David, Lees, Jonathan G., Sillitoe, Ian, Bhat, Prajwal, Nepusz, Tamá, Romero, Alfonso E., Sasidharan, Rajkumar, Yang, Haixuan, Paccanaro, Alberto, Gillis, Jesse, Sedeño-Cortés, Adriana E., Pavlidis, Paul, Feng, Shou, Cejuela, Juan M., Goldberg, Tatyana, Hamp, Tobia, Richter, Lothar, Salamov, Asaf, Gabaldon, Toni, Marcet-Houben, Marina, Supek, Fran, Gong, Qingtian, Ning, Wei, Zhou, Yuanpeng, Tian, Weidong, Falda, Marco, Fontana, Paolo, Lavezzo, Enrico, Toppo, Stefano, Ferrari, Carlo, Giollo, Manuel, Piovesan, Damiano, Tosatto, Silvio C.E., del Pozo, Angela, Fernández, José M., Maietta, Paolo, Valencia, Alfonso, Tress, Michael L., Benso, Alfredo, Di Carlo, Stefano, Politano, Gianfranco, Savino, Alessandro, Rehman, Hafeez Ur, Re, Matteo, Mesiti, Marco, Valentini, Giorgio, Bargsten, Joachim W., van Dijk, Aalt D. J., Gemovic, Branislava, Glisic, Sanja, Perovic, Vladmir, Veljkovic, Veljko, Veljkovic, Nevena, Almeida-e-Silva, Danillo C., Vencio, Ricardo Z. N., Sharan, Malvika, Vogel, Jörg, Kansakar, Lakesh, Zhang, Shanshan, Vucetic, Slobodan, Wang, Zheng, Sternberg, Michael J. E., Wass, Mark N., Huntley, Rachael P., Martin, Maria J., O’Donovan, Claire, Robinson, Peter N., Moreau, Yve, Tramontano, Anna, Babbitt, Patricia C., Brenner, Steven E., Linial, Michal, Orengo, Christine A., Rost, Burkhard, Greene, Casey S., Mooney, Sean D., Friedberg, Iddo, Radivojac, Predrag, Friedberg, Iddo [0000-0002-1789-8000], Apollo - University of Cambridge Repository, (ukupan broj autora: 147), Biotechnology and Biological Sciences Research Council (BBSRC), National Science Foundation (Estados Unidos), United States of Department of Health & Human Services, National Natural Science Foundation of China, Natural Sciences and Engineering Research Council (Canadá), São Paulo Research Foundation, Ministerio de Economía y Competitividad (España), Biotechnology and Biological Sciences Research Council (Reino Unido), Katholieke Universiteit Leuven (Bélgica), Newton International Fellowship Scheme of the Royal Society grant, British Heart Foundation, Ministry of Education, Science and Technological Development (Serbia), Office of Biological and Environmental Research (Estados Unidos), Australian Research Council, University of Padua (Italia), Swiss National Science Foundation, Institute of Biotechnology, Computational genomics, and Bioinformatics

Subjects
0301 basic medicine, Computer science, Disease gene prioritization, Protein function prediction, Ecology, Evolution, Behavior and Systematics, Genetics, Cell Biology, 05 Environmental Sciences, 600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit, computer.software_genre, Quantitative Biology - Quantitative Methods, Wiskundige en Statistische Methoden - Biometris, Field (computer science), Laboratorium voor Plantenveredeling, Function (engineering), Databases, Protein, 1183 Plant biology, microbiology, virology, Quantitative Methods (q-bio.QM), media_common, Genetics & Heredity, Settore BIO/11 - BIOLOGIA MOLECOLARE, Ecology, SISTA, 1184 Genetics, developmental biology, physiology, Life Sciences & Biomedicine, Algorithms, Bioinformatics, Evolution, media_common.quotation_subject, BIOINFORMÁTICA, Machine learning, Bottleneck, Set (abstract data type), BIOS Applied Bioinformatics, 03 medical and health sciences, Annotation, Structure-Activity Relationship, Behavior and Systematics, Human Phenotype Ontology, Humans, ddc:610, DISINTEGRIN, Mathematical and Statistical Methods - Biometris, BIOINFORMATICS, 08 Information And Computing Sciences, Science & Technology, business.industry, Research, ADAM, Proteins, Computational Biology, Molecular Sequence Annotation, 06 Biological Sciences, Data set, ONTOLOGY, Plant Breeding, 030104 developmental biology, Gene Ontology, Biotechnology & Applied Microbiology, FOS: Biological sciences, Artificial intelligence, business, computer, Software
Abstract

BACKGROUND: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. RESULTS: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. CONCLUSIONS: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent., We acknowledge the contributions of Maximilian Hecht, Alexander Grün, Julia Krumhoff, My Nguyen Ly, Jonathan Boidol, Rene Schoeffel, Yann Spöri, Jessika Binder, Christoph Hamm and Karolina Worf. This work was partially supported by the following grants: National Science Foundation grants DBI-1458477 (PR), DBI-1458443 (SDM), DBI-1458390 (CSG), DBI-1458359 (IF), IIS-1319551 (DK), DBI-1262189 (DK), and DBI-1149224 (JC); National Institutes of Health grants R01GM093123 (JC), R01GM097528 (DK), R01GM076990 (PP), R01GM071749 (SEB), R01LM009722 (SDM), and UL1TR000423 (SDM); the National Natural Science Foundation of China grants 3147124 (WT) and 91231116 (WT); the National Basic Research Program of China grant 2012CB316505 (WT); NSERC grant RGPIN 371348-11 (PP); FP7 infrastructure project TransPLANT Award 283496 (ADJvD); Microsoft Research/FAPESP grant 2009/53161-6 and FAPESP fellowship 2010/50491-1 (DCAeS); Biotechnology and Biological Sciences Research Council grants BB/L020505/1 (DTJ), BB/F020481/1 (MJES), BB/K004131/1 (AP), BB/F00964X/1 (AP), and BB/L018241/1 (CD); the Spanish Ministry of Economics and Competitiveness grant BIO2012-40205 (MT); KU Leuven CoE PFV/10/016 SymBioSys (YM); the Newton International Fellowship Scheme of the Royal Society grant NF080750 (TN). CSG was supported in part by the Gordon and Betty Moore Foundation’s Data-Driven Discovery Initiative grant GBMF4552. Computational resources were provided by CSC – IT Center for Science Ltd., Espoo, Finland (TS). This work was supported by the Academy of Finland (TS). RCL and ANM were supported by British Heart Foundation grant RG/13/5/30112. PD, RCL, and REF were supported by Parkinson’s UK grant G-1307, the Alexander von Humboldt Foundation through the German Federal Ministry for Education and Research, Ernst Ludwig Ehrlich Studienwerk, and the Ministry of Education, Science and Technological Development of the Republic of Serbia grant 173001. This work was a Technology Development effort for ENIGMA – Ecosystems and Networks Integrated with Genes and Molecular Assemblies (http://enigma.lbl.gov), a Scientific Focus Area Program at Lawrence Berkeley National Laboratory, which is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Biological & Environmental Research grant DE-AC02-05CH11231. ENIGMA only covers the application of this work to microbial proteins. NSF DBI-0965616 and Australian Research Council grant DP150101550 (KMV). NSF DBI-0965768 (ABH). NIH T15 LM00945102 (training grant for CSF). FP7 FET grant MAESTRA ICT-2013-612944 and FP7 REGPOT grant InnoMol (FS). NIH R01 GM60595 (PCB). University of Padova grants CPDA138081/13 (ST) and GRIC13AAI9 (EL). Swiss National Science Foundation grant 150654 and UK BBSRC grant BB/M015009/1 (COD). PRB2 IPT13/0001 - ISCIII-SGEFI / FEDER (JMF)., This is the final version of the article. It first appeared from BioMed Central at http://dx.doi.org/10.1186/s13059-016-1037-6.

Published
2016

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