TY - JOUR T1 - Community Assessment of the Predictability of Cancer Protein and Phosphoprotein Levels from Genomics and Transcriptomics. JF - Cell Syst Y1 - 2020 A1 - Yang, Mi A1 - Petralia, Francesca A1 - Li, Zhi A1 - Li, Hongyang A1 - Ma, Weiping A1 - Song, Xiaoyu A1 - Kim, Sunkyu A1 - Lee, Heewon A1 - Yu, Han A1 - Lee, Bora A1 - Bae, Seohui A1 - Heo, Eunji A1 - Kaczmarczyk, Jan A1 - Stępniak, Piotr A1 - Warchoł, Michał A1 - Yu, Thomas A1 - Calinawan, Anna P A1 - Boutros, Paul C A1 - Payne, Samuel H A1 - Reva, Boris A1 - Boja, Emily A1 - Rodriguez, Henry A1 - Stolovitzky, Gustavo A1 - Guan, Yuanfang A1 - Kang, Jaewoo A1 - Wang, Pei A1 - Fenyö, David A1 - Saez-Rodriguez, Julio KW - Crowdsourcing KW - Female KW - Genomics KW - Humans KW - Machine Learning KW - Male KW - Neoplasms KW - Phosphoproteins KW - Proteins KW - Proteomics KW - Transcriptome AB -

Cancer is driven by genomic alterations, but the processes causing this disease are largely performed by proteins. However, proteins are harder and more expensive to measure than genes and transcripts. To catalyze developments of methods to infer protein levels from other omics measurements, we leveraged crowdsourcing via the NCI-CPTAC DREAM proteogenomic challenge. We asked for methods to predict protein and phosphorylation levels from genomic and transcriptomic data in cancer patients. The best performance was achieved by an ensemble of models, including as predictors transcript level of the corresponding genes, interaction between genes, conservation across tumor types, and phosphosite proximity for phosphorylation prediction. Proteins from metabolic pathways and complexes were the best and worst predicted, respectively. The performance of even the best-performing model was modest, suggesting that many proteins are strongly regulated through translational control and degradation. Our results set a reference for the limitations of computational inference in proteogenomics. A record of this paper's transparent peer review process is included in the Supplemental Information.

VL - 11 IS - 2 U1 - https://www.ncbi.nlm.nih.gov/pubmed/32710834?dopt=Abstract ER - TY - JOUR T1 - Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. JF - Nat Commun Y1 - 2019 A1 - Menden, Michael P A1 - Wang, Dennis A1 - Mason, Mike J A1 - Szalai, Bence A1 - Bulusu, Krishna C A1 - Guan, Yuanfang A1 - Yu, Thomas A1 - Kang, Jaewoo A1 - Jeon, Minji A1 - Wolfinger, Russ A1 - Nguyen, Tin A1 - Zaslavskiy, Mikhail A1 - Jang, In Sock A1 - Ghazoui, Zara A1 - Ahsen, Mehmet Eren A1 - Vogel, Robert A1 - Neto, Elias Chaibub A1 - Norman, Thea A1 - Tang, Eric K Y A1 - Garnett, Mathew J A1 - Veroli, Giovanni Y Di A1 - Fawell, Stephen A1 - Stolovitzky, Gustavo A1 - Guinney, Justin A1 - Dry, Jonathan R A1 - Saez-Rodriguez, Julio KW - ADAM17 Protein KW - Antineoplastic Combined Chemotherapy Protocols KW - Benchmarking KW - Biomarkers, Tumor KW - Cell Line, Tumor KW - Computational Biology KW - Datasets as Topic KW - Drug Antagonism KW - Drug Resistance, Neoplasm KW - Drug Synergism KW - Genomics KW - Humans KW - Molecular Targeted Therapy KW - mutation KW - Neoplasms KW - pharmacogenetics KW - Phosphatidylinositol 3-Kinases KW - Phosphoinositide-3 Kinase Inhibitors KW - Treatment Outcome AB -

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.

VL - 10 IS - 1 U1 - https://www.ncbi.nlm.nih.gov/pubmed/31209238?dopt=Abstract ER -