Mapping the proteogenomic landscape enables prediction of drug response in acute myeloid leukemia

Pino, James C.; Posso, Camilo; Joshi, Sunil K.; Nestor, Michael; Moon, Jamie; Hansen, Joshua R.; Hutchinson-Bunch, Chelsea; Gritsenko, Marina A.; Weitz, Karl K.; Watanabe-Smith, Kevin; Long, Nicola; McDermott, Jason E.; Druker, Brian J.; Liu, Tao; Tyner, Jeffrey W.; Agarwal, Anupriya; Traer, Elie; Piehowski, Paul D.; Tognon, Cristina E.; Rodland, Karin D.; Gosline, Sara J.C.

doi:10.1016/j.xcrm.2023.101359

Mapping the proteogenomic landscape enables prediction of drug response in acute myeloid leukemia

Journal Article · Tue Jan 16 00:00:00 EST 2024 · Cell Reports Medicine

DOI:https://doi.org/10.1016/j.xcrm.2023.101359· OSTI ID:2282267

Pino, James C. ^[1]; Posso, Camilo ^[1]; Joshi, Sunil K. ^[2]; Nestor, Michael ^[1]; Moon, Jamie ^[1]; Hansen, Joshua R. ^[1]; Hutchinson-Bunch, Chelsea ^[1]; Gritsenko, Marina A. ^[1]; Weitz, Karl K. ^[1]; Watanabe-Smith, Kevin ^[2]; Long, Nicola ^[2]; McDermott, Jason E. ^[3]; Druker, Brian J. ^[2]; Liu, Tao ^[1]; Tyner, Jeffrey W. ^[2]; Agarwal, Anupriya ^[2]; Traer, Elie ^[2]; Piehowski, Paul D. ^[1]; Tognon, Cristina E. ^[2]; Rodland, Karin D. ^[3] more »

Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Oregon Health & Science University, Portland, OR (United States)
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Oregon Health & Science University, Portland, OR (United States)

Acute myeloid leukemia is a poor prognosis cancer commonly stratified by genetic aberrations, but these mutations are often heterogeneous and don’t always predict therapeutic response. Here we combine transcriptomic, proteomic, and phosphoproteomic datasets with ex vivo drug sensitivity data to help understand the underlying pathophysiology of AML beyond mutations. We measured the proteome and phosphoproteome of 210 patients and combined them with genomics and transcriptomic measurements to identify four proteogenomic subtypes that complemented existing genetic subtypes. We then built a predictor to classify samples into subtypes based on 147 molecular features and mapped them to a ‘landscape’. Each region of this landscape corresponded to specific drug response patterns. We then built a drug response prediction model to identify drugs that target distinct subtypes. We can ultimately use these models to predict drug treatment response and prioritize treatments. Finally, we extended our models and mapped a series of cell lines representing various stages of quizartinib resistance into our subtype landscape, predicting and experimentally validating a switch in sensitivity to venetoclax to panobinostat, two drugs with very different mechanisms than quizartinib. Our results show how multi-omics data together with drug sensitivity data can inform therapy stratification and drug combinations in AML.

View Accepted Manuscript (DOE)

Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: American Cancer Society; National Institutes of Health (NIH); USDOE

Grant/Contract Number:: AC05-76RL01830

OSTI ID:: 2282267

Report Number(s):: PNNL-SA--185276

Journal Information:: Cell Reports Medicine, Journal Name: Cell Reports Medicine Journal Issue: 1 Vol. 5; ISSN 2666-3791

Publisher:: Cell PressCopyright Statement

Country of Publication:: United States

Language:: English

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59 BASIC BIOLOGICAL SCIENCES
acute myeloid leukemia
drug response
genomics
linear regression
multi-omics
non-negative matrix factorization
proteomics
transcriptomics

Mapping the proteogenomic landscape enables prediction of drug response in acute myeloid leukemia

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