Proteomic and phosphoproteomic measurements enhance ability to predict ex vivo drug response in AML

Gosline, Sara J. C.; Tognon, Cristina E.; Nestor, Michael D.; Joshi, Sunil K.; Modak, Rucha V.; Damnernsawad, Alisa; Moon, Jamie; Hansen, Joshua R.; Hutchinson-Bunch, Chelsea M.; Pino, James C.; Gritsenko, Marina A.; Weitz, Karl K.; Traer, Elie; Tyner, Jeffrey W.; Drucker, Brian; Agarwal, Anupriya; Piehowski, Paul D.; McDermott, Jason E.; Rodland, Karin D.

doi:10.1186/s12014-022-09367-9

Proteomic and phosphoproteomic measurements enhance ability to predict ex vivo drug response in AML

Journal Article · Wed Jul 27 00:00:00 EDT 2022 · Clinical Proteomics

DOI:https://doi.org/10.1186/s12014-022-09367-9· OSTI ID:1881459

Gosline, Sara J. C. ^[1]; Tognon, Cristina E. ^[2]; Nestor, Michael D. ^[1]; Joshi, Sunil K. ^[2]; Modak, Rucha V. ^[2]; Damnernsawad, Alisa ^[3]; Moon, Jamie ^[1]; ^[1]; Hutchinson-Bunch, Chelsea M. ^[1]; Pino, James C. ^[1]; ^[1]; Weitz, Karl K. ^[1]; Traer, Elie ^[2]; Tyner, Jeffrey W. ^[2]; Drucker, Brian ^[2]; Agarwal, Anupriya ^[2]; ^[1]; ^[4]; ^[4]

Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Oregon Health & Science Univ., Portland, OR (United States)
Oregon Health & Science Univ., Portland, OR (United States); Mahidol Univ., Bangkok (Thailand)
Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Oregon Health & Science Univ., Portland, OR (United States)

Acute Myeloid Leukemia (AML) affects 20,000 patients in the US annually with a five-year survival rate of approximately 25%. One reason for the low survival rate is the high prevalence of clonal evolution that gives rise to heterogeneous sub-populations of leukemic cells with diverse mutation spectra, which eventually leads to disease relapse. This genetic heterogeneity drives the activation of complex signaling pathways that is reflected at the protein level. This diversity makes it difficult to treat AML with targeted therapy, requiring custom patient treatment protocols tailored to each individual’s leukemia. Toward this end, the Beat AML research program prospectively collected genomic and transcriptomic data from over 1000 AML patients and carried out ex vivo drug sensitivity assays to identify genomic signatures that could predict patient-specific drug responses. However, there are inherent weaknesses in using only genetic and transcriptomic measurements as surrogates of drug response, particularly the absence of direct information about phosphorylation-mediated signal transduction. As a member of the Clinical Proteomic Tumor Analysis Consortium, we have extended the molecular characterization of this cohort by collecting proteomic and phosphoproteomic measurements from a subset of these patient samples (38 in total) to evaluate the hypothesis that proteomic signatures can improve the ability to predict response to 26 drugs in AML ex vivo samples. In this work we describe our systematic, multi-omic approach to evaluate proteomic signatures of drug response and compare protein levels to other markers of drug response such as mutational patterns. We explore the nuances of this approach using two drugs that target key pathways activated in AML: quizartinib (FLT3) and trametinib (Ras/MEK), and show how patient-derived signatures can be interpreted biologically and validated in cell lines. In conclusion, this pilot study demonstrates strong promise for proteomics-based patient stratification to assess drug sensitivity in AML.

View Accepted Manuscript (DOE)

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

Sponsoring Organization:: American Cancer Society; National Cancer Institute (NCI); USDOE

Grant/Contract Number:: AC05-76RL01830

OSTI ID:: 1881459

Report Number(s):: PNNL-SA-163043

Journal Information:: Clinical Proteomics, Journal Name: Clinical Proteomics Journal Issue: 1 Vol. 19; ISSN 1542-6416

Publisher:: BioMed CentralCopyright Statement

Country of Publication:: United States

Language:: English

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Additional file 1 of Proteomic and phosphoproteomic measurements enhance ability to predict ex vivo drug response in AML Gosline, Sara J. C.; Tognon, Cristina; Nestor, Michael https://doi.org/10.6084/m9.figshare.20390796 The current record is supplemented by this dataset	dataset	January 2022
Additional file 1 of Proteomic and phosphoproteomic measurements enhance ability to predict ex vivo drug response in AML Gosline, Sara J. C.; Tognon, Cristina; Nestor, Michael https://doi.org/10.6084/m9.figshare.20390796.v1 The current record is supplemented by this dataset	dataset	January 2022
Additional file 2 of Proteomic and phosphoproteomic measurements enhance ability to predict ex vivo drug response in AML Gosline, Sara J. C.; Tognon, Cristina; Nestor, Michael https://doi.org/10.6084/m9.figshare.20390799 The current record is supplemented by this text	text	January 2022
Proteomic and phosphoproteomic measurements enhance ability to predict ex vivo drug response in AML Gosline, Sara J. C.; Tognon, Cristina; Nestor, Michael https://doi.org/10.6084/m9.figshare.c.6121371.v1 The current record is supplemented by this collection	collection	January 2022

Proteomic and phosphoproteomic measurements enhance ability to predict ex vivo drug response in AML

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