Proteomic and phosphoproteomic measurements enhance ability to predict ex vivo drug response in AML
Tóm tắt
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.
Tài liệu tham khảo
Dong Y, et al. Leukemia incidence trends at the global, regional, and national level between 1990 and 2017. Exp Hematol Oncol. 2020;9:14. https://doi.org/10.1186/s40164-020-00170-6.
Board CNE. Leukemia-acute myeloid-AML. Statistics. 2021;562:526–31.
Tyner JW, et al. Functional genomic landscape of acute myeloid leukaemia. Nature. 2018;562:526–31. https://doi.org/10.1038/s41586-018-0623-z.
Nechiporuk T, et al. The TP53 apoptotic network is a primary mediator of resistance to BCL2 inhibition in AML cells. Cancer Discov. 2019;9:910–25. https://doi.org/10.1158/2159-8290.CD-19-0125.
Drusbosky LM, et al. Predicting response to BET inhibitors using computational modeling: A BEAT AML project study. Leuk Res. 2019;77:42–50. https://doi.org/10.1016/j.leukres.2018.11.010.
Rosenberg MW, et al. Genomic markers of midostaurin drug sensitivity in FLT3 mutated and FLT3 wild-type acute myeloid leukemia patients. Oncotarget. 2020;11:2807–18. https://doi.org/10.18632/oncotarget.27656.
Kurtz SE, et al. Dual inhibition of JAK1/2 kinases and BCL2: a promising therapeutic strategy for acute myeloid leukemia. Leukemia. 2018;32:2025–8. https://doi.org/10.1038/s41375-018-0225-7.
Kurtz SE, et al. Molecularly targeted drug combinations demonstrate selective effectiveness for myeloid-and lymphoid-derived hematologic malignancies. Proc Natl Acad Sci. 2017;114:E7554–63. https://doi.org/10.1073/pnas.1703094114.
Wang J, et al. Proteome profiling outperforms transcriptome profiling for coexpression based gene function prediction. Mol Cell Proteomics. 2017;16:121–34. https://doi.org/10.1074/mcp.M116.060301.
Krug K, et al. Proteogenomic landscape of breast cancer tumorigenesis and targeted therapy. Cell. 2020;183:1436-14561.e31. https://doi.org/10.1016/j.cell.2020.10.036.
Hu Y, et al. Integrated proteomic and glycoproteomic characterization of human high-grade serous ovarian carcinoma. Cell Rep. 2020;33: 108276. https://doi.org/10.1016/j.celrep.2020.108276.
Clark DJ, et al. Integrated proteogenomic characterization of clear cell renal cell carcinoma. Cell. 2020;180:207. https://doi.org/10.1016/j.cell.2019.12.026.
Huang C, et al. Proteogenomic insights into the biology and treatment of HPV-negative head and neck squamous cell carcinoma. Cancer Cell. 2021. https://doi.org/10.1016/j.ccell.2020.12.007.
Dou Y, et al. Proteogenomic characterization of endometrial carcinoma. Cell. 2020;180:729-748.e26. https://doi.org/10.1016/j.cell.2020.01.026.
Wang LB, et al. Proteogenomic and metabolomic characterization of human glioblastoma. Cancer Cell. 2021. https://doi.org/10.1016/j.ccell.2021.01.006.
Frejno M, et al. Proteome activity landscapes of tumor cell lines determine drug responses. Nat Commun. 2020;11:3639. https://doi.org/10.1038/s41467-020-17336-9.
van Alphen C, et al. Phosphotyrosine-based phosphoproteomics for target identification and drug response prediction in AML cell lines. Mol Cell Proteomics. 2020;19:884–99. https://doi.org/10.1074/mcp.RA119.001504.
Casado P, et al. Proteomic and genomic integration identifies kinase and differentiation determinants of kinase inhibitor sensitivity in leukemia cells. Leukemia. 2018;32:1818–22. https://doi.org/10.1038/s41375-018-0032-1.
Hoff FW, et al. Clinical relevance of proteomic profiling in de novo pediatric acute myeloid leukemia: a children’s oncology group study. Haematologica. 2022. https://doi.org/10.3324/haematol.2021.279672.
Cucchi DGJ, et al. Phosphoproteomic characterization of primary AML samples and relevance for response toward FLT3-inhibitors. Hemasphere. 2021;5: e606. https://doi.org/10.1097/HS9.0000000000000606.
Harper AR, Topol EJ. Pharmacogenomics in clinical practice and drug development. Nat Biotechnol. 2012;30:1117–24. https://doi.org/10.1038/nbt.2424.
Ben-David U, et al. Genetic and transcriptional evolution alters cancer cell line drug response. Nature. 2018;560:325–30. https://doi.org/10.1038/s41586-018-0409-3.
Seashore-Ludlow B, et al. Harnessing connectivity in a large-scale small-molecule sensitivity dataset. Cancer Discov. 2015;5:1210–23. https://doi.org/10.1158/2159-8290.CD-15-0235.
Iorio F, et al. A landscape of pharmacogenomic interactions in cancer. Cell. 2016;166:740–54. https://doi.org/10.1016/j.cell.2016.06.017.
Nusinow DP, et al. Quantitative proteomics of the cancer cell line encyclopedia. Cell. 2020;180:387-402.e16. https://doi.org/10.1016/j.cell.2019.12.023.
Gao H, et al. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nat Med. 2015;21:1318–25. https://doi.org/10.1038/nm.3954.
Costello JC, et al. A community effort to assess and improve drug sensitivity prediction algorithms. Nat Biotechnol. 2014;32:1202–12. https://doi.org/10.1038/nbt.2877.
Cortes-Ciriano I, et al. Proteochemometric modeling in a Bayesian framework. J Cheminform. 2014;6:35. https://doi.org/10.1186/1758-2946-6-35.
Rampasek L, Hidru D, Smirnov P, Haibe-Kains B, Goldenberg A. Dr.VAE: improving drug response prediction via modeling of drug perturbation effects. Bioinformatics. 2019;35:3743–51. https://doi.org/10.1093/bioinformatics/btz158.
Kuenzi BM, et al. Predicting drug response and synergy using a deep learning model of human cancer cells. Cancer Cell. 2020;38:672-684.e6. https://doi.org/10.1016/j.ccell.2020.09.014.
Gerdes H, et al. Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs. Nat Commun. 2021;12:1850. https://doi.org/10.1038/s41467-021-22170-8.
Rydenfelt M, Wongchenko M, Klinger B, Yan Y, Bluthgen N. The cancer cell proteome and transcriptome predicts sensitivity to targeted and cytotoxic drugs. Life Sci Alliance. 2019. https://doi.org/10.26508/lsa.201900445.
Ali M, Khan SA, Wennerberg K, Aittokallio T. Global proteomics profiling improves drug sensitivity prediction: results from a multi-omics, pan-cancer modeling approach. Bioinformatics. 2018;34:1353–62. https://doi.org/10.1093/bioinformatics/btx766.
Mertins P, et al. Reproducible workflow for multiplexed deep-scale proteome and phosphoproteome analysis of tumor tissues by liquid chromatography-mass spectrometry. Nat Protoc. 2018;13:1632–61. https://doi.org/10.1038/s41596-018-0006-9.
Gibbons BC, Chambers MC, Monroe ME, Tabb DL, Payne SH. Correcting systematic bias and instrument measurement drift with mzRefinery. Bioinformatics. 2015;31:3838–40. https://doi.org/10.1093/bioinformatics/btv437.
Kim S, Pevzner PA. MS-GF+ makes progress towards a universal database search tool for proteomics. Nat Commun. 2014;5:5277. https://doi.org/10.1038/ncomms6277.
Kim S, Gupta N, Pevzner PA. Spectral probabilities and generating functions of tandem mass spectra: a strike against decoy databases. J Proteome Res. 2008;7:3354–63. https://doi.org/10.1021/pr8001244.
Monroe ME, Shaw JL, Daly DS, Adkins JN, Smith RD. MASIC: a software program for fast quantitation and flexible visualization of chromatographic profiles from detected LC-MS(/MS) features. Comput Biol Chem. 2008;32:215–7. https://doi.org/10.1016/j.compbiolchem.2008.02.006.
Beausoleil SA, Villen J, Gerber SA, Rush J, Gygi SP. A probability-based approach for high-throughput protein phosphorylation analysis and site localization. Nat Biotechnol. 2006;24:1285–92. https://doi.org/10.1038/nbt1240.
Tibshirani R. Regression shrinkage and selection via the Lasso. J Roy Stat Soc B Met. 1996;58:267–88. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x.
Zou H, Hastie T. Regression shrinkage and selection via the elastic net, with applications to microarrays. JR Stat Soc Ser B. 2003;67:301–20.
Friedman J, Hastie T, Tibshirani R. Regularization Paths for Generalized Linear Models via Coordinate Descent. J Stat Softw. 2010;33:1–22.
Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16:284–7. https://doi.org/10.1089/omi.2011.0118.
Danna V, et al. leapR: an r package for multiomic pathway analysis. J Proteome Res. 2021. https://doi.org/10.1021/acs.jproteome.0c00963.
GiddingsRisk MB. A user's guide to the encyclopedia of DNA elements ENCODE: The ENCODE Project Consortium (2011) as it is a consortium paper. PLoS Biol. 2011;9:e1001046. https://doi.org/10.1371/journal.pbio.1001046.
Szklarczyk D, et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021;49:D605–12. https://doi.org/10.1093/nar/gkaa1074.
Linding R, et al. NetworKIN: a resource for exploring cellular phosphorylation networks. Nucleic Acids Res. 2008;36:D695-699. https://doi.org/10.1093/nar/gkm902.
Hornbeck PV, et al. 15 years of PhosphoSitePlus(R): integrating post-translationally modified sites, disease variants and isoforms. Nucleic Acids Res. 2019;47:D433–41. https://doi.org/10.1093/nar/gky1159.
Tuncbag N, et al. Network-based Interpretation of diverse high-throughput datasets through the omics integrator software package. PLoS Comput Biol. 2016;12: e1004879. https://doi.org/10.1371/journal.pcbi.1004879.
Akhmedov M, et al. PCSF: An R-package for network-based interpretation of high-throughput data. PLoS Comput Biol. 2017;13: e1005694. https://doi.org/10.1371/journal.pcbi.1005694.
Shannon P, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504. https://doi.org/10.1101/gr.1239303.
Maere S, Heymans K, Kuiper M. BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics. 2005;21:3448–9. https://doi.org/10.1093/bioinformatics/bti551.
Traer E, et al. FGF2 from marrow microenvironment promotes resistance to FLT3 Inhibitors in acute myeloid leukemia. Cancer Res. 2016;76:6471–82. https://doi.org/10.1158/0008-5472.CAN-15-3569.
Arshad OA, et al. An integrative analysis of tumor proteomic and phosphoproteomic profiles to examine the relationships between kinase activity and phosphorylation. Mol Cell Proteomics. 2019;18:S26–36. https://doi.org/10.1074/mcp.RA119.001540.
Nalaskowski MM, et al. Nuclear accumulation of SHIP1 mutants derived from AML patients leads to increased proliferation of leukemic cells. Cell Signal. 2018;49:87–94. https://doi.org/10.1016/j.cellsig.2018.05.006.
Zhang S, Mantel C, Broxmeyer HE. Flt3 signaling involves tyrosyl-phosphorylation of SHP-2 and SHIP and their association with Grb2 and Shc in Baf3/Flt3 cells. J Leukoc Biol. 1999;65:372–80. https://doi.org/10.1002/jlb.65.3.372.
Gu TL, et al. Survey of activated FLT3 signaling in leukemia. PLoS ONE. 2011;6:e19169. https://doi.org/10.1371/journal.pone.0019169.
Lunghi P, et al. Expression and activation of SHC/MAP kinase pathway in primary acute myeloid leukemia blasts. Hematol J. 2001;2:70–80. https://doi.org/10.1038/sj/thj/6200095.
Viny AD, et al. Dose-dependent role of the cohesin complex in normal and malignant hematopoiesis. J Exp Med. 2015;212:1819–32. https://doi.org/10.1084/jem.20151317.
Han L, et al. Concomitant targeting of BCL2 with venetoclax and MAPK signaling with cobimetinib in acute myeloid leukemia models. Haematologica. 2020;105:697–707. https://doi.org/10.3324/haematol.2018.205534.
Joshi SK, et al. The AML microenvironment catalyzes a stepwise evolution to gilteritinib resistance. Cancer Cell. 2021;39(999–1014):e1018. https://doi.org/10.1016/j.ccell.2021.06.003.
Kuusanmaki H, et al. Phenotype-based drug screening reveals association between venetoclax response and differentiation stage in acute myeloid leukemia. Haematologica. 2020;105:708–20. https://doi.org/10.3324/haematol.2018.214882.
Singh Mali R, et al. Venetoclax combines synergistically with FLT3 inhibition to effectively target leukemic cells in FLT3-ITD+ acute myeloid leukemia models. Haematologica. 2021;106:1034–46. https://doi.org/10.3324/haematol.2019.244020.