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A reference profile-free deconvolution method to infer cancer cell-intrinsic subtypes and tumor-type-specific stromal profiles

Authors
  • Wang, Li1, 1, 2
  • Sebra, Robert P.1, 1, 2
  • Sfakianos, John P.1
  • Allette, Kimaada1, 1
  • Wang, Wenhui1, 1
  • Yoo, Seungyeul1, 1
  • Bhardwaj, Nina1, 1
  • Schadt, Eric E.1, 1, 2, 1
  • Yao, Xin3
  • Galsky, Matthew D.1, 1
  • Zhu, Jun1, 1, 2, 1
  • 1 Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA , New York (United States)
  • 2 Sema4, a Mount Sinai venture, Stamford, CT, 06902, USA , Stamford (United States)
  • 3 Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China , Tianjin (China)
Type
Published Article
Journal
Genome Medicine
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Feb 28, 2020
Volume
12
Issue
1
Identifiers
DOI: 10.1186/s13073-020-0720-0
Source
Springer Nature
Keywords
License
Green

Abstract

BackgroundPatient stratification based on molecular subtypes is an important strategy for cancer precision medicine. Deriving clinically informative cancer molecular subtypes from transcriptomic data generated on whole tumor tissue samples is a non-trivial task, especially given the various non-cancer cellular elements intertwined with cancer cells in the tumor microenvironment.MethodsWe developed a computational deconvolution method, DeClust, that stratifies patients into subtypes based on cancer cell-intrinsic signals identified by distinguishing cancer-type-specific signals from non-cancer signals in bulk tumor transcriptomic data. DeClust differs from most existing methods by directly incorporating molecular subtyping of solid tumors into the deconvolution process and outputting molecular subtype-specific tumor reference profiles for the cohort rather than individual tumor profiles. In addition, DeClust does not require reference expression profiles or signature matrices as inputs and estimates cancer-type-specific microenvironment signals from bulk tumor transcriptomic data.ResultsDeClust was evaluated on both simulated data and 13 solid tumor datasets from The Cancer Genome Atlas (TCGA). DeClust performed among the best, relative to existing methods, for estimation of cellular composition. Compared to molecular subtypes reported by TCGA or other similar approaches, the subtypes generated by DeClust had higher correlations with cancer-intrinsic genomic alterations (e.g., somatic mutations and copy number variations) and lower correlations with tumor purity. While DeClust-identified subtypes were not more significantly associated with survival in general, DeClust identified a poor prognosis subtype of clear cell renal cancer, papillary renal cancer, and lung adenocarcinoma, all of which were characterized by CDKN2A deletions. As a reference profile-free deconvolution method, the tumor-type-specific stromal profiles and cancer cell-intrinsic subtypes generated by DeClust were supported by single-cell RNA sequencing data.ConclusionsDeClust is a useful tool for cancer cell-intrinsic molecular subtyping of solid tumors. DeClust subtypes, together with the tumor-type-specific stromal profiles generated by this pan-cancer study, may lead to mechanistic and clinical insights across multiple tumor types.

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