Systems-level network modeling of Small Cell Lung Cancer subtypes identifies master regulators and destabilizers.

Autor: Wooten DJ; Department of Physics, The Pennsylvania State University, University Park, Pennsylvania, United States of America.; Department of Biochemistry, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America., Groves SM; Department of Biochemistry, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America., Tyson DR; Department of Biochemistry, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America., Liu Q; Departments of Biomedical Informatics and Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America., Lim JS; Departments of Pediatrics and Genetics, Stanford University, Stanford, California, United States of America., Albert R; Department of Physics, The Pennsylvania State University, University Park, Pennsylvania, United States of America., Lopez CF; Department of Biochemistry, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America., Sage J; Departments of Pediatrics and Genetics, Stanford University, Stanford, California, United States of America., Quaranta V; Department of Biochemistry, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.
Jazyk: angličtina
Zdroj: PLoS computational biology [PLoS Comput Biol] 2019 Oct 31; Vol. 15 (10), pp. e1007343. Date of Electronic Publication: 2019 Oct 31 (Print Publication: 2019).
DOI: 10.1371/journal.pcbi.1007343
Abstrakt: Adopting a systems approach, we devise a general workflow to define actionable subtypes in human cancers. Applied to small cell lung cancer (SCLC), the workflow identifies four subtypes based on global gene expression patterns and ontologies. Three correspond to known subtypes (SCLC-A, SCLC-N, and SCLC-Y), while the fourth is a previously undescribed ASCL1+ neuroendocrine variant (NEv2, or SCLC-A2). Tumor deconvolution with subtype gene signatures shows that all of the subtypes are detectable in varying proportions in human and mouse tumors. To understand how multiple stable subtypes can arise within a tumor, we infer a network of transcription factors and develop BooleaBayes, a minimally-constrained Boolean rule-fitting approach. In silico perturbations of the network identify master regulators and destabilizers of its attractors. Specific to NEv2, BooleaBayes predicts ELF3 and NR0B1 as master regulators of the subtype, and TCF3 as a master destabilizer. Since the four subtypes exhibit differential drug sensitivity, with NEv2 consistently least sensitive, these findings may lead to actionable therapeutic strategies that consider SCLC intratumoral heterogeneity. Our systems-level approach should generalize to other cancer types.
Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: J.S. receives research funding from StemCentrx/Abbvie, Revolution Medicines, and from Pfizer, and owns stock in Forty Seven Inc.
Databáze: MEDLINE
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