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

Wooten DJ, Groves SM, Tyson DR, Liu Q, Lim JS, Albert R, Lopez CF, Sage J, Quaranta V
PLoS Comput Biol. 2019 15 (10): e1007343

PMID: 31671086 · PMCID: PMC6860456 · DOI:10.1371/journal.pcbi.1007343

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.

MeSH Terms (18)

Algorithms Animals Basic Helix-Loop-Helix Transcription Factors Bayes Theorem Cell Line, Tumor Cluster Analysis Databases, Genetic Drug Resistance, Neoplasm Gene Expression Gene Expression Regulation, Neoplastic Gene Ontology Gene Regulatory Networks Humans Mice Models, Theoretical Small Cell Lung Carcinoma Systems Analysis Transcription Factors

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