DOI: 10.1101/506402Dec 26, 2018Paper

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

BioRxiv : the Preprint Server for Biology
David J WootenVito Quaranta

Abstract

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, while the fourth is a previously undescribed neuroendocrine variant (NEv2). 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. Author summary Small-cell lung cancer (SCLC) ...Continue Reading

Related Concepts

Malignant Neoplasms
Gene Expression
Genes
Neoplasms
Transcription Factor
Small Cell Carcinoma of Lung
Subtype (Attribute)
Patterns
Gene Mutant
Inference

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