Highlights d MuSyC is a synergy framework applicable to any metric of drug combination effect d Unlike other methods, MuSyC decouples synergy of potency and efficacy d It subsumes traditional synergy methods, resolving ambiguities and biases in the field d MuSyC reveals optimal co-targeting strategies in NCSLC and melanoma

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.

Small cell lung cancer (SCLC) is a devastating disease because of its tendency to early invasion and refractory relapse after initial treatment response. These aggressive traits have been associated with phenotypic heterogeneity, which however remains incompletely understood. To fill this knowledge gap, we inferred a set of 33 transcription factors (TFs) associated with gene signatures of the known neuroendocrine/epithelial (NE) and non-neuroendocrine/mesenchymal-like (ML) SCLC phenotypes. The topology of this SCLC TF network was derived from prior knowledge and simulated using Boolean modeling. These simulations predicted that the network settles into attractors (TF expression patterns) correlated with NE or ML phenotypes, suggesting that TF network dynamics underlie emergence of heterogeneous SCLC phenotypes in an epigenetic landscape. However, several cell lines and patient samples did not correlate with either the NE or ML attractors. Flow cytometry indicated that single cells within these cell lines simultaneously express surface markers of both NE and ML differentiation, revealing existence of a “hybrid” phenotype. Upon exposure to standard-of-care cytotoxic drugs or epigenetic modifiers, NE and ML cell populations converged toward the hybrid state, suggesting a possible escape route from treatment. Our findings indicate that SCLC phenotypic heterogeneity can be specified dynamically by attractor states of a master regulatory TF network. Thus, SCLC heterogeneity may be best understood as states within an epigenetic landscape. Understanding phenotypic transitions within this landscape could provide insights to clinical applications.

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