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

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 (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.

SCLC_CellMiner: Integrated Genomics and Therapeutics Predictors of Small Cell Lung Cancer Cell Lines based on their genomic signatures

“…In spite of the resistance of non-neuroendocrine (or variant) SCLC cells (SCLC-P and -Y 726 subgroups) to the standard of care treatments (Gazdar et al, 1992), we find that those subgroups 727 appear responsive to mTOR and AKT inhibitors (see Figure 6D-E). Our result is consistent with a 728 recent study (Wooten et al, 2019) showing that non-neuroendocrine SCLC cell lines are sensitive 729…”

Topological signatures in regulatory network enable phenotypic heterogeneity in small cell lung cancer

“…Table S3: Cell line classification of CCLE dataset using different cluster values for k-means and hierarchical algorithm over four genes of interest (ASCL1, NEUROD1, YAP1 and POU2F3). Also contains the classification as given by Wooten et al 2019. Table S4: Cell line classification of GSE73160 dataset using different cluster values for k-means and hierarchical algorithm over four genes of interest (ASCL1, NEUROD1, YAP1 and POU2F3).…”

“…Table S4: Cell line classification of GSE73160 dataset using different cluster values for k-means and hierarchical algorithm over four genes of interest (ASCL1, NEUROD1, YAP1 and POU2F3). Also contains the classification as given by Wooten et al 2019 (for the cell lines included in both GSE73160 and CCLE) Table S5: Steady-state frequency distribution for asynchronous Boolean update of network for genes corresponding to GROUP A and ELF3. Table S6: Steady-state frequency distribution for asynchronous Boolean update of network for genes corresponding to GROUP A only.…”

Cancer Hallmarks Define a Continuum of Plastic Cell States between Small Cell Lung Cancer Archetypes

“…5,[9][10][11][12][13] We also identified a fifth subtype, A2, which is driven by ASCL1 but is clearly distinct from the SCLC-A neuroendocrine subtype. 27,28 However, stark delineation of bulk cell lines or tumors into single subtypes has proven difficult and may inadequately describe SCLC intratumoral heterogeneity, since it is often the case that either multiple or none of the eponymous TFs are expressed in a population of SCLC cells. For instance, our work using CIBERSORT 29 decomposition showed all tested SCLC tumors are composed of multiple subtypes, and several studies have reported changes of subtype prevalence during tumor progression or in response to treatment.…”