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Systems-level network modeling of Small Cell Lung Cancer subtypes identifies master regulators and destabilizers
David J Wooten1,2, Sarah M Groves2, Darren R Tyson2
1Department of Physics, The Pennsylvania State University, University Park, Pennsylvania, United States of America.
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.
Insights
Researchers identified four small cell lung cancer (SCLC) subtypes, including a new neuroendocrine variant (NEv2). This discovery offers insights into cancer heterogeneity and potential new therapeutic strategies for SCLC treatment.
Area of Science:
- Oncology
- Systems Biology
- Genomics
Background:
- Small cell lung cancer (SCLC) is a heterogeneous disease.
- Understanding intratumoral heterogeneity is crucial for developing effective therapies.
Purpose of the Study:
- To develop a generalizable workflow for defining actionable cancer subtypes using a systems approach.
- To identify and characterize subtypes of SCLC and understand their regulatory mechanisms.
Main Methods:
- A systems-based workflow was applied to SCLC using global gene expression and ontology data.
- A network of transcription factors was inferred, and BooleaBayes, a Boolean rule-fitting approach, was developed.
- In silico perturbations were used to identify master regulators and destabilizers of subtype attractors.
Main Results:
- Four SCLC subtypes were identified: three known (SCLC-A, SCLC-N, SCLC-Y) and one novel ASCL1+ neuroendocrine variant (NEv2/SCLC-A2).
- All subtypes were detectable in human and mouse tumors.
- BooleaBayes predicted ELF3 and NR0B1 as NEv2 master regulators and TCF3 as a master destabilizer.
- The identified subtypes exhibited differential drug sensitivity, with NEv2 being the least sensitive.
Conclusions:
- The study defines actionable SCLC subtypes, including a novel NEv2 variant, contributing to the understanding of SCLC intratumoral heterogeneity.
- The findings suggest potential therapeutic strategies targeting specific SCLC subtypes.
- The developed systems-level approach is potentially generalizable to other cancer types.
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