Quantifying phenotypic heterogeneity in small-cell lung cancer: implications for subtype classification and treatment

Abstract

Oncogenic mechanisms in small-cell lung cancer (SCLC) remain poorly understood leaving this tumor with the worst prognosis among all lung cancers. Unlike other cancer types, sequencing genomic approaches have been of limited success in small-cell lung cancer, i.e., no mutated oncogenes with potential driver characteristics have emerged, as it is the case for activating mutations of epidermal growth factor receptor in non-small-cell lung cancer. Differential gene expression analysis has also produced SCLC signatures with limited application, since they are generally not robust across datasets. We derived an SCLC-specific classifier from weighted gene co-expression network analysis (WGCNA) of a lung cancer dataset. The classifier, termed SCLC-specific hub network (SSHN), robustly separates SCLC from other lung cancer types across multiple datasets and multiple platforms, including RNA-seq and shotgun proteomics. The classifier was also conserved in SCLC cell lines. SSHN is enriched for co-expressed signaling network hubs strongly associated with the SCLC phenotype. Twenty of these hubs are actionable kinases with oncogenic potential, among which spleen tyrosine kinase (SYK) exhibits one of the highest overall statistical associations to SCLC. In patient tissue microarrays and cell lines, SCLC can be separated into SYK-positive and -negative. SYK siRNA decreases proliferation rate and increases cell death of SYK-positive SCLC cell lines, suggesting a role for SYK as an oncogenic driver in a subset of SCLC. The SSHN signature also provided some hints of heterogeneity within SCLC patients and cell lines. Due to the lack of large SCLC patient datasets, we applied WGCNA analysis separately in SCLC cell lines and identified two anti-correlated modules of co-expressed genes that together provide a framework for a heterogeneous phenotypic state space in SCLC. Mathematical modeling of a common transcriptional network regulating these modules predicts a discretization of the seemingly continuous phenotypic state space of SCLC into two distinct attractor basin clusters – neuroendocrine and mesenchymal. Each cluster of attractors is defined by specific stable activation states of neuroendocrine, epithelial and mesenchymal transcription factors (TFs). These attractor states were experimentally validated via differences in protein expression of TFs, surface markers, kinases, and signaling in SCLC cell lines and patient samples. At a single-cell level, multidimensional flow cytometry analysis reconfirms phenotypic heterogeneity in SCLC as two discrete stable attractors. Collectively, a mixed bioinformatic-modeling-experimental approach defines heterogeneity in human SCLC as distinct phenotypic attractor states - neuroendocrine and mesenchymal, and provides a foundation for guiding personalized treatment strategies for SCLC patients in the future.