(22f) A Computational Model of Subtype Interactions in Small Cell Lung Cancer Predicts Factors Controlling Intertumoral Heterogeneity

Small cell lung cancer (SCLC) is an aggressive neuroendocrine carcinoma known for rapid metastasis and recurrence following treatment. The standard of care (etoposide + cis-platinum and radiation) has not changed in over 30 years, with dismal outcomes. There is a great need, therefore, for new and improved therapeutic approaches to treat this deadly disease. While histologically homogeneous, recent work suggests that SCLC tumors are comprised of numerous subtypes that support growth and facilitate treatment evasion. Recently, we identified four distinct SCLC subtypes using consensus clustering and weighted gene co-expression network analysis on transcriptomics data from numerous tumors and cultured cell lines [1]. Three of these subtypes had been described previously [2,3,4], while the fourth shows broad insensitivity to several classes of therapeutic agents, suggesting a possible role in treatment resistance and tumor recurrence. Subsequent analysis using CIBERSORT [5] on human and mouse tumor samples indicates that SCLC tumors are comprised of varying proportions of all four subtypes [1].

Here, we use computational modeling to investigate interactions amongst SCLC subtypes and identify factors that can modulate tumor composition. The model features three neuroendocrine (NE) variants that get trophic support from one non-NE subtype. Cells can reversibly transition between all NE subtypes but only one NE variant can transition into non-NE. Trophic support by non-NE cells is assumed to be through secreted factors (e.g., midkine, fibroblast growth factor) that enhance division and inhibit death of NE cells. NE subtypes are also assumed to secrete factors that inhibit non-NE cell division. We fit the model to CIBERSORT data from numerous human and mouse tumors with distinct subtype compositions to identify driving factors, such as differentiation rates and interaction modes/strengths, which can explain the observed intertumoral heterogeneity. Our results indicate that cell-cell interactions are crucial for maintaining stable subtype content and suggest experimentally testable interventions for modulating tumor composition.

1. Wooten, D. J. et al. Systems-level network modeling of Small Cell Lung Cancer subtypes identifies master regulators and destabilizers. bioRxiv (2019). doi:10.1101/506402
2. Borromeo, M. D. et al. ASCL1 and NEUROD1 reveal heterogeneity in pulmonary neuroendocrine tumors and regulate distinct genetic programs. Cell Rep. 16, 1259–1272 (2016).
3. Lim, J. S. et al. Intratumoural heterogeneity generated by Notch signalling promotes small-cell lung cancer. Nature 545, 360–364 (2017).
4. Mollaoglu, G. et al. MYC drives progression of small cell lung cancer to a variant neuroendocrine subtype with vulnerability to aurora kinase inhibition. Cancer Cell 31, 270–285 (2017).
5. Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).