TITLE: “Unbiased analysis of single cell RNA sequencing data reveals previously uncharacterized heterogeneity in small cell lung cancer cell lines.”
ABSTRACT: Small Cell Lung Cancer (SCLC) accounts for approximately 13% of all new lung cancer diagnoses. SCLC exhibits propensity for early metastasis, rapid cell division, high levels of replication stress, the ability to cope with certain oxidative and metabolic stresses, and evasion of apoptosis and the effector cells of the immune system. Together, these factors contribute to an exceedingly poor prognosis with patient survival measured in months, not years, that has led to a recalcitrant cancer designation for SCLC by the National Cancer Institute. Nevertheless, systemic therapies that include immunotherapy are beginning to show promise in the clinic. Although, these results are encouraging, many patients do not respond to, or rapidly recur after, current regimens, necessitating alternative or complementary therapeutic strategies. Heterogeneity of cancer cells is a key factor by which tumors resist treatment, and It is increasingly appreciated that there are discrete molecular subtypes of SCLC that can differ in their response to different therapies. In my talk, I will describe how we use gene expression data obtained from single-cell RNA sequencing of eight immortalized SCLC cell lines to characterize heterogeneity in SCLC. I will show that, in addition to established classification of cancer sub-types, there is an orthogonal axis of heterogeneity in which exist distinct subpopulations, each with its own unique gene expression profile. One such subpopulation is characterized by stem-like properties and decreased expression of key signaling proteins. This finding suggests that SCLC tumors are highly heterogeneous in make-up, and that consideration of this stem-like subpopulation will be critical for the development of effective therapeutics.
