Abstract 2897: Dynamic transdifferentiation programs in small cell lung carcinoma

Abstract

Abstract Tumors must also have the ability to rapidly adjust to a mutable environment during the lifespan of the cell. This form of phenotypic plasticity is typified by biological programs regulated by a host of transcription factors in a well-coordinated “switch” that is initiated by both cell autonomous and microenvironmental cues. We sought to study these dynamics in small cell lung carcinoma (SCLC). First, we used k-means clustering to identify discrete transcriptional programs that are represented across SCLC using gene expression values from 53 and 71 SCLC cell lines and primary tumors, respectively. To investigate these programs further, we used our inventory of 67 patient-derived xenografts (PDX) to establish an ex vivo cell culture system that reconstitutes with high fidelity the gene expression values observed in the PDXs and the primary tumors from which these cells were derived. Our data demonstrates that gene expression groupings or clusters across samples mark distinct SCLC transcriptional states, representing intertumoral variation. However, in an intriguing demonstration of intratumoral heterogeneity, the very same groupings define morphologically and phenotypically distinct subpopulations within individual samples. Subpopulations were represented by suspending aggregates of very small cells (neuroendocrine or NEn), pre-suspension aggregates that are nested above mesenchymal-like cells and appear to give rise to the suspending aggregates (proneural or PN), pleomorphic cells growing as a monolayer composed of larger cells with visible cytoplasm and spindle-like membrane extensions (mesenchymal or MS), and semi-adherent cells that are physically positioned over the latter (neuroepithelial or NEp). WES demonstrated high genetic fidelity across all subpopulations, indicating that the significant morphologic and phenotypic differences were non-genetic. RNAseq and gene ontology was applied to differentially expressed genes to inform classifications. Using network analyses, we identified transcription factors (TFs) that function as central nodes in the gene expression networks of each population. Namely, we show that ASCL1 marks a rapidly proliferating neuroendocrine population (NEn), NEUROD1 marks a primitive neural population (PN) and YAP1 marks a slow-growing mesenchymal population (MS). We also developed a promoter-reporter fluorescent protein system that positions distinctly colored fluorescent proteins under the regulatory control of the promoters of the three TFs. We show that a Markov process models the transition probability of states. Taken together, our results show that human SCLC is comprised of distinct tumor subpopulations characterized by significant, non-genetic plasticity. We posit that understanding of the SCLC intratumoral ecosystem can create new opportunities to evolutionarily steer this tumor toward improved therapeutic results. Note: This abstract was not presented at the meeting. Citation Format: Priyanka Gopal, Kevin Rogacki, Craig D. Peacock, Mohamed E. Abazeed. Dynamic transdifferentiation programs in small cell lung carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2897.