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Measuring tumor cell heterogeneity in small-cell lung carcinoma across varying tumor microenvironments

Presenters Name: 
Dylan Schaff
Co Presenters Name: 
Primary Research Mentor: 
Kevin Janes
Secondary Research Mentor: 
Shambhavi Singh
Time: 
2:00 - 3:15
Time of Presentation: 
2019 - 2:00pm to 3:15pm
Session: 
4
Location: 
Newcomb Hall Ballroom
Presentation Type: 
Poster
Presentations Academic Category: 
Engineering
Grant Program Recipient: 
Harrison Undergraduate Research Grant
Abstract: 

Small-cell lung carcinoma (SCLC) accounts for 15-20% of all lung cancer cases and is the most lethal form of lung cancer due to lack of effective treatments. One cause of differential and ineffectual responses to therapy is variations between cells that make up a tumor, or intra-tumor heterogeneity. We believe measurements of intra-tumor heterogeneity in SCLC is a crucial next step towards identifying more effective treatment strategies. To measure intra-tumor heterogeneity, we have developed a method for quantifying gene expression globally, accurately, and sensitively by RNA sequencing of 10-cell samples isolated by laser-capture microdissection (10cRNA-seq). 10cRNA-seq selectively measures mRNA in situ and improves gene detection compared to conventional methods for single-cell RNA-seq. We applied this method to a genetically engineered mouse model of SCLC to compare tumor cell heterogeneity in vitro and in vivo. For genes that are reliably measured, we compare expression patterns in groups of 10-cells to matched split-pool controls and identify candidate heterogeneously expressed genes. For in vitro cultured cells, the analytical pipeline identified candidate genes related to the biological processes of cellular metabolism and protein localization. Dysregulation of these biological processes are characteristic of cancer. We will compare the candidate genes identified from the in vitro cultured cells to genes identified from the same SCLC cells grown in vivo as liver metastases in both immunocompetent and immunocompromised mice. The identified expression profiles will provide insight into how the tumor microenvironment and immune cell infiltration impact local tumor behavior.