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Friday, December 3 • 9:55am - 10:05am
OP 19 - Exploring hypotheses of small cell lung cancer growth mechanisms using Bayesian mutlimodel inference

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Exploring hypotheses of small cell lung cancer growth mechanisms using Bayesian mutlimodel inference

Presenting Author: Samantha Beik, Vanderbilt University

Leonard Harris, University of Arkansas
Vito Quaranta, Vanderbilt University
Carlos Lopez, Vanderbilt University

Abstract: Small cell lung cancer (SCLC) is a phenotypically heterogeneous disease, comprising multiple cellular subtypes within a tumor that exhibit differential sensitivity to drug treatments. SCLC heterogeneity is hypothesized to be responsible for rapid development of chemotherapy resistance, leading to the dismal 6% five-year survival rate for this disease. Experimental results from several studies suggest that treatment alters tumor composition from an initial makeup of phenotypic subtype(s) to different, less-treatment-sensitive subtypes. We hypothesize that this change arises from phenotypic transitions, rather than outgrowth of subclone(s) selected for by treatment, and that interactions between subtypes are key for tumor survival. We set out to use mathematical modeling to investigate the theoretical basis for SCLC tumor growth, but soon realized that analysis of only one interpretation of SCLC data (one model) would be flawed, and turned to multimodel inference (MMI) to address this issue. We move beyond traditional information theoretic MMI to a fully Bayesian approach, applying MMI to population dynamics models fit to SCLC tumor steady-state data. We extend our findings beyond a ranking of models toward a probabilistic view of subtype behaviors, determining that the existence of reversible phenotypic transitions is highly likely in SCLC. Our results highlight what knowledge is supported by the data and where more experiments are needed, with an aim to modulate tumor composition and decrease treatment resistance. This is sorely needed in SCLC, for which the survival rate has barely improved in decades. With sensible treatment options, the burden of this aggressive disease can be decreased.

avatar for Samantha Beik

Samantha Beik

Vanderbilt University

Friday December 3, 2021 9:55am - 10:05am MST
Ballroom Salon 1