Using birth-death processes to infer tumor subpopulation structure from live-cell imaging drug screening data

Tumor heterogeneity is a complex and widely recognized trait that poses significant challenges in developing effective cancer therapies. In particular, many tumors harbor a variety of subpopulations with distinct therapeutic response characteristics. Characterizing this heterogeneity by determining the subpopulation structure within a tumor enables more precise and successful treatment strategies. In our prior work, we developed PhenoPop, a computational framework for unravelling the drug-response subpopulation structure within a tumor from bulk high-throughput drug screening data. However, the deterministic nature of the underlying models driving PhenoPop restricts the model fit and the information it can extract from the data. As an advancement, we propose a stochastic model based on the linear birth-death process to address this limitation. Our model can formulate a dynamic variance along the horizon of the experiment so that the model uses more information from the data to provide a more robust estimation. In addition, the newly proposed model can be readily adapted to situations where the experimental data exhibits a positive time correlation. We test our model on simulated data (in silico) and experimental data (in vitro), which supports our argument about its advantages. One of the main reasons tumors can be difficult to treat is the presence of multiple subpopulations each with a distinct response to a given therapy. In particular some of these subpopulations are able to evade anti-cancer therapies and give rise to treatment resistant disease. Therefore it is vitally important to be able to identify these subpopulations and furthermore quantify their response to a therapy of interest, i.e., to quantify a tumors subpopulation structure. A potential tool for quantifying a tumors subpopulation structure are so called high-throughput drug screens (HTDS). In these screens a patients tumor sample is collected and then conditioned to grow in vitro where it can be exposed to a variety of drugs at different concentration levels. In the present work we develop statistical tools that create quantitative estimates of tumor population substructure based on HTDS data. These estimators have better precision than previous results, and furthermore are able to more accurately identify smaller subpopulations than previous estimators.