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The goal of this study is to target pre-existing heterogeneity in gene expression to ultimately overcome resistance to targeted therapy in melanoma. Despite significant advances in the treatment of melanoma, many patients still fail to achieve durable responses to therapy due to the development of drug resistance. Our lab recently discovered that in drug-naive melanoma rare cells transiently enter distinct gene expression states that allows them to ultimately survive treatment with targeted therapy (we refer to these cells as “primed for drug resistance”). The goal of this work is to develop a strategy to overcome this form of resistance by driving cells out of states primed for resistance and into drug-susceptible states. To identify the pathways that regulate the transition to the primed state we combined single-cell RNA sequencing with DNA barcoding to extract the transcriptional profiles and lineage information of over 7,000 drug-naive melanoma cells. We found that cells in lineages that transitioned into states primed for drug resistance express high levels of genes associated with TGFβ and PI3K signaling compared to lineages that stayed in a drug-susceptible state. To test whether TGFβ can directly cause cells to enter the primed state, we treated cells with recombinant TGFβ which led to a significant increase in the number of primed cells. Intriguingly, we also found that treating with TGFB receptor inhibitor or PI3K inhibitor reduced the number of cells in the primed cell state. Further, we sequentially treated these cells first with either the TGFB or PI3K inhibitor followed by targeted therapy and ultimately reduced the number of resistant colonies, demonstrating the potential for this therapeutic paradigm. To better understand how targeting the TGFB and PI3K pathways shift the population dynamics, we used single-cell RNA-seq and cellular barcoding to track lineages following treatment with TGFB, TGFB inhibitor, and PI3K inhibitor. Our analysis reveals the population changes following each therapy and directly demonstrates that treating with PI3K inhibitor decreases the number of primed cells, shifting the population into a drug-susceptible expression state. Ultimately, this work defines two key pathways underlying the primed cell state in melanoma and presents a therapeutic paradigm to eliminate this form of therapy resistance. Citation Format: Guillaume Harmange, Ben Emert, Dylan Schaff, Raul Reyes-Hueros, Sydney Shaffer. Overcoming resistance by driving melanoma cells into drug-susceptible states [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr B028.

Non-genetic factors can cause individual cells to fluctuate substantially in gene expression levels over time. Yet it remains unclear whether these fluctuations can persist for much longer than the time of one cell division. Current methods for measuring gene expression in single cells mostly rely on single time point measurements, making the duration of gene expression fluctuations or cellular memory difficult to measure. Here, we report a method combining Luria and Delbrück’s fluctuation analysis with population-based RNA sequencing (MemorySeq) for identifying genes transcriptome-wide whose fluctuations persist for several cell divisions. MemorySeq revealed multiple gene modules that are expressed together in rare cells within otherwise homogeneous clonal populations. Further, we found that these rare cell subpopulations are associated with biologically distinct behaviors, such as the ability to proliferate in the face of anti-cancer therapeutics, in different cancer cell lines. The identification of non-genetic, multigenerational fluctuations has the potential to reveal new forms of biological memory at the level of single cells and suggests that non-genetic heritability of cellular state may be a quantitative property.