Cooperative adaptation to therapy (CAT) confers resistance in heterogeneous non-small cell lung cancer

Abstract

Understanding intrinsic and acquired resistance is crucial to overcoming cancer chemotherapy failure. While it is well-established that intratumor, subclonal genetic and phenotypic heterogeneity significantly contribute to resistance, it is not fully understood how tumor sub-clones interact with each other to withstand therapy pressure. Here, we report a previously unrecognized behavior in heterogeneous tumors: cooperative adaptation to therapy (CAT), in which cancer cells induce co-resistant phenotypes in neighboring cancer cells when exposed to cancer therapy. Using a CRISPR/Cas9 toolkit we engineered phenotypically diverse non-small cell lung cancer (NSCLC) cells by conferring mutations in Dicer1, a type III cytoplasmic endoribonuclease involved in small non-coding RNA genesis. We monitored three-dimensional growth dynamics of fluorescently-labeled mutant and/or wild-type cells individually or in co-culture using a substrate-free NanoCulture system under unstimulated or drug pressure conditions. By integrating mathematical modeling with flow cytometry, we characterized the growth patterns of mono- and co-cultures using a mathematical model of intra- and interspecies competition. Leveraging the flow cytometry data, we estimated the model’s parameters to reveal that the combination of WT and mutants in co-cultures allowed for beneficial growth in previously drug sensitive cells despite drug pressure via induction of cell state transitions described by a cooperative game theoretic change in the fitness values. Finally, we used an ex vivo human tumor model that predicts clinical response through drug sensitivity analyses and determined that cellular and morphologic heterogeneity correlates to prognostic failure of multiple clinically-approved and off-label drugs in individual NSCLC patient samples. Together, these findings present a new paradox in drug resistance implicating non-genetic cooperation among tumor cells to thwart drug pressure, suggesting that profiling for druggable targets (i.e. mutations) alone may be insufficient to assign effective therapy.

Reciprocal interactions between tumour cell populations enhance growth and reduce radiation sensitivity in prostate cancer

“…We found that both competitive and antagonistic interactions increase bulk tumour growth and resistance to radiation. Our results are consistent with findings from previous studies investigating the impact of ecological interactions in chemotherapy: although the type of interaction (e.g., competitive vs. mutualism) can vary, ecological interactions appear to increase resistance to cancer therapy 14 , 17 , 27 – 29 . Furthermore, in our study, interactions increase the growth and post-radiation survival for the parental population in mixed spheroids in both cell lines.…”

Understanding the role of phenotypic switching in cancer drug resistance

“…Thus, while reversible drug-tolerant cells may arise naturally in drug-naïve cell populations, their emergence can also be directly influenced by anti-cancer treatment. Further complicating the picture, Craig et al [35] have hypothesized that genotypically and phenotypically distinct cells can cooperate to induce the adoption of drug tolerance, although the potential mechanism behind such cooperation remains unclear.…”

Modeling the bidirectional glutamine/ammonium conversion between cancer cells and cancer-associated fibroblasts

“…Full-size  DOI: 10.7717/peerj.10648/ fig-11 immune therapy. One can include a potential interplay between different phenotypes in cancer cells when exposed to therapy (Craig et al, 2019). It would also be interesting to determine the dose effects of various non-cancer cells on both the rate and persistence of tumor growth, and to incorporate not only metabolic interactions but also intercellular crosstalk through growth factors and cytokines, to improve our understanding of the dynamic and highly heterogeneous tumor growth environment.…”

“…In future studies, the model presented herein can be amended to include additional cell types found in the tumor microenvironment, such as vascular endothelial cells and immune cells, to understand their role in tumor metabolism and growth and to predict tumor response to therapies such as cytotoxic chemotherapy, metabolic therapy, and immune therapy. One can include a potential interplay between different phenotypes in cancer cells when exposed to therapy ( Craig et al, 2019 ). It would also be interesting to determine the dose effects of various non-cancer cells on both the rate and persistence of tumor growth, and to incorporate not only metabolic interactions but also intercellular crosstalk through growth factors and cytokines, to improve our understanding of the dynamic and highly heterogeneous tumor growth environment.…”