Ecological interactions in breast cancer: Cell facilitation promotes growth and survival under drug pressure

Abstract

Competition between different lineages of cells within a tumor has been considered in cancer evolution and progression, but positive interactions like facilitation have received far less study. We developed isogenic estrogen positive breast cancer cell lineages (CAMA-1 and MCF7) sensitive and resistant to CDK4/6 inhibition in order to investigate the mechanisms of cell interaction under selective pressure. When these sensitive and resistant lineages are grown in coculture under ribociclib treatment, sensitive cells survive and proliferate better than in monoculture despite the effects of competition. Conditioned media taken from resistant CAMA-1 cells reproduces this advantage, suggesting that secretion of some factor from resistant cells underlies the effect. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) assays were used to identify factors secreted by cell lineages. While the less active estrone metabolite was detected in medium from both lineages, highly active estradiol was only detectable in media that included resistant cells. Because sensitive cells are more dependent on estrogen signaling than resistant cells, they benefit from the increase in local estradiol, leading to facilitated growth when in coculture in the presence of ribociclib. This facilitation of sensitive cells by resistant cells is recapitulated when cells are treated with small molecule inhibitors that target estrogen signaling pathways. We developed a set of mathematical models to quantify the strength of competition and facilitation between cell lines and evaluate the effects of treatments that modify these interactions. These analyses support the conclusion that resistant cell lineages benefit sensitive lineages during therapy through the increased production and sharing of local estradiol, and themselves suffer from increased competition and suppression as a result. Our multidisciplinary approach analyzes and quantifies interactions between ER+ cancer lineages during treatment and provides the framework to evaluate these effects in patients and improve therapy.

A Systems Biology Approach for Addressing Cisplatin Resistance in Non-Small Cell Lung Cancer

“…In the cancer world, drug attrition rates are notorious—several drugs are effective in preclinical studies but only a few are approved for clinical use [ 74 ]. Furthermore, while most approved cancer drugs do help in improving the life expectancy of the patients, cancer cells often develop resistance against these therapies and relapse as resistant and metastatic diseases.…”

Phylogenetic inference from single-cell RNA-seq data

“…Finally, to estimate the phylogeny of the cells, we used BEAST2, a Bayesian phylogenetic inference algorithm (Figure 1D). To verify that genotype smoothing helped recover more accurate phylogenetic relationships, we used a data set where the CAMA-1 breast cancer cell line was experimentally induced to evolve under 6 months of treatment with ribociclib, a CDK4/6 inhibitor, resulting in a resistant cell line (GSE193278) [31]. A sister lineage from the same ancestral population evolved under untreated conditions for the same period and remained ribociclib sensitive.…”

Delaying Emergence of Resistance to KRAS Inhibitors with Adaptive Therapy: “Treatment-to-Contain” Instead of “Treatment-to-Cure”

“…This phenomenon has been studied in vitro in a NSCLC cell line, PC-9, that acquired the point mutation T790M, which is shown to proliferate roughly 1.22 times slower than parental cells [32,33]. As a result, this leads to the general principle of adaptive therapy is that treatment should be used to reduce the total cancer population below a certain symptomatic threshold while sustaining a significant population of sensitive cells, and the extended treatment-free period allows both sensitive and resistant cells to proliferate competitively for survival [34][35][36] (Fig. 1).…”