Abstract Cancer is an evolutionary disease driven by molecular alterations in cancer cells and concomitant tumor microenvironments. Unfortunately, cancer cells often evolve into aggressive tumors that ultimately evade treatment. Thus, in order to improve clinical outcomes, there is an urgent need to define mechanisms by which cancer cells evolve. Recent multi-region sequencing studies, including our own, have inferred phylogenetic evolution across distinct lesions collected from patients. While these studies analyzed the extent of intratumoral heterogeneity across tumor types, the molecular determinants of cancer evolution remain unclear. For example, it is challenging to precisely quantify the adaptive dynamics of a cancer cell lineage before, during, and after a selective pressure. Moreover, tumor microenvironments tend to be spatially and temporally heterogeneous, which complicates evolutionary analyses of cancer cells within these microenvironments. To address these challenges in resolving the evolutionary dynamics of cancer cells, our current work combined bioreactor culturing, longitudinal sampling, single cell sequencing, and metabolomics. Cancer cell lines were selected to represent diverse hematological and solid tumor types, including leukemia, lymphoma, myeloma, colorectal, retinoblastoma, and lung cancers. For four weeks, we consistently maintained multiple environmental parameters of each cancer cell population including temperature, pH, oxygen, and agitation. All cancer cell populations were initiated with the same seeding density in identical media. Since we aimed to quantify growth patterns and to define mechanisms by which cancer cells adapt to nutrient starvation, we allowed each cancer cell population to alter cell density as well as metabolite consumption over the course of the experiment. Every 48 hours, cells and media were collected and preserved to establish a “fossil record” for analysis, and cell density and viability were measured. We found that all cancer cell populations demonstrated exponential growth, plateau and death phases over the course of these experiments, and that each cell line exhibited its own characteristic growth pattern and carrying capacity (range 125 - 250 million cancer cells) despite all cell lines having been grown in the same environmental condition. Moreover, these growth patterns were highly concordant among independently maintained populations. Given our environmental controls, these results suggest that the cancer cell population growth patterns we observed reflected cell-intrinsic features. To explore transcriptional dynamics, we used single cell RNA sequencing to analyze longitudinal samples of the cancer cells. We found that transcriptional subclones emerged over the course of the experiment with altered gene expression profiles, including in genes with functions related to cancer cell metabolism such as biosynthesis, stress responses, and nutrient uptake, indicating putative mechanisms by which the cancer cells were adapting to an increasingly stringent environment. To further define environmental constituents, we analyzed longitudinal media samples that were collected at each timepoint with the cancer cells. Multiple metabolites were consumed within the first seven days of culture, including amino acids, vitamins, and nucleotides. Strikingly, we also observed metabolites that were secreted into the media over the course of the experiment, including nucleotides and signaling molecules. These metabolite patterns were consistent with the concomitant gene expression changes of the cancer cells. Overall, our results showed that the cancer cells were simultaneously adapting to and remodeling their environment rather than solely depleting nutrients. Defining such dynamics, especially in the context of fluctuating environmental conditions, will be essential for mechanistic studies of cancer evolution. Citation Format: Alvin P. Makohon-Moore. Transcriptional and metabolic dynamics of cancer cells under nutrient deprivation. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr NG08.
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Open Access
Apr 30, 2023
Huiming Zhang 
