Systematic alteration of in vitro metabolic environments reveals empirical growth relationships in cancer cell phenotypes

Abstract

SUMMARY Cancer cells, like microbes, live in complex metabolic environments. Recent evidence suggests that microbial behavior across metabolic environments is well described by simple empirical growth relationships, or growth laws. Do such empirical growth relationships also exist in cancer cells? To test this question, we develop a high-throughput approach to extract quantitative measurements of cancer cell behaviors in systematically altered metabolic environments. Using this approach, we examine relationships between growth and three frequently studied cancer phenotypes: drug-treatment survival, cell migration, and lactate overflow. Drug-treatment survival follows simple linear growth relationships, which differ quantitatively between chemotherapeutics and EGFR inhibition. Cell migration follows a weak grow-and-go growth relationship, with substantial deviation in some environments. Finally, lactate overflow is mostly decoupled from growth rate and is instead determined by the cells’ ability to maintain high sugar uptake rates. Altogether, this work provides a quantitative approach for formulating empirical growth laws of cancer.

Resource Allocation in Mammalian Systems

“…In mammalian cells, linear relationships exist between individual genes' abundance with cell size (Miettinen et al, 2014) and between other phenotypes such as migration with growth rate (Kochanowski et al, 2021). Omics measurements of individual genes enable further exploration of proteome re-allocation between translational machinery and other functional protein sectors, such as energy metabolism (Appendix A Figure b).…”

Cell Population Growth Kinetics in the Presence of Stochastic Heterogeneity of Cell Phenotype

“…Many new questions arise following the hypothesis that phenotypic heterogeneity and transi-tions between phenotypes within one genetic clone are important factors in cancer. Can tumors arise, as theoretical considerations indicate, because of a state conversion (within one clone) to a phenotype capable of faster, more autonomous growth as opposed to acquisition of a new genetic mutation that confers such a selectable phenotype ( Zhou et al, 2014a; Angelini et al, 2022; Howard et al, 2018; Sahoo et al, 2021; Pisco and Huang, 2015; Zhou et al, 2014b; Kochanowski et al, 2021 )? Is the macroscopic, apparently sudden outgrowth of a tumor driven by a new fastest-growing clone (or subpopulation) taking off exponentially, or due to the cell population reaching a critical mass that permits positive feedback between its subpopulations that stimulates outgrowth, akin to a collectively autocatalytic set ( Hordijk et al, 2018 )?…”

Nontargeted and targeted metabolomics approaches reveal the key amino acid alterations involved in multiple myeloma

“…Accumulating evidence has confirmed that metabolic alterations are involved in myeloma cell growth and drug resistance ( Maiso et al, 2015 ; Pinto et al, 2020 ). Metabolomics analysis is a comprehensive method of metabolites that can dynamically monitor the intermediate and final products of biochemical reactions and has been widely used in cancer diagnosis, treatment, and prognosis ( Armitage & Southam, 2016 ; Cao et al, 2020 ; Kochanowski et al, 2021 ). By analyzing the metabolic profiles of MM patients at diagnosis and after achieving complete remission, some of the metabolic changes, such as glutamine, cholesterol, and lysine, have been observed, suggesting the potential of metabolic profiles in identifying MM biomarkers or monitoring response to treatment ( Puchades-Carrasco et al, 2013 ).…”