Abstract

Understanding cancer cell metabolism and targeting associated pathways is a field of increasing interest. Helmlinger and colleagues measured average pH and pO(2) as functions of distance from a single blood vessel on the micrometer scale. We show that these results provide unique insight into cancer cell metabolism in vivo when combined with an appropriate mathematical model. We calculate pH as a function of distance from a single blood vessel and for a given metabolism while incorporating a single CO(2) buffer with effective diffusion constants. By assuming that cancer cell metabolism is dominated by respiration with a smaller component of glycolysis in the normoxic state, by more balanced respiration and glycolysis in the hypoxic state, and by glycolysis alone in the anoxic state, we are able to semiquantitatively derive the experimental results of Helmlinger and colleagues. We also apply our model to glycolysis-impaired metabolism and show that the low pH and high pO(2) observed in these tumors may be related to the substantial shift from a respiration-dominated metabolism to one in which glutaminolysis dominates. Based on this, we propose an in vivo experimental measurement of pH in a glycolysis-impaired tumor to validate the modeling results.

Highlights

  • Targeting metabolic pathways in malignant tumors increasingly shows promise as an effective therapeutic strategy [1, 2]

  • We suggest that multitargeted therapeutic strategies that inhibit both glycolysis and glutaminolysis are necessary in the design of efficient therapies

  • It was first postulated more than 80 years ago that cancer cells shift their metabolism toward a glycolytic phenotype even under normal levels of oxygen concentration; this phenomenon, dubbed the Warburg effect [6, 7], has since been confirmed by many studies conducted both in vitro and in vivo

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Summary

Introduction

Targeting metabolic pathways in malignant tumors increasingly shows promise as an effective therapeutic strategy [1, 2]. For a given cell metabolism [PG = PG (CG, CO) and PO = PO (CG, CO)], we first simultaneously solve Eq 1 for oxygen and glucose to derive their consumption rates as functions of distance.

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