Adaptive Therapy

Abstract

A number of successful systemic therapies are available for treatment of disseminated cancers. However, tumor response is often transient, and therapy frequently fails due to emergence of resistant populations. The latter reflects the temporal and spatial heterogeneity of the tumor microenvironment as well as the evolutionary capacity of cancer phenotypes to adapt to therapeutic perturbations. Although cancers are highly dynamic systems, cancer therapy is typically administered according to a fixed, linear protocol. Here we examine an adaptive therapeutic approach that evolves in response to the temporal and spatial variability of tumor microenvironment and cellular phenotype as well as therapy-induced perturbations. Initial mathematical models find that when resistant phenotypes arise in the untreated tumor, they are typically present in small numbers because they are less fit than the sensitive population. This reflects the “cost” of phenotypic resistance such as additional substrate and energy used to up-regulate xenobiotic metabolism, and therefore not available for proliferation, or the growth inhibitory nature of environments (i.e., ischemia or hypoxia) that confer resistance on phenotypically sensitive cells. Thus, in the Darwinian environment of a cancer, the fitter chemosensitive cells will ordinarily proliferate at the expense of the less fit chemoresistant cells. The models show that, if resistant populations are present before administration of therapy, treatments designed to kill maximum numbers of cancer cells remove this inhibitory effect and actually promote more rapid growth of the resistant populations. We present an alternative approach in which treatment is continuously modulated to achieve a fixed tumor population. The goal of adaptive therapy is to enforce a stable tumor burden by permitting a significant population of chemosensitive cells to survive so that they, in turn, suppress proliferation of the less fit but chemoresistant subpopulations. Computer simulations show that this strategy can result in prolonged survival that is substantially greater than that of high dose density or metronomic therapies. The feasibility of adaptive therapy is supported by in vivo experiments.

Extremely high genetic diversity in a single tumor points to prevalence of non-Darwinian cell evolution

“…When mutations can be found in nearly all possible coding regions within a tumor, resistance to most drugs seems highly likely. Read et al (37) pointed out that aggressive strategies against cancerous cells are effective only in the absence of resistance at treatment and various strategies for administering drugs in the face of resistant clones have been proposed (38)(39)(40)(41)(42). Finally, a key feature of the non-Darwinian model is the rapidity with which mutations accrue.…”

Non-canonical HIF-2α function drives autonomous breast cancer cell growth via an AREG–EGFR/ErbB4 autocrine loop

“…In clinical practice, cytotoxic therapies are largely ineffective on cells within the most inhospitable environments, often leading to relapse of the disease after an initial phase of tumor mass reduction. Based on the recently developed concepts of 'adaptive therapy', where a stable tumor burden of higher differentiated tumor cells is sustained to suppress the growth of slowproliferating but therapy-resistant phenotypes (Gatenby et al, 2009), novel therapies maintaining an HIF-2a-dominated phenotype could prove of value to treat breast cancer.…”

Evolutionary medicine: its scope, interest and potential

“…The view of cancer as an evolutionary process driven by natural selection operating on the genetic heterogeneity of clones that originate through somatic mutations has been widely accepted by the scientific community [91 -94] and has attracted the attention of theoreticians [95]. However, an important insight of theory into consequences for therapy has not yet been widely recognized much less accepted [96].…”