Evolution of Drug Resistance: Cancer

Please contact Pia Abel zur Wiesch if you want to choose a paper within this topic.

Introduction:

Merlo, L. M., J. W. Pepper, B. J. Reid, and C. C. Maley. 2006. Cancer as an evolutionary and ecological process. Nat Rev Cancer 6:924-935. [pdf]
Neoplasms are microcosms of evolution. Within a neoplasm, a mosaic of mutant cells compete for space and resources, evade predation by the immune system and can even cooperate to disperse and colonize new organs. The evolution of neoplastic cells explains both why we get cancer and why it has been so difficult to cure. The tools of evolutionary biology and ecology are providing new insights into neoplastic progression and the clinical control of cancer.

Michor, F., M. A. Nowak, and Y. Iwasa. 2006. Evolution of resistance to cancer therapy. Curr Pharm Des 12:261-271. [pdf]
Acquired drug resistance is a major limitation for successful treatment of cancer. Resistance emerges due to drug exclusion, drug metabolism and alteration of the drug target by mutation or overexpression. Depending on therapy, the type of cancer and its stage, one or several genetic or epigenetic alterations are necessary to confer resistance to treatment. The fundamental question is the following: if a genetically diverse population of replicating cancer cells is subjected to chemotherapy that has the potential to eradicate it, what is the probability of emergence of resistance? Here, we review a general mathematical framework based on multi-type branching processes designed to study the dynamics of escape of replicating organisms from selection pressures. We apply the general model to evolution of resistance of cancer cells and discuss examples for diverse mechanisms of resistance. Our theory shows how to estimate the probability of success for any treatment regimen.

General mathematical models:

Komarova, N. L., and D. Wodarz. 2005. Drug resistance in cancer: principles of emergence and prevention. Proc Natl Acad Sci U S A 102:9714-9719. [pdf]
Although targeted therapy is yielding promising results in the treatment of specific cancers, drug resistance poses a problem. We develop a mathematical framework that can be used to study the principles underlying the emergence and prevention of resistance in cancers treated with targeted small-molecule drugs. We consider a stochastic dynamical system based on measurable parameters, such as the turnover rate of tumor cells and the rate at which resistant mutants are generated. We find that resistance arises mainly before the start of treatment and, for cancers with high turnover rates, combination therapy is less likely to yield an advantage over single-drug therapy. We apply the mathematical framework to chronic myeloid leukemia. Early-stage chronic myeloid leukemia was the first case to be treated successfully with a targeted drug, imatinib (Novartis, Basel). This drug specifically inhibits the BCR-ABL oncogene, which is required for progression. Although drug resistance prevents successful treatment at later stages of the disease, our calculations suggest that, within the model assumptions, a combination of three targeted drugs with different specificities might overcome the problem of resistance.

Iwasa, Y., M. A. Nowak, and F. Michor. 2006. Evolution of resistance during clonal expansion. Genetics 172:2557-2566. [pdf]
Acquired drug resistance is a major limitation for cancer therapy. Often, one genetic alteration suffices to confer resistance to an otherwise successful therapy. However, little is known about the dynamics of the emergence of resistant tumor cells. In this article, we consider an exponentially growing population starting from one cancer cell that is sensitive to therapy. Sensitive cancer cells can mutate into resistant ones, which have relative fitness alpha prior to therapy. In the special case of no cell death, our model converges to the one investigated by Luria and Delbruck. We calculate the probability of resistance and the mean number of resistant cells once the cancer has reached detection size M. The probability of resistance is an increasing function of the detection size M times the mutation rate u. If Mu << 1, then the expected number of resistant cells in cancers with resistance is independent of the mutation rate u and increases with M in proportion to M(1-1/alpha) for advantageous mutants with relative fitness alpha>1, to l nM for neutral mutants (alpha = 1), but converges to an upper limit for deleterious mutants (alpha<1). Further, the probability of resistance and the average number of resistant cells increase with the number of cell divisions in the history of the tumor. Hence a tumor subject to high rates of apoptosis will show a higher incidence of resistance than expected on its detection size only.

Mechanisms of drug resistance:

Gambacorti-Passerini, C., et al. 2003. Molecular mechanisms of resistance to imatinib in Philadelphia-chromosome-positive leukaemias. Lancet Oncology 4: 75-85. [pdf]
Imatinib (STI571 or CGP57148B) is an innovative treatment for tumours with a constitutively activated form of c-ABL, c-KIT, or PDGFR. Such tumours include Philadelphiachromosome-positive (Ph-positive) leukaemias, gastrointestinal stromal tumours, and PDGFR-positive leukaemias. Diseases such as primary hypereosinophilia and dermatofibrosarcoma protuberans also seem to respond to imatinib. Clinical trials assessing the therapeutic effects of imatinib have shown that the drug is highly effective with few associated side-effects, achieving durable cytogenetic responses in many patients with chronic-phase BCR-ABL-positive leukaemias. However, the emergence of resistance, particularly in patients with acute leukaemias, has prompted intense research, and many are concerned about the future prospects for imatinib. The resistance has been found in patients with acute-phase disease, but may also occur in patients with chronic-phase disease. Two cellular mechanisms for resistance to imatinib have been identified: amplification of BCR-ABL gene and mutations in the catalytic domain of the protein. In addition, suboptimum inhibition of BCR-ABL in vivo could contribute to the selection of resistant cells. We have summarised all currently available data on resistance to imatinib, both published and unpublished, including the mechanisms of resistance identified so far, and their clinical relevance to the different forms of Phpositive leukaemias is discussed. Furthermore, we discuss strategies to overcome or prevent the development of resistance.

Szakacs, G., J. K. Paterson, J. A. Ludwig, C. Booth-Genthe, and M. M. Gottesman. 2006. Targeting multidrug resistance in cancer. Nat Rev Drug Discov 5:219-234. [pdf]
Effective treatment of metastatic cancers usually requires the use of toxic chemotherapy. In most cases, multiple drugs are used, as resistance to single agents occurs almost universally. For this reason, elucidation of mechanisms that confer simultaneous resistance to different drugs with different targets and chemical structures - multidrug resistance - has been a major goal of cancer biologists during the past 35 years. Here, we review the most common of these mechanisms, one that relies on drug efflux from cancer cells mediated by ATP-binding cassette (ABC) transporters. We describe various approaches to combating multidrug-resistant cancer, including the development of drugs that engage, evade or exploit efflux by ABC transporters.

Meads, M. B., R. A. Gatenby, and W. S. Dalton. 2009. Environment-mediated drug resistance: a major contributor to minimal residual disease. Nat Rev Cancer 9:665-674. [pdf]
Environment-mediated drug resistance is a form of de novo drug resistance that protects tumour cells from the initial effects of diverse therapies. Surviving foci of residual disease can then develop complex and permanent acquired resistance in response to the selective pressure of therapy. Recent evidence indicates that environment-mediated drug resistance arises from an adaptive, reciprocal signalling dialogue between tumour cells and the surrounding microenvironment. We propose that new therapeutic strategies targeting this interaction should be applied during initial treatment to prevent the emergence of acquired resistance.

Resistance vs. Drug Toxicity:

Foo, J., and F. Michor. 2009. Evolution of resistance to targeted anti-cancer therapies during continuous and pulsed administration strategies. PLoS Comput Biol 5:e1000557. [pdf]
The discovery of small molecules targeted to specific oncogenic pathways has revolutionized anti-cancer therapy. However, such therapy often fails due to the evolution of acquired resistance. One long-standing question in clinical cancer research is the identification of optimum therapeutic administration strategies so that the risk of resistance is minimized. In this paper, we investigate optimal drug dosing schedules to prevent, or at least delay, the emergence of resistance. We design and analyze a stochastic mathematical model describing the evolutionary dynamics of a tumor cell population during therapy. We consider drug resistance emerging due to a single (epi)genetic alteration and calculate the probability of resistance arising during specific dosing strategies. We then optimize treatment protocols such that the risk of resistance is minimal while considering drug toxicity and side effects as constraints. Our methodology can be used to identify optimum drug administration schedules to avoid resistance conferred by one (epi)genetic alteration for any cancer and treatment type.