Journal Article

Evolutionary dynamics of cancer in response to targeted combination therapy

Chatterjee, Krishnendu, Nowak, Martin A, Moon, Yo Sup, Kelly, Nicole, Chapman, Paul B, Bozic, Ivana, Vogelstein, Bert, Yaqubie, Amin, Lipson, Evan J, Reiter, Johannes G, Shah, Preya, Antal, Tibor, Le, Dung T, Diaz, Luis A, Allen, Benjamin

2013

Overview

In solid tumors, targeted treatments can lead to dramatic regressions, but responses are often short-lived because resistant cancer cells arise. The major strategy proposed for overcoming resistance is combination therapy. We present a mathematical model describing the evolutionary dynamics of lesions in response to treatment. We first studied 20 melanoma patients receiving vemurafenib. We then applied our model to an independent set of pancreatic, colorectal, and melanoma cancer patients with metastatic disease. We find that dual therapy results in long-term disease control for most patients, if there are no single mutations that cause cross-resistance to both drugs; in patients with large disease burden, triple therapy is needed. We also find that simultaneous therapy with two drugs is much more effective than sequential therapy. Our results provide realistic expectations for the efficacy of new drug combinations and inform the design of trials for new cancer therapeutics. As medicine becomes increasingly personalized, more and more emphasis is being placed on the development of therapies that target specific cancer-causing mutations. But while many of these drugs are effective in the short term, and do extend patient lives, tumors tend to evolve resistance to them within a few months. The key problem is that large tumors are genetically diverse. This means that for any given treatment, there is likely to be a small population of cells within the tumor that is resistant to the effects of the drug. When the drug is given to a patient, these cells will survive and multiply and this will lead ultimately to treatment failure. Given that a single drug is therefore highly unlikely to eradicate a tumor, combinations of two or more drugs may offer a higher chance of cure. This approach has been effective in the treatment of HIV as well as certain forms of leukemia. Here, Bozic et al. present a mathematical model designed to predict the effects of combination targeted therapies on tumors, based on the data obtained from 20 melanoma (skin cancer) patients. Their model revealed that if even 1 of the 6.6 billion base pairs of DNA present in a human diploid cell has undergone a mutation that confers resistance to each of two drugs, treatment with those drugs will not lead to sustained improvement for the majority of patients. This confirms the need to develop drugs that target distinct pathways. The model also reveals that combination therapy with two drugs given simultaneously is far more effective than sequential therapy where the drugs are used one after the other. Indeed, the model of Bozic et al. indicates that sequential treatment offers no chance of a cure, even when there are no cross-resistance mutations present, whereas combination therapy offers some hope of a cure, even in the presence of cross-resistance mutations. By emphasizing the need to develop drugs that target distinct pathways, and to administer them in combination rather than sequentially, the study by Bozic et al. offers valuable advice for drug development and the design of clinical trials, as well as for clinical practice.

• Publisher: eLife Sciences Publications Ltd,eLife Sciences Publications, Ltd
• Subject: Antineoplastic Combined Chemotherapy Protocols - therapeutic use / Apoptosis / Birth rate / Cancer / Cancer therapies / Cell division / Clinical trials / Colorectal cancer / Combination therapy / Cross-resistance / Disease control / Drug development / Drug resistance / Drugs / Evolution, Molecular / genetic / Genomics and Evolutionary Biology / Growth rate / Human Biology and Medicine / Humans / Infectious diseases / Leukemia / Lung cancer / mathematical biology / Mathematical models / Mathematics / Melanoma / Metastases / Mutation / Neoplasms - drug therapy / Neoplasms - genetics / Pancreas / Pancreatic cancer / Patients / Solid tumors / stochastic process / targeted therapy / Tumors