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      Evolutionary dynamics of cancer in response to targeted combination therapy

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          Abstract

          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.

          DOI: http://dx.doi.org/10.7554/eLife.00747.001

          eLife digest

          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.

          DOI: http://dx.doi.org/10.7554/eLife.00747.002

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          Most cited references29

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          Wild-type KRAS is required for panitumumab efficacy in patients with metastatic colorectal cancer.

          Panitumumab, a fully human antibody against the epidermal growth factor receptor (EGFR), has activity in a subset of patients with metastatic colorectal cancer (mCRC). Although activating mutations in KRAS, a small G-protein downstream of EGFR, correlate with poor response to anti-EGFR antibodies in mCRC, their role as a selection marker has not been established in randomized trials. KRAS mutations were detected using polymerase chain reaction on DNA from tumor sections collected in a phase III mCRC trial comparing panitumumab monotherapy to best supportive care (BSC). We tested whether the effect of panitumumab on progression-free survival (PFS) differed by KRAS status. KRAS status was ascertained in 427 (92%) of 463 patients (208 panitumumab, 219 BSC). KRAS mutations were found in 43% of patients. The treatment effect on PFS in the wild-type (WT) KRAS group (hazard ratio [HR], 0.45; 95% CI: 0.34 to 0.59) was significantly greater (P < .0001) than in the mutant group (HR, 0.99; 95% CI, 0.73 to 1.36). Median PFS in the WT KRAS group was 12.3 weeks for panitumumab and 7.3 weeks for BSC. Response rates to panitumumab were 17% and 0%, for the WT and mutant groups, respectively. WT KRAS patients had longer overall survival (HR, 0.67; 95% CI, 0.55 to 0.82; treatment arms combined). Consistent with longer exposure, more grade III treatment-related toxicities occurred in the WT KRAS group. No significant differences in toxicity were observed between the WT KRAS group and the overall population. Panitumumab monotherapy efficacy in mCRC is confined to patients with WT KRAS tumors. KRAS status should be considered in selecting patients with mCRC as candidates for panitumumab monotherapy.
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            Targeted cancer therapy.

            Disruption of the normal regulation of cell-cycle progression and division lies at the heart of the events leading to cancer. Complex networks of regulatory factors, the tumour microenvironment and stress signals, such as those resulting from damaged DNA, dictate whether cancer cells proliferate or die. Recent progress in understanding the molecular changes that underlie cancer development offer the prospect of specifically targeting malfunctioning molecules and pathways to achieve more effective and rational cancer therapy.
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              First-line gefitinib in patients with advanced non-small-cell lung cancer harboring somatic EGFR mutations.

              Somatic mutations in the epidermal growth factor receptor (EGFR) correlate with increased response in patients with non-small-cell lung cancer (NSCLC) treated with EGFR tyrosine kinase inhibitors (TKIs). The multicenter iTARGET trial prospectively examined first-line gefitinib in advanced NSCLC patients harboring EGFR mutations and explored the significance of EGFR mutation subtypes and TKI resistance mechanisms. Chemotherapy-naïve patients with advanced NSCLC with >or= 1 clinical characteristic associated with EGFR mutations underwent direct DNA sequencing of tumor tissue EGFR exons 18 to 21. Patients found to harbor any EGFR mutation were treated with gefitinib 250 mg/d until progression or unacceptable toxicity. The primary outcome was response rate. Ninety-eight patients underwent EGFR screening and mutations were detected in 34 (35%). EGFR mutations were primarily exon 19 deletions (53%) and L858R (26%) though 21% of mutation-positive cases had less common subtypes including exon 20 insertions, T790M/L858R, G719A, and L861Q. Thirty-one patients received gefitinib. The response rate was 55% (95% CI, 33 to 70) and median progression-free survival was 9.2 months (95% CI, 6.2 to 11.8). Therapy was well tolerated; 13% of patients had grade 3 toxicities including one grade 3 pneumonitis. Two patients with classic activating mutations exhibited de novo gefitinib resistance and had concurrent genetic anomalies usually associated with acquired TKI resistance, specifically the T790M EGFR mutation and MET amplification. First-line therapy with gefitinib administered in a genotype-directed fashion to patients with advanced NSCLC harboring EGFR mutations results in very favorable clinical outcomes with good tolerance. This strategy should be compared with combination chemotherapy, the current standard of care.
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                Author and article information

                Contributors
                Role: Reviewing editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                25 June 2013
                2013
                : 2
                Affiliations
                [1 ]Program for Evolutionary Dynamics, Harvard University , Cambridge, United States
                [2 ]Department of Mathematics, Harvard University , Cambridge, United States
                [3 ]Institute of Science and Technology Austria , Klosterneuburg, Austria
                [4 ]Department of Mathematics, Emmanuel College , Boston, United States
                [5 ]School of Mathematics, Edinburgh University , Edinburgh, United Kingdom
                [6 ]Harvard College , Cambridge, United States
                [7 ]Memorial Sloan-Kettering Cancer Center , New York, United States
                [8 ]Department of Medical Oncology, Johns Hopkins University School of Medicine; The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins , Baltimore, United States
                [9 ]Ludwig Center for Cancer Genetics and Therapeutics, Howard Hughes Medical Institute, Johns Hopkins Kimmel Cancer Center , Baltimore, United States
                [10 ]Department of Organismic and Evolutionary Biology, Harvard University , Cambridge, United States
                University of Washington , United States
                University of Washington , United States
                Author notes
                [* ]For correspondence: martin_nowak@ 123456harvard.edu
                [†]

                These authors contributed equally to this work.

                Article
                00747
                10.7554/eLife.00747
                3691570
                23805382
                f9f58f34-14de-4fff-b49d-a6eb0a79a090
                Copyright © 2013, Bozic et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                Product
                Funding
                Funded by: Foundational Questions in Evolutionary Biology Fund
                Award Recipient :
                Funded by: European Research Council Start Grant
                Award ID: 279307
                Award Recipient :
                Funded by: FWF (The Austrian Science Fund) Grant
                Award ID: S11407-N23, P23499-N23
                Award Recipient :
                Funded by: Microsoft Faculty Fellow Award
                Award Recipient :
                Funded by: The John Templeton Foundation
                Award Recipient :
                Funded by: The Danny Federici Melanoma Fund
                Award Recipient :
                Funded by: John Figge Melanoma Fund
                Award Recipient :
                Funded by: The Virginia and D. K. Ludwig Fund for Cancer Research
                Award Recipient :
                Funded by: National Cancer Institute
                Award ID: contract N01-CN-43309
                Award Recipient :
                Funded by: National Institutes of Health
                Award ID: CA129825, CA43460, CA57345
                Award Recipient :
                Funded by: National Colorectal Cancer Research Alliance
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Genomics and Evolutionary Biology
                Human Biology and Medicine
                Custom metadata
                A study that models the evolution of drug resistance in tumors reveals that drugs are more effective when given in combination than sequentially, and that cure is much more likely when the drugs target different pathways.

                Life sciences
                mathematical biology,cancer,stochastic processes,targeted therapy,genetics,none
                Life sciences
                mathematical biology, cancer, stochastic processes, targeted therapy, genetics, none

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