Evolutionary dynamics of cancer in response to targeted combination therapy

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

doi:10.7554/elife.00747

Cited by 557 publications

(564 citation statements)

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“…Recently, a mathematical approach applied to the evolutionary dynamics of solid tumors in response to treatment emphasized the need for concurrently targeting different pathways, or different parts of a pathway, to prevent the establishment of disease resistant to individual drugs (12). Therefore, employing agents directed at distinct components of critical signaling pathways in GIST warrants further investigation.…”

Section: Introductionmentioning

confidence: 99%

Combination of Imatinib Mesylate and AKT Inhibitor Provides Synergistic Effects in Preclinical Study of Gastrointestinal Stromal Tumor

Zook

Pathak

Belinsky

et al. 2017

Clinical Cancer Research

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Purpose Gastrointestinal stromal tumors (GIST) generally harbor activating mutations in the receptor tyrosine kinase KIT or in the related platelet derived growth factor receptor alpha (PDGFRA). GIST treated with imatinib mesylate (IM) or second-line therapies that target mutant forms of these receptors generally escape disease control and progress over time. Inhibiting additional molecular targets may provide more substantial disease control. Recent studies have implicated the PI3-kinase/AKT pathway in the survival of IM-resistant GIST cell lines and tumors. Experimental Design Here, we performed in vitro and in vivo studies evaluating the novel combination of IM with the AKT inhibitor MK-2206 in GIST. Whole-transcriptome sequencing (WTS) of xenografts was performed to explore the molecular aspects of tumor response to this novel combination and to potentially identify additional therapeutic targets in GIST. Results This drug combination demonstrated significant synergistic effects in a panel of IM-sensitive and -resistant GIST cell lines. Furthermore, combination therapy provided significantly greater efficacy, as measured by tumor response and animal survival, in IM-sensitive GIST xenografts as compared to treatment with IM or MK-2206 alone. WTS implicated two neural genes, brain expressed X-linked 1 (BEX1) and neuronal pentraxin I (NPTX1), whose expression was significantly up-regulated in combination-treated tumors compared to tumors treated with the two monotherapies. Conclusion These studies provide strong preclinical justification for combining IM with an AKT inhibitor as a front-line therapy in GIST. In addition, the WTS implicated the BCL-2/BAX/BAD apoptotic pathway as a potential mechanism for this enhanced combination effect.

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Section: Introductionmentioning

confidence: 99%

Combination of Imatinib Mesylate and AKT Inhibitor Provides Synergistic Effects in Preclinical Study of Gastrointestinal Stromal Tumor

Zook

Pathak

Belinsky

et al. 2017

Clinical Cancer Research

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“…These cells would then expand during treatment, repopulate the tumor, and cause treatment failure. Similar conclusions should hold for targeted treatments of other solid cancers (15). Successful treatment requires drugs that are effective against the preexisting resistant subpopulation and must take into account the (possible) heterogeneity of resistance mutations present in the patient's lesions.…”

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confidence: 98%

Timing and heterogeneity of mutations associated with drug resistance in metastatic cancers

Božić

Nowak

2014

Proc. Natl. Acad. Sci. U.S.A.

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103

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Targeted therapies provide an exciting new approach to combat human cancer. The immediate effect is a dramatic reduction in disease burden, but in most cases, the tumor returns as a consequence of resistance. Various mechanisms for the evolution of resistance have been implicated, including mutation of target genes and activation of other drivers. There is increasing evidence that the reason for failure of many targeted treatments is a small preexisting subpopulation of resistant cells; however, little is known about the genetic composition of this resistant subpopulation. Using the novel approach of ordering the resistant subclones according to their time of appearance, here we describe the full spectrum of resistance mutations present in a metastatic lesion. We calculate the expected and median number of cells in each resistant subclone. Surprisingly, the ratio of the medians of successive resistant clones is independent of any parameter in our model; for example, the median of the second clone divided by the median of the first is ffiffiffi 2 p − 1. We find that most radiographically detectable lesions harbor at least 10 resistant subclones. Our predictions are in agreement with clinical data on the relative sizes of resistant subclones obtained from liquid biopsies of colorectal cancer patients treated with epidermal growth factor receptor (EGFR) blockade. Our theory quantifies the genetic heterogeneity of resistance that exists before treatment and provides information to design treatment strategies that aim to control resistance.cancer | drug resistance | heterogeneity | mathematical biology A cquired resistance to treatment is a major impediment to successful eradication of cancer. Patients presenting with early-stage cancers can often be cured surgically, but patients with metastatic disease must be treated with systemic therapies (1). Traditional treatments such as chemotherapy and radiation that exploit the enhanced sensitivity of cancer cells to DNA damage have serious side effects and, although curative in some cases, often fail due to intrinsic or resistance acquired during treatment. Targeted therapies, a new class of drugs, inhibit specific molecules implicated in tumor development and are typically less harmful to normal cells compared with chemotherapy and radiation (2-5). In the case of many targeted treatments, patients initially have a dramatic response (6, 7), only to be followed by a regrowth of most of their lesions several months later (8-10). Acquired resistance is often a consequence of genetic alterations (usually point mutations) in the drug target itself or in other genes (10)(11)(12)(13)(14).Recently, mathematical modeling and clinical data were used to show that acquired resistance to an epidermal growth factor receptor (EGFR) inhibitor panitumumab in metastatic colorectal cancer patients is a fait accompli, because typical detectable metastatic lesions are expected to contain hundreds of cells resistant to the drug before the start of treatment (10). These cells would then expand ...

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“…The second is easily met for both healthy and cancerous human cells, but simulating full‐size clinically detectable tumours (more than 10 8 cells (Bozic et al., 2013)) is of large computational cost, and keeping our model and exercise minimal, we have used smaller populations, modelling smaller subclones or spatially segregated populations where drift comes into play. Such drift produces a nonmonomorphic population where evolution deviates from the gradient trajectory and so proceeds slightly slower than our estimate.…”

Section: Evolution Of Instabilitymentioning

confidence: 99%

Adaptive dynamics of unstable cancer populations: The canonical equation

Aguadé-Gorgorió

Solé

2018

Evolutionary Applications

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In most instances of tumour development, genetic instability plays a role in allowing cancer cell populations to respond to selection barriers, such as physical constraints or immune responses, and rapidly adapt to an always changing environment. Modelling instability is a nontrivial task, since by definition evolving instability leads to changes in the underlying landscape. In this article, we explore mathematically a simple version of unstable tumour progression using the formalism of adaptive dynamics (AD) where selection and mutation are explicitly coupled. Using a set of basic fitness landscapes, the so‐called canonical equation for the evolution of genetic instability on a minimal scenario associated with a population of unstable cells is derived. We obtain explicit expressions for the evolution of mutation probabilities, and the implications of the model on further experimental studies and potential mutagenic therapies are discussed.

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Evolutionary dynamics of cancer in response to targeted combination therapy

Cited by 557 publications

References 50 publications

Combination of Imatinib Mesylate and AKT Inhibitor Provides Synergistic Effects in Preclinical Study of Gastrointestinal Stromal Tumor

Combination of Imatinib Mesylate and AKT Inhibitor Provides Synergistic Effects in Preclinical Study of Gastrointestinal Stromal Tumor

Timing and heterogeneity of mutations associated with drug resistance in metastatic cancers

Adaptive dynamics of unstable cancer populations: The canonical equation

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