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Systematic identification of signaling pathways with potential to confer anticancer drug resistance.

ABSTRACT: Cancer cells can activate diverse signaling pathways to evade the cytotoxic action of drugs. We created and screened a library of barcoded pathway-activating mutant complementary DNAs to identify those that enhanced the survival of cancer cells in the presence of 13 clinically relevant, targeted therapies. We found that activation of the RAS-MAPK (mitogen-activated protein kinase), Notch1, PI3K (phosphoinositide 3-kinase)-mTOR (mechanistic target of rapamycin), and ER (estrogen receptor) signaling pathways often conferred resistance to this selection of drugs. Activation of the Notch1 pathway promoted acquired resistance to tamoxifen (an ER-targeted therapy) in serially passaged breast cancer xenografts in mice, and treating mice with a ?-secretase inhibitor to inhibit Notch signaling restored tamoxifen sensitivity. Markers of Notch1 activity in tumor tissue correlated with resistance to tamoxifen in breast cancer patients. Similarly, activation of Notch1 signaling promoted acquired resistance to MAPK inhibitors in BRAF(V600E) melanoma cells in culture, and the abundance of Notch1 pathway markers was increased in tumors from a subset of melanoma patients. Thus, Notch1 signaling may be a therapeutic target in some drug-resistant breast cancers and melanomas. Additionally, multiple resistance pathways were activated in melanoma cell lines with intrinsic resistance to MAPK inhibitors, and simultaneous inhibition of these pathways synergistically induced drug sensitivity. These data illustrate the potential for systematic identification of the signaling pathways controlling drug resistance that could inform clinical strategies and drug development for multiple types of cancer. This approach may also be used to advance clinical options in other disease contexts.

SUBMITTER: Martz CA

PROVIDER: S-EPMC4353587 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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Systematic identification of signaling pathways with potential to confer anticancer drug resistance.

Martz Colin A CA Ottina Kathleen A KA Singleton Katherine R KR Jasper Jeff S JS Wardell Suzanne E SE Peraza-Penton Ashley A Anderson Gray R GR Winter Peter S PS Wang Tim T Alley Holly M HM Kwong Lawrence N LN Cooper Zachary A ZA Tetzlaff Michael M Chen Pei-Ling PL Rathmell Jeffrey C JC Flaherty Keith T KT Wargo Jennifer A JA McDonnell Donald P DP Sabatini David M DM Wood Kris C KC

Science signaling 20141223 357

Cancer cells can activate diverse signaling pathways to evade the cytotoxic action of drugs. We created and screened a library of barcoded pathway-activating mutant complementary DNAs to identify those that enhanced the survival of cancer cells in the presence of 13 clinically relevant, targeted therapies. We found that activation of the RAS-MAPK (mitogen-activated protein kinase), Notch1, PI3K (phosphoinositide 3-kinase)-mTOR (mechanistic target of rapamycin), and ER (estrogen receptor) signali ...[more]

PMID: 25538079

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Project description:Gene Expression Profiling of Breast Cancer Patients with Brain Metastases Brain metastases confer the worst prognosis of breast cancer as no therapy exists that prevents or eliminates the cancer from spreading to the brain. We developed a new computational modeling method to derive specific downstream signaling pathways that reveal unknown target-disease connections and new mechanisms for specific cancer subtypes. The model enables us to reposition drugs based on available gene expression data of patients. We applied this model to repurpose known or shelved drugs for brain, lung, and bone metastases of breast cancer with the hypothesis that cancer subtypes have their own specific signaling mechanisms. To test the hypothesis, we addressed the specific CSBs for each metastasis that satisfy that (1) CSB proteins are activated by the maximal number of enriched signaling pathways specific to this metastasis, and (2) CSB proteins involve in the most differential expressed coding-genes specific to the specific breast cancer metastasis. The identified signaling networks for the three types of metastases contain 31, 15, and 18 proteins, respectively, and are used to reposition 15, 9, and 2 drug candidates for the brain, lung, and bone metastases of breast cancer. We performed in vitro and in vivo preclinical experiments as well as analysis on patient tumor specimens to evaluate the targets and repositioned drugs. Two known drugs, Sunitinib (FDA approved for renal cell carcinoma and imatinib-resistant gastrointestinal stromal tumor) and Dasatinib (FDA approved for chronic myelogenous leukemia (CML) after imatinib treatment and Philadelphia chromosome-positive acute lymphoblastic leukemia), were shown to prohibit the metastatic colonization in brain. The TMH-52 cohort includes 11 patients who were examined with brain metastasis at the time of breast cancer diagnosis. The other 41 patients were examined with other organ metastasis at the time of breast cancer diagnosis.

Project description:BackgroundEmergence of drug resistant varieties of tuberculosis is posing a major threat to global tuberculosis eradication programmes. Although several approaches have been explored to counter resistance, there has been limited success due to a lack of understanding of how resistance emerges in bacteria upon drug treatment. A systems level analysis of the proteins involved is essential to gain insights into the routes required for emergence of drug resistance.ResultsWe derive a genome-scale protein-protein interaction network for Mycobacterium tuberculosis H37Rv from the STRING database, with proteins as nodes and interactions as edges. A set of proteins involved in both intrinsic and extrinsic drug resistance mechanisms are identified from literature. We then compute shortest paths from different drug targets to the set of resistance proteins in the protein-protein interactome, to derive a sub-network relevant to study emergence of drug resistance. The shortest paths are then scored and ranked based on a new scheme that considers (a) drug-induced gene upregulation data, from microarray experiments reported in literature, for the individual nodes and (b) edge-hubness, a network parameter which signifies centrality of a given edge in the network. High-scoring paths identified from this analysis indicate most plausible pathways for the emergence of drug resistance. Different targets appear to have different propensities for four drug resistance mechanisms. A new concept of 'co-targets' has been proposed to counter drug resistance, co-targets being defined as protein(s) that need to be simultaneously inhibited along with the intended target(s), to check emergence of resistance to a given drug.ConclusionThe study leads to the identification of possible pathways for drug resistance, providing novel insights into the problem of resistance. Knowledge of important proteins in such pathways enables identification of appropriate 'co-targets', best examples being RecA, Rv0823c, Rv0892 and DnaE1, for drugs targeting the mycolic acid pathway. Insights obtained about the propensity of a drug to trigger resistance will be useful both for more careful identification of drug targets as well as to identify target-co-target pairs, both implementable in early stages of drug discovery itself. This approach is also inherently generic, likely to significantly impact drug discovery.

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Systematic identification of signaling pathways with potential to confer anticancer drug resistance.

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Systematic identification of signaling pathways with potential to confer anticancer drug resistance.

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