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      Identification of selective cytotoxic and synthetic lethal drug responses in triple negative breast cancer cells

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          Abstract

          Background

          Triple negative breast cancer (TNBC) is a highly heterogeneous and aggressive type of cancer that lacks effective targeted therapy. Despite detailed molecular profiling, no targeted therapy has been established. Hence, with the aim of gaining deeper understanding of the functional differences of TNBC subtypes and how that may relate to potential novel therapeutic strategies, we studied comprehensive anticancer-agent responses among a panel of TNBC cell lines.

          Method

          The responses of 301 approved and investigational oncology compounds were measured in 16 TNBC cell lines applying a functional profiling approach. To go beyond the standard drug viability effect profiling, which has been used in most chemosensitivity studies, we utilized a multiplexed readout for both cell viability and cytotoxicity, allowing us to differentiate between cytostatic and cytotoxic responses.

          Results

          Our approach revealed that most single-agent anti-cancer compounds that showed activity for the viability readout had no or little cytotoxic effects. Major compound classes that exhibited this type of response included anti-mitotics, mTOR, CDK, and metabolic inhibitors, as well as many agents selectively inhibiting oncogene-activated pathways. However, within the broad viability-acting classes of compounds, there were often subsets of cell lines that responded by cell death, suggesting that these cells are particularly vulnerable to the tested substance. In those cases we could identify differential levels of protein markers associated with cytotoxic responses. For example, PAI-1, MAPK phosphatase and Notch-3 levels associated with cytotoxic responses to mitotic and proteasome inhibitors, suggesting that these might serve as markers of response also in clinical settings. Furthermore, the cytotoxicity readout highlighted selective synergistic and synthetic lethal drug combinations that were missed by the cell viability readouts. For instance, the MEK inhibitor trametinib synergized with PARP inhibitors. Similarly, combination of two non-cytotoxic compounds, the rapamycin analog everolimus and an ATP-competitive mTOR inhibitor dactolisib, showed synthetic lethality in several mTOR-addicted cell lines.

          Conclusions

          Taken together, by studying the combination of cytotoxic and cytostatic drug responses, we identified a deeper spectrum of cellular responses both to single agents and combinations that may be highly relevant for identifying precision medicine approaches in TNBC as well as in other types of cancers.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12943-016-0517-3) contains supplementary material, which is available to authorized users.

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          Most cited references31

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          Organoids as an in vitro model of human development and disease.

          The in vitro organoid model is a major technological breakthrough that has already been established as an essential tool in many basic biology and clinical applications. This near-physiological 3D model facilitates an accurate study of a range of in vivo biological processes including tissue renewal, stem cell/niche functions and tissue responses to drugs, mutation or damage. In this Review, we discuss the current achievements, challenges and potential applications of this technique.
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            Inhibition of mTORC1 leads to MAPK pathway activation through a PI3K-dependent feedback loop in human cancer.

            Numerous studies have established a causal link between aberrant mammalian target of rapamycin (mTOR) activation and tumorigenesis, indicating that mTOR inhibition may have therapeutic potential. In this study, we show that rapamycin and its analogs activate the MAPK pathway in human cancer, in what represents a novel mTORC1-MAPK feedback loop. We found that tumor samples from patients with biopsy-accessible solid tumors of advanced disease treated with RAD001, a rapamycin derivative, showed an administration schedule-dependent increase in activation of the MAPK pathway. RAD001 treatment also led to MAPK activation in a mouse model of prostate cancer. We further show that rapamycin-induced MAPK activation occurs in both normal cells and cancer cells lines and that this feedback loop depends on an S6K-PI3K-Ras pathway. Significantly, pharmacological inhibition of the MAPK pathway enhanced the antitumoral effect of mTORC1 inhibition by rapamycin in cancer cells in vitro and in a xenograft mouse model. Taken together, our findings identify MAPK activation as a consequence of mTORC1 inhibition and underscore the potential of a combined therapeutic approach with mTORC1 and MAPK inhibitors, currently employed as single agents in the clinic, for the treatment of human cancers.
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              Patient-derived models of acquired resistance can identify effective drug combinations for cancer.

              Targeted cancer therapies have produced substantial clinical responses, but most tumors develop resistance to these drugs. Here, we describe a pharmacogenomic platform that facilitates rapid discovery of drug combinations that can overcome resistance. We established cell culture models derived from biopsy samples of lung cancer patients whose disease had progressed while on treatment with epidermal growth factor receptor (EGFR) or anaplastic lymphoma kinase (ALK) tyrosine kinase inhibitors and then subjected these cells to genetic analyses and a pharmacological screen. Multiple effective drug combinations were identified. For example, the combination of ALK and MAPK kinase (MEK) inhibitors was active in an ALK-positive resistant tumor that had developed a MAP2K1 activating mutation, and the combination of EGFR and fibroblast growth factor receptor (FGFR) inhibitors was active in an EGFR mutant resistant cancer with a mutation in FGFR3. Combined ALK and SRC (pp60c-src) inhibition was effective in several ALK-driven patient-derived models, a result not predicted by genetic analysis alone. With further refinements, this strategy could help direct therapeutic choices for individual patients.
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                Author and article information

                Contributors
                krister.wennerberg@fimm.fi
                Journal
                Mol Cancer
                Mol. Cancer
                Molecular Cancer
                BioMed Central (London )
                1476-4598
                10 May 2016
                10 May 2016
                2016
                : 15
                : 34
                Affiliations
                Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
                Article
                517
                10.1186/s12943-016-0517-3
                4862054
                27165605
                45cb1ee0-6f3c-4d90-bd85-68d0e74404bf
                © Gautam et al. 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 29 October 2015
                : 30 April 2016
                Funding
                Funded by: FundRef http://dx.doi.org/http://dx.doi.org/10.13039/501100004012, Jane ja Aatos Erkon Säätiö;
                Funded by: FundRef http://dx.doi.org/10.13039/501100002342, Suomalainen Tiedeakatemia;
                Award ID: 272577, 277293
                Award ID: 272437, 269862, 279163, 292611
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/http://dx.doi.org/10.13039/100007797, Helsingin Yliopisto;
                Categories
                Research
                Custom metadata
                © The Author(s) 2016

                Oncology & Radiotherapy
                tnbc,chemosensitivity,viability,cytotoxicity,dsrt,dss,heterogeneity,synergy,antagonism
                Oncology & Radiotherapy
                tnbc, chemosensitivity, viability, cytotoxicity, dsrt, dss, heterogeneity, synergy, antagonism

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