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      Combating subclonal evolution of resistant cancer phenotypes

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          Abstract

          Metastatic breast cancer remains challenging to treat, and most patients ultimately progress on therapy. This acquired drug resistance is largely due to drug-refractory sub-populations (subclones) within heterogeneous tumors. Here, we track the genetic and phenotypic subclonal evolution of four breast cancers through years of treatment to better understand how breast cancers become drug-resistant. Recurrently appearing post-chemotherapy mutations are rare. However, bulk and single-cell RNA sequencing reveal acquisition of malignant phenotypes after treatment, including enhanced mesenchymal and growth factor signaling, which may promote drug resistance, and decreased antigen presentation and TNF-α signaling, which may enable immune system avoidance. Some of these phenotypes pre-exist in pre-treatment subclones that become dominant after chemotherapy, indicating selection for resistance phenotypes. Post-chemotherapy cancer cells are effectively treated with drugs targeting acquired phenotypes. These findings highlight cancer’s ability to evolve phenotypically and suggest a phenotype-targeted treatment strategy that adapts to cancer as it evolves.

          Abstract

          In metastatic breast cancer, subclonal evolution can drive drug resistance. Here, the authors genetically and transcriptionally follow the evolution of four breast cancers over time and treatment, and suggest a phenotype-targeted treatment strategy to adapt to cancer as it evolves.

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          Most cited references 21

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          Acquired resistance to TKIs in solid tumours: learning from lung cancer.

          The use of advanced molecular profiling to direct the use of targeted therapy, such as tyrosine kinase inhibitors (TKIs) for patients with advanced-stage non-small-cell lung cancer (NSCLC), has revolutionized the treatment of this disease. However, acquired resistance, defined as progression after initial benefit, to targeted therapies inevitably occurs. This Review explores breakthroughs in the understanding and treatment of acquired resistance in NSCLC, focusing on EGFR mutant and ALK rearrangement-positive disease, which may be relevant across multiple different solid malignancies with oncogene-addicted subtypes. Mechanisms of acquired resistance may be pharmacological (that is, failure of delivery of the drug to its target) or biological, resulting from evolutionary selection on molecularly diverse tumours. A number of clinical approaches can maintain control of the disease in the acquired resistance setting, including the use of radiation to treat isolated areas of progression and adding or switching to cytotoxic chemotherapy. Furthermore, novel approaches that have already proven successful include the development of second-generation and third-generation inhibitors and the combination of some of these inhibitors with antibodies directed against the same target. With our increased understanding of the spectrum of acquired resistance, major changes in how we conduct clinical research in this setting are now underway.
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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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              Ras/Raf/MEK/ERK and PI3K/PTEN/Akt/mTOR Inhibitors: Rationale and Importance to Inhibiting These Pathways in Human Health

              The Ras/Raf/MEK/ERK and PI3K/PTEN/Akt/mTOR cascades are often activated by genetic alterations in upstream signaling molecules such as receptor tyrosine kinases (RTK). Integral components of these pathways, Ras, B-Raf, PI3K, and PTEN are also activated/inactivated by mutations. These pathways have profound effects on proliferative, apoptotic and differentiation pathways. Dysregulation of these pathways can contribute to chemotherapeutic drug resistance, proliferation of cancer initiating cells (CICs) and premature aging. This review will evaluate more recently described potential uses of MEK, PI3K, Akt and mTOR inhibitors in the proliferation of malignant cells, suppression of CICs, cellular senescence and prevention of aging. Ras/Raf/MEK/ERK and Ras/PI3K/PTEN/Akt/mTOR pathways play key roles in the regulation of normal and malignant cell growth. Inhibitors targeting these pathways have many potential uses from suppression of cancer, proliferative diseases as well as aging.
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                Author and article information

                Contributors
                andreab@genetics.utah.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                1 November 2017
                1 November 2017
                2017
                : 8
                Affiliations
                [1 ]ISNI 0000 0001 2193 0096, GRID grid.223827.e, Department of Pharmacology and Toxicology, College of Pharmacy, , University of Utah, ; 30 South 2000 East, Salt Lake City, UT 84112 USA
                [2 ]ISNI 0000 0001 2193 0096, GRID grid.223827.e, Department of Biomedical Informatics, School of Medicine, , University of Utah, ; 421 Wakara Way, Salt Lake City, UT 84108 USA
                [3 ]ISNI 0000 0001 2193 0096, GRID grid.223827.e, Department of Oncological Sciences, School of Medicine, , University of Utah, ; 2000 Circle of Hope Drive, Salt Lake City, UT 84112 USA
                [4 ]ISNI 0000 0001 2193 0096, GRID grid.223827.e, Department of Human Genetics, School of Medicine, , University of Utah, ; 15 South 2030 East, Salt Lake City, UT 84112 USA
                [5 ]ISNI 0000 0004 1936 9115, GRID grid.253294.b, Department of Biology, College of Life Sciences, , Brigham Young University, ; Provo, UT 84602 USA
                [6 ]ISNI 0000 0004 1936 7558, GRID grid.189504.1, Division of Computational Biomedicine, School of Medicine, , Boston University, ; 72 East Concord Street, Boston, MA 02218 USA
                [7 ]ISNI 0000 0000 9758 5690, GRID grid.5288.7, Department of Biomedical Engineering, , Oregon Health & Science University, ; 2730 SW Moody Ave, Portland, OR 97201 USA
                [8 ]ISNI 0000 0000 9758 5690, GRID grid.5288.7, Oregon Center for Spatial Systems Biomedicine, , Oregon Health & Science University, ; 2730 SW Moody Ave, Portland, OR 97201 USA
                [9 ]ISNI 0000 0001 1034 1720, GRID grid.410711.2, Department of Genetics and Lineberger Comprehensive Cancer Center, , University of North Carolina, ; 450 West Drive, Chapel Hill, NC 27599 USA
                [10 ]ISNI 0000 0004 0515 3663, GRID grid.412722.0, High-Throughput Genomics and Bioinformatic Analysis, , Huntsman Cancer Institute, ; 2000 Circle of Hope Drive, Salt Lake City, UT 84112 USA
                [11 ]Department of Pathology, Huntsman Cancer Hospital, 1950 Circle of Hope Drive, Salt Lake City, UT 84112 USA
                [12 ]ISNI 0000 0001 2193 0096, GRID grid.223827.e, Department of Internal Medicine, Pulmonary Division, School of Medicine, , University of Utah, ; 26 North Medical Drive, Salt Lake City, UT 84132 USA
                [13 ]ISNI 0000 0001 2193 0096, GRID grid.223827.e, Department of Internal Medicine, Huntsman Cancer Institute, , University of Utah, ; 2000 Circle of Hope Drive, Salt Lake City, UT 84112 USA
                [14 ]ISNI 0000 0001 2193 0096, GRID grid.223827.e, Department of Medicine, Oncology Division, Huntsman Cancer Institute, , University of Utah, ; 2000 Circle of Hope Drive, Salt Lake City, UT 84112 USA
                [15 ]ISNI 0000 0004 0421 8357, GRID grid.410425.6, Department of Medical Oncology and Therapeutics, , City of Hope Comprehensive Cancer Institute, ; 1218 S Fifth Ave, Monrovia, CA 91016 USA
                Article
                1174
                10.1038/s41467-017-01174-3
                5666005
                29093439
                © The Author(s) 2017

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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