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      Activity of distinct growth factor receptor network components in breast tumors uncovers two biologically relevant subtypes

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          Abstract

          Background

          The growth factor receptor network (GFRN) plays a significant role in driving key oncogenic processes. However, assessment of global GFRN activity is challenging due to complex crosstalk among GFRN components, or pathways, and the inability to study complex signaling networks in patient tumors. Here, pathway-specific genomic signatures were used to interrogate GFRN activity in breast tumors and the consequent phenotypic impact of GRFN activity patterns.

          Methods

          Novel pathway signatures were generated in human primary mammary epithelial cells by overexpressing key genes from GFRN pathways ( HER2, IGF1R, AKT1, EGFR, KRAS ( G12V), RAF1, BAD). The pathway analysis toolkit Adaptive Signature Selection and InteGratioN (ASSIGN) was used to estimate pathway activity for GFRN components in 1119 breast tumors from The Cancer Genome Atlas (TCGA) and across 55 breast cancer cell lines from the Integrative Cancer Biology Program (ICBP43). These signatures were investigated for their relationship to pro- and anti-apoptotic protein expression and drug response in breast cancer cell lines.

          Results

          Application of these signatures to breast tumor gene expression data identified two novel discrete phenotypes characterized by concordant, aberrant activation of either the HER2, IGF1R, and AKT pathways (“the survival phenotype”) or the EGFR, KRAS (G12V), RAF1, and BAD pathways (“the growth phenotype”). These phenotypes described a significant amount of the variability in the total expression data across breast cancer tumors and characterized distinctive patterns in apoptosis evasion and drug response. The growth phenotype expressed lower levels of BIM and higher levels of MCL-1 proteins. Further, the growth phenotype was more sensitive to common chemotherapies and targeted therapies directed at EGFR and MEK. Alternatively, the survival phenotype was more sensitive to drugs inhibiting HER2, PI3K, AKT, and mTOR, but more resistant to chemotherapies.

          Conclusions

          Gene expression profiling revealed a bifurcation pattern in GFRN activity represented by two discrete phenotypes. These phenotypes correlate to unique mechanisms of apoptosis and drug response and have the potential of pinpointing targetable aberration(s) for more effective breast cancer treatments.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13073-017-0429-x) contains supplementary material, which is available to authorized users.

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          Most cited references50

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          featureCounts: An efficient general-purpose program for assigning sequence reads to genomic features

          , , (2013)
          Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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            Oncogenic pathway signatures in human cancers as a guide to targeted therapies.

            The development of an oncogenic state is a complex process involving the accumulation of multiple independent mutations that lead to deregulation of cell signalling pathways central to the control of cell growth and cell fate. The ability to define cancer subtypes, recurrence of disease and response to specific therapies using DNA microarray-based gene expression signatures has been demonstrated in multiple studies. Various studies have also demonstrated the potential for using gene expression profiles for the analysis of oncogenic pathways. Here we show that gene expression signatures can be identified that reflect the activation status of several oncogenic pathways. When evaluated in several large collections of human cancers, these gene expression signatures identify patterns of pathway deregulation in tumours and clinically relevant associations with disease outcomes. Combining signature-based predictions across several pathways identifies coordinated patterns of pathway deregulation that distinguish between specific cancers and tumour subtypes. Clustering tumours based on pathway signatures further defines prognosis in respective patient subsets, demonstrating that patterns of oncogenic pathway deregulation underlie the development of the oncogenic phenotype and reflect the biology and outcome of specific cancers. Predictions of pathway deregulation in cancer cell lines are also shown to predict the sensitivity to therapeutic agents that target components of the pathway. Linking pathway deregulation with sensitivity to therapeutics that target components of the pathway provides an opportunity to make use of these oncogenic pathway signatures to guide the use of targeted therapeutics.
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              Extrinsic versus intrinsic apoptosis pathways in anticancer chemotherapy.

              Apoptosis or programmed cell death is a key regulator of physiological growth control and regulation of tissue homeostasis. One of the most important advances in cancer research in recent years is the recognition that cell death mostly by apoptosis is crucially involved in the regulation of tumor formation and also critically determines treatment response. Killing of tumor cells by most anticancer strategies currently used in clinical oncology, for example, chemotherapy, gamma-irradiation, suicide gene therapy or immunotherapy, has been linked to activation of apoptosis signal transduction pathways in cancer cells such as the intrinsic and/or extrinsic pathway. Thus, failure to undergo apoptosis may result in treatment resistance. Understanding the molecular events that regulate apoptosis in response to anticancer chemotherapy, and how cancer cells evade apoptotic death, provides novel opportunities for a more rational approach to develop molecular-targeted therapies for combating cancer.
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                Author and article information

                Contributors
                andreab@genetics.utah.edu
                Journal
                Genome Med
                Genome Med
                Genome Medicine
                BioMed Central (London )
                1756-994X
                26 April 2017
                26 April 2017
                2017
                : 9
                : 40
                Affiliations
                [1 ]ISNI 0000 0001 2193 0096, GRID grid.223827.e, Department of Pharmacology and Toxicology, , University of Utah, ; 30 S 2000 E, Salt Lake City, UT 84108 USA
                [2 ]ISNI 0000 0001 2193 0096, GRID grid.223827.e, Department of Biomedical Informatics, , University of Utah, ; Salt Lake City, UT USA
                [3 ]ISNI 0000 0001 2193 0096, GRID grid.223827.e, Department of Oncological Sciences, , University of Utah, ; Salt Lake City, UT USA
                [4 ]ISNI 0000 0004 0367 5222, GRID grid.475010.7, Division of Computational Biomedicine, , Boston University School of Medicine, ; Boston, MA USA
                [5 ]ISNI 0000 0000 9758 5690, GRID grid.5288.7, Department of Biomedical Engineering, Center for Spatial Systems Biomedicine, Knight Cancer Institute, , Oregon Health and Sciences University, ; Portland, OR USA
                [6 ]ISNI 0000 0004 1936 9115, GRID grid.253294.b, Department of Biology, , Brigham Young University, ; Provo, UT USA
                Article
                429
                10.1186/s13073-017-0429-x
                5406893
                28446242
                fed09bfd-3a29-4cd5-a879-5a3e8db243a6
                © The Author(s). 2017

                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
                : 23 August 2016
                : 11 April 2017
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000092, U.S. National Library of Medicine;
                Award ID: T15LM007124
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000054, National Cancer Institute;
                Award ID: U01CA164720
                Categories
                Research
                Custom metadata
                © The Author(s) 2017

                Molecular medicine
                breast cancer,gene expression signatures,cancer phenotypes,growth factor receptor network,genomics,targeted therapy

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