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      Pan-cancer patterns of somatic copy-number alteration

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          Abstract

          Determining how somatic copy-number alterations (SCNAs) promote cancer is an important goal. We characterized SCNA patterns among 4934 cancers from The Cancer Genome Atlas Pan-Cancer dataset. Whole-genome doubling, observed in 37% of cancers, was associated with higher rates of every other type of SCNA, TP53 mutations, CCNE1 amplifications, and alterations of the PPP2R complex. SCNAs that were internal to chromosomes tended to be shorter than telomere-bounded SCNAs, suggesting different mechanisms of generation. Significantly recurrent focal SCNAs were observed in 140 regions, including 102 without known oncogene or tumor suppressor gene targets and 50 with significantly mutated genes. Amplified regions without known oncogenes are enriched for genes involved in epigenetic regulation. When levels of genomic disruption were accounted for, 7% of region pairs anticorrelated, and these tended to encompass genes whose proteins physically interact, suggesting related functions. These results provide insights into mechanisms of generation and functional consequences of cancer SCNAs.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Mutational heterogeneity in cancer and the search for new cancer genes

            Major international projects are now underway aimed at creating a comprehensive catalog of all genes responsible for the initiation and progression of cancer. These studies involve sequencing of matched tumor–normal samples followed by mathematical analysis to identify those genes in which mutations occur more frequently than expected by random chance. Here, we describe a fundamental problem with cancer genome studies: as the sample size increases, the list of putatively significant genes produced by current analytical methods burgeons into the hundreds. The list includes many implausible genes (such as those encoding olfactory receptors and the muscle protein titin), suggesting extensive false positive findings that overshadow true driver events. Here, we show that this problem stems largely from mutational heterogeneity and provide a novel analytical methodology, MutSigCV, for resolving the problem. We apply MutSigCV to exome sequences from 3,083 tumor-normal pairs and discover extraordinary variation in (i) mutation frequency and spectrum within cancer types, which shed light on mutational processes and disease etiology, and (ii) mutation frequency across the genome, which is strongly correlated with DNA replication timing and also with transcriptional activity. By incorporating mutational heterogeneity into the analyses, MutSigCV is able to eliminate most of the apparent artefactual findings and allow true cancer genes to rise to attention.
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              Is Open Access

              Ki67 Index, HER2 Status, and Prognosis of Patients With Luminal B Breast Cancer

              Background Gene expression profiling of breast cancer has identified two biologically distinct estrogen receptor (ER)-positive subtypes of breast cancer: luminal A and luminal B. Luminal B tumors have higher proliferation and poorer prognosis than luminal A tumors. In this study, we developed a clinically practical immunohistochemistry assay to distinguish luminal B from luminal A tumors and investigated its ability to separate tumors according to breast cancer recurrence-free and disease-specific survival. Methods Tumors from a cohort of 357 patients with invasive breast carcinomas were subtyped by gene expression profile. Hormone receptor status, HER2 status, and the Ki67 index (percentage of Ki67-positive cancer nuclei) were determined immunohistochemically. Receiver operating characteristic curves were used to determine the Ki67 cut point to distinguish luminal B from luminal A tumors. The prognostic value of the immunohistochemical assignment for breast cancer recurrence-free and disease-specific survival was investigated with an independent tissue microarray series of 4046 breast cancers by use of Kaplan–Meier curves and multivariable Cox regression. Results Gene expression profiling classified 101 (28%) of the 357 tumors as luminal A and 69 (19%) as luminal B. The best Ki67 index cut point to distinguish luminal B from luminal A tumors was 13.25%. In an independent cohort of 4046 patients with breast cancer, 2847 had hormone receptor–positive tumors. When HER2 immunohistochemistry and the Ki67 index were used to subtype these 2847 tumors, we classified 1530 (59%, 95% confidence interval [CI] = 57% to 61%) as luminal A, 846 (33%, 95% CI = 31% to 34%) as luminal B, and 222 (9%, 95% CI = 7% to 10%) as luminal–HER2 positive. Luminal B and luminal–HER2-positive breast cancers were statistically significantly associated with poor breast cancer recurrence-free and disease-specific survival in all adjuvant systemic treatment categories. Of particular relevance are women who received tamoxifen as their sole adjuvant systemic therapy, among whom the 10-year breast cancer–specific survival was 79% (95% CI = 76% to 83%) for luminal A, 64% (95% CI = 59% to 70%) for luminal B, and 57% (95% CI = 47% to 69%) for luminal–HER2 subtypes. Conclusion Expression of ER, progesterone receptor, and HER2 proteins and the Ki67 index appear to distinguish luminal A from luminal B breast cancer subtypes.
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                Author and article information

                Journal
                9216904
                2419
                Nat Genet
                Nat. Genet.
                Nature genetics
                1061-4036
                1546-1718
                27 November 2013
                October 2013
                26 March 2015
                : 45
                : 10
                : 1134-1140
                Affiliations
                [1 ]Departments of Cancer Biology and Medical Oncology and The Center for Cancer Genome Discovery, Dana Farber Cancer Institute, 450 Brookline Ave., Boston, MA 02215, USA
                [2 ]The Broad Institute, 7 Cambridge Center, Cambridge, MA 02142
                [3 ]Biophysics Program, Harvard University, Boston, MA 02115, USA
                [4 ]Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
                [5 ]Departments of Medicine and Pathology and Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
                [6 ]USC Epigenome Center, University of Southern California, Los Angeles, CA 90033, USA
                Author notes
                [*,†]

                Authors contributed equally to this work

                Article
                NIHMS517488
                10.1038/ng.2760
                3966983
                24071852
                22bcd922-22b1-41d5-baa2-3a882930df4c

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                Genetics
                Genetics

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