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      The Sweden Cancerome Analysis Network - Breast (SCAN-B) Initiative: a large-scale multicenter infrastructure towards implementation of breast cancer genomic analyses in the clinical routine

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          Abstract

          Background

          Breast cancer exhibits significant molecular, pathological, and clinical heterogeneity. Current clinicopathological evaluation is imperfect for predicting outcome, which results in overtreatment for many patients, and for others, leads to death from recurrent disease. Therefore, additional criteria are needed to better personalize care and maximize treatment effectiveness and survival.

          Methods

          To address these challenges, the Sweden Cancerome Analysis Network - Breast (SCAN-B) consortium was initiated in 2010 as a multicenter prospective study with longsighted aims to analyze breast cancers with next-generation genomic technologies for translational research in a population-based manner and integrated with healthcare; decipher fundamental tumor biology from these analyses; utilize genomic data to develop and validate new clinically-actionable biomarker assays; and establish real-time clinical implementation of molecular diagnostic, prognostic, and predictive tests. In the first phase, we focus on molecular profiling by next-generation RNA-sequencing on the Illumina platform.

          Results

          In the first 3 years from 30 August 2010 through 31 August 2013, we have consented and enrolled 3,979 patients with primary breast cancer at the seven hospital sites in South Sweden, representing approximately 85% of eligible patients in the catchment area. Preoperative blood samples have been collected for 3,942 (99%) patients and primary tumor specimens collected for 2,929 (74%) patients. Herein we describe the study infrastructure and protocols and present initial proof of concept results from prospective RNA sequencing including tumor molecular subtyping and detection of driver gene mutations. Prospective patient enrollment is ongoing.

          Conclusions

          We demonstrate that large-scale population-based collection and RNA-sequencing analysis of breast cancer is feasible. The SCAN-B Initiative should significantly reduce the time to discovery, validation, and clinical implementation of novel molecular diagnostic and predictive tests. We welcome the participation of additional comprehensive cancer treatment centers.

          Trial registration

          ClinicalTrials.gov identifier NCT02306096.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13073-015-0131-9) contains supplementary material, which is available to authorized users.

          Related collections

          Most cited references20

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          Prediction of venous metastases, recurrence, and prognosis in hepatocellular carcinoma based on a unique immune response signature of the liver microenvironment.

          Hepatocellular carcinoma (HCC) is an aggressive malignancy mainly due to metastases or postsurgical recurrence. We postulate that metastases are influenced by the liver microenvironment. Here, we show that a unique inflammation/immune response-related signature is associated with noncancerous hepatic tissues from metastatic HCC patients. This signature is principally different from that of the tumor. A global Th1/Th2-like cytokine shift in the venous metastasis-associated liver microenvironment coincides with elevated expression of macrophage colony-stimulating factor (CSF1). Moreover, a refined 17 gene signature was validated as a superior predictor of HCC venous metastases in an independent cohort, when compared to other clinical prognostic parameters. We suggest that a predominant humoral cytokine profile occurs in the metastatic liver milieu and that a shift toward anti-inflammatory/immune-suppressive responses may promote HCC metastases.
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            Gene-expression profiles in hereditary breast cancer.

            Many cases of hereditary breast cancer are due to mutations in either the BRCA1 or the BRCA2 gene. The histopathological changes in these cancers are often characteristic of the mutant gene. We hypothesized that the genes expressed by these two types of tumors are also distinctive, perhaps allowing us to identify cases of hereditary breast cancer on the basis of gene-expression profiles. RNA from samples of primary tumor from seven carriers of the BRCA1 mutation, seven carriers of the BRCA2 mutation, and seven patients with sporadic cases of breast cancer was compared with a microarray of 6512 complementary DNA clones of 5361 genes. Statistical analyses were used to identify a set of genes that could distinguish the BRCA1 genotype from the BRCA2 genotype. Permutation analysis of multivariate classification functions established that the gene-expression profiles of tumors with BRCA1 mutations, tumors with BRCA2 mutations, and sporadic tumors differed significantly from each other. An analysis of variance between the levels of gene expression and the genotype of the samples identified 176 genes that were differentially expressed in tumors with BRCA1 mutations and tumors with BRCA2 mutations. Given the known properties of some of the genes in this panel, our findings indicate that there are functional differences between breast tumors with BRCA1 mutations and those with BRCA2 mutations. Significantly different groups of genes are expressed by breast cancers with BRCA1 mutations and breast cancers with BRCA2 mutations. Our results suggest that a heritable mutation influences the gene-expression profile of the cancer.
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              Estrogen receptor status in breast cancer is associated with remarkably distinct gene expression patterns.

              To investigate the phenotype associated with estrogen receptor alpha (ER) expression in breast carcinoma, gene expression profiles of 58 node-negative breast carcinomas discordant for ER status were determined using DNA microarray technology. Using artificial neural networks as well as standard hierarchical clustering techniques, the tumors could be classified according to ER status, and a list of genes which discriminate tumors according to ER status was generated. The artificial neural networks could accurately predict ER status even when excluding top discriminator genes, including ER itself. By reference to the serial analysis of gene expression database, we found that only a small proportion of the 100 most important ER discriminator genes were also regulated by estradiol in MCF-7 cells. The results provide evidence that ER+ and ER- tumors display remarkably different gene-expression phenotypes not solely explained by differences in estrogen responsiveness.
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                Author and article information

                Contributors
                lao.saal@med.lu.se
                johan.vallon-christersson@med.lu.se
                jari.hakkinen@med.lu.se
                cecilia.hegardt@med.lu.se
                dorthe.grabau@skane.se
                christof.winter@med.lu.se
                christian.brueffer@med.lu.se
                man-hung_eric.tang@med.lu.se
                christel.reutersward@med.lu.se
                ralph.schulz@med.lu.se
                anna_f.karlsson@med.lu.se
                anna.ehinger@med.lu.se
                janne.malina@skane.se
                jonas.manjer@med.lu.se
                martin.malmberg@med.lu.se
                christer.larsson@med.lu.se
                lisa.ryden@med.lu.se
                niklas.loman@med.lu.se
                ake.borg@med.lu.se
                Journal
                Genome Med
                Genome Med
                Genome Medicine
                BioMed Central (London )
                1756-994X
                2 February 2015
                2 February 2015
                2015
                : 7
                : 1
                : 20
                Affiliations
                [ ]Department of Clinical Sciences, Division of Oncology and Pathology, Lund University, Medicon Village 404-A2, SE-22381 Lund, Sweden
                [ ]Lund University Cancer Center, SE-22381 Lund, Sweden
                [ ]CREATE Health Strategic Centre for Translational Cancer Research, Lund University, SE-22381 Lund, Sweden
                [ ]Department of Pathology, Skåne University Hospital, SE-22185 Lund, Sweden
                [ ]Department of Clinical Sciences, SCIBLU Genomics, Lund University, SE-22381 Lund, Sweden
                [ ]Department of Pathology and Cytology, Blekinge County Hospital, SE-37185 Karlskrona, Sweden
                [ ]Department of Pathology, Skåne University Hospital, SE-20502 Malmö, Sweden
                [ ]Department of Surgery, Lund University and Skåne University Hospital, SE-20502 Malmö, Sweden
                [ ]Department of Oncology, Skåne University Hospital, SE-22185 Lund, Sweden
                [ ]Department of Laboratory Medicine, Division of Molecular Pathology, Lund University, SE-22185 Lund, Sweden
                [ ]Department of Surgery, Lund University and Skåne University Hospital, SE-22185 Lund, Sweden
                Article
                131
                10.1186/s13073-015-0131-9
                4341872
                25722745
                77222dd7-2a4b-45aa-b667-ab223d531d16
                © Saal et al.; licensee BioMed Central. 2015

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 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
                : 18 September 2014
                : 15 January 2015
                Categories
                Research
                Custom metadata
                © The Author(s) 2015

                Molecular medicine
                Molecular medicine

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