Breast Cancer Candidate Genes Research Articles

Mutational Landscape and In-Silico Analysis of TP53, PIK3CA, and PTEN in Patients with Breast Cancer from Khyber Pakhtunkhwa.

Herein, we report the mutational spectrum of three breast cancer candidate genes (TP53, PIK3CA, and PTEN) using WES for identifying potential biomarkers. The WES data were thoroughly analyzed using SAMtools for variant calling and identification of the mutations. Various bioinformatic tools (SIFT, PolyPhen-2, Mutation Taster, ISPRED-SEQ, SAAFEQ-SEQ, ConSurf, PROCHECK etc.) were used to determine the pathogenicity and nature of the SNVs. Selected interaction site (IS) mutations were visualized in PyMOL after building 3D structures in Swiss-Model. Ramachandran plots were generated by using the PROCHECK server. The selected IS mutations were subjected to molecular dynamic simulation (MDS) studies using Gromacs 4.5. STRING and GeneMANIA were used for the prediction of gene-gene interactions and pathways. Our results revealed that the luminal A molecular subtype of the breast cancer was most common, whereas a high percentage of was Her2 negatives. Moreover, the somatic mutations were more common as compared to the germline mutations in TP53, PIK3CA, and PTEN. 20% of the identified mutations are reported for the first time from Khyber Pakhtunkhwa. In the enrolled cohort, 23 mutations were nonsynonymous SNVs. The frequency of mutations was the highest in PIK3CA, followed by TP53 and PTEN. A total of 13 mutations were found to be highly pathogenic. Four novel mutations were identified on PIK3CA and one each on PTEN and TP53. SAAFEQ-SEQ predicted the destabilizing effect for all mutations. ISPRED-SEQ predicted 9 IS mutations (6 on TP53 and 3 on PIK3CA), whereas no IS mutation was predicted on PTEN. The TP53 IS mutations were TP53R43H, TP53Y73X, TP53K93Q, TP53K93R, TP53D149E, and TP53Q199X; whereas for PIK3CA, the IS mutations were PIK3CAL156V, PIK3CAM610K, and PIK3CAH1047R. Analysis from the ConSurf Web server revealed five SNVs with a highly conserved status (conservation score 9) across TP53 and PTEN. TP53P33R was found predominant in the grade 3 tumors, whereas PTENp.C65S was distributed on ER+, ER-, PR+, PR-, Her2+, and Her2- patients. TP53p.P33R mutation was found to be recurring in the 14/19 (73.6%) patients and, therefore, can be considered as a potential biomarker. Finally, these mutations were studied in the context of their potential association with different hormonal and social factors.

Identification of Candidate Genes in Breast Cancer Induced by Estrogen Plus Progestogens Using Bioinformatic Analysis.

Menopausal hormone therapy (MHT) was widely used to treat menopause-related symptoms in menopausal women. However, MHT therapies were controversial with the increased risk of breast cancer because of different estrogen and progestogen combinations, and the molecular basis behind this phenomenon is currently not understood. To address this issue, we identified differentially expressed genes (DEGs) between the estrogen plus progestogens treatment (EPT) and estrogen treatment (ET) using the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) data. As a result, a total of 96 upregulated DEGs were first identified. Seven DEGs related to the cell cycle (CCNE2, CDCA5, RAD51, TCF19, KNTC1, MCM10, and NEIL3) were validated by RT-qPCR. Specifically, these seven DEGs were increased in EPT compared to ET (p < 0.05) and had higher expression levels in breast cancer than adjacent normal tissues (p < 0.05). Next, we found that estrogen receptor (ER)-positive breast cancer patients with a higher CNNE2 expression have a shorter overall survival time (p < 0.05), while this effect was not observed in the other six DEGs (p > 0.05). Interestingly, the molecular docking results showed that CCNE2 might bind to 17β-estradiol (−6.791 kcal/mol), progesterone (−6.847 kcal/mol), and medroxyprogesterone acetate (−6.314 kcal/mol) with a relatively strong binding affinity, respectively. Importantly, CNNE2 protein level could be upregulated with EPT and attenuated by estrogen receptor antagonist, acolbifene and had interactions with cancer driver genes (AKT1 and KRAS) and high mutation frequency gene (TP53 and PTEN) in breast cancer patients. In conclusion, the current study showed that CCNE2, CDCA5, RAD51, TCF19, KNTC1, MCM10, and NEIL3 might contribute to EPT-related tumorigenesis in breast cancer, with CCNE2 might be a sensitive risk indicator of breast cancer risk in women using MHT.

Germline Mutation Analysis in Sporadic Breast Cancer Cases With Clinical Correlations.

Demographics for breast cancers vary widely among nations. The frequency of germline mutations in breast cancers, which reflects the hereditary cases, has not been investigated adequately and accurately in highly-consanguineous Pakistani population. In the present discovery case series, germ-line mutations in twenty-seven breast cancer candidate genes were investigated in eighty-four sporadic breast cancer patients along with the clinical correlations. The germ-line variants were also assessed in two healthy gender-matched controls. The clinico-pathological features were evaluated by descriptive analysis and Pearson χ2 test (with significant p-value <0.05). The most frequent parameters associated with hereditary cancer cases are age and ethnicity. Therefore, the analyses were stratified on the basis of age (≤40 years vs. >40 years) and ethnicity. The breast cancer gene panel assay was carried out by BROCA, which is a genomic capture, massively parallel next generation sequencing assay on Illumina Hiseq2000 with 100bp read lengths. Copy number variations were determined by partially-mapped read algorithm. Once the mutation was identified, it was validated by Sanger sequencing. The ethnic analysis stratified on the basis of age showed that the frequency of breast cancer at young age (≤40 years) was higher in Sindhis (n = 12/19; 64%) in contrast to patients in other ethnic groups. Majority of the patients had stage III (38.1%), grade III (50%), tumor size 2–5 cm (54.8%), and invasive ductal carcinoma (81%). Overall, the analysis revealed germ-line mutations in 11.9% of the patients, which was not significantly associated with younger age or any particular ethnicity. The mutational spectrum was restricted to three genes: BRCA1, BRCA2, and TP53. The identified mutations consist of seven novel germ-line mutations, while three mutations have been reported previously. All the mutations are predicted to result in protein truncation. No mutations were identified in the remaining twenty-four candidate breast cancer genes. The present study provides the framework for the development of hereditary-based preventive and treatment strategies against breast cancers in Pakistani population.

Sequencing for germline mutations in Swedish breast cancer families reveals novel breast cancer risk genes

Identifying genetic cancer risk factors will lead to improved genetic counseling, cancer prevention and cancer care. Analyzing families with a strong history of breast cancer (BC) has been a successful method to identify genes that contribute to the disease. This has led to discoveries of high-risk genes like the BRCA-genes. Nevertheless, many BC incidences are of unknown causes. In this study, exome sequencing on 59 BC patients from 24 Swedish families with a strong history of BC was performed to identify variants in known and novel BC predisposing genes. First, we screened known BC genes and identified two pathogenic variants in the BRIP1 and PALB2 genes. Secondly, to identify novel BC genes, rare and high impact variants and segregating in families were analyzed to identify 544 variants in novel BC candidate genes. Of those, 22 variants were defined as high-risk variants. Several interesting genes, either previously linked with BC or in pathways that when flawed could contribute to BC, were among the detected genes. The strongest candidates identified are the FANCM gene, involved in DNA double-strand break repair, and the RAD54L gene, involved in DNA recombination. Our study shows identifying pathogenic variants is challenging despite a strong family history of BC. Several interesting candidates were observed here that need to be further studied.

Systematic review of family studies of heritable breast cancer in Africa.

e22508 Background: The incidence of breast cancer (BC) in Africa (40.4 per 100,000) is 49% that of Europe and North America (81.9 per 100,000) but the mortality rate is 42% higher (19.4 versus 13.7 per 100,000). The high mortality is due to lack of screening. However, more targeted screening based on genomic risks are also missed because of stigmatization, poor family history, and limited information on BC susceptibility gene mutations. Internationally based family members are influencing cultural attitudes to BC in Africa leading to reduced stigmatization, increased better family history and uptake of genomic screening. Family-based studies of BC in Africa would provide useful information on prevalence of mutations that can inform genomic screening panels. We conducted systemic review of all family-based studies of BC in Africa to document state of the art, identify barriers and facilitators of this research method and methods of advancing the science. Methods: Using a comprehensive research strategy, we reviewed articles indexed in PubMed and African Journals Online (AJOL) to identify studies of familial BC in Africa. Specifically, we searched for family-based, study design papers on BC in each country in Africa using the following keywords: breast cancer, family-studies, hereditary, genetic, gene, mutations, and Africa. We excluded case-control, and registry studies, and clinical trials. We extracted relevant information from each study using standardized criteria: number of first- and second-degree relatives affected by BC, mutations, genetic variations, and polymorphisms. Results: A total of 12 out of 727 studies met our inclusion criteria. 80% of these studies were published after the year 2010. There were 9 publications from North Africa evaluating 191 families in total. One study reported novel breast cancer candidate genes: MMS19, DNAH3, POLK and KATB6 and nine publications investigated the mutation spectrum of BRCA genes. One publication from West Africa and one publication from Southern Africa included 1 and 219 families, respectively. All the studies done in these two regions reported targeted genomic screening for only BRCA genes. No study was done in Central and East Africa. Conclusions: There is paucity of family-based studies of breast cancer in Africa. Given the high level of heterogeneity in African genomes, these types of studies may be needed to identify high penetrance, germline mutations to better understand hereditary BC risk and ultimately develop a multigene panel testing to for diagnosis of heritable BC in this population.

Abstract PO006: Computational identification of female-biased breast cancer candidate genes correlated with tumor-infiltrating immune cell types

Abstract Biological differences between sexes are prevalent in many aspects, including in their reactions to tumor invasion. It is important to identify the prognostic biomarkers in order to better understand the interactions between the host immune system and tumors in different sexes. By examining the correlation between female-biased genes identified from The Cancer Genome Atlas (TCGA) Breast Invasive Carcinoma (BRCA) RNA-seq dataset and tumor-infiltrating immune cell types, we can better understand how female-biased genes exhibit their host immune reactions to cancer antigens. We searched for BRCA candidate genes from significant clusters reported by a recent study against published literature on sex-biased genes expressed in breast tissue, and a total list of 114 female-biased genes in BRCA was compiled. We employed TIMER2.0, a function visualization tool of tumor-infiltrating immune cells for TCGA dataset, to study the association between immune infiltration and gene expression for our selected female-biased genes in the context of CD8 T-cell and B-cell types. Both cell types give similar correlation trends. Indeed, the majority of female-biased genes (45 out of 58) show positive correlation between their expression and immune cell abundance level in both cell types, which could imply strong host immune reaction to female-biased cancer antigens. More than half of the genes (26 out of 45) showed positive correlation in both cell types for exhibiting higher expressions in tumor samples than in normal samples. Specifically, two genes, RUNX3 and CXCR6, have high positive correlation in both immune cell types and higher expressions in tumor samples than normal samples, suggesting that these genes could act as antigens on tumor cells in breast cancer. To pinpoint additional genes that are putatively involved in responding to tumor immune infiltration by CD8 T-cells and B-cells, we ran the DAVID functional annotation tool to identify enriched Gene Ontology (GO) terms and pathways for our selected 45 genes showing positive correlation in both cell types. The major GO terms in significantly enriched functional annotation clusters are “membrane,” “cell membrane,” “receptor,” “domain: SH2,” and “transcription from RNA polymerase II promoter.” The genes sharing these GO terms have been previously shown to be involved in breast cancer prognostic process (e.g. CXCR6, KIT, RUNX3) and also immune cell regulation (e.g. CD8B, DAPP1, LY75). Our result indicates that many identified female-biased genes having positive associations with CD8 T-cell and B-cell abundance levels could serve as alternative therapeutic markers which may guide researchers to develop effective cancer treatment vaccines and design immune checkpoint blockade drugs. Citation Format: Eric Li, Yongsheng Bai. Computational identification of female-biased breast cancer candidate genes correlated with tumor-infiltrating immune cell types [abstract]. In: Abstracts: AACR Virtual Special Conference: Tumor Immunology and Immunotherapy; 2020 Oct 19-20. Philadelphia (PA): AACR; Cancer Immunol Res 2021;9(2 Suppl):Abstract nr PO006.

Breast Cancer Candidate Gene Detection Through Integration of Subcellular Localization Data With Protein-Protein Interaction Networks.

Due to technological advances the quality and availability of biological data has increased dramatically in the last decade. Analysing protein-protein interaction networks (PPINs) in an integrated way, together with subcellular compartment data, provides such biological context, helps to fill in the gaps between a single type of biological data and genes causing diseases and can identify novel genes related to disease. In this study, we present BCCGD, a method for integrating subcellular localization data with PPINs that detects breast cancer candidate genes in protein complexes. We achieve this by defining the significance of the compartment, constructing edge-weighted PPINs, finding protein complexes with a non-negative matrix factorization approach, generating disease-specific networks based on the known disease genes, prioritizing disease candidate genes with a WDC method. As a case study, we investigate the breast cancer but the techniques described here are applicable to other disorders. For the top genes scored by BCCGD approach, we utilize the literature retrieving method to test the correlations of them with the breast cancer. The results show that BCCGD discover some novel breast cancer candidate genes which are valuable references for the biomedical scientists.

An integrative multi-omics network-based approach identifies key regulators for breast cancer

Although genome-wide association studies (GWASs) have successfully identified thousands of risk variants for human complex diseases, understanding the biological function and molecular mechanisms of the associated SNPs involved in complex diseases is challenging. Here we developed a framework named integrative multi-omics network-based approach (IMNA), aiming to identify potential key genes in regulatory networks by integrating molecular interactions across multiple biological scales, including GWAS signals, gene expression-based signatures, chromatin interactions and protein interactions from the network topology. We applied this approach to breast cancer, and prioritized key genes involved in regulatory networks. We also developed an abnormal gene expression score (AGES) signature based on the gene expression deviation of the top 20 rank-ordered genes in breast cancer. The AGES values are associated with genetic variants, tumor properties and patient survival outcomes. Among the top 20 genes, RNASEH2A was identified as a new candidate gene for breast cancer. Thus, our integrative network-based approach provides a genetic-driven framework to unveil tissue-specific interactions from multiple biological scales and reveal potential key regulatory genes for breast cancer. This approach can also be applied in other complex diseases such as ovarian cancer to unravel underlying mechanisms and help for developing therapeutic targets.

Protein interactome network analysis predicts novel breast cancer candidate genes with molecular signatures

Abstract Background Advancement in molecular biology research has allowed the measurement of multiomics data points from a single tumor biopsy sample in a reasonable time frame for making significant clinical decisions. There have been tremendous advances from the bioinformatics and systems biology perspective to analyse integrated multi‐scale analysis of the data will prove invaluable for a combined systems‐level understanding of the important biological network processes contributing to the initiation, progression and management of cancer. Cancer networks have greater challenge due to the aberrant functions of hundreds of genes that are translated to altered protein function within each cancer cell, to understand this complexity. Network-based methods have been used to analyse the complexity molecular interactions in the cell and can contribute to prediction of candidate genes responsible for cancer. Methods HI-II- 14 is the largest experimentally determined binary protein – protein interaction map is used for the study. These interactions were mapped to NCBI Entrez gene ID and excluded self-loop and redundant interactions. The known human breast cancer genes were obtained from the Sanger Cancer Gene Census which is a comprehensive catalogue of genes implicated in breast cancer and from Uniprot database. In total, we obtained 913 genes from both for analyses. The genes degree of connections and the centrality in the protein -protein networks were considered. The genome-wide mutation datasets of breast cancer were downloaded from TCGA as maf files. For each gene, we obtained the mutation frequency for each gene is calculated which is defined as the number of mutated samples divided by the total number of samples. The frequency of the top 100 ranked genes and the bottom 100 ranked genes were compared by Wilcox rank sum test. Results In this study, HI-II-14 human protein-protein interactome network is applied to prediction of novel breast cancer genes. Conclusions Our study suggests integrating interactome networks with multiomics datasets could provide novel insights into breast cancer-associated genes and underlying molecular mechanisms. Legal entity responsible for the study M. Peer Mohammed. Funding Has not received any funding. Disclosure The author has declared no conflicts of interest.

Identification of Prognostic Candidate Genes in Breast Cancer by Integrated Bioinformatic Analysis.

Breast cancer is one of the most common malignancies. However, the molecular mechanisms underlying its pathogenesis remain to be elucidated. The present study aimed to identify the potential prognostic marker genes associated with the progression of breast cancer. Weighted gene coexpression network analysis was used to construct free-scale gene coexpression networks, evaluate the associations between the gene sets and clinical features, and identify candidate biomarkers. The gene expression profiles of GSE48213 were selected from the Gene Expression Omnibus database. RNA-seq data and clinical information on breast cancer from The Cancer Genome Atlas were used for validation. Four modules were identified from the gene coexpression network, one of which was found to be significantly associated with patient survival time. The expression status of 28 genes formed the black module (basal); 18 genes, dark red module (claudin-low); nine genes, brown module (luminal), and seven genes, midnight blue module (nonmalignant). These modules were clustered into two groups according to significant difference in survival time between the groups. Therefore, based on betweenness centrality, we identified TXN and ANXA2 in the nonmalignant module, TPM4 and LOXL2 in the luminal module, TPRN and ADCY6 in the claudin-low module, and TUBA1C and CMIP in the basal module as the genes with the highest betweenness, suggesting that they play a central role in information transfer in the network. In the present study, eight candidate biomarkers were identified for further basic and advanced understanding of the molecular pathogenesis of breast cancer by using co-expression network analysis.

Inherited Breast Cancer in Nigerian Women.

PurposeAmong Nigerian women, breast cancer is diagnosed at later stages, is more frequently triple-negative disease, and is far more frequently fatal than in Europe or the United States. We evaluated the contribution of an inherited predisposition to breast cancer in this population.Patients and MethodsCases were 1,136 women with invasive breast cancer (mean age at diagnosis, 47.5 ± 11.5 years) ascertained in Ibadan, Nigeria. Patients were selected regardless of age at diagnosis, family history, or prior genetic testing. Controls were 997 women without cancer (mean age at interview, 47.0 ± 12.4 years) from the same communities. BROCA panel sequencing was used to identify loss-of-function mutations in known and candidate breast cancer genes.ResultsOf 577 patients with information on tumor stage, 86.1% (497) were diagnosed at stage III (241) or IV (256). Of 290 patients with information on tumor hormone receptor status and human epidermal growth factor receptor 2, 45.9% (133) had triple-negative breast cancer. Among all cases, 14.7% (167 of 1,136) carried a loss-of-function mutation in a breast cancer gene: 7.0% in BRCA1, 4.1% in BRCA2, 1.0% in PALB2, 0.4% in TP53, and 2.1% in any of 10 other genes. Odds ratios were 23.4 (95% CI, 7.4 to 73.9) for BRCA1 and 10.3 (95% CI, 3.7 to 28.5) for BRCA2. Risks were also significantly associated with PALB2 (11 cases, zero controls; P = .002) and TP53 (five cases, zero controls; P = .036). Compared with other patients, BRCA1 mutation carriers were younger (P < .001) and more likely to have triple-negative breast cancer (P = .028).ConclusionAmong Nigerian women, one in eight cases of invasive breast cancer is a result of inherited mutations in BRCA1, BRCA2, PALB2, or TP53, and breast cancer risks associated with these genes are extremely high. Given limited resources, prevention and early detection services should be especially focused on these highest-risk women.

Family specific genetic predisposition to breast cancer: results from Tunisian whole exome sequenced breast cancer cases

BackgroundA family history of breast cancer has long been thought to indicate the presence of inherited genetic events that predispose to this disease. In North Africa, many specific epidemio-genetic characteristics have been observed in breast cancer families when compared to Western populations. Despite these specificities, the majority of breast cancer genetics studies performed in North Africa remain restricted to the investigation of the BRCA1 and BRCA2 genes. Thus, comprehensive data at a whole exome or whole genome level from local patients are lacking.MethodsA whole exome sequencing (WES) of seven breast cancer Tunisian families have been performed using a family-based approach. We focused our analysis on BC-TN-F001 family that included two affected members that have been sequenced using WES. Relevant variants identified in BC-TN-F001 have been confirmed using Sanger sequencing. Then, we conducted an integrative analysis by combining our results with those from other WES studies in order to figure out the genetic transmission model of the newly identified genes. Biological network construction and protein–protein interactions analyses have been performed to decipher the molecular mechanisms likely accounting for the role of these genes in breast cancer risk.ResultsSequencing, filtering strategies, and validation analysis have been achieved. For BC-TN-F001, no deleterious mutations have been identified on known breast cancer genes. However, 373 heterozygous, exonic and rare variants have been identified on other candidate genes. After applying several filters, 12 relevant high-risk variants have been selected. Our results showed that these variants seem to be inherited in a family specific model. This hypothesis has been confirmed following a thorough analysis of the reported WES studies. Enriched biological process and protein–protein interaction networks resulted in the identification of four novel breast cancer candidate genes namely MMS19, DNAH3, POLK and KATB6.ConclusionsIn this first WES application on Tunisian breast cancer patients, we highlighted the impact of next generation sequencing technologies in the identification of novel breast cancer candidate genes which may bring new insights into the biological mechanisms of breast carcinogenesis. Our findings showed that the breast cancer predisposition in non-BRCA families may be ethnic and/or family specific.

Complex Landscape of Germline Variants in Brazilian Patients With Hereditary and Early Onset Breast Cancer

Pathogenic variants in known breast cancer (BC) predisposing genes explain only about 30% of Hereditary Breast Cancer (HBC) cases, whereas the underlying genetic factors for most families remain unknown. Here, we used whole-exome sequencing (WES) to identify genetic variants associated to HBC in 17 patients of Brazil with familial BC and negative for causal variants in major BC risk genes (BRCA1/2, TP53, and CHEK2 c.1100delC). First, we searched for rare variants in 27 known HBC genes and identified two patients harboring truncating pathogenic variants in ATM and BARD1. For the remaining 15 negative patients, we found a substantial vast number of rare genetic variants. Thus, for selecting the most promising variants we used functional-based variant prioritization, followed by NGS validation, analysis in a control group, cosegregation analysis in one family and comparison with previous WES studies, shrinking our list to 23 novel BC candidate genes, which were evaluated in an independent cohort of 42 high-risk BC patients. Rare and possibly damaging variants were identified in 12 candidate genes in this cohort, including variants in DNA repair genes (ERCC1 and SXL4) and other cancer-related genes (NOTCH2, ERBB2, MST1R, and RAF1). Overall, this is the first WES study applied for identifying novel genes associated to HBC in Brazilian patients, in which we provide a set of putative BC predisposing genes. We also underpin the value of using WES for assessing the complex landscape of HBC susceptibility, especially in less characterized populations.

Characteristics of interaction of miRNA with mRNA of breast cancer candidate genes

Characteristics of interaction of miRNA with mRNA of breast cancer candidate genes

Genetic Predisposition to Breast Cancer Due to Mutations Other Than BRCA1 and BRCA2 Founder Alleles Among Ashkenazi Jewish Women

ImportanceAmong Ashkenazi Jewish women, 3 mutations in BRCA1 and BRCA2 severely increase the risk of breast and ovarian cancer. However, among Ashkenazi Jewish patients with breast cancer who do not carry one of these founder mutations, the likelihood of carrying another pathogenic mutation in BRCA1 or BRCA2 or another breast cancer gene is not known. This information would be valuable to the patient and family for cancer prevention and treatment.ObjectiveTo determine the frequency of cancer-predisposing mutations other than the BRCA1 and BRCA2 founder alleles among patients of Ashkenazi Jewish ancestry with breast cancer.Design, Setting, and ParticipantsIn this cohort study, genomic DNA of women from 12 major cancer centers with a first diagnosis of invasive breast cancer who identified themselves and all 4 grandparents as Ashkenazi Jewish and participated in the New York Breast Cancer Study (NYBCS) from 1996 to 2000 was sequenced for known and candidate breast cancer genes. Data analysis was performed from July 10, 2014, to March 10, 2017.Main Outcomes and MeasuresGenomic DNA from all 1007 NYBCS probands was sequenced for 23 known and candidate breast cancer genes using BROCA, a targeted multiplexed gene panel.ResultsOf the 1007 probands in the study, 903 probands had no founder mutations in BRCA1 or BRCA2; of these probands, 7 (0.8%) carried another pathogenic mutation in BRCA1 or BRCA2, and 31 (3.4%) carried a pathogenic mutation in another breast cancer gene (29 in CHEK2, and 1 each in BRIP1 and NBN). Of all inherited predispositions to breast cancer in the NYBCS, 73.8% (104 of 142) were due to a BRCA1 or BRCA2 founder allele, 4.9% (7 of 142) to another BRCA1 or BRCA2 mutation, and 21.8% (31 of 142) to a mutation in another gene. Overall, 14.1% (142 of 1007) of Ashkenazi Jewish patients with breast cancer in the NYBCS carried a germline mutation responsible for their disease: 11.0% (111 of 1007) in BRCA1 or BRCA2, and 3.1% (31 of 1007) in CHEK2 or another breast cancer gene. Of the 111 patients with BRCA1 or BRCA2 mutations, 57 (51.4%) had a mother or sister with breast or ovarian cancer and 54 patients (48.6%) did not.Conclusions and RelevanceComprehensive sequencing would provide complete relevant genetic information for Ashkenazi Jewish patients with breast cancer.

Identification of breast cancer candidate genes using gene co-expression and protein-protein interaction information.

Breast cancer (BC) is one of the most common malignancies that could threaten female health. As the molecular mechanism of BC has not yet been completely discovered, identification of related genes of this disease is an important area of research that could provide new insights into gene function as well as potential treatment targets. Here we used subnetwork extraction algorithms to identify novel BC related genes based on the known BC genes (seed genes), gene co-expression profiles and protein-protein interaction network. We computationally predicted seven key genes (EPHX2, GHRH, PPYR1, ALPP, KNG1, GSK3A and TRIT1) as putative genes of BC. Further analysis shows that six of these have been reported as breast cancer associated genes, and one (PPYR1) as cancer associated gene. Lastly, we developed an expression signature using these seven key genes which significantly stratified 1660 BC patients according to relapse free survival (hazard ratio [HR], 0.55; 95% confidence interval [CI], 0.46–0.65; Logrank p = 5.5e−13). The 7-genes signature could be established as a useful predictor of disease prognosis in BC patients. Overall, the identified seven genes might be useful prognostic and predictive molecular markers to predict the clinical outcome of BC patients.

Abstract B08: Highlighting the maturation of the MSI/NCI P20 South Carolina Cancer Disparities Research Center Partnership

Abstract The South Carolina Cancer Disparities Research Center (SC CaDRe), funded by the NIH/NCI Center to Reduce Cancer Health Disparities (CRCHD) in 2011, is a formal collaboration between the Medical University of South Carolina (MUSC) and South Carolina State University (SCSU, an historically black university). The SC CaDRe focuses on biological mechanisms of disparity in hormone-related cancers (breast and prostate cancer). The Sea Island population plays a prominent role in the three SC CaDRe research projects. The Sea Islanders are an African American ethnic group indigenous to the coastal southeast, with low rates of European American genetic admixture; as such they are ideal for inclusion in biomedical research. Each SC CaDRe research project includes Multiple PIs from MUSC and SCSU. The first research project is titled Biological Implications of DNA Glycation in Prostate Cancer Disparities; the second is titled MicroRNA Mediated Negative Regulation of Caveolin 1 as a Biological Mechanism Driving Breast Cancer Disparities; and the third is titled Feasibility Study of Breast Cancer Candidate Genes in Three Population Groups in South Carolina. The design and results of each project will be presented. The P20 SC CaDRe has led to two funded NIH R21 grants, an R01 grant, an R25E grant, and a DOD research training grant. Three undergraduate students from SCSU and four graduate students from MUSC have participated in SC CaDRe cancer research training, resulting in peer-reviewed publications and presentations at national scientific meetings. As a next step, the SC CaDRe investigators plan to submit an NIH/NCI CRCHD U54 grant application in 2016. Citation Format: Marvella E. Ford, Dennis K. Watson, James Stukes, Mahtabuddin Ahmed, David P. Turner, Ashley Evans Knowell, Victoria J. Findlay, Anita L. Harrison, Kendrea D. Knight, Heidi Varner, Kimberly Cannady, Judith D. Salley. Highlighting the maturation of the MSI/NCI P20 South Carolina Cancer Disparities Research Center Partnership. [abstract]. In: Proceedings of the Eighth AACR Conference on The Science of Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; Nov 13-16, 2015; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2016;25(3 Suppl):Abstract nr B08.

Spherical Self-Organizing Map Detects MYBL 1 As Candidate Gene for Triple-Negative Breast Cancer

Spherical Self-Organizing Map Detects MYBL 1 As Candidate Gene for Triple-Negative Breast Cancer

Systematic Identification of Molecular Links between Core and Candidate Genes in Breast Cancer

Despite the remarkable progress achieved in the identification of specific genes involved in breast cancer (BC), our understanding of their complex functioning is still limited. In this manuscript, we systematically explore the existence of direct physical interactions between the products of BC core and associated genes. Our aim is to generate a protein interaction network of BC-associated gene products and suggest potential molecular mechanisms to unveil their role in the disease. In total, we report 599 novel high-confidence interactions among 44 BC core, 54 BC candidate/associated and 96 newly identified proteins. Our findings indicate that this network-based approach is indeed a robust inference tool to pinpoint new potential players and gain insight into the underlying mechanisms of those proteins with previously unknown roles in BC. To illustrate the power of our approach, we provide initial validation of two BC-associated proteins on the alteration of DNA damage response as a result of specific re-wiring interactions. Overall, our BC-related network may serve as a framework to integrate clinical and molecular data and foster novel global therapeutic strategies.

A study on genetic variants of Fibroblast growth factor receptor 2 (FGFR2) and the risk of breast cancer from North India.

Genome-Wide Association Studies (GWAS) have identified Fibroblast growth factor receptor 2 (FGFR2) as a candidate gene for breast cancer with single nucleotide polymorphisms (SNPs) located in intron 2 region as the susceptibility loci strongly associated with the risk. However, replicate studies have often failed to extrapolate the association to diverse ethnic regions. This hints towards the existing heterogeneity among different populations, arising due to differential linkage disequilibrium (LD) structures and frequencies of SNPs within the associated regions of the genome. It is therefore important to revisit the previously linked candidates in varied population groups to unravel the extent of heterogeneity. In an attempt to investigate the role of FGFR2 polymorphisms in susceptibility to the risk of breast cancer among North Indian women, we genotyped rs2981582, rs1219648, rs2981578 and rs7895676 polymorphisms in 368 breast cancer patients and 484 healthy controls by Polymerase chain reaction-Restriction fragment length polymorphism (PCR-RFLP) assay. We observed a statistically significant association with breast cancer risk for all the four genetic variants (P<0.05). In per-allele model for rs2981582, rs1219648, rs7895676 and in dominant model for rs2981578, association remained significant after bonferroni correction (P<0.0125). On performing stratified analysis, significant correlations with various clinicopathological as well as environmental and lifestyle characteristics were observed. It was evident that rs1219648 and rs2981578 interacted with exogenous hormone use and advanced clinical stage III (after Bonferroni correction, P<0.000694), respectively. Furthermore, combined analysis on these four loci revealed that compared to women with 0–1 risk loci, those with 2–4 risk loci had increased risk (OR = 1.645, 95%CI = 1.152–2.347, P = 0.006). In haplotype analysis, for rs2981578, rs2981582 and rs1219648, risk haplotype (GTG) was associated with a significantly increased risk compared to the common (ACA) haplotype (OR = 1.365, 95% CI = 1.086–1.717, P = 0.008). Our results suggest that intron 2 SNPs of FGFR2 may contribute to genetic susceptibility of breast cancer in North India population.