Expanding the genetic basis of copy number variation in familial breast cancer

Germline Structural Variations in Cancer Predisposition Genes

In addition to single nucleotide variations and small-scale indels, structural variations (SVs) also contribute to the genetic diversity of the genome. SVs, such as deletions, duplications, amplifications, or inversions may also affect coding regions of cancer-predisposing genes. These rearrangements may abrogate the open reading frame of these genes or adversely affect their expression and may thus act as germline mutations in hereditary cancer syndromes. With the capacity of disrupting the function of tumor suppressors, structural variations confer an increased risk of cancer and account for a remarkable fraction of heritability. The development of sequencing techniques enables the discovery of a constantly growing number of SVs of various types in cancer predisposition genes (CPGs). Here, we provide a comprehensive review of the landscape of germline SV types, detection methods, pathomechanisms, and frequency in CPGs, focusing on the two most common cancer syndromes: hereditary breast- and ovarian cancer and gastrointestinal cancers. Current knowledge about the possible molecular mechanisms driving to SVs is also summarized.

Germline copy number variations in BRCA1/2 negative families: Role in the molecular etiology of hereditary breast cancer in Tunisia

Hereditary breast cancer accounts for 5-10% of all breast cancer cases. So far, known genetic risk factors account for only 50% of the breast cancer genetic component and almost a quarter of hereditary cases are carriers of pathogenic mutations in BRCA1/2 genes. Hence, the genetic basis for a significant fraction of familial cases remains unsolved. This missing heritability may be explained in part by Copy Number Variations (CNVs). We herein aimed to evaluate the contribution of CNVs to hereditary breast cancer in Tunisia. Whole exome sequencing was performed for 9 BRCA negative cases with a strong family history of breast cancer and 10 matched controls. CNVs were called using the ExomeDepth R-package and investigated by pathway analysis and web-based bioinformatic tools. Overall, 483 CNVs have been identified in breast cancer patients. Rare CNVs affecting cancer genes were detected, of special interest were those disrupting APC2, POU5F1, DOCK8, KANSL1, TMTC3 and the mismatch repair gene PMS2. In addition, common CNVs known to be associated with breast cancer risk have also been identified including CNVs on APOBECA/B, UGT2B17 and GSTT1 genes. Whereas those disrupting SULT1A1 and UGT2B15 seem to correlate with good clinical response to tamoxifen. Our study revealed new insights regarding CNVs and breast cancer risk in the Tunisian population. These findings suggest that rare and common CNVs may contribute to disease susceptibility. Those affecting mismatch repair genes are of interest and require additional attention since it may help to select candidates for immunotherapy leading to better outcomes.

Predicting breast cancer risk using interacting genetic and demographic factors and machine learning

Breast cancer (BC) is a multifactorial disease and the most common cancer in women worldwide. We describe a machine learning approach to identify a combination of interacting genetic variants (SNPs) and demographic risk factors for BC, especially factors related to both familial history (Group 1) and oestrogen metabolism (Group 2), for predicting BC risk. This approach identifies the best combinations of interacting genetic and demographic risk factors that yield the highest BC risk prediction accuracy. In tests on the Kuopio Breast Cancer Project (KBCP) dataset, our approach achieves a mean average precision (mAP) of 77.78 in predicting BC risk by using interacting genetic and Group 1 features, which is better than the mAPs of 74.19 and 73.65 achieved using only Group 1 features and interacting SNPs, respectively. Similarly, using interacting genetic and Group 2 features yields a mAP of 78.00, which outperforms the system based on only Group 2 features, which has a mAP of 72.57. Furthermore, the gene interaction maps built from genes associated with SNPs that interact with demographic risk factors indicate important BC-related biological entities, such as angiogenesis, apoptosis and oestrogen-related networks. The results also show that demographic risk factors are individually more important than genetic variants in predicting BC risk.

Characterization of potential driver mutations involved in human breast cancer by computational approaches

Breast cancer is the second most frequently occurring form of cancer and is also the second most lethal cancer in women worldwide. A genetic mutation is one of the key factors that alter multiple cellular regulatory pathways and drive breast cancer initiation and progression yet nature of these cancer drivers remains elusive. In this article, we have reviewed various computational perspectives and algorithms for exploring breast cancer driver mutation genes. Using both frequency based and mutational exclusivity based approaches, we identified 195 driver genes and shortlisted 63 of them as candidate drivers for breast cancer using various computational approaches. Finally, we conducted network and pathway analysis to explore their functions in breast tumorigenesis including tumor initiation, progression, and metastasis.

Germline copy number variations are associated with breast cancer risk and prognosis

Breast cancer is one of the most common cancers among women, and susceptibility is explained by genetic, lifestyle and environmental components. Copy Number Variants (CNVs) are structural DNA variations that contribute to diverse phenotypes via gene-dosage effects or cis-regulation. In this study, we aimed to identify germline CNVs associated with breast cancer susceptibility and their relevance to prognosis. We performed whole genome CNV genotyping in 422 cases and 348 controls using Human Affymetrix SNP 6 array. Principal component analysis for population stratification revealed 84 outliers leaving 366 cases and 320 controls of Caucasian ancestry for association analysis; CNVs with frequency > 10% and overlapping with protein coding genes were considered for breast cancer risk and prognostic relevance. Coding genes within the CNVs identified were interrogated for gene- dosage effects by correlating copy number status with gene expression profiles in breast tumor tissue. We identified 200 CNVs associated with breast cancer (q-value < 0.05). Of these, 21 CNV regions (overlapping with 22 genes) also showed association with prognosis. We validated representative CNVs overlapping with APOBEC3B and GSTM1 genes using the TaqMan assay. Germline CNVs conferred dosage effects on gene expression in breast tissue. The candidate CNVs identified in this study warrant independent replication.

BCIP: a gene-centered platform for identifying potential regulatory genes in breast cancer

Breast cancer is a disease with high heterogeneity. Many issues on tumorigenesis and progression are still elusive. It is critical to identify genes that play important roles in the progression of tumors, especially for tumors with poor prognosis such as basal-like breast cancer and tumors in very young women. To facilitate the identification of potential regulatory or driver genes, we present the Breast Cancer Integrative Platform (BCIP, http://omics.bmi.ac.cn/bcancer/). BCIP maintains multi-omics data selected with strict quality control and processed with uniform normalization methods, including gene expression profiles from 9,005 tumor and 376 normal tissue samples, copy number variation information from 3,035 tumor samples, microRNA-target interactions, co-expressed genes, KEGG pathways, and mammary tissue-specific gene functional networks. This platform provides a user-friendly interface integrating comprehensive and flexible analysis tools on differential gene expression, copy number variation, and survival analysis. The prominent characteristic of BCIP is that users can perform analysis by customizing subgroups with single or combined clinical features, including subtypes, histological grades, pathologic stages, metastasis status, lymph node status, ER/PR/HER2 status, TP53 mutation status, menopause status, age, tumor size, therapy responses, and prognosis. BCIP will help to identify regulatory or driver genes and candidate biomarkers for further research in breast cancer.

Germline large genomic alterations on 7q in patients with multiple primary cancers

Patients with multiple primary cancers (MPCs) are suspected to have a hereditary cancer syndrome. However, only a small proportion may be explained by mutations in high-penetrance genes. We investigate two unrelated MPC patients that met Hereditary Breast and Ovaria Cancer criteria, both presenting triple negative breast tumors and no mutations in BRCA1, BRCA2 and TP53 genes. Germline rearrangements on chromosome 7q, involving over 40 Mb of the same region, were found in both patients: one with mosaic loss (80% of cells) and the other with cnLOH (copy-neutral loss of heterozygosity) secondary to maternal allele duplication. Five children tested had no alterations on 7q. The patients shared 330 genes in common on 7q22.1-q34, including several tumor suppressor genes (TSGs) previously related to breast cancer risk and imprinted genes. The analysis of the triple negative BC from one patient revealed a mosaic gain of 7q translated for over-expressed cancer-related genes. The involvement of TSGs and imprinted genes, mapped on 7q, has the potential of being associated to MPC risk, as well as cancer progression. To our knowledge, this is the first description of patients with MPCs that harbor constitutive large alterations on 7q.

A Genome-Wide Association Study to Identify Potential Germline Copy Number Variants for Sporadic Breast Cancer Susceptibility

Breast cancer (BC) predisposition in populations arises from both genetic and nongenetic risk factors. Structural variations such as copy number variations (CNVs) are heritable determinants for disease susceptibility. The primary objectives of this study are (1) to identify CNVs associated with sporadic BC using a genome-wide association study (GWAS) design; (2) to utilize 2 distinct CNV calling algorithms to identify concordant CNVs as a strategy to reduce false positive associations in the hypothesis-generating GWAS discovery phase, and (3) to identify potential candidate CNVs for follow-up replication studies. We used Affymetrix SNP Array 6.0 data profiled on Caucasian subjects (422 cases/348 controls) to call CNVs using algorithms implemented in Nexus Copy Number and Partek Genomics Suite software. Nexus algorithm identified CNVs associated with BC (731 autosomal CNVs with >5% frequency in the total sample and Q < 0.05). Thirteen CNVs were identified when Partek algorithm-called CNVs were overlapped with Nexus-identified CNVs; these CNVs showed concordances for frequency, effect size, and direction. Coding genes present within BC-associated CNVs were known to play a role in disease etiology and prognosis. Long noncoding RNAs identified within CNVs showed tissue-specific expression, indicating potential functional relevance of the findings. The identified candidate CNVs warrant independent replication.

WWOX CNV-67048 Functions as a Risk Factor for Epithelial Ovarian Cancer in Chinese Women by Negatively Interacting with Oral Contraceptive Use

Copy number variations (CNVs) have attracted increasing evidences to represent their roles as cancer susceptibility regulators. However, little is known about the role of CNV in epithelia ovarian cancer (EOC). Recently, the CNV-67048 of WW domain-containing oxidoreductase (WWOX) was reported to alter cancer risks. Considering that WWOX also plays a role in EOC, we hypothesized that the CNV-67048 was associated with EOC risk. In a case-control study of 549 EOC patients and 571 age (±5 years) matched cancer-free controls, we found that the low copy number of CNV-67048 (1-copy and 0-copy) conferred a significantly increased risk of EOC (OR = 1.346, 95% CI = 1.037–1.747) and it determined the risk by means of copy number-dependent dosage effect (P = 0.009). Data from TCGA also confirmed the abovementioned association as the frequency of low copies in EOC group was 3.68 times more than that in healthy group (P = 0.023). The CNV also negatively interacted with oral contraceptive use on EOC risk (P = 0.042). Functional analyses further showed a lower mRNA level of WWOX in tissues with the 0-copy or 1-copy than that in those with the 2-copy (P = 0.045). Our data suggested the CNV-67048 to be a risk factor of EOC in Chinese women.

ROBO1 deletion as a novel germline alteration in breast and colorectal cancer patients

Despite one third of breast (BC) and colorectal cancer (CRC) cases having a hereditary component, only a small proportion can be explained by germline mutations. The aim of this study was to identify potential genomic alterations related to cancer predisposition. Copy number variations (CNVs) were interrogated in 113 unrelated cases fulfilling the criteria for hereditary BC/CRC and presenting non-pathogenic mutations in BRCA1, BRCA2, MLH1, MSH2, TP53, and CHEK2 genes. An identical germline deep intronic deletion of ROBO1 was identified in three index patients using two microarray platforms (Agilent 4x180K and Affymetrix CytoScan HD). The ROBO1 deletion was confirmed by quantitative PCR (qPCR). Six relatives were also evaluated by CytoScan HD Array. Genomic analysis confirmed a co-segregation of the ROBO1 deletion with the occurrence of cancer in two families. Direct sequencing revealed no pathogenic ROBO1 point mutations. Transcriptomic analysis (HTA 2.0, Affymetrix) in two breast carcinomas from a single patient revealed ROBO1 down-expression with no splicing events near the intronic deletion. Deeper in silico analysis showed several enhancer regions and a histone methylation mark in the deleted region. The ROBO1 deletion in a putative transcriptional regulatory region, its down-expression in tumor samples, and the results of the co-segregation analysis revealing the presence of the alteration in affected individuals suggest a pathogenic effect of the ROBO1 in cancer predisposition.

NCG 5.0: updates of a manually curated repository of cancer genes and associated properties from cancer mutational screenings

The Network of Cancer Genes (NCG, http://ncg.kcl.ac.uk/) is a manually curated repository of cancer genes derived from the scientific literature. Due to the increasing amount of cancer genomic data, we have introduced a more robust procedure to extract cancer genes from published cancer mutational screenings and two curators independently reviewed each publication. NCG release 5.0 (August 2015) collects 1571 cancer genes from 175 published studies that describe 188 mutational screenings of 13 315 cancer samples from 49 cancer types and 24 primary sites. In addition to collecting cancer genes, NCG also provides information on the experimental validation that supports the role of these genes in cancer and annotates their properties (duplicability, evolutionary origin, expression profile, function and interactions with proteins and miRNAs).