Recurrent and pathological gene fusions in breast cancer: current advances in genomic discovery and clinical implications

Abstract

Gene fusions have long been considered principally as the oncogenic events of hematologic malignancies, but have recently gained wide attention in solid tumors due to several milestone discoveries and the advancement of deep sequencing technologies. With the progress in deep sequencing studies of breast cancer transcriptomes and genomes, the discovery of recurrent and pathological gene fusions in breast cancer is on the focus. Recently, driven by new deep sequencing studies, several recurrent or pathological gene fusions have been identified in breast cancer, including ESR1-CCDC170, SEC16A-NOTCH1, SEC22B-NOTCH2 and ESR1-YAP1, etc. More important, most of these gene fusions are preferentially identified in the more aggressive breast cancers such as luminal B, basal like, or endocrine resistant breast cancer, suggesting recurrent gene fusions as additional key driver events in these tumors other than the known drivers such as the estrogen-receptor. In this paper, we have comprehensively summarized the newly identified recurrent or pathological gene fusion events in breast cancer, reviewed the contributions of new genomic and deep sequencing technologies to new fusion discovery and the integrative bioinformatics tools to analyze these data, highlighted the biological relevance and clinical implications of these fusion discoveries, and discussed future directions of gene fusion research in breast cancer.

Association of PIN3 16-bp Duplication Polymorphism of TP53 with Breast Cancer Risk in Mali and A Meta-analysis.

“…The criteria for selecting the articles were as follows: (1) Results reported about a case-control study, study published as an original study evaluating the association between PIN3 16-bp duplication polymorphism of TP53 and the risk of breast cancer; (2) No deviation from Hardy-Weinberg Equilibrium (HWE) in controls; (3) No in uence on the pooled odds ratio (OR) and p-values ( Figure 1); and (4) Full text available. Two investigators independently reviewed the abstracts of the initial search and assessed each article for inclusion in the meta-analysis.…”

“…With the advent of genomics, dramatic advances have been made in breast cancer research. Recent report showed that in addition to clinical, lifestyle and environmental risk factors, an individual's genetic background plays a crucial role in the development of breast cancer [3]. Several genes have been shown to be associated with an increased risk of breast cancer, such as damaged DNA repair genes (BRCA1 and BRCA2), tumor protein p53 (TP53), Checkpoint kinase 2 (CHEK2), methylenetetrahydrofolate reductase (MTHFR), broblast growth factor receptor 2 (FGFR2) and glutathione S-transferase mu 1 (GSTM1) [4].…”

Using the Seven Bridges Cancer Genomics Cloud to Access and Analyze Petabytes of Cancer Data

“…There is evidence in the literature that some of these fusion pairs are known to be the recurrent gene fusions detected in Breast Invasive Carcinoma. For example: ESR|CCDC170 and EXTL3|SARAF are known recurrent gene fusion pairs detected in breast invasive carcinoma (Veeraraghavan, Ma, Hu, & Wang, 2016; Yoshihara et al, 2015). …”

“…Many of the gene fusions detected are known recurrent gene fusions in breast cancer. For example, the ESR|CCDC170 gene fusion is a known recurrent gene fusion transcript in breast invasive carcinoma (Veeraraghavan et al, 2016). …”

Complementary information on single nucleotide variants, INDELs and functional translocations can be obtained with RNAseq using different library preparations

“…Unfortunately RNA-seq shows computational criticalities in SNV/INDELs detection, such as those due to splicing [2] and the need of statistical models that are insensitive to variability in read coverage due to unequal transcript expression levels [3–5]. RNA-seq has been also used for the detection of translocations generating functional aberrant proteins (also known as chimeras or fusion transcripts [6]), which could act as driver mutations in cancer [7, 8]. Indeed, the sequencing coverage required for fusion transcripts detection in RNA-seq is much lower than the one needed for Whole Genome Sequencing (WGS) making the RNA-seq a suitable methodology for fusion detection.…”