Functional characterization of the RAD51D E233G genetic variant

OncoPan®: An NGS-Based Screening Methodology to Identify Molecular Markers for Therapy and Risk Assessment in Pancreatic Ductal Adenocarcinoma

Pancreatic cancer has a high morbidity and mortality with the majority being PC ductal adenocarcinomas (PDAC). Whole genome sequencing provides a wide description of genomic events involved in pancreatic carcinogenesis and identifies putative biomarkers for new therapeutic approaches. However, currently, there are no approved treatments targeting driver mutations in PDAC that could produce clinical benefit for PDAC patients. A proportion of 5–10% of PDAC have a hereditary origin involving germline variants of homologous recombination genes, such as Mismatch Repair (MMR), STK11 and CDKN2A genes. Very recently, BRCA genes have been demonstrated as a useful biomarker for PARP-inhibitor (PARPi) treatments. In this study, a series of 21 FFPE PDACs were analyzed using OncoPan®, a strategic next-generation sequencing (NGS) panel of 37 genes, useful for identification of therapeutic targets and inherited cancer syndromes. Interestingly, this approach, successful also on minute pancreatic specimens, identified biomarkers for personalized therapy in five PDAC patients, including two cases with HER2 amplification and three cases with mutations in HR genes (BRCA1, BRCA2 and FANCM) and potentially eligible to PARPi therapy. Molecular analysis on normal tissue identified one PDAC patient as a carrier of a germline BRCA1 pathogenetic variant and, noteworthy, this patient was a member of a family affected by inherited breast and ovarian cancer conditions. This study demonstrates that the OncoPan® NGS-based panel constitutes an efficient methodology for the molecular profiling of PDAC, suitable for identifying molecular markers both for therapy and risk assessment. Our data demonstrate the feasibility and utility of these NGS analysis in the routine setting of PDAC molecular characterization.

A decade of RAD51C and RAD51D germline variants in cancer

Defects in DNA repair genes have been extensively associated with cancer susceptibility. Germline pathogenic variants (GPV) in genes involved in homologous recombination repair pathways predispose to cancers arising mainly in the breast and ovary, but also other tissues. The RAD51 paralogs RAD51C and RAD51D were included in this group 10 years ago when germline variants were associated with non-BRCA1/2 familial ovarian cancer. Here, we have reviewed the landscape of RAD51C and RAD51D germline variants in cancer reported in the literature during the last decade, integrating this list with variants identified by in-house patient screening. A comprehensive catalog of 341 variants that have been classified applying ACMG/AMP criteria has been generated pinpointing the existence of recurrent variants in both genes. Recurrent variants have been extensively discussed compiling data on population frequencies and functional characterization if available, highlighting variants that have not been fully characterized yet to properly establish their pathogenicity. Finally, we have complemented this data with relevant information regarding the conservation of mutated residues among RAD51 paralogs and modeling of putative hotspot areas, which contributes to generating an exhaustive update on these two cancer predisposition genes.

Downregulation of HNF1 homeobox B is associated with drug resistance in ovarian cancer

The expression of HNF1 homeobox B (HNF1B) is associated with cancer risk in several tumors, including ovarian cancer, and its decreased expression play roles in cancer development. However, the study of HNF1B and cancer is limited, and its association with drug resistance in cancer has never been reported. On the basis of array data retrieved from Oncomine and Gene Expression Omnibus (GEO) online database, we found that the mRNA expression of HNF1B in 586 ovarian serous cystadenocarcinomas and in platinum-resistant A2780 epithelial ovarian cancer cells was significantly decreased, indicating a potential role of HNF1B in drug resistance in ovarian cancer. Based on this finding, comprehensive bioinformatics analyses, including protein/gene interaction, protein-small molecule/chemical interaction, biological process annotation, gene co-occurrence and pathway enrichment analysis and microRNA-mRNA interaction, were performed to illustrate the association of HNF1B with drug resistance in ovarian cancer. We found that among the proteins/genes, small molecules/chemicals and microRNAs which directly interacted with HNF1B, the majority was associated with drug resistance in cancer, particularly in ovarian cancer. Biological process annotation revealed that HNF1B closely related to 24 biological processes which were all notably associated with ovarian cancer and drug resistance. These results indicated that the downregulation of HNF1B may contribute to drug resistance in ovarian cancer, via its direct interactions with these drug resistance-related proteins/genes, small molecules/chemicals and microRNAs, and via its regulations on the drug resistance-related biological processes. Pathway enrichment analysis of 36 genes which co-occurred with HNF1B, ovarian cancer and drug resistance indicated that the HNF1B may perform its drug resistance-related functions through 4 pathways including ErbB signaling, focal adhesion, apoptosis and p53 signaling. Collectively, in this study, we illustrated for the first time that HNF1B may contribute to drug resistance in ovarian cancer, potentially through the 4 pathways. The present study may pave the way for further investigation of the drug resistance-related functions of HNF1B in ovarian cancer.

Determining effects of non-synonymous SNPs on protein-protein interactions using supervised and semi-supervised learning

Single nucleotide polymorphisms (SNPs) are among the most common types of genetic variation in complex genetic disorders. A growing number of studies link the functional role of SNPs with the networks and pathways mediated by the disease-associated genes. For example, many non-synonymous missense SNPs (nsSNPs) have been found near or inside the protein-protein interaction (PPI) interfaces. Determining whether such nsSNP will disrupt or preserve a PPI is a challenging task to address, both experimentally and computationally. Here, we present this task as three related classification problems, and develop a new computational method, called the SNP-IN tool (non-synonymous SNP INteraction effect predictor). Our method predicts the effects of nsSNPs on PPIs, given the interaction's structure. It leverages supervised and semi-supervised feature-based classifiers, including our new Random Forest self-learning protocol. The classifiers are trained based on a dataset of comprehensive mutagenesis studies for 151 PPI complexes, with experimentally determined binding affinities of the mutant and wild-type interactions. Three classification problems were considered: (1) a 2-class problem (strengthening/weakening PPI mutations), (2) another 2-class problem (mutations that disrupt/preserve a PPI), and (3) a 3-class classification (detrimental/neutral/beneficial mutation effects). In total, 11 different supervised and semi-supervised classifiers were trained and assessed resulting in a promising performance, with the weighted f-measure ranging from 0.87 for Problem 1 to 0.70 for the most challenging Problem 3. By integrating prediction results of the 2-class classifiers into the 3-class classifier, we further improved its performance for Problem 3. To demonstrate the utility of SNP-IN tool, it was applied to study the nsSNP-induced rewiring of two disease-centered networks. The accurate and balanced performance of SNP-IN tool makes it readily available to study the rewiring of large-scale protein-protein interaction networks, and can be useful for functional annotation of disease-associated SNPs. SNIP-IN tool is freely accessible as a web-server at http://korkinlab.org/snpintool/.

Variants in DNA double-strand break repair genes and risk of familial breast cancer in a South American population

The double-strand break (DSB) DNA repair pathway has been implicated in breast cancer (BC). RAD51 and its paralogs XRCC3 and RAD51D play an important role in the repair of DSB through homologous recombination (HR). Some polymorphisms including XRCC3-Thr241Met, RAD51-135G>C, and RAD51D-E233G have been found to confer increased BC susceptibility. In order to detect novel mutations that may contribute to BC susceptibility, 150 patients belonging to 150 Chilean BRCA1/2-negative families were screened for mutations in XRCC3. No mutations were detected in the XRCC3 gene. In addition, using a case-control design we studied the XRCC3-Thr241Met, and RAD51D-E233G polymorphisms in 267 BC cases and 500 controls to evaluate their possible association with BC susceptibility. The XRCC3 Met/Met genotype was associated with an increased BC risk (P = 0.003, OR = 2.44 [95%CI 1.34-4.43]). We did not find an association between E233G polymorphism and BC risk. We also analyzed the effect of combined genotypes among RAD51-135G>C, Thr241Met, and E233G polymorphisms on BC risk. No interaction was observed between Thr241Met and 135G>C. The combined genotype Thr/Met-E/G was associated with an increased BC risk among women who (a) have a family history of BC, (b) are BRCA1/2-negative, and (c) were <50 years at onset (n = 195) (P = 0.037, OR = 10.5 [95%CI 1.16-94.5]). Our results suggested that the variability of the DNA HR repair genes XRCC3 and RAD51D may play a role in BC risk, but this role may be underlined by a mutual interaction between these genes. These findings should be confirmed in other populations.

Cisplatin resistance conferred by the RAD51D (E233G) genetic variant is dependent upon p53 status in human breast carcinoma cell lines

RAD51D, a paralog of the mammalian RAD51 gene, contributes towards maintaining genomic integrity by homologous recombination DNA repair and telomere maintenance. A RAD51D variant, E233G, was initially identified as a potential susceptibility allele in high-risk, site-specific, familial breast cancer. We describe in this report that the Rad51d (E233G) genetic variant confers increased cisplatin resistance and cell growth phenotypes in human breast carcinoma cell lines with a mutant p53 gene (BT20 and T47D) but not with a wild-type p53 gene (MCF-7). Treatment with a p53 specific inhibitor, pifithrin alpha, restored this resistant phenotype in the MCF-7 cell line. Additionally, Rad51d (E233G) conferred increased cisplatin resistance of an MCF7 cell line in which p53 expression was stably knocked down by shRNAp53, indicating that the effect of this variant is dependent upon p53 status. Further study of Rad51d (E233G) will provide mechanistic insight towards the role of RAD51D in cellular response to anticancer agents and as a potential target for cancer therapy.