Functional dissection of inherited non-coding variation influencing multiple myeloma risk

Thousands of non-coding variants have been associated with increased risk of human diseases, yet the causal variants and their mechanisms-of-action remain obscure. In an integrative study combining massively parallel reporter assays (MPRA), expression analyses (eQTL, meQTL, PCHiC) and chromatin accessibility analyses in primary cells (caQTL), we investigate 1,039 variants associated with multiple myeloma (MM). We demonstrate that MM susceptibility is mediated by gene-regulatory changes in plasma cells and B-cells, and identify putative causal variants at six risk loci (SMARCD3, WAC, ELL2, CDCA7L, CEP120, and PREX1). Notably, three of these variants co-localize with significant plasma cell caQTLs, signaling the presence of causal activity at these precise genomic positions in an endogenous chromosomal context in vivo. Our results provide a systematic functional dissection of risk loci for a hematologic malignancy.

Germline variants at SOHLH2 influence multiple myeloma risk

Multiple myeloma (MM) is caused by the uncontrolled, clonal expansion of plasma cells. While there is epidemiological evidence for inherited susceptibility, the molecular basis remains incompletely understood. We report a genome-wide association study totalling 5,320 cases and 422,289 controls from four Nordic populations, and find a novel MM risk variant at SOHLH2 at 13q13.3 (risk allele frequency = 3.5%; odds ratio = 1.38; P = 2.2 × 10-14). This gene encodes a transcription factor involved in gametogenesis that is normally only weakly expressed in plasma cells. The association is represented by 14 variants in linkage disequilibrium. Among these, rs75712673 maps to a genomic region with open chromatin in plasma cells, and upregulates SOHLH2 in this cell type. Moreover, rs75712673 influences transcriptional activity in luciferase assays, and shows a chromatin looping interaction with the SOHLH2 promoter. Our work provides novel insight into MM susceptibility.

Multigene panel testing beyond BRCA1/2 in breast/ovarian cancer Spanish families and clinical actionability of findings

PURPOSE

Few and small studies have been reported about multigene testing usage by massively parallel sequencing in European cancer families. There is an open debate about what genes should be tested, and the actionability of some included genes is under research.

METHODS

We investigated a panel of 34 known high/moderate-risk cancer genes, including 16 related to breast or ovarian cancer (BC/OC) genes, and 63 candidate genes to BC/OC in 192 clinically suspicious of hereditary breast/ovarian cancer (HBOC) Spanish families without pathogenic variants in BRCA1 or BRCA2 (BRCA1/2).

RESULTS

We identified 16 patients who carried a high- or moderate-risk pathogenic variant in eight genes: 4 PALB2, 3 ATM, 2 RAD51D, 2 TP53, 2 APC, 1 BRIP1, 1 PTEN and 1 PMS2. These findings led to increased surveillance or prevention options in 12 patients and predictive testing in their family members. We detected 383 unique variants of uncertain significance in known cancer genes, of which 35 were prioritized in silico. Eighteen loss-of-function variants were detected in candidate BC/OC genes in 17 patients (1 BARD1, 1 ERCC3, 1 ERCC5, 2 FANCE, 1 FANCI, 2 FANCL, 1 FANCM, 1 MCPH1, 1 PPM1D, 2 RBBP8, 3 RECQL4 and 1 with SLX4 and XRCC2), three of which also carry pathogenic variants in known cancer genes.

CONCLUSIONS

Eight percent of the BRCA1/2 negative patients carry pathogenic variants in other actionable genes. The multigene panel usage improves the diagnostic yield in HBOC testing and it is an effective tool to identify potentially new candidate genes.

Computational Tools for Splicing Defect Prediction in Breast/Ovarian Cancer Genes: How Efficient Are They at Predicting RNA Alterations?

In silico tools for splicing defect prediction have a key role to assess the impact of variants of uncertain significance. Our aim was to evaluate the performance of a set of commonly used splicing in silico tools comparing the predictions against RNA in vitro results. This was done for natural splice sites of clinically relevant genes in hereditary breast/ovarian cancer (HBOC) and Lynch syndrome. A study divided into two stages was used to evaluate SSF-like, MaxEntScan, NNSplice, HSF, SPANR, and dbscSNV tools. A discovery dataset of 99 variants with unequivocal results of RNA in vitro studies, located in the 10 exonic and 20 intronic nucleotides adjacent to exon-intron boundaries of BRCA1, BRCA2, MLH1, MSH2, MSH6, PMS2, ATM, BRIP1, CDH1, PALB2, PTEN, RAD51D, STK11, and TP53, was collected from four Spanish cancer genetic laboratories. The best stand-alone predictors or combinations were validated with a set of 346 variants in the same genes with clear splicing outcomes reported in the literature. Sensitivity, specificity, accuracy, negative predictive value (NPV) and Mathews Coefficient Correlation (MCC) scores were used to measure the performance. The discovery stage showed that HSF and SSF-like were the most accurate for variants at the donor and acceptor region, respectively. The further combination analysis revealed that HSF, HSF+SSF-like or HSF+SSF-like+MES achieved a high performance for predicting the disruption of donor sites, and SSF-like or a sequential combination of MES and SSF-like for predicting disruption of acceptor sites. The performance confirmation of these last results with the validation dataset, indicated that the highest sensitivity, accuracy, and NPV (99.44%, 99.44%, and 96.88, respectively) were attained with HSF+SSF-like or HSF+SSF-like+MES for donor sites and SSF-like (92.63%, 92.65%, and 84.44, respectively) for acceptor sites. We provide recommendations for combining algorithms to conduct in silico splicing analysis that achieved a high performance. The high NPV obtained allows to select the variants in which the study by in vitro RNA analysis is mandatory against those with a negligible probability of being spliceogenic. Our study also shows that the performance of each specific predictor varies depending on whether the natural splicing sites are donors or acceptors.