Predicting the impact of rare variants on RNA splicing in CAGI6.

Variants which disrupt splicing are a frequent cause of rare disease that have been under-ascertained clinically. Accurate and efficient methods to predict a variant's impact on splicing are needed to interpret the growing number of variants of unknown significance (VUS) identified by exome and genome sequencing. Here, we present the results of the CAGI6 Splicing VUS challenge, which invited predictions of the splicing impact of 56 variants ascertained clinically and functionally validated to determine splicing impact. The performance of 12 prediction methods, along with SpliceAI and CADD, was compared on the 56 functionally validated variants. The maximum accuracy achieved was 82% from two different approaches, one weighting SpliceAI scores by minor allele frequency, and one applying the recently published Splicing Prediction Pipeline (SPiP). SPiP performed optimally in terms of sensitivity, while an ensemble method combining multiple prediction tools and information from databases exceeded all others for specificity. Several challenge methods equalled or exceeded the performance of SpliceAI, with ultimate choice of prediction method likely to depend on experimental or clinical aims. One quarter of the variants were incorrectly predicted by at least 50% of the methods, highlighting the need for further improvements to splicing prediction methods for successful clinical application.

The OVAREX study: Establishment of ex vivo ovarian cancer models to validate innovative therapies and to identify predictive biomarkers.

BACKGROUND: Ovarian cancer is the first cause of death from gynecological malignancies mainly due to development of chemoresistance. Despite the emergence of PARP inhibitors, which have revolutionized the therapeutic management of some of these ovarian cancers, the 5-year overall survival rate remains around 45%. Therefore, it is crucial to develop new therapeutic strategies, to identify predictive biomarkers and to predict the response to treatments. In this context, functional assays based on patient-derived tumor models could constitute helpful and relevant tools for identifying efficient therapies or to guide clinical decision making. METHOD: The OVAREX study is a single-center non-interventional study which aims at investigating the feasibility of establishing in vivo and ex vivo models and testing ex vivo models to predict clinical response of ovarian cancer patients. Patient-Derived Xenografts (PDX) will be established from tumor fragments engrafted subcutaneously into immunocompromised mice. Explants will be generated by slicing tumor tissues and Ascites-Derived Spheroids (ADS) will be isolated following filtration of ascites. Patient-derived tumor organoids (PDTO) will be established after dissociation of tumor tissues or ADS, cell embedding into extracellular matrix and culture in specific medium. Molecular and histological characterizations will be performed to compare tumor of origin and paired models. Response of ex vivo tumor-derived models to conventional chemotherapy and PARP inhibitors will be assessed and compared to results of companion diagnostic test and/or to the patient's response to evaluate their predictive value. DISCUSSION: This clinical study aims at generating PDX and ex vivo models (PDTO, ADS, and explants) from tumors or ascites of ovarian cancer patients who will undergo surgical procedure or paracentesis. We aim at demonstrating the predictive value of ex vivo models for their potential use in routine clinical practice as part of precision medicine, as well as establishing a collection of relevant ovarian cancer models that will be useful for the evaluation of future innovative therapies. TRIAL REGISTRATION: The clinical trial has been validated by local research ethic committee on January 25th 2019 and registered at ClinicalTrials.gov with the identifier NCT03831230 on January 28th 2019, last amendment v4 accepted on July 18, 2023.

SPiP: Splicing Prediction Pipeline, a machine learning tool for massive detection of exonic and intronic variant effects on mRNA splicing.

Modeling splicing is essential for tackling the challenge of variant interpretation as each nucleotide variation can be pathogenic by affecting pre-mRNA splicing via disruption/creation of splicing motifs such as 5'/3' splice sites, branch sites, or splicing regulatory elements. Unfortunately, most in silico tools focus on a specific type of splicing motif, which is why we developed the Splicing Prediction Pipeline (SPiP) to perform, in one single bioinformatic analysis based on a machine learning approach, a comprehensive assessment of the variant effect on different splicing motifs. We gathered a curated set of 4616 variants scattered all along the sequence of 227 genes, with their corresponding splicing studies. The Bayesian analysis provided us with the number of control variants, that is, variants without impact on splicing, to mimic the deluge of variants from high-throughput sequencing data. Results show that SPiP can deal with the diversity of splicing alterations, with 83.13% sensitivity and 99% specificity to detect spliceogenic variants. Overall performance as measured by area under the receiving operator curve was 0.986, better than SpliceAI and SQUIRLS (0.965 and 0.766) for the same data set. SPiP lends itself to a unique suite for comprehensive prediction of spliceogenicity in the genomic medicine era. SPiP is available at: https://sourceforge.net/projects/splicing-prediction-pipeline/.

SpliceLauncher: a tool for detection, annotation and relative quantification of alternative junctions from RNAseq data.

SUMMARY: Alternative splicing is an important biological process widely analyzed in molecular diagnostic settings. Indeed, a variant can be pathogenic by splicing alteration and a suspected pathogenic variant (e.g. truncating variant) can be rescued by splicing. In this context, detecting and quantifying alternative splicing is challenging. We developed SpliceLauncher, a fast and easy to use open source tool that aims at detecting, annotating and quantifying alternative splice junctions at high resolution. AVAILABILITY AND IMPLEMENTATION: SpliceLauncher is available at https://github.com/raphaelleman/SpliceLauncher. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Assessment of branch point prediction tools to predict physiological branch points and their alteration by variants.

BACKGROUND: Branch points (BPs) map within short motifs upstream of acceptor splice sites (3'ss) and are essential for splicing of pre-mature mRNA. Several BP-dedicated bioinformatics tools, including HSF, SVM-BPfinder, BPP, Branchpointer, LaBranchoR and RNABPS were developed during the last decade. Here, we evaluated their capability to detect the position of BPs, and also to predict the impact on splicing of variants occurring upstream of 3'ss. RESULTS: We used a large set of constitutive and alternative human 3'ss collected from Ensembl (n = 264,787 3'ss) and from in-house RNAseq experiments (n = 51,986 3'ss). We also gathered an unprecedented collection of functional splicing data for 120 variants (62 unpublished) occurring in BP areas of disease-causing genes. Branchpointer showed the best performance to detect the relevant BPs upstream of constitutive and alternative 3'ss (99.48 and 65.84% accuracies, respectively). For variants occurring in a BP area, BPP emerged as having the best performance to predict effects on mRNA splicing, with an accuracy of 89.17%. CONCLUSIONS: Our investigations revealed that Branchpointer was optimal to detect BPs upstream of 3'ss, and that BPP was most relevant to predict splicing alteration due to variants in the BP area.

Alternative splicing and ACMG-AMP-2015-based classification of PALB2 genetic variants: an ENIGMA report.

BACKGROUND: PALB2 monoallelic loss-of-function germ-line variants confer a breast cancer risk comparable to the average BRCA2 pathogenic variant. Recommendations for risk reduction strategies in carriers are similar. Elaborating robust criteria to identify loss-of-function variants in PALB2-without incurring overprediction-is thus of paramount clinical relevance. Towards this aim, we have performed a comprehensive characterisation of alternative splicing in PALB2, analysing its relevance for the classification of truncating and splice site variants according to the 2015 American College of Medical Genetics and Genomics-Association for Molecular Pathology guidelines. METHODS: Alternative splicing was characterised in RNAs extracted from blood, breast and fimbriae/ovary-related human specimens (n=112). RNAseq, RT-PCR/CE and CloneSeq experiments were performed by five contributing laboratories. Centralised revision/curation was performed to assure high-quality annotations. Additional splicing analyses were performed in PALB2 c.212-1G>A, c.1684+1G>A, c.2748+2T>G, c.3113+5G>A, c.3350+1G>A, c.3350+4A>C and c.3350+5G>A carriers. The impact of the findings on PVS1 status was evaluated for truncating and splice site variant. RESULTS: We identified 88 naturally occurring alternative splicing events (81 newly described), including 4 in-frame events predicted relevant to evaluate PVS1 status of splice site variants. We did not identify tissue-specific alternate gene transcripts in breast or ovarian-related samples, supporting the clinical relevance of blood-based splicing studies. CONCLUSIONS: PVS1 is not necessarily warranted for splice site variants targeting four PALB2 acceptor sites (exons 2, 5, 7 and 10). As a result, rare variants at these splice sites cannot be assumed pathogenic/likely pathogenic without further evidences. Our study puts a warning in up to five PALB2 genetic variants that are currently reported as pathogenic/likely pathogenic in ClinVar.

Novel diagnostic tool for prediction of variant spliceogenicity derived from a set of 395 combined in silico/in vitro studies: an international collaborative effort.

Variant interpretation is the key issue in molecular diagnosis. Spliceogenic variants exemplify this issue as each nucleotide variant can be deleterious via disruption or creation of splice site consensus sequences. Consequently, reliable in silico prediction of variant spliceogenicity would be a major improvement. Thanks to an international effort, a set of 395 variants studied at the mRNA level and occurring in 5' and 3' consensus regions (defined as the 11 and 14 bases surrounding the exon/intron junction, respectively) was collected for 11 different genes, including BRCA1, BRCA2, CFTR and RHD, and used to train and validate a new prediction protocol named Splicing Prediction in Consensus Elements (SPiCE). SPiCE combines in silico predictions from SpliceSiteFinder-like and MaxEntScan and uses logistic regression to define optimal decision thresholds. It revealed an unprecedented sensitivity and specificity of 99.5 and 95.2%, respectively, and the impact on splicing was correctly predicted for 98.8% of variants. We therefore propose SPiCE as the new tool for predicting variant spliceogenicity. It could be easily implemented in any diagnostic laboratory as a routine decision making tool to help geneticists to face the deluge of variants in the next-generation sequencing era. SPiCE is accessible at (https://sourceforge.net/projects/spicev2-1/).

First estimate of the scale of canonical 5' splice site GT>GC variants capable of generating wild-type transcripts.

It has long been known that canonical 5' splice site (5'SS) GT>GC variants may be compatible with normal splicing. However, to date, the actual scale of canonical 5'SSs capable of generating wild-type transcripts in the case of GT>GC substitutions remains unknown. Herein, combining data derived from a meta-analysis of 45 human disease-causing 5'SS GT>GC variants and a cell culture-based full-length gene splicing assay of 103 5'SS GT>GC substitutions, we estimate that ~15-18% of canonical GT 5'SSs retain their capacity to generate between 1% and 84% normal transcripts when GT is substituted by GC. We further demonstrate that the canonical 5'SSs in which substitution of GT by GC-generated normal transcripts exhibit stronger complementarity to the 5' end of U1 snRNA than those sites whose substitutions of GT by GC did not lead to the generation of normal transcripts. We also observed a correlation between the generation of wild-type transcripts and a milder than expected clinical phenotype but found that none of the available splicing prediction tools were capable of reliably distinguishing 5'SS GT>GC variants that generated wild-type transcripts from those that did not. Our findings imply that 5'SS GT>GC variants in human disease genes may not invariably be pathogenic.

Carboplatin in metastatic castration-resistant prostate cancer patients with molecular alterations of the DNA damage repair pathway: the PRO-CARBO phase II trial.

Introduction: DNA damage repair genes are altered in 20-35% of metastatic castration-resistant prostate cancer (mCRPC). Poly-ADP (Adénosine Diphosphate)-ribose polymerase inhibitors (PARPi) showed significant activity for these selected tumors, especially with homologous recombination repair (HRR) deficiency. These alterations could also predict platinum sensitivity. Although carboplatin was inconclusive in unselected mCRPC, the literature suggests an anti-tumoral activity in mCRPC with HHR gene alterations. We aimed to assess the efficacy of carboplatin monotherapy in mCRPC patients with HRR deficiency. Methods: This prospective multicenter single-arm two-stage phase II addressed mCRPC men with HRR somatic and/or germline alterations, pretreated with ⩾2 taxane chemotherapy regimens and one androgen receptor pathway inhibitor. Prior PARPi treatment was allowed. Enrolled patients received intravenous carboplatin (AUC5) every 21 days for 6-9 cycles. The primary endpoint was the best response rate according to adapted PCWG3 guidelines: radiological response (RECIST 1.1 criteria) and/or biological response [⩾50% prostate-specific antigen (PSA) decline]. Results: A total of 15 out of 16 enrolled patients started carboplatin treatment. Genomic alterations were identified for BRCA2 (n = 5), CDK12 (n = 3), ATM (n = 3) CHEK2 (n = 2), CHEK1 (n = 1), and BRCA1 (n = 1) genes. Objective response (partial biological response + stable radiological response) was achieved in one patient (6.7%), carrying a BRCA2 mutation and not pre-treated with PARPi; stable disease was observed for five patients (33.5%). Among seven patients (46.7%) with previous PARPi treatment, four patients (57.1%) had a stable disease. The median progression-free and overall survivals were 1.9 [95% confidence interval (95% CI), 1.8-9.5] and 8.6 months (95% CI, 4.3-19.5), respectively. The most common severe (grade 3-4) treatment-related toxicities were thrombocytopenia (66.7%), anemia (66.7%), and nausea (60%). Overall, 8 (53.3%) patients experienced a severe hematological event. Conclusion: The study was prematurely stopped as pre-planned considering the limited activity of carboplatin monotherapy in heavily pre-treated, HHR-deficient mCRPC patients. Larger experience is needed in mCRPC with BRCA alterations. Trial registration: NCT03652493, EudraCT ID number 2017-004764-35.

Validation of the Clinical Use of GIScar, an Academic-developed Genomic Instability Score Predicting Sensitivity to Maintenance Olaparib for Ovarian Cancer.

PURPOSE: The optimal application of maintenance PARP inhibitor therapy for ovarian cancer requires accessible, robust, and rapid testing of homologous recombination deficiency (HRD). However, in many countries, access to HRD testing is problematic and the failure rate is high. We developed an academic HRD test to support treatment decision-making. EXPERIMENTAL DESIGN: Genomic Instability Scar (GIScar) was developed through targeted sequencing of a 127-gene panel to determine HRD status. GIScar was trained from a noninterventional study with 250 prospectively collected ovarian tumor samples. GIScar was validated on 469 DNA tumor samples from the PAOLA-1 trial evaluating maintenance olaparib for newly diagnosed ovarian cancer, and its predictive value was compared with Myriad Genetics MyChoice (MGMC). RESULTS: GIScar showed significant correlation with MGMC HRD classification (kappa statistics: 0.780). From PAOLA-1 samples, more HRD-positive tumors were identified by GIScar (258) than MGMC (242), with a lower proportion of inconclusive results (1% vs. 9%, respectively). The HRs for progression-free survival (PFS) with olaparib versus placebo were 0.45 [95% confidence interval (CI), 0.33-0.62] in GIScar-identified HRD-positive BRCA-mutated tumors, 0.50 (95% CI, 0.31-0.80) in HRD-positive BRCA-wild-type tumors, and 1.02 (95% CI, 0.74-1.40) in HRD-negative tumors. Tumors identified as HRD positive by GIScar but HRD negative by MGMC had better PFS with olaparib (HR, 0.23; 95% CI, 0.07-0.72). CONCLUSIONS: GIScar is a valuable diagnostic tool, reliably detecting HRD and predicting sensitivity to olaparib for ovarian cancer. GIScar showed high analytic concordance with MGMC test and fewer inconclusive results. GIScar is easily implemented into diagnostic laboratories with a rapid turnaround.

Value of the loss of heterozygosity to BRCA1 variant classification.

At least 10% of the BRCA1/2 tests identify variants of uncertain significance (VUS) while the distinction between pathogenic variants (PV) and benign variants (BV) remains particularly challenging. As a typical tumor suppressor gene, the inactivation of the second wild-type (WT) BRCA1 allele is expected to trigger cancer initiation. Loss of heterozygosity (LOH) of the WT allele is the most frequent mechanism for the BRCA1 biallelic inactivation. To evaluate if LOH can be an effective predictor of BRCA1 variant pathogenicity, we carried out LOH analysis on DNA extracted from 90 breast and seven ovary tumors diagnosed in 27 benign and 55 pathogenic variant carriers. Further analyses were conducted in tumors with PVs yet without loss of the WT allele: BRCA1 promoter hypermethylation, next-generation sequencing (NGS) of BRCA1/2, and BRCAness score. Ninety-seven tumor samples were analyzed from 26 different BRCA1 variants. A relatively stable pattern of LOH (65.4%) of WT allele for PV tumors was observed, while the allelic balance (63%) or loss of variant allele (15%) was generally seen for carriers of BV. LOH data is a useful complementary argument for BRCA1 variant classification.