Estudio de splicing alternativo y variantes sin clasificar en genes de susceptibilidad al cáncer de mama y ovario: implicaciones clínicas

Durante casi 20 años, BRCA1 y BRCA2 han sido los únicos genes que han proporcionado información útil para el asesoramiento de las familias con sospecha de padecer un síndrome hereditario de cáncer de mama y ovario (HBOC), a pesar de que los defectos germinales en estos genes no explican más de un 40% de estos síndromes. La detección de una mutación patogénica en estos dos genes ha permitido individualizar el asesoramiento genético y proponer estrategias reductoras de riesgo más agresivas, como las cirugías profilácticas. Sin embargo, no siempre el resultado de los test genéticos es de carácter informativo, sino que en muchas ocasiones nos encontramos con mutaciones de significado clínico desconocido. Estas variantes son un problema importante, ya que no se puede concluir la relevancia clínica de la variante, y por tanto resulta complicada su clasificación clínica. Hoy en día está bien establecido que los defectos genéticos en PALB2 (cáncer de mama), y RAD51C o RAD51D (cáncer de ovario), tienen una contribución significativa al exceso de riesgo familiar (aunque las estimaciones de riesgo asociadas a cada uno de estos genes son imprecisas), lo que justifica su inclusión junto a BRCA1 y BRCA2 en paneles multigénicos con finalidad diagnóstica. Por ello, esperamos que el número de variantes genéticas de significado clínicamente desconocido en estos genes aumente de forma drástica en un futuro próximo, causando un serio problema en el asesoramiento genético. En la presente tesis doctoral defendemos que la incorporación de los estudios in vitro de splicing a la rutina diagnóstica pueden aportar información importante para la clasificación clínica de variantes genéticas no sólo demostrando que ciertas variantes son patogénicas al alterar el splicing, sino también identificando variantes (en particular variantes sinónimas e intrónicas) que muy probablemente no lo sean al demostrarse que no afectan al splicing. Además, en esta tesis doctoral defendemos la relevancia clínica de estudiar potenciales alteraciones de splicing asociadas a variantes genéticas inequívocamente no patogénicas (clasificación multifactorial), como es el caso de BRCA1 c.594-2A>C (IVS9-2A>C) y BRCA2 c.687T>A (IVS2-7T>A)..

Brca1 Alternative Splicing Landscape In Breast Tissue Samples

Background: BRCA1 is a key protein in cell network, involved in DNA repair pathways and cell cycle. Recently, the ENIGMA consortium has reported a high number of alternative splicing (AS) events at this locus in blood-derived samples. However, BRCA1 splicing pattern in breast tissue samples is unknown. Here, we provide an accurate description of BRCA1 splicing events distribution in breast tissue samples. Methods: BRCA1 splicing events were scanned in 70 breast tumor samples, 4 breast samples from healthy individuals and in 72 blood-derived samples by capillary electrophoresis (capillary EP). Molecular subtype was identified in all tumor samples. Splicing events were considered predominant if their relative expression level was at least the 10% of the full-length reference signal. Results: 54 BRCA1 AS events were identified, 27 of them were annotated as predominant in at least one sample. Delta 5q, Delta 13, Delta 9, Delta 5 and del 1aA were significantly more frequently annotated as predominant in breast tumor samples than in blood-derived samples. Predominant splicing events were, on average, more frequent in tumor samples than in normal breast tissue samples (P = 0.010). Similarly, likely inactivating splicing events (PTC-NMDs, Non-Coding, Delta 5 and Delta 18) were more frequently annotated as predominant in tumor than in normal breast samples (P = 0.020), whereas there were no significant differences for other splicing events (No-Fs) frequency distribution between tumor and normal breast samples (P = 0.689). Conclusions: Our results complement recent findings by the ENIGMA consortium, demonstrating that BRCA1 AS, despite its tremendous complexity, is similar in breast and blood samples, with no evidences for tissue specific AS events. Further on, we conclude that somatic inactivation of BRCA1 through spliciogenic mutations is, at best, a rare mechanism in breast carcinogenesis, albeit our data detects an excess of likely inactivating AS events in breast tumor samples

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

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

Brca1 Alternative Splicing Landscape In Breast Tissue Samples

Background: BRCA1 is a key protein in cell network, involved in DNA repair pathways and cell cycle. Recently, the ENIGMA consortium has reported a high number of alternative splicing (AS) events at this locus in blood-derived samples. However, BRCA1 splicing pattern in breast tissue samples is unknown. Here, we provide an accurate description of BRCA1 splicing events distribution in breast tissue samples. Methods: BRCA1 splicing events were scanned in 70 breast tumor samples, 4 breast samples from healthy individuals and in 72 blood-derived samples by capillary electrophoresis (capillary EP). Molecular subtype was identified in all tumor samples. Splicing events were considered predominant if their relative expression level was at least the 10% of the full-length reference signal. Results: 54 BRCA1 AS events were identified, 27 of them were annotated as predominant in at least one sample. Delta 5q, Delta 13, Delta 9, Delta 5 and del 1aA were significantly more frequently annotated as predominant in breast tumor samples than in blood-derived samples. Predominant splicing events were, on average, more frequent in tumor samples than in normal breast tissue samples (P = 0.010). Similarly, likely inactivating splicing events (PTC-NMDs, Non-Coding, Delta 5 and Delta 18) were more frequently annotated as predominant in tumor than in normal breast samples (P = 0.020), whereas there were no significant differences for other splicing events (No-Fs) frequency distribution between tumor and normal breast samples (P = 0.689). Conclusions: Our results complement recent findings by the ENIGMA consortium, demonstrating that BRCA1 AS, despite its tremendous complexity, is similar in breast and blood samples, with no evidences for tissue specific AS events. Further on, we conclude that somatic inactivation of BRCA1 through spliciogenic mutations is, at best, a rare mechanism in breast carcinogenesis, albeit our data detects an excess of likely inactivating AS events in breast tumor samples

Targeted RNA-seq successfully identifies normal and pathogenic splicing events in breast/ovarian cancer susceptibility and Lynch syndrome genes

A subset of genetic variants found through screening of patients with hereditary breast and ovarian cancer syndrome (HBOC) and Lynch syndrome impact RNA splicing. Through target enrichment of the transcriptome, it is possible to perform deep-sequencing and to identify the different and even rare mRNA isoforms. A targeted RNA-seq approach was used to analyse the naturally-occurring splicing events for a panel of 8 breast and/or ovarian cancer susceptibility genes (BRCA1, BRCA2, RAD51C, RAD51D, PTEN, STK11, CDH1, TP53), 3 Lynch syndrome genes (MLH1, MSH2, MSH6) and the fanconi anaemia SLX4 gene, in which monoallelic mutations were found in non-BRCA families. For BRCA1, BRCA2, RAD51C and RAD51D the results were validated by capillary electrophoresis and were compared to a non-targeted RNA-seq approach. We also compared splicing events from lymphoblastoid cell-lines with those from breast and ovarian fimbriae tissues. The potential of targeted RNA-seq to detect pathogenic changes in RNA-splicing was validated by the inclusion of samples with previously well characterized BRCA1/2 genetic variants. In our study, we update the catalogue of normal splicing events for BRCA1/2, provide an extensive catalogue of normal RAD51C and RAD51D alternative splicing, and list splicing events found for eight other genes. Additionally, we show that our approach allowed the identification of aberrant splicing events due to the presence of BRCA1/2 genetic variants and distinguished between complete and partial splicing events. In conclusion, targeted-RNA-seq can be very useful to classify variants based on their putative pathogenic impact on splicing

Naturally occurring BRCA2 alternative mRNA splicing events in clinically relevant samples

Background BRCA1 and BRCA2 are the two principal tumour suppressor genes associated with inherited high risk of breast and ovarian cancer. Genetic testing of BRCA1/2 will often reveal one or more sequence variants of uncertain clinical significance, some of which may affect normal splicing patterns and thereby disrupt gene function. mRNA analyses are therefore among the tests used to interpret the clinical significance of some genetic variants. However, these could be confounded by the appearance of naturally occurring alternative transcripts unrelated to germline sequence variation or defects in gene function. To understand which novel splicing events are associated with splicing mutations and which are part of the normal BRCA2 splicing repertoire, a study was undertaken by members of the Evidence-based Network for the Interpretation of Germline Mutant Alleles (ENIGMA) consortium to characterise the spectrum of naturally occurring BRCA2 mRNA alternate-splicing events. Methods mRNA was prepared from several blood and breast tissue-derived cells and cell lines by contributing ENIGMA laboratories. cDNA representing BRCA2 alternate splice sites was amplified and visualised using capillary or agarose gel electrophoresis, followed by sequencing. Results We demonstrate the existence of 24 different BRCA2 mRNA alternate-splicing events in lymphoblastoid cell lines and both breast cancer and non-cancerous breast cell lines. Conclusions These naturally occurring alternate-splicing events contribute to the array of cDNA fragments that may be seen in assays for mutation-associated splicing defects. Caution must be observed in assigning alternate-splicing events to potential splicing mutations

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