Folia Biologica
Journal of Cellular and Molecular Biology, Charles University 

Crossref logo

Fol. Biol. 2024, 70, 62-73

https://doi.org/10.14712/fb2024070010062

Parallel DNA/RNA NGS Using an Identical Target Enrichment Panel in the Analysis of Hereditary Cancer Predisposition

Petra Kleiblová1,2, Marta Černá2, Petra Zemánková2,3, Kateřina Matějková2,4, Petr Nehasil2,3,5, Jan Hojný6, Klára Horáčková2, Markéta Janatová2, Jana Soukupová2, Barbora Šťastná2,7, Zdeněk Kleibl2

1Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
2Institute of Medical Biochemistry and Laboratory Diagnostics, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
3Institute of Pathological Physiology, First Faculty of Medicine, Charles University, Prague, Czech Republic
4Department of Genetics and Microbiology, Faculty of Science, Charles University, Prague, Czech Republic
5Department of Paediatrics and Inherited Metabolic Disorders, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
6Institute of Pathology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
7Department of Biochemistry, Faculty of Science, Charles University, Prague, Czech Republic

Received March 2024
Accepted March 2024

References

1. Acedo, A., Hernandez-Moro, C., Curiel-Garcia, A. et al. (2015) Functional classification of BRCA2 DNA variants by splicing assays in a large minigene with 9 exons. Hum. Mutat. 36, 210-221. <https://doi.org/10.1002/humu.22725>
2. Agius, P., Geiger, H., Robine, N. (2019) SCANVIS: a tool for SCoring, ANnotating and VISualizing splice junctions. Bioinformatics 35, 4843-4845. <https://doi.org/10.1093/bioinformatics/btz452>
3. Brandão, R. D., Mensaert, K., López-Perolio, I. et al. (2019) Targeted RNA-seq successfully identifies normal and pathogenic splicing events in breast/ovarian cancer susceptibility and Lynch syndrome genes. Int. J. Cancer 145, 401-414. <https://doi.org/10.1002/ijc.32114>
4. Chen, Y., Lun, A. T., Smyth, G. K. (2016) From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline. F1000Res. 5, 1438.
5. Colombo, M., Blok, M. J., Whiley, P. et al. (2014) Comprehensive annotation of splice junctions supports pervasive alternative splicing at the BRCA1 locus: a report from the ENIGMA consortium. Hum. Mol. Genet. 23, 3666-3680. <https://doi.org/10.1093/hmg/ddu075>
6. Cotto, K. C., Feng, Y. Y., Ramu, A. et al. (2023) Integrated analysis of genomic and transcriptomic data for the discovery of splice-associated variants in cancer. Nat. Commun. 14, 1589. <https://doi.org/10.1038/s41467-023-37266-6>
7. Curion, F., Handel, A. E., Attar, M. et al. (2020) Targeted RNA sequencing enhances gene expression profiling of ultra-low input samples. RNA Biol. 17, 1741-1753. <https://doi.org/10.1080/15476286.2020.1777768>
8. Davy, G., Rousselin, A., Goardon, N. et al. (2017) Detecting splicing patterns in genes involved in hereditary breast and ovarian cancer. Eur. J. Hum. Genet. 25, 1147-1154. <https://doi.org/10.1038/ejhg.2017.116>
9. Deans, Z. C., Ahn, J. W., Carreira, I. M. et al. (2022) Recommendations for reporting results of diagnostic genomic testing. Eur. J. Hum. Genet. 30, 1011-1016. <https://doi.org/10.1038/s41431-022-01091-0>
10. Dobin, A., Davis, C. A., Schlesinger, F. et al. (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15-21. <https://doi.org/10.1093/bioinformatics/bts635>
11. Ekong, R., Nellist, M., Hoogeveen-Westerveld, M. et al. (2016) Variants within TSC2 exons 25 and 31 are very unlikely to cause clinically diagnosable tuberous sclerosis. Hum. Mutat. 37, 364-370. <https://doi.org/10.1002/humu.22951>
12. Farber-Katz, S., Hsuan, V., Wu, S. et al. (2018) Quantitative analysis of BRCA1 and BRCA2 germline splicing variants using a novel RNA-massively parallel sequencing assay. Front. Oncol. 8, 286. <https://doi.org/10.3389/fonc.2018.00286>
13. Foretova, L., Machackova, E., Palacova, M. et al. (2016) Recommended extension of indication criteria for genetic testing of BRCA1 and BRCA2 mutations in hereditary breast and ovarian cancer syndrome. Klin. Onkol. 29 (Suppl. 1), S9-S13. (in Czech) <https://doi.org/10.14735/amko2016S9>
14. Havranek, O., Kleiblova, P., Hojny, J. et al. (2015) Association of germline CHEK2 gene variants with risk and prognosis of non-Hodgkin lymphoma. PLoS One 10, e0140819. <https://doi.org/10.1371/journal.pone.0140819>
15. Hojny, J., Michalkova, R., Krkavcova, E. et al. (2022) Comprehensive quantitative analysis of alternative splicing variants reveals the HNF1B mRNA splicing pattern in various tumour and non-tumour tissues. Sci. Rep. 12, 199. <https://doi.org/10.1038/s41598-021-03989-z>
16. Hojny, J., Zemankova, P., Lhota, F. et al. (2017) Multiplex PCR and NGS-based identification of mRNA splicing variants: analysis of BRCA1 splicing pattern as a model. Gene 637, 41-49. <https://doi.org/10.1016/j.gene.2017.09.025>
17. Hong, M., Tao, S., Zhang, L. et al. (2020) RNA sequencing: new technologies and applications in cancer research. J. Hematol. Oncol. 13, 166. <https://doi.org/10.1186/s13045-020-01005-x>
18. Horackova, K., Frankova, S., Zemankova, P. et al. (2022) Low frequency of cancer-predisposition gene mutations in liver transplant candidates with hepatocellular carcinoma. Cancers (Basel) 15, 201. <https://doi.org/10.3390/cancers15010201>
19. Horton, C., Cass, A., Conner, B. R. et al. (2022) Mutational and splicing landscape in a cohort of 43,000 patients tested for hereditary cancer. NPJ Genom. Med. 7, 49. <https://doi.org/10.1038/s41525-022-00323-y>
20. Horton, C., Hoang, L., Zimmermann, H. et al. (2024) Diagnostic outcomes of concurrent DNA and RNA sequencing in individuals undergoing hereditary cancer testing. JAMA Oncol. 10, 212-219. <https://doi.org/10.1001/jamaoncol.2023.5586>
21. Houdayer, C., Caux-Moncoutier, V., Krieger, S. et al. (2012) Guidelines for splicing analysis in molecular diagnosis derived from a set of 327 combined in silico/in vitro studies on BRCA1 and BRCA2 variants. Hum. Mutat. 33, 1228-1238. <https://doi.org/10.1002/humu.22101>
22. Hujova, P., Soucek, P., Radova, L. et al. (2021) Nucleotides in both donor and acceptor splice sites are responsible for choice in NAGNAG tandem splice sites. Cell. Mol. Life Sci. 78, 6979-6993. <https://doi.org/10.1007/s00018-021-03943-2>
23. Javed, N., Farjoun, Y., Fennell, T. J. et al. (2020) Detecting sample swaps in diverse NGS data types using linkage disequilibrium. Nat. Commun. 11, 3697. <https://doi.org/10.1038/s41467-020-17453-5>
24. Karam, R., LaDuca, H., Richardson, M. E. et al. (2020) RNA-seq analysis is a useful tool in variant classification. JCO Precis. Oncol. 4, 1226-1227. <https://doi.org/10.1200/PO.20.00310>
25. Kleibl, Z., Kristensen, V. N. (2016) Women at high risk of breast cancer: molecular characteristics, clinical presentation and management. Breast 28, 136-144. <https://doi.org/10.1016/j.breast.2016.05.006>
26. Kleiblova, P., Stolarova, L., Krizova, K. et al. (2019) Identification of deleterious germline CHEK2 mutations and their association with breast and ovarian cancer. Int. J. Cancer 145, 1782-1797. <https://doi.org/10.1002/ijc.32385>
27. Kral, J., Jelinkova, S., Zemankova, P. et al. (2023) Germline multigene panel testing of patients with endometrial cancer. Oncol. Lett. 25, 216. <https://doi.org/10.3892/ol.2023.13802>
28. Kraus, C., Hoyer, J., Vasileiou, G. et al. (2017) Gene panel sequencing in familial breast/ovarian cancer patients identifies multiple novel mutations also in genes others than BRCA1/2. Int. J. Cancer 140, 95-102. <https://doi.org/10.1002/ijc.30428>
29. Kuzbari, Z., Bandlamudi, C., Loveday, C. et al. (2023) Germline-focused analysis of tumour-detected variants in 49,264 cancer patients: ESMO Precision Medicine Working Group recommendations. Ann. Oncol. 34, 215-227. <https://doi.org/10.1016/j.annonc.2022.12.003>
30. LaDuca, H., Polley, E. C., Yussuf, A. et al. (2020) A clinical guide to hereditary cancer panel testing: evaluation of gene-specific cancer associations and sensitivity of genetic testing criteria in a cohort of 165,000 high-risk patients. Genet. Med. 22, 407-415. <https://doi.org/10.1038/s41436-019-0633-8>
31. Lattimore, V. L., Pearson, J. F., Currie, M. J. et al. (2018) Investigation of experimental factors that underlie BRCA1/2 mRNA isoform expression variation: recommendations for utilizing targeted RNA sequencing to evaluate potential spliceogenic variants. Front. Oncol. 8, 140. <https://doi.org/10.3389/fonc.2018.00140>
32. Lattimore, V. L., Pearson, J. F., Morley-Bunker, A. E. et al. (2019) Quantifying BRCA1 and BRCA2 mRNA isoform expression levels in single cells. Int. J. Mol. Sci. 20, 693. <https://doi.org/10.3390/ijms20030693>
33. Leman, R., Harter, V., Atkinson, A. et al. (2020) SpliceLauncher: a tool for detection, annotation and relative quantification of alternative junctions from RNAseq data. Bioinformatics 36, 1634-1636. <https://doi.org/10.1093/bioinformatics/btz784>
34. Lhota, F., Zemankova, P., Kleiblova, P. et al. (2016) Hereditary truncating mutations of DNA repair and other genes in BRCA1/BRCA2/PALB2-negatively tested breast cancer patients. Clin. Genet. 90, 324-333. <https://doi.org/10.1111/cge.12748>
35. Lhotova, K., Stolarova, L., Zemankova, P. et al. (2020) Multigene panel germline testing of 1333 Czech patients with ovarian cancer. Cancers (Basel) 12, 956. <https://doi.org/10.3390/cancers12040956>
36. Lopez-Perolio, I., Leman, R., Behar, R. et al. (2019) Alternative splicing and ACMG-AMP-2015-based classification of PALB2 genetic variants: an ENIGMA report. J. Med. Genet. 56, 453-460. <https://doi.org/10.1136/jmedgenet-2018-105834>
37. Machackova, E., Claes, K., Mikova, M. et al. (2019) Twenty years of BRCA1 and BRCA2 molecular analysis at MMCI – current developments for the classification of variants. Klin. Onkol. 32 (Suppl. 2), 51-71. <https://doi.org/10.14735/amko2019S51>
38. McCarthy, D. J., Chen, Y., Smyth, G. K. (2012) Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 40, 4288-4297. <https://doi.org/10.1093/nar/gks042>
39. McKenna, A., Hanna, M., Banks, E. et al. (2010) The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297-1303. <https://doi.org/10.1101/gr.107524.110>
40. Moles-Fernandez, A., Domenech-Vivo, J., Tenes, A. et al. (2021) Role of splicing regulatory elements and in silico tools usage in the identification of deep intronic splicing variants in hereditary breast/ovarian cancer genes. Cancers (Basel) 13, 3341. <https://doi.org/10.3390/cancers13133341>
41. Pohlreich, P., Stribrna, J., Kleibl, Z. et al. (2003) Mutations of the BRCA1 gene in hereditary breast and ovarian cancer in the Czech Republic. Med. Princ. Pract. 12, 23-29. <https://doi.org/10.1159/000068163>
42. Rahman, N. (2014) Realizing the promise of cancer predisposition genes. Nature 505, 302-308. <https://doi.org/10.1038/nature12981>
43. Schafer, S., Miao, K., Benson, C. C. et al. (2015) Alternative splicing signatures in RNA-seq data: percent spliced in (PSI). Curr. Protoc. Hum. Genet. 87, 11.16.1-11.16.14.
44. Soukupova, J., Zemankova, P., Lhotova, K. et al. (2018) Validation of CZECANCA (CZEch CAncer paNel for Clinical Application) for targeted NGS-based analysis of hereditary cancer syndromes. PLoS One 13, e0195761. <https://doi.org/10.1371/journal.pone.0195761>
45. Stadler, Z. K., Maio, A., Chakravarty, D. et al. (2021) Therapeutic implications of germline testing in patients with advanced cancers. J. Clin. Oncol. 39, 2698-2709. <https://doi.org/10.1200/JCO.20.03661>
46. Struzinska, I., Hajkova, N., Hojny, J. et al. (2023) A comprehensive molecular analysis of 113 primary ovarian clear cell carcinomas reveals common therapeutically significant aberrations. Diagn. Pathol. 18, 72. <https://doi.org/10.1186/s13000-023-01358-0>
47. Sung, H., Ferlay, J., Siegel, R. L. et al. (2021) Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71, 209-249. <https://doi.org/10.3322/caac.21660>
48. Szafranski, K., Kramer, M. (2015) It’s a bit over, is that ok? The subtle surplus from tandem alternative splicing. RNA Biol. 12, 115-122. <https://doi.org/10.1080/15476286.2015.1017210>
49. Walker, L. C., Hoya, M., Wiggins, G. A. R. et al. (2023) Using the ACMG/AMP framework to capture evidence related to predicted and observed impact on splicing: recommendations from the ClinGen SVI splicing subgroup. Am. J. Hum. Genet. 110, 1046-1067. <https://doi.org/10.1016/j.ajhg.2023.06.002>
50. Walker, L. C., Lattimore, V. L., Kvist, A. et al. (2019) Comprehensive assessment of BARD1 messenger ribonucleic acid splicing with implications for variant classification. Front. Genet. 10, 1139. <https://doi.org/10.3389/fgene.2019.01139>
51. Wieme, G., Kral, J., Rosseel, T. et al. (2021) Prevalence of germline pathogenic variants in cancer predisposing genes in Czech and Belgian pancreatic cancer patients. Cancers (Basel) 13, 4430. <https://doi.org/10.3390/cancers13174430>
52. Yoon, S., Nam, D. (2017) Gene dispersion is the key determinant of the read count bias in differential expression analysis of RNA-seq data. BMC Genomics 18, 408. <https://doi.org/10.1186/s12864-017-3809-0>
53. Zheng, X., Levine, D., Shen, J. et al. (2012) A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics 28, 3326-3328. <https://doi.org/10.1093/bioinformatics/bts606>
front cover

ISSN 0015-5500 (Print) ISSN 2533-7602 (Online)

Open access journal

Submissions

Archive