Folia Biologica
Journal of Cellular and Molecular Biology, Charles University 

Crossref logo

Fol. Biol. 2026, 72, 16-26

https://doi.org/10.14712/fb2026.0001

Short-Term Lymphocyte Culture Improves the Diagnostic Yield of Targeted RNA NGS in Cancer Predisposition Testing

Marta Černá1ID, Kateřina Matějková1,2ID, Taťána Ptáčková1ID, Petr Nehasil1,3,4ID, Romana Mihalová5ID, Kamila Veselá5ID, Markéta Janatová1ID, Jana Soukupová1ID, Petra Kleiblová1,5ID

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

Received November 19, 2025
Accepted January 27, 2026

References

1. 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>
2. Auerbach, A. D. (2009) Fanconi anemia and its diagnosis. Mutat. Res. 668, 4-10.
3. Ceccaldi, R., Sarangi, P., D’Andrea, A. D. (2016) The fanconi anaemia pathway: new players and new functions. Nat. Rev. Mol. Cell Biol. 17, 337-349. <https://doi.org/10.1038/nrm.2016.48>
4. Černá, M., Šťastná, B., Pešek, P. et al. (2025) Semi-automated RNA isolation from tempus blood RNA tubes using the magcore plus II instrument. Folia Biol. (Praha) 71, 88-94. <https://doi.org/10.14712/fb2025071020088>
5. Claussen, U., Michel, S., Muhlig, P. et al. (2002) Demystifying chromosome preparation and the implications for the concept of chromosome condensation during mitosis. Cytogenet Genome Res. 98, 136-146. <https://doi.org/10.1159/000069817>
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. 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>
8. De Cock, L., D’Haenens, E., Vantomme, L. et al. (2025) Cracking rare disorders: a new minimally invasive RNA-seq protocol. NPJ Genom. Med. 10, 45. <https://doi.org/10.1038/s41525-025-00502-7>
9. 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>
10. Hodson, C., Walden, H. (2012) Towards a molecular understanding of the Fanconi anemia core complex. Anemia 2012, 926787. <https://doi.org/10.1155/2012/926787>
11. 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>
12. 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>
13. 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>
14. Ibraimov, A. I. (1983) Chromosome preparations of human whole blood lymphocytes: an improved technique. Clin. Genet. 24, 240-242. <https://doi.org/10.1111/j.1399-0004.1983.tb00077.x>
15. 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>
16. Kimble, D. C., Lach, F. P., Gregg, S. Q. et al. (2018) A comprehensive approach to identification of pathogenic FANCA variants in Fanconi anemia patients and their families. Hum. Mutat. 39, 237-254. <https://doi.org/10.1002/humu.23366>
17. 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>
18. Kleiblová, P., Černá, M., Zemánková, P. et al. (2024) Parallel DNA/RNA NGS using an identical target enrichment panel in the analysis of hereditary cancer predisposition. Folia Biol. (Praha) 70, 62-73. <https://doi.org/10.14712/fb2024070010062>
19. Kutler, D. I., Singh, B., Satagopan, J. et al. (2003) A 20-year perspective on the International Fanconi Anemia Registry (IFAR). Blood 101, 1249-1256. <https://doi.org/10.1182/blood-2002-07-2170>
20. 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>
21. 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>
22. 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>
23. Mesman, R. L. S., Calleja, F., de la Hoya, M. et al. (2020) Alternative mRNA splicing can attenuate the pathogenicity of presumed loss-of-function variants in BRCA2. Genet. Med. 22, 1355-1365. <https://doi.org/10.1038/s41436-020-0814-5>
24. Rowlands, C. F., Taylor, A., Rice, G. et al. (2022) MRSD: a quantitative approach for assessing suitability of RNA-seq in the investigation of mis-splicing in Mendelian disease. Am. J. Hum. Genet. 109, 210-222. <https://doi.org/10.1016/j.ajhg.2021.12.014>
25. Shahid, M., Firasat, S. (2019) FANCA and contribution of studies from Asian populations to the understanding of fanca mediated Fanconi anemia. Genetika 51, 1197-1225. <https://doi.org/10.2298/GENSR1903197S>
26. Soukupová, J., Šťastna, B., Kanwal, M. et al. (2024) A comprehensive study evaluating germline FANCG variants in predisposition to breast and ovarian cancer. Cancer Med. 13, e70103. <https://doi.org/10.1002/cam4.70103>
27. Soukupová, J., Zemánková, P., Lhotová, 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>
28. 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>
29. Valero-Mora, P. M. (2010) ggplot2:Elegant Graphics for Data Analysis. Journal of Statistical Software 35 (Book Review 1), 1-3.
30. 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>
31. Wen, L., Tang, F. (2025) Single-cell omics sequencing technologies: the long-read generation. Trends Genet. 42, 46-62. <https://doi.org/10.1016/j.tig.2025.07.012>
32. Yang, X., Zhang, X., Jiao, J. et al. (2019) Rare variants in FANCA induce premature ovarian insufficiency. Hum. Genet. 138, 1227-1236. <https://doi.org/10.1007/s00439-019-02059-9>
front cover

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

Open access journal

Submissions

Archive