Detection of known gene fusions in cancer cell lines using whole-genome bisulfite sequencing data

detection-of-known-gene-fusions-in-cancer-cell-lines-using-whole-genome-bisulfite-sequencing-data
Detection of known gene fusions in cancer cell lines using whole-genome bisulfite sequencing data

References

  1. Liu, S. V., Nagasaka, M., Atz, J., Solca, F. & Müllauer, L. Oncogenic gene fusions in cancer: From biology to therapy. Signal Transduct. Target. Ther. 10, 111 (2025).

    Google Scholar 

  2. Belzen, I. A. E. M. van et al. Complex structural variation is prevalent and highly pathogenic in pediatric solid tumors. Cell Genom. 4, (2024).

  3. Tuna, M., Amos, C. I. & Mills, G. B. Molecular mechanisms and pathobiology of oncogenic fusion transcripts in epithelial tumors. Oncotarget 10, 2095–2111 (2019).

    Google Scholar 

  4. Nord, K. H. et al. GRM1 is upregulated through gene fusion and promoter swapping in chondromyxoid fibroma. Nat. Genet. 46, 474–477 (2014).

    Google Scholar 

  5. Van, A.-A. N. et al. Protein kinase C fusion proteins are paradoxically loss of function in cancer. J. Biol. Chem. 296, (2021).

  6. Mitelman, F., Johansson, B. & Mertens, F. The impact of translocations and gene fusions on cancer causation. Nat. Rev. Cancer 7, 233–245 (2007).

    Google Scholar 

  7. Wu, H., Li, X. & Li, H. Gene fusions and chimeric RNAs, and their implications in cancer. Genes Dis. 6, 385–390 (2019).

    Google Scholar 

  8. Mertens, F., Johansson, B., Fioretos, T. & Mitelman, F. The emerging complexity of gene fusions in cancer. Nat. Rev. Cancer 15, 371–381 (2015).

    Google Scholar 

  9. Carvalho, E., Canberk, S., Schmitt, F. & Vale, N. Molecular subtypes and mechanisms of breast cancer: Precision medicine approaches for targeted therapies. Cancers 17, 1102 (2025).

    Google Scholar 

  10. Huang, D. et al. Molecular subtypes and targeted therapeutic strategies in small cell lung cancer: Advances, challenges, and future perspectives. Molecules 30, 1731 (2025).

    Google Scholar 

  11. Singh, M. P., Rai, S., Pandey, A., Singh, N. K. & Srivastava, S. Molecular subtypes of colorectal cancer: An emerging therapeutic opportunity for personalized medicine. Genes Dis. 8, 133–145 (2019).

    Google Scholar 

  12. Quintás-Cardama, A. & Cortes, J. Molecular biology of bcr-abl1–positive chronic myeloid leukemia. Blood 113, 1619–1630 (2009).

    Google Scholar 

  13. St. John, J., Powell, K., Conley-LaComb, M. K. & Chinni, S. R. TMPRSS2-ERG fusion gene expression in prostate tumor cells and its clinical and biological significance in prostate cancer progression. J. Cancer Sci. Ther. 4, 94–101 (2012).

    Google Scholar 

  14. Zhao, S. G. et al. Integrated analyses highlight interactions between the three-dimensional genome and DNA, RNA and epigenomic alterations in metastatic prostate cancer. Nat. Genet. 56, 1689–1700 (2024).

    Google Scholar 

  15. Esteller, M. Aberrant DNA methylation as a cancer-inducing mechanism. Annu. Rev. Pharmacol. Toxicol. 45, 629–656 (2005).

    Google Scholar 

  16. Saghafinia, S., Mina, M., Riggi, N., Hanahan, D. & Ciriello, G. Pan-cancer landscape of aberrant DNA methylation across human tumors. Cell Rep. 25, 1066-1080.e8 (2018).

    Google Scholar 

  17. Lakshminarasimhan, R. & Liang, G. The role of DNA methylation in cancer. Adv. Exp. Med. Biol. 945, 151–172 (2016).

    Google Scholar 

  18. Witte, T., Plass, C. & Gerhauser, C. Pan-cancer patterns of DNA methylation. Genome Med. 6, 66 (2014).

    Google Scholar 

  19. McCabe, M. T., Brandes, J. C. & Vertino, P. M. Cancer DNA methylation: Molecular mechanisms and clinical implications. Clin. Cancer Res. 15, 3927–3937 (2009).

    Google Scholar 

  20. Kim, S. Y. et al. Cancer signature ensemble integrating cfDNA methylation, copy number, and fragmentation facilitates multi-cancer early detection. Exp. Mol. Med. 55, 2445–2460 (2023).

    Google Scholar 

  21. Heo, Y. J., Hwa, C., Lee, G.-H., Park, J.-M. & An, J.-Y. Integrative multi-omics approaches in cancer research: From biological networks to clinical subtypes. Mol. Cells 44, 433–443 (2021).

    Google Scholar 

  22. Gao, Y. et al. Integration of multiomics features for blood-based early detection of colorectal cancer. Mol. Cancer 23, 173 (2024).

    Google Scholar 

  23. Zhang, J., Che, Y., Liu, R., Wang, Z. & Liu, W. Deep learning–driven multi-omics analysis: Enhancing cancer diagnostics and therapeutics. Brief Bioinform. 26, bbaf440 (2025).

    Google Scholar 

  24. Woodhouse, R. et al. Clinical and analytical validation of FoundationOne Liquid CDx, a novel 324-gene cfDNA-based comprehensive genomic profiling assay for cancers of solid tumor origin. PLoS One 15, e0237802 (2020).

    Google Scholar 

  25. Kerbs, P. et al. Fusion gene detection by RNA-sequencing complements diagnostics of acute myeloid leukemia and identifies recurring NRIP1-MIR99AHG rearrangements. Haematologica 107, 100–111 (2021).

    Google Scholar 

  26. Uhrig, S. et al. Accurate and efficient detection of gene fusions from RNA sequencing data. Genome Res. 31, 448–460 (2021).

    Google Scholar 

  27. Roepman, P. et al. Clinical validation of whole genome sequencing for cancer diagnostics. J. Mol. Diagn. 23, 816–833 (2021).

    Google Scholar 

  28. Hafstað, V. et al. Improved detection of clinically relevant fusion transcripts in cancer by machine learning classification. BMC Genom. 24, 783 (2023).

    Google Scholar 

  29. Kint, S., Spiegelaere, W. D., Kesel, J. D., Vandekerckhove, L. & Criekinge, W. V. Evaluation of bisulfite kits for DNA methylation profiling in terms of DNA fragmentation and DNA recovery using digital PCR. PLoS One 13, e0199091 (2018).

    Google Scholar 

  30. Erger, F. et al. cfNOMe — A single assay for comprehensive epigenetic analyses of cell-free DNA. Genome Med. 12, 54 (2020).

    Google Scholar 

  31. Xi, Y. & Li, W. BSMAP: Whole genome bisulfite sequence MAPping program. BMC Bioinform. 10, 232 (2009).

    Google Scholar 

  32. Tsuji, J. & Weng, Z. Evaluation of preprocessing, mapping and postprocessing algorithms for analyzing whole genome bisulfite sequencing data. Brief Bioinform. 17, 938–952 (2016).

    Google Scholar 

  33. Li, S. et al. Comprehensive evaluation of the impact of whole-genome bisulfite sequencing (WGBS) on the fragmentomic characteristics of plasma cell-free DNA. Clin. Chim. Acta 566, 120033 (2025).

    Google Scholar 

  34. Zhao, S. et al. Benchmarking strategies for CNV calling from whole genome bisulfite data in humans. Comput. Struct. Biotechnol. J. 27, 912–919 (2025).

    Google Scholar 

  35. Gong, T. et al. Analysis and performance assessment of the whole genome bisulfite sequencing data workflow: Currently available tools and a practical guide to advance DNA methylation studies. Small Method. 6, e2101251 (2022).

    Google Scholar 

  36. Zhao, S., Hoff, A. M. & Skotheim, R. I. ScaR—a tool for sensitive detection of known fusion transcripts: Establishing prevalence of fusions in testicular germ cell tumors. NAR Genom. Bioinform. 2, lzq025 (2020).

    Google Scholar 

  37. Dorney, R., Dhungel, B. P., Rasko, J. E. J., Hebbard, L. & Schmitz, U. Recent advances in cancer fusion transcript detection. Brief Bioinform. 24, bbac519 (2023).

    Google Scholar 

  38. Lozzio, C. B. & Lozzio, B. B. Human chronic myelogenous leukemia cell-line with positive Philadelphia chromosome. Blood 45, 321–334 (1975).

    Google Scholar 

  39. Hafstað, V. et al. Improved detection of clinically relevant fusion transcripts in cancer by machine learning classification. BMC Genom. 24, 783 (2023).

    Google Scholar 

  40. Gricourt, G. et al. Fusion gene detection and quantification by Asymmetric Capture Sequencing (aCAP-Seq). J. Mol. Diagn. 24, 1113–1127 (2022).

    Google Scholar 

  41. Piazza, R. et al. FusionAnalyser: A new graphical, event-driven tool for fusion rearrangements discovery. Nucl. Acid. Res. 40, e123 (2012).

    Google Scholar 

  42. Haas, B. J. et al. Targeted in silico characterization of fusion transcripts in tumor and normal tissues via FusionInspector. Cell Rep. Method. 3, 100467 (2023).

    Google Scholar 

  43. Loo, S. K. et al. Fusion-associated carcinomas of the breast: Diagnostic, prognostic, and therapeutic significance. Genes Chromosom. Cancer 61, 261–273 (2022).

    Google Scholar 

  44. Rosenbaum, J. N. et al. Genomic heterogeneity of ALK fusion breakpoints in non-small-cell lung cancer. Mod. Pathol. 31, 791–808 (2018).

    Google Scholar 

  45. Linhartova, J. et al. Characterization of 46 patient-specific BCR-ABL1 fusions and detection of SNPs upstream and downstream the breakpoints in chronic myeloid leukemia using next generation sequencing. Mol. Cancer 14, 89 (2015).

    Google Scholar 

  46. Cai, W. et al. Intratumoral heterogeneity of ALK-rearranged and ALK/EGFR coaltered lung adenocarcinoma. J. Clin. Oncol. 33, 3701–3709 (2015).

    Google Scholar 

  47. Levitin, H. M., Yuan, J. & Sims, P. A. Single-cell transcriptomic analysis of tumor heterogeneity. Trend. Cancer 4, 264–268 (2018).

    Google Scholar 

  48. Chen, S., Zhou, Y., Chen, Y. & Gu, J. Fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).

    Google Scholar 

  49. Pedersen, B. S., Eyring, K., De, S., Yang, I. V. & Schwartz, D. A. Fast and accurate alignment of long bisulfite-seq reads. Preprint at https://doi.org/10.48550/arXiv.1401.1129 (2014).

  50. McKenna, A. et al. The genome analysis toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    Google Scholar 

  51. Ryan, D. dpryan79/MethylDackel. (2025).

  52. Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Google Scholar 

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