Journal of medical genetics · 2023
Transcript capture and ultradeep long-read RNA sequencing (CAPLRseq) to diagnose HNPCC/Lynch syndrome.
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Schwenk Vincent, Leal Silva Rafaela Magalhaes, Scharf Florentine, Knaust Katharina, Wendlandt Martin, Häusser Tanja, Pickl Julia M A, Steinke-Lange Verena, Laner Andreas, Morak Monika, Holinski-Feder Elke, Wolf Dieter A
Аннотация
PURPOSE: Whereas most human genes encode multiple mRNA isoforms with distinct function, clinical workflows for assessing this heterogeneity are not readily available. This is a substantial shortcoming, considering that up to 25% of disease-causing gene variants are suspected of disrupting mRNA splicing or mRNA abundance. Long-read sequencing can readily portray mRNA isoform diversity, but its sensitivity is relatively low due to insufficient transcriptome penetration.
METHODS: We developed and applied capture-based target enrichment from patient RNA samples combined with Oxford Nanopore long-read sequencing for the analysis of 123 hereditary cancer transcripts (capture and ultradeep long-read RNA sequencing (CAPLRseq)).
RESULTS: Validating CAPLRseq, we confirmed 17 cases of hereditary non-polyposis colorectal cancer/Lynch syndrome based on the demonstration of splicing defects and loss of allele expression of mismatch repair genes MLH1, PMS2, MSH2 and MSH6. Using CAPLRseq, we reclassified two variants of uncertain significance in MSH6 and PMS2 as either likely pathogenic or benign.
CONCLUSION: Our data show that CAPLRseq is an automatable and adaptable workflow for effective transcriptome-based identification of disease variants in a clinical diagnostic setting.
METHODS: We developed and applied capture-based target enrichment from patient RNA samples combined with Oxford Nanopore long-read sequencing for the analysis of 123 hereditary cancer transcripts (capture and ultradeep long-read RNA sequencing (CAPLRseq)).
RESULTS: Validating CAPLRseq, we confirmed 17 cases of hereditary non-polyposis colorectal cancer/Lynch syndrome based on the demonstration of splicing defects and loss of allele expression of mismatch repair genes MLH1, PMS2, MSH2 and MSH6. Using CAPLRseq, we reclassified two variants of uncertain significance in MSH6 and PMS2 as either likely pathogenic or benign.
CONCLUSION: Our data show that CAPLRseq is an automatable and adaptable workflow for effective transcriptome-based identification of disease variants in a clinical diagnostic setting.