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Genomic landscape of locally advanced rectal adenocarcinoma: Comparison between before and after neoadjuvant chemoradiation and effects of genetic biomarkers on clinical outcomes and tumor response

PURPOSE: To explore genomic biomarkers in rectal cancer by performing whole‐exome sequencing. MATERIALS AND METHODS: Pre‐chemoradiation (CRT) biopsy and post‐CRT surgical specimens were obtained from 27 patients undergoing neoadjuvant CRT followed by definitive resection. Exomes were sequenced to a...

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Detalles Bibliográficos
Autores principales: Lee, Tae Hoon, Jang, Bum‐Sup, Chang, Ji Hyun, Kim, Eunji, Park, Jeong Hwan, Chie, Eui Kyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10417181/
https://www.ncbi.nlm.nih.gov/pubmed/37260182
http://dx.doi.org/10.1002/cam4.6169
Descripción
Sumario:PURPOSE: To explore genomic biomarkers in rectal cancer by performing whole‐exome sequencing. MATERIALS AND METHODS: Pre‐chemoradiation (CRT) biopsy and post‐CRT surgical specimens were obtained from 27 patients undergoing neoadjuvant CRT followed by definitive resection. Exomes were sequenced to a mean coverage of 30×. Somatic single‐nucleotide variants (SNVs) and insertions/deletions (indels) were identified. Tumor mutational burden was defined as the number of SNVs or indels. Mutational signatures were extracted and fitted to COSMIC reference signatures. Tumor heterogeneity was quantified with a mutant‐allele tumor heterogeneity (MATH) score. Genetic biomarkers and frequently occurred copy number alterations (CNAs) were compared between pre‐ and post‐CRT specimens. Their associations with tumor regression grade (TRG) and clinical outcomes were explored. RESULTS: Top five mutated genes were APC, TP53, NF1, KRAS, and NOTCH1 for pre‐CRT samples and APC, TP53, NF1, CREBBP, and ATM for post‐CRT samples. Several gene mutations including RUNX1, EGFR, and TP53 in pre‐CRT samples showed significant association with clinical outcomes, but not with TRG. However, no such association was found in post‐CRT samples. Discordance of driver mutation status was found between pre‐ and post‐CRT samples. In tumor mutational burden analysis, higher number of SNVs or indels was associated with worse treatment outcomes. Six single‐base substitution (SBS) signatures identified were SBS1, SBS30, SBS29, SBS49, SBS3, and SBS44. The MATH score decreased after CRT on paired analysis. Less than half of CNAs frequent in post‐CRT samples were present in pre‐CRT samples. CONCLUSION: Pre‐ and post‐CRT samples showed different genomic landscape. Potential genetic biomarkers of pre‐CRT samples found in the current analysis call for external validation.