Conclusions
Our study highlights the importance of analyzing both tissue and plasma for detecting actionable mutations in CRC, with plasma ctDNA offering added value. Discordance is associated with gender and peritoneal metastases, and TMB analysis can benefit from a combination of tissue and plasma data. This approach provides valuable insights for personalized CRC treatment.
Methods
Our study involved 76 CRC patients, collecting both tissue and plasma samples for NGS. We assessed the concordance of gene mutational status between ctDNA and tissue, focusing on actionable genes such as KRAS, NRAS, PIK3CA, BRAF, and ERBB2. Logistic regression analysis was used to explore variables associated with discordance and positive mutation rates.
Purpose
The advent of circulating tumor DNA (ctDNA) technology has provided a convenient and noninvasive means to continuously monitor cancer genomic data, facilitating personalized cancer treatment. This study aimed to evaluate the supplementary benefits of plasma ctDNA alongside traditional tissue-based next-generation sequencing (NGS) in identifying targetable mutations and tumor mutational burden (TMB) in colorectal cancers (CRC).
Results
In total, 26 cancer-related genes were identified. The most common variants in tumor tissues and plasma samples were in APC (57.9% vs 19.7%), TP53 (55.3% vs 22.4%) and KRAS (47.4% vs 43.4%). Tissue and ctDNA showed an overall concordance of 73.53% in detecting actionable gene mutations. Notably, plasma ctDNA improved detection for certain genes and gene pools. Variables significantly associated with discordance included gender and peritoneal metastases. TMB analysis revealed a higher detection rate in tissues compared to plasma, but combining both increased detection. Conclusions: Our study highlights the importance of analyzing both tissue and plasma for detecting actionable mutations in CRC, with plasma ctDNA offering added value. Discordance is associated with gender and peritoneal metastases, and TMB analysis can benefit from a combination of tissue and plasma data. This approach provides valuable insights for personalized CRC treatment.