作者
Hongxin Niu,Jian Wang,Huirong Xu,Hao Liang,Jianqi Li
摘要
ABSTRACT Hepatocellular carcinoma (HCC) is one of the deadliest cancers worldwide. This study investigates the temporal dynamics of somatic mutations in HCC patients treated with radioactive particle therapy. Using whole‐exome sequencing (WES), we track these mutations at different stages: pre‐therapy (intra‐cancer), post‐therapy 1 week, 1 month, and 6 months. We also assess the clinical relevance of somatic mutation dynamics in evaluating treatment response, considering tumor size, AFP levels, and liver function markers. This study involved six HCC patients who received radioactive particle therapy. Tumor and adjacent normal tissues were collected before therapy, whereas plasma samples were taken during follow‐up visits at 1 week, 1 month, and 6 months after treatment. We extracted DNA from these samples and analyzed them using WES analysis to identify somatic mutations, such as single‐nucleotide variants (SNVs) and insertions/deletions (indels), across different time points. The somatic mutations of HCC changed over time after radioactive particle therapy. Some key mutations became less common after treatment, whereas others remained or even increased. The overall number of mutations also shifted, suggesting that cancer cells might be adapting to the treatment. CTNNB1 mutations showed a clear decline in DNA variant allele frequency (VAF) over time, with mean values decreasing from approximately 0.32 at baseline to 0.13 at intra‐paratumoral samples, indicating a potential treatment‐responsive pattern. TP53 mutations remained relatively stable, with mean VAFs fluctuating only slightly from ∼0.25 to ∼0.20, suggesting possible therapy resistance. In contrast, MYH15 mutations displayed a modest decline (from ∼0.15 to ∼0.10), with a transient increase at 1 week, which may reflect short‐term adaptive dynamics during treatment. This study demonstrates dynamic changes in somatic mutation profiles in HCC patients following radioactive particle therapy. The observed alterations in key mutations and overall mutational burden over time may inform future approaches for early detection, treatment monitoring, and therapeutic optimization.