Comprehensive genomic profiling of small bowel adenocarcinoma with liver metastasis

Abstract

Background: Small bowel adenocarcinoma (SBA) is a malignant tumor with a relatively low prevalence. Metastasis, especially liver metastasis (LM), is an important prognostic indicator of poor prognosis in patients with SBA. Due to the rarity of SBA, there is currently a lack of research on the characteristics of liver metastases in small intestine cancer. The aim of this study is to investigate the molecular characteristics associated with metastasis in SBA and to explore the specific molecular combinations related to LM.

Methods: A retrospective study was performed on patients with SBA who were admitted to Zhejiang Hospital and Affiliated Hangzhou First People's Hospital from July 2013 to July 2022. Sequencing with tissue was performed using a 1,021-gene panel. The least absolute shrinkage and selection operator (LASSO) algorithm was used to identify the genes for predicting LM from SBA.

Results: A total of 97 patients with SBA, including 48 patients without metastasis, 29 with LM, and 20 with extrahepatic metastasis (EHM), were enrolled in this cohort. The five genes with the highest mutation frequency in the overall samples were TP53, KRAS, APC, CDKN2A, and SMAD4, with 176 actionable mutations that had potential impact on therapy being detected in 77 (79%) cases. TP53 was significantly more frequently mutated in the LM than in the non-LM (NLM) groups. Nine genes were selected via LASSO regression to construct the LM prediction model, which generated an area under curve of 0.867. The receiver operating characteristic (ROC) curve for the validation cohort was 0.724.

Conclusions: This study elucidated the molecular characteristics of metastatic SBA and found that TP53 mutations were associated with LM in SBA. These results deepen our understanding of the occurrence and development of SBA and the potential mechanisms of LM in SBA.

Keywords: Small bowel adenocarcinoma (SBA); genomic landscape; liver metastasis (LM); prediction model; risk factor.

Figure 1

Genetic analysis of primary tumors from the SBA cohorts. (A) Mutational landscape of small-bowel cancer. Each row represents a gene, and each column represents a patient. The mutation frequency of each gene is shown on the left side of the mutation heatmap. The number of detected mutations in a single sample is shown above the mutation heatmap. The detection frequency of genes is shown on the right-hand side of the mutation heatmap. (B) Alterations in drug targetable genes in small-bowel cancer samples. EHM, extrahepatic metastasis; LM, liver metastasis; SBA, small-bowel cancer.

Figure 2

Genomic alteration frequencies between the metastatic group and the non-metastatic group (A), LM group and NLM group (C), LM group, EHM group and non-metastatic group (E). The frequencies of actionable genes mutations between the metastatic group and the non-metastatic group (B), LM group and NLM group (D), LM group, EHM group and non-metastatic group (F). EHM, extrahepatic metastasis; LM, liver metastasis; NLM, non-liver metastasis.

Figure 3

Comparisons of the gene mutation rates in the different subgroups. EHM, extrahepatic metastasis; LM, liver metastasis; NLM, non-liver metastasis.

Figure 4

The ROC curve of prediction model in the (A) construction and (B) validation cohort. ROC, receiver operating characteristic; AUC, area under the curve.