Mutations of key driver genes in gastric cancer metastasis risk: a systematic review and meta-analysis.

Objective: Associations between gene mutations and metastasis in gastric cancer (GC) remain contradictory, resulting in the inaccurate estimation of the magnitude of the risk associated with specific genotypes.Methods: In this study, we first screened out four key driver genes (TP53, PIK3CA, APC and ARID1A) by jointly analyzing the mutation levels and searching the literature for genes associated with GC metastasis. We then performed a meta-analysis to demonstrate the relationship between these key driver gene mutations and GC metastasis, including lymphatic and distance metastasis.Results: We found out four key driver genes (TP53, PIK3CA, APC and ARID1A), associated with risk of GC metastasis. The results showed that TP53 (OR 1.39, 95% CI 1.12-1.72) and APC mutations (OR 0.58, 95% CI 0.38-0.89) were associated with lymph node metastasis and distant metastasis in GC. And TP53 mutations (OR 1.65, 95% CI 1.25-2.18) were significantly related to GC metastasis in the Asian population. APC mutations (OR 0.54, 95% CI 0.29-1.00) were also related to GC metastasis in the European and American populations. There was no significant association with GC metastasis in PIK3CA or ARID1A mutations.Expert opinion:Mutations of TP53 and APC play important roles in lymph node metastasis and distant metastasis of GC and may be potential important biomarkers of progression and therapeutic targets. These observations should be further prospectively verified.

Could microtubule inhibitors be the best choice of therapy in gastric cancer with high immune activity: mutant DYNC1H1 as a biomarker.

Immune checkpoint blockade (ICB) has achieved unprecedented breakthroughs in various cancers, including gastric cancer (GC) with high immune activity (MSI-H or TMB-H), yet clinical benefits from ICB were moderate. Here we aimed to identify the most appropriate drugs which can improve outcomes in GC. We firstly compared MSI-H and TMB-H GC samples with normal samples in TCGA-STAD cohort, respectively. After that, Connectivity Map database repurposed nine candidate drugs (CMap score < -90). Then, microtubule inhibitors (MTIs) were screened as the significant candidate drugs with their representative gene sets strongly enriched (p < 0.05) via GSEA. GDSC database validated higher activities of some MTIs in GC cells with MSI-H and TMB-H (p < 0.05). Furthermore, some MTIs activities were positively associated with mutant Dynein Cytoplasmic 1 Heavy Chain 1 (DYNC1H1) (p < 0.05) based on NCI-60 cancer cell line panel. DYNC1H1 was high frequently alteration in GC and was positively associated with TMB-H and MSI-H. Mutant DYNC1H1 may be accompanied with down-regulation of MTIs-related genes in GC or change the binding pocket to sensitize MTIs. Overall, this study suggested that some MTIs may be the best candidate drugs to treat GC with high immune activity, especially patients with DYNC1H1 mutated.

TGFB2 serves as a link between epithelial-mesenchymal transition and tumor mutation burden in gastric cancer.

Immune checkpoint blockade (ICB) has been a major breakthrough in various cancers including gastric cancer (GC), yet the clinical outcomes remain poor. Currently, epithelial-mesenchymal transition (EMT) has been reported to be associated with tumor mutational burden (TMB), which can cause lack of response to ICB. However, the underlying mechanism remains unknown. Members of the transforming growth factorß (TGFB) family are regarded as the main mediators of EMT, yet how TGFB2 drives EMT in GC is not fully understood. In this study, we found that overexpression of TGFB2 was correlated with poor prognosis in TGCA-STAD and four GEO GC datasets.Gene set enrichment analysis revealed that the EMT pathway was significantly enriched in the high TGFB2 expression group, whilst the TMB-related pathways including mismatch repair, base excision repair, and DNA replication were strongly enriched in the low expression group. Furthermore, EMT score analysis, WGCNA and functional analysis showed that TGFB2 was co-expressed with neurite-related pathways that might drive EMT. Also, CIBERSORT analysis revealed that tumor-infiltrating immune cells like T follicular helper cells might participate in the process of TGFB2 affecting TMB levels in GC. Moreover, in other various cancers, TGFB2 was also negatively correlated with TMB levels as well as ICB response. Overall, these results revealed that TGFB2 could play a vital role in linking EMT and TMB in GC, suggesting that TGFB2 may be a predictive therapeutic target for GC.

Prediction of KRAS, NRAS and BRAF status in colorectal cancer patients with liver metastasis using a deep artificial neural network based on radiomics and semantic features.

There is a critical need for development of improved methods capable of accurately predicting the RAS (KRAS and NRAS) and BRAF gene mutation status in patients with advanced colorectal cancer (CRC). The purpose of this study was to investigate whether radiomics and/or semantic features could improve the detection accuracy of RAS/BRAF gene mutation status in patients with colorectal liver metastasis (CRLM). In this retrospective study, 159 patients who had been diagnosed with CRLM in two hospitals were enrolled. All patients received lung and abdominal contrast-enhanced CT (CECT) scans prior to radiation therapy and chemotherapy. Semantic features were independently assessed by two radiologists. Radiomics features were extracted from the portal venous phase (PVP) of the CT scan for each patient. Seven machine learning algorithms were used to establish three scores based on the semantic, radiomics and the combination of both features. Two semantic and 851 radiomics features were used to predict the mutation status of RAS and BRAF using an artificial neural network method (ANN). This approach performed best out of the seven tested algorithms. We constructed three scores which were based on radiomics, semantic features and the combined scores. The combined score could distinguish between wild-type and mutant patients with an AUC of 0.95 in the primary cohort and 0.79 in the validation cohort. This study proved that the application of radiomics together with semantic features can improve non-invasive assessment of the gene mutation status of RAS (KRAS and NRAS) and BRAF in CRLM.