MET amplification and exon 14 skipping are well known as oncogenic drivers in multiple cancer types. However, MET fusions in most cancer types are poorly defined. To explore the profile and analyze the characteristics of MET fusions, a large‐cohort study was conducted to screen MET fusions in clinical samples (n = 10 882) using DNA‐based NGS. A total of 37 potentially functional MET fusions containing the intact tyrosine kinase domain (TKD) of MET were identified in 36 samples. Further, 15 novel MET fusions were identified in five cancer types, and the incidence of novel MET fusions accounted for 40.5% (15/37). Brain cancer had the highest incidence of MET fusion, with PTPRZ1‐MET as the most common fusion (37.0%). All MET breakpoints in brain cancer (n = 27) were also located in intron 1, while those in lung cancer (n = 4) occurred in intron 1, intron 11, intron 14 and exon 14, respectively. The positive consistency of the common fusion group was 100% (11/11), while that of the rare fusion group was 53.8% (7/13). In conclusion, we provided a comprehensive genomic landscape of MET rearrangement and updated the MET fusions database for clinical test. In addition, we revealed that DNA‐based NGS might serve as the clinical test for common MET fusions; however, rare MET fusions must be validated by both DNA‐based NGS and RNA‐based NGS. Prospective trials are necessary to confirm the treatment efficacy of MET inhibitors.

Background Precision medicine highlights the importance of incorporating molecular genetic testing into standard clinical care. Next-generation sequencing can detect cancer-specific gene mutations, and molecular-targeted drugs can be designed to be effective for one or more specific gene mutations. For patients with special site metastases, it is particularly important to use appropriate samples for genetic profiling. This study aimed to determine whether genomic profiling using ASC and PE is effective in detecting genetic mutations. Methods Tissues, plasma, ascites (ASC) supernatants, and pleural effusion (PE) samples from gastrointestinal cancer patients with peritoneal metastasis and lung cancer patients with pleural metastasis were collected for comprehensive genomic profiling. The samples were subjected to next-generation sequencing using a panel of 59 or 1021 cancer-relevant genes panel. Results A total of 156 tissues, 188 plasma samples, 45 ASC supernatants, and 1 PE samples from 304 gastrointestinal cancer patients and 446 PE supernatants, 122 tissues, 389 plasma samples, and 45 PE sediments from 407 lung cancer patients were analyzed. The MSAF was significantly higher in ASC and PE supernatant than that in plasma ctDNA (50.00% vs. 3.00%, p < 0.0001 and 28.5% vs. 1.30%, p < 0.0001, respectively). The ASC supernatant had a higher actionable mutation rate and more actionable alterations than the plasma ctDNA in 26 paired samples. The PE supernatant had a higher total actionable mutation rate than plasma (80.3% vs. 48.4%, p < 0.05). The PE supernatant had a higher frequency of uncommon variations than the plasma regardless of distant organ metastasis. Conclusion ASC and PE supernatants could be better alternative samples when tumor tissues are not available, especially in patients with only peritoneal or pleural metastases.

Objective. To explore the clinical characteristics of metastatic colorectal cancer combined with gastrointestinal perforation and the prognostic value of circulating tumor DNA (ctDNA). Methods. A total of 97 patients with metastatic colorectal cancer and gastrointestinal perforation were enrolled as the research objects between February 2016 and January 2019. Their clinicopathological characteristics were statistically analyzed. Patients were divided into the death group (n = 78) and the survival group (n = 19) according to their survival status at 3 years after surgery. The ctDNA level between the two groups was compared. Also, its evaluation value on patient prognosis was analyzed. The survival time in patients with different levels of ctDNA was compared. Results. The clinical staging was stage T4 in patients with metastatic colorectal cancer combined with gastrointestinal perforation, including 70 cases (72.16%) aged ≥60 years and 27 cases (27.84%) <60 years. There were 61 males (62.89%) and 36 females (37.11%). There were 27 cases (27.84%) with primary site at left colon, 59 cases (60.82%) at right colon and 11 cases (11.34%) at rectum. There were 56 cases (57.73%) with number of metastatic organs ≥2 and 41 cases (42.27%) <2. There were 58 cases (59.79%) treated with VEGF inhibitor before perforation, 40 cases (41.24%) with lung metastasis, 72 cases (74.23%) with liver metastasis, 30 cases (30.93%) with pelvic metastasis, 24 cases (24.74%) with distant lymph node metastasis, 56 cases (57.73%) with obstruction, and 35 cases (36.08%) with diverticulum. According to survival status at 3 years after after surgery, patients were divided into the death group (n = 78) and the survival group (n = 19). The level of plasma ctDNA in the death group was higher than that in the survival group ( P < 0.05 ). The area under curve (AUC) of ctDNA for predicting survival of patients was 0.806. According to ctDNA expression, patients were divided into the high expression group (n = 57) and the low expression group (n = 40). The survival rate in the high expression group was lower than that in the low expression group (7.02% (4/57) vs 36.38% (15/40)) ( P < 0.001 ). The median survival time for the two groups was 18.20 and 28.10 months, respectively. Conclusion. Clinical characteristics of metastatic colorectal cancer combined with gastrointestinal perforation include elderly age, obstruction, and diverticulum. The expression of ctDNA has evaluation value for prognosis of patients.

Background: Distant metastasis is the major cause of treatment failure in locally advanced rectal cancer (LARC). Adjuvant chemotherapy (AC) is usually used for distant control. However, only certain subgroups of patients could benefit from AC. Our aim was to develop a radiomics model for the prediction of survival and chemotherapeutic benefits using pretreatment multiparameter MR images and clinicopathological features in patients with LARC. Methods: 186 consecutive patients with LARC underwent feature extraction from the whole tumor on T2-weighted (T2w), contrast enhanced T1-weighted (cT1w), and ADC images. Feature selection was based on feature stability and the Boruta algorithm. Radiomics signatures for predicting DFS (disease-free survival) were then generated using the selected features. Combining clinical risk factors, a radiomics nomogram was constructed using Cox proportional hazards regression model. The predictive performance was evaluated by Harrell's concordance indices (C-index) and time-independent receiver operating characteristic (ROC) analysis. Results: Four features were selected to construct the radiomics signature, which was significantly associated with DFS (P < 0.001). The radiomics nomogram, incorporating radiomics signature and two clinicopathological variables (pN and tumor differentiation), exhibited better prediction performance for DFS than the clinicopathological model, with C-index of 0.780 (95%CI, 0.718-0.843) and 0.803 (95%CI, 0.717-0.889) in the training and validation cohorts, respectively. The radiomics nomogram-defined high-risk group had a shorter DFS, DMFS, and OS than those in the low-risk group (all P <0.05). Further analysis showed that patients with higher nomogram-defined score exhibited a favorable response to AC while the low-risk could not. Conclusion: This study demonstrated that the newly developed pretreatment multiparameter MRI-based radiomics model could serve as a powerful predictor of prognosis, and may act as a potential indicator for guiding AC in patients with LARC.

Objectives Breast carcinoma (BRCA) has resulted in a huge health burden globally. N1-methyladenosine (m1A) RNA methylation has been proven to play key roles in tumorigenesis. Nevertheless, the function of m1A RNA methylation-related genes in BRCA is indistinct. Methods The RNA sequencing (RNA-seq), copy-number variation (CNV), single-nucleotide variant (SNV), and clinical data of BRCA were acquired via The Cancer Genome Atlas (TCGA) database. In addition, the GSE20685 dataset, the external validation set, was acquired from the Gene Expression Omnibus (GEO) database. 10 m1A RNA methylation regulators were obtained from the previous literature, and further analyzed through differential expression analysis by rank-sum test, mutation by SNV data, and mutual correlation by Pearson Correlation Analysis. Furthermore, the differentially expressed m1A-related genes were selected through overlapping m1A-related module genes obtained by weighted gene co-expression network analysis (WGCNA), differentially expressed genes (DEGs) in BRCA and DEGs between high- and low- m1A score subgroups. The m1A-related model genes in the risk signature were derived by univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. In addition, a nomogram was built through univariate and multivariate Cox analyses. After that, the immune infiltration between the high- and low-risk groups was investigated through ESTIMATE and CIBERSORT. Finally, the expression trends of model genes in clinical BRCA samples were further confirmed by quantitative real-time PCR (RT‒qPCR). Results Eighty-five differentially expressed m1A-related genes were obtained. Among them, six genes were selected as prognostic biomarkers to build the risk model. The validation results of the risk model showed that its prediction was reliable. In addition, Cox independent prognosis analysis revealed that age, risk score, and stage were independent prognostic factors for BRCA. Moreover, 13 types of immune cells were different between the high- and low-risk groups and the immune checkpoint molecules TIGIT, IDO1, LAG3, ICOS, PDCD1LG2, PDCD1, CD27, and CD274 were significantly different between the two risk groups. Ultimately, RT-qPCR results confirmed that the model genes MEOX1, COL17A1, FREM1, TNN, and SLIT3 were significantly up-regulated in BRCA tissues versus normal tissues. Conclusions An m1A RNA methylation regulator-related prognostic model was constructed, and a nomogram based on the prognostic model was constructed to provide a theoretical reference for individual counseling and clinical preventive intervention in BRCA.

Objective To explore the epidemiological characteristics of patients with lymphoepithelial carcinoma (LEC) of the head and neck and the prognostic factors. Methods We conducted a retrospective cohort study of cases of head and neck LEC retrieved from the Surveillance, Epidemiology and End Results database. Kaplan–Meier survival analysis and the log-rank test were employed to assess overall survival (OS) and cancer-specific survival (CSS). Univariate and multivariate analyses were used to construct Cox regression models. We established nomograms to predict OS and CSS among patients with nasopharyngeal LEC, who were divided into high- and low-risk groups based on the OS nomograms to compare the effects of treatment using the restricted mean survival time (RMST). Results The 5-year OS and CSS rates of the cohort were 70.8% and 74.8%, respectively. Advanced age, unmarried status, black race, distant metastasis, and the absence of surgical treatment were significantly associated with decreased survival rates. RMST did not differ between the combined treatment (radiotherapy and chemotherapy) and radiotherapy monotherapy groups, but chemotherapy alone displayed poor efficacy. Conclusions Head and neck LEC is associated with a favorable prognosis. Radiotherapy plays a significant role in managing patients with nasopharyngeal LEC, which is influenced by multiple prognostic factors.