Multigene panel predicting survival of patients with colon cancer.
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ABSTRACT:
SUBMITTER: Li HD
PROVIDER: S-EPMC6909949 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Endometrial cancer (EC) is the most common gynecological malignancy in developed countries. The present study aimed to determine the frequency of germline pathogenic variants (PV) in patients with EC. In this multicenter retrospective cohort study, germline genetic testing (GGT) was performed in 527 patients with EC using a next generation sequencing panel targeting 226 genes, including 5 Lynch syndrome (LS) and 14 hereditary breast and ovarian cancer (HBOC) predisposition genes, and 207 candidate predisposition genes. Gene-level risks were calculated using 1,662 population-matched controls (PMCs). Patients were sub-categorized to fulfill GGT criteria for LS, HBOC, both or none. A total of 60 patients (11.4%) carried PV in LS (5.1%) and HBOC (6.6%) predisposition genes, including two carriers of double PV. PV in LS genes conferred a significantly higher EC risk [odds ratio (OR), 22.4; 95% CI, 7.8-64.3; P=1.8×10-17] than the most frequently altered HBOC genes BRCA1 (OR, 3.9; 95% CI, 1.6-9.5; P=0.001), BRCA2 (OR, 7.4; 95% CI, 1.9-28.9; P=0.002) and CHEK2 (OR, 3.2; 95% CI, 1.0-9.9; P=0.04). Furthermore, >6% of patients with EC not fulfilling LS or HBOC GGT indication criteria carried a PV in a clinically relevant gene. Carriers of PV in LS genes had a significantly lower age of EC onset than non-carriers (P=0.01). Another 11.0% of patients carried PV in a candidate gene (the most frequent were FANCA and MUTYH); however, their individual frequencies did not differ from PMCs (except for aggregated frequency of loss-of-function variants in POLE/POLD1; OR, 10.44; 95% CI, 1.1-100.5; P=0.012). The present study demonstrated the importance of GGT in patients with EC. The increased risk of EC of PV carriers in HBOC genes suggests that the diagnosis of EC should be included in the HBOC GGT criteria.
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Project description:Most of the proposed individual markers had limited clinical utility due to the inherent biological and genetic heterogeneity of gastric cancer. We aimed to build a new molecular-based model to predict prognosis in patients with gastric cancer. A total of 200 patients who underwent gastric resection for gastric cancer were divided into learning and validation cohorts using a table of random numbers in a 1:1 ratio. In the learning cohort, mRNA expression levels of 15 molecular markers in gastric tissues were analyzed and concordance index (C-index) values of all single and combinations of the 15 candidate markers for overall survival were calculated. The multigene expression panel was designed according to C-index values and the subpopulation index. Expression scores were determined with weighting according to the coefficient of each constituent. The reproducibility of the panel was evaluated in the validation cohort. C-index values of the 15 single candidate markers ranged from 0.506-0.653. Among 32,767 combinations, the optimal and balanced expression panel comprised four constituents (MAGED2, SYT8, BTG1, and FAM46) and the C-index value was 0.793. Using this panel, patients were provisionally categorized with scores of 1-3, and clearly stratified into favorable, intermediate, and poor overall survival groups. In the validation cohort, both overall and disease-free survival rates decreased incrementally with increasing expression scores. Multivariate analysis revealed that the expression score was an independent prognostic factor for overall survival after curative gastrectomy. We developed an integrated multigene expression panel that simply and accurately stratified risk of patients with gastric cancer.
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Project description:BACKGROUND The objective of the present study was to identify prognostication biomarkers in patients with cervical cancer. MATERIAL AND METHODS Survival related genes were identified in The Cancer Genome Atlas (TCGA) cervical cancer study, and they were included into an elastic net regularized Cox proportional hazards regression model (CoxPH). The genes that their coefficients that were not zero were combined to build a prognostication combination. The prognostication performance of the multigene combination was evaluated and validated using Kaplan-Meier curve and univariate and multivariable CoxPH model. Meanwhile, a nomogram was built to translate the multigene combination into clinical application. RESULTS There were 37 survival related genes identified, 9 of which were integrated to build a multigene combination. The area under the curve (AUC) of receiver operating characteristic (ROC) curve at 1-year, 3-year, 5-year, and 7-year in the training set were 0.757, 0.744, 0.799, and 0.854, respectively, and the multigene combination could stratify patients into significantly different prognostic groups (hazard ratio [HR]=0.2223, log-rank P<0.0001). Meanwhile, the corresponding AUCs in the test set was 0.767, 0.721, 0.735, and 0.703, respectively, and the multigene combination could classify patients into different risk groups (HR=0.3793, log-rank P=0.0021). The multigene combination could stratify patients with early stage and advanced stage into significantly different survival groups in the training set and test set. The prognostication performance of the multigene combination was better compared with 3 existing prognostic signatures. Finally, a multigene containing nomogram was developed. CONCLUSIONS We developed a multigene combination which could be treated as an independent prognostic factor in cervical cancer and be translated into clinical application.
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Project description:Background: This study aimed to develop an effective prognostic nomogram for predicting non-metastatic colon cancer. Methods: The Surveillance, Epidemiology, and End Results program was utilized to analyze patients who underwent surgical therapy (25,350 for training, 10,860 for validation). Nomograms were created depending upon multivariate analysis in the training cohort and were compared to current American Joint Committee on Cancer (AJCC) classifications. Areas under the receiver-operating characteristic curves (AUCs), Akaike's information criterions (AICs), and calibration curves were used. The clinical benefit was measured using decision curve analyses (DCAs). The validation cohort was used to validate the results. Results: Nomogram 1 included age, gender, histological grade, T stage, number of retrieved lymph nodes, tumor size, and N stage. Nomogram 2 included age, gender, histological grade, T stage, number of retrieved lymph nodes, tumor size, and number of positive lymph nodes. The prognostic discrimination of nomogram 1 (AUC, 0.729, 95% CI, 0.723-0.736) was better than that of nomogram 2 (AUC, 0.704, 95% CI, 0.698-0.710, p < 0.001) in five-year overall survival in the training cohort. Nomogram 1 (AIC, 137,319) also showed superior model-fitting compared to nomogram 2 (AIC, 137,453). Similarity, nomogram 1 was better than the AJCC 6th and 8th TNM classifications. DCA revealed that nomogram 1 had a superior net benefit than other models. These findings were validated using the validation cohort. Conclusions: The proposed nomogram 1 was a better prognostic prediction model with better discrimination and superior model-fitting for patients with non-metastatic colon cancer, which might prove to be clinically helpful.
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