Development and External Validation of Survival Prediction Model for Pancreatic Cancer Using Two Nationwide Databases: Surveillance, Epidemiology and End Results (SEER) and Korea Tumor Registry System-Biliary Pancreas (KOTUS-BP).

Background/Aims: Several prediction models for evaluating the prognosis of nonmetastatic resected pancreatic ductal adenocarcinoma (PDAC) have been developed, and their performances were reported to be superior to that of the 8th edition of the American Joint Committee on Cancer (AJCC) staging system. We developed a prediction model to evaluate the prognosis of resected PDAC and externally validated it with data from a nationwide Korean database. Methods: Data from the Surveillance, Epidemiology and End Results (SEER) database were utilized for model development, and data from the Korea Tumor Registry System-Biliary Pancreas (KOTUS-BP) database were used for external validation. Potential candidate variables for model development were age, sex, histologic differentiation, tumor location, adjuvant chemotherapy, and the AJCC 8th staging system T and N stages. For external validation, the concordance index (C-index) and time-dependent area under the receiver operating characteristic curve (AUC) were evaluated. Results: Between 2004 and 2016, data from 9,624 patients were utilized for model development, and data from 3,282 patients were used for external validation. In the multivariate Cox proportional hazard model, age, sex, tumor location, T and N stages, histologic differentiation, and adjuvant chemotherapy were independent prognostic factors for resected PDAC. After an exhaustive search and 10-fold cross validation, the best model was finally developed, which included all prognostic variables. The C-index, 1-year, 2-year, 3-year, and 5-year time-dependent AUCs were 0.628, 0.650, 0.665, 0.675, and 0.686, respectively. Conclusions: The survival prediction model for resected PDAC could provide quantitative survival probabilities with reliable performance. External validation studies with other nationwide databases are needed to evaluate the performance of this model.

HisCoM-PAGE: Hierarchical Structural Component Models for Pathway Analysis of Gene Expression Data.

Although there have been several analyses for identifying cancer-associated pathways, based on gene expression data, most of these are based on single pathway analyses, and thus do not consider correlations between pathways. In this paper, we propose a hierarchical structural component model for pathway analysis of gene expression data (HisCoM-PAGE), which accounts for the hierarchical structure of genes and pathways, as well as the correlations among pathways. Specifically, HisCoM-PAGE focuses on the survival phenotype and identifies its associated pathways. Moreover, its application to real biological data analysis of pancreatic cancer data demonstrated that HisCoM-PAGE could successfully identify pathways associated with pancreatic cancer prognosis. Simulation studies comparing the performance of HisCoM-PAGE with other competing methods such as Gene Set Enrichment Analysis (GSEA), Global Test, and Wald-type Test showed HisCoM-PAGE to have the highest power to detect causal pathways in most simulation scenarios.

HisCoM-PAGE: software for hierarchical structural component models for pathway analysis of gene expression data.

To identify pathways associated with survival phenotypes using gene expression data, we recently proposed the hierarchical structural component model for pathway analysis of gene expression data (HisCoM-PAGE) method. The HisCoM-PAGE software can consider hierarchical structural relationships between genes and pathways and analyze multiple pathways simultaneously. It can be applied to various types of gene expression data, such as microarray data or RNA sequencing data. We expect that the HisCoM-PAGE software will make our method more easily accessible to researchers who want to perform pathway analysis for survival times.

Ovarian Cancer Prognostic Prediction Model Using RNA Sequencing Data.

Ovarian cancer is one of the leading causes of cancer-related deaths in gynecologic malignancies. Over 70 % of ovarian cancer cases are high-grade serous ovarian cancers (HGSC) and have high death rates due to their resistance to chemotherapy. Despite advances in surgical and pharmaceutical therapies, overall survival rates are not good and accurate prediction of prognosis is not easy because of the highly heterogeneous nature of ovarian cancer. To improve patient's prognosis through proper treatment, we present a prognostic prediction model by integrating the high dimensional RNA sequencing data with their clinical data through the following steps: (1) gene filtration, (2) pre-screening, (3) gene marker selection (4) integrated study of selected gene markers and prediction model building. These steps of the prognostic prediction model can be applied to other types of cancer besides ovarian cancer.