Yang Li1, Shaodong Xu1, Shuangge Ma2, Mengyun Wu3. 1. Center for Applied Statistics, School of Statistics, Statistical Consulting Center, and RSS and China-Re Life Joint Lab on Public Health and Risk Management, Renmin University of China, Beijing 100872, China. 2. Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520, USA. 3. School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200433, China.
Abstract
MOTIVATION: Cancer genetic heterogeneity analysis has critical implications for tumour classification, response to therapy and choice of biomarkers to guide personalized cancer medicine. However, existing heterogeneity analysis based solely on molecular profiling data usually suffers from a lack of information and has limited effectiveness. Many biomedical and life sciences databases have accumulated a substantial volume of meaningful biological information. They can provide additional information beyond molecular profiling data, yet pose challenges arising from potential noise and uncertainty. RESULTS: In this study, we aim to develop a more effective heterogeneity analysis method with the help of prior information. A network-based penalization technique is proposed to innovatively incorporate a multi-view of prior information from multiple databases, which accommodates heterogeneity attributed to both differential genes and gene relationships. To account for the fact that the prior information might not be fully credible, we propose a weighted strategy, where the weight is determined dependent on the data and can ensure that the present model is not excessively disturbed by incorrect information. Simulation and analysis of The Cancer Genome Atlas glioblastoma multiforme data demonstrate the practical applicability of the proposed method. AVAILABILITY AND IMPLEMENTATION: R code implementing the proposed method is available at https://github.com/mengyunwu2020/PECM. The data that support the findings in this paper are openly available in TCGA (The Cancer Genome Atlas) at https://portal.gdc.cancer.gov/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Cancer genetic heterogeneity analysis has critical implications for tumour classification, response to therapy and choice of biomarkers to guide personalized cancer medicine. However, existing heterogeneity analysis based solely on molecular profiling data usually suffers from a lack of information and has limited effectiveness. Many biomedical and life sciences databases have accumulated a substantial volume of meaningful biological information. They can provide additional information beyond molecular profiling data, yet pose challenges arising from potential noise and uncertainty. RESULTS: In this study, we aim to develop a more effective heterogeneity analysis method with the help of prior information. A network-based penalization technique is proposed to innovatively incorporate a multi-view of prior information from multiple databases, which accommodates heterogeneity attributed to both differential genes and gene relationships. To account for the fact that the prior information might not be fully credible, we propose a weighted strategy, where the weight is determined dependent on the data and can ensure that the present model is not excessively disturbed by incorrect information. Simulation and analysis of The Cancer Genome Atlas glioblastoma multiforme data demonstrate the practical applicability of the proposed method. AVAILABILITY AND IMPLEMENTATION: R code implementing the proposed method is available at https://github.com/mengyunwu2020/PECM. The data that support the findings in this paper are openly available in TCGA (The Cancer Genome Atlas) at https://portal.gdc.cancer.gov/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Charles M Rudin; John T Poirier; Lauren Averett Byers; Caroline Dive; Afshin Dowlati; Julie George; John V Heymach; Jane E Johnson; Jonathan M Lehman; David MacPherson; Pierre P Massion; John D Minna; Trudy G Oliver; Vito Quaranta; Julien Sage; Roman K Thomas; Christopher R Vakoc; Adi F Gazdar Journal: Nat Rev Cancer Date: 2019-05 Impact factor: 60.716
Authors: Jeremy S Toma; Konstantina Karamboulas; Matthew J Carr; Adelaida Kolaj; Scott A Yuzwa; Neemat Mahmud; Mekayla A Storer; David R Kaplan; Freda D Miller Journal: eNeuro Date: 2020-06-12