Erica Ponzi1, Magne Thoresen2, Therese Haugdahl Nøst3, Kajsa Møllersen3. 1. Oslo Center for Biostatistics and Epidemiology, UiO, University of Oslo, Oslo, Norway. erica.ponzi@medisin.uio.no. 2. Oslo Center for Biostatistics and Epidemiology, UiO, University of Oslo, Oslo, Norway. 3. Department of Community Medicine, UiT, The Arctic University of Norway, Tromsö, Norway.
Abstract
BACKGROUND: Cancer genomic studies often include data collected from several omics platforms. Each omics data source contributes to the understanding of the underlying biological process via source specific ("individual") patterns of variability. At the same time, statistical associations and potential interactions among the different data sources can reveal signals from common biological processes that might not be identified by single source analyses. These common patterns of variability are referred to as "shared" or "joint". In this work, we show how the use of joint and individual components can lead to better predictive models, and to a deeper understanding of the biological process at hand. We identify joint and individual contributions of DNA methylation, miRNA and mRNA expression collected from blood samples in a lung cancer case-control study nested within the Norwegian Women and Cancer (NOWAC) cohort study, and we use such components to build prediction models for case-control and metastatic status. To assess the quality of predictions, we compare models based on simultaneous, integrative analysis of multi-source omics data to a standard non-integrative analysis of each single omics dataset, and to penalized regression models. Additionally, we apply the proposed approach to a breast cancer dataset from The Cancer Genome Atlas. RESULTS: Our results show how an integrative analysis that preserves both components of variation is more appropriate than standard multi-omics analyses that are not based on such a distinction. Both joint and individual components are shown to contribute to a better quality of model predictions, and facilitate the interpretation of the underlying biological processes in lung cancer development. CONCLUSIONS: In the presence of multiple omics data sources, we recommend the use of data integration techniques that preserve the joint and individual components across the omics sources. We show how the inclusion of such components increases the quality of model predictions of clinical outcomes.
BACKGROUND:Cancer genomic studies often include data collected from several omics platforms. Each omics data source contributes to the understanding of the underlying biological process via source specific ("individual") patterns of variability. At the same time, statistical associations and potential interactions among the different data sources can reveal signals from common biological processes that might not be identified by single source analyses. These common patterns of variability are referred to as "shared" or "joint". In this work, we show how the use of joint and individual components can lead to better predictive models, and to a deeper understanding of the biological process at hand. We identify joint and individual contributions of DNA methylation, miRNA and mRNA expression collected from blood samples in a lung cancer case-control study nested within the Norwegian Women and Cancer (NOWAC) cohort study, and we use such components to build prediction models for case-control and metastatic status. To assess the quality of predictions, we compare models based on simultaneous, integrative analysis of multi-source omics data to a standard non-integrative analysis of each single omics dataset, and to penalized regression models. Additionally, we apply the proposed approach to a breast cancer dataset from The Cancer Genome Atlas. RESULTS: Our results show how an integrative analysis that preserves both components of variation is more appropriate than standard multi-omics analyses that are not based on such a distinction. Both joint and individual components are shown to contribute to a better quality of model predictions, and facilitate the interpretation of the underlying biological processes in lung cancer development. CONCLUSIONS: In the presence of multiple omics data sources, we recommend the use of data integration techniques that preserve the joint and individual components across the omics sources. We show how the inclusion of such components increases the quality of model predictions of clinical outcomes.
Authors: Nozomu Yanaihara; Natasha Caplen; Elise Bowman; Masahiro Seike; Kensuke Kumamoto; Ming Yi; Robert M Stephens; Aikou Okamoto; Jun Yokota; Tadao Tanaka; George Adrian Calin; Chang-Gong Liu; Carlo M Croce; Curtis C Harris Journal: Cancer Cell Date: 2006-03 Impact factor: 31.743
Authors: Yan Zhang; Lutz P Breitling; Yesilda Balavarca; Bernd Holleczek; Ben Schöttker; Hermann Brenner Journal: Int J Cancer Date: 2016-08-22 Impact factor: 7.396
Authors: Gerwin Heller; Marlene Weinzierl; Christian Noll; Valerie Babinsky; Barbara Ziegler; Corinna Altenberger; Christoph Minichsdorfer; György Lang; Balazs Döme; Adelheid End-Pfützenreuter; Britt-Madeleine Arns; Yuliya Grin; Walter Klepetko; Christoph C Zielinski; Sabine Zöchbauer-Müller Journal: Clin Cancer Res Date: 2012-01-26 Impact factor: 12.531
Authors: Baoshan Ma; Elissa H Wilker; Saffron A G Willis-Owen; Hyang-Min Byun; Kenny C C Wong; Valeria Motta; Andrea A Baccarelli; Joel Schwartz; William O C M Cookson; Kamal Khabbaz; Murray A Mittleman; Miriam F Moffatt; Liming Liang Journal: Nucleic Acids Res Date: 2014-01-20 Impact factor: 16.971
Authors: Jessica Gliozzo; Marco Mesiti; Marco Notaro; Alessandro Petrini; Alex Patak; Antonio Puertas-Gallardo; Alberto Paccanaro; Giorgio Valentini; Elena Casiraghi Journal: Brief Bioinform Date: 2022-07-18 Impact factor: 13.994