Integrative analysis of prognosis data on multiple cancer subtypes

Jin Liu; Jian Huang; Yawei Zhang; Qing Lan; Nathaniel Rothman; Tongzhang Zheng; Shuangge Ma

doi:10.1111/biom.12177

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Integrative analysis of prognosis data on multiple cancer subtypes

Journal article

Peer reviewed

Integrative analysis of prognosis data on multiple cancer subtypes

Jin Liu, Jian Huang, Yawei Zhang, Qing Lan, Nathaniel Rothman, Tongzhang Zheng and Shuangge Ma

Biometrics, Vol.70(3), pp.480-488

09/2014

DOI: 10.1111/biom.12177

PMCID: PMC4209207

PMID: 24766212

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Abstract

In cancer research, profiling studies have been extensively conducted, searching for genes/SNPs associated with prognosis. Cancer is diverse. Examining the similarity and difference in the genetic basis of multiple subtypes of the same cancer can lead to a better understanding of their connections and distinctions. Classic meta-analysis methods analyze each subtype separately and then compare analysis results across subtypes. Integrative analysis methods, in contrast, analyze the raw data on multiple subtypes simultaneously and can outperform meta-analysis methods. In this study, prognosis data on multiple subtypes of the same cancer are analyzed. An AFT (accelerated failure time) model is adopted to describe survival. The genetic basis of multiple subtypes is described using the heterogeneity model, which allows a gene/SNP to be associated with prognosis of some subtypes but not others. A compound penalization method is developed to identify genes that contain important SNPs associated with prognosis. The proposed method has an intuitive formulation and is realized using an iterative algorithm. Asymptotic properties are rigorously established. Simulation shows that the proposed method has satisfactory performance and outperforms a penalization-based meta-analysis method and a regularized thresholding method. An NHL (non-Hodgkin lymphoma) prognosis study with SNP measurements is analyzed. Genes associated with the three major subtypes, namely DLBCL, FL, and CLL/SLL, are identified. The proposed method identifies genes that are different from alternatives and have important implications and satisfactory prediction performance.

Data Interpretation, Statistical

Genetic Markers - genetics

Genetic Predisposition to Disease - epidemiology

Genetic Predisposition to Disease - genetics

Humans

Meta-Analysis as Topic

Neoplasms - genetics

Neoplasms - mortality

Polymorphism, Single Nucleotide - genetics

Prevalence

Prognosis

Reproducibility of Results

Risk Assessment - methods

Sensitivity and Specificity

Survival Analysis

Systems Integration

Details

Title: Subtitle: Integrative analysis of prognosis data on multiple cancer subtypes
Creators: Jin Liu - Division of Epidemiology and Biostatistics, UIC School of Public Health, Chicago, Illinois, U.S.A.
Jian Huang - University of Iowa
Yawei Zhang - Yale University
Qing Lan - Division of Cancer Epidemiology and Genetics, NCI, NIH, Bethesda, Maryland, U.S.A.
Nathaniel Rothman - Division of Cancer Epidemiology and Genetics, NCI, NIH, Bethesda, Maryland, U.S.A.
Tongzhang Zheng - Yale University
Shuangge Ma - Yale University
Resource Type: Journal article
Publication Details: Biometrics, Vol.70(3), pp.480-488
DOI: 10.1111/biom.12177
PMID: 24766212
PMCID: PMC4209207
NLM abbreviation: Biometrics
ISSN: 0006-341X
eISSN: 1541-0420
Grant note: R01 CA142774 / NCI NIH HHS CA142774 / NCI NIH HHS R21 CA165923 / NCI NIH HHS R01 CA152301 / NCI NIH HHS CA152301 / NCI NIH HHS CA165923 / NCI NIH HHS
Language: English
Date published: 09/2014
Academic Unit: Statistics and Actuarial Science
Record Identifier: 9984257634302771

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