Faculty, Staff and Student Publications
Publication Date
7-1-2022
Journal
JCO Clinical Cancer Informatics
Abstract
Purpose: Advances in biological measurement technologies are enabling large-scale studies of patient cohorts across multiple omics platforms. Holistic analysis of these data can generate actionable insights for translational research and necessitate new approaches for data integration and mining.
Methods: We present a novel approach for integrating data across platforms on the basis of the shared nearest neighbors algorithm and use it to create a network of multiplatform data from the immunogenomic profiling of non-small-cell lung cancer project.
Results: Benchmarking demonstrates that the shared nearest neighbors-based network approach outperforms a traditional gene-gene network in capturing established interactions while providing new ones on the basis of the interplay between measurements from different platforms. When used to examine patient characteristics of interest, our approach provided signatures associated with and new leads related to recurrence and TP53 oncogenotype.
Conclusion: The network developed offers an unprecedented, holistic view into immunogenomic profiling of non-small-cell lung cancer, which can be explored through the accompanying interactive browser that we built.
Keywords
Carcinoma, Non-Small-Cell Lung, Cluster Analysis, Gene Expression Profiling, Humans, Lung Neoplasms, Software
DOI
10.1200/CCI.22.00040
PMID
35944232
PMCID
PMC9470146
PubMedCentral® Posted Date
8-9-2022
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Included in
Bioinformatics Commons, Biomedical Informatics Commons, Medical Sciences Commons, Oncology Commons