Shared Nearest Neighbors Approach and Interactive Browser for Network Analysis of a Comprehensive Non-Small-Cell Lung Cancer Data Set

ICON Team

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)e2200040
JournalJCO Clinical Cancer Informatics
Volume6
DOIs
StatePublished - Jul 1 2022

ASJC Scopus subject areas

  • Oncology
  • Health Informatics
  • Cancer Research

MD Anderson CCSG core facilities

  • Tissue Biospecimen and Pathology Resource
  • Functional Proteomics Reverse Phase Protein Array Core
  • Bioinformatics Shared Resource
  • ORION

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