TY - JOUR
T1 - Shared Nearest Neighbors Approach and Interactive Browser for Network Analysis of a Comprehensive Non-Small-Cell Lung Cancer Data Set
AU - ICON Team
AU - Schmidt, Stephanie T.
AU - Akhave, Neal
AU - Knightly, Ryan E.
AU - Reuben, Alexandre
AU - Vokes, Natalie
AU - Zhang, Jianhua
AU - Li, Jun
AU - Fujimoto, Junya
AU - Byers, Lauren A.
AU - Sanchez-Espiridion, Beatriz
AU - Diao, Lixia
AU - Wang, Jing
AU - Federico, Lorenzo
AU - Forget, Marie Andree
AU - McGrail, Daniel J.
AU - Weissferdt, Annikka
AU - Lin, Shiaw Yih
AU - Lee, Younghee
AU - Suzuki, Erika
AU - Kovacs, Jeffrey J.
AU - Behrens, Carmen
AU - Wistuba, Ignacio I.
AU - Futreal, Andrew
AU - Vaporciyan, Ara
AU - Sepesi, Boris
AU - Heymach, John V.
AU - Bernatchez, Chantale
AU - Haymaker, Cara
AU - Cascone, Tina
AU - Zhang, Jianjun
AU - Bristow, Christopher A.
AU - Heffernan, Timothy P.
AU - Negrao, Marcelo V.
AU - Gibbons, Don L.
PY - 2022/7/1
Y1 - 2022/7/1
N2 - 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.
AB - 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.
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U2 - 10.1200/CCI.22.00040
DO - 10.1200/CCI.22.00040
M3 - Article
C2 - 35944232
AN - SCOPUS:85135732997
SN - 2473-4276
VL - 6
SP - e2200040
JO - JCO Clinical Cancer Informatics
JF - JCO Clinical Cancer Informatics
ER -