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

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.

FIG 1.

ICON project. (A) Schematic of data collected at the time of surgery through the ICON project. Red denotes tumor samples, and white denotes adjacent uninvolved tissue samples. (B) Heatmap of measurements available in ICON for each platform (rows) across patients (columns). Colors indicate treatment received, stage, histology, and data availability. FC, flow cytometry; ICON, immunogenomic profiling of non–small-cell lung cancer; mIF, multiplex immunofluorescence; NSCLC, non–small-cell lung cancer; RNA-Seq, RNA sequencing; RPPA, reverse phase protein array; TCR-seq, T-cell receptor sequencing; WES, whole-exome sequencing.

FIG 2.

SNN drives integrated ICON data network. (A) Overlap score is determined by the fraction of shared top k correlates from RPPA, FC. (B) SNN matrix is compiled by performing scoring across all gene pairs for RPPA, FC. (C) Gene pairs meeting score threshold are used to construct the overall network. FC, flow cytometry; ICON, immunogenomic profiling of non–small-cell lung cancer; RPPA, reverse phase protein array; SNN, shared nearest neighbors.

FIG 3.

ICON data network combines SNN with RNA-based correlation edges. (A) Selection of SNN with RNA-based edges on the basis of improved performance over SNN, RNA alone. (B) Community detection via Infomap identifies 10 top-level modules and 93 mid-level modules with at least 10 genes. The modules can be examined in tandem with orthogonal data modalities and patient characteristics. (C) Overview of the ICON data network composition. For each top-level module, the most significant annotation (with specific database source in parentheses where applicable) of its mid-level modules is shown. Modules in gray are without annotations meeting FDR < 0.05. (D) Nodes balanced across source modalities. Overlaps in gene nodes from each modality shown along with representative summary annotations. (E) SNN modalities provide more edges per node. Overlaps in network edges from each modality shown. ECM, extracellular matrix; FC, flow cytometry; FDR, false discovery rate; ICON, immunogenomic profiling of non–small-cell lung cancer; KEGG, Kyoto Encyclopedia of Genes and Genomes; PID, Pathway Interaction Database; RPPA, reverse phase protein array; SNN, shared nearest neighbors.

FIG 4.

ICON data browser enables exploration of ICON data and network. (A) The network panel offers a view into the multilayered structures of constituent modules, pathway annotation results, and top correlates from the shared nearest neighbors-based network-building approach tied to module formation. (B) The plotting and modeling panel allows for a selected module's ssGSEA scores or any other measurement across the full ICON data set to be compared against categorical metadata or against a secondary continuous measurement. FDR, false discovery rate; ICON, immunogenomic profiling of non–small-cell lung cancer; KEGG, Kyoto Encyclopedia of Genes and Genomes; N/A, not available; PID, Pathway Interaction Database; RPPA, reverse phase protein array; ssGSEA, single sample gene set enrichment; TID, tumor identifer.

FIG 5.

Network mining identifies module strongly tied to patient recurrence. (A) Distribution of ssGSEA scores by patient response for the neoadjuvant-free cohort. (B) Distribution of ssGSEA scores by patient response for a subset of cohort with nonsquamous, stage II+ tumors. (C) Top pathways for genes driving annotation of the selected module with the false discovery rate < 0.25. (D) Correlation of module scores with the Morisita overlap index from T-cell receptor sequencing (ϱ = 0.53, P = 1.98e^–05). ssGSEA, single sample gene set enrichment.

FIG 6.

Network mining identifies the transfer RNA–focused module strongly associated with functionally significant TP53 mutations in LUSC. (A) Distribution of ssGSEA scores by TP53 status for LUSC tumors. (B) Correlation of module scores with the presence of malignant cells (count/mm²) in the tumor and stroma via multiplex immunofluorescence (ϱ = 0.55, P = 6.39e^–03). (C) Components of the identified module highlight strength of interaction with transfer RNA transcripts. LUSC, lung squamous cell carcinoma; ssGSEA, single sample gene set enrichment.