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Mapping the functional network of human cancer through machine learning and pan-cancer proteogenomics

Nature Cancer volume 6, pages 205–222 (2025) Cite this article

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Abstract

Large-scale omics profiling has uncovered a vast array of somatic mutations and cancer-associated proteins, posing substantial challenges for their functional interpretation. Here we present a network-based approach centered on FunMap, a pan-cancer functional network constructed using supervised machine learning on extensive proteomics and RNA sequencing data from 1,194 individuals spanning 11 cancer types. Comprising 10,525 protein-coding genes, FunMap connects functionally associated genes with unprecedented precision, surpassing traditional protein–protein interaction maps. Network analysis identifies functional protein modules, reveals a hierarchical structure linked to cancer hallmarks and clinical phenotypes, provides deeper insights into established cancer drivers and predicts functions for understudied cancer-associated proteins. Additionally, applying graph-neural-network-based deep learning to FunMap uncovers drivers with low mutation frequency. This study establishes FunMap as a powerful and unbiased tool for interpreting somatic mutations and understudied proteins, with broad implications for advancing cancer biology and informing therapeutic strategies.

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Fig. 1: Protein coexpression is a strong predictor of gene cofunctionality.
The alternative text for this image may have been generated using AI.
Fig. 2: FunMap has high functional relevance, deep proteome coverage and a scale-free, modular and small-world network topology.
The alternative text for this image may have been generated using AI.
Fig. 3: FunMap reveals known and previously unidentified dense modules associated with cancer biology and clinical phenotype.
The alternative text for this image may have been generated using AI.
Fig. 4: Hierarchical modular organization of FunMap statistically linked to cancer hallmarks.
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Fig. 5: In-depth analysis of selected FunMap branches and their clinical associations.
The alternative text for this image may have been generated using AI.
Fig. 6: Connecting somatic mutations to functional protein modules.
The alternative text for this image may have been generated using AI.
Fig. 7: FunMap predicts functions of understudied proteins.
The alternative text for this image may have been generated using AI.
Fig. 8: Discovery of cancer drivers with low mutation frequency using FunMap.
The alternative text for this image may have been generated using AI.

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Data availability

Proteomics and RNAseq data for the ten CPTAC cancer types were derived from the CPTAC pan-cancer study15 (https://proteomic.datacommons.cancer.gov/pdc/cptac-pancancer). Proteomics and RNAseq data for HCC were downloaded from the original publication55. The data tables derived from these resources and used as input for FunMap construction are available from Zenodo (https://doi.org/10.5281/zenodo.7948943)73. Derived feature data for XGBoost model training are available from Zenodo (https://doi.org/10.5281/zenodo.7949374)74. XGBoost prediction scores for all gene pairs are available from Zenodo (https://doi.org/10.5281/zenodo.10080763)75. The FunMap edge list, dense modules and hierarchical modules are available online (https://funmap.linkedomics.org/). The same website also provides visualization tools to explore the gene neighborhoods, dense modules and hierarchical organization of FunMap. Additionally, the FunMap network and modules were integrated into WebGestalt76 for enrichment analysis of user-provided gene lists. Cell line annotations and CRISPR KO dependency scores can be retrieved from the DepMap website (https://www.depmap.org). Other datasets used in the study included the gene cofunctionality gold standard derived from the Reactome pathway database12, ProHD12, BioPlex18, HuRI19, HI-Union19 and BioGRID20. Source data are provided with this paper.

Code availability

The FunMap Python package is fully open source and available for download from the Python Package Index (https://pypi.org/project/funmap). The source code is hosted on GitHub (https://github.com/bzhanglab/funmap). Other supporting software is available as follows: scikit-learn 1.3.2 (https://scikit-learn.org/stable/index.html), ICE 1.0.2 (http://ice.zhang-lab.org), NetSAM 1.44.0 (https://www.bioconductor.org/packages/release/bioc/html/NetSAM.html), WebGestaltR 0.4.6 (https://cran.r-project.org/web/packages/WebGestaltR/index.html) and pytorch_geometric 1.7.2 (https://github.com/pyg-team/pytorch_geometric).

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Acknowledgements

We gratefully acknowledge contributions from the CPTAC and its Pan-Cancer Analysis working group. This work was supported by National Institutes of Health grants from the National Cancer Institute (U24 CA210954, U24 CA271076, R01 CA245903 and U01 CA271247 to B.Z.), by the Cancer Prevention and Research Institute of Texas (CPRIT; award RR160027 to B.Z.) and by the McNair Medical Institute at The Robert and Janice McNair Foundation (to B.Z.). B.Z. is a CPRIT scholar in cancer research and a McNair scholar.

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Author notes

  1. These authors contributed equally: Zhiao Shi, Jonathan T. Lei.

Authors and Affiliations

  1. Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA

    Zhiao Shi, Jonathan T. Lei, John M. Elizarraras & Bing Zhang

  2. Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA

    Zhiao Shi, Jonathan T. Lei, John M. Elizarraras & Bing Zhang

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  1. Zhiao Shi
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Contributions

Conceptualization, B.Z.; methodology, Z.S. and B.Z.; formal analysis, Z.S. and J.T.L.; investigation, Z.S., J.T.L. and B.Z.; resources, Z.S. and J.M.E.; data curation, Z.S. and J.T.L.; writing—original draft, Z.S., J.T.L. and B.Z.; visualization, Z.S., J.T.L. and J.M.E.; supervision, B.Z.; funding acquisition, B.Z.

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Correspondence to Bing Zhang.

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Competing interests

B.Z. received research funding from AstraZeneca and consulting fees from Inotiv. The other authors declare no competing interests.

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Nature Cancer thanks Leeat Keren and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Quantification of inter-sample heterogeneity through gene-wise standard deviation.

A) Distributions of gene-wise standard deviations across individual datasets (n = 17,733 to 19,113 mRNAs and n = 7,961 to 11,815 proteins). For boxplots, centerline indicates the median, box limits indicate upper and lower quartiles, whiskers indicate the 1.5 interquartile range. B) Median values of the median standard deviations across various dataset groups. T: Tumor; N: Normal.

Source data

Extended Data Fig. 2 Breakdown of feature importance in the XGBoost model.

A) Barplot showing importance of individual features. B) Pie chart depicting aggregated importance by data and sample type pairs.

Source data

Extended Data Fig. 3 Characterization of dense modules.

A) Heatmap depicting log2 fold change (log2FC) of average protein abundance of dense modules (cliques) in tumor vs normal for each of the five cancer cohorts shown. All 78 cliques have concordant tumor over- or under-expression in all five cohorts (FDR < 0.01 in each cohort). Table shows the number and maximum number of overlapping edges with other networks as indicated. Gene ontology biological processes (GO_BP) indicates the top enriched term of a given clique (GO_BP_FDR). B-C) Tumor overexpressed, ECM-associated dense modules, Clique 96 (B) and Clique 54 (C). Edge color indicates lack of overlap in BioGRID, BioPlex, HI-union, STRING, and CORUM (pink) or overlap in any of these resources (gray). D-E) Boxplots comparing average protein abundance of Clique 96 (D) and Clique 54 (E) in tumor and normal samples demonstrating tumor overexpression in five cancer cohorts. Number of samples, n, are indicated in parenthesis. P-values determined by two-sided Wilcoxon rank-sum test. F-G) Kaplan-Meier plots depicting overall survival (OS) difference in patients from indicated cohorts stratified by median value of the average abundance of proteins in Clique 96 (F) and Clique 54 (G). Logrank p-values and hazard ratio (HR) shown with 95% confidence intervals derived from Cox-proportional hazard models. Significance is indicated as ****p < 0.0001. For boxplots, centerline indicates the median, box limits indicate upper and lower quartiles, whiskers indicate the 1.5 interquartile range, and number of samples per group indicated in parentheses.

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Extended Data Fig. 4 Connecting somatic mutations to functional protein modules.

A) Average pairwise Pearson’s correlation coefficient for genes in L2_M40 based on mRNA or protein data in different cancer types. B) Average pairwise Pearson’s correlation coefficient for genes in L3_M58 based on mRNA or protein data in different cancer types. C) Comparison of TP53 protein abundance (log2 MS1 intensity) in TP53 wildtype (wt) and mutant (mut) samples across 10 cancer types. Number of samples, n, are indicated in parenthesis. P-values were derived from two-sided Wilcoxon rank-sum test. Significance is indicated as *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, ns: not significant. For boxplots, centerline indicates the median, box limits indicate upper and lower quartiles, whiskers indicate the 1.5 interquartile range, and number of samples per group indicated in parentheses.

Source data

Extended Data Fig. 5 Illuminating understudied cancer proteins RBM34 and RBM12B.

A) Boxplots comparing protein abundance of RBM34 and RBM12B in tumor and normal samples demonstrating tumor over-expression in five cancer cohorts. Number of samples, n, are indicated in parenthesis. P-values determined by two-sided Wilcoxon rank-sum test. For boxplots, centerline indicates the median, box limits indicate upper and lower quartiles, whiskers indicate the 1.5 interquartile range, and number of samples per group indicated in parentheses. B) Barplots depicting frequency of somatic copy number and mutations in RBM34 and RBM12B from TCGA PanCancer Atlas Studies in cBioPortal. C-D) Network neighborhood of RBM34 (C) or RBM12B (D) with genes associated with the enriched GO terms highlighted.

Source data

Extended Data Fig. 6 Illuminating understudied cancer proteins CXorf38 and MAB21L4.

A) Boxplots comparing protein abundance of CXorf38 in tumor and normal samples demonstrating tumor over-expression in five cancer cohorts. Number of samples, n, are indicated in parenthesis. P-values determined by two-sided Wilcoxon rank-sum test. B) Relationship between protein abundance of CXorf38 and RNA-seq inferred ESTIMATE ImmunoScore in eight cancer types. P-values were derived from two-sided Spearman’s rank correlation. Shaded area depicts the 95% confidence interval. C) Single cell data from the Human Protein Atlas showing that CXorf38 is expressed across all cell types, but the highest expression occurs in immune cells. D) Boxplots comparing protein abundance of MAB21L4 in tumor and normal samples in five cancer cohorts. Number of samples, n, are indicated in parenthesis. P-values determined by two-sided Wilcoxon rank-sum test. Significance is indicated as *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, ns: not significant. For boxplots, centerline indicates the median, box limits indicate upper and lower quartiles, whiskers indicate the 1.5 interquartile range, and number of samples per group indicated in parentheses.

Source data

Extended Data Fig. 7 Graph neural network architecture for predicting cancer driver genes based on network topology and mutation data.

The model takes as input mutation data for genes represented in a feature matrix. Nodes in the graph correspond to genes, where pink nodes are known positive driver genes, orange nodes are hidden positive genes, and gray nodes are unlabeled genes. Both the node features and network topology are processed through hidden layers with ReLU activations. The output layer predicts gene classifications, with red nodes indicating predicted positive driver genes and blue nodes indicating predicted negative genes.

Supplementary information

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Supplementary Tables 1–7 (download XLSX )

Supplementary Table 1. RNA and protein expression datasets. Supplementary Table 2. FunMap edges. Supplementary Table 3. FunMap dense modules and their characterization. Supplementary Table 4. FunMap hierarchical modules and their characterization. Supplementary Table 5. Importance of mutant genes in predicting module abundance. Supplementary Table 6. Dark gene characterization using FunMap. Supplementary Table 7. Driver gene prediction.

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Shi, Z., Lei, J.T., Elizarraras, J.M. et al. Mapping the functional network of human cancer through machine learning and pan-cancer proteogenomics. Nat Cancer 6, 205–222 (2025). https://doi.org/10.1038/s43018-024-00869-z

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  • DOI: https://doi.org/10.1038/s43018-024-00869-z

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