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The Cancer Surfaceome Atlas integrates genomic, functional and drug response data to identify actionable targets

Nature Cancer volume 2, pages 1406–1422 (2021)Cite this article

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Abstract

Cell-surface proteins (SPs) are a rich source of immune and targeted therapies. By systematically integrating single-cell and bulk genomics, functional studies and target actionability, in the present study we comprehensively identify and annotate genes encoding SPs (GESPs) pan-cancer. We characterize GESP expression patterns, recurrent genomic alterations, essentiality, receptor–ligand interactions and therapeutic potential. We also find that mRNA expression of GESPs is cancer-type specific and positively correlates with protein expression, and that certain GESP subgroups function as common or specific essential genes for tumor cell growth. We also predict receptor–ligand interactions substantially deregulated in cancer and, using systems biology approaches, we identify cancer-specific GESPs with therapeutic potential. We have made this resource available through the Cancer Surfaceome Atlas (http://fcgportal.org/TCSA) within the Functional Cancer Genome data portal.

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Fig. 1: Definition of the human surfaceome on a genome-wide scale.
Fig. 2: Expression of GESPs across healthy normal tissues and primary tumor specimens.
Fig. 3: Identification of GESPs that are specifically expressed in cancers.
Fig. 4: Evaluation of GESP combinations for logic-gated CAR-T design.
Fig. 5: Characterization of recurrent genomic alterations of GESPs across cancers.
Fig. 6: Characterization of receptor–ligand interactions of the GESPs in cancers.
Fig. 7: Characterization of mIAMs in cancers.
Fig. 8: Evaluation of GESPs as therapeutic targets in anticancer drug development.

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Data availability

The present study is based on genomic profiles generated by TCGA project, which was supported by the NCI and the National Human Genome Research Institute (http://cancergenome.nih.gov). TCGA profiling data are publicly available through TCGA data portal (https://tcga-data.nci.nih.gov/tcga), the Genomic Data Commons portal (GDC, https://gdc-portal.nci.nih.gov), the GDAC Firehose of the Broad Institute (http://gdac.broadinstitute.org), the UCSC Toil RNAseq Recompute Compendium (https://xenabrowser.net/datapages/?hub=https://toil.xenahubs.net:443), TCGA Multi-Center Mutation Calling in Multiple Cancers (MC3) project (https://doi.org/10.7303/syn7214402) and TumorFusions data portal (http://tumorfusions.org/). Proteomics profiles were generated by the NCI’s CPTAC (https://proteomics.cancer.gov/programs/cptac). The CPTAC profiling data are publicly available through the CPTAC data portal (https://cptac-data-portal.georgetown.edu). CRISPR–Cas9 screening profiles in human cancer cell lines are publicly available through the DepMap portal (https://depmap.org/portal) and the Score projects (https://doi.org/10.6084/m9.figshare.c.5289226.v1). ScRNA-seq data are available through http://blueprint.lambrechtslab.org (breast invasive carcinoma, colon adenocarcinoma and ovarian serous cystadenocarcinoma), http://ureca-singlecell.kr (bladder urothelial carcinoma), https://bigd.big.ac.cn/bioproject/browse/PRJCA001063 (pancreatic adenocarcinoma), https://dna-discovery.stanford.edu/research/datasets (follicular lymphoma, and stomach adenocarcinoma), https://science.sciencemag.org/highwire/filestream/713964/field_highwire_adjunct_files/6/aat1699_DataS1.gz.zip (kidney renal clear cell carcinoma) and Gene Expression Omnibus (accession nos. GSE125449, GSE131907, GSE131928 and GSE139829) (cholangiocarcinoma and liver hepatocellular carcinoma, lung adenocarcinoma, glioblastoma multiforme and uveal melanoma), respectively. The data generated by the present study are publicly available through the FCG data portal (http://fcgportal.org/fcgtcsa). All other data supporting the findings of the present study are available from the corresponding author on reasonable request. Source data are provided with this paper.

Code availability

The code for analysis of TCSA is available at https://github.com/fcgportal/TCSA.

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Acknowledgements

The present study was supported, in whole or in part, by the grants from the Pennsylvania Department of Health, Harry Fields Professorship and Abramson Cancer Center. L.Z. was supported by the Basser Center for BRCA and US National Institutes for Health (NIH) grants (nos. R01CA142776, R01CA190415, R01CA225929, R01CA262070, P50CA083638 and P50CA174523). R.H.V. was supported by NIH grants (nos. P01CA210944 and R01CA229803). X.H. was supported by the Ovarian Cancer Research Alliance. X.H. and Y.Z. were supported by the Foundation for Women’s Cancer. Support of the core facilities was provided by an NIH Cancer Centre support grant (no. P30CA016520) to Abramson Cancer Center.

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Author notes

  1. These authors contributed equally: Zhongyi Hu, Jiao Yuan.

Authors and Affiliations

  1. Center for Research on Reproduction & Women’s Health, University of Pennsylvania, Philadelphia, PA, USA

    Zhongyi Hu, Jiao Yuan, Junjie Jiang, Youyou Zhang, Tianli Zhang, Mu Xu, Xiaowen Hu & Lin Zhang

  2. Department of Obstetrics and Gynecology, University of Pennsylvania, Philadelphia, PA, USA

    Zhongyi Hu, Jiao Yuan, Junjie Jiang, Youyou Zhang, Janos L. Tanyi, Xiaowen Hu & Lin Zhang

  3. Division of Hematology, Department of Internal Medicine, Ohio State University, Columbus, OH, USA

    Meixiao Long

  4. Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA

    Yi Fan

  5. Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Kathleen T. Montone

  6. Bioscience, Research and Early Development, Oncology R&D, AstraZeneca, Waltham, MA, USA

    Omid Tavana & Ho Man Chan

  7. Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, USA

    Robert H. Vonderheide & Lin Zhang

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Contributions

Z.H., J.Y., X.H., R.H.V. and L.Z. conceived and designed the research. Z.H. and J.Y. performed the computational analysis and statistical computations. M.L., J.J., Y.Z., T.Z., M.X., F.Y. J.L.T., K.T.M., O.T. and H.M.C. performed raw data collection, dataset integration and general discussion on genomics, immunology, cancer pathology and drug discovery. Z.H., J.Y., X.H., R.H.V. and L.Z. wrote the paper.

Corresponding authors

Correspondence to Xiaowen Hu, Robert H. Vonderheide or Lin Zhang.

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Competing interests

L.Z. and X.H. report having received research funding from AstraZeneca, Bristol-Myers Squibb/Celgene and Prelude Therapeutics. R.H.V. is an inventor on a licensed patent relating to cancer cellular immunotherapy and receives royalties from Children’s Hospital Boston for a licensed research-only monoclonal antibody. O.T. and H.M.C. are employees of AstraZeneca. The remaining authors declare no competing interests.

Additional information

Peer review information Nature Cancer thanks Francesco Iorio and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Tissue specificity of GESPs across normal tissues.

a, Percentages of genes which were detectable (median FPKM value >1) by RNA-seq analysis in 0–6, 7–23, and 24–30 tissue types. Red: GESPs; and gray: non-GESPs. b, The percentages of genes detectable in 0–6, 7–23, and 24–30 tissue types, stratified by subcellular location of gene products. c, Bar plot (left) and bubble plot (right) show enrichment of tissue type-specific genes in the corresponding subgroups based on subcellular location of gene products. P-values were calculated by two-sided Fisher’s exact test. Purple, enriched; green, depleted. The size of the bubble: absolute value of log2(odds ratio).

Source data

Extended Data Fig. 2 GESPs specifically express in distinct cell populations in tumor microenvironment.

a, Bubble plots show expression levels and percentages of the cells expressing ERBB3, EGFR, MET, PDGFRB, KDR, CTLA4, CSF1R, or IL2RB in each cell population across 13 cancer types. Bubble size: percentage of positive cells; intensity of color: expression level. b, Violin plots show gene expression levels of ERBB3, EGFR, MET, PDGFRB, KDR, CTLA4, CSF1R, and IL2RB in each cell population at single cell. Each plot presents expression level in one cell.

Extended Data Fig. 3 Distribution of correlation coefficient between protein and mRNA expression levels.

The empirical null distribution for correlation of mRNA and protein generated by permutating samples is shown for comparison (all p-values < 2.2 × 10–16, two-sided Wilcoxon rank-sum test).

Extended Data Fig. 4 Characteristic of caGESPs stratified by specificity score.

Up panel: pie charts showing percentages of caGESPs in each tier stratified by specificity score. All caGESPs have specificity score ≥ 3. Bottom panel: dot plot showing the relative contribution of each algorithm to identification of caGESPs stratified by specificity score. Size, weighted vote; color intensity, fractional vote.

Source data

Extended Data Fig. 5 Somatic copy number alterations of the GESPs across cancers.

a, The workflow of somatic copy number alteration analysis. Four criteria were used to identify the putative cancer-causing GESPs driven by SCNAs in each cancer type. b and c, Bubble plot shows the SCNA G-scores, which consider both the amplitudes of the aberrations and the frequencies of their occurrence across samples, of the putative cancer-causing GESPs driven by SCNAs in each cancer type. b, copy number gain; c, copy number loss. The size of the bubble: G-score; red: gain; blue: loss. Pubtator scores, which represent the number of publications for a given gene and were retrieved from Pubtator database, are shown next to G-score plot. Green: 1–150 (understudied genes); Red: >150. Target development levels of each gene, which were retrieved from PHAROS database, are shown in the left. Red: Tclin; blue: TChem; green: Tbio; grey: Tdark. Genes are ordered according to overall G-score (from largest values to smallest values). Top 100 GESPs with highest overall G-score were shown.

Extended Data Fig. 6 Somatic mutations of the GESPs across cancers.

a, The workflow of recurrent somatic mutation analysis. Five complementary methods were integrated to identify the putative cancer-causing GESPs driven by mutatons in each cancer type. b, The bubble plot shows the mutation frequencies and mutation indexes of the putative cancer-causing GESPs driven by somatic mutations in each cancer type. The size of the bubble: mutation frequency; intensity of color: mutation index. c, The bubble plot shows the frequencies of hotspot mutation of GESPs in each cancer type. The size of the bubble: hotspot mutation frequency. The locations of hotspot mutated regions were retrieved from cancerhotspots.org. Pubtator scores, which represent the number of publications for a given gene and were retrieved from Pubtator database, are shown the next to bubble plot. Green: 1–150 (understudied genes); Red: >150. Target development levels of each gene, which were retrieved from PHAROS database, are shown in the left. Red: Tclin; blue: TChem; green: Tbio; grey: Tdark. Genes are ordered according to overall M-score (from largest values to smallest values). Top 100 GESPs with highest overall M-score were shown.

Extended Data Fig. 7 Transcript fusions of the GESPs across cancers.

a, Summary of the GESP transcript fusion events across cancers. The size of the bubble: number of the GESP transcript fusion events across 33 cancer types. Pubtator scores, which represent the number of publications for a given gene and were retrieved from Pubtator database, are shown the next to bubble plot. Green: 1–150 (understudied genes); Red: >150. Target development levels of each gene, which were retrieved from PHAROS database, are shown in the left. Red: Tclin; blue: TChem; green: Tbio; grey: Tdark. Genes are ordered according to the overall number of the fusion events (from largest values to smallest values). Top 86 GESPs with highest number of fusion events (≥12) were shown. b, Summary of the GESP transcript fusion pairs across cancers. The size of the bubble: number of the GESP transcript fusion pairs across 33 cancer types. Fusion pairs are ordered according to the overall recurrent pairs number (from largest values to smallest values). Top 86 GESP transcript fusion pairs were shown.

Extended Data Fig. 8 Characterization of dependencies of the GESPs in cancer cell growth.

a, Proportional doughnut graph showing the frequency of common essential and strongly selective genes among GESPs (outer layer) and non-GESPs (inner layer). b, Summary of enrichment for essential genes (common essential and strongly selective) in the corresponding subgroups based on subcellular locations. Bar plot (left) and bubble plot (right) show the odds ratios on a log scale for each subgroup. Purple and green bars indicate that essential genes are enriched and depleted in the corresponding subgroups, respectively. The color intensity of the bars and bubbles indicates the enrichment significance calculated by two-sided Fisher’s exact test. c, Summary of association between mRNA expression levels and dependencies for essential GESPs. The x-axis represents the effect size of each gene. Positive effect size values represent higher dependency in cells expressing higher level of mRNA. The y-axis represents the negative logarithm (base 10) of the p-values from the Bioconductor Limma package. Benjamini-Hochberg (BH) method was used to adjust the p-values. Each circle corresponds to a GESP with size proportional to overall G-score (gain). Red circles represent GESPs whose dependencies are significantly and positively correlated with their mRNA expression levels. GESPs which are recurrently amplified in tumors and whose dependencies are significantly and positively correlated with both copy number and mRNA expression levels are outlined with black border. d, Association between mRNA expression levels and dependencies for EGFR (left) and ERBB2 (right). Red dots represent cells with gene copy number amplification. Density plots of gene expression and gene dependencies are stratified by gene amplification status.

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Extended Data Fig. 9 Characterization of membrane-bound immunological accessory molecules (mIAMs) in cancers.

a, Mosaic plot shows the classification of mIAMs based on their expression patterns across cancer cell lines from non-hematological malignancies. b, Circos plot shows the number of mIAMs-associated interactions between cell types across 13 cancer types. Paired cell types with significant cell-cell interactions identified by CellPhoneDB were connected by lines. The width of the lines indicates normalized number of mIAMs-associated interactions between two cell types.

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Hu, Z., Yuan, J., Long, M. et al. The Cancer Surfaceome Atlas integrates genomic, functional and drug response data to identify actionable targets. Nat Cancer 2, 1406–1422 (2021). https://doi.org/10.1038/s43018-021-00282-w

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