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Protein-structure-guided discovery of functional mutations across 19 cancer types

Nature Genetics volume 48, pages 827–837 (2016)Cite this article

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A Corrigendum to this article was published on 27 July 2017

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Abstract

Local concentrations of mutations are well known in human cancers. However, their three-dimensional spatial relationships in the encoded protein have yet to be systematically explored. We developed a computational tool, HotSpot3D, to identify such spatial hotspots (clusters) and to interpret the potential function of variants within them. We applied HotSpot3D to >4,400 TCGA tumors across 19 cancer types, discovering >6,000 intra- and intermolecular clusters, some of which showed tumor and/or tissue specificity. In addition, we identified 369 rare mutations in genes including TP53, PTEN, VHL, EGFR, and FBXW7 and 99 medium-recurrence mutations in genes such as RUNX1, MTOR, CA3, PI3, and PTPN11, all mapping within clusters having potential functional implications. As a proof of concept, we validated our predictions in EGFR using high-throughput phosphorylation data and cell-line-based experimental evaluation. Finally, mutation–drug cluster and network analysis predicted over 800 promising candidates for druggable mutations, raising new possibilities for designing personalized treatments for patients carrying specific mutations.

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Figure 1: HotSpot3D workflow, robustness simulations, and comparison to SpacePAC.
Figure 2: Significant spatial clusters.
Figure 3: Cancer type specificity of intramolecular and intermolecular clusters.
Figure 4: Intramolecular and intermolecular clusters with unique hotspot variants and new variants.
Figure 5: Polar plots showing discovery of rare and medium-recurrence functional variants in intramolecular and intermolecular clusters.
Figure 6: Functional assessment using phosphorylation data and experimental validation.
Figure 7: Variant–drug interaction heat maps and structures.

Change history

  • 20 March 2017

    In the version of this article initially published, residue R384 was incorrectly highlighted in the protein model depicted in Figure 7e. The correct residue is R394. The error has been corrected in the HTML and PDF versions of the article.

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Acknowledgements

We thank R. Friesel (Maine Medical Center Research Institute) for NIH3T3 clone 2.2 cells. We thank K. Johnson, C. Kandoth, M. Bharadwaj, and K. Huang for suggestions on data analysis. We also thank M. Xie, R. Jayasinghe, and H. Greulich for suggestions on experiments. This work was supported by National Cancer Institute grants R01CA180006 and R01CA178383 and National Human Genome Research Institute grant U01HG006517 to L.D. F.C. is supported in part by US Department of Defense grant PC130118 (W81XWH-14-1-0458) and National Institute of Diabetes and Digestive and Kidney Diseases grant R01DK087960. M.H.B. is in part supported by the Precision Medicine Pathway at the Washington University School of Medicine.

Author information

Author notes

  1. Beifang Niu

    Present address: Present address: Department of High-Performance Computing Technology and Application Development, Computer Network Information Center, Chinese Academy of Sciences, Beijing, China.,

  2. Beifang Niu, Adam D Scott and Sohini Sengupta: These authors contributed equally to this work.

Authors and Affiliations

  1. McDonnell Genome Institute, Washington University, St. Louis, Missouri, USA

    Beifang Niu, Adam D Scott, Sohini Sengupta, Matthew H Bailey, Prag Batra, Jie Ning, Matthew A Wyczalkowski, Wen-Wei Liang, Qunyuan Zhang, Michael D McLellan, Sam Q Sun, Carolyn Lou, Kai Ye, R Jay Mashl, John Wallis, Michael C Wendl & Li Ding

  2. Division of Oncology, Department of Medicine, Washington University, St. Louis, Missouri, USA

    Adam D Scott, Sohini Sengupta, Matthew H Bailey, Matthew A Wyczalkowski, Wen-Wei Liang, Sam Q Sun, Carolyn Lou, R Jay Mashl, Michael C Wendl & Li Ding

  3. Division of Nephrology, Department of Medicine, Washington University, St. Louis, Missouri, USA

    Jie Ning, Piyush Tripathi, Feng Chen & Li Ding

  4. Department of Genetics, Washington University, St. Louis, Missouri, USA

    Qunyuan Zhang, Kai Ye & Michael C Wendl

  5. Department of Mathematics, Washington University, St. Louis, Missouri, USA

    Michael C Wendl

  6. Siteman Cancer Center, Washington University, St. Louis, Missouri, USA

    Feng Chen & Li Ding

  7. Department of Cell Biology and Physiology, Washington University, St. Louis, Missouri, USA

    Feng Chen

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Contributions

L.D. and F.C. designed and supervised research. B.N., A.D.S., S.S., J.N., M.H.B., P.B., J.W., M.D.M., P.T., C.L., K.Y., S.Q.S., W.-W.L., F.C., and L.D. analyzed the data. M.C.W., B.N., A.D.S., and Q.Z. performed statistical analysis. M.A.W., B.N., A.D.S., S.S., and M.H.B. prepared figures and tables. B.N., A.D.S., S.S., J.W., and R.J.M. contributed to HotSpot3D code. L.D., F.C., B.N., A.D.S., S.S., and M.H.B. wrote the manuscript. F.C., M.C.W., A.D.S., S.S., and L.D. revised the manuscript.

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Correspondence to Feng Chen or Li Ding.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Clustered mutations of oncogenes and tumor suppressors.

For each oncogene (red dots) and tumor suppressor (blue dots), the number of mutations found in an intramolecular cluster is shown with the number of total mutations. Labeled genes correspond to those from either category that had at least half of all mutations found in a cluster.

Supplementary Figure 2 Cancer type specificities of clusters and three-dimensional protein structures.

(a) Intramolecular plot of the top three MTOR clusters anchored around C1483 (purple), F1888 (blue), and T1977 (green). Connections link the significant interacting pairs in lung (LUSC and LUAD), endometrial (UCEC), and kidney (KIRC and KIRP) cancers lying within a geodesic cluster radius of 10 Å from the centroid. Bubble position in the individual tracks indicates mutations along the primary (linear) protein sequence, and bubble size corresponds to sample count for each mutation. Gray shading of MTOR indicates sections currently lacking structure information in PDB. All residues in the three clusters (including cancer types not shown above) are highlighted on the three-dimensional structure model. (b) Intermolecular plot for two PIK3CA–PIK3R1 clusters centered at PIK3CA N345 (green) and PIK3CA E545 (yellow). Residues in the cluster (including cancer types not shown above) are depicted in spatial arrangement on the ribbon structure model (below).

Supplementary Figure 3 Drug–mutation clusters involving one drug.

All dasatinib drug–mutation clusters are shown that involve ABL1, BMX, and BTK. Dasatinib (green) is the centroid of each cluster, and distances to each residue where mutations occurred are shown in the radial plot (left) for ABL1 (red), BMX (orange), and BTK (purple). The outer ring segments show the linear protein sequence of each gene, and the inner ring segments show the regions containing mutations. Structures are shown (from left to right) for ABL1, BTK, and BMX.

Supplementary Figure 4 HotSpot3D online visualization portal.

(a) A screenshot of the online visualization portal with a pair of mutations between VHL and TCEB1 shown. (b) Mutations from the ASB9, SOCS4, TCEB1, and VHL intermolecular cluster are shown for a structure of TCEB1 (purple) and VHL (green). Y79 in TCEB1 was recently validated as disrupting interaction with VHL in KIRC cases.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–4 and Supplementary Note. (PDF 932 kb)

Supplementary Table 1

List of curated cancer-related genes. (XLSX 41 kb)

Supplementary Table 2

Gene, pair, and cluster counts at varying proximity-pair significance levels and by cancer gene category. (XLSX 40 kb)

Supplementary Table 3

Intramolecular clusters with annotations. (XLSX 1082 kb)

Supplementary Table 4

Intermolecular clusters with annotations. (XLSX 158 kb)

Supplementary Table 5

Cluster closeness and number of mutations from oncogene clusters. (XLSX 43 kb)

Supplementary Table 6

Cluster closeness and number of mutations from tumor-suppressor clusters. (XLSX 47 kb)

Supplementary Table 7

Oncogene intramolecular clusters with protein domain annotation. (XLSX 71 kb)

Supplementary Table 8

Tumor-suppressor intramolecular clusters with protein domain annotation. (XLSX 80 kb)

Supplementary Table 9

Intramolecular clusters with at least 50% specificity to one cancer type. (XLSX 47 kb)

Supplementary Table 10

Intermolecular clusters with at least 50% specificity to one cancer type. (XLSX 48 kb)

Supplementary Table 11

Experimental validation for predicted functional rare driver mutations. (XLSX 26 kb)

Supplementary Table 12

Intramolecular clusters with hotspot residues and potential novel functional mutations. (XLSX 27 kb)

Supplementary Table 13

Intermolecular clusters with hotspot residues and potential novel functional mutations. (XLSX 49 kb)

Supplementary Table 14

Intramolecular clusters with medium-recurrence novel functional mutations. (XLSX 26 kb)

Supplementary Table 15

Intermolecular clusters with medium-recurrence novel functional mutations. (XLSX 50 kb)

Supplementary Table 16

Singletons In RBX1–CUL1–GLMN cluster. (XLSX 38 kb)

Supplementary Table 17

Quantitative image analysis of intensities from immunoblots. (XLSX 47 kb)

Supplementary Table 18

Drug–mutation clusters with annotations (Cc > 2.5 in blue). (XLS 124 kb)

Supplementary Table 19

HGNC gene families found in drug–mutation clusters. (XLSX 42 kb)

Supplementary Table 20

NIH drug classes observed in drug–mutation clusters. (XLSX 24 kb)

Supplementary Table 21

DrugBank drug classes observed in drug–mutation clusters. (XLSX 34 kb)

Supplementary Table 22

Prioritized putative functional variants in rank order by closeness centrality. (XLSX 44 kb)

Supplementary Table 23

Sample association table of three HUGO genes and UniProt IDs, PDB IDs, and transcript IDs. (XLSX 25 kb)

Supplementary Table 24

Somatic mutations from 19 TCGA cancer types annotated for all transcripts with the minimum columns necessary from a mutation allele format (MAF) to run HotSpot3D. (XLS 223472 kb)

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Niu, B., Scott, A., Sengupta, S. et al. Protein-structure-guided discovery of functional mutations across 19 cancer types. Nat Genet 48, 827–837 (2016). https://doi.org/10.1038/ng.3586

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