Clustered mutations of oncogenes and tumor suppressors.

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1 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.

2 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).

3 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.

4 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.

5 Supplementary Note 1) Performance assessment and comparison to existing tools We evaluated HotSpot3D clustering performance on 50 replicated trials of mutation datasets at 20%, 40%, 60%, and 80% of the full Pan-Cancer mutation set, with mutations for each sample chosen randomly. We observed close to linear reductions in the numbers of clusters relative to the percentage of variants removed (Figure 1b). Mutation-drug clusters decline more slowly than inter- and intra-mutation clusters because the drugs themselves were not down-sampled like the mutations. Linearity suggests that connectivity is relatively evenly distributed and that the algorithm does not experience any catastrophic failures related to data abundance. Incidentally, extrapolation of these curves suggests additional clusters remain to be discovered as additional data accumulate. We also examined the cluster mass (number of mutations or drugs within a cluster) distributions for each dataset size (Figure 1c), where we again observed a general decline, as expected. Smaller cluster masses show faster decline due to the relatively greater importance of each individual member residue. These tests suggest that HotSpot3D is stable and robust. We also sought to evaluate differences with other algorithms in clustering and discovery power for novel mutations. We chose 33 random structures involving cancer genes from a list of 624 cancer genes 1 (Supplementary Table 1) and, using the TCGA 19 cancer mutation data set, ran SpacePAC and HotSpot3D for each structure. We configured HotSpot3D to give as impartial of a comparison to SpacePAC as possible (Online Methods), with results summarized in Figure 1d for significant clusters (P <0.05). Of the 33 structures, 32 had significant HotSpot3D clusters (TP53 had two insignificant clusters in HotSpot3D, and only single residue clusters in SpacePAC). HotSpot3D identified 263 unique residues among 85 clusters versus 105 unique residues in 53 clusters found by SpacePAC. Over half of the SpacePAC clusters (32 clusters) are composed of a single residue, which could likely have been found by primary sequence clustering methods, independent of protein structure. There are 9 structures on which SpacePAC found clusters with at least two residues and, of the 5 structures that had no HotSpot3D cluster, just one had nonsingleton residue clusters. There are 10 structures on which HotSpot3D found clusters, but SpacePAC did not. Finally, while SpacePAC has a hard limit of 3 clusters, HotSpot3D identified more than 3 clusters on 8 structures, demonstrating that larger cluster censuses can occur within tertiary protein structures. Importantly, the clustering objective in HotSpot3D frees the discovery space of pre-defined limitations, for example in numbers of clusters or spherical cluster shapes. SpacePAC is not readily automated, nor is it designed for analyzing large numbers of protein structures or interfaces among quaternary structures. The comparison suggests HotSpot3D is a useful advancement for mutation cluster analysis.

6 2) Intra- and inter-mutation clusters across 19 cancer types We also computed cluster conservation scores (Online Methods) to evaluate whether clusters occur in functionally important/conserved regions. Most clusters (4,083 out of 5,822 intra-molecular clusters) show high conservation (above 0.95), with a significant difference in conservation from mutations not found in clusters (P < 2.2e-16). The difference in cluster conservation between oncogene and TSGs in the clusters with highest cluster closeness (38 clusters) is not significant (P 0.10), suggesting that recurrently mutated clusters are in functionally relevant and conserved regions without regard to gene s specific roles (TSG vs oncogene). 3) Significant mutation clusters with cancer type specificity We identified residues Leu62, Gly63, Glu84, Val85, Arg108, Arg222, Arg252, Phe254, Asp256, Cys264, Ala289, His304 in the extracellular region of EGFR (specific to LGG/GBM) that likely play a role in ligandindependent activation of its extracellular region, as well as residues Phe712, Gly721, Lys747_Glu749, Val769, Ile789, Thr790, Arg831, Arg832, Leu833, Ala839, Leu858, Leu861 (specific to LUAD/LUSC) that play a role in activation of its kinase domain. Importantly, all mutations in these two EGFR clusters collectively contribute to the cancer specificity not just one hotspot residue. We also performed comparative structural analysis of mutations from intra-molecular MTOR and intermolecular PIK3CA/PIK3R1 clusters. MTOR is significantly mutated in renal cell carcinoma 2,3. Three intramolecular clusters with centroids at Cys1483, Phe1888, and Thr1977 exhibited cluster closeness scores within the top 10% (Supplementary Fig. 2a). One contains 4 unique mutations (p.ala1459pro, p.leu1460pro, p.cys1483phe, and p.cys1483tyr) that are highly specific to KIRC. All 3 MTOR clusters collectively represent 50% of all KIRC mutations in the protein. Also, we find enrichment of UCEC mutations in the clusters (19%) that center around Phe1888 (p.phe1888val, p.phe1888ile, p.phe1888leu, p.glu1799lys) and Thr1977 (p.val2006leu, p.thr1977arg, p.thr1977lys, p.tyr1974cys, p.ser2013gly, p.ile1973phe, p.val2006ile, p.leu2230val). The Thr1977 cluster does not reside in one functional domain; rather, spatial mutations reside between and across protein domains (FRB and Kinase domains) (Supplementary Fig. 2a). HotSpot3D identified two separate intermolecular clusters on the PIK3CA/PIK3R1 complex (Supplementary Fig. 2b), which together link 46.3% of their mutations. It is well known that the mutation

7 profile of the PI3K-complex varies widely by cancer type, but nevertheless shares some common hotspots 4-6. We found that only brain tumors LGG and GBM share the mutation burden equally across 3D spatial links between PIK3CA/PIK3R1 (Supplementary Fig. 2b, yellow cluster). If mutations coalescing in threedimensional space have similar consequences for cancer progression, chance would suggest that the mutation burden would be shared roughly equally by both PIK3CA and PIK3R1. However, we found the burden to be largely on PIK3CA in BRCA, HNSC, and UCEC. This indicates selective pressures in these cancers are distinct from those in GBM and LGG (Supplementary Fig. 2b). Thus, HotSpot3D provides evidence for diverse roles of PI3K proteins across different tissues and cell types; however, larger sample sizes from different cancer types will be required to conclusively validate this observation. 4) Mutation-drug networks and clinical implications Of the 359 relevant genes, the top HGNC gene families (genenames.org/cgi-bin/genefamilies/), ranked by number of mutations in drug clusters, are clusters of differentiation (CD) molecules, receptor tyrosine kinases, nuclear hormone receptors, fibronectin type III domain containing, and immunoglobulin-like domain containing genes, at 8.8%, 7.8%, 4.7%, 2.9%, and 2.8%, respectively (Figure 6a and Supplementary Table 19). According to NIH drug name stems, the top five drug classes observed in the 394 clusters are anti-inflammatory agents (acetic acid derivatives; 33.2% of paired mutations), iodine-containing contrast media (11.1%), tyrosine kinase inhibitors (TKI, 5.2%), calcium metabolism regulators (1.9%), and antiasthmatics/antiallergics (1.7%) (Figure 6a and Supplementary Table 20). By DrugBank classifications, the top five classes are antineoplastic agents, dietary supplements, supplements, micronutrients, and vasodilator agents, respectively (Supplementary Table 21). SUPPLEMENTARY REFERENCES 1 Lu, C. et al. Patterns and functional implications of rare germline variants across 12 cancer types. Nature Communications 6 (2015). 2 Cancer Genome Atlas Research Network. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature 499, 43-49, doi: /nature12222 (2013). 3 Grabiner, B. C. et al. A diverse array of cancer-associated MTOR mutations are hyperactivating and can predict rapamycin sensitivity. Cancer Discov 4, , doi: / cd (2014). 4 Samuels, Y. et al. High frequency of mutations of the PIK3CA gene in human cancers. Science 304, 554, doi: /science (2004). 5 Weber, G. L., Parat, M. O., Binder, Z. A., Gallia, G. L. & Riggins, G. J. Abrogation of PIK3CA or PIK3R1 reduces proliferation, migration, and invasion in glioblastoma multiforme cells. Oncotarget 2, (2011). 6 Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455, , doi: /nature07385 (2008).