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GPCR Signaling As a Paradigm

GPCR Signaling As a Paradigm

(2019) 38:6491–6506 https://doi.org/10.1038/s41388-019-0895-2

ARTICLE

Rare, functional, somatic variants in families linked to : GPCR signaling as a paradigm

1,2 3,4 3,4 5 1,2 Francesco Raimondi ● Asuka Inoue ● Francois M. N. Kadji ● Ni Shuai ● Juan-Carlos Gonzalez ● 1,2 6 6 5 3,4 7 Gurdeep Singh ● Alicia Alonso de la Vega ● Rocio Sotillo ● Bernd Fischer ● Junken Aoki ● J. Silvio Gutkind ● Robert B. Russell 1,2

Received: 24 September 2018 / Revised: 4 March 2019 / Accepted: 8 April 2019 / Published online: 23 July 2019 © The Author(s) 2019. This article is published with open access

Abstract Oncodriver genes are usually identified when recur in multiple tumours. Different drivers often converge in the activation or repression of key cancer-relevant pathways. However, as many pathways contain multiple members of the same gene family, individual mutations might be overlooked, as each family member would necessarily have a lower frequency and thus not identified as significant in any one-gene-at-a-time analysis. Here, we looked for mutated, functional sequence positions in gene families that were mutually exclusive (in patients) with another gene in the same pathway, which fi

1234567890();,: 1234567890();,: identi ed both known and new candidate oncodrivers. For instance, many inactivating mutations in multiple G- (particularly Gi/o) coupled receptors, are mutually exclusive with Gαs oncogenic activating mutations, both of which ultimately enhance cAMP signalling. By integrating transcriptomics and interaction data, we show that the Gs pathway is upregulated in multiple cancer types, even those lacking known GNAS activating mutations. This suggests that cancer cells may develop alternative strategies to activate adenylate cyclase signalling in multiple cancer types. Our study provides a mechanistic interpretation for several rare somatic mutations in multi-gene oncodrivers, and offers possible explanations for known and potential off-label cancer treatments, suggesting new therapeutic opportunities.

Introduction

Cancer genome sequencing projects have revealed a growing list of genes and mutations driving tumor initiation These authors contributed equally: Francesco Raimondi, Asuka Inoue and progression [1–5]. However, even when an oncodriver Deceased Bernd Fischer role is well established, it remains difficult to discriminate driver from passenger mutations, especially when they are Lead contact: Francesco Raimondi rare [6]. This task is more daunting when sparse somatic Supplementary information The online version of this article (https:// mutations affect genes not previously linked to cancer by doi.org/10.1038/s41388-019-0895-2) contains supplementary standard approaches based on positive selection. A recent material, which is available to authorized users.

* Francesco Raimondi 4 Advanced Research & Development Programs for Medical [email protected] Innovation (PRIME), Japan Agency for Medical Research and * Robert B. Russell Development (AMED), Chiyoda-ku, Tokyo 100-0004, Japan [email protected] 5 Computational Genome , German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany 1 BioQuant, Heidelberg University, Im Neuenheimer Feld 267, 6 Division of Molecular Thoracic Oncology, German Cancer 69120 Heidelberg, Germany Research Center (DKFZ), Translational Lung Research Center 2 Heidelberg University Biochemistry Centre (BZH), Im (TLRC), Member of the German Center for Lung Research (DZL), Neuenheimer Feld 328, 69120 Heidelberg, Germany 69120 Heidelberg, Germany 7 3 Graduate School of Pharmaceutical Science, Tohoku University, Moores Cancer Center, University of San Diego, San Diego, La Sendai 980-8578 Miyagi, Japan Jolla CA 92093, USA 6492 F. Raimondi et al. systematic survey of somatic mutations under positive families, we then sought protein alignment positions that selection estimated that nearly half of driving events are were mutually exclusive with regard to somatic, non- found in genes not yet linked to cancer [7]. With genome synonymous cancer variants (Methods; Fig. 1b), either in sequencing entering clinical practice, more powerful specific or across all of them (pan-cancer). We approaches able to identify rare driver mutations are considered 1.9 M simple somatic non-synonymous muta- therefore required. tions, from 414 different cancer subtypes (from 37 primary Analysis of mutated genes, in the context of pathways tissues and 49 histologies). A total of 794k mutations (41% and networks [8] or three-dimensional structures [5, 9], has of the total) affect 282k domain positions from 5.7k human aided the detection of driver variants, also illuminating protein families (92% of the total). We defined significantly mechanisms adopted by cancer cells for tumor growth and enriched domain positions according to a gene-level back- spread. For example, frequently mutated oncodrivers rarely ground model (to correct for genes with different mutational participate to the same interaction interface [10]: e.g., TP53 burden; Fig. S1 and Tables S1, S2), and retained pairs of is mutated in many cancers, but its most common interac- positions displaying significant enrichment in a pathway- tion partners are not. However, there are instances where level background model (Tables S3, S4; see Methods). We mutations in different parts of one pathway appear to be chose this model, similar to one used in another recent study responsible for a common cancer. For example, mutations [16], and not models designed to detect positive selection in affecting KRAS/NRAS and BRAF are often found mutually individual genes [3], as we were seeking positions across exclusively along the MAP cascade pathway, parti- families: using other models would have filtered out many cularly in , colorectal, lung and pancreatic can- rare variants that were precisely what we sought. Changing cers [11]. Analysis of cancer genomes has revealed many the parameters of the analysis (i.e., mutation thresholds, set examples of mutually exclusive events concurring with of pathways considered, background model) led only to specific cancer [12]. moderate changes in the number of identified positions. The Given the redundancy of many biological processes, number of dubious genes (i.e., those more tolerant to where multiple can modulate a common up- or mutations [17]) uncovered also does not vary using differ- downstream partner, it is possible that multiple genes could ent parameters and it is moreover always much lower than essentially replace mutations in a single common cancer when using the 1000 Genomes set (see Methods and gene. Indeed, several known oncodrivers have multiple Table S5). regulators. For example, the phosphoinositide 3-kinase It is possible that the mutually exclusive pairs we detect (PI3K) pathway, shows great redundancy in that down- arise because mutations in both partner proteins in one stream target PI3Ks can be activated by many upstream sample would simply not be tolerated in living cells. While signals transduced by and G-protein cou- we cannot rule this out in all instances, evidence that this is pled receptors [13]. not the case comes from the fact that we see co-occurrence Instances of this multi-gene oncodriver phenomenon of mutations, in at least one sample, for 56% of all hotspot would often be overlooked as mutations in multiple pairs (i.e., in addition to the observed exclusivity). This upstream genes necessarily have lower frequencies, and suggests that the mutations are at least, in principle, toler- thus would not be identified as statistically significant in any ated together. Moreover, few of the pairs we identified one-gene-at-a-time analysis. Here we searched for such involve interactions between well-established oncodrivers, examples, by grouping all genes into but most often involve one oncodriver and a larger family of families and looking for pairs of functionally linked families proteins. showing exclusivity of mutations in particular cancers (Fig. Our pan-cancer analysis revealed 414 significantly 1a). We identify several instances of this phenomenon, mutated (Binomial q < 0.1) and mutually exclusive involving both and tumor suppressors, including (Fischer’s exact q < 0.1) pairs, involving a total of 86 sparsely mutated, genes not previously linked to cancer. positions from 23 families in 55 pathways (Fig. 1c), affecting 9.1k (43% of the total) samples. The three most represented pathways were signalling, gen- Results eric , and GPCR downstream signalling (Fig. S2; Table S3). We found that domains with significant A network of mutually exclusive, variant-enriched mutated positions have similar fractions of highly conserved protein family positions in cancer positions compared to those with no mutations (p = 0.17; Table S6). Moreover, only a minority (22%) of the top 50 We defined a set of interacting gene families as those largest sequence families in the human proteome have at sharing a common protein domain [14], and residing in the least one significantly mutated positions, ruling out a bias same pathway (from Reactome; [15]). In each pair of owing to family size (see Table S6). Rare, functional, somatic variants in gene families linked to cancer genes: GPCR signaling as a paradigm 6493

The process of identifying variant-enriched, mutually compared to random selections [18] (Methods). Nearly half exclusive positions also highlights functional positions, as of mutually exclusive position pairs (171 out of 336 mat- they are enriched for sites that bind to other molecules ched to an interaction interface) have similar predicted 6494 F. Raimondi et al.

Fig. 1 a Multi-gene oncodriver hypothesis. b Analysis workflow. c ChIP-seq/ChIP-chip data (Methods; Fig. 2c). These include Network showing functionally related protein families with members growth promoting genes such as , RELA, and STAT3 fi in common Reactome pathways (nodes) displaying signi cantly (Fig. 2d). Remarkably, -fingers SP1, PRDM1, ZNF740 enriched, mutually exclusive mutated positions (edges) pan-cancer. Node diameter is proportional to the total number of nonsynonymous and YY1 all show overlaps of more than 20% with TP53 mutations (number of unique samples) for a family member; thicker target genes (Table S8). Several have already been linked to cyan borders indicate families where at least one highly conserved cancer; for example SP1 is a prognostic factor for lower fi position is signi cantly mutated. Inside each node, mutated members survival in gastric cancer [19], PRDM1 to be a tumor sup- of a given family are displayed, with a diameter proportional to the number of mutations. Edge thickness is proportional to average pressor in lymphomas [20] and YY1 as an initiator of shortest paths between the two families tumorigenesis in several malignancies [21]. Most of these relationships were statistically significant when considering the large pan-cancer dataset, though several are also seen in functional consequences in the of targeting protein, specific cancer tissues (Table S9), including, skin and large small-molecule or nucleic acid binding sites (Fig. S3). 9% intestine. (of 5188) gene-pairs share a specific protein and 39% share a small-molecule interaction partner, indicating a more Multiple GPCRs mutations are exclusive with G- precise functional overlap (Table S7, see below). proteins and other oncogenes The 1055 genes linked in the network (Fig. 1c), involve several instances of small (even single member) families, The other main component of the pan-cancer network (Fig. 1c, which include relations between well-known cancer genes. right) relates broadly to signal and contains several Not every known oncodriver is found, which is expected known oncogenes (e.g., Ras-family members, tyrosine and since the method is very restrictive: requiring gene family -threonine kinases, and G proteins). This also involves pairs each to have a specific position enriched in variants in several hundred GPCR genes that show positional exclusivity a mutually exclusive fashion within at least one common with G-protein hotspots. A major contributor to this is the DRY pathway. Nevertheless, some well-known examples are in (R3.50, superscript denotes the Ballesteros/Weinstein this network. For example, KRAS p.G12D with BRAF p. numbering [22], see Methods), which is the most significantly V600E, and AKT1 p.E17K with PIK3CA p.E545K. The enriched (q = 2×10−35; Table S1) position, and which shows approach also revealed candidate multi-gene oncodrivers, mutual exclusivity with recurrent mutations of the hetero- namely where positional variants scattered across a large trimeric Gα family catalytic switch I (SWI) arginine (mainly family show mutual exclusivity with positions in a single accounted by GNAS p.R201G.hfs2.2, superscript denotes the protein downstream. Common Gα numbering [23]; Fisher exact q < 0.001; Table S3). Mutations in multiple transcription factors are DRY is an important motif for Class A GPCR activation mutually exclusive with TP53, representing [24, 25], mediating intra-molecular polar contacts holding potential tumor suppressors receptors inactive until binding [25]. The arginine is recurrently mutated (Table S10), with a total of 94 class A A major part of the network (Fig. 1c) relates to tumor GPCRs having at least one somatic mutation at this arginine suppressor activities mediated by transcription factors, in 153 unique samples for a total of 189 non-synonymous particularly TP53. Mutations in several genes with tran- mutations (see Table S1). Among them, the most frequently scriptional regulatory functions (i.e., the left side of Fig. 1c) mutated are HCRTR2, GPR174, P2RY12 and LPAR4. The are mutually exclusive (e.g., zf-C2H2 or zf-C2H6 with majority of mutations at the G-alpha SWI Arginine position either or EGF domains). Several known oncodrivers (Table S11) are found in GNAS (109 samples) with others involved in this functional subnetwork are genes with found in GNAI2 (12), GNA13 (7), GNA15 (4) and GNAQ/11 known tumor suppressor properties, such as TP53, PTEN, (6). DRY arginine also displays mutual exclusivity with FBXW7, SMAD3 and SMAD4. positions in genes involved in GPCR-mediated downstream In addition, many of these relationships involve tran- signalling including many oncogenes: AKT1(2,3) p.E17K, scription factors with broadly similar functions, including PIK3CA p.E545K and p.E542K, RAC1 p. P29S, JAK2 p. zinc-fingers with positions that were found to be exclusive V617F. (Fig. 1c and Table S3). with TP53 counterparts. The majority of positions are Several other GPCR positions also show exclusivity, residues involved in DNA-binding in both families, where including P7.50 from another highly conserved NPxxY motif, the predicted effect [18] is a loss of binding, suggesting that which shows exclusivity with the DRY arginine itself (i.e., one could indeed replace the other functionally (Fig. 2a, b). intramolecular; Table S3); these motifs cooperate in In support of this, several zinc-finger proteins show simi- activation [25]. This proline, together with other positions on larities in transcriptional targets with TP53, as identified in the cytosolic side of the GPCR structure (Fig. 3b), also Rare, functional, somatic variants in gene families linked to cancer genes: GPCR signaling as a paradigm 6495

a)

TP53 p.R273G ZNF420 p.R234C

b) c) DNA/protein DNA/Zinc DNA Zinc

Overlap in targetted genes

d) 8 Most common ChIP-seq targets 7 6 5 4 3 2 1 0 S E 2 A 2 P1 C6 2 RA FE SP1 SP4 SP3 CF4 B FO P2A D U MYC CFL1 T CTCF RELA N EGR4 C THRA P SCYL1 FOSL1 STAT3 STAP2 SART1 PT FAP TO T POLR

Negative regulation of apoptotic process (GO) Positive regulation of proliferation (GO) Cellular Senescence (Reactome) Fig. 2 TP53/Zinc-finger mutually exclusive mutations: a 2D repre- Correlation between mutations predicted effect and sentation of patients (columns) affected by TP53 p.R273 mutations similarity of target genes. The upper half of the matrix shows the (blue) and zf-C2H2 r12 position (orange). Genes (rows) are sorted proportion of target genes (Jaccard score) shared by two transcription based on mutation frequency, showing the top 50 most mutated zinc factors. Only the top 13 genes more similar to TP53 are shown, along fingers. 3D cartoon representations of TP53 (PDB ID: 2AC0) and with their Jaccard scores (considering all the possible pairs). Circles in ZNF420 (PDB ID: 1MEY) highlighting the mutated as red the upper half of the matrix indicate overlap of predicted mutation sticks; Note that the structures are placed so they do not cover any non- (Mechismo) effects. Colors indicate the class of interactor affected and white portion of the plot (i.e., in blank parts of the plot). b Mechismo size is proportional to the number of mutually exclusive positions with network representation of predictions for the mutations in TP53 and the same predicted outcome. d Counts of the most common target zinc-fingers. Mutated genes are magenta; DNA is cyan. Red and green genes shared across 8 TP53/zinc-finger pairs with Reactome and gene- links indicate interaction disabling and enabling predicted effects. c ontology (GO) groups indicated below 6496 F. Raimondi et al.

Fig. 3 Class A GPCR and G-protein mutually exclusive mutations: a tagged transforming -α (AP-TGFα)-encoding plasmid as for Fig. 2a, but for GPCR and Gα mutations at either GPCR (DRY) together with an empty plasmid (Mock), WT GPCR-encoding plasmid R3.50 or Gα SWI arginine. Only the top 30 mutated genes are shown. (WT) or DRY-mutant GPCR-encoding plasmid (MT) treated with b GPCR (PDB: 3NYA) and c Gα (1AZT) significant positions indi- titrated ligands for 1 h while quantifiying AP-TGFα release into con- cated as spheres centred on Cα atoms and whose diameter is propor- ditioned media. Symbols and error bars represent mean and SEM, tional to the number of mutations. The right panel shows GPCRs respectively, of three to five independent experiments with each coupling preferences from IUPHAR (maroon and red indicate primary measured in triplicates. For MRGPRX1 and HCRTR2, symbols of MT and secondary coupling respectively). The lower panel shows co- overlap with Mock. Parameters from the concentration-response occurring mutations for the top 10 most mutated signalling onco- curves (EC50 and Emax) are listed in Table S15; e Kaplan-Meier drivers; d Loss of G-protein signalling activity in the DRY mutant curve showing survival analysis for patients affected by R3.50 muta- GPCRs. HEK293 cells transfected with the alkaline - tions (orange curves) in skin melanoma Rare, functional, somatic variants in gene families linked to cancer genes: GPCR signaling as a paradigm 6497 shows mutual exclusivity with the G-alpha SWI arginine DRY arginine mutations in seven representative receptors and positions from many other downstream oncogenes (selected according to the data in Fig. S5) and the pre- (Table S3). Moreover, we also found a tendency for mutual viously described AVPR2 mutations by a TGFα shedding exclusivity of GPCR DRY Arginine and G-alpha SWI assay [34], we find that these positions almost always lead to Arginine in specific tissues, including pancreas (q = 0.02; a loss of ligand-induced function (Fig. 3d, Fig. S8 and Table Table S4), large intestine (p = 0.02, q = 0.23), stomach (p S17). Moreover, while AVPR2 mutations, as expected [32], = 0.13, q = 1) and skin (p = 0.25, q = 1) (Fig. S5). led to constitutive activity, none of the representative The potential importance of these mutations is under- receptors with cancer mutations displayed constitutive acti- scored by their association with poorer survival, either in vation as reported by cAMP assays (Fig. S9). specific cancers (DRY Arginine in skin melanoma; Log- This seemingly counterintuitive result can be explained Rank p = 0.03; Cox’sHR= 1.16; Fig. 3e), or R4.40 muta- by the overall context of GPCR/G-protein mediated sig- tions in all cancers (LogRank p = 0.01; Cox’sHR= 1.03; nalling. We combined mutation, and Fig. S4; Tables S12–S14). GPCR/G-protein coupling data (i.e., which G-protein is The GPCR mutations we find in these cancer samples coupled to each GPCR) [35] to estimate the overall activ- differ greatly from naturally occurring variants in healthy ities of the four main G-protein classes in each cancer (Fig. individuals (Wilcoxon p = 0.001; see Fig. S6), which are 4a; Methods). This revealed that many cancers, beyond most often found in Olfactory receptors [26]. Nevertheless, those showing GNAS hotspot mutations, display a tendency despite the stringent background models adopted, we do see for widespread up-regulation of Gs activity over the other mutual exclusivities involving olfactory receptors (Fig. 1c). G-proteins, which is particularly evident for Gi/o which Among them is OR51E2 (Table S3), which is ectopically never prevails in any investigated cancer type (Fig. 4a, top). expressed in melanocytes [27] as well as in primary mela- Indeed, 71% of TCGA cancer types characterized by GNAS noma and melanoma metastasis [28] and whose activation activating mutations also show overall higher activity of the has been shown to elicit an onco-suppressive effect in Gs pathway, which is the most activated in 75% of the prostate carcinoma [29]. Thus, the emerging information considered cancer types (Fig. 4a). Notably, these include all suggests that at least a subset of these mutations might have malignancies of the gastro-intestinal tract (i.e., esophagus, consequences in cancers. stomach, liver, colon, rectum) present in the differential We also found exclusivity between G-alpha positions expression panel. More specifically, while Gs activity is and downstream signalling partners. In pancreatic tumors, mainly accounted by higher GNAS levels (Fig. 4b), lower for example, GNAS SWI arginine and p.V633M mutations Gi/o levels are seemingly caused by a diminished expression in the downstream adenylate cyclase ADCY8 are mutually of either Gi/o-proteins or their coupled receptors (Figs. S10, exclusive (Fig. S7 and Table S4) in addition to those S11), which are more frequently affected by deleterious affecting DRY Arginine of upstream GPCRs (Fig. S7 and mutations (i.e., stop gains, frameshifts or non-synonymous Table S4). In adenomas GNAS SWI arginine mutations at highly conserved domain positions; see and p.L207R mutations in the cAMP activated Methods) in multiple cancers (Fig. 4a; grey in middle PRKACA are mutually exclusive (Table S4). Remarkably, panel). Overall, Gi/o coupled receptors are the class hit by samples with these GNAS or PRKACA mutations do not the greatest number of deleterious mutations also when have any other known oncogene or signalling oncodriver considering all cancers (Fig. 4c). mutated (Table S16). This immediately suggests an explanation for our observation, as deactivating Gi/o signalling and activating Gs Widespread dysregulation of GPCR-mediated signalling would both lead to an increase in adenylate signaling in cancer cyclase activity and cAMP mediated signalling (Fig. 4e). Indeed, we found that cAMP assays for a selection of G-alpha SWI arginine mutations are usually oncogenic, representative Gi/o coupling GPCRs (UTS2R and leading to increased signaling [30]. The mutual exclusivity HCRTR2) showed that the DRY mutants poorly induced Gi would be tantalizingly explained by DRY mutations (or activation as measured by loss of inhibitory effect of ligand- other mutually exclusive GPCR positions) also being acti- induced cAMP luminescent signals (Fig. 4d, Table S18). vating. This is also an attractive idea as it is known that The much greater expression of GNAS relative to the other some mutations, particularly of the DRY motif (e.g., in genes in all cancers (Fig. 4b), would also imply ADRA1A [31] and AVPR2 [32]), can lead to constitutive that Gs signalling is prevailing (i.e., even in the absence of activation. However, we found that these positions almost any GNAS activation) and hence needs to be strictly always lead to a loss of function (Fig. 3d), as it is known to controlled. be most often the case for mutations to this arginine [33]. To understand more in details the functional con- Indeed, by experimentally testing the effects of observed sequences of DRY arginine mutations, we performed 6498 F. Raimondi et al.

a) b) Expression (pan-cancer) activity Normalized Normalized Fraction of Fraction sample mutated GPCR (by coupling) GPCR (by Median RPKM

s (G ) α q G (Gs) of Fraction sample mutated

c) Mutations in coupling groups d) UTS2R HCRTR2 1.2 1.2 Mock 1.0 1.0 WT MT 0.8 0.8

response 0.6 0.6

Mutations 0.4 0.4 cAMP Corrected fraction Corrected -9 -8 -7 -6 -9 -8 -7 0 -10 0 -10

(fold-luminescent change) 10 10 10 10 10 10 10 10 10 Urotensin II (M) Orexin-A (M)

e) Gi/o coupled Gs coupled Gi/o coupled Gs coupled Gi/o coupled Gs coupled GPCR GPCR GPCR GPCR GPCR GPCR

Adenylate Adenylate Adenylate cyclase cyclase cyclase

β α β α β α β α β α β α γ GDP γ GDP γ GDP γ GDP γ GDP γ GDP signal signal signal Gi/o Gs Gi/o Gs Gi/o Gs Normal situation Activating Gs mutations Loss of Gi/o receptor function

Fig. 4 Functional consequence of GPCR mutations: a integrative Loss of Gi activity in the DRY mutant GPCRs. HEK293 cells trans- analysis of G-protein activities, hotspot mutations and GPCR dele- fected with the cAMP biosensor-encoding plasmid and plasmid terious mutations in different TCGA cancer types. Top panel: G- together with an empty plasmid (Mock), WT GPCR-encoding plasmid protein activities estimated by combining G-proteins and receptors (WT) or DRY-mutant GPCR-encoding plasmid (MT) were loaded differential expression levels through coupling information; middle with D-luciferin for 2 h and treated with titrated ligands in the presence panel: fraction of samples with deleterious GPCR mutations (i.e., of 10 µM forskolin for 10 min. Luminescent signals were measured either highly conserved residues, stop gains or frameshifts); lower before and after ligand addition and data expressed as a change in panel: known activating GNAS (p.R201) and GNAQ (p.Q209) muta- luminescent counts. Symbols and error bars represent mean and SEM, tions. b Median of RPKM values of Gα subunits in 32 TCGA cancer respectively, of six to seven independent experiments with each types. c Number of unique samples (crimson) and fraction of the measured in duplicates; e cartoons of the Adenylate Cyclase pathway coupling group (grey) displaying deleterious mutations pan-cancer; d regulation summarising the mechanisms described in the text differential expression analysis of R3.50 mutations in cancer at least partial functional equivalence between these types where it was found at highest recurrence (i.e., skin, mutually exclusive mutation sets and moreover pointing to gastric and uterine; see Methods). In melanoma 31% (146 biological processes characteristic of more aggressive mel- out 462) of dysregulated genes in R3.50 mutated samples anomas (e.g., thickening, loss of inhibitory endopeptidase overlap with those of G-protein activating mutations activity [36], see Fig. S12a). Comparative differential (GNAQ p.Q209G.s3h2.3 and GNAI2 p.R179G.hfs2.2), indicating expression analysis of R3.50 mutations in multiple cancers Rare, functional, somatic variants in gene families linked to cancer genes: GPCR signaling as a paradigm 6499

(i.e., skin, gastric and uterine) revealed a more context improve the detection of deleterious variants of genetic specific downstream effect of these mutations, with only 15 diseases [45]. Here, we employed a similar approach to genes similarly dysregulated (Fig. S12b). Among these, we analyse non-synonymous mutations and, to further infer on found GNAS-AS1, the antisense RNA for the GNAS , their biological role, we pinpointed causal relationships by which has been reported to regulate its imprinting status systematically analysing their mutual exclusivity along [37], thus suggesting an additional layer of regulation of biological pathways. GNAS’s activity. Intriguingly, GNAS-AS1 downregulation Our pan-cancer network includes just 51 (5%) genes by methylation has been associated to colorectal tumor- from the Cancer Gene Census. Missing genes are from igenesis [38] as well as to susceptibility [39]. protein families lacking either individual mutation-enriched positions, or mutual exclusivity. The majority of the genes An integrated map for GPCR drugs (off-)label are not currently in the census, and could be new (usually prescription rare) oncodriver mutations. For example, a few zinc-finger genes have been classified as oncodrivers in the Census Activation of cAMP signalling via means other than by (e.g., ZNF311 [46]) and there is mounting evidence for their activating GNAS mutations suggests both an explanation for involvement in several cancer types, most often as tumor off-label drugs with potential actions against cancer, and the suppressors, by regulating the transcription of genes means to identify additional cancer targeting drugs (Table 1, important for tumor progression [46]. Table S19; Fig. S13). For instance propranolol, an antago- We propose that novel inhibitory mutations on Gi/o- nist of the GNAS coupled β2 andrenergic receptor coupled receptors may converge to produce a similar out- (ADRAB2), improves prognosis in thick skin melanoma come to GNAS activating mutations, resulting in increased [40] and abrogates EGFR inhibitor resistance acquisition in cAMP signalling. Moreover, these events appear to be part lung cancer [41]. This suggests that other such antagonists of a wider dysregulation of GPCR mediated signalling in could also prove useful, for example of several other multiple cancers (Fig. 4a), which we predict to lead to adrenergic receptors (Table 1). In contrast, agonists of Gi GNAS overactivity, owing to its much higher expression signalling could, in principle, produce similar effects, and than all other G-proteins (Fig. 4a, b). This is also supported interestingly, two drugs targeting Gi-receptors (Pasireotide by our analysis of non-synonymous mutations and copy targeting somatostatin receptors and analogs number variants (CNVs) in genes involved in cAMP sig- targeting ) are already in use based nalling (Fig. S14a). About a third of patients (9k of 28k) on antitumor effects (Table 1). These potential treatments have such mutations, and as expected activating mutations are likely only relevant when cAMP signalling either drives and/or CNV gains prevail in genes increasing cAMP (i.e., the cancer or where it is linked to therapy resistance, as in Gαs subunits and Adenylyl cyclases), with CNV losses [42], and where there is not overexpression of being more common in genes that lower cAMP (i.e., Gαi the GPCR of interest (which could indicate a different subunits and ), (Fig. S14b). Generally, mechanism; e.g., DRD2 in gastric cancers [43]). To aid the many cancers evolve towards higher cAMP by a variety of search for new therapeutic candidates, we integrated data on different mechanisms. GPCR coupling and known antagonists/agonists for all Several GPCRs are involved in cancer progression, coupling groups (Table 1, Fig. S13). metastasis and therapy resistance [47]. GPCRs and their cognate Gα proteins are extensively mutated in cancer samples, though the functional consequences are not always Discussion clear and appear to be context dependent. For example, it is well established that GNAS p.R201G.hfs2.2 (on SWI) and Understanding the role played by somatic mutations in GNAQ/11 p.Q209G.s3h2.3 (on SWII) activating mutations are cancer is critical for the interpretation of large datasets from oncogenic [30]. Recent experiments have begun to decipher high-throughput sequencing projects and the development the functional consequences of the GNAS p.R201C muta- of personalized therapies. Considering functional relation- tion in pancreatic ductal , where it mod- ships, or biological context, can help identify novel driver ulates KRAS p.G12V initiated neoplasia [48, 49]. We also mutations, even for less frequently mutated genes [44]. found SWI mutations in GNAI2 (p.R179), in skin melanoma Previous studies used protein family evolutionary relation- and lymphomas, where it has oncogenic effects by upre- ships to highlight positions significantly affected by somatic gulating ERK1/2 [50], and in GNA13 (p.R200; in bladder mutations [16] and suggested that sparse mutations affect- carcinoma), which is not yet fully understood. ing equivalent domain positions in known drivers might Functionally equivalent mutations on GPCRs have been display similar downstream consequences [16]. Similarly, seen to replace those in Gα proteins. For example, mutually the usage of protein domain information was shown to exclusive P2RY8, GNA13 and RHOA mutations have 6500 F. Raimondi et al.

Table 1 Gi/o-coupled receptor agonists and Gs-coupled receptor antagonists with known or putative anti-cancer activity GPCR Cancers Agonists Evidence for therapeutic benefit in cancer Indications

Gi/o-coupled receptors with high expression, with no change in expression in tumor compared to wild-type, and with approved agonists ADORA3 BLCA 2 Antiproliferative effects of adenosine or synthetic agonist in Coronary vasodilators, pharmacological stress testing melanoma, prostate, colon and liver carcinomas or lymphoma [70, 71] HNSC LUSC PRAD STAD HTR1D STAD 20 Serotonin analogues are inhibitors of breast cancer Agonists used for migrane treatment [72] S1PR1 ESCA 1 Fingolimod efficacy in in-vitro and in-vivo cancer models by Immuno-modulating in Multiple Sclerosis inhibition of sphingosine kinase 1 [73] + READ STAD SSTR1 ESCA 1 Pasireotide (somatostatin analogue) can inhibit non-functioning Treatment of Cushing’s disease pituitary adenomas and neuroendocrine tumors [74, 75] KIRP LIHC STAD UCEC Gs-coupled receptors with no significant under expression and with approved antagonists ADRA2A ESCA 11 None found Many indications for antagonists, including Parkinson’s, schizophrenia, psychosis, depression and erectile dysfunction HNSC LUSC READ STAD THCA UCEC ADRA2B KICH 13 None found As above KIRC UCEC ADRA2C BLCA 12 None found As above BRCA ESCA HNSC LUSC STAD UCEC ADRB1 ESCA 14 None found ADRB2 HNSC 15 Propranolol suppresses pancreatic and breast cancers invasion, Treatment of hypertension or irregular heart rate protects patients with skin melanoma from disease recurrence and death and avoids EGFR inhibitor resistance in lung cancer [40, 41, 76, 77] KIRC KIRP THCA CNR1 ESCA 1 Rimonabant inhibits human breast cancer [78] Anorectic antiobesity (drug withdrawn) THCA HTR7 BLCA 20 None found Many indications, including depression, psychosis and panic disorder

All receptors show mean expression (RPKM) values > = 100. For Gi/o-linked GPCRs we sought only those that showed no significant fold-change when comparing tumors to wild-types (i.e., as overexpression likely indicates an oncogenic activity); for Gs-linked GPCRs we included all that were not significantly under-expressed. Cancer types in italic are those where GNAS activating mutations or Gi/o GPCR down-regulation/ deactivation is observed. Agonist/antagonist classification has been derived from IUPHAR [35]. +Fingolimod (phosphorylated metabolite) is a functional antagonist for S1PR1. It first acts as an agonist, but induces degradation of S1PR1 Rare, functional, somatic variants in gene families linked to cancer genes: GPCR signaling as a paradigm 6501 similar inactivating effects in B-cell derived lymphomas types. We defined cancer types using the COSMIC classi- [51, 52]. Activating mutations of CYSLTR2 (p.L129Q) in fication system considering Primary tissue/Tissue sub-type1 uveal melanoma are mutually exclusive and functionally and Primary histology/Histology sub-type1 specifications. equivalent to mutations in GNAQ/11 (p.Q209) [53]. Com- We used the Pfam [14] database as it has the widest pensatory mutations with opposite functional outcomes coverage of sequence space and provides HMM profiles for have been reported in pituitary tumors, where the effect of easy alignment of query sequences to the pre-existing activating GNAS mutations can be replaced by inactivating alignments. We identified Pfam-A families within the map- mutations on Gi/o proteins [54] or on AIP [55]. ped protein sequences using HMMer [59], defining highly- Thus, widespread inactivating mutations in Gi/o-linked conserved positions as those with one amino-acid recurring GPCRs similarly suggests that their restraining activity on in 50% or more sequences. To identify each position within GNAS is lost and, as part of the tumorigenic process, cAMP the alignment, we employed the Pfam consecutive number- signalling is persistently activated and leads to tumor pro- ing scheme and labelled them using the amino acids letter gression. It has been shown that a cAMP signalling net- from the Hidden Markov Model (HMM) consensus sequence work, involving upstream GPCRs, is responsible of MAP (highly conserved residues are upper case). For GPCRs, we kinase inhibitors resistance in melanomas [42]. The obser- additionally labelled positions through the Ballesteros/ vation that DRY arginine mutations are associated to lower Weinstein scheme [22], using the consensus domain sec- survival in melanoma, suggests that these mutation events ondary structure from the HMM model to number residues in might indeed concur to these mechanisms. helix regions (see Table S20). For G-proteins, we employed Knowledge of rare oncodrivers, including several iden- the established Common Gα numbering [23]. tified here, can impact cancer diagnostics and treatment. For example, of the 26k samples across all cancers lacking Variant enrichment common TP53 mutations, 2.2k (7.8%) show mutations in the Zinc-finger genes and positions mentioned above, thus To define domain positions within protein family align- potentially providing a molecular mechanism for tumor ments enriched in variants we computed the log of the suppressor activities that might have been overlooked. More observed number of variants divided by the expected (log tantalizingly, observation of deactivating mutations in Gi/o odds). We computed the expected number by multiplying coupled GPCRs could indicate suitability for treatment of to the frequency of total alleles (4.6 alleles) in the total pro- be developed Gs inhibitors, or readily available inhibitors of teome length (>11 million amino acids) times the number of Gs coupled receptors, including the recently proposed off domain instances in the proteome. We assessed the statis- label use of beta-blockers (e.g., propranolol) or by tical significance of domain position enrichments, either in exploiting the wealth of agonists activating Gi/o coupled individual cancer types or pan-cancer, through a one-tailed receptors. The search for these in the future must naturally binomial test, computing the prior probability by randomly consider the complete context of GPCR/G-protein signal- shuffling of non-synonymous protein variants from each ling pathways in each cancer. individual across the same protein. Shuffling did not pro- The synthesis of functional information with genetic duce sufficient numbers of all possible combinations we variants can uncover new molecular insights into diseases, observed to generate separate distributions to calculate including new disease genes, and specific molecular observed P-values, but was rather used to get an expected mechanisms. This holds much promise for developing probability to use in the binomial calculation. We corrected personalized diagnoses and therapies across many clinical P-values through the Benjamini-Hochberg or False Discovery subjects. Rate procedure [60] to give a corresponding Q-value (labelled q in the text). For individual cancers, we retained positions having 5 observed or 2.5 expected non-synonymous variants; Materials and methods for pan-cancer we considered positions with 20 observed or 5 expected. For both we defined significantly enriched positions Datasets as those with log odds > = 0andq < = 0.01. These thresholds were chosen heuristically, based on our We extracted confirmed somatic, non-synonymous muta- experience, to narrow down the analysis to more interesting tions from version 79 of COSMIC [56] genomes (http://ca examples and to avoid potential false positives. Indeed, ncer.sanger.ac.uk/cosmic). We mapped 22842 of 28089 using no thresholds, led to an increase in the total number of (81%) of the associated Ensembl [57] transcripts to Uniprot identified positions, in both specific tissues and pan-cancer [58] canonical isoforms, which left 1.5 M unique protein and in the absolute number of dubious genes (see Table mutations (corresponding to a total of 4.6 M mutated S5a), defined as those more tolerant to mutations as asses- alleles), from 21k unique samples from 414 different cancer sed through the RVIS approach [17]. We considered only 6502 F. Raimondi et al. the top 2% of the most tolerant genes ranked through the Assessing biomolecular consequences of mutations RVIS score. To correct for domain mutation rate, we randomly We predicted functional consequences of COSMIC missense shuffled mutations found at domains within positions of the mutations using Mechismo [18] (mechismo.russelllab.org), same domain and the same protein. Mutations found outside which matches protein sequence positions to positions within domain regions, were randomly shuffled within the three-dimensional structures and identifies sites affecting remaining protein sequence portion. We similarly calculated known interactions with other proteins, DNA/RNA or small- the enrichment of mutations at domain positions by using molecules. We considered medium-high confidence predic- this alternative background model. tions, which include known structures or homologs with > = 30% of sequence identity for protein-protein interac- Mutual exclusivity analysis of functionally related tions, > = 35% for protein-chemicals and > = 41% for pro- protein positions tein-DNA/RNA interactions (as defined by Mechismo based on a benchmark for the accuracy of perturbed interfaces). We We defined functionally interacting genes based on their considered any interaction evidence, including interactions membership to a subset of Reactome (http://reactome.org/) from other species, or those coming from indirect experi- pathways [18], defined according to size criteria to represent ments (e.g., affinity purifications or co-expression). specific functional units. We considered pathways with fewer than 200 proteins from the FrontPageItem list. Sub- Analysis of transcription factor targets pathways were included if their size was less than 300. For sub-pathways with >300 proteins, we considered their sub- We retrieved a list of transcription factors putative target pathways. The threshold for the second level is based on the genes from an R package (https://github.com/slowkow/tfta assumption that the lower level pathways should be more rgets), which is a collection of several gene regulatory akin to functional units than those in upper levels. This network experiments (including Chip-Seq data [61–66]). allowed us to define a set of intermediate level biological We then assessed the pairwise similarity of transcription pathways, which we found to be a trade-off between cov- factors target genes with a Jaccard score. erage and lower inner redundancy. We assessed the mutual exclusivity of domain positions Oncodriver co-occurrence and survival time analysis in mutated family members participating to the same bio- logical pathway either in individual cancer types or pan- For each retained domain position, we assessed the co- cancer. We considered only significantly enriched domain occurrence of mutations of known oncodrivers from the positions (see previous section) mutated either in at least Cancer Genes Census (CGC) [1]. We considered simple five unique samples for individual cancers, or 50 unique non-synonymous mutations, copy number variants (CNVs) samples for the pan-cancer set. We assessed the mutual and structural rearrangements for a total of 24k unique exclusivity for each domain position pair within a given samples, corresponding to the 82% of the total. pathway through a one tail Fischer’s exact test, correcting We considered separately oncogenes and tumor sup- P-values through the FDR procedure (Q-values), and con- pressor genes (TSG), and defined a third category, Signal- sidering position pairs with q < = 0.1. We adopted a looser ling Oncodrivers, which we generated by mapping all CGC threshold for FDR correction, with respect to the enrich- genes to the Reactome “” (top hier- ment analysis, as by default mutual exclusivity inspected archy) pathway. We then manually check this list based on through the Fisher’s exact is an extremely stringent test. For literature inspection to include additional candidates and/or each comparison, we restricted our analysis only to unique remove genes with no role in signal transduction. Mutual samples having mutations to the same class of domain exclusivity between domain positions and oncodriver position (i.e., classifying mutations at either conserved or alterations was done through a one tailed Fisher’s exact test, non-conserved positions within the same domain). correcting P-values through the FDR procedure (Q-values), We evaluated the enrichment significance for domain considering as significant those instances having a q < 0.1. positions pairs through a binomial test procedure similar to We collected information for 17323 donors from release that used for individual positions, computing the prior 23 of ICGC (icgc.org), and matched these to the corre- probability by randomly shuffling non-synonymous protein sponding COSMIC sample. We considered vital status variants within members of a particular pathway. (alive/deceased), disease status (complete remission or not – Repeating the same test by considering the entire set of i.e., partial remission, relapse, progression) and survival lowest level pathways from Reactome hierarchy [15] led to time. We used the LogRank test to identify patient groups a moderate decrease of identified positions with no change affected by particular domain positional mutations and in the number of RVIS genes (see Table S5b). having a statistically significant (P < 0.05) difference in Rare, functional, somatic variants in gene families linked to cancer genes: GPCR signaling as a paradigm 6503 survival time. We generated Kaplan-Meier survival analysis transferred volume out of total 100 µl). Compound-induced plots for the most significant examples. We employed Cox’s AP-TGFα release was obtained by subtracting spontaneous proportional hazard model to predict hazard ratios and sur- AP-TGFα release (e.g., vehicle-treated relative AP-TGFα in vival probability of patients affected by interface-perturbing the CM plate). The AP-TGFα release was fitted to a four- mutations, employing age, sex and cancer type as covariates. parameter sigmoidal concentration-response curve (Prism For all the clustering and statistical analysis we used 7 software, GraphPad Prism). python (www.python.org/) through scipy (www.scipy.org/), statsmodels (statsmodels.sourceforge.net/) and lifelines cAMP Glosensor assay (lifelines.readthedocs.org/en/latest/) libraries. An in-house-modified cAMP GlosensorTM assay was per- TGFα shedding assay formed as follows. HEK293A cells (Thermo Fisher Scien- tific) were seeded in a 6-cm culture dish (Greiner Bio-One) Transforming growth factor-α (TGFα) shedding assay was at a density of 2 × 105 cells per well, with 4 ml of Dulbec- performed as described previously [34] with minor mod- co’s Modified Eagle Medium (DMEM), supplemented with ifications. HEK293A cells were seeded in a 6-well culture 10% fetal calf serum (FCS) and 100 U ml−1 penicillin and plate (Greiner Bio-One) at a density of 2 × 105 cells per 100 µg ml−1 streptomycin (complete DMEM), and cultured well, with 2 ml of complete DMEM and cultured for 1 day. for 1 day. Transfection solution was prepared by combining The cells were transfected with a mixture of plasmids 190 µl of Opti-MEM® I Reduced Serum Medium (Thermo encoding a codon-optimized alkaline phosphatase (AP)- Fisher Scientific), plasmids encoding pGlo-22F tagged TGFα (AP-TGFα, 1 µg, pCAGGS vector) and a (2 µg, codon-optimized for human cell expression, Gen- GPCR of interest (200 ng) combined with 5 µl of 10 µl of script, pCAGGS vector) and a GPCR of interest (400 ng, 1mgml−1 PEI solution and 95 µl of Opti-MEM® I Reduced pCAGGS vector or pcDNA3.1 vector), and 10 µl of Serum Medium. For some GPCRs, a plasmid encoding a 1mgml−1 PEI solution (Polyethylenimine “Max”, (Mw chimeric Gαq/s subunit (100 ng, pCAGGS vector) was co- 40,000), Polysciences). The resulting transfection solution transfected. After 1-day incubation, the cells were trypsi- was added to the cells. After 1-day culture, the transfected nized with 0.05% trypsin- and 0.53 mM EDTA-containing cells were detached with 1 ml of 0.53 mM EDTA- D-PBS, neutralized with the complete DMEM, collected in containing Dulbecco’s Phosphate-Buffered Saline (D- a 15-ml tube, centrifuged at 190 g for 5 min, and suspended PBS), mixed with 2 ml of Hank’s Balanced Salt Solution in 6 ml of pre-warmed HEPES-HBSS. The cell suspension (HBSS) containing 5 mM HEPES (pH 7.4) (HEPES-HBSS) was left for 15-min to settle spontaneous AP-TGFα release and centrifuged at 190 g for 5 min. The cell pellet was caused by trypsinization. Afterward, the cells were cen- suspended in 1.2 ml of 0.01% (w/v) bovine serum albumin trifuged and suspended in 6 ml of HEPES-HBSS and see- (BSA, fatty-acid-free and protease-free grade, Serva)-con- ded in a 96-well culture microplate (Greiner Bio-one) at a taining HEPES-HBSS and the cell suspension was seeded volume of 80 μl per well. The cell plate was placed in a CO2 in a 96-well half-are white microplate (Advanced TC, incubator for 30 min and mixed with 10 µl of 10X titrated Greiner Bio-One) at a volume of 30 µl per well. The cells test compounds diluted in 0.01% BSA-containing HEPES- were loaded with D-luciferin (Wako Pure chemicals) at HBSS. After 1-h incubation, the cell plate was centrifuged 10 µl of 8 mM solution and incubated at room temperature at 190 g for 2 min, and 80 µl of conditioned media was for 2 h in dark. After measurement of initial luminescent transferred to an empty 96-well plate (conditioned media signals with a microplate luminometer (SpectraMax L, (CM) plate). AP reaction solution (10 mM p-nitrophenyl- Molecular Devices), 10 µl of 5X titrated test compound phosphate (p-NPP, disodium salt, Wako Pure Chemicals), diluted in 5 µM forskolin were manually added to the cell 120 mM Tris–HCl (pH 9.5), 40 mM NaCl, and 10 mM and incubated at room temperature for 10 min. Afterward, MgCl2) was dispensed into the cell plate and the CM plate luminescent signals were measured and normalized to the at a volume of 80 µl per well. Absorbance at a wavelength initial counts. The luminescent signals were fitted to a four- of 405 nm were measured using a microplate reader parameter sigmoidal concentration-response curve using the (SpectraMax 340 PC384, Molecular Devices), before and Prism 7 software (GraphPad Prism) and the values for after a 1 h incubation at room temperature. For each well pEC50 (equal to −Log10 EC50 [M]) and Emax were calcu- measurement, change in the absorbance unit (Abs405) during lated from the curve. 1 h incubation with p-NPP solution (ΔAbs405) was used as a relative amount of AP-TGFα. Relative AP-TGFα in the CM Differential expression analysis plate was calculated by dividing ΔAbs405 in the CM plate by the total (the CM plate and the cell plate) ΔAbs405, fol- We obtained raw read counts for each gene in all cancer lowed by multiplication by a factor of 1.25 (80 µl types released from TCGA (The Cancer Genome Atlas) 6504 F. Raimondi et al. before 26th February 2016. To avoid unreliable results, we shift insertions/deletions or non-synonymous mutations selected 16 cancer types with at least 10 pairs of tumor- affecting highly conserved 7TM positions. For G-proteins, normal tissue matches. We used Deseq2 [67] for differential we calculated the fraction of samples affected by known gene expression analysis in each cancer type and adjusted p- specific oncogenic mutations. values (padj) for multiple testing by the Benjamini & The column of the matrix in Fig. 4aaswellasthetop Hochberg method [60]. We considered only values with dendrogram were derived from hierarchical, complete- corrected p-values (padj or q) < 0.01. linkage clustering of GPCR coupling group significant LFCs. We also performed differential expression analysis of samples carrying GPCR R3.50 or G-protein known activat- Code availability ing mutations (i.e., on position G.hfs2.2 on SWI, corre- sponding to GNAS p.R201, and on G.s3h2.3 on SWII, Code and datasets to reproduce the analysis available at: corresponding to GNAQ/11 p.Q209). For both, we exclu- http://www.russelllab.org/multigene-oncodriver. ded from the control both normal tissue samples as well as samples carrying mutations of the second class (either Funding FR, JCG, GS and RBR are supported by the Cell Networks GPCR or G-protein) to be compared. Excellence initiative of the Germany Research Foundation (DFG). FR was supported by an Alexander Von Humboldt post-doctoral fellow- We considered the top 3 TCGA cancer types carrying ship and a Michael J. Fox Foundation research grant. RBR is part of 7M R3.50 mutations, i.e., skin melanoma (SKCM: 19 sam- the DFG SFB/TPR186 Molecular Switches in the Spatio-Temporal ples), uterus endometrial carcinoma (UCEC: 11 samples) Control of Cellular Signal Transmission, and a member of the BMBF and stomach (STAD: 10 samples). Only German Network for Bioinfomatics Infrastructure (de.NBI). AI was fi funded by Japan Science and Technology Agency (JST) Grant signi cant genes (q < 0.01) with an absolute Log Fold Number JPMJPR1331, Japan Agency for Medical Research and Change (LFC) > = 2 were retained. Gene enrichment ana- Development (AMED) Grant Number JP17gm5910013 and Japan lysis of the overlapping genes between 7TM R3.50 and Gα Society for the Promotion of Science (JSPS) KAKENHI Grant activating mutations in melanoma was performed through g: Number 17K08264. JA received funding from AMED Grant Number fi JP17gm0710001. The authors thank Ayumi Inoue, Naoko Kuroda and Pro ler [68] and visualized through Enrichment map [69]. Yuko Sugamura (Tohoku University) for plasmid construction and cell culture experiments. We also thank a reviewer for the helpful sug- Integrative analysis of GPCR’s activity in cancer gestion to explore the wider set of genes potentially affecting cAMP in cancer. We grouped GPCRs based on their coupling preferences by Author contributions FR, AI, JSG, and RBR designed the study; FR using primary and secondary coupling information available performed the computational analysis; GS performed mutation clus- from the literature (http://www.guidetopharmacology.org/) tering; JCGS generated the random models for statistical testing; FR, [35]. We then estimated the activity for each G-protein NS, and BF performed differential expression analysis; AI, FMNK, family by combining differential expression data for G- JA, AAV, and RS provided reagents and performed experimental assays; FR, JSG and RBR wrote the manuscript with input from all the protein and GPCRs according to available coupling infor- authors mation. We considered the following G-protein groups: Gs (GNAS, GNAL), Gi/o (GNAI1, GNAI2, GNAI3 GNAO1), Gq/ Compliance with ethical standards 11 (GNAQ, GNA14, GNA15) and G12/13 (GNA12, GNA13). We estimated the activity (AG) of each G-protein family, Conflict of interest The authors declare that they have no conflict of in a given cancer type, according to the following equation: interest.

Xn Publisher’s note: Springer Nature remains neutral with regard to ¼ s à s à ð Þ fi AG mGPROT mGPCR c 1 jurisdictional claims in published maps and institutional af liations. 1 Open Access This article is licensed under a Creative Commons where, m is the base mean of normalized counts for all Attribution 4.0 International License, which permits use, sharing, samples (i.e., cancer and control) used for differential adaptation, distribution and reproduction in any medium or format, as s long as you give appropriate credit to the original author(s) and the expression analysis, normalized for sequencing depth; is a source, provide a link to the Creative Commons license, and indicate if scaling factor corresponding to the Log Fold Change (LFC), changes were made. The images or other third party material in this when significant (i.e., padj < 0.01), alternatively it’s set to 1; article are included in the article’s Creative Commons license, unless c is a coupling constant set to 1 if the coupling is reported indicated otherwise in a credit line to the material. If material is not ’ n included in the article s Creative Commons license and your intended in IUPHAR, elsewhere is 0; is the number of G-protein use is not permitted by statutory regulation or exceeds the permitted for a given family. use, you will need to obtain permission directly from the copyright For GPCRs, we calculated the fraction of samples con- holder. To view a copy of this license, visit http://creativecommons. taining likely deleterious mutations, i.e., stop gains, frame- org/licenses/by/4.0/. Rare, functional, somatic variants in gene families linked to cancer genes: GPCR signaling as a paradigm 6505

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