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Cumulative Haploinsufficiency and Triplosensitivity Drive Aneuploidy Patterns and Shape the Cancer Genome Cell

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Cumulative Haploinsufficiency and Triplosensitivity Drive Aneuploidy Patterns and Shape the Cancer Genome

Teresa Davoli,1,2,5 Andrew Wei Xu,2,4,5 Kristen E. Mengwasser,1,2 Laura M. Sack,1,2 John C. Yoon,2,3 Peter J. Park,2,4 and Stephen J. Elledge1,2,* 1Howard Hughes Medical Institute, Department of Genetics, Harvard Medical School, Boston, MA 02115, USA 2Division of Genetics, Brigham and Women’s Hospital, Boston, MA 02115, USA 3Department of Medicine, Massachusetts General Hospital, Boston, MA 02114, USA 4Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA 5These authors contributed equally to this work *Correspondence: [email protected] http://dx.doi.org/10.1016/j.cell.2013.10.011

SUMMARY such as translocations and chromothripsis. Understanding how these events drive tumorigenesis is a major unmet need in Aneuploidy has been recognized as a hallmark of cancer research. cancer for more than 100 years, yet no general the- Though ostensibly random, these alterations follow a ory to explain the recurring patterns of aneuploidy nonrandom pattern that suggests that they are under selection in cancer has emerged. Here, we develop Tumor and are likely to be cancer drivers rather than passengers. Suppressor and Oncogene (TUSON) Explorer, a If so, we should be able to explain how they drive tumori- computational method that analyzes the patterns genesis. A recent clue as to how this might work came from the integration of a genome-wide RNAi proliferation screen of mutational signatures in tumors and predicts with focal SCNA information (Solimini et al., 2012). The screen the likelihood that any individual gene functions identified STOP and GO genes that are negative and positive as a tumor suppressor (TSG) or oncogene (OG). regulators of cell proliferation, respectively. Hemizygous By analyzing >8,200 tumor-normal pairs, we pro- recurring focal deletions were enriched for STOP genes vide statistical evidence suggesting that many and depleted of GO genes, suggesting that the deletions more genes possess cancer driver properties than maximize their protumorigenic phenotype through cumula- anticipated, forming a continuum of oncogenic tive haploinsufficiency of STOP and GO genes. Haploin- potential. Integrating our driver predictions with sufficiency describes a genetic relationship in a diploid information on somatic copy number alterations, organism in which loss of one copy of a gene causes a pheno- we find that the distribution and potency of TSGs type. The converse is triplosensitivity, in which an additional (STOP genes), OGs, and essential genes (GO copy of a gene produces a phenotype. However, the distribu- tions of STOP and GO genes were not able to predict genes) on chromosomes can predict the complex aneuploidy or chromosome arm SCNA frequencies, perhaps patterns of aneuploidy and copy number varia- because they represent only one aspect of tumorigenesis tion characteristic of cancer genomes. We pro- (proliferation) or are too diluted by nonhaploinsufficient genes. pose that the cancer genome is shaped through We hypothesized that the drivers of sporadic tumorigenesis a process of cumulative haploinsufficiency and might provide a more representative and potent set of STOP triplosensitivity. and GO genes with which to explore this phenomenon. Furthermore, this gene set may possess a higher frequency INTRODUCTION of haploinsufficiency. In this study, we developed methods to identify tumor sup- A key goal of cancer research is to identify genes whose muta- pressor genes (TGSs) and oncogenes (OGs) from tumor DNA tion promotes the oncogenic state. Research over the last 40 sequences. We implicate many new drivers in cancer causation years has identified numerous potent drivers of the cancer and find many more cancer drivers than expected that exist in phenotype (Meyerson et al., 2010; Stratton et al., 2009; Vogel- a continuum of decreasing phenotypic potential. Furthermore, stein et al., 2013). Perhaps the most striking characteristics of we found that the distribution and potency of TSGs, OGs, and cancer genomes are their frequent somatic copy number alter- essential genes on chromosomes can explain copy number ations (SCNAs) and extensive aneuploidies. Deletions and ampli- alterations of whole chromosomes and chromosome arms fications of whole chromosomes, chromosome arms, or focal during cancer evolution through a process of cumulative hap- regions are rampant in cancer, as are other rearrangements loinsufficiency and triplosensitivity.

948 Cell 155, 948–962, November 7, 2013 ª2013 Elsevier Inc. ACMethod for the prediction Figure 1. Prediction of TSGs and OGs of TSGs and OGs Based on Their Mutational Profile (A) Schematic representation of our method for the Tumor Classification of mutations Samples based on their functional impact detection of cancer driver genes based on the assessment of the overall mutational profile of

Selection of traning sets each gene. The somatic mutations in each gene for TSGs,OGs and Neutral genes from all tumor samples are combined and classi- Analysis fied based on their predicted functional impact. Definition of 22 potentially The main classes of mutations (silent, missense, relevant parameters Analysis of Mutation Patterns and LOF) are depicted. for each gene (B) Schematic depicting the most important LOF Identification of the best features of the distributions of mutation types parameters through the LASSO Functional Prediction expected for a typical TSG, OG, and neutral Impact Lasso classification method Missense Prediction of TSGs and OGs gene. Compared to ‘‘neutral’’ genes, TSGs are Silent based on Lasso expected to display a higher number of in- Gene TUSON Explorer Prediction activating mutations relative to their background

Patterns of mutations expected to be Prediction of TSGs mutation rate (benign mutations), and OGs are B predictive of TSGs and OGs and OGs based on the combined significance expected to display a higher number of acti- LOF of the best parameters vating missense mutations and a characteristic Pattern Expected Missense Silent pattern of recurrent missense mutations in spe-

Neutral gene cific residues. D TUSON Explorer LOF (C) A flowchart delineating the main steps in our High ratio of damaging Missense method for identifying TSGs and OGs, from the versus benign mutations Silent Parameters for the Parameters for the classification of the mutations based on their Tumor Suppressor Gene prediction of TSGs prediction of OGs functional impact to the identification of the best LOF 1. LOF/Benign ratio 1. Entropy Score parameters through Lasso and their use for the High level of missense mutations 2. HiFi Missense/Benign ratio 2. HiFi Missense/Benign ratio and clustering of mutations Missense 3. Splicing/Benign ratio prediction of TSGs and OGs by TUSON Explorer Silent (or the Lasso method). Oncogene (D) Schematic related to (C) depicting the pa- rameters selected by Lasso employed by TUSON Explorer for the prediction of TSGs and OGs (HiFI, high functional impact). For TSGs, the parameters are the LOF/Benign ratio, the HiFI/Benign ratio, and the Splicing/Benign ratio, whereas for OGs the parameters are the Entropy score and the HiFI/Benign ratio. Also see Figure S1 and Table S1.

RESULTS eters, and this rate can be estimated by the number of mutations that are unlikely to affect its function (such as silent or func- Cancer driver genes have been described as mountains and hills tionally benign mutations), whose observed frequency is not (Wood et al., 2007). Mountains are driver genes that are very dependent on selective pressure during cancer evolution. The frequently mutated in cancer, whereas hills represent less proportion of functionally relevant mutations of particular classes frequently mutated driver genes. It has become clear from recent compared to this background mutation rate will be dependent on international sequencing efforts that most potent drivers (moun- the degree of selection and will predict the likelihood that a gene tains) have been discovered. A key issue is how to determine the will act as a cancer driver. TSGs and OGs can be distinguished identity of the significant but less frequently mutated drivers, the among the cancer driver genes based on the characteristic hills. A recent analysis searching for very high confidence cancer pattern of the different types of mutations (i.e., loss of function drivers in a database of 400,000 mutations estimated that there [LOF], missense, silent) that are typically observed for those were 71 TSGs and 54 OGs (Vogelstein et al., 2013). It is likely that two classes of drivers relative to neutral genes, as illustrated in there also exist additional functionally significant cancer drivers Figure 1B. with weaker phenotypes and lower probabilities that are selected less frequently. A central question is how to identify Identification of Parameters Predicting TSGs and OGs these genes. In principle, with more samples analyzed, greater We set out to determine the most reliable parameters for the statistical significance can be placed on the outliers, allowing prediction of TSGs and OGs in an unbiased way (Figure 1C). discovery of lower penetrance drivers. However, it is likely that We used sequence data from >8,200 tumors from the COSMIC there is more information present in the current data that may (Forbes et al., 2010) and TCGA (http://cancergenome.nih.gov/) allow these lower frequency events to be detected. databases and a recently published database (Alexandrov To approach this question, we sought to devise a method to et al., 2013) comprising >1,000,000 mutations (Figure S1 and predict TSGs and OGs in cancer based on the properties of Table S1 available online). We defined a list of 22 parameters pri- gene mutation signatures of these two distinct classes of driver marily based on the different classes of mutations and used the genes. We hypothesized that the proportion of the different types classification method Lasso and three training sets of known of mutations with different functional impact would be informa- TSGs and OGs (from the Cancer Gene Census, Futreal et al., tive in predicting these two types of drivers (Figure 1A). Each 2004)(Table S2A) and neutral genes to identify those parameters gene has a background mutation rate that is dependent on tran- that best predict the two classes of driver genes (see Experi- scription, replication timing, and possibly other unknown param- mental Procedures). We employed PolyPhen2 to predict the

Cell 155, 948–962, November 7, 2013 ª2013 Elsevier Inc. 949 functional impact of missense mutations in order to classify them mutations (see Experimental Procedures). Finally, we used an into those with potentially high (HiFI) or low (LoFI) functional extension of Liptak’s method to provide a combined p value impact (Adzhubei et al., 2010). LoFI mutations are typically for the selected parameters for each gene. For TSGs, the com- conservative amino acid changes or changes in poorly bined p values (and q values) were derived from individual values conserved residues. We defined the combination of silent and from the LOF/Benign, Splicing/Benign, and HiFI/Benign param- LoFI missense as ‘‘Benign’’ mutations to provide a larger, more eters. For OGs, the combined values were derived from the reliable value for estimating background mutation rates. We Missense Entropy and the HiFI/Benign parameters (Figure 1D). also defined the LOF mutations as the combination of nonsense The LOF/Benign parameter for discrimination between TSGs and frameshift mutations. As a majority of known OGs show an and OGs was subsequently utilized to define a final list of OGs atypical distribution of recurrent mutations in one or a few key and TSGs (see Experimental Procedures). TUSON Explorer residues, we utilized entropy, a well-defined concept in physics does not take into account SCNA information, and this allows and information theory (Shannon and Weaver, 1949), to measure us to perform a rigorous analysis of our cancer driver genes for the degree of reoccurring mutations within a gene. The Entropy their abilities to predict the frequency of deletion and amplifica- score represents the weighted sum of the probabilities, across tion (see below). a gene, that a site is mutated (see Experimental Procedures). As a second strategy to predict the probability of a given gene The best parameters found by Lasso for the prediction of being a TSG or OG, we employed the Lasso model, which also TSGs and OGs are described below and are visualized in Figures takes into account SCNAs (see Experimental Procedures). The 1D and 2. ranked lists of predicted TSGs and OGs by both Lasso and Tumor Suppressors versus Neutral Genes TUSON Explorer are contained in Tables S3A and S3B. This The most predictive parameters for TSGs are: (1) the ratio of LOF list provides a facile look-up table that can be easily sorted for mutations to Benign (p = 2.51 3 10À28, Wilcoxon, one-tailed different parameters for all those who are interested in the muta- test); (2) the ratio of Splicing to Benign mutations (p = 4.6 3 tional behavior of a given gene in this data set. 10À13); (3) the ratio of HiFI missense to Benign mutations (p = Both ranking strategies performed similarly and eliminated the 3.2 3 10À14); and (4) high-level deletion frequency (p = 1.46 3 problems of inappropriately including giant genes and genes in 10À8). A 20-fold cross-validation shows a high prediction accu- highly mutable regions (Dees et al., 2012), without the need to racy of 93.2% on these training sets (Figure 2A and Table S2B). consider expression level or replication timing (Lawrence et al., Oncogenes versus Neutral Genes 2013). Importantly, both of our strategies distinguish between The most predictive parameters for OGs are: (1) the entropy for TSGs and OGs, which are predicted to have functionally oppo- missense mutations (p = 2.2 3 10À14); (2) the ratio of HiFI site roles in the control of cell growth and have different implica- missense mutations to Benign mutations (p = 1.2 3 10À9); and tions for potential cancer therapeutics. (3) high-level amplification frequency (p = 1.4 3 10À6). The 20-fold cross-validation accuracy is 85.2% (Figure 2B and Estimates of the Numbers of TSGs and OGs Table S2B). A ranking of this nature consists of truly significant genes mixed Tumor Suppressor Genes versus Oncogenes with false-positive genes that obtain low p values by chance un- One important aim of our prediction method is the discrimination der the null hypothesis. Thus, we sought to get an estimate of the between TSGs and OGs. The most predictive parameters be- minimum number of TSGs and OGs by analyzing the distribution tween these two sets are: (1) the ratio of LOF to Benign mutations of the combined p values for each class of cancer driver genes. (p = 2.5 3 10À16); (2) high-level amplification frequency (p = To achieve this, we utilized a histogram-based method (Mosig 1.3 3 10À9); (3) high-level deletion frequency (p = 7.6 3 10À6); et al., 2001) to estimate the number of rejected hypotheses and (4) the ratio of Splicing to Benign mutations (p = 9.9 3 from the distributions of the combined p values calculated for 10À7). The 20-fold cross-validation accuracy is 91.9%. Overall, each gene. With our data set, this method estimated 320 Lasso identified parameters that make intuitive sense for these TSGs and 250 OGs (Extended Experimental Procedures). classes of genes and clearly delineated TSGs and OGs from This long list of TSGs and OGs suggests that there are many each other and from neutral genes. In sum, we identified more drivers than anticipated and that they exist in a continuum independent parameters that strongly predict and distinguish of decreasing potency (Discussion and Figure 7A). For the between TSGs and OGs (Figures 2C and 2D and Table S2B). analyses described below, we considered the top 300 TSGs (q value < 0.18) and 250 OGs (q value < 0.22) as our working lists. Identifying OGs and TSGs Given the fact that the deviation of the mutation signatures from Having identified the most predictive parameters, we developed the normal pattern is a function of the degree of selection and the a method we call Tumor Suppressor and Oncogene Explorer frequency of mutation, increasing the number of tumor samples (TUSON Explorer) that combined selected parameters to derive will detect even more cancer drivers of progressively weaker an overall significance and ranking for each gene as a potential selective pressure. To determine the potential number of TSGs TSG or OG (Figure 1D). First, we derived a p value for each upon additional sequencing, we applied TUSON and estimated gene for the ratios of LOF/Benign, Splicing/Benign, HiFI/Benign, the number of TSGs (using Mosig’s method) on random subsets and Missense Entropy based on the comparison to the neutral of the data set with increasing numbers of samples and gene set (see Experimental Procedures). For the LOF/Benign observed that the number of predicted TSGs continues to parameter, we applied a correction to normalize for the nonuni- increase with additional samples. We observed that the rate of form codon usage among genes for the occurrence of nonsense increase in the predicted number of TSGs decreases slightly at

950 Cell 155, 948–962, November 7, 2013 ª2013 Elsevier Inc. TSG versus Neutral Genes OG versus TSG ACLOF HiFI Splicing Deletion LOF Splicing Deletion Amplification Benign Benign Benign Frequency Benign Benign Frequency Frequency p=3e-28 p=3e-14 p=5e-13 p=1e-8 p=3e-16 p=1e-8 p=8e-6 p=1e-9 0.020 3.00 0.06 3.00 2.50 0.5 0.007 2.75 2.75 2.25 0.4 2.50 2.50 0.05 0.006 2.00 0.4 2.25 0.015 2.25 1.75 2.00 0.3 0.005 0.04 2.00 1.50 0.3 1.75 1.75 0.004 1.50 1.25 0.010 1.50 0.03 0.2 1.25 1.25 LOF/Benign LOF/Benign 1.00 0.2 0.003 Deletion Freq Deletion Freq Splicing/Benign Splicing/Benign HFI Miss/Benign Amplification Freq 1.00 1.00 0.02 0.75 0.002 0.005 0.75 0.75 0.1 0.50 0.1 0.50 0.50 0.01 0.001 0.25 0.25 0.25

0.00 0.00 0.0 0.000 0.00 0.0 0.000 0.00 Neut TSG Neut TSG Neut TSG Neut TSG D OG TSG OG TSG OG TSG OG TSG BRAF IDH1 -10 -5 -5 -10 2.00 <10 10 110<10 OG versus Neutral Genes KRAS B PIK3CA Missense HiFI Mean Missense Amplification Entropy Benign Polyphen2 Score Frequency NRAS pvalue OG pvalue TSG p=2e-14 p=1e-9 p=3e-10 p=1e-6 AKT1 1.00 0.40 2.00 0.06 IDH2 CTNNB1 0.80 0.35 1.75 RAC1 PTEN EZH2 0.75 0.05 TP53 U2AF1 0.30 1.50 0.60 EGFR 0.04 0.50 0.25 1.25 FBXW7 0.40 0.50 SMAD4 0.20 1.00 0.03 0.30 Missense Entropy B2M 0.15 0.75 HFI Miss/Benign

Missense Entropy 0.20 Amplification Freq 0.02 CDKN2A Mean Polyphen2 Score Mean Polyphen2 0.25 0.10 0.50 VHL

0.10 0.01 0.05 0.25 RB1 0.05 MAP3K1 STK11 0.00 0.00 0.00 0.00 NPM1 CASP8 APC Neut OG Neut OG Neut OG Neut OG GATA3 ARID1A CDKN1B RPL22

0.25 0.50 0.75 1.00 1.50 2.00 4.00 LOF/Benign

Figure 2. Best Parameters Selected by Lasso for the Prediction of TSGs and OGs (A–C) Box plot representations of the distribution of the values for the indicated parameters in the neutral genes (gray), TSG (red), and OG training set (green). The median, first quartile, third quartile, and outliers in the distribution are shown. The p value for the difference between the two indicated distributions is shown as derived by the Wilcoxon test. (A) Box plots showing the distribution of LOF/Benign, HiFI missense/Benign, and Splicing/Benign ratios and the high-level frequency of focal deletion among the neutral gene set and the TSG set. (B) Box plots showing the distribution of Missense Entropy, HiFI missense/Benign, mean of PolyPhen2 score, and the high-level frequency of focal amplification among the neutral gene set and the OG set. (C) Box plots showing the distribution of LOF/Benign and Splicing/Benign ratios, high-level frequency of deletion and amplification among the TSG and OG sets. (D) Plot of the LOF/Benign ratio and Missense Entropy for each gene, the best parameters for discriminating between TSGs and OGs. Specific genes with high levels of LOF/Benign or Missense Entropy are shown along with their p values for being a TSG or an OG (TUSON Explorer). Also see Figure S2 and Tables S2 and S3. the highest number of samples examined (Figure S2), indicating relevant to tumorigenesis, including cell-cycle control, embry- a possible plateau at very large number of samples. onic development, promotion of differentiation, apoptosis, and blood vessel development (Table S3C and Figure 3). In addition, PAN-Cancer Mutational Analysis there was a strong enrichment for transcriptional regulation (q = À À Gene Ontology (GO) term and pathway analysis of our list of po- 6.19 3 10 11) and chromatin modification (q = 5.7 3 10 12). tential TSGs showed enrichment for functions that are highly Furthermore, we noticed an enrichment for genes involved in

Cell 155, 948–962, November 7, 2013 ª2013 Elsevier Inc. 951 Tumor Suppressor Genes Oncogenes -100 -10 -5 -2 p value 10 10 10 10

Metabolic WNT TNF Response to Growth Factors −β Pathways Signaling Pathway TGF Signaling GF RSPO2 WNT11 FASLG TNF

PTPRK EGFR FGFR2 ERBB2 ERBB3 KIT1 FZD6 FAS TNFR NF1 GNAS PTPN11 PGM5 IDH2 NF2 FADD TRAF2 RNF43 HRAS NRAS KRAS PIK3R1 PIK3CA PSPH IDH1 RASA1 TGFBR2 DVL1 TGFB1 CUL3 CASP8 MAP3K1 TGFBR1 RALGDS PRKCI BRAF RIT1 PTEN NFE2L2 KEAP1 BID MAP2K4 SMAD2 MAP2K1 GSK3B AKT1 MTOR STK11 ACVR2A RAC1 AXIN1 SMAD4 Activin APC ACVR1B RHOA MAPK1 Mitochondria BAX CHD8 CTNNB1 FOXO1 IKK Ribosome RPL22 RPL18 CytC BCL2 ARHGAP35 NOTCH1 Notch RPL5 LEF TCF7L2 NFKB NOTCH2 Signaling COL4A2 CASP9

PTCH1 SHH Cell Cycle Regulation Altered COS2 GLI SUFU SMO ZFP36L1 PPP2R1A Metabolism Sonic Avoiding FU E2F Hedgehog ZFP36L2 FBXW7 PPP6C Sustaining Apoptosis RB1 Proliferation Antigen HLA-B CDKN2A CCNA/B CCNE1 CCND3 Presentation CDK1 CDK2 CDK4/6 Escaping B2M CDKN2C and Immune Inflammation HLA-A Response ANAPC1 CDKN1A CDKN1B HLA-DRB1

Genome IL32IL32 SF3B1 Instability BRD7 U2AF1 Invasion Splicing MDM2 TP53 Avoiding EGLN1 Hypoxia CUL2 Differentiation Stress HIF1A VHL ATM RNF111 Response

BCOR TBX3 TCF12 KANSL1 UBR5 RNF168 TRIP12 RUNX1 GATA3 SPOP BRCA1 BAP1 USP28 TP53BP1 MBD6 KMT2A KMT2B EP300 CDK12 CIC EZH2 Cell-Cell BRE Adhesion NSD1 MEN1 KDM5C NCOR1 GPS2 CDH1 RBMX Transcription SETD2 KTM2D KTM2C SRC PVRL4 COL4A2 RAD21 SMC4 MCM7 ATR FAT1 STAG2 NIPBL ATRX SMARCA4 ARID1A CHD3 BRCA2 TRRAP MAX MYC ARID1B SMARCB1 ARID2 Cohesin MLH1 CHD4 CTCF FUBP1 SIN3A DNA Damage Response Epigenetic Regulation

Figure 3. Representation of Predicted TSGs and OGs within Their Cellular Pathways Placement of predicted cancer drivers within specific cellular pathways. TSGs and OGs were predicted by TUSON Explorer. The predicted TSGs and OGs belonging to many known cellular pathways or complexes are shown, along with how they generally correspond to the hallmarks of cancer. TSGs are shown in red, whereas OGs are shown in green; color intensity is proportional to the combined p value as indicated. For some pathways, additional genes absent from the predicted TSGs and OGs were added and marked in gray for clarity of the pathway representation. Although several genes are known to affect multiple pathways and hallmarks, only one function is presented for the sake of limiting the complexity of the diagram. An external black box outside of the colored gene box highlights genes previously less well characterized for their roles in tumorigenesis. See also Figure S3 and Table S3.

952 Cell 155, 948–962, November 7, 2013 ª2013 Elsevier Inc. LOF Silent Missense Figure 4. Representation of Mutation Patterns in Representative Predicted TSGs A and OGs USP28 UIM UCH-1 CC UCH-2 (A–F) The mutational patterns of selected TSGs 1 97-116 162-196 400-432 580 649 1077 and OGs are depicted. For TSGs (A–D), the loca- tions of LOF (red) and silent (white) mutations B within the coding regions are shown. For OGs (E and F), the location of recurrent missense mu- MHC I IgC TM HLA-A tations (orange) and of LOF mutations (red) within 1 26 203 207 299 309- 365 332 the coding regions are shown. USP28, TP53BP1, and RASA1 are previously less well-characterized C candidate TSGs in the TUSON PAN-Cancer anal- ysis. SPOP and PPP2R1A are previously less well- TP53BP1 γ-H2AX Binding TUDOR BRCT BRCT characterized candidate OGs. 1 956 1354 1490- 1714- 1865- 1590 1850 1972 See also Table S3. D

RASA1 SH2 SH3 SH2 PH C2 RasGAP 1 161 264 287 339 350 426 494 575 595-689 698 1044 New Potential Cancer Drivers New components of pathways previously linked to tumorigenesis have also been detected (Figure 4). For example, the DNA damage response pathway is cen- S143F R144C T145P R144H R258H R183W S256F W257C R258C E R183Q S256Y tral to the maintenance of genomic P179R stability, and both members of a key PPP2R1A PP2A Subunit B Binding Subunit C-Binding DDR complex, the TP53BP1/USP28 1 8 x18 399 400 589 complex (Zhang et al., 2006), which are substrates of the ATM kinase (Matsuoka et al., 2007), were identified within the top 110 TSGs (q < 0.15, Figures 3 and 4A–4C). Two components that regulate D140G D140N R121Q W131 F133 K134 F E47A E47K E50K ATM-dependent chromatin remodeling, UBR5 and TRIP12 (Gudjonsson et al., SPOP MATH domain BTB domain 2012), are also high on the TSG list (q < 1 28 166 190 294 374 0.25). RBMX, which controls ATR and BRCA2 expression (Adamson et al., 2012), ranked 76th on the list (q = 1.1 3 the immune system (q = 5.8 3 10À3), particularly in antigen pro- 10À4). There are several candidate OGs with enzymatic functions cessing and presentation represented by the MHC class I sys- that could serve as drug targets (Figure 4E and Tables S3B and tem. Two major HLA genes (HLA-A and HLA-B) were in the top S7B), including three phosphatases (PPP6C, PTPN11, PTPRF) 90 candidate TSGs (q < 0.0002), and the b2 microglobulin and regulators such as PPP2R1A, as well as several kinases (B2M) gene, which is an obligatory complex component of (MAPK1, MAPK8, BRSK1 among others). There are many other both HLA proteins, ranked 43rd (q = 9.2 3 10À9) on our TSG new potential TSGs and OGs on these lists that cannot be dis- list, underscoring that escaping from immunosurveillance is a cussed here due to limitations of space, but several of these significant selective force in tumorigenesis (Hanahan and Wein- are presented in Figure 3. berg, 2011)(Figures 3 and 4B). Furthermore, IL32, which stimu- Consistent with the enrichment of cell-cycle and apoptosis GO lates the immune responses of NK cells and CD8+ T cells that terms, integration of the PAN-Cancer analysis with functional monitor MHC status (Conti et al., 2007), is also in the top gene sets revealed that essential genes are significantly 50 TSGs. Unexpectedly, negative regulation of cell adhesion depleted for deleterious mutations (see below). An exception (q = 4.32 3 10À4) was enriched, indicating that increase of cell to that finding was the presence of RPL22, RPL5, and RPL18 adhesion may confer a selective advantage to tumor cells. Tradi- large ribosomal subunit genes in the top 210 TSGs (q < 0.07; tionally it has been thought that reducing adhesion promotes Figure 3). Interestingly, heterozygous mutations in ribosomal tumorigenesis; however, recent findings suggest a potentially genes promote tumorigenesis in zebrafish (Lai et al., 2009). different role for cell-to-cell-adhesion. First, it has been shown Furthermore, familial mutations in ribosomal proteins have that circulating tumor cells exist in clusters in the blood (Hou been associated with Diamond-Blackfan anemia, which is asso- et al., 2011). Second, PVRL4, which ranked well in our Lasso ciated with an increased risk of leukemia (Willig et al., 2000). OG list, was shown to promote transformation through cell adhe- sion, as do several other oncogenes like MYC, KRAS, PI3K, and Analysis of Individual Tumor Types loss of PTEN (Pavlova et al., 2013). Thus, promotion of adhesion Identification of cancer drivers using the PAN-Cancer analysis may be a driving force in tumorigenesis. favors discovery of genes whose functions contribute to many

Cell 155, 948–962, November 7, 2013 ª2013 Elsevier Inc. 953 ABSTOP versus Neutral Genes Essential versus Neutral Genes LOF Splicing HiFI Deletion LOF LOF HiFI Deletion Silent Benign Benign Frequency Silent Kb Benign Frequency p=2e-18 p=2.5e-9 p=1.6e-8 p=1.4e-5 p=5.8e-5 p=7.1e-7 p=1.7e-4 p=8.9e-3

1.2 0.25 3.0 0.005 1.50 3.0 0.005 2.8 2.6 1.25 1.0 2.5 8.0 2.4 0.004 0.20 0.004 2.2 1.00 2.0 0.8 2.0 1.8 0.003 0.15 0.003 0.75 5.5 1.6 0.6 1.5 1.4

LOF/Kb 0.50

LOF/Silent

LOF/Silent HFI/Benign HFI/Benign 1.2 0.002 0.10 0.002

Splicing/Silent Deletion Frequency 0.4 1.0 Deletion Frequency 1.0 3.0 0.25 0.8

0.05 0.001 0.6 0.001 0.2 0.5 0.00 0.4

0.5 0.2

0.0 0.00 0.0 0.000 −0.25 0.0 0.000

Neut STOP Neut STOP Neut STOP Neut STOP Neut Ess Neut Ess Neut Ess Neut Ess

Figure 5. Behavior of Functional Gene Sets Relative to TSG Parameters (A and B) Box plot representation of the distribution of the values for the indicated parameters in the neutral genes compared with the essential genes and STOP genes. The median, first quartile, third quartile, and outliers in the distribution are shown. The p value for the difference between the two indicated distributions is shown as derived by the Wilcoxon test. (A) Box plots showing the distribution (orange) of LOF/Silent, Splicing/Benign, and HiFI missense/Benign ratios (gray) and the high-level frequency of focal deletion among the neutral gene and STOP gene sets. (B) Box plots showing the distribution of LOF/Silent, LOF/Kb, HiFI missense/Benign ratios and the high frequency of focal deletion among the neutral gene set (gray) and the essential gene set (light blue). See also Tables S3A, S5A, and S5B. different types of cancer. Certain cancer drivers may miss the Analysis of TSGs and OGs cutoff for significance in the PAN-Cancer analysis because Behavior of Functional Gene Sets they are primarily involved in controlling tissue-specific differen- The PAN-Cancer mutation data set allows us to interrogate the tiation networks or because they are rate limiting for a particular behavior of functional gene sets derived through experimental function in only certain tissues. Thus, we anticipate that new approaches. We previously showed that STOP genes are over- drivers can be discovered through analysis of mutation signa- represented in regions of deletion (Solimini et al., 2012). Examina- tures in individual tumor types despite their lower numbers. We tion of their abundance in the set of candidate TSGs showed that performed the same analysis as above for each of 20 tumor STOP genes are significantly enriched in the TSG set (p = 0.0031, types (Tables S1, S4A, and S4B). This analysis found many Fisher’s exact test) comprising 10% of the top 300 TSGs (68% TSGs that are specific for one tissue type such as CDH1 and more than expected). The STOP gene set showed a 50% GATA3 in breast adenocarcinoma, VHL and PBMR1 in kidney higher ratio LOF/Silent than the average for the neutral gene set clear renal cell carcinoma, and ID3 and NPM1 in hematological (p = 2.0 3 10À18; Figure 5A). Furthermore, the STOP genes malignances (Table S4A). Genes whose FDRs for the different showed a significant increase in the Splicing/Benign and HiFI/ subtypes were below 0.25 were all already relatively highly Benign ratios, two of the most potent parameters for the predic- ranked in the PAN-Cancer analysis. This indicates that the ma- tion of TSGs (Figure 5A). This analysis further underscores the jority of tissue-specific drivers were detected in the PAN-Cancer fundamental connection between cell proliferation and cancer. analysis. We next investigated a high-confidence set of 145 genes We wanted to determine how many new TSGs might be predicted to be essential at the cellular level based on their expected from the analysis of a new cancer subtype. For this, housekeeping cellular functions and their high evolutionary we calculated the number of TSGs in the whole data set lacking conservation (Table S5A and Experimental Procedures). This an individual tumor type (Tables S4C) and compared this list to set was depleted from regions of recurring deletions (Beroukhim the TSGs in that tumor type, which averaged 14 genes. We found et al., 2010) by 43% (p = 0.0198, Fisher’s exact test), and a larger that the average new cancer type added about five TSGs to the set of 332 essential genes was depleted by 25% (p = 0.014). PAN-Cancer list. Thus, on average, 70% of the TSGs detected Examination of the LOF/Silent ratio showed that, for the set of in a single tumor type were already detected in the PAN-Cancer 145 genes, the frequency of LOF /silent was 27% lower than analysis performed after excluding the mutations in that type of the rate for the neutral gene set (p = 5.8 3 10À5; Figures 5B tumor. This suggests that most cancer genes selected during and S5B). Additionally, the LOF/kb and HiFI/Benign ratios were tumor evolution act in cellular pathways whose role in tumorigen- also significantly decreased in the essential gene set. Given esis is widespread among different tumor types. that the vast majority of the mutations and deletions in question

954 Cell 155, 948–962, November 7, 2013 ª2013 Elsevier Inc. are heterozygous, the reduced LOF mutation and deletion fre- (p = 3.3 3 10À46). This could explain the observation that quency of the essential genes as a group argues that between frequent Y nullisomy is observed in prostate, renal cell, head 25% and 45% are haploinsufficient. Interestingly, our TSGs and neck, Barret’s esophageal adenocarcinoma, bladder, were enriched in recurring focal deletions (68%, p = 0.000281) pancreatic adenocarcinoma, and other cancers at frequencies and were depleted from recurring amplifications (28%, p = of 30%–80% (Bianchi, 2009; Kowalski et al., 2007). 0.015), whereas the OGs were enriched in amplifications (25%, Furthermore, we analyzed the silent mutation rates along p = 0.046) and depleted from focal deletions (23%), indicating entire chromosomes and found an enhanced mutation rate that amplifications are also likely to be Cancer Gene Islands. on the X chromosome relative to autosomes in males (30% increase, p = 1.1 3 10À9). This increase is even greater in General Properties of Cancer Drivers females (77.5%, p = 1.6 3 10À11)(Table S5D). Possible explana- High Interactivity tions for this phenomenon are detailed in the discussion. To search for unique properties of TSGs and OGs, we examined the degree to which these drivers participate in protein com- Distribution and Potency of Cancer Drivers on plexes using the CORUM database of experimentally validated Chromosomes Predict Arm and Chromosome SCNA human protein complexes (Ruepp et al., 2010). We found that Frequencies both TSGs and OGs were significantly more likely to be in protein In addition to focal SCNAs, a less frequent but significant chro- complexes than a typical protein. The 13.4% of all proteins found mosomal alteration is whole-arm loss or gain. We hypothesized in CORUM are in a complex. However, 36.7% of the predicted that the distribution and potency of TSGs and OGs on chromo- TSGs were in complexes (p = 3.1 3 10À24), and 26.4% of the pre- somes might explain the average frequency of chromosomal dicted OGs were in complexes (p = 3.5 10À8; Figure S3A). whole-arm SCNAs seen in cancer. To this end, we generated a High Betweenness chromosome arm score, Charm, that provides an assessment A second property of complexes is the degree to which they are of each arm based on the density of TSGs and OGs and their connected to other proteins and complexes. We explored this potency (weights of TSGs and OGs are based on their rank on by assessing a property called ‘‘betweenness,’’ which is propor- their respective lists and serve as a metric for their potency). tional to the number of times the protein is part of the shortest The Charm score represents a measure of the amount of positive paths between all pairs of proteins in a network. High between- or negative growth and survival potential that wild-type OGs or ness indicates a greater connectivity. The TSG and OG candi- TSGs might normally impart to a given arm and therefore how date gene lists were mapped onto the most current BioGRID SCNAs might impact cancer evolution by altering this balance human protein-protein interaction network (Stark et al., 2006). during tumorigenesis. Importantly, for Charm calculations, we Both the predicted TSGs and OGs show a high degree of employed the parameters from TUSON Explorer, which does betweenness (TSG p = 6.16 3 10À32, OG p = 1.68 3 10À6; Fig- not include copy number information. To lessen the diluting ure S3B), indicating that they are optimally positioned to impact impact of false positives for this analysis, we applied stringency information flow through networks. cutoffs of a q value of 0.25 for TSGs and 0.35 for OGs and a min- Greater Length imum of 10 missense mutations for OGs and 8 LOF mutations for Proteins with greater interactivity often have more domains. TSGs to get a stringent list of 264 TSGs and 219 OGs (see Exper- Thus, we examined gene length. Cancer drivers are significantly imental Procedures). The analysis of the CharmTSG score versus longer than the average gene (1,700 nt), with the mean for TSGs frequency of chromosomal arm deletion revealed a strong posi- at 3,234 nt (p = 2 3 10À21) and OGs at 2,107 nt (p = 9.7 3 10À6). tive correlation (r = 0.578, p = 5.8 3 10À5, Pearson correlation; Importantly, this observation is also characteristic of the genes in Figure 6A and Table S6A). Interestingly, the CharmTSG score our training sets (TSGs, 4,133 nt, p = 6.7 3 10À10; OGs, 2,260 nt, also showed a strong negative correlation with arm amplification p = 0.0016). frequency, and thus a high CharmTSG score indicates a signifi- cantly reduced tendency for a chromosome arm to be amplified An Unusually High Concentration of TSGs on the X (r = À0.59, p = 2.8 3 10À5; Figure 6B). Simple TSG densities Chromosome without weighting by rank also showed correlations with arm While examining the distribution of TSGs across chromosomes, deletions (Figure S4A), but these correlations are improved by we found that the X is unusually enriched for TSGs (p = 0.0042, Charm. In contrast to CharmTSG, the CharmOG score showed a exact binomial test) relative to autosomes. Examining the top negative correlation with arm deletion frequency (r = 0.52, p = 300 TSGs, we find that, although only 3.9% of all genes are on 3.2 3 10À4; Figure 6C). Moreover, the density of OGs positively the X, it contains 7.3% of all predicted TSGs (86% more than ex- correlated with arm amplification frequency (r = 0.45, p = 1.8 3 pected) and was the only chromosome with a significant enrich- 10À3, Figure 6D) but was not improved by the Charm score ment of TSGs (Table S5C). Given the fact that the X is functionally (data not shown). haploid in both males and females, this observation has certain We reasoned that, like GO genes in focal deletions, the chro- implications for evolutionary selection of cancer drivers during mosome arms most frequently deleted in cancer would be tumorigenesis and haploinsufficiency of TSGs (see Discussion). depleted of genes that promote the fitness of cancer cells. Using Interestingly, in the top 400 TSGs, we found two potential our in silico list of essential genes, we estimated their fitness TSGs on the Y, ZFY and UTY (q < 0.22). Both have homologs potency by estimating their avoidance of damaging mutations on the X that escape X inactivation, each of which also displays using the (LOF + HiFI)/Benign ratios. By determining a CharmEss tumor suppressor properties: ZFX (p = 0.019) and UTX/KDM6A score for each arm, we found a negative correlation between

Cell 155, 948–962, November 7, 2013 ª2013 Elsevier Inc. 955 TSG TSG A Charm and Deletion Frequency B Charm and Amplification Frequency 5 r=0.578 p=5.881e-05 r=-0.599 p=2.818e-05 4 10p 17p 4 10p 17p 16q 13q 16q 13q 3 3 6q 3p TSG 15q18q TSG 22q 15q 18q 4p 1p 14q 3p 22q 2q 5q 1p 5q 14q 4p 9p 2 10q 12q 2 12q 2q 9p 17q 2p 2p 17q 10q 4q 11q 16p 12p Charm 6p 4q 21q 21q Charm 11q 11p 6p 12p 19q 16p 19p 11p 1 19q 1 7p 9q 9q 19p 1q 20q 8q 8q 7q 3q 1q 3q 5p 7p 5p 20p 20p 7q 20q 18p 8p 18p 8p 0 0

0.05 0.10 0.15 0.20 0.1 0.2 0.3 Arm Deletion Frequency Arm Amplification Frequency

C OG D OG Charm and Deletion Frequency Dens and Amplification Frequency 0.03 7p 12p 3 r=-0.523 p=0.0003214 r=0.454 p=0.001837

12p 7p 21q 0.02 7q 2 12q 19p 13q 20q OG 7q 2q OG 19p 21q 12q 8q 20q 2p 20p 8q 3p 9q 3p 2p 20p 11p 13q 2q 10p 1q 3q 15q 4q 10q 9q 17q Dens 4q 17q 1q 0.01 6q4p 18q 16p19q 3q Charm 16p 1p 5q6p 8p 1 11q 14q 4p 16q5q 8p 17p 15q11p1p 5p 19q 16q 22q 6q 10q 17p 14q 6p 18q 10p 11q 5p 22q 18p 0 9p 0.00 9p 18p

0.05 0.10 0.15 0.20 0.1 0.2 0.3 Arm Deletion Frequency Arm Amplification Frequency E F CharmTSG-OG-Ess and Deletion Frequency CharmTSG-OG and Amplification Frequency 2.5 10p r=0.771 p=4.774e-09 10p 17p 16q r=-0.651 p=3.627e-06 2 9p 18q 9p 17p 18q 22q 22q 6q 6q 0.0 10q 4p 1p 13q 4p 16q 13q 14q 5q 10q15q 18p 11q 3p 16p 19q 1p 4q 0 17q 18p TSG-OG 6p

TSG-OG-Ess 19q 5q 4q 11q 14q 8p 2q 2p 5p 1q 15q 21q 11p 5p 6p 1q -2.5 2p 11p 3p 17q 21q 12q 3q 3q 16p 9q 8p 7q 2q 9q 19p 20p 8q

Charm 20q

Charm 12q -2 20q 19p 12p 20p 7q -5.0 8q 12p 7p 7p -4 0.05 0.10 0.15 0.20 0.1 0.2 0.3 Arm Deletion Frequency Arm Amplification Frequency

G H ChromTSG-OG-Ess and Deletion Frequency ChromTSG-OG and Amplification Frequency

r=0.804 p=3.2e-06 2 r=-0.648 p=0.0005522

18 22 10 22 0 10 18 16 13 13 17 6 15 14 5 4 6 0 11 1 4 TSG-OG 2 3 15 9 19 5 TSG-OG-Ess 1 16 14 17 -2 11 219 21 12 8 3 2 19 Chrom -2 20

Chrom 8 7 -4 20 7 12

-4

0.04 0.08 0.12 0.16 0.05 0.10 0.15 0.20 0.25 Chromosome Deletion Frequency Chromosome Amplification Frequency

Figure 6. Charm Score, Chrom Score, and Copy Number Alterations: Correlation Analysis (A–F) The Pearson’s correlation analysis of the Charm scores or Density score for the indicated gene sets (A–D) and/or combinations of these sets (E andF) relative to the frequency of arm-level deletion or amplification. Ess, essential genes. (G and H) The correlations of the Chrom scores (ChromTSG-OG-Ess and ChromTSG-OG) relative to the chromosome-level deletion or amplification frequency. The Charm scores refer to a weighted density of TSGs, OGs, or essential genes present on each chromosome arm, where each TSG or OG is weighted based on its rank position within the list of predicted TSGs and OGs ranked by TUSON Explorer and each essential gene is weighted based on its (LOF + 1/2 3 HiFI)/Benign ratio. The Chrom score is the equivalent of the Charm score for whole chromosomes. See also Figures 4 and 5 and Table S6.

956 Cell 155, 948–962, November 7, 2013 ª2013 Elsevier Inc. CharmEss scores and the frequency of arm-level deletions (r = Thus, SCNAs comprise a very large proportion of cancer-driving 0.34, p = 1.6 3 10À2; Figure S4D). No correlation was found events. between CharmEss and amplification frequency, as expected. Because the CharmTSG, CharmOG, and CharmEss scores A Continuum of Cancer Driver Genes correlate with arm-level deletion, we combined them by giving A central conclusion from this study is that there are likely to be a positive weight to the CharmTSG score and a negative weight many more cancer drivers than anticipated. Our estimate of the to the CharmOG and CharmEss scores to derive a cumula- number of TSGs based either on the combined significance of tive CharmTSG-OG-Ess score. The CharmTSG-OG-Ess score gave the different parameters or on the single best parameter for the an even stronger positive correlation with arm deletion fre- prediction of TSGs, i.e., the LOF/Benign ratio, predicted 320 quency (r = 0.77, p = 4.7 3 10À9; Figure 6E and Table S6A). TSGs with the current database from 8,200 tumors. Likewise, For amplification, we used the CharmTSG-OG score and found a we also predict more OGs than anticipated. The view of the can- strong negative correlation with amplification frequency (r = cer landscape emerging from our analysis does not contain a 0.65, p = 3.6 3 10À6; Figure 6F). We also combined amplification clear cutoff for predicting cancer drivers. Instead, there exists and deletion frequencies into a single score for copy number a continuum of decreasing probability of a given gene being a variation on each arm and compared that to the CharmTSG-OG driver (either TSG or OG). This probability is revealed by the score. This also gave a strong significant correlation (r = 0.74, degree of selection that the gene experiences during tumor evo- p = 2.7 3 10À8; Figure S5A). lution, which should be proportional to the phenotypic effect We extended our analysis of cancer driver scores and SCNAs caused by its loss or gain. This continuum of decreasing potency to whole-chromosome aneuploidy using its Charm equivalent of potential cancer drivers is likely to correspond to a continuum score that we call Chrom (Figures 6G, 6H, S4E–S4H, S5B, of increasing numbers of genes with decreasing phenotypic S5E, and S5F). ChromTSG significantly correlated with chromo- severity, as illustrated schematically in Figure 7A. In addition, some deletion frequency (r = 0.66, p = 3.7 3 10À4; Figure S4E) we hypothesize that events that simultaneously affect multiple and anticorrelated with amplification frequency (r = 0.54, p = weak drivers can cumulatively have an effect equal to a single 4.0 3 10À3; Figure S4F). Impressively, when we combined potent driver. Our modeling of the progressively higher number all three classes—TSGs, OGs, and essential genes—the of driver genes identified as increasing numbers of tumors are ChromTSG-OG-Ess was strongly predictive of the frequency of analyzed suggests that this number will continue to climb as chromosome loss (r = 0.80, p = 3.2 3 10À6; Figure 6G), and more sequence information becomes available but may be ChromTSG-OG was predictive of chromosome gains (r = 0.64, beginning to plateau. However, the newly identified drivers are p = 5.5 3 10À4; Figure 6H). Very similar results were obtained likely to display progressively less potency with lower therapeu- using just the TUSON ranking without stringency cutoffs (Figures tic significance. This is analogous to GWAS studies for which S5C–S5F and Table S6B). increasing sample sizes allow the identification of progressively Together, these data strongly argue that a selective force in weaker acting variants. generating chromosomal arm and whole-chromosome SCNAs Our analysis provides a probability of each gene being a can- derives from the integration of the relative densities and cer driver, and as such, there will be false positives regardless of potencies of positively and negatively acting cancer drivers on the threshold of minimum probability that we employ. Identifying a particular chromosome. Thus, the SCNAs in cancer genomes bona fide drivers from the regions with significant p values but may be selected during tumor evolution through cumulative higher FDR values, i.e., weaker phenotypic signatures, can be haploinsufficiency for deletions (as previously proposed for aided by considering other information such as their involvement STOP genes in focal deletions [Solimini et al., 2012]) and through in SCNAs, biochemical connections to known OGs and cumulative triplosensitivity for amplifications (see Discussion). TSGs, and functional information gleaned from the literature. These heuristic methods can be used to increase confidence DISCUSSION and rescue genes onto the likely cancer driver list (Tables S7A and S7B). In this study we analyzed the mutational data from >8,200 spo- radic cancers to predict cancer driver genes. We determined PAN-Cancer and Tissue-Specific Analysis the most predictive parameters for identifying TSGs and OGs Analysis of individual tumor types identified distinct sets of and used them to develop an algorithm called TUSON Explorer drivers in each tumor type, but the majority of these were to predict the probability that an individual gene functions as a also identified in the PAN-Cancer analysis as lower confidence TSG or an OG in cancer. This unbiased approach demonstrated candidates (Tables S4A–S4C). Thus, although there is clearly that the probability of being a cancer driver can be assessed by tissue specificity, there is still significant overlap among the significance of the distortion of its mutational pattern from the different tumor types and a PAN-Cancer analysis samples a pattern expected for a ‘‘neutral’’ gene. Combining data from our sufficient number of similar tumors to detect most of the largely analyses of drivers and copy number changes, the average tissue-specific or tissue-biased cancer drivers. Our analysis tumor in our data set has a mean number of 1 OG mutation, suggests that significantly deeper sequencing of individual tu- 3 TSG mutations (LOF and damaging missense), 3 chromo- mor types is unlikely to uncover many new potent drivers somal arm gains, 5 chromosomal arm losses, 2 whole- beyond what we have already identified and further sequencing chromosome gain, 2 whole-chromosome losses, 12 focal is likely to suffer from diminishing returns. This view is consis- deletions, and 11 focal amplifications (Zack et al., 2013). tent with a recent review that argues that nearly all potent

Cell 155, 948–962, November 7, 2013 ª2013 Elsevier Inc. 957 AB

C D

Figure 7. Cumulative Haploinsufficiency and Triplosensitivity Shape the Cancer Genome Illustrative schematics of different concepts highlighted in the Discussion. (A) The phenotypic continuum of cancer drivers. (B) The cancer gene island model for focal SCNAs. (C) The cumulative gene dosage balance model for predicting the patterns of aneuploidy. (The panel depicts the concept for arm-level SCNAs.) (D) Comparison of the predictions of Knudson’s Two-Hit Hypothesis for TSGs compared to the Haploinsufficiency Hypothesis presented in this study. drivers have been identified (Vogelstein et al., 2013). are longer. Highly connected nodes are better positioned to Sequencing of more rare and relatively unexplored cancer control the flow of information, and their removal or hyperactiva- types may identify a few novel potent drivers that are specific tion will have the highest impact across a network due to their to those tumor types, but the vast majority of potent drivers centrality. will already have been seen in other cancers. The major effects of continued sequencing will likely be to solidify the continuum Unexpected Properties of the X Chromosome by bringing much weaker drivers into the realm of statistical Given the potentially deleterious effects of mutating TSGs, we significance. anticipated that TSGs would be depleted from the X chro- mosome by natural selection, as the X is haploid in males and Properties of New Potential Cancer Driver Genes is functionally haploid in females due to dosage compensation. Analysis of the lists enriched for cancer drivers revealed several However, our analysis revealed just the opposite—namely, that general properties that distinguish them from nondriver genes. the X has 86% more TSGs than expected. Oncogenes, on the The lists of both TSGs and OGs are strongly enriched both for other hand, are not overrepresented on the X. The likely explana- residence in protein complexes and for a property known as tion is that a deleterious mutation in a TSG on the X is more pene- betweenness, which is a measure of the degree to which a set trant because there is not a WT copy to compensate for its loss. of genes is enriched for hubs within an interaction network. This further suggests that natural selection has not completely Thus, the driver genes are much more highly connected depleted TSGs from the X, possibly because cancer is largely than the average protein in the human gene network and a postreproductive disease.

958 Cell 155, 948–962, November 7, 2013 ª2013 Elsevier Inc. We found a higher mutation rate for the X than for autosomes, tive pressure on the X could have reduced that number. Thus, and this is further exaggerated in females. In females, the addi- it is possible that there are actually similar densities of TSGs tional increase in X mutability is likely due to the presence of on the X and autosomes (haploinsufficient sporadic TSGs and the inactive X, which has very little transcription and, hence, haplosufficient potential TSGs), but those on the X realize their less transcription-coupled repair and is enriched in late-repli- tumorigenic potential at a higher rate than do those on the cating heterochromatin, which tends to be more mutagenic autosomes. (Stamatoyannopoulos et al., 2009). The mechanism underlying these differences and their biological significance remains to The PAN-Cancer Mutational Analysis Predicts be determined. However, these differences might indicate that Aneuploidy in Cancer the mutation rates of whole chromosomes are set by evolution Aneuploidy is a hallmark of cancer and can have both advanta- and that the higher mutability of the X is advantageous over geous and deleterious consequences for cells (Tang and evolutionary time if it also occurs in the germline. Amon, 2013; Luo et al., 2009), but there is no general theory that explains how patterns of aneuploidy emerge. Knowing the Haploinsufficiency and Cancer identity and potential potency of cancer drivers has allowed us The clonal expansion theory of tumorigenesis argues that, in to uncover a driving force behind selection of arm- and chromo- order for an individual mutation to be selected, it must cause some-level SCNAs. Our analysis using Charm and Chrom as an an expansion of the clone derived from that mutant cell by integrated assessment of the density and potency of the different increasing its relative proliferation and survival (Vogelstein and classes of cancer driver genes on chromosomes displayed a Kinzler, 1993). This is intuitive for OGs, as they are dominant, robust ability to predict the patterns of whole-arm amplifications but it is less so for TSGs. For a hemizygous mutation in a TSG and deletions and aneuploidy (Figures 6, S4, and S5). The fact to be selected in cancer, we have to assume that either the that the Charm score improves the correlations with SCNAs mutation is dominant negative or the TSG is haploinsufficient. compared to the simple gene density of the different classes of Our current analysis of the degree to which essential genes are genes indicates that the ranking of driver genes by TUSON absent from hemizygous recurring focal deletions, coupled Explorer is likely to represent an accurate estimate of the with the reduced frequency with which essential genes experi- potency of their phenotypic effect in cancer and further supports ence LOF mutations in tumors, conservatively suggests 30% the continuum theory. haploinsufficiency overall among human genes (Experimental DensOG and CharmOG do not predict arm amplification as well Procedures). A recent analysis of haploinsufficiency by the as CharmTSG. This reduced predictive potential is likely to be mouse knockout consortium (White et al., 2013) found that because the OGs were selected on the basis of the ability to 42% of genes examined produced a phenotype when heterozy- be activated by mutation and because simply increasing the gous, similar to our estimates. Evidence suggesting that our dosage by 50% might not strongly impact the networks they sporadic TSG list is largely haploinsufficient comes from a com- control. CharmOG, however, does show a strong negative corre- parison of the enrichment in focal deletions of STOP genes lation with arm deletion frequency, indicating that, normally, the versus our sporadic TSGs. STOP genes, which are TSG-like, WT OGs are acting to promote proliferation and survival and the are enriched by 20% (Solimini et al., 2012). If we assume that cumulative reduction of their levels by 50% is deleterious. In this only 30% of this gene set is haploinsufficient and that all of respect, the OGs are behaving like the essential genes, and the the selective enrichment comes from haploinsufficient genes, inclusion of a high-confidence list of 332 essential genes then a list of purely haploinsufficient STOP genes would be together with OGs and TSGs further improves the predictive abil- expected to be enriched by 67%. Perhaps coincidentally, our ity for arm deletions (Figure 6E). As expected, the essential genes list of TSGs is enriched 68% in recurring focal deletions, sug- have no predictive power for amplifications. gesting that a significant proportion, and possibly all, of sporadic CharmTSG strongly predicts whole-arm deletions. Unexpect- TSGs are haploinsufficient. edly, it also strongly predicts arm amplification, providing a We propose that two classes of TSGs might exist: those that strong negative correlation. This suggests that increasing the are haploinsufficient and contribute to sporadic cancer and gene dosage of a group of TSGs can have deleterious effects those that are haplosufficient and do not significantly contribute on tumorigenesis through the process of cumulative triplosensi- to sporadic cancer through mutation. Circumstances under tivity. If TSGs are truly haploinsufficient, their WT protein levels which organisms inherit only one functional copy of those haplo- may be only marginally sufficient to execute their roles. If so, sufficient TSGs might result in cancer because loss of the sec- TSGs may well be more sensitive to increased gene dosage to ond allele would produce a selectable phenotype. This situation further enhance their pathways than typical genes. In other occurs with familial TSGs and the classic Two-Hit model of words, haploinsufficient genes may be more likely to display tumorigenesis. Our hypothesis is consistent with the fact that, triplosensitivity. This property of sporadic TSGs being both out of a list of 73 familial TSGs culled from the literature, only haploinsufficient and triplosensitive, therefore, may make their 32% of them had a combined q value <0.25 in the PAN-Cancer cumulative Charm score an even better parameter to explain analysis (Table S3D). Another circumstance with only one func- SCNAs of chromosome arms and aneuploidy in general. Devel- tional allele per cell occurs on the X, where we see a 86% oping a combined CharmTSG-OG-Ess and ChromTSG-OG-Ess score higher density of TSGs than on the autosomes. If the predicted can now predict 80% of the frequency of arm and chromo- rate of 30% haploinsufficiency is correct, then one might some loss and 65% of the amplifications observed across all expect a 200% increase over autosomes, but negative selec- cancers.

Cell 155, 948–962, November 7, 2013 ª2013 Elsevier Inc. 959 Although the correlation between Charm/Chrom scores and cancer in the assumption that the first hit is fully recessive and SCNAs is striking, there are several areas for improvement. a second hit is required to contribute to tumorigenesis. While it The first area concerns our lack of knowledge of the full comple- is difficult to measure the frequency with which the Two-Hit ment of essential genes and which of these are haploinsufficient. Hypothesis operates in cancers because the role and extent of Second, only a subset of OGs will be dosage sensitive, and this methylation inactivation is not yet known in each tumor, analysis knowledge would improve the correlation. In addition, there are of LOF mutational events suggests that it may be a relatively two classes of OGs. Class I contains classical oncogenes such infrequent event except in the case of a few genes such as as KRAS that are activated by mutation but whose WT copies TP53 (p53) and CDKN2A (p16), both of which are inducible are not necessarily oncogenic after overexpression and will not responders to oncogenic stress, which can increase during be predictive of amplification. Class II contains genes such as tumorigenesis. In the cases of sporadic cancer, wherein the cyclin D that can be activated by overexpression but are difficult two-hit hypothesis does operate, it is still possible and even to activate by missense mutations and thus lack a mutational probable that the genes involved are haploinsufficient to begin signature. Class II OGs cannot be identified with confidence with. Our results have led us to propose that the vast majority, through mutational signatures yet are likely to display triplosen- if not all, of sporadic TSGs are likely to be haploinsufficient and sitivity and would positively correlate with amplification. Third, that, therefore, sporadic TSGs are most likely to operate through some TSGs can be difficult to distinguish from OGs. These are the Haploinsufficiency Model shown in Figure 7D. It is important TGSs that have low haploinsufficiency but can produce a select- to note that these hypotheses are not mutually exclusive, as loss able phenotype by generation of dominant-negative alleles. of the second allele of a haploinsufficient TSG, the second hit, Such genes will lack a strong LOF signature but will show a sig- will undoubtedly provide a stronger selective pressure than the nificant number of deleterious missense mutations, which are first hit. However, a tumor has multiple paths through which to likely to predominantly occur in one or a few crucial residues, evolve, and it may not require loss of that second allele as it thus conferring a significant Entropy score. In addition, early obtains growth-promoting power through the accumulation of SCNA events might influence subsequent events, as is the other events. case when specific aneuploidy co-occurs (Ozery-Flato et al., In 1914, Theodor Boveri proposed that specific ‘‘chromosome 2011), which would confound our analysis to some degree. constitutions can be produced such that the cells that harbor it Finally, refining these lists of cancer drivers will only improve their are driven to unrestrained proliferation’’ (Boveri, 1929). Recurring predictive power. The current programs for prediction have their patterns of aneuploidy exist in tumors, but whether they exist strengths and weaknesses and are likely to be further improved because of the frequency of occurrence of each individual in the future. More precise knowledge of these essential and SCNA event or because they are selected due to a tumorigenic cancer driver genes should significantly improve SCNA predict- phenotypic effect was not known. Here, we propose that the cu- ability and our understanding of the cancer genome. Finally, the mulative phenotypic effects of gene dosage alterations of STOP SCNA frequencies might vary according to tumor type; thus, and GO genes provide the selective pressure that is responsible comparison of data sets within one tumor type might provide for the recurrent patterns of copy number variation observed in more predictive power. In addition, we do not know the back- cancer. Our findings support the hypothesis put forward by ground frequency of SCNAs upon which selection acts, so the Boveri exactly one century ago that aneuploidy is not only a hall- observed SCNA frequency cannot be normalized like mutation mark of cancer, but is also a driving force during the evolution of rates can, and therefore, the observed SCNA frequency de- human cancer. tected might reflect both frequency of the event and its selective power, which could confound the correlation. EXPERIMENTAL PROCEDURES

Models of Cancer Evolution Somatic Mutation Data Set The data set of somatic mutations included data from The Cancer Genome Our work suggests a very important role for cumulative haploin- Atlas (TCGA, http://cancergenome.nih.gov/) research network and from the sufficiency and triplosensitivity operating during cancer evolu- Catalogue of Somatic Mutations in Cancer (COSMIC, http://cancer.sanger. tion to drive tumorigenesis. In each genomic region, there are ac.uk/cancergenome/projects/cosmic/) and the data set published by Alexan- STOP (TSG) and GO (OG and essential) genes that will exert a drov et al. (2013). The data set contained 1,200,000 mutations from 8,207 negative or positive phenotypic effect on tumorigenesis. Both tumor samples from >20 tumor types (Table S1) and will be available at for focal deletions as illustrated by the Cancer Gene Island Model http://elledgelab.med.harvard.edu/. All data related to SCNAs were derived (Figure 7B) and for chromosomes and chromosomal arm SCNAs from the TCGA Genome Data analysis Center at the Broad Institute (Zack et al., 2013). as indicated by the Charm and Chrom analysis (Figure 7C), the integrated cumulative balance of these positive and negative TUSON Explorer Predictions tumorigenic effects of individual genes affected in each SCNA The PolyPhen2 algorithm (Adzhubei et al., 2010) was used to predict the event provides the selective potency to that event and can pre- functional impact of each missense mutation and to classify them as high dict its frequency across cancers. functional impact (HiFI) or low functional impact (LoFI). We defined the four For the past 40 years, the tumor suppressor field has been following classes of mutations: (1) Benign mutations: Silent + LoFI Missense; (2) Loss of Function mutations (LOF): Nonsense and Frameshift mutations; (3) guided by Knudson’s classical Two-Hit Hypothesis of tumori- Splicing mutations: mutations affecting splicing sites; and (4) HiFI missense genesis for familial cancers. Though there are certainly bona mutations. An additional parameter considered was the Entropy score, which fide examples of the Two-Hit Model in sporadic cancer, this measures the degree of randomness of the distribution of missense model conflicts with the theory of clonal evolution of sporadic mutations.

960 Cell 155, 948–962, November 7, 2013 ª2013 Elsevier Inc. Among 22 potential parameters, we selected the most predictive ones by binding protein RBMX as a component of the DNA-damage response. Nat. using the Lasso prediction model and three training sets of known TSGs and Cell Biol. 14, 318–328. OGs (from the Cancer Gene Census, Futreal et al., 2004) and putative neutral Adzhubei, I.A., Schmidt, S., Peshkin, L., Ramensky, V.E., Gerasimova, A., genes. LOF/Benign, Splicing/Benign, and HiFI/Benign ratios were selected by Bork, P., Kondrashov, A.S., and Sunyaev, S.R. (2010). A method and server Lasso for the prediction of TSGs, and the HiFI/Benign ratio and the Entropy for predicting damaging missense mutations. Nat. Methods 7, 248–249. score were selected for the prediction of OGs. TUSON predictions are based Alexandrov, L.B., Nik-Zainal, S., Wedge, D.C., Aparicio, S.A., Behjati, S., Bian- on the calculation of a combined p value (and q value) of the selected param- kin, A.V., Bignell, G.R., Bolli, N., Borg, A., Børresen-Dale, A.L., et al.; Australian eters by using an extended version of the Liptak method (Tables S3A and S3B). Pancreatic Cancer Genome Initiative; ICGC Breast Cancer Consortium; ICGC Based on the combined p values derived with the TUSON method, we esti- MMML-Seq Consortium; ICGC PedBrain. (2013). Signatures of mutational mated the total number of predicted TSGs and OGs by using a histogram- processes in human cancer. Nature 500, 415–421. based method (Mosig et al., 2001). Beroukhim, R., Mermel, C.H., Porter, D., Wei, G., Raychaudhuri, S., Donovan, Charm and Chrom Score and Correlation with Frequency of SCNAs J., Barretina, J., Boehm, J.S., Dobson, J., Urashima, M., et al. (2010). 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962 Cell 155, 948–962, November 7, 2013 ª2013 Elsevier Inc. OPEN ARTICLE doi:10.1038/nature12634

Mutational landscape and significance across12majorcancertypes

Cyriac Kandoth1*, Michael D. McLellan1*, Fabio Vandin2, Kai Ye1,3, Beifang Niu1, Charles Lu1, Mingchao Xie1, Qunyuan Zhang1,3, Joshua F. McMichael1, Matthew A. Wyczalkowski1, Mark D. M. Leiserson2, Christopher A. Miller1, John S. Welch4,5, Matthew J. Walter4,5, Michael C. Wendl1,3,6, Timothy J. Ley1,3,4,5, Richard K. Wilson1,3,5, Benjamin J. Raphael2 &LiDing1,3,4,5

The Cancer Genome Atlas (TCGA) has used the latest sequencing and analysis methods to identify somatic variants across thousands of tumours. Here we present data and analytical results for point mutations and small insertions/deletions from 3,281 tumours across 12 tumour types as part of the TCGA Pan-Cancer effort. We illustrate the distributions of mutation frequencies, types and contexts across tumour types, and establish their links to tissues of origin, environmental/ carcinogen influences, and DNA repair defects. Using the integrated data sets, we identified 127 significantly mutated genes from well-known(for example, mitogen-activated protein kinase, phosphatidylinositol-3-OH kinase,Wnt/b-catenin and receptor tyrosine kinase signalling pathways, and cell cycle control) and emerging (for example, histone, histone modification, splicing, metabolism and proteolysis) cellular processes in cancer. The average number of mutations in these significantly mutated genes varies across tumour types; most tumours have two to six, indicating that the numberof driver mutations required during oncogenesis is relatively small. Mutations in transcriptional factors/regulators show tissue specificity, whereas histone modifiers are often mutated across several cancer types. Clinical association analysis identifies genes having a significant effect on survival, and investigations of mutations with respect to clonal/subclonal architecture delineate their temporal orders during tumorigenesis. Taken together, these results lay the groundwork for developing new diagnostics and individualizing cancer treatment.

The advancement of DNA sequencing technologies now enables the (GBM), head and neck squamous cell carcinoma (HNSC), colon and processing of thousands of tumours of many types for systematic rectal carcinoma (COAD, READ), bladder urothelial carcinoma (BLCA), mutation discovery. This expansion of scope, coupled with appreciable kidney renal clear cell carcinoma (KIRC), ovarian serous carcinoma progress in algorithms1–5, has led directly to characterization of signifi- (OV) and acute myeloid leukaemia (LAML; conventionally called cant functional mutations, genes and pathways6–18. Cancer encompasses AML) (Supplementary Table 1). A total of 617,354 somatic mutations, more than 100 related diseases19, making it crucial to understand the consisting of 398,750 missense, 145,488 silent, 36,443 nonsense, 9,778 commonalities and differences among various types and subtypes. splice site, 7,693 non-coding RNA, 523 non-stop/readthrough, 15,141 TCGA was founded to address these needs, and its large data sets frameshift insertions/deletions (indels) and 3,538 inframe indels, were are providing unprecedented opportunities for systematic, integrated included for downstream analyses (Supplementary Table 2). analysis. We performed a systematic analysis of 3,281 tumours from 12 cancer Distinct mutation frequencies and sequence context types to investigate underlying mechanisms of cancer initiation and Figure 1a shows that AML has the lowest median mutation frequency progression. We describe variable mutation frequencies and contexts and LUSC the highest (0.28 and 8.15 mutations per megabase (Mb), and their associations with environmental factors and defects in DNA respectively). Besides AML, all types average over 1 mutation per Mb, repair. We identify 127 significantly mutated genes (SMGs) from diverse substantially higher than in paediatric tumours20. Clustering21 illus- signalling and enzymatic processes. The finding of a TP53-driven trates that mutation frequencies for KIRC, BRCA, OV and AML are breast, head and neck, and ovarian cancer cluster with a dearth of other normally distributed within a single cluster, whereas other types have mutations in SMGs suggests common therapeutic strategies might be several clusters (for example, 5 and 6 clusters in UCEC and COAD/ applied for these tumours. We determined interactions among muta- READ, respectively) (Fig. 1a and Supplementary Table 3a, b). In UCEC, tions and correlated mutations in BAP1, FBXW7 and TP53 with det- the largest patient cluster has a frequency of approximately 1.5 muta- rimental phenotypes across several cancer types. The subclonal structure tions per Mb, and the cluster with the highest frequency is more than and transcription status of underlying somatic mutations reveal the 150 times greater. Multiple clusters suggest that factors other than age trajectory of tumour progression in patients with cancer. contribute to development in these tumours14,16. Indeed, there is a significant correlation between high mutation frequency and DNA Standardization of mutation data repair pathway genes (for example, PRKDC, TP53 and MSH6) (Sup- Stringent filters (Methods) were applied to ensure high quality muta- plementary Table 3c). Notably, PRKDC mutations are associated with tion calls for 12 cancer types: breast adenocarcinoma (BRCA), lung high frequency in BLCA, COAD/READ, LUAD and UCEC, whereas adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), TP53 mutations are related with higher frequencies in AML, BLCA, uterine corpus endometrial carcinoma (UCEC), glioblastoma multiforme BRCA, HNSC, LUAD, LUSC and UCEC (all P , 0.05). Mutations in

1The Genome Institute, Washington University in St Louis, Missouri 63108, USA. 2Department of Computer Science, Brown University, Providence, Rhode Island 02912, USA. 3Department of Genetics, Washington University in St Louis, Missouri 63108, USA. 4Department of Medicine, Washington University in St Louis, Missouri 63108, USA. 5Siteman Cancer Center, Washington University in St Louis, Missouri 63108, USA. 6Department of Mathematics, Washington University in St Louis, Missouri 63108, USA. *These authors contributed equally to this work.

17 OCTOBER 2013 | VOL 502 | NATURE | 333 ©2013 Macmillan Publishers Limited. All rights reserved RESEARCH ARTICLE a of C.T transitions are between 59% and 67%, substantially higher than the approximately 40% in other cancer types. Higher frequencies of transition mutations at CpG in gastrointestinal tumours, including 22 100 colorectal, were previously reported . We found three additional cancer types (GBM, AML and UCEC) clustered in the C.T mutation at CpG, consistent with previous findings of aberrant DNA methyla- tion in endometrial cancer23 and glioblastoma24. BLCA has a unique signature for C.T transitions compared to the other types (enriched for TC) (Extended Data Fig. 1).

1 Significantly mutated genes Genes under positive selection, either in individual or multiple tumour

Number of mutations per Mb types, tend to display higher mutation frequencies above background. Our statistical analysis3, guided by expression data and curation (Methods), identified127 such genes (SMGs; Supplementary Table 4). These SMGs are involved in a wide range of cellular processes, broadly classified 0.01 into 20 categories (Fig. 2), including transcription factors/regulators, AML BRCA OV KIRC UCEC GBM COAD/ HNSC BLCA LUAD LUSC histone modifiers, genome integrity, receptor tyrosine kinase signal- b READ ling, cell cycle, mitogen-activated protein kinases (MAPK) signalling, 100 phosphatidylinositol-3-OH kinase (PI(3)K) signalling, Wnt/b-catenin 75 signalling, histones, ubiquitin-mediated proteolysis, and splicing (Fig. 2). 50 The identification of MAPK, PI(3)K and Wnt/b-catenin signalling path- 25 ways is consistent with classical cancer studies. Notably, newer categories 0 (for example, splicing, transcription regulators, metabolism, proteolysis

Ti/Tv frequency (%) C>T C>G C>A A>T A>G A>C c and histones) emerge as exciting guides for the development of new A>C A>G A>T therapeutic targets. Genes categorized as histone modifiers (Z 5 0.57), PI(3)K signalling (Z 5 1.03), and genome integrity (Z 5 0.66) all relate to more than one cancer type, whereas transcription factor/regulator (Z 5 0.40), TGF-b signalling (Z 5 0.66), and Wnt/b-catenin signalling (Z 5 0.55) genes tend to associate with single types (Methods). Notably, 3,053 out of 3,281 total samples (93%) across the Pan- Cancer collection had at least one non-synonymous mutation in at least one SMG. The average number of point mutations and small indels in these genes varies across tumour types, with the highest (,6 C>A C>G C>T mutations per tumour) in UCEC, LUAD and LUSC, and the lowest (,2 mutations per tumour) in AML, BRCA, KIRC and OV. This suggests that the numbers of both cancer-related genes (only 127 identified in this study) and cooperating driver mutations required during oncogenesis are small (most cases only had 2–6) (Fig. 3), although large-scale structural rearrangements were not included in this analysis.

Common mutations Correlation coefficient The most frequently mutated gene in the Pan-Cancer cohort is TP53 0.75 0.85 0.95 (42% of samples). Its mutations predominate in serous ovarian (95%) Figure 1 | Mutation frequencies, spectra and contexts across 12 cancer and serous endometrial carcinomas (89%) (Fig. 2). TP53 mutations types. a, Distribution of mutation frequencies across 12 cancer types. Dashed are also associated with basal subtype breast tumours. PIK3CA is the grey and solid white lines denote average across cancer types and median for second most commonly mutated gene, occurring frequently (.10%) each type, respectively. b, Mutation spectrum of six transition (Ti) and in most cancer types except OV, KIRC, LUAD and AML. PIK3CA transversion (Tv) categories for each cancer type. c, Hierarchically clustered mutations frequented UCEC (52%) and BRCA (33.6%), being speci- mutation context (defined by the proportion of A, T, C and G nucleotides fically enriched in luminal subtype tumours. Tumours lacking PIK3CA within 62 bp of variant site) for six mutation categories. Cancer types mutations often had mutations in PIK3R1, with the highest occur- correspond to colours in a. Colour denotes degree of correlation: yellow (r 5 0.75) and red (r 5 1). rences in UCEC (31%) and GBM (11%) (Fig. 2). Many cancer types carried mutations in chromatin re-modelling genes. In particular, histone-lysine N-methyltransferase genes (MLL2 POLQ and POLE associate with high frequencies in multiple cancer types; (also known as KMT2D), MLL3 (KMT2C) and MLL4 (KMT2B)) clus- POLE association in UCEC is consistent with previous observations14. ter in bladder, lung and endometrial cancers, whereas the lysine (K)- Comparison of spectra across the 12 types (Fig. 1b and Supplemen- specific demethylase KDM5C is prevalently mutated in KIRC (7%). tary Table 3d) reveals that LUSC and LUAD contain increased C.A Mutations in ARID1A are frequent in BLCA, UCEC, LUAD and transversions, a signature of cigarette smoke exposure10. Sequence LUSC, whereas mutations in ARID5B predominate in UCEC (10%) context analysis across 12 types revealed the largest difference being (Fig. 2). in C.T transitions and C.G transversions (Fig. 1c). The frequency KRAS and NRAS mutations are typically mutually exclusive, with of thymine 1-bp (base pair) upstream of C.G transversions is mark- recurrent activating mutations (KRAS (Gly 12), KRAS (Gly 13) and edly higher in BLCA, BRCA and HNSC than in other cancer types NRAS (Gln 61)) common in COAD/READ (30%, 5% and 5%, respect- (Extended Data Fig. 1). GBM, AML, COAD/READ and UCEC have ively), UCEC (15%, 4% and 2%) and LUAD (24%, 1% and 2%). EGFR similar contexts in that the proportions of guanine 1 base downstream mutations are frequent in GBM (27%) and LUAD (11%). Recurrent,

334 | NATURE | VOL 502 | 17 OCTOBER 2013 ©2013 Macmillan Publishers Limited. All rights reserved ARTICLE RESEARCH AML AML BLCA BRCA COAD/READ GBM HNSC KIRC LUAD LUSC OV UCEC Pan−Cancer BLCA BRCA COAD/READ GBM HNSC KIRC LUAD LUSC OV UCEC Pan−Cancer Transcription 0.0 0.0 0.0 0.0 0.0 52.3 0.0 0.0 0.6 0.0 0.9 6.9 VHL MAPK signalling 0.0 0.8 45.1 0.7 0.3 0.2 4.0 26.3 1.2 0.6 20.0 6.7 KRAS factor/regulator 1.0 10.6 1.0 0.0 2.0 0.0 0.0 2.6 2.9 0.3 0.4 3.2 GATA3 7.1 2.5 1.0 11.0 2.7 1.7 1.0 11.8 10.3 3.8 3.5 4.4 NF1 2.0 0.7 3.1 0.7 1.3 1.2 0.5 14.9 6.3 1.0 3.9 2.6 TSHZ3 3.1 7.2 0.0 2.1 1.0 1.2 0.0 1.8 1.7 0.3 3.5 2.7 MAP3K1 17.4 0.8 2.1 0.3 8.0 1.4 0.0 0.9 4.6 0.3 5.2 2.5 EP300 2.0 0.4 3.6 2.1 1.0 0.2 0.0 6.6 4.6 0.6 0.9 1.5 BRAF 2.0 2.4 1.6 0.0 3.3 0.5 0.5 1.3 0.0 0.3 16.5 2.4 CTCF 2.0 0.1 8.8 0.3 0.0 0.0 7.5 1.8 0.6 0.6 2.6 1.5 NRAS 2.0 1.1 1.6 1.4 2.3 1.2 0.0 4.0 6.9 1.6 8.7 2.3 TAF1 0.0 4.1 2.6 0.0 0.3 0.0 0.0 1.3 0.6 0.3 1.3 1.4 MAP2K4 4.1 0.9 3.1 2.4 1.3 0.7 0.0 6.6 3.5 1.0 1.7 1.8 TSHZ2 2.0 0.3 2.1 0.7 0.7 0.5 0.0 1.8 1.2 0.3 0.4 0.7 MAPK8IP1 1.0 3.3 1.0 0.0 0.7 0.0 9.0 0.4 0.0 0.0 1.3 1.6 RUNX1 PI(3)K signalling 17.4 33.6 17.6 11.0 20.6 2.9 0.0 4.4 14.9 0.6 52.2 17.8 PIK3CA 5.1 0.5 1.0 1.4 1.7 1.0 0.0 3.5 4.6 0.6 3.0 1.5 MECOM 3.1 3.8 1.0 30.7 1.3 4.3 0.0 2.2 8.1 0.6 63.5 9.7 PTEN 3.1 2.4 1.0 0.0 0.7 0.0 0.0 4.4 2.9 1.0 1.3 1.4 TBX3 1.0 2.5 2.1 11.4 1.7 0.5 0.0 1.3 0.6 0.3 30.9 4.4 PIK3R1 1.0 0.5 0.5 0.7 0.7 0.5 0.0 1.8 2.9 0.6 5.2 1.1 SIN3A 2.0 1.2 0.0 0.3 2.0 0.5 0.5 11.4 5.8 1.0 0.4 1.9 TLR4 0.0 0.1 1.0 0.7 0.0 0.7 6.0 3.5 2.3 0.0 0.4 1.0 WT1 2.0 0.4 0.5 2.4 2.7 0.7 0.0 5.3 7.5 1.0 1.3 1.7 PIK3CG 2.0 0.5 2.6 0.0 0.0 0.7 0.0 1.8 1.2 0.6 1.3 0.8 EIF4A2 0.0 2.5 0.0 0.3 0.7 0.5 0.0 0.0 0.6 0.0 1.3 0.9 AKT1 4.1 1.7 0.0 1.0 0.7 0.0 0.0 0.4 0.6 0.0 0.0 0.8 FOXA1 TGF-β signalling 2.0 0.4 9.8 0.3 2.0 0.5 0.0 3.1 2.9 0.0 0.0 1.4 SMAD4 3.1 0.4 0.0 0.3 0.3 0.5 3.0 0.9 1.2 0.3 1.3 0.8 PHF6 3.1 0.4 2.6 0.7 3.0 0.2 0.0 0.9 1.7 1.0 1.3 1.1 TGFBR2 1.0 2.1 0.0 0.0 0.0 0.2 1.0 0.4 0.6 0.0 0.4 0.7 CBFB 0.0 0.7 3.6 0.0 1.3 1.0 0.0 2.2 1.2 0.3 1.7 1.0 ACVR1B 0.0 0.1 4.2 1.0 0.7 0.7 0.0 1.3 0.6 0.0 0.4 0.7 SOX9 1.0 0.5 5.7 0.0 1.0 0.5 0.0 0.9 1.2 0.0 1.3 0.9 SMAD2 8.2 0.1 3.6 0.0 0.3 0.0 0.0 0.4 0.0 0.3 0.4 0.6 ELF3 1.0 0.5 2.6 0.0 0.7 0.2 0.0 0.9 1.2 0.0 0.4 0.6 ACVR2A 2.0 0.9 0.0 0.7 0.7 0.0 0.0 0.9 1.7 0.0 0.0 0.6 VEZF1 Wnt/β-catenin 4.1 0.5 81.9 0.3 4.0 1.4 0.0 9.2 4.0 2.2 5.7 7.3 APC 0.0 0.0 0.0 0.0 0.0 0.2 6.5 0.0 0.6 0.0 0.0 0.5 CEBPA signalling 2.0 0.1 4.7 0.3 0.7 0.2 0.0 3.5 1.7 0.6 28.3 2.9 CTNNB1 1.0 0.0 0.5 0.0 0.0 0.0 0.0 0.0 0.0 0.6 4.8 0.5 FOXA2 3.1 0.1 3.6 0.3 1.7 0.2 0.0 0.9 0.6 0.3 2.6 0.9 AXIN2 Histone modifier 24.5 6.4 2.6 3.1 7.3 3.6 0.5 18.4 15.5 1.9 5.2 6.6 MLL3 2.0 1.1 0.00.01.00.70.02.2 1.2 0.3 1.3 0.8 TBL1XR1 25.5 1.6 1.6 1.7 17.9 3.1 0.5 8.8 20.1 0.6 8.3 5.9 MLL2 0.0 0.0 0.5 0.3 0.3 0.0 0.0 0.4 0.0 0.0 3.0 0.3 SOX17 27.6 2.0 5.7 0.7 3.0 2.9 0.5 6.1 6.3 1.0 30.0 5.4 ARID1A Histone 1.00.41.00.71.3 0.2 0.0 0.4 0.6 1.3 0.0 0.6 HIST1H1C 6.1 0.4 0.0 0.7 2.3 32.9 0.0 1.8 3.5 0.3 2.6 5.4 PBRM1 0.0 0.0 0.0 0.7 0.7 0.0 0.0 1.8 1.2 0.0 0.9 0.4 H3F3C 6.1 1.2 2.6 1.7 2.3 11.5 0.5 7.9 2.9 1.9 2.6 3.6 SETD2 1.0 0.0 0.0 0.0 1.3 0.0 0.0 0.0 1.2 0.3 2.6 0.4 HIST1H2BD 6.1 0.3 0.5 0.3 10.6 1.0 0.0 3.1 5.2 0.6 5.7 2.4 NSD1 Proteolysis 9.2 0.8 11.4 0.3 5.0 0.2 0.0 1.3 5.2 1.0 11.7 3.0 FBXW7 2.0 0.4 1.6 1.4 3.0 1.4 1.0 12.7 5.2 0.0 2.2 2.2 SETBP1 3.1 0.1 0.0 0.0 4.0 0.5 0.0 17.1 12.1 0.3 1.3 2.6 KEAP1 1.0 0.5 0.5 0.7 1.0 6.5 0.0 4.8 2.9 1.9 2.2 2.0 KDM5C 1.0 0.1 0.0 0.0 1.0 0.0 0.0 0.4 0.6 0.3 6.5 0.7 SPOP 26.5 1.1 0.0 1.0 2.7 1.0 1.5 0.9 4.0 0.0 0.9 2.0 KDM6A Splicing 4.1 1.8 1.0 0.7 0.7 1.0 0.5 2.2 2.3 0.0 2.2 1.3 SF3B1 7.1 0.7 2.1 2.1 2.7 1.0 0.0 1.8 4.0 0.3 8.3 2.0 MLL4 1.0 0.3 0.5 0.0 1.3 0.0 4.0 2.6 0.0 0.0 0.9 0.8 U2AF1 3.1 0.4 0.0 0.3 3.3 0.7 0.0 2.2 1.7 0.6 9.6 1.6 ARID5B 1.0 0.0 2.6 0.0 0.0 0.2 0.0 0.4 0.0 0.0 0.9 0.3 PCBP1 3.1 0.4 1.6 0.0 3.0 1.0 2.5 1.3 5.2 0.0 0.9 1.3 ASXL1 5.1 7.0 0.5 0.3 1.3 0.5 0.0 1.3 1.7 0.3 3.0 2.5 CDH1 HIPPO signalling 1.0 0.1 0.0 1.0 0.3 0.7 1.5 2.2 2.3 0.0 1.7 0.8 EZH2 2.0 0.1 0.0 0.3 6.0 0.5 0.0 0.9 0.0 0.0 0.0 0.8 AJUBA 50.0 32.9 58.6 28.3 69.8 2.2 7.5 51.8 79.3 94.6 27.8 42.0 TP53 0.0 0.5 1.0 0.0 25.5 4.0 4.0 1.0 1.3 2.8 DNMT3A Genome integrity DNA methylation 1.7 1.2 11.2 2.1 5.7 1.4 2.7 2.9 0.0 7.9 4.0 1.3 6.5 3.3 ATM 3.10.40.00.70.31.98.5 3.1 2.3 0.0 2.2 1.6 TET2 8.2 1.2 1.0 5.5 4.3 1.9 0.0 6.1 5.8 0.6 3.0 2.8 ATRX 3.1 0.3 0.0 5.2 0.3 0.5 9.5 0.9 1.2 0.0 0.9 1.5 IDH1 6.1 1.7 1.6 1.4 3.7 1.9 0.0 5.7 5.8 3.2 4.4 2.7 BRCA2 Metabolism 0.0 0.0 1.6 0.0 0.0 0.0 10.0 0.4 0.0 0.0 0.4 0.8 IDH2 4.1 0.8 2.1 1.4 5.3 1.2 0.0 5.7 4.0 0.6 7.0 2.4 ATR 9.20.10.00.05.31.2 0.0 2.2 14.9 0.0 5.2 2.3 NFE2L2 10.2 0.9 1.0 4.1 0.7 1.7 3.0 2.6 3.5 1.0 3.9 2.2 STAG2 NFE2L 3.1 0.8 0.0 0.3 1.3 0.2 0.0 0.0 2.3 0.3 1.7 0.8 NFE2L3 4.1 0.3 0.0 0.7 1.0 10.1 0.0 1.3 0.6 0.6 2.2 2.0 BAP1 1.00.11.60.01.31.2 0.0 1.3 4.6 1.3 8.7 1.5 PPP2R1A 4.1 1.6 0.0 1.0 2.7 1.0 0.0 3.5 5.2 3.5 0.9 1.9 BRCA1 Protein phosphatase 0.0 0.1 1.0 1.7 0.3 0.2 4.5 2.6 1.7 0.3 0.9 1.0 PTPN11 3.1 0.8 1.6 1.7 1.0 0.5 3.5 1.3 0.6 1.3 4.4 1.5 SMC1A 0.0 0.0 0.0 0.3 0.7 0.5 0.0 0.4 0.0 0.0 10.9 1.0 RPL22 1.0 0.4 0.0 1.4 1.7 1.2 3.5 2.6 2.3 0.3 0.4 1.2 SMC3 Ribosome 0.0 0.4 0.0 2.8 0.0 1.4 0.0 0.4 1.2 0.0 0.9 0.7 RPL5 2.0 0.4 0.0 1.7 2.3 0.7 0.0 0.9 1.2 0.3 1.3 0.9 CHEK2 2.0 1.4 3.6 1.4 1.3 6.0 0.0 7.5 4.6 1.9 5.2 3.0 MTOR 2.0 0.5 1.0 0.3 1.0 0.0 2.5 2.6 1.2 0.3 0.9 0.9 RAD21 TOR signalling 0.0 0.3 0.0 0.0 0.3 0.2 0.0 8.8 1.7 0.0 0.4 0.9 STK11 12.2 0.1 0.5 0.0 0.3 0.2 0.0 1.3 0.0 0.3 0.4 0.7 ERCC2 Other 5.1 1.4 2.1 1.0 7.3 1.4 0.0 21.5 19.0 1.3 5.2 4.6 NAV3 RTK signalling 1.0 0.7 1.6 26.6 4.7 1.7 1.0 11.4 2.9 1.9 1.3 4.6 EGFR 5.1 0.4 0.0 0.0 19.3 1.0 0.5 3.1 8.1 0.6 1.7 3.1 NOTCH1 2.0 0.4 0.0 1.7 0.7 0.5 26.5 4.0 4.0 1.0 0.9 2.7 FLT3 5.1 0.7 2.6 1.0 5.0 1.4 0.0 6.6 11.5 2.9 3.5 2.8 LRRK2 1.0 0.5 3.1 1.0 3.7 0.5 0.5 8.8 6.3 1.0 2.2 2.1 EPHA3 15.3 1.1 0.0 0.0 6.3 1.9 0.0 9.7 5.8 1.0 0.0 2.7 MALAT1 2.0 0.8 3.6 0.3 4.3 1.4 0.0 7.5 5.2 0.0 2.6 2.1 ERBB4 5.1 0.9 0.5 0.7 3.7 1.2 0.54.05.81.610.0 2.5 ARHGAP35 6.1 0.4 1.0 3.8 1.0 1.4 0.5 6.6 4.0 1.0 1.3 1.9 PDGFRA 7.1 0.8 0.5 1.0 4.3 1.2 0.0 5.7 9.2 1.0 3.9 2.4 POLQ 3.1 0.4 0.0 1.4 1.3 1.2 0.0 9.7 3.5 0.3 1.7 1.6 EPHB6 8.2 3.9 0.5 0.7 3.3 0.7 0.0 2.6 3.5 0.3 1.3 2.2 NCOR1 2.0 0.9 0.0 0.3 0.7 0.2 0.0 3.1 2.3 0.0 10.4 1.5 FGFR2 3.1 1.2 0.0 0.7 4.3 1.0 0.5 5.3 4.6 0.3 6.5 2.1 USP9X 1.0 0.5 1.0 1.0 1.0 0.7 4.0 1.8 3.5 1.9 2.2 1.4 KIT 0.0 0.0 0.0 0.3 0.3 0.0 27.0 0.9 0.0 0.0 0.4 1.8 NPM1 8.2 0.1 0.5 1.4 1.7 1.4 0.0 0.4 2.3 0.3 0.4 1.0 FGFR3 1.00.50.00.32.7 0.2 0.0 10.5 5.8 0.6 1.3 1.7 HGF Cell cycle 4.1 0.0 0.5 0.7 21.3 1.0 0.0 6.6 14.9 0.0 0.4 3.6 CDKN2A 2.0 0.3 0.0 2.8 2.7 0.7 0.0 3.1 4.0 0.3 3.0 1.4 EPPK1 14.3 1.8 0.5 8.3 3.0 0.2 0.0 5.3 6.9 1.9 3.9 3.2 RB1 1.0 0.7 2.1 0.0 2.3 0.5 0.0 1.8 3.5 0.3 3.5 1.2 AR 4.1 0.9 1.6 0.3 1.7 1.4 0.0 3.1 0.6 2.9 2.2 1.5 CDK12 1.0 0.8 5.2 0.0 2.7 0.5 0.0 0.9 1.7 0.6 1.7 1.2 LIFR 2.0 0.9 1.0 0.3 0.7 0.0 0.0 1.8 0.0 0.3 0.9 0.7 CDKN1B 5.1 0.5 0.5 0.7 1.7 1.2 0.5 0.4 1.2 0.3 1.3 0.9 PRX 2.0 0.1 0.0 0.0 0.3 0.0 0.0 0.9 0.6 0.0 5.2 0.6 CCND1 2.0 0.3 0.0 0.3 1.0 0.5 0.0 5.3 0.0 0.0 0.4 0.7 CRIPAK 12.2 0.0 0.0 0.3 0.0 0.2 0.0 0.4 1.2 0.3 0.0 0.6 CDKN1A 1.00.30.00.30.00.20.00.41.2 0.0 0.4 0.3 EGR3 0.0 0.0 0.0 1.0 0.0 0.2 0.0 0.0 0.6 0.6 0.0 0.2 CDKN2C 0.0 0.1 0.5 0.0 0.0 0.2 0.0 0.0 1.2 0.0 0.9 0.2 B4GALT3 0.0 0.1 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.2 MIR142 Figure 2 | The 127 SMGs from 20 cellular processes in cancer identified in and Pan-Cancer are shown, with the highest percentage in each gene among 12 12 cancer types. Percentages of samples mutated in individual tumour types cancer types in bold. gain-of-function mutations in IDH1 (Arg 132) and/or IDH2 (Arg 172) Wnt/b-catenin signalling (93% of samples). Several mutations occur typify GBM and AML (Supplementary Table 2 and Fig. 2). Although exclusively in AML, including recurrent mutations in NPM1 (27%) KRAS residues Gly 12 and Gly 13 are commonly mutated in LUAD, and FLT3 (27%), and rare mutations affecting MIR142 (Fig. 2). COAD/READ and UCEC, the proportion of Gly12Cys changes is Mutations of methylation and chromatin modifiers are also typical significantly higher in lung cancer (P , 3.2 3 10210), resulting from in AML, mostly affecting DNMT3A and TET2. BRCA-specific muta- the high C.A transversion rate (Extended Data Fig. 2). tions include GATA3 and MAP3K1, whereas KEAP1 mutations pre- dominate in lung cancer (LUAD 17%, LUSC 12%). EPHA3 (9%), Tumour-type-specific mutations SETBP1 (13%) and STK11 (9%) are characteristic in LUAD. Signature mutations exclusive to KIRC include those affecting VHL (52%) and PBRM1 (33%) (Fig. 2). Mutations to BAP1 (10%) and Shared and cancer type-specific mutation signatures SETD2 (12%) are also most common in KIRC. Transcription factor Cluster analysis on mutations in SMGs (Fig. 4 and Extended Data Fig. 3) CTCF, ribosome component RPL22, and histone modifiers ARID1A showed 72% (1,881 of 2,611) of tumours are adjacent to those from the and ARID5B have the highest frequencies in UCEC. Predominant same tissue type. Notably, clustering identified several dominant groups COAD/READ-specific mutations are those affecting APC (82%) and in UCEC, COAD, GBM, AML, KIRC, OV and BRCA. Two major

17 OCTOBER 2013 | VOL 502 | NATURE | 335 ©2013 Macmillan Publishers Limited. All rights reserved RESEARCH ARTICLE

12 Mutual exclusivity and co-occurrence among SMGs Pairwise exclusivity and co-occurrence analysis for the 127 SMGs 10 found 14 mutually exclusive (false discovery rate (FDR) , 0.05) and 148 co-occurring (FDR , 0.05) pairs (Supplementary Table 6). TP53 8 and CDH1 are exclusive in BRCA, with mutations enriched in different subtypes13, as are TP53 and CTNNB1 in UCEC. Cohort analysis iden- tified pairs where at least one gene has mutations strongly associated 6 (corrected P , 0.05) to one cancer type, and also identifies TP53 and PIK3CA with significant exclusivity (Extended Data Fig. 4). Pairs with 4 significant co-occurrence include IDH1 and ATRX in GBM29, TP53 and CDKN2A in HNSC, and TBX3 and MLL4 in LUAD. 2 Dendrix30 identified a set of five genes (TP53, PTEN, VHL, NPM1 and GATA3) having strong mutual exclusivity (P , 0.01) (Extended 0 Data Fig. 5a and Supplementary Table 7). Not surprisingly, many are Number of non-synonymous mutations in SMGs OV BRCA GBM KIRC AML COAD/ HNSC LUAD LUSC BLCA UCEC associated (P , 0.05) with one cancer type (for example, VHL muta- READ (n = 316) (n = 763) (n = 290) (n = 471) (n = 200) (n = 193) (n = 301) (n = 228) (n = 174) (n = 98) (n = 230) tions in KIRC), demonstrating a strong relationship between exclus- Figure 3 | Distribution of mutations in 127 SMGs across Pan-Cancer ivity and tissue of origin. When 600 non-cancer-type-specific genes cohort. Box plot displays median numbers of non-synonymous mutations, were added to the analysis (Methods), we identified another set con- with outliers shown as dots. In total, 3,210 tumours were used for this analysis sisting of TP53, PIK3CA, PIK3R1, SETD2 and WT1 (P , 0.01; (hypermutators excluded). Extended Data Fig. 5b and Supplementary Table 7). Dendrix also finds genes with strong mutual exclusivity from each cancer type endometrial endometroid clusters were found, one having mutations separately (Extended Data Fig. 5c), allowing calculation of ‘cancer in PIK3CA, PTEN and ARID1A, and the other containing mutations in exclusivity’. KIRC has the strongest exclusivity from the other 11 two additional genes (PIK3R1 and CTNNB1). Five major breast cancer cancer types, followed by AML with clear exclusivity from BRCA clusters were observed, with mutations in CDH1, GATA3, MAP3K1, and UCEC. Conversely, COAD/READ displayed the greatest co- PIK3CA and TP53 as drivers for respective clusters. The TP53-driven occurrence with other cancer types (Extended Data Fig. 5d). cluster is adjacent to an HNSC cluster and an ovarian cancer cluster, all having a dearth of other SMG mutations (Fig. 4). The glioblastoma cluster is characterized by mutations in EGFR. Two kidney clear cell Clinical correlation across tumour types cancer clusters were detected; both have VHL as the common driver We examined how clinical features (Supplementary Table 8) correlate and one has additional mutations in PBRM1 and/or BAP1 (refs 25–27). with somatic events in 127 SMGs within tumour types. Some findings PBRM1 and BAP1 mutations are mutually exclusive in KIRC (P 5 are unsurprising, such as the correlation of TP53 mutations with gene- 0.006), consistent with previous reports26,28. AML has three major clus- rally unfavourable indicators, for example in tumour stage (P 5 0.01, ters represented by various combinations of DNMT3A, NPM1 and Fisher’s exact test) and elapsed time to death (P 5 0.006, Wilcoxon) in FLT3 mutations, and one cluster dominated by RUNX1 mutations. HNSC, age (P 5 0.002, Wilcoxon rank test) and time to death (P 5 One cluster having APC and KRAS mutations was almost exclusively 0.09, Wilcoxon) in AML, and vital status in OV (P 5 0.04, Fisher). In detected in COAD/READ. Tumours from BLCA, HNSC, LUAD and UCEC, mutations in several genes are correlated with the endometrioid LUSC are largely scattered over the Pan-Cancer cohort, indicating rather than serous subtype: PTEN, CTNNB1, PIK3R1, KRAS, ARID1A, extensive heterogeneity in these diseases. CTCF, RPL22 and ARID5B (all P , 0.03) (Supplementary Table 9).

BLCA BRCA COAD/READ GBM HNSC KIRC AML LUAD LUSC OV UCEC

TP53 PIK3CA PTEN PBRM1 VHL KRAS APC MLL3 EGFR ARID1A CTNNB1 PIK3R1 MLL2 NF1 GATA3 SETD2 MAP3K1 NOTCH1 CDKN2A ATM RB1 CDH1 NAV3 FBXW7 DNMT3A NPM1 FLT3 MTOR BAP1 IDH1 ATRX BRCA2 CTCF NCOR1 STAG2 TAF1 RUNX1

Figure 4 | Unsupervised clustering based on mutation status of SMGs. mutations detected in UCEC, COAD, GBM, AML, KIRC, OV and BRCA were Tumours having no mutation or more than 500 mutations were excluded. A highlighted. Complete gene list shown in Extended Data Fig. 3. mutation status matrix was constructed for 2,611 tumours. Major clusters of

336 | NATURE | VOL 502 | 17 OCTOBER 2013 ©2013 Macmillan Publishers Limited. All rights reserved ARTICLE RESEARCH

We examined which genes correlate with survival using the Cox HR 5 2.20, 95% CI: 1.47–3.29, more than 53 mutated tumours out proportional hazards model, first analysing individual cancer types of 888 total), with mutations associating with detrimental outcome in using age and gender as covariates; an average of 2 genes (range: 0–4) four tumour types and notable associations in KIRC (P 5 0.00079), with mutation frequency $2% were significant (P # 0.05) in each consistent with a recent report28, and in UCEC (P 5 0.066). Mutations in type (Supplementary Table 10a and Extended Data Fig. 6). KDM6A several other genes are detrimental, including DNMT3A (HR 5 1.59), and ARID1A mutations correlate with better survival in BLCA previously identified with poor prognosis in AML34,andKDM5C (P 5 0.03, hazard ratio (HR) 5 0.36, 95% confidence interval (CI): (HR 5 1.63), FBXW7 (HR 5 1.57) and TP53 (HR 5 1.19). TP53 has 0.14–0.92) and UCEC (P 5 0.03, HR 5 0.11, 95% CI: 0.01–0.84), significant associations with poor outcome in KIRC (P 5 0.012), AML respectively, but mutations in SETBP1, recently identified with worse (P 5 0.0007) and HNSC (P 5 0.00007). Conversely, BRCA2 (P 5 0.05, prognosis in atypical chronic myeloid leukaemia (aCML)31, have a HR 5 0.62, 95% CI: 0.38 to 0.99) correlates with survival benefit in six significant detrimental effect in HNSC (P 5 0.006, HR 5 3.21, 95% CI: types, including OV and UCEC (Supplementary Table 10a, b). IDH1 1.39–7.44). BAP1 strongly correlates with poor survival (P 5 0.00079, mutations are associated with improved prognosis across the Pan- HR 5 2.17, 95% CI: 1.38–3.41) in KIRC. Conversely, BRCA2 muta- Cancer set (HR 5 0.67, P 5 0.16) and also in GBM (HR 5 0.42, tions (P 5 0.02, HR 5 0.31, 95% CI: 0.12–0.85) associate with better P 5 0.09) (Supplementary Table 10a, b), consistent with previous work35. survival in ovarian cancer, consistent with previous reports32,33; BRCA1 mutations showed positive correlation with better survival, but did not Driver mutations and tumour clonal architecture reach significance here. To understand the temporal order of somatic events, we analysed the We extended our survival analysis across cancer types, restricting variant allele fraction (VAF) distribution of mutations in SMGs across our attention to the subset of 97 SMGs whose mutations appeared AML, BRCA and UCEC (Fig. 5a and Supplementary Table 11a) and in $2% of patients having survival data in $2 tumour types. Taking other tumour types (Extended Data Fig. 7). To minimize the effect of type, age and gender as covariates, we found 7 significant genes: BAP1, copy number alterations, we focused on mutations in copy neutral DNMT3A, HGF, KDM5C, FBXW7, BRCA2 and TP53 (Extended Data segments. Mutations in TP53 have higher VAFs on average in all three Table 1). In particular, BAP1 was highly significant (P 5 0.00013, cancer types, suggesting early appearance during tumorigenesis, although

a AML BRCA UCEC

100 ChrX 100 100

75 75 75

50 50 50 VAF VAF VAF

25 25 25

0 0 0 AR KIT NF1 RB1 ATR WT1 IDH1 IDH2 FLT3 TP53 TP53 TP53 TAF1 AKT1 TET2 MLL4 TBX3 MLL3 PHF6 CDH1 PTEN PTEN KRAS KRAS KRAS CTCF CTCF SMC3 NRAS NRAS SPOP SIN3A SF3B1 U2AF1 RPL22 RAD21 FOXA1 GATA3 SOX17 STAG2 FGFR2 FOXA2 USP9X RUNX1 RUNX1 CEBPA SMC1A CCND1 PIK3R1 PIK3R1 NCOR1 FBXW7 PIK3CA PIK3CA ARID1A ARID1A ARID5B NFE2L2 PTPN11 MECOM MAP3K1 MAP2K4 CTNNB1 DNMT3A TBL1XR1 PPP2R1A ARHGAP35 HIST1H2BD

AML TCGA-AB-2968 BRCA TCGA-BH-A18P UCEC TCGA-B5-A0JV b VAF VAF VAF 0 20 40 60 80 100 0 20 40 60 80 100 0 20 40 60 80 100

2 copies 2 copies 2 copies Model fit Model fit Model fit Component fits Component fits Component fits Density (a.u.) Density (a.u.) Density (a.u.)

2,000 2,000 2,000 1,000 1,000 1,000 500 1 500 3 500 4 5 3 2 2 200 71 2 200 200 3 6 100 100 100 1 50 50 50 20 20

Tumour coverage Tumour coverage 20 Tumour coverage 10 10 10 5 5 5 0 20406080100 0204060 80 100 0 20406080100 1 NRAS (Q61H) Cluster 1 ChrX variants 1 NRAS (Q61K) 5 CTCF (R566H) Cluster 1 ChrX variants 1 FOXA1 (I176M) Cluster 1 ChrX variants 2 DNMT3A (E477 ) Cluster 2 Autosome variants 2 ARID1A (R693 ) 6 PIK3CA (E542K) Cluster 2 Autosome * 2 PIK3R1 (E635K) Cluster 2 Autosome variants * 3 DNMT3A (A741B) 3 PTEN (P96T) 7 PMS2 (N432S) Cluster 3 3 MLL3 (E1992Q) variants Cluster 4 4 KRAS (G12D) Figure 5 | Driver initiation and progression mutations and tumour clonal mutation is in the subclone. In BRCA tumour TCGA-BH-A18P (exome), one architecture. a, Variant allele fraction (VAF) distribution of mutations in FOXA1 mutation is in the founding clone, and PIK3R1 and MLL3 mutations SMGs across tumours from AML, BRCA and UCEC for mutations ($203 are in the subclone. In UCEC tumour TCGA-B5-A0JV (exome), PIK3CA, coverage) in copy neutral segments. SMGs having $5 mutation data points ARID1A and CTCF mutations are in the founding clone, and NRAS, PTEN and were included. ChrX, chromosome X. b, In AML sample TCGA-AB-2968 KRAS mutations are in the secondary clone. Asterisk denotes stop codon. (WGS), two DNMT3A mutations are in the founding clone, and one NRAS

17 OCTOBER 2013 | VOL 502 | NATURE | 337 ©2013 Macmillan Publishers Limited. All rights reserved RESEARCH ARTICLE it is possible that a later mutation contributing to tumour cell expan- human transcript annotation imported from Ensembl release 69. Mutation con- sion might have a high VAF. It is worth noting that copy neutral loss of text (22to12 bp) was calculated for each somatic variant in each mutation heterozygosity is commonly found in classical tumour suppressors category, and hierarchical clustering was then performed using the pairwise such as TP53, BRCA1, BRCA2 and PTEN, leading to increased VAFs mutation context correlation across all cancer types. The mutational significance in cancer (MuSiC)3 package was used to identify significant genes for both indi- 5 in these genes. In AML, DNMT3A (permutation test P 0), RUNX1 vidual tumour types and the Pan-Cancer collective. An R function ‘hclust’ was (P 5 0.0003) and SMC3 (P 5 0.05) have significantly higher VAFs used for complete-linkage hierarchical clustering across mutations and samples, than average among SMGs (Fig. 5a and Supplementary Table 11b). and Dendrix30 was used to identify sets of approximately mutual exclusive muta- In breast cancer, AKT1, CBFB, MAP2K4, ARID1A, FOXA1 and PIK3CA tions. Cross-cancer survival analysis was based on the Cox proportional hazards have relatively high average VAFs. For endometrial cancer, multiple model, as implemented in the R package ‘survival’ (http://cran.r-project.org/web/ SMGs (for example, PIK3CA, PIK3R1, PTEN, FOXA2 and ARID1A) packages/survival/), and the sciClone algorithm (http://github.com/genome/sci- have similar median VAFs. Conversely, KRAS and/or NRAS mutations clone) generated mutation clusters using point mutations from copy number tend to have lower VAFs in all three tumour types (Fig. 5a), suggesting neutral segments. A complete description of the materials and methods used to generate this data set and its results is provided in the Methods. NRAS (for example, P 5 0 in AML) and KRAS (for example, P 5 0.02 in BRCA) have a progression role in a subset of AML, BRCA and Online Content Any additional Methods, Extended Data display items and Source UCEC tumours. For all three cancer types, we clearly observed a shift Data are available in the online version of the paper; references unique to these sections appear only in the online paper. towards higher expression VAFs in SMGs versus non-SMGs, most apparent in BRCA and UCEC (Extended Data Fig. 8a and Methods). Received 21 June; accepted 13 September 2013. Previous analysis using whole-genome sequencing (WGS) detected subclones in approximately 50% of AML cases15,36,37; however, ana- 1. Larson, D. E. et al. SomaticSniper: identification of somatic point mutations in whole genome sequencing data. Bioinformatics 28, 311–317 (2012). lysis is difficult using AML exome owing to its relatively few coding 2. Koboldt, D. C. et al. VarScan 2: somatic mutation and copy number alteration mutations. Using 50 AML WGS cases, sciClone (http://github.com/ discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012). genome/sciclone) detected DNMT3A mutations in the founding clone 3. Dees, N. D. et al. MuSiC: Identifying mutational significance in cancer genomes. Genome Res. 22, 1589–1598 (2012). for 100% (8 out of 8) of cases and NRAS mutations in the subclone for 4. Roth, A. et al. JointSNVMix: a probabilistic model for accurate detection of somatic 75% (3 out of 4) of cases (Extended Data Fig. 8b). Among 304 and 160 mutations in normal/tumour paired next-generation sequencing data. of BRCA and UCEC tumours, respectively, with enough coding muta- Bioinformatics 28, 907–913 (2012). 5. Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and tions for clustering, 35% BRCA and 44% UCEC tumours contained heterogeneous cancer samples. Nature Biotechnol. 31, 213–219 (2013). subclones. Our analysis provides the lower bound for tumour hetero- 6. Jones, S. et al. Core signaling pathways in human pancreatic cancers revealed by geneity, because only coding mutations were used for clustering. In global genomic analyses. Science 321, 1801–1806 (2008). 7. Parsons, D. W. et al. An integrated genomic analysis of human glioblastoma BRCA, 95% (62 out of 65) of cases contained PIK3CA mutations in multiforme. Science 321, 1807–1812 (2008). the founding clone, whereas 33% (3 out of 9) of cases had MLL3 muta- 8. Sjo¨blom, T. et al. The consensus coding sequences of human breast and colorectal tions in the subclone. Similar patterns were found in UCEC tumours, cancers. Science 314, 268–274 (2006). 9. The Cancer Genome Atlas Research Network. Comprehensive genomic with 96% (65 out of 68) and 95% (62 out of 65) of tumours containing characterization defines human glioblastoma genes and core pathways. Nature PIK3CA and PTEN mutations, respectively, in the founding clone, and 455, 1061–1068 (2008). 9% (2 out of 22) of KRAS and 14% (1 out of 7) of NRAS mutations in the 10. Ding, L. et al. Somatic mutations affect key pathways in lung adenocarcinoma. Nature 455, 1069–1075 (2008). subclone (Extended Data Fig. 8b and Supplementary Table 12). 11. Wood, L. D. et al. The genomic landscapes of human breast and colorectal cancers. Science 318, 1108–1113 (2007). Discussion 12. The Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma. Nature 474, 609–615 (2011). We have performed systematic analysis of the TCGA Pan-Cancer muta- 13. The Cancer Genome Atlas Network. Comprehensive molecular portraits of human tion data set, finding key insights for cancer genomes, as summarized in breast tumours. Nature 490, 61–70 (2012). Extended Data Fig. 9. The data set contains 127 diverse SMGs, demon- 14. Cancer Genome Atlas Research Network. Integrated genomic characterization of endometrial carcinoma. Nature 497, 67–73 (2013). strating that many cellular and enzymatic processes are involved in 15. The Cancer Genome Atlas Research Network. Genomic and epigenomic tumorigenesis. Notably, 66 of them are also on the ‘mut-driver genes’ landscapes of adult de novo acute myeloid leukemia. N. Engl. J. Med. 368, list generated by a ratiometric method using COSMIC mutations38. 2059–2074 (2013). 16. The Cancer Genome Atlas Network. Comprehensive molecular characterization of Although a common set of driver mutations exists in each cancer type, human colon and rectal cancer. Nature 487, 330–337 (2012). the combination of drivers within a cancer type and their distribution 17. Ellis, M. J. et al. Whole-genome analysis informs breast cancer response to within the founding clone and subclones varies for individual patients. aromatase inhibition. Nature 486, 353–360 (2012). 18. The Cancer Genome Atlas Research Network. Comprehensive molecular This suggests that knowing the clonal architecture of each patient’s characterization of clear cell renal cell carcinoma. Nature 499, 43–49 (2013). tumour will be crucial for optimizing their treatment. 19. Hanahan, D. & Weinberg, R. A. The hallmarks of cancer. Cell 100, 57–70 (2000). Given the rate at which TCGA and International Cancer Genome 20. Downing, J. R. et al. The Pediatric Cancer Genome Project. Nature Genet. 44, 619–622 (2012). Consortium projects are generating genomic data, there are reas- 21. Ma, Z. & Leijon, A. Bayesian estimation of beta mixture models with variational onable chances of identifying the ‘core’ cancer genes and pathways inference. IEEE Trans. Pattern Anal. Mach. Intell. 33, 2160–2173 (2011). and tumour-type-specific genes and pathways in the near term. These 22. Lawrence, M. S. et al. Mutational heterogeneity in cancer and the search for new results will be immediately circulated within the research community cancer-associated genes. Nature 499, 214–218 (2013). 23. Tao, M. H. & Freudenheim, J. L. DNA methylation in endometrial cancer. to assess their potential for candidate targets for diverse tumour types Epigenetics 5, 491–498 (2010). or for specific tumour type. Ultimately, these data and their associa- 24. Etcheverry, A. et al. DNA methylation in glioblastoma: impact on gene expression tions with different clinical features and subtypes should contribute to and clinical outcome. BMC Genomics 11, 701 (2010). 25. Varela, I. et al. Exome sequencing identifies frequent mutation of the SWI/SNF the formulation of a reference candidate gene panel for all tumour complex gene PBRM1 in renal carcinoma. Nature 469, 539–542 (2011). types that could be helpful for prognosis at various clinical time points. 26. Pen˜ a-Llopis, S. et al. BAP1 loss defines a new class of renal cell carcinoma. Nature Genet. 44, 751–759 (2012). 27. Clapier, C. R. & Cairns, B. R. The biology of chromatin remodeling complexes. Annu. METHODS SUMMARY Rev. Biochem. 78, 273–304 (2009). Mutation data were standardized for 12 cancer types and tracked on Synapse with 28. Kapur, P. et al. Effects on survival of BAP1 and PBRM1 mutations in sporadic clear- documentation (http://dx.doi.org/10.7303/syn1729383.2). All mutation annota- cell renal-cell carcinoma: a retrospective analysis with independent validation. tion format files were downloaded from the TCGA data coordinating centre, each Lancet Oncol. 14, 159–167 (2013). 29. Jiao, Y. et al. Frequent ATRX, CIC, FUBP1 and IDH1 mutations refine the being reprocessed to eliminate known, recurrent false positives and germline classification of malignant gliomas. Oncotarget 3, 709–722 (2012). single nucleotide polymorphisms (SNP) present in the dbSNP database. All vari- 30. Vandin, F., Upfal, E. & Raphael, B. J. De novo discovery of mutated driver pathways ant coordinates were converted to GRCh37 and re-annotated using the Gencode in cancer. Genome Res. 22, 375–385 (2012).

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31. Piazza, R. et al. Recurrent SETBP1 mutations in atypical chronic myeloid leukemia. Research Institute grants R01HG005690 to B.J.R., U54HG003079 to R.K.W., and Nature Genet. 45, 18–24 (2013). U01HG006517 to L.D., and National Science Foundation grant IIS-1016648 to B.J.R. 32. Yang, D. et al. Association of BRCA1 and BRCA2 mutations with survival, We gratefully acknowledge the contributions from TCGA Research Network and its chemotherapy sensitivity, and gene mutator phenotype in patients with ovarian TCGA Pan-Cancer Analysis Working Group. We also thank M. Bharadwaj for technical cancer. J. Am. Med. Assoc. 306, 1557–1565 (2011). assistance. 33. Bolton, K. L. et al. Association between BRCA1 and BRCA2 mutations and survival in women with invasive epithelial ovarian cancer. J. Am. Med. Assoc. 307, 382–390 Author Contributions L.D. and R.K.W. supervised the research. L.D., C.K., M.D.M., F.V., (2012). K.Y., B.N., C.L., M.X., M.D.M.L., M.A.W., J.F.M., M.J.W., C.A.M., J.S.W. and B.J.R. analysed the 34. Ley, T. J. et al. DNMT3A mutations in acute myeloid leukemia. N. Engl. J. Med. 363, data. M.C.W. and Q.Z. performed statistical analysis. M.D.M., C.K., F.V., C.L., M.X., K.Y., 2424–2433 (2010). B.N., Q.Z., M.C.W., J.F.M., M.D.M., M.A.W. and L.D. prepared the figures and tables. L.D., 35. Myung, J. K. et al. IDH1 mutation of gliomas with long-term survival analysis. Oncol. T.J.L., C.K. and B.J.R conceived and designed the experiments. L.D., M.D.M., C.K., F.V., Rep. 28, 1639–1644 (2012). C.L., B.J.R., K.Y. and M.C.W. wrote the manuscript. 36. Ding, L. et al. Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing. Nature 481, 506–510 (2012). Author Information Reprints and permissions information is available at 37. Welch, J. S. et al. The origin and evolution of mutations in acute myeloid leukemia. www.nature.com/reprints. The authors declare no competing financial interests. Cell 150, 264–278 (2012). Readers are welcome to comment on the online version of the paper. Correspondence 38. Vogelstein, B. et al. Cancer genome landscapes. Science 339, 1546–1558 (2013). and requests for materials should be addressed to L.D. ([email protected]). Supplementary Information is available in the online version of the paper. This work is licensed under a Creative Commons Attribution- Acknowledgements This work was supported by the National Cancer Institute grants NonCommercial-Share Alike 3.0 Unported licence. To view a copy of this R01CA180006 to L.D. and PO1CA101937 to T.J.L., and National Human Genome licence, visit http://creativecommons.org/licenses/by-nc-sa/3.0

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METHODS an association (setting indicator variable to 1) if a given gene frequency within a Standardization and tracking of mutation data from 12 cancer types. The three type exceeded a threshold. Otherwise we set the indicator to 0, indicating no TCGA genome sequencing centres (GSCs; Baylor Human Genome Center, Broad association. We took the threshold as a standardized Z-score of 0.2 above the Institute, and The Genome Institute at Washington University) collectively per- mean based on the estimated level of noise in the 127 significant genes as quan- formed exome sequencing on thousands of tumour samples and matched normal tified by the coefficient of variation for each cancer type. We then computed an tissues, the latter being used as controls to distinguish somatic mutations from overall distribution on the indicator variable. The mean for each functional inherited variants. These controls were most often peripheral blood, but skin category having at least five genes was then converted to a Z-score based on the tissue was used in 199 AML samples as well as 1 buccal source, and adjacent descriptive statistics (mean and standard deviation) of the indicator distribution. tumour-free tissue was used for 927 cases, with 120 cases having normal DNA Unsupervised clustering. Somatic point mutations and small indels of 127 SMGs from blood and adjacent solid normal tissue. across the 3,281 tumours were collected. To reduce noise from passenger muta- Exome capture targets may differ among GSCs, as well as across cohorts at the tions, tumours having more than 500 somatic mutations (considered hypermu- same GSC because the capture technologies and sequencing platforms continue tators) were excluded from this analysis. Tumours with zero detected somatic to evolve over time. Therefore, collecting sequencing coverage data for each mutations were also excluded, resulting in mutations from 2,611 tumours for sample is crucial for most variant significance analyses. Somatic variant calling downstream clustering analysis. A mutation status matrix (sample 3 gene) was methods also differ among GSCs for similar reasons, in addition to the fact that constructed and passed to the R function ‘hclust’ for complete-linkage hierarch- filtering strategies may be tuned to emphasize either sensitivity or specificity of ical clustering and a respective heatmap with a dendrogram was plotted. Tumours calls. Finally, the TCGA disease analysis working groups (AWGs) may optionally from BLCA, HNSC, LUAD and LUSC are largely scattered over the Pan-Cancer perform manual curation of the variant calls, in which false positives are removed cohort, indicating extensive heterogeneity in these diseases. For instance, eight and true negatives are recovered. AWGs and GSCs also collaboratively select LUAD and two LUSC tumours are in the solid colorectal cluster, largely owing to putative variants for validation or for recovering variants from regions that their KRAS mutations (Fig. 4). Three UCEC, two GBM, one OV and one HSNC reported low coverage in the first pass of exome sequencing. These steps mean samples are in the BRCA cluster courtesy of TP53 and PIK3CA mutations. The that somatic variant sensitivity and specificity are mostly comparable across resolution of this analysis could be improved by incorporating copy number data, samples of a given TCGA tumour type, but that they differ considerably among structural variants, gene expression, proteomics and methylation. tumour types, creating significant challenges for Pan-Cancer analyses. Dendrix analysis of mutation relationships. We used Fisher’s exact test to Complete standardization of sensitivity could not be attained, as it would have identify pairs of SMGs with significant (FDR 5 0.05 by Benjamini–Hochberg) required a uniform variant calling and filtering workflow across all tumour– exclusivity and co-occurrence. We identified significant pairs by analysing all normal pairs. Instead, publicly available somatic variant calls in mutation annota- samples together and by analysing samples in each cancer type separately. A large tion format (MAF) files from the TCGA were used to both ensure reproducibility number of pairs (142) with significant co-occurring mutations is identified only and take advantage of extensive manual curation performed over the years by considering the Pan-Cancer data set (Extended Data Fig. 4); these pairs include experts in the disease or in genomic sequence analysis and annotation. Specifically, several candidate genes (for example, NAV3, RPL22 and TSHZ3) whose function all MAF files were downloaded from the TCGA data coordinating centre, each in carcinogenesis is not well characterized. being reprocessed to eliminate known, recurrent false positives and germline single We applied our de novo driver exclusivity (Dendrix) algorithm to identify sets nucleotide polymorphisms (SNP) present in the dbSNP database. All variant of approximately mutually exclusive mutations on all samples together. Dendrix coordinates were transferred to GRCh37 and re-annotated using the Gencode finds a set M of genes of maximum weight W(M), in which W(M) is the difference human transcript annotation imported from Ensembl release 69. Per sample, between the number of samples with a mutation in M and the number of samples per gene coverage values were obtained using WIG-formatted reference coverage with more than one mutation in M. The maximum scoring set of genes of a given files associated with the BAMs or by processing the original BAM files directly. size is identified using a Markov chain Monte Carlo approach. We first applied Details were tracked on Synapse with provenance and documentation (https:// Dendrix considering only the 127 SMGs, and then extended our analysis to www.synapse.org/#!Synapse:syn1729383). consider the 1,000 genes of smallest median q value among the three q values Mutation frequency and spectrum analysis. We calculate mutation frequency (convolution, Fisher’s combined test, and likelihood ratio) reported by MuSiC. by dividing the number of validated somatic variants by the number of base pairs From these 1,000 genes, we discarded the ones with mutations strongly associated that have sufficient coverage. Minimum coverage is six and eight reads for normal (Bonferroni corrected P # 0.05 by Fisher’s exact test) with a cancer type, and this and tumour BAMs, respectively. For mutation spectrum we classify the mutation resulted in 600 genes for Dendrix analysis. by six types (transitions/transversions). Mutation context is generated by count- We also applied Dendrix to identify sets of exclusive mutations among the ing the frequency of A, T, C and G nucleotides that are 2 bp 59 and 39 to each SMGs in each cancer type separately. The exclusivity and co-occurrence between variant within the six mutation categories. For the clustering, we pooled all different sets of mutations was assessed using Fisher’s exact test, and the mutation samples (excluding hypermutators having .500 mutations) for each cancer type. status (‘mutated’ if at least one gene in the set is mutated, ‘not mutated’ otherwise) We calculated the mutation context (22to12 bp) for each somatic variant in of the two sets as categories for the 2 3 2 contingency table. each mutation category. A hierarchical clustering was then done using the pair- Cross-cancer-type survival analysis using Cox proportional hazards model. wise correlation of the mutation context across all cancer types. We used correla- We used the clinical correlation module of MuSiC to examine how clinical fea- tion modules in the mutational significance in cancer (MuSiC) package to identify tures correlate with somatic events within individual tumour types. Fisher’s exact genes with mutations that are positively correlated with the number of mutations test was used for analysing categorical features, and the Wilcoxon rank sum test in the tumour sample. This analysis was performed for all 12 cancer types. Only was used for quantitative variables. We used the standard Cox proportional genes mutated in at least 5% of tumours were included in the analysis. A list of hazards model for individual cancer types as well as cross-cancer survival ana- genes known to be involved in DNA mismatch repair is included Supplementary lysis, as implemented in the R package ‘survival’ (http://cran.r-project.org/web/ Table 13. packages/survival/). Here, the effects of all mutations are taken to be constant SMG analysis. We used the SMG test in the MuSiC suite3 to identify significant over time, that is, they are ‘stationary coefficients’. Hazard ratios exceeding 1 genes for each tumour type and also for Pan-Cancer tumours. This test assigns indicate an overall detrimental effect across cancers, whereas those below 1 mutations to seven categories: AT transition, AT transversion, CG transition, CG associate with better outcome. Calculations included only those genes having transversion, CpG transition, CpG transversion and indel, and then uses statist- mutation frequencies of at least 2% (for both individual cancer types and Pan- ical methods based on convolution, the hypergeometric distribution (Fisher’s Cancer) in at least two cancer types (for Pan-Cancer). In practice, this means that test), and likelihood to combine the category-specific binomials to obtain overall although some genes encompass many types, for example, TP53 is calculated over P values. All P values were combined using the methods described previously3. 12 types, most are based on only a few (Extended Data Table 1 and Supplemen- SMGs are listed in Fig. 2. Finally, for the analysis of SMGs, genes not typically tary Table 10a, b). There is no basis for calculation of genes having 2% mutation in expressed in individual tumour type or/and Pan-Cancer tumour samples were only a single type, although single-type analysis was computed similarly for all filtered if they had an average read per kilobase per million (RPKM) # 0.5. For the genes having at least 2% in all cancer types. Analyses were performed using age RNA sequencing (RNA-seq)-based gene expression analysis, we used the and gender as covariates. ‘Pancan12 per-sample log2-RSEM’ matrix from Synapse (https://www.synapse. We also performed Pan-Cancer survival analysis by stratifying on cancer type org/#!Synapse:syn1734155). A gene qualified as ‘expressed’ if it had at least three in the Cox regression model and found the results are largely consistent with reads in at least 70% of samples. Annotation based curation was also performed. taking cancer type as a covariate (14 out of top 15 significant genes are overlap- Tumour specificity analysis. To make quantitative inferences as to the number ping from these two analyses) (Supplementary Table 10b). Furthermore, stage of cancer types with which an individual gene associates, we calculated the was used as a covariate for individual cancer type survival analysis (AML and empirical distributions of frequency for each cancer (tissue) type and declared COAD/READ not included). Again, the results are fairly consistent with taking

©2013 Macmillan Publishers Limited. All rights reserved ARTICLE RESEARCH only age and gender as covariates (for example, 12 out of top 15 significant genes We further investigated the expression level of somatic mutations using avail- are overlapping from these two analyses for UCEC) (Supplementary Table 14). able RNA sequencing data for AML, BRCA and UCEC, and then compared Clonality and mutation VAF analysis. We computed the VAFs of somatic observed mutant allele expressions with expected levels based on DNA VAFs mutations in SMGs using TCGA targeted validation data or/and exome and (assuming no allelic expression bias). A total of 671 BRCA, 170 AML and 190 RNA sequencing data for AML, BRCA and UCEC. An internally developed tool UCEC tumours with RNA-seq BAMs were used for this analysis. Notably, we called Bam2ReadCount (unpublished), which counts the number of reads sup- observed at least a twofold increase of variant allele expressions in 3.9%, 12.9% porting the reference and variant alleles, was used for computing VAFs for point and 5.9% of mutations from SMGs in AML (for example, TP53, STAG2 and mutations and short indels in copy number neutral segments. Only mutation sites SMC3), BRCA (for example, CDH1, TP53, GATA3 and MLL3), and UCEC (for example, ARID1A and FGFR2), respectively (Supplementary Table 11a). We having $203 coverage and SMGs having at least five data points were included further compared expression level distributions across mutations from SMGs in downstream analyses. Permutation and t-tests were used to identify genes and non-SMGs. For all three cancer types, we clearly observed a shift towards with significantly higher or lower VAFs than the average (Supplementary higher expression VAFs in SMGs versus non-SMGs, which was most apparent in Table 11a, b). These indicate chronological order-of-appearance of somatic BRCA and UCEC (Extended Data Fig. 8a). This result suggests potential selection events during tumorigenesis. VAFs for mutations from genes that are not iden- of these mutations during tumorigenesis. tified as significantly mutated were similarly computed for generating control SciClone (http://github.com/genome/sciclone) was used for generating muta- VAF density distribution. We also computed VAF distribution for the other nine tion clusters using point mutations from copy number neutral segments. Only cancer types, and plots are included in Extended Data Fig. 7. In total, 91 BLCA, variants with greater than or equal to 1003 coverage were used for clustering and 772 BRCA, 144 COAD/READ, 62 GBM, 144 HNSC, 195 KIRC, 197 LAML, 216 plotting. Validation data were used for AML, and exome sequencing data were LUAD, 146 LUSC, 278 OV and 248 UCEC tumours were used for SMG VAF used for BRCA and UCEC. SMGs were highlighted automatically by sciClone to distribution analysis. show their clonal association (Extended Data Fig. 8b).

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Extended Data Figure 1 | Mutation context across 12 cancer types. across all 12 cancer types. The y axis denotes the total number of mutations in Mutation context showing proportions of A, T, C and G nucleotides each category. within 65 bp for all validated mutations of type C.G/G.C and C.T/G.A

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Extended Data Figure 2 | The distribution of KRAS hotspot mutations significantly higher proportion of Gly12Cys mutations than other cancers across tumour types. Distribution of changes caused by mutations of the (P , 3.2 3 10210), caused by the increase in C.A transversions in the genomic KRAS hotspot at amino acids 12 and 13. Lung adenocarcinoma has a DNA at that location.

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Extended Data Figure 3 | Unsupervised clustering based on mutation status tumours. Major clusters of mutations detected in UCEC, COAD, GBM, AML, of SMGs. Tumours having no mutation or more than 500 mutations were KIRC, OV and BRCA were highlighted. The shorter version is shown in Fig. 4. excluded to reduce noise. A mutation status matrix was constructed for 2,611

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Extended Data Figure 4 | Mutation relation analysis in individual tumour the pair shows exclusivity or co-occurrence, respectively. b, Exclusivity and types and the Pan-Cancer set. a, Exclusivity and co-occurrence between co-occurrence between genes in the most significant (q , 0.05) pairs in SMGs in each tumour type. The 2log10 P value appears in either red or green if Pan-Cancer set. Colour scheme is as in a.

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Extended Data Figure 5 | Mutually exclusive mutations identified by in the most exclusive set, three include KRAS, and two include PTEN, with the Dendrix in the Pan-Cancer and individual cancer type data sets. a, The remaining genes appearing in only a single type. d, Exclusivity and co- highest scoring exclusive set of mutated genes in 127 SMGs contains several occurrence assessed at the Pan-Cancer level. The 2log10 P value appears in red genes that are strongly associated with one cancer type. b, The highest scoring or green if the pair shows exclusivity or co-occurrence, respectively. KIRC is exclusive set of mutations in the top 600 genes (not enriched for mutations in most exclusive to other tumour types, whereas COAD/READ presented strong one cancer type) reported by MuSiC. c, Relationships between exclusive gene co-occurrence with other types. sets identified by Dendrix in individual cancer types. Eight types include TP53

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Extended Data Figure 6 | Kaplan–Meier plots for genes significantly based on cytogenetics or FLT3 internal tandem duplication status in this associated with survival. Plots are shown for 24 genes showing significant analysis, and cannot discern this effect. Because most patients with OV (95%) (P # 0.05) association in individual cancer types. Although NPM1 mutations in have TP53 mutations, we could not obtain sufficient non-TP53 mutant controls patients with AML having intermediate cytogenetic risk are relatively benign in for confidently dissecting the relationship between TP53 status and survival in the absence of internal tandem duplications in FLT3, we did not stratify patients OV.

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Extended Data Figure 7 | VAF distribution of mutations in SMGs across mutations residing in copy number neutral segments were used for this tumours from BLCA, KIRC, HNSC, LUAD, LUSC, COAD/READ, OV and analysis. Only mutation sites with $203 coverage were used for analysis and GBM. To minimize the effect of copy number alterations on VAFs, only plotting. SMGs with at least five data points were included in the plot.

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Extended Data Figure 8 | Mutation expression and tumour clonal shown for each case: kernel density (top), followed by the plot of tumour VAF architecture in AML, BRCA and UCEC. a, Density plots of expressed VAFs by sequence depth for sites from selected copy number neutral regions. for mutations in SMGs (blue) and non-SMGs (red). b, SciClone clonality Mutations (with annotations) in SMGs were shown. example plots for AML (validation data), BRCA and UCEC. Two plots are

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Extended Data Figure 9 | Summary of major findings in Pan-Cancer 12. SMGs, cancer-related cellular processes, and genes associated with clinical Systematic analysis of the TCGA Pan-Cancer mutation dataset identifies features and tumour progression.

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Extended Data Table 1 | Clinical correlation and survival analysis for genes mutated at $2% frequency in at least 2 tumour types

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Targeting RAF kinases for cancer therapy: BRAF-mutated melanoma and beyond

Matthew Holderfield*, Marian M. Deuker*, Frank McCormick and Martin McMahon Abstract | The identification of mutationally activated BRAF in many cancers altered our conception of the part played by the RAF family of protein kinases in oncogenesis. In this Review, we describe the development of BRAF inhibitors and the results that have emerged from their analysis in both the laboratory and the clinic. We discuss the spectrum of RAF mutations in human cancer and the complex interplay between the tissue of origin and the response to RAF inhibition. Finally, we enumerate mechanisms of resistance to BRAF inhibition that have been characterized and postulate how strategies of RAF pathway inhibition may be extended in scope to benefit not only the thousands of patients who are diagnosed annually with BRAF-mutated metastatic melanoma but also the larger patient population with malignancies harbouring mutationally activated RAF genes that are ineffectively treated with the current generation of BRAF kinase inhibitors.

RAF kinases have been associated with cancer since their MAP kinase signalling cascade, relaying signalling cues discovery in 1983, when Ulf Rapp and colleagues1 first from the extracellular environment throughout the described v‑Raf, a murine retroviral oncogene with a cell, thereby directing cell proliferation, differentiation, mammalian cell homologue, termed CRAF (also known migration and survival. as RAF1). Contemporaneously, Bister and colleagues2 In 2002, sequencing efforts identified a high fre‑ characterized v‑mil, an avian retroviral oncogene that is quency of BRAF point mutations in melanoma and in orthologous to v‑Raf. In 1984, both v‑RAF and v‑MIL other human cancers14. The ensuing decade witnessed oncoproteins were shown to have serine/threonine myriad publications that further characterized the roles kinase activity — they were the first onco­proteins iden‑ of mutant BRAF in numerous solid tumours and hae‑ tified to have such activity3. Two genes that are related matological malignancies. Furthermore, it has become to CRAF were subsequently found in mice and humans: evident that mutations in CRAF and ARAF also occur ARAF and BRAF4,5. Furthermore, homologues of BRAF in cancer, thereby implicating the RAF family protein were identified in Drosophila melanogaster (D‑Raf) and kinases both as drivers of oncogenesis and as direct in Caenorhabditis elegans (lin‑45), which function dur‑ targets for therapeutic intervention. Discovery of the ing development downstream of receptor tyrosine kinase BRAF oncogenes prompted several structure-based Helen Diller Family 6,7 Comprehensive Cancer Center (RTK) signalling . Ten years after the identification of drug design campaigns that have yielded several highly 8–10 and Department of Cell and CRAF , the dual-specificity protein kinase MEK1 potent and selective ATP-competitive small molecule Molecular Pharmacology, was identified as a physiological substrate of CRAF11. BRAF inhibitors. Two compounds (vemurafenib and University of California, Concurrently, several groups identified a direct inter‑ dabrafenib) have achieved approval by the US Food San Francisco, California 94158, USA. action between RAF proteins and GTP-bound RAS and Drug Administration (FDA) for the treatment of *These authors contributed proteins, implicating RAF proteins as direct effectors of metastatic and unresectable BRAF-mutated melano‑ equally to this work. activated RAS12,13. Interaction with RAS–GTP at mem‑ mas. Initially, the success of BRAF inhibitors seemed to Correspondence to branes promotes RAF kinase activation that, in turn, unequivocally reinforce the paradigm of using predic‑ F.M. & M.M. leads to direct RAF-mediated activating phosphorylation tive markers to molecularly stratify patients in clinical e-mails: [email protected]; of MEK1 and MEK2. In turn, MEK1 and MEK2 activate trials testing pathway-targeted therapeutics. However, it [email protected] the ERK1 and ERK2 MAP kinases via phosphory­lation. has since become apparent that BRAF mutational status doi:10.1038/nrc3760 Thus, RAF proteins are crucial regulators of the ERK alone does not predict therapeutic response in all cancers.

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Key points at residues 464–469 (REFS 15,16) (FIG. 1). Crystallographic analysis revealed that the inactive conformation • Mutationally activated BRAF is expressed in melanoma, glioblastoma, thyroid, lung of BRAF is stabilized by interactions between the and colon cancers and in a subset of haematological malignancies. A-loop and P‑loop of the BRAF kinase domain, spe‑ • The most common BRAF mutation leads to the substitution of a glutamic acid for cifically involving V600 interacting with F468 (REF. 17). valine at amino acid 600 (V600E) in the kinase domain of the protein. This substitution Under normal circumstances, reversible phosphory­ mimics phosphorylation of the activation loop, thereby inducing constitutive BRAF protein kinase activity. lation of T599 and S602 in the A‑loop regulates the • Point mutations in the related ARAF and CRAF protein kinases, although very rare, A‑loop–P‑loop interaction, thereby allowing BRAF to have been reported as oncogenic drivers in some human cancers. In addition to point convert back and forth from its kinase-active state mutation, gene fusion events are reported to activate BRAF and CRAF. to its kinase-inactive state. Consequently, BRAF muta‑ • Numerous non‑V600E alterations in BRAF have been reported in cancer and in a tions that lead to amino acid substitutions in either the rare developmental disorder. Many of these promote kinase activity by relieving A‑loop or the P‑loop mimic T599 and S602 phosphory­ autoinhibitory mechanisms or promote activation of other RAF isoforms in a lation and, by disrupting the A‑loop–P‑loop interac‑ RAS-dependent manner. tion, irreversibly shift the equilibrium of BRAF to the • ATP-competitive BRAF kinase inhibitors are currently under investigation for the kinase-active conformation. treatment of BRAF-mutated cancers. However, to date, efficacy is limited to a subset BRAFT1799 point mutations are clearly the most of melanomas owing to primary or adaptive resistance mechanisms in colorectal and common oncogenic driver in melanoma, but BRAF- thyroid cancers that reactivate signalling downstream of receptor tyrosine kinases. V600E melanoma represents only a subset of tumours • In clinical trials of BRAF-mutated melanoma, BRAF inhibitors tend to induce high rates with BRAF alterations. BRAF point mutations also of response that show transient durability due to the onset of drug-resistant disease. occur in 60% of thyroid cancers, 10% of colorectal • Acquired resistance to BRAF‑V600E inhibitors is strikingly complex but frequently carcinomas and in 6% of lung cancers (FIG. 1), as well involves reactivation of MEK–ERK MAP kinase signalling. Drug resistance due to as in nearly all cases of papillary craniopharyngi‑ overexpression of oncogenic BRAF‑V600E leads to ‘oncogene overdose’ following 18 19,20 cessation of drug administration — a phenomenon that could be clinically exploitable oma , classical hairy-cell leukaemia (HCL‑C) and 21 to forestall the onset of drug resistance. metanephric kidney adenoma . Unlike other indica‑ • The efficacy of RAF inhibitors in tumours with other RAF mutations is mostly tions where V600 mutations predominate, BRAF unknown, although preclinical studies indicate varied responses that are alterations in lung cancer often occur in the P‑loop at inhibitor-specific and depend on the biochemical mechanism of oncogene activation. G466 and G469 (FIG. 1). Although the frequencies of BRAF mutation in colon cancer and lung cancer are considerably lower than in the other types of cancer in Efficacy of BRAF inhibitors is limited to a subset of can‑ which BRAF mutations have been found, the relative cer patients with BRAF-mutated metastatic melanoma, morbidity for these indications (50,000 and 158,000 despite the abundance of BRAF-mutated tumours iden‑ deaths per year, respectively, in the United States22) tified in colorectal cancers, thyroid cancers, glioblastoma suggest that an even larger population of patients with and non-small-cell lung cancers (NSCLCs), as well as BRAF-mutated cancers are currently ineffectively the minority of ARAF and CRAF mutations that have treated. If 10% of patients with colorectal cancer and been found in lung adenocarcinoma. Furthermore, the 6% of patients with lung cancer carry BRAF muta‑ durability of responses in BRAF-mutated melanoma is tions, that amounts to nearly 16,000 deaths annually restricted by the onset of drug resistance. Although the owing to BRAF-mutated cancers. era of RAF-targeted therapeutics remains in its infancy, The biochemistry of the various altered BRAF pro‑ the challenge in the coming years lies in determining teins varies substantially. Although the V600E altera‑ how to use RAF inhibitors across multiple tumour tion markedly increases kinase activity, several of the types to achieve the greatest immediate clinical ben‑ less common alterations decrease BRAF kinase activity, efit, while simultaneously forestalling the emergence of but they promote MEK phosphorylation in a CRAF- drug-resistant disease. dependent manner23. In one genetically engineered mouse model (GEMM), conditional melanocyte-specific RAF mutations in cancer expression of either KRAS‑G12D or BRAF‑D594A The spectrum of BRAF mutations. The identification of (a kinase-impaired A‑loop mutation) was insufficient BRAF mutations in cancer ushered in a new era in the to induce nevi or melanomas, but co‑expression of treatment of advanced melanomas. BRAF is mutated both mutant proteins promoted cellular dimerization in ~8% of all cancers, and approximately one-half of all of the catalytically inert BRAF‑D594A with catalyti‑ melanomas harbour a BRAFT1799A transversion, which cally competent CRAF and elicited rapidly growing, encodes the constitutively active BRAF‑V600E oncopro‑ pigmented tumours24. These data strongly indicate tein. In the original description of BRAF mutations in that kinase-impaired BRAF mutations are oncogenic cancer, BRAFT1799A was only one of 14 BRAF alterations drivers but require RAS and a catalytically compe‑ identified in cell lines and primary tumour samples14. tent RAF isoform to activate downstream signalling. Since then, nearly 300 (REF. 15) distinct missense muta‑ Additionally, the P‑loop contains an autophosphory­ tions have been found in tumour samples and cancer lation site that markedly reduces the activity of the cell lines (FIG. 1). These missense mutations encompass wild-type enzyme25. At least some of the BRAF-mutant 115 of the 766 BRAF amino acids, but most of the muta‑ proteins seem to bypass this effect, which may explain tions occur in the activation loop (A‑loop) near V600, why even mutations that impair intrinsic kinase or in the GSGSFG phosphate-binding loop (P‑loop) activity can constitutively activate the enzyme by

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a BRAF b CRAF 100,000 5 10,000 4 1,000 3 100 2 10 Number of mutations Number of mutations 1 1 0 1 100 200 300 400 500 600 700 766 1 100 200 300 400 500 600 648 Amino acid position Amino acid position

1 100 200 300 400 500 600 700 766 1 100 200 300 400 500 600 648 Phosphorylation sites Phosphorylation sites Amino acid position Amino acid position RBD Kinase domain RBD Kinase domain

Functional domains 1 100 200 300 400 500 600 700 766 Functional domains 1 100 200 300 400 500 600 648 Amino acid position Amino acid position P-loop Conserved regions A-loop Fusion points

c 100,000 10,000 1,000 Thyroid cancer (59%, 177 of 299) 100 Skin cancer (51%, 117 of 228) Colon cancer (10%, 30 of 296) 10 Number of mutations Lung cancer (6.2%, 43 of 692) 1 1 100 200 300 400 500 600 700 766 Amino acid position

Figure 1 | BRAF and CRAF mutations in cancer. BRAF amino acid positions (1–766) (part a) and CRAFNature amino Reviews acid | Cancer positions (1–648) (part b) are depicted on the x-axis. In both part a and part b, the top graphs show the number of mutations that are reported for each position15; the middle panels show the position of putative phosphorylation sites that are reported to have a functional consequence on kinase activity, stability or localization; and the bottom panels show BRAF and CRAF functional domains: the RAS-binding domain (RBD) and the kinase domain are highlighted in blue, the phosphate-binding loop (P‑loop) is highlighted in yellow, the activation loop (A‑loop) is highlighted in green and fusion points are highlighted in magenta. Part c shows the spectrum of BRAF mutations compiled from multiple studies75 in thyroid19, skin141,142, colon143,144 and lung40,145,146 cancers (no equivalent graph is shown for CRAF mutations owing to their low frequency across all cancers). The sample size of the compiled sequencing data and the percentage of BRAF mutations for each indication are also shown.

preventing autoinhibition. Therefore, the various BRAF‑V600E in melanocyte lineage also cooperated BRAF P‑loop and A‑loop mutations may have different with loss of tumour suppressors, Pten or p16INK4A (also mechanisms of activation, relying partly on constitutive known as Cdkn2a), to yield melanoma, with meta‑ kinase activity or functioning entirely as a scaffold for static potential in BrafT1799A; Pten–/– melanocytes27,28. RAS signal transduction through CRAF. Targeted expression of BrafT1799A in thyrocytes pro‑ Although the tumour-initiating potential of many duced papillary or anaplastic thyroid cancers29–32. of the rare BRAF mutations has yet to be demon‑ Expression of BRAF‑V600E in the proliferative cells strated in vivo, the oncogenicity of BRAF‑V600E of the mouse gastrointestinal tract has been shown has been validated in numerous GEMMs. To study to function as an early driver mutation in the patho‑ BRAF‑V600E‑driven tumorigenesis, a mouse carry‑ genesis of serrated colorectal cancer33,34. In addition, ing a Cre recombinase-activated allele of Braf (BrafCA) recent years have heralded the publication of addi‑ was developed such that normal BRAF is expressed tional BRAF‑V600E‑driven GEMMs of pancreatic prior to Cre-mediated recombination and mutant ductal adenocarcinoma35, prostate cancer36, paedi‑ BRAF‑V600E is only expressed from the endo­genous atric malignant astrocytoma37, soft tissue sarcoma38 locus after exposure to Cre26. Using this model, and neoplasms of the monocyte–histiocyte lineage39. lung-specific expression of BRAF‑V600E caused lung Together, these models of BRAF‑V600E‑driven malig‑ adenomas, whereas concomitant BRAF‑V600E expres‑ nancies highlight the oncogenicity of the mutant pro‑ sion and homozygous excision of floxed Trp53 alleles tein in a wide array of tissue types and emphasize the caused progression to adenocarcinoma. Expression of therapeutic potential of targeting BRAF oncoproteins.

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CRAF and ARAF mutations. In contrast to BRAF, ARAF thyroid and breast cancers53–58. Early studies demon‑ and CRAF mutations are exceptionally rare in cancer. strated the constitutive activity and transforming poten‑ Recent data indicate that a small subset (~1%) of patients tial of RAF proteins with amino terminus truncations59–61. with lung adenocarcinoma carry activating ARAF In every case in which chromosomal break-points have or CRAF mutations. It has not yet been determined been identified within the BRAF locus in human can‑ whether all ARAF and CRAF mutations constitute onco‑ cer, the carboxy‑terminal portion of the enzyme is fused genic drivers in all cases, but initial cell culture studies in‑frame using the start codon of another gene, resulting in confirmed the transforming potential of ARAF‑S124C, a RAF kinase fusion protein that lacks the N-terminal CRAF‑S257L and CRAF‑S259A, as well as the sensitivity domain. Several 5′ fusion partners have been identified of these mutants to RAF inhibition40. for BRAF, including angiotensin II receptor-associated Although somatic CRAF point mutations are rare in protein (AGTRAP) and solute carrier family 45, mem‑ human cancers, several germline CRAF mutations are the ber 3 (SLC45A3) in prostate cancer53; A-kinase anchor cause of Noonan syndrome (germline mutations in seven protein 9 (AKAP9) in thyroid cancer54; and MARVEL other MAP kinase pathway genes are also reported to domain containing 1 (MARVELD1), acylglycerol kinase cause Noonan syndrome; thus, the disorder is described (AGK)62 and HSPB associated protein 1 (HSPBAP1; also as a ‘RASopathy’ (REFS 41,42)). Noonan syndrome is an known as PASS1) in melanoma63. CRAF fusions with epi‑ autosomal dominant genetic disorder that is character‑ thelial splicing regulatory protein 1 (ESRP1) have also ized by craniofacial deformations, short stature, cardiac been described in prostate cancers53, although most RAF anomalies and a propensity for neurocognitive delay43. kinase fusion events seem to occur in paediatric gliomas. One-third of patients with Noonan syndrome who have Despite the low frequency of BRAF point mutations in CRAF mutations also display multiple nevi, lentigines or low-grade paediatric astrocytomas (~1%), BRAF gene café au lait spots, suggesting that germline CRAF muta‑ fusions are observed in the majority (~70%) of such tions may predispose patients to cutaneous hyperpig‑ cases. To date, the KIAA1549–BRAF fusion is the most mented lesions, similar to the ability of BRAFT1799A to commonly observed64,65, whereas FAM131B–BRAF55 elicit benign nevi27,44. However, most of the CRAF point and SLIT-ROBO RHO GTPase activating protein 3 mutations identified in Noonan syndrome have not been (SRGAP3)–CRAF fusions seem to occur less frequently found in human cancers, and fewer than 2% of human (~2%–8% of cases)58. cancer-derived cell lines harbour mutated CRAF45. The The biochemistry of RAF fusion proteins remains oncogenic impotence of CRAF, compared to the potent poorly characterized, but canonical RAF signalling is oncogenicity of BRAF, is thought to arise because CRAF thought to heavily rely on RAS-mediated dimerization of has low basal kinase activity compared with that of BRAF and CRAF protomers. Deletion of the N-terminal BRAF46. Importantly, one Noonan syndrome‑associated RAS-binding domain (RBD) has long been known mutation, CRAF‑S259F, has also been identified in lung to constitutively activate RAF kinases60,66 and is thought to cancer. Additionally, CRAF‑S259 mutations have been promote dimerization, suggesting that the N terminus identified in patients with either colon (S259P) or ovar‑ of RAF contains an autoinhibitory domain that prevents ian (S259A) cancer. Phosphorylation of CRAF‑S259 is dimerization. Dimerization is associated with increased associated with reduced activity and promotes direct MEK phosphorylation, and disruption of the dimeri‑ binding to 14‑3‑3 proteins, which stabilize the inactive zation interface identified by the crystal structures17,23 state41,47. Thus, dephosphorylation or mutation of S259 nearly abolishes catalytic activity67,68. Indeed, disrupted disrupts the stability of the inactive CRAF conformation dimerization also prevents KIAA1549–BRAF-induced and facilitates binding to RAS–GTP at the plasma mem‑ oncogenic transformation in vitro69, indicating that loss brane48,49. The Melan‑a cell line — a non-transformed of the RAF RBD through fusion of the kinase domain mouse melanocyte-derived cell line that is sensitive to to KIAA1549 may constitutively activate RAF proteins BRAF‑V600E‑induced transformation — is also trans‑ through a similar mechanism. formed by the expression of CRAF‑S259A50. In contrast to the scarcity of CRAF point mutations identified in Development of ATP-competitive RAF inhibitors cancer, increased CRAF expression has been identified In 2000, sorafenib was the first RAF inhibitor to enter clin‑ in several malignancies, the most notable being bladder ical trials; this was prior to the discovery of BRAF, CRAF cancer51. The oncogenic importance and therapeutic or ARAF mutations in cancer. Originally developed as a implications of CRAF amplifications remain unclear. CRAF inhibitor that was intended to treat RAS-mutated cancers, sorafenib was discovered by screening a chemical RAF fusion proteins as oncogenic drivers. Analyses of library for inhibitors of recombinant, activated CRAF70. prostate cancer and paediatric astrocytoma or glioma Sorafenib competes with ATP for binding directly to have revealed that point mutation is not the only mecha‑ the active site of the CRAF kinase domain. Owing to the nism that can reveal the oncogenic potential of RAF shared structural homology of the ATP-binding pocket, protein kinases. The presence of the Philadelphia chro‑ several additional kinase targets have been described, mosome in chronic myelogenous leukaemia is the arche‑ including FMS-like tyrosine kinase 3 (FLT3), platelet- typal example of an oncogenic protein kinase (BCR–ABL) derived growth factor receptor‑β (PDGFRβ), KIT and that is generated via a chromosome translocation52. vascular endothelial growth factor receptor 2 (VEGFR2)71. Similarly, RAF fusion transcripts have been identified Although sorafenib is now approved for the treat‑ in melanoma and gliomas, as well as in prostate, gastric, ment of renal cell, hepatocellular and thyroid cancers,

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a developed specifically to target BRAF‑V600E. Specificity Inhibitor concentration for the mutated oncoprotein was first shown for the vemurafenib analogue PLX‑4720, which has a 50% BRAF- BRAF- BRAF- BRAF- BRAF- BRAF- growth inhibition (GI ) that is approximately 50‑fold V600E V600E V600E V600E V600E V600E 50 more potent for cells containing BRAF-V600E than for Inhibitor cells with wild-type BRAF73. Selectivity for the mutant MEK MEK MEK protein was originally explained by the type I binding mode of vemurafenib (and dabrafenib), which favours the active enzyme conformation that is imparted by the ERK ERK ERK oncogenic mutation. By contrast, sorafenib and other type II inhibitors preferentially bind to the inactive conformation and are thus relatively poor inhibitors of Proliferation Proliferation BRAF‑V600E. Clinically, this oncoprotein binding selec‑ tivity has translated into an unusually high therapeutic index for BRAF‑V600E inhibitors, which allows for high BRAF activity exposures of the drug while avoiding the acute toxici‑ ties that are associated with RAF inhibition74,75. The high b therapeutic index of BRAF‑V600E inhibitors has proven Inhibitor concentration to be crucial for treatment of BRAF-mutated melanomas because >80% target inhibition is required for a clinical 76 RAS RAS RAS RAS response . Recent studies have shown that all ATP- RAF RAF RAF RAF RAF RAF competitive RAF inhibitors — including vemurafenib, P P P dabrafenib and sorafenib — are not only poor inhibitors of wild-type BRAF but also lead to paradoxical activation MEK MEK MEK of the MAP kinase pathway in BRAF wild-type cells24,77,78 (FIG. 2). Thus, the high potency of BRAF‑V600E inhibitors in the treatment of BRAF‑V600E‑mutated melanoma ERK ERK ERK may be attributed to inhibitor binding mode, but the lack of potency in BRAF wild-type cells and the high thera‑ peutic index probably stem from the unique mechanism Proliferation of action for wild-type RAF kinase, as described below.

The RAF inhibitor paradox. The use of RAF inhibitors RAF activity in RAS-mutated cancers has revealed the complexity of RAF signal transduction. In 2009, three groups showed Figure 2 | Biochemistry of RAF inhibitors in BRAF‑V600E andNature BRAF Reviewswild-type | Cancer cells. that catalytic inhibition of RAF kinases resulted in ‘para‑ BRAF oncoproteins promote proliferation by constitutively activating the MAP kinase doxical activation’ of CRAF, increased proliferation of pathway. a | ATP-competitive RAF inhibitors disrupt MEK activation and prevent RAS-mutated cells in vitro and, in some cases, induc‑ proliferation of BRAF‑V600E‑mutated melanomas in a dose-dependent manner. tion of tumorigenesis in vivo24,77,78. Although each group b | Wild-type RAF proteins usually occur in an autoinhibited state. In the context of activated RAS, RAF inhibitors stimulate MEK activation at sub-saturating inhibitor proposed a distinct mechanism to explain the effect, concentrations by relieving autophosphorylation (P) of wild-type RAF, thereby promoting several themes overlapped (FIG. 2b). RAF inhibitors pro‑ RAF dimerization and association with RAS. Saturating inhibitor concentrations prevent moted co‑immunoprecipitation of RAF isoforms, as well MEK phosphorylation by completely blocking RAF catalysis. Part b was adapted from as association with RAS–GTP at cell membranes25,77,79, REF. 148, Nature Publishing Group. suggesting that the inhibitors promote CRAF homo­ dimerization and heterodimerization and RAS-mediated activation. Moreover, site-directed mutagenesis of the it remains unclear if CRAF is the predominant thera‑ putative dimer interface rendered CRAF resistant to peutic target. The efficacy of sorafenib in renal cell car‑ paradoxical activation78. BRAF inhibitors also promoted cinomas is probably due to inhibition of VEGFR2, and binding to kinase suppressor of RAS 1 (KSR1)80, which although responses in hepatocellular carcinomas are cor‑ functions as a scaffold to coordinate a RAF–KSR1–MEK related with ERK phosphorylation72, responses are not complex81 but competes with CRAF for dimerization correlated with RAS mutational status. with BRAF. The drug-induced association of RAF with Melanoma emerged as the ideal test case for RAS–GTP, increased RAF dimerization and binding to BRAF‑V600E kinase inhibitors, not only because the KSR1 probably each contribute to the activation of ERK tumour genetics of melanoma suggest MAP kinase path‑ signalling, but it remains unclear whether each event way dependence but also because patients with metas­tatic alone is sufficient to induce paradoxical activation. melanoma historically suffered a dearth of efficacious Interestingly, paradoxical activation by RAF inhibi‑ treatment options. Unfortunately, sorafenib and other tors can be genetically recapitulated using catalytically compounds that were developed to inhibit CRAF lack impaired BRAF24. In this model, BRAF catalytically sup‑ efficacy in BRAF-mutated melanomas. Therefore, a sec‑ presses CRAF activity in RAS-mutated cells such that a ond generation of ATP-competitive RAF inhibitors was disruption in BRAF function (either genetically or with

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a BRAF inhibitor) alleviates CRAF suppression and acti‑ Cutaneous lesions induced by BRAF inhibitors. The vates it in a RAS-dependent manner. This mechanism ability of RAF inhibitors to paradoxically activate ERK is further supported by the discovery of a RAF auto­ MAP kinase signalling has crucial clinical implications. inhibitory phosphorylation site that is required for para‑ Results from the vemurafenib Phase III clinical trial doxical activation25 (FIG. 2b), indicating that paradoxical indicate that 18% of patients experienced squamous cell activation may be intrinsic to all catalytic RAF inhibitors. carcinomas (SCCs) and/or kerato­acanthomas84. The list of secondary cutaneous lesions that are induced by Clinical use of RAF inhibitors BRAF inhibitors has since expanded to include papil‑ Prior to the approval of BRAF inhibitors, patients with lomas, cystic lesions, milia, eruptive nevi and basal BRAF-mutated melanoma faced a worse prognosis cell carcinomas90. Although all drug-induced lesions than that for patients whose disease expressed wild- are, so far, wild type for BRAF, multiple other genetic type BRAF. However, the recent approval of two BRAF mutations have been identified. Importantly, sam‑ inhibitors (vemurafenib and dabrafenib) and one MEK ples of secondary keratoacanthomas show a high fre‑ inhibitor (trametinib) for the treatment of metastatic quency of HRAS mutations91–93. This suggests that the BRAF-V600E-mutated melanoma has altered the situ‑ ability of BRAF inhibitors to elicit paradoxical MAP ation; patients with BRAF-mutated melanoma who are kinase pathway activation induces a proliferative pro‑ treated with BRAF inhibitors now have a longer median gramme in dormant keratinocytes that harbour a latent survival than that of patients with wild-type BRAF mela‑ RAS mutation. Indeed, studies using the keratin 5 noma82. In Phase I clinical trials, treatment with vemu‑ (K5; also known as Krt5)–SOS–F transgenic mouse rafenib induced disease stabilization, as well as some cases model, in which mice are predisposed to epidermal of marked regression of BRAF-V600E-mutated mela‑ tumours through keratinocyte-specific expression of a nomas74. The objective response rate (ORR) in Phase II dominant active, farnesylated son of sevenless (SOS‑F), increased from 19% in the dacarbazine control arm to have shown that treatment with a RAF inhibitor pro‑ 53% in the vemurafenib arm, and the average duration motes RAF dimerization, activates ERK signalling and of response increased from 2.7 months to 6.7 months83. suppresses RHO-associated, coiled-coil containing pro‑ In 2011, the Phase III trial reported a 48% ORR when tein kinase 2 (ROCK2) to potentiate RAS-driven epi‑ patients were treated with vemurafenib, compared with a dermal tumorigenesis79. In addition to RAS mutations, 5% ORR when patients were treated with dacarbazine, as oncoviruses have been implicated as initiating factors in well as an estimated 5.3‑month median progression-free a subset of cutaneous lesions. Human papilloma virus survival (PFS) in the vemurafenib treatment group, com‑ (HPV) and Merkel cell polyomavirus DNA were identi‑ pared with a 1.6‑month median PFS in the dacarbazine fied in samples from patients who had been treated with treatment group84. Although the overall survival benefit BRAF inhibitors, and vemurafenib promoted tumori­ was limited, approximately 14% of patients experienced genesis in an HPV-driven mouse model of SCC94. So durable responses and remained free from relapse after far, the secondary cutaneous malignancies arising in 18 months of treatment with vemurafenib85. Moreover, patients receiving BRAF inhibitor therapy have been the full measure of the effect of vemurafenib on overall well-differentiated and amenable to local resection with survival could not be calculated owing to the crossover no reports of metastasis95. design of the Phase III trial. A second BRAF inhibitor, More worrisome than the cutaneous toxicities is the dabrafenib, also gained FDA approval in 2013, for the possibility that BRAF inhibitors may induce the prolif‑ treatment of not only BRAF‑V600E‑expressing mela‑ eration of pre-malignant cells that harbour RAS muta‑ nomas but also BRAF‑V600K‑expressing melanomas86. tions in non-cutaneous tissues, such as the lung, colon Phase III results reported that treatment with dabrafenib or pancreas. The ability of BRAF inhibitors to promote resulted in a 50% ORR and a 5.1-month median PFS, the growth of pre-malignant precursor lesions along the compared with a 6% ORR and a 2.7-month median path to adenocarcinoma is of considerable concern. PFS for dacarbazine87. FDA approval was also granted for Indeed, a recent report described a patient with meta‑ trametinib — the first MEK inhibitor marketed for can‑ static BRAF-V600K melanoma who developed NRAS- cer treatment. The median PFS and the ORR increased mutant chronic myelomonocytic leukaemia during the from 1.5 months and 8%, respectively, with dacarbazine course of vemurafenib treatment96,97. Upon withdrawal to 4.8 months and 22%, respectively, with trametinib88. of vemurafenib, the leukaemic cell count of the patient The remarkable efficacy of BRAF inhibition in mela‑ dropped, thereby necessitating an empirically deter‑ noma has been recapitulated in early data that are emerg‑ mined intermittent dosing schedule of vemurafenib ing from the treatment of patients with HCL‑C. In 2011, and cobimetinib (a MEK inhibitor) to simultaneously sequencing of a cohort of samples from patients with control the metastatic melanoma and to prevent the out‑ HCL‑C showed that the BRAFT1799A mutation occurred growth of leukaemia96. A second case report described in virtually all patients with HCL‑C20. Anecdotes from a patient presenting with BRAF-mutated metastatic the clinic suggest that patients with HCL‑C whose dis‑ melanoma who had undergone a prior successful sur‑ ease is refractory to conventional chemotherapy respond gical resection of a KRAS‑G12D colorectal cancer98. well to vemurafenib89. Thus, HCL‑C may be a neoplasm Although the combination of dabrafenib and trametinib in which mutated BRAF is a key oncogenic driver, and caused a partial response of the patient’s melanoma, at the paucity of BRAF-independent signalling pathways 12 weeks the patient presented with a brain metastasis precludes the development of primary chemoresistance. that, upon resection, was characterized as originating

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from a KRAS-mutated colon cancer. After progressive In some cases, inhibition of the BRAF oncopro‑ metastatic colon cancer necessitated cessation of dab‑ tein may activate signalling through wild-type RAF rafenib and trametinib combination therapy, the patient by relieving feedback mechanisms and increasing received trametinib monotherapy and experienced a RAS–GTP levels99. Moreover, results from the use brief improvement in performance status. In vitro studies of BRAF inhibitors in non-melanoma malignancies that were carried out using tissue that was derived from show that the cell of origin of the cancer — and thus the aforementioned patients suggested that MEK inhibi‑ the signalling pathways that are inherently available to the tors suppress the growth of BRAF-V600E melanoma and cancer cell — predicts the efficacy of BRAF inhibition. also prevent expansion of the RAS-mutated malignan‑ For example, although BRAF‑V600E is expressed in cies. These case reports not only emphasize the caution ~10% of patients with metastatic colorectal carcino‑ that must be exercised when considering the use of RAF mas (mCRCs), a Phase I clinical trial of vemurafenib in inhibitors as an adjuvant therapy but also demonstrate patients with BRAF-mutated mCRC was associated with the importance of determining which node of the MAP only a 3.7‑month median PFS — about one-half of the kinase pathway is most appropriate to therapeutically PFS observed in BRAF-mutated advanced melanoma100. target in patients with BRAF-mutated melanoma. Subsequent studies suggested that the decreased effi‑ cacy of vemurafenib in BRAF-mutated mCRC can be Primary resistance to RAF inhibition attributed to increased epidermal growth factor receptor BRAF mutation is not always prognostic of respon‑ (EGFR) signalling in these cells in response to vemu‑ siveness to BRAF inhibitor therapy. Although BRAF- rafenib. At baseline, BRAF‑V600E–MEK–ERK signal‑ mutated melanoma is heralded as the malignancy in ling activates a negative-feedback loop that functions to which BRAF inhibitors prove to be most beneficial, attenuate RTK–EGFR signalling101–103. However, BRAF primary resistance occurs even in BRAF-mutated mela‑ inhibition relieves this negative feedback, and this leads noma. In the dabrafenib Phase II clinical trial, 16% to increased signalling through EGFR (FIG. 3); in sum‑ of patients with BRAF-V600E melanoma and 31% of mary, the on‑target activity of BRAF inhibitors activates patients with BRAF-V600K melanoma experienced a rapid, adaptive mechanism of chemoresistance. In con‑ progressive disease despite treatment with dabrafenib86. trast to mCRC, basal EGFR levels are low in melanoma, thereby allowing for evasion of this resistance mecha‑ RTKs (EGFR, Mitogen nism. Thus, the cellular context in which BRAF inhibitors HER2 or HER3) are used is highly relevant to therapeutic efficacy. Papillary thyroid cancer is another malignancy for which BRAF mutational status does not predict the response to BRAF‑V600E inhibition. By a mechanism that is conceptually concordant with that observed in RAF inhibitor GEFs mCRC, the inhibition of BRAF‑V600E in papillary thy‑ roid cancer causes a relief of negative feedback, lead‑ ing to an induction of human epidermal growth factor BRAF- RAS PI3K receptor 2 (HER2; also known as ERBB2) and HER3 sig‑ V600E nalling104. Fagin and colleagues104 showed that the BRAF inhibitor-mediated induction of HER2 and HER3 sig‑ nalling in thyroid cancer is dependent on the secretion MEK CRAF of the HER2 and HER3 ligand neuregulin 1 (NRG1). This ligand is not expressed in melanoma or colorectal cancer cells, thereby allowing the evasion of HER2- and ERK HER3‑mediated de novo resistance in these tissues. The prognostic value of BRAF mutation for pathway- Proliferation targeted therapy in NSCLC or ovarian cancer remains mostly unknown at this time but, at least with NSCLC, Figure 3 | Loss of feedback inhibition and activation of grounds for optimism are offered by the response of the PI3K pathway mediates primaryNature resistance Reviews | Cancerto BRAF‑V600E‑initiated NSCLCs to pathway-targeted BRAF‑V600E inhibition. Resistance to BRAF inhibitors in blockade in GEMMs and by a case report on the metastatic colorectal and papillary thyroid cancers is response of a patient with ARAF-mutated lung cancer mediated by loss of a feedback loop (epidermal growth to sorafenib26,40,105,106. factor (EGF)–EGF receptor (EGFR) signalling in colorectal cancers or neuregulin 1 (NRG1)–human epidermal growth Acquired resistance to RAF inhibition factor receptor 2 (HER2) and NRG1–HER3 signalling in Although the reported sample sizes remain small, the papillary thyroid cancers), whereby MAP kinase pathway activity inhibits mitogen-dependent signalling (shown in survival benefit in patients with BRAF-mutated mela‑ red). The negative-feedback loop is disrupted upon noma treated with BRAF inhibitors seems to be lim‑ treatment with a RAF inhibitor, and proliferation is restored ited to less than 1 year, with the durability of patient through receptor tyrosine kinase (RTK), RAS and PI3K responses to RAF inhibitors being limited by the signalling (shown in blue). GEFs, guanine nucleotide onset of drug-resistant disease83,84,86. Initial attempts exchange factors. to anticipate resistance mechanisms were informed

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by experience with acquired resistance to imatinib in Despite the absence of T529 mutations in BRAF, BCR–ABL-mutated leukaemia, which occurs when numerous other mechanisms of acquired resistance to mutation of the ‘gatekeeper’ threonine prevents the bind‑ BRAF inhibitors that contribute to clinical drug resist‑ ing of the drug without substantially affecting normal ance have been described. Most resistance mechanisms kinase activity107,108. Surprisingly, although engineering promote reactivation of the MAP kinase signalling of mutations at the analogous gatekeeper residue (T529) pathway in the presence of BRAF inhibition (FIG. 4). in BRAF confers vemurafenib resistance in vitro, T529 For example, mutational activation of NRAS, MEK1 mutations have never been reported in tumour samples or MEK2 can reactivate the MAP kinase pathway in from BRAF inhibitor-resistant patients108. One possible the presence of BRAF inhibition109–112, and increased explanation for the failure to find such an obvious and CRAF protein levels have also been shown to confer highly predicted mechanism of resistance may be that resistance to BRAF inhibition in cell culture models of cells expressing BRAF that is doubly mutated at amino melanoma112,113. Increased levels of CRAF protein have acids 529 and 600 are not viable in the absence of the yet to be identified in clinical samples of BRAF inhib‑ drug. Hence, there would be no reservoir of such cells itor resistance, and it has been shown that, in some prior to drug treatment and, therefore, this would not contexts, CRAF negatively regulates BRAF‑V600E114, occur as a mechanism of drug resistance. Although so further analysis is necessary to determine the clini‑ Marais and colleagues108 have shown that a myeloid cal relevance of increased levels of CRAF protein as cell line (Ba/F3) remains viable in the absence of BRAF a bona fide BRAF inhibitor resistance mechanism. inhibition when transfected with BRAF that is doubly Through an unbiased screen, the serine/threonine mutated at codons 529 and 600, this observation is prob‑ MAP kinase kinase kinase COT (also known as ably cell type-specific and may also reflect the outgrowth MAP3K8) was shown to activate MEK in the presence of cells that are transduced with lower — and, thus, of BRAF inhibition115. Elevated COT copy number and non-toxic — levels of doubly mutated BRAF. mRNA expression was identified in biopsy specimens of metastatic melanoma following vemurafenib treat‑ ment. Overexpression of the mutant BRAF protein HGF itself has also been reported, which further emphasizes the importance of increased expression of the drug Mitogen Stromal cell target as a relevant mechanism of cancer drug resist‑ MET RTK (PDGFR or IGF1R) β ance116,117. Additionally, BRAF‑V600E splice variants, which endow the proteins with the ability to dimer‑ ize in a RAS-independent manner and which increase the kinase activity of the proteins, represent the only GEFs resistance mechanism that involves a structural change to BRAF itself and one of the first examples of how altered mRNA splicing can render an oncoprotein resistant to a drug118. NRAS* RAS RAF inhibitor In addition to MAP kinase pathway reactiva‑ tion in the presence of BRAF inhibition, it has been suggested that about 30% of patients develop MAP ↑BRAF- p61 BRAF- BRAF- kinase pathway-independent mechanisms of resist‑ CRAF ↑ V600E V600E V600E ance119. Initial reports propose that most MAP kinase pathway-independent mechanisms of resistance involve alterations that lead to upregulation of the PTEN–PI3K–AKT signalling axis. Increased expres‑ ↑COT MEK sion of either PDGFRβ or insulin-like growth fac‑ tor 1 receptor (IGF1R) was identified in cultured cells MEK1* and ERK and in specimens from patients with vemurafenib- MEK2* resistant melanomas. It is claimed that PDGFRβ or IGF1R signalling allows for the activation of MAP Proliferation kinase pathway-independent pro-survival signalling pathways, such as the PI3K–AKT axis, which render Figure 4 | Mechanisms of acquired resistance to BRAF‑V600E inhibition that lead 109,120 Nature Reviews | Cancer cells resistant to the effects of BRAF inhibition . to reactivation of the MAP kinase pathway. Multiple mechanisms of acquired Furthermore, activation of HER3 signalling has been resistance (shown in blue) to BRAF‑V600E inhibitors lead to reactivation of the MEK–ERK identified as an adaptive mechanism of resistance in pathway (shown in red) in the presence of a RAF inhibitor. The mechanisms of resistance a subset of patients with melanoma. It is thought that include mutational activation (asterisks) of NRAS, MEK1 and MEK2, overexpression (↑) of CRAF, of BRAF‑V600E itself or of COT, alternative splicing of BRAF that renders the MAP kinase pathway inhibition promotes the upregu‑ protein immune to inhibitors (p61 BRAF‑V600E), ligand-dependent RAS signalling lation of the transcription factor forkhead box protein through either of the receptor tyrosine kinases (RTKs) platelet-derived growth factor D3 (FOXD3), which in turn directs increased expres‑ receptor‑β (PDGFRβ) or insulin-like growth factor 1 receptor (IGF1R), or stromal cell sion of HER3 and allows for enhanced HER2–HER3 secretion of hepatocyte growth factor (HGF) to activate MET (also known as HGF signalling121. In addition, amplification of the melano­ receptor) signalling. GEFs, guanine nucleotide exchange factors. cyte lineage-specific transcription factor MITF or

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its effector, BCL2‑related protein A1 (BCL2A1), has vemurafenib or dabrafenib monotherapy, subsequent been shown to confer resistance to BRAF and MEK treatment with trametinib gave only a 1.8‑month inhibitors in cell culture models, and upregulation of median PFS and a 5.8‑month median overall survival128. MITF occurs in a minority of patients with BRAF- Only two patients within the cohort had a complete or mutated melanoma122–124. Secretion of growth factors partial response, and both of these patients had discon‑ by the tumour stroma has also been shown to confer tinued BRAF inhibitor therapy owing to adverse events resistance to BRAF inhibition. Specifically, stromal rather than the onset of drug-resistant disease. Thus, cell-directed secretion of hepatocyte growth factor second-line MEK inhibitor monotherapy to treat BRAF (HGF) has been shown to signal through its receptor, inhibitor-resistant disease seems limited. These results MET (also known as HGF receptor; which is expressed may be partially explained by a recent report showing on the surface of melanoma cells) and thereby reacti‑ that oncogenic BRAF overexpression or alternative vate MAPK and PI3K–AKT signalling pathways in the splicing — previously known to confer resistance to presence of BRAF inhibition125,126. BRAF inhibitor monotherapy — is also a resistance Although numerous on-pathway or off-pathway mechanism among patients who receive first-line BRAF mechanisms of resistance have been identified and MEK combination therapy129. Another tactic to tar‑ through preclinical studies, the challenge lies in get tumours that have acquired MAP kinase pathway- characterizing which of these resistance mechanisms dependent resistance mechanisms is the use of ERK are clinically relevant and in determining the relative inhibitors. Although ERK inhibitors have only recently frequencies of the resistance mechanisms observed entered clinical trials, preclinical data suggest that cells in the clinic. The development of second-generation that have developed resistance to RAF and MEK inhibi‑ BRAF inhibitors has definitively shown the potential tors remain sensitive to a selective ERK1 and ERK2 of pathway-targeted therapeutics in the treatment of inhibitor130. The acquisition of MAP kinase pathway- BRAF-mutated melanoma. However, the emergence independent mechanisms of resistance may also sen‑ of drug-resistant disease stands as a formidable obstacle sitize tumours to other pathway-targeted therapies. that remains to be addressed to enhance the durability For example, increased PDGFRβ or IGF1R signalling of patient responses. might be combated with RTK or PI3K inhibitors120.

Improving responses to RAF inhibition with other BRAF‑V600E: ‘oncogene overdose’ and intermittent therapeutic targets. It is currently unclear whether drug dosing. Although BRAF‑V600E constitutively survival benefit alone will cause RAF inhibitors to be activates the MEK–ERK pathway, many cancer cells used as single agents, but preclinical and early clini‑ remain sensitive to the quantity of MAP kinase path‑ cal data strongly suggest that some combination of way signalling (as measured by ERK phosphorylation or inhibitors of RAF and either MEK or, conceivably, transcription of target genes). More than ten years ago, ERK will have the greatest efficacy. FDA approval was it was shown that mouse or human fibroblasts display a recently granted for the combination of dabrafenib and peak proliferative response with an intermediate amount trametinib in advanced melanoma, and detailed results of RAF–MEK–ERK pathway activation131,132. Indeed, in of the Phase III trial are forthcoming. Phase I/II non-immortalized human IMR‑90 fibroblasts, sustained results demonstrated that the combination increased RAF activation induced an irreversible cell cycle arrest median PFS from 5.8 months for single agent treatment with features of cellular senescence133. This further sub‑ to 9.4 months in the combination group, and the ORR stantiated the hypothesis that RAF-induced transforma‑ increased from 54% to 76% (REF. 127). Perhaps unex‑ tion is determined not only by activation of the MAP pectedly, the tolerability of either agent improved in the kinase pathway but also by the quantity of pathway combination group. Acneiform dermatitis, which is activation, as reviewed by Marshall134. the most common dose-limiting toxicity that is asso‑ The notion that malignant cells might remain sen‑ ciated with trametinib (8%), was not observed in the sitive to the strength of MAP kinase pathway signal‑ combination group. Likewise, the frequency of hyper‑ ling was recently substantiated using a patient-derived keratosis, cutaneous SCCs and papillomas, which are xenograft model of melanoma. Upon transplanta‑ all RAF inhibitor class effects, each decreased when tion into immunocompromised mice, a chemonaive, dabrafenib was co‑administered with trametinib. BRAF-mutated melanoma showed striking regression Mechanistically, this most probably occurs because in response to treatment with vemurafenib132. However, the paradoxical activation of the MEK–ERK MAP sustained drug treatment led to the emergence of lethal kinase pathway that is induced by RAF inhibitors is drug-resistant disease due to increased BRAF‑V600E suppressed by MEK inhibition. expression. Remarkably, the drug-resistant tumours Tumours that have acquired on‑pathway mecha‑ and cell cultures were not only drug-resistant but also nisms of resistance to BRAF inhibitors constitute drug-dependent for their peak proliferation (FIG. 5a). an important group in which to test the efficacy of Moreover, when vemurafenib was removed from drug- second-line MEK or ERK inhibitors. Unfortunately, resistant cells or tumours, striking anti-proliferative early clinical data suggest a limited efficacy of MEK effects were observed (FIG. 5b). These data indicate that inhibitor monotherapy for patients with BRAF-mutated BRAF‑V600E-‘addicted’ melanoma cells can remain melanoma who have drug-resistant disease. Within a sensitive to the magnitude of BRAF‑V600E–MEK–ERK cohort of 40 patients who had progressed on either signalling pathway activation, such that too much of the

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oncoprotein to which they are addicted can lead to onco‑ and MEK127 may prevent some of the identified gene overdose. The demonstration that vemurafenib- mechanisms of resistance, although it is not clear to resistant melanoma cells can have a fitness deficit in the what extent. Identifying and understanding common absence of drug has led to the design of clinical trials to resistance mechanisms in BRAF and MEK inhibitor- test the efficacy of intermittent dosing to forestall the resistant melanomas will inform additional rational onset of drug-resistant disease and therefore enhance pathway-targeted therapeutic combinations, such as the durability of patient responses. co‑targeting PI3K signalling, as well as dosing strate‑ gies to prevent or delay drug resistance and achieve Conclusions long-term survival benefit. Development of BRAF inhibitors has changed pre‑ A more thorough understanding of the mechanism clinical understanding and clinical treatment of late- (or mechanisms) of oncogenesis has led to potent and stage melanoma, and it offers a more hopeful option selective RAF inhibitors that exploit the unique biol‑ to patients with a historically devastating diagnosis. ogy of BRAF-mutated melanomas73. However, BRAF- Despite the short duration of responses, BRAF inhibitor- mutated melanoma only represents a small subset of treated patients have a high ORR and frequently RAF-mutated cancers. Several RAF fusion proteins experience marked tumour regression. Several com‑ seem to be insensitive to the current generation of plete responses have been observed (by Response BRAF inhibitors and instead show paradoxical activa‑ Evaluation Criteria in Solid Tumours (RECIST))74,84. tion in a similar way to wild-type RAF alleles (TABLE 1). Drug-resistant disease progression is observed in Additionally, CRC and NSCLC comprise a large most cases, often occurring within a few months after cohort of BRAF-mutated cancers that are currently initial response. Current efforts to co‑target BRAF ineffectively treated. The mechanisms of RAF-driven oncogenesis in RAF inhibitor-resistant settings may involve redundant pathways, additional biochemical a functions or additional substrates of RAF oncopro‑ teins135. In the same way as rational structure-based On therapy Off therapy On therapy Off therapy On therapy Off therapy drug design has yielded selective and targeted treat‑ ments for BRAF-mutated melanomas, understanding Drug-resistant Excess tumour cell phospho-ERK the mechanisms of other BRAF oncogenes and CRAF- dependent tumours may lead to effective treatments Optimal in these malignancies. Similarly, although an under‑ phospho-ERK standing of rapid adaptive resistance in BRAF-mutated CRC led to hypotheses for rational combinations using 101,102,104

Phospho-ERK level Drug-sensitive Insufficient RTK or PI3K pathway inhibitors , understand‑ tumour cell phospho-ERK ing BRAF inhibitor resistance in papillary thyroid and Time ovarian cancers will probably yield additional rational therapeutic approaches and predictive biomarkers for b these indications. Drug-sensitive Although BRAF‑V600E and BRAF‑V600K are tumour cell bona fide drug targets in melanoma, it remains unclear Drug-resistant whether ARAF and CRAF alterations constitute driver tumour cell 1 mutations, but there is reason to suspect that they 2 1 2 1 2 1 2 are therapeutic targets. A recent case study indicates Tumour volume Tumour that ARAF mutations could be treated with currently available RAF inhibitors. A patient with NSCLC with Time an ARAF‑S124C mutation and a lack of alterations in any other known oncogenes or tumour suppres‑ Figure 5 | Intermittent BRAF inhibitor therapy to forestall theNature onset Reviews of | Cancer drug-resistant melanoma. Prior to the initiation of BRAF inhibitor therapy for sor genes, achieved a sustained tumour remission 40 BRAF-mutated melanomas, the bulk of the cells have optimal BRAF‑V600E–MEK–ERK (~5 years) with sorafenib treatment , and this gives activity (part a) and are drug-sensitive (blue line), with only rare variant cells with intrinsic some promise of treatment for cancer patients with drug resistance (red line). The addition of a BRAF inhibitor leads to insufficient these rare mutations. Interest has also been renewed BRAF‑V600E–MEK–ERK activity in the bulk of the tumour (part a), which leads to tumour in wild-type CRAF as a therapeutic target for KRAS- regression (response 1 in part b). However, BRAF inhibitor therapy immediately starts to mutated lung cancer after demonstrations of CRAF select for expansion of melanoma cells with increased BRAF‑V600E expression and dependence for the onset of Kras-mutated NSCLC therefore intrinsic drug resistance, because they have an optimal level of BRAF‑V600E– in GEMMs136–138. Interestingly, BRAF was dispensa‑ MEK–ERK activity — and therefore a fitness benefit — in the presence of the drug. ble, but concurrent loss of MEK1 and MEK2 or ERK1 Upon cessation of drug administration, drug-resistant melanoma cells with increased BRAF‑V600E expression have an excess of BRAF‑V600E–MEK–ERK activity (part a) that and ERK2 also prevented tumour initiation, suggest‑ confers a fitness deficit on the cells (response 2 in part b). However, cessation of drug ing that CRAF but not BRAF is essential for tumour administration also allows residual drug-sensitive cells to restore their optimal level of formation in Kras-driven oncogenesis. CRAF but not BRAF‑V600E–MEK–ERK activity (part a) and therefore re‑initiate their proliferation BRAF dependence was also shown in a SOS‑F‑induced (response 2 in part b). Phospho-ERK, phosphorylated ERK. Adapted with permission from model of skin cancer138, further supporting CRAF The Skin Cancer Foundation — SkinCancer.org149. as a therapeutic target in RAS-dependent cancers.

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Table 1 | Oncogenic MAP kinase pathway mutants and the effects of RAF and MEK inhibitors Cancer-associated Biochemical effect of Clinical effect of Biochemical effect Clinical effect of mutation RAF inhibition RAF inhibition of MEK inhibition MEK inhibition BRAF‑V600E or Inhibits MEK Tumour regression Inhibits ERK Tumour regression BRAF‑V600K phosphorylation and and improved phosphorylation and and improved inhibits growth of survival in inhibits growth of survival in late-stage melanoma cells74 late-stage melanoma cells147 melanoma89,129 melanoma84–88 BRAF mutations Inhibits MEK Unknown Unknown Unknown other than V600E phosphorylation in and V600K some settings26; no effect or enhanced MEK phosphorylation in others140 BRAF fusion proteins ‘Paradoxical activation’ of Unknown Inhibits ERK Unknown paediatric astrocytoma- phosphorylation associated fusion in PASS1–BRAF proteins56; sorafenib but fusion-expressing not vemurafenib inhibits melanoma cells64 MEK phosphorylation in a patient-derived cell line with AGK–BRAF fusion63 CRAF fusion proteins Unknown Unknown Unknown Unknown CRAF point Sorafenib inhibits Unknown Inhibits ERK Unknown mutations MEK phosphorylation phosphorylation and inhibits growth and inhibits growth of CRAF‑S257L and of CRAF‑S257L CRAF‑S259A transformed and CRAF‑S259A cells41 transformed cells41 ARAF point ARAF‑S124C transformed 5‑year tumour Unknown Unknown mutations cells are sensitive to RAF remission when inhibition41 treated with sorafenib41 RAS mutations Paradoxical activation25,78,79 Ineffective Inhibits ERK Mostly ineffective, phosphorylation and although early inhibits growth in reports indicate some settings147 responses in some NRAS-mutated melanomas140 AGK, acylglycerol kinase; PASS1, protein associated with small stress protein 1.

However, the usefulness of catalytic CRAF inhibitors paradoxical activation93. In addition, preliminary data remains uncertain owing to the paradoxical activation of suggest that MEK inhibitors may be effective in some ERK MAP kinase signalling by RAF kinase domain inhib‑ NRAS-mutated melanomas140. A deeper understanding itors. Alternatively, the mechanisms that regulate RAF of RAS–RAF–MEK–ERK biochemistry, in conjunction activation may reveal novel modes of therapeutic inter‑ with the identification of new biomarkers to predict vention. Indeed, efforts have begun to develop ‘dimer- response, will be essential for the future success of drug blockers’ (REF. 139) and RAF inhibitors that do not show development programmes in RAF-driven cancers.

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