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High-throughput oncogene mutation profiling in human cancer
Author: R. K. Thomas
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"High-throughput oncogene mutation profiling in human cancer Roman K Thomas 1,2,25?27 , Alissa C Baker 1,27 , Ralph M DeBiasi 1,2,27 , Wendy Winckler 1,2 , Thomas LaFramboise 1,2 , William M Lin 1,2 , Meng Wang 1,2 , Whei Feng 1,2 , Thomas Zander 26 , Laura E MacConaill 1,2 , Jeffrey C Lee 1,2 , Rick Nicoletti 1,2 , Charlie Hatton 1,2 , Mary Goyette 2 , Luc Girard 3 , Kuntal Majmudar 3 , Liuda Ziaugra 2 , Kwok-Kin Wong 1 , Stacey Gabriel 2 , Rameen Beroukhim 1,2 , Michael Peyton 3 , Jordi Barretina 1,2 , Amit Dutt 1,2 , Caroline Emery 1 , Heidi Greulich 1,2 , Kinjal Shah 1,2 , Hidefumi Sasaki 4 , Adi Gazdar 3,5 , John Minna 3,6 , Scott A Armstrong 7 , Ingo K Mellinghoff 8 , F Stephen Hodi 1 , Glenn Dranoff 1 , Paul S Mischel 9 , Tim F Cloughesy 10 , Stan F Nelson 11 , Linda M Liau 12 , Kirsten Mertz 13,14 , Mark A Rubin 13 , Holger Moch 14 , Massimo Loda 1,13 , William Catalona 15 , Jonathan Fletcher 1,13 , Sabina Signoretti 1,13 , Frederic Kaye 16 , Kenneth C Anderson 1 , George D Demetri 1,17 , Reinhard Dummer 18 , Stephan Wagner 19 , Meenhard Herlyn 20 , William R Sellers 1,21 , Matthew Meyerson 1,2,22,23 & Levi A Garraway 1,2,23,24 Systematic efforts are underway to decipher the genetic changes associated with tumor initiation and progression 1,2 . However, widespread clinical application of this information is hampered by an inability to identify critical genetic events across the spectrum of human tumors with adequate sensitivity and scalability. Here, we have adapted high-throughput genotyping to query 238 known oncogene mutations across 1,000 human tumor samples. This approach established robust mutation distributions spanning 17 cancer types. Of 17 oncogenes analyzed, we found 14 to be mutated at least once, and 298 (30%) samples carried at least one mutation. Moreover, we identified previously unrecognized oncogene mutations in several tumor types and observed an unexpectedly high number of co-occurring mutations. These results offer a new dimension in tumor genetics, where mutations involving multiple cancer genes may be interrogated simultaneously and in ?real time? to guide cancer classification and rational therapeutic intervention. Numerous cancer genome characterization efforts have emerged in recent years, empowered by the notion that detailed knowledge of somatic alterations will speed the development of targeted cancer therapeutics 1?3 . These initiatives have relied heavily on large-scale sequencing approaches to characterize the point mutations and short Received 4 August 2006; accepted 11 January 2007; published online 11 February; corrected after print 14 March 2007; doi:10.1038/ng1975 1 Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, 44 Binney Street, Boston, Massachusetts 02115, USA. 2 The Broad Institute of M.I.T. and Harvard, 7 Cambridge Center, Cambridge, Massachusetts 02142, USA. 3 Hamon Center for Therapeutic Oncology Research, University of Texas Southwestern Medical Center at Dallas, 6000 Harry Hines Boulevard, Dallas, Texas 75390-8593, USA. 4 Department of Surgery 2, Nagoya City University Medical School, Nagoya 467-8601, Japan. 5 Department of Pathology, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA. 6 Departments of Internal Medicine and Pharmacology, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA. 7 Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts 02115, USA. 8 Department of Molecular and Medical Pharmacology and Medicine, David Geffen School of Medicine at the University of California, Los Angeles, Los Angeles California 90095-1732, USA. 9 Department of Pathology, 10 Department of Neurology, 11 Department of Human Genetics and 12 Department of Neurosurgery, David Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, California 90095- 1732, USA. 13 Department of Pathology, Brigham and Women?s Hospital, Harvard Medical School, 75 Francis Street, Boston, Massachusetts 02115, USA. 14 Institute of Surgical Pathology, University Hospital Zu�rich, 8091 Zu�rich, Switzerland. 15 Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60637, USA. 16 Genetics Branch, Center for Cancer Research, National Cancer Institute and National Naval Medical Center, Bethesda, Maryland, USA. 17 Ludwig Center for Cancer Research at Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA. 18 Department of Dermatology, University Hospital Zu�rich, 8091 Zu�rich, Switzerland. 19 Division of Immunology, Allergy and Infectious Diseases, Department of Dermatology, Medical University of Vienna, and Center of Molecular Medicine, Austrian Academy of Sciences, Wahringer Gurtel 18-20, A-1090 Vienna, Austria. 20 The Wistar Institute, 3601 Spruce Street, Philadelphia, Pennsylvania 19104, USA. 21 Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA. 22 Department of Pathology, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, Massachusetts 02115, USA. 23 Center for Cancer Genome Discovery, Dana-Farber Cancer Institute, Harvard Medical School, 44 Binney Street, Boston, Massachusetts 02115, USA. 24 Melanoma Program in Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, 44 Binney Street, Boston, Massachusetts 02115, USA. 25 Max Planck Institute for Neurological Research with Klaus Joachim Zu�lch Laboratories of the Max Planck Society and the Medical Faculty of the University of Cologne, Gleueler Str. 50, 50931 Cologne, Germany. 26 Center for Integrated Oncology and Department I for Internal Medicine, University of Cologne, 50931 Cologne, Germany. 27 These authors contributed equally to this work. Correspondence should be addressed to L.A.G. (levi_garraway@dfci.harvard.edu). NATURE GENETICS VOLUME 39 [ NUMBER 3 [ MARCH 2007 347 LETTERS � 200 7 Nature Pub lishing Gr oup http://www .nature .com/natureg enetics insertions or deletions that represent frequent mechanisms of onco- gene activation 2,4?8 . The concomitant expansion in the number of known genetic alterations in tumors has now shifted the bottleneck toward translation of such information into therapeutic benefit. Accomplishing this task will require both rigorous genetic character- ization across all human tumor types and the advent of methods that detect multiple mutations with high accuracy and at acceptable cost. In this regard, systematic cancer gene mutation detection in clinical specimens has often proved difficult, particularly in the context of the ploidy alterations and admixture of non-malignant cells (stroma, lymphocytes, etc.) characteristic of tumor tissue. Gain-of-function point mutations do not occur randomly in most known oncogenes characterized to date; instead, changes affecting a relatively small number of codons often account for the majority of somatic mutations. In principle, then, a limited number of judiciously designed genetic assays should effectively interrogate a large propor- tion of known oncogene mutations. For example, 16?44 assays per gene in RAS, EGFR and BRAF captured 90%?99% of the mutation prevalence observed thus far for these genes in human malignancies (Supplementary Table 1 online). Therefore, we reasoned that high- throughput genotyping might provide an effective means to detect critical and/or ?targetable? cancer mutations on a large scale in clinical specimens. Accordingly, we designed 245 genotyping assays that queried 238 known somatic mutations involving 17 human oncogenes (Supplementary Table 1). For this proof-of-principle approach, we gave priority to mutations with high prevalence (for example, RAS family mutations), proven clinical implications (such as KIT and EGFR) 4,6?8 and/or strong correlation with preclinical sensitivity to targeted agents (for example, BRAF) 9 . To measure its sensitivity for mutation detection in tumor-derived DNA, we compared the mass spectrometric genotyping approach to both Sanger sequencing and a highly sensitive pyrosequencing-by- synthesis method (picotiter plate pyrosequencing) 10 for the detection of EGFR mutations in 22 primary lung tumor samples. Both geno- typing and picotiter plate pyrosequencing detected 12 mutations, including three mutant alleles representing 16%, 12% and 9% of the total DNA as quantified by the pyrosequencing method (data not shown and Supplementary Table 2 online) 10 . In contrast, Sanger sequencing detected only nine EGFR mutations, missing the three aforementioned low-frequency events 10 . We observed similar results for a panel of KRAS mutations in human lung adenocarcinoma samples (data not shown). Thus, the sensitivity of mass spectrometric genotyping is consistent with prior genetic association studies using pooled DNA samples 11,12 , and it may exceed that of Sanger sequencing for mutation profiling in clinical tumor specimens. In considering the specificity of mass spectrometry?based oncogene profiling, we reasoned that the distribution of the mutations identified by this method should reflect patterns observed previously in human tumors. This prediction was borne out by our results (Fig. 1 and Supplementary Table 2). For example, we observed JAK2 mutations in 3 out of 4 polycythemia vera samples 13?16 , we found FGFR3 mutations in 2 out of 23 multiple myelomas 17 and KIT mutations occurred in 4 out of 104 sarcoma samples 18 , all of which were gastro- intestinal stromal tumors (GISTs). None of these mutations occurred in any of the other tumor samples analyzed. Moreover, this high specificity was confirmed through independent validation of 393 mutation calls by Sanger sequencing or other methods (including duplicates; see Supplementary Note online). We found one GIST specimen carrying two KIT mutations, including a D816H mutation recently shown to be associated with resistance to imatinib 19 (Supple- mentary Table 2). Notably, this sample had been obtained from an individual whose tumor relapsed after imatinib treatment. Thus, our approach may facilitate prediction of clinical response and resistance to targeted cancer therapies. 100 F r equency (%) Tu m or type Gene 80 60 40 20 0 PIK3CA NRAS KRAS JA K 2 HRAS FGFR1 EGFR CDK4 BRAF RET PDGFRA KIT ERBB2 Breast ( n = 60) Melanoma ( n = 136) Renal ( n = 83) Lung ( n = 255) Enddometr i al ( n =10) Ov ar ian ( n = 18) P a ncreas ( n = 3) Leuk emia ( n = 45) Colorectal ( n = 12) Prostate ( n = 95) Mesothelioma ( n = 36) Glioma ( n = 99) Medullob l astoma ( n = 10) P o lythemia v e r a ( n = 4) L ymphoma ( n = 7) Multiple m y eloma ( n = 23) Sarcoma ( n = 104) FGFR3 Figure 1 Frequencies of oncogene mutations across human tumor types. Frequencies (y axis) were calculated as percentages of tumor samples (x axis) from a given type that harbored an oncogene mutation (z axis) compared with the total number of samples of that tumor type. Table 1 Rare or novel oncogene point mutations identified by genotyping Sample ID Tumor type Assay Mutation RL95-2 Endometrial OM_00067 EGFR_A289V RL95-2 Endometrial OM_00150 HRAS_Q61H RPMI-8226 Multiple myeloma OM_00190 KRAS_G12A RPMI-8226 Multiple myeloma OM_00079 EGFR_T751I a S002039 Lung OM_00260 RET_M918T S002039 Lung OM_00188 KRAS_G12V b S004154 Medulloblastoma OM_00196 KRAS_G13D WM3682 Melanoma OM_00127 FGFR1_S125L WM3702 Melanoma OM_00127 FGFR1_S125L Meso 986 Mesothelioma OM_00220 NRAS_G13D Meso 713 Mesothelioma OM_00228 NRAS_Q61K b Meso 542 Mesothelioma OM_00227 NRAS_Q61R S003253 Multiple myeloma OM_00246 PIK3CA_E545K OVCAR-8 Ovarian OM_00120 ERBB2_G776V c S003195 Prostate OM_00056 BRAF_K601E S004480 Renal OM_00052 BRAF_V600E S003239 Sarcoma OM_00052 BRAF_V600E S006118 Sarcoma OM_00052 BRAF_V600E S006065 Lentigo simplex OM_00250 PIK3CA_H1047R a The detected mutation was a single-base substitution identified by an assay interrogating the deletion EGFR_E746_A750del, V ins. b Not confirmed by sequencing. c The detected mutation was a single-base substitution identified by an assay interrogating the insertion ERBB2_G776VC. 348 VOLUME 39 [ NUMBER 3 [ MARCH 2007 NATURE GENETICS LETTERS � 200 7 Nature Pub lishing Gr oup http://www .nature .com/natureg enetics In total, we performed oncogene mutation profiling on 1,000 individual tumor samples, including primary tumor specimens, cancer cell lines, short-term cultures and xenografts spanning 17 tumor lineages. We identified at least one mutation in 298 (30%) of the samples and performed confirmatory studies on approximately 90% of mutations identified, as noted above (Supplementary Note). Of the 238 genotyping assays employed here, 81 (34%) were called ?mutant? in at least one sample, and 14 of the 17 oncogenes queried were found mutated at least once. A ?peak-height? analysis of raw spectral data (see Methods) suggested that most of the mutations found were either heterozygous or admixed with stromal DNA; however, a subset of mutations showed spectral patterns consistent with homozygous alleles (Supplementary Fig. 1 and Supplementary Table 2). Although we generally observed a distribution of oncogene muta- tions that was consistent with prior literature reports (Fig. 1, Supple- mentary Figs. 2?4 and Supplementary Table 2 online), our approach also identified many low-frequency events involving both rare and common neoplasms (Fig. 1). Frequently, such mutations constituted rarely or never previously reported alterations in the associated tumors (Table 1). Examples include NRAS mutations in 3 out of 37 mesothelioma cell lines and a PIK3CA kinase-domain mutation in a human skin specimen that contained lentigo simplex (Table 1). The latter suggests that lentigo simplex might be associated with PIK3CA mutations, just as benign melanocytic nevi are associated with BRAF mutations. Addi- tional novel mutations included an ERBB2 (G776V) mutation in an ovarian cancer cell line 20 , PIK3CA mutations in both a multiple myeloma and a metastatic melanoma sample, an FGFR1 mutation in melanoma short-term cultures, an EGFR mutation in a multiple myeloma cell line 20 , a mutation in the region encoding the extracellular domain of EGFR in an endometrial carcinoma cell line 21 ,aRET mutation in a primary non?small cell lung tumor and mutations in codons 600 or 601 of BRAF in sarcoma, breast, ovarian and pros- tate cancer specimens (see also Supplementary Table 2). Thus, despite the well-known uneven distribution of oncogene mutations across tumor types, these results suggest that rare and potentially ?druggable? oncogene mutations might exist in many common tumor types. Oncogene mutations that activate common downstream pathways often occur in a mutually exclusive fashion in human cancers. While confirming this relationship among prevalent oncogene mutations (Fig. 2a), high-throughput mutation profiling also uncovered several co-occurring mutations that had not previously been reported (Fig. 2a). For example, 30% of all PIK3CA mutations identified were coincident with another oncogene mutation. KRAS was the most common partner oncogene (10% of all KRAS mutations co-occurred with a PIK3CA mutation; P � 0.0047; Fig. 2), but EGFR and BRAF mutations were also observed to co-occur with PIK3CA mutations (Supplementary Table 2). Similarly, BRAF muta- tions involving codons other than 600 or 601 were highly likely to co- occur with a RAS family mutation, whereas similar coincident events involving mutations in BRAF codons 600 or 601 were never observed (P � 1.8 C2 10 ?5 ; Fig. 2b). This observation suggests that BRAF V600E may elicit potent oncogenic effects that are also mechanistically distinct from other BRAF kinase domain mutations 22 . Furthermore, despite the strong oncogenic potential of many RAS, BRAF and PIK3CA mutations, as measured by forward in vitro transformation assays, the observed co-occurrences suggest that alterations in the BRAF_464-597 BRAF_600-601 EGFR_T790M EGFR_ECD EGFR_KD PDGFRA PIK3CA_KD PIK3CA_HD RET ERBB2 FRFR1 FGFR3 JAK2 HRAS KRAS NRAS KIT CDK4 BRAF_464-597 BRAF_600-601 EGFR_T790M EGFR_ECD EGFR_KD PDGFRA PIK3CA_KD PIK3CA_HD RET ERBB2 FRFR1 FGFR3 JAK2 HRAS KRAS NRAS KIT CDK4 BRAF_464-597 BRAF_600-601 EGFR_T790M EGFR_ECD EGFR_KD PDGFRA PIK3CA_KD PIK3CA_HD RET ERBB2 FRFR1 FGFR3 JAK2 HRAS KRAS NRAS KIT CDK4 Breast RAS mt + PIK3CA mt + PIK3CA mt ? 077 3 P = 1.8 � 10 ?5 P = 0.0047 4 26 7 907 60 RAS mt ? BRAF 600?601 BRAF non600?601 KRAS mt ? KRAS mt + Melanoma Glioma Lung Leukemia Mesothelioma Colorectal Endometrial Lentigo simplex Lymphoma Medulloblastoma Multiple myeloma Ovarian Pancreas Polycythemia vera Prostate Renal Sarcoma a b c Figure 2 Mutually exclusive and co-occurring oncogene mutations in human cancer. (a) Oncogene mutations were grouped together when they occurred within a given gene (for example, ?KRAS? for all mutations in KRAS)orin the same functional domain of the encoded protein (for example, ?PIK3CA_KD? for kinase domain mutations of PIK3CA). When a distinct phenotype was correlated with a mutation, the mutation was grouped separately (for example, ?EGFR_T790M? for the T790M mutation of EGFR known to be correlated with resistance to EGFR inhibitors). Mutant samples (columns/black bars) are sorted by grouped oncogene mutations and by tumor type (color legend indicated). Red bars indicate co-occurring mutations. EGFR_ECD, extracellular domain mutations of EGFR; EGFR_KD, kinase domain mutations of EGFR; PIK3CA_KD, kinase domain mutation of PIK3CA; PIK3CA_HD, helical domain mutations of PIK3CA.(b) Incidence of BRAF mutations and co-occurring mutations in any RAS gene. (c) Incidence of co-occurring KRAS and PIK3CA mutations (see text for details). NATURE GENETICS VOLUME 39 [ NUMBER 3 [ MARCH 2007 349 LETTERS � 200 7 Nature Pub lishing Gr oup http://www .nature .com/natureg enetics associated pathways may often elicit complementary rather than redundant effects on tumorigenesis in situ. Gain-of-function genetic alterations often cause tumor cells to become ?addicted? to the relevant oncogene or its downstream path- way 23 , thereby exposing a potential therapeutic vulnerability 4,5 . Here, we have shown that high-throughput genotyping enables sensitive and accurate oncogene mutation profiling in human cancer specimens. This approach successfully identified numerous individual and co- occurring genetic alterations that promise to provide new biological and therapeutic insights in several tumor types. Given that discovery- oriented cancer gene resequencing has reached the dimension of all annotated genes in the genome 2 ; large-scale mutation profiling using mass spectrometry or other methods may complement these efforts by enabling new and existing mutation panels to be queried broadly across human malignancies. Moreover, the clinical application of rapid, scalable and cost-effective mutation profiling approaches should facilitate patient stratification for the rational deployment of targeted cancer therapeutics. METHODS Samples. We used 1,000 tumor samples derived from the following 17 tumor types: breast cancer (n � 60), colorectal cancer (n � 12), endometrial cancer (n � 10), glioma (n � 99), leukemia (n � 45), lung cancer (n � 255), lymphoma (n � 7), medulloblastoma (n � 10), melanoma (n � 136), mesothelioma (n � 36), multiple myeloma (n � 23), ovarian cancer (n � 18), pancreatic cancer (n � 3), polycythemia vera (n � 4), prostate cancer (n� 95), renal cell cancer (n� 83) and sarcoma (n� 104). All primary tumor DNA samples were obtained from fresh-frozen tumor specimens based on a 70% cutoff for sample purity. For tumors that could be obtained as actual tumor biopsy specimens from collaborators (for example, all lung tumors), diagnoses were confirmed by independent histopathological review. The quality of all DNA samples was ensured by independent quantification and quantitative PCR. The study was conducted under institutional review board approval. Selection of oncogene mutations and assay design. We queried the following databases for known somatic oncogene mutations: Cosmic 24 , PubMed and an internal database of oncogene mutations discovered through our systematic resequencing efforts in human cancer specimens 6,21,25,26 .Weselectedonly nonsynonymous coding mutations that previously had been reported to occur as somatic mutations in human cancer. The resulting list (Supplementary Table 1) contained 238 individual oncogene mutations, comprising single-base substitutions as well as insertions or deletions. Genomic positions for all mutations were computed using the HG16 build of the human genome and the University of California Santa Cruz (UCSC) genome annotation database. BLAT alignment information and exon structures for the National Center for Biotechnology Information (NCBI) Ref Seq transcripts were downloaded from UCSC, and genomic locations for all assays were determined. Translation accuracy of all candidate mutations was determined by comparing the calculated genomic position of the candidate to the exon and BLAT alignment block information provided by the UCSC annotation information. For each mutation, the discriminating nucleotides for both wild-type and mutant alleles were determined, enabling insertions or deletions to be represented by single- base changes. Subsequently, 250 bases of neighboring DNA were added to each side of the resulting mutation assay to enable primer design. Genotyping assays (primers for PCR amplification and the extension probe) were designed using the Sequenom MassARRAY Assay Design 3.0 software, applying default para- meters (maximum of six multiplexed assays per well). For complex mutations (that is, mutations defined by more than one nucleotide change, such as a deletion of bases 2345?2360 combined with a substitution of base 2364), genotyping assays were designed manually. Mass-spectrometric genotyping. Genomic DNA from all tumor samples was purified and subjected to phi29 polymerase multiple strand-displacement whole-genome amplification, as described previously 27 . After quantification and dilution of genome-amplified DNA, multiplexed PCR was performed in 5-ml volumes containing 0.1 units of Taq polymerase, 5 ng of genome-amplified genomic DNA, 2.5 pmol of each PCR primer and 2.5 mmol of dNTP. Thermocycling was at 95 1C for 15 min followed by 45 cycles of 95 1Cfor 20 s, 56 1C for 30 s and 72 1C for 30 s. Unincorporated dNTPs were deactivated using 0.3 U of shrimp alkaline phosphatase, and primer extension was carried out using 5.4 pmol of each primer extension probe, 50 mmol of the appropriate dNTP/ddNTP combination and 0.5 units of Thermosequenase DNA polymer- ase. Reactions were cycled at 94 1C for 2 min, followed by 40 cycles of 94 1Cfor 5s,501C for 5 s and 72 1C for 5 s. After the addition of a cation exchange resin to remove residual salt from the reactions, 7 nl of the purified primer extension reaction was loaded onto a matrix pad (3-hydroxypicoloinic acid) of a SpectroCHIP (Sequenom). SpectroCHIPs were analyzed using a Bruker Biflex III matrix-assisted laser desorption/ionization?time of flight (MALDI-TOF) mass spectrometer (SpectroREADER, Sequenom). Analytical and statistical methods. Mutation calls for each sample were determined using the default settings of MassArray Typer 3.4 Analyzer (Sequenom). Successful genotyping assays were defined as those in which 75% of all genotyping calls were obtained (based on ?conservative? allele calls according to the manufacturer?s specifications; see below and Supplementary Table 3 online). Unsuccessful assays were repeated after another round of primer design and testing. Automated mutation calls were generated using available computational algorithms for genotyping of diploid samples without further refinement or adaptation (Sequenom, MassArray RTTM software) (n � 437). These were compared with calls made by manual review of the raw mass spectra (n � 448), with a concordance rate of 95%. To measure assay reproducibility, a subset of tumors was interrogated in duplicate, and some mutations were detected using two independent genotyping assays (for example, mutations targeting codon 600 of BRAF). The statistical significance of co-occurring mutations was calculated by applying a Fisher?s exact test. To estimate mutant allele percentage and degree of heterozygosity, the heights of raw spectral peaks corresponding to the mutant and wild-type signal were quantified and compared with those from an independent dataset of germline SNPs (SNP identifiers available upon request) using 39 unique assays. For these reference SNPs, the allele status (homozygous or heterozygous) had been determined previously by mass spectrometric genotyping of 95 prostate cancer specimens (3,403 data points). Peak height ratios (mutant peak/wild- type peak) of the various mutations found in more than one tumor sample of a given tumor type were plotted and compared with the peak-height distribution of the reference SNPs (Supplementary Fig. 1 and Supplementary Table 2). The relative signal was determined as (mutant peak C2 100) / (mutant peak + wild-type peak). The ?positive/negative control? ranges for peak height ratios were determined from the aforementioned independent data set of 95 prostate cancer samples. Calculated peak height ratios from the reference data set were sorted by heterozygous versus homozygous calls. Although the peak height ratio boundary was not absolute between heterozygous and homozygous samples, a value of 5.53 was empirically found to be the maximum hetero- zygous peak height ratio (Supplementary Fig. 1). In total, 1,365 data points had peak-height ratios o5.53 inclusive of all heterozygous alleles (and some homozygous alleles), whereas 1,803 samples had peak-height ratios 45.53 (all homozygous alleles). Some samples were omitted (n � 235) because the peak height of the wild-type allele was measured as 0 (thus, the ratio would have required division by zero). URLs. Cosmic 24 : http:/www.sanger.ac.uk/genetics/CGP/cosmic/; UCSC genome browser: http://genome.ucsc.edu. Note: Supplementary information is available on the Nature Genetics website. ACKNOWLEDGMENTS We thank E. Lander and G. Getz for comments and advice. R.K.T. is a Mildred- Scheel fellow of the Deutsche Krebshilfe. R.K.T. is supported by the International Association for the Study of Lung Cancer (IASLC). R.M.D. is supported by the Swiss national science foundation (no: 3100A0-103671/1). A.G and J.M. are supported by the National Cancer Institute through SPORE grant P50CA70907. G.D.D. is supported by the Virginia and Daniel K. Ludwig Trust for Cancer Research, the Quick Family Fund for Cancer Research and the Ronald O. 350 VOLUME 39 [ NUMBER 3 [ MARCH 2007 NATURE GENETICS LETTERS � 200 7 Nature Pub lishing Gr oup http://www .nature .com/natureg enetics Perelman Fund for Cancer Research at Dana-Farber. I.K.M. and P.S.M. are supported by Accelerate Brain Tumor Cure. I.K.M., L.M.L, T.F.C., and P.S.M. are supported by the Henry E. Singleton Brain Tumor Program. I.K.M., L.M.L, T.F.C., S.F.N., M.M., W.R.S. and P.S.M. are supported by the Brain Tumor Funders? Collaborative. M.M. and L.A.G. are supported by a grant from Genentech, Inc. M.M. is supported by the American Cancer Society. L.A.G is supported by the National Cancer Institute, the Prostate Cancer Foundation, the Burroughs-Wellcome Fund, the Robert Wood Johnson Foundation and the Novartis Institute for Biomedical Research. COMPETING INTERESTS STATEMENT The authors declare that they have no competing financial interests. Published online at http://www.nature.com/naturegenetics Reprints and permissions information is available online at http://npg.nature.com/ reprintsandpermissions 1. National Human Genome Research Institute. Cancer Sequencing. /http://www. genome.gov/cancersequencing/S (2006). 2. Sjoblom, T. et al. The consensus coding sequences of human breast and colorectal cancers. Science 314, 268?274 (2006). 3. National Cancer Institute and National Human Genome Research Institute. The Cancer Genome Atlas. /http://cancergenome.nih.gov/index.aspS (2006). 4. Heinrich, M.C. et al. Kinase mutations and imatinib response in patients with metastatic gastrointestinal stromal tumor. J. Clin. 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NATURE GENETICS VOLUME 39 [ NUMBER 3 [ MARCH 2007 351 LETTERS � 200 7 Nature Pub lishing Gr oup http://www .nature .com/natureg enetics CORRIGENDA Corrigendum: High-throughput oncogene mutation profiling in human cancer Roman K Thomas, Alissa C Baker, Ralph M DeBiasi, Wendy Winckler, Thomas LaFramboise, William M Lin, Meng Wang, Whei Feng, Thomas Zander, Laura E MacConnaill, Jeffrey C Lee, Rick Nicoletti, Charlie Hatton, Mary Goyette, Luc Girard, Kuntal Majmudar, Liuda Ziaugra, Kwok-Kin Wong, Stacey Gabriel, Rameen Beroukhim, Michael Peyton, Jordi Barretina, Amit Dutt, Caroline Emery, Heidi Greulich, Kinjal Shah, Hidefumi Sasaki, Adi Gazdar, John Minna, Scott A Armstrong, Ingo K Mellinghoff, F Stephen Hodi, Glenn Dranoff, Paul S Mischel, Tim F Cloughesy, Stan F Nelson, Linda M Liau, Kirsten Mertz, Mark A Rubin, Holger Moch, Massimo Loda, William Catalona, Jonathan Fletcher, Sabina Signoretti, Frederic Kaye, Kenneth C Anderson, George D Demetri, Reinhard Dummer, Stephan Wagner, Meenhard Herlyn, William R Sellers, Matthew Meyerson & Levi A Garraway Nat. Genet. 39, 347?351 (2007); published online 11 February; corrected after print 14 March 2007 In the version of this article initially published, the name of an author was spelled incorrectly as Laura MacConnaill. The correct spelling is Laura MacConaill. The error has been corrected in the HTML and PDF versions of the article. � 200 7 Nature Pub lishing Gr oup http://www .nature .com/natureg enetics "
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