Accelerating Discovery of Functional Mutant Alleles in Cancer
Most mutations in cancer are rare, which complicates the identification of therapeutically significant mutations and thus limits the clinical impact of genomic profiling in patients with cancer. Here, we analyzed 24,592 cancers including 10,336 prospectively sequenced patients with advanced disease...
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| Published in | Cancer discovery Vol. 8; no. 2; pp. 174 - 183 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
01.02.2018
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2159-8274 2159-8290 2159-8290 |
| DOI | 10.1158/2159-8290.CD-17-0321 |
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| Abstract | Most mutations in cancer are rare, which complicates the identification of therapeutically significant mutations and thus limits the clinical impact of genomic profiling in patients with cancer. Here, we analyzed 24,592 cancers including 10,336 prospectively sequenced patients with advanced disease to identify mutant residues arising more frequently than expected in the absence of selection. We identified 1,165 statistically significant hotspot mutations of which 80% arose in 1 in 1,000 or fewer patients. Of 55 recurrent in-frame indels, we validated that novel AKT1 duplications induced pathway hyperactivation and conferred AKT inhibitor sensitivity. Cancer genes exhibit different rates of hotspot discovery with increasing sample size, with few approaching saturation. Consequently, 26% of all hotspots in therapeutically actionable oncogenes were novel. Upon matching a subset of affected patients directly to molecularly targeted therapy, we observed radiographic and clinical responses. Population-scale mutant allele discovery illustrates how the identification of driver mutations in cancer is far from complete.
Significance: Our systematic computational, experimental, and clinical analysis of hotspot mutations in approximately 25,000 human cancers demonstrates that the long right tail of biologically and therapeutically significant mutant alleles is still incompletely characterized. Sharing prospective genomic data will accelerate hotspot identification, thereby expanding the reach of precision oncology in patients with cancer. Cancer Discov; 8(2); 174–83. ©2017 AACR.
This article is highlighted in the In This Issue feature, p. 127 |
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| AbstractList | Most mutations in cancer are rare, which complicates the identification of therapeutically significant mutations and thus limits the clinical impact of genomic profiling in patients with cancer. Here, we analyzed 24,592 cancers including 10,336 prospectively sequenced patients with advanced disease to identify mutant residues arising more frequently than expected in the absence of selection. We identified 1,165 statistically significant hotspot mutations of which 80% arose in 1 in 1,000 or fewer patients. Of 55 recurrent in-frame indels, we validated that novel AKT1 duplications induced pathway hyperactivation and conferred AKT inhibitor sensitivity. Cancer genes exhibit different rates of hotspot discovery with increasing sample size, with few approaching saturation. Consequently, 26% of all hotspots in therapeutically actionable oncogenes were novel. Upon matching a subset of affected patients directly to molecularly targeted therapy, we observed radiographic and clinical responses. Population-scale mutant allele discovery illustrates how the identification of driver mutations in cancer is far from complete.Significance: Our systematic computational, experimental, and clinical analysis of hotspot mutations in approximately 25,000 human cancers demonstrates that the long right tail of biologically and therapeutically significant mutant alleles is still incompletely characterized. Sharing prospective genomic data will accelerate hotspot identification, thereby expanding the reach of precision oncology in patients with cancer. Cancer Discov; 8(2); 174-83. ©2017 AACR.This article is highlighted in the In This Issue feature, p. 127.Most mutations in cancer are rare, which complicates the identification of therapeutically significant mutations and thus limits the clinical impact of genomic profiling in patients with cancer. Here, we analyzed 24,592 cancers including 10,336 prospectively sequenced patients with advanced disease to identify mutant residues arising more frequently than expected in the absence of selection. We identified 1,165 statistically significant hotspot mutations of which 80% arose in 1 in 1,000 or fewer patients. Of 55 recurrent in-frame indels, we validated that novel AKT1 duplications induced pathway hyperactivation and conferred AKT inhibitor sensitivity. Cancer genes exhibit different rates of hotspot discovery with increasing sample size, with few approaching saturation. Consequently, 26% of all hotspots in therapeutically actionable oncogenes were novel. Upon matching a subset of affected patients directly to molecularly targeted therapy, we observed radiographic and clinical responses. Population-scale mutant allele discovery illustrates how the identification of driver mutations in cancer is far from complete.Significance: Our systematic computational, experimental, and clinical analysis of hotspot mutations in approximately 25,000 human cancers demonstrates that the long right tail of biologically and therapeutically significant mutant alleles is still incompletely characterized. Sharing prospective genomic data will accelerate hotspot identification, thereby expanding the reach of precision oncology in patients with cancer. Cancer Discov; 8(2); 174-83. ©2017 AACR.This article is highlighted in the In This Issue feature, p. 127. Most mutations in cancer are rare, which complicates the identification of therapeutically significant mutations and thus limits the clinical impact of genomic profiling in cancer patients. Here, we analyzed 24,592 cancers including 10,336 prospectively sequenced patients with advanced disease to identify mutant residues arising more frequently than expected in the absence of selection. We identified 1,165 statistically significant hotspot mutations of which 80% arose in 1 in 1000 or fewer patients. Of 55 recurrent in-frame indels, we validated that novel AKT1 duplications induced pathway hyperactivation and conferred AKT inhibitor sensitivity. Cancer genes exhibit different rates of hotspot discovery with increasing sample size, with few approaching saturation. Consequently, 26% of all hotspots in therapeutically actionable oncogenes were novel. Upon matching a subset of affected patients directly to molecularly targeted therapy, we observed radiographic and clinical responses. Population-scale mutant allele discovery illustrates how the identification of driver mutations in cancer is far from complete. Most mutations in cancer are rare, which complicates the identification of therapeutically significant mutations and thus limits the clinical impact of genomic profiling in patients with cancer. Here, we analyzed 24,592 cancers including 10,336 prospectively sequenced patients with advanced disease to identify mutant residues arising more frequently than expected in the absence of selection. We identified 1,165 statistically significant hotspot mutations of which 80% arose in 1 in 1,000 or fewer patients. Of 55 recurrent in-frame indels, we validated that novel AKT1 duplications induced pathway hyperactivation and conferred AKT inhibitor sensitivity. Cancer genes exhibit different rates of hotspot discovery with increasing sample size, with few approaching saturation. Consequently, 26% of all hotspots in therapeutically actionable oncogenes were novel. Upon matching a subset of affected patients directly to molecularly targeted therapy, we observed radiographic and clinical responses. Population-scale mutant allele discovery illustrates how the identification of driver mutations in cancer is far from complete. Significance: Our systematic computational, experimental, and clinical analysis of hotspot mutations in approximately 25,000 human cancers demonstrates that the long right tail of biologically and therapeutically significant mutant alleles is still incompletely characterized. Sharing prospective genomic data will accelerate hotspot identification, thereby expanding the reach of precision oncology in patients with cancer. Cancer Discov; 8(2); 174–83. ©2017 AACR. This article is highlighted in the In This Issue feature, p. 127 Most mutations in cancer are rare, which complicates the identification of therapeutically significant mutations and thus limits the clinical impact of genomic profiling in patients with cancer. Here, we analyzed 24,592 cancers including 10,336 prospectively sequenced patients with advanced disease to identify mutant residues arising more frequently than expected in the absence of selection. We identified 1,165 statistically significant hotspot mutations of which 80% arose in 1 in 1,000 or fewer patients. Of 55 recurrent in-frame indels, we validated that novel duplications induced pathway hyperactivation and conferred AKT inhibitor sensitivity. Cancer genes exhibit different rates of hotspot discovery with increasing sample size, with few approaching saturation. Consequently, 26% of all hotspots in therapeutically actionable oncogenes were novel. Upon matching a subset of affected patients directly to molecularly targeted therapy, we observed radiographic and clinical responses. Population-scale mutant allele discovery illustrates how the identification of driver mutations in cancer is far from complete. Our systematic computational, experimental, and clinical analysis of hotspot mutations in approximately 25,000 human cancers demonstrates that the long right tail of biologically and therapeutically significant mutant alleles is still incompletely characterized. Sharing prospective genomic data will accelerate hotspot identification, thereby expanding the reach of precision oncology in patients with cancer. . |
| Author | Schultz, Nikolaus Bielski, Craig M. Jayakumaran, Gowtham Razavi, Pedram Penson, Alexander Arcila, Maria E. Kundra, Ritika Donoghue, Mark T.A. Rosen, Neal Reales, Dalicia N. Gao, JianJiong Patel, Swati Li, Bob T. Zehir, Ahmet Baselga, José Harris, Christopher Solit, David B. Gorelick, Alexander Phillips, Sarah Benayed, Ryma Ladanyi, Marc Chang, Matthew T. Bhattarai, Tripti Shrestha Jonsson, Philip Socci, Nicholas D. Chakravarty, Debyani Taylor, Barry S. Schram, Alison M. Sumer, Selcuk Onur Kandoth, Cyriac Hyman, David M. Chandarlapaty, Sarat Berger, Michael F. Shamu, Tambudzai |
| AuthorAffiliation | 7 Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY 8 Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, NY 9 Department of Medicine, Weill Cornell Medical College, Cornell University, New York, NY 5 Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 3 Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco 2 Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 4 Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 1 Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY 6 Bioinformatics Core, Memorial Sloan Kettering Cancer Center, New York, NY |
| AuthorAffiliation_xml | – name: 6 Bioinformatics Core, Memorial Sloan Kettering Cancer Center, New York, NY – name: 9 Department of Medicine, Weill Cornell Medical College, Cornell University, New York, NY – name: 3 Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco – name: 1 Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY – name: 4 Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY – name: 8 Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, NY – name: 7 Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY – name: 5 Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY – name: 2 Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY |
| Author_xml | – sequence: 1 givenname: Matthew T. surname: Chang fullname: Chang, Matthew T. – sequence: 2 givenname: Tripti Shrestha surname: Bhattarai fullname: Bhattarai, Tripti Shrestha – sequence: 3 givenname: Alison M. surname: Schram fullname: Schram, Alison M. – sequence: 4 givenname: Craig M. surname: Bielski fullname: Bielski, Craig M. – sequence: 5 givenname: Mark T.A. surname: Donoghue fullname: Donoghue, Mark T.A. – sequence: 6 givenname: Philip surname: Jonsson fullname: Jonsson, Philip – sequence: 7 givenname: Debyani surname: Chakravarty fullname: Chakravarty, Debyani – sequence: 8 givenname: Sarah surname: Phillips fullname: Phillips, Sarah – sequence: 9 givenname: Cyriac surname: Kandoth fullname: Kandoth, Cyriac – sequence: 10 givenname: Alexander surname: Penson fullname: Penson, Alexander – sequence: 11 givenname: Alexander surname: Gorelick fullname: Gorelick, Alexander – sequence: 12 givenname: Tambudzai surname: Shamu fullname: Shamu, Tambudzai – sequence: 13 givenname: Swati surname: Patel fullname: Patel, Swati – sequence: 14 givenname: Christopher surname: Harris fullname: Harris, Christopher – sequence: 15 givenname: JianJiong surname: Gao fullname: Gao, JianJiong – sequence: 16 givenname: Selcuk Onur surname: Sumer fullname: Sumer, Selcuk Onur – sequence: 17 givenname: Ritika surname: Kundra fullname: Kundra, Ritika – sequence: 18 givenname: Pedram surname: Razavi fullname: Razavi, Pedram – sequence: 19 givenname: Bob T. surname: Li fullname: Li, Bob T. – sequence: 20 givenname: Dalicia N. surname: Reales fullname: Reales, Dalicia N. – sequence: 21 givenname: Nicholas D. surname: Socci fullname: Socci, Nicholas D. – sequence: 22 givenname: Gowtham surname: Jayakumaran fullname: Jayakumaran, Gowtham – sequence: 23 givenname: Ahmet surname: Zehir fullname: Zehir, Ahmet – sequence: 24 givenname: Ryma surname: Benayed fullname: Benayed, Ryma – sequence: 25 givenname: Maria E. surname: Arcila fullname: Arcila, Maria E. – sequence: 26 givenname: Sarat surname: Chandarlapaty fullname: Chandarlapaty, Sarat – sequence: 27 givenname: Marc surname: Ladanyi fullname: Ladanyi, Marc – sequence: 28 givenname: Nikolaus surname: Schultz fullname: Schultz, Nikolaus – sequence: 29 givenname: José surname: Baselga fullname: Baselga, José – sequence: 30 givenname: Michael F. surname: Berger fullname: Berger, Michael F. – sequence: 31 givenname: Neal surname: Rosen fullname: Rosen, Neal – sequence: 32 givenname: David B. surname: Solit fullname: Solit, David B. – sequence: 33 givenname: David M. surname: Hyman fullname: Hyman, David M. – sequence: 34 givenname: Barry S. surname: Taylor fullname: Taylor, Barry S. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29247016$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1158/2159-8290.CD-17-1119 10.1016/j.cell.2013.03.002 10.1038/nature19057 10.1038/ng.3549 10.1158/1535-7163.TARG-15-B109 10.1182/blood-2004-11-4430 10.1056/NEJMoa1502309 10.1126/scitranslmed.3003161 10.1038/nbt.3391 10.1038/sj.onc.1203642 10.1016/j.gde.2013.11.014 10.1056/NEJMoa0909530 10.1056/NEJMoa020461 10.1158/2159-8290.CD-16-0160 10.1038/nm.3559 10.1016/j.ccell.2016.02.010 10.1038/nbt.2696 10.1186/s13073-016-0364-2 10.1158/0008-5472.CAN-10-2956 10.1038/ng.2822 10.1056/NEJMp1607591 10.1158/2159-8290.CD-12-0095 10.1016/j.jmoldx.2014.12.006 10.1038/nm.4333 10.1016/j.cell.2009.12.040 10.1093/nar/gkv1222 10.1158/1538-7445.AM2017-CT001 10.1016/j.cell.2015.05.001 10.1002/ijc.25893 10.1126/scisignal.2004088 10.1016/j.ccell.2016.06.022 |
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| SubjectTerms | Alleles Biomarkers, Tumor Codon Genetic Association Studies - methods Genetic Predisposition to Disease Humans INDEL Mutation Mutation Neoplasms - genetics |
| Title | Accelerating Discovery of Functional Mutant Alleles in Cancer |
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