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Haan D, Bergamaschi A, Friedl V, Guler GD, Ning Y, Reggiardo R, Kesling M, Collins M, Gibb B, Hazen K, Bates S, Antoine M, Fraire C, Lopez V, Malta R, Nabiyouni M, Nguyen A, Phillips T, Riviere M, Leighton A, Ellison C, McCarthy E, Scott A, Gigliotti L, Nilson E, Sheard J, Peters M, Bethel K, Chowdhury S, Volkmuth W, Levy S. Epigenomic Blood-Based Early Detection of Pancreatic Cancer Employing Cell-Free DNA. Clin Gastroenterol Hepatol 2023; 21:1802-1809.e6. [PMID: 36967102 DOI: 10.1016/j.cgh.2023.03.016] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 03/05/2023] [Accepted: 03/16/2023] [Indexed: 04/25/2023]
Abstract
BACKGROUND & AIMS Early detection of pancreatic cancer (PaC) can drastically improve survival rates. Approximately 25% of subjects with PaC have type 2 diabetes diagnosed within 3 years prior to the PaC diagnosis, suggesting that subjects with type 2 diabetes are at high risk of occult PaC. We have developed an early-detection PaC test, based on changes in 5-hydroxymethylcytosine (5hmC) signals in cell-free DNA from plasma. METHODS Blood was collected from 132 subjects with PaC and 528 noncancer subjects to generate epigenomic and genomic feature sets yielding a predictive PaC signal algorithm. The algorithm was validated in a blinded cohort composed of 102 subjects with PaC, 2048 noncancer subjects, and 1524 subjects with non-PaCs. RESULTS 5hmC differential profiling and additional genomic features enabled the development of a machine learning algorithm capable of distinguishing subjects with PaC from noncancer subjects with high specificity and sensitivity. The algorithm was validated with a sensitivity for early-stage (stage I/II) PaC of 68.3% (95% confidence interval [CI], 51.9%-81.9%) and an overall specificity of 96.9% (95% CI, 96.1%-97.7%). CONCLUSIONS The PaC detection test showed robust early-stage detection of PaC signal in the studied cohorts with varying type 2 diabetes status. This assay merits further clinical validation for the early detection of PaC in high-risk individuals.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Bill Gibb
- ClearNote Health, San Mateo, California
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Bergamaschi A, Haan D, Collins M, Guler G, Peters M, Gigliotti L, Chowdhury S, Volkmuth W, Levy S. Early detection of pancreatic cancer using 5-hydroxymethylation profiles in plasma-derived cell-free DNA. J Clin Oncol 2023. [DOI: 10.1200/jco.2023.41.4_suppl.672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
672 Background: Pancreatic cancer is one of the deadliest cancers, with approximately 15-20% of patients who present at diagnosis with a resectable disease. The major barrier to better outcomes is the lack of early-detection molecular tools to enable timely intervention. We have developed a test that enables the detection of pancreatic cancer from a simple blood draw. The test incorporates a novel, genome-wide sequencing-based epigenomics detection method that enriches for DNA loci that undergo active de-methylation. The measurement of 5-hydroxymethylcytosine (5hmC) provides a unique and stable biomarker for the early detection of cancer including pancreatic cancer. Methods: Whole-blood was obtained from a training cohort of 660 individuals (consisting of 132 pancreatic cancers (PaCa) and 528 non-cancers) and a validation cohort of 2,150 individuals (consisting of 102 PaCa and 2,048 non-cancers). Cell-free DNA (cfDNA) was isolated from plasma from which 5hmC and whole-genome libraries were generated and sequenced. Logistic regression algorithms were employed using 5hmC feature sets combined with physical characteristics of DNA fragments to optimally partition cancer from non-cancer samples. Results: Cross validation of the training model yielded an overall sensitivity of 65.9%,(95% CI, 57.2%–73.9%), early-stage (stage I-II) sensitivity of 57.1% (95% CI, 44%–69.5%) and a specificity of 98%. The model was further validated in a separate, non-overlapping set of blinded and independently processed samples and yielded an early-stage sensitivity of 68.3% (95% CI, 51.9%–81.9%) and a specificity of 96.9% (95% CI, 96.0%–97.6%). Conclusions: Our results demonstrate that plasma-derived cfDNA 5hmC profiles enable the accurate detection of early-stage PaCa, providing a valuable non-invasive tool especially for those individuals at high risk for the disease, including individuals with genetic predisposition and newly diagnosed type 2 diabetes. A larger clinical study (NODMED - NCT05188586) is ongoing and will provide clinical validation for the detection in individuals at high risk for this deadly disease.
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Gerstung M, Jolly C, Leshchiner I, Dentro SC, Gonzalez S, Rosebrock D, Mitchell TJ, Rubanova Y, Anur P, Yu K, Tarabichi M, Deshwar A, Wintersinger J, Kleinheinz K, Vázquez-García I, Haase K, Jerman L, Sengupta S, Macintyre G, Malikic S, Donmez N, Livitz DG, Cmero M, Demeulemeester J, Schumacher S, Fan Y, Yao X, Lee J, Schlesner M, Boutros PC, Bowtell DD, Zhu H, Getz G, Imielinski M, Beroukhim R, Sahinalp SC, Ji Y, Peifer M, Markowetz F, Mustonen V, Yuan K, Wang W, Morris QD, Spellman PT, Wedge DC, Van Loo P, Tarabichi M, Wintersinger J, Deshwar AG, Yu K, Gonzalez S, Rubanova Y, Macintyre G, Adams DJ, Anur P, Beroukhim R, Boutros PC, Bowtell DD, Campbell PJ, Cao S, Christie EL, Cmero M, Cun Y, Dawson KJ, Demeulemeester J, Donmez N, Drews RM, Eils R, Fan Y, Fittall M, Garsed DW, Getz G, Ha G, Imielinski M, Jerman L, Ji Y, Kleinheinz K, Lee J, Lee-Six H, Livitz DG, Malikic S, Markowetz F, Martincorena I, Mitchell TJ, Mustonen V, Oesper L, Peifer M, Peto M, Raphael BJ, Rosebrock D, 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K, Rozen SG, Rubanova Y, Rubin MA, Rubio-Perez C, Rudneva VA, Rusev BC, Ruzzenente A, Rätsch G, Sabarinathan R, Sabelnykova VY, Zhu H, Sadeghi S, Sahinalp SC, Saini N, Saito-Adachi M, Saksena G, Salcedo A, Salgado R, Salichos L, Sallari R, Saller C, Wang W, Salvia R, Sam M, Samra JS, Sanchez-Vega F, Sander C, Sanders G, Sarin R, Sarrafi I, Sasaki-Oku A, Sauer T, Morris QD, Sauter G, Saw RPM, Scardoni M, Scarlett CJ, Scarpa A, Scelo G, Schadendorf D, Schein JE, Schilhabel MB, Schlesner M, Spellman PT, Schlomm T, Schmidt HK, Schramm SJ, Schreiber S, Schultz N, Schumacher SE, Schwarz RF, Scolyer RA, Scott D, Scully R, Wedge DC, Seethala R, Segre AV, Selander I, Semple CA, Senbabaoglu Y, Sengupta S, Sereni E, Serra S, Sgroi DC, Shackleton M, Van Loo P, Shah NC, Shahabi S, Shang CA, Shang P, Shapira O, Shelton T, Shen C, Shen H, Shepherd R, Shi R, Spellman PT, Shi Y, Shiah YJ, Shibata T, Shih J, Shimizu E, Shimizu K, Shin SJ, Shiraishi Y, Shmaya T, Shmulevich I, Wedge DC, Shorser SI, Short 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Yuen D, Yung CK, Zaikova O, Zamora J, Zapatka M, Zenklusen JC, Ammerpohl O, Zenz T, Zeps N, Zhang CZ, Zhang F, Zhang H, Zhang H, Zhang H, Zhang J, Zhang J, Zhang J, Anderson MJ, Zhang X, Zhang X, Zhang Y, Zhang Z, Zhao Z, Zheng L, Zheng X, Zhou W, Zhou Y, Zhu B, Ang Y, Zhu H, Zhu J, Zhu S, Zou L, Zou X, deFazio A, van As N, van Deurzen CHM, van de Vijver MJ, van’t Veer L, Antonello D, von Mering C, Anur P, Aparicio S, Appelbaum EL, Arai Y, Aretz A, Arihiro K, Ariizumi SI, Armenia J, Arnould L, Asa S, Assenov Y, Atwal G, Aukema S, Auman JT, Aure MRR, Awadalla P, Aymerich M, Bader GD, Baez-Ortega A, Bailey MH, Bailey PJ, Balasundaram M, Balu S, Bandopadhayay P, Banks RE, Barbi S, Barbour AP, Barenboim J, Barnholtz-Sloan J, Barr H, Barrera E, Bartlett J, Bartolome J, Bassi C, Bathe OF, Baumhoer D, Bavi P, Baylin SB, Bazant W, Beardsmore D, Beck TA, Behjati S, Behren A, Niu B, Bell C, Beltran S, Benz C, Berchuck A, Bergmann AK, Bergstrom EN, Berman BP, Berney DM, Bernhart SH, Beroukhim R, Berrios M, Bersani S, Bertl J, Betancourt M, Bhandari V, Bhosle SG, Biankin AV, Bieg M, Bigner D, Binder H, Birney E, Birrer M, Biswas NK, Bjerkehagen B, Bodenheimer T, Boice L, Bonizzato G, De Bono JS, Boot A, Bootwalla MS, Borg A, Borkhardt A, Boroevich KA, Borozan I, Borst C, Bosenberg M, Bosio M, Boultwood J, Bourque G, Boutros PC, Bova GS, Bowen DT, Bowlby R, Bowtell DDL, Boyault S, Boyce R, Boyd J, Brazma A, Brennan P, Brewer DS, Brinkman AB, Bristow RG, Broaddus RR, Brock JE, Brock M, Broeks A, Brooks AN, Brooks D, Brors B, Brunak S, Bruxner TJC, Bruzos AL, Buchanan A, Buchhalter I, Buchholz C, Bullman S, Burke H, Burkhardt B, Burns KH, Busanovich J, Bustamante CD, Butler AP, Butte AJ, Byrne NJ, Børresen-Dale AL, Caesar-Johnson SJ, Cafferkey A, Cahill D, Calabrese C, Caldas C, Calvo F, Camacho N, Campbell PJ, Campo E, Cantù C, Cao S, Carey TE, Carlevaro-Fita J, Carlsen R, Cataldo I, Cazzola M, Cebon J, Cerfolio R, Chadwick DE, Chakravarty D, Chalmers D, Chan CWY, Chan K, 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George J, Gerhard DS, Gerhauser C, Gershenwald JE, Gerstein M, Gerstung M, Getz G, Ghori M, Ghossein R, Giama NH, Gibbs RA, Gibson B, Gill AJ, Gill P, Giri DD, Glodzik D, Gnanapragasam VJ, Goebler ME, Goldman MJ, Gomez C, Gonzalez S, Gonzalez-Perez A, Gordenin DA, Gossage J, Gotoh K, Govindan R, Grabau D, Graham JS, Grant RC, Green AR, Green E, Greger L, Grehan N, Grimaldi S, Grimmond SM, Grossman RL, Grundhoff A, Gundem G, Guo Q, Gupta M, Gupta S, Gut IG, Gut M, Göke J, Ha G, Haake A, Haan D, Haas S, Haase K, Haber JE, Habermann N, Hach F, Haider S, Hama N, Hamdy FC, Hamilton A, Hamilton MP, Han L, Hanna GB, Hansmann M, Haradhvala NJ, Harismendy O, Harliwong I, Harmanci AO, Harrington E, Hasegawa T, Haussler D, Hawkins S, Hayami S, Hayashi S, Hayes DN, Hayes SJ, Hayward NK, Hazell S, He Y, Heath AP, Heath SC, Hedley D, Hegde AM, Heiman DI, Heinold MC, Heins Z, Heisler LE, Hellstrom-Lindberg E, Helmy M, Heo SG, Hepperla AJ, Heredia-Genestar JM, Herrmann C, Hersey P, Hess JM, Hilmarsdottir H, Hinton J, Hirano S, Hiraoka N, Hoadley KA, Hobolth A, Hodzic E, Hoell JI, Hoffmann S, Hofmann O, Holbrook A, Holik AZ, Hollingsworth MA, Holmes O, Holt RA, Hong C, Hong EP, Hong JH, Hooijer GK, Hornshøj H, Hosoda F, Hou Y, Hovestadt V, Howat W, Hoyle AP, Hruban RH, Hu J, Hu T, Hua X, Huang KL, Huang M, Huang MN, Huang V, Huang Y, Huber W, Hudson TJ, Hummel M, Hung JA, Huntsman D, Hupp TR, Huse J, Huska MR, Hutter B, Hutter CM, Hübschmann D, Iacobuzio-Donahue CA, Imbusch CD, Imielinski M, Imoto S, Isaacs WB, Isaev K, Ishikawa S, Iskar M, Islam SMA, Ittmann M, Ivkovic S, Izarzugaza JMG, Jacquemier J, Jakrot V, Jamieson NB, Jang GH, Jang SJ, Jayaseelan JC, Jayasinghe R, Jefferys SR, Jegalian K, Jennings JL, Jeon SH, Jerman L, Ji Y, Jiao W, Johansson PA, Johns AL, Johns J, Johnson R, Johnson TA, Jolly C, Joly Y, Jonasson JG, Jones CD, Jones DR, Jones DTW, Jones N, Jones SJM, Jonkers J, Ju YS, Juhl H, Jung J, Juul M, Juul RI, Juul S, Jäger N, Kabbe R, Kahles A, Kahraman A, Kaiser VB, Kakavand H, Kalimuthu S, von Kalle C, Kang KJ, Karaszi K, Karlan B, Karlić R, Karsch D, Kasaian K, Kassahn KS, Katai H, Kato M, Katoh H, Kawakami Y, Kay JD, Kazakoff SH, Kazanov MD, Keays M, Kebebew E, Kefford RF, Kellis M, Kench JG, Kennedy CJ, Kerssemakers JNA, Khoo D, Khoo V, Khuntikeo N, Khurana E, Kilpinen H, Kim HK, Kim HL, Kim HY, Kim H, Kim J, Kim J, Kim JK, Kim Y, King TA, Klapper W, Kleinheinz K, Klimczak LJ, Knappskog S, Kneba M, Knoppers BM, Koh Y, Komorowski J, Komura D, Komura M, Kong G, Kool M, Korbel JO, Korchina V, Korshunov A, Koscher M, Koster R, Kote-Jarai Z, Koures A, Kovacevic M, Kremeyer B, Kretzmer H, Kreuz M, Krishnamurthy S, Kube D, Kumar K, Kumar P, Kumar S, Kumar Y, Kundra R, Kübler K, Küppers R, Lagergren J, Lai PH, Laird PW, Lakhani SR, Lalansingh CM, Lalonde E, Lamaze FC, Lambert A, Lander E, Landgraf P, Landoni L, Langerød A, Lanzós A, Larsimont D, Larsson E, Lathrop M, Lau LMS, Lawerenz C, Lawlor RT, Lawrence MS, Lazar AJ, Lazic AM, Le X, Lee D, Lee D, Lee EA, Lee HJ, Lee JJK, Lee JY, Lee J, Lee MTM, Lee-Six H, Lehmann KV, Lehrach H, Lenze D, Leonard CR, Leongamornlert DA, Leshchiner I, Letourneau L, Letunic I, Levine DA, Lewis L, Ley T, Li C, Li CH, Li HI, Li J, Li L, Li S, Li S, Li X, Li X, Li X, Li Y, Liang H, Liang SB, Lichter P, Lin P, Lin Z, Linehan WM, Lingjærde OC, Liu D, Liu EM, Liu FFF, Liu F, Liu J, Liu X, Livingstone J, Livitz D, Livni N, Lochovsky L, Loeffler M, Long GV, Lopez-Guillermo A, Lou S, Louis DN, Lovat LB, Lu Y, Lu YJ, Lu Y, Luchini C, Lungu I, Luo X, Luxton HJ, Lynch AG, Lype L, López C, López-Otín C, Ma EZ, Ma Y, MacGrogan G, MacRae S, Macintyre G, Madsen T, Maejima K, Mafficini A, Maglinte DT, Maitra A, Majumder PP, Malcovati L, Malikic S, Malleo G, Mann GJ, Mantovani-Löffler L, Marchal K, Marchegiani G, Mardis ER, Margolin AA, Marin MG, Markowetz F, Markowski J, Marks J, Marques-Bonet T, Marra MA, Marsden L, Martens JWM, Martin S, Martin-Subero JI, Martincorena I, Martinez-Fundichely A, Maruvka YE, Mashl RJ, Massie CE, Matthew TJ, Matthews L, Mayer E, Mayes S, Mayo M, Mbabaali F, McCune K, McDermott U, McGillivray PD, McLellan MD, McPherson JD, McPherson JR, McPherson TA, Meier SR, Meng A, Meng S, Menzies A, Merrett ND, Merson S, Meyerson M, Meyerson W, Mieczkowski PA, Mihaiescu GL, Mijalkovic S, Mikkelsen T, Milella M, Mileshkin L, Miller CA, Miller DK, Miller JK, Mills GB, Milovanovic A, Minner S, Miotto M, Arnau GM, Mirabello L, Mitchell C, Mitchell TJ, Miyano S, Miyoshi N, Mizuno S, Molnár-Gábor F, Moore MJ, Moore RA, Morganella S, Morris QD, Morrison C, Mose LE, Moser CD, Muiños F, Mularoni L, Mungall AJ, Mungall K, Musgrove EA, Mustonen V, Mutch D, Muyas F, Muzny DM, Muñoz A, Myers J, Myklebost O, Möller P, Nagae G, Nagrial AM, Nahal-Bose HK, Nakagama H, Nakagawa H, Nakamura H, Nakamura T, Nakano K, Nandi T, Nangalia J, Nastic M, Navarro A, Navarro FCP, Neal DE, Nettekoven G, Newell F, Newhouse SJ, Newton Y, Ng AWT, Ng A, Nicholson J, Nicol D, Nie Y, Nielsen GP, Nielsen MM, Nik-Zainal S, Noble MS, Nones K, Northcott PA, Notta F, O’Connor BD, O’Donnell P, O’Donovan M, O’Meara S, O’Neill BP, O’Neill JR, Ocana D, Ochoa A, Oesper L, Ogden C, Ohdan H, Ohi K, Ohno-Machado L, Oien KA, Ojesina AI, Ojima H, Okusaka T, Omberg L, Ong CK, Ossowski S, Ott G, Ouellette BFF, P’ng C, Paczkowska M, Paiella S, Pairojkul C, Pajic M, Pan-Hammarström Q, Papaemmanuil E, Papatheodorou I, Paramasivam N, Park JW, Park JW, Park K, Park K, Park PJ, Parker JS, Parsons SL, Pass H, Pasternack D, Pastore A, Patch AM, Pauporté I, Pea A, Pearson JV, Pedamallu CS, Pedersen JS, Pederzoli P, Peifer M, Pennell NA, Perou CM, Perry MD, Petersen GM, Peto M, Petrelli N, Petryszak R, Pfister SM, Phillips M, Pich O, Pickett HA, Pihl TD, Pillay N, Pinder S, Pinese M, Pinho AV. Author Correction: The evolutionary history of 2,658 cancers. Nature 2023; 614:E42. [PMID: 36697833 PMCID: PMC9931577 DOI: 10.1038/s41586-022-05601-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- Moritz Gerstung
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK. .,European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany. .,Wellcome Sanger Institute, Cambridge, UK.
| | - Clemency Jolly
- grid.451388.30000 0004 1795 1830The Francis Crick Institute, London, UK
| | - Ignaty Leshchiner
- grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Stefan C. Dentro
- grid.10306.340000 0004 0606 5382Wellcome Sanger Institute, Cambridge, UK ,grid.451388.30000 0004 1795 1830The Francis Crick Institute, London, UK ,grid.4991.50000 0004 1936 8948Big Data Institute, University of Oxford, Oxford, UK
| | - Santiago Gonzalez
- grid.225360.00000 0000 9709 7726European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Daniel Rosebrock
- grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Thomas J. Mitchell
- grid.10306.340000 0004 0606 5382Wellcome Sanger Institute, Cambridge, UK ,grid.5335.00000000121885934University of Cambridge, Cambridge, UK
| | - Yulia Rubanova
- grid.17063.330000 0001 2157 2938University of Toronto, Toronto, Ontario Canada ,grid.494618.6Vector Institute, Toronto, Ontario Canada
| | - Pavana Anur
- grid.5288.70000 0000 9758 5690Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR USA
| | - Kaixian Yu
- grid.240145.60000 0001 2291 4776The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Maxime Tarabichi
- grid.10306.340000 0004 0606 5382Wellcome Sanger Institute, Cambridge, UK ,grid.451388.30000 0004 1795 1830The Francis Crick Institute, London, UK
| | - Amit Deshwar
- grid.17063.330000 0001 2157 2938University of Toronto, Toronto, Ontario Canada ,grid.494618.6Vector Institute, Toronto, Ontario Canada
| | - Jeff Wintersinger
- grid.17063.330000 0001 2157 2938University of Toronto, Toronto, Ontario Canada ,grid.494618.6Vector Institute, Toronto, Ontario Canada
| | - Kortine Kleinheinz
- grid.7497.d0000 0004 0492 0584German Cancer Research Center (DKFZ), Heidelberg, Germany ,grid.7700.00000 0001 2190 4373Heidelberg University, Heidelberg, Germany
| | - Ignacio Vázquez-García
- grid.10306.340000 0004 0606 5382Wellcome Sanger Institute, Cambridge, UK ,grid.5335.00000000121885934University of Cambridge, Cambridge, UK
| | - Kerstin Haase
- grid.451388.30000 0004 1795 1830The Francis Crick Institute, London, UK
| | - Lara Jerman
- grid.225360.00000 0000 9709 7726European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK ,grid.8954.00000 0001 0721 6013University of Ljubljana, Ljubljana, Slovenia
| | - Subhajit Sengupta
- grid.240372.00000 0004 0400 4439NorthShore University HealthSystem, Evanston, IL USA
| | - Geoff Macintyre
- grid.5335.00000000121885934Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Salem Malikic
- grid.61971.380000 0004 1936 7494Simon Fraser University, Burnaby, British Columbia Canada ,grid.412541.70000 0001 0684 7796Vancouver Prostate Centre, Vancouver, British Columbia Canada
| | - Nilgun Donmez
- grid.61971.380000 0004 1936 7494Simon Fraser University, Burnaby, British Columbia Canada ,grid.412541.70000 0001 0684 7796Vancouver Prostate Centre, Vancouver, British Columbia Canada
| | - Dimitri G. Livitz
- grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Marek Cmero
- grid.1008.90000 0001 2179 088XUniversity of Melbourne, Melbourne, Victoria Australia ,grid.1042.70000 0004 0432 4889Walter and Eliza Hall Institute, Melbourne, Victoria Australia
| | - Jonas Demeulemeester
- grid.451388.30000 0004 1795 1830The Francis Crick Institute, London, UK ,grid.5596.f0000 0001 0668 7884University of Leuven, Leuven, Belgium
| | - Steven Schumacher
- grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Yu Fan
- grid.240145.60000 0001 2291 4776The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Xiaotong Yao
- grid.5386.8000000041936877XWeill Cornell Medicine, New York, NY USA ,grid.429884.b0000 0004 1791 0895New York Genome Center, New York, NY USA
| | - Juhee Lee
- grid.205975.c0000 0001 0740 6917University of California Santa Cruz, Santa Cruz, CA USA
| | - Matthias Schlesner
- grid.7497.d0000 0004 0492 0584German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Paul C. Boutros
- grid.17063.330000 0001 2157 2938University of Toronto, Toronto, Ontario Canada ,grid.419890.d0000 0004 0626 690XOntario Institute for Cancer Research, Toronto, Ontario Canada ,grid.19006.3e0000 0000 9632 6718University of California, Los Angeles, CA USA
| | - David D. Bowtell
- grid.1055.10000000403978434Peter MacCallum Cancer Centre, Melbourne, Victoria Australia
| | - Hongtu Zhu
- grid.240145.60000 0001 2291 4776The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Gad Getz
- grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA ,grid.32224.350000 0004 0386 9924Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA USA ,grid.32224.350000 0004 0386 9924Department of Pathology, Massachusetts General Hospital, Boston, MA USA ,grid.38142.3c000000041936754XHarvard Medical School, Boston, MA USA
| | - Marcin Imielinski
- grid.5386.8000000041936877XWeill Cornell Medicine, New York, NY USA ,grid.429884.b0000 0004 1791 0895New York Genome Center, New York, NY USA
| | - Rameen Beroukhim
- grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA ,grid.65499.370000 0001 2106 9910Dana-Farber Cancer Institute, Boston, MA USA
| | - S. Cenk Sahinalp
- grid.412541.70000 0001 0684 7796Vancouver Prostate Centre, Vancouver, British Columbia Canada ,grid.411377.70000 0001 0790 959XIndiana University, Bloomington, IN USA
| | - Yuan Ji
- grid.240372.00000 0004 0400 4439NorthShore University HealthSystem, Evanston, IL USA ,grid.170205.10000 0004 1936 7822The University of Chicago, Chicago, IL USA
| | - Martin Peifer
- grid.6190.e0000 0000 8580 3777University of Cologne, Cologne, Germany
| | - Florian Markowetz
- grid.5335.00000000121885934Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Ville Mustonen
- grid.7737.40000 0004 0410 2071University of Helsinki, Helsinki, Finland
| | - Ke Yuan
- grid.5335.00000000121885934Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK ,grid.8756.c0000 0001 2193 314XUniversity of Glasgow, Glasgow, UK
| | - Wenyi Wang
- grid.240145.60000 0001 2291 4776The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Quaid D. Morris
- grid.17063.330000 0001 2157 2938University of Toronto, Toronto, Ontario Canada ,grid.494618.6Vector Institute, Toronto, Ontario Canada
| | | | - Paul T. Spellman
- grid.5288.70000 0000 9758 5690Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR USA
| | - David C. Wedge
- grid.4991.50000 0004 1936 8948Big Data Institute, University of Oxford, Oxford, UK ,grid.454382.c0000 0004 7871 7212Oxford NIHR Biomedical Research Centre, Oxford, UK
| | - Peter Van Loo
- The Francis Crick Institute, London, UK. .,University of Leuven, Leuven, Belgium.
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Calabrese C, Davidson NR, Demircioğlu D, Fonseca NA, He Y, Kahles A, Lehmann KV, Liu F, Shiraishi Y, Soulette CM, Urban L, Greger L, Li S, Liu D, Perry MD, Xiang Q, Zhang F, Zhang J, Bailey P, Erkek S, Hoadley KA, Hou Y, Huska MR, Kilpinen H, Korbel JO, Marin MG, Markowski J, Nandi T, Pan-Hammarström Q, Pedamallu CS, Siebert R, Stark SG, Su H, Tan P, Waszak SM, Yung C, Zhu S, Awadalla P, Creighton CJ, Meyerson M, Ouellette BFF, Wu K, Yang H, Brazma A, Brooks AN, Göke J, Rätsch G, Schwarz RF, Stegle O, Zhang Z, Wu K, Yang H, Fonseca NA, Kahles A, Lehmann KV, Urban L, Soulette CM, Shiraishi Y, Liu F, He Y, Demircioğlu D, Davidson NR, Calabrese C, Zhang J, Perry MD, Xiang Q, Greger L, Li S, Liu D, Stark SG, Zhang F, Amin SB, Bailey P, Chateigner A, Cortés-Ciriano I, Craft B, Erkek S, Frenkel-Morgenstern M, Goldman M, Hoadley KA, Hou Y, Huska MR, Khurana E, Kilpinen H, Korbel JO, Lamaze FC, Li C, Li X, Li X, Liu X, Marin MG, Markowski J, Nandi T, Nielsen MM, Ojesina AI, Pan-Hammarström Q, Park PJ, Pedamallu CS, Pedersen JS, Pederzoli P, Peifer M, Pennell NA, Perou CM, Perry MD, Petersen GM, Peto M, Petrelli N, Pedamallu CS, Petryszak R, Pfister SM, Phillips M, Pich O, Pickett HA, Pihl TD, Pillay N, Pinder S, Pinese M, Pinho AV, Pedersen JS, Pitkänen E, Pivot X, Piñeiro-Yáñez E, Planko L, Plass C, Polak P, Pons T, Popescu I, Potapova O, Prasad A, Siebert R, Preston SR, Prinz M, Pritchard AL, Prokopec SD, Provenzano E, Puente XS, Puig S, Puiggròs M, Pulido-Tamayo S, Pupo GM, Su H, Purdie CA, Quinn MC, Rabionet R, Rader JS, Radlwimmer B, Radovic P, Raeder B, Raine KM, Ramakrishna M, Ramakrishnan K, Tan P, Ramalingam S, Raphael BJ, Rathmell WK, Rausch T, Reifenberger G, Reimand J, Reis-Filho J, Reuter V, Reyes-Salazar I, Reyna MA, Teh BT, Reynolds SM, Rheinbay E, Riazalhosseini Y, Richardson AL, Richter J, Ringel M, Ringnér M, Rino Y, Rippe K, Roach J, Wang J, Roberts LR, Roberts ND, Roberts SA, Robertson AG, Robertson AJ, Rodriguez JB, Rodriguez-Martin B, Rodríguez-González FG, Roehrl MHA, Rohde M, Waszak SM, Rokutan H, Romieu G, Rooman I, Roques T, Rosebrock D, Rosenberg M, Rosenstiel PC, Rosenwald A, Rowe EW, Royo R, Xiong H, Rozen SG, Rubanova Y, Rubin MA, Rubio-Perez C, Rudneva VA, Rusev BC, Ruzzenente A, Rätsch G, Sabarinathan R, Sabelnykova VY, Yakneen S, Sadeghi S, Sahinalp SC, Saini N, Saito-Adachi M, Saksena G, Salcedo A, Salgado R, Salichos L, Sallari R, Saller C, Ye C, Salvia R, Sam M, Samra JS, Sanchez-Vega F, Sander C, Sanders G, Sarin R, Sarrafi I, Sasaki-Oku A, Sauer T, Yung C, Sauter G, Saw RPM, Scardoni M, Scarlett CJ, Scarpa A, Scelo G, Schadendorf D, Schein JE, Schilhabel MB, Schlesner M, Zhang X, Schlomm T, Schmidt HK, Schramm SJ, Schreiber S, Schultz N, Schumacher SE, Schwarz RF, Scolyer RA, Scott D, Scully R, Zheng L, Seethala R, Segre AV, Selander I, Semple CA, Senbabaoglu Y, Sengupta S, Sereni E, Serra S, Sgroi DC, Shackleton M, Zhu J, Shah NC, Shahabi S, Shang CA, Shang P, Shapira O, Shelton T, Shen C, Shen H, Shepherd R, Shi R, Zhu S, Shi Y, Shiah YJ, Shibata T, Shih J, Shimizu E, Shimizu K, Shin SJ, Shiraishi Y, Shmaya T, Shmulevich I, Awadalla P, Shorser SI, Short C, Shrestha R, Shringarpure SS, Shriver C, Shuai S, Sidiropoulos N, Siebert R, Sieuwerts AM, Sieverling L, Creighton CJ, Signoretti S, Sikora KO, Simbolo M, Simon R, Simons JV, Simpson JT, Simpson PT, Singer S, Sinnott-Armstrong N, Sipahimalani P, Meyerson M, Skelly TJ, Smid M, Smith J, Smith-McCune K, Socci ND, Sofia HJ, Soloway MG, Song L, Sood AK, Sothi S, Ouellette BFF, Sotiriou C, Soulette CM, Span PN, Spellman PT, Sperandio N, Spillane AJ, Spiro O, Spring J, Staaf J, Stadler PF, Wu K, Staib P, Stark SG, Stebbings L, Stefánsson ÓA, Stegle O, Stein LD, Stenhouse A, Stewart C, Stilgenbauer S, Stobbe MD, Yang H, Stratton MR, Stretch JR, Struck AJ, Stuart JM, Stunnenberg HG, Su H, Su X, Sun RX, Sungalee S, Susak H, Göke J, Suzuki A, Sweep F, Szczepanowski M, Sültmann H, Yugawa T, Tam A, Tamborero D, Tan BKT, Tan D, Tan P, Schwarz RF, Tanaka H, Taniguchi H, Tanskanen TJ, Tarabichi M, Tarnuzzer R, Tarpey P, Taschuk ML, Tatsuno K, Tavaré S, Taylor DF, Stegle O, Taylor-Weiner A, Teague JW, Teh BT, Tembe V, Temes J, Thai K, Thayer SP, Thiessen N, Thomas G, Thomas S, Zhang Z, Thompson A, Thompson AM, Thompson JFF, Thompson RH, Thorne H, Thorne LB, Thorogood A, Tiao G, Tijanic N, Timms LE, Brazma A, Tirabosco R, Tojo M, Tommasi S, Toon CW, Toprak UH, Torrents D, Tortora G, Tost J, Totoki Y, Townend D, Rätsch G, Traficante N, Treilleux I, Trotta JR, Trümper LHP, Tsao M, Tsunoda T, Tubio JMC, Tucker O, Turkington R, Turner DJ, Brooks AN, Tutt A, Ueno M, Ueno NT, Umbricht C, Umer HM, Underwood TJ, Urban L, Urushidate T, Ushiku T, Uusküla-Reimand L, Brazma A, Valencia A, Van Den Berg DJ, Van Laere S, Van Loo P, Van Meir EG, Van den Eynden GG, Van der Kwast T, Vasudev N, Vazquez M, Vedururu R, Brooks AN, Veluvolu U, Vembu S, Verbeke LPC, Vermeulen P, Verrill C, Viari A, Vicente D, Vicentini C, VijayRaghavan K, Viksna J, Göke J, Vilain RE, Villasante I, Vincent-Salomon A, Visakorpi T, Voet D, Vyas P, Vázquez-García I, Waddell NM, Waddell N, Wadelius C, Rätsch G, Wadi L, Wagener R, Wala JA, Wang J, Wang J, Wang L, Wang Q, Wang W, Wang Y, Wang Z, Schwarz RF, Waring PM, Warnatz HJ, Warrell J, Warren AY, Waszak SM, Wedge DC, Weichenhan D, Weinberger P, Weinstein JN, Weischenfeldt J, Stegle O, Weisenberger DJ, Welch I, Wendl MC, Werner J, Whalley JP, Wheeler DA, Whitaker HC, Wigle D, Wilkerson MD, Williams A, Zhang Z, Wilmott JS, Wilson GW, Wilson JM, Wilson RK, Winterhoff B, Wintersinger JA, Wiznerowicz M, Wolf S, Wong BH, Wong T, Aaltonen LA, Wong W, Woo Y, Wood S, Wouters BG, Wright AJ, Wright DW, Wright MH, Wu CL, Wu DY, Wu G, Abascal F, Wu J, Wu K, Wu Y, Wu Z, Xi L, Xia T, Xiang Q, Xiao X, Xing R, Xiong H, Abeshouse A, Xu Q, Xu Y, Xue H, Yachida S, Yakneen S, Yamaguchi R, Yamaguchi TN, Yamamoto M, Yamamoto S, Yamaue H, Aburatani H, Yang F, Yang H, Yang JY, Yang L, Yang L, Yang S, Yang TP, Yang Y, Yao X, Yaspo ML, Adams DJ, Yates L, Yau C, Ye C, Ye K, Yellapantula VD, Yoon CJ, Yoon SS, Yousif F, Yu J, Yu K, Agrawal N, Yu W, Yu Y, Yuan K, Yuan Y, Yuen D, Yung CK, Zaikova O, Zamora J, Zapatka M, Zenklusen JC, Ahn KS, Zenz T, Zeps N, Zhang CZ, Zhang F, Zhang H, Zhang H, Zhang H, Zhang J, Zhang J, Zhang J, Ahn SM, Zhang X, Zhang X, Zhang Y, Zhang Z, Zhao Z, Zheng L, Zheng X, Zhou W, Zhou Y, Zhu B, Aikata H, Zhu H, Zhu J, Zhu S, Zou L, Zou X, deFazio A, van As N, van Deurzen CHM, van de Vijver MJ, van’t Veer L, Akbani R, von Mering C, Akdemir KC, Al-Ahmadie H, Al-Sedairy ST, Al-Shahrour F, Alawi M, Albert M, Aldape K, Alexandrov LB, Ally A, Alsop K, Alvarez EG, Amary F, Amin SB, Aminou B, Ammerpohl O, Anderson MJ, Ang Y, Antonello D, Anur P, Aparicio S, Appelbaum EL, Arai Y, Aretz A, Arihiro K, Ariizumi SI, Armenia J, Arnould L, Asa S, Assenov Y, Atwal G, Aukema S, Auman JT, Aure MRR, Awadalla P, Aymerich M, Bader GD, Baez-Ortega A, Bailey MH, Bailey PJ, Balasundaram M, Balu S, Bandopadhayay P, Banks RE, Barbi S, Barbour AP, Barenboim J, Barnholtz-Sloan J, Barr H, Barrera E, Bartlett J, Bartolome J, Bassi C, Bathe OF, Baumhoer D, Bavi P, Baylin SB, Bazant W, Beardsmore D, Beck TA, Behjati S, Behren A, Niu B, Bell C, Beltran S, Benz C, Berchuck A, Bergmann AK, Bergstrom EN, Berman BP, Berney DM, Bernhart SH, Beroukhim R, Berrios M, Bersani S, Bertl J, Betancourt M, Bhandari V, Bhosle SG, Biankin AV, Bieg M, Bigner D, Binder H, Birney E, Birrer M, Biswas NK, Bjerkehagen B, Bodenheimer T, Boice L, Bonizzato G, De Bono JS, Boot A, Bootwalla MS, Borg A, Borkhardt A, Boroevich KA, Borozan I, Borst C, Bosenberg M, Bosio M, Boultwood J, Bourque G, Boutros PC, Bova GS, Bowen DT, Bowlby R, Bowtell DDL, Boyault S, Boyce R, Boyd J, Brazma A, Brennan P, Brewer DS, Brinkman AB, Bristow RG, Broaddus RR, Brock JE, Brock M, Broeks A, Brooks AN, Brooks D, Brors B, Brunak S, Bruxner TJC, Bruzos AL, Buchanan A, Buchhalter I, Buchholz C, Bullman S, Burke H, Burkhardt B, Burns KH, Busanovich J, Bustamante CD, Butler AP, Butte AJ, Byrne NJ, Børresen-Dale AL, Caesar-Johnson SJ, Cafferkey A, Cahill D, Calabrese C, Caldas C, Calvo F, Camacho N, Campbell PJ, Campo E, Cantù C, Cao S, Carey TE, Carlevaro-Fita J, Carlsen R, Cataldo I, Cazzola M, Cebon J, Cerfolio R, Chadwick DE, Chakravarty D, Chalmers D, Chan CWY, Chan K, Chan-Seng-Yue M, Chandan VS, Chang DK, Chanock SJ, Chantrill LA, Chateigner A, Chatterjee N, Chayama K, Chen HW, Chen J, Chen K, Chen Y, Chen Z, Cherniack AD, Chien J, Chiew YE, Chin SF, Cho J, Cho S, Choi JK, Choi W, Chomienne C, Chong Z, Choo SP, Chou A, Christ AN, Christie EL, Chuah E, Cibulskis C, Cibulskis K, Cingarlini S, Clapham P, Claviez A, Cleary S, Cloonan N, Cmero M, Collins CC, Connor AA, Cooke SL, Cooper CS, Cope L, Corbo V, Cordes MG, Cordner SM, Cortés-Ciriano I, Covington K, Cowin PA, Craft B, Craft D, Creighton CJ, Cun Y, Curley E, Cutcutache I, Czajka K, Czerniak B, Dagg RA, Danilova L, Davi MV, Davidson NR, Davies H, Davis IJ, Davis-Dusenbery BN, Dawson KJ, De La Vega FM, De Paoli-Iseppi R, Defreitas T, Tos APD, Delaneau O, Demchok JA, Demeulemeester J, Demidov GM, Demircioğlu D, Dennis NM, Denroche RE, Dentro SC, Desai N, Deshpande V, Deshwar AG, Desmedt C, Deu-Pons J, Dhalla N, Dhani NC, Dhingra P, Dhir R, DiBiase A, Diamanti K, Ding L, Ding S, Dinh HQ, Dirix L, Doddapaneni H, Donmez N, Dow MT, Drapkin R, Drechsel O, Drews RM, Serge S, Dudderidge T, Dueso-Barroso A, Dunford AJ, Dunn M, Dursi LJ, Duthie FR, Dutton-Regester K, Eagles J, Easton DF, Edmonds S, Edwards PA, Edwards SE, Eeles RA, Ehinger A, Eils J, Eils R, El-Naggar A, Eldridge M, Ellrott K, Erkek S, Escaramis G, Espiritu SMG, Estivill X, Etemadmoghadam D, Eyfjord JE, Faltas BM, Fan D, Fan Y, Faquin WC, Farcas C, Fassan M, Fatima A, Favero F, Fayzullaev N, Felau I, Fereday S, Ferguson ML, Ferretti V, Feuerbach L, Field MA, Fink JL, Finocchiaro G, Fisher C, Fittall MW, Fitzgerald A, Fitzgerald RC, Flanagan AM, Fleshner NE, Flicek P, Foekens JA, Fong KM, Fonseca NA, Foster CS, Fox NS, Fraser M, Frazer S, Frenkel-Morgenstern M, Friedman W, Frigola J, Fronick CC, Fujimoto A, Fujita M, Fukayama M, Fulton LA, Fulton RS, Furuta M, Futreal PA, Füllgrabe A, Gabriel SB, Gallinger S, Gambacorti-Passerini C, Gao J, Gao S, Garraway L, Garred Ø, Garrison E, Garsed DW, Gehlenborg N, Gelpi JLL, George J, Gerhard DS, Gerhauser C, Gershenwald JE, Gerstein M, Gerstung M, Getz G, Ghori M, Ghossein R, Giama NH, Gibbs RA, Gibson B, Gill AJ, Gill P, Giri DD, Glodzik D, Gnanapragasam VJ, Goebler ME, Goldman MJ, Gomez C, Gonzalez S, Gonzalez-Perez A, Gordenin DA, Gossage J, Gotoh K, Govindan R, Grabau D, Graham JS, Grant RC, Green AR, Green E, Greger L, Grehan N, Grimaldi S, Grimmond SM, Grossman RL, Grundhoff A, Gundem G, Guo Q, Gupta M, Gupta S, Gut IG, Gut M, Göke J, Ha G, Haake A, Haan D, Haas S, Haase K, Haber JE, Habermann N, Hach F, Haider S, Hama N, Hamdy FC, Hamilton A, Hamilton MP, Han L, Hanna GB, Hansmann M, Haradhvala NJ, Harismendy O, Harliwong I, Harmanci AO, Harrington E, Hasegawa T, Haussler D, Hawkins S, Hayami S, Hayashi S, Hayes DN, Hayes SJ, Hayward NK, Hazell S, He Y, Heath AP, Heath SC, Hedley D, Hegde AM, Heiman DI, Heinold MC, Heins Z, Heisler LE, Hellstrom-Lindberg E, Helmy M, Heo SG, Hepperla AJ, Heredia-Genestar JM, Herrmann C, Hersey P, Hess JM, Hilmarsdottir H, Hinton J, Hirano S, Hiraoka N, Hoadley KA, Hobolth A, Hodzic E, Hoell JI, Hoffmann S, Hofmann O, Holbrook A, Holik AZ, Hollingsworth MA, Holmes O, Holt RA, Hong C, Hong EP, Hong JH, Hooijer GK, Hornshøj H, Hosoda F, Hou Y, Hovestadt V, Howat W, Hoyle AP, Hruban RH, Hu J, Hu T, Hua X, Huang KL, Huang M, Huang MN, Huang V, Huang Y, Huber W, Hudson TJ, Hummel M, Hung JA, Huntsman D, Hupp TR, Huse J, Huska MR, Hutter B, Hutter CM, Hübschmann D, Iacobuzio-Donahue CA, Imbusch CD, Imielinski M, Imoto S, Isaacs WB, Isaev K, Ishikawa S, Iskar M, Islam SMA, Ittmann M, Ivkovic S, Izarzugaza JMG, Jacquemier J, Jakrot V, Jamieson NB, Jang GH, Jang SJ, Jayaseelan JC, Jayasinghe R, Jefferys SR, Jegalian K, Jennings JL, Jeon SH, Jerman L, Ji Y, Jiao W, Johansson PA, Johns AL, Johns J, Johnson R, Johnson TA, Jolly C, Joly Y, Jonasson JG, Jones CD, Jones DR, Jones DTW, Jones N, Jones SJM, Jonkers J, Ju YS, Juhl H, Jung J, Juul M, Juul RI, Juul S, Jäger N, Kabbe R, Kahles A, Kahraman A, Kaiser VB, Kakavand H, Kalimuthu S, von Kalle C, Kang KJ, Karaszi K, Karlan B, Karlić R, Karsch D, Kasaian K, Kassahn KS, Katai H, Kato M, Katoh H, Kawakami Y, Kay JD, Kazakoff SH, Kazanov MD, Keays M, Kebebew E, Kefford RF, Kellis M, Kench JG, Kennedy CJ, Kerssemakers JNA, Khoo D, Khoo V, Khuntikeo N, Khurana E, Kilpinen H, Kim HK, Kim HL, Kim HY, Kim H, Kim J, Kim J, Kim JK, Kim Y, King TA, Klapper W, Kleinheinz K, Klimczak LJ, Knappskog S, Kneba M, Knoppers BM, Koh Y, Komorowski J, Komura D, Komura M, Kong G, Kool M, Korbel JO, Korchina V, Korshunov A, Koscher M, Koster R, Kote-Jarai Z, Koures A, Kovacevic M, Kremeyer B, Kretzmer H, Kreuz M, Krishnamurthy S, Kube D, Kumar K, Kumar P, Kumar S, Kumar Y, Kundra R, Kübler K, Küppers R, Lagergren J, Lai PH, Laird PW, Lakhani SR, Lalansingh CM, Lalonde E, Lamaze FC, Lambert A, Lander E, Landgraf P, Landoni L, Langerød A, Lanzós A, Larsimont D, Larsson E, Lathrop M, Lau LMS, Lawerenz C, Lawlor RT, Lawrence MS, Lazar AJ, Lazic AM, Le X, Lee D, Lee D, Lee EA, Lee HJ, Lee JJK, Lee JY, Lee J, Lee MTM, Lee-Six H, Lehmann KV, Lehrach H, Lenze D, Leonard CR, Leongamornlert DA, Leshchiner I, Letourneau L, Letunic I, Levine DA, Lewis L, Ley T, Li C, Li CH, Li HI, Li J, Li L, Li S, Li S, Li X, Li X, Li X, Li Y, Liang H, Liang SB, Lichter P, Lin P, Lin Z, Linehan WM, Lingjærde OC, Liu D, Liu EM, Liu FFF, Liu F, Liu J, Liu X, Livingstone J, Livitz D, Livni N, Lochovsky L, Loeffler M, Long GV, Lopez-Guillermo A, Lou S, Louis DN, Lovat LB, Lu Y, Lu YJ, Lu Y, Luchini C, Lungu I, Luo X, Luxton HJ, Lynch AG, Lype L, López C, López-Otín C, Ma EZ, Ma Y, MacGrogan G, MacRae S, Macintyre G, Madsen T, Maejima K, Mafficini A, Maglinte DT, Maitra A, Majumder PP, Malcovati L, Malikic S, Malleo G, Mann GJ, Mantovani-Löffler L, Marchal K, Marchegiani G, Mardis ER, Margolin AA, Marin MG, Markowetz F, Markowski J, Marks J, Marques-Bonet T, Marra MA, Marsden L, Martens JWM, Martin S, Martin-Subero JI, Martincorena I, Martinez-Fundichely A, Maruvka YE, Mashl RJ, Massie CE, Matthew TJ, Matthews L, Mayer E, Mayes S, Mayo M, Mbabaali F, McCune K, McDermott U, McGillivray PD, McLellan MD, McPherson JD, McPherson JR, McPherson TA, Meier SR, Meng A, Meng S, Menzies A, Merrett ND, Merson S, Meyerson M, Meyerson W, Mieczkowski PA, Mihaiescu GL, Mijalkovic S, Mikkelsen T, Milella M, Mileshkin L, Miller CA, Miller DK, Miller JK, Mills GB, Milovanovic A, Minner S, Miotto M, Arnau GM, Mirabello L, Mitchell C, Mitchell TJ, Miyano S, Miyoshi N, Mizuno S, Molnár-Gábor F, Moore MJ, Moore RA, Morganella S, Morris QD, Morrison C, Mose LE, Moser CD, Muiños F, Mularoni L, Mungall AJ, Mungall K, Musgrove EA, Mustonen V, Mutch D, Muyas F, Muzny DM, Muñoz A, Myers J, Myklebost O, Möller P, Nagae G, Nagrial AM, Nahal-Bose HK, Nakagama H, Nakagawa H, Nakamura H, Nakamura T, Nakano K, Nandi T, Nangalia J, Nastic M, Navarro A, Navarro FCP, Neal DE, Nettekoven G, Newell F, Newhouse SJ, Newton Y, Ng AWT, Ng A, Nicholson J, Nicol D, Nie Y, Nielsen GP, Nielsen MM, Nik-Zainal S, Noble MS, Nones K, Northcott PA, Notta F, O’Connor BD, O’Donnell P, O’Donovan M, O’Meara S, O’Neill BP, O’Neill JR, Ocana D, Ochoa A, Oesper L, Ogden C, Ohdan H, Ohi K, Ohno-Machado L, Oien KA, Ojesina AI, Ojima H, Okusaka T, Omberg L, Ong CK, Ossowski S, Ott G, Ouellette BFF, P’ng C, Paczkowska M, Paiella S, Pairojkul C, Pajic M, Pan-Hammarström Q, Papaemmanuil E, Papatheodorou I, Paramasivam N, Park JW, Park JW, Park K, Park K, Park PJ, Parker JS, Parsons SL, Pass H, Pasternack D, Pastore A, Patch AM, Pauporté I, Pea A, Pearson JV. Author Correction: Genomic basis for RNA alterations in cancer. Nature 2023; 614:E37. [PMID: 36697831 PMCID: PMC9931574 DOI: 10.1038/s41586-022-05596-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
| | - Claudia Calabrese
- grid.225360.00000 0000 9709 7726European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Natalie R. Davidson
- grid.5801.c0000 0001 2156 2780ETH Zurich, Zurich, Switzerland ,grid.51462.340000 0001 2171 9952Memorial Sloan Kettering Cancer Center, New York, NY USA ,grid.5386.8000000041936877XWeill Cornell Medical College, New York, NY USA ,grid.419765.80000 0001 2223 3006SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland ,grid.412004.30000 0004 0478 9977University Hospital Zurich, Zurich, Switzerland
| | - Deniz Demircioğlu
- grid.4280.e0000 0001 2180 6431National University of Singapore, Singapore, Singapore ,grid.418377.e0000 0004 0620 715XGenome Institute of Singapore, Singapore, Singapore
| | - Nuno A. Fonseca
- grid.225360.00000 0000 9709 7726European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Yao He
- grid.11135.370000 0001 2256 9319Peking University, Beijing, China
| | - André Kahles
- grid.5801.c0000 0001 2156 2780ETH Zurich, Zurich, Switzerland ,grid.51462.340000 0001 2171 9952Memorial Sloan Kettering Cancer Center, New York, NY USA ,grid.419765.80000 0001 2223 3006SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland ,grid.412004.30000 0004 0478 9977University Hospital Zurich, Zurich, Switzerland
| | - Kjong-Van Lehmann
- grid.5801.c0000 0001 2156 2780ETH Zurich, Zurich, Switzerland ,grid.51462.340000 0001 2171 9952Memorial Sloan Kettering Cancer Center, New York, NY USA ,grid.419765.80000 0001 2223 3006SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland ,grid.412004.30000 0004 0478 9977University Hospital Zurich, Zurich, Switzerland
| | - Fenglin Liu
- grid.11135.370000 0001 2256 9319Peking University, Beijing, China
| | - Yuichi Shiraishi
- grid.26999.3d0000 0001 2151 536XThe University of Tokyo, Minato-ku, Japan
| | - Cameron M. Soulette
- grid.205975.c0000 0001 0740 6917University of California, Santa Cruz, Santa Cruz, CA USA
| | - Lara Urban
- grid.225360.00000 0000 9709 7726European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Liliana Greger
- grid.225360.00000 0000 9709 7726European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Siliang Li
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, Shenzhen, China ,grid.507779.b0000 0004 4910 5858China National GeneBank-Shenzhen, Shenzhen, China
| | - Dongbing Liu
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, Shenzhen, China ,grid.507779.b0000 0004 4910 5858China National GeneBank-Shenzhen, Shenzhen, China
| | - Marc D. Perry
- grid.17063.330000 0001 2157 2938Ontario Institute for Cancer Research, Toronto, Ontario, Canada ,grid.266102.10000 0001 2297 6811University of California, San Francisco, San Francisco, CA USA
| | - Qian Xiang
- grid.17063.330000 0001 2157 2938Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Fan Zhang
- grid.11135.370000 0001 2256 9319Peking University, Beijing, China
| | - Junjun Zhang
- grid.17063.330000 0001 2157 2938Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Peter Bailey
- grid.8756.c0000 0001 2193 314XUniversity of Glasgow, Glasgow, UK
| | - Serap Erkek
- grid.4709.a0000 0004 0495 846XEuropean Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Katherine A. Hoadley
- grid.10698.360000000122483208The University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Yong Hou
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, Shenzhen, China ,grid.507779.b0000 0004 4910 5858China National GeneBank-Shenzhen, Shenzhen, China
| | - Matthew R. Huska
- grid.419491.00000 0001 1014 0849Berlin Institute for Medical Systems Biology, Max Delbruck Center for Molecular Medicine, Berlin, Germany
| | - Helena Kilpinen
- grid.83440.3b0000000121901201University College London, London, UK
| | - Jan O. Korbel
- grid.4709.a0000 0004 0495 846XEuropean Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Maximillian G. Marin
- grid.205975.c0000 0001 0740 6917University of California, Santa Cruz, Santa Cruz, CA USA
| | - Julia Markowski
- grid.419491.00000 0001 1014 0849Berlin Institute for Medical Systems Biology, Max Delbruck Center for Molecular Medicine, Berlin, Germany
| | - Tannistha Nandi
- grid.418377.e0000 0004 0620 715XGenome Institute of Singapore, Singapore, Singapore
| | - Qiang Pan-Hammarström
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, Shenzhen, China ,grid.4714.60000 0004 1937 0626Karolinska Institutet, Stockholm, Sweden
| | - Chandra Sekhar Pedamallu
- grid.66859.340000 0004 0546 1623Broad Institute, Cambridge, MA USA ,grid.65499.370000 0001 2106 9910Dana-Farber Cancer Institute, Boston, MA USA ,grid.38142.3c000000041936754XHarvard Medical School, Boston, MA USA
| | - Reiner Siebert
- grid.410712.10000 0004 0473 882XUlm University and Ulm University Medical Center, Ulm, Germany
| | - Stefan G. Stark
- grid.5801.c0000 0001 2156 2780ETH Zurich, Zurich, Switzerland ,grid.51462.340000 0001 2171 9952Memorial Sloan Kettering Cancer Center, New York, NY USA ,grid.419765.80000 0001 2223 3006SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland ,grid.412004.30000 0004 0478 9977University Hospital Zurich, Zurich, Switzerland
| | - Hong Su
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, Shenzhen, China ,grid.507779.b0000 0004 4910 5858China National GeneBank-Shenzhen, Shenzhen, China
| | - Patrick Tan
- grid.418377.e0000 0004 0620 715XGenome Institute of Singapore, Singapore, Singapore ,grid.428397.30000 0004 0385 0924Duke-NUS Medical School, Singapore, Singapore
| | - Sebastian M. Waszak
- grid.4709.a0000 0004 0495 846XEuropean Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Christina Yung
- grid.17063.330000 0001 2157 2938Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Shida Zhu
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, Shenzhen, China ,grid.507779.b0000 0004 4910 5858China National GeneBank-Shenzhen, Shenzhen, China
| | - Philip Awadalla
- grid.17063.330000 0001 2157 2938Ontario Institute for Cancer Research, Toronto, Ontario, Canada ,grid.17063.330000 0001 2157 2938University of Toronto, Toronto, Ontario Canada
| | - Chad J. Creighton
- grid.39382.330000 0001 2160 926XBaylor College of Medicine, Houston, TX USA
| | - Matthew Meyerson
- grid.66859.340000 0004 0546 1623Broad Institute, Cambridge, MA USA ,grid.65499.370000 0001 2106 9910Dana-Farber Cancer Institute, Boston, MA USA ,grid.38142.3c000000041936754XHarvard Medical School, Boston, MA USA
| | | | - Kui Wu
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, Shenzhen, China ,grid.507779.b0000 0004 4910 5858China National GeneBank-Shenzhen, Shenzhen, China
| | - Huanming Yang
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, Shenzhen, China
| | | | - Alvis Brazma
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK.
| | - Angela N. Brooks
- grid.205975.c0000 0001 0740 6917University of California, Santa Cruz, Santa Cruz, CA USA ,grid.66859.340000 0004 0546 1623Broad Institute, Cambridge, MA USA ,grid.65499.370000 0001 2106 9910Dana-Farber Cancer Institute, Boston, MA USA
| | - Jonathan Göke
- grid.418377.e0000 0004 0620 715XGenome Institute of Singapore, Singapore, Singapore ,grid.410724.40000 0004 0620 9745National Cancer Centre Singapore, Singapore, Singapore
| | - Gunnar Rätsch
- ETH Zurich, Zurich, Switzerland. .,Memorial Sloan Kettering Cancer Center, New York, NY, USA. .,Weill Cornell Medical College, New York, NY, USA. .,SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland. .,University Hospital Zurich, Zurich, Switzerland.
| | - Roland F. Schwarz
- grid.225360.00000 0000 9709 7726European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK ,grid.419491.00000 0001 1014 0849Berlin Institute for Medical Systems Biology, Max Delbruck Center for Molecular Medicine, Berlin, Germany ,grid.7497.d0000 0004 0492 0584German Cancer Consortium (DKTK), partner site Berlin, Germany ,grid.7497.d0000 0004 0492 0584German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Oliver Stegle
- grid.225360.00000 0000 9709 7726European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK ,grid.4709.a0000 0004 0495 846XEuropean Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany ,grid.7497.d0000 0004 0492 0584German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Zemin Zhang
- grid.11135.370000 0001 2256 9319Peking University, Beijing, China
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Reyna MA, Haan D, Paczkowska M, Verbeke LPC, Vazquez M, Kahraman A, Pulido-Tamayo S, Barenboim J, Wadi L, Dhingra P, Shrestha R, Getz G, Lawrence MS, Pedersen JS, Rubin MA, Wheeler DA, Brunak S, Izarzugaza JMG, Khurana E, Marchal K, von Mering C, Sahinalp SC, Valencia A, Reimand J, Stuart JM, Raphael BJ. Author Correction: Pathway and network analysis of more than 2500 whole cancer genomes. Nat Commun 2022; 13:7566. [PMID: 36481610 PMCID: PMC9732045 DOI: 10.1038/s41467-022-32334-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Affiliation(s)
- Matthew A Reyna
- Department of Computer Science, Princeton University, Princeton, NJ, 08540, USA
- Department of Biomedical Informatics, Emory University, Atlanta, GA, 30322, USA
| | - David Haan
- Department of Biomolecular Engineering and UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, 95060, USA
| | - Marta Paczkowska
- Computational Biology Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Lieven P C Verbeke
- Department of Information Technology, IDLab, Ghent University, IMEC, Ghent, the Netherlands
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, the Netherlands
| | - Miguel Vazquez
- Barcelona Supercomputing Center (BSC), Barcelona, 08034, Spain
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Abdullah Kahraman
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, CH-8057, Zurich, Switzerland
- Department of Pathology and Molecular Pathology, University Hospital Zurich, CH-8091, Zurich, Switzerland
| | - Sergio Pulido-Tamayo
- Department of Information Technology, IDLab, Ghent University, IMEC, Ghent, the Netherlands
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, the Netherlands
| | - Jonathan Barenboim
- Computational Biology Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Lina Wadi
- Computational Biology Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Priyanka Dhingra
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Raunak Shrestha
- Vancouver Prostate Centre, 2660 Oak Street, Vancouver, BC, V6H 3Z6, Canada
| | - Gad Getz
- The Broad Institute of MIT and Harvard, Cambridge, MA, 02124, USA
- Massachusetts General Hospital Center for Cancer Research, Charlestown, MA, 02129, USA
- Harvard Medical School, 250 Longwood Avenue, Boston, MA, 02115, USA
- Massachusetts General Hospital, Department of Pathology, Boston, MA, 02114, USA
| | - Michael S Lawrence
- The Broad Institute of MIT and Harvard, Cambridge, MA, 02124, USA
- Massachusetts General Hospital Center for Cancer Research, Charlestown, MA, 02129, USA
| | - Jakob Skou Pedersen
- Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus, Denmark
- Bioinformatics Research Centre (BiRC), Aarhus University, Aarhus, Denmark
| | - Mark A Rubin
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10065, USA
| | - David A Wheeler
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Søren Brunak
- DTU Bioinformatics, Department of Bio and Health Informatics, Technical University of Denmark, Kemitorvet, 2800, Kongens Lyngby, Denmark
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200, Copenhagen, Denmark
| | - Jose M G Izarzugaza
- DTU Bioinformatics, Department of Bio and Health Informatics, Technical University of Denmark, Kemitorvet, 2800, Kongens Lyngby, Denmark
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200, Copenhagen, Denmark
| | - Ekta Khurana
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Kathleen Marchal
- Department of Information Technology, IDLab, Ghent University, IMEC, Ghent, the Netherlands
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, the Netherlands
| | - Christian von Mering
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, CH-8057, Zurich, Switzerland
| | - S Cenk Sahinalp
- Vancouver Prostate Centre, 2660 Oak Street, Vancouver, BC, V6H 3Z6, Canada
- Department of Computer Science, Indiana University, Bloomington, IN, 47405, USA
| | - Alfonso Valencia
- Barcelona Supercomputing Center (BSC), Barcelona, 08034, Spain
- ICREA, Barcelona, 08010, Spain
| | - Jüri Reimand
- Computational Biology Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada.
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
| | - Joshua M Stuart
- Department of Biomolecular Engineering and UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, 95060, USA.
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ, 08540, USA.
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Haan D, Guler G, Bergamaschi A, Volkmuth W, Levy S. Pancreatic cancer detection using 5-hydroxymethylation signatures in plasma-derived cell free DNA in high-risk patients with new onset diabetes. J Clin Oncol 2022. [DOI: 10.1200/jco.2022.40.4_suppl.539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
539 Background: Pancreatic cancer (PaCa) is the third leading cause of cancer death in the United States despite a low incidence rate. It is often diagnosed when cancer has already metastasized to distant organs. Late diagnosis deprives patients of potentially curative treatments such as surgery and impacts survival rates. People with new onset diabetes (NOD) are at 6-8 fold increased risk for PaCa compared to the general population. Indeed, 0.85% of patients with NOD will be diagnosed with PaCa within 3 years. This population of PaCa patients with NOD constitute 25% of all new pancreatic cancer diagnoses. Surveillance of the NOD population for PaCa presents an opportunity to shift PaCa diagnosis to earlier stage. Methods: Whole blood was obtained from a cohort of 167 PaCa patients and 490 patients with cancers other than PaCa as well as 836 non-cancer controls with and without NOD. Plasma was processed to isolate cfDNA and 5hmC libraries were generated and sequenced. 5hmC data is used to generate models for PaCa detection using Bluestar Genomics’s technology platform. Results: To investigate whether PaCa can be detected in plasma, we interrogated plasma-derived cfDNA hydroxymethylation in PaCa patients and non-cancer controls. Models trained using 5hmC-based biomarkers from cfDNA consistently performed with a mean test sensitivity of 61.1% [95% confidence interval (CI): 35.7% to 82.7%] and a test specificity of 97.6% (CI: 93% to 99.5%) measured across 50 cross validation iterations within the training data set, which was composed of 48.3% early stage (Stages I & II) disease. The final model was trained using all of the training data, yielding 58.4% (CI: 47.5% to 68.8%) sensitivity at 98% (CI: 96.5% to 99.0%) specificity. This model was then tested on an independent test set with 22 PaCa patients (51.7% early stage, 15 of which was NOD) and 123 non-cancer control patients (53 of which were NOD) and yielded a classification performance of 59.1% (CI: 36.4% to 79.3%) sensitivity at 95.9% (CI: 90.8% to 98.7%) specificity. The model performance in the subset of patient cohort with NOD was 53.3% (CI: 26.6% to 78.7%) sensitivity at 94.3% (CI: 84.3% to 98.8%) specificity. Lastly, sensitivity observed on an independent validation set, composed of 56 PaCa and 117 ITTP samples, was 46.4% (CI: 33.0% to 60.2%) with 100% (CI: 96.8 to 100%) specificity. Conclusions: Our results demonstrate PaCa detection in plasma-derived cfDNA using 5hmC profiles. Overall, the model performed consistently between the training and independent validation datasets. A larger clinical study is under development to clinically validate the model described in this study with the goal of identifying occult PaCa within the NOD population in order to enable earlier detection and thus improve patient outcomes.
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Creason A, Haan D, Dang K, Chiotti KE, Inkman M, Lamb A, Yu T, Hu Y, Norman TC, Buchanan A, van Baren MJ, Spangler R, Rollins MR, Spellman PT, Rozanov D, Zhang J, Maher CA, Caloian C, Watson JD, Uhrig S, Haas BJ, Jain M, Akeson M, Ahsen ME, Stolovitzky G, Guinney J, Boutros PC, Stuart JM, Ellrott K. A community challenge to evaluate RNA-seq, fusion detection, and isoform quantification methods for cancer discovery. Cell Syst 2021; 12:827-838.e5. [PMID: 34146471 PMCID: PMC8376800 DOI: 10.1016/j.cels.2021.05.021] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 09/15/2020] [Accepted: 05/25/2021] [Indexed: 02/03/2023]
Abstract
The accurate identification and quantitation of RNA isoforms present in the cancer transcriptome is key for analyses ranging from the inference of the impacts of somatic variants to pathway analysis to biomarker development and subtype discovery. The ICGC-TCGA DREAM Somatic Mutation Calling in RNA (SMC-RNA) challenge was a crowd-sourced effort to benchmark methods for RNA isoform quantification and fusion detection from bulk cancer RNA sequencing (RNA-seq) data. It concluded in 2018 with a comparison of 77 fusion detection entries and 65 isoform quantification entries on 51 synthetic tumors and 32 cell lines with spiked-in fusion constructs. We report the entries used to build this benchmark, the leaderboard results, and the experimental features associated with the accurate prediction of RNA species. This challenge required submissions to be in the form of containerized workflows, meaning each of the entries described is easily reusable through CWL and Docker containers at https://github.com/SMC-RNA-challenge. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Allison Creason
- Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, USA
| | - David Haan
- Biomolecular Engineering and UC Santa Cruz Genome Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | | | - Kami E. Chiotti
- Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, USA
| | - Matthew Inkman
- The Genome Institute, Washington University School of Medicine, 4444 Forest Park Avenue, St. Louis, MO 63110, USA
| | | | | | - Yin Hu
- Sage Bionetworks, Seattle, WA, USA
| | | | - Alex Buchanan
- Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, USA
| | - Marijke J. van Baren
- Biomolecular Engineering and UC Santa Cruz Genome Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Ryan Spangler
- Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, USA
| | - M. Rick Rollins
- Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, USA
| | - Paul T. Spellman
- Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, USA
| | - Dmitri Rozanov
- Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, USA
| | - Jin Zhang
- The Genome Institute, Washington University School of Medicine, 4444 Forest Park Avenue, St. Louis, MO 63110, USA
| | - Christopher A. Maher
- The Genome Institute, Washington University School of Medicine, 4444 Forest Park Avenue, St. Louis, MO 63110, USA
| | - Cristian Caloian
- Computational Biology, Ontario Institute for Cancer Research, Toronto, Canada
| | - John D. Watson
- Computational Biology, Ontario Institute for Cancer Research, Toronto, Canada
| | - Sebastian Uhrig
- Division of Applied Bioinformatics, German Cancer Research Center (DKFZ) and Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Brian J. Haas
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Miten Jain
- Biomolecular Engineering and UC Santa Cruz Genome Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Mark Akeson
- Biomolecular Engineering and UC Santa Cruz Genome Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Mehmet Eren Ahsen
- Icahn School of Medicine at Mount Sinai, Department of Genetics and Genomic Sciences, One Gustave Levy Place, New York, NY 1498, USA
| | | | - Gustavo Stolovitzky
- Icahn School of Medicine at Mount Sinai, Department of Genetics and Genomic Sciences, One Gustave Levy Place, New York, NY 1498, USA,IBM T.J. Watson Research Center, 1101 Kitchawan Road, Route 134, Yorktown Heights, NY 10598, USA
| | | | - Paul C. Boutros
- Computational Biology, Ontario Institute for Cancer Research, Toronto, Canada,Departments of Medical Biophysics and Pharmacology & Toxicology, University of Toronto, Toronto, Canada,Departments of Human Genetics and Urology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Joshua M. Stuart
- Biomolecular Engineering and UC Santa Cruz Genome Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Kyle Ellrott
- Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, USA,Lead contact,Correspondence:
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Thornton AM, Fang L, Lo A, McSharry M, Haan D, O’Brien C, Berger AH, Giannakis M, Brooks AN. eVIP2: Expression-based variant impact phenotyping to predict the function of gene variants. PLoS Comput Biol 2021; 17:e1009132. [PMID: 34214079 PMCID: PMC8281988 DOI: 10.1371/journal.pcbi.1009132] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Revised: 07/15/2021] [Accepted: 05/30/2021] [Indexed: 11/25/2022] Open
Abstract
While advancements in genome sequencing have identified millions of somatic mutations in cancer, their functional impact is poorly understood. We previously developed the expression-based variant impact phenotyping (eVIP) method to use gene expression data to characterize the function of gene variants. The eVIP method uses a decision tree-based algorithm to predict the functional impact of somatic variants by comparing gene expression signatures induced by introduction of wild-type (WT) versus mutant cDNAs in cell lines. The method distinguishes between variants that are gain-of-function, loss-of-function, change-of-function, or neutral. We present eVIP2, software that allows for pathway analysis (eVIP Pathways) and usage with RNA-seq data. To demonstrate the eVIP2 software and approach, we characterized two recurrent frameshift variants in RNF43, a negative regulator of Wnt signaling, frequently mutated in colorectal, gastric, and endometrial cancer. RNF43 WT, RNF43 R117fs, RNF43 G659fs, or GFP control cDNA were overexpressed in HEK293T cells. Analysis with eVIP2 predicted that the frameshift at position 117 was a loss-of-function mutation, as expected. The second frameshift at position 659 has been previously described as a passenger mutation that maintains the RNF43 WT function as a negative regulator of Wnt. Surprisingly, eVIP2 predicted G659fs to be a change-of-function mutation. Additional eVIP Pathways analysis of RNF43 G659fs predicted 10 pathways to be significantly altered, including TNF-α via NFκB signaling, KRAS signaling, and hypoxia, highlighting the benefit of a more comprehensive approach when determining the impact of gene variant function. To validate these predictions, we performed reporter assays and found that each pathway activated by expression of RNF43 G659fs, but not expression of RNF43 WT, was identified as impacted by eVIP2, supporting that RNF43 G659fs is a change-of-function mutation and its effect on the identified pathways. Pathway activation was further validated by Western blot analysis. Lastly, we show primary colon adenocarcinoma patient samples with R117fs and G659fs variants have transcriptional profiles similar to BRAF missense mutations with activated RAS/MAPK signaling, consistent with KRAS signaling pathways being GOF in both variants. The eVIP2 method is an important step towards overcoming the current challenge of variant interpretation in the implementation of precision medicine. eVIP2 is available at https://github.com/BrooksLabUCSC/eVIP2.
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Affiliation(s)
- Alexis M. Thornton
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, California, United States of America
- UCSC Genomics Institute, University of California Santa Cruz, Santa Cruz, California, United States of America
| | - Lishan Fang
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, United States of America
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts, United States of America
- Department of Orthopedics, The Eight Affiliated Hospital of Sun Yat-sen University, Shenzhen, China
| | - April Lo
- Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Maria McSharry
- Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - David Haan
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, California, United States of America
- UCSC Genomics Institute, University of California Santa Cruz, Santa Cruz, California, United States of America
| | - Casey O’Brien
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, United States of America
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts, United States of America
| | - Alice H. Berger
- Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Marios Giannakis
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, United States of America
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts, United States of America
| | - Angela N. Brooks
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, California, United States of America
- UCSC Genomics Institute, University of California Santa Cruz, Santa Cruz, California, United States of America
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Guler G, Bergamaschi A, Haan D, Kesling M, Ning Y, Ellison C, Gibb W, Antoine M, Nguyen A, Malta R, Fraire C, Woldeyohanne S, Scott A, Hazen K, Peters M, Sheard J, Volkmuth W, Bethel K, Levy S. Pancreatic cancer detection using EpiDetect signatures in plasma-derived cell free DNA in high-risk patients with new onset diabetes. J Clin Oncol 2021. [DOI: 10.1200/jco.2021.39.15_suppl.e16265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
e16265 Background: Pancreatic cancer (PaCa) is the third leading cause of cancer death in the United States despite its low incidence rate, owing to a 5-year survival rate of 10%. It is often asymptomatic in early stage, resulting in the majority of diagnoses occurring when cancer has already metastasized to distant organs. Late diagnosis deprives patients of potentially curative treatments such as surgery and impacts survival rates. Diabetes can be an early symptom of PaCa. Indeed, 25% of PaCa patients had a preceding diabetes diagnosis. Among all people with new onset diabetes (NOD), 0.85% will be diagnosed with PaCa within 3 years, which represents 6-8 fold increased risk for PaCa compared to the general population. Surveillance of the NOD population for PaCa presents an opportunity to shift PaCa diagnosis to earlier stage by finding it sooner. Methods: Whole blood was obtained from a cohort of 117 PaCa patients as well as 800 non-cancer controls with and without NOD. Plasma was processed to isolate cfDNA and 5hmC and low pass whole genome libraries were generated and sequenced. The EpiDetect assay combines 5hmC and whole genome sequencing data and were generated using Bluestar Genomics’s technology platform. Results: To investigate whether PaCa can be detected in plasma, we interrogated plasma-derived cfDNA epigenomic and genomic signal from PaCa patients and non-cancer controls. We first trained stacked ensemble models on PaCa and non-cancer samples utilizing 5hmC, fragmentation and CNV-based biomarkers from cfDNA. These models performed stably with a median of 72.8% sensitivity and 90.1% specificity measured across 25 outer fold iterations using the training data set, which was composed of 50% early stage (Stages I & II) disease. The final binomial ensemble model was trained using all of the training data, yielding an area under the receiver operating characteristic curve (auROC) of 0.9, with 75% sensitivity and 89% specificity. This model was then tested on an independent validation data set from 33 PaCa patients (24 with diabetes, 15 of which was NOD) and 202 non-cancer control patients (76 with diabetes, 51 of which was NOD) and yielded a classification performance auROC of 0.9 with 67% sensitivity at 92% specificity. Lastly, model performance in the subset of patient cohort with NOD only had an auROC of 0.87 with 60% sensitivity at 88% specificity. Conclusions: Our results indicate that 5hmC profiles along with CNV and fragmentation patterns from cfDNA can be used to detect PaCa in plasma-derived cfDNA. Overall, model performance was stable and consistent between the training and independent validation datasets. A larger clinical study is under development to investigate the utility of the model described in this pilot study in identifying occult PaCa within the NOD population, with the aim of shifting diagnosis to early stage and potentially improving patient outcomes.
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Haan D, Bergamaschi A, Ning Y, Gibb W, Kesling M, Pitea A, Nabiyouni M, Ellison C, Malta R, Nguyen A, Guler G, McCarthy E, Phillips T, Scott A, Hazen K, Sheard J, Peters M, Bethel K, Volkmuth W, Levy S. Genome-wide 5hmC profiles to enable cancer detection and tissue of origin classification in breast, colorectal, lung, ovarian, and pancreatic cancers. J Clin Oncol 2021. [DOI: 10.1200/jco.2021.39.15_suppl.3044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
3044 Background: Epigenomics assays have recently become popular tools for identification of molecular biomarkers, both in tissue and in plasma. In particular 5-hydroxymethyl-cytosine (5hmC) method, has been shown to enable the epigenomic regulation of gene expression and subsequent gene activity, with different patterns, across several tumor and normal tissues types. In this study we show that 5hmC profiles enable discrete classification of tumor and normal tissue for breast, colorectal, lung ovary and pancreas. Such classification was also recapitulated in cfDNA from patient with breast, colorectal, lung, ovarian and pancreatic cancers. Methods: DNA was isolated from 176 fresh frozen tissues from breast, colorectal, lung, ovary and pancreas (44 per tumor per tissue type and up to 11 tumor tissues for each stage (I-IV)) and up to 10 normal tissues per tissue type. cfDNA was isolated from plasma from 783 non-cancer individuals and 569 cancer patients. Plasma-isolated cfDNA and tumor genomic DNA, were enriched for the 5hmC fraction using chemical labelling, sequenced, and aligned to a reference genome to construct features sets of 5hmC patterns. Results: 5hmC multinomial logistic regression analysis was employed across tumor and normal tissues and identified a set of specific and discrete tumor and normal tissue gene-based features. This indicates that we can classify samples regardless of source, with a high degree of accuracy, based on tissue of origin and also distinguish between normal and tumor status.Next, we employed a stacked ensemble machine learning algorithm combining multiple logistic regression models across diverse feature sets to the cfDNA dataset composed of 783 non cancers and 569 cancers comprising 67 breast, 118 colorectal, 210 Lung, 71 ovarian and 100 pancreatic cancers. We identified a genomic signature that enable the classification of non-cancer versus cancers with an outer fold cross validation sensitivity of 49% (CI 45%-53%) at 99% specificity. Further, individual cancer outer fold cross validation sensitivity at 99% specificity, was measured as follows: breast 30% (CI 119% -42%); colorectal 41% (CI 32%-50%); lung 49% (CI 42%-56%); ovarian 72% (CI 60-82%); pancreatic 56% (CI 46%-66%). Conclusions: This study demonstrates that 5hmC profiles can distinguish cancer and normal tissues based on their origin. Further, 5hmC changes in cfDNA enables detection of the several cancer types: breast, colorectal, lung, ovarian and pancreatic cancers. Our technology provides a non-invasive tool for cancer detection with low risk sample collection enabling improved compliance than current screening methods. Among other utilities, we believe our technology could be applied to asymptomatic high-risk individuals thus enabling enrichment for those subjects that most need a diagnostic imaging follow up.
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Carrot-Zhang J, Yao X, Devarakonda S, Deshpande A, Damrauer JS, Silva TC, Wong CK, Choi HY, Felau I, Robertson AG, Castro MA, Bao L, Rheinbay E, Liu EM, Trieu T, Haan D, Yau C, Hinoue T, Liu Y, Shapira O, Kumar K, Mungall KL, Zhang H, Lee JJK, Berger A, Gao GF, Zhitomirsky B, Liang WW, Zhou M, Moorthi S, Berger AH, Collisson EA, Zody MC, Ding L, Cherniack AD, Getz G, Elemento O, Benz CC, Stuart J, Zenklusen J, Beroukhim R, Chang JC, Campbell JD, Hayes DN, Yang L, Laird PW, Weinstein JN, Kwiatkowski DJ, Tsao MS, Travis WD, Khurana E, Berman BP, Hoadley KA, Robine N, Meyerson M, Govindan R, Imielinski M. Whole-genome characterization of lung adenocarcinomas lacking alterations in the RTK/RAS/RAF pathway. Cell Rep 2021; 34:108784. [PMID: 33626341 PMCID: PMC8608252 DOI: 10.1016/j.celrep.2021.108784] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
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Carrot-Zhang J, Yao X, Devarakonda S, Deshpande A, Damrauer JS, Silva TC, Wong CK, Choi HY, Felau I, Robertson AG, Castro MAA, Bao L, Rheinbay E, Liu EM, Trieu T, Haan D, Yau C, Hinoue T, Liu Y, Shapira O, Kumar K, Mungall KL, Zhang H, Lee JJK, Berger A, Gao GF, Zhitomirsky B, Liang WW, Zhou M, Moorthi S, Berger AH, Collisson EA, Zody MC, Ding L, Cherniack AD, Getz G, Elemento O, Benz CC, Stuart J, Zenklusen JC, Beroukhim R, Chang JC, Campbell JD, Hayes DN, Yang L, Laird PW, Weinstein JN, Kwiatkowski DJ, Tsao MS, Travis WD, Khurana E, Berman BP, Hoadley KA, Robine N, Meyerson M, Govindan R, Imielinski M. Whole-genome characterization of lung adenocarcinomas lacking the RTK/RAS/RAF pathway. Cell Rep 2021; 34:108707. [PMID: 33535033 PMCID: PMC8009291 DOI: 10.1016/j.celrep.2021.108707] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 09/08/2020] [Accepted: 01/08/2021] [Indexed: 12/13/2022] Open
Abstract
RTK/RAS/RAF pathway alterations (RPAs) are a hallmark of lung adenocarcinoma (LUAD). In this study, we use whole-genome sequencing (WGS) of 85 cases found to be RPA(-) by previous studies from The Cancer Genome Atlas (TCGA) to characterize the minority of LUADs lacking apparent alterations in this pathway. We show that WGS analysis uncovers RPA(+) in 28 (33%) of the 85 samples. Among the remaining 57 cases, we observe focal deletions targeting the promoter or transcription start site of STK11 (n = 7) or KEAP1 (n = 3), and promoter mutations associated with the increased expression of ILF2 (n = 6). We also identify complex structural variations associated with high-level copy number amplifications. Moreover, an enrichment of focal deletions is found in TP53 mutant cases. Our results indicate that RPA(-) cases demonstrate tumor suppressor deletions and genome instability, but lack unique or recurrent genetic lesions compensating for the lack of RPAs. Larger WGS studies of RPA(-) cases are required to understand this important LUAD subset.
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Affiliation(s)
- Jian Carrot-Zhang
- Dana-Farber Cancer Institute, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Xiaotong Yao
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA; New York Genome Center, New York, NY, USA; Tri-institutional Ph.D. Program in Computational Biology and Medicine, New York, NY, USA; Caryl and Israel Englander Institute for Precision Medicine and Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Siddhartha Devarakonda
- Section of Medical Oncology, Division of Oncology, Washington University School of Medicine, St. Louis, MO, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Aditya Deshpande
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA; New York Genome Center, New York, NY, USA; Tri-institutional Ph.D. Program in Computational Biology and Medicine, New York, NY, USA; Caryl and Israel Englander Institute for Precision Medicine and Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Jeffrey S Damrauer
- Department of Genetics, Computational Medicine Program, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Tiago Chedraoui Silva
- Center for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Christopher K Wong
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Hyo Young Choi
- University of Tennessee Health Science Center, UTHSC Center for Cancer Research, TN, USA
| | - Ina Felau
- National Cancer Institute, Bethesda, MD, USA
| | - A Gordon Robertson
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | - Mauro A A Castro
- Bioinformatics and Systems Biology Laboratory, Federal University of Paraná, Curitiba, PR, Brazil
| | - Lisui Bao
- Ben May Department for Cancer Research, University of Chicago, Chicago, IL, USA
| | - Esther Rheinbay
- Broad Institute of MIT and Harvard, Cambridge, MA, USA; Massachusetts General Hospital Cancer Center, Boston, MA, USA
| | - Eric Minwei Liu
- Caryl and Israel Englander Institute for Precision Medicine and Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Tuan Trieu
- Caryl and Israel Englander Institute for Precision Medicine and Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - David Haan
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Christina Yau
- University of California, San Francisco, San Francisco, CA, USA; Buck Institute for Research on Aging, Novato, CA, USA
| | | | - Yuexin Liu
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ofer Shapira
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kiran Kumar
- Dana-Farber Cancer Institute, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Karen L Mungall
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | - Hailei Zhang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Ashton Berger
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Galen F Gao
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Binyamin Zhitomirsky
- Broad Institute of MIT and Harvard, Cambridge, MA, USA; Massachusetts General Hospital Cancer Center, Boston, MA, USA
| | - Wen-Wei Liang
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, USA
| | - Meng Zhou
- Dana-Farber Cancer Institute, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Harvard Medical School, Boston, MA, USA
| | | | - Alice H Berger
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | | | | | - Li Ding
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, USA
| | - Andrew D Cherniack
- Dana-Farber Cancer Institute, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Gad Getz
- Broad Institute of MIT and Harvard, Cambridge, MA, USA; Massachusetts General Hospital Cancer Center, Boston, MA, USA
| | - Olivier Elemento
- Tri-institutional Ph.D. Program in Computational Biology and Medicine, New York, NY, USA; Caryl and Israel Englander Institute for Precision Medicine and Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | | | - Josh Stuart
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA
| | | | - Rameen Beroukhim
- Dana-Farber Cancer Institute, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Jason C Chang
- Thoracic Pathology, Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Joshua D Campbell
- Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA
| | - D Neil Hayes
- University of Tennessee Health Science Center, UTHSC Center for Cancer Research, TN, USA
| | - Lixing Yang
- Ben May Department for Cancer Research, University of Chicago, Chicago, IL, USA
| | | | - John N Weinstein
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | - Ming S Tsao
- Department of Pathology, University Health Network, Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - William D Travis
- Thoracic Pathology, Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ekta Khurana
- Caryl and Israel Englander Institute for Precision Medicine and Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Benjamin P Berman
- Center for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, Hebrew University, Jerusalem, Israel
| | - Katherine A Hoadley
- Department of Genetics, Computational Medicine Program, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | | | - Matthew Meyerson
- Dana-Farber Cancer Institute, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Harvard Medical School, Boston, MA, USA.
| | - Ramaswamy Govindan
- Section of Medical Oncology, Division of Oncology, Washington University School of Medicine, St. Louis, MO, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA.
| | - Marcin Imielinski
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA; New York Genome Center, New York, NY, USA; Caryl and Israel Englander Institute for Precision Medicine and Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA.
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Bergamaschi A, Ku J, Ning Y, Collin F, Ellison C, Phillips T, McCarthy E, Wang W, Antoine M, Haan D, Scott A, Lloyd P, Guler G, Ashworth A, Quake S, Levy S. Abstract 783: Epigenomic detection of multiple cancers in plasma derived cell free DNA. Cancer Res 2020. [DOI: 10.1158/1538-7445.am2020-783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Background: Our feasibility study employed a novel genomic detection methodology that enriches 5-hydroxymethylcytosine (5hmC) loci in cell free DNA (cfDNA) from the plasma of cancer patients using click chemistry coupled with sequencing and machine learning based classification methods. These classification methods were developed to detect the presence of disease in the plasma of cancer and control subjects. Cancer and control patient cfDNA cohorts were accrued from multiple sites consisting of 48 breast, 55 lung, 32 prostate and 2 pancreatic datasets consisting of 41 and 53 cancer subjects (Set 1 and 2). In addition, a control cohort of 260 subjects (non-cancer) was employed to match cancer patient demographics (age, sex and smoking status) in a case-control study design.
Methods: Machine learning methods, applied to each cancer case cohort individually, with a balancing non-cancer cohort, were able to classify cancer and control samples. Measures of predictive performance using 5-fold cross validation coupled with out-of-fold Area Under the Receiver Operating Characteristic Curve (AUROC) measures were employed. Gene sets selected as part of biomarker discovery were further analyzed for disease relevance using pathway analysis tools (GSEA, mSigDB).
Results: 260 controls and 229 cancers from four disease types (breast, lung, pancreas and prostate) were analyzed; more than 60% of cancer patients had early stage disease (I or II). Predictive performance, employing AUROC measures, was established for breast (0.89), lung (0.84), pancreas (set 1 - 0.95 and 2 - 0.93) and prostate (0.83). The genes defining each of these predictive models were enriched for pathways relevant to disease specific etiology, notably in the control of gene regulation in these same pathways. The breast cancer cohort consisted primarily of stage I and II patients including tumors < 2 cm and these samples exhibited a higher prediction probability score. The prostate cancer cohort consisted of both indolent and aggressive disease sample and prediction performance was equally high for both (AUROC for indolent vs aggressive was 0.81 and 0.77, respectively).
Conclusions: These findings suggest that 5hmC changes in cfDNA enable non-invasive detection of early stage breast, pancreatic, prostate, and lung cancers. Furthermore, 5hmC profiling in cfDNA may enable the prediction of clinically relevant features such as tumor size in breast adenocarcinoma or indolent disease in prostate cancer. Finally, this study identified a suite of 5hmC biomarkers that may be further validated in larger, and more diverse, patient cohorts.
Citation Format: Anna Bergamaschi, Jeremy Ku, Yuhong Ning, Francois Collin, Chris Ellison, Tierney Phillips, Erin McCarthy, Wendy Wang, Michael Antoine, David Haan, Aaron Scott, Paul Lloyd, Gulfem Guler, Alan Ashworth, Stephen Quake, Samuel Levy. Epigenomic detection of multiple cancers in plasma derived cell free DNA [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 783.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | - Alan Ashworth
- 2UCSF Helen Diller Family Comprehensive Cancer Cent, San Francisco, CA
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Guler G, Haan D, Ning Y, Ku J, McCarthy E, Phillips T, Antoine M, Bergamaschi A, Lloyd P, Scott A, Ellison C, Levy S. Monitoring immunotherapy response in non-small cell lung cancer patients using 5-hydroxymethylcytosine signatures in circulating cell free DNA. J Clin Oncol 2020. [DOI: 10.1200/jco.2020.38.15_suppl.e21505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
e21505 Background: Liquid biopsies are gaining prominence for not only cancer diagnosis but also patient monitoring. Mutational signals derived from cell-free DNA (cfDNA) show promise to assess response to cancer treatment, including immunotherapy. However, reliance of these methods on mutational data from tissue biopsies limit their applicability when a tumor biopsy is unavailable, or when mutational landscape of tumor changes under the selective pressures of cancer drug treatment. Epigenomic approaches have the potential to address these shortcomings. Methods: Blood draws were obtained from a cohort of non-small cell lung cancer (NSCLC) patients (n = 19) who went on to anti-PD1 treatment prior to therapy start and while on therapy. cfDNA was isolated from plasma and was subsequently processed to generate 5hmC genome-wide profiles. Results: We analyzed cfDNA from NSCLC patients undergoing anti-PD1 therapy to investigate whether immunotherapy response can be detected from plasma. Using a predictive model trained on lung cancer and non-cancer samples, we were able to detect changes in prediction scores in patient treated with immunotherapy that were consistent with RECIST. Patients with progressive disease (n = 3), determined by RECIST, had prediction scores that increased while they received treatment. On the other hand, majority of the patients that exhibited partial response to treatment (n = 12) had predictive scores that decreased with treatment, again consistent with RECIST. Furthermore, score changes consistent with RECIST was observed one cycle prior to the RECIST timepoint in all except one patient, where an extra blood draw after baseline was available (n = 7). Annotation of the regions that account for differential scoring identified enhancer, 5’UTR and promoter regions. Comparison of partial responders to patients with progressive disease revealed genes involved in metastasis, oncogenes and tumor suppressors that change in opposing directions between these patient groups, consistent with the underlying biology. Conclusions: Our results suggest that 5hmC profiles from cfDNA can be used to determine immunotherapy response in non-small cell lung cancer patients. Compared with mutation based liquid biopsy methods to assess response, epigenomics-based methods have the advantage of being agnostic to starting tumor mutations, and not relying on a mutational analysis from tumor biopsy. Future work will help determine applicability of this method to other kinds of therapies and cancer types.
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Bergamaschi A, Haan D, Ku J, Ning Y, Ellison C, Guler G, Phillips T, McCarthy E, Antoine M, Nguyen A, Scott A, Lloyd P, Ashworth A, Bethel K, Levy S. Effect of detection of epigenomic changes in plasma-derived cell-free DNA on multicancer classification. J Clin Oncol 2020. [DOI: 10.1200/jco.2020.38.15_suppl.1539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
1539 Background: Epigenomic changes in DNA methylation patterns are more precisely delineated by active demethylation events as marked by 5-hydroxymethylation (5hmC) of cytosine residues. 5hmC appears to be dynamically modulated in tumor tissues and can be employed as a cancer biomarker. Strategies which interrogate 5hmC genome-wide patterns in a liquid biopsy context may provide efficient and precise technology for early cancer screening and detection. In this study we identified genome-wide 5hmC changes in plasma based circulating free DNA (cfDNA) from breast, colorectal, lung, pancreatic and prostate cancer patients versus non-cancer individuals. Methods: cfDNA was isolated from plasma, enriched for the 5hmC fraction using novel click-chemistry protocol for labelling followed by sequencing and alignment to a reference genome to construct features sets of 5hmC patterns. Regularized classification models were constructed to classify cancer samples apart from non-cancer. Results: > 500 non-cancer individuals and > 500 cancer patients across five cancer types (breast, colorectal, lung, pancreas and prostate) were included in this study. About 60% of the cancer samples were early stage disease (I or II). The ability to classify non-cancer versus cancer patients was evaluated by 5-fold cross validation of our trained prediction models. Our models were able to classify all breast cancer with a test auROC of 0.86 while prediction model classification for ER negative samples had an auROC of 0.92. Colorectal performance auROC was 0.9; lung auROC = 0.92, pancreatic auROC = 0.97 and prostate auROC = 0.91. Overall sensitivity range, when allowing 2% false positive, was between 85% and 52%. Further using 5hmC signal in blood we were able to identify several signaling pathways specifically relevant to the biology of the cancers investigated. Conclusions: These findings further demonstrate that 5hmC changes in cfDNA enable non-invasive detection of breast, colorectal, lung pancreatic, and prostate cancers. Further, 5hmC signals enabled the identification of a suite of cancer signaling pathways differentially enriched in cancers versus non-cancers. These data suggest that dynamic changes in tumor cell methylation, detectable through 5-hydroxymethylation, are contained in the circulating blood and signal active disease biology.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | - Alan Ashworth
- UCSF Helen Diller Family Comprehensive Cancer Center, San Francisco, CA
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Reyna MA, Haan D, Paczkowska M, Verbeke LPC, Vazquez M, Kahraman A, Pulido-Tamayo S, Barenboim J, Wadi L, Dhingra P, Shrestha R, Getz G, Lawrence MS, Pedersen JS, Rubin MA, Wheeler DA, Brunak S, Izarzugaza JMG, Khurana E, Marchal K, von Mering C, Sahinalp SC, Valencia A, Reimand J, Stuart JM, Raphael BJ. Pathway and network analysis of more than 2500 whole cancer genomes. Nat Commun 2020; 11:729. [PMID: 32024854 PMCID: PMC7002574 DOI: 10.1038/s41467-020-14367-0] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Accepted: 12/18/2019] [Indexed: 12/14/2022] Open
Abstract
The catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notably TERT promoter mutations, have been reported. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancer across 38 tumor types, we perform multi-faceted pathway and network analyses of non-coding mutations across 2583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project that was motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes. While few non-coding genomic elements are recurrently mutated in this cohort, we identify 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression in TP53, TLE4, and TCF4. We find that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing is primarily altered by non-coding mutations in this cohort, and samples containing non-coding mutations in well-known RNA splicing factors exhibit similar gene expression signatures as samples with coding mutations in these genes. These analyses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments.
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Affiliation(s)
- Matthew A Reyna
- Department of Computer Science, Princeton University, Princeton, NJ, 08540, USA
- Department of Biomedical Informatics, Emory University, Atlanta, GA, 30322, USA
| | - David Haan
- Department of Biomolecular Engineering and UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, 95060, USA
| | - Marta Paczkowska
- Computational Biology Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Lieven P C Verbeke
- Department of Information Technology, IDLab, Ghent University, IMEC, Ghent, the Netherlands
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, the Netherlands
| | - Miguel Vazquez
- Barcelona Supercomputing Center (BSC), Barcelona, 08034, Spain
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Abdullah Kahraman
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, CH-8057, Zurich, Switzerland
- Department of Pathology and Molecular Pathology, University Hospital Zurich, CH-8091, Zurich, Switzerland
| | - Sergio Pulido-Tamayo
- Department of Information Technology, IDLab, Ghent University, IMEC, Ghent, the Netherlands
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, the Netherlands
| | - Jonathan Barenboim
- Computational Biology Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Lina Wadi
- Computational Biology Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Priyanka Dhingra
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Raunak Shrestha
- Vancouver Prostate Centre, 2660 Oak Street, Vancouver, BC, V6H 3Z6, Canada
| | - Gad Getz
- The Broad Institute of MIT and Harvard, Cambridge, MA, 02124, USA
- Massachusetts General Hospital Center for Cancer Research, Charlestown, MA, 02129, USA
- Harvard Medical School, 250 Longwood Avenue, Boston, MA, 02115, USA
- Massachusetts General Hospital, Department of Pathology, Boston, MA, 02114, USA
| | - Michael S Lawrence
- The Broad Institute of MIT and Harvard, Cambridge, MA, 02124, USA
- Massachusetts General Hospital Center for Cancer Research, Charlestown, MA, 02129, USA
| | - Jakob Skou Pedersen
- Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus, Denmark
- Bioinformatics Research Centre (BiRC), Aarhus University, Aarhus, Denmark
| | - Mark A Rubin
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10065, USA
| | - David A Wheeler
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Søren Brunak
- DTU Bioinformatics, Department of Bio and Health Informatics, Technical University of Denmark, Kemitorvet, 2800, Kongens Lyngby, Denmark
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200, Copenhagen, Denmark
| | - Jose M G Izarzugaza
- DTU Bioinformatics, Department of Bio and Health Informatics, Technical University of Denmark, Kemitorvet, 2800, Kongens Lyngby, Denmark
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200, Copenhagen, Denmark
| | - Ekta Khurana
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Kathleen Marchal
- Department of Information Technology, IDLab, Ghent University, IMEC, Ghent, the Netherlands
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, the Netherlands
| | - Christian von Mering
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, CH-8057, Zurich, Switzerland
| | - S Cenk Sahinalp
- Vancouver Prostate Centre, 2660 Oak Street, Vancouver, BC, V6H 3Z6, Canada
- Department of Computer Science, Indiana University, Bloomington, IN, 47405, USA
| | - Alfonso Valencia
- Barcelona Supercomputing Center (BSC), Barcelona, 08034, Spain
- ICREA, Barcelona, 08010, Spain
| | - Jüri Reimand
- Computational Biology Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada.
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
| | - Joshua M Stuart
- Department of Biomolecular Engineering and UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, 95060, USA.
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ, 08540, USA.
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Aaltonen LA, Abascal F, Abeshouse A, Aburatani H, Adams DJ, Agrawal N, Ahn KS, Ahn SM, Aikata H, Akbani R, Akdemir KC, Al-Ahmadie H, Al-Sedairy ST, Al-Shahrour F, Alawi M, Albert M, Aldape K, Alexandrov LB, Ally A, Alsop K, Alvarez EG, Amary F, Amin SB, Aminou B, Ammerpohl O, Anderson MJ, Ang Y, Antonello D, Anur P, Aparicio S, Appelbaum EL, Arai Y, Aretz A, Arihiro K, Ariizumi SI, Armenia J, Arnould L, Asa S, Assenov Y, Atwal G, Aukema S, Auman JT, Aure MRR, Awadalla P, Aymerich M, Bader GD, Baez-Ortega A, Bailey MH, Bailey PJ, Balasundaram M, Balu S, Bandopadhayay P, Banks RE, Barbi S, Barbour AP, Barenboim J, Barnholtz-Sloan J, Barr H, Barrera E, Bartlett J, Bartolome J, Bassi C, Bathe OF, Baumhoer D, Bavi P, Baylin SB, Bazant W, Beardsmore D, Beck TA, Behjati S, Behren A, Niu B, Bell C, Beltran S, Benz C, Berchuck A, Bergmann AK, Bergstrom EN, Berman BP, Berney DM, Bernhart SH, Beroukhim R, Berrios M, Bersani S, Bertl J, Betancourt M, Bhandari V, Bhosle SG, Biankin AV, Bieg M, Bigner D, Binder H, Birney E, Birrer M, Biswas NK, Bjerkehagen B, Bodenheimer T, Boice L, Bonizzato G, De Bono JS, Boot A, Bootwalla MS, Borg A, Borkhardt A, Boroevich KA, Borozan I, Borst C, Bosenberg M, Bosio M, Boultwood J, Bourque G, Boutros PC, Bova GS, Bowen DT, Bowlby R, Bowtell DDL, Boyault S, Boyce R, Boyd J, Brazma A, Brennan P, Brewer DS, Brinkman AB, Bristow RG, Broaddus RR, Brock JE, Brock M, Broeks A, Brooks AN, Brooks D, Brors B, Brunak S, Bruxner TJC, Bruzos AL, Buchanan A, Buchhalter I, Buchholz C, Bullman S, Burke H, Burkhardt B, Burns KH, Busanovich J, Bustamante CD, Butler AP, Butte AJ, Byrne NJ, Børresen-Dale AL, Caesar-Johnson SJ, Cafferkey A, Cahill D, Calabrese C, Caldas C, Calvo F, Camacho N, Campbell PJ, Campo E, Cantù C, Cao S, Carey TE, Carlevaro-Fita J, Carlsen R, Cataldo I, Cazzola M, Cebon J, Cerfolio R, Chadwick DE, Chakravarty D, Chalmers D, Chan CWY, Chan K, Chan-Seng-Yue M, Chandan VS, Chang DK, Chanock SJ, Chantrill LA, Chateigner A, Chatterjee N, Chayama K, Chen HW, Chen J, Chen K, Chen Y, Chen Z, Cherniack AD, Chien J, Chiew YE, Chin SF, Cho J, Cho S, Choi JK, Choi W, Chomienne C, Chong Z, Choo SP, Chou A, Christ AN, Christie EL, Chuah E, Cibulskis C, Cibulskis K, Cingarlini S, Clapham P, Claviez A, Cleary S, Cloonan N, Cmero M, Collins CC, Connor AA, Cooke SL, Cooper CS, Cope L, Corbo V, Cordes MG, Cordner SM, Cortés-Ciriano I, Covington K, Cowin PA, Craft B, Craft D, Creighton CJ, Cun Y, Curley E, Cutcutache I, Czajka K, Czerniak B, Dagg RA, Danilova L, Davi MV, Davidson NR, Davies H, Davis IJ, Davis-Dusenbery BN, Dawson KJ, De La Vega FM, De Paoli-Iseppi R, Defreitas T, Tos APD, Delaneau O, Demchok JA, Demeulemeester J, Demidov GM, Demircioğlu D, Dennis NM, Denroche RE, Dentro SC, Desai N, Deshpande V, Deshwar AG, Desmedt C, Deu-Pons J, Dhalla N, Dhani NC, Dhingra P, Dhir R, DiBiase A, Diamanti K, Ding L, Ding S, Dinh HQ, Dirix L, Doddapaneni H, Donmez N, Dow MT, Drapkin R, Drechsel O, Drews RM, Serge S, Dudderidge T, Dueso-Barroso A, Dunford AJ, Dunn M, Dursi LJ, Duthie FR, Dutton-Regester K, Eagles J, Easton DF, Edmonds S, Edwards PA, Edwards SE, Eeles RA, Ehinger A, Eils J, Eils R, El-Naggar A, Eldridge M, Ellrott K, Erkek S, Escaramis G, Espiritu SMG, Estivill X, Etemadmoghadam D, Eyfjord JE, Faltas BM, Fan D, Fan Y, Faquin WC, Farcas C, Fassan M, Fatima A, Favero F, Fayzullaev N, Felau I, Fereday S, Ferguson ML, Ferretti V, Feuerbach L, Field MA, Fink JL, Finocchiaro G, Fisher C, Fittall MW, Fitzgerald A, Fitzgerald RC, Flanagan AM, Fleshner NE, Flicek P, Foekens JA, Fong KM, Fonseca NA, Foster CS, Fox NS, Fraser M, Frazer S, Frenkel-Morgenstern M, Friedman W, Frigola J, Fronick CC, Fujimoto A, Fujita M, Fukayama M, Fulton LA, Fulton RS, Furuta M, Futreal PA, Füllgrabe A, Gabriel SB, Gallinger S, Gambacorti-Passerini C, Gao J, Gao S, Garraway L, Garred Ø, Garrison E, Garsed DW, Gehlenborg N, Gelpi JLL, George J, Gerhard DS, Gerhauser C, Gershenwald JE, Gerstein M, Gerstung M, Getz G, Ghori M, Ghossein R, Giama NH, Gibbs RA, Gibson B, Gill AJ, Gill P, Giri DD, Glodzik D, Gnanapragasam VJ, Goebler ME, Goldman MJ, Gomez C, Gonzalez S, Gonzalez-Perez A, Gordenin DA, Gossage J, Gotoh K, Govindan R, Grabau D, Graham JS, Grant RC, Green AR, Green E, Greger L, Grehan N, Grimaldi S, Grimmond SM, Grossman RL, Grundhoff A, Gundem G, Guo Q, Gupta M, Gupta S, Gut IG, Gut M, Göke J, Ha G, Haake A, Haan D, Haas S, Haase K, Haber JE, Habermann N, Hach F, Haider S, Hama N, Hamdy FC, Hamilton A, Hamilton MP, Han L, Hanna GB, Hansmann M, Haradhvala NJ, Harismendy O, Harliwong I, Harmanci AO, Harrington E, Hasegawa T, Haussler D, Hawkins S, Hayami S, Hayashi S, Hayes DN, Hayes SJ, Hayward NK, Hazell S, He Y, Heath AP, Heath SC, Hedley D, Hegde AM, Heiman DI, Heinold MC, Heins Z, Heisler LE, Hellstrom-Lindberg E, Helmy M, Heo SG, Hepperla AJ, Heredia-Genestar JM, Herrmann C, Hersey P, Hess JM, Hilmarsdottir H, Hinton J, Hirano S, Hiraoka N, Hoadley KA, Hobolth A, Hodzic E, Hoell JI, Hoffmann S, Hofmann O, Holbrook A, Holik AZ, Hollingsworth MA, Holmes O, Holt RA, Hong C, Hong EP, Hong JH, Hooijer GK, Hornshøj H, Hosoda F, Hou Y, Hovestadt V, Howat W, Hoyle AP, Hruban RH, Hu J, Hu T, Hua X, Huang KL, Huang M, Huang MN, Huang V, Huang Y, Huber W, Hudson TJ, Hummel M, Hung JA, Huntsman D, Hupp TR, Huse J, Huska MR, Hutter B, Hutter CM, Hübschmann D, Iacobuzio-Donahue CA, Imbusch CD, Imielinski M, Imoto S, Isaacs WB, Isaev K, Ishikawa S, Iskar M, Islam SMA, Ittmann M, Ivkovic S, Izarzugaza JMG, Jacquemier J, Jakrot V, Jamieson NB, Jang GH, Jang SJ, Jayaseelan JC, Jayasinghe R, Jefferys SR, Jegalian K, Jennings JL, Jeon SH, Jerman L, Ji Y, Jiao W, Johansson PA, Johns AL, Johns J, Johnson R, Johnson TA, Jolly C, Joly Y, Jonasson JG, Jones CD, Jones DR, Jones DTW, Jones N, Jones SJM, Jonkers J, Ju YS, Juhl H, Jung J, Juul M, Juul RI, Juul S, Jäger N, Kabbe R, Kahles A, Kahraman A, Kaiser VB, Kakavand H, Kalimuthu S, von Kalle C, Kang KJ, Karaszi K, Karlan B, Karlić R, Karsch D, Kasaian K, Kassahn KS, Katai H, Kato M, Katoh H, Kawakami Y, Kay JD, Kazakoff SH, Kazanov MD, Keays M, Kebebew E, Kefford RF, Kellis M, Kench JG, Kennedy CJ, Kerssemakers JNA, Khoo D, Khoo V, Khuntikeo N, Khurana E, Kilpinen H, Kim HK, Kim HL, Kim HY, Kim H, Kim J, Kim J, Kim JK, Kim Y, King TA, Klapper W, Kleinheinz K, Klimczak LJ, Knappskog S, Kneba M, Knoppers BM, Koh Y, Komorowski J, Komura D, Komura M, Kong G, Kool M, Korbel JO, Korchina V, Korshunov A, Koscher M, Koster R, Kote-Jarai Z, Koures A, Kovacevic M, Kremeyer B, Kretzmer H, Kreuz M, Krishnamurthy S, Kube D, Kumar K, Kumar P, Kumar S, Kumar Y, Kundra R, Kübler K, Küppers R, Lagergren J, Lai PH, Laird PW, Lakhani SR, Lalansingh CM, Lalonde E, Lamaze FC, Lambert A, Lander E, Landgraf P, Landoni L, Langerød A, Lanzós A, Larsimont D, Larsson E, Lathrop M, Lau LMS, Lawerenz C, Lawlor RT, Lawrence MS, Lazar AJ, Lazic AM, Le X, Lee D, Lee D, Lee EA, Lee HJ, Lee JJK, Lee JY, Lee J, Lee MTM, Lee-Six H, Lehmann KV, Lehrach H, Lenze D, Leonard CR, Leongamornlert DA, Leshchiner I, Letourneau L, Letunic I, Levine DA, Lewis L, Ley T, Li C, Li CH, Li HI, Li J, Li L, Li S, Li S, Li X, Li X, Li X, Li Y, Liang H, Liang SB, Lichter P, Lin P, Lin Z, Linehan WM, Lingjærde OC, Liu D, Liu EM, Liu FFF, Liu F, Liu J, Liu X, Livingstone J, Livitz D, Livni N, Lochovsky L, Loeffler M, Long GV, Lopez-Guillermo A, Lou S, Louis DN, Lovat LB, Lu Y, Lu YJ, Lu Y, Luchini C, Lungu I, Luo X, Luxton HJ, Lynch AG, Lype L, López C, López-Otín C, Ma EZ, Ma Y, MacGrogan G, MacRae S, Macintyre G, Madsen T, Maejima K, Mafficini A, Maglinte DT, Maitra A, Majumder PP, Malcovati L, Malikic S, Malleo G, Mann GJ, Mantovani-Löffler L, Marchal K, Marchegiani G, Mardis ER, Margolin AA, Marin MG, Markowetz F, Markowski J, Marks J, Marques-Bonet T, Marra MA, Marsden L, Martens JWM, Martin S, Martin-Subero JI, Martincorena I, Martinez-Fundichely A, Maruvka YE, Mashl RJ, Massie CE, Matthew TJ, Matthews L, Mayer E, Mayes S, Mayo M, Mbabaali F, McCune K, McDermott U, McGillivray PD, McLellan MD, McPherson JD, McPherson JR, McPherson TA, Meier SR, Meng A, Meng S, Menzies A, Merrett ND, Merson S, Meyerson M, Meyerson W, Mieczkowski PA, Mihaiescu GL, Mijalkovic S, Mikkelsen T, Milella M, Mileshkin L, Miller CA, Miller DK, Miller JK, Mills GB, Milovanovic A, Minner S, Miotto M, Arnau GM, Mirabello L, Mitchell C, Mitchell TJ, Miyano S, Miyoshi N, Mizuno S, Molnár-Gábor F, Moore MJ, Moore RA, Morganella S, Morris QD, Morrison C, Mose LE, Moser CD, Muiños F, Mularoni L, Mungall AJ, Mungall K, Musgrove EA, Mustonen V, Mutch D, Muyas F, Muzny DM, Muñoz A, Myers J, Myklebost O, Möller P, Nagae G, Nagrial AM, Nahal-Bose HK, Nakagama H, Nakagawa H, Nakamura H, Nakamura T, Nakano K, Nandi T, Nangalia J, Nastic M, Navarro A, Navarro FCP, Neal DE, Nettekoven G, Newell F, Newhouse SJ, Newton Y, Ng AWT, Ng A, Nicholson J, Nicol D, Nie Y, Nielsen GP, Nielsen MM, Nik-Zainal S, Noble MS, Nones K, Northcott PA, Notta F, O’Connor BD, O’Donnell P, O’Donovan M, O’Meara S, O’Neill BP, O’Neill JR, Ocana D, Ochoa A, Oesper L, Ogden C, Ohdan H, Ohi K, Ohno-Machado L, Oien KA, Ojesina AI, Ojima H, Okusaka T, Omberg L, Ong CK, Ossowski S, Ott G, Ouellette BFF, P’ng C, Paczkowska M, Paiella S, Pairojkul C, Pajic M, Pan-Hammarström Q, Papaemmanuil E, Papatheodorou I, Paramasivam N, Park JW, Park JW, Park K, Park K, Park PJ, Parker JS, Parsons SL, Pass H, Pasternack D, Pastore A, Patch AM, Pauporté I, Pea A, Pearson JV, Pedamallu CS, Pedersen JS, Pederzoli P, Peifer M, Pennell NA, Perou CM, Perry MD, Petersen GM, Peto M, Petrelli N, Petryszak R, Pfister SM, Phillips M, Pich O, Pickett HA, Pihl TD, Pillay N, Pinder S, Pinese M, Pinho AV, Pitkänen E, Pivot X, Piñeiro-Yáñez E, Planko L, Plass C, Polak P, Pons T, Popescu I, Potapova O, Prasad A, Preston SR, Prinz M, Pritchard AL, Prokopec SD, Provenzano E, Puente XS, Puig S, Puiggròs M, Pulido-Tamayo S, Pupo GM, Purdie CA, Quinn MC, Rabionet R, Rader JS, Radlwimmer B, Radovic P, Raeder B, Raine KM, Ramakrishna M, Ramakrishnan K, Ramalingam S, Raphael BJ, Rathmell WK, Rausch T, Reifenberger G, Reimand J, Reis-Filho J, Reuter V, Reyes-Salazar I, Reyna MA, Reynolds SM, Rheinbay E, Riazalhosseini Y, Richardson AL, Richter J, Ringel M, Ringnér M, Rino Y, Rippe K, Roach J, Roberts LR, Roberts ND, Roberts SA, Robertson AG, Robertson AJ, Rodriguez JB, Rodriguez-Martin B, Rodríguez-González FG, Roehrl MHA, Rohde M, Rokutan H, Romieu G, Rooman I, Roques T, Rosebrock D, Rosenberg M, Rosenstiel PC, Rosenwald A, Rowe EW, Royo R, Rozen SG, Rubanova Y, Rubin MA, Rubio-Perez C, Rudneva VA, Rusev BC, Ruzzenente A, Rätsch G, Sabarinathan R, Sabelnykova VY, Sadeghi S, Sahinalp SC, Saini N, Saito-Adachi M, Saksena G, Salcedo A, Salgado R, Salichos L, Sallari R, Saller C, Salvia R, Sam M, Samra JS, Sanchez-Vega F, Sander C, Sanders G, Sarin R, Sarrafi I, Sasaki-Oku A, Sauer T, Sauter G, Saw RPM, Scardoni M, Scarlett CJ, Scarpa A, Scelo G, Schadendorf D, Schein JE, Schilhabel MB, Schlesner M, Schlomm T, Schmidt HK, Schramm SJ, Schreiber S, Schultz N, Schumacher SE, Schwarz RF, Scolyer RA, Scott D, Scully R, Seethala R, Segre AV, Selander I, Semple CA, Senbabaoglu Y, Sengupta S, Sereni E, Serra S, Sgroi DC, Shackleton M, Shah NC, Shahabi S, Shang CA, Shang P, Shapira O, Shelton T, Shen C, Shen H, Shepherd R, Shi R, Shi Y, Shiah YJ, Shibata T, Shih J, Shimizu E, Shimizu K, Shin SJ, Shiraishi Y, Shmaya T, Shmulevich I, Shorser SI, Short C, Shrestha R, Shringarpure SS, Shriver C, Shuai S, Sidiropoulos N, Siebert R, Sieuwerts AM, Sieverling L, Signoretti S, Sikora KO, Simbolo M, Simon R, Simons JV, Simpson JT, Simpson PT, Singer S, Sinnott-Armstrong N, Sipahimalani P, Skelly TJ, Smid M, Smith J, Smith-McCune K, Socci ND, Sofia HJ, Soloway MG, Song L, Sood AK, Sothi S, Sotiriou C, Soulette CM, Span PN, Spellman PT, Sperandio N, Spillane AJ, Spiro O, Spring J, Staaf J, Stadler PF, Staib P, Stark SG, Stebbings L, Stefánsson ÓA, Stegle O, Stein LD, Stenhouse A, Stewart C, Stilgenbauer S, Stobbe MD, Stratton MR, Stretch JR, Struck AJ, Stuart JM, Stunnenberg HG, Su H, Su X, Sun RX, Sungalee S, Susak H, Suzuki A, Sweep F, Szczepanowski M, Sültmann H, Yugawa T, Tam A, Tamborero D, Tan BKT, Tan D, Tan P, Tanaka H, Taniguchi H, Tanskanen TJ, Tarabichi M, Tarnuzzer R, Tarpey P, Taschuk ML, Tatsuno K, Tavaré S, Taylor DF, Taylor-Weiner A, Teague JW, Teh BT, Tembe V, Temes J, Thai K, Thayer SP, Thiessen N, Thomas G, Thomas S, Thompson A, Thompson AM, Thompson JFF, Thompson RH, Thorne H, Thorne LB, Thorogood A, Tiao G, Tijanic N, Timms LE, Tirabosco R, Tojo M, Tommasi S, Toon CW, Toprak UH, Torrents D, Tortora G, Tost J, Totoki Y, Townend D, Traficante N, Treilleux I, Trotta JR, Trümper LHP, Tsao M, Tsunoda T, Tubio JMC, Tucker O, Turkington R, Turner DJ, Tutt A, Ueno M, Ueno NT, Umbricht C, Umer HM, Underwood TJ, Urban L, Urushidate T, Ushiku T, Uusküla-Reimand L, Valencia A, Van Den Berg DJ, Van Laere S, Van Loo P, Van Meir EG, Van den Eynden GG, Van der Kwast T, Vasudev N, Vazquez M, Vedururu R, Veluvolu U, Vembu S, Verbeke LPC, Vermeulen P, Verrill C, Viari A, Vicente D, Vicentini C, VijayRaghavan K, Viksna J, Vilain RE, Villasante I, Vincent-Salomon A, Visakorpi T, Voet D, Vyas P, Vázquez-García I, Waddell NM, Waddell N, Wadelius C, Wadi L, Wagener R, Wala JA, Wang J, Wang J, Wang L, Wang Q, Wang W, Wang Y, Wang Z, Waring PM, Warnatz HJ, Warrell J, Warren AY, Waszak SM, Wedge DC, Weichenhan D, Weinberger P, Weinstein JN, Weischenfeldt J, Weisenberger DJ, Welch I, Wendl MC, Werner J, Whalley JP, Wheeler DA, Whitaker HC, Wigle D, Wilkerson MD, Williams A, Wilmott JS, Wilson GW, Wilson JM, Wilson RK, Winterhoff B, Wintersinger JA, Wiznerowicz M, Wolf S, Wong BH, Wong T, Wong W, Woo Y, Wood S, Wouters BG, Wright AJ, Wright DW, Wright MH, Wu CL, Wu DY, Wu G, Wu J, Wu K, Wu Y, Wu Z, Xi L, Xia T, Xiang Q, Xiao X, Xing R, Xiong H, Xu Q, Xu Y, Xue H, Yachida S, Yakneen S, Yamaguchi R, Yamaguchi TN, Yamamoto M, Yamamoto S, Yamaue H, Yang F, Yang H, Yang JY, Yang L, Yang L, Yang S, Yang TP, Yang Y, Yao X, Yaspo ML, Yates L, Yau C, Ye C, Ye K, Yellapantula VD, Yoon CJ, Yoon SS, Yousif F, Yu J, Yu K, Yu W, Yu Y, Yuan K, Yuan Y, Yuen D, Yung CK, Zaikova O, Zamora J, Zapatka M, Zenklusen JC, Zenz T, Zeps N, Zhang CZ, Zhang F, Zhang H, Zhang H, Zhang H, Zhang J, Zhang J, Zhang J, Zhang X, Zhang X, Zhang Y, Zhang Z, Zhao Z, Zheng L, Zheng X, Zhou W, Zhou Y, Zhu B, Zhu H, Zhu J, Zhu S, Zou L, Zou X, deFazio A, van As N, van Deurzen CHM, van de Vijver MJ, van’t Veer L, von Mering C. Pan-cancer analysis of whole genomes. Nature 2020; 578:82-93. [PMID: 32025007 PMCID: PMC7025898 DOI: 10.1038/s41586-020-1969-6] [Citation(s) in RCA: 1435] [Impact Index Per Article: 358.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2018] [Accepted: 12/11/2019] [Indexed: 02/07/2023]
Abstract
Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1-3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10-18.
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Haan D, Tao R, Friedl V, Anastopoulos IN, Wong CK, Weinstein AS, Stuart JM. Using Transcriptional Signatures to Find Cancer Drivers with LURE. Pac Symp Biocomput 2020; 25:343-354. [PMID: 31797609 PMCID: PMC6924983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
Cancer genome projects have produced multidimensional datasets on thousands of samples. Yet, depending on the tumor type, 5-50% of samples have no known driving event. We introduce a semi-supervised method called Learning UnRealized Events (LURE) that uses a progressive label learning framework and minimum spanning analysis to predict cancer drivers based on their altered samples sharing a gene expression signature with the samples of a known event. We demonstrate the utility of the method on the TCGA Pan-Cancer Atlas dataset for which it produced a high-confidence result relating 59 new connections to 18 known mutation events including alterations in the same gene, family, and pathway. We give examples of predicted drivers involved in TP53, telomere maintenance, and MAPK/RTK signaling pathways. LURE identifies connections between genes with no known prior relationship, some of which may offer clues for targeting specific forms of cancer. Code and Supplemental Material are available on the LURE website: https://sysbiowiki.soe.ucsc.edu/lure.
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Affiliation(s)
- David Haan
- Dept. of Biomolecular Engineering and UC Santa Cruz Genomics Institute University Of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Ruikang Tao
- Dept. of Biomolecular Engineering and UC Santa Cruz Genomics Institute University Of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Verena Friedl
- Dept. of Biomolecular Engineering and UC Santa Cruz Genomics Institute University Of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Ioannis N Anastopoulos
- Dept. of Biomolecular Engineering and UC Santa Cruz Genomics Institute University Of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Christopher K Wong
- Dept. of Biomolecular Engineering and UC Santa Cruz Genomics Institute University Of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Alana S Weinstein
- Dept. of Biomolecular Engineering and UC Santa Cruz Genomics Institute University Of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Joshua M Stuart
- Dept. of Biomolecular Engineering and UC Santa Cruz Genomics Institute University Of California, Santa Cruz, Santa Cruz, CA 95064, USA
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Rice ES, Kohno S, John JS, Pham S, Howard J, Lareau LF, O'Connell BL, Hickey G, Armstrong J, Deran A, Fiddes I, Platt RN, Gresham C, McCarthy F, Kern C, Haan D, Phan T, Schmidt C, Sanford JR, Ray DA, Paten B, Guillette LJ, Green RE. Improved genome assembly of American alligator genome reveals conserved architecture of estrogen signaling. Genome Res 2017; 27:686-696. [PMID: 28137821 PMCID: PMC5411764 DOI: 10.1101/gr.213595.116] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Accepted: 12/13/2016] [Indexed: 12/12/2022]
Abstract
The American alligator, Alligator mississippiensis, like all crocodilians, has temperature-dependent sex determination, in which the sex of an embryo is determined by the incubation temperature of the egg during a critical period of development. The lack of genetic differences between male and female alligators leaves open the question of how the genes responsible for sex determination and differentiation are regulated. Insight into this question comes from the fact that exposing an embryo incubated at male-producing temperature to estrogen causes it to develop ovaries. Because estrogen response elements are known to regulate genes over long distances, a contiguous genome assembly is crucial for predicting and understanding their impact. We present an improved assembly of the American alligator genome, scaffolded with in vitro proximity ligation (Chicago) data. We use this assembly to scaffold two other crocodilian genomes based on synteny. We perform RNA sequencing of tissues from American alligator embryos to find genes that are differentially expressed between embryos incubated at male- versus female-producing temperature. Finally, we use the improved contiguity of our assembly along with the current model of CTCF-mediated chromatin looping to predict regions of the genome likely to contain estrogen-responsive genes. We find that these regions are significantly enriched for genes with female-biased expression in developing gonads after the critical period during which sex is determined by incubation temperature. We thus conclude that estrogen signaling is a major driver of female-biased gene expression in the post-temperature sensitive period gonads.
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Affiliation(s)
- Edward S Rice
- Department of Biomolecular Engineering, University of California, Santa Cruz, California 95064, USA
| | - Satomi Kohno
- Department of Biology, St. Cloud State University, St. Cloud, Minnesota 56301, USA
| | - John St John
- Driver Group, LLC, San Francisco, California 94158, USA
| | - Son Pham
- BioTuring, Incorporated, San Diego, California 92121, USA
| | - Jonathan Howard
- Department of Biochemistry, Stanford University, Stanford, California 94305, USA
| | - Liana F Lareau
- California Institute for Quantitative Biosciences, University of California, Berkeley, California 94720, USA
| | - Brendan L O'Connell
- Department of Biomolecular Engineering, University of California, Santa Cruz, California 95064, USA.,Dovetail Genomics, LLC, Santa Cruz, California 95060, USA
| | - Glenn Hickey
- Department of Biomolecular Engineering, University of California, Santa Cruz, California 95064, USA
| | - Joel Armstrong
- Department of Biomolecular Engineering, University of California, Santa Cruz, California 95064, USA
| | - Alden Deran
- Department of Biomolecular Engineering, University of California, Santa Cruz, California 95064, USA
| | - Ian Fiddes
- Department of Biomolecular Engineering, University of California, Santa Cruz, California 95064, USA
| | - Roy N Platt
- Department of Biological Sciences, Texas Tech University, Lubbock, Texas 79409, USA
| | - Cathy Gresham
- Institute for Genomics, Biocomputing & Biotechnology, Mississippi State University, Mississippi State, Mississippi 39762, USA
| | - Fiona McCarthy
- School of Animal and Comparative Biomedical Sciences, University of Arizona, Tucson, Arizona 85721, USA
| | - Colin Kern
- Department of Animal Science, University of California, Davis, California 95616, USA
| | - David Haan
- Department of Biomolecular Engineering, University of California, Santa Cruz, California 95064, USA
| | - Tan Phan
- HCM University of Science, Ho Chí Minh, Vietnam 748500
| | - Carl Schmidt
- Department of Animal and Food Sciences, University of Delaware, Newark, Delaware 19717, USA
| | - Jeremy R Sanford
- Department of Molecular, Cell, and Developmental Biology, University of California, Santa Cruz, California 95064, USA
| | - David A Ray
- Department of Biological Sciences, Texas Tech University, Lubbock, Texas 79409, USA
| | - Benedict Paten
- Center for Biomolecular Science and Engineering, University of California, Santa Cruz, California 95064, USA
| | - Louis J Guillette
- Department of Obstetrics and Gynecology, Marine Biomedicine and Environmental Science Center, Hollings Marine Laboratory, Medical University of South Carolina, Charleston, South Carolina 29412, USA
| | - Richard E Green
- Department of Biomolecular Engineering, University of California, Santa Cruz, California 95064, USA.,California Institute for Quantitative Biosciences, University of California, Berkeley, California 94720, USA.,Dovetail Genomics, LLC, Santa Cruz, California 95060, USA
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20
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Harders H, Haan D, Heisig N, Tilsner V. Zur Objektivierung der Lagerungsprobe nach Ratschow durch die Infrarotthermographie. J Vasc Res 2008. [DOI: 10.1159/000157697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
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21
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Ottervanger JP, Haan D, Gans SJ, Hoorntje JC, Stricker BH. [Bronchospasm, apnea and heart arrest following dipyridamole perfusion scintigraphy]. Ned Tijdschr Geneeskd 1993; 137:142-3. [PMID: 8423863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
The occurrence of fatal respiratory insufficiency following dipyridamole-thallium imaging is described. The patient, a 67-year-old man, had a history of chronic obstructive lung disease. Since patients with a history of chronic obstructive lung disease have an increased risk of developing bronchospasm after dipyridamole infusion, it is advised to be cautious in performing dipyridamole-thallium imaging in these patients. Dobutamine may be an acceptable alternative to dipyridamole in these patients.
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Abstract
Cholesterol embolism after left heart catheterisation by the femoral approach was diagnosed in seven men (mean age 59.6 years) out of a total of 4587 catheterisations. Diabetes was present in four patients, systemic hypertension in three, and signs of extensive atherosclerosis in six; five patients were taking anticoagulant drugs. Acute pain in the legs or abdomen occurred in six patients, two of whom had abdominal angina; renal failure was present in six patients, cutaneous manifestations in five, and a cholesterol embolus was seen in the retina in one. Six out of six patients had an appreciable increase in the erythrocyte sedimentation rate and five out of five had eosinophilia within a week of catheterisation. Renal failure was progressive in five patients, one of whom required haemodialysis. Three patients required amputation of the toes because of gangrene. Four patients died within four and a half months of catheterisation from causes not directly related to cholesterol embolism. At necropsy cholesterol emboli were found in all four patients. Cholesterol embolism is a rare but serious complication of left heart catheterisation.
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23
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Haan D. [Reports of the study group for intensive care in internal medicine]. Internist (Berl) 1973; 14:Suppl 4:16-8. [PMID: 4577214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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24
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Hagedoorn A, Haan D, Tiesinga G, Vonhoff DJ, Buys JJ, Hooft RH, van Rhijn S. Binocular vision (the flip-flop phenomenon). Am J Ophthalmol 1972; 74:907-14. [PMID: 4644738 DOI: 10.1016/0002-9394(72)91211-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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25
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Hagedoorn A, Haan D. [Inverse compimetry]. Ned Tijdschr Geneeskd 1972; 116:867-8. [PMID: 5027848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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26
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Dörner V, Augustin J, Buchwald HP, Haan D, Lanser KG, Runge M, Sill V. [Experiences with the beta receptor blockader ICI 50172]. Med Welt 1971; 41:1587-92. [PMID: 5116331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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27
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Haan D, Dörner V. [Diagnosis of cardiogenic shock]. Med Welt 1971; 22:1192-1194. [PMID: 5566149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
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28
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Haan D, Augustin HJ. [Therapy of cardiogenic shock]. Med Welt 1971; 22:1202-5. [PMID: 5566154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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29
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Ulmer, Fricker, Kuhlo W, Mittelbach F, Haan D, G�b A, Gericke D, Ehrhart H, Theisen. Buchbesprechungen. J Mol Med (Berl) 1971. [DOI: 10.1007/bf01494073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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30
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Ossenberg FW, Martin W, Saegler J, Haan D. [Changes in enzyme activities during poisoning]. Cah Med 1970; 11:1055-9. [PMID: 5485217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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31
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Ossenberg FW, Martin W, Saegler J, Haan D. [Variations of enzymatic activity during poisoning]. Rev Clin Esp 1970; 118:521-4. [PMID: 5487330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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32
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Ossenberg FW, Martin W, Saegler J, Haan D. [Enzyme abnormalities in poisoning]. Munch Med Wochenschr 1970; 112:1730-3. [PMID: 5468822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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33
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Jordan A, Runge M, Haan D. [Medical and nursing problems in an internal medicine intensive care unit]. Dtsch Schwesternztg 1970; 23:388-90. [PMID: 5202000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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34
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35
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Haan D, Cayé P. [Registration possibilities in telemetric and long-term electrocardiograms]. Med Welt 1970; 12:494-5. [PMID: 5444238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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36
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Freyberger H, Haan D, Müller-Wieland K. [Areas of psychosomatic tasks in intensive care units]. Internist (Berl) 1969; 10:240-3. [PMID: 4911473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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37
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Bartelheimer H, Haan D, Müller-Wieland K. [Organization and control of intensive therapy in internal medicine]. Internist (Berl) 1969; 10:173-80. [PMID: 4916477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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38
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Haan D. [Early diagnosis of cardial shock]. Ther Umsch 1968; 25:363-8. [PMID: 5712183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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39
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Heinz N, Haan D, Tilsner V. [Influencing of the experimental hypoxia-induced cardiac hypertrophy in the mouse using digitoxin]. Z Kreislaufforsch 1968; 57:675-7. [PMID: 4233698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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40
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Haan D. [Gradual build-up of the equipment for supervision facilities in mecical intensive care units]. Med Klin 1968; 63:633-7. [PMID: 5704897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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41
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Haan D. [Results of a new method for the registration of central venous blood pressure]. Arch Kreislaufforsch 1967; 53:192-264. [PMID: 4876160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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42
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43
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44
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Harders H, Tilsner V, Heisig N, Haan D. [Demonstration of effect of vasoactive substances by infrared thermography]. Med Klin 1967; 62:566-7. [PMID: 5599931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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45
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Sonne H, Haan D. [Examination of pacemaker therapy through extended electrocardiography]. Z Kreislaufforsch 1967; 56:285-94. [PMID: 5588088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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46
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Haan D. [Continuous automatic recording of blood pressure after administration of a new antihypertensive substance]. Arzneimittelforschung 1966; 16:1180-3. [PMID: 6014739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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47
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Haan D, Tilsner V. [Poisoning with neuroleptics and antidepressants?]. Med Klin 1966; 61:1181-1186. [PMID: 5993138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
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48
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49
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Haan D. [Hypotension and cerebrovascular ischemia syndrome--their treatment with a depot vasoactive agent]. Z Kreislaufforsch 1965; 54:1122-32. [PMID: 5887171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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50
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Tilsner V, Haan D. [Case report on thrombolytic therapy of myocardial infarct]. Arztl Forsch 1965; 19:555. [PMID: 5898950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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