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Brown M, O'Connor D, Turkington R, Eatock M, Vince R, Hulme C, Bowdery R, Robinson R, Wadsley J, Maraveyas A, Prue G. Feasibility of delivering supervised exercise training following surgical resection and during adjuvant chemotherapy for pancreatic ductal adenocarcinoma (PRECISE): a case series. BMC Sports Sci Med Rehabil 2023; 15:116. [PMID: 37735664 PMCID: PMC10514993 DOI: 10.1186/s13102-023-00722-3] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 09/05/2023] [Indexed: 09/23/2023]
Abstract
INTRODUCTION Pancreatic ductal adenocarcinoma (PDAC) is an aggressive neoplasm, with surgical resection and adjuvant chemotherapy the only curative treatment. Treatment-related toxicities place a considerable burden on patients although exercise training has shown promise is helping to manage such adversities and facilitate rehabilitation. The feasibility and safety of exercise training as a supportive therapy during adjuvant chemotherapy remains unknown. METHODS Patients with PDAC were screened post-surgical resection and enrolled in a 16-week, progressive, concurrent exercise programme alongside their chemotherapy regimen. Feasibility was the primary objective detailing recruitment, retention and adherence rates throughout as well as the safety and fidelity of the intervention. Secondarily, the impact on functional fitness and patient-reported outcomes was captured at baseline, post-intervention and 3-month follow up. RESULTS Eight patients consented to participate in this trial, with five proceeding to enrol in exercise training. Concurrent exercise training is feasible and safe during adjuvant chemotherapy and prevented an expected decline in functional fitness and patient-reported outcomes during this time. DISCUSSION This case series provides preliminary evidence that concurrent exercise training during adjuvant therapy is safe, feasible and well tolerated, preventing an expected decline in functional fitness, muscular strength and health-related quality of life (HRQoL). Given the adverse effects of treatment, these findings are promising and provide further evidence for the inclusion of exercise training as a standard of care for surgical rehabilitation and managing treatment-related toxicities. Future research should explore the impact of exercise training during neoadjuvant chemotherapy, with prehabilitation now standard practice for borderline resectable disease. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT04305067, prospectively registered 12/03/2020, https://classic. CLINICALTRIALS gov/ct2/show/NCT04305067 .
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Affiliation(s)
- Malcolm Brown
- School of Nursing and Midwifery, Queen's University Belfast Medical Biology Centre, 97 Lisburn Road, Belfast, BT9 7BL, UK.
| | - Dominic O'Connor
- School of Health Sciences, The University of Nottingham, Nottingham, England, UK
| | - Richard Turkington
- The Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, Northern Ireland, UK
- The Northern Ireland Cancer Centre, Belfast Health and Social Care Trust, Belfast, Northern Ireland, UK
| | - Martin Eatock
- The Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, Northern Ireland, UK
- The Northern Ireland Cancer Centre, Belfast Health and Social Care Trust, Belfast, Northern Ireland, UK
| | - Rebecca Vince
- School of Sport, Exercise and Rehabilitation Sciences, University of Hull, Hull, England, UK
| | - Claire Hulme
- Department of Health and Community Sciences, University of Exeter Medical School, Exeter, England, UK
| | - Roy Bowdery
- Pancreatic Cancer UK Research Involvement Network, London, England, UK
| | - Rebecca Robinson
- Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, England, UK
| | - Jonathan Wadsley
- Department of Oncology and Metabolism, The Medical School, University of Sheffield, Sheffield, England, UK
| | | | - Gillian Prue
- School of Nursing and Midwifery, Queen's University Belfast Medical Biology Centre, 97 Lisburn Road, Belfast, BT9 7BL, UK
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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 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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, 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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, 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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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Moore J, Green M, Deere H, Santaolalla A, Evans R, Elshafie M, Lavery A, McGuigan A, McManus D, Horne J, Walker R, Mir H, Terlizzo M, Kamarajah S, Van Hemelrijck M, Maisey N, Lumsden A, Ngan S, Kelly M, Baker C, Kumar S, Lagergren J, Allum W, Gossage J, Griffiths E, Grabsch H, Turkington R, Underwood T, Smyth E, Fitzgerald R, Cunningham D, Davies A. OGC O02 The prognostic effect of pathological lymph node regression after neoadjuvant chemotherapy for oesophageal adenocarcinoma – a multicentre study. Br J Surg 2022. [DOI: 10.1093/bjs/znac404.034] [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: 12/12/2022]
Abstract
Abstract
Background
The prognostic benefits of primary tumour and lymph node (LN) downstaging after neoadjuvant chemotherapy for oesophageal adenocarcinoma are well described. Pathological primary tumour regression grading (TRG) is widely used in the assessment of response to chemotherapy and has been shown to have prognostic value in this patient group. However, there is a lack of robust evidence regarding the prognostic effect of pathological response in LN despite emerging evidence of a discrepancy, up to 25% of patients in some studies, between TRG in the primary tumour and response in regional LNs. Although primary tumour regression is routinely documented as part of the standard pathological reporting of oesophagectomy specimens, LN regression is generally overlooked despite it's potential prognostic value. The aim of this study was to investigate the relationship between pathological response in LN, tumour recurrence and survival.
Methods
Multicentre cohort study including 763 patients with oesophageal adenocarcinoma treated with neoadjuvant chemotherapy followed by surgery at Guy's and St Thomas’ NHS Foundation Trust (NHS-FT), The Royal Marsden NHS-FT, University Hospitals Birmingham NHS-FT, University Hospital Southampton NHS-FT and Belfast Health and Social Care Trust. Tumour regression was assessed in the primary tumour (as described by Mandard) and, retrospectively, in LNs retrieved from oesophagectomy specimens. LN were graded according to the proportion of fibrosis and residual tumour providing a LN regression score (LNRS). LNRS 1, complete response; LNRS 2, < 10% remaining tumour; LNRS 3, 10–50% remaining tumour; LNRS 4, > 50% viable tumour; LNRS 5, no evidence of response. Regression was defined as a LNRS of 1–3. Patients were classified as LN negative (no evidence of tumour or regression in any LN), complete LN-responders (evidence of regression ≥1 LN, no residual tumour in any LN), partial LN-responders (evidence of regression ≥1 LN with residual tumour ≥1 LN) and LN non-responders (no or minimal regression in any LN). Survival analysis was performed using multivariable Cox regression providing hazard ratios (HR) with 95% confidence intervals (CI) adjusting for age, gender, chemotherapy regimen, clinical stage, tumour grade, lympho-vascular invasion and primary tumour response.
Results
Mean age was 63 years with the majority male (86.2%). In total, 17,930 LN from 763 patients were analysed for evidence of response to chemotherapy. Overall, 243 (31.8%) patients were classified as LN negative, 62 (8.1%) as complete LN-responders, 155 (20.3%) as partial LN-responders and 303 (39.7%) as LN non-responders. Less than half (322/763, 42.2%) of patients demonstrated a pathological response in the primary tumour (Mandard score 1–3). Some patients had a LN response in the absence of a response in the primary tumour (97/431, 22.5%). Multivariable Cox regression survival analysis demonstrated improved overall survival in complete LN-responders (HR 0.37 95% CI 0.24–0.58), partial LN-responders (HR 0.70 95% CI 0.55–0.89) and LN negative patients (HR 0.34 95% CI 0.26–0.44) compared to LN non-responders. Similar results were observed for disease-free survival (complete LN-responders, HR 0.34 95% CI 0.22–0.53; partial LN-responders, HR 0.74 95% CI 0.58–0.93; LN negative, HR 0.33 95% CI 0.25–0.42). Rates of tumour recurrence were lower in patients who demonstrated a LN response or had negative LN (LN negative 23.0% vs complete LN-responders 19.4% vs partial LN-responders 50.3% vs LN non-responders 66.7%, p<0.001).
Conclusions
In this cohort of patients with oeosphageal adenocarcinoma treated with neoadjuvant chemotherapy prior to surgical resection, LN regression was a strong predictive factor for better survival. This relationship was independent of primary tumour response, which was discordant in a significant number of patients. Complete LN-responders had equivalent survival to those with negative LN. Complete and partial LN-responders had better survival than LN non-responders. Evaluation and documentation of LN regression should be considered during the standard pathological reporting of oesophagectomy specimens.
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Affiliation(s)
- Jonathan Moore
- Guy's and St Thomas’ NHS Foundation Trust , London , United Kingdom
- King's College London , London , United Kingdom
| | - Michael Green
- Guy's and St Thomas’ NHS Foundation Trust , London , United Kingdom
| | - Harriet Deere
- Guy's and St Thomas’ NHS Foundation Trust , London , United Kingdom
| | | | - Richard Evans
- University Hospitals Birmingham NHS Foundation Trust , Birmingham , United Kingdom
| | - Mona Elshafie
- University Hospitals Birmingham NHS Foundation Trust , Birmingham , United Kingdom
| | - Anita Lavery
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast , Belfast , United Kingdom
| | - Andrew McGuigan
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast , Belfast , United Kingdom
| | - Damian McManus
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast , Belfast , United Kingdom
| | - Joanne Horne
- University Hospital Southampton NHS Foundation Trust , Southampton , United Kingdom
| | - Robert Walker
- University Hospital Southampton NHS Foundation Trust , Southampton , United Kingdom
| | - Hira Mir
- The Royal Marsden NHS Foundation Trust , London , United Kingdom
| | - Monica Terlizzo
- The Royal Marsden NHS Foundation Trust , London , United Kingdom
| | - Sivesh Kamarajah
- University Hospitals Birmingham NHS Foundation Trust , Birmingham , United Kingdom
| | | | - Nick Maisey
- Guy's and St Thomas’ NHS Foundation Trust , London , United Kingdom
| | - Ailsa Lumsden
- Guy's and St Thomas’ NHS Foundation Trust , London , United Kingdom
| | - Sarah Ngan
- Guy's and St Thomas’ NHS Foundation Trust , London , United Kingdom
| | - Mark Kelly
- Guy's and St Thomas’ NHS Foundation Trust , London , United Kingdom
- King's College London , London , United Kingdom
| | - Cara Baker
- Guy's and St Thomas’ NHS Foundation Trust , London , United Kingdom
- King's College London , London , United Kingdom
| | - Sacheen Kumar
- The Royal Marsden NHS Foundation Trust , London , United Kingdom
| | - Jesper Lagergren
- Guy's and St Thomas’ NHS Foundation Trust , London , United Kingdom
- King's College London , London , United Kingdom
- Karolinsa Institutet , Stockholm , Sweden
| | - William Allum
- The Royal Marsden NHS Foundation Trust , London , United Kingdom
| | - James Gossage
- Guy's and St Thomas’ NHS Foundation Trust , London , United Kingdom
- King's College London , London , United Kingdom
| | - Ewan Griffiths
- University Hospitals Birmingham NHS Foundation Trust , Birmingham , United Kingdom
| | - Heike Grabsch
- GROW School for Oncology and Developmental Biology, Maastricht University Medical Center , Maastricht , Netherlands
- Leeds Institute of Medical Research at St James’s, University of Leeds , Leeds , United Kingdom
| | - Richard Turkington
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast , Belfast , United Kingdom
| | - Tim Underwood
- University Hospital Southampton NHS Foundation Trust , Southampton , United Kingdom
| | - Elizabeth Smyth
- Cambridge University Hospitals NHS Foundation Trust , Cambridge , United Kingdom
| | - Rebecca Fitzgerald
- Cambridge University Hospitals NHS Foundation Trust , Cambridge , United Kingdom
| | - David Cunningham
- The Royal Marsden NHS Foundation Trust , London , United Kingdom
| | - Andrew Davies
- Guy's and St Thomas’ NHS Foundation Trust , London , United Kingdom
- King's College London , London , United Kingdom
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5
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Campiani G, Khan T, Ulivieri C, Staiano L, Papulino C, Magnano S, Nathwani S, Ramunno A, Lucena-Agell D, Relitti N, Federico S, Pozzetti L, Carullo G, Casagni A, Brogi S, Vanni F, Galatello P, Ghanim M, McCabe N, Lamponi S, Valoti M, Ibrahim O, O'Sullivan J, Turkington R, Kelly VP, VanWemmel R, Díaz JF, Gemma S, Zisterer D, Altucci L, De Matteis A, Butini S, Benedetti R. Design and synthesis of multifunctional microtubule targeting agents endowed with dual pro-apoptotic and anti-autophagic efficacy. Eur J Med Chem 2022; 235:114274. [PMID: 35344902 DOI: 10.1016/j.ejmech.2022.114274] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 02/28/2022] [Accepted: 03/08/2022] [Indexed: 02/06/2023]
Abstract
Autophagy is a lysosome dependent cell survival mechanism and is central to the maintenance of organismal homeostasis in both physiological and pathological situations. Targeting autophagy in cancer therapy attracted considerable attention in the past as stress-induced autophagy has been demonstrated to contribute to both drug resistance and malignant progression and recently interest in this area has re-emerged. Unlocking the therapeutic potential of autophagy modulation could be a valuable strategy for designing innovative tools for cancer treatment. Microtubule-targeting agents (MTAs) are some of the most successful anti-cancer drugs used in the clinic to date. Scaling up our efforts to develop new anti-cancer agents, we rationally designed multifunctional agents 5a-l with improved potency and safety that combine tubulin depolymerising efficacy with autophagic flux inhibitory activity. Through a combination of computational, biological, biochemical, pharmacokinetic-safety, metabolic studies and SAR analyses we identified the hits 5i,k. These MTAs were characterised as potent pro-apoptotic agents and also demonstrated autophagy inhibition efficacy. To measure their efficacy at inhibiting autophagy, we investigated their effects on basal and starvation-mediated autophagic flux by quantifying the expression of LC3II/LC3I and p62 proteins in oral squamous cell carcinoma and human leukaemia through western blotting and by immunofluorescence study of LC3 and LAMP1 in a cervical carcinoma cell line. Analogues 5i and 5k, endowed with pro-apoptotic activity on a range of hematological cancer cells (including ex-vivo chronic lymphocytic leukaemia (CLL) cells) and several solid tumor cell lines, also behaved as late-stage autophagy inhibitors by impairing autophagosome-lysosome fusion.
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Affiliation(s)
- Giuseppe Campiani
- Department of Biotechnology, Chemistry and Pharmacy, DoE Department of Excellence 2018-2022, University of Siena, via Aldo Moro 2, 53100, Siena, Italy.
| | - Tuhina Khan
- Department of Biotechnology, Chemistry and Pharmacy, DoE Department of Excellence 2018-2022, University of Siena, via Aldo Moro 2, 53100, Siena, Italy
| | - Cristina Ulivieri
- Department of Life Sciences, University of Siena, via Aldo Moro 2, 53100, Siena, Italy; Istituto Toscano Tumori, University of Siena, via Aldo Moro 2, I, 53100, Siena, Italy
| | - Leopoldo Staiano
- Cell Biology and Disease Mechanisms, Telethon Institute of Genetics and Medicine, Via Campi Flegrei, 34, 80078, Pozzuoli, Naples, Italy; Institute for Genetic and Biomedical Research, National Research Council (CNR), via Fratelli Cervi 93, 20054, Segrate, Milan, Italy
| | - Chiara Papulino
- Department of Precision Medicine, University of Campania Luigi Vanvitelli, Vico L, De Crecchio 7, 80138, Naples, IT, Italy
| | - Stefania Magnano
- School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, 152-160, Pearse Street, Dublin 2, Ireland
| | - Seema Nathwani
- School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, 152-160, Pearse Street, Dublin 2, Ireland
| | - Anna Ramunno
- Department of Pharmacy, University of Salerno, via G. Paolo II 132, 84084, Fisciano (SA), Italy
| | - Daniel Lucena-Agell
- Centro de Investigaciones Biologicas Margarita Salas, Consejo Superior de Investigaciones Cientificas, Ramiro de Maeztu 9, 28040, Madrid, Spain
| | - Nicola Relitti
- IRBM Science Park, Via Pontina km 30, 600, 00071, Pomezia, Rome, Italy
| | - Stefano Federico
- Department of Biotechnology, Chemistry and Pharmacy, DoE Department of Excellence 2018-2022, University of Siena, via Aldo Moro 2, 53100, Siena, Italy
| | - Luca Pozzetti
- Department of Biotechnology, Chemistry and Pharmacy, DoE Department of Excellence 2018-2022, University of Siena, via Aldo Moro 2, 53100, Siena, Italy
| | - Gabriele Carullo
- Department of Biotechnology, Chemistry and Pharmacy, DoE Department of Excellence 2018-2022, University of Siena, via Aldo Moro 2, 53100, Siena, Italy
| | - Alice Casagni
- Department of Biotechnology, Chemistry and Pharmacy, DoE Department of Excellence 2018-2022, University of Siena, via Aldo Moro 2, 53100, Siena, Italy
| | - Simone Brogi
- Department of Pharmacy, University of Pisa, 56126, Pisa, Italy
| | - Francesca Vanni
- Department of Life Sciences, University of Siena, via Aldo Moro 2, 53100, Siena, Italy
| | - Paola Galatello
- Department of Pharmacy, University of Salerno, via G. Paolo II 132, 84084, Fisciano (SA), Italy
| | - Magda Ghanim
- School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, 152-160, Pearse Street, Dublin 2, Ireland
| | - Niamh McCabe
- School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, 97 Lisburn Road, Health Sciences Building, BT9 7BL, Belfast, United Kingdom
| | - Stefania Lamponi
- Department of Biotechnology, Chemistry and Pharmacy, DoE Department of Excellence 2018-2022, University of Siena, via Aldo Moro 2, 53100, Siena, Italy
| | - Massimo Valoti
- Department of Life Sciences, University of Siena, via Aldo Moro 2, 53100, Siena, Italy
| | - Ola Ibrahim
- School of Dental Science, Trinity College Dublin, Lincoln Place, Dublin 2, Ireland
| | - Jeffrey O'Sullivan
- School of Dental Science, Trinity College Dublin, Lincoln Place, Dublin 2, Ireland
| | - Richard Turkington
- School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, 97 Lisburn Road, Health Sciences Building, BT9 7BL, Belfast, United Kingdom
| | - Vincent P Kelly
- School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, 152-160, Pearse Street, Dublin 2, Ireland
| | - Ruben VanWemmel
- Centro de Investigaciones Biologicas Margarita Salas, Consejo Superior de Investigaciones Cientificas, Ramiro de Maeztu 9, 28040, Madrid, Spain
| | - J Fernando Díaz
- Centro de Investigaciones Biologicas Margarita Salas, Consejo Superior de Investigaciones Cientificas, Ramiro de Maeztu 9, 28040, Madrid, Spain
| | - Sandra Gemma
- Department of Biotechnology, Chemistry and Pharmacy, DoE Department of Excellence 2018-2022, University of Siena, via Aldo Moro 2, 53100, Siena, Italy
| | - Daniela Zisterer
- School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, 152-160, Pearse Street, Dublin 2, Ireland
| | - Lucia Altucci
- Department of Precision Medicine, University of Campania Luigi Vanvitelli, Vico L, De Crecchio 7, 80138, Naples, IT, Italy; Biogem Institute of Molecular Biology and Genetics, Via Camporeale, 83031, Ariano Irpino, Italy
| | - Antonella De Matteis
- Cell Biology and Disease Mechanisms, Telethon Institute of Genetics and Medicine, Via Campi Flegrei, 34, 80078, Pozzuoli, Naples, Italy
| | - Stefania Butini
- Department of Biotechnology, Chemistry and Pharmacy, DoE Department of Excellence 2018-2022, University of Siena, via Aldo Moro 2, 53100, Siena, Italy; Istituto Toscano Tumori, University of Siena, via Aldo Moro 2, I, 53100, Siena, Italy.
| | - Rosaria Benedetti
- Department of Precision Medicine, University of Campania Luigi Vanvitelli, Vico L, De Crecchio 7, 80138, Naples, IT, Italy
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Oliveira T, Lewis M, Smyth L, Turkington R, McKnight AJ, Cairnduff V, McManus D, Johnston B, Coleman H, Curtius K. P-OGC95 Understanding the molecular age of Barrett’s oesophagus in a population-representative sample of patients. Br J Surg 2021. [DOI: 10.1093/bjs/znab430.222] [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: 11/13/2022]
Abstract
Abstract
Background
The incidence of oesophageal adenocarcinoma (OAC) increases dramatically with patient age but only a small proportion of patients with diagnosed Barrett’s oesophagus (BO), the precursor to OAC, will develop dysplasia and/or cancer. Beyond chronological age, biomarkers of progression that capture biological aging offer largely untapped potential for objectively identifying BO patients at highest risk of progression, who could undergo personalised surveillance at shorter intervals. We have developed computational tools to determine tissue-specific aging using genome-wide methylation data as a “molecular clock” for estimating patient-specific BO dwell times at the time of incident diagnosis that cannot be clinically measured by other means.
Methods
Using the population-based Northern Ireland BO register in a retrospective study, we have identified 46 non-dysplastic BO patients who have 2-4 serial endoscopic biopsies each, and have not progressed to OAC (age range 29-77 years). FFPE biopsies for 10 age-matched patients who had prevalent HGD/OAC at index BO diagnosis were also retrieved. DNA has been extracted, quantified using fluorescence, quality checked through qPCR, and prepared for Illumina EPIC methylation arrays. We created a Python package called “MethylDrift” to determine genome-wide aging rates in patient data. Model outputs are used in the molecular clock for BO tissue age.
Results
We used MethylDrift to quantify aging rates in both cross-sectional data (population-level epigenetic drift) and longitudinal data within the same patients to obtain individual aging rates. Computational analyses using our previously developed Bayesian framework for the BO molecular clock will be applied to estimate the molecular age of BO in patients, i.e., how long the patient has been living with BO since onset of metaplasia. Results will be compared between age groups, birth cohorts, sex, and importantly between dysplastic and non-dysplastic BO to evaluate biomarker potential. Data analysis is ongoing, and the final results will be presented at the meeting.
Conclusions
Our results from this nested case-control study demonstrate feasibility and generate pilot data on molecular age as a proxy of BO duration at the time of incident diagnosis, in a large population-based registry of patients with BO. This will inform our computational tools for determining biological aging and can be applied in future work to investigate progression risk according to molecular age. Ultimately, this biomarker could inform surveillance frequency for BO patients, enable earlier detection of neoplastic progression, leading to improved patient outcomes and optimal distribution of limited endoscopy capacity for surveillance.
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Affiliation(s)
| | | | | | | | | | | | - Damian McManus
- Belfast Health & Social Care Trust, Belfast, United Kingdom
| | - Brian Johnston
- Belfast Health & Social Care Trust, Belfast, United Kingdom
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Scanlon E, Lavery A, Stevenson L, Kennedy C, Byrne R, Walker A, Eatock M, Middleton M, Thomas A, Turkington R. P-OGC08 Translational Insights from the Dual ErbB Inhibition in Oesophago-gastric Cancer (DEBIOC) Clinical Trial - A Bioinformatic Analysis. Br J Surg 2021. [DOI: 10.1093/bjs/znab430.136] [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: 11/14/2022]
Abstract
Abstract
Background
Oesophageal Adenocarcinoma (OAC) incidence in the Western-world has increased markedly over 30 years. 5-year survival rates for patients remains below 20% with dismal response to neo-adjuvant or perioperative chemotherapy for operable tumours. The Dual ErbB Inhibition in Oesophago-gastric Cancer (DEBIOC) clinical trial assessed efficacy of combined oxaliplatin and capecitabine (Xelox) with dual ErbB inhibitor AZD8931 in providing additional benefit to operable patients compared to Xelox alone. We utilised a bioinformatic approach combing Almac Clara-T Transcriptional Discovery software with unsupervised machine learning methods to unveil translational clinical potential and biological insights from DEBIOC patient biopsy and resection specimens.
Methods
Using microarrays of DEBIOC patient specimens with documented clinical observations, we combined unsupervised machine learning techniques with state-of-the-art Almac Clara-T software to assess transcriptional changes between treatment types regarding the 10 hallmarks of cancer, characterised by representative gene-expression signatures and scores. These methods were employed to identify possible mechanisms of treatment resistance, evaluate changes in the tumour-microenvironment and determine clinically significant molecular subgroups in OAC. Differential expression and pathway analytics were used to describe signalling dissimilarities between clusters from unsupervised analysis and phenotypes respective to hallmarks of cancer, with alignment of sensitivities to single-gene drug targets for subgroups of interest.
Results
Unsupervised clustering analysis of biopsy specimens, resulted in the identification of two robust subgroups pre-treatment in OAC, determined to be significantly associated with the prediction of Mandard Score (Tumour Regression Grade 1-5) post-treatment (fishers exact p < 0.05). Differential expression analysis revealed distinguishing biology between subtypes and noted increased ErbB signalling in non-responding patients in addition to increased PI3K signalling, highlighting a potential mechanism of resistance to dual ErbB inhibition (nominal p-value <0.05, FDR p-value <0.2). Semi-supervised clustering revealed hallmark-specific-phenotypes associated with clinical observations including lymph node involvement, EGFR FISH classification, vascular invasion and progression events at BH adjusted p-values <0.05.
Conclusions
Our analysis has revealed translational insights into possible mechanisms of drug resistance as well as cancer hallmark-specific phenotypes significantly associated with clinico-pathological factors during the DEBIOC clinical trial. Continued analysis into resulting phenotypes and clusters combined with the alignment of single gene drug target sensitivities is anticipated to reveal novel molecular pathways driving phenotypic differences in an effort to further inform biological understanding and improve treatment response and survival outcomes in OAC patients.
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Affiliation(s)
- Enya Scanlon
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom
| | - Anita Lavery
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom
| | - Leanne Stevenson
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom
| | - Chloe Kennedy
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom
| | - Ryan Byrne
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom
| | - Andrew Walker
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom
| | - Martin Eatock
- Belfast Health and Social Care Trust, Belfast, United Kingdom
| | - Mark Middleton
- University of Oxford, Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, Oxford, United Kingdom
| | - Anne Thomas
- University of Leicester, Leicester, United Kingdom
| | - Richard Turkington
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom
- Belfast Health and Social Care Trust, Belfast, United Kingdom
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Rahman SA, Walker RC, Lloyd MA, Grace BL, van Boxel GI, Kingma BF, Ruurda JP, van Hillegersberg R, Harris S, Parsons S, Mercer S, Griffiths EA, O'Neill JR, Turkington R, Fitzgerald RC, Underwood TJ. Machine learning to predict early recurrence after oesophageal cancer surgery. Br J Surg 2020; 107:1042-1052. [PMID: 31997313 PMCID: PMC7299663 DOI: 10.1002/bjs.11461] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [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: 08/15/2019] [Revised: 10/11/2019] [Accepted: 11/13/2019] [Indexed: 12/19/2022]
Abstract
BACKGROUND Early cancer recurrence after oesophagectomy is a common problem, with an incidence of 20-30 per cent despite the widespread use of neoadjuvant treatment. Quantification of this risk is difficult and existing models perform poorly. This study aimed to develop a predictive model for early recurrence after surgery for oesophageal adenocarcinoma using a large multinational cohort and machine learning approaches. METHODS Consecutive patients who underwent oesophagectomy for adenocarcinoma and had neoadjuvant treatment in one Dutch and six UK oesophagogastric units were analysed. Using clinical characteristics and postoperative histopathology, models were generated using elastic net regression (ELR) and the machine learning methods random forest (RF) and extreme gradient boosting (XGB). Finally, a combined (ensemble) model of these was generated. The relative importance of factors to outcome was calculated as a percentage contribution to the model. RESULTS A total of 812 patients were included. The recurrence rate at less than 1 year was 29·1 per cent. All of the models demonstrated good discrimination. Internally validated areas under the receiver operating characteristic (ROC) curve (AUCs) were similar, with the ensemble model performing best (AUC 0·791 for ELR, 0·801 for RF, 0·804 for XGB, 0·805 for ensemble). Performance was similar when internal-external validation was used (validation across sites, AUC 0·804 for ensemble). In the final model, the most important variables were number of positive lymph nodes (25·7 per cent) and lymphovascular invasion (16·9 per cent). CONCLUSION The model derived using machine learning approaches and an international data set provided excellent performance in quantifying the risk of early recurrence after surgery, and will be useful in prognostication for clinicians and patients.
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Affiliation(s)
- S. A. Rahman
- Cancer Sciences UnitUniversity of SouthamptonSouthamptonUK
| | - R. C. Walker
- Cancer Sciences UnitUniversity of SouthamptonSouthamptonUK
| | - M. A. Lloyd
- Cancer Sciences UnitUniversity of SouthamptonSouthamptonUK
| | - B. L. Grace
- Cancer Sciences UnitUniversity of SouthamptonSouthamptonUK
| | - G. I. van Boxel
- Department of SurgeryUniversity Medical CentreUtrechtthe Netherlands
| | - B. F. Kingma
- Department of SurgeryUniversity Medical CentreUtrechtthe Netherlands
| | - J. P. Ruurda
- Department of SurgeryUniversity Medical CentreUtrechtthe Netherlands
| | | | - S. Harris
- Department of Public Health Sciences and Medical StatisticsUniversity of SouthamptonSouthamptonUK
| | - S. Parsons
- Department of SurgeryNottingham University Hospitals NHS TrustNottinghamUK
| | - S. Mercer
- Department of SurgeryPortsmouth Hospitals NHS TrustPortsmouthUK
| | - E. A. Griffiths
- Department of Upper Gastrointestinal SurgeryUniversity Hospitals Birmingham NHS Foundation TrustBirminghamUK
| | - J. R. O'Neill
- Cambridge Oesophagogastric CentreAddenbrookes Hospital, Cambridge University Hospitals Foundation TrustCambridgeUK
| | - R. Turkington
- Centre for Cancer Research and Cell BiologyQueen's University BelfastBelfastUK
| | - R. C. Fitzgerald
- Hutchison/Medical Research Council Cancer UnitUniversity of CambridgeCambridgeUK
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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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Bornschein J, Wernisch L, Secrier M, Miremadi A, Perner J, MacRae S, O'Donovan M, Newton R, Menon S, Bower L, Eldridge MD, Devonshire G, Cheah C, Turkington R, Hardwick RH, Selgrad M, Venerito M, Malfertheiner P, Fitzgerald RC. Transcriptomic profiling reveals three molecular phenotypes of adenocarcinoma at the gastroesophageal junction. Int J Cancer 2019; 145:3389-3401. [PMID: 31050820 PMCID: PMC6851674 DOI: 10.1002/ijc.32384] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [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: 12/27/2018] [Revised: 03/24/2019] [Accepted: 04/04/2019] [Indexed: 12/17/2022]
Abstract
Cancers occurring at the gastroesophageal junction (GEJ) are classified as predominantly esophageal or gastric, which is often difficult to decipher. We hypothesized that the transcriptomic profile might reveal molecular subgroups which could help to define the tumor origin and behavior beyond anatomical location. The gene expression profiles of 107 treatment-naïve, intestinal type, gastroesophageal adenocarcinomas were assessed by the Illumina-HTv4.0 beadchip. Differential gene expression (limma), unsupervised subgroup assignment (mclust) and pathway analysis (gage) were undertaken in R statistical computing and results were related to demographic and clinical parameters. Unsupervised assignment of the gene expression profiles revealed three distinct molecular subgroups, which were not associated with anatomical location, tumor stage or grade (p > 0.05). Group 1 was enriched for pathways involved in cell turnover, Group 2 was enriched for metabolic processes and Group 3 for immune-response pathways. Patients in group 1 showed the worst overall survival (p = 0.019). Key genes for the three subtypes were confirmed by immunohistochemistry. The newly defined intrinsic subtypes were analyzed in four independent datasets of gastric and esophageal adenocarcinomas with transcriptomic data available (RNAseq data: OCCAMS cohort, n = 158; gene expression arrays: Belfast, n = 63; Singapore, n = 191; Asian Cancer Research Group, n = 300). The subgroups were represented in the independent cohorts and pooled analysis confirmed the prognostic effect of the new subtypes. In conclusion, adenocarcinomas at the GEJ comprise three distinct molecular phenotypes which do not reflect anatomical location but rather inform our understanding of the key pathways expressed.
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Affiliation(s)
- Jan Bornschein
- MRC Cancer Unit, Hutchison/MRC Research CentreUniversity of CambridgeCambridgeUnited Kingdom
- Department of Gastroenterology, Hepatology and Infectious DiseasesOtto‐von‐Guericke UniversityMagdeburgGermany
- Translational Gastroenterology UnitOxford University HospitalsOxfordUnited Kingdom
| | - Lorenz Wernisch
- MRC Biostatistics UnitUniversity of CambridgeCambridgeUnited Kingdom
| | - Maria Secrier
- Cancer Research UK Cambridge InstituteUniversity of CambridgeCambridgeUnited Kingdom
| | - Ahmad Miremadi
- Department of Histopathology, Addenbrooke's HospitalCambridge University HospitalsCambridgeUnited Kingdom
| | - Juliane Perner
- Cancer Research UK Cambridge InstituteUniversity of CambridgeCambridgeUnited Kingdom
| | - Shona MacRae
- MRC Cancer Unit, Hutchison/MRC Research CentreUniversity of CambridgeCambridgeUnited Kingdom
| | - Maria O'Donovan
- Department of Histopathology, Addenbrooke's HospitalCambridge University HospitalsCambridgeUnited Kingdom
| | - Richard Newton
- MRC Biostatistics UnitUniversity of CambridgeCambridgeUnited Kingdom
| | - Suraj Menon
- Cancer Research UK Cambridge InstituteUniversity of CambridgeCambridgeUnited Kingdom
| | - Lawrence Bower
- Cancer Research UK Cambridge InstituteUniversity of CambridgeCambridgeUnited Kingdom
| | - Matthew D. Eldridge
- Cancer Research UK Cambridge InstituteUniversity of CambridgeCambridgeUnited Kingdom
| | - Ginny Devonshire
- Cancer Research UK Cambridge InstituteUniversity of CambridgeCambridgeUnited Kingdom
| | - Calvin Cheah
- MRC Cancer Unit, Hutchison/MRC Research CentreUniversity of CambridgeCambridgeUnited Kingdom
| | | | - Richard H. Hardwick
- Department of Surgery, Addenbrooke's HospitalCambridge University HospitalsCambridgeUnited Kingdom
| | - Michael Selgrad
- Department of Gastroenterology, Hepatology and Infectious DiseasesOtto‐von‐Guericke UniversityMagdeburgGermany
| | - Marino Venerito
- Department of Gastroenterology, Hepatology and Infectious DiseasesOtto‐von‐Guericke UniversityMagdeburgGermany
| | - Peter Malfertheiner
- Department of Gastroenterology, Hepatology and Infectious DiseasesOtto‐von‐Guericke UniversityMagdeburgGermany
| | - Rebecca C. Fitzgerald
- MRC Cancer Unit, Hutchison/MRC Research CentreUniversity of CambridgeCambridgeUnited Kingdom
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Rahman S, Walker R, Lloyd M, Grace B, van Boxel G, Kingma BF, Ruurda J, van Hillegersberg R, Harris S, Parsons S, Mercer S, Griffiths E, O'Neill J, Turkington R, Fitzgerald R, Underwood T. Machine Learning to Predict Early Recurrence After Oesophageal Cancer Surgery. Eur J Surg Oncol 2019. [DOI: 10.1016/j.ejso.2019.09.073] [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] Open
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Noble F, Lloyd MA, Turkington R, Griffiths E, O'Donovan M, O'Neill JR, Mercer S, Parsons SL, Fitzgerald RC, Underwood TJ. Multicentre cohort study to define and validate pathological assessment of response to neoadjuvant therapy in oesophagogastric adenocarcinoma. Br J Surg 2017; 104:1816-1828. [PMID: 28944954 PMCID: PMC5725679 DOI: 10.1002/bjs.10627] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [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: 04/05/2017] [Revised: 05/04/2017] [Accepted: 05/30/2017] [Indexed: 12/17/2022]
Abstract
BACKGROUND This multicentre cohort study sought to define a robust pathological indicator of clinically meaningful response to neoadjuvant chemotherapy in oesophageal adenocarcinoma. METHODS A questionnaire was distributed to 11 UK upper gastrointestinal cancer centres to determine the use of assessment of response to neoadjuvant chemotherapy. Records of consecutive patients undergoing oesophagogastric resection at seven centres between January 2000 and December 2013 were reviewed. Pathological response to neoadjuvant chemotherapy was assessed using the Mandard Tumour Regression Grade (TRG) and lymph node downstaging. RESULTS TRG (8 of 11 centres) was the most widely used system to assess response to neoadjuvant chemotherapy, but there was discordance on how it was used in practice. Of 1392 patients, 1293 had TRG assessment; data were available for clinical and pathological nodal status (cN and pN) in 981 patients, and TRG, cN and pN in 885. There was a significant difference in survival between responders (TRG 1-2; median overall survival (OS) not reached) and non-responders (TRG 3-5; median OS 2·22 (95 per cent c.i. 1·94 to 2·51) years; P < 0·001); the hazard ratio was 2·46 (95 per cent c.i. 1·22 to 4·95; P = 0·012). Among local non-responders, the presence of lymph node downstaging was associated with significantly improved OS compared with that of patients without lymph node downstaging (median OS not reached versus 1·92 (1·68 to 2·16) years; P < 0·001). CONCLUSION A clinically meaningful local response to neoadjuvant chemotherapy was restricted to the small minority of patients (14·8 per cent) with TRG 1-2. Among local non-responders, a subset of patients (21·3 per cent) derived benefit from neoadjuvant chemotherapy by lymph node downstaging and their survival mirrored that of local responders.
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Affiliation(s)
- F Noble
- Cancer Sciences Unit, University of Southampton, Southampton, UK
| | - M A Lloyd
- Cancer Sciences Unit, University of Southampton, Southampton, UK
| | - R Turkington
- Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, UK
| | - E Griffiths
- Department of Surgery, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - M O'Donovan
- Hutchison/Medical Research Council Cancer Unit, University of Cambridge, Cambridge, UK
| | - J R O'Neill
- Department of Surgery, Royal Infirmary of Edinburgh, Edinburgh, UK
| | - S Mercer
- Department of Surgery, Portsmouth NHS Trust, Portsmouth, UK
| | - S L Parsons
- Department of Surgery, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - R C Fitzgerald
- Hutchison/Medical Research Council Cancer Unit, University of Cambridge, Cambridge, UK
| | - T J Underwood
- Cancer Sciences Unit, University of Southampton, Southampton, UK
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Alvi MA, Loughrey MB, Dunne P, McQuaid S, Turkington R, Fuchs MA, McGready C, Bingham V, Pang B, Moore W, Maxwell P, Lawler M, James JA, Murray GI, Wilson RH, Salto-Tellez M. Molecular profiling of signet ring cell colorectal cancer provides a strong rationale for genomic targeted and immune checkpoint inhibitor therapies. Br J Cancer 2017; 117:203-209. [PMID: 28595259 PMCID: PMC5520517 DOI: 10.1038/bjc.2017.168] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [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] [Revised: 05/06/2017] [Accepted: 05/15/2017] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Signet ring cell colorectal cancer (SRCCa) has a bleak prognosis. Employing molecular pathology techniques we investigated the potential of precision medicine in this disease. METHODS Using test (n=26) and validation (n=18) cohorts, analysis of mutations, DNA methylation and transcriptome was carried out. Microsatellite instability (MSI) status was established and immunohistochemistry (IHC) was used to test for adaptive immunity (CD3) and the immune checkpoint PDL1. RESULTS DNA methylation data split the cohorts into hypermethylated (n=18, 41%) and hypomethylated groups (n=26, 59%). The hypermethylated group predominant in the proximal colon was enriched for CpG island methylator phenotype (CIMP), BRAF V600E mutation and MSI (P<0.001). These cases also had a high CD3+ immune infiltrate (P<0.001) and expressed PDL1 (P=0.03 in intra-tumoural lymphoid cells). The hypomethylated group predominant in the distal colon did not show any characteristic molecular features. We also detected a common targetable KIT mutation (c.1621A>C) across both groups. No statistically significant difference in outcome was observed between the two groups. CONCLUSIONS Our data show that SRCCa phenotype comprises two distinct genotypes. The MSI+/CIMP+/BRAF V600E+/CD3+/PDL1+ hypermethylated genotype is an ideal candidate for immune checkpoint inhibitor therapy. In addition, one fourth of SRCCa cases can potentially be targeted by KIT inhibitors.
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Affiliation(s)
- Muhammad A Alvi
- Northern Ireland Molecular Pathology Laboratory, Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast BT9 7AE, UK
| | - Maurice B Loughrey
- Northern Ireland Molecular Pathology Laboratory, Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast BT9 7AE, UK
- Department of Histopathology, Royal Victoria Hospital, Belfast Health and Social Care Trust, Belfast, UK
| | - Philip Dunne
- Northern Ireland Molecular Pathology Laboratory, Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast BT9 7AE, UK
| | - Stephen McQuaid
- Northern Ireland Molecular Pathology Laboratory, Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast BT9 7AE, UK
| | - Richard Turkington
- Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast, UK
| | - Marc-Aurel Fuchs
- Northern Ireland Molecular Pathology Laboratory, Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast BT9 7AE, UK
| | - Claire McGready
- Northern Ireland Molecular Pathology Laboratory, Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast BT9 7AE, UK
| | - Victoria Bingham
- Northern Ireland Molecular Pathology Laboratory, Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast BT9 7AE, UK
| | - Brendan Pang
- Department of Pathology, National University Hospital, National University Health System, Singapore, Singapore
| | - Wendy Moore
- Northern Ireland Molecular Pathology Laboratory, Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast BT9 7AE, UK
| | - Perry Maxwell
- Northern Ireland Molecular Pathology Laboratory, Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast BT9 7AE, UK
| | - Mark Lawler
- Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast, UK
| | - Jacqueline A James
- Northern Ireland Molecular Pathology Laboratory, Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast BT9 7AE, UK
| | - Graeme I Murray
- Department of Pathology, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen AB25 2ZD, UK
| | - Richard H Wilson
- Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast, UK
| | - Manuel Salto-Tellez
- Northern Ireland Molecular Pathology Laboratory, Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast BT9 7AE, UK
- Department of Histopathology, Royal Victoria Hospital, Belfast Health and Social Care Trust, Belfast, UK
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Walls G, Kearney BÓ, Turkington R, Harrison C. Waiting times, dose fractionation and treatment efficacy for urgent hemostatic radiotherapy in gastro-oesophageal cancer. Clin Oncol (R Coll Radiol) 2017. [DOI: 10.1016/j.clon.2017.04.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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15
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Stevenson L, Cairns L, Douglas R, McCabe N, Gavory G, Harrison T, Kennedy R, Turkington R. Targeting Akt in oesophageal adenocarcinoma. Eur J Cancer 2016. [DOI: 10.1016/s0959-8049(16)33032-5] [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: 10/20/2022]
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16
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Lloyd MA, Noble F, Walker R, McManus D, Turkington R, Bedford M, Griffiths E, Whiting J, Nutzinger B, Grehan N, Robert O'Neill J, Skipworth RJ, Save V, Rupesh S, Mercer S, Saunders JH, Soomro IN, Parsons SL, Fitzgerald RC, Underwood TJ. A multicentre cohort study to redefine and validate pathological assessment of response to neoadjuvant therapy in treated oesophagogastric adenocarcinoma. Eur J Surg Oncol 2016. [DOI: 10.1016/j.ejso.2016.07.130] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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Alvi MA, McArt DG, Kelly P, Fuchs MA, Alderdice M, McCabe CM, Bingham V, McGready C, Tripathi S, Emmert-Streib F, Loughrey MB, McQuaid S, Maxwell P, Hamilton PW, Turkington R, James JA, Wilson RH, Salto-Tellez M. Comprehensive molecular pathology analysis of small bowel adenocarcinoma reveals novel targets with potential for clinical utility. Oncotarget 2016; 6:20863-74. [PMID: 26315110 PMCID: PMC4673235 DOI: 10.18632/oncotarget.4576] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [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: 06/09/2015] [Accepted: 07/20/2015] [Indexed: 01/22/2023] Open
Abstract
Small bowel accounts for only 0.5% of cancer cases in the US but incidence rates have been rising at 2.4% per year over the past decade. One-third of these are adenocarcinomas but little is known about their molecular pathology and no molecular markers are available for clinical use. Using a retrospective 28 patient matched normal-tumor cohort, next-generation sequencing, gene expression arrays and CpG methylation arrays were used for molecular profiling. Next-generation sequencing identified novel mutations in IDH1, CDH1, KIT, FGFR2, FLT3, NPM1, PTEN, MET, AKT1, RET, NOTCH1 and ERBB4. Array data revealed 17% of CpGs and 5% of RNA transcripts assayed to be differentially methylated and expressed respectively (p < 0.01). Merging gene expression and DNA methylation data revealed CHN2 as consistently hypermethylated and downregulated in this disease (Spearman −0.71, p < 0.001). Mutations in TP53 which were found in more than half of the cohort (15/28) and Kazald1 hypomethylation were both were indicative of poor survival (p = 0.03, HR = 3.2 and p = 0.01, HR = 4.9 respectively). By integrating high-throughput mutational, gene expression and DNA methylation data, this study reveals for the first time the distinct molecular profile of small bowel adenocarcinoma and highlights potential clinically exploitable markers.
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Affiliation(s)
- Muhammad A Alvi
- Northern Ireland Molecular Pathology Laboratory, Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, Northern Ireland, UK
| | - Darragh G McArt
- Northern Ireland Molecular Pathology Laboratory, Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, Northern Ireland, UK
| | - Paul Kelly
- Tissue Pathology, Belfast Health and Social Care Trust, Belfast, Northern Ireland, UK
| | - Marc-Aurel Fuchs
- Northern Ireland Molecular Pathology Laboratory, Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, Northern Ireland, UK
| | - Matthew Alderdice
- Northern Ireland Molecular Pathology Laboratory, Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, Northern Ireland, UK
| | - Clare M McCabe
- Northern Ireland Molecular Pathology Laboratory, Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, Northern Ireland, UK
| | - Victoria Bingham
- Northern Ireland Molecular Pathology Laboratory, Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, Northern Ireland, UK
| | - Claire McGready
- Northern Ireland Molecular Pathology Laboratory, Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, Northern Ireland, UK
| | - Shailesh Tripathi
- Computational Biology and Machine Learning Laboratory, Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, Northern Ireland, UK
| | - Frank Emmert-Streib
- Computational Biology and Machine Learning Laboratory, Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, Northern Ireland, UK
| | - Maurice B Loughrey
- Tissue Pathology, Belfast Health and Social Care Trust, Belfast, Northern Ireland, UK
| | - Stephen McQuaid
- Northern Ireland Molecular Pathology Laboratory, Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, Northern Ireland, UK.,Tissue Pathology, Belfast Health and Social Care Trust, Belfast, Northern Ireland, UK
| | - Perry Maxwell
- Northern Ireland Molecular Pathology Laboratory, Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, Northern Ireland, UK.,Tissue Pathology, Belfast Health and Social Care Trust, Belfast, Northern Ireland, UK
| | - Peter W Hamilton
- Northern Ireland Molecular Pathology Laboratory, Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, Northern Ireland, UK
| | - Richard Turkington
- Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, Northern Ireland, UK
| | - Jacqueline A James
- Northern Ireland Molecular Pathology Laboratory, Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, Northern Ireland, UK.,Tissue Pathology, Belfast Health and Social Care Trust, Belfast, Northern Ireland, UK
| | - Richard H Wilson
- Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, Northern Ireland, UK
| | - Manuel Salto-Tellez
- Northern Ireland Molecular Pathology Laboratory, Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, Northern Ireland, UK.,Tissue Pathology, Belfast Health and Social Care Trust, Belfast, Northern Ireland, UK
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Stevenson L, Allen WL, Turkington R, Jithesh PV, Proutski I, Stewart G, Lenz HJ, Van Schaeybroeck S, Longley DB, Johnston PG. Identification of galanin and its receptor GalR1 as novel determinants of resistance to chemotherapy and potential biomarkers in colorectal cancer. Clin Cancer Res 2012; 18:5412-26. [PMID: 22859720 PMCID: PMC3463501 DOI: 10.1158/1078-0432.ccr-12-1780] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
PURPOSE A major factor limiting the effective clinical management of colorectal cancer (CRC) is resistance to chemotherapy. Therefore, the identification of novel, therapeutically targetable mediators of resistance is vital. EXPERIMENTAL DESIGN We used a CRC disease-focused microarray platform to transcriptionally profile chemotherapy-responsive and nonresponsive pretreatment metastatic CRC liver biopsies and in vitro samples, both sensitive and resistant to clinically relevant chemotherapeutic drugs (5-FU and oxaliplatin). Pathway and gene set enrichment analyses identified candidate genes within key pathways mediating drug resistance. Functional RNAi screening identified regulators of drug resistance. RESULTS Mitogen-activated protein kinase signaling, focal adhesion, cell cycle, insulin signaling, and apoptosis were identified as key pathways involved in mediating drug resistance. The G-protein-coupled receptor galanin receptor 1 (GalR1) was identified as a novel regulator of drug resistance. Notably, silencing either GalR1 or its ligand galanin induced apoptosis in drug-sensitive and resistant cell lines and synergistically enhanced the effects of chemotherapy. Mechanistically, GalR1/galanin silencing resulted in downregulation of the endogenous caspase-8 inhibitor FLIP(L), resulting in induction of caspase-8-dependent apoptosis. Galanin mRNA was found to be overexpressed in colorectal tumors, and importantly, high galanin expression correlated with poor disease-free survival of patients with early-stage CRC. CONCLUSION This study shows the power of systems biology approaches to identify key pathways and genes that are functionally involved in mediating chemotherapy resistance. Moreover, we have identified a novel role for the GalR1/galanin receptor-ligand axis in chemoresistance, providing evidence to support its further evaluation as a potential therapeutic target and biomarker in CRC.
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Sinclair ARE, Krebs CJ, Fryxell JM, Turkington R, Boutin S, Boonstra R, Seccombe-Hett P, Lundberg P, Oksanen L. Testing hypotheses of trophic level interactions: a boreal forest ecosystem. OIKOS 2003. [DOI: 10.1034/j.1600-0706.2000.890213.x] [Citation(s) in RCA: 76] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Boutin S, Krebs CJ, Boonstra R, Dale MRT, Hannon SJ, Martin K, Sinclair ARE, Smith JNM, Turkington R, Blower M, Byrom A, Doyle FI, Doyle C, Hik D, Hofer L, Hubbs A, Karels T, Murray DL, Nams V, O'Donoghue M, Rohner C, Schweiger S. Population Changes of the Vertebrate Community during a Snowshoe Hare Cycle in Canada's Boreal Forest. OIKOS 1995. [DOI: 10.2307/3545676] [Citation(s) in RCA: 141] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Krebs CJ, Boutin S, Boonstra R, Sinclair AR, Smith JN, Dale MR, Martin K, Turkington R. Impact of Food and Predation on the Snowshoe Hare Cycle. Science 1995; 269:1112-5. [PMID: 17755536 DOI: 10.1126/science.269.5227.1112] [Citation(s) in RCA: 369] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
Snowshoe hare populations in the boreal forests of North America go through 10-year cycles. Supplemental food and mammalian predator abundance were manipulated in a factorial design on 1-square-kilometer areas for 8 years in the Yukon. Two blocks of forest were fertilized to test for nutrient effects. Predator exclosure doubled and food addition tripled hare density during the cyclic peak and decline. Predator exclosure combined with food addition increased density 11-fold. Added nutrients increased plant growth but not hare density. Food and predation together had a more than additive effect, which suggests that a three-trophic-level interaction generates hare cycles.
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Reader RJ, Wilson SD, Belcher JW, Wisheu I, Keddy PA, Tilman D, Morris EC, Grace JB, McGraw JB, Olff H, Turkington R, Klein E, Leung Y, Shipley B, van Hulst R, Johansson ME, Nilsson C, Gurevitch J, Grigulis K, Beisner BE. Plant Competition in Relation to Neighbor Biomass: An Intercontinental Study with POA Pratensis. Ecology 1994. [DOI: 10.2307/1939634] [Citation(s) in RCA: 94] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Chanway CP, Holl FB, Turkington R. Specificity of association between Bacillus isolates and genotypes of Lolium perenne and Trifolium repens from a grass – legume pasture. ACTA ACUST UNITED AC 1990. [DOI: 10.1139/b90-142] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Specificity between plants and associated rhizosphere bacteria was investigated using species and genotypic mixtures of Lolium perenne L. (perennial ryegrass) and Trifolium repens L. (white clover) inoculated with Bacillus strains isolated from rhizosphere soil of the pasture plants. The genotypic identity of plants was controlled by using stolon tips (Trifolium) and tillers (Lolium) of three genotypes of each species collected from a 45-year-old permanent pasture. Inoculation of plants in the greenhouse with Bacillus isolates that had coexisted in the field with the Lolium component of a Lolium–Trifolium mixture increased Lolium root and shoot weight. Root and nodule weight of Trifolium in mixture regardless of its genotype was also higher when coexistent Bacillus–Lolium combinations were present. The presence of other coexistent Bacillus–plant or plant–plant combinations did not enhance performance of either pasture species. The identity of the Rhizobium leguminosarum biovar trifolii strains that formed Trifolium root nodules was unaffected by inoculation with Bacillus. Key words: Trifolium repens, Lolium perenne, Bacillus, specificity, rhizosphere, yield.
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Thompson JD, Turkington R, Holl FB. The influence of Rhizobium leguminosarum biovar. trifolii on the growth and neighbour relationships of Trifolium repens and three grasses. ACTA ACUST UNITED AC 1990. [DOI: 10.1139/b90-040] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Trifolium repens was sampled from 10 areas dominated by each of Dactylis glomerata, Holcus lanatus, and Lolium perenne in each of three different-aged pastures. Cell extracts of nodule isolates of Rhizobium leguminosarum biovar. trifolii were compared using polyacrylamide gel electrophoresis. Each pasture contained a range of Rhizobium strains as defined by the protein profiles. No single strain was common to Trifolium sampled from the same grass species neighbourhood. The diversity of Rhizobium strains encountered was negatively correlated (r = −0.67; n = 9) with pasture age; old pastures showed a decrease in strain diversity. In addition, in the oldest pasture (45-year-old), tillers of the associated grasses were also sampled. In a glasshouse, ramets from each Trifolium clone and each of the three grass species were grown in all possible combinations with each of the three Rhizobium strains, plus Trifolium monocultures and an uninoculated control. All Trifolium clones gave increased yield in mixture with Lolium compared with yields with Dactylis and Holcus. Trifolium from the Holcus patches, in most cases, had higher yield than Trifolium from other grass patches. Rhizobium inoculation did not consistently increase Trifolium yield; it actually caused a decrease in yield when in mixture with Lolium. A significant Rhizobium × Trifolium interaction indicated a variable performance of each Trifolium genotype with different Rhizobium strains. The identity of the Trifolium clone had a significant effect on the yield of Holcus and Lolium, but not Dactylis. Total mixture yields were highest when Trifolium from a Lolium patch was part of the mixture. These results are discussed in the context of the hypothesis that Trifolium–grass coexistence is significantly influenced by Rhizobium strain differences.
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