X C Li1, M Y Wang2, M Yang3, H J Dai4, B F Zhang2, W Wang4, X L Chu4, X Wang4, H Zheng4, R F Niu1, W Zhang5, K X Chen6. 1. Public Laborato, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China. 2. Beijing Genomics Institute-Shenzhen, Shenzhen, Guangdong, China. 3. Department of Epidemiology and Biostatisti, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China; Wake Forest Baptist Comprehensive Cancer Center, Wake Forest Baptist Medical Center, Winston-Salem, USA; Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, USA. 4. Department of Epidemiology and Biostatisti, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China. 5. Wake Forest Baptist Comprehensive Cancer Center, Wake Forest Baptist Medical Center, Winston-Salem, USA; Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, USA. Electronic address: wezhang@wakehealth.edu. 6. Department of Epidemiology and Biostatisti, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China. Electronic address: chenkexin@tjmuch.com.
Abstract
Background: Esophageal squamous cell carcinoma (ESCC) is often diagnosed at an advanced and incurable stage. Information on driver genes and prognosticators in ESCC remains incomplete. The objective was to elucidate significantly mutated genes (SMGs), mutational signatures, and prognosticators in ESCC. Patients and methods: Three MutSig algorithms (i.e. MutSigCV, MutSigCL and MutSigFN) and '20/20+' ratio-metric were employed to identify SMGs. Nonnegative matrix factorization was used to decipher mutational signatures. Kaplan-Meier survival analysis, multivariate Cox and logistic regression models were applied to analyze association between mutational features and clinical parameters. Results: We identified 26 SMGs, including 8 novel (NAV3, TENM3, PTCH1, TGFBR2, RIPK4, PBRM1, USP8 and BAP1) and 18 that have been previously reported. Three mutational signatures were identified to be prevalent in ESCC including clocklike C>T at CpG, APOBEC overactive C>T at TpCp[A/T], and a signature featured by T>C substitution. The T>C mutational signature was significantly correlated with alcohol consumption (OR: 3.59; 95% CI: 2.30-5.67; P < 0.001). This alcohol consumption signature was also observed in liver cancer and head and neck squamous cell carcinoma, and its mutational activity was substantially higher in samples with mutations in TP53. Survival analysis revealed that TENM3 mutations (HR: 5.54; CI: 2.68-11.45; P < 0.001) and TP53 hotspot mutation p.R213* (HR: 3.37; CI: 1.73-8.06; P < 0.001) were significantly associated with shortened survival outcome. The association remained statistically significant after controlling for age, gender, TNM stage and tumor grade. Conclusions: We have uncovered several new SMGs in ESCC and defined an alcohol consumption related mutational signature. TENM3 mutations and the TP53 hotspot mutation p.R213* are independent prognosticators for poor survival in ESCC.
Background: Esophageal squamous cell carcinoma (ESCC) is often diagnosed at an advanced and incurable stage. Information on driver genes and prognosticators in ESCC remains incomplete. The objective was to elucidate significantly mutated genes (SMGs), mutational signatures, and prognosticators in ESCC. Patients and methods: Three MutSig algorithms (i.e. MutSigCV, MutSigCL and MutSigFN) and '20/20+' ratio-metric were employed to identify SMGs. Nonnegative matrix factorization was used to decipher mutational signatures. Kaplan-Meier survival analysis, multivariate Cox and logistic regression models were applied to analyze association between mutational features and clinical parameters. Results: We identified 26 SMGs, including 8 novel (NAV3, TENM3, PTCH1, TGFBR2, RIPK4, PBRM1, USP8 and BAP1) and 18 that have been previously reported. Three mutational signatures were identified to be prevalent in ESCC including clocklike C>T at CpG, APOBEC overactive C>T at TpCp[A/T], and a signature featured by T>C substitution. The T>C mutational signature was significantly correlated with alcohol consumption (OR: 3.59; 95% CI: 2.30-5.67; P < 0.001). This alcohol consumption signature was also observed in liver cancer and head and neck squamous cell carcinoma, and its mutational activity was substantially higher in samples with mutations in TP53. Survival analysis revealed that TENM3 mutations (HR: 5.54; CI: 2.68-11.45; P < 0.001) and TP53 hotspot mutation p.R213* (HR: 3.37; CI: 1.73-8.06; P < 0.001) were significantly associated with shortened survival outcome. The association remained statistically significant after controlling for age, gender, TNM stage and tumor grade. Conclusions: We have uncovered several new SMGs in ESCC and defined an alcohol consumption related mutational signature. TENM3 mutations and the TP53 hotspot mutation p.R213* are independent prognosticators for poor survival in ESCC.
Authors: Martin Reincke; Silviu Sbiera; Akira Hayakawa; Marily Theodoropoulou; Andrea Osswald; Felix Beuschlein; Thomas Meitinger; Emi Mizuno-Yamasaki; Kohei Kawaguchi; Yasushi Saeki; Keiji Tanaka; Thomas Wieland; Elisabeth Graf; Wolfgang Saeger; Cristina L Ronchi; Bruno Allolio; Michael Buchfelder; Tim M Strom; Martin Fassnacht; Masayuki Komada Journal: Nat Genet Date: 2014-12-08 Impact factor: 38.330
Authors: Jan J Molenaar; Jan Koster; Danny A Zwijnenburg; Peter van Sluis; Linda J Valentijn; Ida van der Ploeg; Mohamed Hamdi; Johan van Nes; Bart A Westerman; Jennemiek van Arkel; Marli E Ebus; Franciska Haneveld; Arjan Lakeman; Linda Schild; Piet Molenaar; Peter Stroeken; Max M van Noesel; Ingrid Ora; Evan E Santo; Huib N Caron; Ellen M Westerhout; Rogier Versteeg Journal: Nature Date: 2012-02-22 Impact factor: 49.962
Authors: Bert Vogelstein; Nickolas Papadopoulos; Victor E Velculescu; Shibin Zhou; Luis A Diaz; Kenneth W Kinzler Journal: Science Date: 2013-03-29 Impact factor: 47.728
Authors: Sarah Moody; Sergey Senkin; S M Ashiqul Islam; Jingwei Wang; Dariush Nasrollahzadeh; Ricardo Cortez Cardoso Penha; Stephen Fitzgerald; Erik N Bergstrom; Joshua Atkins; Yudou He; Azhar Khandekar; Karl Smith-Byrne; Christine Carreira; Valerie Gaborieau; Calli Latimer; Emily Thomas; Irina Abnizova; Pauline E Bucciarelli; David Jones; Jon W Teague; Behnoush Abedi-Ardekani; Stefano Serra; Jean-Yves Scoazec; Hiva Saffar; Farid Azmoudeh-Ardalan; Masoud Sotoudeh; Arash Nikmanesh; Hossein Poustchi; Ahmadreza Niavarani; Samad Gharavi; Michael Eden; Paul Richman; Lia S Campos; Rebecca C Fitzgerald; Luis Felipe Ribeiro; Sheila Coelho Soares-Lima; Charles Dzamalala; Blandina Theophil Mmbaga; Tatsuhiro Shibata; Diana Menya; Alisa M Goldstein; Nan Hu; Reza Malekzadeh; Abdolreza Fazel; Valerie McCormack; James McKay; Sandra Perdomo; Ghislaine Scelo; Estelle Chanudet; Laura Humphreys; Ludmil B Alexandrov; Paul Brennan; Michael R Stratton Journal: Nat Genet Date: 2021-10-18 Impact factor: 38.330
Authors: Luiza Moore; Alex Cagan; Tim H H Coorens; Matthew D C Neville; Rashesh Sanghvi; Mathijs A Sanders; Thomas R W Oliver; Daniel Leongamornlert; Peter Ellis; Ayesha Noorani; Thomas J Mitchell; Timothy M Butler; Yvette Hooks; Anne Y Warren; Mette Jorgensen; Kevin J Dawson; Andrew Menzies; Laura O'Neill; Calli Latimer; Mabel Teng; Ruben van Boxtel; Christine A Iacobuzio-Donahue; Inigo Martincorena; Rakesh Heer; Peter J Campbell; Rebecca C Fitzgerald; Michael R Stratton; Raheleh Rahbari Journal: Nature Date: 2021-08-25 Impact factor: 69.504
Authors: Arnoud Boot; Alvin W T Ng; Fui Teen Chong; Szu-Chi Ho; Willie Yu; Daniel S W Tan; N Gopalakrishna Iyer; Steven G Rozen Journal: Genome Res Date: 2020-07-06 Impact factor: 9.043