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Kurkalang S, Roy S, Acharya A, Mazumder P, Mazumder S, Patra S, Ghosh S, Sarkar S, Kundu S, Biswas NK, Ghose S, Majumder PP, Maitra A. Single-cell transcriptomic analysis of gingivo-buccal oral cancer reveals two dominant cellular programs. Cancer Sci 2023; 114:4732-4746. [PMID: 37792582 PMCID: PMC10728019 DOI: 10.1111/cas.15979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 09/02/2023] [Accepted: 09/13/2023] [Indexed: 10/06/2023] Open
Abstract
Oral squamous cell carcinoma of the gingivo-buccal region (OSCC-GB) is the most common cancer among men in India, and is associated with poor prognosis and frequent recurrence. Cellular heterogeneity in OSCC-GB was investigated by single-cell RNA sequencing of tumors derived from the oral cavity of 12 OSCC-GB patients, 3 of whom had concomitant presence of a precancerous lesion (oral submucous fibrosis [OSMF]). Unique malignant cell types, features, and phenotypic shifts in the stromal cell population were identified in oral tumors with associated submucous fibrosis. Expression levels of FOS, ATP1A, and DUSP1 provided robust discrimination between tumors with or without the concomitant presence of OSMF. Malignant cell populations shared between tumors with and without OSMF were enriched with the expression of partial epithelial-mesenchymal transition (pEMT) or fetal cell type signatures indicative of two dominant cellular programs in OSCC-GB-pEMT and fetal cellular reprogramming. Malignant cells exhibiting fetal cellular and pEMT programs were enriched with the expression of immune-related pathway genes known to be involved in antitumor immune response. In the tumor microenvironment, higher infiltration of immune cells than the stromal cells was observed. The T cell population was large in tumors and diverse subtypes of T cells with varying levels of infiltration were found. We also detected double-negative PLCG2+ T cells and cells with intermediate M1-M2 macrophage polarization. Our findings shed light on unique aspects of cellular heterogeneity and cell states in OSCC-GB.
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Affiliation(s)
| | - Sumitava Roy
- National Institute of Biomedical GenomicsKalyaniIndia
- Regional Centre for BiotechnologyFaridabadIndia
| | - Arunima Acharya
- National Institute of Biomedical GenomicsKalyaniIndia
- Regional Centre for BiotechnologyFaridabadIndia
| | - Paramita Mazumder
- Department of Oral PathologyDr. R. Ahmed Dental College and HospitalKolkataIndia
| | | | - Subrata Patra
- National Institute of Biomedical GenomicsKalyaniIndia
| | - Shekhar Ghosh
- National Institute of Biomedical GenomicsKalyaniIndia
| | | | - Sudip Kundu
- National Institute of Biomedical GenomicsKalyaniIndia
| | | | - Sandip Ghose
- Department of Oral PathologyDr. R. Ahmed Dental College and HospitalKolkataIndia
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Roy VL, Majumder PP. Genomic associations with antibody response to an oral cholera vaccine. Vaccine 2023; 41:6391-6400. [PMID: 37699782 DOI: 10.1016/j.vaccine.2023.09.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 09/03/2023] [Accepted: 09/07/2023] [Indexed: 09/14/2023]
Abstract
Oral cholera vaccine is one of the key interventions used in our fight to end the longest pandemic of our time, cholera. The immune response conferred by the currently available cholera vaccines, as measured by serum antibody levels, is variable amongst its recipients. We undertook a genome wide association study (GWAS) on antibody response to the cholera vaccine; globally, the first GWAS on cholera vaccine response. We identified three clusters of bi-allelic SNPs, in high within-cluster linkage disequilibrium that were moderately (p < 5 × 10-6) associated with antibody response to the cholera vaccine and mapped to chromosomal regions 4p14, 4p16.1 and 6q23.3. Intronic SNPs of TBC1D1 comprised the cluster on 4p14, intronic SNPs of TBC1D14 comprised that on 4p16.1 and SNPs upstream of TNFAIP3 formed the cluster on 6q23.3. SNPs within and around these clusters have been implicated in immune cell function and immunological aspects of autoimmune or infectious diseases (e.g., diseases caused by Helicobacter pylori and malarial parasite). 6q23.3 is a prominent region harbouring many loci associated with immune related diseases, including multiple sclerosis, rheumatoid arthritis and systemic lupus erythematosus, as well as IL2 and INFα response to a smallpox vaccine. The gene clusters identified in this study play roles in vesicle-mediated pathway, autophagy and NF-κB signaling. No significant effect of O blood group on antibody response to the cholera vaccine was observed in this study.
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Affiliation(s)
- Vijay Laxmi Roy
- National Institute of Biomedical Genomics, P.O.: N.S.S., Kalyani, West Bengal 741251, India
| | - Partha P Majumder
- National Institute of Biomedical Genomics, P.O.: N.S.S., Kalyani, West Bengal 741251, India; Indian Statistical Institute, 203, Barrackpore Trunk Road, Kolkata, West Bengal 700108, India.
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Chatterjee A, Chaudhary A, Ghosh A, Arun P, Mukherjee G, Arun I, Maitra A, Biswas N, Majumder PP. Overexpression of CD73 is associated with recurrence and poor prognosis of gingivobuccal oral cancer as revealed by transcriptome and deep immune profiling of paired tumor and margin tissues. Cancer Med 2023; 12:16774-16787. [PMID: 37392167 PMCID: PMC10501293 DOI: 10.1002/cam4.6299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 06/16/2023] [Accepted: 06/18/2023] [Indexed: 07/03/2023] Open
Abstract
BACKGROUND For various cancers, differences in response to treatment and subsequent survival period have been reported to be associated with variation in immune contextures. AIM We sought to identify whether such association exists in respect of gingivobuccal oral cancer. MATERIALS AND METHODS We performed deep immune profiling of tumor and margin tissues collected from 46 treatment naïve, Human Papillomavirus (HPV) negative, patients. Each patient was followed for 24 months and prognosis (recurrence/death) noted. Key findings were validated by comparing with TCGA-HNSC cohort data. RESULTS About 28% of patients showed poor post-treatment prognosis. These patients exhibited a high probability of recurrence even within 1 year and death within 2 years. There was restricted immune cell infiltration in tumor, but not in margin, among these patients. Reduced expression of eight immune-related genes (IRGs) (NT5E, THRA, RBP1, TLR4, ITGA6, BMPR1B, ITGAV, SSTR1) in tumor strongly predicted better quality of prognosis, both in our patient cohort and in TCGA-HNSC cohort. Tumors of patients with better prognosis were associated with (a) lower CD73+ cells with concomitant lower expression level of NT5E/CD73, (b) higher proportions of CD4+ and CD8+ T cells, B cells, NK cells, M1 macrophages, (c) higher %Granzyme+ cells, (d) higher TCR and BCR repertoire diversities. CD73 expression in tumor was associated with low CD8+ and CD4+ T cells, low immune repertoire diversity, and advanced cancer stage. DISCUSSION AND CONCLUSION High infiltration of anti-tumor immune cells in both tumors and margins results in good prognosis, while in patients with minimal infiltration in tumors in spite of high infiltration in margins results in poor prognosis. Targeted CD73 immune-checkpoint inhibition may improve clinical outcome.
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Affiliation(s)
- Ankita Chatterjee
- National Institute of Biomedical GenomicsKalyaniIndia
- John C. Martin Centre for Liver Research and InnovationsKolkataIndia
| | | | - Arnab Ghosh
- National Institute of Biomedical GenomicsKalyaniIndia
| | | | | | | | | | - Nidhan Biswas
- National Institute of Biomedical GenomicsKalyaniIndia
| | - Partha P. Majumder
- National Institute of Biomedical GenomicsKalyaniIndia
- John C. Martin Centre for Liver Research and InnovationsKolkataIndia
- Indian Statistical InstituteKolkataIndia
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Das J, Wadhwa N, Natchu UC, Thiruvengadam R, Kshetrapal P, Bhatnagar S, Majumder PP, Maitra A. Genome-wide temporal landscaping of DNA methylation in pregnant women delivering at term: a GARBH-InI study. Epigenomics 2023. [PMID: 37345372 DOI: 10.2217/epi-2023-0145] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/23/2023] Open
Abstract
Background: We performed an epigenome-wide longitudinal DNA methylation study on an Indian cohort of pregnant women, GARBH-Ini, at three time points during pregnancy and at delivery. Aim & objective: Our aim was to identify temporal DNA methylation changes in maternal peripheral blood during the period of gestation and assess their impact on biological pathways critical for term delivery. Results: Significantly differentially methylated CpGs were identified by linear mixed model analysis (Bonferroni p < 0.01) and classified into two distinct temporal methylation trends: increasing and decreasing during gestation. Genes with upward methylation trend were enriched for T-cell activity, while those with a downward trend were enriched for solute transport and cell structure organization functions. Conclusion: Consistent trends of DNA methylation in maternal peripheral blood point to the sentinel function of T cells in the maintenance of pregnancy, and the importance of coordinated cellular remodeling to facilitate term delivery.
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Affiliation(s)
- Jagyashila Das
- National Institute of Biomedical Genomics, Kalyani, 741251, India
| | - Nitya Wadhwa
- Translational Health Science & Technology Institute, Faridabad, 121001, India
| | - Uma Cm Natchu
- Translational Health Science & Technology Institute, Faridabad, 121001, India
- St. John's Research Institute, Bengaluru, 560034, India
| | - Ramachandran Thiruvengadam
- Translational Health Science & Technology Institute, Faridabad, 121001, India
- Pondicherry Institute of Medical Sciences, Puducherry, 605014, India
| | - Pallavi Kshetrapal
- Translational Health Science & Technology Institute, Faridabad, 121001, India
| | - Shinjini Bhatnagar
- Translational Health Science & Technology Institute, Faridabad, 121001, India
| | - Partha P Majumder
- National Institute of Biomedical Genomics, Kalyani, 741251, India
- Indian Statistical Institute, Kolkata, 700108, India
| | - Arindam Maitra
- National Institute of Biomedical Genomics, Kalyani, 741251, India
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Gerstung M, Jolly C, Leshchiner I, Dentro SC, Gonzalez S, Rosebrock D, Mitchell TJ, Rubanova Y, Anur P, Yu K, Tarabichi M, Deshwar A, Wintersinger J, Kleinheinz K, Vázquez-García I, Haase K, Jerman L, Sengupta S, Macintyre G, Malikic S, Donmez N, Livitz DG, Cmero M, Demeulemeester J, Schumacher S, Fan Y, Yao X, Lee J, Schlesner M, Boutros PC, Bowtell DD, Zhu H, Getz G, Imielinski M, Beroukhim R, Sahinalp SC, Ji Y, Peifer M, Markowetz F, Mustonen V, Yuan K, Wang W, Morris QD, Spellman PT, Wedge DC, Van Loo P, Tarabichi M, Wintersinger J, Deshwar AG, Yu K, Gonzalez S, Rubanova Y, Macintyre G, Adams DJ, Anur P, Beroukhim R, Boutros PC, Bowtell DD, Campbell PJ, Cao S, Christie EL, Cmero M, Cun Y, Dawson KJ, Demeulemeester J, Donmez N, Drews RM, Eils R, Fan Y, Fittall M, Garsed DW, Getz G, Ha G, Imielinski M, Jerman L, Ji Y, Kleinheinz K, Lee J, Lee-Six H, Livitz DG, Malikic S, Markowetz F, Martincorena I, Mitchell TJ, Mustonen V, Oesper L, Peifer M, Peto M, Raphael BJ, Rosebrock D, Sahinalp SC, Salcedo A, Schlesner M, Schumacher S, Sengupta S, Shi R, Shin SJ, Spiro O, Pitkänen E, Pivot X, Piñeiro-Yáñez E, Planko L, Plass C, Polak P, Pons T, Popescu I, Potapova O, Prasad A, Stein LD, Preston SR, Prinz M, Pritchard AL, Prokopec SD, Provenzano E, Puente XS, Puig S, Puiggròs M, Pulido-Tamayo S, Pupo GM, Vázquez-García I, Purdie CA, Quinn MC, Rabionet R, Rader JS, Radlwimmer B, Radovic P, Raeder B, Raine KM, Ramakrishna M, Ramakrishnan K, Vembu S, Ramalingam S, Raphael BJ, Rathmell WK, Rausch T, Reifenberger G, Reimand J, Reis-Filho J, Reuter V, Reyes-Salazar I, Reyna MA, Wheeler DA, Reynolds SM, Rheinbay E, Riazalhosseini Y, Richardson AL, Richter J, Ringel M, Ringnér M, Rino Y, Rippe K, Roach J, Yang TP, Roberts LR, Roberts ND, Roberts SA, Robertson AG, Robertson AJ, Rodriguez JB, Rodriguez-Martin B, Rodríguez-González FG, Roehrl MHA, Rohde M, Yao X, Rokutan H, Romieu G, Rooman I, Roques T, Rosebrock D, Rosenberg M, Rosenstiel PC, Rosenwald A, Rowe EW, Royo R, Yuan K, Rozen SG, Rubanova Y, Rubin MA, Rubio-Perez C, Rudneva VA, Rusev BC, Ruzzenente A, Rätsch G, Sabarinathan R, Sabelnykova VY, Zhu H, Sadeghi S, Sahinalp SC, Saini N, Saito-Adachi M, Saksena G, Salcedo A, Salgado R, Salichos L, Sallari R, Saller C, Wang W, Salvia R, Sam M, Samra JS, Sanchez-Vega F, Sander C, Sanders G, Sarin R, Sarrafi I, Sasaki-Oku A, Sauer T, Morris QD, Sauter G, Saw RPM, Scardoni M, Scarlett CJ, Scarpa A, Scelo G, Schadendorf D, Schein JE, Schilhabel MB, Schlesner M, Spellman PT, Schlomm T, Schmidt HK, Schramm SJ, Schreiber S, Schultz N, Schumacher SE, Schwarz RF, Scolyer RA, Scott D, Scully R, Wedge DC, Seethala R, Segre AV, Selander I, Semple CA, Senbabaoglu Y, Sengupta S, Sereni E, Serra S, Sgroi DC, Shackleton M, Van Loo P, Shah NC, Shahabi S, Shang CA, Shang P, Shapira O, Shelton T, Shen C, Shen H, Shepherd R, Shi R, Spellman PT, Shi Y, Shiah YJ, Shibata T, Shih J, Shimizu E, Shimizu K, Shin SJ, Shiraishi Y, Shmaya T, Shmulevich I, Wedge DC, Shorser SI, Short C, Shrestha R, Shringarpure SS, Shriver C, Shuai S, Sidiropoulos N, Siebert R, Sieuwerts AM, Sieverling L, Van Loo P, Signoretti S, Sikora KO, Simbolo M, Simon R, Simons JV, Simpson JT, Simpson PT, Singer S, Sinnott-Armstrong N, Sipahimalani P, Aaltonen LA, Skelly TJ, Smid M, Smith J, Smith-McCune K, Socci ND, Sofia HJ, Soloway MG, Song L, Sood AK, Sothi S, Abascal F, Sotiriou C, Soulette CM, Span PN, Spellman PT, Sperandio N, Spillane AJ, Spiro O, Spring J, Staaf J, Stadler PF, Abeshouse A, Staib P, Stark SG, Stebbings L, Stefánsson ÓA, Stegle O, Stein LD, Stenhouse A, Stewart C, Stilgenbauer S, Stobbe MD, Aburatani H, Stratton MR, Stretch JR, Struck AJ, Stuart JM, Stunnenberg HG, Su H, Su X, Sun RX, Sungalee S, Susak H, Adams DJ, Suzuki A, Sweep F, Szczepanowski M, Sültmann H, Yugawa T, Tam A, Tamborero D, Tan BKT, Tan D, Tan P, Agrawal N, Tanaka H, Taniguchi H, Tanskanen TJ, Tarabichi M, Tarnuzzer R, Tarpey P, Taschuk ML, Tatsuno K, Tavaré S, Taylor DF, Ahn KS, Taylor-Weiner A, Teague JW, Teh BT, Tembe V, Temes J, Thai K, Thayer SP, Thiessen N, Thomas G, Thomas S, Ahn SM, Thompson A, Thompson AM, Thompson JFF, Thompson RH, Thorne H, Thorne LB, Thorogood A, Tiao G, Tijanic N, Timms LE, Aikata H, Tirabosco R, Tojo M, Tommasi S, Toon CW, Toprak UH, Torrents D, Tortora G, Tost J, Totoki Y, Townend D, Akbani R, Traficante N, Treilleux I, Trotta JR, Trümper LHP, Tsao M, Tsunoda T, Tubio JMC, Tucker O, Turkington R, Turner DJ, Akdemir KC, Tutt A, Ueno M, Ueno NT, Umbricht C, Umer HM, Underwood TJ, Urban L, Urushidate T, Ushiku T, Uusküla-Reimand L, Al-Ahmadie H, 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, Al-Sedairy ST, Veluvolu U, Vembu S, Verbeke LPC, Vermeulen P, Verrill C, Viari A, Vicente D, Vicentini C, VijayRaghavan K, Viksna J, Al-Shahrour F, Vilain RE, Villasante I, Vincent-Salomon A, Visakorpi T, Voet D, Vyas P, Vázquez-García I, Waddell NM, Waddell N, Wadelius C, Alawi M, Wadi L, Wagener R, Wala JA, Wang J, Wang J, Wang L, Wang Q, Wang W, Wang Y, Wang Z, Albert M, Waring PM, Warnatz HJ, Warrell J, Warren AY, Waszak SM, Wedge DC, Weichenhan D, Weinberger P, Weinstein JN, Weischenfeldt J, Aldape K, Weisenberger DJ, Welch I, Wendl MC, Werner J, Whalley JP, Wheeler DA, Whitaker HC, Wigle D, Wilkerson MD, Williams A, Alexandrov LB, Wilmott JS, Wilson GW, Wilson JM, Wilson RK, Winterhoff B, Wintersinger JA, Wiznerowicz M, Wolf S, Wong BH, Wong T, Ally A, Wong W, Woo Y, Wood S, Wouters BG, Wright AJ, Wright DW, Wright MH, Wu CL, Wu DY, Wu G, Alsop K, Wu J, Wu K, Wu Y, Wu Z, Xi L, Xia T, Xiang Q, Xiao X, Xing R, Xiong H, Alvarez EG, Xu Q, Xu Y, Xue H, Yachida S, Yakneen S, Yamaguchi R, Yamaguchi TN, Yamamoto M, Yamamoto S, Yamaue H, Amary F, Yang F, Yang H, Yang JY, Yang L, Yang L, Yang S, Yang TP, Yang Y, Yao X, Yaspo ML, Amin SB, Yates L, Yau C, Ye C, Ye K, Yellapantula VD, Yoon CJ, Yoon SS, Yousif F, Yu J, Yu K, Aminou B, Yu W, Yu Y, Yuan K, Yuan Y, Yuen D, Yung CK, Zaikova O, Zamora J, Zapatka M, Zenklusen JC, Ammerpohl O, Zenz T, Zeps N, Zhang CZ, Zhang F, Zhang H, Zhang H, Zhang H, Zhang J, Zhang J, Zhang J, Anderson MJ, Zhang X, Zhang X, Zhang Y, Zhang Z, Zhao Z, Zheng L, Zheng X, Zhou W, Zhou Y, Zhu B, Ang Y, Zhu H, Zhu J, Zhu S, Zou L, Zou X, deFazio A, van As N, van Deurzen CHM, van de Vijver MJ, van’t Veer L, Antonello D, von Mering C, Anur P, Aparicio S, Appelbaum EL, Arai Y, Aretz A, Arihiro K, Ariizumi SI, Armenia J, Arnould L, Asa S, Assenov Y, Atwal G, Aukema S, Auman JT, Aure MRR, Awadalla P, Aymerich M, Bader GD, Baez-Ortega A, Bailey MH, Bailey PJ, Balasundaram M, Balu S, Bandopadhayay P, Banks RE, Barbi S, Barbour AP, Barenboim J, Barnholtz-Sloan J, Barr H, Barrera E, Bartlett J, Bartolome J, Bassi C, Bathe OF, Baumhoer D, Bavi P, Baylin SB, Bazant W, Beardsmore D, Beck TA, Behjati S, Behren A, Niu B, Bell C, Beltran S, Benz C, Berchuck A, Bergmann AK, Bergstrom EN, Berman BP, Berney DM, Bernhart SH, Beroukhim R, Berrios M, Bersani S, Bertl J, Betancourt M, Bhandari V, Bhosle SG, Biankin AV, Bieg M, Bigner D, Binder H, Birney E, Birrer M, Biswas NK, Bjerkehagen B, Bodenheimer T, Boice L, Bonizzato G, De Bono JS, Boot A, Bootwalla MS, Borg A, Borkhardt A, Boroevich KA, Borozan I, Borst C, Bosenberg M, Bosio M, Boultwood J, Bourque G, Boutros PC, Bova GS, Bowen DT, Bowlby R, Bowtell DDL, Boyault S, Boyce R, Boyd J, Brazma A, Brennan P, Brewer DS, Brinkman AB, Bristow RG, Broaddus RR, Brock JE, Brock M, Broeks A, Brooks AN, Brooks D, Brors B, Brunak S, Bruxner TJC, Bruzos AL, Buchanan A, Buchhalter I, Buchholz C, Bullman S, Burke H, Burkhardt B, Burns KH, Busanovich J, Bustamante CD, Butler AP, Butte AJ, Byrne NJ, Børresen-Dale AL, Caesar-Johnson SJ, Cafferkey A, Cahill D, Calabrese C, Caldas C, Calvo F, Camacho N, Campbell PJ, Campo E, Cantù C, Cao S, Carey TE, Carlevaro-Fita J, Carlsen R, Cataldo I, Cazzola M, Cebon J, Cerfolio R, Chadwick DE, Chakravarty D, Chalmers D, Chan CWY, Chan K, 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. 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, 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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, 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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, 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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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7
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Ghosh A, Ghosh A, Sinha A, Mathai S, Bhaumik J, Mukhopadhyay A, Maitra A, Biswas NK, Majumder PP, Sengupta S. Identification of HPV16 positive cervical cancer subsets characterized by divergent immune and oncogenic phenotypes with potential implications for immunotherapy. Tumour Biol 2023; 45:55-69. [PMID: 37599552 DOI: 10.3233/tub-220035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/22/2023] Open
Abstract
BACKGROUND Cervical cancers (CaCx), like many other cancer types, portray high molecular heterogeneity that affects response to therapy, including immunotherapy. In India and other developing countries, CaCx mortality rates are very high because women report to the clinics with advanced cancers in absence of organized screening programs. This calls for implementation of newer therapeutic regimens for CaCx, like immunotherapy, which is again not used commonly in such countries. OBJECTIVE Therefore, we focused on dissecting tumour immune heterogeneity, if any, identify immune gene-based biomarkers of heterogeneity and subsets of such cancers with the potential for immunotherapy. We also attempted to characterize the cancer-associated phenotypes of such subsets, including viral load, to decipher the relationship of tumour immunogenicity with oncogenicity. METHODS Employing RNA-seq analysis of 44 HPV16 positive CaCx patients, immune subtypes were identified by unsupervised hierarchical clustering of global immune-gene expression profiles. Proportions of tumor infiltrating immune cells in the tumor milieu were estimated, employing Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), using gene expression data from RNA-seq. The oncogenic phenotypes of the immune subtypes of CaCx were deciphered through differential gene expression (DEGs) and pathway enrichment analysis. Viral load was estimated through TaqMan-based qRT-PCR analysis. RESULTS Analysis revealed the presence of two immune subtypes of CaCx, A (26/44; 59.09%) and B (18/44; 40.90%). Compared to Subtype-A, Subtype-B portrayed overexpression of immune genes and high infiltration of immune cells, specifically CD8+ T cells (p < 0.0001). Besides, a significant correlation between PD-1 and PD-L1 co-expression among Subtype-B, as opposed to Subtype-A, confirmed the interactive roles of these immune checkpoint molecules in Subtype B. Stepwise discriminant analysis pin-pointed ten immune-genes that could classify 100% of the patients significantly (p < 0.0001) into the two immune subtypes and serve as potential biomarkers of CaCx immunity. Differential gene expression analysis between the subtypes unveiled that Subtype-B was more biologically aggressive than Subtype-A, reflecting loss of structural integrity and promotion of cancer progression. The viral load was significantly lower in Subtype-B (average viral load = 10.74/100 ng of genomic DNA) compared to Subtype-A (average viral load = 14.29/100 ng of genomic DNA). Thus viral load and the ten-gene panel underscore their association with immunogenicity and oncogenicity. CONCLUSION Our study provides strong evidence that only a subset, about 41% of HPV16 positive CaCx patients in India, portray immune enrichment of the tumor milieu coupled with aggressive phenotypes. Such subtypes are therefore likely to benefit through checkpoint molecule-based or tumor infiltrating lymphocyte-based immunotherapy, which could be a leap forward in tackling aggressive forms of such CaCx in India and other developing countries.
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Affiliation(s)
- Abhisikta Ghosh
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
| | - Arnab Ghosh
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
| | - Abarna Sinha
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
| | - Sonia Mathai
- Tata Medical Center, Kolkata, West Bengal, India
| | | | - Asima Mukhopadhyay
- Kolkata Gynecological Oncology Trials and Translational Research Group, Kolkata, West Bengal, India
| | - Arindam Maitra
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
| | - Nidhan K Biswas
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
| | - Partha P Majumder
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
| | - Sharmila Sengupta
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
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8
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Tagore D, Majumder PP, Chatterjee A, Basu A. Multiple migrations from East Asia led to linguistic transformation in NorthEast India and mainland Southeast Asia. Front Genet 2022; 13:1023870. [PMID: 36303544 PMCID: PMC9592996 DOI: 10.3389/fgene.2022.1023870] [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] [Received: 08/20/2022] [Accepted: 09/27/2022] [Indexed: 11/13/2022] Open
Abstract
NorthEast India, with its unique geographic location in the midst of the Himalayas and Bay of Bengal, has served as a passage for the movement of modern humans across the Indian subcontinent and East/Southeast Asia. In this study we look into the population genetics of a unique population called the Khasi, speaking a language (also known as the Khasi language) belonging to the Austroasiatic language family and residing amidst the Tibeto-Burman speakers as an isolated population. The Khasi language belongs to one of the three major broad classifications or phyla of the Austroasiatic language and the speakers of the three sub-groups are separated from each other by large geographical distances. The Khasi speakers are separated from their nearest Austroasiatic language-speaking sub-groups: the “Mundari” sub-family from East and peninsular India and the “Mon-Khmers” in Mainland Southeast Asia. We found the Khasi population to be genetically distinct from other Austroasiatic speakers, i.e. Mundaris and Mon-Khmers, but relatively similar to the geographically proximal Tibeto Burmans. The possible reasons for this genetic-linguistic discordance lie in the admixture history of different migration events that originated from East Asia and proceeded possibly towards Southeast Asia. We found at least two distinct migration events from East Asia. While the ancestors of today’s Tibeto-Burman speakers were affected by both, the ancestors of Khasis were insulated from the second migration event. Correlating the linguistic similarity of Tibeto-Burman and Sino-Tibetan languages of today’s East Asians, we infer that the second wave of migration resulted in a linguistic transition while the Khasis could preserve their linguistic identity.
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Affiliation(s)
| | - Partha P. Majumder
- National Institute of Biomedical Genomics, Kalyani, India
- Indian Statistical Institute, Kolkata, India
| | - Anupam Chatterjee
- Department of Biotechnology, North-Eastern Hill University, Shillong, India
- School of Biosciences, Royal Global University, Guwahati, India
| | - Analabha Basu
- National Institute of Biomedical Genomics, Kalyani, India
- *Correspondence: Analabha Basu,
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9
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Ghosh A, Das C, Ghose S, Maitra A, Roy B, Majumder PP, Biswas NK. Integrative analysis of genomic and transcriptomic data of normal, tumour and co-occurring leukoplakia tissue triads drawn from patients with gingivobuccal oral cancer identifies signatures of tumour initiation and progression. J Pathol 2022; 257:593-606. [PMID: 35358331 PMCID: PMC9545831 DOI: 10.1002/path.5900] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [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: 12/16/2021] [Revised: 03/25/2022] [Accepted: 03/28/2022] [Indexed: 11/16/2022]
Abstract
A thickened, white patch — leukoplakia — in the oral cavity is usually benign, but sometimes (in ~9% of individuals) it progresses to malignant tumour. Because the genomic basis of this progression is poorly understood, we undertook this study and collected samples of four tissues — leukoplakia, tumour, adjacent normal, and blood — from each of 28 patients suffering from gingivobuccal oral cancer. We performed multiomics analysis of the 112 collected tissues (four tissues per patient from 28 patients) and integrated information on progressive changes in the mutational and transcriptional profiles of each patient to create this genomic narrative. Additionally, we generated and analysed whole‐exome sequence data from leukoplakia tissues collected from 11 individuals not suffering from oral cancer. Nonsynonymous somatic mutations in the CASP8 gene were identified as the likely events to initiate malignant transformation, since these were frequently shared between tumour and co‐occurring leukoplakia. CASP8 alterations were also shown to enhance expressions of genes that favour lateral spread of mutant cells. During malignant transformation, additional pathogenic mutations are acquired in key genes (TP53, NOTCH1, HRAS) (41% of patients); chromosomal‐instability (arm‐level deletions of 19p and q, focal‐deletion of DNA‐repair pathway genes and NOTCH1, amplification of EGFR) (77%), and increased APOBEC‐activity (23%) are also observed. These additional alterations were present singly (18% of patients) or in combination (68%). Some of these alterations likely impact immune‐dynamics of the evolving transformed tissue; progression to malignancy is associated with immune suppression through infiltration of regulatory T‐cells (56%), depletion of cytotoxic T‐cells (68%), and antigen‐presenting dendritic cells (72%), with a concomitant increase in inflammation (92%). Patients can be grouped into three clusters by the estimated time to development of cancer from precancer by acquiring additional mutations (range: 4–10 years). Our findings provide deep molecular insights into the evolutionary processes and trajectories of oral cancer initiation and progression. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Arnab Ghosh
- National Institute of Biomedical Genomics, Kalyani, India
| | | | - Sandip Ghose
- Dr. R. Ahmed Dental College and Hospital, Kolkata, India
| | - Arindam Maitra
- National Institute of Biomedical Genomics, Kalyani, India
| | - Bidyut Roy
- Indian Statistical Institute, Kolkata, India
| | - Partha P Majumder
- National Institute of Biomedical Genomics, Kalyani, India.,Indian Statistical Institute, Kolkata, India
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10
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Chatterjee A, Basu A, Das K, Singh P, Mondal D, Bhattacharya B, Roychoudhury S, Majumder PP, Chowdhury A, Basu P. Hepatic transcriptome signature correlated with HOMA-IR explains early nonalcoholic fatty liver disease pathogenesis. Ann Hepatol 2021; 19:472-481. [PMID: 32682086 DOI: 10.1016/j.aohep.2020.06.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 06/28/2020] [Accepted: 06/29/2020] [Indexed: 02/04/2023]
Abstract
INTRODUCTION AND OBJECTIVES Non-alcoholic fatty liver disease (NAFLD) is multistage with heterogeneous outcomes. We studied the influence of insulin resistance (IR) on the hepatic transcriptome of early NAFLD stages, to understand disease development. MATERIALS AND METHODS In this cross-sectional study, possible clinicopathological risk factors were compared between mild-NAFL (N = 72) and non-alcoholic steatohepatitis (NASH; N = 51) patients. Liver tissue-transcriptome difference was studied between a subset of 25 mild-NAFL and 20 NASH biopsies and validated on another subset of 12 mild-NAFL and 13 NASH biopsies, using RT-PCR. The relationship between IR driven gene expression changes with fibrosis in NASH was investigated. RESULTS Significantly higher weight (p = 0.005) and elevated levels of HbA1c (p = 0.009), FBG (p = 0.03) and HOMA-IR (p = 0.009) were found in NASH patients. Five differentially expressed genes (DEGs, fold change > 1.5) were identified in NASH-FABP4, FABP5L2, CD24, PRAP1, and SPP1. The DEGs were positively associated with disease severity and HOMA-IR, and were found to be efficient classifiers of mild-NAFL and NASH. Additional 1218 genes identified related to IR (IrCGs), which can classify NASH-with-fibrosis patients separately from mild-NAFL and NASH patients. IrCGs can promote intra-hepatic fat accumulation, dysregulation of the lipid metabolism, lipotoxicity, and activation of cell survival pathways including activation of cell proliferation and differentiation pathways. CONCLUSIONS Hepatic expression of genes associated with insulin resistance may drive NAFLD development and progression.
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Affiliation(s)
- Ankita Chatterjee
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
| | - Analabha Basu
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
| | - Kausik Das
- Institute of Post Graduate Medical Education and Research, Kolkata, West Bengal, India
| | - Pankaj Singh
- Institute of Post Graduate Medical Education and Research, Kolkata, West Bengal, India
| | - Dipankar Mondal
- Institute of Post Graduate Medical Education and Research, Kolkata, West Bengal, India
| | | | | | - Partha P Majumder
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
| | - Abhijit Chowdhury
- Institute of Post Graduate Medical Education and Research, Kolkata, West Bengal, India
| | - Priyadarshi Basu
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India.
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11
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Bhattacharyya C, Das C, Ghosh A, Singh AK, Mukherjee S, Majumder PP, Basu A, Biswas NK. SARS-CoV-2 mutation 614G creates an elastase cleavage site enhancing its spread in high AAT-deficient regions. Infect Genet Evol 2021; 90:104760. [PMID: 33556558 PMCID: PMC7863758 DOI: 10.1016/j.meegid.2021.104760] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 02/01/2021] [Accepted: 02/03/2021] [Indexed: 02/07/2023]
Abstract
SARS-CoV-2 was first reported from China. Within three months, it evolved to 10 additional subtypes. Two evolved subtypes (A2 and A2a) carry a non-synonymous Spike protein mutation (D614G). We conducted phylodynamic analysis of over 70,000 SARS-CoV-2 coronaviruses worldwide, sequenced until July2020, and found that the mutant subtype (614G) outcompeted the pre-existing type (614D), significantly faster in Europe and North-America than in East Asia. Bioinformatically and computationally, we identified a novel neutrophil elastase (ELANE) cleavage site introduced in the G-mutant, near the S1-S2 junction of the Spike protein. We hypothesised that elevation of neutrophil elastase level at the site of infection will enhance the activation of Spike protein thus facilitating host cell entry for 614G, but not the 614D, subtype. The level of neutrophil elastase in the lung is modulated by its inhibitor α1-antitrypsin (AAT). AAT prevents lung tissue damage by elastase. However, many individuals exhibit genotype-dependent deficiency of AAT. AAT deficiency eases host-cell entry of the 614G virus, by retarding inhibition of neutrophil elastase and consequently enhancing activation of the Spike protein. AAT deficiency is highly prevalent in European and North-American populations, but much less so in East Asia. Therefore, the 614G subtype is able to infect and spread more easily in populations of the former regions than in the latter region. Our analyses provide a molecular biological and evolutionary model for the higher observed virulence of the 614G subtype, in terms of causing higher morbidity in the host (higher infectivity and higher viral load), than the non-mutant 614D subtype.
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Affiliation(s)
| | - Chitrarpita Das
- National Institute of Biomedical Genomics, Kalyani 741251, India
| | - Arnab Ghosh
- National Institute of Biomedical Genomics, Kalyani 741251, India
| | - Animesh K. Singh
- National Institute of Biomedical Genomics, Kalyani 741251, India
| | - Souvik Mukherjee
- National Institute of Biomedical Genomics, Kalyani 741251, India
| | - Partha P. Majumder
- National Institute of Biomedical Genomics, Kalyani 741251, India,Indian Statistical Institute, Kolkata 700108, India
| | - Analabha Basu
- National Institute of Biomedical Genomics, Kalyani 741251, India
| | - Nidhan K. Biswas
- National Institute of Biomedical Genomics, Kalyani 741251, India,Corresponding author at: National Institute of Biomedical Genomics, P.O.: N.S.S., Kalyani 741251, West Bengal, India
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Pradhan S, Das S, Singh AK, Das C, Basu A, Majumder PP, Biswas NK. dbGENVOC: database of GENomic Variants of Oral Cancer, with special reference to India. Database (Oxford) 2021; 2021:6287646. [PMID: 34048545 PMCID: PMC8163239 DOI: 10.1093/database/baab034] [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] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 05/12/2021] [Accepted: 05/15/2021] [Indexed: 11/18/2022]
Abstract
Oral cancer is highly prevalent in India and is the most frequent cancer type among Indian males. It is also very common in southeast Asia. India has participated in the International Cancer Genome Consortium (ICGC) and some national initiatives to generate large-scale genomic data on oral cancer patients and analyze to identify associations and systematically catalog the associated variants. We have now created an open, web-accessible database of these variants found significantly associated with Indian oral cancer patients, with a user-friendly interface to enable easy mining. We have value added to this database by including relevant data collated from various sources on other global populations, thereby providing opportunities of comparative geographical and/or ethnic analyses. Currently, no other database of similar nature is available on oral cancer. We have developed Database of GENomic Variants of Oral Cancer, a browsable online database framework for storage, retrieval and analysis of large-scale data on genomic variants and make it freely accessible to the scientific community. Presently, the web-accessible database allows potential users to mine data on ∼24 million clinically relevant somatic and germline variants derived from exomes (n = 100) and whole genomes (n = 5) of Indian oral cancer patients; all generated by us. Variant data from The Cancer Genome Atlas and data manually curated from peer-reviewed publications were also incorporated into the database for comparative analyses. It allows users to query the database by a single gene, multiple genes, multiple variant sites, genomic region, patient ID and pathway identities. Database URL: http://research.nibmg.ac.in/dbcares/dbgenvoc/
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Affiliation(s)
- Sanchari Pradhan
- Human Genetics Unit, National Institute of Biomedical Genomics, Kalyani, West Bengal 741251, India
| | - Subrata Das
- Human Genetics Unit, National Institute of Biomedical Genomics, Kalyani, West Bengal 741251, India
| | - Animesh K Singh
- Human Genetics Unit, National Institute of Biomedical Genomics, Kalyani, West Bengal 741251, India
| | - Chitrarpita Das
- Human Genetics Unit, National Institute of Biomedical Genomics, Kalyani, West Bengal 741251, India
| | - Analabha Basu
- Human Genetics Unit, National Institute of Biomedical Genomics, Kalyani, West Bengal 741251, India
| | - Partha P Majumder
- Human Genetics Unit, National Institute of Biomedical Genomics, Kalyani, West Bengal 741251, India.,Human Genetics Unit, Indian Statistical Institute, Kolkata, West Bengal 700108, India
| | - Nidhan K Biswas
- Human Genetics Unit, National Institute of Biomedical Genomics, Kalyani, West Bengal 741251, India
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Mukhopadhyay S, Ghosh S, Das D, Arun P, Roy B, Biswas NK, Maitra A, Majumder PP. Application of Random Forest and data integration identifies three dysregulated genes and enrichment of Central Carbon Metabolism pathway in Oral Cancer. BMC Cancer 2020; 20:1219. [PMID: 33317464 PMCID: PMC7737291 DOI: 10.1186/s12885-020-07709-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [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: 07/26/2020] [Accepted: 12/03/2020] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Studies of epigenomic alterations associated with diseases primarily focus on methylation profiles of promoter regions of genes, but not of other genomic regions. In our past work (Das et al. 2019) on patients suffering from gingivo-buccal oral cancer - the most prevalent form of cancer among males in India - we have also focused on promoter methylation changes and resultant impact on transcription profiles. Here, we have investigated alterations in non-promoter (gene-body) methylation profiles and have carried out an integrative analysis of gene-body methylation and transcriptomic data of oral cancer patients. METHODS Tumor and adjacent normal tissue samples were collected from 40 patients. Data on methylation in the non-promoter (gene-body) regions of genes and transcriptome profiles were generated and analyzed. Because of high dimensionality and highly correlated nature of these data, we have used Random Forest (RF) and other data-analytical methods. RESULTS Integrative analysis of non-promoter methylation and transcriptome data revealed significant methylation-driven alterations in some genes that also significantly impact on their transcription levels. These changes result in enrichment of the Central Carbon Metabolism (CCM) pathway, primarily by dysregulation of (a) NTRK3, which plays a dual role as an oncogene and a tumor suppressor; (b) SLC7A5 (LAT1) which is a transporter dedicated to essential amino acids, and is overexpressed in cancer cells to meet the increased demand for nutrients that include glucose and essential amino acids; and, (c) EGFR which has been earlier implicated in progression, recurrence, and stemness of oral cancer, but we provide evidence of epigenetic impact on overexpression of this gene for the first time. CONCLUSIONS In rapidly dividing cancer cells, metabolic reprogramming from normal cells takes place to enable enhanced proliferation. Here, we have identified that among oral cancer patients, genes in the CCM pathway - that plays a fundamental role in metabolic reprogramming - are significantly dysregulated because of perturbation of methylation in non-promoter regions of the genome. This result compliments our previous result that perturbation of promoter methylation results in significant changes in key genes that regulate the feedback process of DNA methylation for the maintenance of normal cell division.
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Affiliation(s)
| | - Sahana Ghosh
- National Institute of Biomedical Genomics, Kalyani, 741251, India
| | - Debodipta Das
- National Institute of Biomedical Genomics, Kalyani, 741251, India
| | - P Arun
- Tata Medical Centre, Kolkata, India
| | - Bidyut Roy
- Indian Statistical Institute, Kolkata, India
| | - Nidhan K Biswas
- National Institute of Biomedical Genomics, Kalyani, 741251, India
| | - Arindam Maitra
- National Institute of Biomedical Genomics, Kalyani, 741251, India
| | - Partha P Majumder
- National Institute of Biomedical Genomics, Kalyani, 741251, India. .,Indian Statistical Institute, Kolkata, India.
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Nath S, Kumari N, Bandyopadhyay D, Sinha N, Majumder PP, Mitra R, Mukherjee S. Dysbiotic Lesional Microbiome With Filaggrin Missense Variants Associate With Atopic Dermatitis in India. Front Cell Infect Microbiol 2020; 10:570423. [PMID: 33282748 PMCID: PMC7705349 DOI: 10.3389/fcimb.2020.570423] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [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: 06/07/2020] [Accepted: 09/22/2020] [Indexed: 12/26/2022] Open
Abstract
Background: Atopic Dermatitis (AD) has been associated with the loss of function (LoF) mutations in Filaggrin (FLG) gene and increase in relative abundance of specific microbes in the lesional skin, predominantly in Caucasians. Our study aims to determine, in Indian AD patients, (a) the prevalence of FLG LoF and missense mutations, and (b) the nature and extent of dysbiosis and altered microbial pathways with and without mutations in FLG. AD patients (n = 34) and healthy controls (n = 54) were recruited from India in this study and shotgun sequencing was carried out in a subset of samples with adequate microbiome DNA concentration. Host DNA from the same subset of samples was subjected to FLG coding region sequencing and host-microbiome association was estimated. Results: The prevalence of FLG LoFs that are associated with AD globally were significantly lesser in our cases and controls (8.6%, 0%) than those reported in Europeans (27%, 2.6%). Staphylococcus aureus was present only on AD skin [abundance in Pediatric AD: 32.86%; Adult AD: 22.17%], but not on healthy skin on which Staphylococcus hominis (Adult controls: 16.43%, Adult AD: 0.20%; p = 0.002), Cutibacterium acnes (Adult controls:10.84%, Adult AD: 0.90%; p = 0.02), and Malassezia globosa (Adult controls: 8.89%, Adult AD: 0.005%; p = 0.001) were significantly more abundant. Microbial pathways mostly associated with skin barrier permeability, ammonia production and inflammation (Arginine and Proline metabolism, Histidine Metabolism and Staphylococcus aureus infection) were significantly enriched on AD skin metagenome. These pathways are also reported to impair antimicrobial peptide activity. Among AD patients with missense single nucleotide polymorphisms harboring "potentially damaging" alleles in FLG gene, damaging allele dosage was significantly (p < 0.02) positively correlated with relative abundance of phylum_Proteobacteria up to order_Pseudomonadales and negatively correlated with phylum_Firmicutes up to species_Staphylococcus aureus. Conclusion: Our study has provided evidence that host DNA profile is significantly associated with microbiome composition in the development of AD. Species and strain level analysis showed that the microbial pathways enriched in AD cases were mostly found in MRSA strains. These evidences can be harnessed to control AD by modulating the microbiome using a personalized strategy. Our findings on the association of FLG genotypes with the microbiome dysbiosis may pave the way for a personalized strategy to provide a more effective control of AD.
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Affiliation(s)
- Shankha Nath
- National Institute of Biomedical Genomics, Kalyani, India
| | - Naina Kumari
- National Institute of Biomedical Genomics, Kalyani, India
| | | | - Neloy Sinha
- College of Medicine and JNM Hospital, Kalyani, India
| | - Partha P Majumder
- National Institute of Biomedical Genomics, Kalyani, India.,Indian Statistical Institute, Kolkata, India
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Biswas S, Pal S, Majumder PP, Bhattacharjee S. A framework for pathway knowledge driven prioritization in genome-wide association studies. Genet Epidemiol 2020; 44:841-853. [PMID: 32779262 PMCID: PMC7116354 DOI: 10.1002/gepi.22345] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [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: 04/07/2020] [Revised: 06/18/2020] [Accepted: 07/10/2020] [Indexed: 12/27/2022]
Abstract
Many variants with low frequencies or with low to modest effects likely remain unidentified in genome-wide association studies (GWAS) because of stringent genome-wide thresholds for detection. To improve the power of detection, variant prioritization based on their functional annotations and epigenetic landmarks has been used successfully. Here, we propose a novel method of prioritization of a GWAS by exploiting gene-level knowledge (e.g., annotations to pathways and ontologies) and show that it further improves power. Often, disease associated variants are found near genes that are coinvolved in specific biological pathways relevant to disease process. Utilization of this knowledge to conduct a prioritized scan increases the power to detect loci that map to genes clustered in a few specific pathways. We have developed a computationally scalable framework based on penalized logistic regression (termed GKnowMTest-Genomic Knowledge-guided Multiplte Testing) to enable a prioritized pathway-guided GWAS scan with a very large number of gene-level annotations. We demonstrate that the proposed strategy improves overall power and maintains the Type 1 error globally. Our method works on genome-wide summary level data and a user-specified list of pathways (e.g., those extracted from large pathway databases without reference to biology of a specific disease). It automatically reweights the input p values by incorporating the pathway enrichments as "adaptively learned" from the data using a cross-validation technique to avoid overfitting. We used whole-genome simulations and some publicly available GWAS data sets to illustrate the application of our method. The GKnowMTest framework has been implemented as a user-friendly open-source R package.
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Affiliation(s)
| | - Soumen Pal
- National Institute of Biomedical Genomics, Kalyani, India
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Singh R, Das S, Datta S, Mazumdar A, Biswas NK, Maitra A, Majumder PP, Ghose S, Roy B. Study of Caspase 8 mutation in oral cancer and adjacent precancer tissues and implication in progression. PLoS One 2020; 15:e0233058. [PMID: 32492030 PMCID: PMC7269231 DOI: 10.1371/journal.pone.0233058] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [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: 02/21/2020] [Accepted: 04/27/2020] [Indexed: 12/16/2022] Open
Abstract
It is hypothesized that same driver gene mutations should be present in both oral leukoplakia and cancer tissues. So, we attempted to find out mutations at one of the driver genes, CASP8, in cancer and adjacent leukoplakia tissues. Patients (n = 27), affected by both of cancer and adjacent leukoplakia, were recruited for the study. Blood and tissue DNA samples were used to identify somatic mutations at CASP8 by next generation sequencing method. In total, 56% (15 out of 27) cancer and 30% (8 out of 27) leukoplakia tissues had CASP8 somatic mutations. In 8 patients, both cancer and adjacent leukoplakia tissues, located within 2-5 cm of tumor sites, had identical somatic mutations. But, in 7 patients, cancer samples had somatic mutations but none of the leukoplakia tissues, located beyond 5cm of tumor sites, had somatic mutations. Mutated allele frequencies at CASP8 were found to be more in cancer compared to adjacent leukoplakia tissues. This study provides mutational evidence that oral cancer might have progressed from previously grown leukoplakia lesion. Leukoplakia tissues, located beyond 5cm of cancer sites, were free from mutation. The study implies that CASP8 mutation could be one of the signatures for some of the leukoplakia to progress to oral cancer.
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Affiliation(s)
- Richa Singh
- Human Genetics Unit, Indian Statistical Institute, Kolkata, India
| | - Shreya Das
- Dr. R. Ahmed Dental College and Hospital, Kolkata, India
| | - Sila Datta
- Dr. R. Ahmed Dental College and Hospital, Kolkata, India
| | | | - Nidhan K. Biswas
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
| | - Arindam Maitra
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
| | | | - Sandip Ghose
- Dr. R. Ahmed Dental College and Hospital, Kolkata, India
- * E-mail: (BR); (SG)
| | - Bidyut Roy
- Human Genetics Unit, Indian Statistical Institute, Kolkata, India
- * E-mail: (BR); (SG)
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Abstract
BACKGROUND & OBJECTIVES SARS-CoV-2 (Severe acute respiratory syndrome coronavirus-2) is evolving with the progression of the pandemic. This study was aimed to investigate the diversity and evolution of the coronavirus SARS-CoV-2 with progression of the pandemic over time and to identify similarities and differences of viral diversity and evolution across geographical regions (countries). METHODS Publicly available data on type definitions based on whole-genome sequences of the SARS-CoV-2 sampled during December and March 2020 from 3636 infected patients spread over 55 countries were collected. Phylodynamic analyses were performed and the temporal and spatial evolution of the virus was examined. RESULTS It was found that (i) temporal variation in frequencies of types of the coronavirus was significant; ancestral viruses of type O were replaced by evolved viruses belonging to type A2a; (ii) spatial variation was not significant; with the spread of SARS-CoV-2, the dominant virus was the A2a type virus in every geographical region; (iii) within a geographical region, there was significant micro-level variation in the frequencies of the different viral types, and (iv) the evolved coronavirus of type A2a swept rapidly across all continents. INTERPRETATION & CONCLUSIONS SARS-CoV-2 belonging to the A2a type possesses a non-synomymous variant (D614G) that possibly eases the entry of the virus into the lung cells of the host. This may be the reason why the A2a type has an advantage to infect and survive and as a result has rapidly swept all geographical regions. Therefore, large-scale sequencing of coronavirus genomes and, as required, of host genomes should be undertaken in India to identify regional and ethnic variation in viral composition and its interaction with host genomes. Further, careful collection of clinical and immunological data of the host can provide deep learning in relation to infection and transmission of the types of coronavirus genomes.
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Affiliation(s)
- Nidhan K. Biswas
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
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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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Das D, Ghosh S, Maitra A, Biswas NK, Panda CK, Roy B, Sarin R, Majumder PP. Epigenomic dysregulation-mediated alterations of key biological pathways and tumor immune evasion are hallmarks of gingivo-buccal oral cancer. Clin Epigenetics 2019; 11:178. [PMID: 31796082 PMCID: PMC6889354 DOI: 10.1186/s13148-019-0782-2] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [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: 09/10/2019] [Accepted: 11/17/2019] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Gingivo-buccal oral squamous cell carcinoma (OSCC-GB) is the most common cancer among men in India and is associated with high mortality. Although OSCC-GB is known to be quite different from tongue cancer in its genomic presentation and its clinical behavior, it is treated identically as tongue cancer. Predictive markers of prognosis and therapy that are specific to OSCC-GB are, therefore, required. Although genomic drivers of OSCC-GB have been identified by whole exome and whole genome sequencing, no epigenome-wide study has been conducted in OSCC-GB; our study has filled this gap, and has discovered and validated epigenomic hallmarks of gingivobuccal oral cancer. METHODS We have carried out integrative analysis of epigenomic (n = 87) and transcriptomic (n = 72) profiles of paired tumor-normal tissues collected from OSCC-GB patients from India. Genome-wide DNA methylation assays and RNA-sequencing were performed on high-throughput platforms (Illumina) using a half-sample of randomly selected patients to discover significantly differentially methylated probes (DMPs), which were validated on the remaining half-sample of patients. RESULTS About 200 genes showed significant inverse correlation between promoter methylation and expression, of which the most significant genes included genes that act as transcription factors and genes associated with other cancer types. Novel findings of this study include identification of (a) potential immunosuppressive effect in OSCC-GB due to significant promoter hypomethylation driven upregulation of CD274 and CD80, (b) significant dysregulation by epigenetic modification of DNMT3B (upregulation) and TET1 (downregulation); and (c) known drugs that can reverse the direction of dysregulation of gene expression caused by promoter methylation. CONCLUSIONS In OSCC-GB patients, there are significant alterations in expression of key genes that (a) regulate normal cell division by maintenance of balanced DNA methylation and transcription process, (b) maintain normal physiological signaling (PPAR, B cell receptor) and metabolism (arachidonic acid) pathways, and (c) provide immune protection against antigens, including tumor cells. These findings indicate novel therapeutic targets, including immunotherapeutic, for treatment of OSCC-GB.
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Affiliation(s)
- Debodipta Das
- National Institute of Biomedical Genomics, P.O.: N.S.S, Kalyani, 741251, India
| | - Sahana Ghosh
- National Institute of Biomedical Genomics, P.O.: N.S.S, Kalyani, 741251, India
| | - Arindam Maitra
- National Institute of Biomedical Genomics, P.O.: N.S.S, Kalyani, 741251, India
| | - Nidhan K Biswas
- National Institute of Biomedical Genomics, P.O.: N.S.S, Kalyani, 741251, India
| | | | - Bidyut Roy
- Indian Statistical Institute, Kolkata, India
| | - Rajiv Sarin
- Advanced Centre for Treatment Research and Education in Cancer, Mumbai, India
| | - Partha P Majumder
- National Institute of Biomedical Genomics, P.O.: N.S.S, Kalyani, 741251, India. .,Indian Statistical Institute, Kolkata, India.
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Biswas NK, Das C, Das S, Maitra A, Nair S, Gupta T, D'Cruz AK, Sarin R, Majumder PP. Lymph node metastasis in oral cancer is strongly associated with chromosomal instability and DNA repair defects. Int J Cancer 2019; 145:2568-2579. [PMID: 30924133 DOI: 10.1002/ijc.32305] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Revised: 03/19/2019] [Accepted: 03/22/2019] [Indexed: 01/01/2023]
Abstract
Oral squamous cell carcinoma (OSCC) is highly prevalent in south and southeast Asia. Many (30-50%) OSCC patients develop lymph node metastasis (LNM), which is the most important prognostic factor in OSCC. To identify genomic correlates of LNM, we compared exome sequences and copy number variation data of blood and tumor DNA from highly contrasting subgroups of patients to reduce false inferences-(i) patients with LNM and (ii) patients with late stage disease but without LNM. We found that LNM is associated with (i) specific hotspot somatic mutations in TP53 and CASP8; (ii) rare nonsilent germline mutations in BRCA2 and FAT1; (iii) mutations in mito-G2/M and nonhomologous end joining (NHEJ) pathways; (iv) recurrent deletion of genes for DNA repair by homologous recombination; and (v) chromosomal instability. LN+ patients with NHEJ pathway mutations have longer disease-free survival. Five genomic features have a high predictive value of LNM.
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Affiliation(s)
- Nidhan K Biswas
- National Institute of Biomedical Genomics, Netaji Subhas Sanatorium, Kalyani, West Bengal, India
| | - Chitrarpita Das
- National Institute of Biomedical Genomics, Netaji Subhas Sanatorium, Kalyani, West Bengal, India
| | - Subrata Das
- National Institute of Biomedical Genomics, Netaji Subhas Sanatorium, Kalyani, West Bengal, India
| | - Arindam Maitra
- National Institute of Biomedical Genomics, Netaji Subhas Sanatorium, Kalyani, West Bengal, India
| | - Sudhir Nair
- Tata Memorial Center, Advanced Centre for Treatment, Research and Education in Cancer, Navi Mumbai, Maharashtra, India
| | - Tejpal Gupta
- Tata Memorial Center, Advanced Centre for Treatment, Research and Education in Cancer, Navi Mumbai, Maharashtra, India
| | - Anil K D'Cruz
- Tata Memorial Center, Advanced Centre for Treatment, Research and Education in Cancer, Navi Mumbai, Maharashtra, India
| | - Rajiv Sarin
- Tata Memorial Center, Advanced Centre for Treatment, Research and Education in Cancer, Navi Mumbai, Maharashtra, India
| | - Partha P Majumder
- National Institute of Biomedical Genomics, Netaji Subhas Sanatorium, Kalyani, West Bengal, India
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Bhatnagar S, Majumder PP, Salunke DM. A Pregnancy Cohort to Study Multidimensional Correlates of Preterm Birth in India: Study Design, Implementation, and Baseline Characteristics of the Participants. Am J Epidemiol 2019; 188:621-631. [PMID: 30770926 DOI: 10.1093/aje/kwy284] [Citation(s) in RCA: 23] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Revised: 12/19/2018] [Accepted: 12/19/2018] [Indexed: 11/13/2022] Open
Abstract
Globally, preterm birth is a major public health problem. In India, 3.6 million of the 27 million infants born annually are preterm. Risk stratification of women based on multidimensional risk factors assessed during pregnancy is critical for prevention of preterm birth. A cohort study of pregnant women was initiated in May 2015 at the civil hospital in Gurugram, Haryana, India. Women are enrolled within 20 weeks of gestation and are followed until delivery and once postpartum. The objectives are to identify clinical, epidemiologic, genomic, epigenomic, proteomic, and microbial correlates; discover molecular-risk markers by using an integrative -omics approach; and generate a risk-prediction algorithm for preterm birth. We describe here the longitudinal study design, methodology of data collection, and the repositories of data, biospecimens, and ultrasound images being created. A total of 4,326 pregnant women, with documented evidence of recruitment before 20 weeks of gestation, have been enrolled through March 2018. We report baseline characteristics and outcomes of the first 2,000 enrolled participants. A high frequency of preterm births (14.9% among 1,662 live births) is noteworthy. The cohort database and the repositories will become global resources to answer critical questions on preterm birth and other birth outcomes.
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Affiliation(s)
- Shinjini Bhatnagar
- Translational Health Science and Technology Institute, National Capital Region Biotech Cluster, Faridabad, Delhi NCR, India
| | - Partha P Majumder
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
| | - Dinakar M Salunke
- Regional Centre for Biotechnology, National Capital Region Biotech Cluster, Faridabad, Delhi NCR, India
- International Centre for Genetic Engineering and Biotechnology, New Delhi, India
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Chatterjee S, Chaubal R, Maitra A, Gardi N, Dutt A, Gupta S, Badwe RA, Majumder PP, Pandey P. Pre-operative progesterone benefits operable breast cancer patients by modulating surgical stress. Breast Cancer Res Treat 2018; 170:431-438. [PMID: 29564740 DOI: 10.1007/s10549-018-4749-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [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: 01/12/2018] [Accepted: 03/08/2018] [Indexed: 02/08/2023]
Abstract
PURPOSE We have reported a survival benefit of single injection of hydroxyprogesterone prior to surgery for primary tumour in patients with node-positive operable breast cancer. Hydroxyprogesterone was meant to recapitulate the luteal phase of menstrual cycle in these women. We wanted to understand the molecular basis of action of hydroxyprogesterone on primary breast tumours in a peri-operative setting. METHODS We performed whole transcriptome sequencing (RNA-Seq) of primary breast tumour samples collected from patients before and after hydroxyprogesterone exposure and controls. Paired breast cancer samples were obtained from patients who were given hydroxyprogesterone before surgery and a group of patients who were subjected to only surgery. RESULTS A test of significance between the two groups revealed 207 significantly altered genes, after correction for multiple hypothesis testing. We found significantly contrasting gene expression patterns in exposed versus unexposed groups; 142 genes were up-regulated post-surgery among exposed patients, and down-regulated post-surgery among unexposed patients. Significantly enriched pathways included genes that respond to progesterone, cellular stress, nonsense-mediated decay of proteins and negative regulation of inflammatory response. These results suggest that cellular stress is modulated by hydroxyprogesterone. Network analysis revealed that UBC, a mediator of stress response, to be a major node to which many of the significantly altered genes connect. CONCLUSIONS Our study suggests that pre-operative exposure to progesterone favourably modulates the effect of surgical stress, and this might underlie its beneficial effect when administered prior to surgery.
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Affiliation(s)
- Shatakshee Chatterjee
- National Institute of Biomedical Genomics, P.O.: N.S.S., Kalyani, 741251, West Bengal, India
| | - Rohan Chaubal
- Tata Memorial Centre/Hospital, Parel, Mumbai, 400012, India
| | - Arindam Maitra
- National Institute of Biomedical Genomics, P.O.: N.S.S., Kalyani, 741251, West Bengal, India
| | - Nilesh Gardi
- Tata Memorial Centre/Hospital, Parel, Mumbai, 400012, India
| | - Amit Dutt
- Tata Memorial Centre/Hospital, Parel, Mumbai, 400012, India
| | - Sudeep Gupta
- Tata Memorial Centre/Hospital, Parel, Mumbai, 400012, India.
- Department of Medical Oncology, Tata Memorial Centre, Mumbai, 400012, India.
| | - Rajendra A Badwe
- Tata Memorial Centre/Hospital, Parel, Mumbai, 400012, India.
- Department of Surgical Oncology, Tata Memorial Centre, Mumbai, 400012, India.
| | - Partha P Majumder
- National Institute of Biomedical Genomics, P.O.: N.S.S., Kalyani, 741251, West Bengal, India.
| | - Priyanka Pandey
- National Institute of Biomedical Genomics, P.O.: N.S.S., Kalyani, 741251, West Bengal, India.
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Bhattacharya S, Khadilkar SV, Nalini A, Ganapathy A, Mannan AU, Majumder PP, Bhattacharya A. Mutation Spectrum of GNE Myopathy in the Indian Sub-Continent. J Neuromuscul Dis 2018; 5:85-92. [PMID: 29480215 DOI: 10.3233/jnd-170270] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND GNE myopathy is an adult onset recessive genetic disorder that affects distal muscles sparing the quadriceps. GNE gene mutations have been identified in GNE myopathy patients all over the world. Homozygosity is a common feature in GNE myopathy patients worldwide. OBJECTIVES The major objective of this study was to investigate the mutation spectrum of GNE myopathy in India in relation to the population diversity in the country. MATERIALS AND METHODS We have collated GNE mutation data of Indian GNE myopathy patients from published literature and from recently identified patients. We also used data of people of Indian subcontinent from 1000 genomes database, South Asian Genome database and Strand Life Science database to determine frequency of GNE mutations in the general population. RESULTS A total of 67 GNE myopathy patients were studied, of whom 21% were homozygous for GNE variants, while the rest were compound heterozygous. Thirty-five different mutations in the GNE gene were recorded, of which 5 have not been reported earlier. The most frequent mutation was p.Val727Met (65%) found mainly in the heterozygous form. Another mutation, p.Ile618Thr was also common (16%) but was found mainly in patients from Rajasthan, while p.Val727Met was more widely distributed. The latter was also seen at a high frequency in general population of Indian subcontinent in all the databases. It was also present in Thailand but was absent in general population elsewhere in the world. CONCLUSION p.Val727Met is likely to be a founder mutation of Indian subcontinent.
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Affiliation(s)
- Sudha Bhattacharya
- School of Environmental Sciences, Jawaharlal Nehru University, New Delhi, India.,World Without GNE Myopathy (India), New Delhi, India
| | - Satish V Khadilkar
- Department of Neurology, Grant Government Medical College and J.J. Hospital, Byculla, Mumbai, Maharashtra, India
| | - Atchayaram Nalini
- Departments of Neurology and Neuropathology, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India
| | | | | | - Partha P Majumder
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
| | - Alok Bhattacharya
- School of Life Sciences, Jawaharlal Nehru University, New Delhi, India.,World Without GNE Myopathy (India), New Delhi, India
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Jinam TA, Phipps ME, Aghakhanian F, Majumder PP, Datar F, Stoneking M, Sawai H, Nishida N, Tokunaga K, Kawamura S, Omoto K, Saitou N. Discerning the Origins of the Negritos, First Sundaland People: Deep Divergence and Archaic Admixture. Genome Biol Evol 2018; 9:2013-2022. [PMID: 28854687 PMCID: PMC5597900 DOI: 10.1093/gbe/evx118] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [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] [Accepted: 07/09/2017] [Indexed: 12/26/2022] Open
Abstract
Human presence in Southeast Asia dates back to at least 40,000 years ago, when the current islands formed a continental shelf called Sundaland. In the Philippine Islands, Peninsular Malaysia, and Andaman Islands, there exist indigenous groups collectively called Negritos whose ancestry can be traced to the "First Sundaland People." To understand the relationship between these Negrito groups and their demographic histories, we generated genome-wide single nucleotide polymorphism data in the Philippine Negritos and compared them with existing data from other populations. Phylogenetic tree analyses show that Negritos are basal to other East and Southeast Asians, and that they diverged from West Eurasians at least 38,000 years ago. We also found relatively high traces of Denisovan admixture in the Philippine Negritos, but not in the Malaysian and Andamanese groups, suggesting independent introgression and/or parallel losses involving Denisovan introgressed regions. Shared genetic loci between all three Negrito groups could be related to skin pigmentation, height, facial morphology and malarial resistance. These results show the unique status of Negrito groups as descended from the First Sundaland People.
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Affiliation(s)
- Timothy A Jinam
- Division of Population Genetics, National Institute of Genetics, Mishima, Japan
| | - Maude E Phipps
- Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Sunway City, Selangor, Malaysia
| | - Farhang Aghakhanian
- Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Sunway City, Selangor, Malaysia
| | - Partha P Majumder
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
| | - Francisco Datar
- Department of Anthropology, University of the Philippines, Diliman, Quezon City, The Philippines
| | - Mark Stoneking
- Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Hiromi Sawai
- Department of Human Genetics, Graduate School of Medicine, The University of Tokyo, Japan
| | - Nao Nishida
- Department of Human Genetics, Graduate School of Medicine, The University of Tokyo, Japan.,Department of Hepatic Disease, Research Center for Hepatitis and Immunology, National Center for Global Health and Medicine, Chiba, Japan
| | - Katsushi Tokunaga
- Department of Human Genetics, Graduate School of Medicine, The University of Tokyo, Japan
| | - Shoji Kawamura
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
| | - Keiichi Omoto
- Department of Anthropology, Faculty of Science, The University of Tokyo, Japan
| | - Naruya Saitou
- Division of Population Genetics, National Institute of Genetics, Mishima, Japan
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Affiliation(s)
- J.K. Ghosh
- Anthropology and Human Genetics Unit, Indian Statistical Institute, Calcutta 700 035, West Bengal, India
| | - Partha P. Majumder
- Anthropology and Human Genetics Unit, Indian Statistical Institute, Calcutta 700 035, West Bengal, India
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Majumder PP, Nanjundiah V, Rao V. Preface. J Genet 2017. [DOI: 10.1007/s12041-017-0842-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Tagore D, Aghakhanian F, Naidu R, Majumder PP, Phipps ME, Basu A. A genomic insight into the origin and dispersal of Austroasiatic speakers in South and Southeast Asia. Can J Biotech 2017. [DOI: 10.24870/cjb.2017-a124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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Affiliation(s)
- P. Veerraju
- Department of Human Genetics, Andhra University, Visakhapatnam 530 003, Andhra Pradesh, India
| | - T.V. Rao
- Department of Human Genetics, Andhra University, Visakhapatnam 530 003, Andhra Pradesh, India
| | - N. Lakshmi
- Department of Human Genetics, Andhra University, Visakhapatnam 530 003, Andhra Pradesh, India
| | - S. Reshmi
- Department of Human Genetics, Andhra University, Visakhapatnam 530 003, Andhra Pradesh, India
| | - Badal Dey
- Anthropology and Human Genetics Unit, Indian Statistical Institute, Kolkata 700 035, West Bengal, India
| | - Partha P. Majumder
- Anthropology and Human Genetics Unit, Indian Statistical Institute, Kolkata 700 035, West Bengal, India
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Mondal M, Bergström A, Xue Y, Calafell F, Laayouni H, Casals F, Majumder PP, Tyler-Smith C, Bertranpetit J. Y-chromosomal sequences of diverse Indian populations and the ancestry of the Andamanese. Hum Genet 2017; 136:499-510. [PMID: 28444560 DOI: 10.1007/s00439-017-1800-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [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] [Received: 01/09/2017] [Accepted: 04/10/2017] [Indexed: 01/25/2023]
Abstract
We present 42 new Y-chromosomal sequences from diverse Indian tribal and non-tribal populations, including the Jarawa and Onge from the Andaman Islands, which are analysed within a calibrated Y-chromosomal phylogeny incorporating South Asian (in total 305 individuals) and worldwide (in total 1286 individuals) data from the 1000 Genomes Project. In contrast to the more ancient ancestry in the South than in the North that has been claimed, we detected very similar coalescence times within Northern and Southern non-tribal Indian populations. A closest neighbour analysis in the phylogeny showed that Indian populations have an affinity towards Southern European populations and that the time of divergence from these populations substantially predated the Indo-European migration into India, probably reflecting ancient shared ancestry rather than the Indo-European migration, which had little effect on Indian male lineages. Among the tribal populations, the Birhor (Austro-Asiatic-speaking) and Irula (Dravidian-speaking) are the nearest neighbours of South Asian non-tribal populations, with a common origin in the last few millennia. In contrast, the Riang (Tibeto-Burman-speaking) and Andamanese have their nearest neighbour lineages in East Asia. The Jarawa and Onge shared haplogroup D lineages with each other within the last ~7000 years, but had diverged from Japanese haplogroup D Y-chromosomes ~53000 years ago, most likely by a split from a shared ancestral population. This analysis suggests that Indian populations have complex ancestry which cannot be explained by a single expansion model.
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Affiliation(s)
- Mayukh Mondal
- Institute of Evolutionary Biology (CSIC-UPF), Universitat Pompeu Fabra, Doctor Aiguader 88 (PRBB), 08003, Barcelona, Catalonia, Spain
| | - Anders Bergström
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA,, UK
| | - Yali Xue
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA,, UK
| | - Francesc Calafell
- Institute of Evolutionary Biology (CSIC-UPF), Universitat Pompeu Fabra, Doctor Aiguader 88 (PRBB), 08003, Barcelona, Catalonia, Spain
| | - Hafid Laayouni
- Institute of Evolutionary Biology (CSIC-UPF), Universitat Pompeu Fabra, Doctor Aiguader 88 (PRBB), 08003, Barcelona, Catalonia, Spain
- Bioinformatics Studies, ESCI-UPF, Pg. Pujades 1, 08003, Barcelona, Spain
| | - Ferran Casals
- Genomics Core Facility, Departament de Ciencies Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
| | | | - Chris Tyler-Smith
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA,, UK.
| | - Jaume Bertranpetit
- Institute of Evolutionary Biology (CSIC-UPF), Universitat Pompeu Fabra, Doctor Aiguader 88 (PRBB), 08003, Barcelona, Catalonia, Spain.
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Abstract
Study of temporal trajectory of gene expression is important. RNA sequencing is popular in genome-scale studies of transcription. Because of high expenses involved, many time-course RNA sequencing studies are challenged by inadequacy of sample sizes. This poses difficulties in conducting formal statistical tests of significance of null hypotheses. We propose a bootstrap algorithm to identify 'cognizable' 'time-trends' of gene expression. Properties of the proposed algorithm are derived using a simulation study. The proposed algorithm captured known 'time-trends' in the simulated data with a high probability of success, even when sample sizes were small (n < 10). The proposed statistical method is efficient and robust to capture 'cognizable' 'time-trends' in RNA sequencing data.
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Affiliation(s)
- Shatakshee Chatterjee
- National Institute of Biomedical Genomics, Netaji Subhas Sanatorium (T. B. Hospital), P.O.: N.S.S., Kalyani 741 251,
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Bhattacharya S, Katoch VM, Majumder PP, Bhattacharya A. Rare Diseases In India: Current Knowledge and New Possibilities. Proceedings of the Indian National Science Academy 2016. [DOI: 10.16943/ptinsa/2016/48575] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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Agrawal N, Bhattacharyya C, Mukherjee A, Ullah U, Pandit B, Rao KVS, Majumder PP. Dissecting host factors that regulate the early stages of tuberculosis infection. Tuberculosis (Edinb) 2016; 100:102-113. [PMID: 27553417 DOI: 10.1016/j.tube.2016.07.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2015] [Revised: 06/13/2016] [Accepted: 07/10/2016] [Indexed: 10/21/2022]
Abstract
Incomplete understanding of mechanisms involved in the host-pathogen interactions constrains our efforts to eliminate tuberculosis. In many individuals, resulting from immune response to mycobacterial infection organised structures called granulomas are formed. To identify host responses that may control at least the early stages of infection, we employed an in vitro granuloma model. Here, human PBMCs were infected with live Mycobacterium tuberculosis in culture, and the appearance of granuloma-like structures was monitored over the next several days. Production of cytokines and chemokines in culture supernatants was monitored at various times, and the resulting temporal profiles were examined for possible correlations with either granuloma formation, or bacterial growth. While a positive association of TNF-α and IFN-γ secretion levels with extent of granuloma formation could clearly be identified, we were, however, unable to detect any statistically significant relationship between any cytokine/chemokine and bacterial growth. Examination of specific host cellular biochemical pathways revealed that either modulation of neutral lipid homeostasis through inhibition of the Gi-protein coupled receptor GPR109A, or regulation of host metabolic pathways through addition of vitamin D, provided a more effective means of controlling infection. A subsequent genotypic analysis for a select subset of genes belonging to pathways known to be significant for TB pathology revealed associations of polymorphisms with cytokine secretions and bacterial growth independently. Collectively therefore, the present study supports that key metabolic pathways of the host cell, rather than levels of relevant cytokines/chemokines might be more critical for regulating the intracellular mycobacterial load, in the context of granuloma formation.
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Affiliation(s)
- Neha Agrawal
- Immunology Group, International Centre for Genetic Engineering and Biotechnology, 110067 New Delhi, India.
| | | | - Ankur Mukherjee
- National Institute of Biomedical Genomics, Kalyani, 741251 West Bengal, India.
| | - Ubaid Ullah
- Immunology Group, International Centre for Genetic Engineering and Biotechnology, 110067 New Delhi, India.
| | - Bhaswati Pandit
- National Institute of Biomedical Genomics, Kalyani, 741251 West Bengal, India.
| | - Kanury V S Rao
- Immunology Group, International Centre for Genetic Engineering and Biotechnology, 110067 New Delhi, India.
| | - Partha P Majumder
- National Institute of Biomedical Genomics, Kalyani, 741251 West Bengal, India.
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35
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Mondal M, Casals F, Xu T, Dall'Olio GM, Pybus M, Netea MG, Comas D, Laayouni H, Li Q, Majumder PP, Bertranpetit J. Genomic analysis of Andamanese provides insights into ancient human migration into Asia and adaptation. Nat Genet 2016; 48:1066-70. [DOI: 10.1038/ng.3621] [Citation(s) in RCA: 109] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2015] [Accepted: 06/17/2016] [Indexed: 12/19/2022]
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36
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Chatterjee A, Basu A, Chowdhury A, Das K, Sarkar-Roy N, Majumder PP, Basu P. Comparative analyses of genetic risk prediction methods reveal extreme diversity of genetic predisposition to nonalcoholic fatty liver disease (NAFLD) among ethnic populations of India. J Genet 2016; 94:105-13. [PMID: 25846882 DOI: 10.1007/s12041-015-0494-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Nonalcoholic fatty liver disease (NAFLD) is a distinct pathologic condition characterized by a disease spectrum ranging from simple steatosis to steato-hepatitis, cirrhosis and hepatocellular carcinoma. Prevalence of NAFLD varies in different ethnic groups, ranging from 12% in Chinese to 45% in Hispanics. Among Indian populations, the diversity in prevalence is high, ranging from 9% in rural populations to 32% in urban populations, with geographic differences as well. Here, we wished to find out if this difference is reflected in their genetic makeup. To date, several candidate genes and a few genomewide association studies (GWAS) have been carried out, and many associations between single nucleotide polymorphisms (SNPs) and NAFLD have been observed. In this study, the risk allele frequencies (RAFs) of NAFLD-associated SNPs in 20 Indian ethnic populations (376 individuals) were analysed. We used two different measures for calculating genetic risk scores and compared their performance. The correlation of additive risk scores of NAFLD for three Hapmap populations with their weighted mean prevalence was found to be high (R(2) = 0.93). Later we used this method to compare NAFLD risk among ethnic Indian populations. Based on our observation, the Indian caste populations have high risk scores compared to Caucasians, who are often used as surrogate and similar to Indian caste population in disease gene association studies, and is significantly higher than the Indian tribal populations.
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Affiliation(s)
- Ankita Chatterjee
- National Institute of Biomedical Genomics, Netaji Subhas Sanatorium (T. B. Hospital), Kalyani 741 251, India.
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37
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Majumder PP. A Humanitarian and a Great Indian. Genome Biol Evol 2016; 8:467-9. [PMID: 26837547 PMCID: PMC4779617 DOI: 10.1093/gbe/evw012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Affiliation(s)
- Partha P Majumder
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
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Abstract
Considerable variation in antibody response (AR) was observed among recipients of an injectable typhoid vaccine and an oral cholera vaccine. We sought to find whether polymorphisms in genes of the immune system, both innate and adaptive, were associated with the observed variation in response. For both vaccines, we were able to discover and validate several polymorphisms that were significantly associated with immune response. For the typhoid vaccines, these polymorphisms were on genes that belonged to pathways of polysaccharide recognition, signal transduction, inhibition of T-cell proliferation, pro-inflammatory signalling and eventual production of antimicrobial peptides. For the cholera vaccine, the pathways included epithelial barrier integrity, intestinal homeostasis and leucocyte recruitment. Even though traditional wisdom indicates that both vaccines should act as T-cell-independent antigens, our findings reveal that the vaccines induce AR using different pathways.
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Affiliation(s)
- Partha P Majumder
- National Institute of Biomedical Genomics, Kalyani, West Bengal 741251, India
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Rajaraman P, Dey B, Majumder PP, Mayor S, Pillai MR, Ramaswamy S, Shaha C, Johnson M, Sivaram S, Trimble EL, Harlow EE, VijayRaghavan K. First International Workshopson Provocative Questions (PQ) in Cancer Research, October-November 2014, New Delhi, Bengaluru, and Thiruvananthapuram, India. J Cancer Policy 2015; 6:33-36. [PMID: 26568911 DOI: 10.1016/j.jcpo.2015.10.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
In 2011, the National Cancer Institute (NCI, USA) introduced the Provocative Questions (PQ) Initiative, a new approach allowing active researchers to define major unsolved or neglected problems in oncology unaddressed by existing funding. Last year, the U.S. NCI teamed up with the Indian Department of Biotechnology (DBT) to pilot the PQ approach in three cities in India. Workshop outcomes includedthe generation of fundable "PQs" (perplexing questions understudied by the international scientific community), as well as the identification of several non-PQ projects and research-related issues of importance to DBT and other Indian funding groups. The workshops clearly indicated the need to expand beyond crafting "PQs" when considering the best areas for research funding in international settings. Nonetheless, the first set of PQ workshops provided a forum to discuss key issues regarding cancer research in India, including the paucity of cancer research funding, and the lack of relevant human resource training and technology sharing platforms. Continued open debate between researchers, funders and policymakers will be essential to effectively strengthen the cancer research portfolio in India.
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Affiliation(s)
- Preetha Rajaraman
- Center for Global Health, National Cancer Institute, NIH, DHHS, 9609 Medical Center Drive, Bethesda, USA 20892
| | - Bindu Dey
- Department of Biotechnology (DBT), Government of India, New Delhi, India
| | - Partha P Majumder
- National Institute of Biomedical Genomics, Netaji Subhas Sanatorium, 2nd Floor, P.O.: N.S.S., Kalyani 741251, West-Bengal, India
| | - Satyajit Mayor
- Institute for Stem Cell Biology and Regenerative Medicine (inSTEM) and National Centre for Biological Science (NCBS),Bellary Road, Bangalore 560065
| | | | - S Ramaswamy
- Institute for Stem Cell Biology and Regenerative Medicine (inSTEM) and National Centre for Biological Science (NCBS),Bellary Road, Bangalore 560065
| | - Chandrima Shaha
- National Institute of Immunology, ArunaAsaf Ali Marg New Delhi 110067, India
| | - Maureen Johnson
- Office of the Director, National Cancer Institute, NIH, DHHS, Bethesda, USA
| | - Sudha Sivaram
- Center for Global Health, National Cancer Institute, NIH, DHHS, 9609 Medical Center Drive, Bethesda, USA 20892
| | - Edward L Trimble
- Center for Global Health, National Cancer Institute, NIH, DHHS, 9609 Medical Center Drive, Bethesda, USA 20892
| | - Edward E Harlow
- Office of the Director, National Cancer Institute, NIH, DHHS, Bethesda, USA
| | - K VijayRaghavan
- Secretary, Department of Biotechnology (DBT), Government of India, New Delhi, India
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Ghosh S, Barik A, Majumder S, Gorain A, Mukherjee S, Mazumdar S, Chatterjee K, Bhaumik SK, Bandyopadhyay SK, Satpathi B, Majumder PP, Chowdhury A. Health & Demographic Surveillance System Profile: The Birbhum population project (Birbhum HDSS). Int J Epidemiol 2014; 44:98-107. [PMID: 25540150 DOI: 10.1093/ije/dyu228] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [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] Open
Abstract
The Birbhum HDSS was established in 2008 and covers 351 villages in four administrative blocks in rural areas of Birbhum district of West Bengal, India. The project currently follows 54 585 individuals living in 12557 households. The population being followed up is economically underprivileged and socially marginalized. The HDSS, a prospective longitudinal cohort study, has been designed to study changes in population demographic, health and healthcare utilization. In addition to collecting data on vital statistics and antenatal and postnatal tracking, verbal autopsies are being performed. Moreover, periodic surveys capturing socio-demographic and economic conditions have been conducted twice. Data on nutritional status (children as well as adults), non-communicable diseases, smoking etc. have also been collected in special surveys. Currently, intervention studies on anaemia, undernutrition and common preschool childhood morbidities through behavioural changes are under way. For access to the data, a researcher needs to send a request to the Data Manager [suri.shds@gmail.com]. Data are shared in common formats like comma-separated files (csv) or Microsoft Excel (xlsx) or Microsoft Access Database (mdb).The HDSS will soon upgrade its data management system to a more integrated platform, coordinated and guided by INDEPTH data sharing policy.
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Affiliation(s)
- Saswata Ghosh
- Society for Health and Demographic Surveillance, Kolkata, India, Institute of Development Studies, Kolkata, India, Department of Health & Family Welfare, Government of West Bengal, Kolkata, India and National Institute of Biomedical Genomics, Kalyani, India Society for Health and Demographic Surveillance, Kolkata, India, Institute of Development Studies, Kolkata, India, Department of Health & Family Welfare, Government of West Bengal, Kolkata, India and National Institute of Biomedical Genomics, Kalyani, India
| | - Anamitra Barik
- Society for Health and Demographic Surveillance, Kolkata, India, Institute of Development Studies, Kolkata, India, Department of Health & Family Welfare, Government of West Bengal, Kolkata, India and National Institute of Biomedical Genomics, Kalyani, India
| | - Saikat Majumder
- Society for Health and Demographic Surveillance, Kolkata, India, Institute of Development Studies, Kolkata, India, Department of Health & Family Welfare, Government of West Bengal, Kolkata, India and National Institute of Biomedical Genomics, Kalyani, India
| | - Ashoke Gorain
- Society for Health and Demographic Surveillance, Kolkata, India, Institute of Development Studies, Kolkata, India, Department of Health & Family Welfare, Government of West Bengal, Kolkata, India and National Institute of Biomedical Genomics, Kalyani, India
| | - Subrata Mukherjee
- Society for Health and Demographic Surveillance, Kolkata, India, Institute of Development Studies, Kolkata, India, Department of Health & Family Welfare, Government of West Bengal, Kolkata, India and National Institute of Biomedical Genomics, Kalyani, India Society for Health and Demographic Surveillance, Kolkata, India, Institute of Development Studies, Kolkata, India, Department of Health & Family Welfare, Government of West Bengal, Kolkata, India and National Institute of Biomedical Genomics, Kalyani, India
| | - Saibal Mazumdar
- Society for Health and Demographic Surveillance, Kolkata, India, Institute of Development Studies, Kolkata, India, Department of Health & Family Welfare, Government of West Bengal, Kolkata, India and National Institute of Biomedical Genomics, Kalyani, India
| | - Kajal Chatterjee
- Society for Health and Demographic Surveillance, Kolkata, India, Institute of Development Studies, Kolkata, India, Department of Health & Family Welfare, Government of West Bengal, Kolkata, India and National Institute of Biomedical Genomics, Kalyani, India
| | - Sunil Kumar Bhaumik
- Society for Health and Demographic Surveillance, Kolkata, India, Institute of Development Studies, Kolkata, India, Department of Health & Family Welfare, Government of West Bengal, Kolkata, India and National Institute of Biomedical Genomics, Kalyani, India
| | - Susanta Kumar Bandyopadhyay
- Society for Health and Demographic Surveillance, Kolkata, India, Institute of Development Studies, Kolkata, India, Department of Health & Family Welfare, Government of West Bengal, Kolkata, India and National Institute of Biomedical Genomics, Kalyani, India
| | - BiswaRanjan Satpathi
- Society for Health and Demographic Surveillance, Kolkata, India, Institute of Development Studies, Kolkata, India, Department of Health & Family Welfare, Government of West Bengal, Kolkata, India and National Institute of Biomedical Genomics, Kalyani, India
| | - Partha P Majumder
- Society for Health and Demographic Surveillance, Kolkata, India, Institute of Development Studies, Kolkata, India, Department of Health & Family Welfare, Government of West Bengal, Kolkata, India and National Institute of Biomedical Genomics, Kalyani, India Society for Health and Demographic Surveillance, Kolkata, India, Institute of Development Studies, Kolkata, India, Department of Health & Family Welfare, Government of West Bengal, Kolkata, India and National Institute of Biomedical Genomics, Kalyani, India
| | - Abhijit Chowdhury
- Society for Health and Demographic Surveillance, Kolkata, India, Institute of Development Studies, Kolkata, India, Department of Health & Family Welfare, Government of West Bengal, Kolkata, India and National Institute of Biomedical Genomics, Kalyani, India
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Biswas NK, Das S, Maitra A, Sarin R, Majumder PP. Somatic mutations in arachidonic acid metabolism pathway genes enhance oral cancer post-treatment disease-free survival. Nat Commun 2014; 5:5835. [PMID: 25517499 DOI: 10.1038/ncomms6835] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2014] [Accepted: 11/11/2014] [Indexed: 01/17/2023] Open
Abstract
The arachidonic acid metabolism (AAM) pathway promotes tumour progression. Chemical inhibitors of AAM pathway prolong post-treatment survival of cancer patients. Here we test whether non-synonymous somatic mutations in genes of this pathway, acting as natural inhibitors, increase post-treatment survival. We identify loss-of-function somatic mutations in 15 (18%) of 84 treatment-naïve oral cancer patients by whole-exome sequencing, which we map to genes of AAM pathway. Patients (n = 53) who survived ≥ 12 months after surgery without recurrence have significantly (P = 0.007) higher proportion (26% versus 3%) of mutations than those who did not (n = 31). Patients with mutations have a significantly (P = 0.003) longer median disease-free survival (24 months) than those without (13 months). Compared with the presence of a mutation, absence of any mutation increases the hazard ratio for death (11.3) significantly (P = 0.018). The inferences are strengthened when we pool our data with The Cancer Genome Atlas (TCGA) data. In patients with AAM pathway mutations, some downstream pathways, such as the PI3K-Akt pathway, are downregulated.
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Affiliation(s)
- Nidhan K Biswas
- National Institute of Biomedical Genomics, Netaji Subhas Sanatorium (2nd Floor), Kalyani 741251, India
| | - Subrata Das
- National Institute of Biomedical Genomics, Netaji Subhas Sanatorium (2nd Floor), Kalyani 741251, India
| | - Arindam Maitra
- National Institute of Biomedical Genomics, Netaji Subhas Sanatorium (2nd Floor), Kalyani 741251, India
| | - Rajiv Sarin
- Tata Memorial Centre, Advanced Centre for Treatment, Research and Education in Cancer, Navi Mumbai 410210, India
| | - Partha P Majumder
- National Institute of Biomedical Genomics, Netaji Subhas Sanatorium (2nd Floor), Kalyani 741251, India
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Mukherjee S, Ganguli D, Majumder PP. Global footprints of purifying selection on Toll-like receptor genes primarily associated with response to bacterial infections in humans. Genome Biol Evol 2014; 6:551-8. [PMID: 24554585 PMCID: PMC3971583 DOI: 10.1093/gbe/evu032] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Toll-like receptors (TLRs) are directly involved in host–pathogen interactions. Polymorphisms in these genes are associated with susceptibility to infectious diseases. To understand the influence of environment and pathogen diversity on the evolution of TLR genes, we have undertaken a large-scale population-genetic study. Our study included two hunter–gatherer tribal populations and one urbanized nontribal population from India with distinct ethnicities (n = 266) and 14 populations inhabiting four different continents (n = 1,092). From the data on DNA sequences of cell-surface TLR genes, we observed an excess of rare variants and a large number of low frequency haplotypes in each gene. Nonsynonymous changes were few in every population and the commonly used statistical tests for detecting natural selection provided evidence of purifying selection. The evidence of purifying selection acting on the cell-surface TLRs of the innate immune system is not consistent with Haldane’s theory of coevolution of immunity genes, at least of innate immunity genes, with pathogens. Our study provides evidence that genes of the cell-surface TLRs, that is, TLR2 and TLR4, have been so optimized to defend the host against microbial infections that new mutations in these genes are quickly eliminated.
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Affiliation(s)
- Souvik Mukherjee
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
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Abstract
Recent advances in molecular and statistical genetics have enabled the reconstruction of human history by studying living humans. The ability to sequence and study DNA by calibrating the rate of accumulation of changes with evolutionary time has enabled robust inferences about how humans have evolved. These data indicate that modern humans evolved in Africa about 150,000 years ago and, consistent with paleontological evidence, migrated out of Africa. And through a series of settlements, demographic expansions, and further migrations, they populated the entire world. One of the first waves of migration from Africa was into India. Subsequent, more recent, waves of migration from other parts of the world have resulted in India being a genetic melting pot. Contemporary India has a rich tapestry of cultures and ecologies. There are about 400 tribal groups and more than 4000 groups of castes and subcastes, speaking dialects of 22 recognized languages belonging to four major language families. The contemporary social structure of Indian populations is characterized by endogamy with different degrees of porosity. The social structure, possibly coupled with large ecological heterogeneity, has resulted in considerable genetic diversity and local genetic differences within India. In this essay, we provide genetic evidence of how India may have been peopled, the nature and extent of its genetic diversity, and genetic structure among the extant populations of India.
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Affiliation(s)
| | - Analabha Basu
- National Institute of Biomedical Genomics, Kalyani 741251, India
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Das D, Kaur I, Ali MJ, Biswas NK, Das S, Kumar S, Honavar SG, Maitra A, Chakrabarti S, Majumder PP. Exome sequencing reveals the likely involvement of SOX10 in uveal melanoma. Optom Vis Sci 2014; 91:e185-92. [PMID: 24927141 DOI: 10.1097/opx.0000000000000309] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
PURPOSE To identify the spectrum of somatic mutations in an Asian Indian patient with uveal melanoma (UM) without metastasis using exome sequencing. CASE REPORT A 49-year-old man from India was diagnosed as having cilio-choroidal (uveal) melanoma (UM), without metastasis, in his right eye with the help of magnetic resonance imaging. This was later confirmed by histopathological evaluation. Two individuals from India with non-neoplastic blind eyes were recruited as controls. The affected eyes from the UM patient and the two control individuals were enucleated, and uveal tissues were collected. DNA was extracted from uveal tissue, and the matched blood sample from each of the three individuals was followed by exome sequencing. Statistical and bioinformatic analyses were done to identify somatic mutations and their putative associations with UM. Thirty-one somatic mutations (25 amino acid altering) in protein-coding (exonic) regions were detected in the UM patient. Of the amino acid-altering somatic mutations, 16 mutations were predicted to be candidate mutations relevant to UM. Somatic mutations, putatively causal for UM, were identified in GNAQ, SF3B1, and SOX10. CONCLUSIONS Somatic mutations in GNAQ and SF3B1 genes were probable drivers of UM in the Indian patient; these were also reported earlier in some White patients. In addition, a frameshift deletion of 20 base pairs has been identified in SOX10 in the UM patient. Somatic mutations in SOX10, a transcription factor, which acts upstream of microphthalmia-associated transcription factor and synergizes with microphthalmia-associated transcription factor, was identified in some melanoma cell lines. The transcription factor SOX10 was found to have an essential role in melanocyte development and pigmentation. Our finding of the frameshift deletion (p.H387fs) in exon 4 of SOX10 in UM provides an important insight and complements earlier findings of mutations in GNAQ and SF3B1 on the genomic basis of UM.
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Affiliation(s)
- Debodipta Das
- *MSc †PhD ‡MD §MTech National Institute of Biomedical Genomics, Kalyani, India (DD, NKB, SD, SK, AM, PPM); Prof. Brien Holden Eye Research Centre (IK, SC), and Department of Orbits and Ocular Oncology (MJA, SGH), LV Prasad Eye Institute, Hyderabad, India
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Chinnaswamy S, Das K, Bairagya BB, Bhattacharyya C, Shalimar, Duseja A, Amarapurkar D, Rosangluaia, Chowdhury S, Konar A, Majumder PP, Chowdhury A. Association of IL28B single nucleotide polymorphism rs8099917 with response to treatment in genotype 3 HCV-infected patients from India. Trop Gastroenterol 2014; 35:96-102. [PMID: 25470871 DOI: 10.7869/tg.187] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
BACKGROUND AND AIM IL28B gene polymorphisms have been associated with treatment-response (sustained virological response, SVR) in genotype 1 hepatitis C virus (HCV)-infected patients, but only with early phase of viral decline (rapid virological response, RVR) with genotype 3 HCV-infected patients. Association between IL28B variants and SVR in genotype 3 HCV- infected patients is unclear. Our study aimed to replicate the association of IL28Bsingle nucleotide polymorphism (SNP) rs8099917 with SVR and to validate its association with RVR in genotype 3 HCV-infected patients. METHODS 72 patients receiving combination therapy (interferon-alpha and ribavirin) at different Indian centers were retrospectively recruited and their genotype atrs8099917 was determined. The association with RVR and SVR was tested taking in to account the variation in relevant covariates such as age, gender, baseline HCV RNA copy number and liver enzymes. RESULTS The minor allele frequency (MAF) in the pooled samples was 0.17 at rs8099917 (G allele). 68% had TT, 29% had GT and 3% had the GG genotype. SVR was achieved in 71% of patients. A significant association ofrs8099917 with both RVR (p = 0.026) and SVR (p = 0.016) was observed with none of the covariates showing any significant association. The relapse rate was high (20%) but no association of rs8099917 was observed with relapse (p = 0.420). CONCLUSION An IL28B SNP associates with both early phase of viral decline and sustained response in a cohort of genotype 3 HCV-infected patients from India.
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Affiliation(s)
- Partha P Majumder
- National Institute of Biomedical Genomics, Kalyani, West-Bengal, India
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Majumder PP, Sarkar-Roy N, Staats H, Ramamurthy T, Maiti S, Chowdhury G, Whisnant CC, Narayanasamy K, Wagener DK. Genomic correlates of variability in immune response to an oral cholera vaccine. Eur J Hum Genet 2013; 21:1000-6. [PMID: 23249958 PMCID: PMC3746254 DOI: 10.1038/ejhg.2012.278] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2012] [Revised: 10/23/2012] [Accepted: 11/20/2012] [Indexed: 11/09/2022] Open
Abstract
Cholera is endemic to many countries. Recent major outbreaks of cholera have prompted World Health Organization to recommend oral cholera vaccination as a public-health strategy. Variation in percentage of seroconversion upon cholera vaccination has been recorded across populations. Vaccine-induced responses are influenced by host genetic differences. We have investigated association between single-nucleotide polymorphic (SNP) loci in and around 296 immunologically relevant genes and total anti-lipopolysaccharide (LPS) antibody response to a killed whole-cell vaccine, comprising LPS from multiple strains of Vibrio cholerae. Titers derived from standard vibriocidal assays were also analyzed to gain further insights on validated SNP associations. Vaccination was administered to 1000 individuals drawn from India. Data on two independent random subsets, each comprising ∼500 vaccinees, were used for discovery of genomic associations and validation, respectively. Significant associations of four SNPs and haplotypes in three genes (MARCO, TNFAIP3 and CXCL12) with AR were discovered and validated, of which two in TNFAIP3 and CXCL12 were also significantly associated with immunity (fourfold increase in vibriocidal titers). CXCL12 is a neutrophil and lymphocyte chemoattractant that is upregulated in response to V. cholerae infection. LPS in the vaccine possibly provides signals that mimic those of the live bacterium. TNFAIP3 promotes intestinal epithelial barrier integrity and provides tight junction protein regulation; possible requirements for adequate response to the vaccine. LPS is a potent activator of innate immune responses and a ligand of MARCO. Variants in this gene have been found to be associated with LPS response, but not with high vibriocidal titer level.
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Roy N, Mukhopadhyay I, Das K, Pandit P, Majumder PP, Santra A, Datta S, Banerjee S, Chowdhury A. Genetic variants of TNFα, IL10, IL1β, CTLA4 and TGFβ1 modulate the indices of alcohol-induced liver injury in East Indian population. Gene 2012; 509:178-88. [PMID: 22902304 DOI: 10.1016/j.gene.2012.07.077] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2012] [Accepted: 07/31/2012] [Indexed: 02/06/2023]
Abstract
Alcohol induced liver disease or alcoholic liver disease (ALD), a complex trait, encompasses a gamut of pathophysiological alterations in the liver due to continuous exposure to a toxic amount of alcohol (more than 80 g per day). Of all chronic heavy drinkers, only 15-20% develops hepatitis or cirrhosis concomitantly or in succession. Several studies revealed that inter-individual as well as inter-ethnic genetic variation is one of the major factors that predispose to ALD. The role of genetic factors in ALD has long been sought for in ethnically distinct population groups. ALD is fast emerging as an important cause of chronic liver disease in India; even in populations such as "Bengalis" who were "culturally immune" earlier. While the genetic involvement in the pathogenesis of ALD is being sought for in different races, the complex pathophysiology of ALD as well as the knowledge of population level diversity of the relevant alcohol metabolizing and inflammatory pathways mandates the need for well designed studies of genetic factors in ethnically distinct population groups. An array of cytokines plays a critical role as mediators of injury, inflammation, fibrosis and cirrhosis in ALD. We, therefore, studied the association of polymorphisms in five relevant cytokine genes with "clinically significant" ALD in an ethnic "Bengali" population in Eastern India. Compared with "alcoholic" controls without liver disease (n=110), TNFα -238AA genotype, IL1β -511CC genotype, TGFβ1 -509CC genotype and IL10 -592AA genotype were significantly overrepresented in ALD patients (n=181; OR=2.4 and 95% CI 1.2-5.5, P(genotype)=0.042, P(allelic)=0.008; OR=2.7 and 95% CI 1.2-5.9, P(genotype)=0.018, P(allelic)=0.023; OR=4.7 and 95% CI 1.7-13.1, P(genotype)=0.003, P(allelic)=0.014; and OR=2.2 and 95% CI 1.1-4.8, P(genotype)=0.04, P(allelic)=0.039 respectively). Moreover a cumulative genetic risk analysis revealed a significant trend for developing ALD with an increase in the number of risk alleles on IL10 and TGFβ1 loci among alcoholics. The risk genotype of IL1β and TGFβ1 also influences the total bilirubin, albumin and alanine aminotransferase levels among alcoholic "Bengalis". The present study is the first case-control study from Eastern India that comprehensively identified polymorphic markers in TNFα, IL10, IL1β and TGFβ1 genes to be associated with ALD in the Bengali population, accentuating the significance of genetic factors in clinical expressions of ALD.
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Affiliation(s)
- Neelanjana Roy
- Centre for Liver Research, Institute of Post Graduate Medical Education & Research, Kolkata, India.
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Bodhini D, Sandhiya M, Ghosh S, Majumder PP, Rao MRS, Mohan V, Radha V. Association of His1085His INSR gene polymorphism with type 2 diabetes in South Indians. Diabetes Technol Ther 2012; 14:696-700. [PMID: 22775283 DOI: 10.1089/dia.2012.0009] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
BACKGROUND AND OBJECTIVE The INSR gene, which encodes the insulin receptor, is a candidate gene for type 2 diabetes (T2D). The objective of the present study was to sequence some of the crucial exons in the INSR gene such as exon 2, which encodes the insulin-binding domain of the INSR protein, and exons 17-21, which encode the protein tyrosine kinase domain for mutations/polymorphisms, and to study their association with T2D in the South Indian population. SUBJECTS AND METHODS The INSR gene was sequenced in 25 normal glucose-tolerant (NGT) and 25 T2D subjects, and the variant found was genotyped by polymerase chain reaction-restriction fragment length polymorphism in 1,016 NGT and 1,010 T2D subjects, randomly selected from the Chennai Urban Rural Epidemiology Study. RESULTS Only one previously reported polymorphism, His1085His [rs1799817, (C→T)], in exon 17 was detected by sequencing. The frequency of the "T" allele of the His1085His polymorphism was significantly lower in the T2D subjects (31%) compared with the NGT subjects (35%) and showed significant protection against diabetes (odds ratio 0.85, 95% confidence interval 0.75-0.97, P=0.019). Regression analysis according to a recessive model taking the CC+CT genotype as the reference showed that the TT genotype was protective against diabetes (odds ratio 0.71, 95% confidence interval 0.50-0.99, P=0.048). Adjusting this P value by the number of competing models (three) using Bonferroni's correction, we found that the association finding did not remain significant. CONCLUSIONS The "T" allele of the His1085His polymorphism in the INSR gene shows significant protection against diabetes. This study gains importance because there are no data available to date on the role of INSR variants in T2D in the Indian population.
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Affiliation(s)
- Dhanasekaran Bodhini
- Madras Diabetes Research Foundation and Dr. Mohan's Diabetes Specialities Centre, 4 Conran Smith Road, Gopalapuram, Chennai, India
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Datta S, Chowdhury A, Ghosh M, Das K, Jha P, Colah R, Mukerji M, Majumder PP. A genome-wide search for non-UGT1A1 markers associated with unconjugated bilirubin level reveals significant association with a polymorphic marker near a gene of the nucleoporin family. Ann Hum Genet 2011; 76:33-41. [PMID: 22118420 DOI: 10.1111/j.1469-1809.2011.00688.x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Variants in the UGT1A1 gene and its promoter are known to determine levels of unconjugated bilirubin (UCB), but do not explain all cases of unconjugated hyperbilirubinemia. To discover associations with variants in genes other than UGT1A1, we undertook a genome-wide association study. We recruited 200 participants to cover the entire range of quantitative variation in UCB level. The data set -- after data curation, including analyses for population stratification and cryptic relatedness -- comprised genotypes at 512,349 SNP loci on 182 individuals. Quantitative trait locus (QTL) association analyses were performed, after adjusting the UCB level for effects of age, gender, and genotype at the dinucleotide (TA) insertion locus in UGT1A1 that is known to significantly modulate UCB level. A significant association of a polymorphic marker (rs2328136) near the NUP153 gene (which produces a 153 kDa nucleoporin) was obtained (p = 0.002, after multiple-testing correction). The frequency of the variant allele (A) at the rs2328136 locus in our study population is 40%, higher than most global populations. NUP153, whose product is a major regulatory factor in bidirectional transport of biomolecules across nucleus to cytosol, is associated with the transport of biliverdin reductase, which is important for bilirubin conjugation.
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Affiliation(s)
- Shalini Datta
- Human Genetics Unit, Indian Statistical Institute, Kolkata, India
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