Endoscopic tumour morphology impacts survival in adenocarcinoma of the oesophagus

Autor: Fitzgerald, R.C., Noorani, A., Edwards, P.A.W., Grehan, N., Nutzinger, B., Hughes, C., Fidziukiewicz, E., MacRae, S., Northrop, A., Contino, G., Li, X., de la Rue, R., Katz-Summercorn, A., Abbas, S., Loureda, D., O'Donovan, M., Miremadi, A., Malhotra, S., Tripathi, M., Tavaré, S., Lynch, A.G., Eldridge, M., Secrier, M., Devonshire, G., Perner, J., Jammula, S., Davies, J., Crichton, C., Carroll, N., Safranek, P., Hindmarsh, A., Sujendran, V., Hayes, S.J., Ang, Y., Sharrocks, A., Preston, S.R., Oakes, S., Bagwan, I., Save, V., Skipworth, R.J.E., Hupp, T.R., O'Neill, J.R., Tucker, O., Beggs, A., Taniere, P., Puig, S., Underwood, T.J., Walker, R.C., Grace, B.L., Barr, H., Shepherd, N., Old, O., Lagergren, J., Davies, A., Chang, F., Goh, V., Ciccarelli, F.D., Sanders, G., Berrisford, R., Harden, C., Lewis, M., Cheong, E., Kumar, B., Parsons, S.L., Soomro, I., Kaye, P., Saunders, J., Lovat, L., Haidry, R., Igali, L., Scott, M., Sothi, S., Suortamo, S., Lishman, S., Hanna, G.B., Moorthy, K., Peters, C.J., Grabowska, A., Turkington, R., McManus, D., Coleman, H., Khoo, D., Fickling, W., Knight, William R.C., McEwen, Ricardo, Byrne, Ben E., Habib, Wais, Bott, Rebecca, Zylstra, Janine, Mahadeva, Ula, Gossage, James A.

Publikováno v: In European Journal of Surgical Oncology December 2020 46(12):2257-2261

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Transcriptomic profiling reveals three molecular phenotypes of adenocarcinoma at the gastroesophageal junction

Autor: Bornschein, J, Wernisch, L, Secrier, M, Miremadi, A, Perner, J, MacRae, S, O'Donovan, M, Newton, R, Menon, S, Bower, L, Eldridge, MD, Devonshire, G, Cheah, C, Turkington, R, Hardwick, RH, Selgrad, M, Venerito, M, Malfertheiner, P, Fitzgerald, RC, Noorani, A, Elliott, RF, Edwards, PAW, Grehan, N, Nutzinger, B, Crawte, J, Chettouh, H, Contino, G, Li, X, Gregson, E, Zeki, S, De la Rue, R, Malhotra, S, Tavare, S, Lynch, AG, Smith, ML, Davies, J, Crichton, C, Carroll, N, Safranek, P, Hindmarsh, A, Sujendran, V, Hayes, SJ, Ang, Y, Preston, SR, Oakes, S, Bagwan, I, Save, V, Skipworth, RJE, Hupp, TR, O'Neill, JR, Tucker, O, Beggs, A, Taniere, P, Puig, S, Underwood, TJ, Noble, F, Owsley, J, Barr, H, Shepherd, N, Old, O, Lagergren, J, Gossage, J, Davies, A, Chang, F, Zylstra, J, Goh, V, Ciccarelli, FD, Sanders, G, Berrisford, R, Harden, C, Bunting, D, Lewis, M, Cheong, E, Kumar, B, Parsons, SL, Soomro, I, Kaye, P, Saunders, J, Lovat, L, Haidry, R, Eneh, V, Igali, L, Scott, M, Sothi, S, Suortamo, S, Lishman, S, Hanna, GB, Peters, CJ, Grabowska, A

Publikováno v: on behalf of the OCCAMS Consortium 2019, ' Transcriptomic profiling reveals three molecular phenotypes of adenocarcinoma at the gastroesophageal junction ', International Journal of Cancer . https://doi.org/10.1002/ijc.32384
International Journal of Cancer

Cancers occurring at the gastroesophageal junction (GEJ) are classified as predominantly esophageal or gastric, which is often difficult to decipher. We hypothesized that the transcriptomic profile might reveal molecular subgroups which could help to

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::247888413c0d73e27a41f5f76d2e9dd0
https://doi.org/10.1002/ijc.32384

Patient-specific cancer genes contribute to recurrently perturbed pathways and establish therapeutic vulnerabilities in esophageal adenocarcinoma

Autor: Mourikis, TP, Benedetti, L, Foxall, E, Temelkovski, D, Nulsen, J, Perner, J, Cereda, M, Lagergren, J, Howell, M, Yau, C, Fitzgerald, RC, Scaffidi, P, Noorani, A, Edwards, PAW, Elliott, RF, Grehan, N, Nutzinger, B, Hughes, C, Fidziukiewicz, E, Bornschein, J, MacRae, S, Crawte, J, Northrop, A, Contino, G, Li, X, De la Rue, R, Katz-Summercorn, A, Abbas, S, Loureda, D, O'Donovan, M, Miremadi, A, Malhotra, S, Tripathi, M, Tavare, S, Lynch, AG, Eldridge, M, Secrier, M, Bower, L, Devonshire, G, Jammula, S, Davies, J, Crichton, C, Carroll, N, Safranek, P, Hindmarsh, A, Sujendran, V, Hayes, SJ, Ang, Y, Sharrocks, A, Preston, SR, Oakes, S, Bagwan, I, Save, V, Skipworth, RJE, Hupp, TR, O'Neill, JR, Tucker, O, Beggs, A, Taniere, P, Puig, S, Underwood, TJ, Walker, RC, Grace, BL, Barr, H, Shepherd, N, Old, O, Gossage, J, Davies, A, Chang, F, Zylstra, J, Mahadeva, U, Goh, V, Sanders, G, Berrisford, R, Harden, C, Lewis, M, Cheong, E, Kumar, B, Parsons, SL, Soomro, I, Kaye, P, Saunders, J, Lovat, L, Haidry, R, Igali, L, Scott, M, Sothi, S, Suortamo, S, Lishman, S, Hanna, GB, Peters, CJ, Moorthy, K, Grabowska, A, Turkington, R, McManus, D, Khoo, D, Fickling, W, Ciccarelli, FD

The identification of cancer-promoting genetic alterations is challenging particularly in highly unstable and heterogeneous cancers, such as esophageal adenocarcinoma (EAC). Here we describe a machine learning algorithm to identify cancer genes in in

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::56bbcb4834a826025994807c5a97eb9d
https://www.repository.cam.ac.uk/handle/1810/307928

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