AIMS: The utility of p53 as a prognostic assay has been elusive. The aims of this study were to describe a novel, reproducible scoring system and assess the relationship between differential p53 immunohistochemistry (IHC) expression patterns, TP53 mutation status and patient outcomes in breast cancer. METHODS AND RESULTS: Tissue microarrays were used to study p53 IHC expression patterns: expression was defined as extreme positive (EP), extreme negative (EN), and non-extreme (NE; intermediate patterns). Overall survival (OS) was used to define patient outcome. A representative subgroup (n = 30) showing the various p53 immunophenotypes was analysed for TP53 hotspot mutation status (exons 4-9). Extreme expression of any type occurred in 176 of 288 (61%) cases. As compared with NE expression, EP expression was significantly associated (P = 0.039) with poorer OS. In addition, as compared with NE expression, EN expression was associated (P = 0.059) with poorer OS. Combining cases showing either EP or EN expression better predicted OS than either pattern alone (P = 0.028). This combination immunophenotype was significant in univariate but not multivariate analysis. In subgroup analysis, six substitution exon mutations were detected, all corresponding to extreme IHC phenotypes. Five missense mutations corresponded to EP staining, and the nonsense mutation corresponded to EN staining. No mutations were detected in the NE group. CONCLUSIONS: Patients with extreme p53 IHC expression have a worse OS than those with NE expression. Accounting for EN as well as EP expression improves the prognostic impact. Extreme expression positively correlates with nodal stage and histological grade, and negatively with hormone receptor status. Extreme expression may relate to specific mutational status.
AIMS: The utility of p53 as a prognostic assay has been elusive. The aims of this study were to describe a novel, reproducible scoring system and assess the relationship between differential p53 immunohistochemistry (IHC) expression patterns, TP53 mutation status and patient outcomes in breast cancer. METHODS AND RESULTS: Tissue microarrays were used to study p53 IHC expression patterns: expression was defined as extreme positive (EP), extreme negative (EN), and non-extreme (NE; intermediate patterns). Overall survival (OS) was used to define patient outcome. A representative subgroup (n = 30) showing the various p53 immunophenotypes was analysed for TP53 hotspot mutation status (exons 4-9). Extreme expression of any type occurred in 176 of 288 (61%) cases. As compared with NE expression, EP expression was significantly associated (P = 0.039) with poorer OS. In addition, as compared with NE expression, EN expression was associated (P = 0.059) with poorer OS. Combining cases showing either EP or EN expression better predicted OS than either pattern alone (P = 0.028). This combination immunophenotype was significant in univariate but not multivariate analysis. In subgroup analysis, six substitution exon mutations were detected, all corresponding to extreme IHC phenotypes. Five missense mutations corresponded to EP staining, and the nonsense mutation corresponded to EN staining. No mutations were detected in the NE group. CONCLUSIONS:Patients with extreme p53 IHC expression have a worse OS than those with NE expression. Accounting for EN as well as EP expression improves the prognostic impact. Extreme expression positively correlates with nodal stage and histological grade, and negatively with hormone receptor status. Extreme expression may relate to specific mutational status.
Authors: Darragh G McArt; Jaine K Blayney; David P Boyle; Gareth W Irwin; Michael Moran; Ryan A Hutchinson; Peter Bankhead; Declan Kieran; Yinhai Wang; Philip D Dunne; Richard D Kennedy; Paul B Mullan; D Paul Harkin; Mark A Catherwood; Jacqueline A James; Manuel Salto-Tellez; Peter W Hamilton Journal: Mol Oncol Date: 2015-03-04 Impact factor: 6.603
Authors: Simion I Chiosea; Lester D R Thompson; Ilan Weinreb; Julie E Bauman; Alyssa M Mahaffey; Caitlyn Miller; Robert L Ferris; William E Gooding Journal: Cancer Date: 2016-07-05 Impact factor: 6.860
Authors: Harish Chander; Colin D Brien; Peter Truesdell; Kathleen Watt; Jalna Meens; Colleen Schick; Doris Germain; Andrew W B Craig Journal: Breast Cancer Res Date: 2014-12-30 Impact factor: 6.466
Authors: Mark Kriegsmann; Volker Endris; Thomas Wolf; Nicole Pfarr; Albrecht Stenzinger; Sibylle Loibl; Carsten Denkert; Andreas Schneeweiss; Jan Budczies; Peter Sinn; Wilko Weichert Journal: Oncotarget Date: 2014-10-30
Authors: Niamh Buckley; David Boyle; Darragh McArt; Gareth Irwin; D Paul Harkin; Tong Lioe; Stephen McQuaid; Jacqueline A James; Perry Maxwell; Peter Hamilton; Paul B Mullan; Manuel Salto-Tellez Journal: Oncotarget Date: 2015-12-22
Authors: Niamh E Buckley; Paula Haddock; Ricardo De Matos Simoes; Eileen Parkes; Gareth Irwin; Frank Emmert-Streib; Stephen McQuaid; Richard Kennedy; Paul Mullan Journal: Oncotarget Date: 2016-04-12
Authors: Niamh E Buckley; Claire Forde; Darragh G McArt; David P Boyle; Paul B Mullan; Jacqueline A James; Perry Maxwell; Stephen McQuaid; Manuel Salto-Tellez Journal: Sci Rep Date: 2016-03-21 Impact factor: 4.379
Authors: Eileen E Parkes; Matthew P Humphries; Elaine Gilmore; Fatima A Sidi; Victoria Bingham; Su M Phyu; Stephanie Craig; Catherine Graham; Joseph Miller; Daryl Griffin; Manuel Salto-Tellez; Stephen F Madden; Richard D Kennedy; Samuel F Bakhoum; Stephen McQuaid; Niamh E Buckley Journal: NPJ Breast Cancer Date: 2021-06-25