Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications
Therese Sørlie,Charles M. Perou,Robert Tibshirani,Turid Aas,Stephanie Geisler,Hilde Johnsen,Trevor Hastie,Michael B. Eisen,Matt van de Rijn,Stefanie S. Jeffrey,T. Thorsen,Hanne Quist,John C. Matese,Patrick O. Brown,David Botstein,Per Eystein Lønning,Anne Lise Børresen-Dale +16 more
TLDR
Survival analyses on a subcohort of patients with locally advanced breast cancer uniformly treated in a prospective study showed significantly different outcomes for the patients belonging to the various groups, including a poor prognosis for the basal-like subtype and a significant difference in outcome for the two estrogen receptor-positive groups.Abstract:
The purpose of this study was to classify breast carcinomas based on variations in gene expression patterns derived from cDNA microarrays and to correlate tumor characteristics to clinical outcome. A total of 85 cDNA microarray experiments representing 78 cancers, three fibroadenomas, and four normal breast tissues were analyzed by hierarchical clustering. As reported previously, the cancers could be classified into a basal epithelial-like group, an ERBB2-overexpressing group and a normal breast-like group based on variations in gene expression. A novel finding was that the previously characterized luminal epithelial/estrogen receptor-positive group could be divided into at least two subgroups, each with a distinctive expression profile. These subtypes proved to be reasonably robust by clustering using two different gene sets: first, a set of 456 cDNA clones previously selected to reflect intrinsic properties of the tumors and, second, a gene set that highly correlated with patient outcome. Survival analyses on a subcohort of patients with locally advanced breast cancer uniformly treated in a prospective study showed significantly different outcomes for the patients belonging to the various groups, including a poor prognosis for the basal-like subtype and a significant difference in outcome for the two estrogen receptor-positive groups.read more
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Gene expression profiling predicts clinical outcome of breast cancer
Laura J. van't Veer,Hongyue Dai,Marc J. van de Vijver,Yudong D. He,Augustinus A. M. Hart,Mao Mao,Hans Peterse,Karin van der Kooy,Matthew J. Marton,Anke T. Witteveen,George J. Schreiber,Ron M. Kerkhoven,Christopher J. Roberts,Peter S. Linsley,René Bernards,Stephen H. Friend +15 more
TL;DR: DNA microarray analysis on primary breast tumours of 117 young patients is used and supervised classification is applied to identify a gene expression signature strongly predictive of a short interval to distant metastases (‘poor prognosis’ signature) in patients without tumour cells in local lymph nodes at diagnosis, providing a strategy to select patients who would benefit from adjuvant therapy.
Journal ArticleDOI
A Gene-Expression Signature as a Predictor of Survival in Breast Cancer
Marc J. van de Vijver,Yudong D. He,Laura J. van't Veer,Hongyue Dai,Augustinus A. M. Hart,D.W. Voskuil,George J. Schreiber,Johannes L. Peterse,Christopher J. Roberts,Matthew J. Marton,Mark Parrish,Douwe Atsma,Anke T. Witteveen,Annuska M. Glas,Leonie J. M. J. Delahaye,Tony van de Velde,Harry Bartelink,Sjoerd Rodenhuis,Emiel J. Th. Rutgers,Stephen H. Friend,René Bernards +20 more
TL;DR: The gene-expression profile studied is a more powerful predictor of the outcome of disease in young patients with breast cancer than standard systems based on clinical and histologic criteria.
Journal ArticleDOI
A Multigene Assay to Predict Recurrence of Tamoxifen-Treated, Node-Negative Breast Cancer
Soonmyung Paik,Steven Shak,Gong Tang,Chungyeul Kim,Joffre B. Baker,Maureen T. Cronin,Frederick L. Baehner,Michael G. Walker,Drew Watson,Taesung Park,William Hiller,Edwin R. Fisher,D. Lawrence Wickerham,John Bryant,Norman Wolmark +14 more
TL;DR: The recurrence score has been validated as quantifying the likelihood of distant recurrence in tamoxifen-treated patients with node-negative, estrogen-receptor-positive breast cancer and could be used as a continuous function to predict distant recurrent in individual patients.
Journal ArticleDOI
Repeated observation of breast tumor subtypes in independent gene expression data sets
Therese Sørlie,Robert Tibshirani,Joel S. Parker,Trevor Hastie,James Stephen Marron,Andrew B. Nobel,Shibing Deng,Hilde Johnsen,Robert Pesich,Stephanie Geisler,Janos Demeter,Charles M. Perou,Per Eystein Lønning,Patrick O. Brown,Anne Lise Børresen-Dale,David Botstein +15 more
TL;DR: The results strongly support the idea that many of these breast tumor subtypes represent biologically distinct disease entities.
Journal ArticleDOI
The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups
Christina Curtis,Christina Curtis,Sohrab P. Shah,Suet-Feung Chin,Gulisa Turashvili,Oscar M. Rueda,Mark J Dunning,Doug Speed,Doug Speed,Andy G. Lynch,Shamith A. Samarajiwa,Yinyin Yuan,Stefan Gräf,Gavin Ha,Gholamreza Haffari,Ali Bashashati,Roslin Russell,Steven McKinney,Anita Langerød,Andrew R. Green,Elena Provenzano,Gordon C. Wishart,Sarah E Pinder,Peter H. Watson,Peter H. Watson,Florian Markowetz,Leigh C. Murphy,Ian O. Ellis,Arnie Purushotham,Arnie Purushotham,Anne Lise Børresen-Dale,Anne Lise Børresen-Dale,James D. Brenton,Simon Tavaré,Carlos Caldas,Samuel Aparicio +35 more
TL;DR: The results provide a novel molecular stratification of the breast cancer population, derived from the impact of somatic CNAs on the transcriptome, and identify novel subgroups with distinct clinical outcomes, which reproduced in the validation cohort.
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TL;DR: It is shown that there is diversity in gene expression among the tumours of DLBCL patients, apparently reflecting the variation in tumour proliferation rate, host response and differentiation state of the tumour.
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