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Dive into the research topics where Pierre Farmer is active.

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Featured researches published by Pierre Farmer.


Oncogene | 2005

Identification of molecular apocrine breast tumours by microarray analysis

Pierre Farmer; Hervé Bonnefoi; Véronique Becette; Michele Tubiana-Hulin; Pierre Fumoleau; Denis Larsimont; Gaëtan MacGrogan; Jonas Bergh; David Cameron; Darlene R. Goldstein; Stephan Duss; Anne-Laure Nicoulaz; Cathrin Brisken; Maryse Fiche; Mauro Delorenzi; Richard Iggo

Previous microarray studies on breast cancer identified multiple tumour classes, of which the most prominent, named luminal and basal, differ in expression of the oestrogen receptor α gene (ER). We report here the identification of a group of breast tumours with increased androgen signalling and a ‘molecular apocrine’ gene expression profile. Tumour samples from 49 patients with large operable or locally advanced breast cancers were tested on Affymetrix U133A gene expression microarrays. Principal components analysis and hierarchical clustering split the tumours into three groups: basal, luminal and a group we call molecular apocrine. All of the molecular apocrine tumours have strong apocrine features on histological examination (P=0.0002). The molecular apocrine group is androgen receptor (AR) positive and contains all of the ER-negative tumours outside the basal group. Kolmogorov–Smirnov testing indicates that oestrogen signalling is most active in the luminal group, and androgen signalling is most active in the molecular apocrine group. ERBB2 amplification is commoner in the molecular apocrine than the other groups. Genes that best split the three groups were identified by Wilcoxon test. Correlation of the average expression profile of these genes in our data with the expression profile of individual tumours in four published breast cancer studies suggest that molecular apocrine tumours represent 8–14% of tumours in these studies. Our data show that it is possible with microarray data to divide mammary tumour cells into three groups based on steroid receptor activity: luminal (ER+ AR+), basal (ER− AR−) and molecular apocrine (ER− AR+).


Lancet Oncology | 2007

Retraction--Validation of gene signatures that predict the response of breast cancer to neoadjuvant chemotherapy: a substudy of the EORTC 10994/BIG 00-01 clinical trial.

Hervé Bonnefoi; Anil Potti; Mauro Delorenzi; Louis Mauriac; Mario Campone; Michèle Tubiana-Hulin; Thierry Petit; Philippe Rouanet; Jacek Jassem; Emmanuel Blot; Véronique Becette; Pierre Farmer; Sylvie André; Chaitanya R. Acharya; Sayan Mukherjee; David Cameron; Jonas Bergh; Joseph R. Nevins; Richard Iggo

BACKGROUND We have previously described gene-expression signatures that predict growth inhibitory and cytotoxic effects of common chemotherapeutic drugs in vitro. The aim of this study was to confirm the validity of these gene-expression signatures in a large series of patients with oestrogen-receptor-negative breast tumours who were treated in a phase III neoadjuvant clinical trial. METHODS This trial compares a non-taxane regimen (fluorouracil, epirubicin, and cyclophosphamide [FEC] for six cycles) with a taxane regimen (docetaxel for three cycles followed by epirubicin plus docetaxel [TET] for three cycles) in women with oestrogen-receptor-negative breast cancer. The primary endpoint of the study is the difference in progression-free survival based on TP53 status and will be reported later. Predicting response with gene signatures was a planned secondary endpoint of the trial and is reported here. Pathological complete response, defined as complete disappearance of the tumour with no more than a few scattered tumour cells detected by the pathologist in the resection specimen, was used to assess chemosensitivity. RNA was prepared from sections of frozen biopsies taken at diagnosis and hybridised to Affymetrix X3P microarrays. In-vitro single-agent drug sensitivity signatures were combined to obtain FEC and TET regimen-specific signatures. This study is registered on the clinical trials site of the US National Cancer Institute website http://www.clinicaltrials.gov/ct/show/NCT00017095. FINDINGS Of 212 patients with oestrogen-receptor-negative tumours assessed, 87 patients were excluded. 125 oestrogen-receptor-negative tumours (55 that showed pathological complete responses) were tested: 66 in the FEC group (28 that showed pathological complete responses) and 59 in the TET group (27 that showed pathological complete responses). The regimen-specific signatures significantly predicted pathological complete response in patients treated with the appropriate regimen (p<0.0001). The FEC predictor had a sensitivity of 96% (27 of 28 patients [95% CI 82-99]), specificity of 66% (25 of 38 patients [50-79]), positive predictive value (PPV) of 68% (27 of 40 patients [52-80]), and negative predictive value (NPV) of 96% (25 of 26 patients [81-99]). The TET predictor had a sensitivity of 93% (25 of 27 patients [77-98]), specificity 69% (22 of 32 patients [51-82]), PPV of 71% (25 of 35 patients [55-84]), and NPV of 92% (22 of 24 patients [74-98]). Analysis of tumour size, grade, nodal status, age, and regimen-specific signatures showed that the genomic signatures were the only independent variables predicting pathological complete response at p<0.01. Selection of patients with these signatures would increase the proportion of patients with pathological complete responses from 44% to around 70% in the patients studied here. INTERPRETATION We have validated the use of regimen-specific drug sensitivity signatures in the context of a multicentre randomised trial. The high NPV of both signatures may allow early selection of patients with breast cancer who should be considered for trials with new drugs.


Cancer Research | 2008

CYR61 and αVβ5 Integrin Cooperate to Promote Invasion and Metastasis of Tumors Growing in Preirradiated Stroma

Yan Monnier; Pierre Farmer; Grégory Bieler; Natsuko Imaizumi; Thierry Sengstag; Gian Carlo Alghisi; Jean-Christophe Stehle; Laura Ciarloni; Snezana Andrejevic-Blant; Raphaël Moeckli; René-Olivier Mirimanoff; Simon Goodman; Mauro Delorenzi; Curzio Rüegg

Radiotherapy is widely used to treat human cancer. Patients locally recurring after radiotherapy, however, have increased risk of metastatic progression and poor prognosis. The clinical management of postradiation recurrences remains an unresolved issue. Tumors growing in preirradiated tissues have an increased fraction of hypoxic cells and are more metastatic, a condition known as tumor bed effect. The transcription factor hypoxia inducible factor (HIF)-1 promotes invasion and metastasis of hypoxic tumors, but its role in the tumor bed effect has not been reported. Here, we show that tumor cells derived from SCCVII and HCT116 tumors growing in a preirradiated bed, or selected in vitro through repeated cycles of severe hypoxia, retain invasive and metastatic capacities when returned to normoxia. HIF activity, although facilitating metastatic spreading of tumors growing in a preirradiated bed, is not essential. Through gene expression profiling and gain- and loss-of-function experiments, we identified the matricellular protein CYR61 and alphaVbeta5 integrin as proteins cooperating to mediate these effects. The anti-alphaV integrin monoclonal antibody 17E6 and the small molecular alphaVbeta3/alphaVbeta5 integrin inhibitor EMD121974 suppressed invasion and metastasis induced by CYR61 and attenuated metastasis of tumors growing within a preirradiated field. These results represent a conceptual advance to the understanding of the tumor bed effect and identify CYR61 and alphaVbeta5 integrin as proteins that cooperate to mediate metastasis. They also identify alphaV integrin inhibition as a potential therapeutic approach for preventing metastasis in patients at risk for postradiation recurrences.


Journal of Biological Chemistry | 2004

Transcript Profiling Suggests That Differential Metabolic Adaptation of Mice to a High Fat Diet Is Associated with Changes in Liver to Muscle Lipid Fluxes

Valérie de Fourmestraux; Heike Neubauer; Carine Poussin; Pierre Farmer; Rémy Burcelin; Mauro Delorenzi; Bernard Thorens

Genetically homogenous C57Bl/6 mice display differential metabolic adaptation when fed a high fat diet for 9 months. Most become obese and diabetic, but a significant fraction remains lean and diabetic or lean and non-diabetic. Here, we performed microarray analysis of “metabolic” transcripts expressed in liver and hindlimb muscles to evaluate: (i) whether expressed transcript patterns could indicate changes in metabolic pathways associated with the different phenotypes, (ii) how these changes differed from the early metabolic adaptation to short term high fat feeding, and (iii) whether gene classifiers could be established that were characteristic of each metabolic phenotype. Our data indicate that obesity/diabetes was associated with preserved hepatic lipogenic gene expression and increased plasma levels of very low density lipoprotein and, in muscle, with an increase in lipoprotein lipase gene expression. This suggests increased muscle fatty acid uptake, which may favor insulin resistance. In contrast, the lean mice showed a strong reduction in the expression of hepatic lipogenic genes, in particular of Scd-1, a gene linked to sensitivity to diet-induced obesity; the lean and non-diabetic mice presented an additional increased expression of eNos in liver. After 1 week of high fat feeding the liver gene expression pattern was distinct from that seen at 9 months in any of the three mouse groups, thus indicating progressive establishment of the different phenotypes. Strikingly, development of the obese phenotype involved re-expression of Scd-1 and other lipogenic genes. Finally, gene classifiers could be established that were characteristic of each metabolic phenotype. Together, these data suggest that epigenetic mechanisms influence gene expression patterns and metabolic fates.


Ejc Supplements | 2010

A stroma-related gene signature predicts resistance to neoadjuvant chemotherapy in breast cancer

Richard Iggo; Pierre Farmer; Pratyaksha Wirapati; Pascale Anderle; Mauro Delorenzi; Jan Bogaerts; Martine Piccart; David Cameron; Jonas Bergh; Hervé Bonnefoi

Reference EPFL-CONF-172479View record in Web of Science Record created on 2011-12-16, modified on 2017-05-12


Journal of the National Cancer Institute | 2006

Gene Expression Profiling in Breast Cancer: Understanding the Molecular Basis of Histologic Grade To Improve Prognosis

Christos Sotiriou; Pratyaksha Wirapati; Sherene Loi; Adrian L. Harris; Steve Fox; Johanna Smeds; Hans Nordgren; Pierre Farmer; Viviane Praz; Benjamin Haibe-Kains; Christine Desmedt; Denis Larsimont; Fatima Cardoso; Hans Peterse; Dimitry S.A. Nuyten; Marc Buyse; Marc J. van de Vijver; Jonas Bergh; Martine Piccart; Mauro Delorenzi


Breast Cancer Research | 2008

Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures

Pratyaksha Wirapati; Christos Sotiriou; Susanne Kunkel; Pierre Farmer; Sylvain Pradervand; Benjamin Haibe-Kains; Christine Desmedt; Michail Ignatiadis; Thierry Sengstag; Frédéric Schütz; Darlene R. Goldstein; Martine Piccart; Mauro Delorenzi


Cancer Research | 2003

Classification of human astrocytic gliomas on the basis of gene expression: a correlated group of genes with angiogenic activity emerges as a strong predictor of subtypes

Sophie Godard; Gad Getz; Mauro Delorenzi; Pierre Farmer; Hiroyuki Kobayashi; Isabelle Desbaillets; Michimasa Nozaki; Annie-Claire Diserens; Marie-France Hamou; Pierre-Yves Dietrich; Luca Regli; Robert C. Janzer; Philipp Bucher; Roger Stupp; Nicolas de Tribolet; Eytan Domany; Monika E. Hegi


Nutrition | 2004

Nutrigenomic approach to understanding the mechanisms by which dietary long-chain fatty acids induce gene signals and control mechanisms involved in carcinogenesis

Pascale Anderle; Pierre Farmer; Alvin Berger; Matthew-Alan Roberts


Breast Cancer Research | 2005

Gene expression profiling in breast cancer challenges the existence of intermediate histological grade

Christos Sotiriou; Pratyaksha Wirapati; Sherene Loi; Adrian L. Harris; Jonas Bergh; Johanna Smeds; Viviane Praz; Pierre Farmer; Benjamin Haibe-Kains; Françoise Lallemand; Marc Buyse; Martine Piccart; Mauro Delorenzi

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Pratyaksha Wirapati

Swiss Institute of Bioinformatics

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Christos Sotiriou

Université libre de Bruxelles

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Martine Piccart

Université libre de Bruxelles

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Benjamin Haibe-Kains

Princess Margaret Cancer Centre

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Viviane Praz

Swiss Institute of Bioinformatics

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Christine Desmedt

Université libre de Bruxelles

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