Benjamin Haibe-Kains

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BACKGROUND Estrogen receptor positive (ER+) breast cancers (BC) are heterogeneous with regard to their clinical behavior and response to therapies. The ER is currently the best predictor of response to the anti-estrogen agent tamoxifen, yet up to 30-40% of ER+BC will relapse despite tamoxifen treatment. New prognostic biomarkers and further biological(More)
PURPOSE Recently, several prognostic gene expression signatures have been identified; however, their performance has never been evaluated according to the previously described molecular subtypes based on the estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2), and their biological meaning has remained unclear. Here we aimed to perform(More)
Recent technologies have made it cost-effective to collect diverse types of genome-wide data. Computational methods are needed to combine these data to create a comprehensive view of a given disease or a biological process. Similarity network fusion (SNF) solves this problem by constructing networks of samples (e.g., patients) for each available data type(More)
Two large-scale pharmacogenomic studies were published recently in this journal. Genomic data are well correlated between studies; however, the measured drug response data are highly discordant. Although the source of inconsistencies remains uncertain, it has potential implications for using these outcome measures to assess gene-drug associations or select(More)
MOTIVATION Survival prediction of breast cancer (BC) patients independently of treatment, also known as prognostication, is a complex task since clinically similar breast tumors, in addition to be molecularly heterogeneous, may exhibit different clinical outcomes. In recent years, the analysis of gene expression profiles by means of sophisticated data(More)
Adjuvant chemotherapy for breast cancer after surgery has effectively lowered metastatic recurrence rates. However, a considerable proportion of women suffer recurrent cancer at distant metastatic sites despite adjuvant treatment. Identification of the genes crucial for tumor response to specific chemotherapy drugs is a challenge but is necessary to improve(More)
Human cancers exhibit strong phenotypic differences that can be visualized noninvasively by medical imaging. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. Here we present a radiomic analysis of 440 features quantifying tumour image intensity, shape and texture, which are(More)
Breast cancer is a molecularly, biologically and clinically heterogeneous group of disorders. Understanding this diversity is essential to improving diagnosis and optimizing treatment. Both genetic and acquired epigenetic abnormalities participate in cancer, but the involvement of the epigenome in breast cancer and its contribution to the complexity of the(More)
This article introduces a manually curated data collection for gene expression meta-analysis of patients with ovarian cancer and software for reproducible preparation of similar databases. This resource provides uniformly prepared microarray data for 2970 patients from 23 studies with curated and documented clinical metadata. It allows users to efficiently(More)
Ovarian cancer is the fifth leading cause of cancer death for women in the U.S. and the seventh most fatal worldwide. Although ovarian cancer is notable for its initial sensitivity to platinum-based therapies, the vast majority of patients eventually develop recurrent cancer and succumb to increasingly platinum-resistant disease. Modern, targeted cancer(More)