What do we do?
In the Bioinformatics Core Facility (BCF) we promote trans-disciplinary collaborations between research teams in medicine, molecular biology, genetics, genomics, statistics and bioinformatics. In particular, we perform analysis of biomedical-genomics data with a focus on biomarker studies in cancer research, building on our specific expertise in statistical methods for genomics data analysis. Recently, we concentrated on molecular heterogeneity and pathway activation patterns in cancer subtypes, but we are open to any kind of research directions.
Highlights 2016Our team continues to investigate the molecular heterogeneity of colon cancer (CC) with the aim of finding information that is useful to assess 1) the expected risk of metastasis, and 2) the best way to treat the disease after surgical removal. Useful information consists in predicting the benefit of chemotherapy – with respect to its toxicity – and in predicting which drug would be more effective.
A first approach consists in a direct statistical analysis of the relationships between one tumour feature and a variable of clinical interest (such as the risk of metastasis for example). In a second approach, the group begins by subdividing the tumours into several groups which differ more clearly from one another by the characteristics of their gene expression patterns (so-called tumour subtypes). We then test the usefulness of these groups with respect to the clinical interest.
Our team completed a collaborative investigation (Guinney et al. 2015) designed to consolidate previously proposed gene expression subtype systems including our own (Budinska et al. 2013), which we recently expanded (Barras et al. 2016).
After many years, 2016 also saw the completion of the first analysis of the MINDACT trial data, providing a confirmation of the utility of gene-expression-based assessment regarding the risk of metastasis in chemotherapy decisions for breast cancer patients (Cardoso et al. 2016).
Main publications 2016
- Cardoso F et al. 70-Gene Signature as an Aid to Treatment Decisions in Early-Stage Breast Cancer. N Engl J Med. 2016;25;375(8):717-29. PMID: 27557300.
- Barras D et al. BRAF V600E mutant colorectal cancer subtypes based on gene expression.
Clin Cancer Res. 2016 Jun 27. pii: clincanres.0140.2016. PMID: 27354468.
- Bady P, Delorenzi M, Hegi ME. Sensitivity Analysis of the MGMT-STP27 Model and Impact of Genetic and Epigenetic Context to Predict the MGMT Methylation Status in Gliomas and Other Tumors. J Mol Diagn. 2016;18(3):350-61. PMID: 26927331.