LipoCanPredict: Multi-omics biomarker signatures identification in Cancer-Associated Adipocytes in Invasive Breast Cancer in obese patients

Obesity has been related to breast cancer development and progression. The secretion of adipokines and pro-inflammatory molecules by the white adipose tissue is the thought link to carcinogenesis. Some adipocytes, named Cancer Associated Adipocytes (CAAs), come in contact with the tumour and can promote its progression and resistance to treatment. Thus, CAAs are an attractive research target for the identification of signatures and biomarkers. This ongoing project aims at uncovering the mechanisms of the interaction between CAAs and cancer cells.

The comprehensive analysis of multi-omics data, focused on lipidomics, metabolomics and expression data of CAAs and tumour cells in breast cancer. An established model of co-culture of breast cancer cells with primary adipocytes from breast cancer patients is used to retrieve the comprehensive multi-omics characterisation of each cell type. Moreover, existing multi-omics data with profiles from patient biopsies is being integrated. To retrieve cell type-specific signatures, data deconvolution techniques will be applied, to correlate the multi-omics signatures to phenotypic markers. In order to understand the mechanisms behind the multi-omics signatures, functional interpretation by enrichment analyses is performed. For this purpose, two approaches, a data-driven approach, inferring the molecular networks from the data, and a knowledge-based approach, retrieving molecular networks from the literature and analysing the data in the context of the networks, is used.  Collectively, these approaches will lead to the generation of detailed molecular portraits of CCA in breast cancer

 Multi-omics predictive molecular signatures of the CAAs and the tumour cells in vitro and in obesity related breast cancer patients will be delivered. These complex signatures are expected to provide leads for clinical studies, therefore improving the current breast cancer diagnosis strategies.


image equipe Barillot