Speakers

Andrea Rau

Research Director at the French National Institute for Research in Agriculture, Food, and the Environment (INRAE).

From complexity to clarity: Tackling the challenges of multi-omic integration

The increasing availability and affordability of high-throughput sequencing technologies have enabled the generation of large-scale multi-omic data, greatly enhancing our understanding of complex biological systems across hierarchical molecular levels. A great deal of attention has been devoted to developing integrative methods that can fully leverage these multifaceted data, despite numerous statistical challenges such as small sample sizes, high dimensionality, heterogeneous measures, missing data, and complex interdependencies within and between omic layers. To date, many multi-omic integrative approaches have been proposed, reflecting the diversity of omics combinations, definitions of inter-omic anchors, and analysis objectives. In this talk, I will provide an overview of some commonly used methods for multi-omics integration, and I will introduce one of our own recent contributions in this field: idiffomix, a joint mixture model for integrated differential analyses of paired transcriptomic and methylation data. I will conclude by discussing some future opportunities and challenges in integrative multi-omics research.

Etienne Becht

Researcher at the Center for Inflammation Research (CRI).

Deconvolution : principles, methods and applications

Tumors evolve in close interaction with their microenvironment (TME). The TME is a complex ecosystem composed of tumor, immune, stromal, endothelial and other cells. The last few decades have demonstrated the usefulness of targeting the TME using anti-immune checkpoint or anti-angiogenic drugs. Profiling the TME can in addition enhance our ability to predict a patient’s prognosis or response to therapies.

When profiling a sample with bulk transcriptomics (or other bulk omic modalities), each cell population contributes to the observed measurement based on both its frequency and its average transcriptome. Deconvolution methods aim at estimating the frequencies and/or transcriptome of each cell population present in a cellularly heterogeneous sample from its bulk transcriptome.

The goal of this presentation will be to critically discuss the main assumptions, algorithms and datasets required to perform deconvolution analysis in the context of omic data.

Fatima Al-Shahrour

Head of the Bioinformatics Unit at Spanish National Cancer Research Centre (CNIO).

Bioinformatics strategies to target cancer genomes considering inter and intratumor heterogeneity

Tumor heterogeneity presents significant obstacles in cancer treatment, often leading to treatment failure, drug resistance, and unfavorable patient outcomes. Precision oncology seeks to address these challenges by developing personalized treatment strategies through comprehensive data integration.

Single-cell RNA-seq technology has the transformative capabilities to unravel tumor heterogeneity at transcriptional level. This technology enables the high-resolution characterization of cellular composition, dynamics, and the identification of distinct cancer cell subpopulations, biomarkers, drug resistance pathways, and therapeutic targets. However, the integration of scRNA-seq data and drug response profiles lacks robust computational methodologies.