ESR 14: Patient population stratification towards personalized treatment discovery for Chronic Kidney Disease

Supervisor: Anna Reznichenko

PhD Student: Dianne Acoba

Short Summary

Chronic Kidney Disease is highly complex and heterogeneous and includes a wide range of etiologies with a multitude of underlying molecular processes in the kidney. Furthermore, the kidney tissue is structurally heterogeneous and comprised of more than 25 different cell types. These factors make development of treatments for CKD very challenging. This exciting PhD project will use human omics data and advanced data analysis algorithms to group patients into subtypes and characterize underlying biological pathways in the cell type-specific context that will advance our biological understanding of CKD and open new opportunities for drug discovery leading to personalized treatments.

The ESR will conduct bioinformatics analyses of omics (transcriptomics, proteomics, metabolomics) data as well as perform wet-lab experiments to functionally characterize and validate the findings, and will develop a unique combination of both computational and experimental skills. In addition to their individual scientific project, they will benefit from further continuing education, which includes scientific and transferable skills courses, participation in workshops and conferences, and secondments to partner labs.

Scientific strategy


The main aim of ESR14 project will be to address the CKD heterogeneity through data-driven stratification approaches to open up new opportunities for personalized drug discovery. The CKD patient population includes a wide range of diverse aetiologies with a multitude of underlying molecular processes in the kidney. Moreover, the kidney is structurally heterogeneous and comprised of more than 25 different cell types impeding interpretation of whole-tissue omics profiling.

The specific objectives of ESR14 will be:

  • using available kidney transcriptomics data, identify inherent patient molecular subtypes in the CKD population using unsupervised machine learning clustering approaches (k-means, DBSCAN, neural network);
  • perform computational deconvolution of cell-specific signals within the renal tissue transcriptomics data using CellMix algorithms, reconcile with the population stratification findings and perform pathway enrichment analyses to reveal the underlying molecular processes; 
  • per patient subtype, identify candidate drug targets and biomarkers through unbiased data-driven (using statistical feature selection) and knowledge-based (using pre-existing biological and druggability insights) gene prioritization approaches. Ultimately, the scope of ESR14 project will be to validate the targets preclinically using in vivo suitable tool compounds in translatable animal models in collaboration with the other ESRs.