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Working Group 303/06

Pathogenesis and Tumour Profiling

No of members

WG Leader




Biomedical, Bioinformatics

01 Objectives

To unravel brain cancer pathogenesis and optimise preclinical models. Relevant to RCO2 and RCO3.

02 Tasks

T3.1 Improve the state-of-the-art and good practices in single cell techniques for patient stratification; T3.2 Spatial analysis of intra tumoral heterogeneity with respect to subclones and their states and microenvironments upon treatment. T3.3 WG3 meetings

03 Activities

Involved in all MC and WG3 meetings including organising a WG2/WG3-focused conference, contribute to annual and final reports, the final year conference; 2 peer reviewed joint publications, contribute to dissemination, exploitation, training schools


M3.1: WG2/WG3 conference in month 22; M3.2: Mid-term report in month 25.


PATHOGENESIS & TUMOUR PROFILING (Theme-3) A better understanding of tumour pathogenesis and progress is the key to patient stratification and treatment since it helps to select those patients who re most likely to benefit from certain therapies and improve the level of understanding of treatment related toxicities, cancer recurrence and drug resistance. Single-cell resolution has proven to be of utmost importance and has the ability to reveal a remarkable level of regional heterogeneity in cancer.
In order to obtain a complete understanding of brain tumour biology, this Action will seek to apply novel single-cell-based experimental techniques (e.g., scRNAseq, SCP, SCM), together with DNAm, and computational approaches to reveal how the brain TME contributes to drug resistance and tumour recurrence across brain tumour types, from low grade to high grade, and from paediatric (e.g., medulloblastoma) to adult (e.g., glioma). Formalin-Fixed Paraffin-Embedded (FFPE) and fresh tissue samples will be obtained from public depositories such as EuroBioBank, UKbiobank, as well as cancer centres involved in this network. In addition, single-cell spatial epigenetic technologies will be developed and applied for those solid tissues to tag epigenetic status in situ, and decipher single-cell spatial chromatin accessibility, histone modifications and transcriptional factor bindings.
Both data-driven and hypothesis-driven approaches will be applied together with power calculations to estimate sample size before data collection. Comprehensive bioinformatics analysis will then be carried out including the development and application of AI/ML techniques. Different research groups have been carrying on relevant works for many years, their works will be enhanced via new collaborations within this Action.