Our research in the TRACERx Renal study aims to identify drivers and extent of intra-tumour heterogeneity in RCC and to determine how this heterogeneity impacts clinical outcomes.

Kidney cancer poses a significant health challenge globally and is the 7th most common cancer in the UK, with over 13,000 cases and 4,500 deaths annually. The most common and aggressive histological subtype of kidney cancer is clear cell Renal Cell Carcinoma (ccRCC) and it is known for its diverse and unpredictable clinical behaviours, complicating treatment and clinical management.

To improve patient outcomes, it is essential to understand the drivers of metastatic disease and treatment resistance.

We have previously shown that ccRCC tumours exhibit extensive intra-tumour heterogeneity, which influences tumour evolution and resistance to treatment, posing challenges for precision medicine and the effect of tumour sampling bias on prognostic and predictive marker developments.

Our findings in the TRACERx Renal study (Tx100) highlighted that cancer evolution is highly constrained by selection. However, impact of mutation supply, tumour microenvironment (TME) and therapeutic intervention and strength of selection is unknown.


In our extended analysis, on the basis of conserved patterns of mutational ordering, mutual co-occurrence and exclusivity we seek to extend our discovery of novel evolutionary pathways and genetic dependencies, and associate these with clinical outcomes.

The study utilises spatially resolved evaluations of genetically distinct cancer cell populations within their native tumour microenvironment. Our focus is on analysing the tumour transcriptome, histology and employing advanced spatial biology techniques.

This comprehensive approach aims to uncover the evolutionary pathways and genetic dependencies that are crucial for the progression and resistance of ccRCC.

This image provides a detailed overview of the workflow in cancer research starting from patient interaction and sample collection, through histopathology, imaging and genetic analyses, to advanced data modelling techniques. Each element in the workflow is interconnected. It highlights the integration of clinical, radiological, and laboratory data to facilitate a thorough understanding and management of cancer.

Figure 1: The workflow in cancer research, highlighting the integration of clinical, radiological and laboratory data to facilitate a thorough understanding and management of cancer.

Funding

  • The Royal Marsden NHS Foundation Trust

  • National Institute for Health Research (NIHR)

Publications