A platform for predicting response and resistance to immunotherapy

Primary supervisor: Samra Turajlic, Francis Crick Institute

Secondary supervisor: Kevin Litchfield, UCL

Project

Only 5% of anti-cancer drugs tested in phase one trials progress to registration, while approved cancer therapies benefit a minority of patients. In large part this is the result of the lack of robust biomarkers that account for inter-and intra-tumour heterogeneity and the complex relationship between the tumour cells and the tumour microenvironment (TME).

Clear cell renal cell carcinoma (ccRCC), the most common type of kidney cancer that is rising in incidence and causes ~150K deaths globally each year, is an illustrative example of these challenges. ccRCC lacks mutations in targetable oncogenes and is characterised by pervasive intratumour heterogeneity (ITH) [1]. Systemic therapies for metastatic ccRCC target the TME which typically has high levels of angiogenesis and immune infiltration. Approved therapies include inhibitors of the vascular endothelial growth factor (VEGFi), and immune checkpoint inhibitors (ICI); combinations of both are also approved. Biomarkers that predict benefit to either class of drugs or combination are lacking. Decisions about therapy are made using poorly performing clinical markers and lead to very variable outcomes. Similarly, mechanisms of resistance that could be leveraged for future therapeutic targeting are largely unknown. This contrasts with other ICI-treated cancers, or molecularly stratified therapies with well-defined mode of action. We have recently shown [2] in the context of a clinical trial, that intratumoral T cells are the target of anti-PD1 therapy in ccRCC, and that boosting them is associated with response to treatment. Thus, the drug engages immune cells in the tumour rather than peripheral blood. Clinical studies provide limited materials for translational analyses, especially paired samples, while pre-clinical models provide a platform for repeatable testing of functional drug responses, with potential to predict therapy responses in patients. One of the critical disadvantages of current models, especially for ccRCC is the lack of TME.

In this project, the candidate will utilise the unique framework of the TRACERx Renal study (http://tracerx.co.uk/studies/renal/) to develop a patient-derived-explant (PDE) model and evaluate drug responses in a context that closely mirrors that of the tumour in vivo. PDEs preserve the architecture of the tumour and the TME and are accurate in predicting patient responses to therapy [3]. ITH is accounted for by establishing multiple PDEs from spatially separate areas of the tumour, reflecting different niches [4]. We will focus in particular on patients with high risk stage 3 to predict the benefit of adjuvant PD1 therapy as an emerging area of unmet need.

The candidate will use cutting-edge spatial transcriptomics, multiplex immunohistochemistry, high dimensional flow cytometry, cytokine bead array and single cell molecular and immune profiling, including T/B cell receptor sequencing to interrogate changes on ICI therapy. The impact of lineages beyond T cells, including myeloid cells and B cells will be interrogated. For the first time in such a system, the impact of VEGFi on endothelial and immune cells will be evaluated to understand the bases of this combination therapy. Changes in the PDE and the clinical response to therapy in the patient will be correlated, ultimately to identify biomarkers of benefit, but also inform new potential targets.

Candidate background

This project would suit a candidate with a background in immunology and/or molecular biology though any candidates irrespective of their background, with an interest in tumour immunology, immunotherapy and cancer evolution are encouraged to apply. The project will provide comprehensive training in tumour immunology, pre-clinical models, multi-omic data analysis and computational biology.

Potential Research Placements

1. Daniela Thommen, Netherland Cancer Institute
2. Katie Bentley, Francis Crick Institute
3. Benny Chain, Division of Infection & Immunity, UCL

References

1. Turajlic S, Xu H, Litchfield K, et al.: Deterministic Evolutionary Trajectories Influence Primary Tumor Growth: TRACERx Renal. Cell 173:595-610 e11, 2018
2. Au L, Hatipoglu E, Robert de Massy M, et al.: Determinants of anti-PD-1 response and resistance in clear cell renal cell carcinoma. Cancer Cell 39:1497-1518 e11, 2021
3. Voabil P, de Bruijn M, Roelofsen LM, et al.: An ex vivo tumor fragment platform to dissect response to PD-1 blockade in cancer. Nat Med 27:1250-1261, 2021
4. Zhao Y, Fu X, Lopez JI, et al.: Selection of metastasis competent subclones in the tumour interior. Nat Ecol Evol 5:1033-1045, 2021