Kidney tumours are churning out thousands of abnormal RNA molecules that may determine whether a patient responds to immunotherapy. Clear cell renal cell carcinoma (ccRCC) kills 4,700 people in the UK each year. Unlike most cancers, its DNA mutation load is low and cannot predict who will benefit from immune checkpoint inhibitors—drugs that only work for 20–40% of patients. The missing link, researchers suspect, lies not in DNA but in RNA: the tumour’s transcriptome is wildly unstable, producing novel genes, non-canonical splice variants, and fragments from ancient viral DNA. This instability may shape the immune environment around the tumour and dictate drug response. The team will use Nanopore long-read RNA sequencing—an emerging technology that captures full-length RNA molecules—to measure transcriptome-wide instability in ccRCC samples from existing clinical cohorts. They will identify specific instability markers and link them to hallmark mutations and immune responses. If successful, this fundamental science project could deliver the first reliable molecular predictors of immunotherapy response in kidney cancer, sparing non-responders from ineffective treatment and pointing toward new therapeutic targets. The approach may also provide a generalisable model for studying transcriptome instability across other cancers.
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Clear cell renal cell carcinoma (ccRCC) is the most common form of kidney cancer, accounting for 13,300 new cancer cases and 4,700 cancer-associated deaths per year in the UK. Amongst all cancer types, ccRCCs contain one of the highest percentages of tumour-infiltrating immune cells. Immune checkpoint inhibitors (CPIs) are currently used for the treatment of metastatic disease and have been recently approved for treatment of high-risk localised disease after tumour resection. However only 20-40% patients respond to treatment, with no robust predictors of response. In this context, ccRCC represents a unique challenge: in contrast to most other solid cancers, DNA mutation load can neither explain ccRCC immunogenicity (ccRCC demonstrates low mutational burden) nor response to immunotherapy (no correlation between tumour mutational burden and response to CPIs). Aberrant co- and post-transcriptional events, also known as transcriptome instability, are known to drive oncogenesis and tumour immunogenicity. In a pilot study, we used Nanopore long-read RNA sequencing, an emerging technology, to develop tools to study transcriptome instability, overcoming limitations of conventional RNA sequencing. We discovered a striking and unappreciated layer of transcriptome complexity in ccRCC, including thousands of novel genes and transcripts, non-canonical splice variants and transposable element-derived transcripts. This revealed that ccRCC transcriptomes are characterised by widespread instability, leading to the hypothesis that ccRCC transcriptome instability shapes the immune tumour microenvironment (iTME) and determines response to CPIs. To test this hypothesis, we will use our expertise in RNA-driven immune gene regulation and long-read RNA sequencing and access to already available clinical cohorts (co-Investigators: Samra Turajlic, The Crick Institute and Royal Marsden Hospital; Naveen Vasudev, University of Leeds and St James’s Hospital). We will develop measures of transcriptome-wide instability and identify specific transcripts as ccRCC transcriptome instability markers (TIMs). We will determine how ccRCC-associated hallmark mutations (including loss of VHL, PBRM1, SETD2, KDM5C, and BAP1) contribute to emergence of transcriptome instability. We will then characterise the relationship between transcriptome instability and the ccRCC iTME and determine TIMs that correlate with positive responses to CPIs. Overall, this will be the first large-scale application of long-read sequencing to ccRCC and cancer immunology. We anticipate that our findings will (a) transform our understanding of how cancer transcriptomes contribute to anti-tumour immunity, (b) reveal novel molecular predictors of response to CPIs, paving the way for novel therapeutics and diagnostics in ccRCC, and (c) provide a generalisable model for studying transcriptome instability in cancer.
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