Multiomic Analysis of Immunotherapy Features Evidencing Success and Toxicity (MANIFEST)

Harnessing the immune system to treat cancer has revolutionised survival outcomes for many patients. Immune checkpoint inhibitor therapies which unleash the brakes from immune cells to kill cancer cells, have become standard of care for many cancer subtypes. The success of existing, emerging and future immunotherapies and their routine use in the NHS is dependent on the appropriate tools, data and technology to rationalise their use and manage their side effects. Nevertheless, almost no biomarkers today can effectively distinguish responders from non-responders, predict toxicity, or guide treatment choices. Moreover, existing datasets lack standardization, suffer from sampling biases, and fail to integrate 'multi-omics' data with clinical information. Our platform, MANIFEST, leverages existing and also novel scalable methodologies to provide deep profiling of each patient receiving immunotherapy and will deliver on multimodal data integration and modelling. We represent a diverse group of UK-wide experts in cancer research and clinical care comprising 6 major NHS trusts, 14 academic institutes and universities, and with strong upfront top-up investment (>£ 12 million) from industry partners (namely IMU Biosciences, Guardant Health, Natera, Roche-imCORE, Roche-Sequencing, M:M Bio and Microbiotica; among others). To demonstrate the utility of the MANIFEST platform, we will deliver exemplar projects encompassing multiple tumour types (melanoma, renal cell carcinoma, bladder cancer and triple negative breast cancer), where prediction of treatment outcomes and toxicities to both standard of care and emerging immunotherapies remains a significant unmet need. Specifically, we have access to pre-existing longitudinal samples of >3,000 patients across 10 reported studies (RAMPART, MITRE, PRISM, EXACT, CAPTURE, PaVeMenT, ALEXANDRA, neoTRIP, ABACUS and ABACUS-2). In parallel, we will utilise existing governance at partner NHS sites for prospective sample collection (blood, stool and tumour) and processing. With a tiered approach, we aim to profile patient and tumour samples at scale (~3,000 patients over 3 years). We will implement standard of care diagnostic workflows for high-volume biomarker discovery (Tier 1). We reserve in-depth profiling, through Tiers 2 and 3 participation, to further characterise tumours including discovery-focused techniques, such as high-dimensional peripheral immune profiling, liquid biopsy (cfDNA, immune methylation profiling), and spatial image-profiling approaches coupled with molecular profiling (WES, bulk&long-read RNAseq, TCR&BCRseq). Finally, for selected patients, we will apply our expertise in Representative Sequencing (RepSeq), a novel tumour sampling methodology which overcomes pervasive sampling bias in solid tumours; and pre-clinical modelling through patient-derived tumour fragments (PDTFs) for drug sensitivity screening. Finally, we will deploy a team of 12 experts in artificial intelligence and machine learning to deliver on multimodal data acquisition and integration in our in-house Trusted Research Environment (TRE). We are also excited to be teaming up with the National Pathology Imaging Co-operative (NPIC) and Genomics England (GEL) to synergise efforts in translating discoveries for patient benefit.