Title: Harnessing the power of cancer whole genome sequencing for clinical utility
Funder: Department of Health and Social Care

Abstract: Research Question How can we maximise the use of mutational information in whole cancer genomes for clinical benefit? Background Substantial expertise is required to perform analyses and make clinically-useful interpretations of whole genome sequenced (WGS) cancers. My team have pioneering bioinformatic skills in this area, with strong foundations in exploratory data analysis, supported by experimental approaches. We have begun to use our knowledge to design computational algorithms that could have predictive/prognostic value. We have a track record of substantiating these analytical tools through retrospective and prospective clinical studies, to demonstrate patient benefit. The UK 100,000 Genomes Project (UK100kGP) is the largest WGS endeavour in the world focused on rare diseases, common cancers and infectious diseases. My team have recently forged links with the UK100kGP, become familiar with their secure data infrastructure (the Research Embassy (RE)), and begun curation of >15,000 of their WGS cancers. Clearly capable of generating data, the UK100kGP genomics infrastructure could next benefit from the best WGS insights and algorithmic tools, to become thought-leaders in offering WGS as an unbiased, comprehensive, clinically-actionable assay to inform NHS cancer care. To achieve this potential, we must: Be able to provide holistic WGS cancer genome interpretation containing clinically-useful driver, mutational signature, algorithmic scores and germline genome information Be able to distinguish clinically-relevant mutagenesis for development into new algorithms/biomarkers for prognostic/therapeutic intervention. Aims & Objectives Aim I: Accelerate clinical translation of computational tools Objective I.1: Develop and implement Steiner-Open-CGA Holistic Cancer Genome Interpreter for wider NHS clinical research use Objective I.2: Maintain and enhance the Mutational Signatures Reference Database Signal Objective I.3: Take specific algorithms through the final steps of implementation of clinical algorithms into clinical trialsAim II: Utilise computational approaches to identify clinically-relevant mutagenesis Objective II.1: Work with already-published cohorts of cancer genomes to make new discoveries Objective II.2: Work on clinical cohorts with treatment and outcome data in order to make clinically-useful interpretations Objective II.3: Understand the contribution of the germline to somatic mutagenesisAim III: Use experimental approaches on patient-derived pluripotent stem cells and gene-edited human cellular models to gain mechanistic understanding of mutagenesis Objective III.1: Explore mutational mechanisms underpinning on-going mutational signatures because these may be biomarkers/developed into clinical algorithms Objective III.2: Explore tissue specificities and how the genome interacts with other modalities (transcriptome, proteome, metabolome).Methods For computational work we will utilise: Simple and advanced statistical methods e.g. machine-learning, clustering, latent variable analyses Deep data analysis e.g. nucleotide level exploration to make insights on mutational mechanismsFor experimental work, we will utilise: cellular models from DNA repair patients, perform gene editing and/or treat cells with mutagens functional DNA damage response assays, western blotting, proteomics/metabolomics/transcriptomics/sequencingto seek signatures that inform our computational efforts.Impact Nationally, our tools/insights will be available to all UK100kGP RE users For patients, advances made through WGS are imported rapidly into cancer care For my team and I, NIHR Research Professorship support will be a strong impetus to focus on clinical computational translation and national implementation, substantially increasing our authority and reach

Lay abstract: What is the clinical problem?Reading the human genome or DNA 'sequencing', can now be done very quickly, so that it is possible to sequence the whole human genome in just a few days. Today, we face difficulties in understanding the very large amounts of data that come from whole genome sequences (WGS) of each patient. Cancers carry thousands of mutations. Numerous international research teams are using WGS to study cancer mutations, in order to learn how and why cancers arise. However, there remains a considerable gap between research findings and using WGS to improve care for individual patients. My team has focused on WGS cancer research since 2013. We have particular expertise on how to read cancer genomes and were the first to demonstrate that every patient's cancer is entirely unique. We also specialised in studying patterns of mutations called mutational signatures, which can reveal the causes of each person's cancer. Some of the patterns can, for example, tell us whether a patient has been exposed to DNA damaging agents in the environment or whether there are important functions that are not working correctly in the cancer cells. This is helpful information because it gives us clues about how to treat each patient's cancer more effectively. In 2013, the UK 100,000 Genomes Project (UK100kGP) was launched to perform WGS in rare diseases, infectious diseases and cancer. The UK100kGP has successfully performed WGS on over 15,000 cancers of 20 tumour-types from NHS patients, the largest collection of WGS cancers in the world, stored within their secure Research Embassy. Now, it requires expert input to help perform analyses, to increase understanding about these cancers, to develop computer tools that read cancer genomes more efficiently and interpret them usefully for clinical care. My team has the necessary skill set to assist UK100kGP. We have been based in a purely research setting previously. We are increasingly making our computer programmes more clinically useful. Funding through this Research Professorship, would help to: support our efforts to perform analyses on cancer genomes of over 15,000 patients in the UK100kGP develop computer programmes that help us identify specific mutational signatures in each cancer that may be targetable by specialised drugs implement our knowledge into local and national genomics endeavours (e.g. UK100kGP Research Embassy)What is the aim of this research? This research aims to use the knowledge of mutations present in whole cancer genomes for clinical benefit.How will we carry it out? First, we will take whole cancer genome computer tools that we have already developed, test them on over 15,000 NHS-recruited cancer patients in the UK100kGP Research Embassy, and implement the computer tools so that they can be used for all future cancer patients. Second, we will perform additional computer-based studies on UK100kGP data and other available WGS datasets, to identify mutational signatures that tell clinicians something about how to treat patients more effectively, and then develop these into new computer programmes for clinical use. Third, we will conduct experiments to support and confirm what we discover by our computer work.The second and third themes are crucial steps that help to create new computer tools that will benefit patients. These will eventually go into the first theme to be implemented for patients. What is the impact for society?When our computer programmes become successfully embedded in the Research Embassy, they will be widely available nationally. We expect that this work will have a direct impact on NHS cancer care, both for future clinical research studies and patient management.

Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES;7.3 MANAGEMENT AND DECISION MAKING
Health categories: CANCER AND NEOPLASMS