Title: Harnessing large scale clinical-genomic resources for cancer care: Validity, multimorbidity and clinical decision making
Funder: The Academy of Medical Sciences

Abstract: Clinical decisions surrounding patients with cancer are often made without support from real-time patient-centric evidence. Traditionally, such decisions are made from existing clinical guidelines, oncologists’ own experience, knowledge from observational studies and randomised trials. These approaches, however, face challenges in prioritising relevant information, especially when confronted with the diversity of clinical scenarios in a vast majority of cancer patients in whom multimorbidity are common. This project will leverage expanding clinical-genomic resources (electronic health records and genetic data) to reveal real-world evidence that may transform cancer care. I will systematically evaluate the validity and utility of clinical-genomic data for returning new evidence for risk stratification used in predicting response to cancer therapy. I will assess how multimorbidity information would reveal differences in cancer progression patterns and treatment outcomes. I will characterise longitudinal disease trajectories pre- and post-cancer diagnoses and examine cancer-comorbidity networks. I will develop a ‘Smart Consult’ framework to help guide treatment decisions in cancer patients in whom treatment indications and contraindications co-occur. Using data from the British National Formulary, I will enumerate the frequency by which indication-contraindication pairs co-exist in individuals. This project will answer the question of “based on large-scale clinical-genomics resources, how can we diagnose cancer earlier and treat it in most effective ways?" I anticipate continuous innovation in this area where clinical-genomic data would help treatment decisions for patients today, while anticipating treatment options for future patients, to catapult change across the whole gamut of cancer care in years to come.

Lay abstract: The problem: An oncologist sees a 65-year-old woman with breast cancer, diabetes and a history of heart attack in clinic. He wonders if this patient’s risk of dying with cancer is higher than someone without diabetes or heart disease. He also contemplates on how to optimise her medical care given her other illnesses to minimise risk and achieve a favourable outcome. The approach: I want to investigate how routinely collected patient data that spans diseases and demography can be used to guide treatment decisions in patients with cancer and non-cancer illnesses (comorbidities). The NHS is working to embed whole-genome sequence information in 5million individuals, and I aim to evaluate how genetic data linked to clinical data can inform strategies for better treatment. Research aims: Health data is stored in the form of electronic health records (EHRs), which is a treasure trove of information but is disorganised in nature. I will evaluate how EHRs can be used to improve cancer care using statistical approaches. Over 70% of patients with cancer have other comorbidities. I will investigate how comorbidities may influence the timeliness of cancer diagnosis and the efficacy of cancer intervention strategies. Patients with cancer, especially those with comorbidities, often face dilemmas when deciding treatment options. I will develop a “Smart Consult” framework to return estimates on treatment effectiveness in patients, grouped by their health characteristics, for decision support. I anticipate that the results of this project will provide on-demand evidence to optimise care provision in patients with cancer.

Research activity codes: 7.3 MANAGEMENT AND DECISION MAKING;8.1 ORGANISATION AND DELIVERY OF SERVICES
Health categories: CANCER AND NEOPLASMS