Predicting response in cancer patients using machine learning models

Current clinical decision making for colorectal cancer (CRC) patients is based on a relatively small number of clinical and pathological hallmarks. Developments in the cost effectiveness and robustness of genome-wide molecular phenotyping greatly expand the number of features that can feed into decision making. The Computational Biology and Integrative Genomics Lab and associated researchers in the Bioinformatics Hub, use machine learning to develop and validate clinical classifiers, based on multi-omic data, which can predict treatment response for cancer patients. If successful, this would allow treatment to be individually tailored, meaning that patients receive the most effective treatment in the first instance.

Recently, Dr. Sanjay Rathee, postdoctoral working under the direction of Prof. Francesca Buffa, developed a model to predict treatment response for CRC patients undergoing chemotherapy, radiotherapy, and oxaliplatin treatment using data from the S:CORT consortium led by Prof. Tim Maughan. Looking at concordant predictions between different methods, this approach addresses the challenge of discovering decision-making genes with minimum noise, ensuring models are general and reliable.

In the first analysis of S:CORT samples (131 rectal cancer samples) from patients treated with radiotherapy and capecitabine, the model gives an accuracy of 90%. Whilst initial accuracy is quite high on retrospective cohorts, the results needs to be validated in external, independent cohorts. If validated, the model could predict response for a new patient based on a pre-treatment biopsy. Finding out whether a treatment is likely to be effective could greatly minimise the number of unnecessary interventions and associated side effects (and cost) whilst also increasing the chance of the chosen intervention being an effective treatment strategy.

Find out more about our research below

Oxford joins cancer coronavirus registry project

Oncologists in Oxford join Leeds, Birmingham and more universities to help monitor COVID-19 positive cancer patients

Oxford technology holds great promise for a multi-cancer blood test

Ongoing Oxford research aims to improve the sensitivity of cancer blood tests with the goal of earlier detection for a variety of cancers.

Oxford University and Sichuan University form joint Centre for Gastrointestinal Cancer

The University of Oxford-Sichuan University Huaxi Joint Centre for Gastrointestinal Cancer is a new international collaboration that seeks to develop an integrated gastrointestinal cancer plan through the exchanging of ideas and resources.

Dr Eileen Parkes joins Oxford Cancer

Eileen brings research into the body’s innate immune response to cancer and how we can harness these pathways to develop novel clinical treatments

Professor Sir Peter Ratcliffe elected as an AACR Academy Fellow

Sir Peter joins the ranks of the American Association for Cancer Research’s finest scientists.

Tackling oesophageal cancer early detection challenges through AI

Dr Sharib Ali specialises in the applications of AI to early oesophageal cancer detection

Oesophageal Cancer Focus Month: DPhil Spotlight – Pek Kei Im (Becky)

We talk to DPhil student Becky Im about her investigations into oesophageal cancer risks in Asian populations

Centre co-Director Prof Xin Lu honoured by the Royal Society

Co-director of the Oxford Centre, Prof Xin Lu, has been elected as a Fellow of the Royal Society for her contributions to cancer biology.

Novel sequencing techniques reveal microRNA influence on prostate cancer development

A new publication collaboration between three Oxford departments uses a novel screening approach to reveal the influence microRNA have on the spread of cancer