Abstract

Roche: Personalised lung cancer treatment modelling using electronic health records and genomics

Cancer immunotherapy (CIT) is a promising new type of cancer treatment that uses the patient’s own immune system to fight cancer cells. CIT drugs work to stop the cancer cells from turning off the immune system’s T-cells by inhibiting the PD-L1 produced by the tumour cells (PD-L1 is a protein that binds to PD-1 receptors on T-cells and prevents the immune system from attacking the cancer cells).

CIT is currently being used to treat patients with non-small cell lung cancer (NSCLC) for whom chemotherapy or other drugs have failed. CIT is also be-ing used as part of the first-line treatment in patients with advanced NSCLC (aNSCLC - stage III and higher). Theoretically, patients with high PD-L1 ex-pression levels are more likely to respond well to CIT; however, in practice, patient outcomes vary considerably.

In this data study group, we investigated different approaches for predicting survival time for patients treated with CIT as first line of treatment, using both electronic health records and tumour genomic data. We also investigated the causal effects of CIT vs other oncology treatments, and studied treatment heterogeneity. The results contribute to identifying patients who are most likely to benefit from CIT.

Citation information

Data Study Group team. (2020, June 4). Data Study Group Final Report: Roche. Zenodo. http://doi.org/10.5281/zenodo.3876989

Additional information

Maryam Abdollahyan, Barts Health NHS Trust
Alexander Buchholz, University of Cambridge
Susana Conde, University of Warwick
Nathan Cunningham, University of Warwick
John Dennis, University of Exeter
Karla DiazOrdaz, London School of Hygiene and Tropical Medicine
Fiona Grimm, Health Foundation
Snezhana Ilieva, PwC
Franz Kiraly, UCL
Chen Li, University of Cambridge
Weiqi Liao, University of Southampton
Enrico Mossotto, University of Southampton
Hector Page, Privitar
Mario Parreno-Centeno, UCL
Ben Swallow, University of Glasgow

Turing affiliated authors