Personalised Medicine: Establishing predictive machine learning models for drug responses in patient derived cell culture

TL;DR

This paper presents a proof-of-concept for using drug screening on patient-derived cells to predict effective treatments in personalised cancer therapy, bypassing complex multi-omics data analysis.

Contribution

It introduces a novel, efficient method for ranking drugs based on cell activity profiles, demonstrating potential for personalised treatment prediction.

Findings

Drug activity profiles can predict effective treatments.

Method works across diverse tissue types.

Efficient ranking of candidate drugs.

Abstract

The concept of personalised medicine in cancer therapy is becoming increasingly important. There already exist drugs administered specifically for patients with tumours presenting well-defined mutations. However, the field is still in its infancy, and personalised treatments are far from being standard of care. Personalised medicine is often associated with the utilisation of omics data. Yet, implementation of multi-omics data has proven difficult, due to the variety and scale of the information within the data, as well as the complexity behind the myriad of interactions taking place within the cell. An alternative approach to precision medicine is to employ a function-based profile of the cell. This involves screening a range of drugs against patient derived cells. Here we demonstrate a proof-of-concept, where a collection of drug screens against a highly diverse set of patient-derived cell lines, are leveraged to identify putative treatment options for a 'new patient'. We show that this methodology is highly efficient in ranking the drugs according to their activity towards the target cells. We argue that this approach offers great potential, as activities can be efficiently imputed from various subsets of the drug treated cell lines that do not necessarily originate from the same tissue type.