High-Performance Computing Enhances Treatment Precision in Breast Cancer
Many cancer patients fail to respond to their drug treatment, resulting in heavy human and economic loss. This lack of efficacy is mainly attributed to host/tumour variations at the genetic and molecular level, which clinical practice still struggles to integrate. The experiment we propose aims to deliver a tangible solution for boosting the development of the personalized response prediction tool Allied Intelligence for Drug Accuracy AÏDA. This tool will consist in an intelligent diagnostic platform able to analyse the molecular profiles of cancer patients and identify the drugs most likely to achieve high effectiveness.
TECHNOLOGY USED: HPC, ML
COUNTRY: United Kingdom
Breast cancer continues to be a major health issue with 2.3 million annual breast cancer cases and 685,000 annual deaths globally. Many cancer patients fail to respond to their treatment, resulting in heavy human and economic loss. This lack of efficacy is mainly attributed to host/tumour variations at the genetic and molecular level, which clinical practice still struggles to address.
The emergence of new genomic technology combined with digitalization has delivered treatment regimens that assess the DNA, RNA, protein, and metabolites in the individual patient’s tumour and integrate those into therapeutic decision-making. However, current technologies focusing on just one or a few genetic biomarkers or using complex ex vivo laboratory tumour models are predictive of treatment outcomes only in highly selected cases and difficult to implement effectively. Thus, many patients are still in desperate need of enhanced treatment precision.
CHOSA’s aim is to implement an easy-to-use and intelligent platform which can identify the drugs most likely to achieve high effectiveness in each individual patient based on the specific patient’s molecular profiles (i.e. their biopsy results, the tumour’s genome, and RNA information). Building this platform requires the analysis of huge data sets and correspondingly substantial HPC resources. After extensive validation, this new platform can be introduced in the competitive market of diagnostic services.
The experiment carried out extensive analyses of a huge volume of publicly available data (called NCI-60) which would have required a prohibitive amount of time without the employment of HPC. The NCI-60 data set links 60 human cancer cell lines representing different types of cancer to the anticancer activity of over 50,000 compounds (already established drugs or newly developed mixtures of drugs).
Using specific quality criteria, which were defined at the start of the experiment, 5,986 compounds out of those over 50,000 compounds were selected for further analysis, including 335 drugs that are FDA-approved or in trials. Using the JADBio autoML platform and HPC resources, ML models for these selected compounds were built to estimate the models’ performance in predicting treatment outcomes. Focusing on the approved drugs, 119 models showed a predictive power significantly above a random model.
As a means of early validation of the ML models, biological text mining was carried out independently. It revealed eight specific models which are particularly interesting for breast cancer, which was among the promising 119 models also identified by ML. They include models for key anticancer drug classes used in breast cancer, corroborating the value of the HPC-backed ML approach and building the basis for further clinical validation.
After further validation, the models will be used to set up a complete platform called ‘Allied Intelligence for Drug Accuracy’ (AÏDA) which predicts the efficacy of different cancer drugs for each individual patient, based on their biopsy readings. Clinicians will receive a report listing a large number of relevant drugs that highlight those most likely to work for a given patient’s cancer.
Business, Social and Environmental Impact
The AÏDA technology has a huge potential to support clinicians in their choice of treatment. No similar solutions exist at the moment and therefore AÏDA has the opportunity to become a first-in-market product that can truly revolutionize the way cancer patients are treated.
CHOSA is planning to focus on breast cancer initially. With a breast cancer incidence of over 780,000 in 2018 in the EU and USA alone, there is a huge market potential to be exploited with such a commercial response prediction test – even using very conservative assumptions. The market launch is expected in mid-2024 in Germany and Nordic countries, where 23,000 cases of breast cancer are newly diagnosed per year, offering a business potential of up to €69m, based on an anticipated price of €3,000 per service.
Beyond those initial targets, the business model is highly scalable and the system can be applied to any tumour type and any drug that has demonstrated toxicity.
Besides direct economic and clinical benefits, all partners will enjoy increased visibility in the biomedical market and scientific community, generate new intellectual property, and foster company growth. The HPC-based solution can play a role as a use case for promoting other diagnostic/prognostic/predictive applications in the field of personalized medicine, fostering wider application.
- CHOSA targeting a USP in a market worth €69 million in Germany and Nordic countries, leading to an expected additional turnover of several millions Euro from mid-2024.
- More cancer patients with the limited disease get the right treatment which is potentially lifesaving.
- More cancer patients with advanced disease will live longer by avoiding ineffective treatments.