Life science companies have many different data types at their disposal to support research and development, from established literature and clinical trial evidence to a growing range of real-world data (RWD) assets that are opening up new opportunities every day.
Among these novel datasets is clinicogenomic data: a mashup of longitudinal, deidentified clinical information with patient-level genomic test results that has the potential to give life science researchers highly precise insights into the origins of targeted diseases and how therapeutic agents interact with human biology.
There’s a lot to think about when considering how to integrate clinicogenomic data into the R&D process. Where is it most useful? What tools and strategies are most applicable for processing large volumes of data? Is comprehensive whole exome sequencing or the higher sensitivity of more widely used large panel sequencing needed? What about sequential testing to monitor changes over time? And how can life science companies trust that the data is comprehensive, accurate, standardized, and fit-for-purpose for current and future use cases?
The industry is still at the beginning of pondering these questions, but some of the answers are already becoming clear. At the moment, clinicogenomic data is most useful in early- and mid-stage R&D to explore previously-unknown variants, put therapeutic results in context, or expand the utility of existing compounds.
For example, genomic data played an integral part in helping to identify the PCSK9 mutations linked to coronary heart disease and helped accelerate the drug development process for related conditions.
In another case, researchers used both clinicogenomic and claims data to identify shared biology and drug targets for three strains of coronaviruses. The synthesis of multiple data types helped researchers identify two current medications that could be used to improve patient outcomes.
And clinicogenomic data played a vital role in a third team being able to assess the relationship between tumor genomics and clinical outcomes in patients with non-small cell lung cancer (NSCLC). Researchers were able to identify a subset of NSCLC patients who responded best to PDL-1 therapies and identify a specific predictor of immunotherapy response.
These are just some of the intriguing ways R&D teams are using clinicogenomic data to start shedding new light on therapeutic approaches. With the drug development process taking upwards of 15 years with up to a 90% failure rate, it’s no wonder that life science companies are beginning to invest more heavily in non-traditional datasets that can add value while shortening the development timeline.
To maximize the utility of emerging clinicogenomic datasets, life science entities must be confident that the data they’re using is suitable for supporting their unique goals.
Life science leaders should look for clinicogenomic data partners that source their data from multiple vendors to ensure representation and inclusiveness, include both large panel and whole exome/whole transcriptome sequencing to keep options open in the future, and provide real-world expert support to assist with designing studies and leveraging novel types to their fullest potential.
Integrating clinicogenomic data into the R&D lifecycle will take time and dedicated effort from leading-edge members of the life sciences community. However, the benefits could be substantial for those who lean into these innovative research methods and begin to unlock the incredible potential of next-generation real-world datasets to be faster, more precise, and more sophisticated in developing new therapies.
