Our understanding of human disease is progressing and so is the arc of precision medicine. Originally focused on genomics, thanks to the Human Genome Project, precision medicine is unfolding to include other -omics data and patient-level data such as clinical, environmental and behavioral data. And while no one ever claimed precision medicine would be easy, we’re learning just how hard it is to combine multiple, disparate data types for the purpose of improving human health.
Indeed, the idea of precision medicine used to be more simple. In the beginning, there was only clinical data and expression data. Today, precision medicine is moving beyond single modalities and looking at how to assign treatment to patients based on multiple -omics data and other data types.
Adding more complexity is the way we now understand disease. Take cancer, for example. We used to think it was just one disease. But based on the seminal work of Charles Perou, who used expression analysis to subphenotype breast cancer, we now know that it’s many different diseases. Stratifying phenotype and genotype to distinguish disease based on various characteristics is what we must continue to do to develop effective therapies for patients.
How we view our obligations is evolving as well. The U.S., after years of investing in precision medicine initiatives and realizing that its outcomes aren’t significantly better than nations with no investments, is revising the mantra of precision medicine. The “We must learn from every patient,” has shifted to “We must learn from every patient and translate what we learn to larger populations.” Doing otherwise simply isn’t scalable or cost-effective for our healthcare systems.
But we have made significant progress, and that is perhaps why precision medicine now poses the challenges it does. Advances in the -omics fields–transcriptomics, epigenomics, etc.–are continuing. We can begin to take a holistic approach to precision medicine, rather than the reductionist view we have been taking. Systems biology used to be a dirty word, but luckily it has become popular again.
And a new paradigm is emerging — that of population-based precision medicine initiatives, such as UK Biobank and All of Us. These initiatives examine genomics data alongside phenotypic data, but reveal just how sorely we need platforms to standardize, manage, and analyze multiple data types. The platforms must be able to transact multi-omics data along with electronic health records data. They must promote provenance, auditability, scalability, and security. And most of all, these platforms must be accessible to scientists and clinicians from multiple disciplines to transform data into information that helps us translate what we learn from patients to larger populations.
To accommodate these complex needs, DNAnexus has partnered with industry leaders to build DNAnexus Apollo.
For more information, watch the video below.
1. Liu MC, Pitcher BN, Mardis ER, et al. PAM50 gene signatures and breast cancer prognosis with adjuvant anthracycline- and taxane-based chemotherapy: correlative analysis of C9741 (Alliance). Nature News. https://www.nature.com/articles/npjbcancer201523. Published January 6, 2016. Accessed October 4, 2019.
