Precision medicine holds great promise when it comes to solving healthcare’s greatest problems, and oncology is no exception. But with that hope comes numerous challenges.
Via email, Jonathan Hirsch, founder and president of San Francisco, California-based Syapse, discussed what stands in the way of making precision medicine standard across cancer care. Hirsch is a speaker at the upcoming MedCity CONVERGE conference in Philadelphia.
This exchange has been lightly edited.
Why is precision medicine the future of cancer care?
Precision medicine is providing better outcomes and better quality of life for advanced cancer patients today. That is why precision medicine is not only the future of cancer care, but it is also the present reality of cancer care. By understanding the underlying molecular drivers of an individual patient’s cancer, oncologists can select treatment options that are targeted to that patient’s tumor.
However, the pace of disruption is only accelerating: For example, nearly all cancer drugs in late-phase trials target a molecular pathway, with over 20 targeted drugs approved this year. The FDA just approved, for the first time, a cancer drug targeted to a patient’s genomic signature independent of the tumor site of origin. Health systems are going to need to deal with this innovation head-on by integrating precision medicine more comprehensively into their oncology service lines.
How can organizations that utilize precision medicine technology get payers to buy in?
Healthcare providers will need to demonstrate strong evidence that supports the clinical utility and cost benefits of using precision medicine in routine cancer care. We believe that health systems, in particular, large integrated delivery networks, will lead the way in this due to their ability to harness both payer and provider, as well as their operational discipline to implement complex clinical service programs and evaluate the impact.
The good news is that this evaluation of the clinical utility and cost impact of precision medicine in oncology is already happening today. For example, one of our key collaborators, Intermountain Healthcare, published a study last year evaluating the impact of precision medicine on stage IV cancer patients, finding that precision medicine demonstrated a doubling of progression-free survival with a lower cost per progression-free survival week. This is an incredibly promising signal, and more studies are being conducted now by Intermountain, Providence/Swedish Cancer Institute and others.
Based on these early studies, we are currently seeing a shift toward a more collaborative relationship between payers and health systems to come to grips with precision medicine. For example, both payers and providers would like to ensure that patients are receiving the right drug based on their clinical and genomic profile, and that there is evidence to support improved survival. To achieve this, providers and payers are increasingly relying on aggregated, real-world evidence to inform both medical and coverage policies, and to inform treatment decision-making.
What is the correlation between precision oncology and predictive analytics?
While there is a lot of talk about predictive analytics in oncology, we see this as a very early and immature space. There is a lot of research that needs to be done to evaluate the uses for predictive analytics in oncology, and that work is just beginning. Meanwhile, we have well-validated tools at our disposal today to predict risk of developing breast cancer (BRCA1/2 testing), to predict the risk of recurrence (OncotypeDx and MammaPrint), and more, yet physician adoption of these predictive technologies remains challenging. This is a hard problem that needs to be solved.
How can clinical trials be redesigned so the best patients can be selected?
Targeted oncology trials have complex eligibility criteria and require the full scope of patient data — especially including molecular data — to find accurate potential matches. The problem is that this granular level of data is rarely accessible in a single place and in a standardized language. This leads to two problems: one, patients receive unreliable or inaccurate trial recommendations, or two, providers struggle to scale clinical trials because of the significant time it takes to identify the right patients.
That’s changing, though. We have been collaborating with Providence St. Joseph’s Swedish Cancer Institute to give health systems the ability to match patients to trials based on the full array of eligibility criteria, including their demographic data, cancer diagnosis, tumor genomics, tumor biomarkers, clinical lab results and other clinical data elements. The technology automatically surfaces potential matches, after which clinicians can make the patient aware of their candidacy for the trial and finalize the screening process. This significantly reduces the time it takes to identify patients who may match into a clinical trial, while increasing the odds of a potential match, using a high specificity approach — and allows organizations to scale clinical trials. We are now collaborating with ASCO’s TAPUR clinical trial, which runs on the Syapse Platform, to match patients from our health system partners to TAPUR.
As it relates to precision oncology, how can we improve interoperability and better share data?
Data sharing is critical in oncology, but even more so in precision oncology. As a community, we are all working to determine the best practices for use of molecular profiling and targeted therapies, and the best way for the community to get there faster is by sharing data and knowledge.
That’s why we launched an initiative last year, as part of former Vice President Biden’s Cancer Moonshot, to enable health systems to share precision oncology data — specifically, clinical, molecular, treatment and outcomes data on their cancer patients. This data sharing network enables physicians to put a patient into the context of a broader population, allowing them to see the treatments that clinically and molecularly similar patients received, and their outcomes. Data sharing allows physicians to make vastly more informed treatment decisions, which is critical when treating patients with rare mutations with little to no evidence. Through this data sharing network, we are helping to move cancer care into a more real-world evidence-driven practice.
What’s the next step that must be taken so precision medicine can become more feasible for all stakeholders?
Improving access to precision medicine is critical for patients, providers and payers. Firstly, there are a growing number of value-based reimbursement agreements between payers, providers and drug manufacturers that align incentives around value to the patient. Secondly, patient financial assistance programs provided by drug manufacturers and charitable foundations can reduce the out-of-pocket financial burden on patients, allowing them to stay on treatment despite high costs. Lastly, real-world evidence can help to align payers, providers and drug manufacturers around what treatments will provide the best outcomes for patients. Once we know what works best for a particular patient, we can start to develop reimbursement models that provide access at scale.