With More Oncology Data at Our Fingertips, Interpretation Tools Are Key
Contributed Commentary by Sara Patterson and Cara Statz, The Jackson Laboratory
October 14, 2022 | Oncology has been a shining example of the promise of precision medicine, and the tremendous gains to be made in human health when patients can be prescribed the right drug at the right time. But oncology has also shown the challenge of precision medicine: data interpretation. Never before have scientists and clinicians had access to so much data about patients, treatments, and foundational science. With each new source of data, the difficulty of making sense of the aggregated information only increases.
Consider the example of analyzing and interpreting genomic information gleaned from cancer samples collected from patients. These biopsy or blood samples provide essential clues that can help tailor treatments, match patients to clinical trials, and monitor for therapeutic response or disease recurrence.
But all of those benefits rely on drawing the right conclusions from genomic data. This is no simple task. Interpreting complex genomic profiles from cancer samples is challenging. It puts a tremendous burden on analysts to ensure that results are accurate, comprehensive, and reliable, because the information may be used to guide patient care.
With the right tools, though, the interpretation challenge can be managed. It is no longer feasible for any individual physician, analyst, or researcher to keep up with the rush of clinical trials, new scientific findings, and targeted therapies. In the data-driven oncology field, having access to a reliable cancer knowledgebase allows for the benefit of a personalized approach to healthcare in a more robust and scalable workflow.
There are now a number of cancer knowledgebases available, and it can be difficult to choose from so many options. To select the one-stop-shop that works for you, consider the following elements of a reliable interpretation tool.
Comprehensive data: Ensuring the most complete interpretation results means starting with the most thorough collection of data. Look for knowledgebases that span the hundreds of genes known to be associated with cancer, including the thousands of clinically relevant variants within them. Molecular content should be representative of the entire scientific literature, ideally presented to users at the level of the gene and variant while also capturing complex molecular profiles. Beyond molecular data, a robust cancer knowledge should also incorporate real-world clinical data such as targeted therapies and regulatory status in various countries, clinical trials with specific molecular and other enrollment criteria, and efficacy evidence for available treatments.
Maintenance and curation: Too many cancer knowledgebases have been started with the best intentions, but allowed to languish after the initial data was collected. In the world of oncology, knowledgebases can become obsolete very quickly. Look for a tool that is updated regularly — ideally every day — for the latest scientific evidence, clinical data, professional guidelines, and drug approvals. Also, be sure to prioritize expert curation whenever possible. Curation should be performed by scientific or clinical professionals who have the skills and training needed to review new content and ensure that it is properly applied in the knowledgebase. Finally, check for knowledgebases that organize and structure data consistently. Without this, data may not be mapped correctly and queries could fail to return complete results.
Ease of use: It can be easy to overlook this aspect, but any tool that is too hard or clunky to use will not be helpful to researchers or clinicians. Be sure to check that any knowledgebase you’re considering is user-friendly. Search capabilities, for instance, should be sophisticated enough to return all appropriate results while still feeling intuitive for the user.
With a cancer knowledgebase that meets all of these criteria, it is possible to draw detailed, clinically relevant insights that would otherwise be virtually impossible to find. Consider ALK, a gene with several known fusion events that are effectively targeted with precise therapies. In one study, scientists used a comprehensive cancer knowledgebase to evaluate ALK fusion profiles and assess therapeutic efficacy. They found that response to the same ALK inhibitor was quite different for two separate ALK fusions that both had the same mutation for secondary resistance. Because the interpretation was performed with a comprehensive analysis resource, the scientists were able to find evidence that one molecular profile was associated with sensitivity to the drug while the other molecular profile — one that might otherwise have appeared identical to the first — was linked to resistance.
No doubt these closely related but functionally different molecular profiles will be found more often in oncology research as we improve our ability to mine data and spot them. To better understand cancer, and ultimately to tailor treatments as carefully as possible for patients, we have to approach oncology as the data-driven field it has become. Interpretation tools need to be robust, reliable, comprehensive, and readily accessible if we are to bring the promise of precision medicine to the bedside of patients with cancer.