Advances in RWD aid research for new treatments to common cancers
Rising incidence rates for some types of cancer highlight the need to use data from a variety of organizations’ health records
Jul 06 26
5 min read
C.K. Wang, MD
Life sciences teams face an innovation challenge nowadays, as they wrestle with rising incidence rates for many common cancers, unprecedented turbulence in federal funding for research and a therapeutic landscape that, for most early-stage cancers, hasn’t evolved much in the last decade
How can they find effective new approaches and insights to advance research into lifesaving treatments? One approach showing significant promise across all phases of clinical research is the application of real-world data (RWD)
Comprising all health information routinely collected outside of randomized controlled trials, RWD offers a richer, more diverse, longer-term view of patient experiences than is possible with data drawn from randomized controlled trials. With RWD, researchers can gain a more complete and deeper understanding of disease progression, treatments and outcomes in real-world settings and among smaller subpopulations
Recent research advances
Advances in artificial intelligence and with large language models, applied in conjunction with sophisticated, physician-led curation protocols, are providing researchers with expanded access to timely oncology datasets that are variable-rich and research-ready
These robust RWD databases, specific to common cancers including breast, lung, prostate, ovarian and bladder, offer researchers improved avenues to accelerate studies and bring novel therapies to market faster, as well as offering clinicians more precise therapeutic pathways for treating patients
Progress and promise
RWD is increasingly vital to clinical research, from optimizing clinical trials and establishing external control arms, to monitoring safety and pharma-covigilience
High-quality RWD and AI accelerate research by enabling faster data and insight generation, enabling researchers to test drug targets and identify biomarkers before testing a drug in a trial
Across the board, improved real-world databases for common cancers offer oncology researchers new tools for developing much needed lifesaving treatments
For example, a rich breast cancer dataset has validated an AI pathology solution to improve risk stratification and identify patients with recurrence risk
In early-stage lung cancer, where we see a relatively high rate of biomarker testing, RWD can help drive treatment innovation to decrease relapse while decreasing treatment-related morbidity
In urologic cancers where treatment occurs in distinct and separate specialty settings, real-world datasets that include treatment information from both the urology and oncology clinics are uniquely positioned to provide researchers with the necessary disease information required to understand the full disease evolution and journey, from diagnosis and locoregional therapy to molecular subtypes, recurrence, disease progression and treatment outcomes
Making data accessible and meaningful
Although electronic health records are a keyakes assembling the data in meaningful, searchable ways difficult. Prior to the advent of AI and large language models tools, doing so was infeasible at scale
With careful oversight by experts, AI can automate aspects of data curation and abstraction to help prepare datasets for research, reducing the time humans need to spend analyzing medical records to flag inaccuracies or missing data points
Deep and complete biomarker data is a critical component of RWD. Biomarker data plays a vital role in enabling researchers to categorize heterogeneous populations into specific molecular subgroups, revealing why patients respond differently to the same treatments
RWD augmented with biomarker data facilitates hypothesis generation, assessing drug efficacy, monitor safety, early detection and disease prevention. Biomarker data is key to a move beyond standardized <a href="https://healthylife7.com/ut-southwestern-ranked-no-1-in-the-world-for-healthcare-research-by-nature-index/” title=”UT Southwestern ranked No. 1 in the world for healthcare research by Nature Index”>healthcare approaches toward personalized, data-driven therapies designed for an individual’s specific biological makeup
However, most cancer RWD typically has come from the community treatment setting, where biomarker testing is less comprehensive than at academic medical centers. As a result, most real-world datasets for both solid and blood cancers have lacked the necessary complement of biomarker data needed to understand and develop treatments for complex disease states or support precision healthcare
Cultivating RWD from a network of both community and academic cancer centers can bridge that gap
Recent growth in the RWD space and new data partnership networks are making important biomarker and genomic data increasingly available to researchers and physicians, including in RWD databases for common cancers
An opportunity to improve care
Recent advances in real-world oncology databases, made possible through AI-driven tools, sophisticated curation processes and the inclusion of biomarker data from academic medical centers and growing data partnerships, offer real potential to accelerate cost-effective cancer research
With access to rich and high-quality RWD databases, increasingly powerful AI-based analytic platforms will enable researchers to make more efficient, targeted inquiries. With agentic models, researchers can simply type in questions as they would a web search. The model then scans high-quality, disease-specific datasets to answer their query, pulling out relevant trends or novel findings in a matter of minutes, if not seconds
In an era of restricted funding and high cancer rates, it’s increasingly important to concentrate investment on the studies most likely to succeed and positively impact patient care
Fortunately, significant advances in RWD and AI have created opportunities for progress across the drug development lifecycle, driving more precise and effective treatments to common cancers
C.K. Wang, MD, is the general manager of oncology and chief medical officer at Verana Health, where he oversees the oncology division and serves as the clinical lead for the company
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