National Cancer Institute (NCI) Launches Observational Study to Collect Clinical Data on 10,000 Patients Receiving Standard Cancer Treatments
1. Strategic Value of Large-Scale Standard Treatment Data
The observational study (NCT00923065), led by the Center for Cancer Research (CCR) at the National Cancer Institute (NCI), is a large-scale cohort project aimed at accumulating data on standard of care (SoC) treatments for 10,000 cancer patients treated at the NIH Clinical Center. This clinical trial is not an interventional study designed to directly validate the efficacy of a specific new drug, but rather aims to closely observe the clinical trajectory of standard therapies administered in real-world clinical settings. Researchers will collect longitudinal data from the patient cohort to enhance the standard control database for future clinical research. The accumulated real-world evidence (RWE) is expected to serve as an important benchmark for global pharmaceutical and biotechnology companies developing new drugs, significantly reducing early-stage research and development (R&D) costs.
2. Minimizing Gaps in Patient Management Before and After Clinical Trials
This study plays a crucial role in providing uninterrupted access to standard-of-care therapy for patients who may not be immediately eligible for more complex cancer treatment clinical trials or who are experiencing treatment gaps. The Medical Oncology Branch (MOB) at NCI is responsible for patient management, providing regular blood tests, as well as comprehensive monitoring including tumor diagnostic X-rays, computed tomography (CT) scans, and nuclear medicine scans. This comprehensive and continuous monitoring allows for the real-time accumulation of data on adverse events and response rates. As a result, the research institution can comprehensively capture key data on the entire treatment journey of patients with rare and refractory cancers, which are often difficult to track in general healthcare settings.
3. Building an Integrated Infrastructure for Genomic Information and Personalized Medicine
The clinical study also includes genetic education and counseling for research incidental pathogenic variants with clinical significance discovered during treatment. When necessary, a systematic and precise testing system is provided, with final confirmation of patient genetic variants through CLIA-certified laboratories. By proactively securing genomic data from patients and their families, researchers can precisely elucidate the statistical correlations between specific gene variants and responses to standard chemotherapy. This will provide a significant milestone for bio-ventures in the companion diagnostics (CDx) field, predicting the response to targeted therapies.
4. National Data Sharing to Activate the Precision Medicine Market
The systematically structured individual participant data (IPD) generated through the study will be transparently shared with authorized researchers through BTRIS (Biomedical Translational Research Information System), the NIH's dedicated clinical information analysis system. This can be a significant turning point in activating a public-sector-led global bio-health big data ecosystem, serving as a catalyst for multidisciplinary collaborations between domestic and international research institutions and major pharmaceutical companies. Ultimately, it is expected to significantly expand the scope of the overall precision oncology market by expanding the AI-based drug response prediction platform and personalized biomarker discovery market.
This observational study is a non-interventional cohort study led by the National Cancer Institute (NCI), and is a medium- to long-term project to build a database of standard cancer treatment data for 10,000 patients at the NIH Clinical Center. The established real-world evidence (RWE) database is expected to function as a basic standard control dataset in the global precision medicine market, valued at approximately $146 billion, and will be a key benchmark for the development of targeted therapies. In the short term, it is expected to reduce research and development (R&D) costs in the clinical design phase of rare and refractory solid tumors, for which it is extremely difficult to establish a control group. In the medium to long term, clinically and genetically integrated data with reliability proven through CLIA certification will serve as a benchmark dataset for AI-based drug response prediction models, improving clinical success rates. Bio-big data platform companies will have the opportunity to accelerate the validation of their technologies through public data linkage.
Source: ClinicalTrials.gov (api_ct)