Division of Medical Oncology, Duke University School of Medicine, Durham, NC
Bennett Adam Caughey , Anivarya Kumar , Jennifer R. Owen , Nicholette Sloat , Elizabeth Maynard , Vanessa Hill , Christopher Hubbard , Benjamin Neely , Matthew Stuart McKinney , Linda Sutton , Shannon J. McCall , Michael Datto , John H. Strickler , Michelle Green
Background: Comprehensive genomic profiling (CGP) is an important tool to identify actionable biomarkers and guide cancer therapy. However, patient (pt) race, socioeconomic status, and clinical setting are associated with lower access to CGP, targeted therapies, and clinical trials. To address this, we expanded a novel framework for genomic data management and clinical decision support developed at a large academic medical center to a network of affiliated community cancer centers located in rural, traditionally underserved communities. Methods: The Duke Precision Cancer Medicine Initiative (PCMI) developed a centralized database to aggregate and normalize CGP results. This database enables CGP review by a multi-disciplinary Molecular Tumor Board (MTB). PCMI established a protocol that allows the MTB to review CGP results and relevant clinical data for consented patients. This protocol opened enrollment at four DCN-affiliated cancer centers in rural North Carolina. Clinical recommendations are captured in a standard note and returned to DCN providers following each review. Our protocol also includes an anonymous survey sent to oncology providers asking about barriers to access and utilization of CGP. Results: Between Jan 2022 and Jan 2023, 62 pts were enrolled and had CGP results reviewed by the MTB; 13 pts (21.0%) were Native American and 20 (32.3%) were Black. 61 actionable genomic alterations were identified from 38 pts, including 16 (25.6%) that molecularly matched clinical trial inclusion criteria. Most DCN providers reported being comfortable ordering (14/20) CGP though fewer expressed comfort with interpretation (9/20) CGP; provider-reported barriers included cost/insurance for targeted therapies (12/20) and distance to clinical trials (14/20). Conclusions: Successful academic-community partnerships to expand access to CGP, clinical trials, and targeted therapies are feasible when supported by fit-for-purpose informatics tools and scalable workflows. Additional work is required to develop comprehensive solutions that ensure equal access to CGP and precision medicine for all patients.
Female, N (%) | 28 (45.2%) |
---|---|
Age at testing, median (range) | 65 (27-87) |
Race, N (%) | |
Black | 20 (32.3%) |
Native American | 13 (21.0%) |
White | 26 (41.9%) |
Asian | 0 (0%) |
Unknown | 3 (4.8%) |
Hispanic Ethnicity, N (%) | 3 (4.8%) |
Biomarker Clinical Evidence level, N (%) | |
FDA-approved therapy | 13 (21.0%) |
Off-label therapy | 5 (8.1%) |
Clinical trial inclusion | 16 (25.8%) |
Therapeutic resistance | 12 (19.4%) |
Germline implications | 6 (9.7%) |
Other | 9 (14.5%) |
None | 24 (38.7%) |
Days from metastatic disease to NGS, median (range) | |
All Cancers | 64 (0 to 2277) |
NSCLC | 85 (32 to 818) |
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