National Patterns of Biomarker Testing in Colorectal, Lung, and Prostate Cancers: Insights From the NIH Research Program.

[BACKGROUND] Biomarker testing is central to precision oncology, yet real-world implementation across cancer types and populations remains inconsistent. Social determinants of health (SDoH) may influence testing uptake and exacerbate disparities in access to targeted therapies.

[METHODS] We conducted a retrospective cohort study using Version 7 of the NIH Research Program Curated Data Repository. Adults diagnosed with colorectal cancer (CRC), non-small cell lung cancer (NSCLC), or prostate cancer were identified using standardized condition codes. Biomarker testing was determined through Current Procedural Terminology (CPT) and Logical Observation Identifiers Names and Codes (LOINC) for panel-based and single-gene assays. Logistic regression assessed associations between sociodemographic factors and documented biomarker testing, using robust modeling for the combined cohort and stepwise regression for individual cancer types.

[RESULTS] Among 11 415 eligible participants, only 2.4% (n = 277) had documented biomarker testing, with 71.1% receiving panel-based assays. In the combined model, unemployment was significantly associated with higher odds of testing (odds ratio [OR] = 1.68; 95% confidence interval [CI] = 1.06-2.66), while college education showed a marginal association (OR = 1.48; 95% CI = 0.95-2.30). In cancer-specific models, NSCLC testing was predicted by education alone (OR = 1.70), while CRC testing was associated with unemployment (OR = 2.44), higher income (OR = 1.90), and smoking history. No significant predictors were found for prostate cancer.

[CONCLUSION] Despite national guidelines, biomarker testing remains underutilized and unevenly distributed across sociodemographic groups. These findings should be interpreted as exploratory, reflecting the fidelity of structured electronic health record (EHR) documentation rather than definitive utilization. Leveraging the scale and diversity of highlights both equity gaps and documentation limitations, positioning the program as a valuable platform for hypothesis generation in precision oncology.