Systematic analysis of off-label and off-guideline cancer therapy usage in a real-world cohort of 153,122 U.S. patients

Patients with cancer may be given treatments that are not officially approved (off-label) or recommended by guidelines (off-guideline) for multiple reasons including a lack of effective approved treatments. Here we present a systematic characterization of the patterns of off-label and off-guideline usage in 153,122 U.S. patients with 14 common cancer types using a large electronic health record (EHR)-derived de-identified database. We find that 18.3% and 3.9% of patients have received at least one line of off-label and off-guideline cancer drugs, respectively. Out of the 14 malignancies investigated, advanced bladder cancer has the highest proportion with 8.1% of patients receiving off-guideline treatments, most of which are recommended for non-small cell lung cancer. Patients with worse performance status, in later lines, or treated at academic hospitals are significantly more likely to receive off-label and off-guideline drugs. Underrepresented minority patients are less likely to receive off-guideline treatments in several cancer types. To quantify how predictable off-guideline usage is, we developed machine learning models to predict which drug a patient is likely to receive based on their clinical characteristics and previous treatments. Finally, we demonstrate that our systematic analysis of large real-world cohorts can identify interesting candidates for potential label expansion by identifying off-label treatments that demonstrate effectiveness in the real world setting. For example, we find that hormonal agents approved for breast cancer are used off-label in patients with ovarian cancer. Moreover, these hormonal agents show promising effectiveness in ovarian cancer with adjusted hazard ratio 0.53 (0.44, 0.65) compared to standard-of-care. This work demonstrates the power of large-scale computational analysis of real-world data for investigating non-standard cancer treatment usages.