Systematic analysis of off-label
and off-guideline cancer therapy usage
in a real-world cohort of 165,912 US patients
Ruishan Liu,1,5 Lisa Wang,2,6 Shemra Rizzo,2,6 Marius Rene Garmhausen,2,6 Navdeep Pal,2,6 Sarah Waliany,3,6 Sarah McGough,2 Yvonne G. Lin,2 Zhi Huang,4 Joel Neal,3 Ryan Copping,2,* and James Zou1,4,7,*
1Department of Electrical Engineering, Stanford University, Stanford, CA, USA
2Genentech, South San Francisco, CA, USA
3School of Medicine, Stanford University, Stanford, CA, USA
4Department of Biomedical Data Science, Stanford University, Stanford, CA, USA 5Department of Computer Science, University of Southern California, Los Angeles, CA, USA 6These authors contributed equally
7Lead contact
SUMMARY
Patients with cancer may be given treatments that are not officially approved (off-label) or recommended by guidelines (off-guideline). Here we present a data science framework to systematically characterize off-label and off-guideline usages using real-world data from de-identified electronic health records (EHR). We analyze treatment patterns in 165,912 US patients with 14 common cancer types. We find that 18.6% and 4.4% of patients have received at least one line of off-label and off-guideline cancer drugs, respectively. Pa- tients with worse performance status, in later lines, or treated at academic hospitals are significantly more likely to receive off-label and off-guideline drugs. 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 analyses generate hy- potheses about patients’ response to treatments.
https://www.cell.com/cell-reports-medicine/pdf/S2666-3791(24)00067-3.pdf
The noble aspirations of many when faced with the abyss are thrown out the window and their own survival becomes paramount.
However leaving all that to one side I understood that unlike most cancer treatments the affordability of CF33 has always been spoken about by Professor Fong greatly increasing the opportunities for its off label use.
My opinion only DYOR
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