Patient-reported outcomes to inform patient-specific tumor growth inhibition parameters

Background: Patient-reported outcomes (PROs) are defined by the FDA as “any report of the status of a patient's health condition that comes directly from the patient, without interpretation of the patient's response by a clinician or anyone else.” PROs are promising investigative biomarkers for cancer response and progression due to their being non-invasive and low-cost as well as their ease and frequency to be administered to patients. In this study, we seek to elucidate the relationship between tumor size (TS) and PRO dynamics and leverage this information to infer patient-specific tumor growth inhibition (TGI) parameters. Methods: We developed a joint model describing longitudinal PROs using a Markov chain model with TS as a time-dependent covariate in the transition rate matrix. We then modeled TS using the Claret TGI model. We then simulated 10 in silico patients. To test how the informativeness of the simulated PROs, we performed Bayesian inference on the probability distribution of TGI parameters given: 1) no data as input); 2) only simulated TS data; 3) only simulated PRO data; and 4) both TS and PRO data. Results: Considering PRO data with or without TS data increased precision (1 / standard deviation) of patient-specific TGI parameters by a factor of 1.9 (1.4–2.6) and 1.1 (0.7–1.8), respectively. By contrast, considering PRO data with or without TS data only marginally improved accuracy (1 / root mean squared error) of patient-specific TGI parameters by a factor of 1.4 (0.4–4.5) and 1.3 (0.5–3.5). Conclusion: These results suggest that PROs have the promise to be leveraged as a minimally invasive and inexpensive biomarker to inform patient-specific TGI parameters. Future research directions include exploring the effect of sampling frequency, number of patients, number of PROs, and model misspecification, as well as application on a clinical dataset of 63 NSCLC patients treated with immune checkpoint inhibitors.

Patient-reported outcomes to inform patient-specific tumor growth inhibition parameters

Monday, July 14 at 6:00pm

Daniel Glazar

"Patient-reported outcomes to inform patient-specific tumor growth inhibition parameters"

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