Prognostic Role of Dynamic Changes in Serological Markers in Metastatic Hormone Naive Prostate Cancer

Simple Summary In this exploratory analysis of a randomized controlled trial, we found that dynamic changes in simple laboratory-based markers such as prostate-specific antigen (PSA) could be used to predict survival in patients with metastatic prostate cancer that is sensitive to hormonal manipulation. We developed a model that captures information on dynamic changes in PSA along with hemoglobin (Hb), neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), and lymphocyte to monocyte ratio (LMR), and this model was found to be clinically more useful compared to the "treat all" strategy. This model could be used to design future adaptive trials that will investigate sequential treatment personalization in metastatic hormone sensitive prostate cancer patients.Abstract We investigated whether inter-patient variation in the dynamic trajectory of hemoglobin (Hb), neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), lymphocyte to monocyte ratio (LMR), and prostate-specific antigen (PSA) can prognosticate overall survival (OS) in de novo mHSPC. This is a secondary analysis of the LATITUDE trial in which high-risk de novo mHSPC patients were randomly assigned to receive either androgen deprivation therapy (ADT) plus abiraterone or ADT plus placebo. We used a five-fold cross-validated joint model approach to determine the association of temporal changes in the serological markers with OS. Decision curve analysis was applied to determine the net benefit. When dynamic changes in Hb, LMR, NLR, PLR, and PSA were included in a multivariate joint model, an increase in the log of the current value of PSA (HR: 1.24 [1.20-1.28]) was associated with inferior OS. A multivariate joint model that captured dynamic trajectory of Hb, NLR, PLR, LMR, and PSA up to 24 months, showed a net benefit over the "treat all" strategy at a threshold of probability of approximately & GE;30% while no net benefit was seen when dynamic change in PSA was omitted. Our joint model could be used for designing future adaptive trials investigating sequential treatment personalization.