28 research outputs found
Predicting Outcomes of Prostate Cancer Immunotherapy by Personalized Mathematical Models
Therapeutic vaccination against disseminated prostate cancer (PCa) is partially effective in some PCa patients. We hypothesized that the efficacy of treatment will be enhanced by individualized vaccination regimens tailored by simple mathematical models.We developed a general mathematical model encompassing the basic interactions of a vaccine, immune system and PCa cells, and validated it by the results of a clinical trial testing an allogeneic PCa whole-cell vaccine. For model validation in the absence of any other pertinent marker, we used the clinically measured changes in prostate-specific antigen (PSA) levels as a correlate of tumor burden. Up to 26 PSA levels measured per patient were divided into each patient's training set and his validation set. The training set, used for model personalization, contained the patient's initial sequence of PSA levels; the validation set contained his subsequent PSA data points. Personalized models were simulated to predict changes in tumor burden and PSA levels and predictions were compared to the validation set. The model accurately predicted PSA levels over the entire measured period in 12 of the 15 vaccination-responsive patients (the coefficient of determination between the predicted and observed PSA values was R(2) = 0.972). The model could not account for the inconsistent changes in PSA levels in 3 of the 15 responsive patients at the end of treatment. Each validated personalized model was simulated under many hypothetical immunotherapy protocols to suggest alternative vaccination regimens. Personalized regimens predicted to enhance the effects of therapy differed among the patients.Using a few initial measurements, we constructed robust patient-specific models of PCa immunotherapy, which were retrospectively validated by clinical trial results. Our results emphasize the potential value and feasibility of individualized model-suggested immunotherapy protocols
Recommended from our members
Maternal Bacterial Infection During Pregnancy and Offspring Risk of Psychotic Disorders: Variation by Severity of Infection and Offspring Sex
ObjectivePrevious studies suggest that prenatal immune challenges may elevate the risk of schizophrenia and related psychoses in offspring, yet there has been limited research focused on maternal bacterial infection. The authors hypothesized that maternal bacterial infection during pregnancy increases offspring risk of psychotic disorders in adulthood, and that the magnitude of this association varies as a function of severity of infectious exposure and offspring sex.MethodsThe authors analyzed prospectively collected data from 15,421 pregnancies among women enrolled between 1959 and 1966 at two study sites through the Collaborative Perinatal Project. The sample included 116 offspring with confirmed psychotic disorders. The authors estimated associations between maternal bacterial infection during pregnancy and psychosis risk over the subsequent 40 years, stratified by offspring sex and presence of reported parental mental illness, with adjustment for covariates.ResultsMaternal bacterial infection during pregnancy was strongly associated with psychosis in offspring (adjusted odds ratio=1.8, 95% CI=1.2-2.7) and varied by severity of infection and offspring sex. The effect of multisystemic bacterial infection (adjusted odds ratio=2.9, 95% CI=1.3-5.9) was nearly twice that of less severe localized bacterial infection (adjusted odds ratio=1.6, 95% CI=1.1-2.3). Males were significantly more likely than females to develop psychosis after maternal exposure to any bacterial infection during pregnancy.ConclusionsThe study findings suggest that maternal bacterial infection during pregnancy is associated with an elevated risk for psychotic disorders in offspring and that the association varies by infection severity and offspring sex. These findings call for additional investigation and, if the findings are replicated, public health and clinical efforts that focus on preventing and managing bacterial infection in pregnant women