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

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases

Differential profiling of lacrimal cytokines in patients suffering from thyroid-associated orbitopathy.

The aim was to investigate the levels of cytokines and soluble IL-6R in the tears of patients with thyroid-associated orbitopathy (TAO) disease. Schirmer's test was adopted to collect tears from TAO patients (N = 20, 17 women, mean age (±SD): 46.0 years (±13.4)) and healthy subjects (N = 18, 10 women, 45.4 years (±18.7)). Lacrimal cytokines and soluble IL-6R (sIL-6R) were measured using a 10-plex panel (Meso Scale Discovery Company) and Invitrogen Human sIL-6R Elisa kit, respectively. Tear levels of IL-10, IL-12p70, IL-13, IL-6 and TNF-α appeared significantly higher in TAO patients than in healthy subjects. Interestingly, IL-10, IL-12p70 and IL-8 levels increased in tears whatever the form of TAO whereas IL-13, IL-6 and TNF-α levels were significantly elevated in inflammatory TAO patients, meaning with a clinical score activity (CAS) ≥ 3, compared to controls. Furthermore, only 3 cytokines were strongly positively correlated with CAS (IL-13 Spearman coeff. r: 0.703, p = 0.0005; IL-6 r: 0.553, p = 0.011; IL-8 r: 0.618, p = 0.004, respectively). Finally, tobacco use disturbed the levels of several cytokines, especially in patient suffering of TAO. The differential profile of lacrimal cytokines could be useful for the diagnosis of TAO patients. Nevertheless, the tobacco use of these patients should be taken into account in the interpretation of the cytokine levels