FASL polymorphism is associated with response to bacillus Calmette-Guérin immunotherapy in bladder cancer

Objective Deregulation of FAS/FASL system may lead to immune escape and influence bacillus Calmette-Guérin (BCG) immunotherapy outcome, which is currently the gold standard adjuvant treatment for high-risk non–muscle invasive bladder tumors. Among other events, functional promoter polymorphisms of FAS and FASL genes may alter their transcriptional activity. Therefore, we aim to evaluate the role of FAS and FASL polymorphisms in the context of BCG therapy, envisaging the validation of these biomarkers to predict response. Patients and methods DNA extracted from peripheral blood from 125 patients with bladder cancer treated with BCG therapy was analyzed by Polymerase Chain Reaction—Restriction Fragment Length Polymorphism for FAS-670 A/G and FASL-844 T/C polymorphisms. FASL mRNA expression was analyzed by real-time Polymerase Chain Reaction. Results Carriers of FASL-844 CC genotype present a decreased recurrence-free survival after BCG treatment when compared with FASL-844 T allele carriers (mean 71.5 vs. 97.8 months, P = 0.030) and have an increased risk of BCG treatment failure (Hazard Ratio = 1.922; 95% Confidence Interval: [1.064–3.471]; P = 0.030). Multivariate analysis shows that FASL-844 T/C and therapeutics scheme are independent predictive markers of recurrence after treatment. The evaluation of FASL gene mRNA levels demonstrated that patients carrying FASL-844 CC genotype had higher FASL expression in bladder tumors (P = 0.0027). Higher FASL levels were also associated with an increased risk of recurrence after BCG treatment (Hazard Ratio = 2.833; 95% Confidence Interval: [1.012–7.929]; P = 0.047). FAS-670 A/G polymorphism analysis did not reveal any association with BCG therapy outcome. Conclusions Our results suggest that analysis of FASL-844 T/C, but not FAS-670 A/G polymorphisms, may be used as a predictive marker of response to BCG immunotherapy.

Telomerase promoter mutations in cancer: an emerging molecular biomarker?

João Vinagre, Vasco Pinto and Ricardo Celestino contributed equally to the manuscript.

The role of functional polymorphisms in immune response genes as biomarkers of BCG Immunotherapy outcome in bladder cancer: Establishment of a predictive profile in a Southern Europe population

OBJECTIVE: To evaluate the predictive value of genetic polymorphisms in the context of BCG immunotherapy outcome and create a predictive profile that may allow discriminating the risk of recurrence. MATERIAL AND METHODS: In a dataset of 204 patients treated with BCG, we evaluate 42 genetic polymorphisms in 38 genes involved in the BCG mechanism of action, using Sequenom MassARRAY technology. Stepwise multivariate Cox Regression was used for data mining. RESULTS: In agreement with previous studies we observed that gender, age, tumor multiplicity and treatment scheme were associated with BCG failure. Using stepwise multivariate Cox Regression analysis we propose the first predictive profile of BCG immunotherapy outcome and a risk score based on polymorphisms in immune system molecules (SNPs in TNFA-1031T/C (rs1799964), IL2RA rs2104286 T/C, IL17A-197G/A (rs2275913), IL17RA-809A/G (rs4819554), IL18R1 rs3771171 T/C, ICAM1 K469E (rs5498), FASL-844T/C (rs763110) and TRAILR1-397T/G (rs79037040) in association with clinicopathological variables. This risk score allows the categorization of patients into risk groups: patients within the Low Risk group have a 90% chance of successful treatment, whereas patients in the High Risk group present 75% chance of recurrence after BCG treatment. CONCLUSION: We have established the first predictive score of BCG immunotherapy outcome combining clinicopathological characteristics and a panel of genetic polymorphisms. Further studies using an independent cohort are warranted. Moreover, the inclusion of other biomarkers may help to improve the proposed model.

Using data mining techniques to support breast cancer diagnosis

More than ever, there is an increase of the number of decision support methods and computer aided diagnostic systems applied to various areas of medicine. In breast cancer research, many works have been done in order to reduce false-positives when used as a double reading method. In this study, we aimed to present a set of data mining techniques that were applied to approach a decision support system in the area of breast cancer diagnosis. This method is geared to assist clinical practice in identifying mammographic findings such as microcalcifications, masses and even normal tissues, in order to avoid misdiagnosis. In this work a reliable database was used, with 410 images from about 115 patients, containing previous reviews performed by radiologists as microcalcifications, masses and also normal tissue findings. Throughout this work, two feature extraction techniques were used: the gray level co-occurrence matrix and the gray level run length matrix. For classification purposes, we considered various scenarios according to different distinct patterns of injuries and several classifiers in order to distinguish the best performance in each case described. The many classifiers used were Naïve Bayes, Support Vector Machines, k-nearest Neighbors and Decision Trees (J48 and Random Forests). The results in distinguishing mammographic findings revealed great percentages of PPV and very good accuracy values. Furthermore, it also presented other related results of classification of breast density and BI-RADS® scale. The best predictive method found for all tested groups was the Random Forest classifier, and the best performance has been achieved through the distinction of microcalcifications. The conclusions based on the several tested scenarios represent a new perspective in breast cancer diagnosis using data mining techniques.

A Clinical Support System Based on Quality of Life Estimation

Quality of life is a concept influenced by social, economic, psychological, spiritual or medical state factors. More specifically, the perceived quality of an individual's daily life is an assessment of their well-being or lack of it. In this context, information technologies may help on the management of services for healthcare of chronic patients such as estimating the patient quality of life and helping the medical staff to take appropriate measures to increase each patient quality of life. This paper describes a Quality of Life estimation system developed using information technologies and the application of data mining algorithms to access the information of clinical data of patients with cancer from Otorhinolaryngology and Head and Neck services of an oncology institution. The system was evaluated with a sample composed of 3013 patients. The results achieved show that there are variables that may be significant predictors for the Quality of Life of the patient: years of smoking (p value 0.049) and size of the tumor (p value < 0.001). In order to assign the variables to the classification of the quality of life the best accuracy was obtained by applying the John Platt's sequential minimal optimization algorithm for training a support vector classifier. In conclusion data mining techniques allow having access to patients additional information helping the physicians to be able to know the quality of life and produce a well-informed clinical decision.