Abstract
Anal câncer is a rare neoplasm, but is increasing in incidence with currently estimated at 1.4 per 100,000 men and 1.7 per 100,000 women. There is evidence that the main risk factors for cancer of the anal canal are persistently infected by HPV (human papillomavirus), immunosuppression by HIV infection and smoking. In other types of solid tumors, like colon cancer, are known biomarkers that correlate with aggressive disease and benefit of chemotherapy. Knowledge of these markers may allow a targeted and cost-effective treatment cancer of the anal canal. Objective: Identify biomarkers and correlate expression with outcome efficiency (treatment response) in patients with squamous cell carcinoma of the anal canal. As a secondary objective, correlate with progression-free survival, overall survival and verify if there are differences in HIV-positive and HIV-negative patients. Methodology: Protein expression of Ki-67, CDKN1A, thymidylate synthase and ERCC-1 by immunohistochemistry; mutation of EGFR, KRAS, BRAF and TP53 by Sanger sequencing technique will be analyzed in tissue blocks embedded in paraffin of patients with anal cancer treated with chemo-radiotherapy in São Paulo Cancer Institute. HPV genotyping will be performed with the platform HPV INNO-LiPA Extra Genotyping Assay. Will be searched seven types of low-risk HPV (6, 11, 40, 43, 44, 54 and 70), high-risk HPV subtypes (16, 18, 26, 31, 33, 35, 39, 45, 51, 52, 53, 56, 58, 59, 66, 68, 73 and 82) and some additional HPV types (69, 71 and 74). Descriptive statistics will be used to analyze characteristics of the study population and frequency of changes in biomarkers. Correlative analysis between biomarkers and clinical outcomes (response rate, progression-free survival, overall survival and HIV infection) will be analyzed through Chi-square or Student t test. The confidence interval of 95% will be calculated for the frequency of changes in biomarker expression. Descriptive statistics will be used to the characteristics of the study population and the frequency of changes in biomarkers. Time-to-event will be estimated by the Kaplan Meier method. Correlative analysis between biomarkers and clinical outcome of response rate will be evaluated using chi-square or Fisher exact test when indicated. Confidence interval 95% will be calculated for the frequency of changes of biomarkers. Correlative analysis between biomarkers and clinical endpoints (progression-free survival and overall survival) will be assessed by the log rank test (variable time-to-the-event). Bonferroni test will be used for multiple comparisons. (AU)