ERIC recommendations on TP53 mutation analysis in chronic lymphocytic leukemia.

Recent evidence suggests that - in addition to 17p deletion - TP53 mutation is an independent prognostic factor in chronic lymphocytic leukemia (CLL). Data from retrospective analyses and prospective clinical trials show that ∼5% of untreated CLL patients with treatment indication have a TP53 mutation in the absence of 17p deletion. These patients have a poor response and reduced progression-free survival and overall survival with standard treatment approaches. These data suggest that TP53 mutation testing warrants integration into current diagnostic work up of patients with CLL. There are a number of assays to detect TP53 mutations, which have respective advantages and shortcomings. Direct Sanger sequencing of exons 4-9 can be recommended as a suitable test to identify TP53 mutations for centers with limited experience with alternative screening methods. Recommendations are provided on standard operating procedures, quality control, reporting and interpretation. Patients with treatment indications should be investigated for TP53 mutations in addition to the work-up recommended by the International workshop on CLL guidelines. Patients with TP53 mutation may be considered for allogeneic stem cell transplantation in first remission. Alemtuzumab-based regimens can yield a substantial proportion of complete responses, although of short duration. Ideally, patients should be treated within clinical trials exploring new therapeutic agents.

Common polygenic variation enhances risk prediction for Alzheimer's disease.

The identification of subjects at high risk for Alzheimer’s disease is important for prognosis and early intervention. We investigated the polygenic architecture of Alzheimer’s disease and the accuracy of Alzheimer’s disease prediction models, including and excluding the polygenic component in the model. This study used genotype data from the powerful dataset comprising 17 008 cases and 37 154 controls obtained from the International Genomics of Alzheimer’s Project (IGAP). Polygenic score analysis tested whether the alleles identified to associate with disease in one sample set were significantly enriched in the cases relative to the controls in an independent sample. The disease prediction accuracy was investigated in a subset of the IGAP data, a sample of 3049 cases and 1554 controls (for whom APOE genotype data were available) by means of sensitivity, specificity, area under the receiver operating characteristic curve (AUC) and positive and negative predictive values. We observed significant evidence for a polygenic component enriched in Alzheimer’s disease (P = 4.9 × 10−26). This enrichment remained significant after APOE and other genome-wide associated regions were excluded (P = 3.4 × 10−19). The best prediction accuracy AUC = 78.2% (95% confidence interval 77–80%) was achieved by a logistic regression model with APOE, the polygenic score, sex and age as predictors. In conclusion, Alzheimer’s disease has a significant polygenic component, which has predictive utility for Alzheimer’s disease risk and could be a valuable research tool complementing experimental designs, including preventative clinical trials, stem cell selection and high/low risk clinical studies. In modelling a range of sample disease prevalences, we found that polygenic scores almost doubles case prediction from chance with increased prediction at polygenic extremes.