TP53 copy number and protein expression inform mutation status across risk categories in acute myeloid leukemia.

Mutant TP53 is an adverse risk factor in acute myeloid leukemia (AML), but large-scale integrated genomic-proteomic analyses of TP53 alterations in patients with AML remain limited. We analyzed TP53 mutational status, copy number (CN), and protein expression data in AML (N = 528) and provide a compilation of mutation sites and types across disease subgroups among treated and untreated patients. Our analysis shows differential hotspots in subsets of AML and uncovers novel pathogenic variants involving TP53 splice sites. In addition, we identified TP53 CN loss in 70.2% of TP53-mutated AML cases, which have more deleterious TP53 mutations, as well as copy neutral loss of heterozygosity in 5/32 (15.6%) AML patients who had intact TP53 CN. Importantly, we demonstrate that mutant p53 protein expression patterns by immunohistochemistry evaluated using digital image-assisted analysis provide a robust readout that integrates TP53 mutation and allelic states in patients with AML. Expression of p53 by immunohistochemistry informed mutation status irrespective of TP53 CN status. Genomic analysis of comutations in TP53-mutant AML shows a muted landscape encompassing primarily mutations in genes involved in epigenetic regulation (DNMT3A and TET2), RAS/MAPK signaling (NF1, KRAS/NRAS, PTPN11), and RNA splicing (SRSF2). In summary, our data provide a rationale to refine risk stratification of patients with AML on the basis of integrated molecular and protein-level TP53 analyses.

Evaluation of statistical approaches for association testing in noisy drug screening data.

BACKGROUND: Identifying associations among biological variables is a major challenge in modern quantitative biological research, particularly given the systemic and statistical noise endemic to biological systems. Drug sensitivity data has proven to be a particularly challenging field for identifying associations to inform patient treatment. RESULTS: To address this, we introduce two semi-parametric variations on the commonly used concordance index: the robust concordance index and the kernelized concordance index (rCI, kCI), which incorporate measurements about the noise distribution from the data. We demonstrate that common statistical tests applied to the concordance index and its variations fail to control for false positives, and introduce efficient implementations to compute p-values using adaptive permutation testing. We then evaluate the statistical power of these coefficients under simulation and compare with Pearson and Spearman correlation coefficients. Finally, we evaluate the various statistics in matching drugs across pharmacogenomic datasets. CONCLUSIONS: We observe that the rCI and kCI are better powered than the concordance index in simulation and show some improvement on real data. Surprisingly, we observe that the Pearson correlation was the most robust to measurement noise among the different metrics.