Archer, Kellie J.

Abstract Although the 2022 European LeukemiaNet (ELN) acute myeloid leukemia (AML) risk classification reliably predicts outcomes in younger patients treated with intensive chemotherapy, it is unclear whether it applies to adults ≥60 years treated with lower-intensity treatment (LIT). We aimed to test the prognostic impact of ELN risk in patients with newly diagnosed (ND) AML aged ≥60 years given LIT and to further refine risk stratification for these patients. A total of 595 patients were included: 11% had favorable-, 11% intermediate-, and 78% had adverse-risk AML. ELN risk was prognostic for overall survival (OS) (P < .001) but did not stratify favorable- from intermediate-risk (P = .71). Within adverse-risk AML, the impact of additional molecular abnormalities was further evaluated. Multivariable analysis was performed on a training set (n = 316) and identified IDH2 mutation as an independent favorable prognostic factor, and KRAS, MLL2, and TP53 mutations as unfavorable (P < .05). A “mutation score” was calculated for each combination of these mutations, assigning adverse-risk patients to 2 risk groups: −1 to 0 points (“Beat-AML intermediate”) vs 1+ points (“Beat-AML adverse”). In the final refined risk classification, ELN favorable- and intermediate-risk were combined into a newly defined “Beat-AML favorable-risk” group, in addition to mutation scoring within the ELN adverse-risk group. This approach redefines risk for older patients with ND AML and proposes refined Beat-AML risk groups with improved discrimination for OS (2-year OS, 48% vs 33% vs 11%, respectively; P < .001), providing patients and providers additional information for treatment decision-making.

Abstract Motivation: Batch effects are due to probe-specific systematic variation between groups of samples (batches) resulting from experimental features that are not of biological interest. Principal component analysis (PCA) is commonly used as a visual tool to determine whether batch effects exist after applying a global normalization method. However, PCA yields linear combinations of the variables that contribute maximum variance and thus will not necessarily detect batch effects if they are not the largest source of variability in the data. Results: We present an extension of PCA to quantify the existence of batch effects, called guided PCA (gPCA). We describe a test statistic that uses gPCA to test whether a batch effect exists. We apply our proposed test statistic derived using gPCA to simulated data and to two copy number variation case studies: the first study consisted of 614 samples from a breast cancer family study using Illumina Human 660 bead-chip arrays, whereas the second case study consisted of 703 samples from a family blood pressure study that used Affymetrix SNP Array 6.0. We demonstrate that our statistic has good statistical properties and is able to identify significant batch effects in two copy number variation case studies. Conclusion: We developed a new statistic that uses gPCA to identify whether batch effects exist in high-throughput genomic data. Although our examples pertain to copy number data, gPCA is general and can be used on other data types as well. Availability and implementation: The gPCA R package (Available via CRAN) provides functionality and data to perform the methods in this article. Contact: reesese@vcu.edu or eckel@mayo.edu Supplementary information: Supplementary data are available at Bioinformatics online.