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BACKGROUND: Myelosuppression-related infections remain important causes of morbidity and mortality in children with acute lymphoblastic leukemia (ALL). METHODS: By analyzing fecal samples collected at diagnosis and after each of the initial 3 phases of chemotherapy, we evaluated the role of gut microbiota in predicting infections in 199 children with newly diagnosed ALL. The bacterial 16S rRNA gene was analyzed by high-depth sequencing to determine the diversity and composition of the microbiome. RESULTS: After the induction and reinduction I phases of chemotherapy, microbial diversity decreased significantly relative to the prechemotherapy value. After chemotherapy, the relative abundance of certain bacterial taxa (eg, Bacteroidetes) decreased significantly, whereas that of other taxa (eg, Clostridiaceae and Streptococcaceae) increased. A baseline gut microbiome characterized by Proteobacteria predicted febrile neutropenia. Adjusting for the chemotherapy phase and ALL risk level, Enterococcaceae dominance (relative abundance ≥30%) predicted significantly greater risk of subsequent febrile neutropenia and diarrheal illness, whereas Streptococcaceae dominance predicted significantly greater risk of subsequent diarrheal illness. CONCLUSIONS: In children undergoing therapy for newly diagnosed ALL, the relative abundance of Proteobacteria before chemotherapy initiation predicts development of febrile neutropenia, and domination of the gut microbiota by Enterococcaceae or Streptococcaceae at any time during chemotherapy predicts infection in subsequent phases of chemotherapy. CLINICAL TRIAL REGISTRATION: NCT00549848.

Classification of brain tumors using methylation profiling is an important diagnostic advance, reducing subjectivity and improving interpretability of clinical outcome data. Despite the recognized value of methylation profiling in the clinical laboratory, the performance characteristics of different supervised classification models has not been directly compared. We developed 3 methods using methylation profiles to classify CNS tumors: an exact bootstrap k-nearest neighbor (kNN), a multi-layer perceptron neural net (NN), and a random forest classifier (RF). We trained these methods on the publicly available CNS tumor reference cohort (GSE90496) with 2,801 profiles and 91 classes. We evaluated the performance of these methods by leave-out-25% cross-validation. The relative performance of these methods were evaluated in terms of accuracy, precision, and recall for class or class family. The kNN, RF, and NN classifier had an estimate error rate of 10.74%, 4.01%, and 1.89%, respectively for class prediction and an error rate for family prediction of 5.97%, 0.90%, and 0.6%, respectively. At perfect recall for class assignment, the RF and kNN had a precision of 0.96 and 0.89 while the NN reached 0.98. For family assignment, the precision for the three classifiers was almost 1.0 with recall of nearly 0.8. At the recall rate of 1.0, the precision dropped to 0.94, 0.991 and 0.994 for kNN, RF, and NN, respectively. Overall, the NN showed improved performance metrics compared to the kNN and RF in CNS tumor classification for both class and class family assignment.

In many case-control designs of genome-wide association (GWAS) or next generation sequencing (NGS) studies, extensive data on secondary traits that may correlate and share the common genetic variants with the primary disease are available. Investigating these secondary traits can provide critical insights into the disease etiology or pathology, and enhance the GWAS or NGS results. Methods based on logistic regression (LG) were developed for this purpose. However, for the identification of rare variants (RVs), certain inadequacies in the LG models and algorithmic instability can cause severely inflated type I error, and significant loss of power, when the two traits are correlated and the RV is associated with the disease, especially at stringent significance levels. To address this issue, we propose a novel set-valued (SV) method that models a binary trait by dichotomization of an underlying continuous variable, and incorporate this into the genetic association model as a critical component. Extensive simulations and an analysis of seven secondary traits in a GWAS of benign ethnic neutropenia show that the SV method consistently controls type I error well at stringent significance levels, has larger power than the LG-based methods, and is robust in performance to effect pattern of the genetic variant (risk or protective), rare or common variants, rare or common diseases, and trait distributions. Because of the SV method’s striking and profound advantage, we strongly recommend the SV method be employed instead of the LG-based methods for secondary traits analyses in case-control sequencing studies.

Motivation: Normalization is critical in DNA copy number analysis. We propose a new method to correctly identify two-copy probes from the genome to obtain representative references for normalization in single nucleotide polymorphism arrays. The method is based on a two-state Hidden Markov Model. Unlike most currently available methods in the literature, the proposed method does not need to assume that the percentage of two-copy state probes is dominant in the genome, as long as there do exist two-copy probes. Results: The real data analysis and simulation study show that the proposed algorithm is successful in that (i) it performs as well as the current methods (e.g. CGHnormaliter and popLowess) for samples with dominant two-copy states and outperforms these methods for samples with less dominant two-copy states; (ii) it can identify the copy-neutral loss of heterozygosity; and (iii) it is efficient in terms of the computational time used. Availability: R scripts are available at http://publichealth.lsuhsc.edu/PAIR.html. Contact: zfang@lsuhsc.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: There is a substantial body of works in the biology literature that seeks to characterize the cyclic behavior of genes during cell division. Gene expression microarrays made it possible to measure the expression profiles of thousands of genes simultaneously in time-course experiments to assess changes in the expression levels of genes over time. In this context, the commonly used procedures for testing include the permutation test by de Lichtenberg et al. and the Fisher’s G-test, both of which are designed to evaluate periodicity against noise. However, it is possible that a gene of interest may have expression that is neither cyclic nor just noise. Thus, there is a need for a new test for periodicity that can identify cyclic patterns against not only noise but also other non-cyclic patterns such as linear, quadratic or higher order polynomial patterns. Results: To address this weakness, we have introduced an empirical Bayes approach to test for periodicity and compare its performance in terms of sensitivity and specificity with that of the permutation test and Fisher’s G-test through extensive simulations and by application to a set of time-course experiments on the Schizosaccharomyces pombe cell-cycle gene expression. We use ‘conserved’ and ‘cycling’ genes by Lu et al. to assess the sensitivity and CESR genes by Chenet al. to assess the specificity of our new empirical Bayes method. Availability and implementation: The SAS Macro for our empirical Bayes test for periodicity is included in the supplementary materials along with a sample run of the MACRO program. Contact: mkocak1@uthsc.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Acute myeloid leukemia (AML) is a genetically heterogeneous cancer that frequently exhibits aberrant kinase signaling. We investigated a treatment strategy combining sorafenib, a multikinase inhibitor with limited single-agent activity in AML, and cytarabine, a key component of AML chemotherapy.Using 10 human AML cell lines, we determined the effects of sorafenib (10 μM) on antileukemic activity by measuring cell viability, proliferation, ERK1/2 signaling, and apoptosis. We also investigated the effects of sorafenib treatment on the accumulation of cytarabine and phosphorylated metabolites in vitro. A human equivalent dose of sorafenib in nontumor-bearing NOD-SCID-IL2Rγ(null) mice was determined by pharmacokinetic studies using high performance liquid chromatography with tandem mass spectrometric detection, and steady-state concentrations were estimated by the fit of a one-compartment pharmacokinetic model to concentration-time data. The antitumor activity of sorafenib alone (60 mg/kg) twice daily, cytarabine alone (6.25 mg/kg administered intraperitoneally), or sorafenib once or twice daily plus cytarabine was evaluated in NOD-SCID-IL2Rγ(null) mice bearing AML xenografts.Sorafenib at 10 μM inhibited cell viability, proliferation and ERK1/2 signaling, and induced apoptosis in all cell lines studied. Sorafenib also increased the cellular accumulation of cytarabine and metabolites resulting in additive to synergistic antileukemic activity. A dose of 60 mg/kg in mice produced a human equivalent sorafenib steady-state plasma exposure of 10 μM. The more dose-intensive twice-daily sorafenib plus cytarabine (n = 15) statistically significantly prolonged median survival in an AML xenograft model compared with sorafenib once daily plus cytarabine (n = 12), cytarabine alone (n = 26), or controls (n = 27) (sorafenib twice daily plus cytarabine, median survival = 46 days; sorafenib once daily plus cytarabine, median survival = 40 days; cytarabine alone, median survival = 36 days; control, median survival = 19 days; P.001 for combination twice daily vs all other treatments listed).Sorafenib in combination with cytarabine resulted in strong anti-AML activity in vitro and in vivo. These results warrant clinical evaluation of sorafenib with cytarabine-based regimens in molecularly heterogeneous AML.