Multi-Omics after O-GlcNAc Alteration Identifies Cellular Processes Working Synergistically to Promote Aneuploidy.

Pharmacologic or genetic manipulation of O-GlcNAcylation, an intracellular, single sugar post-translational modification, are difficult to interpret due to the pleotropic nature of O-GlcNAc and the vast signaling pathways it regulates. To address this issue, we employed either OGT (O-GlcNAc transferase), OGA (O-GlcNAcase) liver knockouts, or pharmacological inhibition of OGA coupled with multi-Omics analysis and bioinformatics. We identified numerous genes, proteins, phospho-proteins, or metabolites that were either inversely or equivalently changed between conditions. Moreover, we identified pathways in OGT knockout samples associated with increased aneuploidy. To test and validate these pathways, we induced liver growth in OGT knockouts by partial hepatectomy. OGT knockout livers showed a robust aneuploidy phenotype with disruptions in mitosis, nutrient sensing, protein metabolism/amino acid metabolism, stress response, and HIPPO signaling demonstrating how OGT is essential in controlling aneuploidy pathways. Moreover, these data show how a multi-Omics platform can discern how OGT can synergistically fine-tune multiple cellular pathways.

AMEND: active module identification using experimental data and network diffusion.

BACKGROUND: Molecular interaction networks have become an important tool in providing context to the results of various omics experiments. For example, by integrating transcriptomic data and protein-protein interaction (PPI) networks, one can better understand how the altered expression of several genes are related with one another. The challenge then becomes how to determine, in the context of the interaction network, the subset(s) of genes that best captures the main mechanisms underlying the experimental conditions. Different algorithms have been developed to address this challenge, each with specific biological questions in mind. One emerging area of interest is to determine which genes are equivalently or inversely changed between different experiments. The equivalent change index (ECI) is a recently proposed metric that measures the extent to which a gene is equivalently or inversely regulated between two experiments. The goal of this work is to develop an algorithm that makes use of the ECI and powerful network analysis techniques to identify a connected subset of genes that are highly relevant to the experimental conditions. RESULTS: To address the above goal, we developed a method called Active Module identification using Experimental data and Network Diffusion (AMEND). The AMEND algorithm is designed to find a subset of connected genes in a PPI network that have large experimental values. It makes use of random walk with restart to create gene weights, and a heuristic solution to the Maximum-weight Connected Subgraph problem using these weights. This is performed iteratively until an optimal subnetwork (i.e., active module) is found. AMEND was compared to two current methods, NetCore and DOMINO, using two gene expression datasets. CONCLUSION: The AMEND algorithm is an effective, fast, and easy-to-use method for identifying network-based active modules. It returned connected subnetworks with the largest median ECI by magnitude, capturing distinct but related functional groups of genes. Code is freely available at https://github.com/samboyd0/AMEND .

Incidence of breakthrough fungal infections on isavuconazole prophylaxis compared to posaconazole and voriconazole.

BACKGROUND: Invasive fungal infections (IFIs) are a common infectious complication during the treatment of acute myeloid leukemia (AML), high-risk myelodysplastic syndrome (MDS) or post hematopoietic cell transplantation (HCT). For these patients, the National Comprehensive Cancer Network recommends posaconazole or voriconazole for IFI prophylaxis. In clinical practice, however, there has been increased use of isavuconazole due to favorable pharmacokinetic and pharmacodynamic parameters despite limited data for this indication. The comparative prophylactic efficacy of antifungals in this patient population has not been reported, and an analysis is warranted. METHODS: This retrospective, matched cohort, single-center study, included AML, MDS, or HCT patients who began treatment or underwent transplant between January 1, 2015 and July 31, 2021. Isavuconazole patients were matched 1:2 with patients receiving posaconazole or voriconazole prophylaxis. RESULTS: A total of 126 patients were included, 42 received isavuconazole, 81 received posaconazole, and three received voriconazole. The majority of patients were male receiving secondary IFI prophylaxis while receiving steroids for treatment of GVHD. The incidence of possible, probable or proven IFI was 16.7% in the isavuconazole group compared to 10.7% in the posaconazole and voriconazole group (OR 1.28, 95% CI -0.9-1.4; p = .67). Hepatotoxicity occurred in 16 total patients, 14 receiving posaconazole and two receiving isavuconazole. CONCLUSION: Patients who received isavuconazole prophylaxis during AML induction therapy or post-HCT experienced a similar incidence of breakthrough fungal infections compared to those who received posaconazole or voriconazole. These results suggest no difference in antifungal prophylactic efficacy; however larger prospective comparative studies are needed.

Assessing equivalent and inverse change in genes between diverse experiments.

Background: It is important to identify when two exposures impact a molecular marker (e.g., a gene's expression) in similar ways, for example, to learn that a new drug has a similar effect to an existing drug. Currently, statistically robust approaches for making comparisons of equivalence of effect sizes obtained from two independently run treatment vs. control comparisons have not been developed. Results: Here, we propose two approaches for evaluating the question of equivalence between effect sizes of two independent studies: a bootstrap test of the Equivalent Change Index (ECI), which we previously developed, and performing Two One-Sided t-Tests (TOST) on the difference in log-fold changes directly. The ECI of a gene is computed by taking the ratio of the effect size estimates obtained from the two different studies, weighted by the maximum of the two p-values and giving it a sign indicating if the effects are in the same or opposite directions, whereas TOST is a test of whether the difference in log-fold changes lies outside a region of equivalence. We used a series of simulation studies to compare the two tests on the basis of sensitivity, specificity, balanced accuracy, and F1-score. We found that TOST is not efficient for identifying equivalently changed gene expression values (F1-score = 0) because it is too conservative, while the ECI bootstrap test shows good performance (F1-score = 0.95). Furthermore, applying the ECI bootstrap test and TOST to publicly available microarray expression data from pancreatic cancer showed that, while TOST was not able to identify any equivalently or inversely changed genes, the ECI bootstrap test identified genes associated with pancreatic cancer. Additionally, when investigating publicly available RNAseq data of smoking vs. vaping, no equivalently changed genes were identified by TOST, but ECI bootstrap test identified genes associated with smoking. Conclusion: A bootstrap test of the ECI is a promising new statistical approach for determining if two diverse studies show similarity in the differential expression of genes and can help to identify genes which are similarly influenced by a specific treatment or exposure. The R package for the ECI bootstrap test is available at https://github.com/Hecate08/ECIbootstrap.

Geographic differences in community oncology provider and practice location characteristics in the central United States.

PURPOSE: How care delivery influences urban-rural disparities in cancer outcomes is unclear. We sought to understand community oncologists' practice settings to inform cancer care delivery interventions. METHODS: We conducted secondary analysis of a national dataset of providers billing Medicare from June 1, 2019 to May 31, 2020 in 13 states in the central United States. We used Kruskal-Wallis rank and Fisher's exact tests to compare physician characteristics and practice settings among rural and urban community oncologists. FINDINGS: We identified 1,963 oncologists practicing in 1,492 community locations; 67.5% practiced in exclusively urban locations, 11.3% in exclusively rural locations, and 21.1% in both rural and urban locations. Rural-only, urban-only, and urban-rural spanning oncologists practice in an average of 1.6, 2.4, and 5.1 different locations, respectively. A higher proportion of rural community sites were solo practices (11.7% vs 4.0%, P<.001) or single specialty practices (16.4% vs 9.4%, P<.001); and had less diversity in training environments (86.5% vs 67.8% with <2 medical schools represented, P<.001) than urban community sites. Rural multispecialty group sites were less likely to include other cancer specialists. CONCLUSIONS: We identified 2 potentially distinct styles of care delivery in rural communities, which may require distinct interventions: (1) innovation-isolated rural oncologists, who are more likely to be solo providers, provide care at few locations, and practice with doctors with similar training experiences; and (2) urban-rural spanning oncologists who provide care at a high number of locations and have potential to spread innovation, but may face high complexity and limited opportunity for care standardization.