Circulating metabolites associated with tumor hypoxia and early response to treatment in bevacizumab-refractory glioblastoma after combined bevacizumab and evofosfamide.

Glioblastomas (GBM) are the most common and aggressive form of primary malignant brain tumor in the adult population, and, despite modern therapies, patients often develop recurrent disease, and the disease remains incurable with median survival below 2 years. Resistance to bevacizumab is driven by hypoxia in the tumor and evofosfamide is a hypoxia-activated prodrug, which we tested in a phase 2, dual center (University of Texas Health Science Center in San Antonio and Dana Farber Cancer Institute) clinical trial after bevacizumab failure. Tumor hypoxic volume was quantified by 18F-misonidazole PET. To identify circulating metabolic biomarkers of tumor hypoxia in patients, we used a high-resolution liquid chromatography-mass spectrometry-based approach to profile blood metabolites and their specific enantiomeric forms using untargeted approaches. Moreover, to evaluate early response to treatment, we characterized changes in circulating metabolite levels during treatment with combined bevacizumab and evofosfamide in recurrent GBM after bevacizumab failure. Gamma aminobutyric acid, and glutamic acid as well as its enantiomeric form D-glutamic acid all inversely correlated with tumor hypoxia. Intermediates of the serine synthesis pathway, which is known to be modulated by hypoxia, also correlated with tumor hypoxia (phosphoserine and serine). Moreover, following treatment, lactic acid was modulated by treatment, likely in response to a hypoxia mediated modulation of oxidative vs glycolytic metabolism. In summary, although our results require further validation in larger patients' cohorts, we have identified candidate metabolic biomarkers that could evaluate the extent of tumor hypoxia and predict the benefit of combined bevacizumab and evofosfamide treatment in GBM following bevacizumab failure.

i-Modern: Integrated multi-omics network model identifies potential therapeutic targets in glioma by deep learning with interpretability.

Effective and precise classification of glioma patients for their disease risks is critical to improving early diagnosis and patient survival. In the recent past, a significant amount of multi-omics data derived from cancer patients has emerged. However, a robust framework for integrating multi-omics data types to efficiently and precisely subgroup glioma patients and predict survival prognosis is still lacking. In addition, effective therapeutic targets for treating glioma patients with poor prognoses are in dire need. To begin to resolve this difficulty, we developed i-Modern, an integrated Multi-omics deep learning network method, and optimized a sophisticated computational model in gliomas that can accurately stratify patients based on their prognosis. We built a survival-associated predictive framework integrating transcription profile, miRNA expression, somatic mutations, copy number variation (CNV), DNA methylation, and protein expression. This framework achieved promising performance in distinguishing high-risk glioma patients from those with good prognoses. Furthermore, we constructed multiple fully connected neural networks that are trained on prioritized multi-omics signatures or even only potential single-omics signatures, based on our customized scoring system. Together, the landmark multi-omics signatures we identified may serve as potential therapeutic targets in gliomas.

Timing of Resumption of Anticoagulation After Polypectomy and Frequency of Post-procedural Complications: A Post-hoc Analysis.

BACKGROUND: Optimal timing for anticoagulation resumption after polypectomy is unclear. We explored the association between timing of anticoagulation resumption and occurrence of delayed post-polypectomy bleeding (PPB) and thromboembolic (TE) events. METHODS: We performed a post-hoc analysis of patients in an earlier study whose anticoagulants were interrupted for polypectomy. We compared rates of clinically important delayed PPB and TE events in relationship to timing of anticoagulant resumption. Late resumption was defined as > 2 days after polypectomy. RESULTS: Among 437 patients, 351 had early and 86 late resumption. Compared to early resumers, late resumers had greater polypectomy complexity. PPB rate was higher (but not significantly) in the late versus early resumers (2.3% vs. 0.9%, 1.47% greater, 95% CI [- 2.58 to 5.52], p = 0.26). TE events were more frequent in late versus early resumers [0% vs. 1.2% at 30 days, 0% vs. 2.3%, 95% CI 0.3-8, (p = 0.04) at 90 days]. On multivariate analysis, timing of restarting anticoagulation was not a significant predictor of PPB (OR 0.97, 95% CI 0.61-1.44, p = 0.897). Significant predictors were number of polyps ≥ 1 cm (OR 4.14, 95% CI 1.27-13.66, p = 0.014) and use of fulguration (OR 11.43, 95% CI 1.35-80.80, p = 0.014). CONCLUSIONS: Physicians delayed anticoagulation resumption more commonly after complex polypectomies. The timing of restarting anticoagulation was not a significant risk factor for PPB and late resumers had significantly higher rates of TE events within 90 days. Considering the potentially catastrophic consequences of TE events and the generally benign outcome of PPBs, clinicians should be cautious about delaying resumption of anticoagulation after polypectomy.

e-MutPath: computational modeling reveals the functional landscape of genetic mutations rewiring interactome networks.

Understanding the functional impact of cancer somatic mutations represents a critical knowledge gap for implementing precision oncology. It has been increasingly appreciated that the interaction profile mediated by a genomic mutation provides a fundamental link between genotype and phenotype. However, specific effects on biological signaling networks for the majority of mutations are largely unknown by experimental approaches. To resolve this challenge, we developed e-MutPath (edgetic Mutation-mediated Pathway perturbations), a network-based computational method to identify candidate 'edgetic' mutations that perturb functional pathways. e-MutPath identifies informative paths that could be used to distinguish disease risk factors from neutral elements and to stratify disease subtypes with clinical relevance. The predicted targets are enriched in cancer vulnerability genes, known drug targets but depleted for proteins associated with side effects, demonstrating the power of network-based strategies to investigate the functional impact and perturbation profiles of genomic mutations. Together, e-MutPath represents a robust computational tool to systematically assign functions to genetic mutations, especially in the context of their specific pathway perturbation effect.

Integrated Genomic Characterization of the Human Immunome in Cancer.

Alterations in immune-related pathways are common hallmarks of cancer. A comprehensive understanding of how cancer mutations rewire immune signaling networks and functional output across cancer types is instrumental to realize the full potential of immunotherapy. Here, we systematically interrogated somatic mutations involved in immune signaling that alter immune responses in patients with cancer. To do so, we developed a Network-based Integrative model to Prioritize Potential immune respondER genes (NIPPER). Identified mutations were enriched in essential protein domains and genes identified by NIPPER were associated with responsiveness to multiple immunotherapy modalities. These genes were used to devise an interactome network propagation framework integrated with drug-associated gene signatures to identify potential immunomodulatory drug candidates. Together, our systems-level analysis results help interpret the heterogeneous immune responses among patients and serve as a resource for future functional studies and targeted therapeutics. SIGNIFICANCE: This study demonstrates that integration of multi-omics data can help identify critical molecular determinants for effective targeted therapeutics.

The general mRNA exporters Mex67 and Mtr2 play distinct roles in nuclear export of tRNAs in Trypanosoma brucei.

Transfer RNAs (tRNAs) are central players in protein synthesis, which in Eukarya need to be delivered from the nucleus to the cytoplasm by specific transport receptors, most of which belong to the evolutionarily conserved beta-importin family. Based on the available literature, we identified two candidates, Xpo-t and Xpo-5 for tRNA export in Trypanosoma brucei. However, down-regulation of expression of these genes did not disrupt the export of tRNAs to the cytoplasm. In search of alternative pathways, we tested the mRNA export complex Mex67-Mtr2, for a role in tRNA nuclear export, as described previously in yeast. Down-regulation of either exporter affected the subcellular distribution of tRNAs. However, contrary to yeast, TbMex67 and TbMtr2 accumulated different subsets of tRNAs in the nucleus. While TbMtr2 perturbed the export of all the tRNAs tested, silencing of TbMex67, led to the nuclear accumulation of tRNAs that are typically modified with queuosine. In turn, inhibition of tRNA nuclear export also affected the levels of queuosine modification in tRNAs. Taken together, the results presented demonstrate the dynamic nature of tRNA trafficking in T. brucei and its potential impact not only on the availability of tRNAs for protein synthesis but also on their modification status.