Tumor organoids from individual patients with metastatic colorectal cancer can help predict response to irinotecan with or without 5-fluorouracil.

Metagenomics has demonstrated its capability in outbreak investigations and pathogen surveillance and discovery. With high-throughput and effective bioinformatics, many disease-causing agents, as well as novel viruses of humans and animals, have been identified using metagenomic analysis. In this study, a VIDISCA metagenomics workflow was used to identify potential unknown viruses in 33 fecal samples from asymptomatic long-tailed macaques (Macaca fascicularis) in Ratchaburi Province, Thailand. Putatively novel astroviruses, enteroviruses, and adenoviruses were detected and confirmed by PCR analysis of long-tailed macaque fecal samples collected from areas in four provinces, Ratchaburi, Kanchanaburi, Lopburi, and Prachuap Khiri Khan, where humans and monkeys live in proximity (total n = 187). Astroviruses, enteroviruses, and adenoviruses were present in 3.2%, 7.5%, and 4.8% of macaque fecal samples, respectively. One adenovirus, named AdV-RBR-6-3, was successfully isolated in human cell culture. Whole-genome analysis suggested that it is a new member of the species Human adenovirus G, closely related to Rhesus adenovirus 53, with evidence of genetic recombination and variation in the hexon, fiber, and CR1 genes. Sero-surveillance showed neutralizing antibodies against AdV-RBR-6-3 in 2.9% and 11.2% of monkeys and humans, respectively, suggesting cross-species infection of monkeys and humans. Overall, we reported the use of metagenomics to screen for possible new viruses, as well as the isolation and molecular and serological characterization of the new adenovirus with cross-species transmission potential. The findings emphasize that zoonotic surveillance is important and should be continued, especially in areas where humans and animals interact, to predict and prevent the threat of emerging zoonotic pathogens.

After publication of the article, the authors received comments from a member of the Viruses editorial board who is an expert in the field of adenovirus concerning figures and references that should be included in the paper

Introduction Glioblastoma (GBM) remains one of the most aggressive brain cancers, with standard treatment unchanged since 2005. Challenges like the blood-brain barrier, tumor invasiveness, and resistance to standard therapies have hindered the discovery of new treatment options. However, personalized drug repurposing (PDR) and combination drug therapy (CDT) may offer promising solutions. PDR repurposes existing medicines, leveraging known efficacy for faster, cost-effective treatment discovery for GBM in a patient-tailored manner. However, treatment resistance to monotherapy in GBM is prevalent due to its genetic and phenotypic heterogeneity. To combat this, CDT targets multiple pathways simultaneously, improving treatment efficacy and patient outcomes. Methods We developed SPARC (Synergistic Prediction for Anticancer Repurposing and Combination), a tool integrating PDR and CDT to identify personalized drug combinations. SPARC leverages patient-derived GBM tissues and drug screening data. It incorporates two models: (i) Mechanisms of Action Landscape (MOAL), prioritizing drug pairings based on mechanisms of action, and (ii) Augmented Cancer Drug Atlas (ACDA), a machine learning model predicting drug synergy using the ALMANAC dataset of 100+ FDA-approved drugs. Results Using leave-one-out cross-validation, SPARC correctly predicted synergy in 60% of synergistic drug pairs and 65% of non-synergistic drug pairs. These findings suggest that SPARC can serve as a valuable tool for prioritizing drug combinations for experimental validation, ultimately contributing to more efficient drug discovery and personalized treatment strategies. Conclusion The SPARC model is a promising tool to identify personalized drug combinations for glioblastoma. Currently, in silico predicted synergistic drug combinations are being validated on patient-derived GBM cell cultures. SPARC will be extended to include advanced deep learning and predictive models to enhance the functionality.