Publications by authors named "B A Westerman"

Glioblastoma (GB), the most common and aggressive brain tumor, demonstrates intrinsic resistance to current therapies, resulting in poor clinical outcomes. Cancer progression can be partially attributed to the deregulation of protein translation mechanisms that drive cancer cell growth. In this study, we present the translatome landscape of GB as a valuable data resource.

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Genetic heterogeneity in tumors can show a remarkable selectivity when two or more independent genetic events occur in the same gene. This phenomenon, called composite mutation, points toward a selective pressure, which frequently causes therapy resistance to mutation-specific drugs. Since composite mutations have been described to occur in sub-clonal populations, they are not always captured through biopsy sampling.

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Purpose: Combination therapies are a promising approach for improving cancer treatment, but it is challenging to predict their resulting adverse events in a real-world setting.

Experimental Design: We provide here a proof-of-concept study using 15 million patient records from the FDA Adverse Event Reporting System (FAERS). Complex adverse event frequencies of drugs or their combinations were visualized as heat maps onto a two-dimensional grid.

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Article Synopsis
  • The study analyzed the epigenetic changes in gliomas from 132 patients over time, comparing initial and recurrent tumors in both IDH-wildtype (IDHwt) and IDH-mutant (IDHmut) types.
  • IDHwt gliomas remained stable in their epigenetic profile, while IDHmut gliomas showed a notable decrease in DNA methylation, making their profiles more similar to IDHwt tumors.
  • The research identified HOXD13 as crucial for the evolution of IDHmut tumors and found that treatment led to changes in the tumor microenvironment, like increased blood vessel formation and T-cell presence, mimicking the characteristics of IDHwt gliomas.
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Background: In recent years, drug combinations have become increasingly popular to improve therapeutic outcomes in various diseases, including difficult to cure cancers such as the brain cancer glioblastoma. Assessing the interaction between drugs over time is critical for predicting drug combination effectiveness and minimizing the risk of therapy resistance. However, as viability readouts of drug combination experiments are commonly performed as an endpoint where cells are lysed, longitudinal drug-interaction monitoring is currently only possible through combined endpoint assays.

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