Biology subjects

Skowron, A.

Publications and source records attributed to Skowron, A..

3 recordsLinked to original sources

bioRxiv2026

Targeting the TRA-1-60 Glycoepitope Enables Selective ImmunoPET Imaging of Ovarian Cancer

Targeted radiopharmaceutical development for ovarian cancer (OC) has been limited by the lack of molecular targets that combine broad tumor expression with minimal normal-tissue distribution. TRA-1-60 (TRA) is a cancer-associated glycoepitope carried by podocalyxin. Here, we evaluated TRA as a target for OC and developed a TRA-directed immunoPET imaging platform. Immunohistochemical analysis demonstrated significantly higher TRA expression in ovarian tumors than in normal adjacent ovarian tissue, with expression maintained across epithelial OC histotypes and disease stages. An engineered anti-TRA single-chain variable fragment-Fc (scFv-Fc) demonstrated robust penetration of three-dimensional tumor spheroids and selective accumulation in intraperitoneal tumors in an immunocompetent syngeneic OC model. Radiolabeling with zirconium-89 generated [Zr]Zr-DFO-anti-TRA scFv-Fc with >98% radiochemical yield. Serial PET/CT imaging demonstrated progressive and sustained radiotracer accumulation at tumor sites through 96 hours, accompanied by declining liver-associated activity and low uptake in most normal tissues. Together, these findings identify TRA as a broadly expressed and accessible tumor-associated glycoepitope and establish TRA-targeted immunoPET as a promising strategy for noninvasive detection of OC. The selective and sustained tumor localization of this platform further provides a foundation for development of TRA-directed radiopharmaceutical therapy, supporting a potential theranostic approach for OC.

cancer biology↗

bioRxiv2024

Neural variability compresses with increasing belief precision during Bayesian inference

To make optimal decisions, intelligent agents must learn latent environmental states from discrete observations. Bayesian frameworks argue that integration of evidence over time allows us to refine our state belief by reducing uncertainty about alternate possibilities. How is this increasing belief precision during learning reflected in the brain? We propose that moment-to-moment neural variability should scale with the degree of reduction of uncertainty during learning. In a sample of 47 healthy adults, we found that BOLD signal variability (SDBOLD, as measured with functional MRI) indeed compressed with successive exposure to decision-related evidence. Crucially, more accurate participants expressed greater SDBOLD compression primarily in Default Mode Network regions, possibly reflecting the increasing precision of their latent state belief during more efficient learning. Further, computational modeling of behavior suggested that more accurate subjects held a more unbiased (flatter) prior belief over possible states that allowed for larger uncertainty reduction during learning, which was directly reflected in SDBOLD changes. Our results provide first evidence that moment-to-moment neural variability compresses with increasing belief precision during effective learning, proposing a flexible mechanism for how we come to learn the probabilistic nature of the world around us.

neuroscience↗

bioRxiv2022

Dynamic regulation of neural variability during working memory reflects dopamine, functional integration, and decision-making

The regulation of moment-to-moment neural variability may permit effective responses to changing cognitive demands. However, the mechanisms that support variability regulation are unknown. In the context of working memory, we leverage the largest available PET and fMRI dataset to jointly consider three lenses through which neural variability regulation could be understood: dopamine capacity, network-level functional integration, and flexible decision processes. We show that with greater working memory load, upregulation of variability was associated with elevated dopamine capacity and heightened functional integration, effects dominantly expressed in the striato-thalamic system rather than cortex. Strikingly, behavioral modeling revealed that working memory load evoked substantial decision biases during evidence accumulation, and those who jointly expressed a more optimal decision bias and higher dopamine capacity were most likely to upregulate striato-thalamic variability under load. We argue that the ability to align striato-thalamic variability to level of demand may be a hallmark of a well-functioning brain.

neuroscience↗