MacLean P. Nasrallah

Path2Space predicts spatial gene expression and tumor microenvironment from histopathology slides, identifying new breast cancer subgroups and improving biomarker prediction.

Path2Omics predicts gene expression and methylation from histopathology slides across 23 cancer types, outperforming previous models and showing effectiveness in predicting patient outcomes.

This paper introduces EXPAND, an interpretable AI model that predicts breast cancer subtypes and survival using nuclear features, achieving performance comparable to black-box models.

This study identifies genetic changes in IDH-mutant gliomas that correlate with treatment response and tumor evolution using longitudinal genomic data from 206 patient samples.

This study shows human glioblastoma cells integrate into brain-wide neuronal circuits, exhibit diverse connectivity, and respond to neuromodulatory inputs like acetylcholine, which promotes invasion and survival.

The paper shows that patient-derived glioblastoma organoids can reflect CAR-T cell treatment responses in real-time, correlating with clinical outcomes in patients.

This study uses temporal multi-modal single-cell profiling to show CAR-T cells interact dynamically with glioblastoma cells, including antigen-negative ones, and reduce their proliferation through IFN-gamma.