Jason T. Huse

The paper identifies recurrent copy-number alterations in RCC brain metastases and shows their association with poor prognosis and metastatic potential across multiple cancers.

The paper introduces a modular AI framework for ROI selection in spatial profiling, showing hybrid generalist-specialist strategies balance signal preservation, concordance, consistency, and efficiency.

This study analyzes a diverse glioma cohort to show ancestry influences on survival, somatic alterations, and mutational processes in diffuse gliomas.

This study uses single-nucleus RNA-seq to analyze RCC brain metastases, identifying immune evasion mechanisms and metabolic adaptations that contribute to resistance to therapy.

This study uses spatial transcriptomics to identify ECM expression patterns in GBM, revealing region-specific adhesion and signaling networks in the tumor microenvironment.

This study shows ATRX loss in sarcomas correlates with poor outcomes and identifies TEAD inhibition as a potential therapy for ATRX-deficient tumors.

This study identifies prognostic gene markers in recurrent IDH wild-type gliomas using network-based analysis and survival modeling, revealing genes associated with survival outcomes and recurrence patterns.

The paper introduces EGNF, a graph-based framework for biomarker discovery that outperforms traditional methods in classification accuracy and interpretability across cancer datasets.

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 paper develops network-regularized AFT models with inverse-degree penalties for survival analysis in cancer genomics, showing improved biological interpretability and reduced false positives.

This study investigates how oncogenic mutations in gliomas influence the tumor microenvironment through integrative multiomic and spatial transcriptomic analysis of glioma samples.

The paper integrates spatial multi-omics data from diffuse gliomas to identify locoregional metabolomic regulators linked to glioma growth and treatment resistance.

The paper identifies ERBB2 alterations as a recurrent oncogene in esophageal adenocarcinoma brain metastases and links them to monoclonal seeding and treatment response.

The paper introduces scNanoGPS, a toolkit for analyzing long-read single-cell transcriptomes to infer cell genotypes and phenotypes without short-read data, revealing cell-type-specific isoform and mutation patterns in tumors.