Antonio Iavarone

Single-nucleus multiomics unveils malignant cellular states, regulatory architectures and microenvironmental reorganization across the G-CIMP epigenomic transition in IDH-mutant glioma

The paper integrates single-nucleus RNA and ATAC sequencing to identify cellular states and regulatory networks associated with the G-CIMP epigenomic transition in IDH-mutant gliomas.

Tumor-initiating genetics and therapy drive divergent molecular evolution in IDH-mutant gliomas

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.

Targeting ZNF638 activates antiviral immune responses and potentiates immune checkpoint inhibition in glioblastoma

This study shows targeting ZNF638 induces viral mimicry and enhances immune checkpoint inhibition in glioblastoma by increasing dsRNA signaling and PD-L1 expression.

Oncometabolite-driven enhancer hypermethylation promotes a hyporesponsive microglial state in IDH-mutant gliomas

This study shows that oncometabolite D-2HG drives enhancer hypermethylation in IDH-mutant gliomas, leading to hyporesponsive microglia and reduced immune reactivity.

The epigenetic evolution of gliomas is determined by their IDH1 mutation status and treatment regimen

This study analyzes the epigenetic evolution of IDH1-mutant and IDH1-wildtype gliomas under treatment, showing IDHmut tumors undergo epigenomic changes resembling IDHwt tumors, with implications for treatment strategies.

A fast variational algorithm to detect the clonal copy number substructure of tumors from single-cell data

SCEVAN is a fast variational algorithm that deconvolves clonal substructure in tumors from single-cell data, accurately distinguishing malignant and non-malignant cells.

Integrating single-cell RNA-seq and imaging with SCOPE-seq2

SCOPE-seq2 integrates single-cell imaging and RNA-seq with improved throughput and accuracy for analyzing human glioblastoma cells.

A MAP of tumor-host interactions in glioma at single cell resolution

The paper introduces scTHI, a method to identify ligand-receptor interactions in glioma using single-cell RNA sequencing data from six human datasets.

Surveying Brain Tumor Heterogeneity by Single-Cell RNA Sequencing of Multi-sector Biopsies

This study uses single-cell RNA sequencing on multi-sector glioma biopsies to analyze tumor heterogeneity, revealing spatial patterns of invasion, progression branches, and cell-cell communication.

De novo Gene Signature Identification from Single-Cell RNA-Seq with Hierarchical Poisson Factorization

This paper introduces scHPF, a Bayesian method for discovering gene signatures from scRNA-seq without prior normalization, and finds spatially biased expression patterns in glioma regions linked to poor survival.

RGBM: Regularized Gradient Boosting Machines For The Identification of Transcriptional Regulators Of Discrete Glioma Subtypes

RGBM identifies transcriptional regulators of glioma subtypes by inferring gene regulatory networks from diverse data sources and outperforming existing methods on benchmark datasets.

Distinct epigenetic shift in a subset of Glioma CpG island methylator phenotype (G-CIMP) during tumor recurrence

The paper identifies an epigenetic shift from G-CIMP-high to G-CIMP-low in gliomas during recurrence, associated with genomic instability and stem cell-like features, and proposes biomarkers for predicting recurrence.

Detection of statistically significant network changes in complex biological networks

The paper introduces an improved method for detecting statistically significant network changes in biological networks, showing better performance on dense random geometric networks and application to glioma cancer regulatory networks.