Biology subjects

Glatzer, G.

Publications and source records attributed to Glatzer, G..

4 recordsLinked to original sources

bioRxiv2026

Transcriptomic analysis reveals differential regulation of synaptic components in pediatric and adult brain tumors

Neuronal-cancer cell interactions are a key component of brain tumor pathophysiology, yet many fundamental questions remain unanswered, with even more remaining unasked. Here, we present a comprehensive brain tumor reference map generated from bulk RNA-seq data of ~4,300 adult and pediatric brain tumors, including various glioma subtypes, medulloblastomas, ependymomas, and meningiomas, alongside ~1,400 healthy brain samples. By analyzing synaptic processes and synaptic gene expression across tumor types, we reveal the complexity of neuron-tumor crosstalk and its potential impact on patient survival. Our findings identify key synaptic signaling pathways and dysregulated patterns in brain tumors, including HNRNPH2 upregulation, a key factor in RNA processing and metabolism, in gliomas and medulloblastomas. Additionally, we highlight the role of cholinergic signaling, particularly CHRNA9, as a driver of glioma progression, which is also upregulated in WNT and Group 4 medulloblastomas, ependymomas, and meningiomas. Furthermore, P2 purinergic signaling emerges as a key player in glioma progression. These insights lay the groundwork for future studies exploring whether targeting these pathways could reprogram the tumor microenvironment toward an anti-tumor state, ultimately leading to novel therapeutic strategies and improved patient outcomes.

cancer biology↗

bioRxiv2026

A pan-cohort transcriptional landscape of breast cancer maps subtype and microenvironmental programs

Breast cancer comprises heterogeneous transcriptional states that are incompletely captured by discrete clinical or molecular subtype labels. To visualize this heterogeneity in a unified framework, we integrated bulk RNA-seq data from 2,284 patient samples across 13 studies using 18,089 protein coding genes, a harmonized processing pipeline, batch correction, consensus clustering and PaCMAP dimensionality reduction to construct an interactive breast cancer transcriptional landscape. Consensus clustering identified five major regions, which were annotated using PAM50 scores calculated for each sample: Luminal A, Luminal B, HER2 enriched, and two basal associated clusters. The basal clusters separated into an immune rich region marked by T cell-inflamed, tumor-associated macrophages (TAM), and low-purity signatures, and a cell-cycle driven region enriched for proliferation and DNA replication programs. Overlay of marker genes, pathways, kinases, neuronal like signaling programs, cancer associated fibroblasts (CAF) states, and TAM programs revealed spatially organized subtype biology and microenvironmental heterogeneity. Finally, projection of therapy associated resistance signatures identified landscape regions linked to predicted resistance to HER2-targeted therapy and hormone receptor directed endocrine therapies. By enabling interactive exploration of transcriptional states, marker genes, pathways, and therapeutic response programs, this resource provides a community framework for biomarker discovery in breast cancer.

bioinformatics↗

bioRxiv2026

Beyond Histology: A Unified Transcriptomic Atlas Defines Lung Cancer Biologic States and Subtypes

Lung cancer encompasses multiple histological entities with substantial molecular heterogeneity that remain incompletely resolved at population scale. Here, we constructed a unified reference landscape of lung cancer by analyzing raw RNA sequencing data from 1,824 tumors spanning adenocarcinoma (n=966), squamous cell carcinoma (n=628), small cell lung cancer (n=150), and unclassified non small cell lung cancer (n=80). Following batch correction, samples were analyzed using consensus clustering and visualized with PaCMAP to generate a molecular atlas annotated with clinical and biological metadata. Rather than segregating by pathological diagnosis, tumors organized along conserved transcriptional axes defined by tumor-intrinsic biology including proliferative or metabolic programs and immune-infiltrated states. Consensus clustering resolved nine robust molecular clusters, including an adenocarcinoma-associated subgroup, a neuroendocrine-like adenocarcinoma marked by ASCL1 activation, immune-associated regions, and bifurcation of both small cell and squamous carcinomas into biologically distinct states. Spatially restricted expression of selected clinically relevant transcripts nominated state-specific therapeutic hypotheses requiring future functional and clinical validation. Projection of patient tumors and patient-derived xenografts onto the atlas demonstrated preservation of transcriptional identity and enabled quantitative assessment of model fidelity. This integrated framework organizes lung cancer as a structured continuum of transcriptional states and provides a reference resource for biological interpretation and future translational studies.

bioinformatics↗

bioRxiv2024

Transcriptomic landscape identifies two unrecognized ependymoma subtypes and novel pathways in medulloblastoma

Medulloblastoma and ependymoma are common pediatric central nervous system tumors with significant molecular and clinical heterogeneity. We collected bulk RNA sequencing data from 888 medulloblastoma and 370 ependymoma tumors to establish a comprehensive reference landscape. Following rigorous batch effect correction, normalization, and dimensionality reduction, we constructed a unified landscape to explore gene expression, signaling pathways, RNA fusions, and copy number variations. Our analysis revealed distinct clustering patterns, including two primary ependymoma compartments, EPN-E1 and EPN-E2, each with specific RNA fusions and molecular signatures. In medulloblastoma, we observed precise stratification of Group 3/4 tumors by subtype and in SHH tumors by patient age. This landscape serves as a vital resource for identifying biomarkers, refining diagnoses, and enables the mapping of new patients bulk RNA-seq data onto the reference framework to predict biology and outcome from nearest neighbor analysis facilitate accurate disease subtype identification. The landscape is accessible via Oncoscape, an interactive platform, empowering global exploration and application. One Sentence SummaryA landscape built using only Transcriptomic analysis for medulloblastoma and ependymoma reveals novel insights about subtype-specific biology.

bioinformatics↗