Biology subjects

Meyn, P.

Publications and source records attributed to Meyn, P..

4 recordsLinked to original sources

bioRxiv2025

Cross-species analysis identifies genotype-driven vulnerabilities in lung adenocarcinoma

While three major genetic alteration subsets, characterized by mutations in STK11, KRAS, and EGFR, are seminal in driving tumorigenesis in LUAD, their distinct effects on tumor cells and the tumor microenvironment are not fully understood. Here, we map critical oncogenic subset-specific vulnerabilities by identifying conserved cell-type-specific reprogrammings between human and mouse LUAD. Through harmonized scRNA-seq analysis of 57 human and 18 mouse specimens, we unveil that genetic alterations impose genotype-specific immune imprints on the tumor microenvironment: KRAS is associated with a transitional immune state, whereas STK11 and EGFR mutations define discrete and contrasting immune phenotypes. We find that STK11-mutant tumors exhibit complement and interferon-rich immune microenvironments while EGFR-mutant tumors harbor a naive T cell-rich phenotype accompanied by a global HLA downregulation, stress-responsive alveolar macrophages marked by MARCO. In the epithelial compartments, cross-species analysis reveals metabolic dependencies, including OGDH in EGFR- and PDE4D in STK11-mutant cells. Statement of SignificanceThis study delineates oncogenotype-specific molecular and immune imprints in lung adenocarcinoma, revealing STK11-, KRAS-, and EGFR-mutated tumors as transcriptionally and immunologically discrete ecosystems. By mapping epithelial vulnerabilities alongside immune imprints, we unveil genotype-imposed dependencies that inform the rational development of precision therapies with direct translational relevance.

cancer biology↗

bioRxiv2025

Clonal Heterogeneity in Human Pancreatic Ductal Adenocarcinoma and Its Impact on Tumor Progression

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer characterized by profound desmoplasia and cellular heterogeneity, which cannot be fully resolved using traditional bulk sequencing approaches. To understand the contribution of this heterogeneity to PDAC biology, we analyzed a large cohort of primary human PDAC samples (n = 62), profiling 443,451 single cells and 53,236 spatial transcriptomic spots using a combined single-cell RNA sequencing and spatial transcriptomics approach. Our analysis revealed significant intratumoral heterogeneity, with multiple genetically distinct neoplastic clones co-existing within individual tumors. These clones exhibited diverse transcriptional states and subtype profiles, challenging the traditional binary classification of PDAC into basal and classical subtypes; instead, our findings support a transcriptional continuum influenced by clonal evolution and spatial organization. Additionally, these clones each interacted uniquely with surrounding cell types in the tumor microenvironment. Phylogenetic analysis uncovered a rare but consistent classical-to-basal clonal transition associated with MYC amplification and immune response depletion, which were validated experimentally, suggesting a mechanism driving the emergence of a more aggressive basal clonal phenotype. Spatial analyses further revealed dispersed clones enriched for epithelial-to-mesenchymal transition (EMT) activity and immune suppression, correlating with metastatic potential and colonization of lymph node niches. These dispersed clones tended to transition toward a basal phenotype, contributing to disease progression. Our findings highlight the critical role of clonal diversity, transcriptional plasticity, and TME interactions in shaping human PDAC biology. This work provides new insights into the molecular and spatial heterogeneity of PDAC and offers potential avenues for therapeutic intervention targeting clonal evolution and the mechanisms driving metastasis.

cancer biology↗

bioRxiv2023

MethNet: a robust approach to identify regulatory hubs and their distal targets in cancer

Aberrations in the capacity of DNA/chromatin modifiers and transcription factors to bind non-coding regions can lead to changes in gene regulation and impact disease phenotypes. However, identifying distal regulatory elements and connecting them with their target genes remains challenging. Here, we present MethNet, a pipeline that integrates large-scale DNA methylation and gene expression data across multiple cancers, to uncover novel cis regulatory elements (CREs) in a 1Mb region around every promoter in the genome. MethNet identifies clusters of highly ranked CREs, referred to as hubs, which contribute to the regulation of multiple genes and significantly affect patient survival. Promoter-capture Hi-C confirmed that highly ranked associations involve physical interactions between CREs and their gene targets, and CRISPRi based scRNA Perturb-seq validated the functional impact of CREs. Thus, MethNet-identified CREs represent a valuable resource for unraveling complex mechanisms underlying gene expression, and for prioritizing the verification of predicted non-coding disease hotspots.

cancer biology↗

bioRxiv2022

Inflammation in the tumor-adjacent lung as a predictor of clinical outcome in lung adenocarcinoma

Early-stage lung adenocarcinoma is typically treated by surgical resection of the tumor. While in the majority of cases surgery can lead to cure, approximately 30% of patients progress. Despite intense efforts to map the genetic landscape of early-stage lung tumors, there has been limited success in discovering accurate biomarkers that can predict clinical outcomes. Meanwhile, the role of the tumor-adjacent tissue in cancer progression has been largely ignored. To test whether tumor-adjacent tissue can be informative of progression-free survival and to probe the underlying molecular pathways involved, we designed a multi-omic study in both tumor and matched tumor-adjacent histologically normal lung tissue from the same patient. Our study includes 143 treatment naive stage I cases with long-term patient follow-up and is, to our knowledge, the largest such study with the longest follow-up. We performed a comprehensive histologic characterization of all tumors, mapped the mutational landscape and probed the transcriptome of both tumor and adjacent normal tissue. We evaluated the predictive power of each data modality and showed that the transcriptome of tumor-adjacent histologically normal lung tissue is the only reliable predictor of clinical outcome. Unbiased discovery of co-expressed gene modules revealed that inflammatory pathways are upregulated in the tumor-adjacent tissue of patients at high risk for disease progression. Furthermore, single-cell transcriptome analysis in the tumor-adjacent lung demonstrated that progression-associated inflammatory signatures were broadly expressed by both immune and non-immune cells including mesothelial cells, alveolar type 2 cells and fibroblasts, CD1 dendritic cells and MAST cells. Collectively, our studies suggest that molecular profiling of tumor-adjacent tissue can identify patients that are at high risk for disease progression.

bioinformatics↗