Tsao, M. | Search bioRxiv

bioRxiv2026

Tumor Microenvironment Modulates Lineage Plasticity in Lung Squamous Cell Carcinoma

Lung squamous cell carcinoma (LUSC) is the second most common type of lung cancer, yet therapeutic options remain limited. A deeper understanding of its biology and molecular pathogenesis is essential for developing new treatment strategies. Here, we investigated the mechanisms of phenotypic plasticity in LUSC by comparing organoid-derived orthotopic lung models (ODOLs) and subcutaneous xenograft models (ODXs). ODXs showed greater tumor growth, squamous differentiation, and extracellular matrix (ECM) organization compared to ODOLs. Transcriptomic analyses revealed upregulation of multiple HIF1 and SOX2 target genes together with enhanced hypoxia signaling in ODXs. CRISPR/Cas9-mediated HIF1-knockout ODXs showed reduced SOX2 expression, tumor growth, and ECM organization, whereas SOX2-knockout ODXs reduced tumor growth without affecting HIF1 and ECM organization. These results indicate that HIF1 regulates squamous lineage maintenance through SOX2 and ECM remodeling. Spatial transcriptomics revealed enrichment of basal cell-like and squamous-differentiated tumor states in ODXs, whereas ODOLs displayed less differentiated phenotypes. These findings identify the tumor microenvironment as a critical determinant of lineage plasticity in LUSC and provide mechanistic insight into how hypoxia shapes tumor differentiation.

Cancer Biology↗

bioRxiv2022

scAnnotate: an automated cell type annotation tool for single-cell RNA-sequencing data

MotivationSingle-cell RNA-sequencing (scRNA-seq) technology enables researchers to investigate a genome at the cellular level with unprecedented resolution. An organism consists of a heterogeneous collection of cell types, each of which plays a distinct role in various biological processes. Hence, the first step of scRNA-seq data analysis is often to distinguish cell types so they can be investigated separately. Researchers have recently developed several automated cell type annotation tools, requiring neither biological knowledge nor subjective human decisions. Dropout is a crucial characteristic of scRNA-seq data widely used in differential expression analysis. However, dropout information is not explicitly used by any current cell annotation method. Fully utilizing dropout information for cell type annotation motivated this work. ResultsWe present scAnnotate, a cell annotation tool that fully utilizes dropout information. We model every genes marginal distribution using a mixture model, which describes both the dropout proportion and the distribution of the non-dropout expression levels. Then, using an ensemble machine learning approach, we combine the mixture models of all genes into a single model for cell-type annotation. This combining approach can avoid estimating numerous parameters in the high-dimensional joint distribution of all genes. Using fourteen real scRNA-seq datasets, we demonstrate that scAnnotate is competitive against nine existing annotation methods. Furthermore, because of its distinct modelling strategy, scAnnotates misclassified cells are very different from competitor methods. This suggests using scAnnotate together with other methods could further improve annotation accuracy. AvailabilityWe implemented scAnnotate as an R package and made it publicly available from CRAN. ContactXuekui Zhang: xuekui@uvic.ca and Li Xing: li.xing@math.usask.ca

bioinformatics↗