Rub, J. | bioRxiv Science

bioRxiv2025

Functional interrogation uncovers a critical role for a high-plasticity cell state in lung adenocarcinoma

Plasticity--the ability of cells to undergo phenotypic transitions--drives cancer progression and therapy resistance1-3. To date, strategies targeting cancer plasticity have not advanced to the clinic due to a lack of fundamental understanding of the underlying mechanisms. Recent studies have suggested that plasticity in solid tumors is concentrated in a minority subset of cancer cells4-6, yet functional studies interrogating this high plasticity cell state (HPCS) in situ are lacking. Here, we developed mouse models enabling detection, longitudinal lineage tracing, and ablation of the HPCS in autochthonous lung tumors in vivo. Using lineage tracing, we uncover the HPCS cells are dedifferentiated but possess high capacity for cell state transitions, giving rise to both early neoplastic (differentiated) and advanced lung cancer cell states in situ. Longitudinal lineage tracing using secreted luciferases reveals HPCS-derived cells harbor high capacity for growth when compared to bulk cancer cells or another defined cancer cell state with features of differentiated lung epithelium. Suicide gene-mediated ablation of the HPCS in early neoplasias abrogates tumor progression. Ablating HPCS cells in established tumors by suicide gene or HPCS-directed CAR T cells robustly reduces tumor burden, whereas ablation of a differentiated lung cancer cell state had no effect. We further demonstrate that the HPCS gives rise to therapy-resistant cell states, whereas ablation of the HPCS abrogates resistance to chemotherapy and oncoprotein-targeted therapy. Interestingly, an HPCS-like state is ubiquitous in regenerating epithelia and in carcinomas of multiple other tissues, revealing a convergence of plasticity programs. Our work establishes the HPCS as a critical hub enabling reciprocal transitions between cancer cell states, including acquisition of states adapted to cancer therapies. Targeting the HPCS in lung cancer and in other carcinomas may suppress cancer progression and eradicate treatment resistance.

cancer biology↗

bioRxiv2024

A deep-learning tool for species-agnostic integration of cancer cell states

Genetically engineered mouse models (GEMM) of cancer are a useful tool for exploring the development and biological composition of human tumors and, when combined with single-cell RNA-sequencing (scRNA-seq), provide a transcriptomic snapshot of cancer data to explore heterogeneity of cell states in an immunocompetent context. However, cross-species comparison often suffers from biological batch effect and inherent differences between mice and humans decreases the signal of biological insights that can be gleaned from these models. Here, we develop scVital, a computational tool that uses a variational autoencoder and discriminator to embed scRNA-seq data into a species-agnostic latent space to overcome batch effect and identify cell states shared between species. We introduce the latent space similarity (LSS) score, a new metric designed to evaluate batch correction accuracy by leveraging pre-labeled clusters for scoring instead of the current method of creating new clusters. Using this new metric, we demonstrate scVital performs comparably well relative to other deep learning algorithms and rapidly integrates scRNA-seq data of normal tissues across species with high fidelity. When applying scVital to pancreatic ductal adenocarcinoma or lung adenocarcinoma data from GEMMs and primary patient samples, scVital accurately aligns biologically similar cell states. In undifferentiated pleomorphic sarcoma, a test case with no a priori knowledge of cell state concordance between mouse and human, scVital identifies a previously unknown cell state that persists after chemotherapy and is shared by a GEMM and human patient-derived xenografts. These findings establish the utility of scVital in identifying conserved cell states across species to enhance the translational capabilities of mouse models.

cancer biology↗