Cancer Cells and Metastasis

The paper uses mass cytometry to analyze stemness states in PDAC, revealing molecular heterogeneity and identifying a subset with plastic phenotypes.

The paper investigates extravasation mechanisms in metastatic tumors using a 3D microvessel model and time-lapse imaging, revealing vascular remodeling as a key process in polyclonal colonization.

This paper describes methods to study xenografted human cancer in genetically diverse mice to better understand tumor behavior and identify suitable hosts.

The paper assesses molecular EMT signatures in cancer cell lines to identify drug susceptibilities, showing EMT can predict responses to HSP90 inhibitors and enhance drug sensitivity through TGF-β stimulation.

This study shows hybrid cancer stem cells use vascular tracks for collective invasion in a metastasis-on-a-chip model of oral squamous cell carcinoma.

This study screens for metastasis-related genes in mouse melanoma cells using sequential tail vein injection and identifies differentially expressed genes associated with metastasis.

This study compares 3D tumoroid and 2D cultures of lung cancer cells to assess how cell-cell orientation affects cancer-related features and finds that 3D cultures better mimic primary human lung cancer.

The paper models the dynamics of E-M heterogeneity in breast cancer cells, finding that cell-state transitions, not growth differences, are essential for explaining observed population dynamics.

The paper identifies treatment-resistant Cancer Stem-Like Cells in breast cancer using single-cell RNA sequencing and validates a drug combination to target them effectively.

The paper analyzes the antagonistic roles of RKIP and BACH1 in cancer cell plasticity using transcriptomic data and a gene regulatory network model, showing their association with EMT, metabolic reprogramming, immune evasion, and clinical outcomes.

The paper uses multi-modal transcriptomic data to show basal breast cancer exhibits hybrid epithelial/mesenchymal states and higher phenotypic heterogeneity compared to luminal breast cancer.

The paper shows that stromal cells, particularly fibroblasts, dominate EMT-related gene expression signature scores in cancer, rather than tumor-intrinsic properties.

The paper identifies four pan-cancer subtypes based on embryonic and hematopoietic stem cell signatures, showing distinct clinical and molecular features and prognostic implications.

The paper builds a Boolean model of mechanosensitive EMT and its reversal, reproducing key phenotypes and offering testable predictions on EMT dynamics and biomechanical effects.

The paper develops a mathematical model showing bidirectional crosstalk between EMT and IFNγ-induced PD-L1 expression, revealing how they influence tumor progression and immunotherapy resistance.

This study uses image-based profiling and multi-omics integration to identify morphological axes of variation in colorectal cancer organoids and link them to biological mechanisms and drug responses.

The paper shows that epithelial-mesenchymal plasticity is regulated by inflammatory signalling networks linked to cell morphology through changes in microtubule and nuclear organization.

The paper models phenotypic switching in cancer cells to explain spontaneous epithelial-mesenchymal heterogeneity through stochastic SNAIL partitioning during cell division.

The paper identifies heterogeneous drug-tolerant persisters (DTPs) as population-level states with distinct division and death rates, showing increased ferroptosis susceptibility.

This study shows that NRF2-dependent epigenetic feedback can stabilize hybrid epithelial/mesenchymal states in EMT by affecting SNAIL, while other NRF2 links have less impact.

The paper compares three transcriptomic metrics to quantify epithelial-mesenchymal plasticity across cancer and non-cancer contexts, finding high concordance and applicability beyond cancer.

The paper analyzes RNA-seq data from TCGA to assess stemness and proliferation gene expression across tumor types, finding high heterogeneity and questioning the use of stemness markers in cancer diagnostics.

The paper maps EMT transitions through multi-omic profiling, identifies key regulatory factors, and reveals discordance between molecular layers in human mammary epithelial cells.

The paper develops a mathematical model to explain how glucose regulates Nanog expression and drives differential proliferation in cancer stem cells through STAT3 and p53 dynamics.

This study shows that AP-2γ is essential for maintaining pluripotent mammary stem cells and their ability to form mammary gland structures.

The paper simulates microenvironments to show that RPTP activation by cell contacts can control EMT and promote MET under specific conditions.

The paper models EMT-immune dynamics in epithelial cancer progression, identifying key factors that influence invasion-free survival and predicting new genes affecting cancer outcomes.

The paper investigates how intermediate states in EMT affect cancer metastasis by using a hidden Markov model to show that more intermediate states accelerate the process.

This paper introduces probabilistic phenotype metrics to assess drug efficacy and combination effectiveness by analyzing single-cell responses, showing they reduce bias and improve sensitivity to heterogeneity.

The paper maps EMT and MET states in lung cancer using single-cell resolution and a novel computational tool, showing differences in trajectories and providing a framework for clinical sample characterization.