PHENOSTAMP

PHENOSTAMP is a computational tool that enables the projection of clinical cancer samples onto a reference map of epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET) states. The reference map is constructed using mass cytometry time-course analysis of lung cancer cells undergoing EMT through TGFβ-treatment and MET through TGFβ-withdrawal.

This tool utilizes a neural network algorithm to characterize the phenotypic profile of individual cells within clinical samples in the context of the in vitro EMT-MET analysis.

PHENOSTAMP provides a framework for assessing the clinical relevance of EMT and MET states in cancer progression and drug resistance by allowing researchers to compare the phenotypic profiles of clinical samples with the well-defined in vitro EMT-MET states. PHENOSTAMP offers single-cell resolution insights into the spectrum of EMT and MET states present in clinical cancer samples, potentially aiding in the development of targeted therapies and personalized treatment strategies.

Topic

Oncology;Genotype and phenotype;Machine learning;Proteomics;Systems biology

Detail

  • Operation: Essential dynamics;Mapping;Sorting

  • Software interface: Command-line user interface

  • Language: R

  • License: Not stated

  • Cost: Free of charge

  • Version name: -

  • Credit: -

  • Input: -

  • Output: -

  • Contact: Sylvia K. Plevritis sylvia.plevritis@stanford.edu

  • Collection: -

  • Maturity: -

Publications

  • Mapping lung cancer epithelial-mesenchymal transition states and trajectories with single-cell resolution.
  • Karacosta LG, et al. Mapping lung cancer epithelial-mesenchymal transition states and trajectories with single-cell resolution. Mapping lung cancer epithelial-mesenchymal transition states and trajectories with single-cell resolution. 2019; 10:5587. doi: 10.1038/s41467-019-13441-6
  • https://doi.org/10.1038/S41467-019-13441-6
  • PMID: 31811131
  • PMC: PMC6898514

Download and documentation

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