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Related Concept Videos

The Tumor Microenvironment02:17

The Tumor Microenvironment

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Every normal cell or tissue is embedded in a complex local environment called stroma, consisting of different cell types, a basal membrane, and blood vessels. As normal cells mutate and develop into cancer cells, their local environment also changes to allow cancer progression. The tumor microenvironment (TME) consists of a complex cellular matrix of stromal cells and the developing tumor. The cross-talk between cancer cells and surrounding stromal cells is critical to disrupt normal tissue...
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Enrichment and Characterization of the Tumor Immune and Non-immune Microenvironments in Established Subcutaneous Murine Tumors
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Deciphering the Tumor Microenvironment Composition Using Bulk Transcriptomics: A Guide to Recent Advances and Open

Sotiris Ouzounis1, Donya Zojaji2, Sandra García-Mulero3

  • 1Institute of Chemical Biology, National Hellenic Research Foundation, Athens, Greece.

Methods in Molecular Biology (Clifton, N.J.)
|September 30, 2025
PubMed
Summary
This summary is machine-generated.

Tumor microenvironment (TME) deconvolution analysis estimates cell types from bulk transcriptomics. Benchmarking CIBERSORTx and BayesPrism on triple-negative breast cancer (TNBC) data reveals factors impacting accuracy and reproducibility.

Keywords:
BulkCancerCell typeChallengesDeconvolutionImmuneMicroenvironmentTMETranscriptomics

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Area of Science:

  • Cancer Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Tumors are complex ecosystems with diverse cell types influencing cancer progression and treatment response.
  • Understanding the tumor microenvironment (TME) is crucial, especially with immunotherapy's clinical use.
  • Single-cell and spatial technologies offer high resolution but are costly and complex.

Purpose of the Study:

  • To introduce deconvolution analysis for estimating cell type prevalence from bulk transcriptomics.
  • To present advancements, challenges, and benchmarking methodologies in deconvolution analysis.
  • To provide practical guidance for optimizing deconvolution quality and interpreting results.

Main Methods:

  • Review of deconvolution analysis steps, advancements, and challenges.
  • Emphasis on robust benchmarking, parameter definition, and metric selection.
  • Practical analysis using CIBERSORTx and BayesPrism on triple-negative breast cancer (TNBC) datasets (TCGA).

Main Results:

  • Demonstration of how preprocessing, reference datasets, and software choice influence deconvolution outcomes.
  • Highlighting the importance of quality control for reliable deconvolution analysis.
  • Identification of factors affecting the accuracy and reproducibility of deconvolution methods.

Conclusions:

  • Deconvolution analysis is a valuable tool for exploring TME complexity using bulk transcriptomics.
  • Further research is needed to improve the accuracy and reproducibility of deconvolution methods.
  • Optimizing deconvolution analysis requires careful consideration of methodology and data quality.