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Related Concept Videos

Tumor Progression02:07

Tumor Progression

Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
Tumor Progression02:07

Tumor Progression

Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
The Tumor Microenvironment02:17

The Tumor Microenvironment

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...
The Tumor Microenvironment02:17

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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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Updated: Jun 23, 2026

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence
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Published on: October 25, 2011

QeITH: Quantifies Tumor Ecosystem Heterogeneity to Predict Cancer Progression and Treatment Benefit.

Qiqi Lu1,2, Jiangti Luo1,2, Jiawei Wang1,2

  • 1Biomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 211198, China.

Computational and Structural Biotechnology Journal
|June 22, 2026
PubMed
Summary

Quantifying Ecosystem Intratumor Heterogeneity (QeITH) measures tumor complexity. High QeITH scores indicate aggressiveness but also predict better treatment response by revealing an active immune state.

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Area of Science:

  • Oncology
  • Computational Biology
  • Genomics

Background:

  • Intratumor heterogeneity (ITH) drives therapeutic failure.
  • Tumor ecosystem complexity is crucial for understanding ITH.
  • Precise quantification of ITH is needed for clinical implications.

Purpose of the Study:

  • To develop a computational framework, QeITH, for quantifying ecosystem intratumor heterogeneity.
  • To analyze the role of ITH in cancer progression and therapeutic response.

Main Methods:

  • Developed QeITH, a computational framework using Shannon entropy.
  • Applied QeITH to single-cell, bulk, and spatial transcriptomics data.
  • Quantified cellular composition and functional state diversity and entropy.

Main Results:

  • Elevated ITH at single-cell resolution marks malignant transformation and therapy sensitivity.
  • High QeITH scores correlate with neoantigen burden, PD-L1 expression, and poor prognosis.
  • Spatial transcriptomics shows heterogeneity peaks at invasive fronts and within tertiary lymphoid structures (TLS), modulating therapeutic vulnerability.

Conclusions:

  • QeITH reveals a dual role for ITH: aggressiveness and predictive marker for favorable treatment response.
  • High QeITH scores capture an immunologically active tumor ecosystem.
  • This framework integrates single-cell and spatial data to understand cancer drivers and optimize personalized therapies.