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Related Concept Videos

Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
Targeted Cancer Therapies02:57

Targeted Cancer Therapies

The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
There are several types of targeted therapies against specific...
Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
Targeted Cancer Therapies02:57

Targeted Cancer Therapies

The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
There are several types of targeted therapies against specific...
Cytotoxic T Cells-mediated Immune Response01:27

Cytotoxic T Cells-mediated Immune Response

Cytotoxic T cells are a vital component of the immune system. They have the remarkable ability to identify and target antigens on infected or abnormal cells. These antigens often originate from intracellular pathogens such as viruses or abnormal proteins cancer cells produce.
Immunological surveillance is the ability of immune cells to monitor and eliminate infected cells with intracellular pathogens, neoplastically transformed cells, and cells with non-self antigens. Cytotoxic T cells and NK...
Tumor Immunotherapy01:27

Tumor Immunotherapy

Immunotherapy is a treatment that boosts or manipulates the immune system to fight diseases, including cancer. For instance, by stimulating an immune response through vaccinations against viruses that cause cancers, like hepatitis B virus and human papillomavirus, these diseases can be prevented. Nonetheless, some cancer cells can avoid the immune system due to their rapid mutation and division. The immune response to many cancers involves three phases: elimination, equilibrium, and escape.

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Related Experiment Video

Updated: Jun 6, 2026

Using X-ray Crystallography, Biophysics, and Functional Assays to Determine the Mechanisms Governing T-cell Receptor Recognition of Cancer Antigens
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The Deep Learning Framework iCanTCR Enables Early Cancer Detection Using the T-cell Receptor Repertoire in Peripheral

Yideng Cai1, Meng Luo1, Wenyi Yang1

  • 1School of Life Science and Technology, Harbin Institute of Technology, Harbin, China.

Cancer Research
|March 27, 2024
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Summary

A new deep learning framework, iCanTCR, analyzes T-cell receptor (TCR) repertoires in blood to detect cancer early. This approach shows promise for noninvasive, liquid biopsy-based cancer diagnosis.

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Using X-ray Crystallography, Biophysics, and Functional Assays to Determine the Mechanisms Governing T-cell Receptor Recognition of Cancer Antigens
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Area of Science:

  • Immunology
  • Bioinformatics
  • Artificial Intelligence

Background:

  • T cells and their T-cell receptors (TCRs) play a crucial role in recognizing tumor antigens and initiating anticancer immune responses.
  • Monitoring the TCR repertoire in peripheral blood offers a potential strategy for early cancer detection due to its link to tumor antigen specificity.

Purpose of the Study:

  • To develop and validate a deep learning framework, iCanTCR, for identifying cancer patients based on their TCR repertoire.
  • To assess the efficacy of iCanTCR in detecting various cancer types, including early-stage disease, using peripheral blood samples.

Main Methods:

  • Developed the iCanTCR deep learning framework utilizing TCRβ sequences as input to predict cancer probability.
  • Trained the model on over 2,000 publicly available TCR repertoires from 11 cancer types and healthy controls.
  • Validated the framework on additional independent datasets to assess its performance in distinguishing cancer patients from noncancer individuals.

Main Results:

  • The iCanTCR framework accurately distinguished cancer patients from healthy individuals across multiple cancer types.
  • The model demonstrated significant capability in classifying various cancers.
  • iCanTCR achieved an AUC of 86% in identifying individuals with early-stage cancer.

Conclusions:

  • The study presents iCanTCR as a novel deep learning-based liquid biopsy approach for noninvasive cancer diagnosis.
  • Monitoring circulating immune signals via TCR repertoire analysis holds potential for early and accurate cancer detection.
  • This framework facilitates the capture of immune signals from peripheral blood for improved cancer screening and diagnosis.