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Combination Therapies and Personalized Medicine

Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
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Machine Learning-Based Prognostic Signature in Breast Cancer: Regulatory T Cells, Stemness, and Deep Learning for

Samina Gul1,2, Jianyu Pang1, Yongzhi Chen1

  • 1Laboratory of Molecular Genetics of Aging & Tumor, Medical School, Kunming University of Science and Technology, 727 Jingming South Road, Kunming 650500, China.

International Journal of Molecular Sciences
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This study reveals how cancer stemness interacts with regulatory T cells (Tregs) in breast cancer. Targeting this interaction may offer a new strategy to overcome immune resistance and improve cancer treatment.

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breast cancerdeep learningmachine learningregulatory T cellsstemness

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Area of Science:

  • Immunology
  • Oncology
  • Bioinformatics

Background:

  • Regulatory T cells (Tregs) play a dual role in the tumor microenvironment, balancing autoimmunity and immunosuppression.
  • Understanding the interplay between cancer stemness and Tregs is crucial for breast cancer immunotherapy.

Purpose of the Study:

  • To investigate the interaction between cancer stemness and Regulatory T cells (Tregs) within the breast cancer tumor immune microenvironment.
  • To develop a prognostic risk model for breast cancer based on Treg differentiation and stemness-related genes.

Main Methods:

  • Calculated breast cancer stemness using one-class logistic regression.
  • Identified three subsets of Regulatory T cells (Tregs) associated with immune regulation and metabolic pathways.
  • Generated a prognostic risk model using LASSO and univariate Cox regression on intersecting Treg differentiation and stemness genes.

Main Results:

  • A prognostic signature comprising 7 genes (MEA1, MTFP1, PASK, PSENEN, PSME2, RCC2, SH2D2A) was developed.
  • The model accurately predicted patient survival with high AUC values (0.96 training, 0.831 validation).
  • The prognostic signature demonstrated efficacy in predicting immunotherapy response in ICI RNA-Seq cohorts and identified potential synergistic drug treatments.

Conclusions:

  • The developed prognostic signature can predict survival and immunotherapy efficacy in breast cancer.
  • Blocking the interaction between cancer stemness and Tregs presents a novel therapeutic approach for breast cancer treatment.
  • This research offers a potential strategy to overcome immune resistance in breast cancer.