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T-reg transcriptomic signatures identify response to check-point inhibitors

María Del Mar Noblejas-López1,2,3, Elena García-Gil1, Pedro Pérez-Segura4

  • 1Translational Research Unit, Translational Oncology Laboratory, Albacete University Hospital, 02008, Albacete, Spain.

Scientific Reports
|May 6, 2024
PubMed

Insights

Regulatory T cells (Tregs) in breast tumors correlate with specific gene expression, predicting better outcomes for patients receiving checkpoint inhibitor therapies. These findings offer new insights into immune responses and cancer treatment.

Area of Science:

  • Immunology
  • Oncology
  • Genomics

Background:

  • Regulatory T cells (Tregs) are CD4+ T cells that suppress immune responses.
  • Understanding the genomic landscape of tumors with high Treg infiltration is crucial for predicting treatment efficacy.

Purpose of the Study:

  • To identify key transcripts associated with Treg presence in breast tumors.
  • To explore the correlation of these transcripts with patient prognosis and response to checkpoint inhibitors.

Main Methods:

  • Analysis of genomic datasets from breast tumors.
  • Transcriptomic profiling to identify genes correlated with Treg expression.
  • Correlation analysis with clinical outcomes and immunotherapy response.

Main Results:

  • Four transcripts (BIRC6, MAP3K2, USP4, SMG1) were consistently associated with Tregs across breast cancer subtypes.
  • A combination of these genes predicted favorable outcomes and improved prognosis with checkpoint inhibitors.
  • Upregulated genes involved in cell membrane functions, neutrophil activation, and macrophage regulation were identified, with specific associations in basal-like and HER2+ tumors.
  • Several genes (MSR1, CD80, OLR1, ABCA1, TMEM245, ATP13A3) predicted response to anti-PD(L)1 and anti-CTLA4 therapies.

Conclusions:

  • Specific gene signatures are strongly linked to Treg presence in breast tumors.
  • These genes modulate the response to checkpoint inhibitor therapies, offering potential biomarkers for treatment selection and outcome prediction.
Keywords:
Anti-CTLA4Anti-PD1Breast cancerRegulatory T cellsTranscriptomic analyses

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