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Tumor-Infiltrating Lymphocyte Scoring in Neoadjuvant-Treated Breast Cancer.

Noémie Thomas1, Soizic Garaud1, Mireille Langouo1

  • 1Molecular Immunology Unit, Institut Jules Bordet, 1070 Brussels, Belgium.

Cancers
|August 29, 2024
PubMed
Summary

Tumor-infiltrating lymphocyte (TIL) scoring is reliable in residual breast cancer after neoadjuvant chemotherapy (NAC). This finding supports its use in evaluating treatment response and prognosis in patients with remaining disease.

Keywords:
breast cancerdigital pathologyimmune biomarkerslymphoid aggregatesneoadjuvant chemotherapytertiary lymphoid structurestumor-infiltrating lymphocytes

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Area of Science:

  • Oncology
  • Pathology
  • Cancer Research

Background:

  • Neoadjuvant chemotherapy (NAC) is standard for locally advanced breast cancer (BC).
  • Tumor-infiltrating lymphocyte (TIL) scoring is prognostic and predictive after NAC.
  • Reliability of TIL scoring in post-NAC samples requires investigation due to treatment-induced tumor changes.

Purpose of the Study:

  • To assess the reliability of TIL scoring in breast cancer tissues post-NAC.
  • To compare manual and digital TIL scoring methods.
  • To evaluate TIL scoring concordance in patients with residual disease versus complete response.

Main Methods:

  • Scoring of stromal and intra-tumoral TILs on pre- and post-NAC H&E and IHC-stained tissues.
  • Utilized two experienced pathologists and HALO image analysis software for digital scoring.
  • Assessed inter-pathologist correlation and concordance across methods.

Main Results:

  • Good inter-pathologist correlation for stromal TILs on H&E (CCC 0.73) in residual disease.
  • Good correlation between staining methods (CCC 0.81) and digital scoring (CCC 0.77).
  • Poor concordance for TIL scoring in patients achieving complete response.

Conclusions:

  • TIL scoring demonstrates good reliability in post-NAC breast cancer samples with residual disease.
  • Reliability is comparable to scoring in untreated breast cancer.
  • Digital TIL scoring shows consistency, suggesting potential for validated algorithmic development.