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Background <p>The immune checkpoint targeting is nowadays an integral part of cancer therapies. However, only a minority of patients experience long-term benefits. Thus, the identification of predictive biomarkers contributing to therapy response is urgently needed.</p> Methods <p>Here, we analyzed different immune and tumor specific expression and secretion profiles in the peripheral blood and tumor samples of 50 breast cancer patients by multicolor flow cytometry and bead-based immunoassays at the time of diagnosis. Due to individual phenotype variations, we quantitatively scored 25 expressed and secreted immune-associated (e.g., LAG-3, PD-1, TIM-3, CD27) and tumor relevant markers (e.g., PD-L1, CD44, MHC-I, MHC-II) in immune checkpoint-treated triple negative breast cancer patients based on the current literature. The calculated score divided the patients into individuals with predicted pCR (total score of &gt; 0) or predicted residual disease (total score of ≤ 0). At the end of the neoadjuvant therapy, the truly achieved pathological complete response (pCR; end of observation) was determined.</p> Results <p>The calculated score was 79% in accordance with the achieved pCR at the time of surgery. Moreover, the sensitivity was 83.3%, the specificity 76.9%, the positive predictive value 62.5%, and the negative predictive value 90.9%. In addition, we identified a correlation of PD-1 and LAG-3 expression between tumor-associated and peripheral immune cells, which was independent of the subtype. Overall, PD-1 was the most frequently expressed checkpoint. However, in a number of patient-derived tumors, additional checkpoints as LAG-3 and TIM-3 were substantially (co-)expressed, which potentially compromises anti-PD-(L)1 mono-therapy.</p> Conclusions <p>This study represents a proof-of-principle to identify potential checkpoint therapy responders in advance at the time of diagnosis. The work was based on a scoring derived from a multiplexed marker profiling. However, larger patient cohorts need to be prospectively evaluated for further validation.</p>

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Breast cancer scoring based on a multiplexed profiling of soluble and cell-associated (immune) markers facilitates the prediction of pembrolizumab therapy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月27日

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  • Verena Schweihofer,
  • Christina Bruss,
  • Stephan Seitz,
  • Gunther Glehr,
  • Madeleine Hetterich,
  • Florian Weber,
  • Maria Hatzipanagiotou,
  • Miriam Fernández-Pacheco Álvarez,
  • Olaf Ortmann,
  • Gero Brockhoff,
  • Richard J. Bauer,
  • Anja Kathrin Wege

摘要

Background

The immune checkpoint targeting is nowadays an integral part of cancer therapies. However, only a minority of patients experience long-term benefits. Thus, the identification of predictive biomarkers contributing to therapy response is urgently needed.

Methods

Here, we analyzed different immune and tumor specific expression and secretion profiles in the peripheral blood and tumor samples of 50 breast cancer patients by multicolor flow cytometry and bead-based immunoassays at the time of diagnosis. Due to individual phenotype variations, we quantitatively scored 25 expressed and secreted immune-associated (e.g., LAG-3, PD-1, TIM-3, CD27) and tumor relevant markers (e.g., PD-L1, CD44, MHC-I, MHC-II) in immune checkpoint-treated triple negative breast cancer patients based on the current literature. The calculated score divided the patients into individuals with predicted pCR (total score of > 0) or predicted residual disease (total score of ≤ 0). At the end of the neoadjuvant therapy, the truly achieved pathological complete response (pCR; end of observation) was determined.

Results

The calculated score was 79% in accordance with the achieved pCR at the time of surgery. Moreover, the sensitivity was 83.3%, the specificity 76.9%, the positive predictive value 62.5%, and the negative predictive value 90.9%. In addition, we identified a correlation of PD-1 and LAG-3 expression between tumor-associated and peripheral immune cells, which was independent of the subtype. Overall, PD-1 was the most frequently expressed checkpoint. However, in a number of patient-derived tumors, additional checkpoints as LAG-3 and TIM-3 were substantially (co-)expressed, which potentially compromises anti-PD-(L)1 mono-therapy.

Conclusions

This study represents a proof-of-principle to identify potential checkpoint therapy responders in advance at the time of diagnosis. The work was based on a scoring derived from a multiplexed marker profiling. However, larger patient cohorts need to be prospectively evaluated for further validation.