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<p>Wilms tumor 1 (WT1) is a tumor-associated antigen expressed in solid tumors and hematological malignancies. T-cell immunotherapies targeting WT1 are currently under development. To analyze endogenous T-cell responses against WT1, we trained computational models capable of detecting WT1-specific T-cell responses from T-cell receptor (TCR) sequencing data. We peptide-pulsed healthy donor and acute myeloid leukemia (AML) patient samples with VLDFAPPGA (VLD, WT1<sub>37-45</sub>) and RMFPNAPYL (RMF, WT1<sub>126-134</sub>) peptides, then sequenced the WT1 dextramer-positive CD8 + T-cells with single-cell RNA + TCRαβ sequencing. The TCRGP machine-learning TCR-classification method was trained with epitope-specific and control TCR repertoires, and we obtained AUROC values of 0.74 (VLD) and 0.75 (RMF), allowing reliable identification of WT1-specific T-cells. In bulk TCRβ sequenced patient samples (AML n = 21, chronic myeloid leukemia (CML) n = 26, and myelodysplastic syndrome n = 25), the median WT1-specific T-cell abundance was similar to healthy controls, but their VLD and RMF-specific TCR repertoires exhibited higher clonality with two patients presenting up to 13% of WT1-specific T-cells. ScRNA+TCRαβ sequencing of AML bone marrow T-cells revealed that WT1-specific T-cells predominantly exhibit an effector or terminal effector memory phenotype. In conclusion, our novel computational models enable large-scale WT1-specific T-cell identification from TCR sequencing datasets and leukemia-antigen-specific immune response monitoring.</p>

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Predicting antigen-specific T-cell immunity against Wilms tumor 1 in hematologic cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月22日

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  • Brittany L. Ford,
  • Emmi Jokinen,
  • Jani Huuhtanen,
  • Sofia Forstén,
  • Jay Klievink,
  • Gabriella Antignani,
  • Oscar Brück,
  • Vincenzo Cerullo,
  • Karita Peltonen,
  • Satu Mustjoki

摘要

Wilms tumor 1 (WT1) is a tumor-associated antigen expressed in solid tumors and hematological malignancies. T-cell immunotherapies targeting WT1 are currently under development. To analyze endogenous T-cell responses against WT1, we trained computational models capable of detecting WT1-specific T-cell responses from T-cell receptor (TCR) sequencing data. We peptide-pulsed healthy donor and acute myeloid leukemia (AML) patient samples with VLDFAPPGA (VLD, WT137-45) and RMFPNAPYL (RMF, WT1126-134) peptides, then sequenced the WT1 dextramer-positive CD8 + T-cells with single-cell RNA + TCRαβ sequencing. The TCRGP machine-learning TCR-classification method was trained with epitope-specific and control TCR repertoires, and we obtained AUROC values of 0.74 (VLD) and 0.75 (RMF), allowing reliable identification of WT1-specific T-cells. In bulk TCRβ sequenced patient samples (AML n = 21, chronic myeloid leukemia (CML) n = 26, and myelodysplastic syndrome n = 25), the median WT1-specific T-cell abundance was similar to healthy controls, but their VLD and RMF-specific TCR repertoires exhibited higher clonality with two patients presenting up to 13% of WT1-specific T-cells. ScRNA+TCRαβ sequencing of AML bone marrow T-cells revealed that WT1-specific T-cells predominantly exhibit an effector or terminal effector memory phenotype. In conclusion, our novel computational models enable large-scale WT1-specific T-cell identification from TCR sequencing datasets and leukemia-antigen-specific immune response monitoring.