{
  "abstract": "Background Chimeric antigen receptor (CAR) T therapies have transformed the treatment options for patients with hematological malignancies, yet more than 50% of patients do not derive a durable benefit. The variability in clinical outcomes is partially driven by heterogeneity in the composition and state of infused cellular products.Methods Here, we introduce tcellMIL, a biologically informed machine learning framework designed to predict patient-level CAR T cell therapy outcomes from infusion product single-cell RNA-sequencing (scRNA-seq) data and identify genetic engineering strategies predicted to enhance patient outcomes. tcellMIL represents each infusion product as a ‘bag of cells’ using multiple instance learning (MIL) and incorporates regulatory priors via the SCENIC algorithm-inferred transcription factor (TF) regulon activity. The TF regulon activity scores are denoised via a self-supervised autoencoder and combined with explicit batch encoding to improve cross-cohort generalization. An attention-based MIL mechanism identifies the most outcome-relevant sub-populations, providing interpretability at cell and regulon levels.Results To train tcellMIL, we assembled a scRNA-seq dataset comprising axicabtagene ciloleucel (axi-cel; Yescarta) CAR T cell infusion products from 64 patients with relapse/refractory large B cell lymphoma (LBCL) treated across publicly available or internal clinical cohorts spanning 3 U.S. institutions. By comparing the adjusted Rand index based on batch labels, we found that data represented as TF regulon activity scores had significantly reduced batch effects compared to normalized scRNA-seq data representation (p = 2.87 × 10^ (-5); Wilcoxon signed-rank test). In addition, computing TF regulon activity scores distilled noisy transcriptomics data to a set of 154 biologically interpretable features. In leave-one-patient-out cross-validation, tcellMIL outperformed (i) multiple pseudobulk classifiers, (ii) foundation models, and (iii) other MIL baseline methods in predicting response at 3 months for unseen patients (accuracy: 0.72 vs. 0.53-0.69; F1 score: 0.74 vs. 0.58-0.73).To identify specific genetic edits predicted to improve patient outcomes following axi-cel for LBCL, we applied attention-based model interpretation methods and leveraged tcellMIL to perform in silico perturbation screens. This approach nominated TBX21 (T-bet) overexpression as a consistently beneficial edit, improving predicted response probabilities across all analyses. Notably, this finding is supported by prior experimental studies demonstrating enhanced in vitro and in vivo efficacy with T-bet overexpression in CAR T cells for lymphoma1 and CD19-low leukemia,2 thus validating our approach.Conclusions In summary, this study offers a generalizable framework to leverage primary scRNA-seq datasets for predictive modeling and rational engineering of CAR T cell therapies.References Gacerez AT, Sentman CL. T-bet promotes potent antitumor activity of CD4+ CAR T cells. Cancer Gene Ther. 2018 Jun;25(5-6):117–128. doi: 10.1038/s41417-018-0012-7. Epub 2018 Mar 7. PMID: 29515240; PMCID: PMC6021366.Cimons JM, DeGolier KR, Burciaga SD, Yarnell MC, Novak AJ, Rivera-Reyes AM, Kohler ME, Fry TJ. T-bet overexpression enhances CAR T cell effector functions and antigen sensitivity. J Immunother Cancer. 2025 Apr 17;13(4):e010962. doi: 10.1136/jitc-2024-010962. PMID: 40246581; PMCID: PMC12007057.",
  "authors": [
    {
      "affiliations": [
        "Division of Immunology and Rheumatology, Department of Medicine, Stanford University, Stanford, CA, USA",
        "Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, USA",
        "Stanford Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University, Stanford, CA, USA"
      ],
      "name": "Kristin CY Tsui"
    },
    {
      "affiliations": [
        "Division of Immunology and Rheumatology, Department of Medicine, Stanford University, Stanford, CA, USA",
        "Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, USA",
        "Stanford Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University, Stanford, CA, USA"
      ],
      "name": "Kameron B Rodrigues"
    },
    {
      "affiliations": [
        "Division of Immunology and Rheumatology, Department of Medicine, Stanford University, Stanford, CA, USA",
        "Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, USA",
        "Stanford Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University, Stanford, CA, USA",
        "Department of Biomedical Data Science, Stanford University, Stanford, CA, USA"
      ],
      "name": "Xianghao Zhan"
    },
    {
      "affiliations": [
        "Stanford Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University, Stanford, CA, USA"
      ],
      "name": "Yiyun Chen"
    },
    {
      "affiliations": [
        "Division of Immunology and Rheumatology, Department of Medicine, Stanford University, Stanford, CA, USA",
        "Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, USA",
        "Stanford Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University, Stanford, CA, USA"
      ],
      "name": "Kelvin C Mo"
    },
    {
      "affiliations": [
        "Stanford Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University, Stanford, CA, USA",
        "Division of Blood and Marrow Transplantation and Cellular Therapy, Department of Medicine, Stanford University, Stanford, CA, USA",
        "Division of Hematology and Oncology, Department of Pediatrics, Stanford University, Stanford, CA, USA",
        "Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA"
      ],
      "name": "Crystal L Mackall"
    },
    {
      "affiliations": [
        "Stanford Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University, Stanford, CA, USA",
        "Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA"
      ],
      "name": "David B Miklos"
    },
    {
      "affiliations": [
        "Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, USA",
        "Department of Biomedical Data Science, Stanford University, Stanford, CA, USA"
      ],
      "name": "Olivier Gevaert"
    },
    {
      "affiliations": [
        "Division of Immunology and Rheumatology, Department of Medicine, Stanford University, Stanford, CA, USA",
        "Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, USA",
        "Stanford Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University, Stanford, CA, USA",
        "Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA"
      ],
      "name": "Zinaida Good"
    }
  ],
  "title": "1126 Rational CAR T cell design via attention-based multiple instance learning of infusion product scRNA-seq data",
  "uid": "87c3b070-9abf-5096-a793-70b120a0db9c"
}