Abstract PR-12: Single-cell profiling of tumor-infiltrating B cells reveals autoantibody repertoires and potential cross-talk with T cells
Bibliographic record
Abstract
Abstract A growing body of evidence suggests that tumor-infiltrating B lymphocytes (TIBs) play important roles in anti-tumor immunity. However, their functions and characteristics—particularly the antigens they recognize—remain poorly understood. In this study, we aimed to comprehensively characterize TIBs, including their immunoglobulin repertoire, antigen specificity, and phenotypic diversity. We performed single-cell profiling of both the transcriptome and immunoglobulin repertoire of TIBs isolated from 11 gastric adenocarcinoma tissues. Using single-cell immunoglobulin repertoire sequencing (scRepertoire-seq), we obtained thousands of paired heavy- and light-chain sequences per case. We observed hallmarks of antigen-specific stimulation—including class switching to IgG, somatic hypermutation, clonal expansion, and differentiation into plasma cells—underscoring the importance of antigen identification for understanding TIB biology. Based on the scRepertoire-seq data, we reconstructed human monoclonal antibodies and explored their target antigens. To focus on cases with strong humoral response, we selected three tumors with high plasma cell infiltration and generated 6–10 immunoglobulins per case from dominant TIB clones. We screened these antibodies against human cell line lysates via immunoprecipitation and mass spectrometry, complemented by ELISA and human protein arrays, and identified 10 autoantigens (2–5 per case). The identified autoantigens displayed diverse functions and subcellular localizations, while several were notably linked to nucleic acid–related proteins. Notably, whole-genome sequencing revealed that several identified autoantigens harbored somatic missense mutations in the same tumors where the corresponding autoantibodies were detected. In these tumors, multiple distinct TIB clones recognized the same antigen, suggesting high immunogenicity of the mutated proteins. However, the mutations do not appear to be essential for antibody recognition, as the antigen sources in our experiments were generic human cell lines lacking these mutations. These findings raise the possibility that oligoclonal expansion of TIBs toward mutated proteins is driven by neoantigen-specific T cell responses. Such coordination may involve linked recognition, where T cell–mediated B cell activation leads to both B and T cells recognizing the same or physically associated antigens, potentially at different epitopes. To investigate this, we assessed the MHC binding affinity of the mutated peptides using bioinformatic analysis. Collectively, our findings provide new insights into TIB-related autoimmunity in cancer, suggesting that these responses are not merely non-specific reactions to abundant self-proteins, but may reflect coordinated, antigen-specific interactions with neoantigen-driven T cell immunity. These insights may inform the development of more effective strategies to harness tumor immunity. Citation Format: Mikiya Takata, Hiroto Katoh, Daisuke Komura, Miwako Kakiuchi, Shumpei Ishikawa. Single-cell profiling of tumor-infiltrating B cells reveals autoantibody repertoires and potential cross-talk with T cells [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Mechanisms of Cancer Immunity and Cancer-related Autoimmunity; 2025 Sep 24-27; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2025;13(9 Suppl):Abstract nr PR-12.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".