Abstract 3973: Cancer B cell blueprint reveals dysfunctional extra-follicular dominance with therapeutic opportunity
Bibliographic record
Abstract
Abstract B cells are essential components of humoral immunity but harbor multifaceted functions across cancers. To decode tumor-infiltrating B cells, we generated the B cell blueprint encompassing single-cell transcriptome, BCR repertoire, and chromatin accessibility data across 20 cancer types (483 samples, 266 patients). Strikingly, B cells harbored extraordinary heterogeneity and composed of 15 subsets, which were grouped into two independent developmental paths (extra-follicular [EF] versus germinal-center [GC]) exhibiting organ preference. Of note, colon adenocarcinoma and hepatocellular carcinoma are the two representative types of cancer enriched for GC and EF pathways, respectively. We affirm that EF-dominant cancers correlate with dysregulated immune responses and worse clinical outcomes. We then identify the dynamic metabolic-epigenetic-signaling networks in fine-tuning tumor-infiltrating B cell differentiation and managing the balance between the EF and GC pathways. Atypical memory (AtM) B cells, the primary progenitors of EF-derived ASCs, exhibit an exhausted and bystander phenotype and develop independently of the GC pathway. We find that the AtM B cells reside in the center of immature TLSs and spatially relocate to the periphery during TLS maturation. Finally, we mechanistically link these findings to specific transcription factors and epigenomic regulations. We demonstrate that the glutamine-derived metabolite α-ketoglutarate (αKG) could increase the expression of AtM B cell associated transcription factors Tbet and BATF and promote their differentiation, accompanied by the activation of mammalian target of rapamycin complex 1 (mTORC1) signaling. Consequently, AtM B cells acquire an immunoregulatory function that dampens anti-tumor T cell responses and fosters an immunosuppressive microenvironment. Citation Format: Jiaqiang Ma, Yingcheng Wu, Lifeng Ma, Xupeng Yang, Tiancheng Zhang, Guoji Guo, Jia Fan, Xiaoming Zhang, Qiang Gao. Cancer B cell blueprint reveals dysfunctional extra-follicular dominance with therapeutic opportunity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3973.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".