Tumor microenvironmental stress modulates membrane trafficking to regulate T cell receptor complex aggregation in Epithelial Ovarian Cancer 3967
Bibliographic record
Abstract
Abstract Description High-grade serous ovarian cancer (HGSOC) remains refractory to current immunotherapeutics despite its immunogenicity. Here we describe a novel mechanism in HGSOC in which aberrant endosomal trafficking promotes disruptions in T cell receptor (TCR) nanoclustering to drive T cell paralysis in HGSOC through functionally impaired TCR:pMHC avidities. We have identified a poorly characterized molecule, BLTP3A, as instrumental in impairing immune synapse formation in tumor beds by shuttling antigen-specific TCR complexes to lysosomes for their degradation. We found that BLTP3A is aberrantly expressed in tumor infiltrating T cells through an ATF4-dependent mechanism, where it associates with RAB7+ endosomes and the endosomal cargo-retrieval retromer complex. Critically, a single nucleotide polymorphism (M1098T) in BLTP3A is commonly found in HGSOC. Leveraging multiplex IF, we found that HGSOCs expressing BLTP3AM1098T demonstrate elevated Granzyme B+ effector T cells in tumor epithelial islets, while the conditional ablation of Bltp3a in post-thymic T cells results in more effective antitumor T cell responses in murine models. Mechanistically, BLTP3A impairs TCR complex recycling to the cell surface from late endosomes in tumor beds, leading to lysosomal degradation of antigen-reactive TCR complexes and promoting tumor-immune evasion. Collectively, this work reveals a novel immunosuppressive mechanism in HGSOC and the potential of silencing BLTP3A for engineered T cell therapeutics. Funding Sources The Pershing Square Sohn Cancer Research Alliance Ovarian Cancer Research Alliance V Foundation American Cancer Society The New Jersey Commission on Cancer Research (NJCCR) Topic Categories Tumor Immunology: Cellular Responses and Tumor Microevironment (TIME)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 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".