Defining molecular determinants of T cell spatial localization and clonal response throughout PDAC malignant progression 3030
Bibliographic record
Abstract
Abstract Description Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy, poised to become the second leading cause of cancer-related deaths in North America. The paucity of neoantigen presentation in PDAC has been hypothesized to promote the development of an “immunologically-cold” tumor microenvironment (TME), driving poor clinical responses to immunotherapy. Despite the abundant immune infiltrate observed in PDAC, T cells display a limited cytotoxic capacity to kill malignant tumor cells which further diminishes throughout disease progression. Utilizing single cell RNA-sequencing coupled to T cell receptor profiling, we have characterized T cell populations in clinical specimens of inflammatory pre-malignant intraductal papillary mucinous neoplasia (IPMN) lesions and advanced stage PDAC tumors. Striking phenotypic and clonal dynamic differences were observed in both CD8 and CD4 T cell populations between IPMN and PDAC specimens which underly their distinct functional capabilities in the evolving TME. The spatial interactions and microenvironmental neighborhoods of neoantigen-reactive T cells in PDAC have yet to be explored in-depth and hold unexploited therapeutic potential. Utilizing the 10X Genomics Xenium platform with custom probes designed to hybridize with neo-antigen-specific T cell clones, we are interrogating the cellular interactions which drive T cell clonal expansion to identify therapeutically relevant vulnerabilities in PDAC malignant progression. Funding Sources Supported by National Insitutes of Health, Canadian Institutes of Health Research, and Princess Margaret Cancer Center Catalyst Grant. Topic Categories Tumor Immunology: Cellular Responses and Tumor Microevironment (TIME)
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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) | 0.002 | 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".