1091 Decoding clinical and molecular determinants of tertiary lymphoid structure heterogeneity in pancreatic cancer by integrating multimodal spatial transcriptomics, proteomics, and histopathology imaging
Bibliographic record
Abstract
Background Pancreatic ductal adenocarcinoma (PDAC) is a malignant neoplasm of the pancreas characterized by late-stage detection, with few treatment options and prognostic biomarkers. Tertiary lymphoid structures (TLS) have been found to be predictive of survival in PDAC, however, their phenotypic heterogeneity, functional significance, and relationship to genomic and transcriptomic subtypes remain poorly understood.Methods Using TLS histopathology detection methods we identified TLS across a cohort of over 600 patients and linked their presence to matched genomics and clinical metadata. We generated GeoMx spatial transcriptomics data for 13 PDAC patients with over 200 regions of interest (ROIs) focused on TLS and tumour. We stained and digitized matched whole-slide hematoxylin and eosin (H&E) images and single-cell spatial proteomic data using Imaging Mass Cytometry (IMC) on serial sections of the same ROIs profiled with GeoMx. Using unsupervised clustering and differential expression analyses we characterized TLS into subgroups.Results We show that we can automate TLS detection in H&E from primary tumour resections and liver metastases biopsies. We quantified the percentage of PDAC samples with TLS, the number of TLS per patient and linked their presence to PDAC transcriptomic subtypes, genomic aberrations and patient metadata. We identified three distinct TLS subgroups based on whole-transcriptome TLS expression profiles, and find that TLS subgroup identity is determined by tumour proximity, and specific pathway activation within the adjacent tumour. Finally, we use IMC to deconvolve the single cell content of each subgroup.Conclusions TLS are a known prognostic factor in PDAC, however, they have never been thoroughly characterized or linked to genomic and transcriptomic PDAC subtypes. We find previously unappreciated heterogeneity in TLS phenotypes and link these to tumour phenotypes in what is the most comprehensive characterization of TLS to date.Ethics Approval Samples were taken from previous studies with patient informed consent and approval from Institutional Review or Research Ethics Boards (REB # 20-0170-E, Sinai Health).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".