Abstract C109: Nanostring-based subtyping of pancreatic ductal adenocarcinoma is strongly influenced by the stromal compartment
Bibliographic record
Abstract
Abstract The prognostic significance of basal-like and classical subtypes of pancreatic ductal adenocarcinoma (PDAC) is well-established, and ongoing clinical trials are poised to validate the predictive utility of such subtypes in guiding first-line treatment decision-making. While the Purity-independent (PurIST)-based subtyping strategy provides a method for PDAC subtype determination based on the Nanostring gene expression quantification platform, the utility of this platform has not been tested in a real-world patient setting. Here, we leverage a retrospective dataset of patients with resectable PDAC (n=299) that received Nanostring sequencing in combination with comprehensive clinical metadata curation and histopathological analysis. All sequencing analyses were performed using formalin-fixed paraffin-embedded (FFPE) tumor whole sections. The proportion of basal-like cases (15%) was comparable with other resectable PDAC cohorts, and patients with basal-like tumors showed reduced overall survival (p=3.9e-4). Histopathological quantification of epithelial, stromal and normal components in each sample revealed significantly (p=0.007) reduced classical gene expression in stroma-high samples. Out of 29 patients with repeat sampling from different FFPE blocks derived from the same tumor and surgery, 31% of tumors received discordant subtypes calls and increases in the amount of stroma sequenced between two samples from the same tumor was negatively correlated with expression of classical subtype genes (p=0.02). Taken together, these data demonstrate the presence of intra-tumoral subtype heterogeneity, highlight the quantity of stroma sequenced as a major contributing factor in PurIST-based subtype determination and generate insight into the reproducibility of this subtyping strategy in a real-world clinical setting. Citation Format: James T. Topham, Steve E. Kalloger, Joanna M. Karasinska, Jenny E. Chu, Hassan Ali, Dongxia Gao, Christine Chow, Andrew Metcalfe, Jonathan M. Loree, David F. Schaeffer, Daniel J. Renouf. Nanostring-based subtyping of pancreatic ductal adenocarcinoma is strongly influenced by the stromal compartment [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr C109.
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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".