Abstract B032: Evaluating T cell-inflammation in pancreatic neuroendocrine tumors
Bibliographic record
Abstract
Abstract Background: Pancreatic neuroendocrine tumors (pNETs) are rare tumors arising from neuroendocrine cells in the pancreas. Their clinical behaviour and prognosis are variable and durable responses are often difficult to achieve with conventional therapies. Immunotherapy approaches may be efficacious in a subgroup of pNETs. In a recent study that did not include pNETs, we showed that a four-chemokine signature (c-Score: CCL4, CCL5, CXCL9, CXCL10) predicts T cell-inflammation and response to immune checkpoint inhibitors across cancers. We have hypothesized that the c-Score will also identify pNET subgroups with T cell-inflammation. Methods: The c-Score and hallmarks of the cancer-immunity cycle are being evaluated in pNETs as a cohort study (n=100 cases) of the Marathon of Hope Cancer Centres Network whole genome and transcriptome tumor sequencing (WGTS) project. The cohort study will compare pNET profiles with those of other pancreatic and peri-ampullary tumors. Results: As of June 1, 2024, primary tumor data from 23 cases were available for analysis, including 16 pNETs, 2 pancreatic ductal adenocarcinomas (PDAC), 2 pancreatic adenosquamous carcinoma (PASC), 2 distal cholangiocarcinomas (dCCA), and 1 ampullary carcinoma (AC). Dimension reduction by principle component analysis (PCA) revealed clustering based on histological subtype. Interestingly, PCA using immune genes also demonstrate subgrouping that overlapped with the histological subtypes, suggesting that immunity of pancreatic/peri-ampullary neoplasms may be directed by histological types rather than the pancreas gland itself. These cancers showed a broad distribution of c-Score expression, with pNETs having the lowest c-Score. Tumors with high expression of the c-Score, including 2 pNETs, had increased expression of cancer-immunity cycle gene sets. DUX4, FAM186A, and IGFN1 were amongst the most common genes mutated in all tumors. Conclusions: Early data suggest that pNETs generally harbor low c-Score values and hallmarks of cancer-immunity, with the existence of a subgroup of pNETs displaying T cell-inflammation. Citation Format: Joan Miguel Romero, James Vafiadis, Tess Aalto, Ruomeng Fang, Yifan Wang, George Zogopoulos. Evaluating T cell-inflammation in pancreatic neuroendocrine tumors [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr B032.
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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 | 0.001 | 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".