Abstract A041: Characterizing antigen presentation associated immune escape mechanisms in pancreatic adenocarcinoma using an integrative computational approach
Bibliographic record
Abstract
Abstract Introduction: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal neoplasm of the pancreas characterized by a low survival rate and limited treatment options. Despite the success of immunotherapy in various cancer types, its efficacy in PDAC remains low. The underlying reasons for this discrepancy are not fully understood, despite PDAC exhibiting a moderate mutation burden. One possible determinant could be the loss of heterozygosity (LOH) at the human leukocyte antigen (HLA) loci, resulting in compromised antigen presentation. In this work, we aim to identify and validate HLA LOH events in two independent cohorts to gain insights into both their prevalence and clinical and phenotypic impact on PDAC. Methods: We applied LOHHLA, a specialized pipeline to identify HLA LOH events from paired whole genome sequencing to two independent cohorts comprising over 650 patients. We contextualized these results with the paired transcriptome-wide gene expression data and immunohistochemistry stains. In addition, we developed a machine learning classifier to predict HLA LOH from transcriptomic data. We transfer this classifier to single cell RNA sequencing data to identify the impact of (subclonal) HLA LOH on the tumor microenvironment. Results: HLA LOH events occur in approximately 30% of PDAC patients, with about 50% of these deletions being focal deletions. These events are early genetic alterations and lead to a dosage compensation of the retained HLA genes. We observed a significant association between HLA LOH and the Basal PDAC expression subtype. In addition, we find that HLA expression is the most important determinant of lymphocyte infiltration followed by non-LOH at the HLA locus. Moreover, our transcriptomic classifier allowed us to accurately identify HLA LOH events from RNA sequencing data, which we successfully validated in an independent cohort. Finally, we applied this classifier to single cell sequencing data and identified phenotypic differences in multiple compartments of the tumor microenvironment between samples with intact HLA and HLA LOH. Conclusions: In conclusion, our study provides the most in depth characterization of the consequences of altered antigen presentation in PDAC to date. We have demonstrated that HLA LOH occurs in a substantial proportion of patients. Overall, these findings contribute to a better understanding of the immune landscape in PDAC and may have implications for the development of immunotherapeutic strategies tailored to this challenging cancer type. Citation Format: Michael J. Geuenich, Barbara Gruenwald, Amy Zhang, Oumaima Hamza, Gun Ho Jang, Grainne O’Kane, Faiyaz Notta, Steven Gallinger, Kieran R. Campbell. Characterizing antigen presentation associated immune escape mechanisms in pancreatic adenocarcinoma using an integrative computational approach [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 A041.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".