Abstract B57: Early-onset pancreatic ductal adenocarcinomas are characterized by a distinct mutational landscape
Bibliographic record
Abstract
Abstract Background: Advanced pancreatic ductal adenocarcinoma (PDAC) remains a leading cause of cancer-related mortality. There has been a rising incidence of early-onset pancreatic cancer (EOPC; ≤55 years). Reported treatment and survival outcomes in EOPC remain limited and have only been reported in the pre-FOLFIRINOX era. We characterized the genomic and transcriptomic landscapes of EOPC, while also leveraging provincial health data to investigate survival outcomes in advanced EOPC in a separate dataset. Methods: We generated a comprehensive and integrative dataset utilizing RNA-seq data and matched clinical metadata for 402 PDAC patients across 5 distinct studies and 4 sequencing centers, encompassing both resectable (ICGC, TCGA) and advanced (Personalized OncoGenomics and COMPASS) disease. 345 (85.8%) and 371 (92.3%) of samples had SNV/indel and CNV data available, respectively. Patients were stratified into EOPC (n=96), average-onset pancreatic cancer (AOPC, ≥70 years; n=121), and intermediate (>55 and <70 years; n=185) groups. Batch correction was performed on RNA-seq data using an Empirical Bayes approach and verified by principal components analysis. Mutation enrichment and differential gene expression analyses were conducted using Fisher’s exact test and Spearman correlation, respectively. All significance values were corrected for multiple comparisons using the Benjamini-Hochberg procedure. Survival analysis in a separate provincial dataset was conducted using 676 patients who received systemic therapy between January 2012-December 2015 across 6 cancer centers in British Columbia, Canada. Kaplan-Meier survival analysis was performed to compare overall survival (OS) between EOPC (n=102) vs. AOPC (n=239) vs. intermediate (n=335). Results: CDKN2A SNV/indels were identified in 22% and 26% of intermediate and AOPC patients, and in only 7% of EOPC patients (p<0.01). SNV/indels in epigenetic modifiers KMT2C/D trended towards lower frequency in EOPC (4% and 5%, respectively) compared to intermediate (13% and 13%) and AOPC (13% and 13%), although these observations did not pass multiple test correction (p=0.09, 0.20). Differential expression analysis and subsequent gene set enrichment analysis revealed EOPC-specific upregulation of genes belonging to pathways related to synaptic signal transduction. In a separate analysis of provincial data for survival outcomes, median OS revealed improved outcomes in EOPC compared to AOPC and intermediate groups (10.9, 7.5, and 8.3 months respectively, p=0.002). Conclusions: Using an extensive PDAC sequencing dataset, we highlight a novel association between CDKN2A SNV/indel frequency and EOPC. Transcriptome-based analysis identified significant associations between age of onset and expression of synaptic signal transduction pathways. Collectively, these data indicate potential age-specific differences in the mutational and developmental trajectories of PDAC and generate novel hypotheses for further study of EOPC. Citation Format: Erica S. Tsang, James T. Topham, Joanna M. Karasinska, Michael K.C. Lee, Shehara Mendis, Luka Culibrk, Robert Denroche, Gun Ho Jang, Steve E. Kalloger, Richard A. Moore, Andrew J. Mungall, Janessa Laskin, Grainne M. O'Kane, Jennifer J. Knox, Steven Gallinger, Steven J. Jones, Marco A. Marra, Jonathan M. Loree, David F. Schaeffer, Daniel J. Renouf. Early-onset pancreatic ductal adenocarcinomas are characterized by a distinct mutational landscape [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Advances in Science and Clinical Care; 2019 Sept 6-9; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2019;79(24 Suppl):Abstract nr B57.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".