Abstract IA15: What the surgeon has learned from pancreas cancer genomics, and vice versa
Bibliographic record
Abstract
Abstract Recent advances in molecular profiling and fundamental science discoveries are impacting care of PDAC patients, including surgical decision making. Our multidisciplinary group at the Ontario Institute for Cancer Research and the Princess Margaret in Toronto has focused on elucidating both germline and somatic genomic changes in PDAC, with aims to understand genetic predisposition to the disease, foster early detection and conduct trials for improved selection of standard and novel therapies. Using laser capture microdissection overseen by expert pathologists, seamless tumor banking and aggressive biopsy of chemo-naïve and progressive metastases, we have generated high epithelial tumor cell content whole genomes and transcriptomes (WGTS) on over 600 PDAC patients with all stages of the disease. Germline sequencing of all PDAC patients should be considered standard of care to detect patients with uniquely actionable genomic changes due to less common mutational pathways. The genetic basis of familial pancreas cancer remains elusive and we continue to collaborate with the PACGENE Network spearheaded by colleagues at Mayo, Hopkins and McGill to discover novel genes in high risk cases. Our genomic work and others explains the clinical conundrum of late presentation of PDAC due to rapid punctuated evolution which challenges the ‘holy grail’ of early detection. Thus, screening high risk groups is still within the realm of investigational studies such as the PRECEDE consortium, of which we are an active member. Focused clinical trials (eg COMPASS and PASS-01) using WGTS has revealed both large scale changes in genomes (eg. HRD, tandem duplicators, etc) and transcriptomes (the Moffit ‘continuum’) and discreet mutations (eg BRAF mutations in Kras wild type tumors) that are important to identify as early as possible in the patient’s clinical course. Subspecialty PDAC surgeons need to keep abreast of recent developments of both prognostic and predictive transcriptomic biomarkers that will soon be important in selecting appropriate patients for appropriate neoadjuvant therapies. A few clinical vignettes will be shown to provide evidence for the importance of WGTS and other profiling approaches for almost all PDAC patients. Citation Format: Steven Gallinger. What the surgeon has learned from pancreas cancer genomics, and vice versa [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 IA15.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.010 | 0.024 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".