Abstract 5071: The genomic landscape of pancreatic cancer: from discovery to patient care
Bibliographic record
Abstract
Abstract Pancreatic Cancer is the fourth leading cause of cancer death in our society, with a mortality that virtually parallels its incidence, a median survival of < 12 months even with maximal therapy, and a 5-year survival rate of < 5%. The diversity of clinical outcomes and the molecular heterogeneity of histopathologically similar cancer types, incomplete knowledge of the genomic aberrations that drive carcinogenesis and the lack of therapeutics that specifically target most known genomic aberrations necessitates large scale detailed analysis of cancer genomes to identify novel potential therapeutic strategies. As part of the International Cancer Genome Consortium (ICGC), the Australian Pancreatic Cancer Genome Initiative (APGI), together with the Baylor College of Medicine and the Ontario Institute of Cancer Research used exomic sequencing and copy number analysis to define genomic aberrations that characterise a large clinically focused prospectively accrued cohort of patients with pancreatic cancer (n = 142). The cohort consists of early (clinical stages I and II) non-pretreated patients with pancreatic ductal adenocarcinoma who underwent operative resection with curative intent. We devise approaches to adjust for low epithelial content in primary tumours and define the genomic landscape of pancreatic cancer to identify novel candidate driver genes and mechanisms. We develop stratified, molecular phenotype guided therapeutic strategies using existing therapeutics that are either rescued, repurposed, in development, or are known to be effective in an undefined subgroup of PC patients. These are then tested in primary patient derived xenografts and cell lines from the above deeply characterised cohort. In addition, we are launching a clinical trial named IMPaCT (Individualised Molecular Pancreatic Cancer Therapy). This trial randomises patients with metastatic disease to either standard first line therapy with gemcitabine, or a molecular phenotype guided approach using next generation sequencing strategies to screen for actionable mutations defined through the ICGC effort. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5071. doi:1538-7445.AM2012-5071
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".