Abstract 5185: Redefining the somatic landscape of pancreatic adenocarcinoma
Bibliographic record
Abstract
Abstract Efforts to catalogue genomic abnormalities in pancreatic ductal adenocarcinoma (PDAC) have been challenging due to low tumor cellularity which requires deep sequencing to obtain adequate coverage of tumor DNA. To make whole genome sequencing (WGS) feasible with PDAC tumors, we have developed protocols for sequencing enriched tumor material by laser capture microdissection (LCM) from frozen samples and by flow sorting when fresh tissue is available. The enriched samples have a median tumor cellularity of 88% compared to a median cellularity of 12% in the bulk samples. Furthermore, the enrichment methods may also provide a source of normal or reference material, non-tumor cells in the case of LCM and CD45+ blood cells in the case of flow sorting, and eliminate the need for matched blood samples to call somatic variation. In conjunction with low quantity DNA input protocols for library construction, high quality next generation sequencing libraries are readily produced without sacrificing library complexity that enables somatic mutation, copy-number and structural variant detection at modest sequencing depths (∼50x). These methods are applicable to all tumor types where significant stromal contamination hinders somatic mutation detection. In addition to redefining the rates of known somatically altered genes in PDAC, such as CDKN2A and SMAD4, the high cellularity data set has also allowed us to evaluate PDAC tumor heterogeneity with the ability to detect sub-clonal variation that is not masked by non-tumor DNA. Moreover, in 50% of the samples sequenced to date, we observe evidence of punctuated evolution suggesting a catastrophic event during mutagenesis, which may be associated with the clinically observed rapid progression of the disease. The result of enhanced somatic mutation detection is a more comprehensive picture of the PDAC genomic landscape. Citation Format: Andrew M.K. Brown, Faiyaz Notta, Mathieu Lemire, Ilinca Lungu, Robert E. Denroche, Christina Yung, Kristen Geras, Lincoln Stein, John M. Bartlett, Thomas J. Hudson, Michael H.A. Roehrl, Steven Gallinger, John D. McPherson. Redefining the somatic landscape of pancreatic adenocarcinoma. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 5185. doi:10.1158/1538-7445.AM2014-5185
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".