Abstract 3157: Loss of heterozygosity as a molecular ‘second hit’ In familial pancreatic cancer.
Bibliographic record
Abstract
Abstract BACKGROUND/HYPOTHESIS: Familial Pancreatic Cancer (FPC) has an autosomal dominant, variable penetrant mode of inheritance with >80% of its genetic cause yet to be discovered. We hypothesize that a high density DNA microarray analysis of Formalin-Fixed Paraffin Embedded (FFPE) FPC tumors will yield novel regions of genomic loss harboring disease causing FPC gene(s). METHODS: 158 FFPE FPC tumor specimens with matched normal tissue were reviewed by a pancreatic pathologist and tumors with <70% cellularity underwent laser capture microdissection. The samples with >70% cellularity were microdissected directly from marked unstained slides. A total of 74 samples were DNA extracted, whole genome amplified and processed on the Affymetrix 660K Oncoscan Microarray. Copy number analysis was performed using Nexus Copy Number Variation Version 6.1 software employing the SNP-FASST2 segmentation and Allele Specific Copy Number Analysis of Tumors (ASCAT) algorithms. PRELIMINARY RESULTS: A pair-wise analysis of 55 FPC samples with matched normal tissue was performed. Recurrent regions of loss of heterozygosity (LOH) were known loci of importance in pancreatic tumorigenesis, including CDKN2A, p53 and SMAD4. Copy neutral/gain LOH was observed throughout the genome and may account for >35% of chromosomal loss. Recurrent novel genetic loci displaying LOH was observed on chromosomes: 3p(25%), 5q(18%), 6p(22%), 12q(18%) and Xq (42%). Examination of 2 FPC siblings elucidates shared loss regions spanning the 3p22.2-3p22.3 and 17p13.3 loci. Candidate tumor suppressor genes (TSGs) of interest include DCLK3, SERPINF1, SERPINF2 and SMYD4. CONCLUSION: We have described for the first time the presence of copy neutral/gain LOH in FPC highlighting previously unknown regions displaying genetic loss. Further confirmation with quantitative PCR, integrating with germline exome sequencing data, and validation of putative TSGs shared by members of high risk FPC kindreds will help uncover novel genes associated with this disease. Citation Format: Zaheer S. Kanji, Stefano Serra, Spring Holter, Ayelet Borgida, Robert Grant, Steven Gallinger. Loss of heterozygosity as a molecular ‘second hit’ In familial pancreatic cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3157. doi:10.1158/1538-7445.AM2013-3157
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".