Abstract LB-404: Genomic analysis reveals roles for chromatin modification and axon guidance in pancreatic cancer
Bibliographic record
Abstract
Abstract Pancreatic cancer (PC) is the fourth leading cause of cancer death with an overall 5-year survival rate of < 5%, a statistic that has not changed in almost 50 years. A deeper understanding of the underlying molecular pathophysiology is expected to advance the urgent need to develop effective therapeutic and early detection strategies for this disease. Genomic characterisation of Pancreatic Ductal Adenocarcinoma (PDAC), which accounts for over 90% of PC has to date relied on targeted PCR based exome sequencing of primary and metastatic lesions propagated as xenografts or cell lines. Here we use exome sequencing and copy number analysis to define genomic aberrations in a prospectively accrued clinical cohort (n = 142) of early (Stage I and II) PDAC. Detailed analysis of 99 informative tumours identified 1984 non-silent mutations and 1628 significant CNV events, and defined 439 significantly mutated genes based on stringent Significant Mutated Gene and GISTIC analysis. Integration with functional data from in vitro shRNA and in vivo Sleeping Beauty-mediated somatic mutagenesis screens provided supportive evidence for 182 of these as candidate driver mutations. Pathway based analysis recapitulated clustering of mutations in core signalling pathways in PDAC, and identified multiple new components in each, particularly in DNA damage repair mechanisms (ATM, TOP2A, RPA1, BRCC3). We also identified frequent and diverse somatic aberrations in genes involved in chromatin modification (SWI/SNF complex members, SETD2, EPC1), and novel mechanisms such as axon guidance (Semaphorin, Slit, Netrin and Ephrin signalling), extending the number of core perturbed pathways in PDAC. Aberrant expression of axon guidance genes co-segregated with poor patient survival, and in animal models was associated with disease development and progression, further implicating perturbation of the axon guidance pathway as a novel mechanism important in PDAC. 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 LB-404. doi:1538-7445.AM2012-LB-404
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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 | 0.000 |
| 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) | 0.002 | 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".