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Abstract B2-57: Genomic analysis of metachronous pancreatic ductal adenocarcinoma

2015· article· en· W2403998717 on OpenAlexaff
Ashton A. Connor, Michelle Chan‐Seng‐Yue, Robert E. Denroche, Ayelet Borgida, Sheng‐Ben Liang, Faiyaz Notta, Lincoln Stein, Michael H. A. Roehrl, John D. McPherson, Steven Gallinger

Bibliographic record

VenueCancer Research · 2015
Typearticle
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsOntario Institute for Cancer ResearchUniversity of Toronto
Fundersnot available
KeywordsBiologyAdenocarcinomaPancreasCopy number analysisPathologyDNA sequencingGenomeCopy-number variationCancerMedicineGeneticsDNAGene

Abstract

fetched live from OpenAlex

Abstract Background: Improving outcomes for pancreatic ductal adenocarcinoma (PDAC) requires more complete understanding of pancreatic carcinogenesis, including tumor heterogeneity and mutational evolution. Such studies in PDAC are hampered by presentation at advanced stages and rarely operable recurrences. We present a case of metachronous PDAC managed by serial resections and interrogated by whole genome sequencing of all three lesions. Methods: Tumors underwent either laser capture microscopy or flow cytometry of fresh frozen tissue. Germline DNA was obtained from normal tissue. Genomic DNA was isolated, and sequencing libraries were prepared. Whole genome sequencing was performed on the Illumina HiSeq2500 system. Pipeline analysis included quality control, alignment to the reference genome (hg19), variant calling and annotation. Structural variants were called by the union of two tools, DELLY and CREST. Copy number variants were called using an in-house tool. Single nucleotide polymorphisms and short insertions and deletions were identified by the intersection of two tools, Strelka and MuTect, and annotated using ANNOVAR. All structural, copy number and non-silent variants were manually reviewed in the Integrated Genome Viewer. Secondary analyses were done in R. Representative sections of the three tumors also underwent formalin fixation, paraffin embedding, sectioning at five micron thickness and hematoxylin and eosin staining for microscopic analysis. Results: The first tumor was located in the head of the pancreas and resected by Whipple operation, the second and third in the body and tail, respectively, and resected by completion pancreatectomy 14 months later. The patient received chemotherapy in the interim. Histologically, all 3 tumors were moderately differentiated adenocarcinomas with focal mucinous areas and extensive lymphovascular and perineural invasion. There were no distinguishing morphological features. For the 3 lesions, we achieved average sequence coverage of 52.3x, 54.3x, and 70.7x, respectively. A total of 69 structural variants and 62 copy number variants were identified, with concordance of 75% and 92% in all three lesions. A total of 61 non-silent exonic variants were identified, with concordance of 33% in all three lesions. A KRAS (NM_004985) c.G34C missense and TP53 (NM_001126115) c.G418A missense were present in all three, whereas a SMAD4 (NM_005359) c.118delC frameshift was present in only the second and third tumors. Hierarchical clustering by non-silent variants, copy number variants, and an intersect of all variants showed the tumors in the head and tail to be more closely related to one another than to the body lesion, whereas clustering by structural variants showed little difference between the three. Conclusions: The highly similar structural and copy number profiles across the three lesions suggests that these tumors arose from a common precursor, and that large chromosomal changes occurred early in its mutational evolution. The poor concordance of simple somatic mutations imply that these evolved gradually and are responsible for tumor heterogeneity and possibly acquired phenotypes, include therapeutic resistance. This study also demonstrates that isolated, intra-parenchymal metastases (as opposed to de novo tumors within a “field defect”) are possible with PDAC, which has been suggested in case reports but never definitively proven, further implying that these patients may benefit from re-operation despite disease relapse. Further analysis will include mathematical modeling of mutation rates and validation in three additional metachronous cases identified in our biospecimen repository and in specimens obtained by our rapid autopsy program. Citation Format: Ashton A. Connor, Michelle Chan-Seng-Yue, Robert E. Denroche, Ayelet Borgida, Sheng-Ben Liang, Faiyaz Notta, Lincoln Stein, Michael H. Roehrl, John McPherson, Steven Gallinger. Genomic analysis of metachronous pancreatic ductal adenocarcinoma. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B2-57.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.199
GPT teacher head0.468
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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