MétaCan
Menu
Back to cohort
Record W2011734597 · doi:10.1158/1538-7445.am2013-2003

Abstract 2003: A molecular portrait of potentially curable prostate cancer.

2013· article· en· W2011734597 on OpenAlexaff
Michael Fraser, Richard de Borja, Dominique Trudel, Nicholas J. Harding, Pablo H. Hennings-Yeomans, Alice Meng, Emilie Lalonde, Andrew Brown, Natalie S. Fox, Taryne Chong, Amin Zia, Michelle Sam, Jianxin Wang, Michelle Chan‐Seng‐Yue, Jeremy Johns, Lee E. Timms, Nicholas Buchner, Ada Wong, Fouad Yousif, Rob Denroche, Gaetano Zafarana, Maud H. W. Starmans, Hanbert Chen, Shaylan K. Govind, Francis Nguyen, Melania Pintilie, Neil Fleshner, Stanislav Volik, Lakshmi Muthuswamy, Colin C. Collins, Thomas J. Hudson, Lincoln Stein, Timothy A. Beck, John D. McPherson, Theodorus van der Kwast, Paul C. Boutros, Robert G. Bristow

Bibliographic record

VenueCancer Research · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMolecular Biology Techniques and Applications
Canadian institutionsUniversity Health NetworkUniversity of TorontoOntario Institute for Cancer Research
Fundersnot available
KeywordsProstate cancerProstatectomyMedicineCancerGermlinePathologyBiologyOncologyInternal medicineGeneticsGene

Abstract

fetched live from OpenAlex

Abstract Intermediate risk prostate cancer (CaP) with Gleason score (GS) of 7 show up to 100x variability in genetic instability. As CaP is multifocal and likely multiclonal, there is a need to characterize heterogeneity for patient stratification, which would increase the ability to act on genomic information by adding adjuvant therapies to offset systemic occult metastases that currently limit cure in ∼30% of patients. Individual genetic portraits could be used to improve cure on combined clinical-molecular staging criteria. We undertook a pilot study to assess the genetic heterogeneity of potentially curable GS=7 CaP. We selected 10 men with GS=7 CaP; 5 treated with external beam radiotherapy (frozen pre-treatment biopsies) and 5 treated with radical prostatectomy (RadP, frozen tumour). Additionally, DNA from 18 distinct formalin-fixed, paraffin-embedded (FFPE) foci from the 5 RadP were analysed. Each of these 28 foci were subjected to whole-genome sequencing (WGS) and OncoScan SNP arrays to yield comprehensive genetic profiles. mRNA expression was evaluated on frozen RadP by microarray. Germline DNA from whole-blood was also analysed. Following independent pathology reviews and manual macro-dissection of tumour areas of ≥70% cellularity, WGS (≥50x tumour, ≥30x germline) was performed on as little as 50 ng genomic DNA, and OncoScan arrays were performed using as little as 30ng DNA using either amplified or innate genomic DNA. Regions of CaP in FFPE RadP were recorded using a tissue map to identify independent malignant foci, and ERG immunostaining was performed to assist in the identification. In cases where ERG-positive and -negative foci were adjacent, ERG staining was repeated on an un-stained slide to confirm separate foci based on 3D multi-section analyses. ERG fusion status was also assessed in frozen samples by aCGH or IHC. Validation of SNVs via SNP array and deep-resequencing showed ∼99% accuracy. Tumour cellularity was estimated using Qpure and was >60% for all samples. Phylogenetic techniques were used to demonstrate clear multi-clonality in two tumours. Across all tumours, ∼50% of SNVs were specific to an individual tumour-region. Phylogenies were confirmed with both SNVs and CNAs, but CNAs generally exhibited greater concordance amongst different regions of the same tumour. Some previously observed recurrent mutations were previously identified as recurrent in CaP (e.g. SPOP), and the overall mutation rate for intermediate-risk CaP was only somewhat below that reported for castrate-resistant disease (11,230 somatic SNVs per tumour). Our studies support the concept that a complete characterization of inter- and intra-CaP heterogeneity is possible in fresh and archival tissues; the latter is important for correlations to clinical outcome. These approaches can then be streamlined for high-throughput analyses within personalized medicine laboratories leading to “point of care” molecular tests and individualization of therapy. Citation Format: Michael E. Fraser, Richard de Borja, Dominique Trudel, Nicholas J. Harding, Pablo H. Hennings-Yeomans, Alice Meng, Emilie R. Lalonde, Andrew Brown, Natalie S. Fox, Taryne Chong, Amin Zia, Michelle Sam, Jianxin Wang, Michelle A. Chan-Seng-Yue, Jeremy Johns, Lee Timms, Nicholas Buchner, Ada Wong, Fouad Yousif, Rob Denroche, Gaetano Zafarana, Maud HW Starmans, Hanbert Chen, Shaylan Govind, Francis Nguyen, Melania Pintilie, Neil Fleshner, Stanislav Volik, Lakshmi Muthuswamy, Colin C. Collins, Thomas J. Hudson, Lincoln D. Stein, Timothy Beck, John D. McPherson, Theodorus van der Kwast, Paul C. Boutros, Rob G. Bristow. A molecular portrait of potentially curable prostate 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 2003. doi:10.1158/1538-7445.AM2013-2003

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.024
GPT teacher head0.374
Teacher spread0.350 · 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 designBench or experimental
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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueCancer ResearchSame topicMolecular Biology Techniques and ApplicationsFrench-language works237,207