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Gene-expression signatures as prognostic for relapse in stage I testicular germ cell tumors (TGCT).

2016· article· en· W2590257863 on OpenAlexaff
Jeremy Lewin, Philippe L. Bédard, Robert J. Hamilton, Peter Chung, Malcolm J. Moore, Michael A.S. Jewett, Lynn Anson‐Cartwright, Carl Virtanen, Neil Winegarden, Benjamin Haibe‐Kains, Padraig Warde, Joan Sweet, Aaron R. Hansen

Bibliographic record

VenueJournal of Clinical Oncology · 2016
Typearticle
Languageen
FieldMedicine
TopicTesticular diseases and treatments
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsSeminomaStage (stratigraphy)MedicineGene expressionPathologicalOncologyCancerGeneRNA extractionInternal medicinePathologyBiologyGeneticsChemotherapy

Abstract

fetched live from OpenAlex

493 Background: Genomic signatures may compliment pathological features in identifying appropriate patients who may benefit from adjuvant therapy in Stage I (SI) TGCT. This study aimed to identify a gene expression pattern to differentiate between relapsed (R) and non-relapsed (NR) SI TGCT. Methods: Patients with SI non-seminoma (NS) and seminoma (S) were identified from an institutional database from 2000 to 2012. All patients were managed with active surveillance. NR-NS and NR-S patients were defined as having no evidence of relapse after 2 and 3 years of surveillance respectively. Following pathology review, RNA extraction and gene expression analysis was performed on archived paraffin embedded tumor and normal testicular tissue using Illumina Whole Genome DASL Human HT-12 V4 BeadChip. Hierarchical clustering analysis, ANOVA and t-tests were used to evaluate candidate genes and expression patterns that could differentiate NR and R samples. Results: 57 patients (12 R-NS, 15 R-S, 15 NR-NS, 15 NR-S) were identified with median relapse time of 5.6 (2.5-18.1) and 19.3 (4.7-65.3) months in NS and S cohorts respectively. 3 additional normal testis samples were included. Poor prognostic factors were more frequent in R versus NR cases (NS: vascular invasion [5/12 vs 0/15]; S: median size [4cm vs 2.8cm]). Unsupervised hierarchical clustering of 22822 probes randomly separated S from NS, indicating no batch effect. One-way ANOVA revealed 4525 significantly varying probes (p < 0.05) however, no statistically significant gene expression profile differentiated the 4 cohorts. A discriminative gene expression profile between R and NR cases was discovered when combining NS and S samples using 10 (p = 0.03) and 30 (p = 0.03) probe signatures with a 10 fold cross-validation. However, this profile was not observed in the S and NS cohorts individually. Conclusions: A discriminating signature for R and NR was identified for SI testis tumors, but not separately for NS and S. Biological relevance of these signatures is to be determined. Further studies are required to corroborate this profile in NS and S. If validated, these expression patterns could help identify patients beyond standard pathological risk algorithms for optimal management.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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)0.0010.000

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 head0.061
GPT teacher head0.436
Teacher spread0.375 · 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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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