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Record W2315123780 · doi:10.1158/1538-7445.am10-2136

Abstract 2136: Combined 8q gain and 10q loss predicts for relapse following radical radiotherapy in intermediate risk prostate cancer

2010· article· en· W2315123780 on OpenAlexaff
Gaetano Zafarana, Adrian Ishkanian, Chad Maloff, John Thoms, Jeremy A. Squire, Melania Pintile, Michael Milosevic, Wan L. Lam, Theo van der Kvast, Robert G. Bristow

Bibliographic record

VenueCancer Research · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related molecular mechanisms research
Canadian institutionsUniversity of TorontoQueen's UniversityUniversity of British ColumbiaUniversity Health Network
Fundersnot available
KeywordsPTENProstate cancerProstateRadiation therapyMedicineOncologyBiochemical recurrenceCancerCancer researchInternal medicineErgUrologyBiologyProstatectomyPI3K/AKT/mTOR pathwayGeneticsApoptosis

Abstract

fetched live from OpenAlex

Abstract Introduction: Gains on 8q with amplification of c-MYC and loss of 10q and PTEN have been associated with aggression in a number of cancers, including prostate cancer. Pre-clinical studies support that altered PTEN and c-MYC expression can alter cellular radiosensitivity. A definitive study of genetic loci containing PTEN deletions and c-Myc as potential predictors of relapse following radiotherapy has not been reported. We hypothesized that increased radiotherapy relapse was associated with 10q deletion and 8q gain (associated with loss of PTEN and amplification of c-MYC). Methods Biopsies were derived from 115 men with intermediate risk prostate cancer (T1-T2 disease and a Gleason score 7 and PSA less than 20 ng/ml, or a Gleason score less than 7 and PSA between 10 and 20 ng/ml). We used high-resolution array comparative genomic hybridization (arrayCGH) to identify copy number alterations (CNAs) in intermediate risk prostate cancer patients treated with 75.6 or 79.8 Gy of conformal radiotherapy. Biochemical failure, defined by Phoenix criteria or the initiation of salvage therapy, was observed in 35 patients after median follow-up of 5.4 years (range 0.9-8.8). Results: In our cohort, the percentage of a patient's genome which is altered (PGA) ranges from <1% to 35% (median 6.7%). Multiple high-frequency CNAs were observed, including del8p23.1-8p21.1 in 55%, del21q22.2 in 28% (containing the TMPRSS2:ERG fusion transcript), del10q23.31 in 24% (containing PTEN), and amplification at 8q21.3-24.3 in 30% (containing c-Myc). Patients with 8q and 10q alterations had significantly increased CNA values. There was a trend towards CNA associating with increased Gleason score in patients with 8q gains, but not with 10q deletions. In a multivariate model when adjusting for the clinical factors of pre-treatment PSA, T-stage and Gleason Score, we observed that increased CNA predicted for relapse following radiotherapy (Hazard Ratio (HR): 1.11; p = 0.00016). Additionally, patients with 8q gain and 10q deletion (c-MYC + PTEN alterations) had increased relapse following radiotherapy (HR: 2.52; p= 0.016). Conclusions: This is the first report of altered loci pertaining to the PTEN and c-MYC genes as determinants of radiotherapy outcome in intermediate risk prostate cancer. Future goals are directed towards using all aCGH hits and FISH validation to stratify the same patients into responders and non-responders. If validated in other radical radiotherapy series, PTEN-cMYC status could be used to individualize patient therapy for localized prostate cancer. (Supported by grants from CCSRI, the Terry Fox Foundation, PCC and CFI). Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 2136.

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 score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.362
Teacher spread0.345 · 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
Published2010
Admission routes1
Has abstractyes

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