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Relationships between an androgen receptor output signature (ARoS), AR expression, and poor prostate cancer prognosis in RP tissues.

2017· article· en· W2599694538 on OpenAlexaff
Mohammed Alshalalfa, María Santiago‐Jiménez, Nicholas Erho, Nicholas Fishbane, Kasra Yousefi, Hussam Al-Deen Ashab, Elai Davicioni, Ashley E. Ross, Edward M. Schaeffer

Bibliographic record

VenueJournal of Clinical Oncology · 2017
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsGenome British Columbia
Fundersnot available
KeywordsProstate cancerAndrogen receptorMedicineProspective cohort studyInternal medicineAndrogen deprivation therapyCohortCancerAndrogenOncologyRetrospective cohort studyEndocrinologyHormone

Abstract

fetched live from OpenAlex

38 Background: The Androgen-receptor (AR) signaling plays a pivotal role in prostate cancer initiation and progression. AR amplification and overexpression have been associated with lethal PCa and CRPC; however, much of the impact of these events on cancer progression is poorly understood with an unclear prognostic significance of down-stream targets of AR. Methods: A total of 2,555 RP expression profiles were extracted from the Decipher GRID database; 262 of which were from retrospective natural history cohort with known metastasic outcomes and the remaining from anonymized prospective cases with basic demographic and pathological data available. We built a 9 gene AR-output signature (ARoS) score based on canonical androgen regulated genes to represent AR-output activity in RP tissues. The 9 genes were weighted based on their distribution skewness in the prospective data (n = 2,293) and then scores were calculating by summing the weighted expression of the 9 genes. Results: AR expression and ARoS score were not strongly correlated with approximately 50% of samples with low ARoS have relatively high AR expression. In a prospective cohort of 2,293 patients, low ARoS was associated with higher Decipher metastasis risk scores (OR: 3.1, p < 0.001), higher Gleason grade (OR: 2.4, p < 0.001), ERG-fusion negative (OR: 1.8, p < 0.001) and SVI (OR: 2.1, p < 0.001). Additionally, low ARoS was associated with higher expression of neuroendocrine biomarkers (NCAM1, ENO2). In a large retrospective cohort with long term clinical follow-up, patients with low ARoS score had a higher probability of 10-year metastasis rate (p = 0.002). In 55 patients from this cohort who received ADT post metastasis, the ARoS signature derived from primary tissue was lower in patients who developed CRPC (p < 0.001). Furthermore, ARoS was significantly lower in mCRPC samples (p < 0.001) compared to primary PCa tissues. Conclusions: ARoS derived from primary prostate cancer tissue is correlated with metastatic outcomes and progression to CRPC. With further validation, such a signature may better predict those patients which require early intensification of systemic therapy.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalmedium
models agreeAgreement compares identical category sets and study designs across arms.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.235
GPT teacher head0.510
Teacher spread0.275 · 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

Labeled directly by 2 models reading the full record.

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

Citations5
Published2017
Admission routes1
Has abstractyes

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