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Record W2601615263 · doi:10.1200/jco.2017.35.6_suppl.3

Luminal and basal subtyping of prostate cancer.

2017· article· en· W2601615263 on OpenAlexaff
Felix Y. Feng, Shuang G. Zhao, S. Laura Chang, Nicholas Erho, Jonathan Lehrer, Mohammed Alshalalfa, Matthew R. Cooperberg, Won Bae Kim, Charles J. Ryan, Robert B. Den, Stephen J. Freedland, Edwin M. Posadas, Eric A. Klein, Elai Davicioni, Ashley E. Ross, Edward M. Schaeffer, Paul L. Nguyen, Peter R. Carroll, Jeffrey Karnes, Daniel E. Spratt

Bibliographic record

VenueJournal of Clinical Oncology · 2017
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsGenome British Columbia
Fundersnot available
KeywordsProstate cancerMedicineSubtypingProstateOncologyInternal medicineTissue microarrayBasal (medicine)Androgen deprivation therapyCancerPathology

Abstract

fetched live from OpenAlex

3 Background: There is a clear need to develop a clinically relevant molecular subtyping approach for prostate cancer. We hypothesized that prostate cancer can be subtyped based on luminal versus basal lineage. Methods: We applied the PAM50 classifier, which is used clinically to identify luminal and basal cancers in breast cancer, to subtype a total of 3,782 prostate cancer samples using a high-density microarray platform run in a CLIA-certified laboratory. We examined the associations of these subtypes and clinical outcomes. Results: We demonstrate that PAM50 segregates prostate cancer into three reproducible subtypes in both retrospective cohorts and on prospective validation: luminal A (33.3%-34.3%), luminal B (28.5%-32.6%), and basal (34.1%-37.1%). Luminal B prostate cancers exhibited the worst clinical prognoses, followed by basal and luminal A subtypes (10-year biochemical recurrence-free survival: 29/39/41%; distant metastasis-free survival: 53/73/73%; prostate cancer-specific survival: 78/86/89%; overall survival: 69/80/82% respectively) on both univariable and multivariable analyses accounting for standard clinicopathologic prognostic factors. Known luminal lineage markers, such as NKX3.1 and KRT18, and the basal lineage CD49f signature, were enriched in luminal- and basal-like cancers respectively, demonstrating the connection between these subtypes and established prostate cancer biology. While both luminal-like subtypes were associated with increased AR expression and signaling, only luminal B prostate cancers were significantly associated with post-operative response to androgen deprivation therapy (ADT) in a subset analysis matching patients based on clinicopathologic variables (interaction p = 0.006, luminal B 10-year metastasis: 33% (treated) vs. 55% (untreated), non-luminal B: 37% (treated) vs. 21% (untreated)). Conclusions: These findings contribute novel insight into the biology of prostate cancer, and provide translatable clinical tools for personalizing post-operative ADT for patients with prostate cancer. Similar to breast cancer, these findings suggest that luminal/basal subtyping may be useful in treatment selection in prostate cancer.

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)0.001
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 score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.219
GPT teacher head0.560
Teacher spread0.341 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

Explore more

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