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AR nuclear localization and microtubule bundling as markers of docetaxel and cabazitaxel sensitivity in metastatic castration-resistant prostate cancer (mCRPC): Prospective biomarker analysis from TAXYNERGY.

2017· article· en· W2602035838 on OpenAlexaff
Paraskevi Giannakakou, Emmanuel S. Antonarakis, Giuseppe Galletti, Daniel Worroll, John A. Stewart, Atef Zaher, Ted P. Szatrowski, Wei Zhou, Karla V. Ballman, Marie Vanhuyse, Ada Gjyrezi, Shinsuke Tasaki, Yang Bai, Luigi Portella, Brian J. Kirby, Guru Sonpavde, Mario A. Eisenberger, David M. Nanus, Fred Saad, Scott T. Tagawa

Bibliographic record

VenueJournal of Clinical Oncology · 2017
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsMontreal General HospitalUniversité de MontréalSanofi (Canada)
Fundersnot available
KeywordsTaxaneCabazitaxelMedicineDocetaxelProstate cancerCirculating tumor cellBiomarkerOncologyInternal medicineCancerBreast cancerMetastasisAndrogen deprivation therapyBiology

Abstract

fetched live from OpenAlex

134 Background: A better understanding of taxane sensitivity/resistance in mCRPC is needed to optimize treatment. Preclinically, taxane efficacy has been linked to the ability of microtubules (MT) to inhibit AR nuclear trafficking. In this prospective biomarker study, we used circulating tumor cells (CTCs) from patients (pts) in TAXYNERGY to perform real-time analysis of AR nuclear localization (ARNL) and MT stabilization (bundling; MTB) in order to predict taxane sensitivity. Methods: TAXYNERGY (NCT01718353) is a phase 2 trial randomizing chemo-naïve mCRPC pts 2:1 to docetaxel or cabazitaxel, with a switch to the alternative taxane in the absence of a ≥ 30% PSA drop by C4. Here we present the co-primary biomarker endpoints. CTCs at baseline (C1D1) were compared to CTCs after 1 week of taxane treatment (C1D8), and were analyzed by multiplex confocal microscopy for %ARNL (integrated AR intensity in the cell and nuclear areas) and MTB (assessed for increase compared to C1D1 on a scale from 0–3 from no to most MTB increase). Associations between %ARNL and MTB with clinical outcomes were sought. Results: Of 63 randomized pts, 26 had evaluable CTCs both at C1D1 and C1D8. At C1D8, mean %ARNL was significantly lower in pts achieving a ≥ 50% PSA drop by C4 vs those without (44% vs 64%; p = 0.004). A taxane-induced decrease in mean %ARNL (C1D8 vs C1D1) was associated with a higher rate of ≥ 50% PSA response (73% vs 13%; p = 0.009); mean %ARNL decreased by 18% in responders and increased by 2% in non-responders (p = 0.02). Finally, a taxane-induced increase in mean MTB trended higher in pts achieving a ≥ 30% PSA drop by C4 vs those without (0.69 vs 0.09; p = 0.09); increase in mean MTB score was indicative of response and observed in pts who did not require a taxane switch after C4 (0.75 vs 0.09; p = 0.06). Conclusions: We provide the first prospective data suggesting that taxane-induced shifts in ARNL and MTB (measured in CTCs) may serve as an early biomarker of taxane sensitivity. Consistent with preclinical data, AR nuclear exclusion caused by microtubule bundling may be a clinically-actionable marker of taxane efficacy. Funding: Sanofi Genzyme. Clinical trial information: NCT01718353.

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 score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.082
GPT teacher head0.455
Teacher spread0.373 · 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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