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Record W2893560583 · doi:10.1136/jclinpath-2018-205404

Immune infiltrates and PD-L1 expression in treatment-naïve acinar prostatic adenocarcinoma: an exploratory analysis

2018· article· en· W2893560583 on OpenAlexaff
Elan Hahn, Stanley K. Liu, Danny Vesprini, Bin Xu, Michelle R. Downes

Bibliographic record

VenueJournal of Clinical Pathology · 2018
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsProstate cancerImmune checkpointImmune systemCD163ImmunosuppressionPD-L1MedicineCD68ProstateImmunotherapyAdenocarcinomaBlockadeCancerCancer researchImmunologyImmunohistochemistryInternal medicineMacrophageBiologyReceptor

Abstract

fetched live from OpenAlex

Tumour-induced immunosuppression plays a role in the development and progression of cancer. Of interest is the interaction between programmed death-1 and programmed death ligand-1 (PD-L1) which can be targeted through immune checkpoint blockade; however, there are limited data surrounding the composition of the immune milieu in prostate cancer. We preliminarily assessed 21 radical prostatectomies in therapy-naïve patients for immune markers and PD-L1 expression. The immune infiltrates were higher in adenocarcinoma than benign prostate (lymphocytes p<0.001, macrophages p=0.010) with 5% of cases being PD-L1 high (≥5% expression). Increased peritumoural CD68 and CD163 expression correlated with lower grade group (GG) (p=0.024 and p=0.014, respectively) with a trend towards increased CD68 expression in lower stage cases (p=0.086). There was also increased CD45 expression in lower GGs (p=0.063). We found the immune infiltrate in acinar prostate cancer to be extremely heterogeneous with an overall immunophenotype unlikely to respond to immune checkpoint blockade.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.066
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.074
GPT teacher head0.411
Teacher spread0.337 · 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 teacher head, 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

Citations13
Published2018
Admission routes1
Has abstractyes

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