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Record W4367184214 · doi:10.1080/2162402x.2023.2204745

Early response evaluation of PD-1 blockade in NSCLC patients through FDG-PET-CT and T cell profiling of tumor-draining lymph nodes

2023· article· en· W4367184214 on OpenAlexaff
Frank J. Borm, Jasper N. Smit, Joyce Bakker, Maurits Wondergem, Egbert F. Smit, Adrianus J. de Langen, Tanja D. de Gruijl

Bibliographic record

VenueOncoImmunology · 2023
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsInstitute of Infection and Immunity
FundersBristol-Myers Squibb
KeywordsMedicineBlockadeCD8LymphPositron emission tomographyPathologicalOncologyImaging biomarkerPathologyInternal medicineCancer researchImmune systemNuclear medicineReceptorImmunologyRadiologyMagnetic resonance imaging

Abstract

fetched live from OpenAlex

Better biomarkers for programmed death - (ligand) 1 (PD-(L)1) checkpoint blockade in non-small cell lung cancer (NSCLC) are needed. We explored the predictive value of early response evaluation using Fluor-18-deoxyglucose positron emission tomography and pre- and on-treatment flowcytometric T-cell profiling in peripheral blood and tumor-draining lymph nodes (TDLN). The on-treatment evaluation was performed 7–14 days after the start of PD-1 blockade in NSCLC patients. These data were related to (pathological) tumor response, progression-free survival, and overall survival (OS). We found that increases in total lesion glycolysis (TLG) had a strong reverse correlation with OS (r = −0.93, p = 0.022). Additionally, responders showed decreased and progressors increased Treg frequencies on-treatment. Frequencies of detectable PD-1-expressing CD8+ T cells decreased in responders but remained stable in progressors. This was especially found in the TDLN. Changes in activated Treg rates in TDLN were strongly but, due to low numbers of data points, non-significantly correlated with ΔTLG and reversely correlated with OS.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.664
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

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

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 head0.036
GPT teacher head0.319
Teacher spread0.283 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

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