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The effect of circadian rhythm on clinical outcome in patients receiving pembrolizumab in the INSPIRE pan-cancer trial.

2022· article· en· W4286295232 on OpenAlexafffund
Helena Jacoba Janse van Rensburg, Zhihui Liu, Albiruni Ryan Abdul Razak, Anna Spreafico, Philippe L. Bédard, Aaron R. Hansen, Stéphanie Lheureux, Marcus O. Butler, Lillian L. Siu

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer CentreUniversity Health Network
FundersPrincess Margaret Cancer Foundation
KeywordsPembrolizumabMedicineCohortInternal medicineOncologyMelanomaAdverse effectCancerImmunotherapyBreast cancerImmune systemImmunologyCancer research

Abstract

fetched live from OpenAlex

2589 Background: The molecular networks comprising circadian rhythm are expressed in immune cells where they affect immune-related processes. Within the emerging field of chrono-immunotherapy, it has been proposed that immunotherapies should be applied at certain times of day to optimize efficacy. Two recent reports have suggested that earlier administration of immune checkpoint inhibitors (ICIs) in non-small cell lung cancer and melanoma may offer improved survival outcomes (Karaboué et al. ASCO 2021, PMID 34780711). Whether this observation applies to other tumour types, or occurs in geographic regions with differing seasonality, is not known. Methods: We retrospectively analyzed the time of administration data from the INSPIRE single-centre, phase II, multi-cohort study of pembrolizumab (200 mg IV over 1 hour, q3w to maximum 35 cycles) in patients with advanced solid tumours (NCT02644369). Kaplan-Meier methods were used to estimate PFS and OS. Cox proportional hazards models were fitted to assess the association between time of administration and PFS or OS, adjusting for cohort. Fisher’s exact test was used to test for association with immune-related adverse events (irAEs). Results: A total of 106 patients (19 head and neck squamous cell, 22 triple negative breast, 21 epithelial ovarian, 12 melanoma, 32 other solid tumours) were accrued between March 21, 2016, and May 9, 2018. Median time of follow-up was 11.5 months. Start of infusion times were obtained for 806 total doses. The median time of administration was 15h06 for the first dose and 15h11 for all doses (range 09h19 – 18h34). No differences in PFS or OS were observed between patients who received their first dose before or after noon, or before or after 15h06. Furthermore, no differences in PFS or OS were observed between patients who received ≥ 50% of their doses before or after noon, or before or after 15h11. There was also no difference in PFS or OS between patients who did or did not have a significant proportion of doses (≥ 20%) after 16h30 (“evening” in previous reports). There were no differences in the frequency of grade ≥ 2 irAEs amongst the various groups. No differences in efficacy were found when individual cohorts were evaluated separately. Finally, no differences in PFS or OS were observed when participants were grouped by season of first dose. Conclusions: Despite previous reports of improved survival with earlier ICI dosing, we did not identify any association of time of pembrolizumab administration with clinical outcomes. Our analysis is limited by small sample size and patient heterogeneity which may hinder identification of smaller associations. It is also unclear whether lower response rates in a pan-cancer population (relative to prior reports in lung cancer and melanoma) might impact correlation analysis. Further studies will be necessary to interrogate this phenomenon and ensure that ICI are optimally applied.

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)0.002
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.116
GPT teacher head0.489
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".

Quick stats

Citations9
Published2022
Admission routes2
Has abstractyes

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