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Impact of concurrent antibiotics on survival with immunotherapy in metastatic colorectal and pancreatic cancer.

2024· article· en· W4401325730 on OpenAlexaffabout
Cynthia Yeung, Emma Titmuss, Dongsheng Tu, Daniel J. Renouf, Sharlene Gill, Jennifer J. Knox, Eric Xueyu Chen, Christopher J. O’Callaghan, Jonathan M. Loree

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Treatments and Studies
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoQueen's University
Fundersnot available
KeywordsMedicineColorectal cancerCetuximabImmunotherapyOncologyInternal medicineOverall survivalPancreatic cancerCancerAntibiotics

Abstract

fetched live from OpenAlex

2609 Background: The interplay between antibiotics and the microbiome has been hypothesized to modify the immune response to cancer and may negatively affect immune checkpoint inhibitor (ICI) efficacy. Methods: We retrospectively reviewed concurrent antibiotics received in the randomized phase II Canadian Cancer Trials Group (CCTG) CO.26 and PA.7 clinical trials. CO.26 evaluated dual programmed death-ligand 1 (PD-L1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) inhibition (with durvalumab and tremelimumab) versus best supportive care only (BSC) in metastatic refractory colorectal cancer (CRC). PA.7 evaluated gemcitabine and nab-paclitaxel +/- durvalumab and tremelimumab in metastatic pancreatic ductal adenocarcinoma. In CO.26, T cell receptor sequencing (TCR-seq) was available at baseline and week 8. Results: In CO.26 (n=180), median (range) age was 65 (36-87) years, 33% (n=59) were female, and 29% (n=50) were ECOG 0. Concurrent exposure to antibiotics was associated with improved overall survival (OS) (7.4 vs 6.2 months, adjusted hazard ratio (aHR) 0.66 (95% confidence interval (CI) 0.46-0.93), p=0.018) in patients with metastatic CRC who received ICIs, but not in patients who received BSC (3.5 vs 4.5 months, HR 1.49 (95% CI 0.81-2.73), p= 0.20); (p-interaction=0.094). In antibiotic class analysis, concurrent exposure of ICIs to fluoroquinolones (9.4 vs 6.2 months, aHR 0.50 (95% CI 0.30-0.82), p=0.0067), but not penicillins or cephalosporins, was associated with improved OS. There was no difference in RAS/RAF status or tumor mutation burden in patients who received antibiotics/fluoroquinolones or not. The results were consistent when also controlling for TMB (antibiotics aHR 0.63 (0.44, 0.91), p=0.013; fluoroquinolones aHR 0.51 (0.31, 0.84), p=0.009). Patients treated with antibiotics did not have a different TCR diversity or clonality between baseline and 8 weeks (p=0.37 and p=0.47) compared to those who did not receive antibiotics (p=0.26 and p=0.60). In PA.7 (n=180), median (range) age was 65 (29-84) years, 48% (n=87) were female, and 24% (n=44) were ECOG 0. Antibiotics were not associated with OS in patients who did/did not receive ICIs (9.7 vs 10.8 months, HR 0.97 (95% CI 0.64, 1.46), p=0.87; 7.4 vs 10.3 months HR 1.22 (95% CI 0.72, 2.09), p=0.46, respectively). Fluoroquinolones were also not associated with OS regardless of whether or not the patient received ICI therapy (10.9 vs 9.8 months, HR 0.74 (95% CI 0.47, 1.15), p=0.18; 7.2 vs 10.2 months, HR 1.18 (95% CI 0.62, 2.27), p=0.62). Conclusions: Concurrent exposure to antibiotics, and specifically fluoroquinolones during ICI therapy was associated with a statistically significant improvement in OS in patients with metastatic CRC, but not metastatic pancreatic cancer. This is discrepant from prior reports in other tumor types and suggests that the gut microbiome may impact ICI efficacy uniquely in patient with CRC. Clinical trial information: NCT02870920 , NCT02879318 .

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)0.003
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.113
GPT teacher head0.512
Teacher spread0.398 · 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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Citations2
Published2024
Admission routes2
Has abstractyes

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