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Systemic immune signature of inflammation in metastatic melanoma (MM) patients treated with ipilimumab (IPI) and carboplatin/paclitaxel (CP).

2018· article· en· W2790931951 on OpenAlexaff
Wilson H. Miller, Rahima Jamal, Eftihia Cocolakis, Paméla Thébault, Jennifer Friedmann, Shirin Kazemi, Jeanne Dionne, Jean‐François Cailhier, Stéphanie Lepage, Karl Bélanger, Jean-Pierre M. Ayoub, HB Le, Caroline Lambert, Jida El Hajjar, Léon C.L.T. van Kempen, Alan Spatz, Réjean Lapointe

Bibliographic record

VenueJournal of Clinical Oncology · 2018
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsUniversité de MontréalHôpital Notre-DameCentre Hospitalier de l’Université de MontréalMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsMedicineInternal medicineIpilimumabImmune systemCD8GastroenterologyNeutrophil to lymphocyte ratioCarboplatinInflammationImmunologyChemokinePaclitaxelSystemic inflammationLog-rank testOncologyLymphocyteSurvival analysisCancerChemotherapyImmunotherapy

Abstract

fetched live from OpenAlex

185 Background: Only a subset of MM patients benefit from treatment with IPI. In an effort to increase response and identify predictive immune biomarkers, we combined IPI with CP. We here report a significant correlation between overall survival (OS) and biomarkers of a pre-existing inflammatory state. Methods: 30 patients were treated with C (AUC = 6) and P (175mg/m2) every 3 weeks x 5 and IPI (3mg/kg) every 3 weeks x 4. Blood was collected throughout. OS Kaplan–Meier curves were compared by the log-rank test. Cutoff thresholds defining high or low levels of chemokines, B cell populations and PD-1+CD8+ T cell populations were established from the mean value of a given variable from all patients. Neutrophil to Lymphocyte Ratio (NLR) and Systemic Immune Inflammation Index defined as Platelet x Neutrophil to Lymphocyte Ratio (SII) were calculated at baseline and tested for association with OS. SII ≥ 1375 and NLR ≥ 5 were considered as high risk groups. Results: Median OS was 16.2 months, with a 3-year OS of 36.7% for all patients. High levels of CCL3 (HR = 2.79, p = 0.0159), CCL4 (HR = 8.36, p < 0.0001) and CXCL8 (HR = 3.52, p = 0.0037) were associated with worse OS. Advanced B cell differentiation before treatment was associated with worse patient outcomes. High levels of early differentiated Bm2 ( > 57%) were strongly associated with better OS: HR = 0.26 p = 0.004 whereas high eBm5+Bm5 ( > 14%) levels were strongly associated with worse OS: HR = 2.65, p = 0.029. Patients with higher proportions of PD-1+CD8+ T cells in circulation during treatment had poorer OS (HR = 3.84, p = 0.004) at week 10; (HR = 3.53, p = 0.005) at week 13 and (HR = 2.84, p = 0.040) at week 24. High risk SII (HR = 3.30, p = 0.0192) and NLR (HR = 2.367, p = 0.0486) were associated with worse OS. Conclusions: We have identified a significant correlation between pre-existing systemic inflammatory state and a poor response to IPI and CP. Specifically elevated CCL3, CCL4 and CXCL8, baseline B lymphocyte subset skewing, increased CD8+PD-1+ T lymphocytes, increased NLR and SII were all strongly associated to worse OS. Comprehensive immune monitoring provides evidence for new circulating biomarkers predicting outcome in MM patients treated with IPI and CP. Clinical trial information: NCT01676649.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.041
GPT teacher head0.379
Teacher spread0.338 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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