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Uveal melanoma: Real-world analysis of immune checkpoint inhibitors efficacy and lymphocyte-to-monocyte ratio as a biomarker of response.

2022· article· en· W4281787837 on OpenAlexaffabout
Antoine Desîlets, Chloé Béland, Valerie Hladky, Steph A. Pang, Arielle Elkrief, Andreea Stepanov, Bertrand Routy, Karl Bélanger, Wilson H. Miller, Olivier LaRochelle, Rahima Jamal

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicOcular Oncology and Treatments
Canadian institutionsJewish General HospitalUniversité LavalHôtel-Dieu de QuébecMcGill UniversityCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsMedicineHazard ratioInternal medicineMelanomaOncologyProportional hazards modelBiomarkerUnivariate analysisIpilimumabLog-rank testGastroenterologyImmunotherapyCancerMultivariate analysisConfidence intervalCancer research

Abstract

fetched live from OpenAlex

e21590 Background: Uveal melanoma (UM) is a rare subtype of melanoma. Real-world data is needed to guide management since patients with advanced UM were excluded from immune checkpoint inhibition (ICI) phase III clinical trials. Lymphopenia and elevated monocyte count, which represents a surrogate marker of systemic inflammation and a source of pro-angiogenic tumor-associated macrophages (TAM), have been associated with poor treatment outcomes in various solid tumors. We sought to characterize ICI efficacy in patients with UM vs cutaneous melanoma (CM) and to investigate pre-treatment lymphocyte-to-monocyte ratio (LMR) as a biomarker of response. Methods: We conducted a multicentric cohort study across 3 academic centers in Canada. Best overall responses as per the investigators were recorded. Overall survival (OS) and progression-free survival (PFS) were compared using the log rank (Mantel-Cox) test, with univariate analyses performed using Cox proportional hazard regression model. Results: A total of 122 patients with metastatic melanoma were included, either UM (n = 60) or CM (n = 62). UM patients were treated either with anti-PD-1 monotherapy (69%) or combination with anti-CTLA-4 (31%). Median OS was 35.4 months for CM versus 8.0 months for UM (HR 0.38, 95%CI 0.24-0.60, p = 0.0001). Median PFS was 10.7 months for CM versus 3.5 months for UM (HR 0.38, 95%CI 0.22-0.65, p = 0.0001). Best overall response in the UM group was progressive disease (74%), stable disease (10%) or partial response (16%). In UM patients, higher baseline LMR (HR 0.48, 95%CI 0.24-0.97, p = 0.04) and presence of immune-related adverse events (HR 0.42, 95%CI 0.20-0.89, p = 0.02) were associated with improved OS in univariate analysis. In CM, such association with high LMR was not significant (HR 0.58, 95% 0.28-1.21, p = 0.10) unless excluding patients with corticosteroid-induced lymphopenia due to recently treated brain metastases (HR 0.42, 95%CI 0.19-0.92, p = 0.03). Conclusions: Advanced UM treated with ICI have inferior survival outcomes compared with cutaneous primaries. Higher pre-treatment LMR is associated with improved OS to ICI in UM patients and could represent a surrogate marker of immune activation. Our findings reinforce the need for new treatment strategies in UM.

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)0.003
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 score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.075
GPT teacher head0.448
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".

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Citations0
Published2022
Admission routes2
Has abstractyes

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