Evidence map›Paper›PMID 37055772›Full record

ArticleJournal of translational medicine2023

Cross-platform comparison of immune signatures in immunotherapy-treated patients with advanced melanoma using a rank-based scoring approach.

Yizhe Mao, Tuba N Gide, Nurudeen A Adegoke, Camelia Quek, Nigel Maher, Alison Potter, Ellis Patrick, Robyn P M Saw, John F Thompson, Andrew J Spillane and 8 more

Registry-linked trialOpen access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of translational medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06536257 (Personalised Immunotherapy Platform), which is not on this map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.5field-weighted citation impact, top 17% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT06536257 recruitingnot on this map

Personalised Immunotherapy Platform (PIP) - Implementation of a Predictive Model of Response to Immunotherapies in Melanoma

TypeobservationalSponsorMelanoma Institute AustraliaRan2021 to 2037Enrolled1,000ConditionsMelanoma, Cutaneous Squamous Cell Carcinoma, Basal Cell Carcinoma, Merkel Cell CarcinomaArmsPredictive model
3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
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  9. Machine Learning Methods for Gene Selection in Uveal Melanoma.International journal of molecular sciences · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors at 1 institution in 1 country.

Yizhe Mao *Melanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia.
Tuba N Gide *Melanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia.
Nurudeen A AdegokeMelanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia.
Camelia QuekMelanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia.
Nigel MaherMelanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia.
Alison PotterMelanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia.
Ellis PatrickSchool of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia.
Robyn P M SawMelanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia.
John F ThompsonMelanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia.
Andrew J SpillaneMelanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia.
Kerwin F ShannonMelanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia.
Matteo S CarlinoMelanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia.
Serigne N LoMelanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia.
Alexander M MenziesMelanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia.
Inês Pires da SilvaMelanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia.
Georgina V Long *Melanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia.
Richard A Scolyer *Melanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia.
James S Wilmott *Melanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia. james.wilmott@sydney.edu.au.ORCID 0000-0002-6750-5244
The University of Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGene expression profiling is increasingly being utilised as a diagnostic, prognostic and predictive tool for managing cancer patients. Single-sample scoring approach has been developed to alleviate instability of signature scores due to variations from sample composition. However, it is a challenge to achieve comparable signature scores across different expressional platforms.

methodsThe pre-treatment biopsies from a total of 158 patients, who have received single-agent anti-PD-1 (n = 84) or anti-PD-1 + anti-CTLA-4 therapy (n = 74), were performed using NanoString PanCancer IO360 Panel. Multiple immune-related signature scores were measured from a single-sample rank-based scoring approach, singscore. We assessed the reproducibility and the performance in reporting immune profile of singscore based on NanoString assay in advance melanoma. To conduct cross-platform analyses, singscores between the immune profiles of NanoString assay and the previous orthogonal whole transcriptome sequencing (WTS) data were compared through linear regression and cross-platform prediction.

resultssingscore-derived signature scores reported significantly high scores in responders in multiple PD-1, MHC-1-, CD8 T-cell-, antigen presentation-, cytokine- and chemokine-related signatures. We found that singscore provided stable and reproducible signature scores among the repeats in different batches and cross-sample normalisations. The cross-platform comparisons confirmed that singscores derived via NanoString and WTS were comparable. When singscore of WTS generated by the overlapping genes to the NanoString gene set, the signatures generated highly correlated cross-platform scores (Spearman correlation interquartile range (IQR) [0.88, 0.92] and r

conclusionsOverall, the outcome of this study confirms that singscore based on NanoString data is a feasible approach to produce reliable signature scores for determining patients' immune profiles and the potential clinical utility in biomarker implementation, as well as to conduct cross-platform comparisons, such as WTS.

Indexed as

MelanomaBiomarkersGene Expression ProfilingHumansImmunotherapyReproducibility of ResultsBiomarkersAdvanced melanomaCross-platform analysesGene expression profileImmune signatureImmunotherapySingle-sample signature score

Identifiers

PMID37055772
PMCPMC10103529
OpenAlexW4365450758

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.