Evidence map›Paper›PMID 32610166›Full record

SynthesisAnnals of oncology : official journal of the European Society for Medical Oncology2020

Neoantigen prediction and computational perspectives towards clinical benefit: recommendations from the ESMO Precision Medicine Working Group.

L De Mattos-Arruda, M Vazquez, F Finotello, R Lepore, E Porta, J Hundal, P Amengual-Rigo, C K Y Ng, A Valencia, J Carrillo and 5 more

Open access · bronzeAbstract readSystematic Review
In one paragraph

Synthesis in Annals of oncology : official journal of the European Society for Medical Oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 104 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
104citing papers in PubMed, 1 pooled it
6.8field-weighted citation impact, top 2% 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

104 citing papers in PubMed, 1 synthesis or guideline pooled it, 148 citations in OpenAlex.

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  17. Personalizing DNA Cancer Vaccines.Journal of personalized medicine · 2025
    Review
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44 more citing papers are in PubMed but not listed here.

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

15 authors at 10 institutions in 5 countries.

L De Mattos-ArrudaIrsiCaixa, Hospital Universitari Trias i Pujol, Badalona, Spain; Germans Trias i Pujol Research Institute (IGTP), Badalona, Spain. Electronic address: education@esmo.org.
M VazquezBarcelona Supercomputing Center, Barcelona, Spain.
F FinotelloBiocenter, Institute of Bioinformatics, Medical University of Innsbruck, Innsbruck, Austria.
R LeporeBarcelona Supercomputing Center, Barcelona, Spain.
E PortaBarcelona Supercomputing Center, Barcelona, Spain; Josep Carreras Leukaemia Research Institute (IJC), Badalona, Spain.
J HundalThe McDonnell Genome Institute, Washington University in St Louis, USA.
P Amengual-RigoBarcelona Supercomputing Center, Barcelona, Spain.
C K Y NgDepartment for BioMedical Research, University of Bern, Bern, Switzerland.
A ValenciaBarcelona Supercomputing Center, Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.
J CarrilloIrsiCaixa, Hospital Universitari Trias i Pujol, Badalona, Spain; Germans Trias i Pujol Research Institute (IGTP), Badalona, Spain.
T A ChanCenter for Immunotherapy and Precision-Immuno-Oncology, Cleveland Clinic, Cleveland, USA.
V GuallarBarcelona Supercomputing Center, Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.
N McGranahanCancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, University College, London, UK; Cancer Genome Evolution Research Group, University College London Cancer Institute, University College London, London, UK.
J BlancoIrsiCaixa, Hospital Universitari Trias i Pujol, Badalona, Spain; Germans Trias i Pujol Research Institute (IGTP), Badalona, Spain; Universitat de Vic-Universitat Central de Catalunya (UVic-UCC), Vic, Spain.
M GriffithDepartment of Medicine, Washington University School of Medicine, St. Louis, USA.
Barcelona Supercomputing Center · ESInstitució Catalana de Recerca i Estudis Avançats · ESIrsiCaixa · ESCancer Research UK · GBCleveland Clinic · USInnsbruck Medical University · ATJames S. McDonnell Foundation · USUniversitat de Vic - Universitat Central de Catalunya · ESUniversity of Bern · CHWashington University in St. Louis · US

Funding

Integrated Analysis & Interpretation of Whole Genome Exome & Transcriptome SequenR00HG007940 · NHGRI · WASHINGTON UNIVERSITY · PI GRIFFITH, MALACHI · 2017 to 2019
$724k
Austrian Science Fund FWF T 974NHGRI NIH HHS R00 HG007940Wellcome Trust
6 · The paper itself

Abstract

backgroundThe use of next-generation sequencing technologies has enabled the rapid identification of non-synonymous somatic mutations in cancer cells. Neoantigens are mutated peptides derived from somatic mutations not present in normal tissues that may result in the presentation of tumour-specific peptides capable of eliciting antitumour T-cell responses. Personalised neoantigen-based cancer vaccines and adoptive T-cell therapies have been shown to prime host immunity against tumour cells and are under clinical trial development. However, the optimisation and standardisation of neoantigen identification, as well as its delivery as immunotherapy are needed to increase tumour-specific T-cell responses and, thus, the clinical efficacy of current cancer immunotherapies.

methodsIn this recommendation article, launched by the European Society for Medical Oncology (ESMO), we outline and discuss the available framework for neoantigen prediction and present a systematic review of the current scientific evidence.

resultsA number of computational pipelines for neoantigen prediction are available. Most of them provide peptide major histocompatibility complex (MHC) binding affinity predictions, but more recent approaches incorporate additional features like variant allele fraction, gene expression, and clonality of mutations. Neoantigens can be predicted in all cancer types with high and low tumour mutation burden, in part by exploiting tumour-specific aberrations derived from mutational frameshifts, splice variants, gene fusions, endogenous retroelements and other tumour-specific processes that could yield more potently immunogenic tumour neoantigens. Ongoing clinical trials will highlight those cancer types and combinations of immune therapies that would derive the most benefit from neoantigen-based immunotherapies.

conclusionsImproved identification, selection and prioritisation of tumour-specific neoantigens are needed to increase the scope of benefit from cancer vaccines and adoptive T-cell therapies. Novel pipelines are being developed to resolve the challenges posed by high-throughput sequencing and to predict immunogenic neoantigens.

Indexed as

Cancer VaccinesNeoplasmsAntigens, NeoplasmHumansImmunotherapyMedical OncologyPractice Guidelines as TopicPrecision MedicineAntigens, NeoplasmCancer Vaccinescancercomputationalimmunotherapymutationneoantigenpersonalised vaccine

Identifiers

PMID32610166
PMCPMC7885309
OpenAlexW3037875124

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.