Evidence map›Paper›PMID 38253617›Full record

ArticleNature communications2024

MARS an improved de novo peptide candidate selection method for non-canonical antigen target discovery in cancer.

Hanqing Liao, Carolina Barra, Zhicheng Zhou, Xu Peng, Isaac Woodhouse, Arun Tailor, Robert Parker, Alexia Carré, Persephone Borrow, Michael J Hogan and 5 more

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
3.7field-weighted citation impact, top 6% 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

14 citing papers in PubMed, 18 citations in OpenAlex.

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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 5 institutions in 5 countries.

Hanqing LiaoThe Jenner Institute, University of Oxford, Oxford, OX3 7BN, UK.
Carolina BarraTechnical University Denmark, Copenhagen, Denmark.ORCID 0000-0002-6836-4906
Zhicheng ZhouUniversité Paris Cité, Institut Cochin, CNRS, INSERM, 75014, Paris, France.ORCID 0000-0001-9137-7951
Xu PengThe Jenner Institute, University of Oxford, Oxford, OX3 7BN, UK.
Isaac WoodhouseThe Jenner Institute, University of Oxford, Oxford, OX3 7BN, UK.ORCID 0000-0002-8221-7697
Arun TailorThe Jenner Institute, University of Oxford, Oxford, OX3 7BN, UK.ORCID 0000-0002-9698-3480
Robert ParkerThe Jenner Institute, University of Oxford, Oxford, OX3 7BN, UK.
Alexia CarréUniversité Paris Cité, Institut Cochin, CNRS, INSERM, 75014, Paris, France.ORCID 0000-0001-8144-7194
Persephone BorrowCentre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7DQ, UK.ORCID 0000-0002-3877-9780
Michael J HoganDepartment of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.ORCID 0000-0002-6244-2993
Wayne PaesThe Jenner Institute, University of Oxford, Oxford, OX3 7BN, UK.ORCID 0000-0002-0529-2765
Laurence C EisenlohrDepartment of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.ORCID 0000-0002-8475-7910
Roberto MalloneUniversité Paris Cité, Institut Cochin, CNRS, INSERM, 75014, Paris, France.ORCID 0000-0002-9846-8861
Morten Nielsen *Technical University Denmark, Copenhagen, Denmark.ORCID 0000-0001-7885-4311
Nicola Ternette *The Jenner Institute, University of Oxford, Oxford, OX3 7BN, UK. nicola.ternette@ndm.ox.ac.uk.ORCID 0000-0002-9283-0743
University of Oxford · GBCentre National de la Recherche Scientifique · FRChildren's Hospital of Philadelphia · USTechnical University of Denmark · DKUniversity of Applied Sciences Utrecht · NL

Funding

Virus biology scientific research support componentUM1AI100645 · NIAID · DUKE UNIVERSITY · PI SANTRA, SAMPA · 2012 to 2018
$204.7M
Targeting 5' leader-encoded defective ribosomal products for HIV T cell vaccinesR01AI118549 · NIAID · UNIVERSITY OF PENNSYLVANIA · PI SHAW, GEORGE M · 2016 to 2020
$3.6M
Delineating the non-conventional MHC class I and class II peptidome of influenzaR21AI153978 · NIAID · CHILDREN'S HOSP OF PHILADELPHIA · PI EISENLOHR, LAURENCE CRANE · 2020 to 2021
$477k
Cancer Research UK 28736Medical Research Council MR/K012037NIAID NIH HHS R01 AI118549NIAID NIH HHS R21 AI153978NIAID NIH HHS UM1 AI100645Wellcome Trust
6 · The paper itself

Abstract

Understanding the nature and extent of non-canonical human leukocyte antigen (HLA) presentation in tumour cells is a priority for target antigen discovery for the development of next generation immunotherapies in cancer. We here employ a de novo mass spectrometric sequencing approach with a refined, MHC-centric analysis strategy to detect non-canonical MHC-associated peptides specific to cancer without any prior knowledge of the target sequence from genomic or RNA sequencing data. Our strategy integrates MHC binding rank, Average local confidence scores, and peptide Retention time prediction for improved de novo candidate Selection; culminating in the machine learning model MARS. We benchmark our model on a large synthetic peptide library dataset and reanalysis of a published dataset of high-quality non-canonical MHC-associated peptide identifications in human cancer. We achieve almost 2-fold improvement for high quality spectral assignments in comparison to de novo sequencing alone with an estimated accuracy of above 85.7% when integrated with a stepwise peptide sequence mapping strategy. Finally, we utilize MARS to detect and validate lncRNA-derived peptides in human cervical tumour resections, demonstrating its suitability to discover novel, immunogenic, non-canonical peptide sequences in primary tumour tissue.

Indexed as

PeptidesUterine Cervical NeoplasmsAmino Acid SequenceBenchmarkingFemaleHumansPeptide LibraryPeptide LibraryPeptides

Identifiers

PMID38253617
PMCPMC10803737
OpenAlexW4391103249

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.