Evidence map›Paper›PMID 31775766›Full record

ArticleBMC medical genomics2019

Identification and ranking of recurrent neo-epitopes in cancer.

Eric Blanc, Manuel Holtgrewe, Arunraj Dhamodaran, Clemens Messerschmidt, Gerald Willimsky, Thomas Blankenstein, Dieter Beule

Abstract read
In one paragraph

Article in BMC medical genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

7 authors.

Eric BlancCore Unit Bioinformatics, Berlin Institute of Health, Charitéplatz 1, Berlin, 10117, Germany.
Manuel HoltgreweCore Unit Bioinformatics, Berlin Institute of Health, Charitéplatz 1, Berlin, 10117, Germany.
Arunraj DhamodaranMax Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Robert-Rössle-Str. 10, Berlin, 13092, Germany.
Clemens MesserschmidtCore Unit Bioinformatics, Berlin Institute of Health, Charitéplatz 1, Berlin, 10117, Germany.
Gerald WillimskyInstitute of Immunology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Lindenberger Weg 80, Berlin, 13125, Germany.
Thomas BlankensteinInstitute of Immunology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Lindenberger Weg 80, Berlin, 13125, Germany.
Dieter BeuleCore Unit Bioinformatics, Berlin Institute of Health, Charitéplatz 1, Berlin, 10117, Germany. dieter.beule@bihealth.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundImmune escape is one of the hallmarks of cancer and several new treatment approaches attempt to modulate and restore the immune system's capability to target cancer cells. At the heart of the immune recognition process lies antigen presentation from somatic mutations. These neo-epitopes are emerging as attractive targets for cancer immunotherapy and new strategies for rapid identification of relevant candidates have become a priority.

methodsWe carefully screen TCGA data sets for recurrent somatic amino acid exchanges and apply MHC class I binding predictions.

resultsWe propose a method for in silico selection and prioritization of candidates which have a high potential for neo-antigen generation and are likely to appear in multiple patients. While the percentage of patients carrying a specific neo-epitope and HLA-type combination is relatively small, the sheer number of new patients leads to surprisingly high reoccurence numbers. We identify 769 epitopes which are expected to occur in 77629 patients per year.

conclusionWhile our candidate list will definitely contain false positives, the results provide an objective order for wet-lab testing of reusable neo-epitopes. Thus recurrent neo-epitopes may be suitable to supplement existing personalized T cell treatment approaches with precision treatment options.

Indexed as

AllelesAnimalsComputational BiologyDatabases, FactualEpitopesGenes, MHC Class IHumansMiceNeoplasmsEpitopesCancerImmunotherapyNeo-antigenNeo-epitopePrecision treatment

Identifiers

PMID31775766
PMCPMC6882202

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Registered trials

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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.