Evidence map›Paper›PMID 37019174›Full record

ArticleJournal of molecular biology2023

Concerted Antibody and Antigen Discovery by Differential Whole-cell Phage Display Selections and Multi-omic Target Deconvolution.

Matthew G Cyr, Henry D Wilson, Anna-Lena Spierling, Jing Chang, Haiyong Peng, Peter Steinberger, Christoph Rader

Abstract read
In one paragraph

Article in Journal of molecular biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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.

Matthew G CyrSkaggs Graduate School of Chemical and Biological Sciences, The Scripps Research Institute, Jupiter, FL, USA; Department of Immunology and Microbiology, The Herbert Wertheim UF Scripps Institute for Biomedical Innovation & Technology, University of Florida, Jupiter, FL, USA. Electronic address: https://twitter.com/CyrialDilutions.
Henry D WilsonSkaggs Graduate School of Chemical and Biological Sciences, The Scripps Research Institute, Jupiter, FL, USA.
Anna-Lena SpierlingDepartment of Immunology and Microbiology, The Herbert Wertheim UF Scripps Institute for Biomedical Innovation & Technology, University of Florida, Jupiter, FL, USA.
Jing ChangDepartment of Immunology and Microbiology, The Herbert Wertheim UF Scripps Institute for Biomedical Innovation & Technology, University of Florida, Jupiter, FL, USA.
Haiyong PengDepartment of Immunology and Microbiology, The Herbert Wertheim UF Scripps Institute for Biomedical Innovation & Technology, University of Florida, Jupiter, FL, USA.
Peter SteinbergerInstitute of Immunology, Medical University of Vienna, Vienna, Austria.
Christoph RaderSkaggs Graduate School of Chemical and Biological Sciences, The Scripps Research Institute, Jupiter, FL, USA; Department of Immunology and Microbiology, The Herbert Wertheim UF Scripps Institute for Biomedical Innovation & Technology, University of Florida, Jupiter, FL, USA. Electronic address: crader@scripps.edu.

Funding

Novel Enediyne-Based Antibody-Drug Conjugates for CancersR01CA204484 · NCI · UNIVERSITY OF FLORIDA · PI Natarajan Muthusamy, Ben Shen · 2017 to 2026
$4.6M
A Drug Delivery Strategy for Targeted Therapy of Chronic Lymphocytic LeukemiaR01CA174844 · NCI · UNIVERSITY OF FLORIDA · PI SHEN, BEN · 2018 to 2022
$3.0M
Chemically Programmed Bispecific Antibodies for Cancer TherapyR01CA181258 · NCI · SCRIPPS FLORIDA · PI RADER, CHRISTOPH · 2014 to 2018
$2.0M
T-cell engaging bispecific antibodies designed for proteolytic activation in the tumor microenvironmentR21CA263240 · NCI · UNIVERSITY OF FLORIDA · PI RADER, CHRISTOPH · 2021 to 2022
$471k
Validation of Siglec-6 as a novel target for cancer immunotherapyR21CA229961 · NCI · SCRIPPS FLORIDA · PI MUTHUSAMY, NATARAJAN, RADER, CHRISTOPH · 2018 to 2019
$426k
NCI NIH HHS R01 CA174844NCI NIH HHS R01 CA181258NCI NIH HHS R01 CA204484NCI NIH HHS R21 CA229961NCI NIH HHS R21 CA263240
6 · The paper itself

Abstract

Monoclonal antibody (mAb)-based biologics are well established treatments of cancer. Antibody discovery campaigns are typically directed at a single target of interest, which inherently limits the possibility of uncovering novel antibody specificities or functionalities. Here, we present a target-unbiased approach for antibody discovery that relies on generating mAbs against native target cell surfaces via phage display. This method combines a previously reported method for improved whole-cell phage display selections with next-generation sequencing analysis to efficiently identify mAbs with the desired target cell reactivity. Applying this method to multiple myeloma cells yielded a panel of >50 mAbs with unique sequences and diverse reactivities. To uncover the identities of the cognate antigens recognized by this panel, representative mAbs from each unique reactivity cluster were used in a multi-omic target deconvolution approach. From this, we identified and validated three cell surface antigens: PTPRG, ICAM1, and CADM1. PTPRG and CADM1 remain largely unstudied in the context of multiple myeloma, which could warrant further investigation into their potential as therapeutic targets. These results highlight the utility of optimized whole-cell phage display selection methods and could motivate further interest in target-unbiased antibody discovery workflows.

Indexed as

Antibodies, MonoclonalAntigensPeptide LibraryAntibody SpecificityCell Adhesion Molecule-1Cell Surface Display TechniquesHumansMultiomicsMultiple MyelomaAntibodies, MonoclonalAntigensCADM1 protein, humanCell Adhesion Molecule-1Peptide LibraryCADM1FabICAM1multiple myelomaPTPRG

Identifiers

PMID37019174
PMCPMC10148915

What OpenQuestion holds

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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.