Evidence map›Paper›PMID 40448719›Full record

ArticleCancer immunology, immunotherapy : CII2025

Cancer therapy via neoepitope-specific monoclonal antibody cocktails.

Colin J Hartman, Asmaa O Mohamed, Girja S Shukla, Stephanie C Pero, Yu-Jing Sun, Roberto S Rodríguez-Jimenez, Nicholas F Genovese, Nico M Kohler, Thomas R Hemphill, Yina H Huang and 2 more

Abstract read
In one paragraph

Article in Cancer immunology, immunotherapy : CII, 2025. 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

12 authors.

Colin J HartmanDepartment of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, 14 Engineering Drive, Hanover, NH, 03755, USA.
Asmaa O MohamedDepartment of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, 14 Engineering Drive, Hanover, NH, 03755, USA.
Girja S ShuklaLarner College of Medicine, University of Vermont, Burlington, VT, USA.
Stephanie C PeroLarner College of Medicine, University of Vermont, Burlington, VT, USA.
Yu-Jing SunLarner College of Medicine, University of Vermont, Burlington, VT, USA.
Roberto S Rodríguez-JimenezThayer School of Engineering, Dartmouth College, Hanover, NH, USA.
Nicholas F GenoveseThayer School of Engineering, Dartmouth College, Hanover, NH, USA.
Nico M KohlerThayer School of Engineering, Dartmouth College, Hanover, NH, USA.
Thomas R HemphillThayer School of Engineering, Dartmouth College, Hanover, NH, USA.
Yina H HuangDepartment of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, 14 Engineering Drive, Hanover, NH, 03755, USA.
David N KragLarner College of Medicine, University of Vermont, Burlington, VT, USA.
Margaret E AckermanDepartment of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, 14 Engineering Drive, Hanover, NH, 03755, USA. margaret.e.ackerman@dartmouth.edu.

Funding

Translational Engineering in Cancer (TEC)P30CA023108 · NCI · DARTMOUTH COLLEGE · PI Fred W Kolling IV · 1985 to 2026
$91.3M
Understanding the role of RNA-binding protein mutations in cancerP20GM113132 · NIGMS · DARTMOUTH COLLEGE · PI MIERKE, DALE F · 2016 to 2025
$25.9M
Molecular characterization and modeling efficient antibody effector functionR01AI186995 · NIAID · DARTMOUTH COLLEGE · PI Margaret E Ackerman · 2025 to 2026
$884k
NCI NIH HHS P30 CA023108NIAID NIH HHS R01 AI186995NIGMS NIH HHS P20 GM113132NIH HHS R01AI186995
6 · The paper itself

Abstract

Cellular heterogeneity presents a significant challenge to cancer treatment. Antibody therapies targeting individual tumor-associated antigens can be extremely effective but are not suited for all patients and often fail against tumors with heterogeneous expression as tumor cells with low or no antigen expression escape targeting and develop resistance. Simultaneously targeting multiple tumor-specific proteins with multiple antibodies has the potential to overcome this barrier and improve efficacy, but relatively few widely expressed cancer-specific antigens are known. In contrast, neoepitopes, which arise from mutations unique to tumor cells, are considerably more abundant. However, since neoepitopes are not commonly shared between individuals, a patient-customized approach is necessary and motivates efforts to develop an efficient means to identify suitable target mutations and isolate neoepitope-specific monoclonal antibodies. Here, focusing on the latter goal, we use directed evolution in yeast and phage display systems to engineer antibodies from nonimmune, human antibody fragment libraries that are specific for neoepitopes previously reported in the B16F10 melanoma model. We demonstrate proof-of-concept for a pipeline that supports rapid isolation and functional enhancement of multiple neoepitope peptide-targeted monoclonal antibodies and demonstrate their robust binding to B16F10 cells and potent effector functions in vitro. These antibodies were combined and evaluated in vivo for anticancer activity in tumor-bearing mice, where they suppressed B16F10 tumor growth and prolonged survival. These findings emphasize the potential for clinical application of patient-customized antibody cocktails in the treatment of the many cancers poorly addressed by current therapies.

Indexed as

Antibodies, MonoclonalAntigens, NeoplasmEpitopesMelanoma, ExperimentalAnimalsCell Line, TumorFemaleHumansMiceMice, Inbred C57BLAntibodies, MonoclonalAntigens, NeoplasmEpitopesEffector functionMelanomaMonoclonal antibodyNeoepitopePhage displayYeast display

Identifiers

PMID40448719
PMCPMC12126454

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.