Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 371

The DOI matches its Crossref record; 180 of the 180 references it cites were found in a registry or an index.

Targeting cancer with mRNA–lipid nanoparticles: key considerations and future prospects

Open access
Kon, Edo, Ad-El, Nitay, Hazan-Halevy, Inbal, Stotsky-Oterin, Lior, Peer, Dan·2023·Nature Reviews Clinical Oncology

Abstract

Harnessing mRNA–lipid nanoparticles (LNPs) to treat patients with cancer has been an ongoing research area that started before these versatile nanoparticles were successfully used as COVID-19 vaccines. Currently, efforts are underway to harness this platform for oncology therapeutics, mainly focusing on cancer vaccines targeting multiple neoantigens or direct intratumoural injections of mRNA–LNPs encoding pro-inflammatory cytokines. In this Review, we describe the opportunities of using mRNA–LNPs in oncology applications and discuss the challenges for successfully translating the findings of preclinical studies of these nanoparticles into the clinic. We critically appraise the potential of various mRNA–LNP targeting and delivery strategies, considering physiological, technological and manufacturing challenges. We explore these approaches in the context of the potential clinical applications best suited to each approach and highlight the obstacles that currently need to be addressed to achieve these applications. Finally, we provide insights from preclinical and clinical studies that are leading to this powerful platform being considered the next frontier in oncology treatment. In oncology, mRNA–lipid nanoparticles (LNPs) have been used either to achieve intratumoural expression of immune-stimulating cytokine combinations or as cancer vaccines, and new strategies are in development to enable the selective delivery of payloads into cancer cells previously considered unreachable. The authors of this Review present various approaches for delivering mRNA–LNPs to tumours and discuss improvements that will improve the selective targeting of cancer cells with mRNA–LNPs.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41571-023-00811-9

Cites

180 of 180 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 180 references

Cited by

See all 371

Metadata

Authors
Kon, E., Ad-El, N., Hazan-Halevy, I., Stotsky-Oterin, L., & Peer, D.
Venue
Nature Reviews Clinical Oncology
Published
2023-08-16
Volume
20
Issue
11
Pages
739-754
ISSN
  • 1759-4774 (ISSN-L)
  • 1759-4782
OpenAlex
W4385879053
Cited by
371
References
180
FWCI
9.27
Type
journal article
Language
en
Open access
hybrid

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Targeting cancer with mRNA–lipid nanoparticles: key considerations and future prospects. https://app.cochat.ai/papers/10.1038/s41571-023-00811-9
The paper
Kon, E., Ad-El, N., Hazan-Halevy, I., Stotsky-Oterin, L., & Peer, D. (2023). Targeting cancer with mRNA–lipid nanoparticles: key considerations and future prospects. Nature Reviews Clinical Oncology. https://doi.org/10.1038/s41571-023-00811-9

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.