Evidence map›Paper›PMID 32034288›Full record

ReviewNature reviews. Clinical oncology2020

Improving cancer immunotherapy using nanomedicines: progress, opportunities and challenges.

John D Martin, Horacio Cabral, Triantafyllos Stylianopoulos, Rakesh K Jain

Open access · greenAbstract readReview
In one paragraph

Review in Nature reviews. Clinical oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 289 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
289citing papers in PubMed, 1 pooled it
33.5field-weighted citation impact, top 1% 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

289 citing papers in PubMed, 1 synthesis or guideline pooled it, 650 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Green Synthesis and Characterization of SiOApplied biochemistry and biotechnology · 2026
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  6. Review
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  16. Modular Vaccine Robots for Spatiotemporal Programming of the CD4Small (Weinheim an der Bergstrasse, Germany) · 2026
    Article
  17. Review
  18. Article
  19. Article
  20. Review

229 more citing papers are in PubMed but not listed here.

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

4 authors at 3 institutions in 3 countries.

John D MartinDepartment of Bioengineering, Graduate School of Engineering, The University of Tokyo, Bunkyo, Tokyo, Japan.ORCID http://orcid.org/0000-0002-9828-8203
Horacio CabralDepartment of Bioengineering, Graduate School of Engineering, The University of Tokyo, Bunkyo, Tokyo, Japan.
Triantafyllos StylianopoulosCancer Biophysics Laboratory, Department of Mechanical and Manufacturing Engineering, University of Cyprus, Nicosia, Cyprus. tstylian@ucy.ac.cy.ORCID http://orcid.org/0000-0002-3093-1696
Rakesh K JainEdwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. jain@steele.mgh.harvard.edu.ORCID http://orcid.org/0000-0001-7571-3548
The University of Tokyo · JPHarvard University · USUniversity of Cyprus · CY

Funding

Vascular Normalization: Rolse of Perivascular CellsP01CA080124 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI FUKUMURA, DAI · 2000 to 2016
$35.0M
Dissecting Pediatric Brain Tumor Microenvironment to Improve TreatmentR35CA197743 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI JAIN, RAKESH K. · 2015 to 2020
$5.7M
Reprogramming PDAC tumor microenvironment to improve immunotherapyU01CA224348 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI BOUCHER, YVES, JAIN, RAKESH K. · 2017 to 2021
$2.9M
Viral Vectors in Tumors: Effects of Taxol and RadiationR01CA098706 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI BOUCHER, YVES · 2003 to 2013
$2.8M
Reengineering obesity-induced abnormal microenvironment to improve PDAC treatmentR01CA208205 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI FUKUMURA, DAI, JAIN, RAKESH K. · 2017 to 2020
$2.2M
NCI NIH HHS P01 CA080124NCI NIH HHS R01 CA098706NCI NIH HHS R01 CA208205NCI NIH HHS R35 CA197743NCI NIH HHS U01 CA224348
6 · The paper itself

Abstract

Multiple nanotherapeutics have been approved for patients with cancer, but their effects on survival have been modest and, in some examples, less than those of other approved therapies. At the same time, the clinical successes achieved with immunotherapy have revolutionized the treatment of multiple advanced-stage malignancies. However, the majority of patients do not benefit from the currently available immunotherapies and many develop immune-related adverse events. By contrast, nanomedicines can reduce - but do not eliminate - the risk of certain life-threatening toxicities. Thus, the combination of these therapeutic classes is of intense research interest. The tumour microenvironment (TME) is a major cause of the failure of both nanomedicines and immunotherapies that not only limits delivery, but also can compromise efficacy, even when agents accumulate in the TME. Coincidentally, the same TME features that impair nanomedicine delivery can also cause immunosuppression. In this Perspective, we describe TME normalization strategies that have the potential to simultaneously promote the delivery of nanomedicines and reduce immunosuppression in the TME. Then, we discuss the potential of a combined nanomedicine-based TME normalization and immunotherapeutic strategy designed to overcome each step of the cancer-immunity cycle and propose a broadly applicable 'minimal combination' of therapies designed to increase the number of patients with cancer who are able to benefit from immunotherapy.

Indexed as

Drug Delivery SystemsHumansImmune ToleranceImmunotherapyModels, ImmunologicalMolecular Targeted TherapyNanomedicineNeoplasmsNeovascularization, PathologicTumor Microenvironment

Identifiers

PMID32034288
PMCPMC8272676
OpenAlexW3004769637

What OpenQuestion holds

Textmetadata
LicenceTDM
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.