Evidence map›Paper›PMID 38023788›Full record

ArticleBio-protocol2023

Reprogramming Cancer Cells to Antigen-presenting Cells.

Alexandra G Ferreira, Olga Zimmermannova, Ilia Kurochkin, Ervin Ascic, Fritiof Åkerström, Carlos-Filipe Pereira

Open access · diamondAbstract read
In one paragraph

Article in Bio-protocol, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.2field-weighted citation impact, top 20% 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

4 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
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

6 authors at 1 institution in 1 country.

Alexandra G FerreiraMolecular Medicine and Gene Therapy, Lund Stem Cell Center, Lund University, Lund, Sweden.
Olga ZimmermannovaMolecular Medicine and Gene Therapy, Lund Stem Cell Center, Lund University, Lund, Sweden.
Ilia KurochkinMolecular Medicine and Gene Therapy, Lund Stem Cell Center, Lund University, Lund, Sweden.
Ervin AscicMolecular Medicine and Gene Therapy, Lund Stem Cell Center, Lund University, Lund, Sweden.
Fritiof ÅkerströmAsgard Therapeutics AB, Medicon Village, Lund, Sweden.
Carlos-Filipe PereiraMolecular Medicine and Gene Therapy, Lund Stem Cell Center, Lund University, Lund, Sweden.
Lund University · SE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer cells evade the immune system by downregulating antigen presentation. Although immune checkpoint inhibitors (ICI) and adoptive T-cell therapies revolutionized cancer treatment, their efficacy relies on the intrinsic immunogenicity of tumor cells and antigen presentation by dendritic cells. Here, we describe a protocol to directly reprogram murine and human cancer cells into tumor-antigen-presenting cells (tumor-APCs), using the type 1 conventional dendritic cell (cDC1) transcription factors PU.1, IRF8, and BATF3 delivered by a lentiviral vector. Tumor-APCs acquire a cDC1 cell-like phenotype, transcriptional and epigenetic programs, and function within nine days (Zimmermannova et al., 2023). Tumor-APCs express the hematopoietic marker CD45 and acquire the antigen presentation complexes MHC class I and II as well as co-stimulatory molecules required for antigen presentation to T cells, but do not express high levels of negative immune checkpoint regulators. Enriched tumor-APCs present antigens to Naïve CD8

Indexed as

Antigen presentationCancer vaccineCell reprogrammingConventional type 1 dendritic cellsImmunogenicityImmunotherapyMagnetic-activated cell sortingTumor-APCs

Identifiers

PMID38023788
PMCPMC10665638
OpenAlexW4388812779

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.