Evidence map›Paper›PMID 36935289›Full record

ArticleCytotherapy2023

Reference gene selection for clinical chimeric antigen receptor T-cell product vector copy number assays.

Jinxia Ma, Lipei Shao, Tatyana Fuksenko, Hui Liu, Rongye Shi, Anh Dinh, Steven L Highfill, Nan Zhang, Sandhya R Panch, Robert P Somerville and 2 more

Open access · hybridAbstract read
In one paragraph

Article in Cytotherapy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
2.8field-weighted citation impact, top 9% 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

9 citing papers in PubMed, 12 citations in OpenAlex.

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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 at 2 institutions in 2 countries.

Jinxia MaCenter for Cellular Engineering, Department of Transfusion Medicine and Center for Cellular Engineering, National Instates of Health Clinical Center, Bethesda, Maryland, USA.
Lipei ShaoCenter for Cellular Engineering, Department of Transfusion Medicine and Center for Cellular Engineering, National Instates of Health Clinical Center, Bethesda, Maryland, USA.
Tatyana FuksenkoCenter for Cellular Engineering, Department of Transfusion Medicine and Center for Cellular Engineering, National Instates of Health Clinical Center, Bethesda, Maryland, USA.
Hui LiuCenter for Cellular Engineering, Department of Transfusion Medicine and Center for Cellular Engineering, National Instates of Health Clinical Center, Bethesda, Maryland, USA.
Rongye ShiCenter for Cellular Engineering, Department of Transfusion Medicine and Center for Cellular Engineering, National Instates of Health Clinical Center, Bethesda, Maryland, USA.
Anh DinhCenter for Cellular Engineering, Department of Transfusion Medicine and Center for Cellular Engineering, National Instates of Health Clinical Center, Bethesda, Maryland, USA.
Steven L HighfillCenter for Cellular Engineering, Department of Transfusion Medicine and Center for Cellular Engineering, National Instates of Health Clinical Center, Bethesda, Maryland, USA.
Nan ZhangCenter for Cellular Engineering, Department of Transfusion Medicine and Center for Cellular Engineering, National Instates of Health Clinical Center, Bethesda, Maryland, USA.
Sandhya R PanchDepartment of Medicine (Hematology Division), University of Washington/Fred Hutchinson Cancer Center, Seattle, Washington, USA.
Robert P SomervilleCenter for Cellular Engineering, Department of Transfusion Medicine and Center for Cellular Engineering, National Instates of Health Clinical Center, Bethesda, Maryland, USA.
David F StroncekCenter for Cellular Engineering, Department of Transfusion Medicine and Center for Cellular Engineering, National Instates of Health Clinical Center, Bethesda, Maryland, USA.
Ping JinCenter for Cellular Engineering, Department of Transfusion Medicine and Center for Cellular Engineering, National Instates of Health Clinical Center, Bethesda, Maryland, USA. Electronic address: PJin@mail.cc.nih.gov.
Cellular Therapeutics (United Kingdom) · GBUniversity of Washington · US

Funding

Develop novel assays for assessing cellular and gene therapiesZIACL002119 · CLC · CLINICAL CENTER · PI STRONCEK, DAVID · 2010 to 2025
$0k
Intramural NIH HHS Z99 CL999999
6 · The paper itself

Abstract

BACKGROUND

aimsReference genes are an essential part of clinical assays such as droplet digital polymerase chain reaction (ddPCR), which measure the number of copies of vector integrated into genetically engineered cells and the loss of plasmids in reprogrammed cells used in clinical cell therapies. Care should be taken to select reference genes, because it has been discovered that there may be thousands of variations in copy number from genomic segments among different individuals. In addition, within the same person in the context of cancer and other proliferative disorders, substantial parts of the genome also can differ in copy number between cells from diseased and healthy people. The purpose of this study was to identify reference genes that could be used for copy number variation analysis of transduced chimeric antigen receptor T cells and for plasmid loss analysis in induced pluripotent stem cells using ddPCR.

methodsWe used The Cancer Genome Atlas (TCGA) to evaluate candidate reference genes. If TCGA found a candidate gene to have low copy number variance in cancer, ddPCR was used to measure the copy numbers of the potential reference gene in cells from healthy subjects, cancer cell lines and patients with acute lymphocytic leukemia, lymphoma, multiple myeloma and human papillomavirus-associated cancers.

resultsIn addition to the rPP30 gene, which we have has been using in our copy number assays, three other candidate reference genes were evaluated using TCGA, and this analysis found that none of the four gene regions (AGO1, AP3B1, MKL2 and rPP30) were amplified or deleted in all of the cancer cell types that are currently being treated with cellular therapies by our facility. The number of copies of the genes AP3B1, AGO1, rPP30 and MKL2 measured by ddPCR was similar among cells from healthy subjects. We found that AGO1 had copy number alteration in some of the clinical samples, and the number of copies of the genes AP3B1, MKL2 and rPP30 measured by ddPCR was similar among cells from patients with the cancer cell types that are currently being treated with genetically engineered T-cell therapies by our facility.

conclusionsBased on our current results, the three genes, AP3B1, MKL2 and rPP30, are suitable for use as reference genes for assays measuring vector copy number in chimeric antigen receptor T cells produced from patients with acute leukemia, lymphoma, multiple myeloma and human papillomavirus-associated cancers. We will continue to evaluate AGO1 on our future samples.

Indexed as

Multiple MyelomaReceptors, Chimeric AntigenDNA Copy Number VariationsHumansPolymerase Chain ReactionT-LymphocytesReceptors, Chimeric Antigenchimeric antigen receptor T-cellsdroplet digital PCRvector copy number

Identifiers

PMID36935289
PMCPMC10159958
OpenAlexW4327892784

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.