Evidence map›Paper›PMID 38704515›Full record

ArticleTherapeutic innovation & regulatory science2024

Challenges and Lessons Learned in Autologous Chimeric Antigen Receptor T-Cell Therapy Development from a Statistical Perspective.

Daniel Li, Zhenzhen Xu, Shihua Wen, Revathi Ananthakrishnan, Yeonhee Kim, Khadija Rerhou Rantell, Patricia Anderson, James Whitmore, Alan Chiang

Abstract read
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In one paragraph

Article in Therapeutic innovation & regulatory science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

9 authors.

Daniel LiBristol Myers Squibb, Seattle, WA, USA. daniel.li1@bms.com.
Zhenzhen XuUS Food and Drug Administration, Silver Spring, MD, USA.
Shihua WenNovartis Pharmaceuticals, East Hanover, NJ, USA.
Revathi AnanthakrishnanBristol Myers Squibb, Summit, NJ, USA.
Yeonhee KimLyell Immunopharma, Seattle, WA, USA.
Khadija Rerhou RantellMedicines and Healthcare Products Regulatory Agency, London, UK.
Patricia AndersonICON Centre for Cell and Gene Therapy, Houston, TX, USA.
James WhitmoreKite, Santa Monica, CA, USA.
Alan ChiangLyell Immunopharma, Seattle, WA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chimeric antigen receptor (CAR) T-cell therapy is a human gene therapy product where T cells from a patient are genetically modified to enable them to recognize desired target antigen(s) more effectively. In recent years, promising antitumor activity has been seen with autologous CAR T cells. Since 2017, six CAR T-cell therapies for the treatment of hematological malignancies have been approved by the Food and Drug Administration (FDA). Despite the rapid progress of CAR T-cell therapies, considerable statistical challenges still exist for this category of products across all phases of clinical development that need to be addressed. These include (but not limited to) dose finding strategy, implementation of the estimand framework, use of real-world data in contextualizing single-arm CAR T trials, analysis of safety data and long-term follow-up studies. This paper is the first step in summarizing and addressing these statistical hurdles based on the development of the six approved CAR T-cell products.

Indexed as

Immunotherapy, AdoptiveReceptors, Chimeric AntigenHematologic NeoplasmsHumansT-LymphocytesUnited StatesUnited States Food and Drug AdministrationReceptors, Chimeric AntigenCAR T-cellCell and gene therapyDose-findingEstimandLong-term follow-upReal-world evidence

Identifiers

PMID38704515

What OpenQuestion holds

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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.