Evidence map›Paper›PMID 36248860›Full record

ArticleFrontiers in immunology2022

Single-cell sequencing reveals effects of chemotherapy on the immune landscape and TCR/BCR clonal expansion in a relapsed ovarian cancer patient.

Yanyu Ren, Runrong Li, Hanxiao Feng, Jieying Xie, Lin Gao, Shuai Chu, Yan Li, Fanliang Meng, Yunshan Ning

Abstract read
In one paragraph

Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

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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

9 authors.

Yanyu RenSchool of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou, China.
Runrong LiSchool of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou, China.
Hanxiao FengThe First Clinical Medical School, Southern Medical University, Guangzhou, China.
Jieying XieSchool of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou, China.
Lin GaoSchool of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou, China.
Shuai ChuDepartment of Clinical Laboratory, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Yan LiSchool of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou, China.
Fanliang MengThe First Clinical Medical School, Southern Medical University, Guangzhou, China.
Yunshan NingSchool of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer recurrence and chemoresistance are the leading causes of death in high-grade serous ovarian cancer (HGSOC) patients. However, the unique role of the immune environment in tumor progression for relapsed chemo-resistant patients remains elusive. In single-cell resolution, we characterized a comprehensive multi-dimensional cellular and immunological atlas from tumor, ascites, and peripheral blood of a chemo-resistant patient at different stages of treatment. Our results highlight a role in recurrence and chemoresistance of the immunosuppressive microenvironment in ascites, including MDSC-like myeloid and hypo-metabolic γδT cells, and of peripheral CD8

Indexed as

Antineoplastic AgentsOvarian NeoplasmsAscitesCarcinoma, Ovarian EpithelialCD8-Positive T-LymphocytesFemaleHumansNeoplasm Recurrence, LocalReceptors, Antigen, T-CellTumor MicroenvironmentAntineoplastic AgentsReceptors, Antigen, T-Cellchemotherapyclonal expansionimmune microenvironmentovarian cancersingle-cell sequencingTCR/BCR repertoire

Identifiers

PMID36248860
PMCPMC9555851

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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.