Evidence map›Paper›PMID 32654419›Full record

ReviewCancer communications (London, England)2020

Single-cell RNA sequencing in breast cancer: Understanding tumor heterogeneity and paving roads to individualized therapy.

Shuning Ding, Xiaosong Chen, Kunwei Shen

Registry-linked trialOpen access · goldAbstract readReview
In one paragraph

Review in Cancer communications (London, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06125834 (Trastuzumab Emtansine), which is not on this map. Cited by 168 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
168citing papers in PubMed, 1 pooled it
11.6field-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.

NCT06125834 phase2unknown statusnot on this mapstarted 2023, after this paper: background citation

Trastuzumab Emtansine (T-DM1) Treatment in HER2-positive Breast Cancer Patients With Progressive Disease After TKIs or HP Therapy: a Multicenter, Single-arm, Phase II Study

TypeinterventionalSponsorThe First Affiliated Hospital with Nanjing Medical UniversityRan2023 to 2026Enrolled36ConditionsAdvanced Breast Cancer, Objective Response Rate, Trastuzumab EmtansineArmsTrastuzumab Emtansine (T-DM1)
3 · Its place in the literature

Who cites it

168 citing papers in PubMed, 1 synthesis or guideline pooled it, 256 citations in OpenAlex.

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  11. Single-cell sequencing: accurate disease detection.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
    Review
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108 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

3 authors at 1 institution in 1 country.

Shuning DingDepartment of General Surgery, Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, P. R. China.
Xiaosong ChenDepartment of General Surgery, Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, P. R. China.ORCID 0000-0002-3286-0035
Kunwei ShenDepartment of General Surgery, Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, P. R. China.
Shanghai Jiao Tong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Single-cell RNA sequencing (scRNA-seq) is a novel technology that allows transcriptomic analyses of individual cells. During the past decade, scRNA-seq sensitivity, accuracy, and efficiency have improved due to innovations including more sensitive, automated, and cost-effective single-cell isolation methods with higher throughput as well as ongoing technological development of scRNA-seq protocols. Among the variety of current approaches with distinct features, researchers can choose the most suitable method to carry out their research. By profiling single cells in a complex population mix, scRNA-seq presents great advantages over traditional sequencing methods in dissecting heterogeneity in cell populations hidden in bulk analysis and exploring rare cell types associated with tumorigenesis and metastasis. scRNA-seq studies in recent years in the field of breast cancer research have clustered breast cancer cell populations with different molecular subtypes to identify distinct populations that may correlate with poor prognosis and drug resistance. The technology has also been used to explain tumor microenvironment heterogeneity by identifying distinct immune cell subsets that may be associated with immunosurveillance and are potential immunotherapy targets. Moreover, scRNA-seq has diverse applications in breast cancer research besides exploring heterogeneity, including the analysis of cell-cell communications, regulatory single-cell states, immune cell distributions, and more. scRNA-seq is also a promising tool that can facilitate individualized therapy due to its ability to define cell subsets with potential treatment targets. Although scRNA-seq studies of therapeutic selection in breast cancer are currently limited, the application of this technology in this field is prospective. Joint efforts and original ideas are needed to better implement scRNA-seq technologies in breast cancer research to pave the way for individualized treatment management. This review provides a brief introduction on the currently available scRNA-seq approaches along with their corresponding strengths and weaknesses and may act as a reference for the selection of suitable methods for research. We also discuss the current applications of scRNA-seq in breast cancer research for tumor heterogeneity analysis, individualized therapy, and the other research directions mentioned above by reviewing corresponding published studies. Finally, we discuss the limitations of current scRNA-seq technologies and technical problems that remain to be overcome.

Indexed as

Breast NeoplasmsSequence Analysis, RNASingle-Cell AnalysisFemaleGene Expression ProfilingHumansProspective StudiesTumor Microenvironmentbreast cancerindividualized therapySingle-cell sequencingtumor heterogeneity

Identifiers

PMID32654419
PMCPMC7427308
OpenAlexW3041348098

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

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.