Evidence map›Paper›PMID 30718356›Full record

ArticleClinical cancer research : an official journal of the American Association for Cancer Research2019

Single-Cell Profiling of Cutaneous T-Cell Lymphoma Reveals Underlying Heterogeneity Associated with Disease Progression.

Nicholas Borcherding, Andrew P Voigt, Vincent Liu, Brian K Link, Weizhou Zhang, Ali Jabbari

Open access · greenAbstract read
In one paragraph

Article in Clinical cancer research : an official journal of the American Association for Cancer Research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 88 papers.

0numbers the graph read from it
0cells of the map it votes in
88citing papers in PubMed
13.5field-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.

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

88 citing papers in PubMed, 126 citations in OpenAlex.

  1. Trial
  2. Mapping malignant T-cell states and immune circuits in Sézary syndrome by single-cell analysis.Journal of the European Academy of Dermatology and Venereology : JEADV · 2026
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  3. Review
  4. Single-Cell Sequencing Reveals That CCL2+ Adipose-Derived Stem Cells Promote Diabetic Wound Healing Through the CCL2-ACKR1 Signaling Axis.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026
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28 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

6 authors at 1 institution in 1 country.

Nicholas BorcherdingDepartment of Pathology, University of Iowa, College of Medicine, Iowa City, Iowa.
Andrew P VoigtMedical Scientist Training Program, University of Iowa, College of Medicine, Iowa City, Iowa.
Vincent LiuDepartment of Pathology, University of Iowa, College of Medicine, Iowa City, Iowa.
Brian K LinkHolden Comprehensive Cancer Center, University of Iowa, College of Medicine, Iowa City, Iowa.
Weizhou ZhangDepartment of Pathology, University of Iowa, College of Medicine, Iowa City, Iowa.
Ali JabbariCancer Biology Graduate Program, University of Iowa, College of Medicine, Iowa City, Iowa. ali-jabbari@uiowa.edu.
University of Iowa · US

Funding

Viral VectorP30CA086862 · NCI · UNIVERSITY OF IOWA · PI Douglas Robert Spitz · 2000 to 2026
$70.0M
The Role of Monocytes in non-Hodgkin LymphomaP50CA097274 · NCI · UNIVERSITY OF IOWA · PI WEINER, GEORGE J. · 2002 to 2021
$45.7M
MEDICAL SCIENTIST TRAINING PROGRAMT32GM007337 · NIGMS · UNIVERSITY OF IOWA · PI GEYER, PAMELA K., LENTZ, STEVEN R · 1985 to 2020
$15.9M
Obesity, inflammation and breast cancerR01CA200673 · NCI · UNIVERSITY OF IOWA · PI ZHANG, WEIZHOU · 2016 to 2020
$1.7M
CD177 suppresses breast-cancer development by inhibiting beta-cateninR01CA203834 · NCI · UNIVERSITY OF IOWA · PI ZHANG, WEIZHOU · 2016 to 2020
$1.7M
Defining T helper cell associated cytokines and mechanisms in Alopecia AreataK08AR069111 · NIAMS · UNIVERSITY OF IOWA · PI JABBARI, ALI · 2016 to 2020
$854k
The role of paracrine WNT5A/RYK in ErbB2-driven tumorigenesisF30CA206255 · NCI · UNIVERSITY OF IOWA · PI BORCHERDING, NICHOLAS · 2017 to 2020
$143k
NCI NIH HHS F30 CA206255NCI NIH HHS P30 CA086862NCI NIH HHS R01 CA200673NCI NIH HHS R01 CA203834NIAMS NIH HHS K08 AR069111NIGMS NIH HHS T32 GM007337
6 · The paper itself

Abstract

purposeCutaneous T-cell lymphomas (CTCL), encompassing a spectrum of T-cell lymphoproliferative disorders involving the skin, have collectively increased in incidence over the last 40 years. Sézary syndrome is an aggressive form of CTCL characterized by significant presence of malignant cells in both the blood and skin. The guarded prognosis for Sézary syndrome reflects a lack of reliably effective therapy, due, in part, to an incomplete understanding of disease pathogenesis. EXPERIMENTAL

designUsing single-cell sequencing of RNA and the machine-learning reverse graph embedding approach in the Monocle package, we defined a model featuring distinct transcriptomic states within Sézary syndrome. Gene expression used to differentiate the unique transcriptional states were further used to develop a boosted tree classification for early versus late CTCL disease.

resultsOur analysis showed the involvement of

conclusionsThis work offers insight into the heterogeneity of Sézary syndrome, providing better understanding of the transcriptomic diversities within a clonal tumor. This transcriptional heterogeneity can predict tumor stage and thereby offer guidance for therapy.

Indexed as

Biomarkers, TumorGene Expression ProfilingGenetic HeterogeneityArtificial IntelligenceCD4-Positive T-LymphocytesComputational BiologyDisease ProgressionGene Expression Regulation, NeoplasticHumansLymphoma, T-Cell, CutaneousMachine LearningNeoplasm StagingSingle-Cell AnalysisSkin NeoplasmsTranscriptomeBiomarkers, Tumor

Identifiers

PMID30718356
PMCPMC6659117
OpenAlexW2913008832

What OpenQuestion holds

Textmetadata
LicenceTDM
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.