Evidence map›Paper›PMID 34755136›Full record

ArticleCell reports. Medicine2021

Platelet transcriptome identifies progressive markers and potential therapeutic targets in chronic myeloproliferative neoplasms.

Zhu Shen, Wenfei Du, Cecelia Perkins, Lenn Fechter, Vanita Natu, Holden Maecker, Jesse Rowley, Jason Gotlib, James Zehnder, Anandi Krishnan

Open access · goldAbstract read
In one paragraph

Article in Cell reports. Medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed
5.3field-weighted citation impact, top 3% 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

37 citing papers in PubMed, 51 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors at 3 institutions in 1 country.

Zhu ShenDepartment of Statistics, Stanford University, Stanford, CA, USA.
Wenfei DuDepartment of Statistics, Stanford University, Stanford, CA, USA.
Cecelia PerkinsStanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
Lenn FechterStanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
Vanita NatuStanford Functional Genomics Facility, Stanford University School of Medicine, Stanford, CA, USA.
Holden MaeckerDepartment of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA.
Jesse RowleyDepartment of Internal Medicine, University of Utah, Salt Lake City, UT, USA.
Jason GotlibStanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
James ZehnderStanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
Anandi KrishnanStanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
Stanford University · USStanford Medicine · USUniversity of Utah · US

Funding

Stanford Center for Clinical & Translational Education and Research (Spectrum)UL1TR003142 · NCATS · STANFORD UNIVERSITY · PI O'HARA, RUTH M · 2019 to 2023
$45.0M
Spectrum Stanford Center for clinical and Translational Research and EducationUL1TR001085 · NCATS · STANFORD UNIVERSITY · PI CULLEN, MARK RICHARD, GREENBERG, HARRY BERNARD · 2013 to 2017
$37.1M
Mitochondrial fusion protein MFN2 prevents platelet death and dysfunctionR01HL144957 · NHLBI · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI JESSE ROWLEY · 2019 to 2026
$3.2M
Genomics of Megakaryocyte and Platelet BiologyR01HL139825 · NHLBI · WASHINGTON UNIVERSITY · PI DI PAOLA, JORGE A · 2020 to 2023
$2.3M
Integrated Clinical and Transcriptomic Profiling to Characterize Disease PhenotypeK08HG010061 · NHGRI · STANFORD UNIVERSITY · PI KRISHNAN, ANANDI · 2018 to 2022
$955k
High Throughput DNA Sequencer UpgradeS10OD018220 · OD · STANFORD UNIVERSITY · PI COLLER, JOHN · 2014 to 2014
$596k
Object Storage for Secure Data SharingS10OD025082 · OD · STANFORD UNIVERSITY · PI DATTA, SOMALEE · 2018 to 2018
$593k
Computer ClusterS10OD021763 · OD · STANFORD UNIVERSITY · PI JI, HANLEE P · 2016 to 2016
$481k
NCATS NIH HHS UL1 TR001085NCATS NIH HHS UL1 TR003142NHGRI NIH HHS K08 HG010061NHLBI NIH HHS R01 HL139825NHLBI NIH HHS R01 HL144957NIH HHS S10 OD018220NIH HHS S10 OD021763NIH HHS S10 OD025082
6 · The paper itself

Abstract

Predicting disease progression remains a particularly challenging endeavor in chronic degenerative disorders and cancer, thus limiting early detection, risk stratification, and preventive interventions. Here, profiling the three chronic subtypes of myeloproliferative neoplasms (MPNs), we identify the blood platelet transcriptome as a proxy strategy for highly sensitive progression biomarkers that also enables prediction of advanced disease via machine-learning algorithms. The MPN platelet transcriptome reveals an incremental molecular reprogramming that is independent of patient driver mutation status or therapy. Subtype-specific markers offer mechanistic and therapeutic insights, and highlight impaired proteostasis and a persistent integrated stress response. Using a LASSO model with validation in two independent cohorts, we identify the advanced subtype MF at high accuracy and offer a robust progression signature toward clinical translation. Our platelet transcriptome snapshot of chronic MPNs demonstrates a proof-of-principle for disease risk stratification and progression beyond genetic data alone, with potential utility in other progressive disorders.

Indexed as

TranscriptomeAdolescentAdultAgedAged, 80 and overBiomarkers, TumorBlood PlateletsCellular ReprogrammingChildChild, PreschoolCohort StudiesDiagnosis, DifferentialDisease ProgressionFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticBiomarkers, Tumorbiomarkerblood plateletsMPNmyeloproliferative neoplasmsplatelet RNA-seqplatelet transcriptomeprediction algorithmsprogression signaturesproteostasisruxolitinib

Identifiers

PMID34755136
PMCPMC8561315
OpenAlexW3206984067

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.