Evidence map›Paper›PMID 34597583›Full record

ArticleCell2021

Atlas of clinically distinct cell states and ecosystems across human solid tumors.

Bogdan A Luca, Chloé B Steen, Magdalena Matusiak, Armon Azizi, Sushama Varma, Chunfang Zhu, Joanna Przybyl, Almudena Espín-Pérez, Maximilian Diehn, Ash A Alizadeh and 3 more

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 245 papers.

0numbers the graph read from it
0cells of the map it votes in
245citing papers in PubMed
17.4field-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

245 citing papers in PubMed, 318 citations in OpenAlex.

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  18. Multi-omic Profiling of Recurrence Risk Across Breast Cancer Subtypes.medRxiv : the preprint server for health sciences · 2026
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185 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors at 3 institutions in 1 country.

Bogdan A LucaStanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA.
Chloé B SteenDivision of Oncology, Department of Medicine, Stanford University, Stanford, CA 94305, USA; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA.
Magdalena MatusiakDepartment of Pathology, Stanford University, Stanford, CA 94305, USA.
Armon AziziStanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA 94305, USA.
Sushama VarmaDepartment of Pathology, Stanford University, Stanford, CA 94305, USA.
Chunfang ZhuDepartment of Pathology, Stanford University, Stanford, CA 94305, USA.
Joanna PrzybylDepartment of Pathology, Stanford University, Stanford, CA 94305, USA.
Almudena Espín-PérezStanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA 94305, USA.
Maximilian DiehnInstitute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA; Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA.
Ash A AlizadehDivision of Oncology, Department of Medicine, Stanford University, Stanford, CA 94305, USA; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA; Division of Hematology, Department of Medicine, Stanford University, Stanford, CA 94305, USA.
Matt van de RijnDepartment of Pathology, Stanford University, Stanford, CA 94305, USA.
Andrew J GentlesStanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA. Electronic address: andrewg@stanford.edu.
Aaron M NewmanInstitute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA. Electronic address: amnewman@stanford.edu.
Stanford University · USCalifornia Institute for Regenerative Medicine · USStanford Medicine · US

Funding

Stanford Cancer Immune Monitoring and Analysis Center (CIMAC)U24CA224309 · NCI · STANFORD UNIVERSITY · PI Sean Curtis Bendall, Holden T. Maecker · 2017 to 2026
$19.9M
Spatial-Genomic Integrative Multi-Species Analysis of Lymph Node MetastasisU54CA209971 · NCI · STANFORD UNIVERSITY · PI NOLAN, GARRY P, PLEVRITIS, SYLVIA KATINA · 2016 to 2021
$11.0M
Studying macrophage polarization in search for predictive and prognostic markers of breast cancerR01CA229529 · NCI · STANFORD UNIVERSITY · PI Robert michael Angelo, Magdalena Matusiak · 2018 to 2026
$3.9M
A Genomic Framework for Molecular Risk Prediction & Individualized Lymphoma TherapyR01CA233975 · NCI · STANFORD UNIVERSITY · PI ALIZADEH, ASH ARASH, DIEHN, MAXIMILIAN · 2019 to 2023
$2.8M
Delineating developmental programs driving tumorigenesis in triple-negative breast cancerR01CA255450 · NCI · STANFORD UNIVERSITY · PI NEWMAN, AARON M · 2021 to 2025
$2.5M
Ultrasensitive Quantitation of Circulating Tumor DNAR00CA187192 · NCI · STANFORD UNIVERSITY · PI NEWMAN, AARON M · 2017 to 2019
$747k
The prognostic landscape of gender- and ethnicity-specific immune influences on cancer outcomesR21CA238971 · NCI · STANFORD UNIVERSITY · PI GENTLES, ANDREW J. · 2019 to 2020
$374k
NCI NIH HHS R00 CA187192NCI NIH HHS R01 CA229529NCI NIH HHS R01 CA233975NCI NIH HHS R01 CA255450NCI NIH HHS R21 CA238971NCI NIH HHS U24 CA224309NCI NIH HHS U54 CA209971
6 · The paper itself

Abstract

Determining how cells vary with their local signaling environment and organize into distinct cellular communities is critical for understanding processes as diverse as development, aging, and cancer. Here we introduce EcoTyper, a machine learning framework for large-scale identification and validation of cell states and multicellular communities from bulk, single-cell, and spatially resolved gene expression data. When applied to 12 major cell lineages across 16 types of human carcinoma, EcoTyper identified 69 transcriptionally defined cell states. Most states were specific to neoplastic tissue, ubiquitous across tumor types, and significantly prognostic. By analyzing cell-state co-occurrence patterns, we discovered ten clinically distinct multicellular communities with unexpectedly strong conservation, including three with myeloid and stromal elements linked to adverse survival, one enriched in normal tissue, and two associated with early cancer development. This study elucidates fundamental units of cellular organization in human carcinoma and provides a framework for large-scale profiling of cellular ecosystems in any tissue.

Indexed as

Tumor MicroenvironmentCell SurvivalGene Expression Regulation, NeoplasticHumansImmunotherapyInflammationLigandsNeoplasmsPhenotypePrognosisTranscription, GeneticLigandscancer genomicscell statescellular communitiesCIBERSORTxecosystemsEcoTyperecotypesexpression deconvolutiontumor immunologytumor microenvironment

Identifiers

PMID34597583
PMCPMC8526411
OpenAlexW3202521679

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.