Evidence map›Paper›PMID 39227745›Full record

ArticleNature cancer2024

A protein expression atlas on tissue samples and cell lines from cancer patients provides insights into tumor heterogeneity and dependencies.

Jun Li, Wei Liu, Kamalika Mojumdar, Hong Kim, Zhicheng Zhou, Zhenlin Ju, Shwetha V Kumar, Patrick Kwok-Shing Ng, Han Chen, Michael A Davies and 4 more

Abstract read
In one paragraph

Article in Nature cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Review
  6. Functional, Pharmacogenomic, and Immune Landscapes of Long Non-Coding RNAs in Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Enter sandman: how to tackle dormant breast cancer cells.Medical oncology (Northwood, London, England) · 2025
    Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
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

14 authors.

Jun LiDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID http://orcid.org/0000-0002-1171-7141
Wei LiuDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Kamalika MojumdarDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID http://orcid.org/0000-0003-3420-4258
Hong KimDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Zhicheng ZhouDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Zhenlin JuDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Shwetha V KumarDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Patrick Kwok-Shing NgInstitute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Han ChenDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Michael A DaviesDepartment of Melanoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID http://orcid.org/0000-0002-0977-0912
Yiling LuDepartment of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Rehan AkbaniDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. rakbani@mdanderson.org.ORCID http://orcid.org/0000-0003-2686-7721
Gordon B MillsKnight Cancer Institute and Cell, Developmental and Cancer Biology, Oregon Health and Science University, Portland, OR, USA. millsg@ohsu.edu.ORCID http://orcid.org/0000-0002-0144-9614
Han LiangDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. hliang1@mdanderson.org.ORCID http://orcid.org/0000-0001-7633-286X

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Fabien Delerue · 1985 to 2026
$290.8M
UNIVERSITY OF TEXAS--SPORE IN LUNG CANCERP50CA070907 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI HEYMACH, JOHN V., MINNA, JOHN D. · 1996 to 2024
$57.4M
The University of Texas MD Anderson Cancer Center SPORE in Ovarian CancerP50CA281701 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI ROBERT C BAST, ANIL K SOOD · 2023 to 2026
$11.5M
The University of Texas MD Anderson Cancer Center SPORE in MelanomaP50CA221703 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI DAVIES, MICHAEL · 2019 to 2023
$10.3M
TCPA: an Integrated Bioinformatics Resource for Functional Cancer Proteomic DataU24CA209851 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI LIANG, HAN, MILLS, GORDON B. · 2016 to 2020
$4.0M
The Cancer Proteome Atlas: an Integrated Bioinformatics Resource for Functional Cancer Proteomic DataU24CA264128 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Rehan Akbani, Han Liang · 2022 to 2026
$3.9M
Integrative bioinformatics and functional characterization of oncogenic driver aberrations in cancerU01CA217842 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI DENEEN, BENJAMIN, MILLS, GORDON B. · 2017 to 2021
$3.6M
Systematic Characterization and Targeting of Neomorphic Drivers in CancerU01CA281902 · NCI · OREGON HEALTH & SCIENCE UNIVERSITY · PI Benjamin Deneen, Han Liang · 2023 to 2026
$3.5M
Characterization and modeling of m6A RNA methylation in cancerR01CA251150 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI LIANG, HAN · 2020 to 2024
$2.7M
Mechanistic maps of adaptive responses to therapeutic stress to optimize combination therapies.U01CA253472 · NCI · OREGON HEALTH & SCIENCE UNIVERSITY · PI KORKUT, ANIL, LIANG, HAN · 2021 to 2025
$2.6M
Batch effects in molecular profiling data on cancers: detection, quantitation, interpretation, and correctionU24CA210949 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI AKBANI, REHAN, MILLS, GORDON B. · 2016 to 2020
$1.9M
A Genome Data Analysis Center Focused on Batch Effect Analysis and Data IntegrationU24CA264006 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI AKBANI, REHAN, WEINSTEIN, JOHN N · 2021 to 2025
$1.9M
NCI NIH HHS P30 CA016672NCI NIH HHS P50 CA070907NCI NIH HHS P50 CA221703NCI NIH HHS P50 CA281701NCI NIH HHS R01 CA251150NCI NIH HHS R50 CA221675NCI NIH HHS U01 CA217842NCI NIH HHS U01 CA253472NCI NIH HHS U01 CA281902NCI NIH HHS U24 CA210949NCI NIH HHS U24 CA210950NCI NIH HHS U24 CA264006NCI NIH HHS U24 CA264128U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI) U01CA217842U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI) U24CA209851
6 · The paper itself

Abstract

The Cancer Genome Atlas (TCGA) and the Cancer Cell Line Encyclopedia (CCLE) are foundational resources in cancer research, providing extensive molecular and phenotypic data. However, large-scale proteomic data across various cancer types for these cohorts remain limited. Here, we expand upon our previous work to generate high-quality protein expression data for approximately 8,000 TCGA patient samples and around 900 CCLE cell line samples, covering 447 clinically relevant proteins, using reverse-phase protein arrays. These protein expression profiles offer profound insights into intertumor heterogeneity and cancer dependency and serve as sensitive functional readouts for somatic alterations. We develop a systematic protein-centered strategy for identifying synthetic lethality pairs and experimentally validate an interaction between protein kinase A subunit α and epidermal growth factor receptor. We also identify metastasis-related protein markers with clinical relevance. This dataset represents a valuable resource for advancing our understanding of cancer mechanisms, discovering protein biomarkers and developing innovative therapeutic strategies.

Indexed as

Biomarkers, TumorNeoplasmsCell Line, TumorErbB ReceptorsGene Expression Regulation, NeoplasticHumansProteomicsBiomarkers, TumorErbB Receptors

Identifiers

PMID39227745
PMCPMC11999798

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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.