Evidence map›Paper›PMID 39936522›Full record

ArticleJournal of proteome research2025

Deciphering Proteoform Landscape of Mammary Carcinoma by Top-Down Proteomics.

Samantha J Knott, Trisha Tucholski, Harini Josyer, David Inman, Andreas Friedl, Yanlong Zhu, Ying Ge, Suzanne M Ponik

Abstract read
In one paragraph

Article in Journal of proteome research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Samantha J KnottDepartment of Chemistry, University of Wisconsin-Madison, 1101 University Ave., Madison, Wisconsin 53706, United States.
Trisha TucholskiDepartment of Chemistry, University of Wisconsin-Madison, 1101 University Ave., Madison, Wisconsin 53706, United States.
Harini JosyerDepartment of Cell and Regenerative Biology, University of Wisconsin-Madison, 1111 Highland Ave., Madison, Wisconsin 53705, United States.
David InmanDepartment of Cell and Regenerative Biology, University of Wisconsin-Madison, 1111 Highland Ave., Madison, Wisconsin 53705, United States.
Andreas FriedlDepartment of Pathology and Laboratory Medicine, University of Wisconsin-Madison, 1685 Highland Ave., Madison, Wisconsin 53705, United States.
Yanlong ZhuDepartment of Cell and Regenerative Biology, University of Wisconsin-Madison, 1111 Highland Ave., Madison, Wisconsin 53705, United States.ORCID 0000-0003-4909-7336
Ying GeDepartment of Chemistry, University of Wisconsin-Madison, 1101 University Ave., Madison, Wisconsin 53706, United States.ORCID 0000-0001-5211-6812
Suzanne M PonikDepartment of Cell and Regenerative Biology, University of Wisconsin-Madison, 1111 Highland Ave., Madison, Wisconsin 53705, United States.ORCID 0000-0003-1367-4349

Funding

Matrix density promotes pro-tumorigenic hormone actions in breast cancerR01CA179556 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI PONIK, SUZANNE MARIE, SCHULER, LINDA A. · 2014 to 2024
$4.9M
Exosome secretion in breast cancer progressionR01CA206458 · NCI · VANDERBILT UNIVERSITY · PI PONIK, SUZANNE MARIE, WEAVER, ALISSA M · 2016 to 2025
$4.6M
Enabling Top-Down Proteomics through Materials Chemistry and NanotechnologyR01GM117058 · NIGMS · UNIVERSITY OF WISCONSIN-MADISON · PI GE, YING, JIN, SONG · 2015 to 2023
$2.7M
Ultra High Resolution Mass Spectrometer for Biomedical ResearchS10OD018475 · OD · UNIVERSITY OF WISCONSIN-MADISON · PI GE, YING · 2015 to 2015
$2.0M
MASH Explorer, a Comprehensive Software Environment for Top-Down ProteomicsR01GM125085 · NIGMS · UNIVERSITY OF WISCONSIN-MADISON · PI GE, YING · 2018 to 2021
$1.2M
NCI NIH HHS R01 CA179556NCI NIH HHS R01 CA206458NIGMS NIH HHS R01 GM117058NIGMS NIH HHS R01 GM125085NIH HHS S10 OD018475
6 · The paper itself

Abstract

Defining the proteoform landscape of breast cancer can provide unique insights into the signaling pathways driving disease progression. While bottom-up proteomics has been utilized to profile breast cancer, it lacks the ability to capture intact proteoforms that may underpin the disease. Top-down proteomics is ideally suited to characterize intact proteoforms; however, most top-down proteomics studies have been limited to low molecular weight (MW) proteins (<50 kDa). Herein, we employed a two-dimensional (2D) liquid chromatography combining size exclusion chromatography (SEC) with reverse phase chromatography (RPC) followed by high-resolution mass spectrometry (MS) to expand the coverage for high MW proteoforms. Using this 2D-SEC-RPC-MS approach, we observed a 5-fold increase in the detection of high MW proteoforms (>50 kDa) compared to the conventional 1D-RPC-MS. SEC separation significantly enhanced the detection of high MW proteoforms (>104 kDa), including intermediate filament proteins, vimentin and keratins. Based on accurate mass measurements and MS/MS data, we identified 775 proteoforms from both TFA and HEPES extracts and detected PTMs, such as acetylation, glutathionylation, and myristoylation. Pathway analysis uncovered many proteoforms involved in processes dysregulated in cancer progression. Overall, our findings illustrate the power of top-down proteomics in defining the proteoform landscape of breast carcinoma.

Indexed as

Breast NeoplasmsProteomeProteomicsAnimalsChromatography, GelChromatography, LiquidChromatography, Reverse-PhaseFemaleHumansTandem Mass SpectrometryProteomebreast cancercytoskeletal organizationmetabolic pathwaysMMTV-PyMT mammary tumorsmyristolationpost-translational modificationsproteoformssize exclusion chromatographytop-down proteomicstwo-dimensional liquid chromatography

Identifiers

PMID39936522
PMCPMC12006981

What OpenQuestion holds

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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.