Evidence map›Paper›PMID 42426806›Full record

ArticleGenome biology2026

Bimodality in pan-cancer proteomics reveals new opportunities for biomarker discovery.

Wen Jiang, Damian Bikiel, Jan Zaucha, Jixin Wang, Sanddhya Jayabalan, Yongjin Li, Matthew Sung, James Harper, Jimmy Zhao, Krishna Bulusu and 1 more

Abstract read
PubMed Publisher
In one paragraph

Article in Genome biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

11 authors.

Wen Jiang *Oncology Data Science, Oncology R&D, AstraZeneca, Gaithersburg, MD, USA.
Damian Bikiel *Oncology Data Science, Oncology R&D, AstraZeneca, New York, NY, USA.
Jan Zaucha *Oncology Data Science, Oncology R&D, AstraZeneca, Cambridge, UK.
Jixin WangOncology Data Science, Oncology R&D, AstraZeneca, Gaithersburg, MD, USA.
Sanddhya JayabalanOncology Data Science, Oncology R&D, AstraZeneca, Waltham, MA, USA.
Yongjin LiOncology Data Science, Oncology R&D, AstraZeneca, Waltham, MA, USA.
Matthew SungOncology Targeted Discovery, Oncology R&D, AstraZeneca, Waltham, MA, USA.
James HarperTranslational Medicine, Oncology R&D, AstraZeneca, Cambridge, UK.
Jimmy ZhaoLate Development Oncology, Oncology R&D, AstraZeneca, New York, NY, USA.
Krishna BulusuOncology Data Science, Oncology R&D, AstraZeneca, Cambridge, UK.
Wenyan ZhongOncology Data Science, Oncology R&D, AstraZeneca, New York, NY, USA. wenyan.zhong@astrazeneca.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBimodal protein expression, defined as the distribution of protein expression with two modes, is linked to phenotypic variation across various biological systems. To advance the identification of cancer biomarkers and targets for precision oncology beyond RNA-based studies, we developed a proteomics-specific bimodality model.

resultsBy analyzing proteomics data from various cancer types, 2401 tumor-associated bimodal proteins significantly linked to critical cancer pathways were identified, including amino acid metabolism, extracellular matrix-receptor interaction, and central carbon metabolism. Utilizing an AI-enhanced knowledge graph, we further delineated common patterns among pan-cancer tumor-associated bimodal proteins. A case study on TROP2 in colon adenocarcinoma highlights up-regulation of MYC and WNT/β-catenin pathways and down-regulation of inflammatory pathways in TROP2-high groups.

conclusionThis research highlights the biological differences impacting cancer heterogeneity and vulnerability, ultimately aiding treatment decisions. Our findings illustrate the value of proteomics in uncovering novel biomarkers and advancing precision medicine, paving the way for multi-omics integration and clinical validation.

Indexed as

Biomarkers, TumorNeoplasmsProteomicsCell Adhesion MoleculesGene Expression Regulation, NeoplasticHumansBiomarkers, TumorCell Adhesion Molecules

Identifiers

PMID42426806

What OpenQuestion holds

Textmetadata
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.