Evidence map›Paper›PMID 35173148›Full record

ArticleNature communications2022

Proteomic analysis of archival breast cancer clinical specimens identifies biological subtypes with distinct survival outcomes.

Karama Asleh, Gian Luca Negri, Sandra E Spencer Miko, Shane Colborne, Christopher S Hughes, Xiu Q Wang, Dongxia Gao, C Blake Gilks, Stephen K L Chia, Torsten O Nielsen and 1 more

Registry-linked trialAbstract read
In one paragraph

Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05985577 (Mechanism of Gastric Signet Ring Cell Carcinoma Based on Microproteomics), which is not on this map. Cited by 74 papers.

0numbers the graph read from it
0cells of the map it votes in
74citing 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.

NCT05985577 unknown statusnot on this mapstarted 2023, after this paper: background citation

Mechanism of Gastric Signet Ring Cell Carcinoma Based on Microproteomics

Typeobservational_patient_registrySponsorZhejiang Cancer HospitalRan2023 to 2024Enrolled300ConditionsGastric Cancer, Signet Ring Cell CarcinomaArmsWithout any intervention
3 · Its place in the literature

Who cites it

74 citing papers in PubMed.

  1. Review
  2. Review
  3. Assessing the Effects of a 3-Dimensional (3D) Pathology Tissue-Processing Workflow on Downstream Molecular Analyses.Laboratory investigation; a journal of technical methods and pathology · 2026
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  10. Observational
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  15. Review
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14 more citing papers are in PubMed but not listed here.

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.

Karama Asleh *Genetic Pathology Evaluation Centre, Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada.ORCID 0000-0002-2873-7253
Gian Luca Negri *Canada's Michael Smith Genome Sciences Centre, BC Cancer Research Institute, University of British Columbia, Vancouver, BC, Canada.ORCID 0000-0001-7722-8888
Sandra E Spencer MikoCanada's Michael Smith Genome Sciences Centre, BC Cancer Research Institute, University of British Columbia, Vancouver, BC, Canada.ORCID 0000-0001-6446-1584
Shane ColborneCanada's Michael Smith Genome Sciences Centre, BC Cancer Research Institute, University of British Columbia, Vancouver, BC, Canada.
Christopher S HughesDepartment of Molecular Oncology, BC Cancer Research Institute, University of British Columbia, Vancouver, BC, Canada.ORCID 0000-0002-6980-4459
Xiu Q WangGenetic Pathology Evaluation Centre, Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada.
Dongxia GaoGenetic Pathology Evaluation Centre, Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada.
C Blake GilksDivision of Anatomical Pathology, Vancouver General Hospital, University of British Columbia, Vancouver, Canada.
Stephen K L ChiaDivision of Medical Oncology, British Columbia Cancer Centre, University of British Columbia, Vancouver, BC, Canada.
Torsten O NielsenGenetic Pathology Evaluation Centre, Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada.ORCID 0000-0003-3769-2517
Gregg B MorinCanada's Michael Smith Genome Sciences Centre, BC Cancer Research Institute, University of British Columbia, Vancouver, BC, Canada. gmorin@bcgsc.ca.ORCID 0000-0001-8949-4374

Funding

CIHR
6 · The paper itself

Abstract

Despite advances in genomic classification of breast cancer, current clinical tests and treatment decisions are commonly based on protein level information. Formalin-fixed paraffin-embedded (FFPE) tissue specimens with extended clinical outcomes are widely available. Here, we perform comprehensive proteomic profiling of 300 FFPE breast cancer surgical specimens, 75 of each PAM50 subtype, from patients diagnosed in 2008-2013 (n = 178) and 1986-1992 (n = 122) with linked clinical outcomes. These two cohorts are analyzed separately, and we quantify 4214 proteins across all 300 samples. Within the aggressive PAM50-classified basal-like cases, proteomic profiling reveals two groups with one having characteristic immune hot expression features and highly favorable survival. Her2-Enriched cases separate into heterogeneous groups differing by extracellular matrix, lipid metabolism, and immune-response features. Within 88 triple-negative breast cancers, four proteomic clusters display features of basal-immune hot, basal-immune cold, mesenchymal, and luminal with disparate survival outcomes. Our proteomic analysis characterizes the heterogeneity of breast cancer in a clinically-applicable manner, identifies potential biomarkers and therapeutic targets, and provides a resource for clinical breast cancer classification.

Indexed as

Biomarkers, TumorBreastFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansProteomeProteomicsTreatment OutcomeTriple Negative Breast NeoplasmsBiomarkers, TumorProteome

Identifiers

PMID35173148
PMCPMC8850446

What OpenQuestion holds

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Registered trials

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.