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Updated: Aug 1, 2026
Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence
Published on: October 25, 2011
Automated Classification of Breast Cancer Across the Spectrum of ERBB2 Expression Focusing on Heterogeneous Tumors
1Department of Surgery, Division of Endocrine & Oncologic Surgery, Harrison Department of Surgical Research, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
Purpose:
Although pharmaceutical companies conduct clinical trials of novel human epidermal growth factor receptor 2 (HER2)-low-directed drugs, diagnosing HER2-low cancer by immunohistochemistry (IHC) and in situ hybridization (ISH) remains challenging. This study investigates the performance of first-in-kind computerized intelligence to classify samples across gene expression levels and differentiate HER2-low tumors.
Materials And Methods:
We classified 251 samples: 142 primary invasive breast cancers (IBCs), 75 ductal carcinomas in situ (DCIS), and 34 mammaplasties (reference) using mRNA expression data from the QuantiGene Plex 2.0 assay. We used g3mclass probabilistic software to assess the number of classes in the assay data, the mean and the variance in each class, diagnostic cutoffs, and the prevalence of each class in the study population.
Results:
HER2-low (IHC score of 1+ or 2+/ISH-) accounted for 31% of IBC. First, we discovered that HER2-low tumors were represented by cases with normal ERBB2 transcript levels that were expected to produce physiologic levels of HER2 (70%) and cases with abnormally upregulated unamplified ERBB2 (30%). We termed the latter cancers ERBB2-up as they do not meet the standard definitions for ERBB2 overexpression and amplification. Second, HER2-low IBC classified as ERBB2-up had not only abnormally increased luminal growth and adhesion markers (ERBB2, ESR1, PGR, IGF1R, VAV2, VAV3, KRT8, CDH1) but also downregulated myoepithelial marker (KRT5). The vascularization (RAP1 and C3G), immune cell infiltration (VAV1), and mesenchymal transition (CDH2) markers were dysregulated. Finally, in the independent cohort of DCIS, 40% of HER2-low DCIS shared similar traits with HER2-low IBC except for rare downregulation of KRT5 and no change in C3G, VAV1, and CDH2.
Conclusion:
We demonstrated how innovative bioinformatic tools could help diagnose cancer across the spectrum of ERBB2 expression to aid decision making for HER2-low.
Insights
This study introduces computerized intelligence to classify HER2-low breast cancer, identifying a new subtype called ERBB2-up. This aids in diagnosing challenging HER2-low tumors for targeted therapies.
Area of Science:
- Oncology
- Bioinformatics
- Genomics
Background:
- Diagnosing human epidermal growth factor receptor 2 (HER2)-low breast cancer is challenging using standard immunohistochemistry (IHC) and in situ hybridization (ISH) methods.
- Clinical trials for novel HER2-low targeted therapies necessitate accurate diagnostic tools.
Purpose of the Study:
- To investigate the performance of computerized intelligence in classifying samples across gene expression levels.
- To differentiate HER2-low tumors, including a newly identified subtype, using advanced bioinformatic approaches.
Main Methods:
- Classified 251 samples (142 primary invasive breast cancers (IBCs), 75 ductal carcinomas in situ (DCIS), 34 mammaplasties) using mRNA expression data.
- Employed probabilistic software (g3mclass) to analyze gene expression data, identify classes, and establish diagnostic cutoffs.
Main Results:
- HER2-low IBC constituted 31% of cases, with 70% showing normal ERBB2 transcript levels and 30% exhibiting abnormally upregulated, unamplified ERBB2 (termed ERBB2-up).
- ERBB2-up HER2-low IBC displayed dysregulated markers for luminal growth, adhesion, myoepithelial function, vascularization, immune infiltration, and mesenchymal transition.
- HER2-low DCIS shared similarities with HER2-low IBC, with distinct differences in specific marker expression.
Conclusions:
- Innovative bioinformatic tools can accurately diagnose cancers across the spectrum of ERBB2 expression.
- This approach aids in the decision-making process for HER2-low breast cancer diagnosis and treatment.
