Automated Classification of Breast Cancer Across the Spectrum of Expression Focusing on Heterogeneous Tumors With Low Human Epidermal Growth Factor Receptor 2 Expression.

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.

The g3mclass is a practical software for multiclass classification on biomarkers.

The analytes qualified as biomarkers are potent tools to diagnose various diseases, monitor therapy responses, and design therapeutic interventions. The early assessment of the diverseness of human disease is essential for the speedy and cost-efficient implementation of personalized medicine. We developed g3mclass, the Gaussian mixture modeling software for molecular assay data classification.

CDH2/N-cadherin and early diagnosis of invasion in patients with ductal carcinoma in situ.

Purpose: This proof-of-concept study investigates gene expression in core needle biopsies (CNB) to predict whether individuals diagnosed with ductal carcinoma in situ (DCIS) on CNB were affected by invasion at the time of diagnosis.

Methods: Using a QuantiGene Plex 2.0 assay, 14 gene expression profiling was performed in 303 breast tissue samples.

Improving patient classification and biomarker assessment using Gaussian Mixture Models and Bayes' rule.

In clinical research, determining cutoff values for continuous variables in test results remains challenging, particularly when considering candidate biomarkers or therapeutic targets for disease. Distribution of a continuous variable into two populations is known as dichotomization and has been commonly used in clinical studies. We recently reported a new method for determining multiple cutoffs for continuous variables.

Gaussian Mixture Models for Probabilistic Classification of Breast Cancer.

In the era of omics-driven research, it remains a common dilemma to stratify individual patients based on the molecular characteristics of their tumors. To improve molecular stratification of patients with breast cancer, we developed the Gaussian mixture model (GMM)-based classifier. This probabilistic classifier was built on mRNA expression data from more than 300 clinical samples of breast cancer and healthy tissue and was validated on datasets of , and , which encode standard clinical markers and therapeutic targets.