Molecular Classification of Lobular Carcinoma of the Breast

Abstract

The morphology of breast tumors is complicated and diagnosis can be difficult. We present here a novel diagnostic model which we validate on both array-based and RNA sequencing platforms which reliably distinguishes this tumor type across multiple cohorts. We also examine how this molecular classification predicts sensitivity to common chemotherapeutics in cell-line based assays. A total of 1845 invasive breast cancer cases in six cohorts were collected, split into discovery and validation cohorts, and a classifier was created and compared to pathological diagnosis, grade and survival. In the validation cohorts the concordance of predicted diagnosis with a pathological diagnosis was 92%, and 97% when inconclusively classified cases were excluded. Tumor-derived cell lines were classified with the model as having predominantly ductal or lobular-like molecular physiologies, and sensitivity of these lines to relevant compounds was analyzed. A diagnostic tool can be created that reliably distinguishes lobular from ductal carcinoma and allows the classification of cell lines on the basis of molecular profiles associated with these tumor types. This tool may assist in improved diagnosis and aid in explorations of the response of lobular type breast tumor models to different compounds.

Uterine metastasis of lobular breast carcinoma under tamoxifen therapy: A case report

“…Frequent metastasis of ILC is caused by reduced levels of expression of the cellular adhesion molecule E-cadherin, which is not observed in IDC (12). ILC is also characterized by reduced sensitivity to neoadjuvant chemotherapy compared with IDC (13).…”

The combined signatures of the tumour microenvironment and nucleotide metabolism-related genes provide a prognostic and therapeutic biomarker for gastric cancer

“…Additionally, RNA-seq and clinical data of the GSE84437 cohort, comprising 433 GC samples, were downloaded from the Gene Expression Omnibus (GEO) database as a validation set 24 . Log transformed expression data from raw hybridisation arrays were downloaded and normalised using robust multi-array averaging 25 .…”

Feature Selection for Longitudinal Data by Using Sign Averages to Summarize Gene Expression Values over Time

“…Given that CD has a linearly increased computing burden with the number of genes, it presents excellent power to optimize penalized regression problems. The CD method has been widely utilized in many studies [29–31]. Its key component is the soft-threshold operator S(x, y) defined below.…”