title: 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.
authors.affiliation: Institute of Genomic and Personalized Medicine, College of Life Science, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.
authors.forename: Denggang
authors.initials: D
authors.lastname: Fu
authors.affiliation: Institute of Genomic and Personalized Medicine, College of Life Science, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.
authors.forename: Qi
authors.initials: Q
authors.lastname: Zuo
authors.affiliation: Institute of Genomic and Personalized Medicine, College of Life Science, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.
authors.forename: Qi
authors.initials: Q
authors.lastname: Huang
authors.affiliation: Key Laboratory of Molecular Biophysics, Ministry of Education, College of Life Science, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.
authors.forename: Li
authors.initials: L
authors.lastname: Su
authors.affiliation: Institute of Genomic and Personalized Medicine, College of Life Science, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.
authors.forename: Huijun Z
authors.initials: HZ
authors.lastname: Ring
authors.affiliation: Institute of Genomic and Personalized Medicine, College of Life Science, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.
authors.forename: Brian Z
authors.initials: BZ
authors.lastname: Ring
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000970:Antineoplastic Agents / Q000494:pharmacology; D001943:Breast Neoplasms / Q000145:classification* / Q000175:diagnosis / Q000235:genetics / Q000473:pathology*; D018275:Carcinoma, Lobular / Q000145:classification* / Q000175:diagnosis / Q000235:genetics / Q000473:pathology*; D045744:Cell Line, Tumor; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D008875:Middle Aged; D016019:Survival Analysis; D055815:Young Adult
chemical_list: D000970:Antineoplastic Agents