Refinement of Triple-Negative Breast Cancer Molecular Subtypes: Implications for Neoadjuvant Chemotherapy Selection

Abstract

Triple-negative breast cancer (TNBC) is a heterogeneous disease that can be classified into distinct molecular subtypes by gene expression profiling. Considered a difficult-to-treat cancer, a fraction of TNBC patients benefit significantly from neoadjuvant chemotherapy and have far better overall survival. Outside of BRCA1/2 mutation status, biomarkers do not exist to identify patients most likely to respond to current chemotherapy; and, to date, no FDA-approved targeted therapies are available for TNBC patients. Previously, we developed an approach to identify six molecular subtypes TNBC (TNBCtype), with each subtype displaying unique ontologies and differential response to standard-of-care chemotherapy. Given the complexity of the varying histological landscape of tumor specimens, we used histopathological quantification and laser-capture microdissection to determine that transcripts in the previously described immunomodulatory (IM) and mesenchymal stem-like (MSL) subtypes were contributed from infiltrating lymphocytes and tumor-associated stromal cells, respectively. Therefore, we refined TNBC molecular subtypes from six (TNBCtype) into four (TNBCtype-4) tumor-specific subtypes (BL1, BL2, M and LAR) and demonstrate differences in diagnosis age, grade, local and distant disease progression and histopathology. Using five publicly available, neoadjuvant chemotherapy breast cancer gene expression datasets, we retrospectively evaluated chemotherapy response of over 300 TNBC patients from pretreatment biopsies subtyped using either the intrinsic (PAM50) or TNBCtype approaches. Combined analysis of TNBC patients demonstrated that TNBC subtypes significantly differ in response to similar neoadjuvant chemotherapy with 41% of BL1 patients achieving a pathological complete response compared to 18% for BL2 and 29% for LAR with 95% confidence intervals (CIs; [33, 51], [9, 28], [17, 41], respectively). Collectively, we provide pre-clinical data that could inform clinical trials designed to test the hypothesis that improved outcomes can be achieved for TNBC patients, if selection and combination of existing chemotherapies is directed by knowledge of molecular TNBC subtypes.

Integrin-β4 identifies cancer stem cell-enriched populations of partially mesenchymal carcinoma cells

“…Multiple distinct subtypes of TNBC cells have been described based on global gene expression analyses (19,20); however, little information existed in the literature regarding how such subtypes of TNBC cells could be grouped or physically segregated based on their cell-surface marker profiles. To address this issue, we performed a series of analyses using highly epithelial immortalized mammary epithelial cells (HMLEs) (21), and their derived naturally arising mesenchymal epithelial cell (NAMEC) population, NAMEC8 (22), which led to the identification of ITGB4 as a cell surface protein that could be used for fluorescence-activated cell sorting (FACS) to segregate highly epithelial and mesenchymal-like mammary epithelial cells according to their relative epithelial versus mesenchymal cell states (SI Appendix, Fig.…”

Multidimensional phenotyping of breast cancer cell lines to guide preclinical research

“…These studies proposed various frameworks in which each line could be categorized. For example, expression array studies identified three major intrinsic subtypes (Luminal, Basal-A and Basal-B/claudin-low) [1,4,7], and 4-6 triplenegative (TN) cell line subgroups [3,10]. Expression of breast epithelial lineage commitment markers can also discriminate phenotypic and functional classes, such as CD24 and CD44 (associated with differentiation and tumourigenicity); and EpCAM and CD49f, which exhibit restricted expression in the two major functional compartments (luminal or myoepithelial) of the mammary gland [2,11,12].…”

Identification of Genes Differentially Associated with Triple Negative Breast Cancers

“…DNA microarrays allow (a) the investigator to interrogate genes that are being expressed in a particular sample, at a defined interval, (b) gene expression profiles for different samples to be generated and compared utilizing bioinformatics methods, (c) for investigators to identify genes that are differentially associated between patient samples and (d) for investigators to identify cell signaling pathways that differ between particular sample types. Utilizing DNA microarray and hierarchical clustering methods, Lehmann et al (11) and others (12) (13) convincingly establish that TNBC does not represent a single group of patients but instead is a heterogeneous subtype representing diverse types of patients. Lehmann et al found that TNBC patient samples can be subdivided into six subclasses, based on their relationships to particular cellular processes.…”