Abstract P6-07-07: Triple negative breast cancer classification according to cancer stem cell hypothesis

Background:

Triple negative breast cancer (TNBC) is described by lack of ER and PR expression and HER2 overexpression. This subgroup has no targeted therapies and its prognosis its worse than other breast cancer subtypes. The Cancer Stem Cell hypothesis lies in two ideas; first, that breast cancers can initiate in different cell types, which can be any epithelial stem cells or any of their progeny, and, that this original cell will give the tumor a specific molecular profile. Our work proposes a division in molecular groups of TNBC looking for this tissue of origin molecular profile through gene expression data and probabilistic graphical models analyses.

Material and methods:

TNBC gene expression data was obtained from GSE31519 (n=494). 2000 most variable genes were selected for subsequent analysis. A functional network was built using a probabilistic graphical model approach. Functional nodes were defined, and its function was explored by Gene Ontology using DAVID. Then, a new molecular classification was generated using the activity of functional nodes and k-means. Subgroups were characterized and compared with previous TNBC molecular classifications.

Results:

Probabilistic graphical models defined a functional structure comprising 27 functional nodes. We found some Luminal, some Basal and a claudin-enriched node. Based on these nodes molecular subgroups were defined, following the cancer stem cell hypothesis. Thus, four subtypes: (Luminal Androgen receptor (LAR), basal, claudin-low and claudin-high were defined matching tumor origin characteristics with those in the actual tumor sample. Tumors with low expression of claudins (CLDN-low subtype) had been differentiated in the first steps of the mammary epithelial development. Tumors with high expression of claudins (CLDN-High) had been originated at the second step of the development. Next step in the development are basal-epithelial cells, which will generate Basal subtype of TNBC. And finally, the last step in development is the differentiation to luminal cell, which is the origin of the Luminal subtype. Immune status, determined by immune functional nodes, showed prognostic value (p>0.05). Finally, we compared our classification with previous ones defined by Lehmann, Burstein and the PAM50.

Table 1Cellular ClassificacionTumor sizeGradeNodal T1RestG3G1 or G2N0N1Basal761631994516835CLDN-High227315194CLDN-Low103221202811LAR115429333618Total9927628010325168

Conclusion:

Functional networks can provide a relevant molecular knowledge which complements the TNBC classification. From this approach we establish a new classification taking into account the cancer stem cell hypothesis. Besides, this deep knowledge will allow a more accurate prediction of outcome and can also be used for diagnostic purposes and therapy selection.

Citation Format: Zamora-Auñón P, Trilla-Fuertes L, Diaz-Almiron M, Gamez-Pozo A, Prado-Vázquez G, Zapater-Moros A, Llorente-Armijo S, Gaya Romero F, Espinosa Arranz E, Fresno-Vara JA. Triple negative breast cancer classification according to cancer stem cell hypothesis [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P6-07-07.

Abstract P4-05-04: Proteomic patterns unravel a new luminal-A breast cancer molecular subgroup with prognostic value

Background: Breast cancer is a heterogeneous disease including a variety of entities with different genetic background. Estrogen receptor-positive, HER2 negative tumors (ER+) usually have a favorable outcome, although some patients eventually relapse, which suggests some heterogeneity within this category.

In the last years, proteomic approaches have been incorporated to the study of clinical samples as a way to complement the information provided by gene analysis. Shotgun proteomics allows measuring over 1,000 proteins in clinical samples. In the present study, we combined genomic and proteomic techniques to characterize a set of breast tumors.

Methods: The study population consisted of 102 patients with lymph-node positive breast cancer who had received anthracycline-based adjuvant chemotherapy. Protein extracts from FFPE samples were prepared in 2% SDS buffer and digested with trypsin. SDS was removed from digested lysates, and resulting peptides were analyzed in an Orbitrap Velos. Protein abundance was calculated on the basis of the normalized spectral protein intensity (LFQ intensity) using MaxQuant. A prognostic protein signature was built. Findings were verified using whole genome gene expression data from 1,141 patients included in public repositories. To this purpose, the protein signature was converted to a gene signature. Data analysis was done using MeV, BRBArray Tools, R and Cytoscape software suites and Uniprot (http://www.uniprot.org/) and DAVID (http://david.abcc.ncifcrf.gov) webtools.

Results: We identified 3,000 protein groups in FFPE breast cancer samples and selected 1,000 that were identified at least in 75% of the samples. Significance Analysis for Microarrays analysis revealed 224 protein groups differentially expressed between ER+ and triple-negative (TN) samples (False Discovery Rate set at <0.001). Hierarchical clustering analyses of protein expression showed that some ER+ samples had a protein expression profile similar to that of TN samples: patients with TN-like tumors had a clinical outcome similar to those with TN disease. Gene ontology analyses unraveled a reduced expression of proteins related with cellular adhesion in the TN-like and the TN groups. A TN-like predictive protein signature was built, converted to a gene signature and evaluated in the whole-genome expression data. The signature had prognostic value in patients with luminal-A breast cancer. This prognostic information was independent from that provided by standard genomic tests for breast cancer, such as MammaPrint, OncoType Dx and the 8-gene Score.

Conclusions: Proteomic profiling showed that cellular adhesion is a differential process between ER+ and TN breast cancer, and is reduced in the TN tumors. A group of ER+ breast tumors with reduced cellular adhesion was identified (TN-like). Patients with this luminal-A, TN-like breast cancer type had a poor outcome. This prognostic information was complementary to that offered by genomic tests such as OncoType.

Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P4-05-04.