Abstract P3-07-10: Triple negative breast cancer tumors subtyping by means of integrated transcriptome and proteome analyses
Authors: Campone M , Guette C , Lasla H , Gouraud W , Guérin-Charbonnel C , Jézéquel P 
Published: May 2019

Abstract
Background: Heterogeneity and lack of targeted therapies represent the two main impediments to precision treatment of triple-negative breast cancer (TNBC) and therefore, molecular subtyping and identification of therapeutic pathways are required to optimize medical care. The aim of the present work was to confirm unsupervised analysis of TNBC transcriptomic data by means of proteomics.
Methods: Transcriptome and proteome of 83 TNBC macrodissected tumors were screened in parallel. These patients were described in a previous study [Jézéquel, et al., Breast Cancer research (2015) 17, 43). Transcriptome screening was performed using Affymetrix Human Genome U133 Plus 2.0 Arrays (AffymetrixÒ, Santa Clara, CA). Proteome profiling was performed by means of iTRAQ-OFFGEL-LC-MS/MS approach [Campone, et al, Mol Cell Proteomics (2015) 14, 2936-2946].
Results: Unsupervised analysis of transcriptomic data identified three molecular clusters within TNBC: one molecular apocrine (C1: 20%) and two basal-like-enriched (C2: 47% and C3: 33%). C2 presented pro-tumorigenic immune response and C3 exhibited adaptive immune response.
iTRAQ-OFFGEL-LC-MS/MS screening identified 366 out of 1,929 unique proteins, which were quantified in at least 70% of TNBC tumors and therefore could be used for analysis. Principal component analysis (PCA) with projection of 83 TNBC onto the first principal plane showed inhomogeneous distribution: one largest group (n = 77) and two outlier groups composed of three tumors, which have been eliminated for the rest of the work. In order to look for the existence of a partition of 77 TNBC cohort based on proteomics data, we performed clustering analysis using fuzzy clustering. Gap statistic was used to estimate the optimal number of clusters. This number was equal to one, whatever the metric. PCA and estimation of the number of clusters results lead us to conclude that iTRAQ-OFFGEL-LC-MS/MS data could not be used alone to subtype our 77 TNBC cohort most probably due to insufficient information content of proteomics matrix.
TNBC cluster assignment, based on transcriptomics was applied to these tumors. Sixty-two out 366 ANOVA analyses were significant between the three clusters (p &lt; 0.05). Twenty-two differentially expressed proteins between C1, C2 and C3 belonged to biological categories, which characterized these TNBC clusters. Gene Ontology enrichment analysis based on the set of proteins highly expressed in C2 compared to C1 and C3 (n = 21) displayed enrichment in genes coding for protein involved in extracellular matrix, wound response and RNA splicing.
Table 1.Proteins found differentially expressed between TNBC cluster defined by means of transcriptomicsProteinsBiological categoriesClustersK2C7, K2C8, K1C18, K1C19LuminalC1FAS, UGDHAndrogen induced (molecular apocrine) LDHBBasal-likeC2PLMN, POSTN, FLNB, TENA, PLOD3, FSCN1, SERPH, FINCInvasion, extracellular matrix MOESBasal-likeC3STAT1, SYWC, AMPL, SAMH1Interferon pathway IGKC, IGHMImmunoglobulines
Conclusion: Although iTRAQ-OFFGEL-LC-MS/MS screening did not contain enough information for cluster identification, 22 proteins, which were differentially expressed between the three clusters corroborate transcriptomic subtyping of TNBC.
Citation Format: Campone M, Guette C, Lasla H, Gouraud W, Guérin-Charbonnel C, Jézéquel P. Triple negative breast cancer tumors subtyping by means of integrated transcriptome and proteome analyses [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P3-07-10.