MamaPred: A new and innovative approach to determine recurrence risk in HR+/HER2- early-stage breast cancer using HTG EdgeSeq technology.
Authors: Lopez Guerrero Jose Antonio , Loucera Carlos , Ramírez-Calvo Marta , Peña María , Fernandez-Serra Antonio , Guerrero-Zotano Angel , Palacios José , Burgues Octavio , Servitja Sonia , Tusquets Ignacio , Peiro Gloria , Bermejo Begoña , Albanell Joan , Llombart-Cussac Antonio , Dopazo Joaquin 
Published: June 2021

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Background: Genomic platforms, such as Mammaprint (Agendia) (MP) and OncoType (Genomic Health) (OT), have been validated to determine the risk of relapse in therapeutic decision-making in early-stage hormone receptor positive (HR+), epidermal growth factor receptor 2 (HER2) negative breast cancer (BC). Discordances in risk allocation between these platforms affect up to 30% of patients. This study aims to develop the MamaPred test to improve the diagnostic performance of recurrence risk in HR+/HER2- early-stage BC. Methods: A total of 606 HR+/HER2- early-stage BC previously tested with OT [n = 287; Low Risk (LR) = 165, Intermediate Risk (IR) = 103 and High Risk (HR) = 19] and MP (n = 319; LR = 217 and HR = 102) were included. A retrospective independent series of 144 HR+/HER2- early-stage BC [median follow-up: 10.53 years (range: 3.1-23.1 yrs); age (median = 62.9 yrs (33-89 yrs); systemic relapse 10.5% (n = 15)] was used as validation set.The expression levels of 2560 cancer-related mRNAs were evaluated from one 5 μm thin-section of a FFPE block (15 mm2 tumor area) using the Oncology Biomarker Panel (OBP) and the HTG EdgeSeq System (HTG Molecular Diagnostics. Inc) and quantified by NGS on a NextSeq550 sequencer (Illumina). A predictive model was built from normalized and logarithmically transformed values (rescaled to [0, 1]) using as response a binary meta-variable constructed by taking the values -1 (for LR of MP and OT together the OT IR) and 1 (for HR MP and OT). Differential expression, GSEA and visualization were performed with DESeq2, gage and pathview packages respectively in R v4.0.1. Results: MamaPred consists of a logistic regression classifier with an elastic net penalty (mix of L1 and L2 priors as regularizer) where the mixing parameter is optimized along with regularization strength by selecting the ones that minimize the area under the precision and recall curve over a validation split for each training fold. Metrics of MamaPred were: balanced accuracy, 80.5%; Kappa, 0.562; specificity, 80.7%; and NPV, 91.4%. GSE analysis on differentially expressed genes (q < 0.1) showed four KEGG pathways overrepresented in HR (p < 0.05): adherens junction, tight junction, glutathione metabolism and focal adhesion; and two underrepresented: DNA replication (p = 0.0765) and pyrimidine metabolism (p = 0.086).The prognostic prediction of MamaPred was validated on the independent retrospective series, distant disease-free survival for HR and LR being 88.63% (95% IC: 78.72%-99.78%) and 98.1% (95% IC: 95.6%-100%) respectively (p = 0.00603). Correlation between the probabilities assigned to any given sample and its replicas was extremely high (r > 0.9 p < 1e-5). Conclusions: MamaPred identifies HR+/HER2- early-stage BC patients with high-risk of distant relapse improving the prognostic value of those studies that compare MP and OT, suggesting a more precise risk classification.