Prediction of prognostic signatures in triple-negative breast cancer based on the differential expression analysis via NanoString nCounter immune panel

• Published in: BMC cancer Vol. 20; no. 1; pp. 1052 - 13
• Main Authors: Lim, Gyeong Back, Kim, Young-Ae, Seo, Jeong-Han, Lee, Hee Jin, Gong, Gyungyub, Park, Sung Hee
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 02.11.2020; BioMed Central; BMC
• Subjects: Accuracy; Biological markers; Biomarkers; Biomarkers, Tumor - genetics; Biopsy; Breast cancer; Cancer therapies; Care and treatment; Chemotherapy; Data analysis; Datasets; Diagnosis; Differential expression; ErbB-2 protein; Extracellular Matrix Proteins - genetics; Feature selection; Female; Gene expression; Gene Expression Profiling - instrumentation; Gene Expression Profiling - methods; Gene Expression Regulation, Neoplastic; Genetic aspects; Health aspects; Humans; Identification and classification; Immune response; Immunity; Immunology; Medical prognosis; Models, Genetic; NanoString nCounter immune panel; Pathological complete response; Prediction models; Progesterone; Prognosis; Prognostic marker; Proportional Hazards Models; Receptor, Endothelin B - genetics; Receptors, IgE - genetics; Relapse; Survival; Survival Analysis; Transforming Growth Factor beta - genetics; Triple negative breast cancer; Triple Negative Breast Neoplasms - genetics; Triple Negative Breast Neoplasms - mortality; Tumors; South Korea; Relapse; NanoString nCounter immune panel; Prediction model; Pathological complete response; Differential expression; Prognostic marker; Triple negative breast cancer; Random Forest
• ISSN: 1471-2407; 1471-2407
• DOI: 10.1186/s12885-020-07399-8

Triple-Negative Breast Cancer (TNBC) is an aggressive and complex subtype of breast cancer. The current biomarkers used in the context of breast cancer treatment are highly dependent on the targeting of oestrogen receptor, progesterone receptor, or HER2, resulting in treatment failure and disease recurrence and creating clinical challenges. Thus, there is still a crucial need for the improvement of TNBC treatment; the discovery of effective biomarkers that can be easily translated to the clinics is essential. We report an approach for the discovery of biomarkers that can predict tumour relapse and pathologic complete response (pCR) in TNBC on the basis of mRNA expression quantified using the NanoString nCounter Immunology Panel. To overcome the limited sample size, prediction models based on random Forest were constructed using the differentially expressed genes (DEGs) as selected features. We also evaluated the differences between pre- and post-treatment groups aiming for the combinatorial assessment of pCR and relapse using additive models in edgeR. We identify nine and 13 DEGs strongly associated with pCR and relapse, respectively, from 579 immune genes in a small number of samples (n = 55) using edgeR. An additive model for the comparison of pre- and post-treatment groups via the adjustment of the independent subject in the relapse group revealed associations for 41 genes. Comprehensive analysis indicated that our prediction models outperformed those constructed using features extracted from the existing feature selection model Elastic Net in terms of accuracy. The prediction models were assessed using a randomization test to validate the robustness (empirical P for the model of pCR = 0.015 and empirical P for the model of relapse = 0.018). Furthermore, three DEGs (FCER1A, EDNRB, and TGFBI) in the model of relapse showed prognostic significance for predicting the survival of patients with cancer through Cox proportional hazards regression model-based survival analysis. Gene expression quantified via the NanoString nCounter Immunology Panel can be seamlessly analysed using edgeR, even considering small sample sizes. Our approach provides a scalable framework that can easily be applied for the discovery of biomarkers based on the NanoString nCounter Immunology Panel. The source code will be available from github at https://github.com/sungheep/nanostring .