Breast Cancer Biomarker Prediction Model Based on Principal Component Extraction and Deep Convolutional Network Integration Learning

Kun Ruan,Tanke Wang,Yuhao Peng,Yuhan Kang,Shun Zhao,Tao Liu,Yuchi Zhang,L Yang,Z Xu

doi:10.1051/e3sconf/202018504028

Kun Ruan, Tanke Wang + Show 7 more

Open Access

https://doi.org/10.1051/e3sconf/202018504028

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Abstract

Effective extraction of characteristic information from sequencing data of cancer patients is an essential application for cancer research. Several prognostic classification models for breast cancer sequencing data have been established to assist patients in their treatment. However, these models still have problems such as poor robustness and low precision. Based on the convolutional network model in deep learning, we construct a new classifier PCA-1D LeNet-Ada (PLA) by using principal component extraction method, Le-Net convolution network, and Adaptive Boosting method. PLA predicts three biomarkers for breast cancer patients based on their somatic cell copy number variations and gene expression profiles.

Highlights

Determining the relationship between sequencing data and cancer characterization is significant for clinical decision-making and diagnosis and treatment
Based on progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and estrogen receptor (ER), breast cancer can be divided into four molecular subtypes: Luminal A (ER+, PR+, and HER2-), Luminal B (ER+, PR±, and Her2±), Her2-overexpression (ER, PR- and HER2+) and triple-negative (ER, PR- and HER2-) [2, 3]
This paper compares the classification results with traditional models, and the results show that the Principal component analysis (PCA)-1D LeNet-Ada (PLA) model is better for molecular receptor classification

Summary

Introduction

Determining the relationship between sequencing data and cancer characterization is significant for clinical decision-making and diagnosis and treatment. Based on progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and estrogen receptor (ER), breast cancer can be divided into four molecular subtypes: Luminal A (ER+, PR+, and HER2-), Luminal B (ER+, PR±, and Her2±), Her2-overexpression (ER-, PR- and HER2+) and triple-negative (ER-, PR- and HER2-) [2, 3]. Many scholars have combined deep learning with gene sequencing data for cancer analysis experiments. Xiaofan Ding et al tested the possible correlation between recurrent CNV in the genome and the huge risk of cancer [7], and Md. Mohaiminul Islam et al established a prediction of breast cancer molecular subtypes based on changes in somatic copy number Deep learning model [8]

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