Immunosignature Screening for Multiple Cancer Subtypes Based on Expression Rule.

Lei Chen,Yunhua Zhang,Tao Zeng,Tao Huang,Yu-Hang Zhang,Xiaoyong Pan,Yu-Dong Cai

doi:10.3389/fbioe.2019.00370

Abstract

Liquid biopsy (i.e., fluid biopsy) involves a series of clinical examination approaches. Monitoring of cancer immunological status by the “immunosignature” of patients presents a novel method for tumor-associated liquid biopsy. The major work content and the core technological difficulties for the monitoring of cancer immunosignature are the recognition of cancer-related immune-activating antigens by high-throughput screening approaches. Currently, one key task of immunosignature-based liquid biopsy is the qualitative and quantitative identification of typical tumor-specific antigens. In this study, we reused two sets of peptide microarray data that detected the expression level of potential antigenic peptides derived from tumor tissues to avoid the detection differences induced by chip platforms. Several machine learning algorithms were applied on these two sets. First, the Monte Carlo Feature Selection (MCFS) method was used to analyze features in two sets. A feature list was obtained according to the MCFS results on each set. Second, incremental feature selection method incorporating one classification algorithm (support vector machine or random forest) followed to extract optimal features and construct optimal classifiers. On the other hand, the repeated incremental pruning to produce error reduction, a rule learning algorithm, was applied on key features yielded by the MCFS method to extract quantitative rules for accurate cancer immune monitoring and pathologic diagnosis. Finally, obtained key features and quantitative rules were extensively analyzed.

Highlights

Liquid biopsy involves a series of clinical examination approaches, including sampling and analysis, on non-solid suspected pathogenic tissues, such as blood (Crowley et al, 2013), amniotic fluid (Ilas et al, 2000), and cerebrospinal fluid (Hiemcke-Jiwa et al, 2018)
The Matthew’s correlation coefficient (MCC) on different feature subsets is illustrated in Figure 2A, from which we can see that the highest MCC is obtained when top 50 features are used
We run incremental feature selection (IFS) with an integrated Support vector machine (SVM) on the samples consisting of features from the generated feature subsets with a step 10

Summary

Introduction

Liquid biopsy (i.e., fluid biopsy) involves a series of clinical examination approaches, including sampling and analysis, on non-solid suspected pathogenic tissues, such as blood (Crowley et al, 2013), amniotic fluid (Ilas et al, 2000), and cerebrospinal fluid (Hiemcke-Jiwa et al, 2018). Liquid biopsy is applied in three main fields: cancer studies (Condello et al, 2018; Mithraprabhu and Spencer, 2018), heart attack diagnosis (Ogawa et al, 1983), and prenatal. As for prenatal diagnosis, cell-free fetal DNA reflects the genomic characteristics of the infant, applicable for the development of monitoring, and diagnosis of genetic disorders (Sun et al, 2015). Liquid biopsy has been used for the identification of cancer biomarkers to monitor the progression of tumorigenesis and predict the prognosis. In 2014, a specific study (Stafford et al, 2014) on the evaluation of immune status of cancer presented a novel method for tumor-associated liquid biopsy, i.e., monitoring of cancer immunological status by the “immunosignature” of patients

Methods

Results

Conclusion