A Machine Learning Method to Trace Cancer Primary Lesion Using Microarray-Based Gene Expression Data

Abstract

Cancer of unknown primary site (CUP) is a heterogeneous group of cancers whose tissue of origin remains unknown after detailed investigation by conventional clinical methods. The number of CUP accounts for roughly 3%–5% of all human malignancies. CUP patients are usually treated with broad-spectrum chemotherapy, which often leads to a poor prognosis. Recent studies suggest that the treatment targeting the primary lesion of CUP will significantly improve the prognosis of the patient. Therefore, it is urgent to develop an efficient method to accurately detect tissue of origin of CUP in clinical cancer research. In this work, we developed a novel framework that uses Extreme Gradient Boosting (XGBoost) to trace the primary site of CUP based on microarray-based gene expression data. First, we downloaded the microarray-based gene expression profiles of 59,385 genes for 57,08 samples from The Cancer Genome Atlas (TCGA) and 6,364 genes for 3,101 samples from the Gene Expression Omnibus (GEO). Both data were divided into training and independent testing data with a ratio of 4:1. Then, we obtained in the training data 200 and 290 genes from TCGA and the GEO datasets, respectively, to train XGBoost models for the identification of the primary site of CUP. The overall 5-fold cross-validation accuracies of our methods were 96.9% and 95.3% on TCGA and GEO training datasets, respectively. Meanwhile, the macro-precision for the independent dataset reached 96.75% and 98.8% on, respectively, TCGA and GEO. Experimental results demonstrated that the XGBoost framework not only can reduce the cost of clinical cancer traceability but also has high efficiency, which might be useful in clinical usage.

New techniques to identify the tissue of origin for cancer of unknown primary in the era of precision medicine: progress and challenges

“…Using genomic profiling data as an example, bioinformatics and ML algorithms are applied to score and rank the most relevant genes for creating tumor–gene associations and constructing TOO classifiers. Several ML algorithms to identify the TOO of CUP have been applied in this context [ 28 , 29 , 33 , 42 , 44 , 45 , 52–54 , 57 , 58 , 64 , 73 , 96 ] ( Supplementary Figure 3 and Table 2 ). These associations are subsequently assessed through independent validation sets, and the classifier’s efficacy is further verified with challenging clinical cases.…”