A Novel XGBoost Method to Identify Cancer Tissue-of-Origin Based on Copy Number Variations - Zhang, Yulin; Feng, Tong; Wang, Shudong; Dong, Ruyi; Yang, Jialiang; Su, Jionglong; Wang, Bo (Frontiers in Genetics, 2020)
The discovery of cancer of unknown primary (CUP) is of great significance to designing more effective treatments and improving the diagnostic efficiency in cancer patients. In the study, we develop an appropriate machine learning model for tracing the tissue of origin of CUP with high accuracy after feature engineering and model evaluation. Based on a copy number variation data consisting of 4566 training cases and 1262 independent validation cases, an XGBoost classifier is applied to ten types of cancer. Extremely random trees are used for dimension reduction so that fewer variables replace the original high-dimensional variables. Features with top 300 weights are selected and principal component analysis is applied to eliminate noise. We find that XGBoost classifier achieves the highest overall accuracy of 0.8913 in the 10 fold cross-validation for training samples and 0.7421 on independent validation datasets for predicting tumor tissue of origin. Furthermore, by contrasting various performance indices such as precision and recall rate, the experimental results show that XGBoost classifier significantly improves the classification performance of various tumors with less prediction error, as compared to other classifiers such as KNN, Bayes, SVM and Adaboost. Our method can infer tissue of origin for the 10 cancer types with acceptable accuracy in both cross validation and independent validate data. It may be used as an auxiliary diagnostic method to determine the actual clinicopathological status of specific cancer.