A Novel XGBoost Method to Identify Cancer Tissue-of-Origin Based on Copy Number Variations

Zhang, Yulin; Feng, Tingting; Wang, Shudong; Dong, Ruyi; Yang, Jialiang; Su, Jionglong; Wang, Bo

doi:10.3389/fgene.2020.585029

Cited by 23 publications

(19 citation statements)

References 32 publications

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“…In previous studies, these models have been shown to be of great value in medical imaging (5)(6)(7)(8). In oncology, they are used in the diagnosis of malignant tumors (9,10), prediction of clinical burden before and after cancer surgery (11), and prediction of adverse reactions to adjuvant therapy (12), among other applications. They have also been shown to be effective in the prognostic prediction of malignant tumors (13,14).…”

Section: Introductionmentioning

confidence: 99%

Application of machine learning approaches to predict the 5-year survival status of patients with esophageal cancer

Gong¹,

Zheng²,

Xu³

et al. 2021

J Thorac Dis

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Background:Accurate prognostic estimation for esophageal cancer (EC) patients plays an important role in the process of clinical decision-making. The objective of this study was to develop an effective model to predict the 5-year survival status of EC patients using machine learning (ML) algorithms. Methods:We retrieved the information of patients diagnosed with EC between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) Program, including 24 features. A total of 8 ML models were applied to the selected dataset to classify the EC patients in terms of 5-year survival status, including 3 newly developed gradient boosting models (GBM), XGBoost, CatBoost, and LightGBM, 2 commonly used tree-based models, gradient boosting decision trees (GBDT) and random forest (RF), and 3 other ML models, artificial neural networks (ANN), naive Bayes (NB), and support vector machines (SVM).A 5-fold cross-validation was used in model performance measurement. Results: After excluding records with missing data, the final study population comprised 10,588 patients.Feature selection was conducted based on the χ 2 test, however, the experiment results showed that the complete dataset provided better prediction of outcomes than the dataset with removal of non-significant features. Among the 8 models, XGBoost had the best performance [area under the receiver operating characteristic (ROC) curve (AUC): 0.852 for XGBoost, 0.849 for CatBoost, 0.850 for LightGBM, 0.846 for GBDT, 0.838 for RF, 0.844 for ANN, 0.833 for NB, and 0.789 for SVM]. The accuracy and logistic loss of XGBoost were 0.875 and 0.301, respectively, which were also the best performances. In the XGBoost model, the SHapley Additive exPlanations (SHAP) value was calculated and the result indicated that the four features:reason no cancer-directed surgery, Surg Prim Site, age, and stage group had the greatest impact on predicting the outcomes. Conclusions:The XGBoost model and the complete dataset can be used to construct an accurate prognostic model for patients diagnosed with EC which may be applicable in clinical practice in the future.

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Section: Introductionmentioning

confidence: 99%

Application of machine learning approaches to predict the 5-year survival status of patients with esophageal cancer

Gong¹,

Zheng²,

Xu³

et al. 2021

J Thorac Dis

View full text Add to dashboard Cite

show abstract

“…While in this work we focused on tumor transcriptome data which can be measured with high precision over a wide dynamic range of transcript abundances by RNA-seq, we note that TCGA datasets of tumor somatic mutations and copy number alteration events are also available (Hutter and Zenklusen, 2018). Given the voluminous literature on the use of tumor somatic genomic data for precision cancer diagnosis (Mitchel et al , 2019; Zhang et al , 2020; Lee et al , 2019), tumor DNA datasets are fertile ground for developing a semi-supervised, multi-omics model for predicting response to chemotherapy.…”

Section: Discussionmentioning

confidence: 99%

Predicting chemotherapy response using a variational autoencoder approach

Wei

Ramsey

2021

Preprint

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MotivationMultiple studies have shown the utility of transcriptome-wide RNA-seq profiles as features for machine learning-based prediction of response to chemotherapy in cancer. While tumor transcriptome profiles are publicly available for thousands of tumors for many cancer types, a relatively modest number of tumor profiles are clinically annotated for response to chemotherapy. The paucity of labeled examples and high dimension of the feature data limit performance for predicting therapeutic response using fully-supervised classification methods. Recently, multiple studies have established the utility of a deep neural network approach, the variational autoencoder (VAE), for generating meaningful latent features from original data. Here, we report first study of a semi-supervised approach using VAE-encoded tumor transcriptome features and regularized gradient boosted decision trees (XGBoost) to predict chemotherapy drug response for five cancer types: colon adenocarcinoma, pancreatic adenocarcinoma, bladder carcinoma, sarcoma, and breast invasive carcinoma.ResultsWe found: (1) VAE-encoding of the tumor transcriptome preserves the cancer type identity of the tumor, suggesting preservation of biologically relevant information; and (2) as a feature-set for supervised classification to predict response-to-chemotherapy, the unsupervised VAE encoding of the tumor’s gene expression profile leads to better area under the receiver operating characteristic curve (AUROC) classification performance than either the original gene expression profile or the PCA principal components of the gene expression profile, in four out of five cancer types that we tested.Availabilitygithub.com/ATHED/VAE_for_chemotherapy_drug_response_predictionContactramseyst@oregonstate.eduSupplementary informationSupplementary data are available at Bioinformatics online.

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“…While in this work we focused on tumor transcriptome data which can be measured with high precision over a wide dynamic range of transcript abundances by RNA-seq, we note that TCGA datasets of tumor somatic mutations and copy number alteration events are also available [17]. Given the voluminous literature on the use of tumor somatic genomic data for precision cancer diagnosis [37][38][39], tumor DNA datasets are fertile ground for developing a semi-supervised, multi-omics model for predicting response to chemotherapy. Second, for decision tree-based response-to-chemotherapy prediction, the performance of VAE-encoded transcriptome features is somewhat sensitive to the type of normalization used for the gene expression levels (data not shown).…”

Section: Discussionmentioning

confidence: 99%

Predicting chemotherapy response using a variational autoencoder approach

Wei

Ramsey

2021

BMC Bioinformatics

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Background Multiple studies have shown the utility of transcriptome-wide RNA-seq profiles as features for machine learning-based prediction of response to chemotherapy in cancer. While tumor transcriptome profiles are publicly available for thousands of tumors for many cancer types, a relatively modest number of tumor profiles are clinically annotated for response to chemotherapy. The paucity of labeled examples and the high dimension of the feature data limit performance for predicting therapeutic response using fully-supervised classification methods. Recently, multiple studies have established the utility of a deep neural network approach, the variational autoencoder (VAE), for generating meaningful latent features from original data. Here, we report the first study of a semi-supervised approach using VAE-encoded tumor transcriptome features and regularized gradient boosted decision trees (XGBoost) to predict chemotherapy drug response for five cancer types: colon, pancreatic, bladder, breast, and sarcoma. Results We found: (1) VAE-encoding of the tumor transcriptome preserves the cancer type identity of the tumor, suggesting preservation of biologically relevant information; and (2) as a feature-set for supervised classification to predict response-to-chemotherapy, the unsupervised VAE encoding of the tumor’s gene expression profile leads to better area under the receiver operating characteristic curve and area under the precision-recall curve classification performance than the original gene expression profile or the PCA principal components or the ICA components of the gene expression profile, in four out of five cancer types that we tested. Conclusions Given high-dimensional “omics” data, the VAE is a powerful tool for obtaining a nonlinear low-dimensional embedding; it yields features that retain biological patterns that distinguish between different types of cancer and that enable more accurate tumor transcriptome-based prediction of response to chemotherapy than would be possible using the original data or their principal components.

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A Novel XGBoost Method to Identify Cancer Tissue-of-Origin Based on Copy Number Variations

Cited by 23 publications

References 32 publications

Application of machine learning approaches to predict the 5-year survival status of patients with esophageal cancer

Application of machine learning approaches to predict the 5-year survival status of patients with esophageal cancer

Predicting chemotherapy response using a variational autoencoder approach

Predicting chemotherapy response using a variational autoencoder approach

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