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pmid:30823599
Developing a Prognostic Gene Panel of Epithelial Ovarian Cancer Patients by a Machine Learning Model.
Tzu-Pin Lu · Kuan-Ting Kuo · Ching-Hsuan Chen · Ming-Cheng Chang · Hsiu-Ping Lin · Yu-Hao Hu · Ying-Cheng Chiang · Wen-Fang Cheng · Chi-An Chen
Cancers · 2019 doi:10.3390/cancers11020270
Paper
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Experiment
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Data
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Analysis
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Execution
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Complete file
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We utilized the support vector machine algorithm to develop a model for the chemo-response using the Cancer Cell Line Encyclopedia (CCLE) and validated the model in The Cancer Genome Atlas (TCGA) and the GSE9891 dataset. Finally, we evaluated the feasibility of the model using ovarian cancer patients from our institute. The 10-gene predictive model demonstrated that the high response group had a longer recurrence-free survival (RFS) (log-rank test, p = 0.015 for TCGA, p = 0.013 for GSE9891 and p = 0.039 for NTUH) and overall survival (OS) (log-rank test, p = 0.002 for TCGA and p = 0.016 for NTUH). In a multivariate Cox hazard regression model, the predictive model (HR: 0.644, 95% CI: 0.436⁻0.952, p = 0.027) and residual tumor size < 1 cm (HR: 0.312, 95% CI: 0.170⁻0.573, p < 0.001) were significant factors for recurrence. 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"version": null, "note": "Clinical characteristics of the epithelial ovarian cancer patients at NTUH.", "structure": {"pmcid": "PMC6406249", "section_index": 6, "section_title": "2.4. 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Validation of The Predictive Model by QRT-PCR in Ovarian Cancer Tissue of NTUH", "row_count": 19, "column_count": 10}, "table": {"label": "Table 2", "caption": "Cox regression model for the clinical features and predictive model in recurrence and death of NTUH patients.", "rows": [["Feature", "Numbers", "Recurrence", "Death"], ["Univariate", "Multivariate", "Univariate", "Multivariate"], ["HR (95% CI)", "p Value", "HR (95% CI)", "p Value", "HR (95% CI)", "p Value", "HR (95% CI)", "p Value"], ["Predictive model", "", "", "", "", "", "", "", "", ""], ["Low", "19", "1.00", "", "1.00", "", "1.00", "", "1.00", ""], ["Medium + High", "65", "0.643(0.415–0.998)", "0.049", "0.644(0.436–0.952)", "0.027", "0.559(0.351–0.890)", "0.014", "0.511(0.334–0.783)", "0.002"], ["Residual tumor size < 1 cm", "", "", "", "", "", "", "", "", ""], ["No", "42", "1.00", "", "1.00", "", "1.00", "", "1.00", ""], ["Yes", "42", "0.273(0.160–0.468)", "<0.001", "0.312(0.170–0.573)", "<0.001", "0.198(0.107–0.365)", "<0.001", "0.252(0.128–0.496)", "<0.001"], ["FIGO stage", "", "", "", "", "", "", "", "", ""], ["Early (I + II)", "11", "1.00", "", "1.00", "", "1.00", "", "1.00", ""], ["Advanced (III + IV)", "73", "7.954(1.934–32.71)", "0.004", "2.149(0.257–17.93)", "0.480", "6.13(1.489–25.24)", "0.012", "1.732(0.201–14.90)", "0.617"], ["Tumor grade", "", "", "", "", "", "", "", "", ""], ["1", "4", "1.00", "", "1.00", "", "1.00", "", "1.00", ""], ["2 + 3", "67", "2.289(1.156–4.530)", "0.018", "2.125(0.992–4.552)", "0.053", "1.756(0.953–3.234)", "0.071", "1.533 (0.761–3.09)", "0.232"], ["Histological subtype", "", "", "", "", "", "", "", ""], ["Serous", "59", "1.00", "", "1.00", "", "1.00", "", "1.00", ""], ["Endometrioid", "12", "0.454(0.193–1.067)", "0.070", "1.023(0.422–2.475)", "0.960", "0.562(0.239–1.323)", "0.187", "1.077(0.443–2.618)", "0.870"], ["Clear cell", "13", "0.531(0.239–1.180)", "0.120", "NA", "NA", "0.284(0.102–0.793)", "0.016", "NA", "NA"]], "row_count": 19, "column_count": 10, "numeric_cells": 82, "header_rows": 3, "locator": "section[6]/table-wrap[2]#cancers-11-00270-t002"}}], "gaps": ["asset:data:geo:GSE36133 not downloaded", "asset:data:geo:GSE36133 licence not read", "asset:data:geo:GSE9891 not downloaded", "asset:data:geo:GSE9891 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "kaplan_meier", "label": "Survival analysis", "dsl_operations": ["survival_km"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "Kaplan Meier"}}, {"id": "method-2", "family": "survival_analysis", "specific": "log_rank_test", "label": "Survival analysis", "dsl_operations": ["logrank_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.7, "band": "medium", "quote": "log-rank"}}, {"id": "method-3", "family": "machine_learning", "specific": "support_vector_machine", "label": "Machine learning", "dsl_operations": ["train_classifier", "cross_validate", "roc_auc"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.7, "band": "medium", "quote": "support vector machine"}}, {"id": "method-4", "family": null, "specific": null, "label": "permutation test", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "To address this issue, a permutation test was repeated 100,000 times to evaluate the probability of identifying a model with the same prediction accuracy."}}], "targets": [], "gaps": ["method.reader_named_unmapped"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["survival_km"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: kaplan_meier."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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