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pmid:39100839

Machine learning developed an intratumor heterogeneity signature for predicting clinical outcome and immunotherapy benefit in bladder cancer.

Cheng Chen · Jun Zhang · Xiaoshuang Liu · Qianfeng Zhuang · Hao Lu · Jianquan Hou

Translational andrology and urology · 2024 doi:10.21037/tau-24-5

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title
Machine learning developed an intratumor heterogeneity signature for predicting clinical outcome and immunotherapy benefit in bladder cancer.
doi
10.21037/tau-24-5
pmid
39100839
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Cheng Chen
[1]
Jun Zhang
[2]
Xiaoshuang Liu
[3]
Qianfeng Zhuang
[4]
Hao Lu
[5]
Jianquan Hou
journal
Translational andrology and urology
year
2024
abstract
Background: Bladder cancer is a common malignancy with high invasion and poor clinical outcome. Intratumor heterogeneity (ITH) is linked to cancer progression and metastasis and high ITH can accelerate tumor evolution. Our objective is to develop an ITH-related signature (IRS) for predicting clinical outcome and immunotherapy benefit in bladder cancer. Methods: Integrative procedure containing ten machine learning methods was applied to develop an IRS with The Cancer Genome Atlas (TCGA), gene series expression (GSE)13507, GSE31684, GSE32984 and GSE48276 datasets. To evaluate the performance of IRS in predicting the immunotherapy benefit, we also used several predicting scores and three immunotherapy datasets, including GSE91061, GSE78220 and IMvigor210. Results: The predicting model constructed with Enet (alpha =0.2) algorithm had a highest average C-index of 0.69, which was suggested as the optimal IRS. As an independent risk factor for bladder cancer, IRS had a powerful performance in predicting the overall survival (OS) rate of patients, with an area under curve of 1-, 3- and 5-year receiver operating characteristic (ROC) curve being 0.744, 0.791 and 0.816 in TCGA dataset. Bladder cancer patients with low IRS score presented with a higher level of immune-activated cells, cytolytic function and T cell co-stimulation. We also found a lower tumor immune dysfunction and exclusion (TIDE) score, lower immune escape score, higher programmed cell death protein 1 (PD-1) & cytotoxic T-lymphocyte associated protein 4 immunophenoscore, higher tumor mutation burden (TMB) score, higher response rate and better prognosis in bladder cancer with low IRS score. Bladder cancer cases with high IRS score had a higher half maximal inhibitory concentration value of common chemotherapy and targeted therapy regimens. Conclusions: The current study developed an optimal IRS for bladder cancer patients, which acted as an indicator for predicting prognosis, stratifying risk and guiding treatment for bladder cancer patients. Further analysis should be focused on the exploration the differentially expressed genes (DEGs) and related underlying mechanism mediating the development of bladder cancer in different IRS score group.
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Integrative procedure containing 10 machine learning methods was applied to develop an IRS.
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Following inclusion criteria (pathological diagnosis of bladder urothelium carcinoma with completed prognostic information) and exclusion criteria (followed up for less than one month or died within one month after surgery or with other malignancies), we acquired mRNA data of bladder cancer patients from TCGA dataset (n=396), gene series expression (GSE)13507 (n=165), GSE31684 (n=90), GSE32984 (n=223) and GSE48276 (n=73) dataset.
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sections[4] "Data acquisition and ITH score of bladder cancer cases" paragraph 2
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Following inclusion criteria (pathological diagnosis of bladder urothelium carcinoma with completed prognostic information) and exclusion criteria (followed up for less than one month or died within one month after surgery or with other malignancies), we acquired mRNA data of bladder cancer patients from TCGA dataset (n=396), gene series expression (GSE)13507 (n=165), GSE31684 (n=90), GSE32984 (n=223) and GSE48276 (n=73) dataset.
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sections[12] "Machine learning developed a prognostic IRS" paragraph 2
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The IRS constructed by Enet (alpha =0.2) algorithm had a highest average C-index of 0.69, which was suggested as the optimal IRS (Figure 2A).
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Following inclusion criteria (pathological diagnosis of bladder urothelium carcinoma with completed prognostic information) and exclusion criteria (followed up for less than one month or died within one month after surgery or with other malignancies), we acquired mRNA data of bladder cancer patients from TCGA dataset (n=396), gene series expression (GSE)13507 (n=165), GSE31684 (n=90), GSE32984 (n=223) and GSE48276 (n=73) dataset.
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Following inclusion criteria (pathological diagnosis of bladder urothelium carcinoma with completed prognostic information) and exclusion criteria (followed up for less than one month or died within one month after surgery or with other malignancies), we acquired mRNA data of bladder cancer patients from TCGA dataset (n=396), gene series expression (GSE)13507 (n=165), GSE31684 (n=90), GSE32984 (n=223) and GSE48276 (n=73) dataset.
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title
Gene expression profiling of urothelial carcinoma
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2026-11-01T15:39:54Z
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Following inclusion criteria (pathological diagnosis of bladder urothelium carcinoma with completed prognostic information) and exclusion criteria (followed up for less than one month or died within one month after surgery or with other malignancies), we acquired mRNA data of bladder cancer patients from TCGA dataset (n=396), gene series expression (GSE)13507 (n=165), GSE31684 (n=90), GSE32984 (n=223) and GSE48276 (n=73) dataset.
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Following inclusion criteria (pathological diagnosis of bladder urothelium carcinoma with completed prognostic information) and exclusion criteria (followed up for less than one month or died within one month after surgery or with other malignancies), we acquired mRNA data of bladder cancer patients from TCGA dataset (n=396), gene series expression (GSE)13507 (n=165), GSE31684 (n=90), GSE32984 (n=223) and GSE48276 (n=73) dataset.
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mRNA expressions in pre-treatment melanomas undergoing anti-PD-1 checkpoint inhibition therapy
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Following inclusion criteria (pathological diagnosis of bladder urothelium carcinoma with completed prognostic information) and exclusion criteria (followed up for less than one month or died within one month after surgery or with other malignancies), we acquired mRNA data of bladder cancer patients from TCGA dataset (n=396), gene series expression (GSE)13507 (n=165), GSE31684 (n=90), GSE32984 (n=223) and GSE48276 (n=73) dataset.
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Following inclusion criteria (pathological diagnosis of bladder urothelium carcinoma with completed prognostic information) and exclusion criteria (followed up for less than one month or died within one month after surgery or with other malignancies), we acquired mRNA data of bladder cancer patients from TCGA dataset (n=396), gene series expression (GSE)13507 (n=165), GSE31684 (n=90), GSE32984 (n=223) and GSE48276 (n=73) dataset.
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Molecular portraits of tumor mutational and micro-environmental sculpting by immune checkpoint blockade therapy
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2026-11-01T15:39:55Z
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Following inclusion criteria (pathological diagnosis of bladder urothelium carcinoma with completed prognostic information) and exclusion criteria (followed up for less than one month or died within one month after surgery or with other malignancies), we acquired mRNA data of bladder cancer patients from TCGA dataset (n=396), gene series expression (GSE)13507 (n=165), GSE31684 (n=90), GSE32984 (n=223) and GSE48276 (n=73) dataset.
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The paper names GSE13507, GSE31684, GSE32984, GSE48276, GSE91061, GSE78220 in Gene Expression Omnibus and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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assay:1
Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39100839", "kind": "science-record", "revision": 3, "content_hash": "sha256:d56b04dd04464612efabac2673b3fecd5a3401a9c75b4a552fb27328dfc39900", "created_at": "2026-11-01T20:29:10Z", "updated_at": "2026-11-01T22:12:22Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Machine learning developed an intratumor heterogeneity signature for predicting clinical outcome and immunotherapy benefit in bladder cancer.", "doi": "10.21037/tau-24-5", "pmid": "39100839", "authors": ["Cheng Chen", "Jun Zhang", "Xiaoshuang Liu", "Qianfeng Zhuang", "Hao Lu", "Jianquan Hou"], "journal": "Translational andrology and urology", "year": 2024, "abstract": "Background: Bladder cancer is a common malignancy with high invasion and poor clinical outcome. Intratumor heterogeneity (ITH) is linked to cancer progression and metastasis and high ITH can accelerate tumor evolution. Our objective is to develop an ITH-related signature (IRS) for predicting clinical outcome and immunotherapy benefit in bladder cancer.\nMethods: Integrative procedure containing ten machine learning methods was applied to develop an IRS with The Cancer Genome Atlas (TCGA), gene series expression (GSE)13507, GSE31684, GSE32984 and GSE48276 datasets. To evaluate the performance of IRS in predicting the immunotherapy benefit, we also used several predicting scores and three immunotherapy datasets, including GSE91061, GSE78220 and IMvigor210.\nResults: The predicting model constructed with Enet (alpha =0.2) algorithm had a highest average C-index of 0.69, which was suggested as the optimal IRS. As an independent risk factor for bladder cancer, IRS had a powerful performance in predicting the overall survival (OS) rate of patients, with an area under curve of 1-, 3- and 5-year receiver operating characteristic (ROC) curve being 0.744, 0.791 and 0.816 in TCGA dataset. Bladder cancer patients with low IRS score presented with a higher level of immune-activated cells, cytolytic function and T cell co-stimulation. We also found a lower tumor immune dysfunction and exclusion (TIDE) score, lower immune escape score, higher programmed cell death protein 1 (PD-1) & cytotoxic T-lymphocyte associated protein 4 immunophenoscore, higher tumor mutation burden (TMB) score, higher response rate and better prognosis in bladder cancer with low IRS score. Bladder cancer cases with high IRS score had a higher half maximal inhibitory concentration value of common chemotherapy and targeted therapy regimens.\nConclusions: The current study developed an optimal IRS for bladder cancer patients, which acted as an indicator for predicting prognosis, stratifying risk and guiding treatment for bladder cancer patients. Further analysis should be focused on the exploration the differentially expressed genes (DEGs) and related underlying mechanism mediating the development of bladder cancer in different IRS score group.", "claims": [{"id": "claim-method", "statement": "Integrative procedure containing 10 machine learning methods was applied to develop an IRS.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39100839", "locator": "sections[9] \"Statistical analysis\" paragraph 2", "quote": "Integrative procedure containing 10 machine learning methods was applied to develop an IRS."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Following inclusion criteria (pathological diagnosis of bladder urothelium carcinoma with completed prognostic information) and exclusion criteria (followed up for less than one month or died within one month after surgery or with other malignancies), we acquired mRNA data of bladder cancer patients from TCGA dataset (n=396), gene series expression (GSE)13507 (n=165), GSE31684 (n=90), GSE32984 (n=223) and GSE48276 (n=73) dataset.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39100839", "locator": "sections[4] \"Data acquisition and ITH score of bladder cancer cases\" paragraph 2", "quote": "Following inclusion criteria (pathological diagnosis of bladder urothelium carcinoma with completed prognostic information) and exclusion criteria (followed up for less than one month or died within one month after surgery or with other malignancies), we acquired mRNA data of bladder cancer patients from TCGA dataset (n=396), gene series expression (GSE)13507 (n=165), GSE31684 (n=90), GSE32984 (n=223) and GSE48276 (n=73) dataset."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The IRS constructed by Enet (alpha =0.2) algorithm had a highest average C-index of 0.69, which was suggested as the optimal IRS", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39100839", "locator": "sections[12] \"Machine learning developed a prognostic IRS\" paragraph 2", "quote": "The IRS constructed by Enet (alpha =0.2) algorithm had a highest average C-index of 0.69, which was suggested as the optimal IRS (Figure 2A)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The IRS constructed by Enet (alpha =0.2) algorithm had a highest average C-index of 0.69, which was suggested as the optimal IRS", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:39100839"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE13507"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE13507"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39100839", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/tau-24-5", "sha256": 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