Radiomics-based prediction model for outcomes of PD-1/PD-L1 immunotherapy in metastatic urothelial carcinoma

Park, Kye Jin; Lee, Jae‐Lyun; Yoon, Shinkyo; Heo, Changhoe; Park, Bum Woo; Kim, Jeong Kon

doi:10.1007/s00330-020-06847-0

Cited by 48 publications

(26 citation statements)

References 35 publications

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“…Based on six genes, some researchers have constructed a lung cancer risk score model to provide a reference for individualized immunotherapy for patients (46). Some studies construct the prediction model of immunotherapy response for urothelial carcinoma or lung cancer using deep learning of noninvasive radionics biomarkers (47,48). However, a user-friendly web tool is still not available for TNBC patients.…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning Model to Predict the Triple Negative Breast Cancer Immune Subtype

et al. 2021

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BackgroundImmune checkpoint blockade (ICB) has been approved for the treatment of triple-negative breast cancer (TNBC), since it significantly improved the progression-free survival (PFS). However, only about 10% of TNBC patients could achieve the complete response (CR) to ICB because of the low response rate and potential adverse reactions to ICB.MethodsOpen datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were downloaded to perform an unsupervised clustering analysis to identify the immune subtype according to the expression profiles. The prognosis, enriched pathways, and the ICB indicators were compared between immune subtypes. Afterward, samples from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset were used to validate the correlation of immune subtype with prognosis. Data from patients who received ICB were selected to validate the correlation of the immune subtype with ICB response. Machine learning models were used to build a visual web server to predict the immune subtype of TNBC patients requiring ICB.ResultsA total of eight open datasets including 931 TNBC samples were used for the unsupervised clustering. Two novel immune subtypes (referred to as S1 and S2) were identified among TNBC patients. Compared with S2, S1 was associated with higher immune scores, higher levels of immune cells, and a better prognosis for immunotherapy. In the validation dataset, subtype 1 samples had a better prognosis than sub type 2 samples, no matter in overall survival (OS) (p = 0.00036) or relapse-free survival (RFS) (p = 0.0022). Bioinformatics analysis identified 11 hub genes (LCK, IL2RG, CD3G, STAT1, CD247, IL2RB, CD3D, IRF1, OAS2, IRF4, and IFNG) related to the immune subtype. A robust machine learning model based on random forest algorithm was established by 11 hub genes, and it performed reasonably well with area Under the Curve of the receiver operating characteristic (AUC) values = 0.76. An open and free web server based on the random forest model, named as triple-negative breast cancer immune subtype (TNBCIS), was developed and is available from https://immunotypes.shinyapps.io/TNBCIS/.ConclusionTNBC open datasets allowed us to stratify samples into distinct immunotherapy response subgroups according to gene expression profiles. Based on two novel subtypes, candidates for ICB with a higher response rate and better prognosis could be selected by using the free visual online web server that we designed.

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Section: Discussionmentioning

confidence: 99%

A Machine Learning Model to Predict the Triple Negative Breast Cancer Immune Subtype

et al. 2021

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“…[36][37][38] Many studies have also applied radiomics in the diagnosis, staging, grading, treatment efficacy evaluation, prognostic evaluation, and biological behavior assessment of BLCA. [39][40][41][42][43][44] For instance, Lin et al developed a prognostic model for predicting the clinical outcome of BLCA. 45 Xu et al constructed a radiomics score model based on T2W, DW, ADC, and DCE images to predict the recurrence risk of BLCA.…”

Section: Discussionmentioning

confidence: 99%

Radiomics Analysis Based on Ultrasound Images to Distinguish the Tumor Stage and Pathological Grade of Bladder Cancer

Gao

Wen

et al. 2021

J of Ultrasound Medicine

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Objectives-To identify the clinical value of ultrasound radiomic features in the preoperative prediction of tumor stage and pathological grade of bladder cancer (BLCA) patients.Methods-We retrospectively collected patients who had been diagnosed with BLCA by pathology. Ultrasound-based radiomic features were extracted from manually segmented regions of interest. Participants were randomly assigned to a training cohort and a validation cohort at a ratio of 7:3. Radiomic features were Z-score normalized and submitted to dimensional reduction analysis (including Spearman's correlation coefficient analysis, the random forest algorithm, and statistical testing) for core feature selection. Classifiers for tumor stage and pathological grade prediction were then constructed. Prediction performance was estimated by the area under the curve (AUC) of the receiver operating characteristic curve and was verified by the validation cohort.Results-A total of 5936 radiomic features were extracted from each of the ultrasound images obtained from 157 patients. The BLCA tumor stage and pathological grade prediction models were developed based on 30 and 35 features, respectively. Both models showed good predictive ability. For the tumor stage prediction model, the AUC was 0.94 in the training cohort and 0.84 in the validation cohort. For the pathological grade model, the AUCs obtained were 0.84 in the training cohort and 0.75 in the validation cohort.Conclusions-The ultrasound-based radiomics models performed well in the preoperative tumor staging and pathological grading of BLCA. These findings should be applied clinically to optimize treatment and to assess prognoses for BLCA.

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“…Therapeutic blockade of the T cell negative regulator PD-L1 could enhance T cell effector function when PD-L1 was expressed in tumors, as it happens in the tumor microenvironment after IR [ 22 , 23 ]. PD-L1 expression in the tumor microenvironment provided an opportunity for therapeutic intervention using immunotherapies such as anti PD-L1, anti PD-1 and CTLA-4 [ 24 , 25 ].…”

Section: Discussionmentioning

confidence: 99%

A Tumor-Immune Interaction Model for Synergistic Combinations of Anti PD-L1 and Ionizing Irradiation Treatment

et al. 2020

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Combination therapy with immune checkpoint blockade and ionizing irradiation therapy (IR) generates a synergistic effect to inhibit tumor growth better than either therapy does alone. We modeled the tumor-immune interactions occurring during combined IT and IR based on the published data from Deng et al. The mathematical model considered programmed cell death protein 1 and programmed death ligand 1, to quantify data fitting and global sensitivity of critical parameters. Fitting of data from control, IR and IT samples was conducted to verify the synergistic effect of a combination therapy consisting of IR and IT. Our approach using the model showed that an increase in the expression level of PD-1 and PD-L1 was proportional to tumor growth before therapy, but not after initiating therapy. The high expression level of PD-L1 in T cells may inhibit IT efficacy. After combination therapy begins, the tumor size was also influenced by the ratio of PD-1 to PD-L1. These results highlight that the ratio of PD-1 to PD-L1 in T cells could be considered in combination therapy.

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Radiomics-based prediction model for outcomes of PD-1/PD-L1 immunotherapy in metastatic urothelial carcinoma

Cited by 48 publications

References 35 publications

A Machine Learning Model to Predict the Triple Negative Breast Cancer Immune Subtype

A Machine Learning Model to Predict the Triple Negative Breast Cancer Immune Subtype

Radiomics Analysis Based on Ultrasound Images to Distinguish the Tumor Stage and Pathological Grade of Bladder Cancer

A Tumor-Immune Interaction Model for Synergistic Combinations of Anti PD-L1 and Ionizing Irradiation Treatment

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