Dataset Information

A blood-based miRNA signature with prognostic value for overall survival in advanced stage non-small cell lung cancer treated with immunotherapy.

ABSTRACT: Immunotherapies have recently gained traction as highly effective therapies in a subset of late-stage cancers. Unfortunately, only a minority of patients experience the remarkable benefits of immunotherapies, whilst others fail to respond or even come to harm through immune-related adverse events. For immunotherapies within the PD-1/PD-L1 inhibitor class, patient stratification is currently performed using tumor (tissue-based) PD-L1 expression. However, PD-L1 is an accurate predictor of response in only ~30% of cases. There is pressing need for more accurate biomarkers for immunotherapy response prediction. We sought to identify peripheral blood biomarkers, predictive of response to immunotherapies against lung cancer, based on whole blood microRNA profiling. Using three well-characterized cohorts consisting of a total of 334 stage IV NSCLC patients, we have defined a 5 microRNA risk score (miRisk) that is predictive of overall survival following immunotherapy in training and independent validation (HR 2.40, 95% CI 1.37-4.19; P < 0.01) cohorts. We have traced the signature to a myeloid origin and performed miRNA target prediction to make a direct mechanistic link to the PD-L1 signaling pathway and PD-L1 itself. The miRisk score offers a potential blood-based companion diagnostic for immunotherapy that outperforms tissue-based PD-L1 staining.

SUBMITTER: Rajakumar T

PROVIDER: S-EPMC8971493 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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A blood-based miRNA signature with prognostic value for overall survival in advanced stage non-small cell lung cancer treated with immunotherapy.

Rajakumar Timothy T Horos Rastislav R Jehn Julia J Schenz Judith J Muley Thomas T Pelea Oana O Hofmann Sarah S Kittner Paul P Kahraman Mustafa M Heuvelman Marco M Sikosek Tobias T Feufel Jennifer J Skottke Jasmin J Nötzel Dennis D Hinkfoth Franziska F Tikk Kaja K Daniel-Moreno Alberto A Ceiler Jessika J Mercaldo Nathaniel N Uhle Florian F Uhle Sandra S Weigand Markus A MA Elshiaty Mariam M Lusky Fabienne F Schindler Hannah H Ferry Quentin Q Sauka-Spengler Tatjana T Wu Qianxin Q Rabe Klaus F KF Reck Martin M Thomas Michael M Christopoulos Petros P Steinkraus Bruno R BR

NPJ precision oncology 20220331 1

Immunotherapies have recently gained traction as highly effective therapies in a subset of late-stage cancers. Unfortunately, only a minority of patients experience the remarkable benefits of immunotherapies, whilst others fail to respond or even come to harm through immune-related adverse events. For immunotherapies within the PD-1/PD-L1 inhibitor class, patient stratification is currently performed using tumor (tissue-based) PD-L1 expression. However, PD-L1 is an accurate predictor of response ...[more]

PMID: 35361874

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Project description:BackgroundThe role of artificial intelligence and radiomics in prediction model development in cancer has been increasing every passing day. Cervical cancer is the 4th most common cancer in women worldwide, contributing to 6.5% of all cancer types. The treatment outcome of cervical cancer patients varies and individualized prediction of disease outcome is of paramount importance.PurposeThe purpose of this study is to develop and validate the digital signature for 5-year overall survival prediction in cervical cancer using robust CT radiomic and clinical features.Materials and methodsPretreatment clinical features and CT radiomic features of 68 patients, who were treated with chemoradiation therapy in our hospital, were used in this study. Radiomic features were extracted using an in-house developed python script and pyradiomic package. Clinical features were selected by the recursive feature elimination technique. Whereas radiomic feature selection was performed using a multi-step process i.e., step-1: only robust radiomic features were selected based on our previous study, step-2: a hierarchical clustering was performed to eliminate feature redundancy, and step-3: recursive feature elimination was performed to select the best features for prediction model development. Four machine algorithms i.e., Logistic regression (LR), Random Forest (RF), Support vector classifier (SVC), and Gradient boosting classifier (GBC), were used to develop 24 models (six models using each algorithm) using clinical, radiomic and combined features. Models were compared based on the prediction score in the internal validation.ResultsThe average prediction accuracy was found to be 0.65 (95% CI: 0.60-0.70), 0.72 (95% CI: 0.63-0.81), and 0.77 (95% CI: 0.72-0.82) for clinical, radiomic, and combined models developed using four prediction algorithms respectively. The average prediction accuracy was found to be 0.69 (95% CI: 0.62-0.76), 0.79 (95% CI: 0.72-0.86), 0.71 (95% CI: 0.62-0.80), and 0.72 (95% CI: 0.66-0.78) for LR, RF, SVC and GBC models developed on three datasets respectively.ConclusionOur study shows the promising predictive performance of a robust radiomic signature to predict 5-year overall survival in cervical cancer patients.

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Dataset Information

A blood-based miRNA signature with prognostic value for overall survival in advanced stage non-small cell lung cancer treated with immunotherapy.

Publications

A blood-based miRNA signature with prognostic value for overall survival in advanced stage non-small cell lung cancer treated with immunotherapy.

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