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A predictive biomarker panel for bone metastases: Liquid biopsy approach.

ABSTRACT: Bone metastases is one of the common metastatic site and leading cause of cancer-related mortality in progressive cancer patients. The purpose of the present study is to establish a liquid biopsy based multi-gene classifier and associated signalling pathways for early diagnosis of bone metastases. We used publically available microarray datasets and analysed them in a platform/chip-specific manner using GeneSpring software. Analyses of gene expression datasets identified 15 consistently over-expressed genes with statistical significance. Further, expression profile of same set of 15 genes were compared in breast and lung cancer exosome derived mRNA with (n = 10) and without (n = 10) bone metastases against healthy controls. ROC curve analysis performed individually for all the 15 genes shortlisted the 5 most relevant genes with significant sensitivity and specificity in both cancers. This liquid biopsy-based bone metastases predictor using multi-gene panel is a unique approach with potential clinical applications for effective management of aggressive cancers.

SUBMITTER: Bhadresha KP

PROVIDER: S-EPMC8220227 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Study designRetrospective analysis of a registered cohort of patients treated and irradiated for metastases in the spinal column in a single institute.ObjectiveThis is the first study to develop and internally validate radiomics features for predicting six-month survival probability for patients with spinal bone metastases (SBM).Background dataExtracted radiomics features from routine clinical CT images can be used to identify textural and intensity-based features unperceivable to human observers and associate them with a patient survival probability or disease progression.MethodsA study was conducted on 250 patients treated for metastases in the spinal column irradiated for the first time between 2014 and 2016, at the MAASTRO clinic in Maastricht, the Netherlands. The first 150 available patients were used to develop the model and the subsequent 100 patient were considered as a test set for the model. A bootstrap (B = 400) stepwise model selection, which combines both the forward and backward variable elimination procedure, was used to select the most useful predictive features from the training data based on the Akaike information criterion (AIC). The stepwise selection procedure was applied to the 400 bootstrap samples, and the results were plotted as a histogram to visualize how often each variable was selected. Only variables selected more than 90 % of the time over the bootstrap runs were used to build the final model.A prognostic index (PI) called radiomics score (radscore) and clinical score (clinscore) was calculated for each patient. The prognostic index was not scaled, the original values were used which can be extracted from the model directly or calculated as a linear combination of the variables in the model multiplied by the respective beta value for each patient.ResultsThe clinical model had a good discrimination power. The radiomics model, on the other hand, had an inferior performance with no added predictive power to the clinical model. The internal imaging characteristics do not seem to have a value in the prediction of survival. However, the Shape features were excluded from further analyses in our study since all biopsies had a standard shape hence no variability.

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A predictive biomarker panel for bone metastases: Liquid biopsy approach.

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