Development and validation of genomic predictors of radiation sensitivity using preclinical data

Manem, Venkata S. K.

doi:10.1186/s12885-021-08652-4

Cited by 9 publications

(8 citation statements)

References 27 publications

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“…It is widely accepted that the next wave of clinical gains will be from designing biologically-guided radiation regimens. The availability of OMICS data has accelerated research towards developing data-driven OMICS-based biomarkers using gene expression profiles from in-vitro or cell line data [ 13 , 20 ]. Several studies in the literature have developed radiation sensitivity gene signatures using cell data obtained from clonogenic survival assays, and an overview of these signatures can be found in a recent work by Manem et al [ 11 ].…”

Section: Discussionmentioning

confidence: 99%

Gene expression signature predicts radiation sensitivity in cell lines using the integral of dose–response curve

Kolnohuz,

Ebrahimpour,

Yolchuyeva

et al. 2024

BMC Cancer

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Background Although substantial efforts have been made to build molecular biomarkers to predict radiation sensitivity, the ability to accurately stratify the patients is still limited. In this study, we aim to leverage large-scale radiogenomics datasets to build genomic predictors of radiation response using the integral of the radiation dose–response curve. Methods Two radiogenomics datasets consisting of 511 and 60 cancer cell lines were utilized to develop genomic predictors of radiation sensitivity. The intrinsic radiation sensitivity, defined as the integral of the dose–response curve (AUC) was used as the radioresponse variable. The biological determinants driving AUC and SF2 were compared using pathway analysis. To build the predictive model, the largest and smallest datasets consisting of 511 and 60 cancer cell lines were used as the discovery and validation cohorts, respectively, with AUC as the response variable. Results Utilizing a compendium of three pathway databases, we illustrated that integral of the radiobiological model provides a more comprehensive characterization of molecular processes underpinning radioresponse compared to SF2. Furthermore, more pathways were found to be unique to AUC than SF2—30, 288 and 38 in KEGG, REACTOME and WIKIPATHWAYS, respectively. Also, the leading-edge genes driving the biological pathways using AUC were unique and different compared to SF2. With regards to radiation sensitivity gene signature, we obtained a concordance index of 0.65 and 0.61 on the discovery and validation cohorts, respectively. Conclusion We developed an integrated framework that quantifies the impact of physical radiation dose and the biological effect of radiation therapy in interventional pre-clinical model systems. With the availability of more data in the future, the clinical potential of this signature can be assessed, which will eventually provide a framework to integrate genomics into biologically-driven precision radiation oncology.

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Section: Discussionmentioning

confidence: 99%

Gene expression signature predicts radiation sensitivity in cell lines using the integral of dose–response curve

Kolnohuz,

Ebrahimpour,

Yolchuyeva

et al. 2024

BMC Cancer

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“…In the validation phase, we trained each of the models on the CHUM dataset with best features, then evaluated them on the IUCPQ dataset with the best features. Finally, we computed the C-index for OS and PFS for the validation phase (similar to our previous works [ 17 , 29 ]). In addition, the hyperparameter tuning of the classifiers was carried out through the process of cross-validation with the help of the GridSearchCV class provided by scikit-learn.…”

Section: Methodsmentioning

confidence: 99%

Multi-institutional prognostic modeling of survival outcomes in NSCLC patients treated with first-line immunotherapy using radiomics

Yolchuyeva,

Ebrahimpour,

Tonneau

et al. 2024

J Transl Med

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Background Immune checkpoint inhibitors (ICIs) have emerged as one of the most promising first-line therapeutics in the management of non-small cell lung cancer (NSCLC). However, only a subset of these patients responds to ICIs, highlighting the clinical need to develop better predictive and prognostic biomarkers. This study will leverage pre-treatment imaging profiles to develop survival risk models for NSCLC patients treated with first-line immunotherapy. Methods Advanced NSCLC patients (n = 149) were retrospectively identified from two institutions who were treated with first-line ICIs. Radiomics features extracted from pretreatment imaging scans were used to build the predictive models for progression-free survival (PFS) and overall survival (OS). A compendium of five feature selection methods and seven machine learning approaches were utilized to build the survival risk models. The concordance index (C-index) was used to evaluate model performance. Results From our results, we found several combinations of machine learning algorithms and feature selection methods to achieve similar performance. K-nearest neighbourhood (KNN) with ReliefF (RL) feature selection was the best-performing model to predict PFS (C-index = 0.61 and 0.604 in discovery and validation cohorts), while XGBoost with Mutual Information (MI) feature selection was the best-performing model for OS (C-index = 0.7 and 0.655 in discovery and validation cohorts). Conclusion The results of this study highlight the importance of implementing an appropriate feature selection method coupled with a machine learning strategy to develop robust survival models. With further validation of these models on external cohorts when available, this can have the potential to improve clinical decisions by systematically analyzing routine medical images.

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“…The C-index metric is defined as the probability that two variables will rank a random pair of samples in the same order and is a generalization of the area under the ROC curve. A random predictor would result in an index of 0.5, while a perfect predictor yields an index of 1 [35]. AUC stands for "area under the ROC curve" and measures the entire two-dimensional area underneath the entire ROC curve from (0,0) to (1,1).…”

Section: Model Performancementioning

confidence: 99%

A Radiomics-Clinical Model Predicts Overall Survival of Non-Small Cell Lung Cancer Patients Treated with Immunotherapy: A Multicenter Study

Yolchuyeva

Giacomazzi

Tonneau

et al. 2023

Cancers

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Background: Immune checkpoint inhibitors (ICIs) are a great breakthrough in cancer treatments and provide improved long-term survival in a subset of non-small cell lung cancer (NSCLC) patients. However, prognostic and predictive biomarkers of immunotherapy still remain an unmet clinical need. In this work, we aim to leverage imaging data and clinical variables to develop survival risk models among advanced NSCLC patients treated with immunotherapy. Methods: This retrospective study includes a total of 385 patients from two institutions who were treated with ICIs. Radiomics features extracted from pretreatment CT scans were used to build predictive models. The objectives were to predict overall survival (OS) along with building a classifier for short- and long-term survival groups. We employed the XGBoost learning method to build radiomics and integrated clinical-radiomics predictive models. Feature selection and model building were developed and validated on a multicenter cohort. Results: We developed parsimonious models that were associated with OS and a classifier for short- and long-term survivor groups. The concordance indices (C-index) of the radiomics model were 0.61 and 0.57 to predict OS in the discovery and validation cohorts, respectively. While the area under the curve (AUC) values of the radiomic models for short- and long-term groups were found to be 0.65 and 0.58 in the discovery and validation cohorts. The accuracy of the combined radiomics-clinical model resulted in 0.63 and 0.62 to predict OS and in 0.77 and 0.62 to classify the survival groups in the discovery and validation cohorts, respectively. Conclusions: We developed and validated novel radiomics and integrated radiomics-clinical survival models among NSCLC patients treated with ICIs. This model has important translational implications, which can be used to identify a subset of patients who are not likely to benefit from immunotherapy. The developed imaging biomarkers may allow early prediction of low-group survivors, though additional validation of these radiomics models is warranted.

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Development and validation of genomic predictors of radiation sensitivity using preclinical data

Cited by 9 publications

References 27 publications

Gene expression signature predicts radiation sensitivity in cell lines using the integral of dose–response curve

Gene expression signature predicts radiation sensitivity in cell lines using the integral of dose–response curve

Multi-institutional prognostic modeling of survival outcomes in NSCLC patients treated with first-line immunotherapy using radiomics

A Radiomics-Clinical Model Predicts Overall Survival of Non-Small Cell Lung Cancer Patients Treated with Immunotherapy: A Multicenter Study

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