Computational analysis of whole slide images predicts PD-L1 expression and progression-free survival in immunotherapy-treated non-small cell lung cancer patients
Abstract Background Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment by significantly improving the efficacy of treatments and tolerability for patients with non-small cell lung cancer (NSCLC). However, even after meticulous selection based on molecular criteria, only 20–30%...
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| Format: | Article |
| Language: | English |
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BMC
2025-05-01
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| Series: | Journal of Translational Medicine |
| Online Access: | https://doi.org/10.1186/s12967-025-06487-2 |
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| author | Abdou Khadir Dia Alona Kolnohuz Sevinj Yolchuyeva Marion Tonneau Fabien Lamaze Michele Orain Andréanne Gagné Florence Blais François Coulombe Julie Malo Wiam Belkaid Arielle Elkrief Drew Williamson Bertrand Routy Philippe Joubert Mathieu Laplante Steve Bilodeau Venkata SK. Manem |
| author_facet | Abdou Khadir Dia Alona Kolnohuz Sevinj Yolchuyeva Marion Tonneau Fabien Lamaze Michele Orain Andréanne Gagné Florence Blais François Coulombe Julie Malo Wiam Belkaid Arielle Elkrief Drew Williamson Bertrand Routy Philippe Joubert Mathieu Laplante Steve Bilodeau Venkata SK. Manem |
| author_sort | Abdou Khadir Dia |
| collection | DOAJ |
| description | Abstract Background Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment by significantly improving the efficacy of treatments and tolerability for patients with non-small cell lung cancer (NSCLC). However, even after meticulous selection based on molecular criteria, only 20–30% of the patients respond to ICIs. This highlights the urgent clinical need to develop more precise biomarkers to better identify individuals who will benefit from these expensive therapies. Methods Data from NSCLC patients treated with immunotherapy were collected from two institutions. From the histological images of tumors, pathomics features were extracted. We employed six machine learning models and seven feature selection methods to predict expression of the programmed death-ligand 1 (PD-L1), a current biomarker used to select patients for immunotherapy, and progression-free survival (PFS). The association between pathomics features and biological pathways was explored to validate pathomics-based signatures. We performed gene set enrichment analysis to identify the pathways enriched with the predictive signatures. Results Handcrafted histological features were extracted from the whole slide images (WSI). The Support Vector Machines model with the SurfStar feature selection method, offered the best results, with an area under the curve (AUC) of around 0.66 for both the training and validation sets to predict PD-L1. For the prediction of PFS, the most effective model was linear discriminant analysis using the Multi Surf feature selection method with an AUC of 0.71 for the training set and 0.62 for the validation set. We found immune pathways to be upregulated in the high PD-L1 and high PFS groups, confirming the utility of image analysis for predicting clinical endpoints in patients treated with immunotherapy. Conclusion Our models, based on the analysis of histological images, can serve as predictive biomarkers for PD-L1 and PFS. This approach, focused on histological images, enables the distinction of patients based on treatment response, thus providing clinicians with a valuable tool for patient management. With further validation on external cohorts, these models could enhance clinical decision-making through analysis of routine medical images. |
| format | Article |
| id | doaj-art-29ba5c92685d42c1966bcda0d8bb12b3 |
| institution | Kabale University |
| issn | 1479-5876 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj-art-29ba5c92685d42c1966bcda0d8bb12b32025-08-20T03:52:55ZengBMCJournal of Translational Medicine1479-58762025-05-0123111310.1186/s12967-025-06487-2Computational analysis of whole slide images predicts PD-L1 expression and progression-free survival in immunotherapy-treated non-small cell lung cancer patientsAbdou Khadir Dia0Alona Kolnohuz1Sevinj Yolchuyeva2Marion Tonneau3Fabien Lamaze4Michele Orain5Andréanne Gagné6Florence Blais7François Coulombe8Julie Malo9Wiam Belkaid10Arielle Elkrief11Drew Williamson12Bertrand Routy13Philippe Joubert14Mathieu Laplante15Steve Bilodeau16Venkata SK. Manem17Centre de Recherche du CHU de Québec - Université LavalQuebec Heart & Lung Institute Research CenterCentre de Recherche du CHU de Québec - Université LavalCentre de Recherche du Centre Hospitalier Universitaire de MontréalQuebec Heart & Lung Institute Research CenterQuebec Heart & Lung Institute Research CenterUniversité LavalUniversité LavalUniversité LavalCentre de Recherche du Centre Hospitalier Universitaire de MontréalCentre de Recherche du Centre Hospitalier Universitaire de MontréalCentre de Recherche du Centre Hospitalier Universitaire de MontréalDepartment of Pathology and Laboratory Medicine, Emory University School of MedicineCentre de Recherche du Centre Hospitalier Universitaire de MontréalQuebec Heart & Lung Institute Research CenterQuebec Heart & Lung Institute Research CenterCentre de Recherche du CHU de Québec - Université LavalCentre de Recherche du CHU de Québec - Université LavalAbstract Background Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment by significantly improving the efficacy of treatments and tolerability for patients with non-small cell lung cancer (NSCLC). However, even after meticulous selection based on molecular criteria, only 20–30% of the patients respond to ICIs. This highlights the urgent clinical need to develop more precise biomarkers to better identify individuals who will benefit from these expensive therapies. Methods Data from NSCLC patients treated with immunotherapy were collected from two institutions. From the histological images of tumors, pathomics features were extracted. We employed six machine learning models and seven feature selection methods to predict expression of the programmed death-ligand 1 (PD-L1), a current biomarker used to select patients for immunotherapy, and progression-free survival (PFS). The association between pathomics features and biological pathways was explored to validate pathomics-based signatures. We performed gene set enrichment analysis to identify the pathways enriched with the predictive signatures. Results Handcrafted histological features were extracted from the whole slide images (WSI). The Support Vector Machines model with the SurfStar feature selection method, offered the best results, with an area under the curve (AUC) of around 0.66 for both the training and validation sets to predict PD-L1. For the prediction of PFS, the most effective model was linear discriminant analysis using the Multi Surf feature selection method with an AUC of 0.71 for the training set and 0.62 for the validation set. We found immune pathways to be upregulated in the high PD-L1 and high PFS groups, confirming the utility of image analysis for predicting clinical endpoints in patients treated with immunotherapy. Conclusion Our models, based on the analysis of histological images, can serve as predictive biomarkers for PD-L1 and PFS. This approach, focused on histological images, enables the distinction of patients based on treatment response, thus providing clinicians with a valuable tool for patient management. With further validation on external cohorts, these models could enhance clinical decision-making through analysis of routine medical images.https://doi.org/10.1186/s12967-025-06487-2 |
| spellingShingle | Abdou Khadir Dia Alona Kolnohuz Sevinj Yolchuyeva Marion Tonneau Fabien Lamaze Michele Orain Andréanne Gagné Florence Blais François Coulombe Julie Malo Wiam Belkaid Arielle Elkrief Drew Williamson Bertrand Routy Philippe Joubert Mathieu Laplante Steve Bilodeau Venkata SK. Manem Computational analysis of whole slide images predicts PD-L1 expression and progression-free survival in immunotherapy-treated non-small cell lung cancer patients Journal of Translational Medicine |
| title | Computational analysis of whole slide images predicts PD-L1 expression and progression-free survival in immunotherapy-treated non-small cell lung cancer patients |
| title_full | Computational analysis of whole slide images predicts PD-L1 expression and progression-free survival in immunotherapy-treated non-small cell lung cancer patients |
| title_fullStr | Computational analysis of whole slide images predicts PD-L1 expression and progression-free survival in immunotherapy-treated non-small cell lung cancer patients |
| title_full_unstemmed | Computational analysis of whole slide images predicts PD-L1 expression and progression-free survival in immunotherapy-treated non-small cell lung cancer patients |
| title_short | Computational analysis of whole slide images predicts PD-L1 expression and progression-free survival in immunotherapy-treated non-small cell lung cancer patients |
| title_sort | computational analysis of whole slide images predicts pd l1 expression and progression free survival in immunotherapy treated non small cell lung cancer patients |
| url | https://doi.org/10.1186/s12967-025-06487-2 |
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