Radiomics signature for dynamic monitoring of tumor inflamed microenvironment and immunotherapy response prediction
Background The efficacy of immune checkpoint inhibitors (ICIs) depends on the tumor immune microenvironment (TIME), with a preference for a T cell-inflamed TIME. However, challenges in tissue-based assessments via biopsies have triggered the exploration of non-invasive alternatives, such as radiomic...
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BMJ Publishing Group
2025-01-01
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| Series: | Journal for ImmunoTherapy of Cancer |
| Online Access: | https://jitc.bmj.com/content/13/1/e009140.full |
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| author | Enriqueta Felip Paolo Nuciforo Elena Garralda Joan Frigola Ramon Amat Garazi Serna Francesco Grussu Kinga Bernatowicz Olivia Prior Marta Ligero Christina Zatse Rodrigo Toledo Manel Escobar Raquel Perez-Lopez |
| author_facet | Enriqueta Felip Paolo Nuciforo Elena Garralda Joan Frigola Ramon Amat Garazi Serna Francesco Grussu Kinga Bernatowicz Olivia Prior Marta Ligero Christina Zatse Rodrigo Toledo Manel Escobar Raquel Perez-Lopez |
| author_sort | Enriqueta Felip |
| collection | DOAJ |
| description | Background The efficacy of immune checkpoint inhibitors (ICIs) depends on the tumor immune microenvironment (TIME), with a preference for a T cell-inflamed TIME. However, challenges in tissue-based assessments via biopsies have triggered the exploration of non-invasive alternatives, such as radiomics, to comprehensively evaluate TIME across diverse cancers. To address these challenges, we develop an ICI response signature by integrating radiomics with T cell-inflamed gene-expression profiles.Methods We conducted a pan-cancer investigation into the utility of radiomics for TIME assessment, including 1360 tumors from 428 patients. Leveraging contrast-enhanced CT images, we characterized TIME through RNA gene expression analysis, using the T cell-inflamed gene expression signature. Subsequently, a pan-cancer CT-radiomic signature predicting inflamed TIME (CT-TIME) was developed and externally validated. Machine learning was employed to select robust radiomic features and predict inflamed TIME. The study also integrated independent cohorts with longitudinal CT images, baseline biopsies, and comprehensive immunohistochemistry panel evaluation to assess the pan-cancer biological associations, spatiotemporal landscape and clinical utility of the CT-TIME.Results The CT-TIME signature, comprising four radiomic features linked to a T-cell inflamed microenvironment, demonstrated robust performance with AUCs (95% CI) of 0.85 (0.73 to 0.96) (training) and 0.78 (0.65 to 0.92) (external validation). CT-TIME scores exhibited positive correlations with CD3, CD8, and CD163 expression. Intrapatient analysis revealed considerable heterogeneity in TIME between tumors, which could not be assessed using biopsies. Evaluation of aggregated per-patient CT-TIME scores highlighted its promising clinical utility for dynamically assessing the immune microenvironment and predicting immunotherapy response across diverse scenarios in advanced cancer. Despite demonstrating progression disease at the first follow-up, patients within the inflamed status group, identified by CT-TIME, exhibited significantly prolonged progression-free survival (PFS), with some surpassing 5 months, suggesting a potential phenomenon of pseudoprogression. Cox models using aggregated CT-TIME scores from baseline images revealed a statistically significant reduction in the risk of PFS in the pan-cancer cohort (HR 0.62, 95% CI 0.44 to 0.88, p=0.007), and Kaplan-Meier analysis further confirmed substantial differences in PFS between patients with inflamed and uninflamed status (log-rank test p=0.009).Conclusions The signature holds promise for impacting clinical decision-making, pan-cancer patient stratification, and treatment outcomes in immune checkpoint therapies. |
| format | Article |
| id | doaj-art-b5156977bb144aee8b8a3bbf43ebe032 |
| institution | Kabale University |
| issn | 2051-1426 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | Journal for ImmunoTherapy of Cancer |
| spelling | doaj-art-b5156977bb144aee8b8a3bbf43ebe0322025-01-14T23:45:08ZengBMJ Publishing GroupJournal for ImmunoTherapy of Cancer2051-14262025-01-0113110.1136/jitc-2024-009140Radiomics signature for dynamic monitoring of tumor inflamed microenvironment and immunotherapy response predictionEnriqueta Felip0Paolo Nuciforo1Elena Garralda2Joan Frigola3Ramon Amat4Garazi Serna5Francesco Grussu6Kinga Bernatowicz7Olivia Prior8Marta Ligero9Christina Zatse10Rodrigo Toledo11Manel Escobar12Raquel Perez-Lopez1315Vall d’Hebron University Hospital and Vall d’Hebron Institute of Oncology, Barcelona, Spain4 Molecular Oncology Laboratory, Vall d`Hebron Institute of Oncology (VHIO), Barcelona, SpainVall d`Hebron Institute of Oncology, Barcelona, SpainThoracic Cancers Translational Genomics Unit, Vall d`Hebron Institute of Oncology (VHIO), Barcelona, SpainVall d`Hebron Institute of Oncology, Barcelona, SpainMolecular Oncology Group, Vall d`Hebron Institute of Oncology, Barcelona, SpainCentre for Medical Imaging Computing, Department of Computer Science, University College London, London, UKVall d`Hebron Institute of Oncology, Barcelona, SpainVall d`Hebron Institute of Oncology, Barcelona, SpainVall d`Hebron Institute of Oncology, Barcelona, SpainVall d`Hebron Institute of Oncology, Barcelona, SpainVall d`Hebron Institute of Oncology, Barcelona, SpainVall d`Hebron University Hospital, Barcelona, SpainVall d`Hebron Institute of Oncology, Barcelona, SpainBackground The efficacy of immune checkpoint inhibitors (ICIs) depends on the tumor immune microenvironment (TIME), with a preference for a T cell-inflamed TIME. However, challenges in tissue-based assessments via biopsies have triggered the exploration of non-invasive alternatives, such as radiomics, to comprehensively evaluate TIME across diverse cancers. To address these challenges, we develop an ICI response signature by integrating radiomics with T cell-inflamed gene-expression profiles.Methods We conducted a pan-cancer investigation into the utility of radiomics for TIME assessment, including 1360 tumors from 428 patients. Leveraging contrast-enhanced CT images, we characterized TIME through RNA gene expression analysis, using the T cell-inflamed gene expression signature. Subsequently, a pan-cancer CT-radiomic signature predicting inflamed TIME (CT-TIME) was developed and externally validated. Machine learning was employed to select robust radiomic features and predict inflamed TIME. The study also integrated independent cohorts with longitudinal CT images, baseline biopsies, and comprehensive immunohistochemistry panel evaluation to assess the pan-cancer biological associations, spatiotemporal landscape and clinical utility of the CT-TIME.Results The CT-TIME signature, comprising four radiomic features linked to a T-cell inflamed microenvironment, demonstrated robust performance with AUCs (95% CI) of 0.85 (0.73 to 0.96) (training) and 0.78 (0.65 to 0.92) (external validation). CT-TIME scores exhibited positive correlations with CD3, CD8, and CD163 expression. Intrapatient analysis revealed considerable heterogeneity in TIME between tumors, which could not be assessed using biopsies. Evaluation of aggregated per-patient CT-TIME scores highlighted its promising clinical utility for dynamically assessing the immune microenvironment and predicting immunotherapy response across diverse scenarios in advanced cancer. Despite demonstrating progression disease at the first follow-up, patients within the inflamed status group, identified by CT-TIME, exhibited significantly prolonged progression-free survival (PFS), with some surpassing 5 months, suggesting a potential phenomenon of pseudoprogression. Cox models using aggregated CT-TIME scores from baseline images revealed a statistically significant reduction in the risk of PFS in the pan-cancer cohort (HR 0.62, 95% CI 0.44 to 0.88, p=0.007), and Kaplan-Meier analysis further confirmed substantial differences in PFS between patients with inflamed and uninflamed status (log-rank test p=0.009).Conclusions The signature holds promise for impacting clinical decision-making, pan-cancer patient stratification, and treatment outcomes in immune checkpoint therapies.https://jitc.bmj.com/content/13/1/e009140.full |
| spellingShingle | Enriqueta Felip Paolo Nuciforo Elena Garralda Joan Frigola Ramon Amat Garazi Serna Francesco Grussu Kinga Bernatowicz Olivia Prior Marta Ligero Christina Zatse Rodrigo Toledo Manel Escobar Raquel Perez-Lopez Radiomics signature for dynamic monitoring of tumor inflamed microenvironment and immunotherapy response prediction Journal for ImmunoTherapy of Cancer |
| title | Radiomics signature for dynamic monitoring of tumor inflamed microenvironment and immunotherapy response prediction |
| title_full | Radiomics signature for dynamic monitoring of tumor inflamed microenvironment and immunotherapy response prediction |
| title_fullStr | Radiomics signature for dynamic monitoring of tumor inflamed microenvironment and immunotherapy response prediction |
| title_full_unstemmed | Radiomics signature for dynamic monitoring of tumor inflamed microenvironment and immunotherapy response prediction |
| title_short | Radiomics signature for dynamic monitoring of tumor inflamed microenvironment and immunotherapy response prediction |
| title_sort | radiomics signature for dynamic monitoring of tumor inflamed microenvironment and immunotherapy response prediction |
| url | https://jitc.bmj.com/content/13/1/e009140.full |
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