Radiomics for characterization of the glioma immune microenvironment

Khalili, Nastaran; Kazerooni, Anahita Fathi; Familiar, Ariana; Haldar, Debanjan; Kraya, Adam; Foster, Jessica; Koptyra, Mateusz; Storm, Phillip B.; Resnick, Adam; Nabavizadeh, Ali

doi:10.1038/s41698-023-00413-9

Cited by 13 publications

(7 citation statements)

References 103 publications

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“…Several advanced radiographic techniques are emerging for studying immune cells within the GBM TME. Radiomics extracts and analyzes many quantitative features from medical images that can predict the presence and density of immune cells within the TME ( 42 ). PET has significantly advanced our understanding of neuroinflammation thanks to tracers that selectively bind to activated microglia and macrophages ( 43 ).…”

Section: Discussionmentioning

confidence: 99%

The FKBP51s Splice Isoform Predicts Unfavorable Prognosis in Patients with Glioblastoma

Giordano,

Marrone,

Romano

et al. 2024

Cancer Research Communications

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The primary treatment for glioblastoma (GBM) is removing the tumor mass as defined by magnetic resonance imaging (MRI). However, MRI has limited diagnostic and predictive value. Tumor-associated macrophages (TAMs) are abundant in GBM microenvironment (TME) and are found in peripheral blood (PB). FKBP51 expression, with its canonical and spliced isoforms, is constitutive in immune cells and aberrant in GBM. Spliced FKBP51s supports M2-polarization. To find an immunological signature that combined with MRI could advance in diagnosis, we immunophenotyped the macrophages of TME and PB from 37 GBM patients using FKBP51s and classical M1-M2 markers. We also determined the tumor levels of FKBP51s, PD-L1, and HLA-DR. Tumors expressing FKBP51s showed an increase in various M2 phenotypes and Tregs in PB, indicating immunosuppression. Tumors expressing FKBP51s also activated STAT3 and were associated with reduced survival. Correlative studies with MRI and tumor/macrophages co-cultures allowed to interpret TAMs. Tumor volume correlated with M1 infiltration of TME. Co-cultures with spheroids produced M1 polarization, suggesting that M1 macrophages may infiltrate alongside cancer stem-cells. Co-cultures of adherent cells developed the M2 phenotype CD163/FKBP51s expressing pSTAT6, a transcription factor enabling migration and invasion. In patients with recurrences, increased counts of CD163/FKBP51s monocyte/macrophages in PB correlated with callosal infiltration and was accompanied by a concomitant decrease in TME-infiltrating M1 macrophages. PB PD-L1/FKBP51s connoted necrotic tumors. In conclusion, FKBP51s identifies a GBM subtype that significantly impairs the immune system. Moreover, FKBP51s marks PB macrophages associated with MRI features of glioma malignancy that can aid in patient monitoring.

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Section: Discussionmentioning

confidence: 99%

The FKBP51s Splice Isoform Predicts Unfavorable Prognosis in Patients with Glioblastoma

Giordano,

Marrone,

Romano

et al. 2024

Cancer Research Communications

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“…The association between radiomics and cancer biology, especially regarding the tumor immune microenvironment, has been recently investigated in some cancer types, especially in lung and breast cancers [60][61][62][63][64][65][66][67]. Some studies have focused on HNSCC [60,68], with most of them investigating the relevance of radiomics to capture histological features related to HPV status [25] and immune cell infiltration [40].…”

Section: Discussionmentioning

confidence: 99%

A Radiomics Approach to Identify Immunologically Active Tumor in Patients with Head and Neck Squamous Cell Carcinomas

Nguyen,

Bertolus,

Giraud

et al. 2023

Cancers

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Background: We recently developed a gene-expression-based HOT score to identify the hot/cold phenotype of head and neck squamous cell carcinomas (HNSCCs), which is associated with the response to immunotherapy. Our goal was to determine whether radiomic profiling from computed tomography (CT) scans can distinguish hot and cold HNSCC. Method: We included 113 patients from The Cancer Genome Atlas (TCGA) and 20 patients from the Groupe Hospitalier Pitié-Salpêtrière (GHPS) with HNSCC, all with available pre-treatment CT scans. The hot/cold phenotype was computed for all patients using the HOT score. The IBEX software (version 4.11.9, accessed on 30 march 2020) was used to extract radiomic features from the delineated tumor region in both datasets, and the intraclass correlation coefficient (ICC) was computed to select robust features. Machine learning classifier models were trained and tested in the TCGA dataset and validated using the area under the receiver operator characteristic curve (AUC) in the GHPS cohort. Results: A total of 144 radiomic features with an ICC >0.9 was selected. An XGBoost model including these selected features showed the best performance prediction of the hot/cold phenotype with AUC = 0.86 in the GHPS validation dataset. Conclusions and Relevance: We identified a relevant radiomic model to capture the overall hot/cold phenotype of HNSCC. This non-invasive approach could help with the identification of patients with HNSCC who may benefit from immunotherapy.

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“…This knowledge is also essential in preventing paradoxical tumor growth as seen with the sorafenib kinase inhibitor drug in BRAF-altered pLGGs 4 . Accordingly, with increasingly encouraging reports on the efficacy of immunotherapies in a number of solid pediatric brain tumors 6 , it is vital to understand the immunological characteristics of brain tumors. The tumor immune microenvironment (TIME) -composed of a diverse set of immune and stromal cells exhibiting complex interactions with each other and with tumor cellsinfluences the suppression or promotion of tumor growth.…”

Section: Introductionmentioning

confidence: 99%

Multiparametric MRI Along with Machine Learning Predicts Prognosis and Treatment Response in Pediatric Low-Grade Glioma

Fathi Kazerooni,

Kraya,

Rathi

et al. 2024

Preprint

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In this study, we present a comprehensive radiogenomic analysis of pediatric low-grade gliomas (pLGGs), combining treatment-naive multiparametric MRI and RNA sequencing. We identified three immunological clusters using XCell enrichment scores, highlighting an 'immune-hot' group correlating with poorer prognosis, suggesting potential benefits from immunotherapies. A radiomic signature predicting immunological profiles showed balanced accuracies of 81.5% and 84.4% across discovery and replication cohorts, respectively. Our clinicoradiomic model predicted progression-free survival with concordance indices of 0.71 and 0.77 in these cohorts, and the clinicoradiomic scores correlated with treatment response (p = 0.001). We also explored germline variants and transcriptomic pathways related to clinicoradiomic risk, identifying those involved in tumor growth and immune responses. This is the first radiogenomic analysis in pLGGs that enhances prognostication by prediction of immunological profiles, assessment of patients' risk of progression, prediction of treatment response to standard-of-care therapies, and early stratification of patients to identify potential candidates for novel therapies targeting specific pathways.

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Radiomics for characterization of the glioma immune microenvironment

Cited by 13 publications

References 103 publications

The FKBP51s Splice Isoform Predicts Unfavorable Prognosis in Patients with Glioblastoma

The FKBP51s Splice Isoform Predicts Unfavorable Prognosis in Patients with Glioblastoma

A Radiomics Approach to Identify Immunologically Active Tumor in Patients with Head and Neck Squamous Cell Carcinomas

Multiparametric MRI Along with Machine Learning Predicts Prognosis and Treatment Response in Pediatric Low-Grade Glioma

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