Multiple Faces of the Glioblastoma Microenvironment

The tumor microenvironment is a highly dynamic accumulation of resident and infiltrating tumor cells, responsible for growth and invasion. The authors focused on the leading-edge concepts regarding the glioblastoma microenvironment. Due to the fact that the modern trend in the research and treatment of glioblastoma is represented by multiple approaches that target not only the primary tumor but also the neighboring tissue, the study of the microenvironment in the peritumoral tissue is an appealing direction for current and future therapies.

NIMG-22. PREDICTION OF GLIOBLASTOMA CELLULAR INFILTRATION AND RECURRENCE USING MACHINE LEARNING AND MULTI-PARAMETRIC MRI ANALYSIS: RESULTS FROM THE MULTI-INSTITUTIONAL RESPOND CONSORTIUM

PURPOSE Multi-parametric MRI and artificial intelligence (AI) methods were previously used to predict peritumoral neoplastic cell infiltration and risk of future recurrence in glioblastoma, in single-institution studies. We hypothesize that important characteristics of peritumoral tissue heterogeneity captured, engineered/selected, and quantified by these methods relate to predictions generalizable in the multi-institutional ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium. METHODS To support further development, generalization, and clinical translation of our proposed method, we trained the AI model on a retrospective cohort of 29 de novo glioblastoma patients from the Hospital of the University of Pennsylvania (UPenn) (Male/Female:20/9, age:22-78 years) followed by evaluation on a prospective multi-institutional cohort of 84 glioblastoma patients (Male/Female:51/33, age:34-89 years) from Case Western Reserve University/University Hospitals (CWRU/UH, 25), New York University (NYU, 13), Ohio State University (OSU, 13), University Hospital Río Hortega (RH, 2), and UPenn (31). Features extracted from pre-resection MRI (T1, T1-Gd, T2, T2-FLAIR, ADC) were used to build our model predicting the spatial pattern of subsequent tumor recurrence. These predictions were evaluated against regions of pathology-confirmed post-resection recurrence. RESULTS Our model predicted the locations that later harbored tumor recurrence with sensitivity 83%, AUC 0.83 (99% CI, 0.73-0.93), and odds ratio 7.23 (99% CI, 7.09-7.37) in the prospective cohort. Odds ratio (99% CI)/AUC(99% CI) per institute were: CWRU/UH, 7.8(7.6-8.1)/0.82(0.75-0.89); NYU, 3.5(3.3-3.6)/0.84(074-0.93); OSU, 7.9(7.6-8.3)/0.8(0.67-0.94); RH, 22.7(20-25.1)/0.94(0.27-1); UPenn, 7.1(6.8-7.3)/0.83(0.76-0.91). CONCLUSION This is the first study that provides relatively extensive multi-institutional validated evidence that AI can provide good predictions of peritumoral neoplastic cell infiltration and future recurrence, by dissecting the MRI signal heterogeneity in peritumoral tissue. Our analyses leveraged the unique dataset of the ReSPOND consortium, which aims to develop and evaluate AI-based biomarkers for individualized prediction and prognostication, by moving from single-institution studies to generalizable, well-validated multi-institutional predictive biomarkers.

Distinct tumor signatures using deep learning-based characterization of the peritumoral microenvironment in glioblastomas and brain metastases

Tumor types are classically distinguished based on biopsies of the tumor itself, as well as a radiological interpretation using diverse MRI modalities. In the current study, the overarching goal is to demonstrate that primary (glioblastomas) and secondary (brain metastases) malignancies can be differentiated based on the microstructure of the peritumoral region. This is achieved by exploiting the extracellular water differences between vasogenic edema and infiltrative tissue and training a convolutional neural network (CNN) on the Diffusion Tensor Imaging (DTI)-derived free water volume fraction. We obtained 85% accuracy in discriminating extracellular water differences between local patches in the peritumoral area of 66 glioblastomas and 40 metastatic patients in a cross-validation setting. On an independent test cohort consisting of 20 glioblastomas and 10 metastases, we got 93% accuracy in discriminating metastases from glioblastomas using majority voting on patches. This level of accuracy surpasses CNNs trained on other conventional DTI-based measures such as fractional anisotropy (FA) and mean diffusivity (MD), that have been used in other studies. Additionally, the CNN captures the peritumoral heterogeneity better than conventional texture features, including Gabor and radiomic features. Our results demonstrate that the extracellular water content of the peritumoral tissue, as captured by the free water volume fraction, is best able to characterize the differences between infiltrative and vasogenic peritumoral regions, paving the way for its use in classifying and benchmarking peritumoral tissue with varying degrees of infiltration.

Spatial immunoprofiling of the intratumoral and peritumoral tissue of renal cell carcinoma patients

While the abundance and phenotype of tumor-infiltrating lymphocytes are linked with clinical survival, their spatial coordination and its clinical significance remain unclear. Here, we investigated the immune profile of intratumoral and peritumoral tissue of clear cell renal cell carcinoma patients (n = 64). We trained a cell classifier to detect lymphocytes from hematoxylin and eosin stained tissue slides. Using unsupervised classification, patients were further classified into immune cold, hot and excluded topographies reflecting lymphocyte abundance and localization. The immune topography distribution was further validated with The Cancer Genome Atlas digital image dataset. We showed association between PBRM1 mutation and immune cold topography, STAG1 mutation and immune hot topography and BAP1 mutation and immune excluded topography. With quantitative multiplex immunohistochemistry we analyzed the expression of 23 lymphocyte markers in intratumoral and peritumoral tissue regions. To study spatial interactions, we developed an algorithm quantifying the proportion of adjacent immune cell pairs and their immunophenotypes. Immune excluded tumors were associated with superior overall survival (HR 0.19, p = 0.02) and less extensive metastasis. Intratumoral T cells were characterized with pronounced expression of immunological activation and exhaustion markers such as granzyme B, PD1, and LAG3. Immune cell interaction occurred most frequently in the intratumoral region and correlated with CD45RO expression. Moreover, high proportion of peritumoral CD45RO+ T cells predicted poor overall survival. In summary, intratumoral and peritumoral tissue regions represent distinct immunospatial profiles and are associated with clinicopathologic characteristics.

Contrast Administration Impacts CT-Based Radiomics of Colorectal Liver Metastases and Non-Tumoral Liver Parenchyma Revealing the “Radiological” Tumour Microenvironment

The impact of the contrast medium on the radiomic textural features (TF) extracted from the CT scan is unclear. We investigated the modification of TFs of colorectal liver metastases (CLM), peritumoral tissue, and liver parenchyma. One hundred and sixty-two patients with 409 CLMs undergoing resection (2017–2020) into a single institution were considered. We analyzed the following volumes of interest (VOIs): The CLM (Tumor-VOI); a 5-mm parenchyma rim around the CLM (Margin-VOI); and a 2-mL sample of parenchyma distant from CLM (Liver-VOI). Forty-five TFs were extracted from each VOI (LIFEx®®). Contrast enhancement affected most TFs of the Tumor-VOI (71%) and Margin-VOI (62%), and part of those of the Liver-VOI (44%, p = 0.010). After contrast administration, entropy increased and energy decreased in the Tumor-VOI (0.93 ± 0.10 vs. 0.85 ± 0.14 in pre-contrast; 0.14 ± 0.03 vs. 0.18 ± 0.04, p < 0.001) and Margin-VOI (0.89 ± 0.11 vs. 0.85 ± 0.12; 0.16 ± 0.04 vs. 0.18 ± 0.04, p < 0.001), while remaining stable in the Liver-VOI. Comparing the VOIs, pre-contrast Tumor and Margin-VOI had similar entropy and energy (0.85/0.18 for both), while Liver-VOI had lower values (0.76/0.21, p < 0.001). In the portal phase, a gradient was observed (entropy: Tumor > Margin > Liver; energy: Tumor < Margin < Liver, p < 0.001). Contrast enhancement affected TFs of CLM, while it did not modify entropy and energy of parenchyma. TFs of the peritumoral tissue had modifications similar to the Tumor-VOI despite its radiological aspect being equal to non-tumoral parenchyma.

Evidence of Reelin Signaling in GBM and Its Derived Cancer Stem Cells

Glioblastoma (GBM) is the most aggressive and malignant form of primary brain cancer, characterized by an overall survival time ranging from 12 to 18 months. Despite the progress in the clinical treatment and the growing number of experimental data aimed at investigating the molecular bases of GBM development, the disease remains characterized by a poor prognosis. Recent studies have proposed the existence of a population of GBM cancer stem cells (CSCs) endowed with self-renewal capability and a high tumorigenic potential that are believed to be responsible for the resistance against common chemotherapy and radiotherapy treatments. Reelin is a large secreted extracellular matrix glycoprotein, which contributes to positioning, migration, and laminar organization of several central nervous system structures during brain development. Mutations of the reelin gene have been linked to disorganization of brain structures during development and behavioral anomalies. In this study, we explored the expression of reelin in GBM and its related peritumoral tissue and performed the same analysis in CSCs isolated from both GBM (GCSCs) and peritumoral tissue (PCSCs) of human patients. Our findings reveal (i) the higher expression of reelin in GBM compared to the peritumoral tissue by immunohistochemical analysis, (ii) the mRNA expression of both reelin and its adaptor molecule Dab1 in either CSC subtypes, although at a different extent; and (iii) the contribution of CSCs-derived reelin in the migration of human primary GBM cell line U87MG. Taken together, our data indicate that the expression of reelin in GBM may represent a potential contribution to the regulation of GBM cancer stem cells behavior, further stimulating the interest on the reelin pathway as a potential target for GBM treatment.

Changes of the redox-regulatory system in tumor and its surrounding tissues in patients with soft tissue sarcoma (STS) after neoadjuvant chemotherapy (NC).

e23522 Background: The disruptions in redox homeostasis in the nonmalignant tissues surrounding neoplasm can promote the tumor progression. The aim of this work was to assess the changes of the redox-regulatory system in the tumor and tumor-surrounding tissues in STS patients (pts) under the influence of a mofified metod of NC. Methods: The activity of glutathione S-transferase (GST), glutathione peroxidase (GPx), glutathione reductase (GR), content of reduced glutathione (GSH) and malondialdehyde (MDA) were determined by spectrophotometric methods. All markers were measured in the samples of tumor, peritumoral area and healthy tissues (taken along the line of resection) obtained during the surgery from 42 STS pts (T2a-bN0M0). The control group consisted of 21 primary pts who underwent resection only. Patients of the experimental group (21) received NC comprising systemic and local administration of antitumor drugs. Doxorubicin (40 mg/m2) was injected intravenously on the 1st and 7th days with autologous red blood cells as drug carriers; at the same time, cyclophosphamide (600 mg/m2) and methotrexate (40 mg/m2) were injected along the tumor periphery, on autologous plasma as a carrier. After 14 days, tumor removal surgery performed. All STS pts received standard postoperative chemoradiotherapy. Results: The level of GSH in tumor without NC treatment was higher than in the healthy and peritumoral tissue (by 2.3-2.5 times), and the activity of all glutathione-dependent enzymes was higher by 53.0-147.0 % (p = 0.0413-0.00124). The content of MDA in tumor was lower than in other tissues by 30.0-46.0 % (p = 0.00061). We did not find any differences between the healthy and peritumoral areas. In tumor samples of the experimental group, we also observed statistically significant increase in the level of GSH (by 2.8–3.0 times) and activation of GPO (by 37.3-95.8 %) and GR (by 2.0-3.2 times) vs. other tissues. However, after NC, the studied samples showed an increase in GSH by 3.1–3.8 times (p = 0.0143–0.00112), compared with the corresponding control samples. Also, the activity of GPO (by 54.5 %) and GsT (by 38.9 %) was significantly increased in peritumoral tissue vs similar area in the control group. After NC, the content of MDA was reduced in the healthy and tumor tissues vs control by 52.0 % (p = 0.0074) and 30.6 % (p = 0.04815), respectively. Clinical efficacy of NC was confirmed by reduced tumor volume in most patients by 30-40 %; the 5-year monitoring of STS pts showed that local recurrence and metastasis occurred in 14 of 21 pts in the control group, and in 6 of 21 in the main group (p = 0.0294). Conclusions: The NC treatment modifies the redox balance in the tumor-surrounding tissues and, as a result, decreases the oxidation damages in the healthy tissues. This effect, apparently, is an additional factor that improves the effectiveness of the proposed NC method.

A Multiparametric MRI-Based Radiomics Analysis to Efficiently Classify Tumor Subregions of Glioblastoma: A Pilot Study in Machine Learning

Glioblastoma multiforme (GBM) carries a poor prognosis and usually presents with heterogenous regions of a necrotic core, solid part, peritumoral tissue, and peritumoral edema. Accurate demarcation on magnetic resonance imaging (MRI) between the active tumor region and perifocal edematous extension is essential for planning stereotactic biopsy, GBM resection, and radiotherapy. We established a set of radiomics features to efficiently classify patients with GBM and retrieved cerebral multiparametric MRI, including contrast-enhanced T1-weighted (T1-CE), T2-weighted, T2-weighted fluid-attenuated inversion recovery, and apparent diffusion coefficient images from local patients with GBM. A total of 1316 features on the raw MR images were selected for each annotated area. A leave-one-out cross-validation was performed on the whole dataset, the different machine learning and deep learning techniques tested; random forest achieved the best performance (average accuracy: 93.6% necrosis, 90.4% solid part, 95.8% peritumoral tissue, and 90.4% peritumoral edema). The features from the enhancing tumor and the tumor shape elongation of peritumoral edema region for high-risk groups from T1-CE. The multiparametric MRI-based radiomics model showed the efficient classification of tumor subregions of GBM and suggests that prognostic radiomic features from a routine MRI exam may also be significantly associated with key biological processes that affect the response to chemotherapy in GBM.