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The aggressive and recurrent nature of glioblastoma is multifactorial and has been attributed to its biological heterogeneity, dysfunctional metabolic signaling pathways, rigid blood-brain barrier, inherent resistance to standard therapy due to the stemness property of the gliomas cells, immunosuppressive tumor microenvironment, hypoxia and neoangiogenesis which are very well orchestrated and create the tumor's own highly pro-tumorigenic milieu. Once the relay of events starts amongst these components, eventually it becomes difficult to control the cascade using only the balanced contemporary care of treatment consisting of maximal resection, radiotherapy and chemotherapy with temozolamide. Over the past few decades, implementation of contemporary treatment modalities has shown benefit to some extent, but no significant overall survival benefit is achieved. Therefore, there is an unmet need for advanced multifaceted combinatorial strategies. Recent advances in molecular biology, development of innovative therapeutics and novel delivery platforms over the years has resulted in a paradigm shift in gliomas therapeutics. Decades of research has led to emergence of several treatment molecules, including immunotherapies such as immune checkpoint blockade, oncolytic virotherapy, adoptive cell therapy, nanoparticles, CED and BNCT, each with the unique proficiency to overcome the mentioned challenges, present research. Recent years are seeing innovative combinatorial strategies to overcome the multifactorial resistance put forth by the GBM cell and its TME. This review discusses the contemporary and the investigational combinatorial strategies being employed to treat GBM and summarizes the evidence accumulated till date.Glioblastoma is a form of brain tumor which typically leads to death in almost all patients. Over the last two decades, traditional treatment strategies such as surgery, radiotherapy and chemotherapy have been combined as standard therapy. Together, these aggressive treatment strategies have provided modest survival benefit with acceptable toxicity. However, relapse is the invariable norm resulting in death in the overwhelming majority of patients. Relapse occurs due to multiple factors such as inability of drugs to cross blood–brain barrier, immunosuppressive tumor microenvironment, stemness nature of glioma cells, tumor heterogeneity and enhanced hypoxia and angiogenic factors. Therefore, there is an urgent need to develop an innovative treatment approach to treat glioblastoma. Recently, several treatment strategies known as immunotherapies including CAR T cell therapy, dendritic cell vaccines, immune checkpoints blockade and oncolytic virus, nano particles and gene editing/silencing technology have demonstrated promising results in preclinical and few clinical trials. Furthermore, to increase the efficacy of these novel strategies, combinatorial approaches are being implemented for the treatment. This includes CAR T cell therapy in combination with small molecules, immune checkpoint inhibitors and oncolytic virus and nanoparticles plus gene editing, silencing or immune checkpoints inhibitors. These treatments have shown exciting results in preclinical settings and few of these trials are in progress. The review summarizes these combinatorial novel approaches and discusses them in detail.

Orbital mucosa-associated lymphoid tissue (MALT) lymphoma, which are rare and indolent, often present at an early stage. We report the efficacy and safety outcomes of treatment in these patients.We analyzed adult patients with stage IE or IIE orbital MALT lymphoma between 1999 and 2017 treated at our institute. We assessed local control (LC) rates, overall survival (OS), relapse-free survival (RFS) using Kaplan Meier method and the incidence of late toxicities.Seventy patients were analyzed for clinical outcomes. The median age at diagnosis was 52 years (IQR-45-62 years). Radiotherapy was offered to 97% of patients and the dose ranged from 36 to 45 Gy. Chemotherapy was administered in 5(7.1%) patients. Relapse occurred in 8 patients (local: 2, distant: 6). At a median follow-up of 101 months (IQR-47-146 months), the median OS and RFS was not reached. 8-year OS, RFS and LC rates were 96.5%, 88.5%, 96.7% respectively. Univariate analysis showed age ≤60 years and lacrimal involvement significantly correlated with better OS (P = .01 and .04, respectively). Cataract was the most common sequelae observed in 31 patients (44.3%).Moderate doses of radiotherapy are curative in early-stage orbital MALT lymphoma with favorable clinical outcomes. Lower doses of radiation can reduce the toxicity further, without compromising efficacy.

Spatiotemporal targeting of anti-glioma drugs remains a pressing issue in glioblastoma (GBM) treatment. We challenge this issue by developing a minimally invasive

Glioblastoma is one of the most difficult tumor types to manage, having high morbidity and mortality with available therapies (surgery, radiotherapy and chemotherapy). Immunotherapeutic agents like Oncolytic Viruses (OVs), Immune Checkpoint Inhibitors (ICIs), Chimeric Antigen Receptor (CAR) T cells and Natural Killer (NK) cell therapies are now being extensively used as experimental therapies in the management of glioblastoma. Oncolytic virotherapy is an emerging form of anti-cancer therapy, employing nature’s own agents to target and destroy glioma cells. Several oncolytic viruses have demonstrated the ability to infect and lyse glioma cells by inducing apoptosis or triggering an anti-tumor immune response. In this mini-review, we discuss the role of OV therapy (OVT) in malignant gliomas with a special focus on ongoing and completed clinical trials and the ensuing challenges and perspectives thereof in subsequent sections.

supplementary document for Journal of Personalised Medicine

The unprecedented clinical success of Chimeric Antigen Receptor (CAR) T cell therapy in hematological malignancies has led researchers to study its role in solid tumors. Although, its utility in solid tumors especially in neuroblastoma has begun to emerge, preclinical studies of its efficacy in other solid tumors like osteosarcomas or gliomas has caught the attention of oncologist to be tried in clinical trials. Malignant high-grade brain tumors like glioblastomas or midline gliomas in children represent some of the most difficult malignancies to be managed with conventionally available therapeutics, while relapsed gliomas continue to have the most dismal prognosis due to limited therapeutic options. Innovative therapies such as CAR T cells could give an additional leverage to the treating oncologists by potentially improving outcomes and ameliorating the toxicity of the currently available therapies. Moreover, CAR T cell therapy has the potential to be integrated into the therapeutic paradigm for aggressive gliomas in the near future. In this review we discuss the challenges in using CAR T cell therapy in brain tumors, enumerate the completed and ongoing clinical trials of different types of CAR T cell therapy for different brain tumors with special emphasis on glioblastoma and also discuss the future role of CAR T cells in Brain tumors.

BackgroundGlioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) are common in elderly yet difficult to differentiate on MRI. Their management and prognosis are quite different. Recent surge of interest in predictive analytics, using machine learning (ML) from radiomic features and deep learning (DL) for diagnosing, predicting response and prognosticating disease has evinced interest among radiologists and clinicians. The objective of this systematic review and meta-analysis was to evaluate the deep learning & ML algorithms in classifying PCNSL from GBM.MethodsThe authors performed a systematic review of the literature from MEDLINE, EMBASE and the Cochrane central trials register for the search strategy in accordance with PRISMA guidelines to select and evaluate studies that included themes of ML, DL, AI, GBM, PCNSL. All studies reporting on ML algorithms or DL that for differentiating PCNSL from GBM on MR imaging were included. These studies were further narrowed down to focus on works published between 2018 and 2021. Two researchers independently conducted the literature screening, database extraction and risk bias assessment. The extracted data was synthesised and analysed by forest plots. Outcomes assessed were test characteristics such as accuracy, sensitivity, specificity and balanced accuracy.ResultsTen articles meeting the eligibility criteria were identified addressing use of ML and DL in training and validation classifiers to distinguish PCNSL from GBM on MR imaging. The total sample size was 1311 in the included studies. ML approach was used in 6 studies while DL in 4 studies. The lowest reported sensitivity was 80%, while the highest reported sensitivity was 99% in studies in which ML and DL was directly compared with the gold standard histopathology. The lowest reported specificity was 87% while the highest reported specificity was 100%. The highest reported balanced accuracy was 100% and the lowest was 84%.ConclusionsExtensive search of the database revealed a limited number of studies that have applied ML or DL to differentiate PCNSL from GBM. Of the currently published studies, Both DL & ML algorithms have demonstrated encouraging results and certainly have the potential to aid neurooncologists in taking preoperative decisions in the future leading to not only reduction in morbidities but also be cost effective.

Grading of gliomas is a piece of critical information related to prognosis and survival. Classifying glioma grade by semantic radiological features is subjective, requires multiple MRI sequences, is quite complex and clinically demanding, and can very often result in erroneous radiological diagnosis. We used a radiomics approach with machine learning classifiers to determine the grade of gliomas. Eighty-three patients with histopathologically proven gliomas underwent MRI of the brain. Whenever available, immunohistochemistry was additionally used to augment the histopathological diagnosis. Segmentation was performed manually on the T2W MR sequence using the TexRad texture analysis softwareTM, Version 3.10. Forty-two radiomics features, which included first-order features and shape features, were derived and compared between high-grade and low-grade gliomas. Features were selected by recursive feature elimination using a random forest algorithm method. The classification performance of the models was measured using accuracy, precision, recall, f1 score, and area under the curve (AUC) of the receiver operating characteristic curve. A 10-fold cross-validation was adopted to separate the training and the test data. The selected features were used to build five classifier models: support vector machine, random forest, gradient boost, naive Bayes, and AdaBoost classifiers. The random forest model performed the best, achieving an AUC of 0.81, an accuracy of 0.83, f1 score of 0.88, a recall of 0.93, and a precision of 0.85 for the test cohort. The results suggest that machine-learning-based radiomics features extracted from multiparametric MRI images can provide a non-invasive method for predicting glioma grades preoperatively. In the present study, we extracted the radiomics features from a single cross-sectional image of the T2W MRI sequence and utilized these features to build a fairly robust model to classify low-grade gliomas from high-grade gliomas (grade 4 gliomas).

Objective: Image-based prediction of molecular subgroups of Medulloblastoma (MB) has the potential to optimize and personalize therapy. The objective of the study is to distinguish between broad molecular subgroups of MB using MR–Texture analysis. Methods: Thirty-eight MB patients treated between 2007 and 2020 were retrospectively analyzed. Texture analysis was performed on contrast enhanced T1(T1C) and T2 weighted (T2W) MR images. Manual segmentation was performed on all slices and radiomic features were extracted which included first order, second order (GLCM - Grey level co-occurrence matrix) and shape features. Feature enrichment was done using LASSO (Least Absolute Shrinkage and Selection Operator) regression and thereafter Support Vector Machine (SVM) and a 10-fold cross-validation strategy was used for model development. The area under Receiver Operator Characteristic (ROC) curve was used to evaluate the model. Results: A total of 174 and 170 images were obtained for analysis from the Axial T1C and T2W image datasets. One hundred and sixty-four MR based texture features were extracted. The best model was arrived at by using a combination of 30 GLCM and six shape features on T1C MR sequence. A 10-fold cross-validation demonstrated an AUC of 0.93, 0.9, 0.93, and 0.93 in predicting WNT, SHH, Group 3, and Group 4 MB subgroups, respectively. Conclusion: Radiomic analysis of MR images in MB can predict molecular subgroups with acceptable degree of accuracy. The strategy needs further validation in an external dataset for its potential use in ab initio management paradigms of MBs. Advances in knowledge: Medulloblastoma can be classified into four distinct molecular subgroups using radiomic feature classifier from non-invasive Multiparametric Magnetic resonance imaging. This can have future ramifications in the extent of surgical resection of Medulloblastoma which can ultimately result in reduction of morbidity.

Background and purposeSemantic imaging features have been used for molecular subclassification of high-grade gliomas. Radiomics-based prediction of molecular subgroups has the potential to strategize and individualize therapy. Using MRI texture features, we propose to distinguish between IDH wild type and IDH mutant type high grade gliomas.MethodsBetween 2013 and 2020, 100 patients were retrospectively analyzed for the radiomics study. Immunohistochemistry of the pathological specimen was used to initially identify patients for the IDH mutant/wild phenotype and was then confirmed by Sanger’s sequencing. Image texture analysis was performed on contrast-enhanced T1 (T1C) and T2 weighted (T2W) MR images. Manual segmentation was performed on MR image slices followed by single-slice multiple sampling image augmentation. Both whole tumor multislice segmentation and single-slice multiple sampling approaches were used to arrive at the best model. Radiomic features were extracted, which included first-order features, second-order (GLCM—Grey level co-occurrence matrix), and shape features. Feature enrichment was done using LASSO (Least Absolute Shrinkage and Selection Operator) regression, followed by radiomic classification using Support Vector Machine (SVM) and a 10-fold cross-validation strategy for model development. The area under the Receiver Operator Characteristic (ROC) curve and predictive accuracy were used as diagnostic metrics to evaluate the model to classify IDH mutant and wild-type subgroups.ResultsMultislice analysis resulted in a better model compared to the single-slice multiple-sampling approach. A total of 164 MR-based texture features were extracted, out of which LASSO regression identified 14 distinctive GLCM features for the endpoint, which were used for further model development. The best model was achieved by using combined T1C and T2W MR images using a Quadratic Support Vector Machine Classifier and a 10-fold internal cross-validation approach, which demonstrated a predictive accuracy of 89% with an AUC of 0.89 for each IDH mutant and IDH wild subgroup.ConclusionA machine learning classifier of radiomic features extracted from multiparametric MRI images (T1C and T2w) provides important diagnostic information for the non-invasive prediction of the IDH mutant or wild-type phenotype of high-grade gliomas and may have potential use in either escalating or de-escalating adjuvant therapy for gliomas or for using targeted agents in the future.

Although cancer stem cells (CSCs) have been reported across solid tumors, there is a dearth of data regarding CSC and its impact on outcomes of cervical cancer.From October 2013 to December 2015, patients with squamous cancer of the cervix (stage IB2-IVA) were included. Pretreatment and posttreatment biopsy was obtained and immunohistochemistry was performed for SOX-2, OCT-4, Nanog, CD44, and Podoplanin. All patients received concurrent radiation and brachytherapy to an equivalent dose of 80 to 84 Gy to point A with concurrent weekly cisplatin. Correlation of CSC expression was performed with known prognostic factors. The effect of stem cell expression on disease outcomes was tested within multivariate analysis.One hundred fifty patients were included. The median dose to point A was 83 Gy (46-89 Gy) and a median of 4 cycles (range, 0-6 cycles) of chemotherapy was administered. At baseline, moderate to strong immunohistochemical expression of SOX-2, OCT-4, Nanog, CD44, and Podoplanin was observed in 12.8%, 4.8%, 24.4%, 15.5%, and 1.3% of patients, respectively. At median follow-up of 30 months (range, 3-51 months), locoregional and distant relapse was observed in 12.2% and 23.1% of patients, of whom 4.7% had both local and distant relapse. The 3-year disease-free survival rate was 87%. On multivariate analysis, moderate to high CSC expression and CD44 low status (hazard ratio [HR] = 8.8; 95% confidence interval [CI], 1.0-77.2; P .04) independently predicted for locoregional relapse-free survival. International Federation of Gynecology and Obstetrics stage (HR = 2.6; 95% CI, 1.3-5.4; P = .004) and presence of residual tumor after external radiation (HR = 3.5; 95% CI, 1.8-6.5; P = .0001) predicted for a detriment in disease-free survival.The presence of stem cell proteins and loss of CD44 independently predicts for reduced locoregional control in locally advanced cervical cancer. Further investigation into the interaction of stem cell and CD44 biology is warranted.

Previously we have shown, using a primary glioblastoma (GBM) cell model, that a subpopulation of innately radiation resistant (RR) GBM cells survive radiotherapy and form multinucleated and giant cells (MNGCs) by homotypic fusions. We also showed that MNGCs may cause relapse. Here, we set out to explore whether molecular characteristics of RR cells captured from patient-derived primary GBM cultures bear clinical relevance.Primary cultures were derived from 19 naive GBM tumor samples. RR cells generated from these cultures were characterized using various cell biological assays. We also collected clinicopathological data of the 19 patients and assessed associations with RR variables using Spearman's correlation test and with patient survival using Kaplan-Meier analysis. Significance was determined using a log-rank test.We found that SF2 (surviving fraction 2) values (p = 0.029), days of RR cell formation (p = 0.019) and percentage of giant cells (p = 0.034) in the RR population independently correlated with a poor patient survival. We also found that low ATM (Ataxia-telangiectasia mutated) expression levels in RR cells showed a significant (p = 0.002) negative correlation with SF2 values. A low ATM expression level in RR cells along with a high tumor volume was also found to negatively correlate with patient survival (p = 0.011). Finally, we found that the ATM expression levels in RR cells independently correlated with a poor patient survival (p = 0.014).Our data indicate that molecular features of innately radiation resistant GBM cells independently correlate with clinical outcome. Our study also highlights the relevance of using patient-derived primary GBM cultures for the characterization of RR cells that are otherwise inaccessible for analysis.