top of page
Clouds in the Sky
Divyalakshmi

Divyalakshmi Ramakrishna, MDS

Content writer – Clinical

Healthy heart for healthy long life.

Published on: January 10, 2024

Life expectancy is a measure of population health. It is related to the longevity of an individual and is interdependent on a number of measurable factors. One of the most important factor is cardiovascular health. Cardiovascular health is based on four health behaviors and four health factors. The health behaviors include healthy diet, participation in physical activity avoidance of nicotine, healthy sleep and health factors include healthy weight, healthy levels of blood lipids, blood glucose levels and blood pressure. An updated approach for measuring, monitoring and modifying the cardiovascular health is proposed by the American Heart Association. Various factors which are measures of cardiovascular health may promote cardiovascular disease. These factors are implicated in the aging process and play a pivotal role in predicting longevity of an individual. Cardiovascular (CV) aging and longevity are inter related and are inversely proportional to each other. The major factors that contribute to cardiovascular aging and in turn longevity share common pathophysiological mechanisms/ pathways such as oxidative stress, dyslipidemia, hyperglycemia, insulin resistance and arterial hypertension. Also, the genetic, dietary, and environmental characteristics of long-living populations plays a role in determining longevity. Scientific research on the mechanisms of aging and means of achieving longevity is constantly growing. In this arena, five places in the world, termed as “Blue Zones,” have been identified as the areas with the highest percentage of centenarians. These places are Loma Linda, California, USA, Nicoya Cost Rica, Sardinia Italy, Ikaria Greece, and Okinawa Japan. In this context, one of the important study is the IKARIA study on the pathophysiological mechanisms of cardiovascular aging and longevity, their interaction, and their translation into lifestyle behaviors. Discussing the various health factors, increased oxidative stress effectively promotes cardiovascular aging by inflammaging and causing aging related diseases hence short lifespan. The use of anti-inflammaging agents slows down cardiovascular aging thus promoting longevity. The role of hyperinsulinemia, hyperglycemia and insulin resistance are related to increased cardiovascular mortality and short lifespan. However, normal glucose metabolism enhances longevity by reducing cardiovascular mortality. Several age-related mechanisms are believed to mediate alterations in lipoprotein synthesis and activity resulting in dyslipidemia. The aging-observed hypercholesterolemia and the genetic modulation of lipoproteins is implicated in the relationship of longevity. Of the genetic factors telomere length is attributed to have an impact on cardiovascular aging and longevity. Short telomere length is associated with many aging-related diseases that promote cardiovascular aging and increased cardiovascular mortality. Short telomere length is also associated with short life span. On the contrary, long telomere length has been associated with both decreased CV mortality and longevity. Further the role of various health behaviors, is summarized which shows that abstaining from smoking is fundamental for healthy aging and longevity. Also, physical activity and smoking cessation reduce CV risk and may favorably modulate duration of life span. Physical activity and exercise are considered essential factors contributing to healthy aging and prolonged life span. Environmental factor of inhaling air pollutants increase oxidative stress and induce inflammation. There is evidence between ambient temperature and cardiovascular mortality, suggesting that both cold and hot temperatures may affect cardiovascular mortality, although the effect of cold is stronger than hot temperatures. Mediterranean diet, possibly accompanied by coffee intake, seems the most appropriate dietary choice for CV and holistic health. Low-calorie intake is also a promising dietary path to longevity. Thus to conclude, delaying cardiovascular aging increases the likelihood of longevity. However, Mediterranean diet, low-calorie intake, physical activity, smoking cessation, and a favorable genetic and environmental background are features of long-living populations. Also, the genetic, molecular, and biochemical pathways of aging may help to introduce interventions that might delay cardiovascular aging, and hence help to achieve the goal of longevity. References: 1. Panagiota Pietri, Christodoulos Stefanadis. Cardiovascular Aging and Longevity: JACC State-of-the-Art Review. Journal of the American College of Cardiology, Volume 77, Issue 2, 19 January 2021, Pages 189-204. https://doi.org/10.1016/j.jacc.2020.11.023. 2. Hao Ma, Xuan Wang, Qiaochu Xue, Xiang Li , Zhaoxia Liang, Yoriko Heianza, Oscar H. Franco, Lu Qi. Cardiovascular Health and Life Expectancy among Adults in the United States. Circulation. 2023; 147:1137–1146. doi: 10.1161/circulationaha.122.062457 3. Chenjie Xu, Pengjie Zhang, Zhi Cao. Cardiovascular health and healthy longevity in people with and without cardiometabolic disease: A prospective cohort study. www.thelancet.com Vol 45, March, 2022. https://doi.org/10.1016/j. eclinm.2022.101329 4. Donald M. Lloyd-Jones, Norrina B. Allen, Cheryl A.M. Anderson, Terrie Black, LaPrincess C. Brewer, Randi E. Foraker, Michael A. Grandner, Helen Lavretsky, Amanda Marma Perak, Garima Sharma, Wayne Rosamond. Life’s Essential 8: Updating and Enhancing the American Heart Association’s Construct of Cardiovascular Health: A Presidential Advisory From the American Heart Association. Circulation, Vol 146, No.5, e18-e43, August 2022. https://doi.org/10.1161/CIR.0000000000001078 5. Michelle C. Odden, Yongmei Li, Roland J. Thorpe Jr., Annabel Tan, Kendra D. Sims, Jourdan Ratcliff, Hoda S. Abdel Magid, Mario Sims. Neighborhood factors and survival to old age: The Jackson Heart Study. Preventive Medicine Reports 35, August 2023. https://doi.org/10.1016/j.pmedr.2023.102360

Artificial intelligence–based prediction of lung cancer risk using nonimaging electronic medical records: Deep learning approach.

Published on: September 20, 2023

Original author: Marvin Chia-Han Yeh, et al., 2021 (doi: 10.2196/26256)

Lung cancer is the most common cause of death worldwide. So, early detection is crucial which may be helpful to reduce mortality rate. Low dose computed tomography (LDCT) screening may reduce the lung cancer associated mortality by 20%. But the potential harm due to radiation exposure, false-positive results, and costs associated with LDCT, most organizations only recommend annual screening that targets high-risk individuals. However, due to the potential harm associated with false-positive results, the cost-effectiveness of implementing annual LDCT screening remains controversial. In order to overcome this problem, multiple research groups have attempted to develop risk prediction models to classify patients who might benefit from LDCT screening. In this scenario, the use of artificial intelligence (AI) has resulted in good performance of predicting image related tasks, specifically the use of convolutional neural networks (CNNs). In lung cancer research, CNNs have been applied to LDCT and chest radiographic images to facilitate detection. Also, in predicting lung cancer risk, the electronic medical records (EMR) is suited to the task of identifying high-risk individuals. The goal of the present study was to generate a model that facilitated the prospective identification of individuals at higher risk for developing lung cancer; these individuals might then undergo further follow-up examinations, including LDCT. The use of a predictive model to identify individuals at high risk could serve to limit unnecessary radiation exposure and reduce costs associated with LDCT and related interventions. Methodology The patients EMRs were initially sampled from the Taiwan National Health Insurance Research Database (NHIRD). These EMRs included the demographic information, diagnoses, and procedure codes from the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) and prescriptions from both outpatient clinical declaration files and in-hospital declaration files. This study included participants between the ages of 20 and 90 years who had at least 4 years of medical records on file. Participants with missing data were excluded. The index date for patients with lung cancer was defined as the date of first diagnosis. The inputs included age, gender, and an image representing the patient’s 3-year history of diagnosis and medication. The image was input into Xception, a 126-layer neural network, in which feature extraction was performed. The final layer of the Xception network was connected to an average pooling layer and then connected to a fully connected layer with the patient’s age and gender. A three subgroup analyses was performed to investigate the performance of the model in different populations. According to the age criteria used in previous trials focused on lung cancer screening, patients above and below 55 years of age were included among the subgroups. Also, patients both with and without previous lung disease, including subgroups of patients diagnosed with asbestosis, bronchiectasis, chronic bronchitis, chronic obstructive pulmonary disease (COPD), emphysema, fibrosis, pneumonia, sarcoidosis, silicosis, and tuberculosis were examined. Finally, to focus on the discriminative power of the diagnosis and medication without the confounding effects of age, a subgroup of age- and gender-matched controls was identified. All patient data were split into training, validation, and testing sets based on their respective index dates. To understand the model prediction, occlusion sensitivity analysis was performed by iteratively masking information from a single diagnosis or medication followed by evaluating any changes in the model prediction. In addition, a dimensional reduction technique, t-distributed stochastic neighbor embedding (t-SNE), was performed on the fully connected hidden layer output of the final testing data. The model construction, data preprocessing, model training, and statistical processing were performed using the Python programming language, version 3.6. Results A total of 11,617 lung cancer patients and 1,423,154 control patients were identified in our data set. The mean age of the lung cancer group was 66.62 years; the overall data set included 856,558 (59.7%) men and 578,213 (40.3%) women. For all patients, the model revealed an AUC of 0.821 when the input image-like array included sequential diagnostic information only. By contrast, the AUC was 0.894 when the input features included sequential medication information only; when the sequential diagnostic and medication information was simplified to binary variables, the model performance decreased (AUC=0.827). When both sequential diagnostic and medication information were integrated, the model reached an AUC of 0.902 on prospective testing, with a sensitivity of 0.804 and specificity of 0.837. The model performance at different age cut offs was then investigated. Screening using an age cut off of 55 years revealed a superior AUC of 0.871 compared to those obtained when cut offs of 50 or 60 years were used (0.866 and 0.863, respectively). Analyses of the subgroups included one that was both age and-gender-matched, those at ages above and below 55 years, and those with or without lung disease were performed. This model revealed an AUC of 0.818 with a sensitivity of 0.647 and a specificity of 0.873. For patients above 55 years of age, the model revealed an AUC of 0.869 with a sensitivity of 0.784 and a specificity of 0.785. The discriminatory powers of these models were both excellent among patients with and without a history of lung disease; the AUCs for these subgroups were 0.914 and 0.887, respectively. Among all the subgroups, the model had the weakest performance in patients below 55 years of age who had no history of lung disease. By contrast, the model provided the strongest performance for individuals above the age of 55 years with a history of lung disease, which revealed the highest PPV of 14.3%. The model exhibited the lowest PPV of 1.0% for individuals less than 55 years of age with no history of lung disease. Conclusion CNN model exhibited robust performance with respect to the 1-year prospective prediction of the risk of developing lung cancer. This model may be deployed as a means to analyze medical databases, thus paving the way for efficient population-based screening and digital precision medicine. A future randomized controlled trial will be required to explore the clinical benefit of this model in diverse populations. Impact of research 1. The model could be readily deployed as a means to evaluate centralized health care databases. 2. To perform efficient population-based screening of risk prediction of lung cancer. 3. The similar models may be incorporated for detection of other types of cancer as well.

A molecularly integrated grade for meningioma.

Published on: May 31, 2023

Original author: Joseph Driver, et al., 2022 (doi: 10.1093/neuonc/noab213)

Meningiomas are one of the most common primary intracranial tumors in adults. Ass there is presence of more number of cases worldwide, there is a need for a reliable, widely accessible and acceptable predictor of clinical outcome after a diagnosis. At present, the World Health Organization (WHO) places meningioma into 3 grades based on the histopathological features, and is the primary measure used to predict outcome and guide postsurgical treatment decision making. It is well recognized, however, that the behavior of a number of meningiomas does not conform to their assigned WHO grade, with some histologically benign meningiomas developing repeated recurrences despite aggressive treatment while other higher-grade meningiomas remain stable after surgical resection. The limitations of WHO grading is dependent on the interobserver variability in histological assessment, the potential for under-sampling of a tumor type with known histologic and molecular heterogeneity, and the possibility that malignant potential may not be uniformly reflected in assessment of the histological features. Hence, molecular based approaches were incorporated to develop a grading system for meningiomas. This grading system incorporates mitotic index and multiple high-risk copy-number alterations for identification of patients at risk for tumor recurrence, despite complete tumor resection, and in some cases, despite benign-appearing histopathology (WHO grade I). A critical aspect of this approach is that the features are transparent, and can be assessed by a number of genomic platforms that have proliferated across medical centers. Methodology A total number of 699 meningiomas with detailed clinical, imaging, histological, and molecular annotation, were evaluated inorder to formulate a molecularly integrated grade that is simple to apply, transparent, scalable, and accurate in long-term prediction of clinical behavior. Out of which, 527 meningiomas resected from unique patients evaluated at the Brigham and Women’s Hospital (BWH) from 2003 to 2019 as the discovery cohort. An additional 172 patients with surgically resected meningioma, including 117 from BWH and 55 from the University of Toronto, were examined as independent validation cohorts. The clinical history, tumor location, and radiographic recurrence were assessed. Preoperative and postoperative MRIs underwent volumetric contouring to define the extent of resection, with gross total resection (GTR) defined by no residual enhancing nodular tumor on imaging, and all others classified as subtotal resection. All follow-up MRIs were independently reviewed by 3 authors (W.L.B., J.D., and S.T.) to evaluate for recurrence. The histopathological review of all the tumors was performed by board-certified neuropathologists and tumor grade was abstracted from the pathology reports issued by the BWH Neuropathology division according to the WHO Classification of Tumors (2007 and 2016). The whole-genome microarray analysis for DNA copy-number profiling was available for 527 tumors from the discovery cohort and 83 samples from the BWH validation cohort. Targeted mutational profiling of 227-447 cancer-associated genes (OncoPanel, versions 1-3) was available for 118 samples from the discovery cohort and all 117 of the BWH validation set. Using all of the discovery cohort, which included tumors with varied treatment histories, the Integrated Grade was included along with extent of resection, tumor size, and tumor status (primary vs recurrent) into a Cox proportional hazards model to generate a nomogram for recurrence risk. A decision curve analysis was performed to evaluate the clinical utility of the nomogram. Results The results were subdivided into: 1) Development of a molecularly based Integrated grade An Integrated grade was devised, which accounts for mitotic count, focal hemizygous or homozygous loss of CDKN2A, and loss of 1p, 3p, 4p/q, 6p/q, 10p/q, 14q, 18p/q, and 19p/q. Several high-risk chromosomes, including 4, 6, 10, 18, and 19, exhibited synchronous loss of the short and long arms when altered, mitotic count exhibited less variance and all these features were assigned 1 point for the presence of any of the above chromosomal losses. Also, for CDKN2A loss and a mitotic count of 4-19; 2 points were assigned for mitotic count >20. Tumors were divided into three Integrated Grades based on their point score: Integrated Grade 1 (0-1 pt), Integrated Grade 2 (2-3 pts), and Integrated Grade 3 (>4 pts). 2) Association between WHO grade and Integrated grade The association of WHO grade and Integrated Grade for 527 meningiomas, showed 87% concordance between WHO grade I and Integrated Grade 1, 31% concordance between WHO grade II and Integrated Grade 2, and 72% concordance between WHO grade III and Integrated Grade 3. 3) Integrated Grade and Clinical Outcome The authors reported that in the subset of 338 primary non-irradiated meningiomas with gross total resection (GTR), tumors demonstrated distinct progression-free survival as stratified by either WHO grade or Integrated grade. The Integrated Grade was compared to WHO grade for predicting tumor recurrence using time-dependent receiver operator curves (ROC), time-dependent average precision (AP) curves, and Brier curves. Also, they found that the Integrated grading scheme significantly improved the ability to predict recurrence risk compared to the WHO grade, as evaluated by time-dependent (ROC) area under the curve and Brier score even when restricted to the prospectively collected cases. Notably, the predictive capacity of the Integrated grade compared to WHO grade strengthened with follow-up time. In each of these cohorts, the study reported that the Integrated grade was superior to WHO grade in predicting recurrence. In addition, the Integrated Grade was superior to WHO grade in assessing overall survival on long-term follow-up. Conclusion The present study shows that mitotic index and copy-number profile can appraise tumor behavior with satisfactory results. Taken together, the modular nature of the proposed risk stratified Integrated grading scheme lends itself to future refinement with incorporation of additional axes of genomic data as scientific discovery advances and has immediate relevance to management of meningioma patients. Also, the authors propose that the Integrated grading system can enhance the clinical care of meningioma patients and aid in the design of future prospective clinical trials. Impact of research 1. Improve clinical outcome and better patient management. 2. Help to identify recurrence of tumors and progression free survival rate. 3. Provide more effective grading of meningiomas, and genomically informed clinical trials.

Prognostic factors associated with survival in patients with diffuse astrocytoma.

Published on: February 08, 2023

Original author: Liu S, Liu X, and Zhuang W (2021) (DOI: 10.3389/fsurg.2021.712350)

Astrocytomas are the most common primary tumors in the central nervous system (CNS). These tumors arise from astrocytes—star-shaped cells that make up the “glue-like” or supportive tissue of the brain. The diagnosis is based primarily on histopathological criteria defined by the World Health Organisation (WHO) that grades astrocytomas as pilocytic astrocytoma (grade I), diffuse astrocytoma (grade II), anaplastic astrocytoma (grade III), and glioblastoma (grade IV).​ Diffuse astrocytomas (DA) are grade II astrocytomas also known as low-grade astrocytomas. DA consists of fibrillary astrocytoma, protoplasmic astrocytoma, and gemistocytic astrocytoma. Diffuse astrocytoma is a slow-growing brain tumor. They are infiltrating tumors with ill-defined borders. Although diffuse astrocytoma is a relatively slow-growing tumor with a median survival time of 5-8 years, they have a high recurrence rate due to diffuse infiltration of brain tissue and an inherent malignant potential to transform into high-grade astrocytomas. Clinical symptoms of DA vary depending on the location of the tumor. Seizures, headaches, and focal neurologic deficits are the most frequent presenting symptoms. The histopathological diagnosis of a diffuse astrocytoma can be challenging due to its pronounced heterogeneity. Hence, the tumor characteristics and the factors associated with the prognosis are inadequately understood. However, studies about the clinicopathological characteristics of DA are scarce in the literature at present. Factors influencing the prognosis of DA are also unclear. Thus, identifying the factors associated with prognosis and survival rate in DA patients is necessary. Methodology Given this, a population-based cohort study was conducted, utilizing prospectively extracted data from the Surveillance, Epidemiology, and End Results (SEER) database. The patients were collected from the SEER database, documented from 1973-2017. In this retrospective study, the patients diagnosed with primary tumor as DA, according to the International Classification of Diseases for Oncology, Third Edition (ICD-O-3), were identified. The demographic features as well as the clinicopathological characteristics of the patients were also collected. The age of the patient at diagnosis, race, sex, marital status, primary tumor site, histological type, tumor size, surgical treatment, survival duration in months, and survival status were collected in this study. Patients with unclear information on any of the collected variables were excluded.​ Kaplan–Meier analysis was used to assess the cancer-specific survival (CSS) stratified by each factor. The clinicopathological factors and CSS were analyzed using Cox proportional hazards model. Statistically significant variables in univariate Cox analysis were further included in multivariate Cox analysis. For each patient, significant prognostic factors were further utilized to prepare a nomogram and then put into the nomogram calculator to get a predicted survival rate at 5- and 10 years. The C-index and receiver operating characteristic (ROC) curve were utilized to evaluate the accuracy of the nomogram. R software (version 3.5.0) was utilized to perform the statistical analysis. Results A total of 799 participants with DA were included, consisting of 95.9% fibrillary astrocytoma and 4.1% protoplasmic variants. The average age of participants was 41.9 years, with 57.2% being male. The majority of the population was white (87.5%). More than half (53.9%) of the patients were married. DA arose mostly in the cerebrum (63.8%). Around 71.6% of the population had received surgical treatment. The overall 1-, 3-, 5-, and 10-year survival rates were 73.7, 55.2, 49.4, and 37.6%, respectively. Kaplan–Meier analysis showed that age at diagnosis, marital status, primary tumor site, tumor size, and surgery was possibly associated with cancer-specific survival (CSS) (p < 0.05). Multivariate Cox proportional hazard analysis indicated that surgery was a protective factor whereas older age, larger tumor size, and tumors in the brainstem were harmful factors for patients with DA. Moreover, a nomogram predicting 5- and 10-year survival probability for DA was developed. Conclusion In conclusion, the authors proposed that the present study is the largest one to date to investigate the clinicopathological characteristics and survival of patients with DA. They concluded that age, primary tumor site, tumor size, and surgery were associated with the survival of patients with DA. Thus, these outcomes may contribute to the future management of DA patients. Impact of the research 1. Help in systematic treatment planning 2. Predict the prognosis and better treatment outcomes 3. Personalized or individualistic approach to the management of DA patients 4. The survival rate of the patients 5. All the above factors will impact the quality of life of DA patients <

Reassessing diabetes and APOE genotype as potential interacting risk factors for Alzheimer’s disease.

Published on: November 2, 2022

Original author: Ravipati K, Chen Y, Manns JR. et al. (2022) (DOI: 10.1177/15333175211070912)

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder in which the death of brain cells causes memory loss and cognitive decline. Alzheimer's disease is the most common cause of dementia — a continuous decline in thinking, behavioral and social skills that affect a person's ability to function independently. Out of the approximately 50 million people worldwide with dementia, between 60% and 70% are estimated to have Alzheimer's disease. Approximately 5.8 million people in the United States aged 65 years and older live with Alzheimer's disease. The early signs of the disease include forgetting recent events or conversations. As the disease progresses, a person with Alzheimer's disease will develop severe memory impairment and lose the ability to carry out everyday tasks along with difficulty concentrating and thinking, especially about abstract concepts such as numbers. Hence, there is a need for a better early diagnosis of AD.​ There are various/multiple risk factors associated with AD. Accurate assessment of risk factors is the key focus for early diagnosis of AD. Researchers believe that genetics play a major role in the development of Alzheimer's disease. One of the best-characterized genetic risk factors for AD is the Apolipoprotein E (APOE) ε4 allele, one of three polymorphisms of the apolipoprotein E gene. Diabetes has also been proposed to be another risk factor for AD, and several recent studies have attempted to characterize the physiological relationship between diabetes and AD. However, an important unanswered question is an extent to which diabetes alone or in combination with APOE ε4 carriage predicts AD diagnosis. Methodology The present study assessed the interaction between APOE ε4 and diabetes in the context of AD diagnosis by analyzing a large and diverse participant population. A retrospective cohort study was conducted on participants from the NIA-funded National Alzheimer’s Coordinating Center (NACC) database, which included data (n = 33456) collected (longitudinally for many participants) from 2005 to 2016 at 29 Alzheimer’s disease Centers (ADCs). A standardized clinical evaluation that included neuropsychological tests was used to diagnose participants into three categories: AD, cognitive impairment without AD, and cognitively unimpaired. Data were also collected regarding health, cognition, and demographics. Diagnosis of diabetes was based on patient self-reporting. No other confirmatory testing, including biomarkers or medication history, was available regarding diabetic status. APOE genotype was available for 24336 participants and was presented as 0, 1, or 2 APOE ε4 alleles in the NACC database. Of the 4204 who self-reported diabetes, 2803 (66.6%) had APOE genotype information. Results All the data collected were tabulated and multiple statistical analysis models were used to assess the relationship between APOE genotype, diabetes, and AD diagnosis. All statistical analyses were performed using R software, version 3.4.1. Multinomial Logistic Regression Models were used to assess the participant’s first and last Alzheimer’s disease center (ADC) visit, as well as the association between the APOE 4 genotype and AD along with the association of diabetes and AD. A mixed effects model, was used to assess the risk of being diagnosed with AD at any visit. Structural Equation Modelling (SEM) was used to model how various factors mediated the influence of APOE genotype and diabetes on conversion to AD diagnosis in individuals not diagnosed with AD on their first visit.​ In the present study, APOE 4 genotype showed a strong association with AD diagnosis and was significant whereas the association between diabetes and AD diagnosis was weak and non-significant. Conclusion In conclusion, the present study reported that the APOE ε4 genotype was strongly associated with AD risk and the finding that the APOE ε4 genotype correlates with impairment of long-term memory (LTM) loss provides clinicians with additional insight to make the most accurate diagnosis possible. Also, it was suggested that diabetes was not a potential risk factor for AD, and associating diabetes with working memory (WM) rather than LTM impairments will aid to distinguish AD from other types of dementia. Impact of the research 1. The identification of risk factors associated with AD may help in better and early diagnosis. 2. It helps to differentiate different types of dementia to a certain extent. 3. It also paves the way to unveil the pathophysiological link between APOE 4 genotype, diabetes, and AD pathology.

Intratumoral IL-12 delivery empowers CAR-T cell immunotherapy in a pre-clinical model of glioblastoma.

Published on: July 29, 2022

Original author: Giulia Agliardi et al. (2021) (DOI: 10.1038/s41467-020-20599-x)

Glioblastoma multiforme (GBM) is the most common and aggressive form of primary brain cancer in adults. It accounts for about 60-70% of gliomas. The standard treatment regimen for GBM is tumor resection followed by radiotherapy and concomitant chemotherapy with temozolomide. However, GBM has high recurrence attributed to the infiltrative nature of the tumor, and also a dismal prognosis with a poor median survival of around 14 months, hence there is a need for more effective therapies. In this arena, immunotherapy treatment directed towards T-cells with tumor specificity may be a promising therapeutic strategy. Research overview The treatment with chimeric antigenic receptor (CAR) T-cells is preferable to treat intracranial tumors due to the ability of T- cells to access the central nervous system (CNS), and penetrate the infiltrative sites of the tumor, and show antitumor activity. But the CAR-T cells efficacy may be impaired by various adaptive immune-suppressive responses. CAR – T cell therapy alone was not sufficient to eradicate GBM. However, an additional or a combinational therapeutic option was added to overcome tumor heterogeneity and the altered tumor microenvironment (TME). In this scenario, Interleukin 12 (IL – 12), a proinflammatory cytokine with potent tumor suppressor activity has been implemented as a combination agent with CAR T- cells in the present study. IL – 12 directly supports the cytotoxic activity of T- cells as well as plays a role in reshaping the TME to achieve antitumor immunity. Methodology In the present study, in the first phase, an immunocompetent orthotopic syngeneic mouse model of GBM was used to evaluate the anti-tumor activity of EGFRvIII-specific murine CAR-T cells as a single agent which was administered by intravenous injection. Also, the efficacy of EGFRvIII-directed CAR-T cells in vivo in mice was tested. The first phase consisted of an evaluation of recombinant single-chain IL-12 fused to the Fc portion of murine IgG3 (hereafter called IL-12:Fc) as a single agent in an orthotopic syngeneic mouse model with GBM. In the second phase, a combinatorial immunotherapy approach was designed where the local intratumoral delivery of a single dose of IL-12:Fc was administered along with systemic (intravenous) CAR – T cell therapy. Tumor growth was monitored using a magnetic resonance imaging (1T-MRI) system by measuring the tumor volumes.​ The results of the present study demonstrated that both the single therapies were only able to delay tumor growth to a certain extent, whereas the combination of systemic EGFRvIII-specific CAR infusion and local IL-12:Fc administration eliminated tumors in most treated mice and showed a synergistic effect on the overall survival. Moreover, combination treatment with IL-12:Fc and CAR-T cell therapy showed significantly improved survival as compared to either treatment alone. Also, local delivery of a single dose of IL-12 improves the efficacy of CAR-T cells. Therefore, the authors have reported that the data has demonstrated that combined IL-12 and CAR-T cell therapy promotes an effective and persistent anti-tumor response, even in the context of a poorly immunogenic model. Impact of the research The benefit of combined therapy of CAR - T cells and local IL – 12, is multifaceted as the administration of IL -12 at the tumor site improves the efficacy of CAR T – cells in aggressive and poorly immunogenic tumors and thus also affect the transferred CAR –T cells, T – cell compartment and the myeloid cells in TME in preclinical models which helps in reshaping the TME and achieving antitumor immunity. Hence, the present study showed that the local administration of IL – 12, overcomes the barriers which are encountered by the CAR-T cell therapy in GBM. Thus, the authors have suggested trials for clinical studies of the combinational approach of systemic CAR –T cell therapy along with intratumoral administration of IL-12 in patients with GBM.

Connecting Dots

Get the latest updates

Join Cell talk!

Subscribe us today & receive latest updates on your mail !

bottom of page