scholarly journals Subgroup-Specific Diagnostic, Prognostic, and Predictive Markers Influencing Pediatric Medulloblastoma Treatment

Diagnostics ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 61
Author(s):  
Sutapa Ray ◽  
Nagendra K. Chaturvedi ◽  
Kishor K. Bhakat ◽  
Angie Rizzino ◽  
Sidharth Mahapatra

Medulloblastoma (MB) is the most common malignant central nervous system tumor in pediatric patients. Mainstay of therapy remains surgical resection followed by craniospinal radiation and chemotherapy, although limitations to this therapy are applied in the youngest patients. Clinically, tumors are divided into average and high-risk status on the basis of age, metastasis at diagnosis, and extent of surgical resection. However, technological advances in high-throughput screening have facilitated the analysis of large transcriptomic datasets that have been used to generate the current classification system, dividing patients into four primary subgroups, i.e., WNT (wingless), SHH (sonic hedgehog), and the non-SHH/WNT subgroups 3 and 4. Each subgroup can further be subdivided on the basis of a combination of cytogenetic and epigenetic events, some in distinct signaling pathways, that activate specific phenotypes impacting patient prognosis. Here, we delve deeper into the genetic basis for each subgroup by reviewing the extent of cytogenetic events in key genes that trigger neoplastic transformation or that exhibit oncogenic properties. Each of these discussions is further centered on how these genetic aberrations can be exploited to generate novel targeted therapeutics for each subgroup along with a discussion on challenges that are currently faced in generating said therapies. Our future hope is that through better understanding of subgroup-specific cytogenetic events, the field may improve diagnosis, prognosis, and treatment to improve overall quality of life for these patients.

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Tuo Wang ◽  
Yao Sun ◽  
Zichao Xiong ◽  
Jiamin Wu ◽  
Xiaoying Ding ◽  
...  

Abstract Background Astrocytoma is a common type of central nervous system tumor. In this study, we investigated the correlation between ST6GAL1 and CYP19A1 polymorphisms and the risk and prognosis of astrocytoma. Methods A total of 365 astrocytoma patients and 379 healthy controls were genotyped using the Agena MassARRAY system. The correlation between ST6GAL1 and CYP19A1 variants and astrocytoma risk was calculated using logistic regression. The survival rate of patients with astrocytoma was analyzed to evaluate prognosis. Results We found that the ST6GAL1-rs2239611 significantly decreased the risk of astrocytoma in the codominant model (p = 0.044) and dominant model (p = 0.049). In stratified analyses, CYP19A1-rs2255192 might be associated with a higher risk of astrocytoma among the low-grade subgroup under recessive (p = 0.034) and additive (p = 0.030) models. However, CYP19A1-rs4646 had a risk-decreasing effect on the high-grade subgroup in the codominant model (p = 0.044). The results of Cox regression analysis showed that the CYP19A1-rs2239611 and -rs1042757 polymorphisms were significantly correlated with the prognosis of astrocytoma. Conclusion Our results suggest that ST6GAL1 and CYP19A1 genes may be a potential biomarker of genetic susceptibility and prognosis to astrocytoma in the Chinese Han population.


2012 ◽  
Vol 01 (01) ◽  
pp. 083-085 ◽  
Author(s):  
Pankaj Ailawadhi ◽  
M.C. Sharma ◽  
A.K. Mahapatra ◽  
P. Sarat Chandra

Abstract Cerebellar liponeurocytoma consists of well-differentiated neurons with the cytology of neurocytes in addition to a population of lipidized cells. Hence it is biphasic in appearance and has been included in the category of glioneuronal tumors of the central nervous system by the WHO working group on the Classification of Tumors of the Nervous System. However, liponeurocytoma is not exclusive to the cerebellar or fourth ventricular location. Since its inclusion in the central nervous system tumor classification, nine cases with similar histological and immunohistochemical features have also been described in the lateral ventricles. We describe here such a lateral ventricular tumour in a 30-year-old woman, characteristically showing divergent glio-neuronal differentiation and lipidized neoplastic cells. Therefore, we suggest that future WHO tumor classification should consider that liponeurocytomas are not entirely restricted to the cerebellum and henceforth change of nomenclature might be considered, as also pointed out by other authors.


Author(s):  
Beatriz de las Heras Polo

Natural products have historically contributed to drug discovery as a source of bioactive molecules, due to their great diversity and structural complexity. They have provided “lead” molecules for the development of drugs in different therapeutic areas, with a very prominent representation in the treatment of pain and inflammation, coagulation disorders, metabolic disorders, as well as in the treatment of cancer and infectious diseases. In recent decades there has been a paradigm shift in drug discovery strategies that has allowed the identification of new active natural products in therapeutic targets. Combinatorial Chemistry and biological tests (High Throughput Screening), together with the development of computational techniques, have contributed decisively to the design and optimization of libraries of natural product derivatives based on their biological activity. In parallel, technological advances in the field of Omics sciences and in data processing lead to a multidimensional approach in the drug discovery process. These powerful tools will allow the analysis of the pharmacological potential of natural products and their derivatives for the conversion of these molecules to active products with low toxicity. In the Precision Medicine era, natural products continue to be molecules with great potential in pharmaceutical development, since, unlike other therapeutic strategies, they have a favorable cost-benefit ratio, which will allow their future use in this discipline.


1997 ◽  
Vol 99 ◽  
pp. S251
Author(s):  
M.E. Kusak ◽  
J.M. Alonso ◽  
D. Santamarta ◽  
I. Recio ◽  
J.M. Borrás ◽  
...  

1991 ◽  
Vol 181 (1) ◽  
pp. 151-158 ◽  
Author(s):  
Kei Tashiro ◽  
Toru Nakano ◽  
Tasuku Honjo ◽  
Tomokazu Aoki ◽  
Shin-ichi Miyatake ◽  
...  

2017 ◽  
Vol 20 (1) ◽  
pp. 3-9
Author(s):  
Ram Kumar Shrestha ◽  
Bibek KC ◽  
Gopal Sedain ◽  
Gita Sayami ◽  
Sushil Shilpakar ◽  
...  

Introduction: CNS tumor requires intraoperative decision making regarding the extent of tumor removal. Clinical examination and imaging studies are not sufficient enough to predict the biological behavior of the tumors. Squash cytology is a quick method of evaluation of cytomorphologic features prepared from smear technique and provide the preliminary diagnosis and aid in intraoperative decision making by differentiating neoplastic from non neoplastic and benign from malignant lesions. The aim of this study is compare the diagnostic accuracy of squash cytology to that of histopathological examination. Methods: This study consists of 36 specimens from both brain and spine subjected to both squash cytology and histopathological evaluation. The squash preparation and histopathological finding were later compared and diagnostic accuracy calculated. Results: Gliomas are the most common tumor encountered and the accuracy of Squash cytology obtained was 71%. In meningioma, 100% diagnostic accuracy was obtained however, there was limitation in accurately predict the subgroup of tumor by squash cytology alone. Other neoplastic lesions included in this study were Schwannoma, Oligodendroglioma, Ependymoma, mixed tumors and others. Overall, the accuracy predicted by squash cytology is found to be 77.8 % in this study. Conclusion: Squash cytology is rapid and reliable method of tissue diagnosis that aid in intraoperative decision making regarding the extent of Central Nervous System tumor excision


2018 ◽  
Vol 20 (suppl_2) ◽  
pp. i168-i168
Author(s):  
Kathleen Dorris ◽  
Jessica Channell ◽  
Molly Hemenway ◽  
Angelina Baroffio ◽  
Michael Ellison ◽  
...  

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