Exploring Notch Pathway to Elucidate Phenotypic Plasticity and Intra-tumor Heterogeneity in Gliomas

Genome-wide profiling of patient-derived glioblastoma stem-like cells reveals recurrent genetic and transcriptomic signatures associated with brain tumors

PURPOSE

Patient-derived cancer cell lines can be very useful to investigate genetic as well as epigenetic mechanisms of transformation and to test new drugs. In this multi-centric study, we performed genomic and transcriptomic characterization of a large set of patient-derived glioblastoma (GBM) stem-like cells (GSCs).

METHODS

94 (80 I surgery/14 II surgery) and 53 (42 I surgery/11 II surgery) GSCs lines underwent whole exome and trascriptome analysis, respectively.

RESULTS

Exome sequencing revealed TP53 as the main mutated gene (41/94 samples, 44%), followed by PTEN (33/94, 35%), RB1 (16/94, 17%) and NF1 (15/94, 16%), among other genes associated to brain tumors. One GSC sample bearing a BRAF p.V600E mutation showed sensitivity in vitro to a BRAF inhibitor. Gene Ontology and Reactome analysis uncovered several biological processes mostly associated to gliogenesis and glial cell differentiation, S - adenosylmethionine metabolic process, mismatch repair and methylation. Comparison of I and II surgery samples disclosed a similar distribution of mutated genes, with an overrepresentation of mutations in mismatch repair, cell cycle, p53 and methylation pathways in I surgery samples, and of mutations in receptor tyrosine kinase and MAPK signaling pathways in II surgery samples. Unsupervised hierarchical clustering of RNA-seq data produced 3 clusters characterized by distinctive sets of up-regulated genes and signaling pathways.

CONCLUSION

The availability of a large set of fully molecularly characterized GCSs represents a valuable public resource to support the advancement of precision oncology for the treatment of GBM.

Understanding cancer stem cells and plasticity: Towards better therapeutics

The ability of cancer cells to finally overcome various lines of treatment in due course has always baffled the scientific community. Even with the most promising therapies, relapse is ultimately seen, and this resilience has proved to be a major hurdle in the management of cancer. Accumulating evidence now attributes this resilience to plasticity. Plasticity is the ability of cells to change their properties and is substantial as it helps in normal tissue regeneration or post-injury repair processes. It also helps in the overall maintenance of homeostasis. Unfortunately, this critical ability of cells, when activated incorrectly, can lead to numerous diseases, including cancer. Therefore, in this review, we focus on the plasticity aspect with an emphasis on cancer stem cells (CSCs). We discuss the various forms of plasticity that provide survival advantages to CSCs. Moreover, we explore various factors that affect plasticity. Furthermore, we provide the therapeutic implications of plasticity. Finally, we provide an insight into the future targeted therapies involving plasticity for better clinical outcomes.

Emerging landscape of molecular interaction networks: Opportunities, challenges and prospects

Network biology finds application in interpreting molecular interaction networks and providing insightful inferences using graph theoretical analysis of biological systems. The integration of computational bio-modelling approaches with different hybrid network-based techniques provides additional information about the behaviour of complex systems. With increasing advances in high-throughput technologies in biological research, attempts have been made to incorporate this information into network structures, which has led to a continuous update of network biology approaches over time. The newly minted centrality measures accommodate the details of omics data and regulatory network structure information. The unification of graph network properties with classical mathematical and computational modelling approaches and technologically advanced approaches like machine-learning- and artificial intelligence-based algorithms leverages the potential application of these techniques. These computational advances prove beneficial and serve various applications such as essential gene prediction, identification of drug–disease interaction and gene prioritization. Hence, in this review, we have provided a comprehensive overview of the emerging landscape of molecular interaction networks using graph theoretical approaches. With the aim to provide information on the wide range of applications of network biology approaches in understanding the interaction and regulation of genes, proteins, enzymes and metabolites at different molecular levels, we have reviewed the methods that utilize network topological properties, emerging hybrid network-based approaches and applications that integrate machine learning techniques to analyse molecular interaction networks. Further, we have discussed the applications of these approaches in biomedical research with a note on future prospects.

Targeting Notch Pathway in Cancer Diagnostics and Therapeutics: An Emerging Approach

The Notch signaling pathway is an evolutionarily conserved pathway usually present in multicellular organisms, which plays a pivotal role in cell fate determination and proliferation. Due to this property, it is highly oncogenic, especially in the dysregulated version of the Notch pathway, where apoptosis is inhibited, and abnormal cell growth is supported. Notch receptors and ligand proteins play an essential role in cancers, for instance, myeloid leukemia, T-cell lymphoblastic leukemia, and organ-specific, i.e., breast, colon, pancreas, and skin cancers. Any type of cancer generates as a result of genetic defects, including epigenetic alterations as well as mutations. These alterations can be used by the researchers to find a promising diagnostic as well as therapeutic tool for cancer. The successful inhibition of the Notch pathway with the help of specific biomarkers or suppression of gene expression represents a new remedy in the field of cancer research. This article focuses on the various remedies hidden within the Notch pathway's mechanism, primarily based on different patents published in recent years for assisting cancer diagnosis and succeeding treatment.

Adult immuno-oncology: using past failures to inform the future

In oncology, "immunotherapy" is a broad term encompassing multiple means of utilizing the patient's immune system to combat malignancy. Prominent among these are immune checkpoint inhibitors, cellular therapies including chimeric antigen receptor T-cell therapy, vaccines, and oncolytic viruses. Immunotherapy for glioblastoma (GBM) has had mixed results in early trials. In this context, the past, present, and future of immune oncology for the treatment of GBM was discussed by clinical, research, and thought leaders as well as patient advocates at the first annual Remission Summit in 2019. The goal was to use current knowledge (published and unpublished) to identify possible causes of treatment failures and the best strategies to advance immunotherapy as a treatment modality for patients with GBM. The discussion focuses on past failures, current limitations, failure analyses, and proposed best practices moving forward.