Brain tumors are the leading cause of cancer-related death in childhood. To combat this, the Bandopadhayay Lab uses  cutting-edge genomics to: 1) Characterize driver alterations that contribute to tumor formation and 2) Develop strategies to therapeutically target these tumors. We also study the evolution of cancer cells in pediatric brain tumors during treatment to understand how tumors become resistant to treatments, and to determine how we can therapeutically overcome it.

Our goal is to find better treatments for children with brain tumors that result in improved survival rates with fewer long-term side effects.

Our current research endeavors include:

Identifying and characterizing driver genomic alterations in pediatric brain tumors. We apply next-generation sequencing approaches to identify recurrent somatic driver mutations and structural alterations across a range of pediatric brain tumors. The Bandopadhayay Lab combines genomic approaches (including the use of genome-scale functional modifier screens) with cancer-biology techniques to unravel the mechanisms through which these alterations contribute to tumor formation and to determine how these can be leveraged to guide diagnostic approaches and develop novel therapeutic strategies.

Characterizing chromatin complexes as therapeutic targets in pediatric brain tumors. Many pediatric brain tumors are driven by oncogenic fusion proteins or transcription factors that cannot be inhibited with small molecules. An alternate approach is to develop strategies that target the complexes with which these proteins interact. We have made significant efforts to explore the use of inhibitors that target chromatin complexes as an alternate therapeutic strategy.

Determining resistance mechanisms to current and novel therapies. Resistance to anti-cancer treatments is a major obstacle to a cure for many children. We aim to study how cancer genomes change with therapy (by comparing pre-treatment and post-treatment tumor samples) and to apply functional genomic approaches to predict how cancer cells are expected to overcome treatments. We also have an interest in developing new tools (such as the use of DNA-barcoding technology) to allow us to track the evolution of cancer cells (at single cell resolution) with treatments.

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