Sharon J. Diskin

Dr. Diskin is trained in genomics and computational biology and has a multi-disciplinary research program. Along with her colleagues, she couples integrative computational analyses of large-scale data such as next generation DNA/RNA sequencing, single nucleotide polymorphism (SNP) genotyping, epigenetic profiling, 3D chromatin looping, and mass spectrometry, with rigorous experimental validation. Her goal is to help improve outcomes for children diagnosed with high-risk childhood cancers.

She is currently working to identify genetic risk factors and their biological relevance in neuroblastoma, and her lab identified the first germline copy number variant (CNV) associated with neuroblastoma. Collaboratively, her team also established the genetic basis of sporadic neuroblastoma and demonstrated that genes targeted by neuroblastoma-associated germline variants not only influence tumor initiation by promoting malignant transformation, but are also required for maintenance of the malignant phenotype.

Other projects in Dr. Diskin's lab focus on defining the role of structural variation in neuroblastoma tumorigenesis, with her team identifying a rare ~550 kb germline deletion on chromosome 16 associated with neuroblastoma. In parallel, the lab found that somatic structural variant (SV) breakpoints are enriched in genes involved in neurodevelopment, and identified a novel tumor suppressor disrupted by SVs in neuroblastoma.

In addition, Dr. Diskin is investigating optimal immunotherapeutic drug targets for neuroblastoma, and has developed an integrative proteogenomic approach that combines cell surface mass spectrometry with RNA sequencing from neuroblastoma and a large set of normal pediatric and adult tissues. As a result, several proteins have been prioritized as candidate immunotherapeutic targets and are undergoing additional validation and functional studies.

Finally, Dr. Diskin often collaborates to develop new computational methods for cancer genomics and proteomics, with recent examples include CODEX, a method to improve normalization and DNA copy number detection from exome sequencing data, and GiaPronto, a web-based analytical tool for mass spectrometry data used worldwide.

Data Science