We are dedicated to solving the problem of pediatric cancer through integration of big data and wet-bench validation. Through the combinatorial lens of multi-omics (incorporating genomics, epigenomics, transcriptomics, and proteomics data), we study 1) genetic predisposition, 2) somatic drivers of cancer and tumor biology, and we also seek to 3) identify optimal targets for immunotherapy. Our primary focus has been on neuroblastoma, though we are now branching out to include additional high-risk pediatric malignancies.
Please contact us if you are interested in rotating in the lab or joining us for a research internship!
Childhood cancers are inherently malignancies arising during development, sometimes at very early stages. As a result, many pediatric cancers exhibit a relative paucity of recurrent somatic mutations in tumors compared to adult cancers. By studying germline variants – those present in DNA of “all” cells (not unique to cancer cells), we seek to identify genetic variants that predispose to cancer. These can include common single nucleotide variants (SNVs) and insertions/deletions (indels), rare SNVs/indels, and larger structural variants (SVs). They can be inherited from a parent, but can also arise de novo very early in development. We use a combination of large-scale genotyping and next generation sequencing (NGS) data to elucidate the genetic basis of neuroblastoma. We do this through a combination of genome-wide association studies (GWAS) to discovery common risk variants, and large next-generation sequencing (NGS) projects to identify rare variants with larger effect sizes. Experimental approaches are then applied to understand the biological relevance of newly discovered genetic risk factors. Collectively, this work has made lasting impacts on the field by defining the genetic basis of neuroblastoma and demonstrating that many of the genes targeted by neuroblastoma-associated variants not only influence tumor initiation by promoting malignant transformation, but are also required for maintenance of the malignant phenotype.
Understanding differences and the complex interplay between tumors and healthy normal tissue is essential in finding effective treatments and understanding the genetic basis of cancers. We use a variety of techniques and approaches to study somatic structural variation, the non-coding genome, long non-coding RNAs (lncRNAs), whole proteomes of tumors, and tumor evolution.
Immunotherapy approaches hold great promise for the treatment and potential cure of childhood and adult malignancies. However, the optimal cell surface proteins to target in many childhood cancers remain unknown. With the support of the W.W. Smith Charitable Trust and an Innovation Award from Alex’s Lemonade Stand Foundation (ALSF), we have developed an integrative multi-omic approach to identify and prioritize candidate immunotherapeutic drug targets in neuroblastoma. Specifically, we are generating and integrating plasma membrane enriched mass spectrometry-based proteomics data with RNA sequencing and epigenetic data from both tumor and normal tissues, with the goal of identifying protein expressed uniquely (or preferentially) on cancer cells. We have applied our approach to human-derived neuroblastoma cell lines, patient derived xenograft (PDX) models, and frozen tumor samples from neuroblastoma patients at diagnosis and relapse. Our lab also validates cell surface expression using orthogonal approaches and performs the necessary experiments to demonstrate functional relevance of prioritized cell surface candidates. Through this research, we are delivering (1) the definable landscape of cell surface protein expression (surfaceome) in neuroblastoma, including how these proteins evolve under the selective pressure of chemotherapy, and (2) a high confidence set of validated cell surface proteins to undergo subsequent antibody-based or Chimeric Antigen Receptor (CAR) T-cell immunotherapy development.