Exploring cancer genomics
As a computational biologist, my research focuses on understanding the mechanisms of cancer initiation, progression, and prognosis through developing and utilizing computational methods.
My recent research areas include:
- Identifying driver mutation, etiology, mechanism, and heterogeneity in human and mouse cancer genomes
- Ultra-sensitive detection of cancerous and precancerous lesions using somatic mutations
- Developing innovative and integrative open-source computational software for analyzing genomics data
- Accurately interpreting complex genomic variants by haplotype-aware annotations
Cancer genomics & tumor evolution
Next-generation sequencing (NGS) has become widely accessible in research and clinical laboratories. By using whole-genome, whole-exome and transcriptomes sequencing, we can detect various types of variants.
Ultra-sensitive detection of mutations in tumor and normal tissues
We’ve conducted innovative studies aiming to expand our detection limits to very-low-frequency (<5%) mutations, which are important for cancer early detection and residual disease monitoring.
Tumor-specific neoantigents for immunoprofiling
We designed the Christmas Light Plot (CLP) for easy visualization of identified neoantigens. The CLP incorporates pre-defined criteria for neoantigen prioritization.
Mutational landscapes in preclinical models
Analyses of mouse somatic mutations pose a unique challenge due to the diverse genetic background in GEM and mouse stromal contamination in PDX models.
In the News
Contact the Wei Lab
Department of Biostatistics and Bioinformatics
Roswell Park Comprehensive Cancer Center
Elm and Carlton Streets
Buffalo, NY 14263