The Han Lab at McMaster works at the interface of cancer biology, RNA and multilayer gene regulation, and innovative high-throughput technologies.
Our interdisciplinary research focuses on high-throughput discovery and characterization of coordinated multilayer gene regulation in health and disease, and developing novel approaches for cancer therapeutics. Dr. Han and her team have pioneered developing and applying several integrated technological platforms for large-scale genetic/drug screening and ultra-high-throughput single-cell profiling. Leveraging the power of these systematic experimental and computational approaches, together with in vitro, in vivo, and patient cohort studies, we uncover multilayer gene regulatory maps of key cell fate control, including master gene and RNA/splicing regulators of cancer development, recurrence, and metastasis. In addition, we identify small molecule modulators and mechanistic insights to advance therapeutics for treatment-resistant cancers.
1. Alternative splicing regulation in cancer
Alternative splicing represents a critical layer of gene regulation, which acts widely to expand transcriptomic and proteomic complexity. Transcripts from over 95% of human multi-exon genes are alternatively spliced. Misregulation of alternative splicing impacts every hallmark of cancer. Our research is directed to high-throughput discovery and characterization of alternative splicing regulatory networks during cancer initiation, progression, and therapy response, with an initial focus on glioblastoma and prostate cancer. In addition, we study how alternative splicing is integrated with other gene regulatory layers, such as (epi)genetics, transcription, and translation, to control key cell fate and state transitions during cancer ecosystem evolution. Modulating alternative splicing and its coordination with other layers of gene regulation offers us many opportunities to uncover new cancer biology and therapeutic targets.
2. Multilayer mechanisms of glioblastoma heterogeneity and microenvironment evolution
Cancer is a highly complex ecosystem. It involves dynamic interactions between malignant and non-malignant compartments, such as crosstalk between tumour, immune, vascular, and other surrounding tissue cells. Major challenges of treatment-resistant cancer include immense genetic and cellular tumour heterogeneity as well as the rapid evolution of both tumour and its microenvironment through time and therapy. My team establishes and employs ultra-high-throughput single-cell multi-omics (e.g., mutation-expression-splicing) and integrated bioinformatics platforms to reveal multilayer cellular and molecular mechanisms underlying glioblastoma, the most common and lethal primary adult brain tumour. Exploring how these coordinated regulation networks evolve in time and space to specifically impact glioblastoma progression and patient outcomes enables us to systematically discover biomarkers, driver events, and candidate therapeutic targets.
3. Multiplexed screening of gene/isoform function in treatment-resistant cancer and therapeutic discovery
Despite recent advances, a challenge with current genetic screening and drug discovery remains scalability in terms of the number of perturbations and multimodal readouts that are amenable to advance disease models. We develop an innovative multiplexed screening approach to dissect gene/isoform function and mechanisms underlying cancer recurrence and metastasis. In addition, we extend this cost-effective, massively multiplexed screening platform to accelerate the discovery of candidate therapeutics by simultaneously interrogating the effects of different effectors and/or strategies. The established platform can be used for a broad spectrum of therapeutic screens. For example, it can range from the in-depth screening of dozens of conditions (e.g., CAR T-cells and engineered myeloid cells) to rapid screening of thousands of conditions (e.g., small molecules, antisense oligonucleotides, and novel biologics). Applications of these integrated genetic and screening strategies with preclinical and translational cancer models allow the rapid discovery of novel modulators, therapeutics, and mechanisms.
