Overarching Goal
We aim to advance the understanding of the genetic and epigenetic alterations that drive the pathogenesis of different leukemias,
and develop faithful experimental models and novel diagnostic and therapeutic approaches.
Research Backgroud
Our research focuses on acute lymphoblastic leukemia (ALL), the most common pediatric cancer.
ALL consists of dozens of distinct subtypes featured with various genetic alterations and gene expression profiles (GEPs).
Accurate classification of ALL subtypes is of central importance for prognostication and therapeutic decision-making.
In our recent work, we performed integrative genomic analysis of over 3,000 RNA-seq of B-cell ALL (B-ALL) and
devised a molecular classification system solely based on bulk RNA-seq. This system can automatically classify
all the reported B-ALL subtypes and it has been widely applied in both basic research and clinical practice.
Through the prism of the granular B-ALL classification system, the etiology of this highly heterogeneous leukemia has been
revealed at an unprecedented rate, which will largely facilitate the development of tailored therapies.
Among the B-ALL subtypes, two novel ones discovered in our studies are defined by various genetic alterations in PAX5,
a gene that encodes a key transcription factor for B-cell development. We specifically focus on the B-ALL driven by PAX5
point mutations, which were previously considered secondary events in leukemogenesis. Over 10% of B-ALL have PAX5 point mutations,
and the most common one is PAX5 P80R, which defines a subtype with highly distinct GEP. Patients with PAX5 P80R mutation are
observed with better outcomes compared to the ones carrying other PAX5 mutations. However, the mechanisms responsible for such
differences are still unknown. With a limited understanding of the PAX5-mutant B-ALL, generic chemotherapies are applied,
and many patients' outcomes are still poor.
With hybrid expertise in computational biology and leukemia genomics, the Gu Lab is focusing on the following research areas:
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Identify novel leukemia subtypes and develop clinically applicable classification system
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Curate and maintain well-annotated leukemia genomic databases to serve as a public resource
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Develop machine-learning based bioinformatics tools for leukemia/cancer genomic analysis
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Study the mechanisms and molecular features of PAX5-mutant B-ALL and develop therapeutic strategies