Wang Lab includes a computational lab and an experimental lab. Traditionally Wang Lab was mainly working on computational systems biology, machine learning and AI. Since 2016, Wang Lab has shifted toward on conducting both computational and experimental systems biology in cancer and immunology.
The computational work includes: (1) big medical data analysis (2) machine learning, deep learning and artificial intelligence (3) AI-based predictive model construction (4) wet-lab work on single-cell genomics and mouse models in cancer and immunology. We are developing novel AI algorithms (machine learning, deep learning) for modeling of molecular networks and cancer biomarker discovery, and also developing new concepts for data analysis toward interpreting data, generating, prioritizing and testing new hypotheses. We also conduct wet-lab work of single-cell omics and integrate data for analysis and experimental validation.
Examples of the computationally driven work:
Cancer hallmark network framework
Cancer hallmarks represent the most important understanding of cancer in the past of 50 years. However, they have been largely descriptive. We proposed that cancer hallmarks can be represented, quantified and further modeled computationally using cancer hallmark networks in an evolutionary context (Wang et al., 2014; Wang, 2010). Hallmark networks interact each other during tumor evolution, and furthermore, possess distinctive and complementary capabilities that enable tumor growth and metastasis, which constitute organizing principles that provide a logical framework for understanding the remarkable diversity of the diseases. We have proposed 'network operational signatures' and implemented these quantitative models by examining the collective effects of genomic alterations on cancer hallmark networks for predicting clinical features. Using this framework and a systems approach, we could predict personalized drug targets, drug resistance, and metastasis for cancer patients, as well as cancer risks for healthy individuals by modeling of cancer hallmark networks at tumor subclonal levels (Wang et al., 2013a; Wang et al., 2013b; Zaman et al., 2013).
eTumor (electronic tumor) projects for precision medicine
A collection of projects which aim to develop algorithms and computational tools (based on the cancer hallmark network framework) for predicting drug targets, metastasis and drug resistance for individual subclones within a tumor based on the tumor's exome-sequencing data. For example, we have developed a tool, named eTumorKiller for drug target prediction of subclones within a tumor.
For a given patient's genome exome sequences, eTumorKiller first identifies subclones for that tumor: how many subclones are; the volume of each subclone is; which mutations of each subclone are. In the second step, based on the profile of functional mutations of each subclone, eTumorKiller will predict drug targets for every subclone. eTumorKiller is built upon network-based approaches and captures the complex relationships, redundancies and subtle interdependencies of the various components of cancer hallmark networks. The systems biology approach of eTumorKiller transforms collections of sequencing data into insights and predictive models. It brings a whole new level of robustness, generality and accuracy to drug target identification and patient stratification.
Other cancer hallmark network framework-based eTumor projects are under development:
eTumorStroma, which predicts potential proteins secreted from tumor stroma that induce drug resistance for a given drug for treating a tumor or subclone.
eTumorMetastasis, which predicts cancer recurrence and metastasis based on genome sequencing data.
eTumorMonitor, which predicts and detects early stage of cancers based on genome sequencing of germline and tumor circulating DNAs in blood.
eTumorRisk, which predicts personalized cancer risks of health individuals using their blood-sample genome sequencing data.
All these work will have experimental validations.
Single-cell genomics of cancer immunology
We are applying bioinformatics, single-cell genomics and mouse models to understand cancer immunology and develop novel applications for boosting cancer immunotherapy, identifying immunotherapy response gene signatures, and identifying targets to improve tumor-infiltrating immune cells and new checkpoints for modulating cancer immunity.
Generating, prioritizing and testing new hypotheses
We are working on biological problems for interpreting data, generating and prioritizing new hypotheses using omic data and systems biology concepts. Interesting hypotheses are tested by us or our collaborators.
iHeathcare Systems (intelligent/personalized Healthcare Systems)
The long-term goal of this project is to timely monitor people's health, warn disease and provide information for clinicians and individuals for prevention, diagnosis and treatment by constructing predictive models using the data of host genomics, epigenetics and oral/gut microbiome (linking to diet) and other information which represent life style information and physiological data collected by smart devices such as iPhone and iWatch.
Examples of the experimentally driven work:
Single-cell sequencing for tumor subclonal network evolution and immune system
We are applying cutting-edge technologies to profile tumor subclonal gene regulatory networks, and investigate network evolution at the subclonal level under immune pressure.
MORE INFORMATION: Lab News //// Publications
Edwin has a undergraduate training in Computer Science and a PhD training in Molecular Genetics (UBC - University of British Columbia, 2002). After one-year postdoc training at FlyBase, a genome database of fly, he moved to NRC as a PI. In 2016, he became an AISH Chair Professor at University of Calgary. His pioneering work of cancer network motifs has been featured in the college textbook, GENETICS (2014/2017) written by a Nobel Laureate, Dr. Hartwell and the father of systems biology, Dr. Hood. His pioneering work of microRNA of signaling networks opens the new research area: network biology of non-coding RNAs.