HUANG LAB | COMPUTATIONAL OMICS

Research Philosophy: Integrate Diversity

We believe in advancing science and technology for the right cause. In our research, like how we structure our team and ideas, we integrate diverse components that make the biological world endlessly fascinating. 

(I) Inclusion of diverse populations. We tackle the health challenges faced by diverse populations.

(II) Integration of diverse big data. We resolve biological questions by integrating multi-modal big data (ex. genome, epigenome, proteomes, microbiome, EHR) at the single-cell and bulk-tissue level.

(III) Individual-based consideration of diverse diseases and health parameters. We consider multiple clinical, molecular, and disease parameters pertaining to each individual’s precision health.

Below are some of our team’s research topics of interest. For the cited peer-reviewed publications, PI Huang’s first-author research articles are in bold; PI Huang’s last-author research articles are in italic.

Pathogenic Variant Identification in Diverse Populations

Germline pathogenic variants contribute to a substantial fraction of disease risk. However, our knowledge of pathogenic variants remains far from complete in diverse populations, limiting the yield of genetic testing. Our contribution includes identifying pathogenic germline variants leading to cancer in large genomic cohorts (Cell 2018), characterizing pathogenic variants in diverse populations (Cancer Cell 2020, Genome Med 2020, Genome Med 2021) and young adults (Cell Reports 2021), and finally linking mutations to response to targeted or immuno-therapy (Cell Rep Med 2021, Cancers 2021). We have expanded this line of research to utilize National/Mount Sinai biobank cohorts and develop accurate disease prediction models based on integrating common/rare genetic risks.

Post-Transcriptional/Translational Biology and Treatment Targets

Proteins carry out cellular functions and are often the direct drug target. However, the precision medicine paradigm has focused on genomic analyses and neglected post-transcriptional/translational events. We have developed openly-available multi-omic software (MCP 2019Nat Comm 2021PSB 2021) and discovered multiple protein-level treatment targets not found at genomic levels (Nat Comm 2017Comm Bio 2021). In addition to genomic data, we continue to integrate global proteomic and functional screening data at both the bulk- and single-cell levels to identify the most promising drug targets. 

Machine-learning Models Combining Multi-modal Data for Risk & Treatment Predictions

Personalized medicine—whereby individuals’ care is determined based on their unique molecular features—has transformed how patients are treated. But, our ability to predict the best drug and likely clinical outcomes for each patient remains limited. As treatment response to most drugs is multi-factorial—considerable fractions of patients with the targeted biomarker still fail to respond. Based on our work identifying predictive genes to targeted or immuno-therapy response (Cell Reports Med 2021Cancers 2021), we are actively developing multi-factorial models to predict clinical risks and optimized treatment options for diseases, including cancer, Alzheimer’s disease, and COVID-19 (Sci Reports 2021).

System Biology of the Microbe-Immune-Tissue Axis in Human Disease

Emerging evidence has shown considerable involvement of the immune system, together with its interaction with the microbiome, in multiple complex diseases like cancer and Alzheimer’s disease (AD). We have previously established a myeloid transcription network in AD (Nat Neuro 2017), associated driver genes with cancer immune response (Cell Reports Med 2021), and investigated how multi-factorial factors impact COVID-19 outcomes (iScience 2021, Comm Med 2021, Sci Reports 2021, Viruses 2022, Chaos Solitons Fractals 2022). Onwards, we are integrating multi-omics data to decode the Microbe-Immune-Tissue axis and identify disease-driving interactions that may serve as new treatment targets.