Core Philosophy [WHY]: the three Is to advance precision medicine.
We understand biology of by integrating different molecular big data (ex. GWAS array, epigenomics, DNA-Seq, RNA-Seq, MS proteomics).
We resolve health challenges faced by diverse populations.
(III) Individual-based medicine and wellness.
We take an individual-centric approach by joining often-separated findings across diseases and phenotypes for each person.
Approach [HOW]: Discovery Powered by Intersecting New Biology & Data & Algorithm
The hedgehog concept intersecting passion/economy/strength is derived from data-driven studies of companies. We effectively drive innovation in biomedical science by adopting the concept. Our projects share these attributes to effectively advance science:
(1) Based on biological context, we derive biomedical hypotheses that we are deeply curious about.
(2) We seek the best high-dimensional data that are generated by cutting-edge technologies.
(3) We develop/adopt new computational approaches that are the best to address the challenge using the data.
Genetic Predisposition to Complex Disease
- What are the common/rare genetic factors predisposing to Alzheimer’s disease and cancer?
- How do they interact with intrinsic (ex. immune system) and extrinsic factors (ex. diet, environment) to give rise to somatic events?
- How do they collaborate with the somatic genome, gene/protein expression, and network alterations to induce oncogenesis?
- Huang et al. Pathogenic Germline Variants in 10,389 Adult Cancers. Cell 2018.
- Huang et al. A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer’s disease. Nat Neuro 2017.
- Scott, Huang et al. CharGer: Clinical Characterization of Germline Variants. Bioinformatics 2018.
- Mashl, Scott, Huang et al. GenomeVIP: a cloud platform for genomic variant discovery and interpretation. Genome Res 2017.
- How do genomic drivers connect through various omics levels to affect phenotype?
- What proteomic events arise post-transcriptionally that may give rise to cancer?
- How do we combine somatic genome, transcriptome, and proteome data to best design treatment strategies and predict prognosis?
- Mundt*, Rajput* et al. Mass Spectrometry-Based Proteomics Reveals Potential Roles of NEK9 and MAP2K4 in Resistance to PI3K Inhibition in Triple-Negative Breast Cancers. Cancer Res 2018.
- Huang*, Li*, Mertins* et al. Proteogenomic integration reveals therapeutic targets in breast cancer xenografts. Nat Commun 2017.
- Mertins P*, Mani DR*, Ruggles KV*, Gillette MA* et al. Proteogenomics connects somatic mutations to signalling in breast cancer. Nature 2016.
- What are the phosphorylation events important in driving cancer?
- How are kinase and substrate linked in signaling cascades that drive specific functionalities?
- Can we combine genomic, proteomic, and phosphoproteomic data to systematically depict signaling alteration in cancer?
- Huang et al. Regulated phosphosignaling associated with breast cancer subtypes and druggability. Submitted.
- Huang et al. HotPho: Systematic Discovery of Spatially Interacting Phosphorylation Sites and Mutations in Cancer. Submitted.