Science

Core Philosophy [WHY]: the three Is to advance precision medicine.  

(I) Integration.

We understand biology of by integrating different molecular big data (ex. GWAS array, epigenomics, DNA-Seq, RNA-Seq, MS proteomics).

(II) Inclusion.

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

Key Questions:

1. What are the common/rare genetic factors predisposing to Alzheimer’s disease and cancer?
2. How do they interact with intrinsic (ex. immune system) and extrinsic factors (ex. diet, environment) to give rise to somatic events?
3. 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.

Proteogenomic Integration

Key Questions:

1. How do genomic drivers connect through various omics levels to affect phenotype? 
2. What proteomic events arise post-transcriptionally that may give rise to cancer? 
3. 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.

Phosphoproteomics

Key Questions:

1. What are the phosphorylation events important in driving cancer? 
2. How are kinase and substrate linked in signaling cascades that drive specific functionalities?
3. 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.