# Systems Biology based Proteogenomic Translator for Cancer Marker Discovery towards Precision Medicine

> **NIH NIH U24** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2020 · $907,614

## Abstract

PROJECT SUMMARY/ABSTRACT
The goal of our PGDAC is to improve understanding of the proteogenomic complexity of tumors. Towards this
goal, our First Aim is to apply network based system learning to reveal causative molecular regulatory
relationships contributing to varieties of phenotypes in cancer using CPTAC proteomic/genomic data. We will
start with a mixed effects model to (1) fix the batch effects in data from multi-plex proteomics experiments; and
(2) handle the large amount of missing data from abundance-dependent missing mechanisms in proteomic
data (Aim 1.1). We will then utilize a multivariate penalized regression framework to construct the global
regulatory networks between genomic alterations (such as DNA mutations, CNA, methylations), and protein as
well as their PTM (post translational modification) abundances (Aim 1.2). Such regulatory networks help to
elucidate how protein or pathway activities are shaped by genomic alterations in tumor cells. We will also
construct protein co-expression networks based on global-, phosphor-, glyco- and other PTM-proteomics data
(Aim 1.3). When constructing these networks, we will use advanced computational tools to effectively borrow
information from literatures, databases, and transcriptome profiles. In addition, we will model tumor and normal
tissues jointly, so that tumor specific interactions and network modules will be inferred with better accuracy.
Both Aims 1.2 and 1.3 will lead to a big collection of network modules, as well as functionally related protein
sets (e.g. proteins regulated by the same genomic alteration). These network modules and protein sets will
then be tested for their associations with disease phenotypes (Aim 1.4). In the end, we will derive a more
integrated view of commonalities and differences across multiple tumor types via a Pan-cancer analysis (Aim
1.5). Our Second Aim is to further develop methods, software, and web-tools to optimize the data analysis in
our PGDAC. We will develop novel statistical/computational tools tailored to CPTAC proteomics data;
implement these methods as computationally efficient software; and construct an integrated data analysis
pipeline (Aim 2.1). We also plan to develop a set of web service tools for visualization and biological annotation
of protein networks and clinical interpretation of proteomic data (Aim 2.2). Our Third Aim is to nominate novel
protein-based cancer biomarkers and drug targets for further investigation by targeted proteomics assays. We
will first utilize a prediction based scoring system to identify protein biomarkers that predict altered cancer
pathways, network modules and individual oncogenes; disease outcome and drug resistance; and
therapeutically distinct disease subtypes (Aim 3.1) We will then utilize network based tools to identify driver
players in selected proteins signature sets (Aim 3.2). These driver proteins could play important roles in
shaping the overall function of regulatory system, and thus serve a...

## Key facts

- **NIH application ID:** 9994849
- **Project number:** 5U24CA210993-05
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** ERIC E SCHADT
- **Activity code:** U24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $907,614
- **Award type:** 5
- **Project period:** 2016-09-19 → 2021-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9994849

## Citation

> US National Institutes of Health, RePORTER application 9994849, Systems Biology based Proteogenomic Translator for Cancer Marker Discovery towards Precision Medicine (5U24CA210993-05). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9994849. Licensed CC0.

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