# Michigan Center for Translational Cancer Proteogenomics

> **NIH NIH U24** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2022 · $768,826

## Abstract

ABSTRACT
This application aims to establish a Proteogenomic Data Analysis Center at the University of Michigan for the
Clinical Proteomic Tumor Analysis Consortium (CPTAC). Our Center is anchored at the Michigan Center for
Translational Pathology and brings together a multi-disciplinary team of leading scientific experts in the
foundational areas of proteomics, cancer genomics, immunomics, and integrative systems biology. Our team
established the foundations for precision oncology and proteogenomics at the University of Michigan and has
a long history of successful inter-institutional collaborations. This positions us well to apply, working in close
collaboration with other CPTAC groups, our innovative algorithms, comprehensive computational
infrastructure, and expert knowledge to carry out high-impact translational proteogenomics research that is a
core mission of the CPTAC. We have developed a balanced approach for integrative proteogenomic
analyses, with a blend of both state-of-art and novel pipelines and tools. Our analytics support dual purpose
- to perform both cohort-wide and patient centric (personalized) investigations – a unique future and a strength
of our proposal. Our experience in support of our real-time precision oncology program and past CPTAC
efforts will ensure both the fidelity of detecting diverse proteogenomic cancer driver events and rigorous
ascertainment of their biological implications. Both of these features are of paramount importance to
understand disease mechanisms and discover prognostic markers and therapeutic targets. Our proposed
strategy combines well-established and innovative data analyses and modeling approaches, cognizant of
continuing developments in the corresponding areas. In addition, we propose a conceptually novel approach
of “integrative cellular network analysis” and advanced data visualization modules, capitalizing on recent
advances in single cell and spatial proteogenomics research. These features will refine inference from the
bulk tissue omics data in terms of tumor microenvironment, ploidy and cellularity, identification of cell of origin
and clonal expansion, cell-cell interactions, distinguishing lineage versus cancer-specific biomarkers, and
gene signatures associated with genetic and epigenetic alterations. Such precise and refined integrative
analyses across genome and proteome data require advanced bioinformatics tools and stringent quality
control measures. Our integrated genome/transcriptome/proteome pipelines – already in wide use by the
research community - will be further optimized for speed and accuracy and enhanced with data visualization
and report generation capabilities for presenting the findings to cancer biologists in a transparent and readily-
interpreted manner. Furthermore, our extensive experience in the area of biomarker discovery and precision
oncology, further enhanced through participation of our investigators in the EDRN, SPORE, and other NIH
initiatives, puts us in...

## Key facts

- **NIH application ID:** 10440158
- **Project number:** 1U24CA271037-01
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Saravana Mohan Dhanasekaran
- **Activity code:** U24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $768,826
- **Award type:** 1
- **Project period:** 2022-06-06 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10440158

## Citation

> US National Institutes of Health, RePORTER application 10440158, Michigan Center for Translational Cancer Proteogenomics (1U24CA271037-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10440158. Licensed CC0.

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