1. Proteomic-based integrated subject-specific networks in cancer
Min Jin Ha
Department of Biostatistics
University of Texas MD Anderson Cancer Center

2. TEAM
Key Personnel
Min Jin Ha (PI) – Department of Biostatistics, UT MD Anderson Cancer Center
Veerabhadran Baladandayuthapani (Co-PI) – Department of Biostatistics, UT MD Anderson Cancer Center
Rehan Akbani (Co-I) – Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center
Abishek Saha (post-doctoral fellow) – Department of Biostatistics, UT MD Anderson Cancer Center

3. Broad Objectives
The cancer functional genome and proteome provides a rich source of information to identify patient-specific variations in signaling pathways and activities within and across tumor
Patient-specific approaches have recently emerged, within a precision medicine paradigm, that acknowledges the fact that pathway structures and activity might be considerably different within and across tumor
The overall objective is to develop general analytic frameworks to characterize patient-specific pathway signatures by sequential estimations of cancer-specific (global) and patient-specific (local) networks

4. ANALYTICAL Schema & Specific Aims

5. Data Resources
Reverse-phase protein array (RPPA)-based proteomic data from The Cancer Genome Atlas (TCGA) across 33 cancer types and MD Anderson Cell Lines Project (MCLP) across 19 tumor lineages
Dr. Akbani (Co-I) have led the generation and curation of these comprehensive databases
The MDACC TCGA RPPA platform includes extensively validated 181 high- quality antibodies that target proteins covering major signaling pathways, including apoptosis, cell cycle, DNA damage, PI3K, MAPK and mTOR pathways

6. Breast cancer subtypes
Of the patients with HER2-positive breast cancer in cluster C1,75% showed activation of the RTK pathway that includes HER2 (ERBB2) protein
All patients in C3 had HER2-negative breast cancer
66%, 82%, and 72% of the patients in C3 showed activation of the apoptosis, breast reactive, and RAS/MAPK pathways, respectively

7. Higher prognostic power of PRECISE scores as compared to naïve/existing approaches

8. timeline
Methods were developed and the application to TCGA data across 33 cancer types are under revision in Scientific Reports