As one of the five current CPTAC PCCs, we propose to leverage our established center to further our research in the comprehensive characterization of biospecimens from human and preclinical models for additional cancer types. We have assembled an outstanding team of basic and clinical scientists to discover and confirm proteins and protein modifications associated with cancer progression using genoproteomic approaches. This multidisciplinary team will be managed by the same three Principal Investigators (PI) and consists of internationally recognized experts in proteomic technologies, genomics, proteogenomics-specific bioinformatics, biostatistics, oncology, pathology, cancer biology, assay development, quality control, technology optimization, and clinical laboratory science/clinical chemistry. Our team has complementary and integrated expertise with previous and ongoing successful collaborations. The plan is to integrate the genomics findings with proteomic analysis (genoproteomics) and to overcome the caveats and challenges in the comprehensive understanding of tumor biology. We believe that genomic data provides a highly valuable molecular route towards the identification of genes and pathways that could be useful for the detection, differential diagnosis, outcome prediction and therapeutic targets of cancer. The proteomic approaches will provide the identification of unique features that are inherent to proteins including post-translational modifications, such as glycosylation and phosphorylation. We propose to use the technology platform validated during the current CPTAC which is high throughput, robust and state of the art. These technologies are proven, with the capability of generating reproducible results across labs. We will use a two-step strategy to characterize defined sets of genomically-characterized samples. The first step is the discovery of target proteins from both biological and clinical specimens using mass spectrometry and affinity based technologies. The second step is the confirmation of these targets using high-throughput, CPTAC Tier 2 analytically validated targeted assays, for example, Multiple Reaction Monitoring Mass Spectrometry (MRM-MS). In addition, we propose to develop pilot studies for technology improvement. The goal is to generate accurate, reproducible, sensitive, quantitative, and multiplexed assays using optimized and standardized high-throughput technologies for the discovered targets. While this PCC application is focused on the proteomic characterization of biological and clinical specimens, we believe that the understanding of and expertise in proteogenomic data analysis and translation will be critical for the success of the PCC and the overall CPTAC network. Investigators/PIs from our proposed PCC will be part of applications for the CPTAC Proteogenomic Data Analysis (PGDAC) and Translational Research (PTCR) Centers. Collaboration and team work are key to the success of the CPTAC program. With this multidisciplinary team of outstanding basic and clinical scientists, our PCC offers the best opportunity for the successful characterization of biological and clinical specimens to discover and confirm cancer targets to advance precision cancer medicine.

Public Health Relevance

Understanding the molecular network in carcinogenesis is essential for the development of a successful strategy to reduce cancer mortality. Based on the genomic alterations, this application will focus on the characterization of biological and clinical specimens to discover and confirm cancer protein targets using state-of-the-art proteomics technologies for the advancement of precision cancer medicine.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Resource-Related Research Projects--Cooperative Agreements (U24)
Project #
5U24CA210985-05
Application #
9998921
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Rodriguez, Henry
Project Start
2016-09-16
Project End
2021-08-31
Budget Start
2020-09-01
Budget End
2021-08-31
Support Year
5
Fiscal Year
2020
Total Cost
Indirect Cost
Name
Johns Hopkins University
Department
Pathology
Type
Schools of Medicine
DUNS #
001910777
City
Baltimore
State
MD
Country
United States
Zip Code
21205
Pertea, Mihaela; Shumate, Alaina; Pertea, Geo et al. (2018) CHESS: a new human gene catalog curated from thousands of large-scale RNA sequencing experiments reveals extensive transcriptional noise. Genome Biol 19:208
Höti, Naseruddin; Yang, Shuang; Hu, Yingwei et al. (2018) Overexpression of ? (1,6) fucosyltransferase in the development of castration-resistant prostate cancer cells. Prostate Cancer Prostatic Dis 21:137-146
Kim, Dae In; Cutler, Jevon A; Na, Chan Hyun et al. (2018) BioSITe: A Method for Direct Detection and Quantitation of Site-Specific Biotinylation. J Proteome Res 17:759-769
Wu, Xinyan; Zahari, Muhammad Saddiq; Renuse, Santosh et al. (2018) Quantitative phosphoproteomic analysis reveals reciprocal activation of receptor tyrosine kinases between cancer epithelial cells and stromal fibroblasts. Clin Proteomics 15:21
Mertins, Philipp; Tang, Lauren C; Krug, Karsten et al. (2018) Reproducible workflow for multiplexed deep-scale proteome and phosphoproteome analysis of tumor tissues by liquid chromatography-mass spectrometry. Nat Protoc 13:1632-1661
Na, Chan Hyun; Barbhuiya, Mustafa A; Kim, Min-Sik et al. (2018) Discovery of noncanonical translation initiation sites through mass spectrometric analysis of protein N termini. Genome Res 28:25-36
Yang, Shuang; Hu, Yingwei; Sokoll, Lori et al. (2017) Simultaneous quantification of N- and O-glycans using a solid-phase method. Nat Protoc 12:1229-1244
Yang, Weiming; Shah, Punit; Hu, Yingwei et al. (2017) Comparison of Enrichment Methods for Intact N- and O-Linked Glycopeptides Using Strong Anion Exchange and Hydrophilic Interaction Liquid Chromatography. Anal Chem 89:11193-11197
McCall, Matthew N; Kim, Min-Sik; Adil, Mohammed et al. (2017) Toward the human cellular microRNAome. Genome Res 27:1769-1781
Yang, Shuang; Clark, David; Liu, Yang et al. (2017) High-throughput analysis of N-glycans using AutoTip via glycoprotein immobilization. Sci Rep 7:10216

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