The advent of molecular biology and molecular profiling in clinical medicine has transformed our understanding of the molecular basis of human cancer. As a result, we are increasingly improving the classification of human tumors based on their specific genetic and molecular mechanisms of pathogenesis. However, currently only a small number of mutant alleles guide treatment decisions, while most observed mutations remain of unknown pathologic and clinical significance. In addition, even for recently approved drugs, such as those targeting activated kinase signaling, clinical efficacy is highly varied, with no currently satisfactory means to identify molecular markers of response and resistance. Quantitative measurements of the abundance of proteins and stoichiometry of their regulatory post-translational modifications can be used to determine activation states of of pathways and cells. However, current quantitative mass spectrometry techniques are limited by peptide ion fragmentation, duty cycles that restrict assays to about 100 proteins, and limited scalability to permit high- throughput clinical applications. To address this need, and broadly enable transformative future advances in precision oncology and patient outcomes, we have recently developed a new method with 3 orders of magnitude improvement in sensitivity, termed accumulated ion monitoring (AIM).
Using AIM, we developed the Quantitative Cancer Proteomics Atlas (QCPA) for functional profiling of biochemical processes mediating aberrant survival of cancer cells. In principle, this technology permits highly multiplexed, quantitative analysis of the expression and biochemical activity of thousands of proteins, covering most recurrently mutated and known pathogenic pathways in cancer cells, and designed to be applied to clinically-accessible, microgram patient specimens. The objective of this proposal is to develop scalable and high-throughput mass spectrometry technology for proteome-wide and pathway-scale profiling of hundreds of clinical specimens on the hours time scale. Our central hypothesis is that implementation of high-efficiency duty-cycle and ion multiplexing quantitative proteomics will enable high-throughput functional molecular profiling for both basic science and clinical applications.
Aim 1 will develop pathway-scale functional mass spectrometry proteomic mapping technology based on multiplex and triggered ion monitoring.
Aim 2 will implement tandem mass tagging for high-throughput quantitative mass spectrometry for scalable functional profiling of clinical cancer specimens. Successful completion of this proposal is expected to close the technical gap currently preventing the use of mass spectrometry for comprehensive functional profiling of clinical specimens. This research will have broad significance because improved quantitative functional measures of cell signaling are needed to overcome persistent challenges that limit progress in cancer biology and clinical oncology.

Public Health Relevance

Cancer patients have poor outcomes with current therapy. The planned research project is relevant to public health because the development of clinical functional proteomic profiling will lead to improved diagnosis and targeted therapy selection for cancer patients. The proposed research is highly relevant to the NIH mission and the urgent unmet need for new and improved capabilities for advancing precise clinical diagnosis of cancer patients, thereby accelerating strategies to overcome persistent challenges in clinical oncology.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21CA235285-01
Application #
9657875
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Knowlton, John R
Project Start
2019-06-01
Project End
2022-05-31
Budget Start
2019-06-01
Budget End
2020-05-31
Support Year
1
Fiscal Year
2019
Total Cost
Indirect Cost
Name
Sloan-Kettering Institute for Cancer Research
Department
Type
DUNS #
064931884
City
New York
State
NY
Country
United States
Zip Code
10065