Cancer Proteomics Core
The Cancer Proteomics Core (CPC) provides all DF/HCC members access to comprehensive interdisciplinary, translational, and clinically-oriented facilities for high-sensitivity, high-resolution, and high-throughput proteomics and metabolomics services. The core has a particular emphasis on clinical sample analysis and in-depth scientific consultation for proteomics, metabolomics, lipidomics, and data analysis. The CPC also provides access to proteomic services for basic cancer research exploring signaling pathways and other aspects of cancer biology related to the goals of DF/HCC. The CPC is the exclusive service provider in the Boston/Longwood Medical Area for the exciting and powerful new, state-of-the-art biomarker discovery platform, SOMAscan.
The Core allows investigators to use the most cost-effective, state-of-the-art proteomics strategies for extracting the most relevant information for each project. It provides assistance in study design, experimental execution, data analysis, and systems biology, while also serving as a forum for education, scientific interactions, and cross-fertilization in proteomics and metabolomics.
- High multiplex, high sensitivity aptamer-based SOMAscan-1310 protein biomarker discovery
- Protein sample preparation and digestion
- Protein and peptide profiling of bodily fluids and tissue or cell extracts
- Profiling by µLC/MS
- Multidimensional protein and peptide fractionation by µLC
- Protein identification by µLC/MS/MS
- Coomassie and silver-stained gel bands
- Identification of protein and peptide post-translational modifications
- Phosphorylation sites
- Protein modifications, such as acetylation, methylation, and ubiqitination
- By µLC/MS/MS
- Coomassie stain only, purified proteins
- Protein-protein interaction (PPI) determination using antibody and tagged based IP-MS
- Relative quantitation of proteins and peptides, including modified peptides, by µLC/MS/MS
- SILAC: Stable isotope labeling of amino acids in cell culture (a biosynthetic approach)
- GIST: Global internal standard technology, a post-digestion peptide level labeling technique
- ICAT: Isotope coded affinity tag-based protein profiling
- ITRAQ: Isobaric peptide tagging system that enables labeling all primary amines, regardless of peptide class
- Targeted proteomics
- Metabolomic profiling of cancer cells and tissues
- Lipidomic profiling of cancer cells and tissues
- Analytic procedures to explore the data sets for further hypothesis generation
- SOMAscan Protein and Biomarker Discovery Platform: SOMAscan (SomaLogic) is a high multiplex, high sensitivity aptamer-based immune-like protein and biomarker discovery platform that simultaneously quantifies 1,310 human proteins in all types of protein extracts from bodily fluids such as serum, plasma, urine, saliva, CSF, cyst fluid, and tissue, cells, lavage, animal models, exosomes, etc. The assay needs very low amounts of starting material such as 65μl human plasma or serum. SOMAscan advantages are a median lower limit of detection of 40 fM (<1pg/ml), an exceptional dynamic range of >8 logs (femtomolar to micromolar), high reproducibility (~5% median CV), and throughput. SOMAscan’s depth of coverage offers unprecedented power for biomarker discovery with more multiplex capability, and a >10,000-fold greater dynamic range than other proteomic technologies.
- Quantitative proteomics: Relative and absolute protein quantitation applying isotopic, isobaric, and label-free methods, such as iTRAQ, ICAT, SILAC, TMT, GIST, TIC ratio, and spectral counting
- Targeted proteomics: With the acquisition of the 5500 QTRAP instrument, the core offers a service for targeted quantitative proteomics involving multiple reaction monitoring (MRM)
- Metabolomics: A service for targeted metabolomics using selected reaction monitoring (SRM) triple quadruple MS technology, which has become very popular for investigating more than 250 polar metabolites in cancer using cell cultures, bodily fluids, and tumor tissues. The CPC is the first to provide these analyses at Harvard.
- Sources for biomarker discovery: While originally focused on serum, the core has now optimized workflows for quantitative proteomics analysis of urine, saliva, tumor tissue, proximal tumor fluids such as cyst fluid, CSF, and isolated subfractions of cells, such as mitochondria
- Data analysis: New services for bioinformatics and systems biology analysis of proteomic data, including pathway analysis tools (Ingenuity, GeneGo) and quantitative proteomics analysis tools (Protein Pilot 4.0)
- The core now offers automated Western analysis service using the ProteinSimple Peggy instrument. The core offers this new automated process (Simple WesternTM) using a novel capillary electrophoresis (CE) based immunodetection platform for protein separation and characterization. Proteins can be separated by size as in traditional PAGE Western or by their pI as in traditional CE. This high throughput system is able to multiplex and normalize to standard proteins and is highly reproducible.
- ELISA analysis for high throughput validation of individual biomarkers
- Personalized consulting
- Customized data analysis and interpretation
- Web-based bioinformatics tools
- Comprehensive and innovative bioinformatics and visualization tools
- Comprehensive annotation tools
- Identification of global relationships
- Incorporation of biological knowledge
- Pathway analysis
Project Title: Identification of biomarkers in pancreatic cancer, triple-negative breast cancer, renal cell cancer, prostate cancer, and glioblastoma using SOMAscan protein biomarker analyses.
Principal Investigators: Towia Libermann, PhD (BIDMC); Marsha Moses, PhD (Boston Children’s Hosptital); Rebecca Miksad, MD (BIDMC); Eric Wong, MD (BIDMC), Rupal Bhatt, MD PhD (BIDMC), Andrea Bullock, MD (BIDMC)
Description of the Project: The goal is to identify biomarkers for early detection of pancreatic adenocarcinoma (PDAC) and for monitoring therapeutic response or predict outcome. The focus of this project is to identify minimally-invasive protein biomarkers in urine and serum/plasma that correlate with the early stages of PDAC and can, in high risk populations such as families with hereditary pancreatitis or hereditary pancreatic cancer or patients with intraductal papillary mucinous neoplasms (IPMNs) and mucinous cystic neoplasms (MCNs), assess the risk of malignant transformation and also detect the early stages of PDAC development. These population groups as well as some others are considered to have the highest risks for PDAC development, but currently there is a complete lack of biomarkers to detect within these populations who is at the highest risk for PDAC and who already harbors early stages of PDAC. IPMNs and MCNs, while mostly benign, can progress from benign lesions to pancreatic cancer in 2-14% of cases. To discover protein biomarkers that can accurately identify patients with the early stages of PDAC SOMAscan analysis is being performed now on urine samples from patients with PDAC and healthy controls and eventually on serum/plasma samples from patients with PDAC, chronic pancreatitis, IPMNs, MCNs, and healthy controls. The first studies of urine samples have already identified a set of novel, candidate biomarker proteins that are significantly differentially expressed in pancreatic cancer patients. Similar studies are being performed on triple-negative breast cancer, renal cell cancer, prostate cancer, and glioblastoma patients.
Contribution of the Core: The Core, one of few facilities in the world with the SOMAscan technology, is in a unique position to perform and analyze such samples using the SOMAscan platform. This is an ongoing project that will engage various additional DF/HCC investigators from other DF/HCC institutions and is one of the first cancer projects to be tested on the SOMAscan platform by the Core. The CPC performed all of the SOMAscan and mass spectrometry experiments, as well as complete bioinformatic and systems biology analysis of the data. The latest 1310 protein biomarker SOMAscan array was run using human urine from patients diagnosed with pancreatic cancer or with triple-negative breast cancer.
- Bhasin MK, Ndebele K, Bucur O, Yee EU, Otu HH, Plati J, Bullock A, Gu X, Castan E, Zhang P, Najarian R, Muraru MS, Miksad R, Khosravi-Far R, Libermann TA. Meta-analysis of transcriptome data identifies a novel 5-gene pancreatic adenocarcinoma classifier. Oncotarget. 2016 Mar 16.
- Jedinak A, Curatolo A, Zurakowski D, Dillon S, Bhasin MK, Libermann TA, Roy R, Sachdev M, Loughlin KR and Moses MA. Novel non-invasive biomarkers that distinguish between benign prostate hyperplasia and prostate cancer. BMC Cancer 2015; Apr 11;15:259.
- Roy R, Zurakowski D, Wischhusen J, Frauenhoffer C, Hooshmand S, Kulke M, Moses MA. Urinary TIMP-1 and MMP-2 levels detect the presence of pancreatic malignancies. Br J Cancer. 2014 Oct 28;111(9):1772-79.
Project Title: Metabolomics (13C glucose/glutamine flux): Oncogenic Kras maintains pancreatic tumors through regulation of anabolic glucose metabolism.
Principal Investigators: Alec Kimmelman, MD, PhD (DFCI); Lewis Cantley, PhD; Ronald Dephino, MD
Description of the Project: Transcriptome and metabolomic analyses indicate that Kras(G12D) serves a vital role in controlling tumor metabolism through stimulation of glucose uptake and channeling of glucose intermediates into the hexosamine biosynthesis and pentose phosphate pathways (PPP). These studies reveal that oncogenic Kras promotes ribose biogenesis and unlike canonical models, we demonstrate that Kras(G12D) drives glycolysis intermediates to the nonoxidative PPP, thereby decoupling ribose biogenesis from NADP/NADPH-mediated redox control.
Contribution of the Core: The CPC, primarily with the help from John Asara, performed all the LC/MS/MS experiments. The CPC developed a qualitative and quantitative metabolomics approach for studying metabolomics differences involved in pancreatic cancer development using the AB Sciex 5500 QTrap mass spectrometer (figure 1). The CPC has been working closely with the investigators to approach the challenges and to develop new methods for quantitative analysis of metabolites.
Publication: Ying H, Kimmelman AC, Lyssiotis CA, Hua S, Chu GC, Fletcher-Sananikone E, Locasale JW, Son J, Zhang H, Coloff JL, Yan H, Wang W, Chen S, Viale A, Zheng H, Paik JH, Lim C, Guimaraes AR, Martin ES, Chang J, Hezel AF, Perry SR, Hu J, Gan B, Xiao Y, Asara JM, Weissleder R, Wang YA, Chin L, Cantley LC, DePinho RA. Oncogenic Kras maintains pancreatic tumors through regulation of anabolic glucose metabolism. Cell. 2012 Apr 27;149(3):656-70.
Project Title: Proteomics (Targeted Phosphorylation in Lung Cancer): MEK inhibition leads to PI3K/AKT activation by relieving a negative feedback on ERBB receptors
Principal Investigator: Jeffrey Engelman, MD, PhD (MGH)
Description of the Project: The PI3K/AKT and RAF/MEK/ERK signaling pathways are activated in a wide range of human cancers. In many cases, concomitant inhibition of both pathways is necessary to block proliferation and induce cell death. In this study we describe a feedback mechanism in which MEK inhibition leads to activation of PI3K/AKT signaling in EGFR and HER2-driven cancers. We found that MEK inhibitor-induced activation of PI3K/AKT resulted from hyperactivation of ERBB3 as a result of the loss of an inhibitory threonine phosphorylation in the conserved juxtamembrane (JM) domains of EGFR and HER2.
Contribution of Core: The CPC played a key role in this project and helped with all the LC/MS/MS experiments to determine the phosphorylation sites using the Thermo LTQ Orbitrap mass spectrometer.
Publication: Turke AB, Song Y, Costa C, Cook R, Arteaga CL, Asara JM, Engelman JA. MEK inhibition leads to PI3K/AKT activation by relieving a negative feedback on ERBB receptors. Cancer Res. 2012 Jul 1;72(13):3228-37.
If research supported by this core facility results in publication, please acknowledge this support by including the following in your publication(s):
We thank Dana-Farber/Harvard Cancer Center in Boston, MA, for the use of the Cancer Proteomics Core Facility, which provided __________ service. Dana-Farber/Harvard Cancer Center is supported in part by a NCI Cancer Center Support Grant # NIH 5 P30 CA06516.