Core Spotlight: Cancer Proteomics
October 17, 2018
In this DF/HCC eNews Spotlight Edition, we present the DF/HCC Cancer Proteomics Core which is dedicated to provide access for all DF/HCC members to comprehensive interdisciplinary, translational, and clinically oriented facilities for high sensitivity, high resolution and high throughput proteomics, metabolomics, and lipidomics services, with particular emphasis on clinical and animal model sample analysis, in depth scientific consultation for proteomics, metabolomics, lipidomics, and comprehensive data and systems biology analysis.
We now offer comprehensive approaches to develop biomarker predictors and modeling using various Machine Learning techniques. We also offer a full pipeline for protein biomarker discovery, validation, and diagnostic model development employing our state-of-the-art protein biomarker discovery platform, SOMAscan, combined with biomarker validation using immunoassays and modeling of multiplex diagnostic biomarker panels.
- High multiplex, high sensitivity aptamer-based SOMAscan biomarker discovery
- Protein sample preparation and digestion
- Depletion of abundant proteins
- Lipid and metabolite sample preparation
- Protein and peptide profiling of bodily fluids, tissue, cell extracts, exosomes, and subcellular fractions
- Multi-dimensional protein and peptide fractionation
- Protein and peptide identification
- Identification of post-translational modifications
- Protein-protein interaction (PPI) determination using antibody and tagged-based IP-MS
- Relative quantitation of proteins and peptides, including modified peptides: 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 quantitative proteomics: multiple reaction monitoring (MRM)
- Metabolomic profiling of cancer cells, tissues, and bodily fluids including metabolomics flux analysis
- Lipidomic profiling of cancer cells and tissues
- Analytic procedures to explore the data sets for further hypothesis generation
- Discovery and validation of diagnostic, predictive, or prognostic biomarker predictors
stability and reproducibility of proteomic profiles measured with an aptamer-based platform
PIs: Meir J. StampferBWH(Cancer Epidemiology Program), Andrew T. ChanMGH(Gastrointestinal Malignancies program), Towia A. LibermannBIDMC(Kidney Cancer Program), A. Heather EliassenBWH(Breast Cancer Program)
The feasibility of SOMAscan, a multiplex, high sensitivity proteomics platform, for use in studies using archived plasma samples has not yet been assessed. We quantified 1,305 proteins from plasma samples donated by 16 Nurses’ Health Study (NHS) participants, 40 NHSII participants, and 12 local volunteers. We assessed assay reproducibility using coefficients of variation (CV) from duplicate samples and intraclass correlation coefficients (ICC) and Spearman correlation coefficients (r) of samples processed (i.e., centrifuged and aliquoted into separate components) immediately, 24, and 48 hours after collection, as well as those of samples collected from the same individuals 1 year apart. CVs were < 20% for 99% of proteins overall and < 10% for 92% of proteins in heparin samples compared to 66% for EDTA samples. We observed ICC or Spearman r (comparing immediate vs. 24-hour delayed processing) ≥ 0.75 for 61% of proteins, with some variation by anticoagulant (56% for heparin and 70% for EDTA) and protein class (ranging from 49% among kinases to 83% among hormones). Within-person stability over 1 year was good (ICC or Spearman r ≥ 0.4) for 91% of proteins. These results demonstrate the feasibility of SOMAscan for analyses of archived plasma samples.
Contribution of the Core: Drs. Libermann and Dillon applied the new, cutting edge aptamer-based protein biomarker discovery platform, SOMAscan, to a large set of samples from the Nurses Health Study to evaluate and validate the potential and quality of the SOMAscan platform for biomarker discovery in prospective epidemiological study cohorts.
Publication: Kim CH, Tworoger SS, Stampfer MJBWH, Dillon ST, Gu X, Sawyer SJ, Chan ATMGH, Libermann TABIDMC, Eliassen AHBWH. Stability and reproducibility of proteomic profiles measured with an aptamer-based platform. Sci Rep. 2018 May 30;8(1):8382. PubMed PMID: 29849057; PubMed Central PMCID: PMC5976624.
Novel non-invasive biomarkers that distinguish between benign prostate hyperplasia and prostate cancer
PIs: K. LoughlinBWH(Prostate Cancer Program), M. MosesBCH (Breast Cancer Program)
The objective of this study was to discover and to validate novel noninvasive biomarkers that distinguish between benign prostate hyperplasia (BPH) and localized prostate cancer (PCa), thereby helping to solve the diagnostic dilemma confronting clinicians who treat these patients. Within the CPC, Drs. Libermann and Dillon developed a quantitative iTRAQ LC/LC/MS/MS analysis strategy on the AB/Sciex 4800 MALDI TOF/TOF MS that was used to identify proteins that are differentially expressed in the urine of men with BPH compared with thosewho have localized PCa. These identified proteins were validated in 173 urine samples from patients diagnosed with BPH (N = 83) and PCa (N = 90). Multivariate logistic regression analysis was used to identify the predictive biomarkers. Three proteins, β2M, PGA3, and MUC3 were identified by iTRAQ and validated by immunoblot analyses. Univariate analysis demonstrated significant elevations in urinary β2M (P < 0.001), PGA3 (P = 0.006), and MUC3 (P = 0.018) levels found in the urine of PCa patients. Multivariate logistic regression analysis revealed AUC values ranging from 0.618 for MUC3 (P = 0.009), 0.625 for PGA3 (P < 0.008), and 0.668 for β2M (P < 0.001). The combination of all three demonstrated an AUC of 0.710 (95% CI: 0.631 - 0.788, P < 0.001); diagnostic accuracy improved even more when these data were combined with PSA categories (AUC = 0.812, (95% CI: 0.740 - 0.885, P < 0.001). Urinary β2M, PGA3, and MUC3, when analyzed alone or when multiplexed with clinically defined categories of PSA, may be clinically useful in noninvasively resolving the dilemma of effectively discriminating between BPH and localized PCa.
Contribution of the Core: Drs. Libermann and Dillon developed a quantitative iTRAQ LC/LC/MS/MS analysis strategy on the AB/Sciex 4800 MALDI TOF/TOF MS that was used to identify proteins that are differentially expressed in the urine of men with BPH compared with those who have localized PCa.
Publication: Jedinak A, Curatolo A, Zurakowski D, Dillon S, Bhasin MK, Libermann TABIDMC, Roy R, Sachdev M, Loughlin KRBWH, Moses MABCH. Novel non-invasive biomarkers that distinguish between benign prostate hyperplasia and prostate cancer. BMC Cancer. 2015 Apr 11;15:259. PMC4433087.
Impairment of gamma-glutamyl transferase 1 activity in the metabolic pathogenesis of chromophobe renal cell carcinoma
PIs: Asara JMBIDMC (Cancer Genetics Program), Kwiatkowski DJBWH (Lung Cancer Program), Henske EPBWH(Cancer Genetics Program)
Chromophobe renal cell carcinoma (ChRCC) accounts for 5% of all sporadic renal cancers and can also occur in genetic syndromes including Birt-Hogg-Dube (BHD) and tuberous sclerosis complex (TSC). ChRCC has a distinct accumulation of abnormal mitochondria, accompanied by characteristic chromosomal imbalances and relatively few "driver" mutations. Metabolomic profiling of ChRCC and oncocytomas (benign renal tumors that share pathological features with ChRCC) revealed both similarities and differences between these tumor types, with principal component analysis (PCA) showing a distinct separation. ChRCC have a striking decrease in intermediates of the glutathione salvage pathway (also known as the gamma-glutamyl cycle) compared with adjacent normal kidney, as well as significant changes in glycolytic and pentose phosphate pathway intermediates.
We also found that gamma glutamyl transferase 1 (GGT1), the key enzyme of the gamma-glutamyl cycle, is expressed at ∼100-fold lower levels in ChRCC compared with normal kidney, while no change in GGT1 expression was found in clear cell RCC (ccRCC). Significant differences in specific metabolite abundance were found in ChRCC vs. ccRCC, including the oxidative stress marker ophthalmate. Down-regulation of GGT1 enhanced the sensitivity to oxidative stress and treatment with buthionine sulfoximine (BSO), which was associated with changes in glutathione-pathway metabolites. These data indicate that impairment of the glutathione salvage pathway, associated with enhanced oxidative stress, may have key therapeutic implications for this rare tumor type for which there are currently no specific targeted therapies.
Contribution of the Core: All the metabolomics mass spectrometric assays were performed and analyzed by the Core using the AB/Sciex 5500 QTRAP mass spectrometer coupled to a Shimadzu UFLC using normal phase amino Hydrophilic interaction chromatography (HILIC) at pH=9.0 with positive/negative switching within the same experimental 20 minute LC/MS/MS run.
Publication: Priolo C, Khabibullin D, Reznik E, Filippakis H, Ogórek B, Kavanagh TR, Nijmeh J, Herbert ZT, Asara JMBIDMC, Kwiatkowski DJBWH, Wu CL, Henske EP BWH. Impairment of gamma-glutamyl transferase 1 activity in the metabolic pathogenesis of chromophobe renal cell carcinoma. Proc Natl Acad Sci U S A. 2018 Jul 3;115(27):E6274-E6282. PubMed PMID: 29891694.
For more information or to contact the Cancer Proteomics Core, visit the core website here.