Quantitative Proteomics: From Technology to Cancer Biology

Transcription

1 Quantitative Proteomics: From Technology to Cancer Biology Beyond the Genetic Prescription Pad: Personalizing Cancer Medicine in 2014 February 10-11, 2014 Thomas Kislinger

2 Molecular Biomarkers in Body Fluids 1Circulating Tumor Cells 2Circulating Tumor DNA, mirnas 3Tumor-derived Extracellular Vesicles 4Soluble Proteins & PTMs 5Metabolites

3 Biomarker Development Pipeline # of samples # of candidates Cost Discovery Assay Development Retrospective Verification, Validation Prospective Validation Bench Bedside Statistical Samples Challenges Analytical Figure adapted from Clin Chem 2013; Pavlou Pepe et al. JNCI; 2001;93:

4 Discovery Proteomics Proteins Peptides Liquid Chromatography Mass Spectrometry Trypsin YDGPIK Identification MS and MS/MS spectra are used SGGPTR KPQIMDLK Protein 1 Protein 2 Parent ion mass spectrum QLFIPDGWK CHGIPLMHR Quantification MS or MS/MS spectra are used Tandem mass spectra

5 Targeted Proteomics Spike-in Heavy Endogenous Light Integrated Quantification

6 Complexity of the Proteome PSA Schiess et al. Mol Oncol. 2009

7 Dynamic Range: Possible Solutions 1 Divide and conquer 2 Deplete high abundance proteins 3 Tissue proximal fluids: higher local concentration 4 Enrich specific sub-proteome 1 N-glycoproteome 2 Tumor-derived Extracellular Vesicles

8 Prostate Cancer Diagnosis 1Serum PSA and DRE 2PSA organ not cancer specific a. Low specificity- unnecessary biopsy b. Screening leads to over detection 27-56% c. Clinically Insignificant disease 30-50% 3Strong need for cancer-specific and prognostic biomarkers a. Active surveillance

9 Expressed Prostatic Secretions - EPS Direct-EPS Fluid that is produced by the prostate EPS-urine EPS is expelled via DRE and voided in urine Contains proteins and shed cells of the prostate Contains prostatic and urine proteins Concentrated source of prostaterelated biomarkers Clinically attractive: can be collected frequently in large volumes and is non-invasive Collected from patients immediately prior to prostatectomy Amenable to large-scale screening

10 EPS-based Peptide Database Drake et al. 2009, J Prot Res Kim et al. 2012, Mol Cell Proteomics Principe et al. 2013, J Prot Res. Principe et al. 2013, Proteomics EPS Database 2,000 proteins

11 Workflow for EPS-based Biomarkers Selection of proteotypic peptides for synthesis based on experimentally-derived data Unpurified C13 and N15-labeled isotope peptide standards Optimization of peptides in matrix of choice (EPS-urine) Relative quantification of endogenous peptides in retrospective test cohort Select panel of signature peptides Assay optimization (linearity, LOD/LOQ) AQUA peptides Absolute quantification in verification cohort

12 Peptide Selection & Assay Optimization Selection of proteotypic peptides for synthesis based on experimentally-derived data Unpurified C13 and N15-labeled isotope peptide standards Optimization of peptides in matrix of choice (EPS-urine) SRM assay optimization 110 proteins / 229 peptides 80 proteins / 144 peptides

13 Peptide Selection & Assay Optimization Selection of proteotypic peptides for synthesis based on experimentally-derived data Unpurified C13 and N15-labeled isotope peptide standards Optimization of peptides in matrix of choice (EPS-urine)

14 Application to EPS-urine cohort Selection of proteotypic peptides for synthesis based on experimentally-derived data Diagnostic Controls vs. PCa 40 peptides Unpurified C13 and N15-labeled isotope peptide standards Optimization of peptides in matrix of choice (EPS-urine) Relative quantification of endogenous peptides in retrospective test cohort Rel. Quantification EPS-urine Prognostic OC vs. EC OC N = 42 EC N = 14 Control N = 26 Collaboration with P. Boutros (OICR)

15 Workflow for EPS-based Biomarkers Selection of proteotypic peptides for synthesis based on experimentally-derived data Unpurified C13 and N15-labeled isotope peptide standards Optimization of peptides in matrix of choice (EPS-urine) Relative quantification of endogenous peptides in retrospective test cohort Select panel of signature peptides Assay optimization (linearity, LOD/LOQ) 40 AQUA peptides

16 AQUA Peptide Quantification

17 Verification in EPS-urine cohort Selection of proteotypic peptides for synthesis based on experimentally-derived data Unpurified C13 and N15-labeled isotope peptide standards Optimization of peptides in matrix of choice (EPS-urine) Relative quantification of endogenous peptides in retrospective test cohort Select panel of signature peptides Abs. Quantification EPS-urine Assay optimization (linearity, LOD/LOQ) AQUA peptides Absolute quantification in verification cohort OC N = 70 EC N = 30 Control N = 40 Verification cohort

18 Summary & Conclusion 1. In-depth peptide library of prostatic fluids 2. Systematic SRM development for selected proteins 3. Quantification in EPS-urines 4. Quantification in longitudinally collected EPS-urines 5. Additional applications a. Tissue imaging MS, glycomics/glyco-proteomics & TDV b. Best technology or multi-panel markers? c. Tissue proteomics integration with next-gen sequencing

19 Acknowledgments Yunee Kim Dr. Simona Principe Alex Ignatchenko Vladimir Ignatchenko OICR Dr. Paul Boutros Cindy Yao EVMS, Virginia Dr. Richard Drake Dr. John Semmes Dr. Raymond Lance UHN/PMH Dr. Neil Fleshner Dr. Rob Bristow Dr. Theo van der Kwast