The search for novel clinically relevant disease-specific protein biomarkers that can be measured in an easily accessible body fluid such as the blood remains a huge challenge for researches because of the complexity of the circulatory proteome and the likely extremely low concentration of any candidate biomarkers coupled with the lability of the markers in vivo and ex vivo. The intensity of biomarker discovery efforts has lead to the increased investigation of the peptidome/LMW proteome because of the facile nature of this archive to easily pass between the tissue and bloodstream. This information archive may be especially a rich source of disease-specific information for the early detection of cancer, toxicity, and drug abuse/doping whereby complex changes within the tissue microenvironment may be reflected in altered pathophysiology of the affected organ(s).
To that end, we developed a novel workflow for LMW proteome/peptidome biomarker research of low-molecular-weight proteins/peptides by the novel application of hydrogel nanoparticle technology as a tool to rapidly capture and concentrate analytes in solution coupled with high-sensitivity mass spectrometry analysis (Fig. ). The novel aspect of this new biomarker discovery method lies within the capacity of the core–shell nanoparticles (described in (26
)) to perform both an affinity capture step and size exclusion chromatography in solution in just one step. In this instance, we used an acrylic acid affinity capture reagent that is immobilized within the core region of the nanoparticle, which is then covered with a shell matrix with tuned porosity to allow in analytes <30 kDa.
Consequently, for this feasibility study, the particles will bind only protein analytes that are net positively charged at the buffered pH of 7.0. Other marker capture nanoparticles with different affinity “baits” can be easily constructed with other types of affinity dye reagents, for example Cibacron blue dye which we showed could effectively capture 100% of hGH in urine (27
). The ability of the particles to perform two-dimensional separation in just one step, whereby subsets of the peptidome (in this case the positively charged archive) are captured from hundreds of microliters of serum along with excluding all of the high abundance resident proteins such as albumin and immunoglobulins, provides a concentration step that is equivalent to injecting in 400 μL (for ovarian cancer study) and 200 μL (for prostate cancer study) into the Orbitrap at once, without the need for any of the types of pre-fractionation step normally required to analyze blood proteins by mass spectrometry (29
). Most of the procedures commonly used can result in the loss of part of important information, either because many LMW serum proteins are bound to high-molecular-weight carrier proteins or because of lengthy sample handling procedures. Moreover, mass spectrometry normally works in a dynamic range of three to four orders of magnitude, meaning that highly abundant proteins such as albumin mask most of the lower abundance proteins.
We applied the nanoparticle-based biomarker capture technique for the determination of differentially abundant peptidomes from two important human cancers as a feasibility case study and not a definitive biomarker discovery effort. We utilized a unique set of pretreatment serum samples taken from CaP patients with organ-confined disease who were undergoing radical prostatectomy whereby patient-matched pre- and post-prostatectomy samples were obtained. This type of serum collection represents a powerful opportunity to understand the changes in protein expression from the same individual with and without organ-confined cancer. We utilized a second study set serum obtained from women with early-stage ovarian cancer and women with benign gynecologic conditions as an age-matched control group. The use of differentially spiked-in cytokine standards into the starting serum sample that were then analyzed at the end of the entire process by MS/MS allowed us to evaluate the robustness of the overall process, with all samples showing the expected 10:1 or 1:10 ratios as seen by BioSieve analysis (Fig. ). While each individual independently run sample did not show the expected ion abundance ratios for peptides corresponding to the two spiked proteins, the average of the case and control sample ratios was approximately equal to the expected ratios and indicated overall process fidelity. Based on this result, we have higher confidence in the biomarker candidate information generated by MS/MS and differential spectral counting methods within the cancer study sets utilized. Furthermore, to evaluate the specificity of the candidate markers found by nanoparticle harvesting, we compared the resultant candidates within the two cancers to each other. We used the results of both Scaffold and BioSieve software, the former to analyze the MS2 data and the other to analyze the MS1 data and integrate the information from both methods. An interesting aspect of a MS1-based approach is that the comparison is not related to an MS2 identification, and as long as the independent MS chromatograms align well, the software performs a statistically driven analysis on any reproducibly seen peak without regard to whether or not an MS2 spectrum is not available, and thus, lower abundant analytes are also analyzed. Peptides that are found to be potentially differentially abundant can then be identified searching the obtained MS2 data against a human protein database (NCBI) with SEQUEST. An MS targeting experiment could be performed when the MS2 spectra were not obtained from the first analysis and differentially abundant peptides taken forward for further validation by mass spectrometry-based methods such as multiple reaction monitoring (MRM).
We chose a label-free spectral counting approach since this method has been found to provide a cost-efficient reproducible means of obtaining accurate differences in relative peptide abundances between different input samples based on the differences in the number of times a specific tryptic fragment is selected for MS/MS within a data-dependent experiment (30
An overview of the proteins identified for both OC and CaP, found by either Scaffold (results shown in Tables and ) or Biosieve (not shown) and in aggregate summary of both (Table ), reveals low-molecular-weight protein such as Profilin 1, Ras suppressor protein 1, S100A7, Ribonuclease RNAse Family, Platelet factor 4, and enzymes. The nanoparticles captured LMW fragments of high-molecular-weight proteins (e.g., ceruloplasmin, fibronectin 1, Talin 1, actinin alpha 1, apolipoprotein B) that were found to be differentially abundant in cancer vs
. control sera as defined by spectral counting differences. The inferred proteins, corresponding to identified peptides, included serum plasma proteins such as serpin peptidase inhibitor, clade A, member 3 precursor, angiotensinogen, and orosomucoid 1. The identified tryptic fragments originate from both cytoplasmic and nuclear proteins which reinforce the value of the LMW proteome/peptidome as being populated by cellular information, reflecting ongoing tissue pathophysiology. The presence in the serum of peptide fragments of intracellular proteins may yield valuable information about the processes and signaling networks operating within the tumor cells. Integrins, LRG, and LPB have been identified in this pilot study as proteins differentially abundant uniquely in ovarian cancer. Integrins constitute a family of transmembrane receptor proteins that mediate the extracellular matrix influence on cell growth and differentiation (31
). Decreased or increased integrin expression has been shown to be associated with cytoskeletal changes coincident with tumor transformation, and a role for integrins in tumor growth and metastasis has repeatedly been proposed (35
If these results are extensively validated in larger independent study sets, development of the expression profiles of LMW peptide fragments of integrin might be useful as tumor progression markers for prognostic and for diagnostic purposes. Moreover, for individual tumors, this may have further potential in identifying a cell surface signature for a specific tumor type and/or stage. (39
). Leucine-rich alpha-2-glycoprotein-1 (LRG) is a serum glycoprotein of unknown function that has shown promise based on qualitative assessments as a biomarker for certain diseases, including microbial infections and cancer (40
Increased serum LRG was observed in patients with several types of cancer, including pancreatic (43
), liver (44
), lung (45
), and epithelial ovarian cancers (46
). However, the lack of a quantitative assay for LRG has so far limited its application. (48
). Lipopolysaccharide-binding protein gene has been found upregulated in clear cell ovarian cancer compared to other major histological types (49
Differentially abundant peptides/LMW fragments found in the prostate cancer study set include proteins already correlated with carcinogenesis or prostate cancer. Vitronectin and cadherin 1 are cell adhesion proteins whose involvement in cancer progression and cellular spreading has been investigated and demonstrated (50
). Vitronectin is recognized by certain members of the integrin family and acts as cell-to-substrate adhesion molecule, while cadherin 1 is involved in mechanisms regulating cell–cell adhesions, mobility, and proliferation of epithelial cells (52
). The presence of vitronectin and cadherin 1 in the circulation could indicate the local loss of diffusion barriers, such as cell junction and the basement membrane. (54
). Vitronectin has recently been identified as an extrinsic inducer of stem cell differentiation through an integrin alpha(v)beta(3)-dependent mechanism (55
) and may be important in mediating bone metastasis in CaP (56
). Moreover, high expression levels of cadherin 1 fragment in serum have been observed in advanced metastatic prostate cancer (57
The spiked-in internal standard standards, MEC and SDF-1, were both found to be differentially abundant in the correct expected pattern by both Scaffold (shown) and BioSieve (not shown), which provides increased confidence to the validity of the newly identified LMW candidates found. Of course, further analysis will be required for full validation. Future work will encompass antibody-based (e.g., ELISA) and antibody-independent (MRM-MS) verification and validation of differential expression in independent blinded serum study sets, including benign and inflammatory diseases as well as other cancers, to understand the specificity and sensitivity of each candidate alone and in combination with each other.
Overall, we identified more differentially abundant proteins in the OC study sets than with CaP samples, and this is very likely due to the fact that we used a smaller input starting volume of serum for CaP studies (due to limitations in sample available) compared to OC samples. This is in keeping with expected results from nanoparticle capture since the entire contents are analyzed from the particle eluates: more input sample, more LMW peptidome analyzed/MS run. Consequently, it is highly likely that the number of CaP-specific candidates would increase if input starting sample volume increased. Moreover, this then highlights the potential for this workflow to enable detection of lower level endogenous proteins and protein fragments as larger volumes of starting sample are used. To that end, among other investigations, we are evaluating experimental results whereby very large volumes (e.g., 8 cc) of body fluids such as serum are used as the input into the nanoparticle capture workflow. The results described in this study are not meant in any way to reflect a comprehensive analysis of the serum peptidome or the entirety of the prostate and ovarian cancer LMW biomarker candidates that exist. Indeed, the identified analyte content is entirely dependent on the bait chemistry used within the nanoparticle. We used an acrylic acid capture chemistry that will selectively bind only analytes with a net positive charge at pH 7.0. Of course, the charge structure of the LMW analyte is dependent on the amino acid composition of the peptide/LMW protein. In the future, one can imagine the use of cocktails of particles with differential binding chemistries for positively charged proteins, negatively charged proteins, phosphoproteins, glycoproteins, etc. that will provide larger screening capabilities and wider coverage of the peptidome for biomarker discovery.