Cancer genetics-guided discovery of serum biomarker signatures for diagnosis and prognosis of prostate cancer

Abstract

A key barrier to the realization of personalized medicine for cancer is the identification of biomarkers. Here we describe a two-stage strategy for the discovery of serum biomarker signatures corresponding to specific cancer-causing mutations and its application to prostate cancer (PCa) in the context of the commonly occurring phosphatase and tensin homolog (PTEN) tumor-suppressor gene inactivation. In the first stage of our approach, we identified 775 N-linked glycoproteins from sera and prostate tissue of wild-type and Pten-null mice. Using label-free quantitative proteomics, we showed that Pten inactivation leads to measurable perturbations in the murine prostate and serum glycoproteome. Following bioinformatic prioritization, in a second stage we applied targeted proteomics to detect and quantify 39 human ortholog candidate biomarkers in the sera of PCa patients and control individuals. The resulting proteomic profiles were analyzed by machine learning to build predictive regression models for tissue PTEN status and diagnosis and grading of PCa. Our approach suggests a general path to rational cancer biomarker discovery and initial validation guided by cancer genetics and based on the integration of experimental mouse models, proteomics-based technologies, and computational modeling.serum biomarkers | mass spectrometry | Pten conditional knockout mouse model

Translational Analysis of Mouse and Human Placental Protein and mRNA Reveals Distinct Molecular Pathologies in Human Preeclampsia

“…This suggests that it may be possible to generate mouse models of the three identified human subgroups of PE. The mouse models may be useful in longitudinal studies to identify candidate blood biomarkers for human PE, similar to a recent strategy in prostate cancer biomarker discovery (59), as well as serving as models to study disease progression, understand mechanisms and test new therapies. Ultimately, knowledge of the unique proteins at the interface between maternal blood and fetal trophoblast reported here will help propel discovery of novel biomarkers to stratify disease by etiology, and may lead to novel targeted treatments based on a greater understanding of the molecular basis of placental pathology in this common and life-threatening disease of pregnancy.…”

Integrating biomarkers across omic platforms: an approach to improve stratification of patients with indolent and aggressive prostate cancer

“…For instance, coefficients for the two individual peptides DYVSQFEGSALGK and LLDNWDSVTSTFSK, of −1.33E‐06 and −8.03E‐07, respectively, are consistent with Apolipoprotein A‐1 inverse association with PCa risk (Van Hemelrijck et al ., 2011). The zinc‐alpha‐2‐glycoprotein, which has a model coefficient value of −1.79E‐01 for aggressive disease, has been shown to be included in a panel of five proteins for the prediction of Gleason 7 or higher (Cima et al ., 2011). Of the proteins that had two peptides identified the majority had coefficients in agreement between the two peptides; for example, Apolipoprotein E had coefficients of −6.23E‐05 and −9.73E‐06 for significant disease VQAAVGTSAAPVPSDNH and WVQTLSEQVQEELLSSQVTQELR, respectively.…”

Applications of targeted proteomics in systems biology and translational medicine

“…Cima and coworkers successfully applied this strategy by initially using shotgun data to establish a protein list of 44 candidates. Consistent quantification of these 44 proteins across a large patient cohort allowed the establishment of four N‐glycosylated protein makers which differentiate patients with a Gleasson score above or below 7 from blood serum 75.…”

“…One such enrichment strategy is the enrichment of N‐glycosylated peptides and the subsequent PNGase F‐catalyzed conversion of Asn to Asp using solid state extraction method 90, 91 a method which was successfully applied to serum samples 75, 92. Upon purification, only the modified peptide is quantified.…”

“…Cima and coworkers followed a two‐stage strategy for biomarker discovery, starting with a discovery scan comparing the serum N‐linked glycoproteome of PTEN conditional knockout model of prostate cancer to wild‐type and then developed targeted assays for 39 human orthologs, which were then quantified in the sera of 143 prostate cancer patients and controls over an abundance range of six orders of magnitude 75. Computational analysis derived a signature for diagnosis and prognosis of prostate cancer.…”