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A major challenge in lung cancer prevention and cure hinges on identifying the at-risk population who ultimately develops lung cancer. Previously we reported proteomic alterations in the cytologically normal bronchial epithelial cells collected from the bronchial brushings of individuals at-risk for lung cancer. Proteins were identified by shotgun proteomics using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and selected candidate biomarkers of risk were validated in an independent sample cohort by parallel reaction monitoring. The purpose of the current study is to validate, in independent cohorts from Vanderbilt University Medical Center, a selected list of 54 candidate proteins associated with risk for lung cancer with sensitive targeted proteomics using selected reaction monitoring (SRM) at the Pacific Northwest National Laboratory. We tested proteins from bronchial brushings and bronchial biopsies collected from individuals at low risk (n=10) and high risk (n=10) based on the Tammemagi risk stratification model as well as from patients with lung cancer (n=10). Multiplexed LC-SRM assays were developed for all 54 proteins and the protein concentrations were determined by LC-SRM using heavy isotope-labeled peptides for 44 and 49 proteins in the brushings and biopsy samples, respectively. Pairwise comparisons by Wilcoxon rank sum test demonstrated significant overexpression, in the high-risk group compared to the low-risk group, of ALDH3A1 and AKR1B10 in both brushings and biopsies, and of SFN/YWHAS only in the biopsies. Significant overexpression of ALDH1A1, ALDH3A1, AKR1B10, ANXA1, CTNNB1, G6PD, LGALS7B, ALOX15, PGK1, SFN/YWHAS, and UBA1 were observed in the lung cancer group compared to the low-risk group in the brushings only. LGALS7B was also overexpressed in the brushings of high risk compared to lung cancer group. In conclusion, these SRM results revealed promise of selected candidate proteins to stratify the at-risk population for lung cancer. Next, our validation efforts will test the promising candidates in second, larger independent cohort; methods with increased sensitivity such as PRISM (high-pressure, high-resolution separations with intelligent selection and multiplexing)-SRM for analysis of the previously undetected low abundant protein candidates and carrier-assisted SRM approaches for analysis of very small-sized samples. Ultimately, we hope to deliver a signature of risk that may provide the basis for lung cancer risk assessment and possibly novel future prevention strategies. This work is supported by UO1CA152662. Citation Format: SM Jamshedur Rahman, Yuqian Gao, Athena A. Schepmoes, Yi-Ting Wang, Tujin Shi, Sheau-Chiann Chen, Heidi Chen, Karin D. Rodland, Tao Liu, Pierre P. Massion. Validation of a proteomic signature of lung cancer risk from bronchial brushings [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1620.

Introduction: Although many (~40%) of the screen-detected prostate cancers are indolent (Gleason Score Material and Methods: To identify a panel of protein markers that could predict prostate cancer progression, we developed ultra-sensitive, high-pressure, high-resolution separations coupled with intelligent selection and multiplexing-selected reaction monitoring (PRISM-SRM) assays for 52 protein markers. Candidate protein markers were identified and selected from existing prostate cancer genomics data sets and validated lists of known prostate cancer drivers. The PRISM-SRM assays used heavy isotope-labeled synthetic peptides as internal standards for quantitative proteomics analysis. Study comprised of a prostate cancer patient cohort with organ confined primary tumors (N=338) presenting following post-surgery features: 53 (15.7%) metastatic progression, 124 (36.7%) biochemical recurrence (BCR), and 161 (47.6%) no progression after more than ten years of follow-up after radical prostatectomy. Index tumor region for each case was scraped from representative 10-m sections of formalin-fixed paraffin embedded (FFPE)-whole-mounted prostatectomy specimens and processed for PRISM-SRM analysis. Results: Overall, PRISM-SRM analysis of the FFPE tissue samples enabled the detection of 42 (80.8%) out of 52 biomarker candidates; in comparison regular LC-SRM without the front-end chromatographic enrichment could detect only 21 (40.4%) of these candidates at the protein level. Kruskal-Wallis testing was used for statistical evaluation of the PRISM-SRM results and comparison of relative protein levels between the “no progression”, BCR and “metastatic progression” groups. Several prostate differentiation/androgen receptor signaling related proteins (FOLH1, PSA and NCOA) and tumor progression-related proteins (TGFB1, CCND1 and SPRC) had significantly different expression levels between the three groups, and showed initial promise in predicting progression to invasive cancer, BCR and metastasis. Conclusion: Ultra-sensitive targeted proteomics can be used to select and verify performance of early prognostic markers based on the analysis of prostatectomy specimens. The top performing markers appeared able to predict progression from organ confined cancer to BCR and metastasis. Citation Format: Yuqian Gao, Yi-Ting Wang, Hui Wang, Denise Young, Jennifer Cullen, Yingjie Song, Yongmei Chen, Athena Schepmoes, Gyorgy Petrovics, Thomas Fillmore, Tujin Shi, Wei-Jun Qian, Richard Smith, Sudhir Srivastava, Jacob Kagan, Albert Dobi, Inger Rosner, Karin Rodland, Isabell Sesterhenn, Shiv Srivastava, Tao Liu. Identification of candidate biomarkers for aggressive prostate cancer using targeted proteomics and FFPE tissue samples with outcomes data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3165.

Mass spectrometry based targeted proteomics such as selected reaction monitoring (SRM) provides an effective, antibody-independent strategy for sensitive, specific and multiplexed verification of genomics biomarker candidates at the protein level. In order to identify a panel of proteins with the potential to predict prostate cancer progression, we have selected 52 protein candidates from existing prostate cancer genomics data sets and validated cancer drivers, and performed quantitative proteomics analysis in tissue samples using the highly sensitive PRISM (high-pressure, high-resolution separations coupled with intelligent selection and multiplexing)-SRM approach. PRISM-SRM assays have been developed for the 52 prostate cancer biomarker candidates including: prostate cancer relevant genes and common cancer drivers. One set of 105 formalin-fixed paraffin-embedded (FFPE) whole mount prostate tissue specimens were analyzed using PRISM-SRM with heavy isotope-labeled synthetic peptides as internal standards: 20 primary tumors from patients showing metastatic progression, 37 primary tumors from patients who showed biochemical recurrence (BCR), and 48 primary tumors from patients with no BCR or metastatic progression after more than ten years of follow-up after radical prostatectomy. Overall, PRISM-SRM analyses of the FFPE tissue samples enabled the detection of 42 out of 52 biomarker candidates; in comparison regular LC-SRM without the front-end chromatographic enrichment could detect only 21 of these candidates at the protein level. Kruskal-Wallis test of the PRISM-SRM results provided a statistical evaluation of comparison of relative protein levels among the “no progression”, BCR and “metastatic progression” groups. Prostate differentiation/AR signaling related proteins (FOLH1, PSA and NCOA) or tumor progression (TGFB1, CCND1 and SPRC) were significantly different between the three groups. These promising biomarker candidates for early detection of aggressive prostate cancer are being further evaluated, individually and in panels, in an independent cohort of 234 samples for their potential prognostic applications. In summary, PRISM-SRM provides a highly sensitive method for quantification and rapid screening of promising cancer biomarker candidates defined by multiomics platforms. Citation Format: Yuqian Gao, Hui Wang, Jennifer Cullen, Yongmei Chen, Athena Schepmoes, Gyorgy Petrovics, Thomas Fillmore, Tujin Shi, Wei-Jun Qian, Richard Smith, Sudhir Srivastava, Jacob Kagan, Albert Dobi, Karin Rodland, Shiv Srivastava, Tao Liu. Selection of candidate biomarkers for aggressive prostate cancer based on targeted proteomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2573.

Mass spectrometry (MS) based targeted proteomics such as selected reaction monitoring (SRM) provides an antibody-independent strategy for sensitive, specific and multiplexed verification of genomics biomarker candidates at the protein level. In order to identify a panel of proteins with the potential to discriminate between aggressive and indolent forms of prostate cancer and predict prostate cancer progression, we have selected 52 protein candidates from existing prostate cancer genomics data sets and validated cancer drivers, and performed quantitative proteomics analysis in tumor and control tissue samples using the highly sensitive PRISM (high-pressure, high-resolution separations coupled with intelligent selection and multiplexing)-SRM approach. PRISM-SRM assays have been developed for the 52 prostate cancer biomarker candidates including: prostate cancer prognosis associated genes, prostate cancer associated genes that were up-regulated in transcriptomics studies, and other cancer-related genes (including the ERG or ETV1 isoforms). Two sets of tissue samples were analyzed using PRISM-SRM with heavy isotope-labeled synthetic peptides as internal standards: 1) 10 high Gleason-score (7-9) primary prostate tumors and 10 benign prostatic hyperplasia (BPH) tissues (OCT-embedded specimens); and 2) 10 primary tumors from patients showing metastatic progression, 10 primary tumors from patients who showed biochemical recurrence (BCR), and 10 primary tumors from patients with no BCR or metastatic progression after more than ten years of follow-up after radical prostatectomy (FFPE whole mount prostate specimens). Overall, PRISM-SRM analyses of all the patient tissue samples enabled the detection of 48 out of 52 biomarker candidates, suggesting extremely low level of expression of the remaining 6 genes (HXC6, OSTP, TWST1, and ERG8); in comparison regular LC-SRM can only detect 21 of these candidates at the protein level. In the 10 high Gleason-score tumors and 10 BPH controls, 13 proteins were found differentially abundant with P Citation Format: Hui Wang, Yuqian Gao, Athena Schepmoes, Gyorgy Petrovics, Jennifer Cullen, Thomas Fillmore, Tujin Shi, Wei-Jun Qian, Richard Smith, Brandi Weaver, Robin Leach, Ian Thompson, Sudhir Srivastava, Jacob Kagan, Albert Dobi, Karin Rodland, Shiv Srivastava, Tao Liu. Verification of prostate cancer genomics biomarker candidates at protein level using PRISM-SRM [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 226. doi:10.1158/1538-7445.AM2017-226

Speckle-type POZ protein (SPOP) is an E3 ubiquitin ligase adaptor protein that functions as a potential tumor suppressor, and SPOP mutations have been identified in ∼10% of human prostate cancers. However, it remains unclear if mutant SPOP proteins can be utilized as biomarkers for diagnosis, prognosis or targeted therapy of prostate cancer. To address this issue, selected reaction monitoring (SRM) and PRISM (high-pressure, high-resolution separations coupled with intelligent selection and multiplexing) -SRM mass spectrometry assays have been developed for quantifying mutant SPOP proteins. SRM assays for wild-type SPOP protein and 11 prostate cancer-derived mutations were developed. The presence of multiple lysine residues in the mutation regions precludes the use of conventional tryptic digestion. Arg-C was selected instead due to its superior performance in generating mutation site(s) containing SPOP peptides that are more suitable for SRM analysis comparing to other proteases (e.g., Asp-N). Although the resulting Arg-C peptides are longer and more hydrophobic than typical tryptic peptides, all the SRM assays showed a linear dynamic range of more than two orders of magnitude. The limits of quantification for the mutation site(s) containing peptides range from 10 to 100 fmol/μg of total protein in the cell lysate. Applying these SRM assays to analyze 293T cells with and without expression of the three most frequent SPOP mutations in prostate cancer (Y87N, F102C or F133V) led to confident detection of all three SPOP mutations in corresponding positive cell lines but not in the negative cell lines. Expression of the F133V mutation and wild-type SPOP was at much lower levels compared to that of F102C and Y87N mutations, which agrees with RT-PCR results. It is unknown if this is related to activity of the SPOP protein. PRISM-SRM assays have shown further improvement in sensitivity. SRM enables multiplexed, isoform-specific detection of mutant SPOP proteins in cell lysates, which holds great potential in biomarker development for prostate cancer. Citation Format: Hui Wang, Christopher Barbieri, Jintang He, Yuqian Gao, Chaochao Wu, Athena Schepmoes, Thomas Fillmore, Tujin Shi, Sung-Suk Chae, Dennis Huang, Juan Miguel Mosquera, Wei-Jun Qian, Richard Smith, Sudhir Srivastava, Jacob Kagan, David Camp, Karin Rodland, Mark Rubin, Tao Liu. Quantification of mutant SPOP proteins in prostate cancer using targeted proteomics. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3872.

Current molecular tests that aid in the diagnosis of prostate cancer (PCa) are based on the detection of PSA and PSA isoforms (blood), overexpression of PCA3 (urine), and overexpression of AMACR (tissue) as biomarkers. The discovery of cancer specific fusion transcripts, which are detected in over 50% of prostate cancers, revolutionized the field. As such, the cancer specific TMPRSS2-ERG gene fusion drives the overexpression of the ERG oncoprotein. Based on this, we hypothesized that ERG protein may serve as a valuable biomarker and for which quantification methods can be applied for the diagnosis of prostate cancer. An antibody-free assay for ERG protein detection was developed based on PRISM (high-pressure high-resolution separations with intelligent selection and multiplexing)-SRM (selected reaction monitoring) mass spectrometry. We utilized TMPRSS2-ERG positive VCaP and TMPRSS2-ERG negative LNCaP cells to simulate three different sample types (cells, tissue, and post-DRE urine sediment) for evaluation. Recombinant ERG protein spiked into LNCaP cell lysates could be detected at levels as low as 20 pg by PRISM-SRM analysis. The sensitivity of the PRISM-SRM assay was approximately 10,000 VCaP cells in a mixed cell population model of VCaP and LNCaP cells. Interestingly, ERG protein could be detected in as few as 600 VCaP cells spiked into female urine, presumably due to more effective loading of ERG protein and the reduced sample complexity. In comparison the detection limit of the in-house enzyme-linked immunosorbent assay was 30 pg of recombinant ERG protein and 10,000 VCaP cells, while Western blot analysis demonstrated the detection of 195 pg of recombinant ERG protein and 10,000 VCaP cells in these two sample types, respectively. On the other hand, qRT-PCR exhibited a higher sensitivity, as TMPRSS2-ERG transcripts were detected in as few as 250 VCaP cells spiked into female urine, in comparison to NanoString methodologies which detected 10,000 VCaP cells in the same sample. In summary the data presented here suggest that qRT-PCR and PRISM-SRM platforms are highly sensitive in detecting TMPRSS2-ERG transcripts and proteins, respectively. Compared to other RNA (without preamplifcation step) and protein detection technologies, the PRISM-SRM assay has several significant advantages: it is not affected by RNA stability, it does not rely on a specific antibody, and it is ideal for isoform-specific detection with high multiplexing capability. Therefore, PRISM-SRM assays can be adapted to detect ERG protein in cells present in clinical specimens (e.g., tissue and urine), and cell-free ERG protein present in the blood sera, providing an opportunity for their use in the clinical setting for detection of ERG in prostate cancer patients and for defining appropriate treatment strategies. Citation Format: Jintang He, Athena Schepmoes, Anshu Rastogi, Shyh-Han Tan, Wusheng Yan, Wei Huang, Sreedatta Banerjee, Tujin Shi, Chaochao Wu, Thomas Fillmore, Yuqian Gao, Jacob Kagan, Sudhir Srivastava, Richard Smith, Wei-Jun Qian, David McLeod, Gyorgy Petrovics, Albert Dobi, Alagarsamy Srinivasan, Shiv Srivastava, Karin Rodland, Tao Liu, David Camp. Analytical platform evaluation for quantification of ERG oncoprotein in prostate cancer using protein and mRNA detection methods. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1818. doi:10.1158/1538-7445.AM2015-1818