Novel Stool-Based Protein Biomarkers for Improved Colorectal Cancer Screening: A Case-Control Study.

BACKGROUND: The fecal immunochemical test (FIT) for detecting hemoglobin is used widely for noninvasive colorectal cancer (CRC) screening, but its sensitivity leaves room for improvement. OBJECTIVE: To identify novel protein biomarkers in stool that outperform or complement hemoglobin in detecting CRC and advanced adenomas. DESIGN: Case-control study. SETTING: Colonoscopy-controlled referral population from several centers. PARTICIPANTS: 315 stool samples from one series of 12 patients with CRC and 10 persons without colorectal neoplasia (control samples) and a second series of 81 patients with CRC, 40 with advanced adenomas, and 43 with nonadvanced adenomas, as well as 129 persons without colorectal neoplasia (control samples); 72 FIT samples from a third independent series of 14 patients with CRC, 16 with advanced adenomas, and 18 with nonadvanced adenomas, as well as 24 persons without colorectal neoplasia (control samples). MEASUREMENTS: Stool samples were analyzed by mass spectrometry. Classification and regression tree (CART) analysis and logistic regression analyses were performed to identify protein combinations that differentiated CRC or advanced adenoma from control samples. Antibody-based assays for 4 selected proteins were done on FIT samples. RESULTS: In total, 834 human proteins were identified, 29 of which were statistically significantly enriched in CRC versus control stool samples in both series. Combinations of 4 proteins reached sensitivities of 80% and 45% for detecting CRC and advanced adenomas, respectively, at 95% specificity, which was higher than that of hemoglobin alone (P < 0.001 and P = 0.003, respectively). Selected proteins could be measured in small sample volumes used in FIT-based screening programs and discriminated between CRC and control samples (P < 0.001). LIMITATION: Lack of availability of antibodies prohibited validation of the top protein combinations in FIT samples. CONCLUSION: Mass spectrometry of stool samples identified novel candidate protein biomarkers for CRC screening. Several protein combinations outperformed hemoglobin in discriminating CRC or advanced adenoma from control samples. Proof of concept that such proteins can be detected with antibody-based assays in small sample volumes indicates the potential of these biomarkers to be applied in population screening. PRIMARY FUNDING SOURCE: Center for Translational Molecular Medicine, International Translational Cancer Research Dream Team, Stand Up to Cancer (American Association for Cancer Research and the Dutch Cancer Society), Dutch Digestive Foundation, and VU University Medical Center.

Identification of Differentially Expressed Splice Variants by the Proteogenomic Pipeline Splicify.

Proteogenomics, i.e. comprehensive integration of genomics and proteomics data, is a powerful approach identifying novel protein biomarkers. This is especially the case for proteins that differ structurally between disease and control conditions. As tumor development is associated with aberrant splicing, we focus on this rich source of cancer specific biomarkers. To this end, we developed a proteogenomic pipeline, Splicify, which can detect differentially expressed protein isoforms. Splicify is based on integrating RNA massive parallel sequencing data and tandem mass spectrometry proteomics data to identify protein isoforms resulting from differential splicing between two conditions. Proof of concept was obtained by applying Splicify to RNA sequencing and mass spectrometry data obtained from colorectal cancer cell line SW480, before and after siRNA-mediated downmodulation of the splicing factors SF3B1 and SRSF1. These analyses revealed 2172 and 149 differentially expressed isoforms, respectively, with peptide confirmation upon knock-down of SF3B1 and SRSF1 compared with their controls. Splice variants identified included RAC1, OSBPL3, MKI67, and SYK. One additional sample was analyzed by PacBio Iso-Seq full-length transcript sequencing after SF3B1 downmodulation. This analysis verified the alternative splicing identified by Splicify and in addition identified novel splicing events that were not represented in the human reference genome annotation. Therefore, Splicify offers a validated proteogenomic data analysis pipeline for identification of disease specific protein biomarkers resulting from mRNA alternative splicing. Splicify is publicly available on GitHub (https://github.com/NKI-TGO/SPLICIFY) and suitable to address basic research questions using pre-clinical model systems as well as translational research questions using patient-derived samples, e.g. allowing to identify clinically relevant biomarkers.

Molecular imaging of aurora kinase A (AURKA) expression: Synthesis and preclinical evaluation of radiolabeled alisertib (MLN8237).

INTRODUCTION: Survival of patients after resection of colorectal cancer liver metastasis (CRCLM) is 36%-58%. Positron emission tomography (PET) tracers, imaging the expression of prognostic biomarkers, may contribute to assign appropriate management to individual patients. Aurora kinase A (AURKA) expression is associated with survival of patients after CRCLM resection. METHODS: We synthesized [(3)H]alisertib and [(11)C]alisertib, starting from [(3)H]methyl nosylate and [(11)C]methyl iodide, respectively. We measured in vitro uptake of [(3)H]alisertib in cancer cells with high (Caco2), moderate (A431, HCT116, SW480) and low (MKN45) AURKA expression, before and after siRNA-mediated AURKA downmodulation, as well as after inhibition of P-glycoprotein (P-gp) activity. We measured in vivo uptake and biodistribution of [(11)C]alisertib in nude mice, xenografted with A431, HCT116 or MKN45 cells, or P-gp knockout mice. RESULTS: [(3)H]Alisertib was synthesized with an overall yield of 42% and [(11)C]alisertib with an overall yield of 23%±9% (radiochemical purity ≥99%). Uptake of [(3)H]alisertib in Caco2 cells was higher than in A431 cells (P=.02) and higher than in SW480, HCT116 and MKN45 cells (P<.01). Uptake in A431 cells was higher than in SW480, HCT116 and MKN45 cells (P<.01). Downmodulation of AURKA expression reduced [(3)H]alisertib uptake in Caco2 cells (P<.01). P-gp inhibition increased [(3)H]alisertib uptake in Caco2 (P<.01) and MKN45 (P<.01) cells. In vivo stability of [(11)C]alisertib 90min post-injection was 94.7%±1.3% and tumor-to-background ratios were 2.3±0.8 (A431), 1.6±0.5 (HCT116) and 1.9±0.5 (MKN45). In brains of P-gp knockout mice [(11)C]alisertib uptake was increased compared to uptake in wild-type mice (P<.01) CONCLUSIONS: Radiolabeled alisertib can be synthesized and may have potential for the imaging of AURKA, particularly when AURKA expression is high. However, the exact mechanisms underlying alisertib accumulation need further investigation. ADVANCES IN KNOWLEDGE AND IMPLICATIONS FOR PATIENT CARE: Radiolabeled alisertib may be used for non-invasively measuring AURKA protein expression and to stratify patients for treatment accordingly.

A prognostic classifier for patients with colorectal cancer liver metastasis, based on AURKA, PTGS2 and MMP9.

BACKGROUND: Prognosis of patients with colorectal cancer liver metastasis (CRCLM) is estimated based on clinicopathological models. Stratifying patients based on tumor biology may have additional value. METHODS: Tissue micro-arrays (TMAs), containing resected CRCLM and corresponding primary tumors from a multi-institutional cohort of 507 patients, were immunohistochemically stained for 18 candidate biomarkers. Cross-validated hazard rate ratios (HRRs) for overall survival (OS) and the proportion of HRRs with opposite effect (P(HRR < 1) or P(HRR > 1)) were calculated. A classifier was constructed by classification and regression tree (CART) analysis and its prognostic value determined by permutation analysis. Correlations between protein expression in primary tumor-CRCLM pairs were calculated. RESULTS: Based on their putative prognostic value, EGFR (P(HRR < 1) = .02), AURKA (P(HRR < 1) = .02), VEGFA (P(HRR < 1) = .02), PTGS2 (P(HRR < 1) = .01), SLC2A1 (P(HRR > 1) < 01), HIF1α (P(HRR > 1) = .06), KCNQ1 (P(HRR > 1) = .09), CEA (P (HRR > 1) = .05) and MMP9 (P(HRR < 1) = .07) were included in the CART analysis (n = 201). The resulting classifier was based on AURKA, PTGS2 and MMP9 expression and was associated with OS (HRR 2.79, p < .001), also after multivariate analysis (HRR 3.57, p < .001). The prognostic value of the biomarker-based classifier was superior to the clinicopathological model (p = .001). Prognostic value was highest for colon cancer patients (HRR 5.71, p < .001) and patients not treated with systemic therapy (HRR 3.48, p < .01). Classification based on protein expression in primary tumors could be based on AURKA expression only (HRR 2.59, p = .04). CONCLUSION: A classifier was generated for patients with CRCLM with improved prognostic value compared to the standard clinicopathological prognostic parameters, which may aid selection of patients who may benefit from adjuvant systemic therapy.