Annemieke C. Hiemstra

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.

Abstract 4523: Stool proteomics reveals new candidate biomarkers for colorectal cancer screening

Background Colorectal Cancer (CRC) screening can save many lives. Many invitational large scale screening programs worldwide use stool tests like the fecal immunochemical tests (FIT), which detects human hemoglobin. Tumor-specific biomarkers have the potential to improve the performance of these tests but so far, no protein-based fecal test has proved better than the FIT. Although most biomarker discoveries are done in tumor-tissues, the presence and/or chemical nature of biomarkers may be different in samples that ultimately will be used for screening like stool. Measuring biomarkers directly in stool samples may therefore yield candidate CRC biomarkers that are stable in the fecal environment. Aim The aim of the present study was to identify tumor-specific protein based biomarkers for the early detection of CRC, by applying in-depth proteomics to stool samples from CRC patients and healthy controls. Material and method Stool samples were obtained from 10 subjects with negative colonoscopy and from 12 CRC patients. Proteins were analyzed by in-depth proteomics using gel electrophoresis and nano Liquid Chromatography coupled to tandem mass spectrometry (nano LC-MS/MS). Resulting MS/MS spectra were searched against the human IPI database (version 3.62). Proteins were analyzed by hierarchical cluster analysis and visualized in a heat map. Non-paired statistical analysis of spectral count data from human proteins was performed using a beta-binomial test. Verification of candidate biomarkers was performed by Selected Reaction Monitoring Mass Spectometry (SRM-MS). Results In total 830 human proteins were identified of which 221 were present at different levels in stool samples from CRC patients compared to control subjects. Of these, 134 proteins were significantly enriched in CRC. Unsupervised hierarchical cluster analysis revealed two clusters. One cluster contained nine CRC stool samples, the other cluster contained all ten control stool samples together with three CRC stool samples. SRM-MS analysis of selected candidate biomarkers on the same stool samples verified the results obtained by LC-MS/MS. Conclusion Proteome profiling on stool revealed 134 proteins significantly enriched in CRC compared to control stool samples, of which a sub set could be verified by SRM-MS. Validation in an independent series of stool samples collection (n=200) by SRM-MS is in process. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4523. doi:1538-7445.AM2012-4523

Novel stool-based protein biomarkers for improved colorectal cancer screening : a case-control study

Screening aims to lower the burden of colorectal cancer (CRC) by either preventing cancer from developing or detecting it at a curable stage (1). Although colonoscopy remains the gold standard for detecting colorectal tumors, for reasons of compliance and cost, most population-wide screening programs use noninvasive stool-based tests for triage to colonoscopy (2). The guaiac-based fecal occult blood test has been proven to reduce mortality from CRC (35). The newer fecal immunochemical test (FIT), which uses an antibody against human hemoglobin, outperforms the guaiac-based fecal occult blood test and is now used widely (69). Yet, the sensitivity of FIT for detecting CRC is suboptimal (79%) and even poorer (31%) for finding advanced colonic adenomas, precursor lesions with an increased risk for progression (10, 11). Molecular screening tests have the potential to detect colorectal tumors better than FIT (12, 13). Indeed, multitarget stool DNA testing combined with FIT has been shown to have a higher sensitivity than FIT alone (14), but it is too costly to implement in population-wide screening programs. In contrast, like hemoglobin in FIT, protein biomarkers can be translated into simple and cost-effective antibody-based screening tests (15). Ideally, these biomarkers also could be quantified in the small stool sample volumes used in FIT-based screening programs. However, thus far, alternative protein biomarkers have failed to improve current hemoglobin-based CRC stool-screening tests (12). Technologic advancements in mass spectrometry now allow for in-depth proteomics for biomarker discovery in complex biological samples (16, 17). A classic approach is to first identify discriminating markers in tissue or cell-line material and then validate them in the final analyte, such as stool. However, constituents of the analyte ultimately used for screeningsuch as bacterial proteases and glycosidases in stoolmay affect test performance, possibly leading to validation failure (12, 18). Therefore, discovering biomarkers directly in the biological sample taken for screening, namely stool, may be a powerful alternative. In the present study, we set out to identify proteins in stool that outperform or complement fecal hemoglobin as a biomarker for early detection of CRC and advanced adenomas. Methods A brief overview of the methods is given here; full sample details and methods are provided in Supplements 1 and 2. For an overview of the workflow, see Figure 1 of Supplement 1. Supplement 1. Supplementary Appendix Supplement 2. Supplementary Tables Sample Series Written informed consent was obtained from all persons who provided stool samples. The study was conducted in compliance with institutional ethical regulations. See the Appendix Table for clinicopathologic characteristics. Appendix Table. Clinicopathologic Characteristics of Stool Sample Series 1 and 2 Sample Series 1 Twenty-two stool subsamples (12 from patients with CRC and 10 from persons without colorectal neoplasia) were collected from a colonoscopy-controlled referral population at VU University Medical Center in Amsterdam, the Netherlands, between 2003 and 2006. Sample Series 2 Whole stool samples from 293 persons with CRC (n= 81), with advanced adenomas (n= 40), with nonadvanced adenomas (n= 43), or without colorectal neoplasia (n= 129) were collected from a colonoscopy-controlled referral population at several centers in the Netherlands and Germany between 2005 and 2012. Sample Series 3 Fecal immunochemical test samples from 72 persons with CRC (n= 14), advanced adenoma (n= 16), or nonadvanced adenoma (n= 18) or who did not have colorectal neoplasia (n= 24) from a colonoscopy-controlled referral population at Kennemer Gasthuis Hospital in Haarlem, the Netherlands, between 2012 and 2014. Proteomics Analysis With Nanoscale Liquid Chromatography Coupled to Tandem Mass Spectrometry Proteins were extracted from stool as described previously (19), with a few adaptations. Equal amounts of protein were run through sodium dodecyl sulfate polyacrylamide gel electrophoresis (Invitrogen) and processed further by in-gel tryptic digestion (20). Peptides were separated by nanoscale liquid chromatography and detected on an LTQ-FT hybrid mass spectrometer (Thermo Fisher) (sample series 1) or a Q Exactive mass spectrometer (Thermo Fisher) (sample series 2). Data acquisition was not successful for 2 CRC samples in series 2. Spectra obtained from tandem mass spectrometry were searched against the UniProt human reference proteome FASTA file, release January 2014, by using MaxQuant 1.4.1.2 (21). Protein abundance was quantified by label-free spectral counting (22). Antibody-Based Assays Fecal immunochemical test fluids from sample series 3 were analyzed by using antibody-based assays for myeloperoxidase (MPO), alpha-2-macroglobulin (A2M), retinol binding protein 4 (RBP4), and adiponectin and analyzed with Discovery Workbench 4.0 software (Meso Scale Diagnostics). Statistical Analysis Statistical analyses were performed in the R computing environment, version 3.1.1 (The R Foundation), including the packages rpart, pROC, gplots, and ggplot2 (2326). Spectral counts were subjected to global normalization (27). Hierarchical clustering was performed on log2 (normalized expression values plus 1) by using the Euclidean distance for sample clustering, the Spearman distance for protein clustering, and complete linkage in both. Heat maps show the Z scores for individual proteins. Univariate differential abundance analysis was performed by using the beta-binomial test (27). Proteins consistently more abundant in CRC than control samples in both series 1 and 2 (BenjaminiHochbergcorrected P value; Q 0.05) constituted input for selecting specific biomarker panels. Biomarker panels were defined by using 2 statistical methods on sample series 2, namely logistic regression (exhaustive search) and classification and regression tree (CART) analysis. Receiver-operating characteristic (ROC) analysis was used to evaluate the performance of protein panels in discriminating between samples with advanced adenoma or CRC and those without colorectal neoplasia. Areas under the curve (AUCs) from ROC curves were compared and evaluated for statistical difference by using the bootstrap method from the pROC package. To assess the statistical significance of the difference in sensitivity between any marker panel and hemoglobin at 95% specificity, the McNemar test was used. Spearman rank correlation, MannWhitney, or KruskalWallis tests were used to assess the relation between protein abundancy and tumor characteristics, such as size, location, stage (CRC), histology (advanced adenoma), and grade of dysplasia (advanced adenoma). Role of the Funding Source The funding sources had no role in the design, conduct, or reporting of the study or in the decision to submit the manuscript for publication. Results Proteomics Analysis of Human Stool Samples A total of 468 human proteins were identified in sample series 1 (Table 1 of Supplement 2 and Figure 2 of Supplement 1). Spectral counts for the alpha and beta chains of hemoglobin, known to be present in equal amounts (28), showed a strong correlation (= 0.95, P < 0.001) (Figure 3A of Supplement 1). Likewise, the S100 calcium binding protein A8 and A9 (S100A8 and S100A9) calprotectin subunits were strongly correlated (= 0.91, P < 0.001) (Figure 3B of Supplement 1). Unsupervised cluster analysis revealed that the protein profile of most CRC stool samples differed from that of control samples (Appendix Figure, A). Because these results confirm the feasibility of quantifying CRC-specific human proteins in stool samples, the analysis was extended to the second, larger series of samples. Appendix Figure. Unsupervised hierarchical cluster analysis of human protein levels in stool samples. Shown are clusters and heat maps for sample series 1 (A) and 2 (B). The bluered color scale of the heat maps depicts protein levels as measured in spectral counts (blue: low; red: high). Green- and red-coded samples in the legend bar represent control and CRC stool samples, respectively. CRC = colorectal cancer. Subsequent analysis of 291 stool samples (Appendix Table) revealed a total of 733 human proteins (Table 2 of Supplement 2 and Figure 2B of Supplement 1) and reidentified 78% of the proteins (367 of 468) from sample series 1. Also in this second sample series, protein levels of hemoglobin alpha and beta, as well as the calprotectin subunits, were highly correlated (= 0.94, P < 0.001, and = 0.8, P < 0.001, respectively) (Figure 3C and 3D of Supplement 1). Again, the CRC stool samples had a protein profile different from that of the control samples (Appendix Figure, B). In sample series 1 and 2, a total of 834 human proteins were detected, 367 (44%) of which were common to both series (Table 3 of Supplement 2 and Figure 2C of Supplement 1). Proteins Discriminating CRC From Control Samples Signals that arise during tumor development are most suitable as biomarkers. Therefore, most cancer screening tests are based on positive signals, so we focused on proteins that were more abundant in the CRC than the control samples. Differential abundance analysis (CRC vs. control samples; fold change >0, P 0.05) yielded 93 and 213 proteins in sample series 1 and 2, respectively, with 55 proteins common to both (Table 3 of Supplement 2). This list of 55 proteins decreased to 29 after correction for multiple testing (that is, Q 0.05) (Table). These 29 proteins included hemoglobin subunits alpha 1, beta, and delta (HBA1, HBB, and HBD). Because population-based screening requires high sensitivity combined with high specificity, specificity was fixed at 95% to evaluate the corresponding sensitivities. At a specificity of 95%, 6 proteinscomplement C3 (C3), A2M, haptoglobin (HP), complement C5 (C5), fibronectin 1 (FN1), and ceruloplasmin (CP)had a statistically significantly higher sensitivity than HBA1 (Figure

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.

Abstract 848: Proteogenomic analysis of alternative splicing: the search for novel biomarkers for colorectal cancer

Introduction Early detection of colorectal cancer (CRC) and its precursor lesions (adenomas) is crucial to reduce mortality rates. The fecal immunochemical test (FIT) is a non-invasive CRC screening test detecting blood-derived protein hemoglobin. However, FIT sensitivity is suboptimal especially in detection of CRC precursor lesions. As adenoma-to-carcinoma progression is accompanied by alternative splicing, tumor-specific proteins derived from alternatively spliced RNA transcripts might serve as candidate biomarkers for CRC detection. Materials and methods RNA and proteins were isolated from CRC cell line SW480 before and after siRNA-mediated down-modulation of the splicing machinery: SF3B1, U2AF1, and SRSF1. To identify splice variants, mRNA was sequenced (Illumina HiSeq) and analyzed. RNA-seq analysis included quality checks (FASTQ, RSeQC), reads mapping (STAR), differential gene expression (DESeq2) and differential expression of splicing isoforms between conditions (MATS). Results from the in silico analysis were validated by qRT-PCR. Proteins were analyzed by in-depth tandem mass spectrometry (QExactive). A proteogenomic data analysis pipeline was established to enrich the sequence database, against which the mass spectra are searched, with the predicted protein splice variants and identify protein isoforms. To further extend the splice-variant database, PacBio sequencing of full-length transcripts is being performed for the SW480 siSF3B1- and control-samples. Results Differential expression analysis on RNA and protein level proved that the knock-down experiments were performed successfully. The RNA-seq analysis revealed hundreds of mRNA splice variants, including positive controls described in literature. For example, down-modulation of SF3B1 resulted in quantitative mRNA changes of spliced isoforms of ADD3 both in RNA-seq and RT-PCR data. The proteomics experiment yielded over 6000 proteins per sample, among which a number of protein isoforms resulting from alternative splicing. Conclusions and discussion We established a proteogenomic pipeline for the analysis of alternative splicing and provided experimental proof of concept. We expect, however, that the true complexity of RNA variant information remains highly underestimated. We therefore are performing PacBio long-read RNA-seq to validate our approach and to identify additional (novel) splicing events. In future studies we will apply the mRNA-seq based proteogenomic pipeline for detection of protein alterations to a series of adenomas at low- and high-risk of progression, and CRCs to validate the isoforms in a clinically relevant setting. Novel findings will be evaluated for their performance as screening markers for CRC. This work was financially supported by KWF project nr: VU 2013-6025. Citation Format: Malgorzata A. Komor, Annemieke C. Hiemstra, Thang V. Pham, Sander R. Piersma, Robert P. Sebra, Bo W. Han, Meredith Ashby, Beatriz Carvalho, Gerrit A. Meijer, Connie R. Jimenez, Remond JA Fijneman. Proteogenomic analysis of alternative splicing: the search for novel biomarkers for colorectal cancer. [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 848.

Abstract 1559: Proteogenomic analysis of alternative splicing in colorectal adenoma-to-carcinoma progression

Background Early diagnosis of colorectal cancer (CRC) and identification of its precursor lesions (adenomas) is crucial in reducing CRC mortality rates. The fecal immunochemical test (FIT) is a non-invasive CRC screening test that detects human protein hemoglobin. Although FIT is beneficial in its current form with a sensitivity of ~65% for detection of CRC and ~27% for adenomas, its performance is still suboptimal and needs to be further improved. Adenoma-to-carcinoma progression is accompanied by alternative splicing, which results in expression of tumor-specific protein variants. These may yield novel biomarkers suitable for improving detection of progressive adenomas and CRCs. Aim We aim to identify novel biomarkers to improve early detection of CRC. Materials and methods RNA was isolated from 3D organoid cultures derived from 5 adenomas and 4 CRC tissues. RNA and proteins were isolated from 18 healthy human colon tissues, 30 adenomas and 30 CRCs. Samples were analyzed by RNA sequencing (Illumina) and in-depth tandem mass spectrometry proteomics (QExactive). For both organoid- and tissue-datasets differential splicing analysis was performed on RNA level to enrich the sequence database, against which mass spectra were searched, with predicted protein isoforms. Results Comparative splicing analysis between CRC and adenoma organoids revealed ~90 differentially spliced genes, yielding candidate biomarkers from epithelial origin. In the tissues, differential splicing analysis between CRCs and controls and between CRCs and adenomas identified over 1000 of splice variants. These include known alternatively spliced genes involved in cancer such as CD44 and VEGFA and a number of candidates overlapping with the isoforms derived from the organoids. Proteomics analysis revealed that approximately 150 of the splice variants were expressed on protein level. Conclusion and Discussion We have confirmed that adenoma-to-carcinoma progression is accompanied by aberrant splicing. Analysis of the organoid cultures allowed us to identify gene isoforms from (neoplastic) epithelial origin. Tissue analysis yielded tumor-specific splice variants that represent novel protein candidate biomarkers for early detection of CRC. The diagnostic performance of these splice variant proteins will be validated in series of stool and FIT samples. Citation Format: Malgorzata A. Komor, Thang V. Pham, Sander R. Piersma, Anne S. Bolijn, Tim Schelfhorst, Pien M. Delis-van Diemen, Marianne Tijssen, Annemieke C. Hiemstra, Meike de Wit, Beatriz Carvalho, Gerrit A. Meijer, Connie R. Jimenez, Remond J. Fijneman. Proteogenomic analysis of alternative splicing in colorectal adenoma-to-carcinoma progression [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 1559. doi:10.1158/1538-7445.AM2017-1559

Abstract 112: Synthesis and preclinical evaluation of radiolabeled alisertib as an investigational aurora kinase A PET tracer

Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA Background: High expression of the aurora kinase A (AURKA) protein is associated with colorectal adenoma-to-carcinoma progression and with poor prognosis of patients with stage III colorectal cancer (CRC) and patients with CRC liver metastases. Several agents have been developed that specifically target AURKA kinase activity, among those the investigational agent alisertib. To identify CRC patients with high AURKA expression levels a dedicated imaging method would be preferred, since only then a whole body assessment of AURKA expression in primary tumors as well as in metastases can be achieved. Positron emission tomography (PET) using a dedicated radiotracer allows for this. To this end, [3H]alisertib has been synthesized for in vitro experiments and [11C]alisertib for in vivo imaging with PET in xenograft mouse models. Methods: [3H]alisertib was synthesized starting from [3H]methyl nosylate under similar conditions as [11C]alisertib, which was synthesized starting from [11C]CH3I in a two-step procedure, purified and formulated within 45 minutes. Four CRC cell lines with different levels of AURKA expression were selected, HCT116, SW480, SW1398 and Caco-2, and the in vitro dynamic uptake of [3H]alisertib in these cell lines was measured before and after siRNA-mediated AURKA downmodulation. Next, the uptake of [11C]alisertib was assessed with PET in xenografted mice using the same four CRC cell lines. Results: The synthesis of [3H]alisertib was optimized to an overall yield of 42%, while an overall yield of 23 ± 4%, starting from [11C]CH3I, was obtained in an optimized synthesis of [11C]alisertib. The CRC cell line with high expression of AURKA, Caco-2, showed a significantly higher uptake of [3H]alisertib compared to the lower expressing AURKA CRC cell lines, HCT116, SW480 and SW1398. In addition, the uptake of [3H]alisertib was reduced in all cell lines upon downmodulation of AURKA. The stability of [11C]alisertib in rodents was determined at 97.8% ± 1.3% intact tracer in blood at 45 minutes after iv injection (n=4). Preliminary data using a HCT116 xenograft mouse model indicated a tumor-to-background ratio of 1.56 ± 0.12, with an uptake of 1.00 %ID/g at 90 minutes after injection. PET studies with the SW480, SW1398 and Caco-2 xenografts are currently in progress. Conclusions: Both [3H]alisertib and [11C]alisertib were obtained with good purity and yield. In vitro studies using [3H]alisertib indicate good correlation of cellular uptake with AURKA expression. [11C]alisertib is stable in vivo and HCT116 xenografted mice show a fair tumor uptake of [11C]alisertib. Citation Format: Joost Verbeek, Jeroen ACM Goos, Albert A. Geldof, Annemieke C. Hiemstra, Otto S. Hoekstra, Gerrit A. Meijer, Steven Stroud, Daniel Bradley, Remond JA Fijneman, Albert D. Windhorst. Synthesis and preclinical evaluation of radiolabeled alisertib as an investigational aurora kinase A PET tracer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 112. doi:10.1158/1538-7445.AM2014-112

Abstract 2853: Epidermal growth factor receptor (EGFR) and prostaglandin-endoperoxide synthase 2 (PTGS2) are prognostic biomarkers for metastatic colorectal cancer

Background: Resection of colorectal cancer liver metastasis (CRCLM) with curative intent is beneficial in approximately 30% of cases, indicating the need for prognostic biomarkers to improve clinical management of CRCLM patients. Protein expression levels of epidermal growth factor receptor (EGFR) and prostaglandin-endoperoxide synthase 2 (PTGS2; also known as cyclooxygenase-2 or COX2) have been associated with carcinogenesis, metastases and survival. EGFR and PTGS2 are targets for molecular drugs and exhibit complex molecular interactions. Aim: We aimed to determine the prognostic value of EGFR and PTGS2 expression in CRCLM of patients who underwent liver resection. Patients and methods: Formalin-fixed paraffin-embedded CRCLM tissue and corresponding primary tumor specimens from a multi-institutional cohort of patients who underwent liver resection between 1990 and 2010 were incorporated into tissue microarrays (TMAs). TMAs were stained for EGFR and PTGS2 by immunohistochemistry and a hazard rate ratio (HRR) for the association between expression in CRCLM and overall survival (OS) was calculated. Results were validated by 500-fold cross-validation. Results: EGFR expression could be evaluated in 323 patients and PTGS2 expression in 351 patients. EGFR expression in CRCLM was associated with poor prognosis in both univariate analysis (average HRR 1.47; P=0.03) and multivariate analysis with standard clinicopathological prognostic variables (average HRR 1.54; P=0.02). PTGS2 expression was also associated with poor prognosis in both univariate (average HRR 1.63; P Conclusion: EGFR and PTGS2 expression are prognostic molecular biomarkers with added value to standard clinicopathological variables for patients with CRCLM. Citation Format: Jeroen A.C.M. Goos, Annemieke C. Hiemstra, Veerle M.H. Coupe, Begona Diosdado, Wendy Kooijman, Pien M. Delis-van Diemen, Cemile Karga, Jeroen A.M. Belien, C. Willemien Menke-van der Houven van Oordt, Albert A. Geldof, Gerrit A. Meijer, Otto S. Hoekstra, Remond J.A. Fijneman. Epidermal growth factor receptor (EGFR) and prostaglandin-endoperoxide synthase 2 (PTGS2) are prognostic biomarkers for metastatic colorectal cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2853. doi:10.1158/1538-7445.AM2014-2853