Functional characterization of the placenta specific protein PLAC1 and its use for cancer immunotherapy

Summary Antibody-based cancer therapies have been successfully introduced into the clinic and have emerged as the most promising therapeutics in oncology. The limiting factor regarding the development of therapeutical antibody vaccines is the identification of tumor-associated antigens. PLAC1, the placenta-specific protein 1, was categorized for the first time by the group of Prof. Sahin as such a tumor-specific antigen. Within this work PLAC1 was characterized using a variety of biochemical methods. The protein expression profile, the cellular localization, the conformational state and especially the interacting partners of PLAC1 and its functionality in cancer were analyzed. Analysis of the protein expression profile of PLAC1 in normal human tissue confirms the published RT-PCR data. Except for placenta no PLAC1 expression was detectable in any other normal human tissue. Beyond, an increased PLAC1 expression was detected in several cancer cell lines derived of trophoblastic, breast and pancreatic lineage emphasizing its properties as tumor-specific antigen. rnThe cellular localization of PLAC1 revealed that PLAC1 contains a functional signal peptide which conducts the propeptide to the endoplasmic reticulum (ER) and results in the secretion of PLAC1 by the secretory pathway. Although PLAC1 did not exhibit a distinct transmembrane domain, no unbound protein was detectable in the cell culture supernatant of overexpressing cells. But by selective isolation of different cellular compartments PLAC1 was clearly enriched within the membrane fraction. Using size exclusion chromatography PLAC1 was characterized as a highly aggregating protein that forms a network of high molecular multimers, consisting of a mixture of non-covalent as well as covalent interactions. Those interactions were formed by PLAC1 with itself and probably other cellular components and proteins. Consequently, PLAC1 localize outside the cell, where it is associated to the membrane forming a stable extracellular coat-like structure.rnThe first mechanistic hint how PLAC1 promote cancer cell proliferation was achieved identifying the fibroblast growth factor FGF7 as a specific interacting partner of PLAC1. Moreover, it was clearly shown that PLAC1 as well as FGF7 bind to heparin, a glycosaminoglycan of the ECM that is also involved in FGF-signaling. The participation of PLAC1 within this pathway was approved after co-localizing PLAC1, FGF7 and the FGF7 specific receptor (FGFR2IIIb) and identifying the formation of a trimeric complex (PLAC1, FGF7 and the specific receptor FGFR2IIIb). Especially this trimeric complex revealed the role of PLAC1. Binding of PLAC1 together with FGF7 leads to the activation of the intracellular tyrosine kinase of the FGFR2IIIb-receptor and mediate the direct phosphorylation of the AKT-kinase. In the absence of PLAC1, no FGF7 mediated phosphorylation of AKT was observed. Consequently the function of PLAC1 was clarified: PLAC1 acts as a co-factor by stimulating proliferation by of the FGF7-FGFR2 signaling pathway.rnAll together, these novel biochemical findings underline that the placenta specific protein PLAC1 could be a new target for cancer immunotherapy, especially considering its potential applicability for antibody therapy in tumor patients.

Matrix metalloproteinases and angiogenic factors: predictors of survival after radical prostatectomy for clinically organ-confined prostate cancer?

The aim of the present study was to investigate whether biomarkers improve the prediction of recurrence-free, disease-specific, and overall survival in patients with clinically localized prostate cancer. A tissue microarray was constructed from prostate specimens of 278 patients who underwent open radical retropubic prostatectomy for clinically localized prostate cancer. For immunohistochemical studies, antibodies were used against matrix metalloproteinase (MMP)-2, MMP-3, MMP-7, MMP-9, MMP-13, and MMP-19, as well as against vascular endothelial growth factor, hypoxia-induced factor 1 , basic fibroblast growth factor, and cluster of differentiation 31. Univariate and multivariable analyses were performed to evaluate the potential predictors of overall, disease-specific, and recurrence-free survival. In univariate analysis of patients with clinically organ-confined prostate cancer, only higher expression levels of MMP-9 (hazard ratio [0.6], 95% CI 0.45-0.8) had a protective effect in terms of overall survival. This positive effect of high MMP-9 expression was also observed for recurrence-free (HR 0.88, 95% CI 0.78-0.99) and disease-specific survival (HR 0.5, 95% CI 0.36-0.73). In multivariable analysis, none of these potential markers was found to be an independent prognostic factor of survival. Of all MMPs and angiogenic factors tested, MMP-9 expression has the potential as a prognostic marker in patients undergoing radical prostatectomy for clinically organ-confined cases of prostate cancer.

Body mass index, hormone replacement therapy, and endometrial cancer risk: a meta-analysis

Background: Body mass index (BMI) is a risk factor for endometrial cancer. We quantified the risk and investigated whether the association differed by use of hormone replacement therapy (HRT), menopausal status, and histologic type. Methods: We searched MEDLINE and EMBASE (1966 to December 2009) to identify prospective studies of BMI and incident endometrial cancer. We did random-effects meta-analyses, meta-regressions, and generalized least square regressions for trend estimations assuming linear, and piecewise linear, relationships. Results: Twenty-four studies (17,710 cases) were analyzed; 9 studies contributed to analyses by HRT, menopausal status, or histologic type, all published since 2003. In the linear model, the overall risk ratio (RR) per 5 kg/m2 increase in BMI was 1.60 (95% CI, 1.52–1.68), P < 0.0001. In the piecewise model, RRs compared with a normal BMI were 1.22 (1.19–1.24), 2.09 (1.94–2.26), 4.36 (3.75–5.10), and 9.11 (7.26–11.51) for BMIs of 27, 32, 37, and 42 kg/m2, respectively. The association was stronger in never HRT users than in ever users: RRs were 1.90 (1.57–2.31) and 1.18 (95% CI, 1.06–1.31) with P for interaction ¼ 0.003. In the piecewise model, the RR in never users was 20.70 (8.28–51.84) at BMI 42 kg/m2, compared with never users at normal BMI. The association was not affected by menopausal status (P ¼ 0.34) or histologic type (P ¼ 0.26). Conclusions: HRT use modifies the BMI-endometrial cancer risk association. Impact: These findings support the hypothesis that hyperestrogenia is an important mechanism underlying the BMI-endometrial cancer association, whilst the presence of residual risk in HRT users points to the role of additional systems. Cancer Epidemiol Biomarkers Prev; 19(12); 3119–30.

Potential biomarkers for hepatoblastoma: Results from the SIOPEL-3 study

Hepatoblastoma (HB) is a rare malignant liver tumour found in infants. Many heterogenous histological tumour subtypes exist. Although survival rates have improved dramatically in recent years with the use of platinum-based chemotherapy, there still exists a subset of HB that does not respond to treatment. There are currently no tumour biomarkers in use and in this study we aim to evaluate potential biomarkers to aid identification of relapse cases that would otherwise be overlooked by current prognostication. This may identify patients that would benefit from more aggressive therapy and could improve overall survival rates. We used immunohistochemistry to analyse the expression of β-catenin, E-cadherin, Cyclin D1, Ki-67 and alpha-fetoprotein (AFP) protein in tumours from 91 patients prospectively enroled into the SIOPEL 3 clinical trial. The relationship between these biomarkers and clinicopathologic features and patient survival were statistically analysed. We identified one biomarker, Cyclin D1, which has a correlation with mixed epithelial/mesenchymal HB approaching significance (P=0.07). Survival analysis using these markers has revealed two potential prognostic indicators; Cyclin D1 and Ki-67 (P=0.01, 0.01).

Serum amino acid profiles and their alterations in colorectal cancer

Mass spectrometry-based serum metabolic profiling is a promising tool to analyse complex cancer associated metabolic alterations, which may broaden our pathophysiological understanding of the disease and may function as a source of new cancer-associated biomarkers. Highly standardized serum samples of patients suffering from colon cancer (n = 59) and controls (n = 58) were collected at the University Hospital Leipzig. We based our investigations on amino acid screening profiles using electrospray tandem-mass spectrometry. Metabolic profiles were evaluated using the Analyst 1.4.2 software. General, comparative and equivalence statistics were performed by R 2.12.2. 11 out of 26 serum amino acid concentrations were significantly different between colorectal cancer patients and healthy controls. We found a model including CEA, glycine, and tyrosine as best discriminating and superior to CEA alone with an AUROC of 0.878 (95% CI 0.815-0.941). Our serum metabolic profiling in colon cancer revealed multiple significant disease-associated alterations in the amino acid profile with promising diagnostic power. Further large-scale studies are necessary to elucidate the potential of our model also to discriminate between cancer and potential differential diagnoses. In conclusion, serum glycine and tyrosine in combination with CEA are superior to CEA for the discrimination between colorectal cancer patients and controls.

Tumour budding: a promising parameter in colorectal cancer

In 2011, the Tumour Node Metastasis (TNM) staging system still remains the gold standard for stratifying colorectal cancer (CRC) patients into prognostic subgroups, and is considered a solid basis for treatment management. Nevertheless, there is still a challenge with regard to therapeutic strategy; stage II patients are not typically selected for postoperative adjuvant chemotherapy, although some stage II patients have a comparable outcome to stage III patients who, themselves do receive such treatment. Consequently, there has been an inundation of 'prognostic biomarker' studies aiming to improve the prognostic stratification power of the TNM staging system. Most proposed biomarkers are not implemented because of lack of reproducibility, validation and standardisation. This problem can be partially resolved by following the REMARK guidelines. In search of novel prognostic factors for patients with CRC, one might glance at a table in the book entitled 'Prognostic Factors in Cancer' published by the International Union against Cancer (UICC) in 2006, in which TNM stage, L and V classifications are considered 'essential' prognostic factors, whereas tumour grade, perineural invasion, tumour budding and tumour-border configuration among others are proposed as 'additional' prognostic factors. Histopathology reports normally include the 'essential' features and are accompanied by tumour grade, histological subtype and information on perineural invasion, but interestingly, the tumour-border configuration (i.e., growth pattern) and especially tumour budding are rarely reported. Although scoring systems such as the 'BRE' in breast and 'Gleason' in prostate cancer are solidly based on histomorphological features and used in daily practice, no such additional scoring system to complement TNM staging is available for CRC. Regardless of differences in study design and methods for tumour-budding assessment, the prognostic power of tumour budding has been confirmed by dozens of study groups worldwide, suggesting that tumour budding may be a valuable candidate for inclusion into a future prognostic scoring system for CRC. This mini-review therefore attempts to present a short and concise overview on tumour budding, including morphological, molecular and prognostic aspects underlining its inter-disciplinary relevance.

Cancer classification using the Immunoscore: a worldwide task force

Prediction of clinical outcome in cancer is usually achieved by histopathological evaluation of tissue samples obtained during surgical resection of the primary tumor. Traditional tumor staging (AJCC/UICC-TNM classification) summarizes data on tumor burden (T), presence of cancer cells in draining and regional lymph nodes (N) and evidence for metastases (M). However, it is now recognized that clinical outcome can significantly vary among patients within the same stage. The current classification provides limited prognostic information, and does not predict response to therapy. Recent literature has alluded to the importance of the host immune system in controlling tumor progression. Thus, evidence supports the notion to include immunological biomarkers, implemented as a tool for the prediction of prognosis and response to therapy. Accumulating data, collected from large cohorts of human cancers, has demonstrated the impact of immune-classification, which has a prognostic value that may add to the significance of the AJCC/UICC TNM-classification. It is therefore imperative to begin to incorporate the 'Immunoscore' into traditional classification, thus providing an essential prognostic and potentially predictive tool. Introduction of this parameter as a biomarker to classify cancers, as part of routine diagnostic and prognostic assessment of tumors, will facilitate clinical decision-making including rational stratification of patient treatment. Equally, the inherent complexity of quantitative immunohistochemistry, in conjunction with protocol variation across laboratories, analysis of different immune cell types, inconsistent region selection criteria, and variable ways to quantify immune infiltration, all underline the urgent requirement to reach assay harmonization. In an effort to promote the Immunoscore in routine clinical settings, an international task force was initiated. This review represents a follow-up of the announcement of this initiative, and of the J Transl Med. editorial from January 2012. Immunophenotyping of tumors may provide crucial novel prognostic information. The results of this international validation may result in the implementation of the Immunoscore as a new component for the classification of cancer, designated TNM-I (TNM-Immune).

Plasma biomarkers as predictors of outcome in patients with advanced hepatocellular carcinoma

Validated biomarkers of prognosis and response to drug have not been identified for patients with hepatocellular carcinoma (HCC). One of the objectives of the phase III, randomized, controlled Sorafenib HCC Assessment Randomized Protocol (SHARP) trial was to explore the ability of plasma biomarkers to predict prognosis and therapeutic efficacy.

Pancreatic carcinoma, pancreatitis, and healthy controls: metabolite models in a three-class diagnostic dilemma

Metabolomics as one of the most rapidly growing technologies in the "-omics" field denotes the comprehensive analysis of low molecular-weight compounds and their pathways. Cancer-specific alterations of the metabolome can be detected by high-throughput mass-spectrometric metabolite profiling and serve as a considerable source of new markers for the early differentiation of malignant diseases as well as their distinction from benign states. However, a comprehensive framework for the statistical evaluation of marker panels in a multi-class setting has not yet been established. We collected serum samples of 40 pancreatic carcinoma patients, 40 controls, and 23 pancreatitis patients according to standard protocols and generated amino acid profiles by routine mass-spectrometry. In an intrinsic three-class bioinformatic approach we compared these profiles, evaluated their selectivity and computed multi-marker panels combined with the conventional tumor marker CA 19-9. Additionally, we tested for non-inferiority and superiority to determine the diagnostic surplus value of our multi-metabolite marker panels. Compared to CA 19-9 alone, the combined amino acid-based metabolite panel had a superior selectivity for the discrimination of healthy controls, pancreatitis, and pancreatic carcinoma patients [Formula: see text] We combined highly standardized samples, a three-class study design, a high-throughput mass-spectrometric technique, and a comprehensive bioinformatic framework to identify metabolite panels selective for all three groups in a single approach. Our results suggest that metabolomic profiling necessitates appropriate evaluation strategies and-despite all its current limitations-can deliver marker panels with high selectivity even in multi-class settings.

Classification and selection of biomarkers in genomic data using LASSO

High-throughput gene expression technologies such as microarrays have been utilized in a variety of scientific applications. Most of the work has been on assessing univariate associations between gene expression with clinical outcome (variable selection) or on developing classification procedures with gene expression data (supervised learning). We consider a hybrid variable selection/classification approach that is based on linear combinations of the gene expression profiles that maximize an accuracy measure summarized using the receiver operating characteristic curve. Under a specific probability model, this leads to consideration of linear discriminant functions. We incorporate an automated variable selection approach using LASSO. An equivalence between LASSO estimation with support vector machines allows for model fitting using standard software. We apply the proposed method to simulated data as well as data from a recently published prostate cancer study.

A Data-Analytic Strategy for Protein-Biomarker Discovery: Profiling of High-Dimensional Proteomic Data for Cancer Detection

With recent advances in mass spectrometry techniques, it is now possible to investigate proteins over a wide range of molecular weights in small biological specimens. This advance has generated data-analytic challenges in proteomics, similar to those created by microarray technologies in genetics, namely, discovery of "signature" protein profiles specific to each pathologic state (e.g., normal vs. cancer) or differential profiles between experimental conditions (e.g., treated by a drug of interest vs. untreated) from high-dimensional data. We propose a data analytic strategy for discovering protein biomarkers based on such high-dimensional mass-spectrometry data. A real biomarker-discovery project on prostate cancer is taken as a concrete example throughout the paper: the project aims to identify proteins in serum that distinguish cancer, benign hyperplasia, and normal states of prostate using the Surface Enhanced Laser Desorption/Ionization (SELDI) technology, a recently developed mass spectrometry technique. Our data analytic strategy takes properties of the SELDI mass-spectrometer into account: the SELDI output of a specimen contains about 48,000 (x, y) points where x is the protein mass divided by the number of charges introduced by ionization and y is the protein intensity of the corresponding mass per charge value, x, in that specimen. Given high coefficients of variation and other characteristics of protein intensity measures (y values), we reduce the measures of protein intensities to a set of binary variables that indicate peaks in the y-axis direction in the nearest neighborhoods of each mass per charge point in the x-axis direction. We then account for a shifting (measurement error) problem of the x-axis in SELDI output. After these pre-analysis processing of data, we combine the binary predictors to generate classification rules for cancer, benign hyperplasia, and normal states of prostate. Our approach is to apply the boosting algorithm to select binary predictors and construct a summary classifier. We empirically evaluate sensitivity and specificity of the resulting summary classifiers with a test dataset that is independent from the training dataset used to construct the summary classifiers. The proposed method performed nearly perfectly in distinguishing cancer and benign hyperplasia from normal. In the classification of cancer vs. benign hyperplasia, however, an appreciable proportion of the benign specimens were classified incorrectly as cancer. We discuss practical issues associated with our proposed approach to the analysis of SELDI output and its application in cancer biomarker discovery.

CA15-3 and alkaline phosphatase as predictors for breast cancer recurrence: a combined analysis of seven International Breast Cancer Study Group trials

BACKGROUND: We evaluated the ability of CA15-3 and alkaline phosphatase (ALP) to predict breast cancer recurrence. PATIENTS AND METHODS: Data from seven International Breast Cancer Study Group trials were combined. The primary end point was relapse-free survival (RFS) (time from randomization to first breast cancer recurrence), and analyses included 3953 patients with one or more CA15-3 and ALP measurement during their RFS period. CA15-3 was considered abnormal if >30 U/ml or >50% higher than the first value recorded; ALP was recorded as normal, abnormal, or equivocal. Cox proportional hazards models with a time-varying indicator for abnormal CA15-3 and/or ALP were utilized. RESULTS: Overall, 784 patients (20%) had a recurrence, before which 274 (35%) had one or more abnormal CA15-3 and 35 (4%) had one or more abnormal ALP. Risk of recurrence increased by 30% for patients with abnormal CA15-3 [hazard ratio (HR) = 1.30; P = 0.0005], and by 4% for those with abnormal ALP (HR = 1.04; P = 0.82). Recurrence risk was greatest for patients with either (HR = 2.40; P < 0.0001) and with both (HR = 4.69; P < 0.0001) biomarkers abnormal. ALP better predicted liver recurrence. CONCLUSIONS: CA15-3 was better able to predict breast cancer recurrence than ALP, but use of both biomarkers together provided a better early indicator of recurrence. Whether routine use of these biomarkers improves overall survival remains an open question.

Body-mass index and incidence of cancer: a systematic review and meta-analysis of prospective observational studies

BACKGROUND: Excess bodyweight, expressed as increased body-mass index (BMI), is associated with the risk of some common adult cancers. We did a systematic review and meta-analysis to assess the strength of associations between BMI and different sites of cancer and to investigate differences in these associations between sex and ethnic groups. METHODS: We did electronic searches on Medline and Embase (1966 to November 2007), and searched reports to identify prospective studies of incident cases of 20 cancer types. We did random-effects meta-analyses and meta-regressions of study-specific incremental estimates to determine the risk of cancer associated with a 5 kg/m2 increase in BMI. FINDINGS: We analysed 221 datasets (141 articles), including 282,137 incident cases. In men, a 5 kg/m2 increase in BMI was strongly associated with oesophageal adenocarcinoma (RR 1.52, p<0.0001) and with thyroid (1.33, p=0.02), colon (1.24, p<0.0001), and renal (1.24, p <0.0001) cancers. In women, we recorded strong associations between a 5 kg/m2 increase in BMI and endometrial (1.59, p<0.0001), gallbladder (1.59, p=0.04), oesophageal adenocarcinoma (1.51, p<0.0001), and renal (1.34, p<0.0001) cancers. We noted weaker positive associations (RR <1.20) between increased BMI and rectal cancer and malignant melanoma in men; postmenopausal breast, pancreatic, thyroid, and colon cancers in women; and leukaemia, multiple myeloma, and non-Hodgkin lymphoma in both sexes. Associations were stronger in men than in women for colon (p<0.0001) cancer. Associations were generally similar in studies from North America, Europe and Australia, and the Asia-Pacific region, but we recorded stronger associations in Asia-Pacific populations between increased BMI and premenopausal (p=0.009) and postmenopausal (p=0.06) breast cancers. INTERPRETATION: Increased BMI is associated with increased risk of common and less common malignancies. For some cancer types, associations differ between sexes and populations of different ethnic origins. These epidemiological observations should inform the exploration of biological mechanisms that link obesity with cancer.

Wild-type and splice-variant secretin receptors in lung cancer: overexpression in carcinoid tumors and peritumoral lung tissue

Gastrointestinal peptide hormone receptors, like somatostatin receptors, are often overexpressed in human cancer, allowing receptor-targeted tumor imaging and therapy. A novel candidate for these applications is the secretin receptor recently identified in pancreatic and cholangiocellular carcinomas. In the present study, secretin receptors were assessed in a non-gastrointestinal tissue, the human lung. Non-small-cell lung cancers (n=26), small-cell lung cancers (n=10), bronchopulmonary carcinoid tumors (n=29), and non-neoplastic lung (n=46) were investigated for secretin receptor protein expression with in vitro receptor autoradiography, using (125)I-[Tyr(10)] rat secretin and for secretin receptor transcripts with RT-PCR. Secretin receptor protein expression was found in 62% of bronchopulmonary carcinoids in moderate to high density, in 12% of non-small cell lung cancers in low density, but not in small cell lung cancers. In tumors found to be secretin receptor positive by autoradiography, RT-PCR revealed transcripts for the wild-type secretin receptor and for novel secretin receptor splice variants. In the non-neoplastic lung, secretin receptor protein expression was observed in low density along the alveolar septa in direct tumor vicinity in cases of acute inflammation, but not in histologically normal lung. In the autoradiographically positive peritumoral lung, RT-PCR showed transcripts for the wild-type secretin receptor and for a secretin receptor spliceoform different from those occurring in lung and gut tumors. In conclusion, secretin receptors are new markers for bronchopulmonary carcinoid tumors, and represent the molecular basis for an in vivo targeting of carcinoid tumors for diagnosis and therapy. Furthermore, secretin receptors may play a role in peritumoral lung pathophysiology. Secretin receptor mis-splicing specifically occurs in tumor and non-tumor lung pathology.

Alternative splicing of pre-mRNA in cancer: focus on G protein-coupled peptide hormone receptors

Through alternative splicing, multiple different transcripts can be generated from a single gene. Alternative splicing represents an important molecular mechanism of gene regulation in physiological processes such as developmental programming as well as in disease. In cancer, splicing is significantly altered. Tumors express a different collection of alternative spliceoforms than normal tissues. Many tumor-associated splice variants arise from genes with an established role in carcinogenesis or tumor progression, and their functions can be oncogenic. This raises the possibility that products of alternative splicing play a pathogenic role in cancer. Moreover, cancer-associated spliceoforms represent potential diagnostic biomarkers and therapeutic targets. G protein-coupled peptide hormone receptors provide a good illustration of alternative splicing in cancer. The wild-type forms of these receptors have long been known to be expressed in cancer and to modulate tumor cell functions. They are also recognized as attractive clinical targets. Recently, splice variants of these receptors have been increasingly identified in various types of cancer. In particular, alternative cholecystokinin type 2, secretin, and growth hormone-releasing hormone receptor spliceoforms are expressed in tumors. Peptide hormone receptor splice variants can fundamentally differ from their wild-type receptor counterparts in pharmacological and functional characteristics, in their distribution in normal and malignant tissues, and in their potential use for clinical applications.