Immunofluorescence and fluorescent-protein tagging show high correlation for protein localization in mammalian cells.

Imaging techniques such as immunofluorescence (IF) and the expression of fluorescent protein (FP) fusions are widely used to investigate the subcellular distribution of proteins. Here we report a systematic analysis of >500 human proteins comparing the localizations obtained in live versus fixed cells using FPs and IF, respectively. We identify systematic discrepancies between IF and FPs as well as between FP tagging at the N and C termini. The analysis shows that for 80% of the proteins, IF and FPs yield the same subcellular distribution, and the locations of 250 previously unlocalized proteins were determined by the overlap between the two methods. Approximately 60% of proteins localize to multiple organelles for both methods, indicating a complex subcellular protein organization. These results show that both IF and FP tagging are reliable techniques and demonstrate the usefulness of an integrative approach for a complete investigation of the subcellular human proteome.

Majority of differentially expressed genes are down-regulated during malignant transformation in a four-stage model.

The transformation of normal cells to malignant, metastatic tumor cells is a multistep process caused by the sequential acquirement of genetic changes. To identify these changes, we compared the transcriptomes and levels and distribution of proteins in a four-stage cell model of isogenically matched normal, immortalized, transformed, and metastatic human cells, using deep transcriptome sequencing and immunofluorescence microscopy. The data show that ∼6% (n = 1,357) of the human protein-coding genes are differentially expressed across the stages in the model. Interestingly, the majority of these genes are down-regulated, linking malignant transformation to dedifferentiation. The up-regulated genes are mainly components that control cellular proliferation, whereas the down-regulated genes consist of proteins exposed on or secreted from the cell surface. As many of the identified gene products control basic cellular functions that are defective in cancers, the data provide candidates for follow-up studies to investigate their functional roles in tumor formation. When we further compared the expression levels of four of the identified proteins in clinical cancer cohorts, similar differences were observed between benign and cancer cells, as in the cell model. This shows that this comprehensive demonstration of the molecular changes underlying malignant transformation is a relevant model to study the process of tumor formation.

Tumour islet Foxp3+ T-cell infiltration predicts poor outcome in nonsmall cell lung cancer.

The impact of host immunity on outcome in nonsmall cell lung cancer (NSCLC) is controversial. We examined the relationship between lymphoid infiltration patterns in NSCLC and prognosis.Tumour- and stroma-infiltrating CD3(+), CD8(+) and forkhead box P3 (Foxp3)(+) T-lymphocytes were identified using immunohistochemistry and a novel image analysis algorithm to assess total, cytotoxic and regulatory T-lymphocyte counts, respectively, in 196 NSCLC cases. The median cell count was selected as a cut-point to define patient subgroups and the ratio of the corresponding tumour islet:stroma (TI/S) counts was determined.There was a positive association between overall survival and increased CD8(+) TI/S ratio (hazard ratio (HR) for death 0.44, p<0.001) but an inverse relationship between Foxp3(+) TI/S ratio and overall survival (HR 4.86, p<0.001). Patients with high CD8(+) islet (HR 0.48, p<0.001) and Foxp3(+) stromal (HR 0.23, p<0.001) counts had better survival, whereas high CD3(+) and CD8(+) stromal counts and high Foxp3(+) islet infiltration conferred a worse survival (HR 1.55, 2.19 and 3.14, respectively). By multivariate analysis, a high CD8(+) TI/S ratio conferred an improved survival (HR 0.48, p=0.002) but a high Foxp3(+) TI/S ratio was associated with worse survival (HR 3.91, p<0.001).Microlocalisation of infiltrating T-lymphocytes is a powerful predictor of outcome in resected NSCLC.

Systematic antibody generation and validation via tissue microarray technology leading to identification of a novel protein prognostic panel in breast cancer.

BACKGROUND: Although omic-based discovery approaches can provide powerful tools for biomarker identification, several reservations have been raised regarding the clinical applicability of gene expression studies, such as their prohibitive cost. However, the limited availability of antibodies is a key barrier to the development of a lower cost alternative, namely a discrete collection of immunohistochemistry (IHC)-based biomarkers. The aim of this study was to use a systematic approach to generate and screen affinity-purified, mono-specific antibodies targeting progression-related biomarkers, with a view towards developing a clinically applicable IHC-based prognostic biomarker panel for breast cancer. METHODS: We examined both in-house and publicly available breast cancer DNA microarray datasets relating to invasion and metastasis, thus identifying a cohort of candidate progression-associated biomarkers. Of these, 18 antibodies were released for extended analysis. Validated antibodies were screened against a tissue microarray (TMA) constructed from a cohort of consecutive breast cancer cases (n = 512) to test the immunohistochemical surrogate signature. RESULTS: Antibody screening revealed 3 candidate prognostic markers: the cell cycle regulator, Anillin (ANLN); the mitogen-activated protein kinase, PDZ-Binding Kinase (PBK); and the estrogen response gene, PDZ-Domain Containing 1 (PDZK1). Increased expression of ANLN and PBK was associated with poor prognosis, whilst increased expression of PDZK1 was associated with good prognosis. A 3-marker signature comprised of high PBK, high ANLN and low PDZK1 expression was associated with decreased recurrence-free survival (p < 0.001) and breast cancer-specific survival (BCSS) (p < 0.001). This novel signature was associated with high tumour grade (p < 0.001), positive nodal status (p = 0.029), ER-negativity (p = 0.006), Her2-positivity (p = 0.036) and high Ki67 status (p < 0.001). However, multivariate Cox regression demonstrated that the signature was not a significant predictor of BCSS (HR = 6.38; 95% CI = 0.79-51.26, p = 0.082). CONCLUSIONS: We have developed a comprehensive biomarker pathway that extends from discovery through to validation on a TMA platform. This proof-of-concept study has resulted in the identification of a novel 3-protein prognostic panel. Additional biochemical markers, interrogated using this high-throughput platform, may further augment the prognostic accuracy of this panel to a point that may allow implementation into routine clinical practice.

Evaluation of the prognostic significance of MSMB and CRISP3 in prostate cancer using automated image analysis.

Despite prostate cancer being the most frequent cancer in men in the Western world, tissue biomarkers for predicting disease recurrence after surgery have not been incorporated into clinical practice. Our group has previously identified ß-microseminoprotein (MSMB) and cysteine-rich secretory protein-3 (CRISP3) as independent predictors of biochemical recurrence after radical prostatectomy. The purpose of the present study was to use automated image analysis, enabling quantitative determination of MSMB and CRISP3 expressions in a large cohort and to validate the previous findings. MSMB and CRISP3 protein expressions were assessed on tissue microarrays constructed from 3268 radical prostatectomy specimens. Whole-slide digital images were captured, and a novel cytoplasmic algorithm was used to develop a quantitative scoring model for cytoplasmic staining. Classification regression tree analysis was used to group patients, with different risk for biochemical recurrence, depending on level of protein expression. Patients with tumors expressing high levels of MSMB had a significantly reduced risk for biochemical recurrence after radical prostatectomy (HR=0.468; 95% CI 0.394-0.556; P<0.001). Multivariate analysis adjusted for clinicopathological parameters revealed that MSMB expression was an independent predictor of decreased risk of recurrence (HR=0.710; 95% CI 0.578-0.872; P<0.001). We found no correlation between CRISP3 expression and biochemical recurrence. In this current study, we applied a novel image analysis on a large independent cohort and successfully verified that MSMB is a strong independent factor, predicting favorable outcome after radical prostatectomy for localized prostate cancer.

The tumour suppressor SOX11 is associated with improved survival among high grade epithelial ovarian cancers and is regulated by reversible promoter methylation.

BACKGROUND: The neural transcription factor SOX11 has been described as a prognostic marker in epithelial ovarian cancers (EOC), however its role in individual histological subtypes and tumour grade requires further clarification. Furthermore, methylation-dependent silencing of SOX11 has been reported for B cell lymphomas and indicates that epigenetic drugs may be used to re-express this tumour suppressor, but information on SOX11 promoter methylation in EOC is still lacking. METHODS: SOX11 expression and clinicopathological data was compared using χ² test in a cohort of 154 cases of primary invasive EOC. Kaplan-Meier analysis and the log rank test were applied to evaluate ovarian cancer-specific survival (OCSS) and overall survival (OS) in strata, according to SOX11 expression. Also, the methylation status of the SOX11 promoter was determined by sodium bisulfite sequencing and methylation specific PCR (MSP). Furthermore, the effect of ectopic overexpression of SOX11 on proliferation was studied through [3H]-thymidine incorporation. RESULTS: SOX11 expression was associated with an improved survival of patients with high grade EOC, although not independent of stage. Further analyses of EOC cell lines showed that SOX11 mRNA and protein were expressed in two of five cell lines, correlating with promoter methylation status. Demethylation was successfully performed using 5'-Aza-2'deoxycytidine (5-Aza-dC) resulting in SOX11 mRNA and protein expression in a previously negative EOC cell line. Furthermore, overexpression of SOX11 in EOC cell lines confirmed the growth regulatory role of SOX11. CONCLUSIONS: SOX11 is a functionally associated protein in EOC with prognostic value for high-grade tumours. Re-expression of SOX11 in EOC indicates a potential use of epigenetic drugs to affect cellular growth in SOX11-negative tumours.

Identification of a myometrial molecular profile for dystocic labor.

BACKGROUND: The most common indication for cesarean section (CS) in nulliparous women is dystocia secondary to ineffective myometrial contractility. The aim of this study was to identify a molecular profile in myometrium associated with dystocic labor. METHODS: Myometrial biopsies were obtained from the upper incisional margins of nulliparous women undergoing lower segment CS for dystocia (n = 4) and control women undergoing CS in the second stage who had demonstrated efficient uterine action during the first stage of labor (n = 4). All patients were in spontaneous (non-induced) labor and had received intrapartum oxytocin to accelerate labor. RNA was extracted from biopsies and hybridized to Affymetrix HuGene U133A Plus 2 microarrays. Internal validation was performed using quantitative SYBR Green Real-Time PCR. RESULTS: Seventy genes were differentially expressed between the two groups. 58 genes were down-regulated in the dystocia group. Gene ontology analysis revealed 12 of the 58 down-regulated genes were involved in the immune response. These included (ERAP2, (8.67 fold change (FC)) HLA-DQB1 (7.88 FC) CD28 (2.60 FC), LILRA3 (2.87 FC) and TGFBR3 (2.1 FC)) Hierarchical clustering demonstrated a difference in global gene expression patterns between the samples from dystocic and non-dystocic labours. RT-PCR validation was performed on 4 genes ERAP2, CD28, LILRA3 and TGFBR3 CONCLUSION: These findings suggest an underlying molecular basis for dystocia in nulliparous women in spontaneous labor. Differentially expressed genes suggest an important role for the immune response in dystocic labor and may provide important indicators for new diagnostic assays and potential intrapartum therapeutic targets.

Tumour-specific HMG-CoAR is an independent predictor of recurrence free survival in epithelial ovarian cancer.

BACKGROUND: Our group previously reported that tumour-specific expression of the rate-limiting enzyme in the mevalonate pathway, 3-hydroxy-3-methylglutharyl-coenzyme A reductase (HMG-CoAR) is associated with more favourable tumour parameters and a good prognosis in breast cancer. In the present study, the prognostic value of HMG-CoAR expression was examined in tumours from a cohort of patients with primary epithelial ovarian cancer. METHODS: HMG-CoAR expression was assessed using immunohistochemistry (IHC) on tissue microarrays (TMA) consisting of 76 ovarian cancer cases, analysed using automated algorithms to develop a quantitative scoring model. Kaplan Meier analysis and Cox proportional hazards modelling were used to estimate the risk of recurrence free survival (RFS). RESULTS: Seventy-two tumours were suitable for analysis. Cytoplasmic HMG-CoAR expression was present in 65% (n = 46) of tumours. No relationship was seen between HMG-CoAR and age, histological subtype, grade, disease stage, estrogen receptor or Ki-67 status. Patients with tumours expressing HMG-CoAR had a significantly prolonged RFS (p = 0.012). Multivariate Cox regression analysis revealed that HMG-CoAR expression was an independent predictor of improved RFS (RR = 0.49, 95% CI (0.25-0.93); p = 0.03) when adjusted for established prognostic factors such as residual disease, tumour stage and grade. CONCLUSION: HMG-CoAR expression is an independent predictor of prolonged RFS in primary ovarian cancer. As HMG-CoAR inhibitors, also known as statins, have demonstrated anti-neoplastic effects in vitro, further studies are required to evaluate HMG-CoAR expression as a surrogate marker of response to statin treatment, especially in conjunction with current chemotherapeutic regimens.

Validation of cytoplasmic-to-nuclear ratio of survivin as an indicator of improved prognosis in breast cancer.

BACKGROUND: Conflicting data exist regarding the prognostic and predictive impact of survivin (BIRC5) in breast cancer. We previously reported survivin cytoplasmic-to-nuclear ratio (CNR) as an independent prognostic indicator in breast cancer. Here, we validate survivin CNR in a separate and extended cohort. Furthermore, we present new data suggesting that a low CNR may predict outcome in tamoxifen-treated patients. METHODS: Survin expression was assessed using immunhistochemistry on a breast cancer tissue microarray (TMA) containing 512 tumours. Whole slide digital images were captured using an Aperio XT scanner. Automated image analysis was used to identify tumour from stroma and then to quantify tumour-specific nuclear and cytoplasmic survivin. A decision tree model selected using a 10-fold cross-validation approach was used to identify prognostic subgroups based on nuclear and cytoplasmic survivin expression. RESULTS: Following optimisation of the staining procedure, it was possible to evaluate survivin protein expression in 70.1% (n = 359) of the 512 tumours represented on the TMA. Decision tree analysis predicted that nuclear, as opposed to cytoplasmic, survivin was the most important determinant of overall survival (OS) and breast cancer-specific survival (BCSS). The decision tree model confirmed CNR of 5 as the optimum threshold for survival analysis. Univariate analysis demonstrated an association between a high CNR (>5) and a prolonged BCSS (HR 0.49, 95% CI 0.29-0.81, p = 0.006). Multivariate analysis revealed a high CNR (>5) was an independent predictor of BCSS (HR 0.47, 95% CI 0.27-0.82, p = 0.008). An increased CNR was associated with ER positive (p = 0.045), low grade (p = 0.007), Ki-67 (p = 0.001) and Her2 (p = 0.026) negative tumours. Finally, a high CNR was an independent predictor of OS in tamoxifen-treated ER-positive patients (HR 0.44, 95% CI 0.23-0.87, p = 0.018). CONCLUSION: Using the same threshold as our previous study, we have validated survivin CNR as a marker of good prognosis in breast cancer in a large independent cohort. These findings provide robust evidence of the importance of survivin CNR as a breast cancer biomarker, and its potential to predict outcome in tamoxifen-treated patients.

Altered cytoplasmic-to-nuclear ratio of survivin is a prognostic indicator in breast cancer.

PURPOSE: Survivin (BIRC5) is a promising tumor biomarker. Conflicting data exist on its prognostic effect in breast cancer. These data may at least be partly due to the manual interpretation of immunohistochemical staining, especially as survivin can be located in both the nucleus and cytoplasm. Quantitative determination of survivin expression using image analysis offers the opportunity to develop alternative scoring models for survivin immunohistochemistry. Here, we present such a model. EXPERIMENTAL DESIGN: A breast cancer tissue microarray containing 102 tumors was stained with an anti-survivin antibody. Whole-slide scanning was used to capture high-resolution images. These images were analyzed using automated algorithms to quantify the staining. RESULTS: Increased nuclear, but not cytoplasmic, survivin was associated with a reduced overall survival (OS; P = 0.038) and disease-specific survival (P = 0.0015). A high cytoplasmic-to-nuclear ratio (CNR) of survivin was associated with improved OS (P = 0.005) and disease-specific survival (P = 0.05). Multivariate analysis revealed that the survivin CNR was an independent predictor of OS (hazard ratio, 0.09; 95% confidence interval, 0.01-0.76; P = 0.027). A survivin CNR of >5 correlated positively with estrogen receptor (P = 0.019) and progesterone receptor (P = 0.033) levels, whereas it was negatively associated with Ki-67 expression (P = 0.04), p53 status (P = 0.005), and c-myc amplification (P = 0.016). CONCLUSION: Different prognostic information is supplied by nuclear and cytoplasmic survivin in breast cancer. Nuclear survivin is a poor prognostic marker in breast cancer. Moreover, CNR of survivin, as determined by image analysis, is an independent prognostic factor.

Novel image analysis approach for quantifying expression of nuclear proteins assessed by immunohistochemistry: application to measurement of oestrogen and progesterone receptor levels in breast cancer.

INTRODUCTION: Manual interpretation of immunohistochemistry (IHC) is a subjective, time-consuming and variable process, with an inherent intra-observer and inter-observer variability. Automated image analysis approaches offer the possibility of developing rapid, uniform indicators of IHC staining. In the present article we describe the development of a novel approach for automatically quantifying oestrogen receptor (ER) and progesterone receptor (PR) protein expression assessed by IHC in primary breast cancer. METHODS: Two cohorts of breast cancer patients (n = 743) were used in the study. Digital images of breast cancer tissue microarrays were captured using the Aperio ScanScope XT slide scanner (Aperio Technologies, Vista, CA, USA). Image analysis algorithms were developed using MatLab 7 (MathWorks, Apple Hill Drive, MA, USA). A fully automated nuclear algorithm was developed to discriminate tumour from normal tissue and to quantify ER and PR expression in both cohorts. Random forest clustering was employed to identify optimum thresholds for survival analysis. RESULTS: The accuracy of the nuclear algorithm was initially confirmed by a histopathologist, who validated the output in 18 representative images. In these 18 samples, an excellent correlation was evident between the results obtained by manual and automated analysis (Spearman's rho = 0.9, P < 0.001). Optimum thresholds for survival analysis were identified using random forest clustering. This revealed 7% positive tumour cells as the optimum threshold for the ER and 5% positive tumour cells for the PR. Moreover, a 7% cutoff level for the ER predicted a better response to tamoxifen than the currently used 10% threshold. Finally, linear regression was employed to demonstrate a more homogeneous pattern of expression for the ER (R = 0.860) than for the PR (R = 0.681). CONCLUSIONS: In summary, we present data on the automated quantification of the ER and the PR in 743 primary breast tumours using a novel unsupervised image analysis algorithm. This novel approach provides a useful tool for the quantification of biomarkers on tissue specimens, as well as for objective identification of appropriate cutoff thresholds for biomarker positivity. It also offers the potential to identify proteins with a homogeneous pattern of expression.

Creation of a digital slide and tissue microarray resource from a multi-institutional predictive toxicology study in the rat: an initial report from the PredTox group.

The widespread use of digital slides has only recently come to the fore with the development of high-throughput scanners and high performance viewing software. This development, along with the optimisation of compression standards and image transfer techniques, has allowed the technology to be used in wide reaching applications including integration of images into hospital information systems and histopathological training, as well as the development of automated image analysis algorithms for prediction of histological aberrations and quantification of immunohistochemical stains. Here, the use of this technology in the creation of a comprehensive library of images of preclinical toxicological relevance is demonstrated. The images, acquired using the Aperio ScanScope CS and XT slide acquisition systems, form part of the ongoing EU FP6 Integrated Project, Innovative Medicines for Europe (InnoMed). In more detail, PredTox (abbreviation for Predictive Toxicology) is a subproject of InnoMed and comprises a consortium of 15 industrial (13 large pharma, 1 technology provider and 1 SME) and three academic partners. The primary aim of this consortium is to assess the value of combining data generated from 'omics technologies (proteomics, transcriptomics, metabolomics) with the results from more conventional toxicology methods, to facilitate further informed decision making in preclinical safety evaluation. A library of 1709 scanned images was created of full-face sections of liver and kidney tissue specimens from male Wistar rats treated with 16 proprietary and reference compounds of known toxicity; additional biological materials from these treated animals were separately used to create 'omics data, that will ultimately be used to populate an integrated toxicological database. In respect to assessment of the digital slides, a web-enabled digital slide management system, Digital SlideServer (DSS), was employed to enable integration of the digital slide content into the 'omics database and to facilitate remote viewing by pathologists connected with the project. DSS also facilitated manual annotation of digital slides by the pathologists, specifically in relation to marking particular lesions of interest. Tissue microarrays (TMAs) were constructed from the specimens for the purpose of creating a repository of tissue from animals used in the study with a view to later-stage biomarker assessment. As the PredTox consortium itself aims to identify new biomarkers of toxicity, these TMAs will be a valuable means of validation. In summary, a large repository of histological images was created enabling the subsequent pathological analysis of samples through remote viewing and, along with the utilisation of TMA technology, will allow the validation of biomarkers identified by the PredTox consortium. The population of the PredTox database with these digitised images represents the creation of the first toxicological database integrating 'omics and preclinical data with histological images.

Compound Functional Prediction Using Multiple Unrelated Morphological Profiling Assays.

Phenotypic cell-based assays have proven to be efficient at discovering first-in-class therapeutic drugs mainly because they allow for scanning a wide spectrum of possible targets at once. However, despite compelling methodological advances, posterior identification of a compound's mechanism of action (MOA) has remained difficult and highly refractory to automated analyses. Methods such as the cell painting assay and multiplexing fluorescent dyes to reveal broadly relevant cellular components were recently suggested for MOA prediction. We demonstrated that adding fluorescent dyes to a single assay has limited impact on MOA prediction accuracy, as monitoring only the nuclei stain could reach compelling levels of accuracy. This observation suggested that multiplexed measurements are highly correlated and nuclei stain could possibly reflect the general state of the cell. We then hypothesized that combining unrelated and possibly simple cell-based assays could bring a solution that would be biologically and technically more relevant to predict a drug target than using a single assay multiplexing dyes. We show that such a combination of past screen data could rationally be reused in screening facilities to train an ensemble classifier to predict drug targets and prioritize a possibly large list of unknown compound hits at once.

Contribution of DNA and tissue microarray technology to the identification and validation of biomarkers and personalised medicine in breast cancer.

Completion of the human genome project has revolutionised translational medicine. High-throughput technology now permits investigators to systematically interrogate the genome, transcriptome, proteome and metabolome. It is expected that these advances will eventually be translated into new more sensitive diagnostic tests and less toxic therapeutics. A major shift is expected in clinical oncology over the next few decades as we start to move away from currently practiced, population-based approaches to personalised medicine. In this emerging approach, the molecular and pathophysiological characteristics of an individual patient and tumour will be measured and tailored therapeutic regimens will be administered based on these profiles. One of the key steps in this process will be the identification and validation of biomarkers. Whilst great advances have been made in the discovery of putative biomarkers, disappointingly few have been translated into clinically applicable assays. It is widely believed that this is due to a lack of well-designed, thorough validation studies. Here, we review the role of DNA microarrays and tissue microarrays in the validation of biomarkers in breast cancer, with emphasis on their potential application to determine mode of personalised therapy in the future.

Smooth 2D manifold extraction from 3D image stack.

Three-dimensional fluorescence microscopy followed by image processing is routinely used to study biological objects at various scales such as cells and tissue. However, maximum intensity projection, the most broadly used rendering tool, extracts a discontinuous layer of voxels, obliviously creating important artifacts and possibly misleading interpretation. Here we propose smooth manifold extraction, an algorithm that produces a continuous focused 2D extraction from a 3D volume, hence preserving local spatial relationships. We demonstrate the usefulness of our approach by applying it to various biological applications using confocal and wide-field microscopy 3D image stacks. We provide a parameter-free ImageJ/Fiji plugin that allows 2D visualization and interpretation of 3D image stacks with maximum accuracy.

A texture based pattern recognition approach to distinguish melanoma from non-melanoma cells in histopathological tissue microarray sections.

AIMS: Immunohistochemistry is a routine practice in clinical cancer diagnostics and also an established technology for tissue-based research regarding biomarker discovery efforts. Tedious manual assessment of immunohistochemically stained tissue needs to be fully automated to take full advantage of the potential for high throughput analyses enabled by tissue microarrays and digital pathology. Such automated tools also need to be reproducible for different experimental conditions and biomarker targets. In this study we present a novel supervised melanoma specific pattern recognition approach that is fully automated and quantitative. METHODS AND RESULTS: Melanoma samples were immunostained for the melanocyte specific target, Melan-A. Images representing immunostained melanoma tissue were then digitally processed to segment regions of interest, highlighting Melan-A positive and negative areas. Color deconvolution was applied to each region of interest to separate the channel containing the immunohistochemistry signal from the hematoxylin counterstaining channel. A support vector machine melanoma classification model was learned from a discovery melanoma patient cohort (n = 264) and subsequently validated on an independent cohort of melanoma patient tissue sample images (n = 157). CONCLUSION: Here we propose a novel method that takes advantage of utilizing an immuhistochemical marker highlighting melanocytes to fully automate the learning of a general melanoma cell classification model. The presented method can be applied on any protein of interest and thus provides a tool for quantification of immunohistochemistry-based protein expression in melanoma.

Tissue microarrays and digital image analysis.

Tissue microarrays (TMAs) have recently emerged as very valuable tools for high-throughput pathological assessment, especially in the cancer research arena. This important technology, however, has yet to fully penetrate into the area of toxicology. Here, we describe the creation of TMAs representative of samples produced from conventional toxicology studies within a large-scale, multi-institutional pan-European project, PredTox. PredTox, short for Predictive Toxicology, formed part of an EU FP6 Integrated Project, Innovative Medicines for Europe (InnoMed), and aimed to study pre-clinically 16 compounds of known liver and/or kidney toxicity. In more detail, TMAs were constructed from materials corresponding to the full face sections of liver and kidney from rats treated with different drug candidates by members of the consortium. We also describe the process of digital slide scanning of kidney and liver sections, in the context of creating an online resource of histopathological data.

RBM3-regulated genes promote DNA integrity and affect clinical outcome in epithelial ovarian cancer.

The RNA-binding motif protein 3 (RBM3) was initially discovered as a putative cancer biomarker based on its differential expression in various cancer forms in the Human Protein Atlas (HPA). We previously reported an association between high expression of RBM3 and prolonged survival in breast and epithelial ovarian cancer (EOC). Because the function of RBM3 has not been fully elucidated, the aim of this study was to use gene set enrichment analysis to identify the underlying biologic processes associated with RBM3 expression in a previously analyzed EOC cohort (cohort 1, n = 267). This revealed an association between RBM3 expression and several cellular processes involved in the maintenance of DNA integrity. RBM3-regulated genes were subsequently screened in the HPA to select for putative prognostic markers, and candidate proteins were analyzed in the ovarian cancer cell line A2780, whereby an up-regulation of Chk1, Chk2, and MCM3 was demonstrated in siRBM3-treated cells compared to controls. The prognostic value of these markers was assessed at the messenger RNA level in cohort 1 and the protein level in an independent EOC cohort (cohort 2, n = 154). High expression levels of Chk1, Chk2, and MCM3 were associated with a significantly shorter survival in both cohorts, and phosphorylated Chk2 was an adverse prognostic marker in cohort 2. These results uncover a putative role for RBM3 in DNA damage response, which might, in part, explain its cisplatin-sensitizing properties and good prognostic value in EOC. Furthermore, it is demonstrated that Chk1, Chk2, and MCM3 are poor prognostic markers in EOC.

Leukocyte complexity predicts breast cancer survival and functionally regulates response to chemotherapy.

UNLABELLED: Immune-regulated pathways influence multiple aspects of cancer development. In this article we demonstrate that both macrophage abundance and T-cell abundance in breast cancer represent prognostic indicators for recurrence-free and overall survival. We provide evidence that response to chemotherapy is in part regulated by these leukocytes; cytotoxic therapies induce mammary epithelial cells to produce monocyte/macrophage recruitment factors, including colony stimulating factor 1 (CSF1) and interleukin-34, which together enhance CSF1 receptor (CSF1R)-dependent macrophage infiltration. Blockade of macrophage recruitment with CSF1R-signaling antagonists, in combination with paclitaxel, improved survival of mammary tumor-bearing mice by slowing primary tumor development and reducing pulmonary metastasis. These improved aspects of mammary carcinogenesis were accompanied by decreased vessel density and appearance of antitumor immune programs fostering tumor suppression in a CD8+ T-cell-dependent manner. These data provide a rationale for targeting macrophage recruitment/response pathways, notably CSF1R, in combination with cytotoxic therapy, and identification of a breast cancer population likely to benefit from this novel therapeutic approach. SIGNIFICANCE: These findings reveal that response to chemotherapy is in part regulated by the tumor immune microenvironment and that common cytotoxic drugs induce neoplastic cells to produce monocyte/macrophage recruitment factors, which in turn enhance macrophage infiltration into mammary adenocarcinomas. Blockade of pathways mediating macrophage recruitment, in combination with chemotherapy, significantly decreases primary tumor progression, reduces metastasis, and improves survival by CD8+ T-cell-dependent mechanisms, thus indicating that the immune microenvironment of tumors can be reprogrammed to instead foster antitumor immunity and improve response to cytotoxic therapy.