The Tissue Microarray Data Exchange Specification (TMA DES) is an eXtensible Markup Language (XML) specification for encoding TMA experiment data in a machine-readable format that is also human readable. TMA DES defines Common Data Elements (CDEs) that form a basic vocabulary for describing TMA data. TMA data are routinely subjected to univariate and multivariate statistical analysis to determine differences or similarities between pathologically distinct groups of tumors for one or more markers or between markers for different groups. Such statistical analysis tests include the t-test, ANOVA, Chi-square, Mann-Whitney U, and Kruskal-Wallis tests. All these generate output that needs to be recorded and stored with TMA data.
Materials and Methods:
We propose extending the TMA DES to include syntactic and semantic definitions of CDEs for describing the results of statistical analyses performed upon TMA DES data. These CDEs are described in this paper and it is illustrated how they can be added to the TMA DES. We created a Document Type Definition (DTD) file defining the syntax for these CDEs, and a set of ISO 11179 entries providing semantic definitions for them. We describe how we wrote a program in R that read TMA DES data from an XML file, performed statistical analyses on that data, and created a new XML file containing both the original XML data and CDEs representing the results of our analyses. This XML file was submitted to XML parsers in order to confirm that they conformed to the syntax defined in our extended DTD file. TMA DES XML files with deliberately introduced errors were also parsed in order to verify that our new DTD file could perform error checking. Finally, we also validated an existing TMA DES XML file against our DTD file in order to demonstrate the backward compatibility of our DTD.
Our experiments demonstrated the encoding of analysis results using our proposed CDEs. We used XML parsers to confirm that these XML data were syntactically correct and conformed to the rules specified in our extended TMA DES DTD. We also demonstrated that this extended DTD was capable of being used to successfully perform error checking, and was backward compatible with pre-existing TMA DES data which did not use our new CDEs.
The TMA DES allows Tissue Microarray data to be shared. A variety of statistical tests are used to analyze such data. We have proposed a set of CDEs as an extension to the TMA DES which can be used to annotate TMA DES data with the results of statistical analyses performed on that data. We performed experiments which demonstrated the usage of TMA DES data containing our proposed CDEs.
CDEs; DTD; statistical analysis; tissue microarray; TMA Data Exchange Specification; XML
Prostate cancer diagnosis is routinely made by the histopathological examination of formalin fixed needle biopsy specimens. Frequently this is the only cancer tissue available from the patient for the analysis of diagnostic and prognostic biomarkers. There is, therefore, an urgent need for methods that allow the high-throughput analysis of these biopsy samples using immunohistochemical (IHC) markers and fluorescence in situ hybridisation (FISH) analysis based markers.
A method that allows the construction of tissue microarrays (TMAs) from diagnostic prostate needle biopsy cores has previously been reported. However, the technique only allows the production of low-density biopsy TMAs with a maximum of 20 cores per TMA. Here two methods are presented that allow the rapid and uniform production of biopsy TMAs containing between 54 and 72 biopsy cores. IHC and FISH techniques were used to detect biomarker status.
Biopsy TMAs were constructed from prostate needle biopsy specimens taken from 102 patients entered into an active surveillance trial and 201 patients in a radiotherapy trial. The detection rate for cancer in slices of these biopsy TMAs was 66% and 79% respectively. Slices of a biopsy TMA prepared from biopsies from active surveillance patients were used to detect multiple IHC markers and to score TMPRSS2-ERG fusion status in a FISH-based assay.
The construction of biopsy TMAs provides an effective method for the multiplex analysis of IHC and FISH markers and for their assessment as prognostic biomarkers in the context of clinical trials.
There is critical need for improved biomarker assessment platforms which integrate traditional pathological parameters (TNM stage, grade and ER/PR/HER2 status) with molecular profiling, to better define prognostic subgroups or systemic treatment response. One roadblock is the lack of semi-quantitative methods which reliably measure biomarker expression. Our study assesses reliability of automated immunohistochemistry (IHC) scoring compared to manual scoring of five selected biomarkers in a tissue microarray (TMA) of 63 human breast cancer cases, and correlates these markers with clinico-pathological data. TMA slides were scanned into an Ariol Imaging System, and histologic (H) scores (% positive tumor area x staining intensity 0–3) were calculated using trained algorithms. H scores for all five biomarkers concurred with pathologists’ scores, based on Pearson correlation coefficients (0.80–0.90) for continuous data and Kappa statistics (0.55–0.92) for positive vs. negative stain. Using continuous data, significant association of pERK expression with absence of LVI (p = 0.005) and lymph node negativity (p = 0.002) was observed. p53 over-expression, characteristic of dysfunctional p53 in cancer, and Ki67 were associated with high grade (p = 0.032 and 0.0007, respectively). Cyclin D1 correlated inversely with ER/PR/HER2-ve (triple negative) tumors (p = 0.0002). Thus automated quantitation of immunostaining concurs with pathologists’ scoring, and provides meaningful associations with clinico-pathological data.
breast cancer; p53/cyclin D1/Ki67/pERK; tissue microarray; automated image analysis; clinico-pathological parameters
Tissue microarray (TMA) technology has been developed to facilitate high-throughput immunohistochemical and in situ hybridization analysis of tissues by inserting small tissue biopsy cores into a single paraffin block. Several studies have revealed novel prognostic biomarkers in esophageal squamous cell carcinoma (ESCC) by means of TMA technology, although this technique has not yet been validated for these tumors. Because representativeness of the donor tissue cores may be a disadvantage compared to full sections, the aim of this study was to assess if TMA technology provides representative immunohistochemical results in ESCC. A TMA was constructed containing triplicate cores of 108 formalin-fixed, paraffin-embedded squamous cell carcinomas of the esophagus. The agreement in the differentiation grade and immunohistochemical staining scores of CK5/6, CK14, E-cadherin, Ki-67, and p53 between TMA cores and a subset of 64 randomly selected donor paraffin blocks was determined using kappa statistics. The concurrence between TMA cores and donor blocks was moderate for Ki-67 (κ = 0.42) and E-cadherin (κ = 0.47), substantial for differentiation grade (κ = 0.65) and CK14 (κ = 0.71), and almost perfect for p53 (κ = 0.86) and CK5/6 (κ = 0.93). TMA technology appears to be a valid method for immunohistochemical analysis of molecular markers in ESCC provided that the staining pattern in the tumor is homogeneous.
Biological markers; Esophageal neoplasms; Protein microarray analysis; Squamous cell carcinoma; Validation studies
Cyclin A has in some studies been associated with poor breast cancer survival, although all studies have not confirmed this. Its prognostic significance in breast cancer needs evaluation in larger studies. Tissue microarray (TMA) technique allows a simultaneous analysis of large amount of tumours on a single microscopic slide. This makes a rapid screening of molecular markers in large amount of tumours possible. Because only a small tissue sample of each tumour is punched on an array, the question has arisen about the representativeness of TMA when studying markers that are expressed in only a small proportion of cells. For this reason, we wanted to compare cyclin A expression on TMA and on traditional large sections. Two breast cancer TMAs were constructed of 200 breast tumours diagnosed between 1997–1998. TMA slides and traditional large section slides of these 200 tumours were stained with cyclin A antibody and analysed by two independent readers. The reproducibility of the two readers' results was good or even very good, with kappa values 0.71–0.87. The agreement of TMA and large section results was good with kappa value 0.62–0.75. Cyclin A overexpression was significantly (P<0.001) associated with oestrogen receptor and progesterone receptor negativity and high grade both on TMA and large sections. Cyclin A overexpression was significantly associated with poor metastasis-free survival both on TMA and large sections. The relative risks for metastasis were similar on TMA and large sections. This study suggests that TMA technique could be useful to study histological correlations and prognostic significance of cyclin A on breast cancer on a large scale.
breast cancer; cyclin A; tissue microarray
Tissue microarrays (TMAs) allow multiplexed analysis of tissue samples and are frequently used to estimate biomarker protein expression in tumour biopsies. TMA Navigator (www.tmanavigator.org) is an open access web application for analysis of TMA data and related information, accommodating categorical, semi-continuous and continuous expression scores. Non-biological variation, or batch effects, can hinder data analysis and may be mitigated using the ComBat algorithm, which is incorporated with enhancements for automated application to TMA data. Unsupervised grouping of samples (patients) is provided according to Gaussian mixture modelling of marker scores, with cardinality selected by Bayesian information criterion regularization. Kaplan–Meier survival analysis is available, including comparison of groups identified by mixture modelling using the Mantel-Cox log-rank test. TMA Navigator also supports network inference approaches useful for TMA datasets, which often constitute comparatively few markers. Tissue and cell-type specific networks derived from TMA expression data offer insights into the molecular logic underlying pathophenotypes, towards more effective and personalized medicine. Output is interactive, and results may be exported for use with external programs. Private anonymous access is available, and user accounts may be generated for easier data management.
Needle biopsies are taken as standard diagnostic specimens for many cancers, but no technique exists for the high-throughput analysis of multiple individual immunohistochemical (IHC) markers using these samples. Here we present a simple and highly reliable technique for constructing tissue microarrays (TMAs) from prostatic needle biopsies. Serial sectioning of the TMAs, called ‘Checkerboard TMAs', facilitated expression analysis of multiple proteins using IHC markers. In total, 100% of the analysed biopsies within the TMA both preserved their antigenicity and maintained their morphology. Checkerboard TMAs will allow the use of needle biopsies (i) alongside other tissue specimens (trans-urethral resection of prostates and prostatectomies in the case of prostate cancer) in clinical correlation studies when searching for new prognostic markers, and (ii) in a diagnostic context for assessing expression of multiple proteins in cancers from patients prior to treatment.
prostate; cancer; needle biopsy; tissue microarray; immunohistochemistry
Tissue Microarrays (TMAs) measure tumor-specific protein expression via high-density immunohistochemical staining assays. They provide a proteomic platform for validating cancer biomarkers emerging from large-scale DNA microarray studies. Repeated observations within each tumor result in substantial biological and experimental variability. This variability is usually ignored when associating the TMA expression data with patient survival outcome. It generates biased estimates of hazard ratio in proportional hazards models. We propose a Latent Expression Index (LEI) as a surrogate protein expression estimate in a two-stage analysis. Several estimators of LEI are compared: an Empirical Bayes (EB), a Full Bayes (FB), and a Varying Replicate Number (VRN) estimator. In addition, we jointly model survival and TMA expression data via a shared random effects model. Bayesian estimation is carried out using a Markov Chain Monte Carlo (MCMC) method. Simulation studies were conducted to compare the two-stage methods and the joint analysis in estimating the Cox regression coefficient. We show the two-stage methods reduce bias relative to the naive approach, but still lead to under-estimated hazard ratios. The joint model consistently outperforms the two-stage methods in terms of both bias and coverage property in various simulation scenarios. In case studies using prostate cancer TMA data sets, the two stage methods yields a good approximation in one data set while an insufficient one in the other. A general advice is to use the joint model inference whenever results differ between the two-stage methods and the joint analysis.
Biomarker; Empirical Bayes; Joint modeling; Mixed effects; Tissue microarray; Varying
Triple-negative breast cancer (TNBC) occurs in approximately 15% of all breast cancer patients, and the incidence of TNBC is greatly increased in BRCA1 mutation carriers. This study aimed to assess the impact of BRCA1 promoter methylation with respect to breast cancer subtypes in sporadic disease. Tissue microarrays (TMAs) were constructed representing tumors from 303 patients previously screened for BRCA1 germline mutations, of which a subset of 111 sporadic tumors had previously been analyzed with respect to BRCA1 methylation. Additionally, a set of eight tumors from BRCA1 mutation carriers were included on the TMAs. Expression analysis was performed on TMAs by immunohistochemistry (IHC) for BRCA1, pRb, p16, p53, PTEN, ER, PR, HER2, CK5/6, CK8, CK18, EGFR, MUC1, and Ki-67. Data on BRCA1 aberrations and IHC expression was examined with respect to breast cancer-specific survival. The results demonstrate that CpG island hypermethylation of BRCA1 significantly associates with the basal/triple-negative subtype. Low expression of pRb, and high/intense p16, were associated with BRCA1 promoter hypermethylation, and the same effects were seen in BRCA1 mutated tumors. The expression patterns of BRCA1, pRb, p16 and PTEN were highly correlated, and define a subgroup of TNBCs characterized by BRCA1 aberrations, high Ki-67 (≥ 40%) and favorable disease outcome. In conclusion, our findings demonstrate that epigenetic inactivation of the BRCA1 gene associates with RB/p16 dysfunction in promoting TNBCs. The clinical implications relate to the potential use of targeted treatment based on PARP inhibitors in sporadic TNBCs, wherein CpG island hypermethylation of BRCA1 represents a potential marker of therapeutic response.
BRCA1; methylation; epigenetics; triple negative; breast cancer; retionblastoma tumor suppressor gene; pRb; p16
Previous investigations have linked decreased nuclear expression of the cell cycle inhibitor p27 with poor outcome in prostate cancer. However, these reports are inconsistent regarding the magnitude of that association and its independence from other predictors. Moreover, cytoplasmic translocation of p27 has been proposed as a negative prognostic sign. Given the cost and accuracy limitations of manual scoring, particularly of tissue microarrays (TMAs), we determined if laser-based fluorescence microscopy could provide automated analysis of p27 in both nuclear and cytoplasmic locations, and thus clarify its significance as a prognostic biomarker.
We constructed TMAs covering 202 recurrent cases (rising PSA) and 202 matched controls without recurrence. Quadruplicate tumor samples encompassed 5 slides and 1,616 cancer histospots. Cases and controls matched on age, Gleason grade, stage and hospital. We immunolabeled epithelial cytoplasm with Alexa647®; p27 with Alexa488®; and nuclei with DAPI. Slides were scanned on an iCys® laser scanning cytometer. Nuclear crowding required a stereological approach - random arrays of circles (phantoms) were layered on images and the content of each phantom analyzed in scatterplots.
Both nuclear and cytoplasmic p27 were significantly lower in cases vs. controls (P=0.014, P=0.004, respectively). Regression models controlling for matching variables plus PSA showed strong linear trends for increased risk of recurrence with lower p27 in both nucleus and cytoplasm (highest vs lowest quartile, OR=0.35, P=0.006). Manual scoring identified an inverse association between p27 expression and tumor grade, but no independent association with recurrence.
In conclusion, we developed an automated method for subcellular scoring of p27 without the need to segment individual cells. Our method identified a strong relationship, independent of tumor grade, stage and PSA, between p27 expression – regardless of subcellular location - and prostate cancer recurrence. This relationship was not observed with manual scoring.
prostate cancer; prognosis; p27Kip1; automated image analysis; tissue microarray
Tissue microarray (TMA) is a high throughput analysis tool to identify new diagnostic and prognostic markers in human cancers. However, standard automated method in tumour detection on both routine histochemical and immunohistochemistry (IHC) images is under developed. This paper presents a robust automated tumour cell segmentation model which can be applied to both routine histochemical tissue slides and IHC slides and deal with finer pixel-based segmentation in comparison with blob or area based segmentation by existing approaches. The presented technique greatly improves the process of TMA construction and plays an important role in automated IHC quantification in biomarker analysis where excluding stroma areas is critical. With the finest pixel-based evaluation (instead of area-based or object-based), the experimental results show that the proposed method is able to achieve 80% accuracy and 78% accuracy in two different types of pathological virtual slides, i.e., routine histochemical H&E and IHC images, respectively. The presented technique greatly reduces labor-intensive workloads for pathologists and highly speeds up the process of TMA construction and provides a possibility for fully automated IHC quantification.
Tissue microarrays (TMAs) are commonly used to study biomarker expression in invasive breast cancers. Whether or not TMAs may also be a potentially useful platform for assessing biomarkers in benign proliferative breast lesions (BPBL) and normal breast tissue has not been previously studied in detail.
We evaluated the success of capturing the targeted areas in TMAs constructed from benign breast biopsy blocks of 368 Nurses' Health Study (NHS) and NHS II participants. Areas targeted included 214 BPBL and 361 normal terminal duct lobular units (TDLUs). At least three 0.6mm cores were obtained from the areas of interest from each donor paraffin block and arrayed into a recipient block. Sections cut from TMA blocks were stained with hematoxylin-and-eosin. Each TMA slide was examined to determine the number of cores/case in which the targeted area was represented.
Overall, the targeted area was present in 776 of 1,800 TMA cores (43%). At least one of the cores contained the area of interest for 401 of the 575 targeted foci (70%), including 76%, 66%, 60% and 40% of cases in which the targeted area was normal TDLUs, usual ductal hyperplasia, atypical lobular hyperplasia and atypical ductal hyperplasia respectively.
In TMAs constructed from BPBL and normal TDLUs, the targeted area was present on at least one core in 70% of cases. Our findings indicate that it is feasible to construct TMAs from donor tissue blocks consisting of BPBLs and normal breast tissue with a relatively high rate of capture of the targeted area.
Benign breast disease; Tissue microarrays
Tissue MicroArrays (TMAs) represent a potential high-throughput platform for the analysis and discovery of tissue biomarkers. As TMA slides are produced manually and subject to processing and sectioning artefacts, the layout of TMA cores on the final slide and subsequent digital scan (TMA digital slide) is often disturbed making it difficult to associate cores with their original position in the planned TMA map. Additionally, the individual cores can be greatly altered and contain numerous irregularities such as missing cores, grid rotation and stretching. These factors demand the development of a robust method for de-arraying TMAs which identifies each TMA core, and assigns them to their appropriate coordinates on the constructed TMA slide.
This study presents a robust TMA de-arraying method consisting of three functional phases: TMA core segmentation, gridding and mapping. The segmentation of TMA cores uses a set of morphological operations to identify each TMA core. Gridding then utilises a Delaunay Triangulation based method to find the row and column indices of each TMA core. Finally, mapping correlates each TMA core from a high resolution TMA whole slide image with its name within a TMAMap.
This study describes a genuine robust TMA de-arraying algorithm for the rapid identification of TMA cores from digital slides. The result of this de-arraying algorithm allows the easy partition of each TMA core for further processing. Based on a test group of 19 TMA slides (3129 cores), 99.84% of cores were segmented successfully, 99.81% of cores were gridded correctly and 99.96% of cores were mapped with their correct names via TMAMaps. The gridding of TMA cores were also extensively tested using a set of 113 pseudo slide (13,536 cores) with a variety of irregular grid layouts including missing cores, rotation and stretching. 100% of the cores were gridded correctly.
Aims: To evaluate the use of tissue microarray (TMA) technology as a validation tool for HER2 testing by both immunocytochemistry (ICC) and fluorescence in situ hybridisation (FISH) in the diagnostic setting.
Methods: TMA constructs from 57 cases of breast cancer were evaluated for HER2 (by ICC and FISH) by two centres. The results were compared.
Results: There was a high level of concordance for both ICC and FISH. In five “discrepant” cases only three would have had a potential impact on patient management.
Conclusions: Validation of HER2 analysis in the clinical setting by ICC and FISH is essential. The use of TMAs provides for an economy of scale and would be practical in the setting of interlaboratory and intralaboratory validation. It is suggested that routine HER2 ICC and FISH should continue to be performed in laboratories on whole sections. Following this, TMAs would be constructed for all cases of breast cancer. ICC and FISH would be performed on these to validate the results. The TMAs would be available for circulation to other centres for validation purposes. The standardisation of testing between centres, the potential difficulty of minimum case numbers, and the workload issues surrounding validation would all be facilitated by this approach.
HER2; immunocytochemistry; fluorescence in situ hybridisation; validation
A novel method for high-throughput proteomic analysis of formalin-fixed paraffin-embedded (FFPE) tissue microarrays (TMA) is described using on-tissue tryptic digestion followed by MALDI imaging MS. A TMA section containing 112 needle core biopsies from lung-tumor patients was analyzed using MS and the data were correlated to a serial hematoxylin and eosin (H&E)-stained section having various histological regions marked, including cancer, non-cancer, and normal ones. By correlating each mass spectrum to a defined histological region, statistical classification models were generated that can sufficiently distinguish biopsies from adenocarcinoma from squamous cell carcinoma biopsies. These classification models were built using a training set of biopsies in the TMA and were then validated on the remaining biopsies. Peptide markers of interest were identified directly from the TMA section using MALDI MS/MS sequence analysis. The ability to detect and characterize tumor marker proteins for a large cohort of FFPE samples in a high-throughput approach will be of significant benefit not only to investigators studying tumor biology, but also to clinicians for diagnostic and prognostic purposes.
Cancer; Formalin-fixed paraffin-embedded; Imaging mass spectrometry; Lung; Tissue
Tissue microarray (TMA) is a promising technique in the evaluation of immunohistochemical markers in tumors and may be used as an alternative for whole sections. However, only a few studies have correlated a clinical outcome with both TMA and results obtained from whole sections. This study compared immunostaining for Ki-67 and p16 in TMA (3 cores from each specimen) and whole sections of 171 cases of stage III epithelial ovarian cancer with clinical data. A high expression of Ki-67 was identified in 85.0, 85.5, 85.8, 90.5 and 84% of cores 1, 2 and 3, TMAs and whole tissue sections, respectively. A high p16 expression was found in 36.5, 31.4, 30.3, 46.3 and 31.0% of cores 1, 2 and 3, TMAs and whole tissue sections, respectively. The high expression of Ki-67 and p16 in whole tissue sections significantly correlated with that of Ki-67 and p16 in core 1 (P<0.0001 and P<0.0001, respectively), core 2 (P<0.0001 and P<0.0001, respectively), core 3 (P<0.0001 and P<0.0001, respectively), and TMAs (P<0.0001 and P<0.0001, respectively). In univariate analysis, a high expression of Ki-67 and p16 in two of the cores; TMA and the whole tissue sections were significantly correlated to disease-related survival (Ki-67: P=0.008, 0.012, 0.012 and 0.0001, respectively, and p16: P=0.0007, 0.0005, 0.0008 and 0.005, respectively). However, in the multivariate analysis only Ki-67 on whole tissue sections retained an independent prognostic significance (P=0.025). We concluded that more studies, with a higher number of cores, are necessary to determine the efficacy of TMA in reflecting the prognostic value of different antibodies. Morever, evaluation of this method is crucial for each type of tumor and each separate antigen. It is also essential to confirm the clinical correlations on the whole sections before investigating the same parameters on TMA.
tissue microarrays; whole tissue sections; immunohistochemistry; prognosis; Ki-67; p16
Quiescin sulfhydryl oxidase 1 (QSOX1) oxidizes sulfhydryl groups to form disulfide bonds in proteins. Tumor specific expression of QSOX1 has been reported for numerous tumor types. In this study, we investigate QSOX1 as a marker of breast tumor progression and evaluate the role of QSOX1 as it relates to breast tumor growth and metastasis.
Correlation of QSOX1 expression with breast tumor grade, subtype and estrogen receptor (ER) status was gathered through informatic analysis using the "Gene expression based Outcome for Breast cancer Online" (GOBO) web-based tool. Expression of QSOX1 protein in breast tumors tissue microarray (TMA) and in a panel of breast cancer cell lines was used to confirm our informatics analysis. To investigate malignant cell mechanisms for which QSOX1 might play a key role, we suppressed QSOX1 protein expression using short hairpin (sh) RNA in ER+ Luminal A-like MCF7, ER+ Luminal B-like BT474 and ER- Basal-like BT549 breast cancer cell lines.
GOBO analysis revealed high levels of QSOX1 RNA expression in ER+ subtypes of breast cancer. In addition, Kaplan Meyer analyses revealed QSOX1 RNA as a highly significant predictive marker for both relapse and poor overall survival in Luminal B tumors. We confirmed this finding by evaluation of QSOX1 protein expression in breast tumors and in a panel of breast cancer cell lines. Expression of QSOX1 in breast tumors correlates with increasing tumor grade and high Ki-67 expression. Suppression of QSOX1 protein slowed cell proliferation as well as dramatic inhibition of MCF7, BT474 and BT549 breast tumor cells from invading through Matrigel™ in a modified Boyden chamber assay. Inhibition of invasion could be rescued by the exogenous addition of recombinant QSOX1. Gelatin zymography indicated that QSOX1 plays an important role in the function of MMP-9, a key mediator of breast cancer invasive behavior.
Taken together, our results suggest that QSOX1 is a novel biomarker for risk of relapse and poor survival in Luminal B breast cancer, and has a pro-proliferative and pro-invasive role in malignant progression partly mediated through a decrease in MMP-9 functional activity.
Thrombotic microangiopathies (TMAs) represent a heterogeneous group of diseases characterized by a microangiopathic hemolytic anemia, peripheral thrombocytopenia, and organ failure of variable severity. TMAs encompass thrombotic thrombocytopenic purpura (TTP), typically characterized by fever, central nervous system manifestations and hemolytic uremic syndrome (HUS), in which renal failure is the prominent abnormality. In patients with cancer TMAs may be related to various antineoplastic drugs or to the malignant disease itself. The reported series of patients with TMAs directly related to cancer are usually heterogeneous, retrospective, and encompass patients with hematologic malignancies with solid tumors or receiving chemotherapy, each of which may have distinct presentations and pathophysiological mechanisms. Patients with disseminated malignancy who present with microangiopathic hemolytic anemia and thrombocytopenia may be misdiagnosed as thrombotic thrombocytopenic purpura (TTP) Only a few cases of TTP secondary to metastatic adenocarcinoma are known in the literature. We present a case of a 34-year-old man with TTP syndrome secondary to metastatic small-bowel adenocarcinoma. Patients with disseminated malignancy had a longer duration of symptoms, more frequent presence of respiratory symptoms, higher lactate dehydrogenase levels, and more often failed to respond to plasma exchange treatment. A search for systemic malignancy, including a bone marrow biopsy, is appropriate when patients with TTP have atypical clinical features or fail to respond to plasma exchange.
metastatic cancer; microangiopathic hemolysis; thrombocytopenia; thrombotic thrombocytopenic purpura; ADAMTS13.
Survival in small cell lung cancer (SCLC) is limited by the development of chemoresistance. Factors associated with chemoresistance in vitro have been difficult to validate in vivo. Both Bcl-2 and β1-integrin have been identified as in vitro chemoresistance factors in SCLC but their importance in patients remains uncertain. Tissue microarrays (TMAs) are useful to validate biomarkers but no large TMA exists for SCLC. We designed an SCLC TMA to study potential biomarkers of prognosis and then used it to clarify the role of both Bcl-2 and β1-integrin in SCLC.
A TMA was constructed consisting of 184 cases of SCLC and stained for expression of Bcl-2 and β1-integrin. The slides were scored and the role of the proteins in survival was determined using Cox regression analysis. A meta-analysis of the role of Bcl-2 expression in SCLC prognosis was performed based on published results.
Both proteins were expressed at high levels in the SCLC cases. For Bcl-2 (n=140), the hazard ratio for death if the staining was weak in intensity was 0.55 (0.33–0.94, P=0.03) and for β1-integrin (n=151) was 0.60 (0.39–0.92, P=0.02). The meta-analysis showed an overall hazard ratio for low expression of Bcl-2 of 0.91(0.74–1.09).
Both Bcl-2 and β1-integrin are independent prognostic factors in SCLC in this cohort although further validation is required to confirm their importance. A TMA of SCLC cases is feasible but challenging and an important tool for biomarker validation.
small cell lung cancer; tissue microarray; Bcl-2; β1-integrin
A set of proteins reflecting the prognosis of patients have clinical significance since they could be utilized as predictive biomarkers and/or potential therapeutic targets. With the aim of finding novel diagnostic and prognostic markers for glioblastoma (GBM), a tissue microarray (TMA) library consisting of 62 GBMs and 28 GBM-associated normal spots was constructed. Immunohistochemistry against 78 GBM-associated proteins was performed. Expression levels of each protein for each patient were analyzed using an image analysis program and converted to H-score [summation of the intensity grade of staining (0–3) multiplied by the percentage of positive cells corresponding to each grade]. Based on H-score and hierarchical clustering methods, we divided the GBMs into two groups (n=19 and 37) that had significantly different survival lengths (p<0.05). In the two groups, expression of nine proteins (survivin, cyclin E, DCC, TGF-β, CDC25B, histone H1, p-EGFR, p-VEGFR2/3, p16) was significantly changed (q<0.05). Prognosis-predicting potential of these proteins were validated with another independent library of 82 GBM TMAs and a public GBM DNA microarray dataset. In addition, we determined 32 aberrant or mislocalized subcellular protein expression patterns in GBMs compared with relatively normal brain tissues, which could be useful for diagnostic biomarkers of GBM. We therefore suggest that these proteins can be used as predictive biomarkers and/or potential therapeutic targets for GBM.
biomarker; therapeutic target; glioblastoma; tissue micro-array; bioinformatics; automated image analysis
In this study we aimed to confirm the emerging role of Chromatin Assembly Factor 1 (CAF-1 p60) as a new proliferation and prognostic marker for cancer and to test the usefulness of the tissue microarray technique (TMA) for CAF-1 p60 rapid screening in several human malignancies. CAF-1 is a histone chaperone, regulating chromatin dynamics during DNA replication and repair in eukaryotics. TMA is a powerful high-throughput methodology in the study of cancer, allowing simultaneous assessment of different biomarkers within large numbers of tissue specimens. We generated TMA taking 3 mm diameter-core biopsies from oral squamous cell carcinoma, prostate cancer, salivary gland tumours and skin melanoma specimens, which had been previously tested for CAF-1 p60 on routine tissue sections. We also analysed, for the first time, 30 larynx and 30 skin squamous cell carcinomas. CAF-1 p60 resulted over-expressed in both the tissue sections and the TMA specimens, with the highest levels of expression in tumours which were more aggressive and metastasizing. Notably, a high degree of agreement was found between the CAF-1 p60 assessment on TMAs and on routine tissue sections. Our findings confirm the prognostic role of CAF-1 p60 and indicate TMA as a really advantageous method for CAF-1 p60 immunohistochemical screening, allowing savings on both tissue quantity and operator-time.
Chromatin Assembly Factor-1; tissue microarray; immunohistochemistry; cancer screening
Digital whole-slide scanning of tissue specimens produces large images demanding increasing storing capacity. To reduce the need of extensive data storage systems image files can be compressed and scaled down. The aim of this article is to study the effect of different levels of image compression and scaling on automated image analysis of immunohistochemical (IHC) stainings and automated tumor segmentation.
Two tissue microarray (TMA) slides containing 800 samples of breast cancer tissue immunostained against Ki-67 protein and two TMA slides containing 144 samples of colorectal cancer immunostained against EGFR were digitized with a whole-slide scanner. The TMA images were JPEG2000 wavelet compressed with four compression ratios: lossless, and 1:12, 1:25 and 1:50 lossy compression. Each of the compressed breast cancer images was furthermore scaled down either to 1:1, 1:2, 1:4, 1:8, 1:16, 1:32, 1:64 or 1:128. Breast cancer images were analyzed using an algorithm that quantitates the extent of staining in Ki-67 immunostained images, and EGFR immunostained colorectal cancer images were analyzed with an automated tumor segmentation algorithm. The automated tools were validated by comparing the results from losslessly compressed and non-scaled images with results from conventional visual assessments. Percentage agreement and kappa statistics were calculated between results from compressed and scaled images and results from lossless and non-scaled images.
Both of the studied image analysis methods showed good agreement between visual and automated results. In the automated IHC quantification, an agreement of over 98% and a kappa value of over 0.96 was observed between losslessly compressed and non-scaled images and combined compression ratios up to 1:50 and scaling down to 1:8. In automated tumor segmentation, an agreement of over 97% and a kappa value of over 0.93 was observed between losslessly compressed images and compression ratios up to 1:25.
The results of this study suggest that images stored for assessment of the extent of immunohistochemical staining can be compressed and scaled significantly, and images of tumors to be segmented can be compressed without compromising computer-assisted analysis results using studied methods.
The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/2442925476534995
Breast cancer; Colorectal cancer; Immunohistochemistry; Texture analysis; Image processing; Computer-Assisted; Image compression; Image scaling
The use of tissue microarrays (TMA) and advances in digital scanning microscopy has enabled the collection of thousands of tissue images. There is a need for software tools to annotate, query and share this data amongst researchers in different physical locations.
We have developed an open source web-based application for remote scoring of TMA images, which exploits the value of Microsoft Silverlight Deep Zoom to provide a intuitive interface for zooming and panning around digital images. We use and extend existing XML-based standards to ensure that the data collected can be archived and that our system is interoperable with other standards-compliant systems.
The application has been used for multi-centre scoring of TMA slides composed of tissues from several Phase III breast cancer trials and ten different studies participating in the International Breast Cancer Association Consortium (BCAC). The system has enabled researchers to simultaneously score large collections of TMA and export the standardised data to integrate with pathological and clinical outcome data, thereby facilitating biomarker discovery.
Tissue microarray; TMA; Metadata; XML; Software
Adipocytokines, such as leptin, resistin, and adiponectin, are associated with obesity and breast cancer. Several studies have indicated that adipocytokines may influence tumor growth or differentiation. The aims of this study were to determine the expression of leptin, leptin receptor (ObR), adiponectin and adiponectin receptor (AdipoR) in human breast cancer, to evaluate their prognostic significance in the breast cancer.
from 198 patients with primary breast cancer were enrolled, and representative paraffin tumor blocks were selected for constructing tissue microarrarys (TMA). Immunohistochemical staining for leptin, ObR, adiponectin, and AdipoR was performed using TMA, and the clinicopathologic characteristics were evaluated from the patient's medical records.
Stage 0 breast cancer accounted for 41 cases, and 157 cases were invasive cancer. Positive rates of leptin and ObR expression in the ductal carcinoma in situ (DCIS) group were significantly higher than those of the invasive cancer group (97.4% vs. 34.0%, p<0.001; 74.4% vs. 29.8%, p<0.001). However, positive rates of adiponectin and AdipoR expression in the invasive cancer group were significantly higher than those in the DCIS group (53.7% vs. 33.3%, p=0.024; 59.9% vs. 26.3%, p<0.001). High leptin expression was significantly associated with high Ki-67 expression (p=0.016). High adiponectin expression was significantly correlated with smaller tumor size (p=0.001).
We suggest that losses of leptin and ObR expression could be associated with invasive cancer, whereas high adiponectin and AdipoR expression may be associated with breast cancer invasiveness.
Adipocytokine; Adiponectin; Breast neoplasms; Leptin
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.
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.
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).
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.