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This study analyzed the chromatin organization state in histologically normal urothelium in patients with synchronous papillary carcinoma using digital texture analysis. The quantitative evaluation was carried out on hematoxylin and eosin-stained sections from 17 cases of urothelial papillary carcinoma in which a simultaneous biopsy specimen featuring histologically normal urothelium was available. Five bladder biopsy specimens of histologically normal urothelium from patients with prostate pathology in whom cystoscopy revealed a normal bladder mucosa were also analyzed. Karyometry showed that the 17 cases of papillary carcinoma, morphologically classified according to the 1973 World Health Organization scheme, belonged to a continuous spectrum or trend curve spanning grade 1 to grade 3. An abnormal pattern and distribution of the nuclear chromatin was seen in the normal-looking urothelium from the 17 bladders with papillary lesions. When this population was plotted along the trend curve, it occupied an intermediate position between the normal samples and samples from grade 1 carcinoma. When the nuclei were considered individually, the changes were detected only in a subpopulation of nuclei with chromatin alteration pointing toward that seen in grade 1 cases, even though distinct from them. In conclusion, karyometry can detect an abnormal chromatin pattern and distribution in the normal-looking urothelium adjacent to papillary carcinoma. Such alterations correspond to the so-called “malignancy-associated change.”

An international consultation on the diagnosis of non-invasive urothelial neoplasms was held in Ancona, Italy in May 2001. Besides histology and problems of classification, one group of experts (Committee no. 3) discussed the molecular pathology and cytometry of non-invasive urothelial carcinomas. In the following first part, special immunohistochemical and molecular markers for stratifications in bladder cancer were discussed including different cytokeratins (clone 34betaE12, CK 20), cell proliferation markers (Ki67/MIB-1, PCNA, AgNOR, DNA-cytometry), tumor suppressor genes and oncogenes (p53, p21, erb-B2, bcl-2), different receptor expressions of epidermal growth factor and vascular endothelial growth factor and others. These molecular markers were analyzed in diagnosis of urothelial carcinomas, recurrences, progression and response to treatment.

Accurate morphological classification of endometrial hyperplasia is crucial as treatments vary widely between the different categories of hyperplasia and are dependent, in part, on the histological diagnosis. However, previous studies have shown considerable inter-observer variation in the classification of endometrial hyperplasias. The aim of this study was to develop a decision support system (DSS) for the classification of endometrial hyperplasias. The system used a Bayesian belief network to distinguish proliferative endometrium, simple hyperplasia, complex hyperplasia, atypical hyperplasia and grade 1 endometrioid adenocarcinoma. These diagnostic outcomes were held in the decision node. Four morphological features were selected as diagnostic clues used routinely in the discrimination of endometrial hyperplasias. These represented the evidence nodes and were linked to the decision node by conditional probability matrices. The system was designed with a computer user interface (CytoInform) where reference images for a given clue were displayed to assist the pathologist in entering evidence into the network. Reproducibility of diagnostic classification was tested on 50 cases chosen by a gynaecological pathologist. These comprised ten cases each of proliferative endometrium, simple hyperplasia, complex hyperplasia, atypical hyperplasia and grade 1 endometrioid adenocarcinoma. The DSS was tested by two consultant pathologists, two junior pathologists and two medical students. Intra- and inter-observer agreement was calculated following conventional histological examination of the slides on two occasions by the consultants and junior pathologists without the use of the DSS. All six participants then assessed the slides using the expert system on two occasions, enabling inter- and intra-observer agreement to be calculated. Using unaided conventional diagnosis, weighted kappa values for intra-observer agreement ranged from 0.645 to 0.901. Using the DSS, the results for the four pathologists ranged from 0.650 to 0.845. Both consultant pathologists had slightly worse weighted kappa values using the DSS, while both junior pathologists achieved slightly better values using the system. The grading of morphological features and the cumulative probability curve provided a quantitative record of the decision route for each case. This allowed a more precise comparison of individuals and identified why discordant diagnoses were made. Taking the original diagnoses of the consultant gynaecological pathologist as the ‘gold standard’, there was excellent or moderate to good inter-observer agreement between the ‘gold standard’ and the results obtained by the four pathologists using the expert system, with weighted kappa values of 0.586–0.872. The two medical students using the expert system achieved weighted kappa values of 0.771 (excellent) and 0.560 (moderate to good) compared to the ‘gold standard’. This study illustrates the potential of expert systems in the classification of endometrial hyperplasias. Copyright © 2002 John Wiley & Sons, Ltd.

Objectives: The association between aspirin use and improved survival after colorectal cancer diagnosis may be more pronounced in tumors that have PIK3CA mutations or high PTGS2 expression. However, the evidence of a difference in association by biomarker status lacks consistency. In this population-based colon cancer cohort study the interaction between these biomarkers, aspirin use, and survival was assessed.Methods: The cohort consisted of 740 stage II and III colon cancer patients diagnosed between 2004 and 2008. Aspirin use was determined through clinical note review. Tissue blocks were retrieved to determine immunohistochemical assessment of PTGS2 expression and the presence of PIK3CA mutations. Cox proportional hazards models were used to calculate hazard ratios (HR) and 95% confidence intervals (CI) for colorectal cancer-specific and overall survival.Results: In this cohort aspirin use was associated with a 31% improvement in cancer-specific survival compared to non-use (adjusted HR=0.69, 95% CI 0.47–0.98). This effect was more pronounced in tumors with high PTGS2 expression (PTGS2-high adjusted HR=0.55, 95% CI 0.32–0.96) compared to those with low PTGS2 expression (PTGS2-low adjusted HR=1.19, 95% CI 0.68–2.07, P for interaction=0.09). The aspirin by PTGS2 interaction was significant for overall survival (PTGS2-high adjusted HR=0.64, 95% CI 0.42–0.98 vs. PTGS2-low adjusted HR=1.28, 95% CI 0.80–2.03, P for interaction=0.04). However, no interaction was observed between aspirin use and PIK3CA mutation status for colorectal cancer-specific or overall survival.Conclusions: Aspirin use was associated with improved survival outcomes in this population-based cohort of colon cancer patients. This association differed according to PTGS2 expression but not PIK3CA mutation status. Limiting adjuvant aspirin trials to PIK3CA-mutant colorectal cancer may be too restrictive.

Aim(1) A pilot study to determine the accuracy of interpretation of whole slide digital images in a broad range of general histopathology cases of graded complexity. (2) To survey the participating histopathologists with regard to acceptability of digital pathology.Materials and methodsGlass slides of 100 biopsies and minor resections were digitally scanned in their entirety, producing digital slides. These cases had been diagnosed by light microscopy at least 1 year previously and were subsequently reassessed by the original reporting pathologist (who was blinded to their original diagnosis) using digital pathology. The digital pathology-based diagnosis was compared with the original glass slide diagnosis and classified as concordant, slightly discordant (without clinical consequence) or discordant. The participants were surveyed at the end of the study.ResultsThere was concordance between the original light microscopy diagnosis and digital pathology-based diagnosis in 95 of the 100 cases while the remaining 5 cases showed only slight discordance (with no clinical consequence). None of the cases were categorised as discordant. Participants had mixed experiences using digital pathology technology.ConclusionsIn the broad range of cases we examined, digital pathology is a safe and viable method of making a primary histopathological diagnosis.

Objective To examine mitomycin-C (MMC)-induced apoptosis in an ex vivo model of superficial TCC, and relate it to the in vivo response to chemotherapy.Materials and methods Dose- and time-response curves were constructed to determine the optimal conditions for the induction of apoptosis by MMC in an ex vivo model of superficial bladder cancer. Subsequently, 41 individual tumours were exposed to MMC in the model and the effects assessed by measuring of apoptosis before and after chemotherapy. The relationships between tumour grade and stage and the intrinsic and induced apoptotic counts were determined. In tandem, in a clinical study, the relationship between in vivo response of a marker tumour to MMC and the ex vivo induction of apoptosis was determined.Results In the ex vivo model, apoptosis was induced at a MMC concentration of 0.5 mg/mL after an incubation time of 8 h. In 41 tumours the intrinsic apoptotic index (AI) was higher with increased grade and stage of tumour (P = 0.048). There was no correlation between the intrinsic AI and the AI after treatment with MMC (induced AI). In 21 tumours (51%) the induced AI did not increase above a predetermined response threshold and these tumours were considered resistant to MMC. Resistance to MMC was related to tumour grade (P = 0.037) with a trend for G3 pT1 tumours to be resistant to the therapy. There was a significant association between ex vivo sensitivity and in vivo marker tumour response (P = 0.02).Conclusions Apoptosis is differentially induced in an ex vivo incubation model of superficial TCC by MMC and evidence suggests that this response matches that seen in vivo. The measurement of apoptosis before therapy does not predict the apoptotic response of a tumour to chemotherapy. The ability to undergo apoptosis correlates with clinical outcome.

The development of prognostic models for assisting medical practitioners with decision making is not a trivial task. Models need to possess a number of desirable characteristics and few, if any, current modelling approaches based on statistical or artificial intelligence can produce models that display all these characteristics. The inability of modelling techniques to provide truly useful models has led to interest in these models being purely academic in nature. This in turn has resulted in only a very small percentage of models that have been developed being deployed in practice. On the other hand, new modelling paradigms are being proposed continuously within the machine learning and statistical community and claims, often based on inadequate evaluation, being made on their superiority over traditional modelling methods. We believe that for new modelling approaches to deliver true net benefits over traditional techniques, an evaluation centric approach to their development is essential. In this paper we present such an evaluation centric approach to developing extensions to the basic k-nearest neighbour (k-NN) paradigm. We use standard statistical techniques to enhance the distance metric used and a framework based on evidence theory to obtain a prediction for the target example from the outcome of the retrieved exemplars. We refer to this new k-NN algorithm as Censored k-NN (Ck-NN). This reflects the enhancements made to k-NN that are aimed at providing a means for handling censored observations within k-NN.

Fine-needle aspiration (FNA) cytology is a rapid and inexpensive technique used extensively in the diagnosis of breast disease. To remove diagnostic subjectivity, a diagnostic decision support system (DDSS) called CytoInform© has been developed, based on a Bayesian belief network (BBN) for the diagnosis of breast FNAs. In addition to acting as a DDSS, the system implements a computer-based training (CBT) system, providing a novel approach to breast cytology training. The system guides the trainee cytopathologist through the diagnostic process, allowing the user to grade each diagnostic feature using a set of on-screen reference images as visual clues. The trainee positions a slider on a spectrum relative to these images, reflecting the similarity between the reference image and the microscope image. From this, an evidence vector is generated, allowing the current diagnostic probability to be updated by the BBN. As the trainee assesses each clue, the evidence entered is compared with that of the expert through the use of a defined teaching file. This file records the relative severity of each clue and a tolerance band within which the trainee must position the slider. When all clues in the teaching case have been completed, the system informs the user of inaccuracies and offers the ability to reassess problematic features. In trials with two pathologists of different experience and a series of ten cases, the system provided an effective tool in conveying diagnostic evidence and protocols to trainees. This is evident from the fact that each pathologist only misinterpreted one case and a total of 86%/88% (experienced/inexperienced) of all clues assessed were interpreted correctly. Significantly, in all cases that produced the correct final diagnostic probability, the route taken to that solution was consistent with the expert's solution. Copyright © 2001 John Wiley & Sons, Ltd.

Non-small cell lung carcinoma remains by far the leading cause of cancer-related deaths worldwide. Overexpression of FLIP, which blocks the extrinsic apoptotic pathway by inhibiting caspase-8 activation, has been identified in various cancers. We investigated FLIP and procaspase-8 expression in NSCLC and the effect of HDAC inhibitors on FLIP expression, activation of caspase-8 and drug resistance in NSCLC and normal lung cell line models. Immunohistochemical analysis of cytoplasmic and nuclear FLIP and procaspase-8 protein expression was carried out using a novel digital pathology approach. Both FLIP and procaspase-8 were found to be significantly overexpressed in tumours, and importantly, high cytoplasmic expression of FLIP significantly correlated with shorter overall survival. Treatment with HDAC inhibitors targeting HDAC1-3 downregulated FLIP expression predominantly via post-transcriptional mechanisms, and this resulted in death receptor- and caspase-8-dependent apoptosis in NSCLC cells, but not normal lung cells. In addition, HDAC inhibitors synergized with TRAIL and cisplatin in NSCLC cells in a FLIP- and caspase-8-dependent manner. Thus, FLIP and procaspase-8 are overexpressed in NSCLC, and high cytoplasmic FLIP expression is indicative of poor prognosis. Targeting high FLIP expression using HDAC1-3 selective inhibitors such as entinostat to exploit high procaspase-8 expression in NSCLC has promising therapeutic potential, particularly when used in combination with TRAIL receptor-targeted agents.

The long term objective of this research was the development of objective, digitally defined procedures for histopathologic assessment. The development of procedures based on digital knowledge had as its first aim the design of a machine vision system with image understanding capability (ie, capable of autonomous processing and analyses of histopathologic imagery). Next, histometric and karyometric diagnostic information extraction led to highly specific characterization of nuclei and lesions. Based on such detailed characterizations, we were able to derive progression curves for prostatic, colonic, breast epithelial, and esophageal lesions. The specific signatures of nuclei and lesions revealed s~tbstantial diversity among lesions of the same visual-diagnostic grade; profiles of deviation of nuclei from a normal standard were derived to provide a novel, additional level of diagnostically discriminating features. Knowledge guided machine vision opens the way to an extremely specific characterization of nuclei and lesions, which may allow better prediction of biological behavior and, thus, more accurate individual patient targeted prognosis. (The J Histoteclzizol 23: 183, 2000)

Background To identify nuclei and lesions with great specificity, a large set of karyometric features is arranged in the form of a linear profile, called a nuclear signature. The karyometric feature values are normalized as z-values. Their ordering along the profile axis is arbitrary but consistent. The profile of the nuclear signature is distinctive; it can be characterized by a new set of variables called contour features. A number of data reduction methods are introduced and their performance is compared with that of the karyometric features in the classification of prostatic, colonic, and esophageal lesions. Methods Contour characteristics were reduced to descriptive statistics of the set of z-values in the nuclear signature and to sequence information. The contour features derived were (1) relative frequencies of occurrence of z-values and of their differences and (2) co-occurrence statistics, run lengths of z-values, and statistics of higher-order dependencies. Performance was evaluated by comparing classification scores of diagnostic groups. Results Rates for correct classification by karyometric features alone and contour features alone indicate equivalent performance. Classification by a combined set of features led to an increase in correct classification. Conclusions Image analysis and subsequent data reduction of nuclear signatures of contour features is a novel method, providing quantitative information that may lead to an effective identification of nuclei and lesions. Cytometry 41:133–138, 2000 © 2000 Wiley-Liss, Inc.

The histological grading of cervical intraepithelial neoplasia (CIN) remains subjective, resulting in inter- and intra-observer variation and poor reproducibility in the grading of cervical lesions. This study has attempted to develop an objective grading system using automated machine vision. The architectural features of cervical squamous epithelium are quantitatively analysed using a combination of computerized digital image processing and Delaunay triangulation analysis; 230 images digitally captured from cases previously classified by a gynaecological pathologist included normal cervical squamous epithelium (n=30), koilocytosis (n=46), CIN 1 (n=52), CIN 2 (n=56), and CIN 3 (n=46). Intra- and inter-observer variation had kappa values of 0.502 and 0.415, respectively. A machine vision system was developed in KS400 macro programming language to segment and mark the centres of all nuclei within the epithelium. By object-oriented analysis of image components, the positional information of nuclei was used to construct a Delaunay triangulation mesh. Each mesh was analysed to compute triangle dimensions including the mean triangle area, the mean triangle edge length, and the number of triangles per unit area, giving an individual quantitative profile of measurements for each case. Discriminant analysis of the geometric data revealed the significant discriminatory variables from which a classification score was derived. The scoring system distinguished between normal and CIN 3 in 98.7% of cases and between koilocytosis and CIN 1 in 76.5% of cases, but only 62.3% of the CIN cases were classified into the correct group, with the CIN 2 group showing the highest rate of misclassification. Graphical plots of triangulation data demonstrated the continuum of morphological change from normal squamous epithelium to the highest grade of CIN, with overlapping of the groups originally defined by the pathologists. This study shows that automated location of nuclei in cervical biopsies using computerized image analysis is possible. Analysis of positional information enables quantitative evaluation of architectural features in CIN using Delaunay triangulation meshes, which is effective in the objective classification of CIN. This demonstrates the future potential of automated machine vision systems in diagnostic histopathology. Copyright © 2000 John Wiley & Sons, Ltd.

The evaluation of prostate cancer histology following hormonal therapy often represents a diagnostic problem for the pathologist. Previous studies have shown that an inference or Bayesian belief network (BBN) offers a descriptive classifier useful for the accurate analysis of morphological changes in individual cases of prostate neoplasia. Three different BBNs were evaluated in 94 cancer foci present in 20 radical prostatectomy (RP) specimens and in the matching biopsies in which the initial diagnosis of prostatic adenocarcinoma was made. Ten RP specimens were from patients treated with total androgen ablation or combination endocrine therapy (CET) before surgery. The first and second BBN allowed the identification with high certainty of the cancer foci present in the biopsies and RP specimens, as well as their Gleason grade, the belief value often being close to 1.0. The results of the second BBN showed a good correspondence between the Gleason grade given in the biopsies and that in the RP specimens, except in the surgical material of the treated patients, in which upgrading was always present. The third BBN showed the existence of three subgroups in treated RP specimens, one with morphological effect, another with poor effect, and the third with the histology of untreated (i.e. unaffected) cancer. In conclusion, an inference network-based analysis allows the characterization of treated prostate cancers according to the degree of histopathological change.

The aims of this investigation were to compare quantitative with qualitative analysis of fluorescent in situ hybridization (FISH) centromere signals in interphase breast cancer cell nuclei and to evaluate the possible clinical utility of detecting numerical abnormalities of chromosomes 11 and 17 by FISH in the preoperative prediction of breast cancer histological grade. Commercial digoxigenin-labeled centromere probes to chromosomes 11 and 17 were hybridized to 69 malignant aspirates with histological follow-up. Aspirates were categorized as disomic or aneusomic for chromosomes 11 and 17 qualitatively; a subset of aspirates was also analyzed quantitatively. The quantitative and qualitative approaches resulted in almost identical categorisation. There was a significant association between the qualitative categorization of aspirates as aneusomic or disomic, the histological grade of the excised tumours (P = .0695, n = 69), and the cytological grade of the clinical aspirates (P = .006, n = 35). Although histological grade III tumors were almost invariably polysomic for one or both chromosomes, polysomy was also detected in grade I and II tumors. Qualitative FISH analysis was shown to be more sensitive than cytological grading in predicting histological grade III but was of lower specificity and was therefore not clinically useful.

Aims Metallothioneins (MTs) are a group of ubiquitous low molecular weight proteins with a high affinity for heavy metal ions. The aim of the present study was to investigate MT expression in a series of endometrial carcinomas. We wished to determine whether MT expression in endometrial carcinoma was related to established prognostic factors such as tumour grade, stage and histological type. We also wanted to establish if high MT expression in curettings of endometrial carcinoma was predictive of high expression in the subsequent hysterectomy specimen. Methods and results Sixty-three cases of endometrial carcinoma were included in the study. These comprised 57 endometrioid adenocarcinomas (15 grade 1, 25 grade 2, 17 grade 3), three papillary serous adenocarcinomas, two mucinous adenocarcinomas and one clear cell adenocarcinoma. Forty-five tumours were stage I, 10 were stage II and eight were stage III. In 28 cases, diagnostic endometrial curettings, performed prior to hysterectomy, were available for study. Immunohistochemical staining was performed using the anti-MT monoclonal antibody E9. The intensity and distribution of MT staining were assessed using a semiquantitative method. This resulted in an intensity distribution (ID) score out of a maximum of 300. The mean ID score of grade 1 and 2 endometrioid adenocarcinomas was 67 and 63, respectively, while for grade 3 tumours the mean ID score was 114. This was statistically significant (P = 0.05). The three papillary serous adenocarcinomas had high ID scores with a mean of 208. The mean ID score of stage I tumours was 69. This was lower than those of stage II and III tumours which had mean ID scores of 116 and 128, respectively. However, these differences were not statistically significant (P = 0.288). A significant correlation was observed between MT ID scores in endometrial curettings and in the subsequent hysterectomy (P = 0.013). Conclusions MT isoforms can be demonstrated in most endometrial adenocarcinomas. High MT ID scores are associated with high grade and high stage endometrial adenocarcinomas and with the aggressive papillary serous adenocarcinoma. Whether this is of value as an independent prognostic factor has yet to be established.

Primary synovial chondromatosis (PSC) is thought to be a cartilaginous metaplasia, but it may recur locally and malignant change has been reported. Histologically, the cartilage is usually cellular, with binucleate forms. These findings suggest that the disease is not simply a metaplasia but imply a proliferative component. In this study, immunohistochemical detection of Ki-67 protein using an antigen retrieval microwave heating technique and DNA image cytometry (VIDAS image analysis system) has been used to assess the proliferative activity in 20 cases of PSC and the results have been compared with those obtained in other cartilage tissues: ten enchondromas, ten chondrosarcomas, and ten samples of normal articular cartilage. There was no detectable staining for Ki-67 protein in cases of PSC or in benign tissues, but there was a significant association between Ki-67 labelling index and grade in the chondrosarcomas (P

Automation in histopathology is an attractive concept and recent advances in the application of computerized expert systems and machine vision have made automated image analysis of histological images possible. Systems capable of complete automation not only require the ability to segment tissue features and grade histological abnormalities, but, must also be capable of locating diagnostically useful areas from within complex histological scenes. This is the first stage of the diagnostic process. The object of this study was to develop criteria for the automatic identification of focal areas of colorectal dysplasia from a background of histologically normal tissue. Fields of view representing normal colorectal mucosa (n = 120) and dysplastic mucosa (n = 120) were digitally captured and subjected to image texture analysis. Two features were selected as being the most important in the discrimination of normal and adenomatous colorectal mucosa. The first was a feature of the co-occurrence matrix and the second was the number of low optical density pixels in the image. A linear classification rule defined using these two features was capable of correctly classifying 86 per cent of a series of training images into their correct groups. In addition, large histological scenes were digitally captured, split into their component images, analysed according to texture, and classified as normal or abnormal using the previously defined classification rule. Maps of the histological scenes were constructed and in most cases, dysplastic colorectal mucosa was correctly identified on the basis of image texture: 83 per cent of test images were correctly classified. This study demonstrates that abnormalities in low-power tissue morphology can be identified using quantitative image analysis. The identification of diagnostically useful fields advances the potential of automated systems in histopathology: these regions could than be scrutinized at high power using knowledge-guided image segmentation for disease grading. Systems of this kind have the potential to provide objectivity, unbiased sampling, and valuable diagnostic decision support.

The distinction between ductal hyperplasia (DH) and ductal carcinoma in situ (DCIS) still remains a problem in the histological diagnosis of non-invasive breast lesions. In this study, a method was developed for the automatic segmentation and quantitative analysis of breast ducts using knowledge-guided machine vision. This permitted duct profiles and intraduct lumina to be identified and their shape, size, and number computed. These were used to derive measures of duct cribriformity and architectural complexity which were examined as an objective tool in the characterization of duct pattern in proliferative lesions. A total of 215 images of ducts were digitally captured from 22 cases of DCIS and 21 cases of DH diagnosed independently by two pathologists. The cribriformity index proved to be a useful measure of duct architecture, showing a nosotonic increase with increasing duct complexity. The number of lumins also increased with increasing overgrowth of ductal epithelium until the duct was filled. Discriminant analysis of the duct characteristics for benign and malignant groups selected the lumen area/duct area ratio and the duct area as significant discriminatory variables and they were combined into a discriminant function. Of the lumens features, the mean area of the lumen and the polar average (mean of the distribution of the number of events with an increasing spiral from the centre of the duct) were combined into a second discriminant function. Plotting cases against these two functions provided good separation of DH and DCIS groups, with correct classification estimated on the training sample as being over 80 per cent. With an increasing incidence of complex proliferative lesions arising from mammography, the ability to diagnose these lesions correctly is more important than ever. The use of expert system-guided machine vision facilitates the quantitative evaluation of breast duct architecture; along with established histological and cytological criteria, it is hoped that this will lead to a more objective means of diagnosis and disease classification.

Current analytic methodologies allow the extraction, even from small tumor masses, of extensive information on the biologic characteristics of malignant lesions, such as tumor aggressivity, metastatic potential, drug resistance, and host interactions. Clinical practice now offers a wide range of therapeutic strategies. Information technological advances offer the opportunity to refer to very large data bases of patient anamnestic data, response to treatment and clinical outcome. There is a need to formulate therapy and prognosis for each individual case. Case based reasoning is a knowledge based methodology where the outcome for complex situations can be predicted by referring to a large data base of cases of known outcomes. The preliminary data obtained from this study suggest that case based reasoning may offer a promising approach to individual targeted prognosis.

The advent of next generation sequencing technologies (NGS) has expanded the area of genomic research, offering high coverage and increased sensitivity over older microarray platforms. Although the current cost of next generation sequencing is still exceeding that of microarray approaches, the rapid advances in NGS will likely make it the platform of choice for future research in differential gene expression. Connectivity mapping is a procedure for examining the connections among diseases, genes and drugs by differential gene expression initially based on microarray technology, with which a large collection of compound-induced reference gene expression profiles have been accumulated. In this work, we aim to test the feasibility of incorporating NGS RNA-Seq data into the current connectivity mapping framework by utilizing the microarray based reference profiles and the construction of a differentially expressed gene signature from a NGS dataset. This would allow for the establishment of connections between the NGS gene signature and those microarray reference profiles, alleviating the associated incurring cost of re-creating drug profiles with NGS technology. We examined the connectivity mapping approach on a publicly available NGS dataset with androgen stimulation of LNCaP cells in order to extract candidate compounds that could inhibit the proliferative phenotype of LNCaP cells and to elucidate their potential in a laboratory setting. In addition, we also analyzed an independent microarray dataset of similar experimental settings. We found a high level of concordance between the top compounds identified using the gene signatures from the two datasets. The nicotine derivative cotinine was returned as the top candidate among the overlapping compounds with potential to suppress this proliferative phenotype. Subsequent lab experiments validated this connectivity mapping hit, showing that cotinine inhibits cell proliferation in an androgen dependent manner. Thus the results in this study suggest a promising prospect of integrating NGS data with connectivity mapping.

Background Modern cancer research often involves large datasets and the use of sophisticated statistical techniques. Together these add a heavy computational load to the analysis, which is often coupled with issues surrounding data accessibility. Connectivity mapping is an advanced bioinformatic and computational technique dedicated to therapeutics discovery and drug re-purposing around differential gene expression analysis. On a normal desktop PC, it is common for the connectivity mapping task with a single gene signature to take > 2h to complete using sscMap, a popular Java application that runs on standard CPUs (Central Processing Units). Here, we describe new software, cudaMap, which has been implemented using CUDA C/C++ to harness the computational power of NVIDIA GPUs (Graphics Processing Units) to greatly reduce processing times for connectivity mapping. Results cudaMap can identify candidate therapeutics from the same signature in just over thirty seconds when using an NVIDIA Tesla C2050 GPU. Results from the analysis of multiple gene signatures, which would previously have taken several days, can now be obtained in as little as 10 minutes, greatly facilitating candidate therapeutics discovery with high throughput. We are able to demonstrate dramatic speed differentials between GPU assisted performance and CPU executions as the computational load increases for high accuracy evaluation of statistical significance. Conclusion Emerging ‘omics’ technologies are constantly increasing the volume of data and information to be processed in all areas of biomedical research. Embracing the multicore functionality of GPUs represents a major avenue of local accelerated computing. cudaMap will make a strong contribution in the discovery of candidate therapeutics by enabling speedy execution of heavy duty connectivity mapping tasks, which are increasingly required in modern cancer research. cudaMap is open source and can be freely downloaded from http://purl.oclc.org/NET/cudaMap.

Three-dimensional (3D) reconstruction can be used to study the structural relationships between various tissue components and can help in the understanding of disease processes. Examples of perineural invasion have been reconstructed in two cases of adenocarcinoma of the extrahepatic bile duct. Ninety-six serial 5 microns sections were taken from both cases stored on file and 3D images were constructed using a Kontron VIDAS image analysis system. Both cases showed continuity of tumour cells within the perineural space. The isolated islands of malignant glands seen by conventional microscopy were shown to be in continuity with larger tumour cell masses via a complex branching network. In addition, direct continuity was demonstrated between malignant glands within the perineural space and those within the surrounding stromal tissue. During growth, the tumour appeared to have followed the plane of least resistance, although the availability of the perineural space may itself have been shaped by pressure effects and/or proteolytic enzyme secretion. Three-dimensional reconstruction of perineural invasion in adenocarcinoma of the extrahepatic bile ducts shows the value of this technique in demonstrating the structural relationships between the tumour and the host nerve bundle.

The aim of this investigation was to explore the relationships between p53 mutation, DNA aneuploidy, 17p deletions, and clinical stage in ovarian cancer. Nuclear suspensions were obtained by tissue disaggregation, stained with propidium iodide, and analysed on a Coulter EPICS Elite flow cytometer. DNA cell cycle analysis was performed using Multicycle software (Phoenix Flow Systems). DNA extracted from paraffin-embedded archival carcinomas/non-tumour tissue was used as template for PCR amplification of the microsatellite dinucleotide repeat polymorphism D17S513, a locus telomeric to p53 on 17p13.1. Allele loss at D17S513 was detected in 64.5 per cent of carcinomas (20 of 31 informative cases). DNA aneuploidy was detected in 20 of 54 (37 per cent) carcinomas. Eight of ten cases previously shown to harbour p53 mutations showed aneuploid DNA content. Although ten other DNA aneuploid cases had shown no p53 mutations, the results show a statistically significant association between p53 mutation and DNA aneuploidy (P < 0.01). Furthermore, the mean DNA index of the DNA aneuploid cases was significantly higher in p53 mutant cases compared with those showing no p53 mutation (P = 0.02). There was also a significant association between p53 mutations and stage, between ploidy and stage, and between allelic deletions at D17S513 or p53 and stage, but not between these allelic deletions and ploidy. p53 mutations appear to be associated with DNA aneuploidy in ovarian cancer independently of 17p deletions. p53 mutations, DNA aneuploidy, and 17p deletions are associated with late stage.

The assessment of cell proliferation in colorectal tissue may provide information with both prognostic and therapeutic implications. A variety of methods are available, including flow cytometric estimations of S phase fraction, immunohistochemical and autoradiographic visualization of exogenous and endogenous proliferation proteins, and morphological and stathmokinetic techniques. There is some correlation between Dukes stage and proliferation state features, and there is increased proliferative activity throughout the adenoma–carcinoma sequence. Data on cell proliferation rates are difficult to obtain. When correctly applied, the metaphase arrest technique remains the ‘gold standard’ of measuring proliferation, but its usefulness in clinical practice is limited. Recent studies have employed dual measurement flow cytometry and double labelling techniques to produce rate data.

Prostatic intraepithelial neoplasia (PIN) diagnosis and grading are affected by uncertainties which arise from the fact that almost all knowledge of PIN histopathology is expressed in concepts, descriptive linguistic terms, and words. A Bayesian belief network (BBN) was therefore used to reduce the problem of uncertainty in diagnostic clue assessment, while still considering the dependences between elements in the reasoning sequence. A shallow network was used with an open-tree topology. with eight first-level descendant nodes for the diagnostic clues (evidence nodes), each independently linked by a conditional probability matrix to a root node containing the diagnostic alternatives (decision node). One of the evidence nodes was based on the tissue architecture and the others were based on cell features. The system was designed to be interactive, in that the histopathologist entered evidence into the network in the form of likelihood ratios for outcomes at each evidence node. The efficiency of the network was tested on a series of 110 prostate specimens. subdivided as follows : 22 cases of non-neoplastic prostate or benign prostatic tissue (NP), 22 PINs of low grade (PINlow), 22 PINs of high grade (PINhigh), 22 prostatic adenocarcinomas with cribriform pattern (PACcri), and 22 prostatic adenocarcinomas with large acinar pattern (PAClgac). The results obtained in the benign and malignant categories showed that the belief for the diagnostic alternatives is very high, the values being in general more than 0.8 and often dose to 1.0. When considering the PIN lesions, the network classified and graded most of the cases with high certainty. However, there were some cases which showed values less than 0.8 (13 cases out of 44), thus indicating that there are situations in which the feature changes are intermediate between contiguous categories or grades. Discrepancy between morphological grading and the BBN results was observed in four out of 44 PIN cases : one PINlow was classified as PINhigh and three PlNhigh were classified as PINlow. In conclusion, the network can grade PIN lesions and differentiate them from other prostate lesions with certainty. In particular, it offers a descriptive classifier which is readily implemented and which allows the use of linguistic, fuzzy variables.

Basal cell adenoma is a well recognized histopathologic variant of monomorphic adenoma of salivary gland, but in recent years there have been occasional reports of malignant basal cell tumors of major salivary glands. We present a case report of one such basal cell adenocarcinoma arising in the submandibular gland and discuss the differential diagnosis and distinction from a basal cell adenoma. We also review the published literature on basal cell adenocarcinoma. This is the first case of basal cell adenocarcinoma to be studied in detail by both immunohistochemistry and electron microscopy. There has been debate in the past as to the cell of origin of salivary gland basal cell tumors and specifically as to whether myoepithelial differentiation occurs in these tumors. The immunohistochemical and ultrastructural features of this case provide evidence for a dual population of ductal and myoepithelial cells. A flow cytometric analysis of nuclear deoxyribonucleic acid content showed the presence of DNA aneuploidy within one tissue block.

Background: Co-localisation is a widely used measurement in immunohistochemical analysis to determine if fluorescently labelled biological entities, such as cells, proteins or molecules share a same location. However the measurement of co-localisation is challenging due to the complex nature of such fluorescent images, especially when multiple focal planes are captured. The current state-of-art co-localisation measurements of 3-dimensional (3D) image stacks are biased by noise and cross-overs from non-consecutive planes.Method: In this study, we have developed Co-localisation Intensity Coefficients (CICs) and Co-localisation Binary Coefficients (CBCs), which uses rich z-stack data from neighbouring focal planes to identify similarities between image intensities of two and potentially more fluorescently-labelled biological entities. This was developed using z-stack images from murine organotypic slice cultures from central nervous system tissue, and two sets of pseudo-data. A large amount of non-specific cross-over situations are excluded using this method. This proposed method is also proven to be robust in recognising co-localisations even when images are polluted with a range of noises.Results: The proposed CBCs and CICs produce robust co-localisation measurements which are easy to interpret, resilient to noise and capable of removing a large amount of false positivity, such as non-specific cross-overs. Performance of this method of measurement is significantly more accurate than existing measurements, as determined statistically using pseudo datasets of known values. This method provides an important and reliable tool for fluorescent 3D neurobiological studies, and will benefit other biological studies which measure fluorescence co-localisation in 3D.

Background: Digital pathology provides a digital environment for the management and interpretation of pathological images and associated data. It is becoming increasing popular to use modern computer based tools and applications in pathological education, tissue based research and clinical diagnosis. Uptake of this new technology is stymied by its single user orientation and its prerequisite and cumbersome combination of mouse and keyboard for navigation and annotation.Methodology: In this study we developed SurfaceSlide, a dedicated viewing platform which enables the navigation and annotation of gigapixel digitised pathological images using fingertip touch. SurfaceSlide was developed using the Microsoft Surface, a 30 inch multitouch tabletop computing platform. SurfaceSlide users can perform direct panning and zooming operations on digitised slide images. These images are downloaded onto the Microsoft Surface platform from a remote server on-demand. Users can also draw annotations and key in texts using an on-screen virtual keyboard. We also developed a smart caching protocol which caches the surrounding regions of a field of view in multi-resolutions thus providing a smooth and vivid user experience and reducing the delay for image downloading from the internet. We compared the usability of SurfaceSlide against Aperio ImageScope and PathXL online viewer.Conclusion: SurfaceSlide is intuitive, fast and easy to use. SurfaceSlide represents the most direct, effective and intimate human–digital slide interaction experience. It is expected that SurfaceSlide will significantly enhance digital pathology tools and applications in education and clinical practice.

Evasion of apoptosis contributes to both tumourigenesis and drug resistance in non-small cell lung carcinoma (NSCLC). The pro-apoptotic BCL-2 family proteins BAX and BAK are critical regulators of mitochondrial apoptosis. New strategies for targeting NSCLC in a mitochondria-independent manner should bypass this common mechanism of apoptosis block. BRCA1 mutation frequency in lung cancer is low; however, decreased BRCA1 mRNA and protein expression levels have been reported in a significant proportion of lung adenocarcinomas. BRCA1 mutation/deficiency confers a defect in homologous recombination DNA repair that has been exploited by synthetic lethality through inhibition of PARP (PARPi) in breast and ovarian cells; however, it is not known whether this same synthetic lethal mechanism exists in NSCLC cells. Additionally, it is unknown whether the mitochondrial apoptotic pathway is required for BRCA1/PARPi-mediated synthetic lethality. Here we demonstrate that silencing of BRCA1 expression by RNA interference sensitizes NSCLC cells to PARP inhibition. Importantly, this sensitivity was not attenuated in cells harbouring mitochondrial apoptosis block induced by co-depletion of BAX and BAK. Furthermore, we demonstrate that BRCA1 inhibition cannot override platinum resistance, which is often mediated by loss of mitochondrial apoptosis signalling, but can still sensitize to PARP inhibition. Finally we demonstrate the existence of a BRCA1-deficient subgroup (11-19%) of NSCLC patients by analysing BRCA1 protein levels using immunohistochemistry in two independent primary NSCLC cohorts. Taken together, the existence of BRCA1-immunodeficient NSCLC suggests that this molecular subgroup could be effectively targeted by PARP inhibitors in the clinic and that PARP inhibitors could be used for the treatment of BRCA1-immunodeficient, platinum-resistant tumours.

Background: 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.Methodology: 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.Conclusion: 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.

Thirteen nuclear and cellular morphometric variables were measured in 312 cases of colorectal adenocarcinoma. All variables, except nuclear shape factors, differed significantly (P

Background: Tissue MicroArrays (TMAs) are a valuable platform for tissue based translational research and the discovery of tissue biomarkers. The digitised TMA slides or TMA Virtual Slides, are ultra-large digital images, and can contain several hundred samples. The processing of such slides is time-consuming, bottlenecking a potentially high throughput platform.Methods: A High Performance Computing (HPC) platform for the rapid analysis of TMA virtual slides is presented in this study. Using an HP high performance cluster and a centralised dynamic load balancing approach, the simultaneous analysis of multiple tissue-cores were established. This was evaluated on Non-Small Cell Lung Cancer TMAs for complex analysis of tissue pattern and immunohistochemical positivity.Results: The automated processing of a single TMA virtual slide containing 230 patient samples can be significantly speeded up by a factor of circa 22, bringing the analysis time to one minute. Over 90 TMAs could also be analysed simultaneously, speeding up multiplex biomarker experiments enormously.Conclusion: The methodologies developed in this paper provide for the first time a genuine high throughput analysis platform for TMA biomarker discovery that will significantly enhance the reliability and speed for biomarker research. This will have widespread implications in translational tissue based research.

The main curative therapy for patients with nonsmall cell lung cancer is surgery. Despite this, the survival rate is only 50%, therefore it is important to more efficiently diagnose and predict prognosis for lung cancer patients. Raman spectroscopy is useful in the diagnosis of malignant and premalignant lesions. The aim of this study is to investigate the ability of Raman microscopy to diagnose lung cancer from surgically resected tissue sections, and predict the prognosis of these patients. Tumor tissue sections from curative resections are mapped by Raman microscopy and the spectra analzsed using multivariate techniques. Spectra from the tumor samples are also compared with their outcome data to define their prognostic significance. Using principal component analysis and random forest classification, Raman microscopy differentiates malignant from normal lung tissue. Principal component analysis of 34 tumor spectra predicts early postoperative cancer recurrence with a sensitivity of 73% and specificity of 74%. Spectral analysis reveals elevated porphyrin levels in the normal samples and more DNA in the tumor samples. Raman microscopy can be a useful technique for the diagnosis and prognosis of lung cancer patients receiving surgery, and for elucidating the biochemical properties of lung tumors.

Reliable pathological interpretation is vital to so many aspects of tissue-based research as well as being central to patient care. Understanding the complex processes involved in decision-making is the starting point to improve both diagnostic reproducibility and the definition of diagnostic groups that underpin our experiments. Unfortunately, there is a paucity of research in this field and it is encouraging to see The Journal of Pathology publishing work in this area. This review attempts to highlight the opportunities that exist in this field and the technologies that are now available to support this type of research. Key amongst these are the use of decision analysis tools such as inference networks, and virtual microscopy that allows us to simulate diagnostic decision-making. These tools have roles, not only in studying the subtleties of diagnostic decision-making, but also in delivering new methods of training and proficiency testing. Research which helps us to better understand what we see, why we see it, and standardizing interpretative reasoning in pathological classification is essential for improving the wide range of activities that pathologists support, including clinical diagnosis, teaching, training, and experimental research.

Objective: The study summarizes results of karyometric measurements in epithelial cells of the colorectal mucosa to document evidence of a field effect of preneoplastic development among patients with colorectal adenocarcinoma or adenoma. Methods: Karyometric analyses were done on high-resolution images of histologic sections from 48 patients with colorectal adenocarcinomas and 44 patients with adenomas and on images from matching normal-appearing mucosa directly adjacent to such lesions, at a 1-cm and 10-cm distance from the lesions or from the rectal mucosa of adenoma patients, as well as from 24 healthy normal controls with no family history of colonic disease. Results: The nuclei recorded in the histologically normal-appearing mucosa of patients with either colorectal adenoma or adenocarcinoma exhibited differences in karyometric features in comparison with nuclei recorded in rectal mucosa from patients who were free of a colonic lesion. These differences were expressed to the same extent in tissue adjacent to the lesions and in normal-appearing tissue as distant as the rectum. Conclusions: The nuclear chromatin pattern may serve as an integrating biomarker for a preneoplastic development. The field effect might provide an end point in chemopreventive intervention trials. (Cancer Epidemiol Biomarkers Prev 2007;16(12):2704–16)

Cervical virtual slides are ultra-large, can have size up to 120 K times 80 K pixels. This paper introduces an image segmentation method for the automated identification of Squamous epithelium from such virtual slides. In order to produce the best segmentation results, in addition to saving processing time and memory, a multiresolution segmentation strategy was developed. The Squamous epithelium layer is first segmented at a low resolution (2X magnification). The boundaries of segmented Squamous epithelium are further fine tuned at the highest resolution of 40X magnification, using an iterative boundary expanding-shrinking method. The block- based segmentation method uses robust texture feature vectors in combination with a Support Vector Machine (SVM) to perform classification. Medical histology rules are finally applied to remove misclassifications. Results demonstrate that, with typical virtual slides, classification accuracies of between 94.9% and 96.3% are achieved.

OBJECTIVE To explore the nuclear chromatin phenotype, overall epigenetic mechanisms, chromatin remodelling enzymes and their role as diagnostic biomarkers in prostate lesions, using high-resolution computerized quantitative digital image analysis (DIA). MATERIALS AND METHODS A tissue microarray (TMA) was constructed using paraffin wax-embedded prostatic tissues from 78 patients, containing 30 cores of benign prostatic hyperplasia (BPH), 10 of low-grade prostatic intraepithelial neoplasia (LGPIN), 38 of prostate adenocarcinoma, 20 of BPH tissue excised at 0.6–1 mm from LGPIN lesions, and 10 of BPH prostatic tissues obtained 0.6–1 mm from high-grade PIN (HGPIN) lesions. Chromatin phenotype was assessed on haematoxylin-stained sections using high-resolution texture analysis. For quantitative immunohistochemistry, antibodies raised against acetylated histone H3 lysine 9 (AcH3K9), 5′methylcytidine (5MeC) and the chromatin remodelling ATPase ISWI (SNF2H and SNF2L) were used. The immunodensity was measured using DIA to determine the epigenetic profile of the cases. At least 60 nuclei were measured from each case. RESULTS There were many statistically significant differences in staining intensity and nuclear distribution patterns in chromatin phenotype and immunostaining (p ≤ 0.001). These changes allowed the differentiation between the various pathological subgroups with a classification accuracy of 76–100% using chromatin phenotype or immunostaining measuring epigenetic and chromatin remodelling changes (5MeC, AcH3K9 and ISWI). In PIN lesions, there was a high chromatin content with DNA-hypermethylation, while in prostatic adenocarcinoma there was a lower chromatin content with DNA-hypomethylation and H3K9-hypoacetylation. There was significantly more ISWI protein in neoplastic tissues. There were malignancy-associated changes (MAC) in chromatin phenotype and overall epigenetic events in BPH tissues adjacent to PIN lesions. CONCLUSIONS The present study confirms the ability of high-resolution computerized digital imaging of nuclear texture features to detect changes in chromatin phenotype, epigenetic events and the presence of chromatin remodelling, factors that can be used to distinguish between different prostatic pathologies, i.e. BPH, LGPIN, HGPIN and prostate adenocarcinoma, and further allow the detection of MAC near PIN lesions. These results provide a base for future diagnostic applications of DIA combined with immunohistochemistry. Our experiments underscore the importance of epigenetic mechanisms during carcinogenesis. Further studies are needed to elucidate the complex interplay between chromatin structure, its modifications and the progression of prostate cancer.

Chromatin phenotype is known to be significantly disrupted in cancer. This has been demonstrated in many morphologic studies on cancer and in recent years by the application of digital texture analysis for quantitative evaluation of chromatin phenotype in neoplasia. Studies have consistently demonstrated the role of chromatin phenotype as a biomarker of diagnosis and prognosis. The underlying molecular mechanisms for chromatin reorganization and its role as a biomarker are largely unknown, but epigenetic processes are likely to be a main factor that not only modify chromatin arrangement but in doing so alter gene expression profiles in a reversible fashion. Of the range of epigenetic modifications that might control chromatin phenotype, histone acetylation is a strong candidate because of its role in the direct modification of chromatin, both through local relaxation of nucleosomal structure and recruitment of chromatin remodeling complexes. The reversible nature of histone acetylation is therapeutically attractive for treatment of aberrant histone acetylation; however, it still remains to be seen whether histone deacetylase inhibitors are clinically applicable or for use primarily as valuable research tools. This review explores the role of histone acetylation in cancer development, as a potential therapeutic candidate and a potential biomarker in tissue pathology.

Small bowel accounts for only 0.5% of cancer cases in the US; a third of which are adenocarcinomas. But incidence rates have been rising at a rate of 2.4% per year over the last decade. Because of the rarity of this cancer, little is known about its molecular pathology and there are no molecular markers for diagnosis, predicting prognosis or therapeutic intervention. The aim of this study was therefore to look into this disease at a molecular level to better understand its biology and identify biomarkers and potential points of therapeutic intervention. Using a retrospective 28 patient matched normal-tumor cohort next generation sequencing (NGS) was performed using a 50 gene cancer hotspot panel, gene expression arrays were used to profile ∼29,000 RNA transcripts and 450k CpG methylation arrays were used to carry out DNA methylation analysis. We also looked at microsatellite instability (MSI), HER2 and p53 expression. NGS identified novel mutations in IDH1, CDH1, KIT, NRAS, FGFR2, FLT3, NPM1, PTEN, MET, AKT1, RET, NOTCH1 and ERBB4. Previously known mutations such as high-frequency KRAS and TP53 and low-frequency HER2 were also confirmed in our cohort. The average patient had 2.6 mutations with eight patients having only a single mutation to one having seven. Array data revealed 17% of CpGs and 5% of RNA transcripts assayed to be differentially methylated and expressed respectively (p This study has for the first time highlighted the extent of molecular changes taking place in SBA. The clinical potential of TP53 mutations and Kazald1 hypomethylation as prognostic biomarkers and CHN2 as a diagnostic biomarker are focus areas for further research by our group. Citation Format: Muhammad A. Alvi, Darragh G. McArt, Paul Kelly, Marc-Aurel Fuchs, Matthew Alderdice, Clare M. McCabe, Victoria Bingham, Claire McGready, Stephen McQuaid, Perry Maxwell, Peter Hamilton, Jacqueline A. James, Richard Wilson, Manuel Salto-Tellez. Comprehensive molecular pathology analysis of small bowel adenocarcinoma reveals novel targets with clinical utility. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4792. doi:10.1158/1538-7445.AM2015-4792

3573 Background: Despite the use of 5-FU-based adjuvant therapies, a large proportion of stage III (locally advanced) colorectal cancer (CRC) patients will relapse and die of metastatic disease. Re...

Background: Nuclear texture analysis measures phenotypic changes in chromatin distribution within a cell nucleus, while the alkaline Comet assay is a sensitive method for measuring the extent of DNA breakage in individual cells. The authors aim to use both methods to provide information about the sensitivity of cells to ionizing radiation. Methods: The alkaline Comet assay was performed on six human bladder carcinoma cell lines and one human urothelial cell line exposed to γ-radiation doses from 0 to 10 Gy. Nuclear chromatin texture analysis of 40 features was then performed in the same cell lines exposed to 0, 2, and 6 Gy to explore if nuclear phenotype was related to radiation sensitivity. Results: Comet assay results demonstrated that the cell lines exhibited different levels of radiosensitivity and could be divided into a radiosensitive and a radioresistant group at >6 Gy. Using stepwise discriminant analysis, a subset of important nuclear texture features that best discriminated between sensitive and resistant cell lines were identified A classification function, defined using these features, correctly classified 81.75% of all cells into their radiosensitive or radioresistant groups based on their pretreatment chromatin phenotype. Posttreatment chromatin changes also varied between cell lines, with sensitive cell lines showing a relaxed chromatin conformation following radiation, whereas resistant cell lines exhibited chromatin condensation. Conclusions: The authors conclude that the alkaline Comet assay and nuclear texture methodologies may prove to be valuable aids in predicting the response of tumor cells to radiotherapy. © 2006 International Society for Analytical Cytology

Breast cancer remains a frequent cause of female cancer death despite the great strides in elucidation of biological subtypes and their reported clinical and prognostic significance. We have defined a general cohort of breast cancers in terms of putative actionable targets, involving growth and proliferative factors, the cell cycle, and apoptotic pathways, both as single biomarkers across a general cohort and within intrinsic molecular subtypes. We identified 293 patients treated with adjuvant chemotherapy. Additional hormonal therapy and trastuzumab was administered depending on hormonal and HER2 status respectively. We performed immunohistochemistry for ER, PR, HER2, MM1, CK5/6, p53, TOP2A, EGFR, IGF1R, PTEN, p-mTOR and e-cadherin. The cohort was classified into luminal (62%) and non-luminal (38%) tumors as well as luminal A (27%), luminal B HER2 negative (22%) and positive (12%), HER2 enriched (14%) and triple negative (25%). Patients with luminal tumors and co-overexpression of TOP2A or IGF1R loss displayed worse overall survival (p=0.0251 and p=0.0008 respectively). Non-luminal tumors had much greater heterogeneous expression profiles with no individual markers of prognostic significance. Non-luminal tumors were characterised by EGFR and TOP2A overexpression, IGF1R, PTEN and p-mTOR negativity and extreme p53 expression. Our results indicate that only a minority of intrinsic subtype tumors purely express single novel actionable targets. This lack of pure biomarker expression is particular prevalent in the triple negative subgroup and may allude to the mechanism of targeted therapy inaction and myriad disappointing trial results. Utilising a combinatorial biomarker approach may enhance studies of targeted therapies providing additional information during design and patient selection while also helping decipher negative trial results. Citation Format: Manuel Salto-Tellez, David P. Boyle, Darragh McArt, Gareth Irwin, Charlotte Charlotte Wilhelm-Benartzi, Tong G. LIoe, Martha Minter, Stephen McQuaid, Paul Mullan, Richard D. Kennedy, Peter Hamilton, D P. Harkin. Defining a therapeutic classification of breast cancer by actionable targets. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1905. doi:10.1158/1538-7445.AM2014-1905

A large body of evidence has implicated mitochondria in control of cell death, where key apoptotic mechanisms involve change in mitochondrial membrane permeability and depolarisation of mitochondrial membrane potential (Δψm). Assessment of Δψm is traditionally conducted using the lipophilic cation JC-1 on the flow cytometer or by fluorescent microscopy. Here we assess JC-1 aggregation using the novel tool of digital texture analysis to establish mitochondrial phenotypic changes induced by the K+ ionophore, valinomycin in a unique model comprising SW480 and SW620 cell lines. This provides an opportunity to study these phenomena in the context of colorectal cancer. Valinomycin-induced apoptosis was detected using morphology and analysis of DNA content. Cells were treated with valinomycin, images digitally recorded on a calibrated video photometer and subjected to high resolution digital texture analysis. This demonstrated that the HARAM texture features (Mean of the Haralick texture features) were highly valuable in describing the transition of Δψm as the cell undergoes apoptosis. In Conclusion this study illustrates the potential of texture analysis as a novel and additional technique for quantifying JC-1 aggregation and revealing the spectrum of collapse of Δψm during apoptosis.