Embracing an integromic approach to tissue biomarker research in cancer: Perspectives and lessons learned.

Modern approaches to biomedical research and diagnostics targeted towards precision medicine are generating 'big data' across a range of high-throughput experimental and analytical platforms. Integrative analysis of this rich clinical, pathological, molecular and imaging data represents one of the greatest bottlenecks in biomarker discovery research in cancer and other diseases. Following on from the publication of our successful framework for multimodal data amalgamation and integrative analysis, Pathology Integromics in Cancer (PICan), this article will explore the essential elements of assembling an integromics framework from a more detailed perspective. PICan, built around a relational database storing curated multimodal data, is the research tool sitting at the heart of our interdisciplinary efforts to streamline biomarker discovery and validation. While recognizing that every institution has a unique set of priorities and challenges, we will use our experiences with PICan as a case study and starting point, rationalizing the design choices we made within the context of our local infrastructure and specific needs, but also highlighting alternative approaches that may better suit other programmes of research and discovery. Along the way, we stress that integromics is not just a set of tools, but rather a cohesive paradigm for how modern bioinformatics can be enhanced. Successful implementation of an integromics framework is a collaborative team effort that is built with an eye to the future and greatly accelerates the processes of biomarker discovery, validation and translation into clinical practice.

Integrated tumor identification and automated scoring minimizes pathologist involvement and provides new insights to key biomarkers in breast cancer.

Digital image analysis (DIA) is becoming central to the quantitative evaluation of tissue biomarkers for discovery, diagnosis and therapeutic selection for the delivery of precision medicine. In this study, automated DIA using a new purpose-built software platform (QuPath) is applied to a cohort of 293 breast cancer patients to score five biomarkers in tissue microarrays (TMAs): ER, PR, HER2, Ki67 and p53. This software is able to measure IHC expression following fully automated tumor recognition in the same immunohistochemical (IHC)-stained tissue section, as part of a rapid workflow to ensure objectivity and accelerate biomarker analysis. The digital scores produced by QuPath were compared with manual scores by a pathologist and shown to have a good level of concordance in all cases (Cohen's κ>0.6), and almost perfect agreement for the clinically relevant biomarkers ER, PR and HER2 (κ>0.86). To assess prognostic value, cutoff thresholds could be applied to both manual and automated scores using the QuPath software, and survival analysis performed for 5-year overall survival. DIA was shown to be capable of replicating the statistically significant stratification of patients achieved using manual scoring across all biomarkers (P<0.01, log-rank test). Furthermore, the image analysis scores were shown to consistently lead to statistical significance across a wide range of potential cutoff thresholds, indicating the robustness of the method, and identify sub-populations of cases exhibiting different expression patterns within the p53 and Ki67 data sets that warrant further investigation. These findings have demonstrated QuPath's suitability for fast, reproducible, high-throughput TMA analysis across a range of important biomarkers. This was achieved using our tumor recognition algorithms for IHC-stained sections, trained interactively without the need for any additional tumor recognition markers, for example, cytokeratin, to obtain greater insight into the relationship between biomarker expression and clinical outcome applicable to a range of cancer types.

Validation of the systematic scoring of immunohistochemically stained tumour tissue microarrays using QuPath digital image analysis.

AIMS: Output from biomarker studies involving immunohistochemistry applied to tissue microarrays (TMA) is limited by the lack of an efficient and reproducible scoring methodology. In this study, we examine the functionality and reproducibility of biomarker scoring using the new, open-source, digital image analysis software, QuPath. METHODS AND RESULTS: Three different reviewers, with varying experience of digital pathology and image analysis, applied an agreed QuPath scoring methodology to CD3 and p53 immunohistochemically stained TMAs from a colon cancer cohort (n = 661). Manual assessment was conducted by one reviewer for CD3. Survival analyses were conducted and intra- and interobserver reproducibility assessed. Median raw scores differed significantly between reviewers, but this had little impact on subsequent analyses. Lower CD3 scores were detected in cases who died from colorectal cancer compared to control cases, and this finding was significant for all three reviewers (P-value range = 0.002-0.02). Higher median p53 scores were generated among cases who died from colorectal cancer compared with controls (P-value range = 0.04-0.12). The ability to dichomotise cases into high versus low expression of CD3 and p53 showed excellent agreement between all three reviewers (kappa score range = 0.82-0.93). All three reviewers produced dichotomised expression scores that resulted in very similar hazard ratios for colorectal cancer-specific survival for each biomarker. Results from manual and QuPath methods of CD3 scoring were comparable, but QuPath scoring revealed stronger prognostic stratification. CONCLUSIONS: Scoring of immunohistochemically stained tumour TMAs using QuPath is functional and reproducible, even among users of limited experience of digital pathology images, and more accurate than manual scoring.

Statin use, candidate mevalonate pathway biomarkers, and colon cancer survival in a population-based cohort study.

BACKGROUND: Statin use after colorectal cancer diagnosis may improve survival but evidence from observational studies is conflicting. The anti-cancer effect of statins may be restricted to certain molecular subgroups. In this population-based cohort study, the interaction between p53 and 3-hydroxy-3-methylglutaryl coenzyme-A reductase (HMGCR) expression, KRAS mutations, and the association between statin use and colon cancer survival was assessed. METHODS: The cohort consisted of 740 stage II and III colon cancer patients diagnosed between 2004 and 2008. Statin use was determined through clinical note review. Tissue blocks were retrieved to determine immunohistochemical expression of p53 and HMGCR in tissue microarrays and the presence of KRAS mutations in extracted DNA. Cox proportional hazards models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for colorectal cancer-specific and overall survival. RESULTS: Statin use was not associated with improved cancer-specific survival in this cohort (HR=0.91, 95% CI 0.64-1.28). Statin use was also not associated with improved survival when the analyses were stratified by tumour p53 (wild-type HR=1.31, 95% CI 0.67-2.56 vs aberrant HR=0.80, 95% CI 0.52-1.24), HMGCR (HMGCR-high HR=0.69, 95% CI 0.40-1.18 vs HMGCR-low HR=1.10, 95% CI 0.66-1.84), and KRAS (wild-type HR=0.73, 95% CI 0.44-1.19 vs mutant HR=1.21, 95% CI 0.70-2.21) status. CONCLUSIONS: Statin use was not associated with improved survival either independently or when stratified by potential mevalonate pathway biomarkers in this population-based cohort of colon cancer patients.

A gene-signature progression approach to identifying candidate small-molecule cancer therapeutics with connectivity mapping.

BACKGROUND: Gene expression connectivity mapping has gained much popularity recently with a number of successful applications in biomedical research testifying its utility and promise. Previously methodological research in connectivity mapping mainly focused on two of the key components in the framework, namely, the reference gene expression profiles and the connectivity mapping algorithms. The other key component in this framework, the query gene signature, has been left to users to construct without much consensus on how this should be done, albeit it has been an issue most relevant to end users. As a key input to the connectivity mapping process, gene signature is crucially important in returning biologically meaningful and relevant results. This paper intends to formulate a standardized procedure for constructing high quality gene signatures from a user's perspective. RESULTS: We describe a two-stage process for making quality gene signatures using gene expression data as initial inputs. First, a differential gene expression analysis comparing two distinct biological states; only the genes that have passed stringent statistical criteria are considered in the second stage of the process, which involves ranking genes based on statistical as well as biological significance. We introduce a "gene signature progression" method as a standard procedure in connectivity mapping. Starting from the highest ranked gene, we progressively determine the minimum length of the gene signature that allows connections to the reference profiles (drugs) being established with a preset target false discovery rate. We use a lung cancer dataset and a breast cancer dataset as two case studies to demonstrate how this standardized procedure works, and we show that highly relevant and interesting biological connections are returned. Of particular note is gefitinib, identified as among the candidate therapeutics in our lung cancer case study. Our gene signature was based on gene expression data from Taiwan female non-smoker lung cancer patients, while there is evidence from independent studies that gefitinib is highly effective in treating women, non-smoker or former light smoker, advanced non-small cell lung cancer patients of Asian origin. CONCLUSIONS: In summary, we introduced a gene signature progression method into connectivity mapping, which enables a standardized procedure for constructing high quality gene signatures. This progression method is particularly useful when the number of differentially expressed genes identified is large, and when there is a need to prioritize them to be included in the query signature. The results from two case studies demonstrate that the approach we have developed is capable of obtaining pertinent candidate drugs with high precision.

QUADrATiC: scalable gene expression connectivity mapping for repurposing FDA-approved therapeutics.

BACKGROUND: Gene expression connectivity mapping has proven to be a powerful and flexible tool for research. Its application has been shown in a broad range of research topics, most commonly as a means of identifying potential small molecule compounds, which may be further investigated as candidates for repurposing to treat diseases. The public release of voluminous data from the Library of Integrated Cellular Signatures (LINCS) programme further enhanced the utilities and potentials of gene expression connectivity mapping in biomedicine. RESULTS: We describe QUADrATiC ( http://go.qub.ac.uk/QUADrATiC ), a user-friendly tool for the exploration of gene expression connectivity on the subset of the LINCS data set corresponding to FDA-approved small molecule compounds. It enables the identification of compounds for repurposing therapeutic potentials. The software is designed to cope with the increased volume of data over existing tools, by taking advantage of multicore computing architectures to provide a scalable solution, which may be installed and operated on a range of computers, from laptops to servers. This scalability is provided by the use of the modern concurrent programming paradigm provided by the Akka framework. The QUADrATiC Graphical User Interface (GUI) has been developed using advanced Javascript frameworks, providing novel visualization capabilities for further analysis of connections. There is also a web services interface, allowing integration with other programs or scripts. CONCLUSIONS: QUADrATiC has been shown to provide an improvement over existing connectivity map software, in terms of scope (based on the LINCS data set), applicability (using FDA-approved compounds), usability and speed. It offers potential to biological researchers to analyze transcriptional data and generate potential therapeutics for focussed study in the lab. QUADrATiC represents a step change in the process of investigating gene expression connectivity and provides more biologically-relevant results than previous alternative solutions.

Whole slide image cytometry: a novel method to detect abnormal DNA content in Barrett's esophagus.

Barrett's esophagus (BE) is a precursor of esophageal adenocarcinoma (EAC). Both low-grade dysplasia (LGD) and high-grade dysplasia (HGD) are associated with an increased risk of progression to EAC. However, histological interpretation and grading of dysplasia (particularly LGD) is subjective and poorly reproducible. This study has combined whole slide imaging with DNA image cytometry to provide a novel method for the detection of abnormal DNA content through image analysis of tissue sections. A total of 20 cases were evaluated, including 8 negative for dysplasia (NFD), 6 LGD, and 6 HGD. Feulgen-stained esophageal sections were scanned in their entirety. Barrett's mucosa was interactively chosen for automatic nuclei segmentation where irrelevant cell types were ignored. The combined DNA content histogram for all nuclei within selected image regions was then obtained. In addition, three histogram measurements were computed, including xER-5C, 2cDI, and DNA-MG. Visual evaluation suggested the shape of DNA content histograms from NFD, LGD, and HGD cases exhibiting identifiable differences. The histogram measurements, xER-5C, 2cDI, and DNA-MG, were shown to be effective in differentiating metaplastic from dysplastic cases with statistical significance. Moreover, they also successfully separated NFD, LGD, and HGD patients with statistical significance. Whole slide image cytometry is a novel and effective method for the detection of abnormal DNA content in BE. Compared with histological review, it is more objective. Compared with flow cytometry and cytology-preparation image cytometry, it is low cost, simple to use, only requires a single 1 µm section, and facilitates selection of tissue and topographical correlation. Whole slide image cytometry can detect differences in DNA content between NFD, LGD, and HGD patients in this cross-sectional study. Abnormal DNA content detection by whole slide image cytometry is a promising biomarker of progression that could affect future diagnostics in BE.

Epidermal growth factor receptor immunohistochemistry: new opportunities in metastatic colorectal cancer.

The treatment of cancer is becoming more precise, targeting specific oncogenic drivers with targeted molecular therapies. The epidermal growth factor receptor has been found to be over-expressed in a multitude of solid tumours. Immunohistochemistry is widely used in the fields of diagnostic and personalised medicine to localise and visualise disease specific proteins. To date the clinical utility of epidermal growth factor receptor immunohistochemistry in determining monoclonal antibody efficacy has remained somewhat inconclusive. The lack of an agreed reproducible scoring criteria for epidermal growth factor receptor immunohistochemistry has, in various clinical trials yielded conflicting results as to the use of epidermal growth factor receptor immunohistochemistry assay as a companion diagnostic. This has resulted in this test being removed from the licence for the drug panitumumab and not performed in clinical practice for cetuximab. In this review we explore the reasons behind this with a particular emphasis on colorectal cancer, and to suggest a way of resolving the situation through improving the precision of epidermal growth factor receptor immunohistochemistry with quantitative image analysis of digitised images complemented with companion molecular morphological techniques such as in situ hybridisation and section based gene mutation analysis.

Digital pathology and image analysis in tissue biomarker research.

Digital pathology and the adoption of image analysis have grown rapidly in the last few years. This is largely due to the implementation of whole slide scanning, advances in software and computer processing capacity and the increasing importance of tissue-based research for biomarker discovery and stratified medicine. This review sets out the key application areas for digital pathology and image analysis, with a particular focus on research and biomarker discovery. A variety of image analysis applications are reviewed including nuclear morphometry and tissue architecture analysis, but with emphasis on immunohistochemistry and fluorescence analysis of tissue biomarkers. Digital pathology and image analysis have important roles across the drug/companion diagnostic development pipeline including biobanking, molecular pathology, tissue microarray analysis, molecular profiling of tissue and these important developments are reviewed. Underpinning all of these important developments is the need for high quality tissue samples and the impact of pre-analytical variables on tissue research is discussed. This requirement is combined with practical advice on setting up and running a digital pathology laboratory. Finally, we discuss the need to integrate digital image analysis data with epidemiological, clinical and genomic data in order to fully understand the relationship between genotype and phenotype and to drive discovery and the delivery of personalized medicine.

The prognostic significance of the aberrant extremes of p53 immunophenotypes in breast cancer.

AIMS: The utility of p53 as a prognostic assay has been elusive. The aims of this study were to describe a novel, reproducible scoring system and assess the relationship between differential p53 immunohistochemistry (IHC) expression patterns, TP53 mutation status and patient outcomes in breast cancer. METHODS AND RESULTS: Tissue microarrays were used to study p53 IHC expression patterns: expression was defined as extreme positive (EP), extreme negative (EN), and non-extreme (NE; intermediate patterns). Overall survival (OS) was used to define patient outcome. A representative subgroup (n = 30) showing the various p53 immunophenotypes was analysed for TP53 hotspot mutation status (exons 4-9). Extreme expression of any type occurred in 176 of 288 (61%) cases. As compared with NE expression, EP expression was significantly associated (P = 0.039) with poorer OS. In addition, as compared with NE expression, EN expression was associated (P = 0.059) with poorer OS. Combining cases showing either EP or EN expression better predicted OS than either pattern alone (P = 0.028). This combination immunophenotype was significant in univariate but not multivariate analysis. In subgroup analysis, six substitution exon mutations were detected, all corresponding to extreme IHC phenotypes. Five missense mutations corresponded to EP staining, and the nonsense mutation corresponded to EN staining. No mutations were detected in the NE group. CONCLUSIONS: Patients with extreme p53 IHC expression have a worse OS than those with NE expression. Accounting for EN as well as EP expression improves the prognostic impact. Extreme expression positively correlates with nodal stage and histological grade, and negatively with hormone receptor status. Extreme expression may relate to specific mutational status.

PARP inhibition induces BAX/BAK-independent synthetic lethality of BRCA1-deficient non-small cell lung cancer.

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.

Hyperacetylation in prostate cancer induces cell cycle aberrations, chromatin reorganization and altered gene expression profiles.

Histone acetylation is a fundamental mechanism in the regulation of local chromatin conformation and gene expression. Research has focused on the impact of altered epigenetic environments on the expression of specific genes and their pathways. However, changes in histone acetylation also have a global impact on the cell. In this study we used digital texture analysis to assess global chromatin patterns following treatment with trichostatin A (TSA) and have observed significant alterations in the condensation and distribution of higher-order chromatin, which were associated with altered gene expression profiles in both immortalised normal PNT1A prostate cell line and androgen-dependent prostate cancer cell line LNCaP. Furthermore, the extent of TSA-induced disruption was both cell cycle and cell line dependent. This was illustrated by the identification of sub-populations of prostate cancer cells expressing high levels of H3K9 acetylation in the G(2)/M phase of the cell cycle that were absent in normal cell populations. In addition, the analysis of enriched populations of G(1) cells showed a global decondensation of chromatin exclusively in normal cells.

Do we see what we think we see? The complexities of morphological assessment.

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.

Changes in chromatin phenotype predict the response to hormonal deprivation therapy in patients with prostate cancer.

OBJECTIVE: To assess the value of studying chromatin organization using high-resolution digital image analysis to predict the response to hormonal-deprivation therapy (HDT) in patients with prostate cancer, using pretreatment prostate tissues. MATERIALS AND METHODS: A tissue microarray (TMA) was constructed using pretreatment paraffin-embedded tissues from transurethral resection of the prostate (TURP) samples (48 patients, 96 cores). None of the patients had received any treatment for prostate cancer before TURP. The patients' medical records for 5 years after treatment were assessed; patients were divided, based on their prostatic specific antigen (PSA) levels after treatment, into those optimally responsive to HDT (14) and those resistant to HDT (34). The latter were further subclassified based on their nadir PSA level. Imaging comprised a calibrated digital image-analysis system with software for densitometric and texture analysis, the latter being assessed on manually segmented nuclei (> or =30 nuclei/core). RESULTS: Most of the measured digital texture features assessing chromatin density and distribution were significantly different between the prognostic groups (P = 0.001). In the training set, 12 of 14 HDT-responsive and 23 (68%) of HDT-resistant patients were accurately predicted. However, all HDT-resistant patients with a nadir PSA level of >5 ng/mL were accurately predicted. The overall classification sensitivity was 47%, specificity 94% with a positive predictive value of 85%. However, the sensitivity was 100% between patients optimally responsive to HDT and those poorly responsive with a nadir PSA level of >5 ng/mL. CONCLUSION: Quantitative image analysis of chromatin phenotype showed promising value in predicting before treatment the response to HDT in patients diagnosed with prostatic adenocarcinoma. However, further work using larger data sets is required before adapting the technique in routine clinical practice.

Chromatin phenotype karyometry can predict recurrence in papillary urothelial neoplasms of low malignant potential.

BACKGROUND: A preceding exploratory study (J. Clin. Pathol. 57(2004), 1201-1207) had shown that a karyometric assessment of nuclei from papillary urothelial neoplasms of low malignant potential (PUNLMP) revealed subtle differences in phenotype which correlated with recurrence of disease. AIM OF THE STUDY: To validate the results from the exploratory study on a larger sample size. MATERIALS: 93 karyometric features were analyzed on haematoxylin and eosin-stained sections from 85 cases of PUNLMP. 45 cases were from patients who had a solitary PUNLMP lesion and were disease-free during a follow-up period of at least 8 years. The other 40 were from patients with a unifocal PUNLMP, with one or more recurrences in the follow-up. A combination of the previously defined classification functions together with a new P-index derived classification method was used in an attempt to classify cases and identify a biomarker of recurrence in PUNLMP lesions. RESULTS: Validation was pursued by a number of separate approaches. First, the exact procedure from the exploratory study was applied to the large validation set. Second, since the discriminant function 2 of the exploratory study had been based on a small sample size, a new discriminant function was derived. The case classification showed a correct classification of 61% for non-recurrent and 74% for recurrent cases, respectively. Greater success was obtained by applying unsupervised learning technologies to take advantage of phenotypical composition (correct classification of 92%). This approach was validated by dividing the data into training and test sets with 2/3 of the cases assigned to the training sets, and 1/3 to the test sets, on a rotating basis, and validation of the classification rate was thus tested on three separate data sets by a leave-k-out process. The average correct classification was 92.8% (training set) and 84.6% (test set). CONCLUSIONS: Our validation study detected subvisual differences in chromatin organization state between non-recurrent and recurrent PUNLMP, thus allowing a very stable method of predicting recurrence of papillary urothelial neoplasms of low malignant potential by karyometry.

KRAS mutant colorectal cancer gene signatures identified angiotensin II receptor blockers as potential therapies.

Colorectal cancer (CRC) is a life-threatening disease with high prevalence and mortality worldwide. The KRAS oncogene is mutated in approximately 40% of CRCs. While antibody based EGFR inhibitors (cetuximab and panitumumab) represent a major treatment strategy for advanced KRAS wild type (KRAS-WT) CRCs, there still remains no effective therapeutic course for advanced KRAS mutant (KRAS-MT) CRC patients.In this study, we employed a novel and comprehensive approach of gene expression connectivity mapping (GECM) to identify candidate compounds to target KRAS-MT tumors. We first created a combined KRAS-MT gene signature with 248 ranked significant genes using 677 CRC clinical samples. A series of 248 sub-signatures was then created containing an increasing number of the top ranked genes. As an input to GECM analysis, each sub-signature was translated into a statistically significant therapeutic drugs list, which was finally combined to obtain a single list of significant drugs.We identify four antihypertensive angiotensin II receptor blockers (ARBs) within the top 30 significant drugs indicating that these drugs have a mechanism of action that can alter the KRAS-MT CRC oncogenic signaling. A hypergeometric test (p-value = 6.57 × 10-6) confirmed that ARBs are significantly enriched in our results. These findings support the hypothesis that ARB antihypertensive drugs may directly block KRAS signaling resulting in improvement in patient outcome or, through a reversion to a KRAS wild-type phenotype, improve the response to anti-EGFR treatment. Antihypertensive angiotensin II receptor blockers are therefore worth further investigation as potential therapeutic candidates in this difficult category of advanced colorectal cancers.

IHC-based subcellular quantification provides new insights into prognostic relevance of FLIP and procaspase-8 in non-small-cell lung cancer.

In this study, we developed an image analysis algorithm for quantification of two potential apoptotic biomarkers in non-small-cell lung cancer (NSCLC): FLIP and procaspase-8. Immunohistochemical expression of FLIP and procaspase-8 in 184 NSCLC tumors were assessed. Individual patient cores were segmented and classified as tumor and stroma using the Definiens Tissue Studio. Subsequently, chromogenic expression of each biomarker was measured separately in the nucleus and cytoplasm and reported as a quantitative histological score. The software package pROC was applied to define biomarker thresholds. Cox proportional hazards analysis was applied to generate hazard ratios (HRs) and associated 95% CI for survival. High cytoplasmic expression of tumoral (but not stromal) FLIP was associated with a 2.5-fold increased risk of death in lung adenocarcinoma patients, even when adjusted for known confounders (HR 2.47, 95% CI 1.14-5.35). Neither nuclear nor cytoplasmic tumoral procaspase-8 expression was associated with overall survival in lung adenocarcinoma patients; however, there was a significant trend (P for trend=0.03) for patients with adenocarcinomas with both high cytoplasmic FLIP and high cytoplasmic procaspase-8 to have a multiplicative increased risk of death. Notably, high stromal nuclear procaspase-8 expression was associated with a reduced risk of death in lung adenocarcinoma patients (adjusted HR 0.31, 95% CI 0.15-0.66). On further examination, the cells with high nuclear procaspase-8 were found to be of lymphoid origin, suggesting that the better prognosis of patients with tumors with high stromal nuclear procaspase-8 is related to immune infiltration, a known favorable prognostic factor. No significant associations were detected in analysis of lung squamous cell carcinoma patients. Our results suggest that cytoplasmic expression of FLIP in the tumor and nuclear expression of procaspase-8 in the stroma are prognostically relevant in non-small-cell adenocarcinomas but not in squamous cell carcinomas of the lung.

QuPath: Open source software for digital pathology image analysis.

QuPath is new bioimage analysis software designed to meet the growing need for a user-friendly, extensible, open-source solution for digital pathology and whole slide image analysis. In addition to offering a comprehensive panel of tumor identification and high-throughput biomarker evaluation tools, QuPath provides researchers with powerful batch-processing and scripting functionality, and an extensible platform with which to develop and share new algorithms to analyze complex tissue images. Furthermore, QuPath's flexible design makes it suitable for a wide range of additional image analysis applications across biomedical research.

Evaluation of PTGS2 Expression, PIK3CA Mutation, Aspirin Use and Colon Cancer Survival in a Population-Based Cohort Study.

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.