Search

Supplementary figure 1: A +B. Hierarchical clustering of microdissected stromal and epithelial (A) and FACS sorted endothelial, fibroblast, epithelial and leukocyte (B) cell populations using previously published non-epithelial derived gene list confirms purity of cell populations. C and D. PCA plot of data presented in top panels highlights clear stratification of samples based on cell lineage using this gene signature. E. Expression values for IFNG expression according to cell-of-origin confirms immune-derived nature of signal. Supplementary figure 2: Association of high PD-L1 expression with MSI subtype and high immune infiltrate. Supplementary figure 3: A. Dot and boxplots of PD-L1 mean gene expression values stratified by MSI status highlights significant association between PD-L1 transcription levels and MSI genotype. B. (Left) Hierarchical clustering of stage II/III CRC cohort based on expression profiles of CD274, CTLA4, LAG3 and IDO1. (Right) Significant upregulation of CD274, IDO1, CTLA4 and LAG3 genes in the PD-L1high subgroup. C. (Left) Dot plot and boxplot of IFNG expression levels stratified by PD-L1-subgroup. (Right) Significant upregulation of IFNG gene in the PD-L1high subgroup.

A recent phase II study of patients with metastatic colorectal carcinoma showed that mismatch repair gene status was predictive of clinical response to PD-1–targeting immune checkpoint blockade. Further examination revealed strong correlation between PD-L1 protein expression and microsatellite instability (MSI) in stage IV colorectal carcinoma, suggesting that the amount of PD-L1 protein expression could identify late-stage patients who might benefit from immunotherapy. To assess whether the clinical associations between PD-L1 gene expression and MSI identified in metastatic colorectal carcinoma are also present in stage II/III colorectal carcinoma, we used in silico analysis to elucidate the cell types expressing the PD-L1 gene. We found a statistically significant association of PD-L1 gene expression with MSI in early-stage colorectal carcinoma (P < 0.001) and show that, unlike in non–colorectal carcinoma tumors, PD-L1 is derived predominantly from the immune infiltrate. We demonstrate that PD-L1 gene expression has positive prognostic value in the adjuvant disease setting (PD-L1low vs. PD-L1high HR = 9.09; CI, 2.11–39.10). PD-L1 gene expression had predictive value, as patients with high PD-L1 expression appear to be harmed by standard-of-care treatment (HR = 4.95; CI, 1.10–22.35). Building on the promising results from the metastatic colorectal carcinoma PD-1–targeting trial, we provide compelling evidence that patients with PD-L1high/MSI/immunehigh stage II/III colorectal carcinoma should not receive standard chemotherapy. This conclusion supports the rationale to clinically evaluate this patient subgroup for PD-1 blockade treatment. Cancer Immunol Res; 4(7); 582–91. ©2016 AACR.

This Supplementary Figure indicates the project plan

Supplementary Table 4: Differentially expressed gene list contrasting tumour region of origin between Lymph Node and Invasive Front.

ACE white paper

Purpose: A number of independent gene expression profiling studies have identified transcriptional subtypes in colorectal cancer with potential diagnostic utility, culminating in publication of a colorectal cancer Consensus Molecular Subtype classification. The worst prognostic subtype has been defined by genes associated with stem-like biology. Recently, it has been shown that the majority of genes associated with this poor prognostic group are stromal derived. We investigated the potential for tumor misclassification into multiple diagnostic subgroups based on tumoral region sampled.Experimental Design: We performed multiregion tissue RNA extraction/transcriptomic analysis using colorectal-specific arrays on invasive front, central tumor, and lymph node regions selected from tissue samples from 25 colorectal cancer patients.Results: We identified a consensus 30-gene list, which represents the intratumoral heterogeneity within a cohort of primary colorectal cancer tumors. Using a series of online datasets, we showed that this gene list displays prognostic potential HR = 2.914 (confidence interval 0.9286–9.162) in stage II/III colorectal cancer patients, but in addition, we demonstrated that these genes are stromal derived, challenging the assumption that poor prognosis tumors with stem-like biology have undergone a widespread epithelial–mesenchymal transition. Most importantly, we showed that patients can be simultaneously classified into multiple diagnostically relevant subgroups based purely on the tumoral region analyzed.Conclusions: Gene expression profiles derived from the nonmalignant stromal region can influence assignment of colorectal cancer transcriptional subtypes, questioning the current molecular classification dogma and highlighting the need to consider pathology sampling region and degree of stromal infiltration when employing transcription-based classifiers to underpin clinical decision making in colorectal cancer. Clin Cancer Res; 22(16); 4095–104. ©2016 AACR.See related commentary by Morris and Kopetz, p. 3989

Modern methods of acquiring molecular data have improved rapidly in recent years, making it easier for researchers to collect large volumes of information. However, this has increased the challenge of recognizing interesting patterns within the data. Atlas Correlation Explorer (ACE) is a user-friendly workbench for seeking associations between attributes in The Cancer Genome Atlas (TCGA) database. It allows any combination of clinical and genomic data streams to be searched using an evolutionary algorithm approach. To showcase ACE, we assessed which RNA sequencing transcripts were associated with estrogen receptor (ESR1) in the TCGA breast cancer cohort. The analysis revealed already well-established associations with XBP1 and FOXA1, but also identified a strong association with CT62, a potential immunotherapeutic target with few previous associations with breast cancer. In conclusion, ACE can produce results for very large searches in a short time and will serve as an increasingly useful tool for biomarker discovery in the big data era.Significance:ACE uses an evolutionary algorithm approach to perform large searches for associations between any combinations of data in the TCGA database.

This Supplementary Table indicates the sample source for the study

ACE Video demonstration

Supplementary Data. Exported ACE outputs

Supplementary Table 3: Differentially expressed gene list contrasting tumour region of origin between Central Tumor and Lymph Node.

Supplementary Table 2: Differentially expressed gene list contrasting tumour region of origin between Central Tumor and Invasive Front. Supplementary Table 3: Differentially expressed gene list contrasting tumour region of origin between CT-LN. Supplementary Table 4: Differentially expressed gene list contrasting tumour region of origin between LN-IF.

Longitudinal next-generation sequencing of cancer patient samples has enhanced our understanding of the evolution and progression of various cancers. As a result, and due to our increasing knowledge of heterogeneity, such sampling is becoming increasingly common in research and clinical trial sample collections. Traditionally, the evolutionary analysis of these cohorts involves the use of an aligner followed by subsequent stringent downstream analyses. However, this can lead to large levels of information loss due to the vast mutational landscape that characterizes tumor samples. Here, we propose an alignment-free approach for sequence comparison—a well-established approach in a range of biological applications including typical phylogenetic classification. Such methods could be used to compare information collated in raw sequence files to allow an unsupervised assessment of the evolutionary trajectory of patient genomic profiles. In order to highlight this utility in cancer research we have applied our alignment-free approach using a previously established metric, Jensen–Shannon divergence, and a metric novel to this area, Hellinger distance, to two longitudinal cancer patient cohorts in glioma and clear cell renal cell carcinoma using our software, NUQA. We hypothesize that this approach has the potential to reveal novel information about the heterogeneity and evolutionary trajectory of spatiotemporal tumor samples, potentially revealing early events in tumorigenesis and the origins of metastases and recurrences. Key words: alignment-free, Hellinger distance, exome-seq, evolution, phylogenetics, longitudinal.

Modern methods of acquiring molecular data have improved rapidly in recent years, making it easier for researchers to collect large volumes of information. However, this has increased the challenge of recognizing interesting patterns within the data. Atlas Correlation Explorer (ACE) is a user-friendly workbench for seeking associations between attributes in The Cancer Genome Atlas (TCGA) database. It allows any combination of clinical and genomic data streams to be searched using an evolutionary algorithm approach. To showcase ACE, we assessed which RNA sequencing transcripts were associated with estrogen receptor (ESR1) in the TCGA breast cancer cohort. The analysis revealed already well-established associations with XBP1 and FOXA1, but also identified a strong association with CT62, a potential immunotherapeutic target with few previous associations with breast cancer. In conclusion, ACE can produce results for very large searches in a short time and will serve as an increasingly useful tool for biomarker discovery in the big data era. Significance: ACE uses an evolutionary algorithm approach to perform large searches for associations between any combinations of data in the TCGA database.

Simple Summary In this study, we propose a general artificial intelligence (AI) based workflow for applying deep learning to the problem of cell identification in immunohistochemistry-stained slides as a basis for quantifying nuclear staining biomarkers. Our approach consists of two main parts: a simplified but robust annotation process, and the application of cell identification models. This results in an optimised process with a new user-friendly tool that can interact with other open-source software and assists pathologists and scientists in creating and exporting data for deep learning. We present a set of architectures for cell identification to quantify and analyse the trade-offs between different deep learning architectures, providing a more accurate and less time-consuming tool than using traditional methods. This approach can identify the best tool to deliver AI tools for clinical utility. Abstract Biomarkers identify patient response to therapy. The potential immune-checkpoint biomarker, Inducible T-cell COStimulator (ICOS), expressed on regulating T-cell activation and involved in adaptive immune responses, is of great interest. We have previously shown that open-source software for digital pathology image analysis can be used to detect and quantify ICOS using cell detection algorithms based on traditional image processing techniques. Currently, artificial intelligence (AI) based on deep learning methods is significantly impacting the domain of digital pathology, including the quantification of biomarkers. In this study, we propose a general AI-based workflow for applying deep learning to the problem of cell segmentation/detection in IHC slides as a basis for quantifying nuclear staining biomarkers, such as ICOS. It consists of two main parts: a simplified but robust annotation process, and cell segmentation/detection models. This results in an optimised annotation process with a new user-friendly tool that can interact with1 other open-source software and assists pathologists and scientists in creating and exporting data for deep learning. We present a set of architectures for cell-based segmentation/detection to quantify and analyse the trade-offs between them, proving to be more accurate and less time consuming than traditional methods. This approach can identify the best tool to deliver the prognostic significance of ICOS protein expression.

Background The presence of hypoxia is a poor prognostic factor in prostate cancer and the hypoxic tumor microenvironment promotes radioresistance. There is potential for drug radiotherapy combinations to improve the therapeutic ratio. We aimed to investigate whether hypoxia-associated genes could be used to identify FDA approved drugs for repurposing for the treatment of hypoxic prostate cancer. Methods Hypoxia associated genes were identified and used in the connectivity mapping software QUADrATIC to identify FDA approved drugs as candidates for repurposing. Drugs identified were tested in vitro in prostate cancer cell lines (DU145, PC3, LNCAP). Cytotoxicity was investigated using the sulforhodamine B assay and radiosensitization using a clonogenic assay in normoxia and hypoxia. Results Menadione and gemcitabine had similar cytotoxicity in normoxia and hypoxia in all three cell lines. In DU145 cells, the radiation sensitizer enhancement ratio (SER) of menadione was 1.02 in normoxia and 1.15 in hypoxia. The SER of gemcitabine was 1.27 in normoxia and 1.09 in hypoxia. No radiosensitization was seen in PC3 cells. Conclusion Connectivity mapping can identify FDA approved drugs for potential repurposing that are linked to a radiobiologically relevant phenotype. Gemcitabine and menadione could be further investigated as potential radiosensitizers in prostate cancer.

RAS mutant (MT) metastatic colorectal cancer (mCRC) is resistant to MEK1/2 inhibition and remains a difficult-to-treat group. Therefore, there is an unmet need for novel treatment options for RASMT mCRC. RALA and RALB GTPases function downstream of RAS and have been found to be key regulators of several cell functions implicated in KRAS-driven tumorigenesis. However, their role as regulators of the apoptotic machinery remains to be elucidated. Here, we found that inhibition of RALB expression, but not RALA, resulted in Caspase-8-dependent cell death in KRASMT CRC cells, which was not further increased following MEK1/2 inhibition. Proteomic analysis and mechanistic studies revealed that RALB depletion induced a marked upregulation of the pro-apoptotic cell surface TRAIL Death Receptor 5 (DR5) (also known as TRAIL-R2), primarily through modulating DR5 protein lysosomal degradation. Moreover, DR5 knockdown or knockout attenuated siRALB-induced apoptosis, confirming the role of the extrinsic apoptotic pathway as a regulator of siRALB-induced cell death. Importantly, TRAIL treatment resulted in the association of RALB with the death-inducing signalling complex (DISC) and targeting RALB using pharmacologic inhibition or RNAi approaches triggered a potent increase in TRAIL-induced cell death in KRASMT CRC cells. Significantly, high RALB mRNA levels were found in the poor prognostic Colorectal Cancer Intrinsic Subtypes (CRIS)-B CRC subgroup. Collectively, this study provides to our knowledge the first evidence for a role for RALB in apoptotic priming and suggests that RALB inhibition may be a promising strategy to improve response to TRAIL treatment in poor prognostic RASMT CRIS-B CRC.

High expression of the HOXA cluster correlates with poor clinical outcome in acute myeloid leukemias, particularly those harboring rearrangements of the mixed-lineage-leukemia gene (MLLr). Whilst decreased HOXA expression acts as a readout for candidate experimental therapies, the necessity of the HOXA cluster for leukemia maintenance has not been fully explored. Primary leukemias were generated in hematopoietic stem/progenitor cells from Cre responsive transgenic mice for conditional deletion of the Hoxa locus. Hoxa deletion resulted in reduced proliferation and colony formation in which surviving leukemic cells retained at least one copy of the Hoxa cluster, indicating dependency. Comparative transcriptome analysis of Hoxa wild type and deleted leukemic cells identified a unique gene signature associated with key pathways including transcriptional mis-regulation in cancer, the Fanconi anemia pathway and cell cycle progression. Further bioinformatics analysis of the gene signature identified a number of candidate FDA-approved drugs for potential repurposing in high HOXA expressing cancers including MLLr leukemias. Together these findings support dependency for an MLLr leukemia on Hoxa expression and identified candidate drugs for further therapeutic evaluation.

High expression of the HOXA cluster correlates with poor clinical outcome in acute myeloid leukemias, particularly those harboring rearrangements of the mixed-lineage-leukemia gene (MLLr). Whilst decreased HOXA expression acts as a readout for candidate experimental therapies, the necessity of the HOXA cluster for leukemia maintenance has not been fully explored. Primary leukemias were generated in hematopoietic stem/progenitor cells from Cre responsive transgenic mice for conditional deletion of the Hoxa locus. Hoxa deletion resulted in reduced proliferation and colony formation in which surviving leukemic cells retained at least one copy of the Hoxa cluster, indicating dependency. Comparative transcriptome analysis of Hoxa wild type and deleted leukemic cells identified a unique gene signature associated with key pathways including transcriptional mis-regulation in cancer, the Fanconi anemia pathway and cell cycle progression. Further bioinformatics analysis of the gene signature identified a number of candidate FDA-approved drugs for potential repurposing in high HOXA expressing cancers including MLLr leukemias. Together these findings support dependency for an MLLr leukemia on Hoxa expression and identified candidate drugs for further therapeutic evaluation.

Objective High grade serous carcinoma (HGSC) is the most common and most aggressive, subtype of epithelial ovarian cancer. It presents as advanced stage disease with poor prognosis. Recent pathological evidence strongly suggests HGSC arises from the fallopian tube via the precursor lesion; serous tubal intraepithelial carcinoma (STIC). However, further definition of the molecular evolution of HGSC has major implications for both clinical management and research. This study aims to more clearly define the molecular pathogenesis of HGSC. Methods Six cases of HGSC were identified at the Northern Ireland Gynaecological Cancer Centre (NIGCC) that each contained ovarian HGSC (HGSC), omental HGSC (OMT), STIC, normal fallopian tube epithelium (FTE) and normal ovarian surface epithelium (OSE). The relevant formalin-fixed paraffin embedded (FFPE) tissue samples were retrieved from the pathology archive via the Northern Ireland Biobank following attaining ethical approval (NIB11:005). Full microarray-based gene expression profiling was performed on the cohort. The resulting data was analysed bioinformatically and the results were validated in a HGSC-specific in-vitro model. Results The carcinogenesis of HGSC was investigated and showed the molecular profile of HGSC to be more closely related to normal FTE than OSE. STIC lesions also clustered closely with HGSC, indicating a common molecular origin. Conclusion This study provides strong evidence suggesting that extrauterine HGSC arises from the fimbria of the distal fallopian tube. Furthermore, several potential pathways were identified which could be targeted by novel therapies for HGSC. These findings have significant translational relevance for both primary prevention and clinical management of the disease.

A recent phase II study of patients with metastatic colorectal carcinoma showed that mismatch repair gene status was predictive of clinical response to PD-1–targeting immune checkpoint blockade. Further examination revealed strong correlation between PD-L1 protein expression and microsatellite instability (MSI) in stage IV colorectal carcinoma, suggesting that the amount of PD-L1 protein expression could identify late-stage patients who might benefit from immunotherapy. To assess whether the clinical associations between PD-L1 gene expression and MSI identified in metastatic colorectal carcinoma are also present in stage II/III colorectal carcinoma, we used in silico analysis to elucidate the cell types expressing the PD-L1 gene. We found a statistically significant association of PD-L1 gene expression with MSI in early-stage colorectal carcinoma (P < 0.001) and show that, unlike in non–colorectal carcinoma tumors, PD-L1 is derived predominantly from the immune infiltrate. We demonstrate that PD-L1 gene expression has positive prognostic value in the adjuvant disease setting (PD-L1low vs. PD-L1high HR = 9.09; CI, 2.11–39.10). PD-L1 gene expression had predictive value, as patients with high PD-L1 expression appear to be harmed by standard-of-care treatment (HR = 4.95; CI, 1.10–22.35). Building on the promising results from the metastatic colorectal carcinoma PD-1–targeting trial, we provide compelling evidence that patients with PD-L1high/MSI/immunehigh stage II/III colorectal carcinoma should not receive standard chemotherapy. This conclusion supports the rationale to clinically evaluate this patient subgroup for PD-1 blockade treatment. Cancer Immunol Res; 4(7); 582–91. ©2016 AACR.

The Colorectal Cancer (CRC) Subtyping Consortium (CRCSC) recently published four consensus molecular subtypes (CMS’s) representing the underlying biology in CRC. The Microsatellite Instable (MSI) immune group, CMS1, has a favorable prognosis in early stage disease, but paradoxically has the worst prognosis following relapse, suggesting the presence of factors enabling neoplastic cells to circumvent this immune response. To identify the genes influencing subsequent poor prognosis in CMS1, we analyzed this subtype, centered on risk of relapse.In a cohort of early stage colon cancer (n=460), we examined, in silico, changes in gene expression within the CMS1 subtype and demonstrated for the first time the favorable prognostic value of chemokine-like factor (CKLF) gene expression in the adjuvant disease setting [HR=0.18, CI=0.04-0.89]. In addition, using transcription profiles originating from cell sorted CRC tumors, we delineated the source of CKLF transcription within the colorectal tumor microenvironment to the leukocyte component of these tumors. Further to this, we confirmed that CKLF gene expression is confined to distinct immune subsets in whole blood samples and primary cell lines, highlighting CKLF as a potential immune cell-derived factor promoting tumor immune-surveillance of nascent neoplastic cells, particularly in CMS1 tumors. Building on the recently reported CRCSC data, we provide compelling evidence that leukocyte-infiltrate derived CKLF expression is a candidate biomarker of favorable prognosis, specifically in MSI-immune stage II/III disease.

Modern methods in generating molecular data have dramatically scaled in recent years, allowing researchers to efficiently acquire large volumes of information. However, this has increased the challenge of recognising interesting patterns within the data. Atlas Correlation Explorer (ACE) is a user-friendly workbench for seeking associations between attributes in the cancer genome atlas (TCGA) database. It allows any combination of clinical and genomic data streams to be selected for searching, and highlights significant correlations within the chosen data. It is based on an evolutionary algorithm which is capable of producing results for very large searches in a short time.

Around 12–15% of patients with locally advanced rectal cancer undergo a pathologically complete response (tumor regression grade 4) to long-course preoperative chemoradiotherapy; the remainder exhibit a spectrum of tumor regression (tumor regression grade 1–3). Understanding therapy-related transcriptional alterations may enable better prediction of response as measured by progression-free and overall survival, in addition to aiding the development of improved strategies based on the underlying biology of the disease. To this end, we performed high-throughput gene expression profiling in 40 pairs of formalin-fixed paraffin-embedded rectal cancer biopsies and matched resections following long-course preoperative chemoradiotherapy (discovery cohort). Differential gene expression analysis was performed contrasting tumor regression grades in resections. Enumeration of the tumor microenvironment cell population was undertaken using in silico analysis of the transcriptional data, and real-time PCR validation of NCR1 undertaken. Immunohistochemistry and survival analysis was used to measure CD56+ cell populations in an independent cohort (n=150). Gene expression traits observed following long-course preoperative chemoradiotherapy in the discovery cohort suggested an increased abundance of natural killer cells in tumors that displayed a clinical response to CRT in a tumor regression grade-dependent manner. CD56+ natural killer-cell populations were measured by immunohistochemistry and found to be significantly higher in tumor regression grade 3 patients compared with tumor regression grade 1–2 in the validation cohort. Furthermore, it was observed that patients positive for CD56 cells after therapy had a better overall survival (HR=0.282, 95% CI=0.109–0.729, χ2=7.854, P=0.005). In conclusion, we have identified a novel post-therapeutic natural killer-like transcription signature in patients responding to long-course preoperative chemoradiotherapy. Furthermore, patients with a higher abundance of CD56-positive natural killer cells post long-course preoperative chemoradiotherapy had better overall survival. Therefore, harnessing a natural killer-like response after therapy may improve outcomes for locally advanced rectal cancer patients. Finally, we hypothesize that future assessment of this natural killer-like response in on-treatment biopsy material may inform clinical decision-making for treatment duration.

// Qing Wen 1 , Philip D. Dunne 1 , Paul G. O’Reilly 1 , Gerald Li 1 , Anthony J. Bjourson 2 , Darragh G. McArt 1 , Peter W. Hamilton 1, * , Shu-Dong Zhang 1, 2, * 1 Centre for Cancer Research and Cell Biology, Queen’s University Belfast, UK 2 Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, C-TRIC, Londonderry, UK * Joint Senior Author Correspondence to: Qing Wen, email: qwen02@qub.ac.uk Peter W. Hamilton, email: P.Hamilton@qub.ac.uk Keywords: connectivity mapping, differentially expressed genes, colorectal cancer, KRAS mutation, FDA approved drugs Received: May 15, 2016 Accepted: November 30, 2016 Published: December 10, 2016 ABSTRACT 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.

Glioblastoma (GBM) is the most prevalent and aggressive of malignant primary brain tumours. Despite multi-modal therapy, including surgical resection, radiotherapy and chemotherapy, GBM continues to be uniformly lethal, with a median survival of 12–18 months from the time of diagnosis. The resistance to therapy and ultimate recurrence is thought to originate from a sub-population of cells residing in the initial tumour, identified as glioma-initiating or glioma-stem cells (GSC). These GSC have been shown to be intrinsically more invasive, with enhanced repair and drug-efflux pathways mediating resistance to chemo-radiation therapies. The purpose of this study was to use and validate a novel connectivity mapping procedure to identify candidate compounds targeting therapy-resistant and recurrent GBM. This procedure was developed by O’Reilly and colleagues (“QUADrATiC: A Scalable Approach to Repurposing FDA-approved therapeutics”, under review in Bioinformatics), and in this study, was performed on publicly available NCBI GEO microarray datasets. Based on the cancer stem cell theory whereby the resistant GSCs repopulate and are enriched in recurrent tumours, primary and recurrent GBM gene expression profiles were compared and the connections between the identified gene lists and drug responses were mapped. To validate that this connectivity mapping procedure is reliable at identifying effective treatments to target therapeutic resistance and recurrence, we tested one of the top ten compounds identified, rosiglitazone (rosi), a ligand of the peroxisome proliferator activated receptor gamma (PPARγ). The efficacy of rosi alone and in combination with the current standard of care therapies, temozolomide and radiation, at killing GSC was assessed in vitro using the clonogenic survival assay. Our data indicated that rosi significantly reduced survival of both non-stem glioma cells and GSC, alone and in combination with radiation and temozolomide. The p53 pathway was identified as a pathway by which rosi may induce permanent cell cycle arrest and cell death. Further work is ongoing to fully elucidate the downstream mechanisms of cell death. This work is proof-of-principle that the current connectivity mapping procedure can be instrumental in identifying more targeted therapeutic interventions for GBM resistance and prevent recurrence.

Purpose: A number of independent gene expression profiling studies have identified transcriptional subtypes in colorectal cancer with potential diagnostic utility, culminating in publication of a colorectal cancer Consensus Molecular Subtype classification. The worst prognostic subtype has been defined by genes associated with stem-like biology. Recently, it has been shown that the majority of genes associated with this poor prognostic group are stromal derived. We investigated the potential for tumor misclassification into multiple diagnostic subgroups based on tumoral region sampled. Experimental Design: We performed multiregion tissue RNA extraction/transcriptomic analysis using colorectal-specific arrays on invasive front, central tumor, and lymph node regions selected from tissue samples from 25 colorectal cancer patients. Results: We identified a consensus 30-gene list, which represents the intratumoral heterogeneity within a cohort of primary colorectal cancer tumors. Using a series of online datasets, we showed that this gene list displays prognostic potential HR = 2.914 (confidence interval 0.9286–9.162) in stage II/III colorectal cancer patients, but in addition, we demonstrated that these genes are stromal derived, challenging the assumption that poor prognosis tumors with stem-like biology have undergone a widespread epithelial–mesenchymal transition. Most importantly, we showed that patients can be simultaneously classified into multiple diagnostically relevant subgroups based purely on the tumoral region analyzed. Conclusions: Gene expression profiles derived from the nonmalignant stromal region can influence assignment of colorectal cancer transcriptional subtypes, questioning the current molecular classification dogma and highlighting the need to consider pathology sampling region and degree of stromal infiltration when employing transcription-based classifiers to underpin clinical decision making in colorectal cancer. Clin Cancer Res; 22(16); 4095–104. ©2016 AACR. See related commentary by Morris and Kopetz, p. 3989

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.

BACKGROUND: Gene expression profiling can uncover biological mechanisms underlying disease and is important in drug development. RNA-seq is routinely used to assess gene expression but costs remain high. Sample multiplexing reduces RNA-seq costs, however, multiplexed samples have lower cDNA sequencing depth, which can hinder accurate differential gene expression detection. The impact of sequencing depth alteration on RNA-seq-based downstream analyses such as gene expression connectivity mapping is not known, where connectivity mapping can be used to identify potential therapeutic compounds for repurposing. MATERIAL AND METHODS: In this study, published RNA-seq profiles from brain tumour (glioma) patients were assembled into two disease progression gene signature contrasts for astrocytoma. Available treatments for glioma have limited effectiveness rendering this a disease of poor clinical outcome. Gene signatures were subsampled to simulate sequencing alterations and analysed in connectivity mapping to investigate target compound robustness. RESULTS: Data loss to gene signatures led to the loss, gain and consistent identification of significant connections. The most accurate gene signature contrast with consistent patient gene expression profiles was more resilient to data loss and identified robust target compounds. Target compounds lost included candidate compounds of potential clinical utility in glioma (e.g. Suramin, Dasatinib). Lost connections may have been linked to low abundance genes in the gene signature that closely characterised the disease phenotype. Consistently identified connections may have been related to highly expressed abundant genes that were ever-present in gene signatures, despite data reductions. Potential noise surrounding findings included false positive connections that were gained as a result of gene signature modification with data loss. CONCLUSION: Findings highlight the necessity for gene signature accuracy for connectivity mapping, which should improve the clinical utility of future target compound discoveries.

The application of transcriptional gene signatures to stratify tumors into prognostic and predictive subtypes has evolved rapidly since the original landmark studies demonstrated the clinical utility of this approach in breast cancer. While the number of published signatures continues to increase, the likelihood of individual signatures achieving clinical utility has remained very low, with some estimates putting this figure below 1%. Transcriptional intratumoral heterogeneity (ITH), resulting from variation in stromal components at different regions of a tumor, has been shown to undermine molecular stratification of colorectal cancer (CRC) patients into prognostic and predictive subgroups. The degree of gene expression changes associated with variation in tumor microenvironment (TME) content may even mask the relatively more subtle changes associated with genetic variability and heterogeneity within the tumor epithelium. This variation in TME content, and subsequent ITH, at different regions of the tumor has long been recognised; however, in the era of precision medicine, the implications of such microenvironmental alterations in confounding patient classification are of increased importance. To examine this issue, a panel of clinically relevant prognostic and predictive transcriptional gene signatures were selected in order to assess the potential for discordant classification of patient samples based on the tumoral region profiled. Using these clinically relevant CRC gene expression signatures, we assessed the susceptibility of each signature to the confounding effects of ITH using gene expression microarray data obtained from multiple tumor regions of a cohort of 24 patients, including central tumor, the tumor invasive front and lymph node metastasis. Consensus annotated gene lists for each of the gene signatures were used, followed by in silico analysis within our multiregional clinical dataset. Sample clustering alongside correlative assessment revealed variation in the ability of each signature to cluster samples according to patient-of-origin rather than region-of-origin within the multiregion dataset. Signatures focused on cancer-cell intrinsic gene expression were found to produce more clinically useful, patient-centred classifiers, as exemplified by the recently developed CRC intrinsic signature (CRIS), which robustly clustered samples by patient rather than sample region. These findings highlight the potential of cancer-cell intrinsic signatures to reliably stratify CRC patients by minimising the confounding effects of ITH. In conclusion, our data support the clinical evaluation of signatures based on intrinsic gene expression, such as CRIS, which are unaffected by the confounding variable of transcriptional ITH and therefore have the potential to be used clinically to inform precision medicine decisions, ultimately leading to improved patient outcomes. Citation Format: Philip D. Dunne, Paul O'Reilly, Aideen Roddy, Matthew Alderdice, Susan Richman, Tim Maughan, Simon McDade, Patrick Johnston, Daniel Longley, Elaine Kay, Darragh McArt, Mark Lawler. Cancer cell intrinsic gene expression signatures minimize the confounding effects of intratumoral heterogeneity in colorectal cancer patient classification [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-042. doi:10.1158/1538-7445.AM2017-LB-042

The problem of measuring high frequency variations in temperature is described, and the need for some form of reconstruction introduced. One method of reconstructing temperature measurements is to use the signals from two thermocouples of differing diameter. Two existing methods for processing such measurements and reconstructing the higher frequency components are described. These are compared to a novel reconstruction algorithm based on a nonlinear extended Kalman filter. The performance of this filter is found to compare favorably, in a number of ways, with the existing techniques, and it is suggested that such a technique would be viable for the online reconstruction of temperatures in real time.

Three alternative approaches are investigated for probability estimation for use in a self-validating sensor. The three methods are Stochastic Approximation (SA), a Reduced Bias Estimate (RBE) of this same approach and a method based on the Bayesian Self-Organising Map using Gaussian Kernels (GK). Simulation studies show that the GK-based method gives superior results when compared to the RBE algorithm. It has also been demonstrated that the GK method is more computationally efficient and requires storage space for fewer variables. The techniques are demonstrated using data from a thermocouple sensor experiencing a change in time constant.

This paper demonstrates the potential of the B-spline neural network (BSNN) for the modelling of nonlinear processes. The performance of this paradigm is compared with the multi-layer perceptron (MLP), for the modelling of experimental and industrial case-studies. The experimental pH neutralisation plant of the University of California at Santa Barbara (UCSB) is modelled using both networks. This case-study demonstrates the higher performance of the B-spline network, for rapid on-line model adaptation. The second casestudy focuses on the prediction of viscosity for an industrial polymerisation reactor. Predictive models of viscosity are developed, based on both networks to predict over and hence remove the measurement time-delay introduced at the viscometer. A novel disturbance modelling approach is also developed here, and demonstrated to perform excellently in online tests

Glioblastoma (GBM) is the most prevalent and aggressive of malignant primary brain tumours. Despite multi-modal therapy, including surgical resection, radiotherapy and chemotherapy, GBM continues to be uniformly lethal, with a median survival of 12-18 months from the time of diagnosis. The resistance to therapy and ultimate recurrence is thought to originate from a sub-population of cells residing in the initial tumour, identified as glioma-initiating or glioma-stem cells (GSC). These GSC have been shown to be intrinsically more invasive, with enhanced repair and drug-efflux pathways mediating resistance to chemo-radiation therapies. The purpose of this study was to use and validate a novel connectivity mapping procedure to identify candidate compounds targeting therapy-resistant and recurrent GBM. This procedure was developed by O’Reilly and colleagues (“QUADrATiC: A Scalable Approach to Repurposing FDA-approved therapeutics”, under review in Bioinformatics), and in this study, was performed on publicly available NCBI GEO microarray datasets. Based on the cancer stem cell theory whereby the resistant GSCs repopulate and are enriched in recurrent tumours, primary and recurrent GBM gene expression profiles were compared and the connections between the identified gene lists and drug responses were mapped. To validate that this connectivity mapping procedure is reliable at identifying effective treatments to target therapeutic resistance and recurrence, we tested one of the top ten compounds identified, rosiglitazone (rosi), a ligand of the peroxisome proliferator activated receptor gamma (PPARγ). The efficacy of rosi alone and in combination with the current standard of care therapies, temozolomide and radiation, at killing GSC was assessed in vitro using the clonogenic survival assay. Our data indicated that rosi significantly reduced survival of both non-stem glioma cells and GSC, alone and in combination with radiation and temozolomide. The p53 pathway was identified as a pathway by which rosi may induce permanent cell cycle arrest and cell death. Further work is ongoing to fully elucidate the downstream mechanisms of cell death. This work is proof-of-principle that the current connectivity mapping procedure can be instrumental in identifying more targeted therapeutic interventions for GBM resistance and prevent recurrence. Citation Format: Shahnaz T. Al Rashid, Paul O’Reilly, Philip D. Dunne, Matthew Alderdice, Thomas J. Flannery, Kevin M. Prise, Shu-Dong Zhang, Darragh G. McArt. Therapeutic compound discovery targeting a recurrent glioblastoma (GBM) phenotype using LINCS compounds via QUADrATiC analyses. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 72.

A new method is described for online detection of parameter changes in a sensor. This is based on work by Yung and Clarke (1989) which employs a local ARIMA model of the sensor output to generate an innovation sequence. A statistical test, which quantifies the change to the variance of an innovation sequence, is developed and used to provide a decision process based on a likelihood ratio of probabilities. Real-time experimental results for detecting a change in a thermocouple time-constant are presented.

If current research efforts worldwide on neural networks are to gain recognition and continue at their current levels, it is essential that theoretical advances are accompanied by industrial applications where the advantages/disadvantages of this new technology can be properly assessed against more conventional techniques. The past few years have seen a marked shift towards practical, as opposed to purely simulation, studies in the field of control and systems engineering, which is a healthy sign both of a maturing technology and successful technology transfer. A good selection of such applications are included in this book and the present chapter contains the results of two studies, one concerned with nonlinear modelling of a 200 MW boiler in an electrical power station [1] the other with inferential estimation of viscosity, a key quality indication in a chemical polymerisation reactor [2, 3].

This paper is concerned with viscosity control in a polymerisation reactor. Here the measurement from the viscometer is subject to a significant time delay but the torque from a variable speed drive provides an instantaneous, if noisy, indication of the reactor viscosity. The aim of the research was to investigate neural network based inferential estimation, where the network is trained to predict the polymer viscosity from past torque and viscosity data thus removing the delay and providing instantaneous information to the operators. The paper presents results from off-line training of a feedforward network and describes the study on online viscosity estimation using B-Spline networks. (6 pages)

BACKGROUND: While the discovery of new drugs is a complex, lengthy and costly process, identifying new uses for existing drugs is a cost-effective approach to therapeutic discovery. Connectivity mapping integrates gene expression profiling with advanced algorithms to connect genes, diseases and small molecule compounds and has been applied in a large number of studies to identify potential drugs, particularly to facilitate drug repurposing. Colorectal cancer (CRC) is a commonly diagnosed cancer with high mortality rates, presenting a worldwide health problem. With the advancement of high throughput omics technologies, a number of large scale gene expression profiling studies have been conducted on CRCs, providing multiple datasets in gene expression data repositories. In this work, we systematically apply gene expression connectivity mapping to multiple CRC datasets to identify candidate therapeutics to this disease.RESULTS: We developed a robust method to compile a combined gene signature for colorectal cancer across multiple datasets. Connectivity mapping analysis with this signature of 148 genes identified 10 candidate compounds, including irinotecan and etoposide, which are chemotherapy drugs currently used to treat CRCs. These results indicate that we have discovered high quality connections between the CRC disease state and the candidate compounds, and that the gene signature we created may be used as a potential therapeutic target in treating the disease. The method we proposed is highly effective in generating quality gene signature through multiple datasets; the publication of the combined CRC gene signature and the list of candidate compounds from this work will benefit both cancer and systems biology research communities for further development and investigations.