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Oncogenic KRAS mutations and inactivation of the APC tumor suppressor co-occur in colorectal cancer (CRC). Despite efforts to target mutant KRAS directly, most therapeutic approaches focus on downstream pathways, albeit with limited efficacy. Moreover, mutant KRAS alters the basal metabolism of cancer cells, increasing glutamine utilization to support proliferation. We show that concomitant mutation of Apc and Kras in the mouse intestinal epithelium profoundly rewires metabolism, increasing glutamine consumption. Furthermore, SLC7A5, a glutamine antiporter, is critical for colorectal tumorigenesis in models of both early- and late-stage metastatic disease. Mechanistically, SLC7A5 maintains intracellular amino acid levels following KRAS activation through transcriptional and metabolic reprogramming. This supports the increased demand for bulk protein synthesis that underpins the enhanced proliferation of KRAS-mutant cells. Moreover, targeting protein synthesis, via inhibition of the mTORC1 regulator, together with Slc7a5 deletion abrogates the growth of established Kras-mutant tumors. Together, these data suggest SLC7A5 as an attractive target for therapy-resistant KRAS-mutant CRC.

Cancer cells’ ability to inhibit apoptosis is key to malignant transformation and limits response to therapy. Here, we performed multiplexed immunofluorescence analysis on tissue microarrays with 373 cores from 168 patients, segmentation of 2.4 million individual cells, and quantification of 18 cell lineage and apoptosis proteins. We identified an enrichment for BCL2 in immune, and BAK, SMAC, and XIAP in cancer cells. Ordinary differential equation-based modeling of apoptosis sensitivity at single-cell resolution was conducted and an atlas of inter- and intra-tumor heterogeneity in apoptosis susceptibility generated. Systems modeling at single-cell resolution identified an enhanced sensitivity of cancer cells to mitochondrial permeabilization and executioner caspase activation compared to immune and stromal cells, but showed significant inter- and intra-tumor heterogeneity.

Motivation Multiplexed immunofluorescence bioimaging of single-cells and their spatial organization in tissue holds great promise to the development of future precision diagnostics and therapeutics. Current multiplexing pipelines typically involve multiple rounds of immunofluorescence staining across multiple tissue slides. This introduces experimental batch effects that can hide underlying biological signal. It is important to have robust algorithms that can correct for the batch effects while not introducing biases into the data. Performance of data normalization methods can vary among different assay pipelines. To evaluate differences, it is critical to have a ground truth dataset that is representative of the assay. Results A new immunoFLuorescence Image NOrmalization method is presented and evaluated against alternative methods and workflows. Multiround immunofluorescence staining of the same tissue with the nuclear dye DAPI was used to represent virtual slides and a ground truth. DAPI was restained on a given tissue slide producing multiple images of the same underlying structure but undergoing multiple representative tissue handling steps. This ground truth dataset was used to evaluate and compare multiple normalization methods including median, quantile, smooth quantile, median ratio normalization and trimmed mean of the M-values. These methods were applied in both an unbiased grid object and segmented cell object workflow to 24 multiplexed biomarkers. An upper quartile normalization of grid objects in log space was found to obtain almost equivalent performance to directly normalizing segmented cell objects by the middle quantile. The developed grid-based technique was then applied with on-slide controls for evaluation. Using five or fewer controls per slide can introduce biases into the data. Ten or more on-slide controls were able to robustly correct for batch effects. Availability and implementation The data underlying this article along with the FLINO R-scripts used to perform the evaluation of image normalizations methods and workflows can be downloaded from https://github.com/GE-Bio/FLINO. Supplementary information Supplementary data are available at Bioinformatics online.

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.

Objective Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), histological grading is a poor predictor of disease progression, and consensus molecular subtypes (CMSs) cannot be distinguished without gene expression profiling. We hypothesise that image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data and to resolve unclassifiable or heterogeneous cases. In this study, we present an image-based approach to predict CRC CMS from standard H&E sections using deep learning. Design Training and evaluation of a neural network were performed using a total of n=1206 tissue sections with comprehensive multi-omic data from three independent datasets (training on FOCUS trial, n=278 patients; test on rectal cancer biopsies, GRAMPIAN cohort, n=144 patients; and The Cancer Genome Atlas (TCGA), n=430 patients). Ground truth CMS calls were ascertained by matching random forest and single sample predictions from CMS classifier. Results Image-based CMS (imCMS) accurately classified slides in unseen datasets from TCGA (n=431 slides, AUC)=0.84) and rectal cancer biopsies (n=265 slides, AUC=0.85). imCMS spatially resolved intratumoural heterogeneity and provided secondary calls correlating with bioinformatic prediction from molecular data. imCMS classified samples previously unclassifiable by RNA expression profiling, reproduced the expected correlations with genomic and epigenetic alterations and showed similar prognostic associations as transcriptomic CMS. Conclusion This study shows that a prediction of RNA expression classifiers can be made from H&E images, opening the door to simple, cheap and reliable biological stratification within routine workflows.

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.

BACKGROUNDSignet ring cell colorectal cancer (SRCCa) has a bleak prognosis. Employing molecular pathology techniques we investigated the potential of precision medicine in this disease.METHODSUsing test (n=26) and validation (n=18) cohorts, analysis of mutations, DNA methylation and transcriptome was carried out. Microsatellite instability (MSI) status was established and immunohistochemistry (IHC) was used to test for adaptive immunity (CD3) and the immune checkpoint PDL1.RESULTS DNA methylation data split the cohorts into hypermethylated (n=18, 41%) and hypomethylated groups (n=26, 59%). The hypermethylated group predominant in the proximal colon was enriched for CpG island methylator phenotype (CIMP), BRAF V600E mutation and MSI (PC) across both groups. No statistically significant difference in outcome was observed between the two groups.CONCLUSIONS Our data show that SRCCa phenotype comprises two distinct genotypes. The MSI(+)/CIMP(+)/BRAF V600E(+)/CD3(+)/PDL1(+) hypermethylated genotype is an ideal candidate for immune checkpoint inhibitor therapy. In addition, one fourth of SRCCa cases can potentially be targeted by KIT inhibitors.

Introduction International guidelines recommend consideration of early-TIPSS for high-risk patients (Childs-Pugh grade B and C) presenting with oesophageal variceal bleeding (VB), following initial haemostasis with endotherapy and drugs. However, this is based on a single randomised controlled trial (RCT). Methods An open-label, parallel-group, RCT was performed at 2 experienced UK centres. Cirrhotic patients with a Childs Pugh (CP) score ≥8 presenting to either centre with VB were considered for inclusion in the study. Following haemostasis with endoscopic band ligation (EBL) and vasoactive drugs, consenting participants were randomised to either continued EBL ± non-selective beta-blockers (standard care) or early-TIPSS (ie within 72hrs). All patients were followed up for 1 year, or until death. The primary endpoint was survival at 1 year. Secondary endpoints included early and late re-bleeding ( Results 216 patients were screened with 58 eligible participants ultimately included (29 per group). EBL and early-TIPSS were well matched for age (53.9 vs 49.4); CP score (10.1 vs 9.8); and MELD score (16.8 vs 16.4) respectively. On intention to treat, 1-year survival rates were 75.8% in the EBL group versus 79.3% in the early-TIPSS group (p=0.79) (see figure 1). 1 patient in the EBL group received TIPSS as rescue therapy, but later died. 4 patients who were randomised to early-TIPSS did not receive this intervention due to time delays and therefore underwent standard care. However, on per-protocol analysis, no survival benefit was observed between groups (p=0.85). In patients with Childs-Pugh C cirrhosis (n=33) no difference in survival was seen between groups (p=0.81). At 6 weeks, rebleeding occurred in 2 patients in both the EBL and early-TIPSS groups. At 1-year, rebleeding occurred in 8 patients in the EBL group compared with 6 patients in the early-TIPSS group. No significant differences in rates of rebleeding were seen at the specified time points. Additionally, no significant differences were observed between the two treatment groups with respect to serious adverse events. Conclusions Contrary to the previous study, our findings do not support the use of early-TIPSS in the reduction of mortality or rebleeding in patients with cirrhosis who are hospitalised for acute variceal bleeding. Given the challenges of resourcing the widespread provision of early-TIPSS, further multi-centred studies with larger patient numbers are required to resolve this important issue.

Introduction International guidelines recommend consideration of early-TIPSS ( Methods This is a single centre cohort study of prospectively collected data in patients undergoing covered TIPSS following urgent ( 72hrs. Primary outcome is all cause mortality. All cases were followed up from the date of OVB necessitating TIPSS insertion for a minimum of 4 years or until death/liver transplant. Results 281 patients were included in the study with 199 undergoing early-TIPSS and 82 having late-TIPSS. Analysis was carried out on 70 patients in each group following propensity score matching. For early and late TIPSS respectively, there were no statistical differences between age (51.2 vs 54.0), female sex (31.4% vs 35.7%), Childs Pugh (CP) score (10.3 vs 10.0), MELD (16.8 vs 18.1), rates of ascites (71.4% vs 65.7%) or encephalopathy (31.4% vs 28.6%) on admission, portal pressure gradient fall (15.1 vs 14.9), rates of post-TIPSS encephalopathy (28.6% vs 24.3%) and rebleeding (24.3% vs 20%). Using competing risk analysis, median follow-up days were as follows: Early TIPSS dead: 259, alive: 5503, liver transplant: 651; Late-TIPSS dead: 322, alive: 5134, liver transplant:172. Equality of cumulative incidence function across groups was non-significant and there was no statistical significance in the incidence of death between the two cohorts (figure 1). On multivariate competing risk regression analysis for the outcome of death, MELD, CP score, ascites and post-TIPSS rebleeding were all statistically significant predictors. Conclusions To our knowledge this is the largest real-world study, with the longest follow-up times, carried out on patients undergoing TIPSS following urgent endoscopic treatment for acute OVB. With advanced statistical analysis, our data suggest that timing of TIPSS does not alter survival in this group of patients.

Objective Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), histological grading is a poor predictor of disease progression, and consensus molecular subtypes (CMSs) cannot be distinguished without gene expression profiling. We hypothesise that image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data and to resolve unclassifiable or heterogeneous cases. In this study, we present an image-based approach to predict CRC CMS from standard H&E sections using deep learning. Design Training and evaluation of a neural network were performed using a total of n=1206 tissue sections with comprehensive multi-omic data from three independent datasets (training on FOCUS trial, n=278 patients; test on rectal cancer biopsies, GRAMPIAN cohort, n=144 patients; and The Cancer Genome Atlas (TCGA), n=430 patients). Ground truth CMS calls were ascertained by matching random forest and single sample predictions from CMS classifier. Results Image-based CMS (imCMS) accurately classified slides in unseen datasets from TCGA (n=431 slides, AUC)=0.84) and rectal cancer biopsies (n=265 slides, AUC=0.85). imCMS spatially resolved intratumoural heterogeneity and provided secondary calls correlating with bioinformatic prediction from molecular data. imCMS classified samples previously unclassifiable by RNA expression profiling, reproduced the expected correlations with genomic and epigenetic alterations and showed similar prognostic associations as transcriptomic CMS. Conclusion This study shows that a prediction of RNA expression classifiers can be made from H&E images, opening the door to simple, cheap and reliable biological stratification within routine workflows.

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.

Purpose: EphA2, a member of the Eph receptor tyrosine kinases family, is an important regulator of tumor initiation, neovascularization, and metastasis in a wide range of epithelial and mesenchymal cancers; however, its role in colorectal cancer recurrence and progression is unclear. Experimental Design: EphA2 expression was determined by immunohistochemistry in stage II/III colorectal tumors (N = 338), and findings correlated with clinical outcome. The correlation between EphA2 expression and stem cell markers CD44 and Lgr5 was examined. The role of EphA2 in migration/invasion was assessed using a panel of KRAS wild-type (WT) and mutant (MT) parental and invasive colorectal cancer cell line models. Results: Colorectal tumors displayed significantly higher expression levels of EphA2 compared with matched normal tissue, which positively correlated with high CD44 and Lgr5 expression levels. Moreover, high EphA2 mRNA and protein expression were found to be associated with poor overall survival in stage II/III colorectal cancer tissues, in both univariate and multivariate analyses. Preclinically, we found that EphA2 was highly expressed in KRASMT colorectal cancer cells and that EphA2 levels are regulated by the KRAS-driven MAPK and RalGDS-RalA pathways. Moreover, EphA2 levels were elevated in several invasive daughter cell lines, and downregulation of EphA2 using RNAi or recombinant EFNA1 suppressed migration and invasion of KRASMT colorectal cancer cells. Conclusions: These data show that EpHA2 is a poor prognostic marker in stage II/III colorectal cancer, which may be due to its ability to promote cell migration and invasion, providing support for the further investigation of EphA2 as a novel prognostic biomarker and therapeutic target. Clin Cancer Res; 22(1); 230–42. ©2015 AACR.

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.

Histopathology-based staging of colorectal cancer (CRC) has utility in assessing the prognosis of patient subtypes, but as yet cannot accurately predict individual patient's treatment response. Transcriptomics approaches, using array based or next generation sequencing (NGS) platforms, of formalin fixed paraffin embedded tissue can be harnessed to develop multi-gene biomarkers for predicting both prognosis and treatment response, leading to stratification of treatment. While transcriptomics can shape future biomarker development, currently

We thank Park et al for their interest in our work and for bringing to our attention their alternative microenvironmental score for assessing colorectal cancer prognosis. 1-3 We are largely in agreement with many of the points raised. The scoring systems proposed by these two studies share many similarities, highlighting the importance of the non-epithelial tumour components and combining assessments of peritumoral inflammatory response and tumour stromal percentage to derive a prognostically valuable fibroinflammatory or microenvironmental score. Despite the overall similarity, a number of small, albeit clinically important, differences lie in the methodologies chosen for assessment of each individual parameter, and in the statistical approaches used. This article is protected by copyright. All rights reserved.

Background: The tumor microenvironment is a key feature to understand cancer biology and may be used clinically. Quantification of tissue composition is usually based either on visual pathological review (VPR) or deconvolution of whole genome molecular data. Although the former is a direct measurement it has modest reproducibility while the latter is an indirect measurement of unclear accuracy, expensive and not always available. Here we test digital pathology coupled with machine learning as a new tool to assess tissue composition. Methods: As part of the Stratification in COloRecTal cancer (S:CORT) programme, a set of over 500 colorectal cancer (CRC) archival paraffin blocks from resections and biopsies were sequentially sectioned for Hematoxylin and Eosin staining (H&E), RNA extraction, a second H&E and DNA extraction. RNA expression microarrays, targeted DNA sequencing and DNA methylation arrays were applied. Tissue composition from the H&Es was obtained by VPR of expert pathologists and by a deep neural net (DNN) algorithm after supervised training on >1,500 tissue areas from S:CORT, TCGA, TEM and CORGI CRC cohorts. Tumor purity estimates were obtained from RNA and methylation arrays. Results: DNN estimates including area and cell counts were obtained for tumor, desmoplastic stroma, inflamed stroma, mucin/hypocellular stroma, muscle, necrosis and white space. An average of 6.8x105 total cells (range: 1.2x104-2.8x106) and 1.2x105 (range: 7.2x104-1.8x106) were classified for resections and biopsies respectively. Analyses performed twice on the same H&Es obtained matching results (r=1.0). Comparison of the paired first and second H&E showed very high correlations (r~0.9) and total cell counts correlated with DNA and RNA extraction yields (r~0.6). Tumor purity estimates by VPR mildly correlated with DNN (r~0.5) but they were underestimated and very variable. As a result, copy number adjusted by VPR purity tended to be overestimated compared to adjustment with DNN estimates. The improved performance of DNN is reflected in an accurate capture of non-linear association between area and cell counts in invasive cancer. In contrast, tumor purity estimates derived from RNA or DNA methylation arrays showed better correlations compared with DNN (r~0.6) but both overestimated purity in cases with low cell counts by up to a three-fold difference. Conclusions: Tissue composition analysis with DNN allows analytical robustness, automatization and standardization and provides very high reproducibility at single cell resolution. DNN-based estimation of tumor purity is more accurate than VPR or extrapolation from molecular data derived from genome-wide omic platforms which tend to under and overestimate tumor purity respectively. DNN could be used to better plan and asses downstream molecular analyses and to investigate tissue-based metrics as potential clinical biomarkers in clinical trials. Citation Format: Enric Domingo, Aikaterini Chatzipli, Susan Richman, Andrew Blake, Claire Hardy, Celina Whalley, Keara Redmon, Ian Tomlinson, Philip Dunne, Steven Walker, Andrew Beggs, Ultan McDermott, Graeme I. Murray, Leslie M. Samuel, Matthew Seymour, Philip Quirke, Tim Maughan, Viktor H. Koelzer. Assessment of tissue composition with digital pathology in colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4446.

BACKGROUND: Combined MET and BRAF inhibition showed clinical benefit in a patient with rectal cancer carrying BRAF(V600E) and MET amplification. However after 4 months, acquired resistance emerged and the patient deceased shortly after disease progression. The mechanism of resistance to this drug combination is unknown.METHODS: We analysed plasma circulating tumour DNA obtained at progression by exome sequencing and digital PCR. MET gene and mRNA in situ hybridisation analyses in two bioptic specimens obtained at progression were used to confirm the plasma data.RESULTS: We identified in plasma MET gene hyper-amplification as a potential mechanism underlying therapy resistance. Increased MET gene copy and transcript levels were detected in liver and lymph node metastatic biopsies. Finally, transduction of MET in BRAF mutant colorectal cancer cells conferred refractoriness to BRAF and MET inhibition.CONCLUSIONS: We identified in a rectal cancer patient MET gene hyper-amplification as mechanism of resistance to dual BRAF and MET inhibition.

// 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.

AimsBoth morphological and molecular approaches have highlighted the biological and prognostic importance of the tumour microenvironment in colorectal cancer (CRC). Despite this, microscopic assessment of tumour microenvironment has not been adopted into routine practice.Methods and ResultsBased on existing evidence, we selected specific morphological features relating to peritumoral inflammatory and stromal responses, agreed criteria for scoring, and assessed these in representative H&E-stained whole tumour sections, from a population-based cohort of 445 stage II/III colon cancer cases. Moderate/severe peritumoral diffuse lymphoid inflammation and Crohn's disease-like reaction were associated with significantly reduced risks of CRC-specific death (adjusted HR 0.48, 95% CI 0.31-0.76, and HR 0.60, 95% CI 0.42-0.84, respectively). The presence of >50% tumour stromal percentage, assessed by global evaluation of tumour area, was associated with a significantly increased risk of CRC-specific death (HR 1.60 95% CI 1.06-2.41). A composite, “fibroinflammatory score” (0-3), combining dichotomised scores of these three features, showed a highly significant association in relation to survival outcomes. Those with a score ≥2 had an almost 2.5 fold increased risk of CRC-specific death (HR 2.44, 95% CI: 1.56-3.81), compared with those scoring zero. These associations were stronger in MSI-high tumours, potentially identifying a subset of MSI-high colon cancers which lack characteristic morphological features and have an associated worse prognosis.ConclusionsIn summary, reporting on H&E staining of selected microscopic features of the tumour microenvironment, independently or in combination, offers valuable prognostic information in stage II/III colon cancer and may allow morphological correlation with developing molecular classifications of prognostic and predictive relevance.

// Conor A. Bradley 1, * , Philip D. Dunne 1, * , Victoria Bingham 1 , Stephen McQuaid 1, 2 , Hajrah Khawaja 1 , Stephanie Craig 1 , Jackie James 1, 2 , Wendy L. Moore 1 , Darragh G. McArt 1 , Mark Lawler 1 , Sonali Dasgupta 1 , Patrick G. Johnston 1 , Sandra Van Schaeybroeck 1 1 Drug Resistance Group, Centre for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Science, Queen’s University Belfast, Belfast, UK 2 Tissue Pathology, Belfast Health and Social Care Trust, Belfast City Hospital, Belfast, UK * Joint first authors Correspondence to: Sandra Van Schaeybroeck, email: s.vanschaeybroeck@qub.ac.uk Keywords: c-MET, colorectal cancer (CRC), tumor budding, invasion, metastasis Received: July 06, 2016 Accepted: October 19, 2016 Published: October 26, 2016 ABSTRACT c-MET and its ligand HGF are frequently overexpressed in colorectal cancer (CRC) and increased c-MET levels are found in CRC liver metastases. This study investigated the role of the HGF/c-MET axis in regulating migration/invasion in CRC, using pre-clinical models and clinical samples. Pre-clinically, we found marked upregulation of c-MET at both protein and mRNA levels in several invasive CRC cells. Down-regulation of c-MET using RNAi suppressed migration/invasion of parental and invasive CRC cells. Stimulation of CRC cells with rh-HGF or co-culture with HGF-expressing colonic myofibroblasts, resulted in significant increases in their migratory/invasive capacity. Importantly, HGF-induced c-MET activation promoted rapid downregulation of c-MET protein levels, while the MET transcript remained unaltered. Using RNA in situ hybridization (RNA ISH), we further showed that MET mRNA, but not protein levels, were significantly upregulated in tumor budding foci at the invasive front of a cohort of stage III CRC tumors ( p < 0.001). Taken together, we show for the first time that transcriptional upregulation of MET is a key molecular event associated with CRC invasion and tumor budding. This data also indicates that RNA ISH, but not immunohistochemistry, provides a robust methodology to assess MET levels as a potential driving force of CRC tumor invasion and metastasis.

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

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.