Validation of Next Generation Sequencing Technologies in Comparison to Current Diagnostic Gold Standards for BRAF, EGFR and KRAS Mutational Analysis

Next Generation Sequencing (NGS) has the potential of becoming an important tool in clinical diagnosis and therapeutic decision-making in oncology owing to its enhanced sensitivity in DNA mutation detection, fast-turnaround of samples in comparison to current gold standard methods and the potential to sequence a large number of cancer-driving genes at the one time. We aim to test the diagnostic accuracy of current NGS technology in the analysis of mutations that represent current standard-of-care, and its reliability to generate concomitant information on other key genes in human oncogenesis. Thirteen clinical samples (8 lung adenocarcinomas, 3 colon carcinomas and 2 malignant melanomas) already genotyped for EGFR, KRAS and BRAF mutations by current standard-of-care methods (Sanger Sequencing and q-PCR), were analysed for detection of mutations in the same three genes using two NGS platforms and an additional 43 genes with one of these platforms. The results were analysed using closed platform-specific proprietary bioinformatics software as well as open third party applications. Our results indicate that the existing format of the NGS technology performed well in detecting the clinically relevant mutations stated above but may not be reliable for a broader unsupervised analysis of the wider genome in its current design. Our study represents a diagnostically lead validation of the major strengths and weaknesses of this technology before consideration for diagnostic use.

AXL Is a Key Regulator of Inherent and Chemotherapy-Induced Invasion and Predicts a Poor Clinical Outcome in Early-Stage Colon Cancer

Purpose: Despite the use of 5-fluorouracil (5-FU)–based adjuvant treatments, a large proportion of patients with high-risk stage II/III colorectal cancer will relapse. Thus, novel therapeutic strategies are needed for early-stage colorectal cancer. Residual micrometastatic disease from the primary tumor is a major cause of patient relapse.

Experimental Design: To model colorectal cancer tumor cell invasion/metastasis, we have generated invasive (KRASMT/KRASWT/+chr3/p53-null) colorectal cancer cell subpopulations. Receptor tyrosine kinase (RTK) screens were used to identify novel proteins that underpin the migratory/invasive phenotype. Migration/invasion was assessed using the XCELLigence system. Tumors from patients with early-stage colorectal cancer (N = 336) were examined for AXL expression.

Results: Invasive colorectal cancer cell subpopulations showed a transition from an epithelial-to-mesenchymal like phenotype with significant increases in migration, invasion, colony-forming ability, and an attenuation of EGF receptor (EGFR)/HER2 autocrine signaling. RTK arrays showed significant increases in AXL levels in all invasive sublines. Importantly, 5-FU treatment resulted in significantly increased migration and invasion, and targeting AXL using pharmacologic inhibition or RNA interference (RNAi) approaches suppressed basal and 5-FU–induced migration and invasion. Significantly, high AXL mRNA and protein expression were found to be associated with poor overall survival in early-stage colorectal cancer tissues.

Conclusions: We have identified AXL as a poor prognostic marker and important mediator of cell migration/invasiveness in colorectal cancer. These findings provide support for the further investigation of AXL as a novel prognostic biomarker and therapeutic target in colorectal cancer, in particular in the adjuvant disease in which EGFR/VEGF–targeted therapies have failed.

cudaMap: a GPU accelerated program for gene expression 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.

The opportunities and limitations of using CFD in the development of wave energy converters

Most Wave Energy Converters (WECs) being developed are fundamentally different from known marine structures. Limited experience is a fundamental challenge for the design, especially issues concerning load assumptions and power estimates. Reynolds-Averaged Navier-Stokes (RANS) CFD methods are being used successfully in many areas of marine engineering. They have been shown to accurately simulate many hydrodynamic effects and are a helpful tool for investigating complex flows. The major drawback is the significant computational power required and the associated overhead with pre and post-processing. This paper presents the challenges and advantages in the application of RANS CFD methods in the design process of a wave energy converter and compares the time, labour and ultimately financial requirements for obtaining practical results.

The effect of the spectral distribution of wave energy on the performance of a bottom hinged flap type wave energy converter

The power output from a wave energy converter is typically predicted using experimental and/or numerical modelling techniques. In order to yield meaningful results the relevant characteristics of the device, together with those of the wave climate must be modelled with sufficient accuracy.

The wave climate is commonly described using a scatter table of sea states defined according to parameters related to wave height and period. These sea states are traditionally modelled with the spectral distribution of energy defined according to some empirical formulation. Since the response of most wave energy converters vary at different frequencies of excitation, their performance in a particular sea state may be expected to depend on the choice of spectral shape employed rather than simply the spectral parameters. Estimates of energy production may therefore be affected if the spectral distribution of wave energy at the deployment site is not well modelled. Furthermore, validation of the model may be affected by differences between the observed full scale spectral energy distribution and the spectrum used to model it.

This paper investigates the sensitivity of the performance of a bottom hinged flap type wave energy converter to the spectral energy distribution of the incident waves. This is investigated experimentally using a 1:20 scale model of Aquamarine Power’s Oyster wave energy converter, a bottom hinged flap type device situated at the European Marine Energy Centre (EMEC) in approximately 13m water depth. The performance of the model is tested in sea states defined according to the same wave height and period parameters but adhering to different spectral energy distributions.

The results of these tests show that power capture is reduced with increasing spectral bandwidth. This result is explored with consideration of the spectral response of the device in irregular wave conditions. The implications of this result are discussed in the context of validation of the model against particular prototype data sets and estimation of annual energy production.

TBX2 represses CST6 resulting in uncontrolled legumain activity to sustain breast cancer proliferation: a novel cancer-selective target pathway with therapeutic opportunities.

TBX2 is an oncogenic transcription factor known to drive breast cancer proliferation. We have identified the cysteine protease inhibitor Cystatin 6 (CST6) as a consistently repressed TBX2 target gene, co-repressed through a mechanism involving Early Growth Response 1 (EGR1). Exogenous expression of CST6 in TBX2-expressing breast cancer cells resulted in significant apoptosis whilst non-tumorigenic breast cells remained unaffected. CST6 is an important tumor suppressor in multiple tissues, acting as a dual protease inhibitor of both papain-like cathepsins and asparaginyl endopeptidases (AEPs) such as Legumain (LGMN). Mutation of the CST6 LGMN-inhibitory domain completely abrogated its ability to induce apoptosis in TBX2-expressing breast cancer cells, whilst mutation of the cathepsin-inhibitory domain or treatment with a pan-cathepsin inhibitor had no effect, suggesting that LGMN is the key oncogenic driver enzyme. LGMN activity assays confirmed the observed growth inhibitory effects were consistent with CST6 inhibition of LGMN. Knockdown of LGMN and the only other known AEP enzyme (GPI8) by siRNA confirmed that LGMN was the enzyme responsible for maintaining breast cancer proliferation. CST6 did not require secretion or glycosylation to elicit its cell killing effects, suggesting an intracellular mode of action. Finally, we show that TBX2 and CST6 displayed reciprocal expression in a cohort of primary breast cancers with increased TBX2 expression associating with increased metastases. We have also noted that tumors with altered TBX2/CST6 expression show poor overall survival. This novel TBX2-CST6-LGMN signaling pathway, therefore, represents an exciting opportunity for the development of novel therapies to target TBX2 driven breast cancers.

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

Aims: The utility of p53 as a prognostic assay has been elusive. The aims of this study were to describe a novel, reproducible scoring system and assess the relationship between differential p53 immunohistochemistry (IHC) expression patterns, TP53 mutation status and patient outcomes in breast cancer.

Methods and Results: Tissue microarrays were used to study p53 IHC expression patterns: expression was defined as extreme positive (EP), extreme negative (EN), and non-extreme (NE; intermediate patterns). Overall survival (OS) was used to define patient outcome. A representative subgroup (n = 30) showing the various p53 immunophenotypes was analysed for TP53 hotspot mutation status (exons 4-9). Extreme expression of any type occurred in 176 of 288 (61%) cases. As compared with NE expression, EP expression was significantly associated (P = 0.039) with poorer OS. In addition, as compared with NE expression, EN expression was associated (P = 0.059) with poorer OS. Combining cases showing either EP or EN expression better predicted OS than either pattern alone (P = 0.028). This combination immunophenotype was significant in univariate but not multivariate analysis. In subgroup analysis, six substitution exon mutations were detected, all corresponding to extreme IHC phenotypes. Five missense mutations corresponded to EP staining, and the nonsense mutation corresponded to EN staining. No mutations were detected in the NE group.

Conclusions: Patients with extreme p53 IHC expression have a worse OS than those with NE expression. Accounting for EN as well as EP expression improves the prognostic impact. Extreme expression positively correlates with nodal stage and histological grade, and negatively with hormone receptor status. Extreme expression may relate to specific mutational status.

Analysis of wntless (WLS) expression in gastric, ovarian, and breast cancers reveals a strong association with HER2 overexpression

The oncogenic role of WNT is well characterized. Wntless (WLS) (also known as GPR177, or Evi), a key modulator of WNT protein secretion, was recently found to be highly overexpressed in malignant astrocytomas. We hypothesized that this molecule may be aberrantly expressed in other cancers known to possess aberrant WNT signaling such as ovarian, gastric, and breast cancers. Immunohistochemical analysis using a TMA platform revealed WLS overexpression in a subset of ovarian, gastric, and breast tumors; this overexpression was associated with poorer clinical outcomes in gastric cancer (P=0.025). In addition, a strong correlation was observed between WLS expression and human epidermal growth factor receptor 2 (HER2) overexpression. Indeed, 100% of HER2-positive intestinal gastric carcinomas, 100% of HER2-positive serous ovarian carcinomas, and 64% of HER2-positive breast carcinomas coexpressed WLS protein. Although HER2 protein expression or gene amplification is an established predictive biomarker for trastuzumab response in breast and gastric cancers, a significant proportion of HER2-positive tumors display resistance to trastuzumab, which may be in part explainable by a possible mechanistic link between WLS and HER2.

Digital pathology and image analysis in tissue biomarker research

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

Abstract 1905: Defining a therapeutic classification of breast cancer by actionable targets

Breast cancer remains a frequent cause of female cancer death despite the great strides in elucidation of biological subtypes and their reported clinical and prognostic significance. We have defined a general cohort of breast cancers in terms of putative actionable targets, involving growth and proliferative factors, the cell cycle, and apoptotic pathways, both as single biomarkers across a general cohort and within intrinsic molecular subtypes.

We identified 293 patients treated with adjuvant chemotherapy. Additional hormonal therapy and trastuzumab was administered depending on hormonal and HER2 status respectively. We performed immunohistochemistry for ER, PR, HER2, MM1, CK5/6, p53, TOP2A, EGFR, IGF1R, PTEN, p-mTOR and e-cadherin. The cohort was classified into luminal (62%) and non-luminal (38%) tumors as well as luminal A (27%), luminal B HER2 negative (22%) and positive (12%), HER2 enriched (14%) and triple negative (25%). Patients with luminal tumors and co-overexpression of TOP2A or IGF1R loss displayed worse overall survival (p=0.0251 and p=0.0008 respectively). Non-luminal tumors had much greater heterogeneous expression profiles with no individual markers of prognostic significance. Non-luminal tumors were characterised by EGFR and TOP2A overexpression, IGF1R, PTEN and p-mTOR negativity and extreme p53 expression.

Our results indicate that only a minority of intrinsic subtype tumors purely express single novel actionable targets. This lack of pure biomarker expression is particular prevalent in the triple negative subgroup and may allude to the mechanism of targeted therapy inaction and myriad disappointing trial results. Utilising a combinatorial biomarker approach may enhance studies of targeted therapies providing additional information during design and patient selection while also helping decipher negative trial results.

PICan: An integromics framework for dynamic cancer biomarker discovery

Modern cancer research on prognostic and predictive biomarkers demands the integration of established and emerging high-throughput technologies. However, these data are meaningless unless carefully integrated with patient clinical outcome and epidemiological information. Integrated datasets hold the key to discovering new biomarkers and therapeutic targets in cancer. We have developed a novel approach and set of methods for integrating and interrogating phenomic, genomic and clinical data sets to facilitate cancer biomarker discovery and patient stratification. Applied to a known paradigm, the biological and clinical relevance of TP53, PICan was able to recapitulate the known biomarker status and prognostic significance at a DNA, RNA and protein levels.

The prognostic value of the stem-like group in colorectal cancer using a panel of immunohistochemistry markers

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths in the Western world. It is becoming increasingly clear that CRC is a diverse disease, as exemplified by the identification of subgroups of CRC tumours that are driven by distinct biology. Recently, a number of studies have begun to define panels of diagnostically relevant markers to align patients into individual subgroups in an attempt to give information on prognosis and treatment response. We examined the immunohistochemical expression profile of 18 markers, each representing a putative role in cancer development, in 493 primary colorectal carcinomas using tissue microarrays. Through unsupervised clustering in stage II cancers, we identified two cluster groups that are broadly defined by inflammatory or immune-related factors (CD3, CD8, COX-2 and FOXP3) and stem-like factors (CD44, LGR5, SOX2, OCT4). The expression of the stem-like group markers was associated with a significantly worse prognosis compared to cases with lower expression. In addition, patients classified in the stem-like subgroup displayed a trend towards a benefit from adjuvant treatment. The biologically relevant and poor prognostic stem-like group could also be identified in early stage I cancers, suggesting a potential opportunity for the identification of aggressive tumors at a very early stage of the disease.