Paul O'Reilly

Challenging the cancer molecular stratification dogma: Intratumoral heterogeneity undermines consensus molecular subtypes and potential diagnostic value in colorectal cancer

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

Neural modelling of chemical plant using MLP and B-spline networks

Abstract 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

Immune-derived PD-L1 gene expression defines a subgroup of stage II/III colorectal cancer patients with favorable prognosis that may be harmed by adjuvant chemotherapy

A subgroup of patients with colorectal cancer was defined by high PD-L1 gene expression on their tumor-infiltrating immune cells. These patients may be harmed by standard chemotherapy and may benefit from immunotherapy that targets the PD-1 immune checkpoint. 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.

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

Abstract 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.

Connectivity mapping using a combined gene signature from multiple colorectal cancer datasets identified candidate drugs including existing chemotherapies

BackgroundWhile 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.ResultsWe 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.

Probability estimation algorithms for self-validating sensors☆

Abstract 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.

Cancer-cell intrinsic gene expression signatures overcome intratumoural heterogeneity bias in colorectal cancer patient classification

Stromal-derived intratumoural heterogeneity (ITH) has been shown to undermine molecular stratification of patients into appropriate prognostic/predictive subgroups. Here, using several clinically relevant colorectal cancer (CRC) gene expression signatures, we assessed the susceptibility of these signatures to the confounding effects of ITH using gene expression microarray data obtained from multiple tumour regions of a cohort of 24 patients, including central tumour, the tumour invasive front and lymph node metastasis. 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 multi-region dataset. Signatures focused on cancer-cell intrinsic gene expression were found to produce more clinically useful, patient-centred classifiers, as exemplified by the CRC intrinsic signature (CRIS), which robustly clustered samples by patient-of-origin rather than region-of-origin. These findings highlight the potential of cancer-cell intrinsic signatures to reliably stratify CRC patients by minimising the confounding effects of stromal-derived ITH.

A case study in on-line intelligent sensing

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.

On-Line Neural Inferential Prediction of Viscosity

Abstract Neural network paradigms such as B-Splines have been suggested for adaptive on-line modelling of nonlinear dynamic systems because of their local support properties. This paper describes the use of such networks for multi-step-ahead prediction. A new disturbance modelling technique is proposed, and the resulting prediction strategy is applied to a real industrial problem.

ACE: A Workbench using Evolutionary Genetic Algorithms for analysing association in TCGA Data

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.