A next-generation -omics analysis of the radiotherapy response in rectal cancer

Rectal cancer (RC) accounts for over a third of all cases of colorectal cancer (CRC), itself the second-leading cause of cancer-related death worldwide. Fundamental to the treatment armamentarium in locally advanced RC is the application of neoadjuvant radiotherapy (RT). The exact response to this therapy is highly individualised and unpredictable. As we enter an era where the role for RT in RC is set to increase, the critical unmet need remains both the lack of a predictive or prognosticating RT biomarker, and an incomplete understanding of the mechanisms that govern radioresistance in RC. Multiple, Next-Generation ‘-omics’ platforms now provide us with the tools to obtain a more holistic snapshot of the biology at play, in ways that until very recently were not feasible. This body of work represents the design and implementation of a novel multi-omics pipeline to address a greater understanding of the contributing and complementary mechanisms at play in RC radioresistance. Firstly, I have demonstrated the value in application of machine-learning algorithms to the vast amount of existing experimental data to reveal previously untapped areas likely to be of biological significance. I have subsequently utilised a soft-ionisation mass spectrometry imaging platform to gain maximally representative biological insights into the tumour lipidome in RC in response to RT. I have further complemented this with a holistic whole-genome sequencing (WGS) approach of pre- and post-RT RC specimens, with a targeted view on the likely genomic aberrations that may be at play. My findings have demonstrated that: (1) Gene Ontology (GO)-based network mapping of published, investigated, candidate biomarkers demonstrates areas of significant promise in the research of this problem. GO domains of cellular metabolism, response to stimuli and cell communication are not only the areas most implicated as being significantly correlated to RT response, but they are all heavily influenced by the actions of complex cellular lipids. (2) Gene Set Enrichment Analysis (GSEA) demonstrates previously unidentified enrichment in gene sets associated with glycerophospholipid (GPL) metabolism in radioresistant RC in a legacy microarray dataset (3) Desorption electrospray ionisation mass spectrometry imaging (DESI MSI) reveals increased abundances of phosphatidylethanolamines (PE), phosphatidylserines (PS) and phosphatidylglycerols (PG) in radioresistant RC, with increased integration of polyunsaturated fatty acids (FAs) into these GPLs as compared to RT-naïve tissues (4) Low-pass whole-genome sequencing (lpWGS) reveals a significant burden of somatic copy number aberration (SCNA) in pre-RT RC, correlating with a CMS2 or CMS4 phenotype. Furthermore, there is significant heterogeneity in SCNA burden in separately sequenced macroscopic regions of tumour prior to RT, and an increased genomic divergence correlates with a superior response to RT.Open Acces

Genomic-driven nutritional interventions for radiotherapy-resistant rectal cancer patient

Abstract Radiotherapy response of rectal cancer patients is dependent on a myriad of molecular mechanisms including response to stress, cell death, and cell metabolism. Modulation of lipid metabolism emerges as a unique strategy to improve radiotherapy outcomes due to its accessibility by bioactive molecules within foods. Even though a few radioresponse modulators have been identified using experimental techniques, trying to experimentally identify all potential modulators is intractable. Here we introduce a machine learning (ML) approach to interrogate the space of bioactive molecules within food for potential modulators of radiotherapy response and provide phytochemically-enriched recipes that encapsulate the benefits of discovered radiotherapy modulators. Potential radioresponse modulators were identified using a genomic-driven network ML approach, metric learning and domain knowledge. Then, recipes from the Recipe1M database were optimized to provide ingredient substitutions maximizing the number of predicted modulators whilst preserving the recipe’s culinary attributes. This work provides a pipeline for the design of genomic-driven nutritional interventions to improve outcomes of rectal cancer patients undergoing radiotherapy

Network mapping of molecular biomarkers influencing radiation response in rectal cancer

Preoperative radiotherapy (RT) plays an important role in the management of locally advanced rectal cancer (RC). Tumor regression after RT shows marked variability, and robust molecular methods are needed to help predict likely response. The aim of this study was to review the current published literature and use Gene Ontology (GO) analysis to define key molecular biomarkers governing radiation response in RC. A systematic review of electronic bibliographic databases (Medline, Embase) was performed for original articles published between 2000 and 2015. Biomarkers were then classified according to biological function and incorporated into a hierarchical GO tree. Both significant and nonsignificant results were included in the analysis. Significance was binarized on the basis of univariate and multivariate statistics. Significance scores were calculated for each biological domain (or node), and a direct acyclic graph was generated for intuitive mapping of biological pathways and markers involved in RC radiation response. Seventy-two individual biomarkers across 74 studies were identified. On highest-order classification, molecular biomarkers falling within the domains of response to stress, cellular metabolism, and pathways inhibiting apoptosis were found to be the most influential in predicting radiosensitivity. Homogenizing biomarker data from original articles using controlled GO terminology demonstrated that cellular mechanisms of response to RT in RC—in particular the metabolic response to RT—may hold promise in developing radiotherapeutic biomarkers to help predict, and in the future modulate, radiation response.</p

High resolution ambient MS imaging of biological samples by desorption electro-flow focussing ionization

In this study, we examine the suitability of desorption electro-flow focusing ionization (DEFFI) for mass spectrometry imaging (MSI) of biological tissue. We also compare the performance of desorption electrospray ionization (DESI) with and without the flow focusing setup. The main potential advantages of applying the flow focusing mechanism in DESI is its rotationally symmetric electrospray jet, higher intensity, more controllable parameters, and better portability due to the robustness of the sprayer. The parameters for DEFFI have therefore been thoroughly optimized, primarily for spatial resolution but also for intensity. Once the parameters have been optimized, DEFFI produces similar images to the existing DESI. MS images for mouse brain samples, acquired at a nominal pixel size of 50 μm, are comparable for both DESI setups, albeit the new sprayer design yields better sensitivity. Furthermore, the two methods are compared with regard to spectral intensity as well as the area of the desorbed crater on rhodamine-coated slides. Overall, the implementation of a flow focusing mechanism in DESI is shown to be highly suitable for imaging biological tissue and has potential to overcome some of the shortcomings experienced with the current geometrical design of DESI