Management Strategies and Outcomes for Small Intestinal Neuroendocrine Tumours with Involvement of the Superior Mesenteric Vessels: A Systematic Review.

Small intestinal neuroendocrine tumours (SI-NETs) are the most common small intestinal tumours. A particularly challenging subset of these tumours is those that involve the superior mesenteric artery or vein for which the role and feasibility of surgery are often questioned. This systematic review aimed to identify and evaluate the management strategies used for these complex SI-NETs. The identified studies showed positive outcomes with surgery and multimodality therapy.

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

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.

A Systematic Review of Surgical Management Strategies in the Treatment of Peritoneal Carcinomatosis of Neuroendocrine Origin.

Cytoreductive surgery (CRS) represents the cornerstone of surgical management for peritoneal carcinomatosis (PC) and involves peritonectomy procedures aimed at complete peritoneal tumour resection. Frequently, CRS is combined with hyperthermic intraperitoneal chemotherapy (HIPEC). The combination of CRS + HIPEC is now considered the standard of care in patients with colorectal and ovarian PC. However, the role of this multi-modality treatment approach in patients with PC of neuroendocrine tumour origin (NET-PC) is less well understood. This systematic review provides a summary of available evidence on management strategies for patients with NET-PC. A systematic literature search was performed using Ovid Medline, EMBASE and Cochrane Library databases to identify studies reporting outcomes for patients with NET-PC undergoing surgical treatment. Eligible studies were assessed for methodological quality and design and evaluated for a method of surgical treatment, method of HIPEC delivery, oncological outcomes, and treatment-related morbidity. Eight studies, including a total of 1240 patients with NET-PC, met predefined inclusion criteria and have been included in this review. In three of the included studies, CRS alone was performed for patients with NET-PC, while five studies reported outcomes with combined treatment using CRS plus HIPEC. All studies were performed at tertiary peritoneal malignancy centres. Only one study directly compared outcomes in patients with NET-PC undergoing CRS plus HIPEC compared with CRS in isolation, with no significant difference in overall survival reported. Carefully selected patients with NET-PC may benefit from aggressive surgical treatment in the form of CRS +/- HIPEC. These procedures are best undertaken at centres with expertise in the management of both neuroendocrine tumours and peritoneal malignancy, as both are conditions that require tertiary-level care. The additional benefit of the HIPEC component in this group of patients remains unclear and warrants further investigation in clinical trials. Overall, the quality of data on this subject is restricted by the low number of studies and the variability in treatment methods employed. A multi-national data registry for patients with NET-PC may offer the opportunity to better define treatment algorithms. Translational research efforts in parallel should focus on developing a better biological understanding of NET-PC, with a view to identifying more effective intraperitoneal cytocidal agents.

Assessing the Impact of Primary Tumour Location on Survival After Resection of Colorectal Liver Metastases: A Propensity Weighted Retrospective Cohort Study.

BACKGROUND: Right-sided colonic tumours appear to have poorer survival after resection of colorectal liver metastases, although this may be confounded by various factors including advanced stage and emergency presentation. METHODS: Medical records of consecutive patients undergoing resection of colorectal liver metastases at a single centre from 2008 to 2015 were retrospectively reviewed. Cases were categorised by primary tumour location (right colon, left colon, rectum). Each primary location was weighted using propensity scores to balance covariates, including staging and mode of presentation. Cox regression was then applied to derive multivariable hazard ratios (HR) of survival after liver resection. Primary outcomes were 10-year overall survival (OS) and 5-year disease-free survival (DFS) after liver resection based on PTL. RESULTS: 414 patients were included in the analysis. Left colonic tumours were significantly associated with higher rates of bilobar liver metastasis (36.2% vs. 20.1% and 30.1%) and larger maximum size of liver metastases compared with rectal and right-sided tumours, respectively. There was no difference in rates of extra-hepatic metastases, recurrence in the liver after resection or RAS, BRAF or p53 mutational status. After propensity weighting and Cox-regression, right-sided tumours were independently associated with significantly reduced 10 year OS (HR 1.56, 95% CI 1.03-2.36, p = 0.04) but not 5 year DFS (HR 1.36, 95% CI 0.89-2.08, p = 0.15). CONCLUSIONS: Compared with left colonic and rectal tumours, right-sided colonic tumours are independently associated with inferior OS after resection of CRLM. This is despite higher rates of bilobar liver metastases and larger metastases with left-sided tumours.

Phytochemically rich dietary components and the risk of colorectal cancer: A systematic review and meta-analysis of observational studies.

BACKGROUND: Personalized nutrition and protective diets and lifestyles represent a key cancer research priority. The association between consumption of specific dietary components and colorectal cancer (CRC) incidence has been evaluated by a number of population-based studies, which have identified certain food items as having protective potential, though the findings have been inconsistent. Herein we present a systematic review and meta-analysis on the potential protective role of five common phytochemically rich dietary components (nuts, cruciferous vegetables, citrus fruits, garlic and tomatoes) in reducing CRC risk. AIM: To investigate the independent impact of increased intake of specific dietary constituents on CRC risk in the general population. METHODS: Medline and Embase were systematically searched, from time of database inception to January 31, 2020, for observational studies reporting CRC incidence relative to intake of one or more of nuts, cruciferous vegetables, citrus fruits, garlic and/or tomatoes in the general population. Data were extracted by two independent reviewers and analyzed in accordance with the Meta-analysis of Observational Studies in Epidemiology (MOOSE) and Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) reporting guidelines and according to predefined inclusion/exclusion criteria. Effect sizes of studies were pooled using a random-effects model. RESULTS: Forty-six studies were identified. CRC risk was significantly reduced in patients with higher vs lower consumption of cruciferous vegetables [odds ratio (OR) = 0.90; 95% confidence interval (CI): 0.85-0.95; P < 0.005], citrus fruits (OR = 0.90; 95%CI: 0.84-0.96; P < 0.005), garlic (OR = 0.83; 95%CI: 0.76-0.91; P < 0.005) and tomatoes (OR = 0.89; 95%CI: 0.84-0.95; P < 0.005). Subgroup analysis showed that this association sustained when looking at case-control studies alone, for all of these four food items, but no significant difference was found in analysis of cohort studies alone. Nut consumption exhibited a similar trend, but overall results were not significant (OR = 0.72; 95%CI: 0.50-1.03; P < 0.07; I 2 = 90.70%). Putative anticarcinogenic mechanisms are proposed using gene-set enrichment analysis of gene/protein perturbations caused by active compounds within each food item. CONCLUSION: Increased cruciferous vegetable, garlic, citrus fruit and tomato consumption are all inversely associated with CRC risk. These findings highlight the potential for developing precision nutrition strategies for CRC prevention.

Network machine learning maps phytochemically rich "Hyperfoods" to fight COVID-19.

In this paper, we introduce a network machine learning method to identify potential bioactive anti-COVID-19 molecules in foods based on their capacity to target the SARS-CoV-2-host gene-gene (protein-protein) interactome. Our analyses were performed using a supercomputing DreamLab App platform, harnessing the idle computational power of thousands of smartphones. Machine learning models were initially calibrated by demonstrating that the proposed method can predict anti-COVID-19 candidates among experimental and clinically approved drugs (5658 in total) targeting COVID-19 interactomics with the balanced classification accuracy of 80-85% in 5-fold cross-validated settings. This identified the most promising drug candidates that can be potentially "repurposed" against COVID-19 including common drugs used to combat cardiovascular and metabolic disorders, such as simvastatin, atorvastatin and metformin. A database of 7694 bioactive food-based molecules was run through the calibrated machine learning algorithm, which identified 52 biologically active molecules, from varied chemical classes, including flavonoids, terpenoids, coumarins and indoles predicted to target SARS-CoV-2-host interactome networks. This in turn was used to construct a "food map" with the theoretical anti-COVID-19 potential of each ingredient estimated based on the diversity and relative levels of candidate compounds with antiviral properties. We expect this in silico predicted food map to play an important role in future clinical studies of precision nutrition interventions against COVID-19 and other viral diseases.

Application of robotic technologies in lower gastrointestinal tract endoscopy: A systematic review.

BACKGROUND: Conventional optical colonoscopy is considered the gold standard investigation for colorectal tract pathology including colorectal malignancy, polyps and inflammatory bowel disease. Inherent limitations exist with current generation endoscopic technologies, including, but not limited to, patient discomfort, endoscopist fatigue, narrow field of view and missed pathology behind colonic folds. Rapid developments in medical robotics have led to the emergence of a variety of next-generation robotically-augmented technologies that could overcome these limitations. AIM: To provide a comprehensive summary of recent developments in the application of robotics in lower gastrointestinal tract endoscopy. METHODS: A systematic review of the literature was performed from January 1, 2000 to the January 7, 2021 using EMBASE, MEDLINE and Cochrane databases. Studies reporting data on the use of robotic technology in ex vivo or in vivo animal and human experiments were included. In vitro studies (studies using synthetic colon models), studies evaluating non-robotic technology, robotic technology aimed at the upper gastrointestinal tract or paediatric endoscopy were excluded. System ergonomics, safety, visualisation, and diagnostic/therapeutic capabilities were assessed. RESULTS: Initial literature searching identified 814 potentially eligible studies, from which 37 were deemed suitable for inclusion. Included studies were classified according to the actuation modality of the robotic device(s) as electromechanical (EM) (n = 13), pneumatic (n = 11), hydraulic (n = 1), magnetic (n = 10) and hybrid (n = 2) mechanisms. Five devices have been approved by the Food and Drug Administration, however most of the technologies reviewed remain in the early phases of testing and development. Level 1 evidence is lacking at present, but early reports suggest that these technologies may be associated with improved pain and safety. The reviewed devices appear to be ergonomically capable and efficient though to date no reports have convincingly shown diagnostic or therapeutic superiority over conventional colonoscopy. CONCLUSION: Significant progress in robotic colonoscopy has been made over the last couple of decades. Improvements in design together with the integration of semi-autonomous and autonomous systems over the next decade will potentially result in robotic colonoscopy becoming more commonplace.

Impact of primary tumour location on colorectal liver metastases: A systematic review.

BACKGROUND: Colorectal cancer (CRC) is the third most common cause of cancer-related death worldwide. Despite significant advances in screening, surgical management and adjuvant therapies, average 5-year survival seldom exceeds 60% in most developed nations. Metastatic disease represents the primary cause of mortality in patients with CRC, and the liver is the most common location for distant tumour spread. Up to 25% of patients are found to have synchronous liver metastases at the time of diagnosis and a further 30%-40% will develop metachronous disease in the course of follow-up. It has been suggested that primary tumour location [right side versus left side, primary tumour location (PTL)] can influence oncological outcomes in this patient group and that this should be considered in prognostic models and therapeutic decision-making algorithms. This suggestion is not universally accepted and there have been conflicting reports in the literature to date. AIM: To provide a comprehensive summary of the available evidence regarding the impact of PTL on oncological outcomes in patients with colorectal cancer liver metastases (CRCLM). METHODS: MEDLINE, EMBASE and COCHRANE were searched for relevant publications using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology. Data on oncological outcomes was then extracted from full text articles that met the predefined inclusion criteria. RESULTS: A total of 41 studies were identified that met predefined inclusion criteria for this review. In 21 out of 38 studies that provided data on overall survival, a statistically significant improvement in overall survival was reported in patients with left sided primary tumours. These studies included a total of 13897 patients compared with 4306 patients in the studies that did not show a significant difference. Eight studies noted a similar trend towards improved disease-free or progression-free survival. Several authors observed distinct patterns of relapse after treatment of hepatic metastases according to PTL; for example hepatic recurrence after treatment of CRCLM appears to occur more aggressively with right-sided CRC. CONCLUSION: Taken together, the findings of the present review indicate that PTL may have a role as an independent prognostic factor when determining treatment and disease surveillance strategies in CRC. The mechanisms responsible for this variation remain poorly understood, but are likely to relate to molecular, histological and embryological differences, as well as inherent differences in therapeutic sensitivity.

HyperFoods: Machine intelligent mapping of cancer-beating molecules in foods.

Recent data indicate that up-to 30-40% of cancers can be prevented by dietary and lifestyle measures alone. Herein, we introduce a unique network-based machine learning platform to identify putative food-based cancer-beating molecules. These have been identified through their molecular biological network commonality with clinically approved anti-cancer therapies. A machine-learning algorithm of random walks on graphs (operating within the supercomputing DreamLab platform) was used to simulate drug actions on human interactome networks to obtain genome-wide activity profiles of 1962 approved drugs (199 of which were classified as "anti-cancer" with their primary indications). A supervised approach was employed to predict cancer-beating molecules using these 'learned' interactome activity profiles. The validated model performance predicted anti-cancer therapeutics with classification accuracy of 84-90%. A comprehensive database of 7962 bioactive molecules within foods was fed into the model, which predicted 110 cancer-beating molecules (defined by anti-cancer drug likeness threshold of >70%) with expected capacity comparable to clinically approved anti-cancer drugs from a variety of chemical classes including flavonoids, terpenoids, and polyphenols. This in turn was used to construct a 'food map' with anti-cancer potential of each ingredient defined by the number of cancer-beating molecules found therein. Our analysis underpins the design of next-generation cancer preventative and therapeutic nutrition strategies.

Colorectal Peritoneal Metastases: A Systematic Review of Current and Emerging Trends in Clinical and Translational Research.

Colorectal peritoneal metastases (CPM) are associated with abbreviated survival and significantly impaired quality of life. In patients with CPM, radical multimodality treatment consisting of cytoreductive surgery (CRS) combined with hyperthermic intraperitoneal chemotherapy (HIPEC) has demonstrated oncological superiority over systemic chemotherapy alone. In highly selected patients undergoing CRS + HIPEC, overall survival of over 60% has been reported in some series. These are patients in whom the disease burden is limited and where the diagnosis is made at an early stage in the disease course. Early diagnosis and a deeper understanding of the biological mechanisms that regulate CPM are critical to refining patient selection for radical treatment, personalising therapeutic approaches, enhancing prognostication, and ultimately improving long-term survivorship. In the present study, we outline three broad themes which represent critical future research targets in CPM: (1) enhanced radiological strategies for early detection and staging; (2) identification and validation of translational biomarkers for diagnostic, prognostic, and therapeutic deployment; and (3) development of optimized approaches for surgical cytoreduction as well as more precise strategies for intraperitoneal drug selection and delivery. Herein, we provide a contemporary narrative review of the state of the art in these three areas. A systematic review in accordance with PRISMA guidelines was undertaken on all English language studies published between 2007 and 2017. In vitro and animal model studies were deemed eligible for inclusion in the sections pertaining to biomarkers and therapeutic optimisation, as these areas of research currently remain in the early stages of development. Acquired data were then divided into hierarchical thematic categories (imaging modalities, translational biomarkers (diagnostic/prognostic/therapeutic), and delivery techniques) and subcategories. An interactive sunburst figure is provided for intuitive interrogation of the CPM research landscape.

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.

Novel biomarkers for patient stratification in colorectal cancer: A review of definitions, emerging concepts, and data.

Colorectal cancer (CRC) treatment has become more personalised, incorporating a combination of the individual patient risk assessment, gene testing, and chemotherapy with surgery for optimal care. The improvement of staging with high-resolution imaging has allowed more selective treatments, optimising survival outcomes. The next step is to identify biomarkers that can inform clinicians of expected prognosis and offer the most beneficial treatment, while reducing unnecessary morbidity for the patient. The search for biomarkers in CRC has been of significant interest, with questions remaining on their impact and applicability. The study of biomarkers can be broadly divided into metabolic, molecular, microRNA, epithelial-to-mesenchymal-transition (EMT), and imaging classes. Although numerous molecules have claimed to impact prognosis and treatment, their clinical application has been limited. Furthermore, routine testing of prognostic markers with no demonstrable influence on response to treatment is a questionable practice, as it increases cost and can adversely affect expectations of treatment. In this review we focus on recent developments and emerging biomarkers with potential utility for clinical translation in CRC. We examine and critically appraise novel imaging and molecular-based approaches; evaluate the promising array of microRNAs, analyze metabolic profiles, and highlight key findings for biomarker potential in the EMT pathway.

Translational -omics: Future potential and current challenges in precision medicine.

Rapid advances in computational science and biotechnology are paving the way for precision medicine - a vision in next-generation healthcare that promises to provide a care package uniquely tailored to each individual's molecular make-up. Until relatively recently, the focus has been firmly centred around the genome; however, over the past two decades there has been a surge in the study of molecular activity within other biological domains (proteome/transcriptome/metabolome) involved in health and pathogenesis. The term '-omics' is broadly applied to these disciplines and 'translational -omics' refers to clinical utilisation of data derived from these scientific approaches. Translational -omics represents the cornerstone of the precision medicine initiative and offers positively disruptive solutions in global healthcare from a humanitarian, scientific and economic standpoint. However, there are unique challenges anticipated for all stakeholders within the precision medicine community, and addressing these early on in the adoption of precision approaches is critical. Herein, we outline the potential for translational -omics in precision medicine, highlight key roadblocks to successful implementation and propose potential solutions to current and expected problems.

BASIS: High-performance bioinformatics platform for processing of large-scale mass spectrometry imaging data in chemically augmented histology.

Mass Spectrometry Imaging (MSI) holds significant promise in augmenting digital histopathologic analysis by generating highly robust big data about the metabolic, lipidomic and proteomic molecular content of the samples. In the process, a vast quantity of unrefined data, that can amount to several hundred gigabytes per tissue section, is produced. Managing, analysing and interpreting this data is a significant challenge and represents a major barrier to the translational application of MSI. Existing data analysis solutions for MSI rely on a set of heterogeneous bioinformatics packages that are not scalable for the reproducible processing of large-scale (hundreds to thousands) biological sample sets. Here, we present a computational platform (pyBASIS) capable of optimized and scalable processing of MSI data for improved information recovery and comparative analysis across tissue specimens using machine learning and related pattern recognition approaches. The proposed solution also provides a means of seamlessly integrating experimental laboratory data with downstream bioinformatics interpretation/analyses, resulting in a truly integrated system for translational MSI.

ChemDistiller: an engine for metabolite annotation in mass spectrometry.

Motivation: High-resolution mass spectrometry permits simultaneous detection of thousands of different metabolites in biological samples; however, their automated annotation still presents a challenge due to the limited number of tailored computational solutions freely available to the scientific community. Results: Here, we introduce ChemDistiller, a customizable engine that combines automated large-scale annotation of metabolites using tandem MS data with a compiled database containing tens of millions of compounds with pre-calculated 'fingerprints' and fragmentation patterns. Our tests using publicly and commercially available tandem MS spectra for reference compounds show retrievals rates comparable to or exceeding the ones obtainable by the current state-of-the-art solutions in the field while offering higher throughput, scalability and processing speed. Availability and implementation: Source code freely available for download at https://bitbucket.org/iAnalytica/chemdistillerpython. Supplementary information: Supplementary data are available at Bioinformatics online.

Exosomal microRNAs derived from colorectal cancer-associated fibroblasts: role in driving cancer progression.

Colorectal cancer is a global disease with increasing incidence. Mortality is largely attributed to metastatic spread and therefore, a mechanistic dissection of the signals which influence tumor progression is needed. Cancer stroma plays a critical role in tumor proliferation, invasion and chemoresistance. Here, we sought to identify and characterize exosomal microRNAs as mediators of stromal-tumor signaling. In vitro, we demonstrated that fibroblast exosomes are transferred to colorectal cancer cells, with a resultant increase in cellular microRNA levels, impacting proliferation and chemoresistance. To probe this further, exosomal microRNAs were profiled from paired patient-derived normal and cancer-associated fibroblasts, from an ongoing prospective biomarker study. An exosomal cancer-associated fibroblast signature consisting of microRNAs 329, 181a, 199b, 382, 215 and 21 was identified. Of these, miR-21 had highest abundance and was enriched in exosomes. Orthotopic xenografts established with miR-21-overexpressing fibroblasts and CRC cells led to increased liver metastases compared to those established with control fibroblasts. Our data provide a novel stromal exosome signature in colorectal cancer, which has potential for biomarker validation. Furthermore, we confirmed the importance of stromal miR-21 in colorectal cancer progression using an orthotopic model, and propose that exosomes are a vehicle for miR-21 transfer between stromal fibroblasts and cancer cells.

Translational utility of a hierarchical classification strategy in biomolecular data analytics.

Hierarchical classification (HC) stratifies and classifies data from broad classes into more specific classes. Unlike commonly used data classification strategies, this enables the probabilistic prediction of unknown classes at different levels, minimizing the burden of incomplete databases. Despite these advantages, its translational application in biomedical sciences has been limited. We describe and demonstrate the implementation of a HC approach for "omics-driven" classification of 15 bacterial species at various taxonomic levels achieving 90-100% accuracy, and 9 cancer types into morphological types and 35 subtypes with 99% and 76% accuracy, respectively. Unknown bacterial species were probabilistically assigned with 100% accuracy to their respective genus or family using mass spectra (n = 284). Cancer types were predicted by mRNA data (n = 1960) for most subtypes with 95-100% accuracy. This has high relevance in clinical practice where complete datasets are difficult to compile with the continuous evolution of diseases and emergence of new strains, yet prediction of unknown classes, such as bacterial species, at upper hierarchy levels may be sufficient to initiate antimicrobial therapy. The algorithms presented here can be directly translated into clinical-use with any quantitative data, and have broad application potential, from unlabeled sample identification, to hierarchical feature selection, and discovery of new taxonomic variants.

A prospective analysis of mucosal microbiome-metabonome interactions in colorectal cancer using a combined MAS 1HNMR and metataxonomic strategy.

Colon cancer induces a state of mucosal dysbiosis with associated niche specific changes in the gut microbiota. However, the key metabolic functions of these bacteria remain unclear. We performed a prospective observational study in patients undergoing elective surgery for colon cancer without mechanical bowel preparation (n = 18). Using 16 S rRNA gene sequencing we demonstrated that microbiota ecology appears to be cancer stage-specific and strongly associated with histological features of poor prognosis. Fusobacteria (p < 0.007) and ε- Proteobacteria (p < 0.01) were enriched on tumour when compared to adjacent normal mucosal tissue, and fusobacteria and ß-Proteobacteria levels increased with advancing cancer stage (p = 0.014 and 0.002 respecitvely). Metabonomic analysis using 1H Magic Angle Spinning Nuclear Magnetic Resonsance  (MAS-NMR) spectroscopy, demonstrated increased abundance of taurine, isoglutamine, choline, lactate, phenylalanine and tyrosine and decreased levels of lipids and triglycerides in tumour relative to adjacent healthy tissue. Network analysis revealed that bacteria associated with poor prognostic features were not responsible for the modification of the cancer mucosal metabonome. Thus the colon cancer mucosal microbiome evolves with cancer stage to meet the demands of cancer metabolism. Passenger microbiota may play a role in the maintenance of cancer mucosal metabolic homeostasis but these metabolic functions may not be stage specific.

Detecting colorectal cancer using electrical impedance spectroscopy: an ex vivo feasibility study.

OBJECTIVE: Colorectal cancer is the fourth most common cancer worldwide, with a lifetime risk of around 20%. Current techniques do not allow clinicians to objectively assess tissue abnormality during endoscopy and perioperatively. A method capable of objectively assessing samples in real time and which can be included in minimally invasive diagnostic and management strategies would be highly transformative. Electrical impedance spectroscopy (EIS) may provide such a solution. This paper presents a feasibility study on using EIS in assessing colorectal tissue. APPROACH: We performed tetrapolar EIS using ZedScan on excised human colorectal tumour tissue and the matched normal colonic mucosa in 22 freshly resected specimens following elective surgery for colorectal cancer. Histopathological examination was used to confirm the final diagnosis. Statistical significance was assessed using the Wilcoxon signed rank test. MAIN RESULTS: Tetrapolar EIS could discriminate cancer with statistically significant results when applying frequencies between 305 Hz and 625 kHz (p < 0.05). 300 Ω was set as the transfer impedance threshold to detect cancer. Thus, the area under the corresponding receiver operating characteristic curve for this threshold was 0.7105. SIGNIFICANCE: This feasibility study demonstrates that impedance spectra changes in colorectal cancer tissue are detectable and may be statistically significant, suggesting that EIS has the potential to be the core technology in a novel non-invasive point of care test for detecting colorectal cancer. These results warrant further development by increasing the size of the study with a device specifically designed for colorectal cancer.