The genomic landscape of 2,023 colorectal cancers.

Colorectal carcinoma (CRC) is a common cause of mortality1, but a comprehensive description of its genomic landscape is lacking2-9. Here we perform whole-genome sequencing of 2,023 CRC samples from participants in the UK 100,000 Genomes Project, thereby providing a highly detailed somatic mutational landscape of this cancer. Integrated analyses identify more than 250 putative CRC driver genes, many not previously implicated in CRC or other cancers, including several recurrent changes outside the coding genome. We extend the molecular pathways involved in CRC development, define four new common subgroups of microsatellite-stable CRC based on genomic features and show that these groups have independent prognostic associations. We also characterize several rare molecular CRC subgroups, some with potential clinical relevance, including cancers with both microsatellite and chromosomal instability. We demonstrate a spectrum of mutational profiles across the colorectum, which reflect aetiological differences. These include the role of Escherichia colipks+ colibactin in rectal cancers10 and the importance of the SBS93 signature11-13, which suggests that diet or smoking is a risk factor. Immune-escape driver mutations14 are near-ubiquitous in hypermutant tumours and occur in about half of microsatellite-stable CRCs, often in the form of HLA copy number changes. Many driver mutations are actionable, including those associated with rare subgroups (for example, BRCA1 and IDH1), highlighting the role of whole-genome sequencing in optimizing patient care.

Automated curation of large-scale cancer histopathology image datasets using deep learning.

BACKGROUND: Artificial intelligence (AI) has numerous applications in pathology, supporting diagnosis and prognostication in cancer. However, most AI models are trained on highly selected data, typically one tissue slide per patient. In reality, especially for large surgical resection specimens, dozens of slides can be available for each patient. Manually sorting and labelling whole-slide images (WSIs) is a very time-consuming process, hindering the direct application of AI on the collected tissue samples from large cohorts. In this study we addressed this issue by developing a deep-learning (DL)-based method for automatic curation of large pathology datasets with several slides per patient. METHODS: We collected multiple large multicentric datasets of colorectal cancer histopathological slides from the United Kingdom (FOXTROT, N = 21,384 slides; CR07, N = 7985 slides) and Germany (DACHS, N = 3606 slides). These datasets contained multiple types of tissue slides, including bowel resection specimens, endoscopic biopsies, lymph node resections, immunohistochemistry-stained slides, and tissue microarrays. We developed, trained, and tested a deep convolutional neural network model to predict the type of slide from the slide overview (thumbnail) image. The primary statistical endpoint was the macro-averaged area under the receiver operating curve (AUROCs) for detection of the type of slide. RESULTS: In the primary dataset (FOXTROT), with an AUROC of 0.995 [95% confidence interval [CI]: 0.994-0.996] the algorithm achieved a high classification performance and was able to accurately predict the type of slide from the thumbnail image alone. In the two external test cohorts (CR07, DACHS) AUROCs of 0.982 [95% CI: 0.979-0.985] and 0.875 [95% CI: 0.864-0.887] were observed, which indicates the generalizability of the trained model on unseen datasets. With a confidence threshold of 0.95, the model reached an accuracy of 94.6% (7331 classified cases) in CR07 and 85.1% (2752 classified cases) for the DACHS cohort. CONCLUSION: Our findings show that using the low-resolution thumbnail image is sufficient to accurately classify the type of slide in digital pathology. This can support researchers to make the vast resource of existing pathology archives accessible to modern AI models with only minimal manual annotations.

Annotation and automated segmentation of single-molecule localisation microscopy data.

Single Molecule Localisation Microscopy (SMLM) is becoming a widely used technique in cell biology. After processing the images, the molecular localisations are typically stored in a table as xy (or xyz) coordinates, with additional information, such as number of photons, etc. This set of coordinates can be used to generate an image to visualise the molecular distribution, for example, a 2D or 3D histogram of localisations. Many different methods have been devised to analyse SMLM data, among which cluster analysis of the localisations is popular. However, it can be useful to first segment the data, to extract the localisations in a specific region of a cell or in individual cells, prior to downstream analysis. Here we describe a pipeline for annotating localisations in an SMLM dataset in which we compared membrane segmentation approaches, including Otsu thresholding and machine learning models, and subsequent cell segmentation. We used an SMLM dataset derived from dSTORM images of sectioned cell pellets, stained for the membrane proteins EGFR (epidermal growth factor receptor) and EREG (epiregulin) as a test dataset. We found that a Cellpose model retrained on our data performed the best in the membrane segmentation task, allowing us to perform downstream cluster analysis of membrane versus cell interior localisations. We anticipate this will be generally useful for SMLM analysis.

A comparison of frailty measures in population-based data for patients with colorectal cancer.

BACKGROUND: Numerous studies have revealed age-related inequalities in colorectal cancer care. Increasing levels of frailty in an ageing population may be contributing to this, but quantifying frailty in population-based studies is challenging. OBJECTIVE: To assess the feasibility, validity and reliability of the Hospital Frailty Risk Score (HFRS), the Secondary Care Administrative Records Frailty (SCARF) index and the frailty syndromes (FS) measures in a national colorectal cancer cohort. DESIGN: Retrospective population-based study using 136,008 patients with colorectal cancer treated within the English National Health Service. METHODS: Each measure was generated in the dataset to assess their feasibility. The diagnostic codes used in each measure were compared with those in the Charlson Comorbidity Index (CCI). Validity was assessed using the prevalence of frailty and relationship with 1-year survival. The Brier score and the c-statistic were used to assess performance and discriminative ability of models with included each measure. RESULTS: All measures demonstrated feasibility, validity and reliability. Diagnostic codes used in SCARF and CCI have considerable overlap. Prevalence of frailty determined by each differed; SCARF allocating 55.4% of the population to the lowest risk group compared with 85.1% (HFRS) and 81.2% (FS). HFRS and FS demonstrated the greatest difference in 1-year overall survival between those with the lowest and highest measured levels of frailty. Differences in model performance were marginal. CONCLUSIONS: HFRS, SCARF and FS all have value in quantifying frailty in routine administrative health care datasets. The most suitable measure will depend on the context and requirements of each individual epidemiological study.

Analysis of an Indian colorectal cancer faecal microbiome collection demonstrates universal colorectal cancer-associated patterns, but closest correlation with other Indian cohorts.

It is increasingly being recognised that changes in the gut microbiome have either a causative or associative relationship with colorectal cancer (CRC). However, most of this research has been carried out in a small number of developed countries with high CRC incidence. It is unknown if lower incidence countries such as India have similar microbial associations.Having previously established protocols to facilitate microbiome research in regions with developing research infrastructure, we have now collected and sequenced microbial samples from a larger cohort study of 46 Indian CRC patients and 43 healthy volunteers.When comparing to previous global collections, these samples resemble other Asian samples, with relatively high levels of Prevotella. Predicting cancer status between cohorts shows good concordance. When compared to a previous collection of Indian CRC patients, there was similar concordance, despite different sequencing technologies between cohorts.These results show that there does seem to be a global CRC microbiome, and that some inference between studies is reasonable. However, we also demonstrate that there is definite regional variation, with more similarities between location-matched comparisons. This emphasises the importance of developing protocols and advancing infrastructure to allow as many countries as possible to contribute to microbiome studies of their own populations.

Weakly supervised annotation-free cancer detection and prediction of genotype in routine histopathology.

Deep learning is a powerful tool in computational pathology: it can be used for tumor detection and for predicting genetic alterations based on histopathology images alone. Conventionally, tumor detection and prediction of genetic alterations are two separate workflows. Newer methods have combined them, but require complex, manually engineered computational pipelines, restricting reproducibility and robustness. To address these issues, we present a new method for simultaneous tumor detection and prediction of genetic alterations: The Slide-Level Assessment Model (SLAM) uses a single off-the-shelf neural network to predict molecular alterations directly from routine pathology slides without any manual annotations, improving upon previous methods by automatically excluding normal and non-informative tissue regions. SLAM requires only standard programming libraries and is conceptually simpler than previous approaches. We have extensively validated SLAM for clinically relevant tasks using two large multicentric cohorts of colorectal cancer patients, Darmkrebs: Chancen der Verhütung durch Screening (DACHS) from Germany and Yorkshire Cancer Research Bowel Cancer Improvement Programme (YCR-BCIP) from the UK. We show that SLAM yields reliable slide-level classification of tumor presence with an area under the receiver operating curve (AUROC) of 0.980 (confidence interval 0.975, 0.984; n = 2,297 tumor and n = 1,281 normal slides). In addition, SLAM can detect microsatellite instability (MSI)/mismatch repair deficiency (dMMR) or microsatellite stability/mismatch repair proficiency with an AUROC of 0.909 (0.888, 0.929; n = 2,039 patients) and BRAF mutational status with an AUROC of 0.821 (0.786, 0.852; n = 2,075 patients). The improvement with respect to previous methods was validated in a large external testing cohort in which MSI/dMMR status was detected with an AUROC of 0.900 (0.864, 0.931; n = 805 patients). In addition, SLAM provides human-interpretable visualization maps, enabling the analysis of multiplexed network predictions by human experts. In summary, SLAM is a new simple and powerful method for computational pathology that could be applied to multiple disease contexts. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

Deep learning identifies inflamed fat as a risk factor for lymph node metastasis in early colorectal cancer.

The spread of early-stage (T1 and T2) adenocarcinomas to locoregional lymph nodes is a key event in disease progression of colorectal cancer (CRC). The cellular mechanisms behind this event are not completely understood and existing predictive biomarkers are imperfect. Here, we used an end-to-end deep learning algorithm to identify risk factors for lymph node metastasis (LNM) status in digitized histopathology slides of the primary CRC and its surrounding tissue. In two large population-based cohorts, we show that this system can predict the presence of more than one LNM in pT2 CRC patients with an area under the receiver operating curve (AUROC) of 0.733 (0.67-0.758) and patients with any LNM with an AUROC of 0.711 (0.597-0.797). Similarly, in pT1 CRC patients, the presence of more than one LNM or any LNM was predictable with an AUROC of 0.733 (0.644-0.778) and 0.567 (0.542-0.597), respectively. Based on these findings, we used the deep learning system to guide human pathology experts towards highly predictive regions for LNM in the whole slide images. This hybrid human observer and deep learning approach identified inflamed adipose tissue as the highest predictive feature for LNM presence. Our study is a first proof of concept that artificial intelligence (AI) systems may be able to discover potentially new biological mechanisms in cancer progression. Our deep learning algorithm is publicly available and can be used for biomarker discovery in any disease setting. © 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Exploring the utility and acceptability of Faecal immunochemical testing (FIT) as a novel intervention for the improvement of colorectal Cancer (CRC) surveillance in individuals with lynch syndrome (FIT for lynch study): a single-arm, prospective, multi-centre, non-randomised study.

BACKGROUND: Lynch Syndrome (LS) is an inherited cancer predisposition syndrome defined by pathogenic variants in the mismatch repair (MMR) or EPCAM genes. In the United Kingdom, people with LS are advised to undergo biennial colonoscopy from as early as 25 until 75 years of age to mitigate a high lifetime colorectal cancer (CRC) risk, though the consideration of additional surveillance intervention(s) through the application of non-invasive diagnostic devices has yet to be longitudinally observed in LS patients. In this study, we will examine the role of annual faecal immunochemical testing (FIT) alongside biennial colonoscopy for CRC surveillance in people with LS. METHODS/DESIGN: In this single-arm, prospective, non-randomised study, 400 LS patients will be recruited across 11 National Health Service (NHS) Trusts throughout the United Kingdom. Study inclusion requires a LS diagnosis, between 25 and 73 years old, and a routine surveillance colonoscopy scheduled during the recruitment period. Eligible patients will receive a baseline OC-Sensor™ FIT kit ahead of their colonoscopy, and annually for 3 years thereafter. A pre-paid envelope addressed to the central lab will be included within all patient mailings for the return of FIT kits and relevant study documents. A questionnaire assessing attitudes and perception of FIT will also be included at baseline. All study samples received by the central lab will be assayed on an OC-Sensor™ PLEDIA Analyser. Patients with FIT results of ≥6 µg of Haemoglobin per gram of faeces (f-Hb) at Years 1 and/or 3 will be referred for colonoscopy via an urgent colonoscopy triage pathway. 16S rRNA gene V4 amplicon sequencing will be carried out on residual faecal DNA of eligible archived FIT samples to characterise the faecal microbiome. DISCUSSION: FIT may have clinical utility alongside colonoscopic surveillance in people with LS. We have designed a longitudinal study to examine the efficacy of FIT as a non-invasive modality. Potential limitations of this method will be assessed, including false negative or false positive FIT results related to specific morphological features of LS neoplasia or the presence of post-resection anastomotic inflammation. The potential for additional colonoscopies in a subset of participants may also impact on colonoscopic resources and patient acceptability. TRIAL REGISTRATION: Trial Registration: ISRCTN, ISRCTN15740250 . Registered 13 July 2021.

Characterisation of dysplastic liver nodules using low-pass DNA sequencing and detection of chromosome arm-level abnormalities in blood-derived cell-free DNA.

High-grade dysplasia carries significant risk of transformation to hepatocellular carcinoma (HCC). Despite this, at the current standard of care, all non-malignant hepatic nodules including high-grade dysplastic nodules are managed similarly. This is partly related to difficulties in distinguishing high-risk pathology in the liver. We aimed to identify chromosome arm-level somatic copy number alterations (SCNAs) that characterise the transition of liver nodules along the cirrhosis-dysplasia-carcinoma axis. We validated our findings on an independent cohort using blood-derived cell-free DNA. A repository of non-cancer DNA sequences obtained from patients with HCC (n = 389) was analysed to generate cut-off thresholds aiming to minimise false-positive SCNAs. Tissue samples representing stages from the multistep process of hepatocarcinogenesis (n = 184) were subjected to low-pass whole genome sequencing. Chromosome arm-level SCNAs were identified in liver cirrhosis, dysplastic nodules, and HCC to assess their discriminative capacity. Samples positive for 1q+ or 8q+ arm-level duplications were likely to be either HCC or high-grade dysplastic nodules as opposed to low-grade dysplastic nodules or cirrhotic tissue with an odds ratio (OR) of 35.5 (95% CI 11.5-110) and 16 (95% CI 6.4-40.2), respectively (p < 0.0001). In an independent cohort of patients recruited from Nottingham, UK, at least two out of four alterations (1q+, 4q-, 8p-, and 8q+) were detectable in blood-derived cell-free DNA of patients with HCC (n = 22) but none of the control patients with liver cirrhosis (n = 9). Arm-level SCNAs on 1q+ or 8q+ are associated with high-risk liver pathology. These can be detected using low-pass sequencing of cell-free DNA isolated from blood, which may be a future early cancer screening tool for patients with liver cirrhosis. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

Deep learning detects genetic alterations in cancer histology generated by adversarial networks.

Deep learning can detect microsatellite instability (MSI) from routine histology images in colorectal cancer (CRC). However, ethical and legal barriers impede sharing of images and genetic data, hampering development of new algorithms for detection of MSI and other biomarkers. We hypothesized that histology images synthesized by conditional generative adversarial networks (CGANs) retain information about genetic alterations. To test this, we developed a 'histology CGAN' which was trained on 256 patients (training cohort 1) and 1457 patients (training cohort 2). The CGAN synthesized 10 000 synthetic MSI and non-MSI images which contained a range of tissue types and were deemed realistic by trained observers in a blinded study. Subsequently, we trained a deep learning detector of MSI on real or synthetic images and evaluated the performance of MSI detection in a held-out set of 142 patients. When trained on real images from training cohort 1, this system achieved an area under the receiver operating curve (AUROC) of 0.742 [0.681, 0.854]. Training on the larger cohort 2 only marginally improved the AUROC to 0.757 [0.707, 0.869]. Training on purely synthetic data resulted in an AUROC of 0.743 [0.658, 0.801]. Training on both real and synthetic data further increased AUROC to 0.777 [0.715, 0.821]. We conclude that synthetic histology images retain information reflecting underlying genetic alterations in colorectal cancer. Using synthetic instead of real images to train deep learning systems yields non-inferior classifiers. This approach can be used to create large shareable data sets or to augment small data sets with rare molecular features. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

Experiences of running a stratified medicine adaptive platform trial: Challenges and lessons learned from 10 years of the FOCUS4 trial in metastatic colorectal cancer.

BACKGROUND: Complex innovative design trials are becoming increasingly common and offer potential for improving patient outcomes in a faster time frame. FOCUS4 was the first molecularly stratified trial in metastatic colorectal cancer and it remains one of the first umbrella trial designs to be launched globally. Here, we aim to describe lessons learned from delivery of the trial over the last 10 years. METHODS: FOCUS4 was a Phase II/III molecularly stratified umbrella trial testing the safety and efficacy of targeted therapies in metastatic colorectal cancer. It used adaptive statistical methodology to decide which sub-trial should close early, and new therapies were added as protocol amendments. Patients with newly diagnosed metastatic colorectal cancer were registered, and central laboratory testing was used to stratify their tumour into molecular subtypes. Following 16 weeks of first-line therapy, patients with stable or responding disease were eligible for randomisation into either a molecularly stratified sub-trial (FOCUS4-B, C or D) or non-stratified FOCUS4-N. The primary outcome for all studies was progression-free survival comparing the intervention with active monitoring/placebo. At the close of the trial, feedback was elicited from all investigators through surveys and interviews and consolidated into a series of recommendations and lessons learned for the delivery of similar future trials. RESULTS: Between January 2014 and October 2020, 1434 patients were registered from 88 UK hospitals. Of the 20 drug combinations that were explored for inclusion in the platform trial, three molecularly targeted sub-trials were activated: FOCUS4-D (February 2014-March 2016) evaluated AZD8931 in the BRAF-PIK3CA-RAS wildtype subgroup; FOCUS4-B (February 2016-July 2018) evaluated aspirin in the PIK3CA mutant subgroup and FOCUS4-C (June 2017-October 2020) evaluated adavosertib in the RAS+TP53 double mutant subgroup. FOCUS4-N was active throughout and evaluated capecitabine monotherapy versus a treatment break. A total of 361 (25%) registered patients were randomised into a sub-trial. Feedback on the experiences of delivery of FOCUS4 could be grouped into three main areas of challenge: funding/infrastructure, biomarker testing procedures and trial design efficiencies within which 20 recommendations are summarised. CONCLUSION: Adaptive stratified medicine platform studies are feasible in common cancers but present challenges. Our stakeholder feedback has helped to inform how these trial designs can succeed and answer multiple questions efficiently, providing resource is adequate.

Addressing the variation in adjuvant chemotherapy treatment for colorectal cancer: Can a regional intervention promote national change?

Analysis of routine population-based data has previously shown that patterns of surgical treatment for colorectal cancer can vary widely, but there is limited evidence available to determine if such variation is also seen in the use of chemotherapy. This study quantified variation in adjuvant chemotherapy across both England using cancer registry data and in more detail across the representative Yorkshire and Humber regions. Individuals with Stages II and III colorectal cancer who underwent major resection from 2014 to 2015 were identified. Rates of chemotherapy were calculated from the Systemic Anticancer Treatment database using multilevel logistic regression. Additionally, questionnaires addressing different clinical scenarios were sent to regional oncologists to investigate the treatment preferences of clinicians. The national adjusted chemotherapy treatment rate ranged from 2% to 46% (Stage II cancers), 19% to 81% (Stage III cancers), 24% to 75% (patients aged <70 years) and 5% to 46% (patients aged ≥70 years). Regionally, the rates of treatment and the proportions of treated patients receiving combination chemotherapy varied by stage (Stage II 4%-26% and 0%-55%, Stage III 48%-71% and 40%-84%) and by age (<70 years 35%-68% and 49%-91%; ≥70 years 15%-39% and 6%-75%). Questionnaire responses showed significant variations in opinions for high-risk Stage II patients with both deficient and proficient mismatch repair tumours and Stage IIIB patients aged ≥70 years. Following a review of the evidence, open discussion in our region has enabled a consensus agreement on an algorithm for colorectal cancer that is intended to reduce variation in practice.

Clinical-Grade Detection of Microsatellite Instability in Colorectal Tumors by Deep Learning.

BACKGROUND & AIMS: Microsatellite instability (MSI) and mismatch-repair deficiency (dMMR) in colorectal tumors are used to select treatment for patients. Deep learning can detect MSI and dMMR in tumor samples on routine histology slides faster and less expensively than molecular assays. However, clinical application of this technology requires high performance and multisite validation, which have not yet been performed. METHODS: We collected H&E-stained slides and findings from molecular analyses for MSI and dMMR from 8836 colorectal tumors (of all stages) included in the MSIDETECT consortium study, from Germany, the Netherlands, the United Kingdom, and the United States. Specimens with dMMR were identified by immunohistochemistry analyses of tissue microarrays for loss of MLH1, MSH2, MSH6, and/or PMS2. Specimens with MSI were identified by genetic analyses. We trained a deep-learning detector to identify samples with MSI from these slides; performance was assessed by cross-validation (N = 6406 specimens) and validated in an external cohort (n = 771 specimens). Prespecified endpoints were area under the receiver operating characteristic (AUROC) curve and area under the precision-recall curve (AUPRC). RESULTS: The deep-learning detector identified specimens with dMMR or MSI with a mean AUROC curve of 0.92 (lower bound, 0.91; upper bound, 0.93) and an AUPRC of 0.63 (range, 0.59-0.65), or 67% specificity and 95% sensitivity, in the cross-validation development cohort. In the validation cohort, the classifier identified samples with dMMR with an AUROC of 0.95 (range, 0.92-0.96) without image preprocessing and an AUROC of 0.96 (range, 0.93-0.98) after color normalization. CONCLUSIONS: We developed a deep-learning system that detects colorectal cancer specimens with dMMR or MSI using H&E-stained slides; it detected tissues with dMMR with an AUROC of 0.96 in a large, international validation cohort. This system might be used for high-throughput, low-cost evaluation of colorectal tissue specimens.

Lynch syndrome screening in colorectal cancer: results of a prospective 2-year regional programme validating the NICE diagnostics guidance pathway throughout a 5.2-million population.

AIMS: Screening all patients newly diagnosed with colorectal cancer (CRC) for possible Lynch syndrome (LS) has been recommended in the United Kingdom since the National Institute for Health and Care Excellence (NICE) released new diagnostics guidance in February 2017. We sought to validate the NICE screening pathway through a prospective regional programme throughout a 5.2-million population during a 2-year period. METHODS AND RESULTS: Pathology departments at 14 hospital trusts in the Yorkshire and Humber region of the United Kingdom were invited to refer material from patients with newly diagnosed CRC aged 50 years or over between 1 April 2017 and 31 March 2019 for LS screening. Testing consisted of immunohistochemistry for MLH1, PMS2, MSH2 and MSH6 followed by BRAF mutation analysis ± MLH1 promoter methylation testing in cases showing MLH1 loss. A total of 3141 individual specimens were submitted for testing from 12 departments consisting of 3061 unique tumours and 2791 prospectively acquired patients with CRC. Defective mismatch repair (dMMR) was observed in 15% of cases. In cases showing MLH1 loss, 76% contained a detectable BRAF mutation and, of the remainder, 77% showed MLH1 promoter hypermethylation. Of the patients included in the final analysis, 81 (2.9%) had an indication for germline testing. CONCLUSION: LS screening using the NICE diagnostics guidance pathway is deliverable at scale identifying significant numbers of patients with dMMR. This information is used to refer patients to regional clinical genetics services in addition to informing treatment pathways including the use of adjuvant/neoadjuvant chemotherapy and immunotherapy.

Luminal Bioavailability of Orally Administered ω-3 PUFAs in the Distal Small Intestine, and Associated Changes to the Ileal Microbiome, in Humans with a Temporary Ileostomy.

BACKGROUND: Oral administration of purified omega-3 (ω-3) PUFAs is associated with changes to the fecal microbiome. However, it is not known whether this effect is associated with increased PUFA concentrations in the gut. OBJECTIVES: We investigated the luminal bioavailability of oral ω-3 PUFAs (daily dose 1 g EPA and 1g DHA free fatty acid equivalents as triglycerides in soft-gel capsules, twice daily) and changes to the gut microbiome, in the ileum. METHODS: Ileostomy fluid (IF) and blood were obtained at baseline, after first capsule dosing (median 2 h), and at a similar time after final dosing on day 28, in 11 individuals (median age 63 y) with a temporary ileostomy. Fatty acids were measured by LC-tandem MS. The ileal microbiome was characterized by 16S rRNA PCR and Illumina sequencing. RESULTS: There was a mean 6.0 ± 9.8-fold and 6.6 ± 9.6-fold increase in ileal EPA and DHA concentrations (primary outcome), respectively, at 28 d, which was associated with increased RBC ω-3 PUFA content (P ≤ 0.05). The first oral dose did not increase the ileal ω-3 PUFA concentration except in 4 individuals, who displayed high luminal EPA and DHA concentrations, which reduced to concentrations similar to the overall study population at day 28, suggesting physiological adaptation. Bacteroides, Clostridium, and Streptococcus were abundant bacterial genera in the ileum. Ileal microbiome variability over time and between individuals was large, with no consistent change associated with acute ω-3 PUFA dosing. However, high concentrations of EPA and DHA in IF on day 28 were associated with higher abundance of Bacteroides (r2 > 0.86, P < 0.05) and reduced abundance of other genera, including Actinomyces (r2 > 0.94, P < 0.05). CONCLUSIONS: Oral administration of ω-3 PUFAs leads to increased luminal ω-3 PUFA concentrations and changes to the microbiome, in the ileum of individuals with a temporary ileostomy. This study is registered on the ISRCTN registry as ISRCTN14530452.

Influence of age on surgical treatment and postoperative outcomes of patients with colorectal cancer in Denmark and Yorkshire, England.

AIM: Denmark and Yorkshire are demographically similar and both have undergone changes in their management of colorectal cancer to improve outcomes. The differential provision of surgical treatment, especially in the older age groups, may contribute to the magnitude of improved survival rates. This study aimed to identify differences in the management of colorectal cancer surgery and postoperative outcomes according to patient age between Denmark and Yorkshire. METHOD: This was a retrospective population-based study of colorectal cancer patients diagnosed in Denmark and Yorkshire between 2005 and 2016. Proportions of patients undergoing major surgical resection, postoperative mortality and relative survival were compared between Denmark and Yorkshire across several age groups (18-59, 60-69, 70-79 and ≥80 years) and over time. RESULTS: The use of major surgical resection was higher in Denmark than in Yorkshire, especially for patients aged ≥80 years (70.5% versus 50.5% for colon cancer, 49.3% versus 38.1% for rectal cancer). Thirty-day postoperative mortality for Danish patients aged ≥80 years was significantly higher than that for Yorkshire patients with colonic cancer [OR (95% CI) = 1.22 (1.07, 1.38)] but not for rectal cancer or for 1-year postoperative mortality. Relative survival significantly increased in all patients aged ≥80 years except for Yorkshire patients with colonic cancer. CONCLUSION: This study suggests that there are major differences between the management of elderly patients with colorectal cancer between the two populations. Improved selection for surgery and better peri- and postoperative care in these patients appears to improve long-term outcomes, but may come at the cost of a higher 30-day mortality.

The role of the longitudinal muscle in the anal sphincter complex: Implications for the Intersphincteric Plane in Low Rectal Cancer Surgery?

Intersphincteric resection (ISR) enables radical sphincter-preserving surgery in a subset of low rectal tumors impinging on the anal sphincter complex (ASC). Excellent anatomical knowledge is essential for optimal ISR. This study describes the role of the longitudinal muscle (LM) in the ASC and implications for ISR and other low rectal and anal pathologies. Six human adult en bloc cadaveric specimens (three males, three females) were obtained from the University of Leeds GIFT Research Tissue Programme. Paraffin-embedded mega blocks containing the ASC were produced and serially sectioned at 250 µm intervals. Whole mount microscopic sections were histologically stained and digitally scanned. The intersphincteric plane was shown to be potentially very variable. In some places adipose tissue is located between the external anal sphincter (EAS) and internal anal sphincter (IAS), whereas in others the LM interdigitates to obliterate the plane. Elsewhere the LM is (partly) absent with the intersphincteric plane lying on the IAS. The LM gave rise to the formation of the submucosae and corrugator ani muscles by penetrating the IAS and EAS. In four of six specimens, striated muscle fibers from the EAS curled around the distal IAS reaching the anal submucosa. The ASC formed a complex structure, varying between individuals with an inconstant LM affecting the potential location of the intersphincteric plane as well as a high degree of intermingling striated and smooth muscle fibers potentially further disrupting the plane. The complexity of identifying the correct pathological staging of low rectal cancer is also demonstrated. Clin. Anat. 33:567-577, 2020. © 2019 Wiley Periodicals, Inc.

A prospective phase II study of pre-operative chemotherapy then short-course radiotherapy for high risk rectal cancer: COPERNICUS.

BACKGROUND: Neoadjuvant chemotherapy (NAC) allows earlier treatment of rectal cancer micro-metastases but is not standard of care. There are currently no biomarkers predicting long-term progression-free survival (PFS) benefit from NAC. PATIENTS AND METHODS: In this single arm phase II trial, patients with non-metastatic magnetic resonance imaging (MRI)-defined operable rectal adenocarcinoma at high risk of post-operative metastatic recurrence, received 8 weeks of oxaliplatin/fluorouracil NAC then short-course preoperative radiotherapy (SCPRT) before immediate surgery. Sixteen weeks of post-operative adjuvant chemotherapy (AC) was planned. A pelvic MRI was performed at week 9 immediately post-NAC, before SCPRT. The primary end point was feasibility assessed by completion of protocol treatment up to and including surgery. Secondary endpoints included compliance, toxicity, downstaging efficacy, and PFS. RESULTS: In total 60 patients were recruited May 2012-June 2014. In total 57 patients completed protocol treatment, meeting the primary endpoint. Compliance with NAC was much better than AC: Comparing NAC vs. AC, the median percentage dose intensity for fluoropyrimidine was 100% vs. 63% and for oxaliplatin 100% vs. 45%. Treatment-related toxicity was acceptable with no treatment-related deaths. Post-NAC MRI showed 44 tumours (73%) were T-downstaged and 22 (37%) had excellent MRI tumour regression grade (mrTRG 1-2). Median follow-up was 27 months with 2-year PFS of 86.2% (10 events). On exploratory analysis, post-NAC mrTRG predicted PFS with no event among those with excellent regression. CONCLUSION: The regimen was well tolerated with effective downstaging and encouraging PFS. mrTRG response to NAC may be a new prognostic factor for long-term PFS, but needs validation in larger studies.

Intratumoral stromal morphometry predicts disease recurrence but not response to 5-fluorouracil-results from the QUASAR trial of colorectal cancer.

AIMS: The biological importance of tumour-associated stroma is becoming increasingly apparent, but its clinical utility remains ill-defined. For stage II/Dukes B colorectal cancer (CRC), clinical biomarkers are urgently required to direct therapeutic options. We report here prognostic/predictive analyses, and molecular associations, of stromal morphometric quantification in the Quick and Simple and Reliable (QUASAR) trial of CRC. METHODS AND RESULTS: Relative proportions of tumour epithelium (PoT) or stroma (PoS) were morphometrically quantified on digitised haematoxylin and eosin (H&E) sections derived from 1800 patients enrolled in QUASAR, which randomised 3239 (91% stage II) CRC patients between adjuvant fluorouracil/folinic acid (FUFA) chemotherapy and observation. The prognostic and predictive values of PoT/PoS measurements were determined by the use of stratified log-rank analyses. A high proportion of tumour stroma (≥50%) was associated with an increased recurrence risk: 31.3% (143/457) recurrence for ≥50% versus 21.9% (294/1343) for <50% [rate ratio (RR) 1.62; 95% confidence interval (CI) 1.30-2.02; P < 0.0001]. Of patients with stromal proportions of ≥65%, 40% (46/115) had recurrent disease within 10 years. The adverse prognostic effect of a high stromal proportion was independent of established prognostic variables, and was maintained in stage II/Dukes B patients (RR 1.62; 95% CI 1.26-2.08; P = 0.0002). KRAS mutation in the presence of a high stromal proportion augmented recurrence risk (RR 2.93; 95% CI 1.87-4.59; P = 0.0005). Stromal morphometry did not predict response to FUFA chemotherapy. CONCLUSIONS: Simple digital morphometry applied to a single representative H&E section identifies CRC patients with a >50% higher risk of disease recurrence. This technique can reliably partition patients into subpopulations with different risks of tumour recurrence in a simple and cost-effective manner. Further prospective validation is warranted.

Colon cancer surgery: pathological quality control is essential for optimal outcomes.

Following Professor Madoff's overview lecture and the experts' round table discussion Dr West and Professor Quirke provide a commentary of the major gains which could be made by improving and standardizing the quality of colon cancer surgery.