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.

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.

Minimally Invasive Surgery for Colorectal Cancer: Benchmarking Uptake for a Regional Improvement Programme.

BACKGROUND: The uptake of minimally invasive surgery (MIS) for patients with colorectal cancer has progressed at differing rates, both across countries, and within countries. This study aimed to investigate uptake for a regional colorectal cancer improvement programme in England. METHOD: We calculated the proportion of patients receiving elective laparoscopic and robot-assisted surgery amongst those diagnosed with colorectal cancer over 3 time periods (2007-2011, 2012-2016 and 2017-2021) in hospitals participating in the Yorkshire Cancer Research Bowel Cancer Improvement Programme (YCR BCIP). These were benchmarked against national rates. Regression analysis and funnel plots were used to develop a data driven approach for analysing trends in the use of MIS at hospitals in the programme. RESULTS: In England, resections performed by MIS increased from 34.9% to 72.9% for colon cancer and from 28.8% to 72.5% for rectal cancer. Robot-assisted surgery increased from 0.1% to 2.7% for colon cancer and from 0.2% to 7.9% for rectal cancer. Wide variation in the uptake of MIS was observed at a hospital level. Detailed analysis of the YCR BCIP region identified a decreasing number of surgical departments, since the start of the programme, as potential outliers for MIS when compared to the English national average. CONCLUSION: Wide variation in use of MIS for colorectal cancer exists within the English National Health Service and a data-driven approach can help identify outlying hospitals. Addressing some of the challenges behind the uptake of MIS, such as ensuring adequate provision of surgical training and equipment, could help increase its use.

Evaluation of CD3 and CD8 T-Cell Immunohistochemistry for Prognostication and Prediction of Benefit From Adjuvant Chemotherapy in Early-Stage Colorectal Cancer Within the QUASAR Trial.

PURPOSE: High densities of tumor infiltrating CD3 and CD8 T-cells are associated with superior prognosis in colorectal cancer (CRC). Their value as predictors of benefit from adjuvant chemotherapy is uncertain. PATIENTS AND METHODS: Tumor tissue from 868 patients in the QUASAR trial (adjuvant fluorouracil/folinic acid v observation in stage II/III CRC) was analyzed by CD3 and CD8 immunohistochemistry. Pathologists, assisted by artificial intelligence, calculated CD3 and CD8 cell densities (cells/mm2) in the core tumor (CT) and invasive margin (IM). Participants were randomly partitioned into training and validation sets. The primary outcome was recurrence-free interval (RFI), 2-year RFI for assessment of biomarker-treatment interactions. Maximum-likelihood methods identified optimal high-risk/low-risk group cutpoints in the training set. Prognostic analyses were repeated in the validation set. RESULTS: In the training set, the recurrence rate in the high-risk group was twice that in the low-risk group for all measures (CD3-CT: rate ratio [RR], 2.00, P = .0008; CD3-IM: 2.38, P < .00001; CD8-CT: 2.17, P = .0001; CD8-IM: 2.13, P = .0001). This was closely replicated in the validation set (RR, 1.96, 1.79, 1.72, 1.72, respectively). In multivariate analyses, prognostic effects were similar in colon and rectal cancers, and in stage II and III disease. Proportional reductions in recurrence with adjuvant chemotherapy were of similar magnitude in the high- and low-recurrence risk groups. Combining information from CD3-IM and CD3-CT (CD3 Score) generated high-, intermediate-, and low-risk groups with numbers needed to treat (NNTs) to prevent one disease recurrence being 11, 21, and 36, respectively. CONCLUSION: Recurrence rates in the high-risk CD3/CD8 groups are twice those in the low-risk groups. Proportional reductions with chemotherapy are similar, allowing NNTs derived in QUASAR to be updated using contemporary, nonrandomized data sets.

Deep learning for dual detection of microsatellite instability and POLE mutations in colorectal cancer histopathology.

In the spectrum of colorectal tumors, microsatellite-stable (MSS) tumors with DNA polymerase ε (POLE) mutations exhibit a hypermutated profile, holding the potential to respond to immunotherapy similarly to their microsatellite-instable (MSI) counterparts. Yet, due to their rarity and the associated testing costs, systematic screening for these mutations is not commonly pursued. Notably, the histopathological phenotype resulting from POLE mutations is theorized to resemble that of MSI. This resemblance not only could facilitate their detection by a transformer-based Deep Learning (DL) system trained on MSI pathology slides, but also indicates the possibility for MSS patients with POLE mutations to access enhanced treatment options, which might otherwise be overlooked. To harness this potential, we trained a Deep Learning classifier on a large dataset with the ground truth for microsatellite status and subsequently validated its capabilities for MSI and POLE detection across three external cohorts. Our model accurately identified MSI status in both the internal and external resection cohorts using pathology images alone. Notably, with a classification threshold of 0.5, over 75% of POLE driver mutant patients in the external resection cohorts were flagged as "positive" by a DL system trained on MSI status. In a clinical setting, deploying this DL model as a preliminary screening tool could facilitate the efficient identification of clinically relevant MSI and POLE mutations in colorectal tumors, in one go.

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.

Image-based consensus molecular subtyping in rectal cancer biopsies and response to neoadjuvant chemoradiotherapy.

The development of deep learning (DL) models to predict the consensus molecular subtypes (CMS) from histopathology images (imCMS) is a promising and cost-effective strategy to support patient stratification. Here, we investigate whether imCMS calls generated from whole slide histopathology images (WSIs) of rectal cancer (RC) pre-treatment biopsies are associated with pathological complete response (pCR) to neoadjuvant long course chemoradiotherapy (LCRT) with single agent fluoropyrimidine. DL models were trained to classify WSIs of colorectal cancers stained with hematoxylin and eosin into one of the four CMS classes using a multi-centric dataset of resection and biopsy specimens (n = 1057 WSIs) with paired transcriptional data. Classifiers were tested on a held out RC biopsy cohort (ARISTOTLE) and correlated with pCR to LCRT in an independent dataset merging two RC cohorts (ARISTOTLE, n = 114 and SALZBURG, n = 55 patients). DL models predicted CMS with high classification performance in multiple comparative analyses. In the independent cohorts (ARISTOTLE, SALZBURG), cases with WSIs classified as imCMS1 had a significantly higher likelihood of achieving pCR (OR = 2.69, 95% CI 1.01-7.17, p = 0.048). Conversely, imCMS4 was associated with lack of pCR (OR = 0.25, 95% CI 0.07-0.88, p = 0.031). Classification maps demonstrated pathologist-interpretable associations with high stromal content in imCMS4 cases, associated with poor outcome. No significant association was found in imCMS2 or imCMS3. imCMS classification of pre-treatment biopsies is a fast and inexpensive solution to identify patient groups that could benefit from neoadjuvant LCRT. The significant associations between imCMS1/imCMS4 with pCR suggest the existence of predictive morphological features that could enhance standard pathological assessment.

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.

Encrypted federated learning for secure decentralized collaboration in cancer image analysis.

Artificial intelligence (AI) has a multitude of applications in cancer research and oncology. However, the training of AI systems is impeded by the limited availability of large datasets due to data protection requirements and other regulatory obstacles. Federated and swarm learning represent possible solutions to this problem by collaboratively training AI models while avoiding data transfer. However, in these decentralized methods, weight updates are still transferred to the aggregation server for merging the models. This leaves the possibility for a breach of data privacy, for example by model inversion or membership inference attacks by untrusted servers. Somewhat-homomorphically-encrypted federated learning (SHEFL) is a solution to this problem because only encrypted weights are transferred, and model updates are performed in the encrypted space. Here, we demonstrate the first successful implementation of SHEFL in a range of clinically relevant tasks in cancer image analysis on multicentric datasets in radiology and histopathology. We show that SHEFL enables the training of AI models which outperform locally trained models and perform on par with models which are centrally trained. In the future, SHEFL can enable multiple institutions to co-train AI models without forsaking data governance and without ever transmitting any decryptable data to untrusted servers.

End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre study.

BACKGROUND: Precise prognosis prediction in patients with colorectal cancer (ie, forecasting survival) is pivotal for individualised treatment and care. Histopathological tissue slides of colorectal cancer specimens contain rich prognostically relevant information. However, existing studies do not have multicentre external validation with real-world sample processing protocols, and algorithms are not yet widely used in clinical routine. METHODS: In this retrospective, multicentre study, we collected tissue samples from four groups of patients with resected colorectal cancer from Australia, Germany, and the USA. We developed and externally validated a deep learning-based prognostic-stratification system for automatic prediction of overall and cancer-specific survival in patients with resected colorectal cancer. We used the model-predicted risk scores to stratify patients into different risk groups and compared survival outcomes between these groups. Additionally, we evaluated the prognostic value of these risk groups after adjusting for established prognostic variables. FINDINGS: We trained and validated our model on a total of 4428 patients. We found that patients could be divided into high-risk and low-risk groups on the basis of the deep learning-based risk score. On the internal test set, the group with a high-risk score had a worse prognosis than the group with a low-risk score, as reflected by a hazard ratio (HR) of 4·50 (95% CI 3·33-6·09) for overall survival and 8·35 (5·06-13·78) for disease-specific survival (DSS). We found consistent performance across three large external test sets. In a test set of 1395 patients, the high-risk group had a lower DSS than the low-risk group, with an HR of 3·08 (2·44-3·89). In two additional test sets, the HRs for DSS were 2·23 (1·23-4·04) and 3·07 (1·78-5·3). We showed that the prognostic value of the deep learning-based risk score is independent of established clinical risk factors. INTERPRETATION: Our findings indicate that attention-based self-supervised deep learning can robustly offer a prognosis on clinical outcomes in patients with colorectal cancer, generalising across different populations and serving as a potentially new prognostic tool in clinical decision making for colorectal cancer management. We release all source codes and trained models under an open-source licence, allowing other researchers to reuse and build upon our work. FUNDING: The German Federal Ministry of Health, the Max-Eder-Programme of German Cancer Aid, the German Federal Ministry of Education and Research, the German Academic Exchange Service, and the EU.

Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study.

Deep learning (DL) can accelerate the prediction of prognostic biomarkers from routine pathology slides in colorectal cancer (CRC). However, current approaches rely on convolutional neural networks (CNNs) and have mostly been validated on small patient cohorts. Here, we develop a new transformer-based pipeline for end-to-end biomarker prediction from pathology slides by combining a pre-trained transformer encoder with a transformer network for patch aggregation. Our transformer-based approach substantially improves the performance, generalizability, data efficiency, and interpretability as compared with current state-of-the-art algorithms. After training and evaluating on a large multicenter cohort of over 13,000 patients from 16 colorectal cancer cohorts, we achieve a sensitivity of 0.99 with a negative predictive value of over 0.99 for prediction of microsatellite instability (MSI) on surgical resection specimens. We demonstrate that resection specimen-only training reaches clinical-grade performance on endoscopic biopsy tissue, solving a long-standing diagnostic problem.

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.

Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: A retrospective multi-centric study.

Deep learning (DL) can predict microsatellite instability (MSI) from routine histopathology slides of colorectal cancer (CRC). However, it is unclear whether DL can also predict other biomarkers with high performance and whether DL predictions generalize to external patient populations. Here, we acquire CRC tissue samples from two large multi-centric studies. We systematically compare six different state-of-the-art DL architectures to predict biomarkers from pathology slides, including MSI and mutations in BRAF, KRAS, NRAS, and PIK3CA. Using a large external validation cohort to provide a realistic evaluation setting, we show that models using self-supervised, attention-based multiple-instance learning consistently outperform previous approaches while offering explainable visualizations of the indicative regions and morphologies. While the prediction of MSI and BRAF mutations reaches a clinical-grade performance, mutation prediction of PIK3CA, KRAS, and NRAS was clinically insufficient.

Tumor deposits in colorectal cancer: Refining their definition in the TNM system.

Tumor deposits (TDs) are discontinuous tumor spread in the mesocolon/mesorectum which is found in approximately 20% of colorectal cancer (CRC) and negatively affects survival. We have a history of repeated revisions on TD definition and categorization in the tumor-node-metastasis (TNM) system leading to stage migration. Since 1997, TDs have been categorized as T or N factors depending on their size (TNM5) or contour (TNM6). In 2009, TNM7 provided the category of N1c for TDs in a case without positive lymph nodes (LNs), which is also used in TNM8. However, increasing evidence suggests that these revisions are suboptimal and only "partially" successful. Specifically, the N1c rule is certainly useful for oncologists who are having difficulty with TDs in a case with no positive LNs. However, it has failed to maximize the value of the TNM system because of the underused prognostic information of individual TDs. Recently, the potential value of an alternative staging method has been highlighted in several studies using the "counting method." For this method, all nodular type TDs are individually counted together with positive LNs to derive the final pN, yielding a prognostic and diagnostic value that is superior to existing TNM systems. The TNM system has long stuck to the origin of TDs in providing its categorization, but it is time to make way for alternative options and initiate an international discussion on optimal treatment of TDs in tumor staging; otherwise, a proportion of patients end up missing an opportunity to receive the optimal adjuvant treatment.

Preoperative Chemotherapy for Operable Colon Cancer: Mature Results of an International Randomized Controlled Trial.

PURPOSE: Neoadjuvant chemotherapy (NAC) has potential advantages over standard postoperative chemotherapy for locally advanced colon cancer but requires formal evaluation. METHODS: Patients with radiologically staged T3-4, N0-2, M0 colon cancer were randomly allocated (2:1) to 6 weeks oxaliplatin-fluoropyrimidine preoperatively plus 18 postoperatively (NAC group) or 24 weeks postoperatively (control group). Patients with RAS-wildtype tumors could also be randomly assigned 1:1 to receive panitumumab or not during NAC. The primary end point was residual disease or recurrence within 2 years. Secondary outcomes included surgical morbidity, histopathologic stage, regression grade, completeness of resection, and cause-specific mortality. Log-rank analyses were by intention-to-treat. RESULTS: Of 699 patients allocated to NAC, 674 (96%) started and 606 (87%) completed NAC. In total, 686 of 699 (98.1%) NAC patients and 351 of 354 (99.2%) control patients underwent surgery. Thirty patients (4.3%) allocated to NAC developed obstructive symptoms requiring expedited surgery, but there were fewer serious postoperative complications with NAC than with control. NAC produced marked T and N downstaging and histologic tumor regression (all P < .001). Resection was more often histopathologically complete: 94% (648/686) versus 89% (311/351), P < .001. Fewer NAC than control patients had residual or recurrent disease within 2 years (16.9% [118/699] v 21.5% [76/354]; rate ratio, 0.72 [95% CI, 0.54 to 0.98]; P = .037). Tumor regression correlated strongly with freedom from recurrence. Panitumumab did not enhance the benefit from NAC. Little benefit from NAC was seen in mismatch repair-deficient tumors. CONCLUSION: Six weeks of preoperative oxaliplatin-fluoropyrimidine chemotherapy for operable colon cancer can be delivered safely, without increasing perioperative morbidity. This chemotherapy regimen, when given preoperatively, produces marked histopathologic down-staging, fewer incomplete resections, and better 2-year disease control. Histologic regression after NAC is a strong predictor of lower postoperative recurrence risk so has potential use as a guide for postoperative therapy. Six weeks of NAC should be considered as a treatment option for locally advanced colon cancer.

FOCUS4 biomarker laboratories: from the benefits to the practical and logistical issues faced during 6 years of centralised testing.

AIMS: FOCUS4 was a phase II/III umbrella trial, recruiting patients with advanced or metastatic colorectal cancer, between 2014 and 2020. Molecular profiling of patients' formalin-fixed, paraffin-embedded tumour blocks was undertaken at two centralised biomarker laboratories (Leeds and Cardiff), and the results fed directly to the Medical Research Council Clinical Trials Unit, and used for subsequent randomisation. Here the laboratories discuss their experiences. METHODS: Following successful tumour content assessment, blocks were sectioned for DNA extraction and immunohistochemistry (IHC). Pyrosequencing was initially used to determine tumour mutation status (KRAS, NRAS, BRAF and PIK3CA), then from 2018 onwards, next-generation sequencing was employed to allow the inclusion of TP53. Protein expression of MLH1, MSH2, MSH6, PMS2 and pTEN was determined by IHC. An interlaboratory comparison programme was initiated, allowing sample exchanges, to ensure continued assay robustness. RESULTS: 1291 tumour samples were successfully analysed. Assay failure rates were very low; 1.9%-3.3% for DNA sequencing and 0.9%-1.3% for IHC. Concordance rates of >98% were seen for the interlaboratory comparisons, where a result was obtained by both laboratories. CONCLUSIONS: Practical and logistical problems were identified, including poor sample quality and difficulties with sample anonymisation. The often last-minute receipt of a sample for testing and a lack of integration with National Health Service mutation analysis services were challenging. The laboratories benefitted from both pretrial validations and interlaboratory comparisons, resulting in robust assay development and provided confidence during the implementation of new sequencing technologies. We conclude that our centralised approach to biomarker testing in FOCUS4 was effective and successful.

The risk of colorectal cancer in individuals with mutations of the cystic fibrosis transmembrane conductance regulator (CFTR) gene: An English population-based study.

BACKGROUND: Studies have demonstrated a higher risk of developing colorectal cancer (CRC) in individuals with Cystic Fibrosis (CF), and also a potentially increased risk in carriers of cystic fibrosis transmembrane conductance regulator (CFTR) mutations. Life expectancy for those with CF is rising, increasing the number at risk of developing CRC. METHODS: The incidence of CRC amongst individuals with CF was calculated using data from CORECT-R and linked UK CF Registry and Secondary User Services (SUS) data. Crude, age-specific and age-standardised rates were compared to those without CF. The presence of CFTR mutations in individuals with CRC was assessed using 100,000 Genomes Project data. FINDINGS: The crude incidence rate of CRC in the CF population was 0.29 per 1,000 person-years (28 cases). The CF population were significantly younger than those without (median age at CRC diagnosis 52 years versus 73 years; p<0·01). When age-adjusted, there was a 5-fold increased CRC incidence amongst individuals with CF compared to those without (SIR 5.0 95%CI 3.2-6.9). When compared to other population studies the overall prevalence of CFTR mutations in the CRC population was significantly higher than expected (p<0·01). INTERPRETATION: CF is linked to an increased risk of CRC. The incidence of CFTR mutations in the CRC population is higher than would be expected, suggesting an association between CFTR function and CRC risk. Further research is needed to develop effective screening strategies for these populations. FUNDING: Cancer Research UK (grants C23434/A23706 & C10674/A27140).

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.

Quality-of-life outcomes in older patients with early-stage rectal cancer receiving organ-preserving treatment with hypofractionated short-course radiotherapy followed by transanal endoscopic microsurgery (TREC): non-randomised registry of patients unsuitable for total mesorectal excision.

BACKGROUND: Older patients with early-stage rectal cancer are under-represented in clinical trials and, therefore, little high-quality data are available to guide treatment in this patient population. The TREC trial was a randomised, open-label feasibility study conducted at 21 centres across the UK that compared organ preservation through short-course radiotherapy (SCRT; 25 Gy in five fractions) plus transanal endoscopic microsurgery (TEM) with standard total mesorectal excision in adults with stage T1-2 rectal adenocarcinoma (maximum diameter ≤30 mm) and no lymph node involvement or metastasis. TREC incorporated a non-randomised registry offering organ preservation to patients who were considered unsuitable for total mesorectal excision by the local colorectal cancer multidisciplinary team. Organ preservation was achieved in 56 (92%) of 61 non-randomised registry patients with local recurrence-free survival of 91% (95% CI 84-99) at 3 years. Here, we report acute and long-term patient-reported outcomes from this non-randomised registry group. METHODS: Patients considered by the local colorectal cancer multidisciplinary team to be at high risk of complications from total mesorectal excision on the basis of frailty, comorbidities, and older age were included in a non-randomised registry to receive organ-preserving treatment. These patients were invited to complete questionnaires on patient-reported outcomes (the European Organisation for Research and Treatment of Cancer Quality of Life [EORTC-QLQ] questionnaire core module [QLQ-C30] and colorectal cancer module [QLQ-CR29], the Colorectal Functional Outcome [COREFO] questionnaire, and EuroQol-5 Dimensions-3 Level [EQ-5D-3L]) at baseline and at months 3, 6, 12, 24, and 36 postoperatively. To aid interpretation, data from patients in the non-randomised registry were compared with data from those patients in the TREC trial who had been randomly assigned to organ-preserving therapy, and an additional reference cohort of aged-matched controls from the UK general population. This study is registered with the ISRCTN registry, ISRCTN14422743, and is closed. FINDINGS: Between July 21, 2011, and July 15, 2015, 88 patients were enrolled onto the TREC study to undergo organ preservation, of whom 27 (31%) were randomly allocated to organ-preserving therapy and 61 (69%) were added to the non-randomised registry for organ-preserving therapy. Non-randomised patients were older than randomised patients (median age 74 years [IQR 67-80] vs 65 years [61-71]). Organ-preserving treatment was well tolerated among patients in the non-randomised registry, with mild worsening of fatigue; quality of life; physical, social, and role functioning; and bowel function 3 months postoperatively compared with baseline values. By 6-12 months, most scores had returned to baseline values, and were indistinguishable from data from the reference cohort. Only mild symptoms of faecal incontinence and urgency, equivalent to less than one episode per week, persisted at 36 months among patients in both groups. INTERPRETATION: The SCRT and TEM organ-preservation approach was well tolerated in older and frailer patients, showed good rates of organ preservation, and was associated with low rates of acute and long-term toxicity, with minimal effects on quality of life and functional status. Our findings support the adoption of this approach for patients considered to be at high risk from radical surgery. FUNDING: Cancer Research UK.