Targeting PIKfyve-driven lipid homeostasis as a metabolic vulnerability in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) subsists in a nutrient-deregulated microenvironment, making it particularly susceptible to treatments that interfere with cancer metabolism12. For example, PDAC utilizes and is dependent on high levels of autophagy and other lysosomal processes3-5. Although targeting these pathways has shown potential in preclinical studies, progress has been hampered by the challenge of identifying and characterizing favorable targets for drug development6. Here, we characterize PIKfyve, a lipid kinase integral to lysosomal functioning7, as a novel and targetable vulnerability in PDAC. In human patient and murine PDAC samples, we discovered that PIKFYVE is overexpressed in PDAC cells compared to adjacent normal cells. Employing a genetically engineered mouse model, we established the essential role of PIKfyve in PDAC progression. Further, through comprehensive metabolic analyses, we found that PIKfyve inhibition obligated PDAC to upregulate de novo lipid synthesis, a relationship previously undescribed. PIKfyve inhibition triggered a distinct lipogenic gene expression and metabolic program, creating a dependency on de novo lipid metabolism pathways, by upregulating genes such as FASN and ACACA. In PDAC, the KRAS-MAPK signaling pathway is a primary driver of de novo lipid synthesis, specifically enhancing FASN and ACACA levels. Accordingly, the simultaneous targeting of PIKfyve and KRAS-MAPK resulted in the elimination of tumor burden in a syngeneic orthotopic model and tumor regression in a xenograft model of PDAC. Taken together, these studies suggest that disrupting lipid metabolism through PIKfyve inhibition induces synthetic lethality in conjunction with KRAS-MAPK-directed therapies for PDAC.

Comparative impact assessment of COVID-19 policy interventions in five South Asian countries using reported and estimated unreported death counts during 2020-2021.

There has been raging discussion and debate around the quality of COVID death data in South Asia. According to WHO, of the 5.5 million reported COVID-19 deaths from 2020-2021, 0.57 million (10%) were contributed by five low and middle income countries (LMIC) countries in the Global South: India, Pakistan, Bangladesh, Sri Lanka and Nepal. However, a number of excess death estimates show that the actual death toll from COVID-19 is significantly higher than the reported number of deaths. For example, the IHME and WHO both project around 14.9 million total deaths, of which 4.5-5.5 million were attributed to these five countries in 2020-2021. We focus our gaze on the COVID-19 performance of these five countries where 23.5% of the world population lives in 2020 and 2021, via a counterfactual lens and ask, to what extent the mortality of one LMIC would have been affected if it adopted the pandemic policies of another, similar country? We use a Bayesian semi-mechanistic model developed by Mishra et al. (2021) to compare both the reported and estimated total death tolls by permuting the time-varying reproduction number (Rt) across these countries over a similar time period. Our analysis shows that, in the first half of 2021, mortality in India in terms of reported deaths could have been reduced to 96 and 102 deaths per million compared to actual 170 reported deaths per million had it adopted the policies of Nepal and Pakistan respectively. In terms of total deaths, India could have averted 481 and 466 deaths per million had it adopted the policies of Bangladesh and Pakistan. On the other hand, India had a lower number of reported COVID-19 deaths per million (48 deaths per million) and a lower estimated total deaths per million (80 deaths per million) in the second half of 2021, and LMICs other than Pakistan would have lower reported mortality had they followed India's strategy. The gap between the reported and estimated total deaths highlights the varying level and extent of under-reporting of deaths across the subcontinent, and that model estimates are contingent on accuracy of the death data. Our analysis shows the importance of timely public health intervention and vaccines for lowering mortality and the need for better coverage infrastructure for the death registration system in LMICs.

BaySyn: Bayesian Evidence Synthesis for Multi-system Multiomic Integration.

The discovery of cancer drivers and drug targets are often limited to the biological systems - from cancer model systems to patients. While multiomic patient databases have sparse drug response data, cancer model systems databases, despite covering a broad range of pharmacogenomic platforms, provide lower lineage-specific sample sizes, resulting in reduced statistical power to detect both functional driver genes and their associations with drug sensitivity profiles. Hence, integrating evidence across model systems, taking into account the pros and cons of each system, in addition to multiomic integration, can more efficiently deconvolve cellular mechanisms of cancer as well as learn therapeutic associations. To this end, we propose BaySyn - a hierarchical Bayesian evidence synthesis framework for multi-system multiomic integration. BaySyn detects functionally relevant driver genes based on their associations with upstream regulators using additive Gaussian process models and uses this evidence to calibrate Bayesian variable selection models in the (drug) outcome layer. We apply BaySyn to multiomic cancer cell line and patient datasets from the Cancer Cell Line Encyclopedia and The Cancer Genome Atlas, respectively, across pan-gynecological cancers. Our mechanistic models implicate several relevant functional genes across cancers such as PTPN6 and ERBB2 in the KEGG adherens junction gene set. Furthermore, our outcome model is able to make higher number of discoveries in drug response models than its uncalibrated counterparts under the same thresholds of Type I error control, including detection of known lineage-specific biomarker associations such as BCL11A in breast and FGFRL1 in ovarian cancers. All our results and implementation codes are freely available via an interactive R Shiny dashboard at tinyurl.com/BaySynApp. The supplementary materials are available online at tinyurl.com/BaySynSup.

Randomized controlled trial of EndoRings assisted colonoscopy versus standard colonoscopy.

OBJECTIVES: The EndoRings device is a distal attachment consisting of two layers of circular flexible rings that evert mucosal folds. The aim of this study was to investigate whether EndoRing assisted colonoscopy (ER) improves polyp and adenoma detection compared to standard colonoscopy (SC). METHODS: Multicenter, parallel group, randomized controlled trial. RESULTS: Total of 556 patients randomized to ER (n = 275) or SC (n = 281). Colonoscopy completed in 532/556 (96%) cases. EndoRings removed in 74/275 (27%) patients. Total number of polyps in ER limb 582 vs. 515 in SC limb, P = 0.04. Total number of adenomas in ER limb 361 vs. 343 for SC limb, P = 0.49. A statistically significant difference in the mean number of polyps per patient in both the intention to treat (1.84 SC vs. 2.10 ER, P = 0.027) and per protocol (PP) (1.84 SC vs. 2.25 ER, P = 0.004). CONCLUSIONS: Our study shows promise for the EndoRings device to improve polyp detection.

Role of multiresolution vulnerability indices in COVID-19 spread in India: a Bayesian model-based analysis.

OBJECTIVES: COVID-19 has differentially affected countries, with health infrastructure and other related vulnerability indicators playing a role in determining the extent of its spread. Vulnerability of a geographical region to COVID-19 has been a topic of interest, particularly in low-income and middle-income countries like India to assess its multifactorial impact on incidence, prevalence or mortality. This study aims to construct a statistical analysis pipeline to compute such vulnerability indices and investigate their association with metrics of the pandemic growth. DESIGN: Using publicly reported observational socioeconomic, demographic, health-based and epidemiological data from Indian national surveys, we compute contextual COVID-19 Vulnerability Indices (cVIs) across multiple thematic resolutions for different geographical and spatial administrative regions. These cVIs are then used in Bayesian regression models to assess their impact on indicators of the spread of COVID-19. SETTING: This study uses district-level indicators and case counts data for the state of Odisha, India. PRIMARY OUTCOME MEASURE: We use instantaneous R (temporal average of estimated time-varying reproduction number for COVID-19) as the primary outcome variable in our models. RESULTS: Our observational study, focussing on 30 districts of Odisha, identified housing and hygiene conditions, COVID-19 preparedness and epidemiological factors as important indicators associated with COVID-19 vulnerability. CONCLUSION: Having succeeded in containing COVID-19 to a reasonable level during the first wave, the second wave of COVID-19 made greater inroads into the hinterlands and peripheral districts of Odisha, burdening the already deficient public health system in these areas, as identified by the cVIs. Improved understanding of the factors driving COVID-19 vulnerability will help policy makers prioritise resources and regions, leading to more effective mitigation strategies for the present and future.

Lessons from SARS-CoV-2 in India: A data-driven framework for pandemic resilience.

India experienced a massive surge in SARS-CoV-2 infections and deaths during April to June 2021 despite having controlled the epidemic relatively well during 2020. Using counterfactual predictions from epidemiological disease transmission models, we produce evidence in support of how strengthening public health interventions early would have helped control transmission in the country and significantly reduced mortality during the second wave, even without harsh lockdowns. We argue that enhanced surveillance at district, state, and national levels and constant assessment of risk associated with increased transmission are critical for future pandemic responsiveness. Building on our retrospective analysis, we provide a tiered data-driven framework for timely escalation of future interventions as a tool for policy-makers.

A comparison of five epidemiological models for transmission of SARS-CoV-2 in India.

BACKGROUND: Many popular disease transmission models have helped nations respond to the COVID-19 pandemic by informing decisions about pandemic planning, resource allocation, implementation of social distancing measures, lockdowns, and other non-pharmaceutical interventions. We study how five epidemiological models forecast and assess the course of the pandemic in India: a baseline curve-fitting model, an extended SIR (eSIR) model, two extended SEIR (SAPHIRE and SEIR-fansy) models, and a semi-mechanistic Bayesian hierarchical model (ICM). METHODS: Using COVID-19 case-recovery-death count data reported in India from March 15 to October 15 to train the models, we generate predictions from each of the five models from October 16 to December 31. To compare prediction accuracy with respect to reported cumulative and active case counts and reported cumulative death counts, we compute the symmetric mean absolute prediction error (SMAPE) for each of the five models. For reported cumulative cases and deaths, we compute Pearson's and Lin's correlation coefficients to investigate how well the projected and observed reported counts agree. We also present underreporting factors when available, and comment on uncertainty of projections from each model. RESULTS: For active case counts, SMAPE values are 35.14% (SEIR-fansy) and 37.96% (eSIR). For cumulative case counts, SMAPE values are 6.89% (baseline), 6.59% (eSIR), 2.25% (SAPHIRE) and 2.29% (SEIR-fansy). For cumulative death counts, the SMAPE values are 4.74% (SEIR-fansy), 8.94% (eSIR) and 0.77% (ICM). Three models (SAPHIRE, SEIR-fansy and ICM) return total (sum of reported and unreported) cumulative case counts as well. We compute underreporting factors as of October 31 and note that for cumulative cases, the SEIR-fansy model yields an underreporting factor of 7.25 and ICM model yields 4.54 for the same quantity. For total (sum of reported and unreported) cumulative deaths the SEIR-fansy model reports an underreporting factor of 2.97. On October 31, we observe 8.18 million cumulative reported cases, while the projections (in millions) from the baseline model are 8.71 (95% credible interval: 8.63-8.80), while eSIR yields 8.35 (7.19-9.60), SAPHIRE returns 8.17 (7.90-8.52) and SEIR-fansy projects 8.51 (8.18-8.85) million cases. Cumulative case projections from the eSIR model have the highest uncertainty in terms of width of 95% credible intervals, followed by those from SAPHIRE, the baseline model and finally SEIR-fansy. CONCLUSIONS: In this comparative paper, we describe five different models used to study the transmission dynamics of the SARS-Cov-2 virus in India. While simulation studies are the only gold standard way to compare the accuracy of the models, here we were uniquely poised to compare the projected case-counts against observed data on a test period. The largest variability across models is observed in predicting the "total" number of infections including reported and unreported cases (on which we have no validation data). The degree of under-reporting has been a major concern in India and is characterized in this report. Overall, the SEIR-fansy model appeared to be a good choice with publicly available R-package and desired flexibility plus accuracy.

Incorporating false negative tests in epidemiological models for SARS-CoV-2 transmission and reconciling with seroprevalence estimates.

Susceptible-Exposed-Infected-Removed (SEIR)-type epidemiologic models, modeling unascertained infections latently, can predict unreported cases and deaths assuming perfect testing. We apply a method we developed to account for the high false negative rates of diagnostic RT-PCR tests for detecting an active SARS-CoV-2 infection in a classic SEIR model. The number of unascertained cases and false negatives being unobservable in a real study, population-based serosurveys can help validate model projections. Applying our method to training data from Delhi, India, during March 15-June 30, 2020, we estimate the underreporting factor for cases at 34-53 (deaths: 8-13) on July 10, 2020, largely consistent with the findings of the first round of serosurveys for Delhi (done during June 27-July 10, 2020) with an estimated 22.86% IgG antibody prevalence, yielding estimated underreporting factors of 30-42 for cases. Together, these imply approximately 96-98% cases in Delhi remained unreported (July 10, 2020). Updated calculations using training data during March 15-December 31, 2020 yield estimated underreporting factor for cases at 13-22 (deaths: 3-7) on January 23, 2021, which are again consistent with the latest (fifth) round of serosurveys for Delhi (done during January 15-23, 2021) with an estimated 56.13% IgG antibody prevalence, yielding an estimated range for the underreporting factor for cases at 17-21. Together, these updated estimates imply approximately 92-96% cases in Delhi remained unreported (January 23, 2021). Such model-based estimates, updated with latest data, provide a viable alternative to repeated resource-intensive serosurveys for tracking unreported cases and deaths and gauging the true extent of the pandemic.

Comprehensive public health evaluation of lockdown as a non-pharmaceutical intervention on COVID-19 spread in India: national trends masking state-level variations.

OBJECTIVES: To evaluate the effect of four-phase national lockdown from March 25 to May 31 in response to the COVID-19 pandemic in India and unmask the state-wise variations in terms of multiple public health metrics. DESIGN: Cohort study (daily time series of case counts). SETTING: Observational and population based. PARTICIPANTS: Confirmed COVID-19 cases nationally and across 20 states that accounted for >99% of the current cumulative case counts in India until 31 May 2020. EXPOSURE: Lockdown (non-medical intervention). MAIN OUTCOMES AND MEASURES: We illustrate the masking of state-level trends and highlight the variations across states by presenting evaluative evidence on some aspects of the COVID-19 outbreak: case fatality rates, doubling times of cases, effective reproduction numbers and the scale of testing. RESULTS: The estimated effective reproduction number R for India was 3.36 (95% CI 3.03 to 3.71) on 24 March, whereas the average of estimates from 25 May to 31 May stands at 1.27 (95% CI 1.26 to 1.28). Similarly, the estimated doubling time across India was at 3.56 days on 24 March, and the past 7-day average for the same on 31 May is 14.37 days. The average daily number of tests increased from 1717 (19-25 March) to 113 372 (25-31 May) while the test positivity rate increased from 2.1% to 4.2%, respectively. However, various states exhibit substantial departures from these national patterns. CONCLUSIONS: Patterns of change over lockdown periods indicate the lockdown has been partly effective in slowing the spread of the virus nationally. However, there exist large state-level variations and identifying these variations can help in both understanding the dynamics of the pandemic and formulating effective public health interventions. Our framework offers a holistic assessment of the pandemic across Indian states and union territories along with a set of interactive visualisation tools that are daily updated at covind19.org.

Predictions, role of interventions and effects of a historic national lockdown in India's response to the COVID-19 pandemic: data science call to arms.

With only 536 cases and 11 fatalities, India took the historic decision of a 21-day national lockdown on March 25. The lockdown was first extended to May 3 soon after the analysis of this paper was completed, and then to May 18 while this paper was being revised. In this paper, we use a Bayesian extension of the Susceptible-Infected-Removed (eSIR) model designed for intervention forecasting to study the short- and long-term impact of an initial 21-day lockdown on the total number of COVID-19 infections in India compared to other less severe non-pharmaceutical interventions. We compare effects of hypothetical durations of lockdown on reducing the number of active and new infections. We find that the lockdown, if implemented correctly, can reduce the total number of cases in the short term, and buy India invaluable time to prepare its healthcare and disease-monitoring system. Our analysis shows we need to have some measures of suppression in place after the lockdown for increased benefit (as measured by reduction in the number of cases). A longer lockdown between 42-56 days is preferable to substantially "flatten the curve" when compared to 21-28 days of lockdown. Our models focus solely on projecting the number of COVID-19 infections and, thus, inform policymakers about one aspect of this multi-faceted decision-making problem. We conclude with a discussion on the pivotal role of increased testing, reliable and transparent data, proper uncertainty quantification, accurate interpretation of forecasting models, reproducible data science methods and tools that can enable data-driven policymaking during a pandemic. Our software products are available at covind19.org.

A Comprehensive Public Health Evaluation of Lockdown as a Non-pharmaceutical Intervention on COVID-19 Spread in India: National Trends Masking State Level Variations.

Introduction: India has been under four phases of a national lockdown from March 25 to May 31 in response to the COVID-19 pandemic. Unmasking the state-wise variation in the effect of the nationwide lockdown on the progression of the pandemic could inform dynamic policy interventions towards containment and mitigation. Methods: Using data on confirmed COVID-19 cases across 20 states that accounted for more than 99% of the cumulative case counts in India till May 31, 2020, we illustrate the masking of state-level trends and highlight the variations across states by presenting evaluative evidence on some aspects of the COVID-19 outbreak: case-fatality rates, doubling times of cases, effective reproduction numbers, and the scale of testing. Results: The estimated effective reproduction number R for India was 3.36 (95% confidence interval (CI): [3.03, 3.71]) on March 24, whereas the average of estimates from May 25 - May 31 stands at 1.27 (95% CI: [1.26, 1.28]). Similarly, the estimated doubling time across India was at 3.56 days on March 24, and the past 7-day average for the same on May 31 is 14.37 days. The average daily number of tests have increased from 1,717 (March 19-25) to 131,772 (May 25-31) with an estimated testing shortfall of 4.58 million tests nationally by May 31. However, various states exhibit substantial departures from these national patterns. Conclusions: Patterns of change over lockdown periods indicate the lockdown has been effective in slowing the spread of the virus nationally. The COVID-19 outbreak in India displays large state-level variations and identifying these variations can help in both understanding the dynamics of the pandemic and formulating effective public health interventions. Our framework offers a holistic assessment of the pandemic across Indian states and union territories along with a set of interactive visualization tools that are daily updated at covind19.org.

Personalized Network Modeling of the Pan-Cancer Patient and Cell Line Interactome.

PURPOSE: Personalized network inference on diverse clinical and in vitro model systems across cancer types can be used to delineate specific regulatory mechanisms, uncover drug targets and pathways, and develop individualized predictive models in cancer. METHODS: We developed TransPRECISE (personalized cancer-specific integrated network estimation model), a multiscale Bayesian network modeling framework, to analyze the pan-cancer patient and cell line interactome to identify differential and conserved intrapathway activities, to globally assess cell lines as representative models for patients, and to develop drug sensitivity prediction models. We assessed pan-cancer pathway activities for a large cohort of patient samples (> 7,700) from the Cancer Proteome Atlas across ≥ 30 tumor types, a set of 640 cancer cell lines from the MD Anderson Cell Lines Project spanning 16 lineages, and ≥ 250 cell lines' response to > 400 drugs. RESULTS: TransPRECISE captured differential and conserved proteomic network topologies and pathway circuitry between multiple patient and cell line lineages: ovarian and kidney cancers shared high levels of connectivity in the hormone receptor and receptor tyrosine kinase pathways, respectively, between the two model systems. Our tumor stratification approach found distinct clinical subtypes of the patients represented by different sets of cell lines: patients with head and neck tumors were classified into two different subtypes that are represented by head and neck and esophagus cell lines and had different prognostic patterns (456 v 654 days of median overall survival; P = .02). High predictive accuracy was observed for drug sensitivities in cell lines across multiple drugs (median area under the receiver operating characteristic curve > 0.8) using Bayesian additive regression tree models with TransPRECISE pathway scores. CONCLUSION: Our study provides a generalizable analytic framework to assess the translational potential of preclinical model systems and to guide pathway-based personalized medical decision making, integrating genomic and molecular data across model systems.

Network-Based Matching of Patients and Targeted Therapies for Precision Oncology.

The extensive acquisition of high-throughput molecular profiling data across model systems (human tumors and cancer cell lines) and drug sensitivity data, makes precision oncology possible - allowing clinicians to match the right drug to the right patient. Current supervised models for drug sensitivity prediction, often use cell lines as exemplars of patient tumors and for model training. However, these models are limited in their ability to accurately predict drug sensitivity of individual cancer patients to a large set of drugs, given the paucity of patient drug sensitivity data used for testing and high variability across different drugs. To address these challenges, we developed a multilayer network-based approach to impute individual patients' responses to a large set of drugs. This approach considers the triplet of patients, cell lines and drugs as one inter-connected holistic system. We first use the omics profiles to construct a patient-cell line network and determine best matching cell lines for patient tumors based on robust measures of network similarity. Subsequently, these results are used to impute the "missing link" between each individual patient and each drug, called Personalized Imputed Drug Sensitivity Score (PIDS-Score), which can be construed as a measure of the therapeutic potential of a drug or therapy. We applied our method to two subtypes of lung cancer patients, matched these patients with cancer cell lines derived from 19 tissue types based on their functional proteomics profiles, and computed their PIDS-Scores to 251 drugs and experimental compounds. We identified the best representative cell lines that conserve lung cancer biology and molecular targets. The PIDS-Score based top sensitive drugs for the entire patient cohort as well as individual patients are highly related to lung cancer in terms of their targets, and their PIDS-Scores are significantly associated with patient clinical outcomes. These findings provide evidence that our method is useful to narrow the scope of possible effective patient-drug matchings for implementing evidence-based personalized medicine strategies.

Endocuff-assisted vs. standard colonoscopy in the fecal occult blood test-based UK Bowel Cancer Screening Programme (E-cap study): a randomized trial.

Background and study aims Up to 25 % colorectal adenomas are missed during colonoscopy. The aim of this study was to investigate whether the endocuff could improve polyp detection in an organized bowel cancer screening program (BCSP). Patients and methods This parallel group, single-blinded, randomized controlled trial included patients with positive fecal occult blood test (FOBT) who were attending for BCSP colonoscopy. The primary outcome was the number of polyps per patient. Secondary outcomes included the number of adenomas per patient, adenoma and polyp detection rates, and withdrawal times. Results A total of 534 BCSP patients were randomized to endocuff-assisted or standard colonoscopy. The mean age was 67 years and the male to female ratio was 1.8:1. We detected no significant difference in the number of polyps per patient (standard 1.8, endocuff 1.6; P = 0.44), adenomas per patient (standard 1.4, endocuff 1.3; P = 0.54), polyp detection rate (standard 69.8 %, endocuff 70.3 %; P = 0.93), adenoma detection rate (standard 63.0 %, endocuff 60.9 %; P = 0.85), advanced adenoma detection rate (standard 18.5 %, endocuff 16.9 %; P = 0.81), and cancer detection rate (standard 5.7 %, endocuff 5.3 %; P = 0.85). The mean withdrawal time was significantly shorter among patients in the endocuff group compared with the standard colonoscopy group (16.9 vs. 19.5 minutes; P < 0.005). The endocuff had to be removed in 17/266 patients (6.4 %) because of inability to pass through the sigmoid colon. Conclusions This study did not find improved polyp or adenoma detection with endocuff-assisted colonoscopy in the FOBT-positive BCSP population. A shorter withdrawal time with endocuff may reflect improved views and stability provided by the endocuff.Trial registered at ClinicalTrials.gov (NCT02529007).

A randomized controlled trial of pre-procedure simethicone and N-acetylcysteine to improve mucosal visibility during gastroscopy - NICEVIS.

Background and study aims: Mucosal views can be impaired by residual bubbles and mucus during gastroscopy. This study aimed to determine whether a pre-gastroscopy drink containing simethicone and N-acetylcysteine improves mucosal visualisation. Patients and methods: We conducted a randomized controlled trial recruiting 126 subjects undergoing routine gastroscopy. Subjects were randomized 1:1:1 to receive: A-pre-procedure drink of water, simethicone and N-acetylcysteine (NAC); B-water alone; or C-no preparation. Study endoscopists were blinded to group allocation. Digital images were taken at 4 locations (lower esophagus/upper gastric body/antrum/fundus), and rated for mucosal visibility (MV) using a 4-point scale (1 = best, 4 = worst) by 4 separate experienced endoscopists. The primary outcome measure was mean mucosal visibility score (MVS). Secondary outcome measures were procedure duration and volume of fluid flush required to achieve adequate mucosal views. Results: Mean MVS for Group A was significantly better than for Group B (1.35 vs 2.11, P < 0.001) and Group C (1.35 vs 2.21, P < 0.001). Mean flush volume required to achieve adequate mucosal views was significantly lower in Group A than Group B (2.0 mL vs 31.5 mL, P = 0.001) and Group C (2.0 mL vs 39.2 mL P < 0.001). Procedure duration did not differ significantly between any of the 3 groups. MV scores at each of the 4 locations demonstrated significantly better mucosal visibility in Group A compared to Group B and Group C (P < 0.0025 for all comparisons). Conclusions: A pre-procedure drink containing simethicone and NAC significantly improves mucosal visibility during gastroscopy and reduces the need for flushes during the procedure. Effectiveness in the lower esophagus demonstrates potential benefit in Barrett's oesophagus surveillance gastroscopy.

Knife-assisted snare resection (KAR) of large and refractory colonic polyps at a Western centre: Feasibility, safety and efficacy study to guide future practice.

OBJECTIVE: Endoscopic mucosal resection (EMR) is widely practiced in western countries. Endoscopic submucosal dissection (ESD) is very effective for treating complex polyps but colonic ESD in the western setting remains challenging. We have developed a novel technique of knife-assisted snare resection (KAR) for the resection of these complex lesions. Here we aim to describe the technique, evaluate its outcomes, identify outcome predictors and define its learning curve. METHODS: We conducted a prospective cohort study of patients who had large and refractory polyps resected by KAR at our institution from 2007 to 2013. Polyp characteristics and procedure details were recorded. Endoscopic follow-up was performed to identify recurrence. RESULTS: A total of 170 patients with polyps 20-170 mm in size were treated by KAR and followed up for a mean of 31.5 months (range 12-84 months). 29% of the polyps were >50 mm, 22% had fibrosis from previous unsuccessful interventions and 25% were in the right colon. The perforation rate (1.2%) and bleeding rate (4.7%) were acceptable and managed conservatively in most patients. Recurrence rate after the first attempt was 13.1%. Recurrence was significantly increased by polyp size >50 mm (p = 0.008; OR 5.03, 95% CI 1.54-16.48), presence of fibrosis (p = 0.002; OR 6.59, 95% CI 1.97-22.07) and piecemeal resection (p < 0.001; OR 0.31, CI 0.078-1.12). Cure rates were 87% after the first attempt, improving to 95.6% with further attempts. En bloc resection rate showed a linear increase and reached almost 80% as the endoscopist's cumulative experience approached 100 cases. CONCLUSION: This is the largest reported Western series on KAR in the colon. We have demonstrated the feasibility, efficacy and safety of this technique in the treatment of complex polyps, with or without fibrosis and at all sites. KAR has shown better outcomes than either EMR or ESD. We have also managed to identify significant outcome predictors and define the learning curve.

Knife-assisted snare resection: a novel technique for resection of scarred polyps in the colon.

BACKGROUND AND STUDY AIMS: There have been significant advances in the management of complex colorectal polyps. Previous failed resection or polyp recurrence is associated with significant fibrosis, making endoscopic resection extremely challenging; the traditional approach to these lesions is surgery. The aim of this study was to evaluate the efficacy of a novel, knife-assisted snare resection (KAR) technique in the resection of scarred colonic polyps. PATIENTS AND METHODS: This was a prospective cohort study of patients, in whom the KAR technique was used to resect scarred colonic polyps > 2  cm in size. Patients had previously undergone endoscopic mucosal resection (EMR) and developed recurrence, or EMR had been attempted but was aborted as a result of technical difficulty. RESULTS: A total of 42 patients underwent KAR of large (median 40  mm) scarred polyps. Surgery for benign disease was avoided in 38 of 41 patients (93 %). No life-threatening complications occurred. Recurrence was seen in six patients (16 %), five of whom underwent further endoscopic resection. The overall cure rate for KAR in complex scarred colonic polyps was 90 %. CONCLUSIONS: KAR of scarred colonic polyps by an expert endoscopist was an effective and safe technique with low recurrence rates.

The role of acetic acid in the management of Barrett's oesophagus.

Barrett's oesophagus is of significant importance due to its premalignant potential. Acetic acid chromoendoscopy is a simple technique that can be used with any endoscope system. It has been utilised for the identification of Barrett's intestinal metaplasia; and more importantly, for the localisation of early neoplasia within Barrett's, which is often focal, subtle and very easy to miss by random quadrantic biopsies alone. Acetic acid is routinely utilised in specialised centres and its use is expanding. This article examines the evidence base behind acetic acid chromoendoscopy and looks at where further research needs to be directed.