Survey of UK histopathology consultants’ attitudes towards academic and molecular pathology

Objective: Academic pathology is facing a crisis; an ongoing decline in academic pathology posts, a paucity of academic pathologist’s in-training and unfilled posts at a time when cellular pathology departments are challenged to deliver increasing numbers of molecular tests. The National Cancer Research Institute initiative in Cellular & Molecular Pathology commissioned a survey to assess attitudes of cellular pathology consultants towards research in order to understand barriers and identify possible solutions to improve this situation. As cellular pathology is encompassing an increasing number of diagnostic molecular tests, we also surveyed the current approach to and extent of training in molecular pathology. Methods: The survey was distributed to all UK-based consultant pathologists via the Pathological Society of Great Britain & Ireland and Royal College of Pathologist networks. Heads of Department were contacted separately to obtain figures for number of academic training and consultant posts. Results: 302 cellular pathologists completed the survey which represents approximately 21% of the total cellular histopathology workforce. Most respondents (89%) had been involved in research at some point; currently, 22% were undertaking research formally, and 41% on an informal basis. Of those previously involved in research, 57% stopped early in their consultant career. The majority of substantive academic posts were Professors of which 60% had been in post for >20 years. Most respondents (84%) used molecular pathology in diagnostic work, independent of where they worked or the length of time in post. Notably, 53% of consultants had not received molecular pathology training, particularly more senior consultants and consultants in district general hospitals. Conclusions: The survey reveals that the academic workforce is skewed towards senior individuals, many of whom are approaching retirement, with a missing cohort of ‘junior consultant’ academic pathologists to replace them. Most pathologists stop formal research activity at the beginning of a consultant career. While molecular pathology is an increasing part of a pathologist’s workload, the majority of consultant cellular pathologists have not received any formal molecular training

Artificial intelligence for detection of microsatellite instability in colorectal cancer-a multicentric analysis of a pre-screening tool for clinical application.

BACKGROUND Microsatellite instability (MSI)/mismatch repair deficiency (dMMR) is a key genetic feature which should be tested in every patient with colorectal cancer (CRC) according to medical guidelines. Artificial intelligence (AI) methods can detect MSI/dMMR directly in routine pathology slides, but the test performance has not been systematically investigated with predefined test thresholds. METHOD We trained and validated AI-based MSI/dMMR detectors and evaluated predefined performance metrics using nine patient cohorts of 8343 patients across different countries and ethnicities. RESULTS Classifiers achieved clinical-grade performance, yielding an area under the receiver operating curve (AUROC) of up to 0.96 without using any manual annotations. Subsequently, we show that the AI system can be applied as a rule-out test: by using cohort-specific thresholds, on average 52.73% of tumors in each surgical cohort [total number of MSI/dMMR = 1020, microsatellite stable (MSS)/ proficient mismatch repair (pMMR) = 7323 patients] could be identified as MSS/pMMR with a fixed sensitivity at 95%. In an additional cohort of N = 1530 (MSI/dMMR = 211, MSS/pMMR = 1319) endoscopy biopsy samples, the system achieved an AUROC of 0.89, and the cohort-specific threshold ruled out 44.12% of tumors with a fixed sensitivity at 95%. As a more robust alternative to cohort-specific thresholds, we showed that with a fixed threshold of 0.25 for all the cohorts, we can rule-out 25.51% in surgical specimens and 6.10% in biopsies. INTERPRETATION When applied in a clinical setting, this means that the AI system can rule out MSI/dMMR in a quarter (with global thresholds) or half of all CRC patients (with local fine-tuning), thereby reducing cost and turnaround time for molecular profiling

The rapid evolution of alternative splicing in plants

Alternative pre-mRNA splicing (AS) is prevalent among all plants and is involved in many interactions with environmental stresses. However, the evolutionary patterns and underlying mechanisms of AS in plants remain unclear. By analyzing the transcriptomes of six plant species, we revealed that AS diverged rapidly among closely related species, largely due to the gains and losses of AS events among orthologous genes. Furthermore, AS that generates transcripts containing premature termination codons (PTC), although only representing a small fraction of the total AS, are more conserved than those that generate non-PTC containing transcripts, suggesting that AS coupled with nonsense-mediated decay (NMD) might play an important role in regulating mRNA levels post-transcriptionally. With a machine learning approach we analyzed the key determinants of AS to understand the mechanisms underlying its rapid divergence. Among the studied species, the presence/absence of alternative splicing site (SS) within the junction, the distance between the authentic SS and the nearest alternative SS, the size of exon-exon junctions were the major determinants for both alternative donor site and acceptor site, suggesting a relatively conserved AS mechanism. Comparative analysis further demonstrated that variations of the identified AS determinants, mostly are located in introns, significantly contributed to the AS turnover among closely related species in both Solanaceae and Brassicaceae taxa. These new mechanistic insights into the evolution of AS in plants highlight the importance of post-transcriptional regulation in mediating plant-environment interactions

Additional loss of MSH2 and MSH6 expression in sporadic deficient mismatch repair colorectal cancer due to MLH1 promoter hypermethylation

Colorectal cancer (CRC) is common with 3% of cases associated with germline mutations in the mismatch repair pathway characteristic of Lynch syndrome (LS). The UK National Institute for Health and Care Excellence recommends screening for LS in all patients newly diagnosed with CRC, irrespective of age. The Yorkshire Cancer Research Bowel Cancer Improvement Programme includes a regional LS screening service for all new diagnoses of CRC. In the first 829 cases screened, 80 cases showed deficient mismatch repair (dMMR) including four cases showing areas with loss of expression of all four mismatch repair proteins by immunohistochemistry. The cases demonstrated diffuse MLH1 loss associated with BRAF mutations and MLH1 promoter hypermethylation in keeping with sporadic dMMR, with presumed additional double hit mutations in MSH2+/−MSH6 rather than underlying LS. Recognition and accurate interpretation of this unusual phenotype is important to prevent unnecessary referrals to clinical genetics and associated patient anxiety

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. (c) 2021 The Pathological Society of Great Britain and Ireland