bluuebunny/crossref_metadata_2025 huggingface dataset:
Reference-free structural variant detection in microbiomes via long-read co-assembly graphs
Authors: Curry Kristen D , Yu Feiqiao Brian , Vance Summer E , Segarra Santiago , Bhaya Devaki , Chikhi Rayan , Rocha Eduardo P C , Treangen Todd J
Published: June 2024

Motivation: The study of bacterial genome dynamics is vital for understanding the mechanisms underlying microbial adaptation, growth, and their impact on host phenotype. Structural variants (SVs), genomic alterations of 50 base pairs or more, play a pivotal role in driving evolutionary processes and maintaining genomic heterogeneity within bacterial populations. While SV detection in isolate genomes is relatively straightforward, metagenomes present broader challenges due to the absence of clear reference genomes and the presence of mixed strains. In response, our proposed method rhea, forgoes reference genomes and metagenome-assembled genomes (MAGs) by encompassing all metagenomic samples in a series (time or other metric) into a single co-assembly graph. The log fold change in graph coverage between successive samples is then calculated to call SVs that are thriving or declining.

Results: We show rhea to outperform existing methods for SV and horizontal gene transfer (HGT) detection in two simulated mock metagenomes, particularly as the simulated reads diverge from reference genomes and an increase in strain diversity is incorporated. We additionally demonstrate use cases for rhea on series metagenomic data of environmental and fermented food microbiomes to detect specific sequence alterations between successive time and temperature samples, suggesting host advantage. Our approach leverages previous work in assembly graph structural and coverage patterns to provide versatility in studying SVs across diverse and poorly characterized microbial communities for more comprehensive insights into microbial gene flux.

Availability and implementation: rhea is open source and available at: https://github.com/treangenlab/rhea.
Fast multiple sequence alignment via multi-armed bandits
Authors: Mazooji Kayvon , Shomorony Ilan
Published: June 2024

Multiple sequence alignment is an important problem in computational biology with applications that include phylogeny and the detection of remote homology between protein sequences. UPP is a popular software package that constructs accurate multiple sequence alignments for large datasets based on ensembles of hidden Markov models (HMMs). A computational bottleneck for this method is a sequence-to-HMM assignment step, which relies on the precise computation of probability scores on the HMMs. In this work, we show that we can speed up this assignment step significantly by replacing these HMM probability scores with alternative scores that can be efficiently estimated. Our proposed approach utilizes a multi-armed bandit algorithm to adaptively and efficiently compute estimates of these scores. This allows us to achieve similar alignment accuracy as UPP with a significant reduction in computation time, particularly for datasets with long sequences.

Availability and implementation

The code used to produce the results in this paper is available on GitHub at: https://github.com/ilanshom/adaptiveMSA.
Label-guided seed-chain-extend alignment on annotated De Bruijn graphs
Authors: Mustafa Harun , Karasikov Mikhail , Mansouri Ghiasi Nika , Rätsch Gunnar , Kahles André
Published: June 2024

Motivation

Exponential growth in sequencing databases has motivated scalable De Bruijn graph-based (DBG) indexing for searching these data, using annotations to label nodes with sample IDs. Low-depth sequencing samples correspond to fragmented subgraphs, complicating finding the long contiguous walks required for alignment queries. Aligners that target single-labelled subgraphs reduce alignment lengths due to fragmentation, leading to low recall for long reads. While some (e.g. label-free) aligners partially overcome fragmentation by combining information from multiple samples, biologically irrelevant combinations in such approaches can inflate the search space or reduce accuracy.

Results

We introduce a new scoring model, ‘multi-label alignment’ (MLA), for annotated DBGs. MLA leverages two new operations: To promote biologically relevant sample combinations, ‘Label Change’ incorporates more informative global sample similarity into local scores. To improve connectivity, ‘Node Length Change’ dynamically adjusts the DBG node length during traversal. Our fast, approximate, yet accurate MLA implementation has two key steps: a single-label seed-chain-extend aligner (SCA) and a multi-label chainer (MLC). SCA uses a traditional scoring model adapting recent chaining improvements to assembly graphs and provides a curated pool of alignments. MLC extracts seed anchors from SCAs alignments, produces multi-label chains using MLA scoring, then finally forms multi-label alignments. We show via substantial improvements in taxonomic classification accuracy that MLA produces biologically relevant alignments, decreasing average weighted UniFrac errors by 63.1%–66.8% and covering 45.5%–47.4% (median) more long-read query characters than state-of-the-art aligners. MLAs runtimes are competitive with label-combining alignment and substantially faster than single-label alignment.

Availability and implementation

The data, scripts, and instructions for generating our results are available at https://github.com/ratschlab/mla.
Joint inference of cell lineage and mitochondrial evolution from single-cell sequencing data
Authors: Sashittal Palash , Chen Viola , Pasarkar Amey , Raphael Benjamin J
Published: June 2024

Motivation

Eukaryotic cells contain organelles called mitochondria that have their own genome. Most cells contain thousands of mitochondria which replicate, even in nondividing cells, by means of a relatively error-prone process resulting in somatic mutations in their genome. Because of the higher mutation rate compared to the nuclear genome, mitochondrial mutations have been used to track cellular lineage, particularly using single-cell sequencing that measures mitochondrial mutations in individual cells. However, existing methods to infer the cell lineage tree from mitochondrial mutations do not model “heteroplasmy,” which is the presence of multiple mitochondrial clones with distinct sets of mutations in an individual cell. Single-cell sequencing data thus provide a mixture of the mitochondrial clones in individual cells, with the ancestral relationships between these clones described by a mitochondrial clone tree. While deconvolution of somatic mutations from a mixture of evolutionarily related genomes has been extensively studied in the context of bulk sequencing of cancer tumor samples, the problem of mitochondrial deconvolution has the additional constraint that the mitochondrial clone tree must be concordant with the cell lineage tree.

Results

We formalize the problem of inferring a concordant pair of a mitochondrial clone tree and a cell lineage tree from single-cell sequencing data as the Nested Perfect Phylogeny Mixture (NPPM) problem. We derive a combinatorial characterization of the solutions to the NPPM problem, and formulate an algorithm, MERLIN, to solve this problem exactly using a mixed integer linear program. We show on simulated data that MERLIN outperforms existing methods that do not model mitochondrial heteroplasmy nor the concordance between the mitochondrial clone tree and the cell lineage tree. We use MERLIN to analyze single-cell whole-genome sequencing data of 5220 cells of a gastric cancer cell line and show that MERLIN infers a more biologically plausible cell lineage tree and mitochondrial clone tree compared to existing methods.

Availability and implementation

https://github.com/raphael-group/MERLIN.
Epidemiological topology data analysis links severe COVID-19 to RAAS and hyperlipidemia associated metabolic syndrome conditions
Authors: Platt Daniel , Bose Aritra , Rhrissorrakrai Kahn , Levovitz Chaya , Parida Laxmi
Published: June 2024

Motivation

The emergence of COVID-19 (C19) created incredible worldwide challenges but offers unique opportunities to understand the physiology of its risk factors and their interactions with complex disease conditions, such as metabolic syndrome. To address the challenges of discovering clinically relevant interactions, we employed a unique approach for epidemiological analysis powered by redescription-based topological data analysis (RTDA).

Results

Here, RTDA was applied to Explorys data to discover associations among severe C19 and metabolic syndrome. This approach was able to further explore the probative value of drug prescriptions to capture the involvement of RAAS and hypertension with C19, as well as modification of risk factor impact by hyperlipidemia (HL) on severe C19. RTDA found higher-order relationships between RAAS pathway and severe C19 along with demographic variables of age, gender, and comorbidities such as obesity, statin prescriptions, HL, chronic kidney failure, and disproportionately affecting Black individuals. RTDA combined with CuNA (cumulant-based network analysis) yielded a higher-order interaction network derived from cumulants that furthered supported the central role that RAAS plays. TDA techniques can provide a novel outlook beyond typical logistic regressions in epidemiology. From an observational cohort of electronic medical records, it can find out how RAAS drugs interact with comorbidities, such as hypertension and HL, of patients with severe bouts of C19. Where single variable association tests with outcome can struggle, TDA’s higher-order interaction network between different variables enables the discovery of the comorbidities of a disease such as C19 work in concert.

Availability and Implementation

Code for performing TDA/RTDA is available in https://github.com/IBM/Matilda and code for CuNA can be found in https://github.com/BiomedSciAI/Geno4SD/.

Supplementary Information

Supplementary data are available at Bioinformatics online.
PhiHER2: phenotype-informed weakly supervised model for HER2 status prediction from pathological images
Authors: Yan Chaoyang , Sun Jialiang , Guan Yiming , Feng Jiuxin , Liu Hong , Liu Jian
Published: June 2024

Motivation

Human epidermal growth factor receptor 2 (HER2) status identification enables physicians to assess the prognosis risk and determine the treatment schedule for patients. In clinical practice, pathological slides serve as the gold standard, offering morphological information on cellular structure and tumoral regions. Computational analysis of pathological images has the potential to discover morphological patterns associated with HER2 molecular targets and achieve precise status prediction. However, pathological images are typically equipped with high-resolution attributes, and HER2 expression in breast cancer (BC) images often manifests the intratumoral heterogeneity.

Results

We present a phenotype-informed weakly supervised multiple instance learning architecture (PhiHER2) for the prediction of the HER2 status from pathological images of BC. Specifically, a hierarchical prototype clustering module is designed to identify representative phenotypes across whole slide images. These phenotype embeddings are then integrated into a cross-attention module, enhancing feature interaction and aggregation on instances. This yields a phenotype-based feature space that leverages the intratumoral morphological heterogeneity for HER2 status prediction. Extensive results demonstrate that PhiHER2 captures a better WSI-level representation by the typical phenotype guidance and significantly outperforms existing methods on real-world datasets. Additionally, interpretability analyses of both phenotypes and WSIs provide explicit insights into the heterogeneity of morphological patterns associated with molecular HER2 status.

Availability and implementation

Our model is available at https://github.com/lyotvincent/PhiHER2
Improving medical reasoning through retrieval and self-reflection with retrieval-augmented large language models
Authors: Jeong Minbyul , Sohn Jiwoong , Sung Mujeen , Kang Jaewoo
Published: June 2024

Recent proprietary large language models (LLMs), such as GPT-4, have achieved a milestone in tackling diverse challenges in the biomedical domain, ranging from multiple-choice questions to long-form generations. To address challenges that still cannot be handled with the encoded knowledge of LLMs, various retrieval-augmented generation (RAG) methods have been developed by searching documents from the knowledge corpus and appending them unconditionally or selectively to the input of LLMs for generation. However, when applying existing methods to different domain-specific problems, poor generalization becomes apparent, leading to fetching incorrect documents or making inaccurate judgments. In this paper, we introduce Self-BioRAG, a framework reliable for biomedical text that specializes in generating explanations, retrieving domain-specific documents, and self-reflecting generated responses. We utilize 84k filtered biomedical instruction sets to train Self-BioRAG that can assess its generated explanations with customized reflective tokens. Our work proves that domain-specific components, such as a retriever, domain-related document corpus, and instruction sets are necessary for adhering to domain-related instructions. Using three major medical question-answering benchmark datasets, experimental results of Self-BioRAG demonstrate significant performance gains by achieving a 7.2% absolute improvement on average over the state-of-the-art open-foundation model with a parameter size of 7B or less. Similarly, Self-BioRAG outperforms RAG by 8% Rouge-1 score in generating more proficient answers on two long-form question-answering benchmarks on average. Overall, we analyze that Self-BioRAG finds the clues in the question, retrieves relevant documents if needed, and understands how to answer with information from retrieved documents and encoded knowledge as a medical expert does. We release our data and code for training our framework components and model weights (7B and 13B) to enhance capabilities in biomedical and clinical domains.

Availability and implementation

Self-BioRAG is available at https://github.com/dmis-lab/self-biorag.
Integrating patients in time series clinical transcriptomics data
Authors: Hasanaj Euxhen , Mathur Sachin , Bar-Joseph Ziv
Published: June 2024

Motivation

Analysis of time series transcriptomics data from clinical trials is challenging. Such studies usually profile very few time points from several individuals with varying response patterns and dynamics. Current methods for these datasets are mainly based on linear, global orderings using visit times which do not account for the varying response rates and subgroups within a patient cohort.

Results

We developed a new method that utilizes multi-commodity flow algorithms for trajectory inference in large scale clinical studies. Recovered trajectories satisfy individual-based timing restrictions while integrating data from multiple patients. Testing the method on multiple drug datasets demonstrated an improved performance compared to prior approaches suggested for this task, while identifying novel disease subtypes that correspond to heterogeneous patient response patterns.

Availability and implementation

The source code and instructions to download the data have been deposited on GitHub at https://github.com/euxhenh/Truffle.
Integration of background knowledge for automatic detection of inconsistencies in gene ontology annotation
Authors: Chen Jiyu , Goudey Benjamin , Geard Nicholas , Verspoor Karin
Published: June 2024

Motivation

Biological background knowledge plays an important role in the manual quality assurance (QA) of biological database records. One such QA task is the detection of inconsistencies in literature-based Gene Ontology Annotation (GOA). This manual verification ensures the accuracy of the GO annotations based on a comprehensive review of the literature used as evidence, Gene Ontology (GO) terms, and annotated genes in GOA records. While automatic approaches for the detection of semantic inconsistencies in GOA have been developed, they operate within predetermined contexts, lacking the ability to leverage broader evidence, especially relevant domain-specific background knowledge. This paper investigates various types of background knowledge that could improve the detection of prevalent inconsistencies in GOA. In addition, the paper proposes several approaches to integrate background knowledge into the automatic GOA inconsistency detection process.

Results

We have extended a previously developed GOA inconsistency dataset with several kinds of GOA-related background knowledge, including GeneRIF statements, biological concepts mentioned within evidence texts, GO hierarchy and existing GO annotations of the specific gene. We have proposed several effective approaches to integrate background knowledge as part of the automatic GOA inconsistency detection process. The proposed approaches can improve automatic detection of self-consistency and several of the most prevalent types of inconsistencies.

 

This is the first study to explore the advantages of utilizing background knowledge and to propose a practical approach to incorporate knowledge in automatic GOA inconsistency detection. We establish a new benchmark for performance on this task. Our methods may be applicable to various tasks that involve incorporating biological background knowledge.

Availability and implementation

https://github.com/jiyuc/de-inconsistency.
Predicting single-cell cellular responses to perturbations using cycle consistency learning
Authors: Huang Wei , Liu Hui
Published: June 2024

Phenotype-based drug screening emerges as a powerful approach for identifying compounds that actively interact with cells. Transcriptional and proteomic profiling of cell lines and individual cells provide insights into the cellular state alterations that occur at the molecular level in response to external perturbations, such as drugs or genetic manipulations. In this paper, we propose cycleCDR, a novel deep learning framework to predict cellular response to external perturbations. We leverage the autoencoder to map the unperturbed cellular states to a latent space, in which we postulate the effects of drug perturbations on cellular states follow a linear additive model. Next, we introduce the cycle consistency constraints to ensure that unperturbed cellular state subjected to drug perturbation in the latent space would produces the perturbed cellular state through the decoder. Conversely, removal of perturbations from the perturbed cellular states can restore the unperturbed cellular state. The cycle consistency constraints and linear modeling in the latent space enable to learn transferable representations of external perturbations, so that our model can generalize well to unseen drugs during training stage. We validate our model on four different types of datasets, including bulk transcriptional responses, bulk proteomic responses, and single-cell transcriptional responses to drug/gene perturbations. The experimental results demonstrate that our model consistently outperforms existing state-of-the-art methods, indicating our method is highly versatile and applicable to a wide range of scenarios.

Availability and implementation

The source code is available at: https://github.com/hliulab/cycleCDR.
Optimal phylogenetic reconstruction of insertion and deletion events
Authors: Tule Sanjana , Foley Gabriel , Zhao Chongting , Forbes Michael , Bodén Mikael
Published: June 2024

Motivation

Insertions and deletions (indels) influence the genetic code in fundamentally distinct ways from substitutions, significantly impacting gene product structure and function. Despite their influence, the evolutionary history of indels is often neglected in phylogenetic tree inference and ancestral sequence reconstruction, hindering efforts to comprehend biological diversity determinants and engineer variants for medical and industrial applications.

Results

We frame determining the optimal history of indel events as a single Mixed-Integer Programming (MIP) problem, across all branch points in a phylogenetic tree adhering to topological constraints, and all sites implied by a given set of aligned, extant sequences. By disentangling the impact on ancestral sequences at each branch point, this approach identifies the minimal indel events that jointly explain the diversity in sequences mapped to the tips of that tree. MIP can recover alternate optimal indel histories, if available. We evaluated MIP for indel inference on a dataset comprising 15 real phylogenetic trees associated with protein families ranging from 165 to 2000 extant sequences, and on 60 synthetic trees at comparable scales of data and reflecting realistic rates of mutation. Across relevant metrics, MIP outperformed alternative parsimony-based approaches and reported the fewest indel events, on par or below their occurrence in synthetic datasets. MIP offers a rational justification for indel patterns in extant sequences; importantly, it uniquely identifies global optima on complex protein data sets without making unrealistic assumptions of independence or evolutionary underpinnings, promising a deeper understanding of molecular evolution and aiding novel protein design.

Availability and implementation

The implementation is available via GitHub at https://github.com/santule/indelmip.
TA-RNN: an attention-based time-aware recurrent neural network architecture for electronic health records
Authors: Al Olaimat Mohammad , Bozdag Serdar ,   for the Alzheimer’s Disease Neuroimaging Initiative
Published: June 2024

Motivation

Electronic health records (EHRs) represent a comprehensive resource of a patient’s medical history. EHRs are essential for utilizing advanced technologies such as deep learning (DL), enabling healthcare providers to analyze extensive data, extract valuable insights, and make precise and data-driven clinical decisions. DL methods such as recurrent neural networks (RNN) have been utilized to analyze EHR to model disease progression and predict diagnosis. However, these methods do not address some inherent irregularities in EHR data such as irregular time intervals between clinical visits. Furthermore, most DL models are not interpretable. In this study, we propose two interpretable DL architectures based on RNN, namely time-aware RNN (TA-RNN) and TA-RNN-autoencoder (TA-RNN-AE) to predict patient’s clinical outcome in EHR at the next visit and multiple visits ahead, respectively. To mitigate the impact of irregular time intervals, we propose incorporating time embedding of the elapsed times between visits. For interpretability, we propose employing a dual-level attention mechanism that operates between visits and features within each visit.

Results

The results of the experiments conducted on Alzheimer’s Disease Neuroimaging Initiative (ADNI) and National Alzheimer’s Coordinating Center (NACC) datasets indicated the superior performance of proposed models for predicting Alzheimer’s Disease (AD) compared to state-of-the-art and baseline approaches based on F2 and sensitivity. Additionally, TA-RNN showed superior performance on the Medical Information Mart for Intensive Care (MIMIC-III) dataset for mortality prediction. In our ablation study, we observed enhanced predictive performance by incorporating time embedding and attention mechanisms. Finally, investigating attention weights helped identify influential visits and features in predictions.

Availability and implementation

https://github.com/bozdaglab/TA-RNN.
CPC11 A case of C3 bullous dermatosis
Authors: Gaglani Bindi , De Mozzi Paola , Watts Sophia
Published: June 2024

A 44-year-old Asian man was seen as an urgent referral in dermatology for insidious-onset multiple pruritic tense serous-filled blisters. These were of varying sizes and occurred over acral sites and the body, sparing mucosal sites. He has a history of eczema as a child, which was in remission now, and no other medical problems and no regular medications. He denied any bowel problems. A punch biopsy showed subepidermal blistering with few eosinophils and a perivascular lymphocytic infiltrate. A perilesional indirect immunofluorescence was negative for IgG, IgM, IgA and C3 initially, and was repeated later to show granular C3 deposition at the basement membrane. Indirect immuno­fluorescence and enzyme-linked immunosorbent assay were negative, as was as an autoimmune screen including antinuclear antibodies, extractable nuclear antigen, dsDNA and coeliac screen. The condition was very sensitive to oral steroids, with relapse of the condition after slow wean of prednisolone. C3 bullous dermatosis is a rare form of bullous dermatosis first described by Hashimoto et al. in 2016 (Hashimoto T, Tsuruta D, Yasukochi A et al. Granular C3 dermatosis. Acta Derm Venereol 2016; 96: 748–53). Since its initial report, only 21 such patients have been reported in the literature in total. This condition appears to exhibit clinical characteristics resembling dermatitis herpetiformis in the absence of other diagnostic criteria for coeliac disease. It also presents histopathological features marked by subepidermal blister formation, oedema, and liquefaction degeneration accompanied by infiltrations of lymphocytes, eosinophils and neutrophils in varying combinations. Despite its benign disease progression, it demonstrates a positive response to oral dapsone and topical steroids. Additionally, granular deposition of C3 and C5–C9 is observed, while other complement components do not show significant deposition. Notably, there is a slight reduction in the expression of C3 in the epidermis.
Bristol Cup PostersP001 A descriptive, observational and cross-sectional study of clinicoepidemiological, dermoscopic and histopathological aspects of primary localized cutaneous amyloidosis and its effect on the quality of life of the patient
Authors: Rane Meghana , Mahajan Sunanda
Published: June 2024

The aims of this study were to examine the clinicoepidemiological factors related to primary localized cutaneous amyloidosis; to investigate its dermoscopic and histopathological findings; to determine the clinicohistopathological and dermoscopic correlation; and to study its effect on the quality of life of the patients (Al-Ratrout JT, Satti MB. Primary localized cutaneous amyloidosis: a clinicopathological study from Saudi Arabia. Int J Dermatol 1997; 36: 428–34; Rasi A, Khatami A, Javaheri SM. Macular amyloidosis: an assessment of prevalence, sex and age. Int J Dermatol 2004; 43: 898–9). An open, prospective, descriptive and observational study was conducted in patients with primary cutaneous localized amyloidosis suspected clinically. It was carried out for a period of 18 months, with 100 patients in the study. Only patients who gave written informed consent for the study were included. Pregnant and lactating patients were also excluded. Clinical examination with clinical photographs, skin biopsy and dermoscopy of the same lesions was done using a disposable handheld biopsy punch under local anaesthesia. The final diagnosis was then made after compiling all of the clinical, dermoscopic and histopathological features. Microsoft Excel and SPSS version 19 (IBM, Armonk, NY, USA) were used for statistical analysis. Of the 100 patients included in the study, 56 had macular amyloidosis, 33 had lichenoid amyloidosis, eight had biphasic amyloidosis, two had dyschromic amyloidosis and had primary amyloidosis of the concha. The most common age group was 40–60 years, and the condition was more common in women. A history of use of scrubbing and rubbing while bathing was reported in 45 patients: 33 (73%) of these had macular amyloidosis, nine (20%) had lichenoid amyloidosis, two (4%) had mixed amyloidosis and one (2%) had dyschromic amyloidosis. Pruritus was seen in 92 patients (92%). Dermoscopy of amyloidosis showed a hub-and-spoke pattern and amorphous pigmented globules; 11 patients showed nonspecific pigmented globules and seven showed scales and a central scar pattern. On histopathology of macular amyloidosis, 94 patients showed amyloid deposits stained positively with Congo red. The mean Dermatology Life Quality Index was 3 in macular amyloidosis, and 4 in lichenoid and biphasic amyloidosis. Studies using confocal laser scanning microscopy and direct immunofluorescence could further provide insight and can allow better outcomes. Primary cutaneous amyloidosis has a small-to-moderate effect on the quality of life of patients as it is pruritic and disfiguring. Dermoscopy is a valuable tool to diagnose characteristic features and thus helps to make quick and accurate diagnosis and avoid skin biopsies, as shown in this study.
P007 Secretory carcinoma arising from axillary accessory breast: a rare presentation
Authors: Li Chia-Lun , Huang Chi-Cheng , Kao Tzu-Hang , Chang Tsung-Hsien
Published: June 2024

A 61-year-old woman presented with a 7-month history of a subcutaneous nodule in her left axilla. On examination, a 1.5 × 1.5-cm mobile rubbery nodule was noted in her left axilla. Biopsy of the lesion revealed neoplastic cuboidal epithel­ial cells in a microcystic and papillary-cystic pattern adjacent to non-neoplastic accessory breast tissue in the deep subcutaneous layer. The cells had monotonous nuclei, eosinophilic cytoplasm and distinct nucleoli. Immunohistochemically, the tumour cells were diffusely positive for S100, CK5/7, GATA3 and pan-TRK, while oestrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 (HER2) stained negative. Further image investigation revealed no evidence of malignancy in the breasts, lymph nodes or salivary glands. A diagnosis of secretory carcinoma (SC) arising from the accessory breast was made based on the clinicopathological correlation. The main differential diagnoses included primary cutaneous SC (PCSC), metastatic breast SC, and metastatic salivary SC. In this case, the presence of accessory breast tissue adjacent to the neoplasm, tumour involvement in the deep dermal–subcutaneous layer, and negative primary tumour in the breast and salivary glands favoured SC of the accessory breast. A wide excision with 1-cm safety margin was performed. The next-generation sequencing analysis confirmed presence of neurotrophic tyrosine receptor kinase 3 (NTRK3) fusion genes. SC of the breast is a very rare subtype of breast carcinoma, accounting for only 0.15% of all breast cancers. Clinically, it manifests as an indolent subcutaneous mass on the breast. Pathologically, SC is characterized by tumour cells exhibiting intracellular and extracellular secretory materials, forming patterns including cribriform, papillary-cystic, lobulated and microcystic. Immunohistochemically, SC expresses S100, basal-like markers (such as CK5/6), epidermal growth factor receptor and pan-TRK, and is usually negative for oestrogen receptor, progesterone receptor and HER2. Studies have shown that these carcinomas develop due to t(12,15) ETV6–NTRK3 gene translocation. Notably, the genetic alteration is not exclusive to a specific origin site, emphasizing the importance of clinicopathological correlation for accurate diagnosis. Furthermore, while most PCSCs are superficially located in the dermis, SC originating from accessory breast is found in the deeper subcutaneous layer, often adjacent to breast tissue. To our knowledge, this is the third report of SC arising from the axillary accessory breast. Our patient highlights the significance of considering SC of the accessory breast as a potential differential diagnosis for indolent axillary masses.
P017 Localized penile bullous pemphigoid: a case report
Authors: Barry Rory , Boggs Jennifer , Mellerick Lisa , Barry Rupert
Published: June 2024

A 69-year-old man presented with a 3-month history of recurrent, itchy blisters on the penile shaft. Past medical history included ischaemic heart disease and hypertension. Medications included amlodipine, aspirin, atorvastatin, bendroflumethiazide and bisoprolol. Examination revealed discrete, deroofed bullae on a slightly eroded base on the penile shaft, with evidence of postinflammatory pigmentary changes. The penile glans and scrotum were unaffected. The remaining full-skin examination, including oral mucosa, was noncontributory. Histological analysis by punch biopsy showed skin with dermal eosinophils and scattered intraepidermal eosinophils. A rare eosinophil was seen at the base of the epidermis. No definite neutrophils were seen. No bulla was identified. Direct immunofluorescence analysis of perilesional skin showed strong linear C3, linear IgG and patchy linear IgA deposition on the dermoepidermal junction, suggestive of a bullous disease. Serum indirect immunofluorescence was negative for anti-BP180 and anti-BP230 antibodies, with normal full blood count and renal and liver profiles. Clinically, the working diagnosis was a localized bullous pemphigoid (BP), and the patient was prescribed clobetasol propionate ointment topically once daily for 4 weeks and oral doxycycline 100 mg once daily and nicotinamide 500 mg three times daily for 3 months. After 2 weeks the bullae had resolved, with no new bulla development. Two months following treatment initiation the patient remains symptom free, and clinically without disease recurrence and no reported adverse drug event. BP is the most common autoimmune subepidermal blistering skin disease, with autoantibodies targeting BP180 or BP230 proteins. BP usually presents in older patients, with an increased prevalence in those with a history of stroke, Parkinson disease and dementia. Clinical presentation typically describes painful, itchy, tense bullae of the skin, either localized or widespread across the trunk and proximal limbs. Genital BP is a rare subgroup of localized BP, typically affecting women and children. Differential diagnoses for BP include fixed drug eruptions, contact dermatitis, dermatitis herpetiformis, linear IgA disease and infective causes such as bullous tinea. From a literature search, this is the fifth case of BP localized solely to the male genitalia. Dermatologists should consider localized BP in male patients presenting with bullae on the penis with confirmation by skin biopsy and immunofluorescence analysis, to initiate prompt treatment and disease control and avoid significant morbidity for this patient group.
P027 Are skin cancer teaching ‘super clinics’ safe and effective? Results of a UK pilot study
Authors: Greenish Huw , Shirwac Hafsa Faarax , Morgan Hannah , Heath Harry , Carswell Sarah , Anderson Alexander D G
Published: June 2024

The ‘super clinic’ model is a response to skin cancer capacity pressures facing the NHS and the need to deliver educational opportunities and supervision to trainees, specialty and specialist (SAS) doctors and specialist nurses. This model involves a single specialist supervising multiple trainees and personally reviewing a high number of patients. It is unknown whether the rapid decision making inherent in this way of working affects clinical performance. This pilot study aimed to assess the implementation of an educational super clinic in an NHS hospital dermatology department and to compare performance against standard practice. Super clinics were run over a 6-month period in 2022. A single permanent associate specialist oversaw up to three trainees, each of whom had their own list of cases. Patients were referred via the suspected skin cancer pathway. After reviewing each patient, trainees presented relevant findings and a working diagnosis to the specialist, who examined the patient, then confirmed the diagnosis and agreed a management plan. All patients were offered a full skin check. Both standard and super clinics lasted 3–4 h. Electronic patient records and histology results were reviewed to establish ultimate diagnoses. The proportions of patients in whom histology was obtained and any form of skin cancer was ultimately diagnosed were compared with data from the same specialist’s standard 2-week-wait clinics during the study period. In total, 345 patients were seen in 12 super clinics (average 29 patients per clinic, maximum 41), while 589 patients were seen in standard clinics (14 patients per clinic) over the same period (no trainees present). Clinicians under supervision included two dermatology ST3 doctors, one dermatology trust grade doctor, one genitourinary medicine consultant, three ST1–2 doctors and two clinical nurse specialists. Histology was obtained in 174 of 345 (50%) patients in the super clinic group vs. 262 of 589 (44%) in the regular clinic group (P = 0.08). There was no significant difference in the number of patients ultimately diagnosed with a malignancy in the super clinics (100 of 345, 29%) vs. the regular clinics (159 of 589, 27%) (P = 0.51). The super clinic model facilitated face-to-face supervision and education for multiple trainees, while simultaneously more than doubling new patient capacity. Compared with standard clinics, there was no negative impact on the specialist’s diagnostic confidence (histology rate) or cancer pick up rate. These findings suggest that the super clinic model is both safe and effective.
P050 Certificate of Eligibility for Specialist Registration (CESR) (portfolio pathway) satisfaction survey for dermatology trainees: a comparison of current CESR vs. national trainees, within a large UK teaching hospital
Authors: Hughes Sam , Lakhan Manpreet , Morris-Jones Rachael
Published: June 2024

Over the past 10 years, the UK has seen a consistent rise in the number of dermatology trainees pursuing alternative training pathways to the traditional national training scheme. During this period, the number of trainees awarded a Certificate of Eligibility for Specialist Registration (CESR) more than tripled. This trend appears to be multifactorial, driven by a shortage of national training numbers (NTNs), geographical preferences and a rising number of vacant consultant posts nationally. The CESR route was renamed the ‘portfolio pathway’ in November 2023. It is equivalent to a Certificate of Completion of Training in dermatology. Since 2018, this large teaching hospital has created a CESR training programme that mirrors the NTN programme. In June 2023, a survey was distributed to all dermatology registrars (ST3–ST6) working within the dermatology department. The responses comprised six CESR trainees and 13 NTNs. Modelled on the General Medical Council’s annual National Training Survey, questions were written with grading on a Likert scale, capturing information including training and research opportunities, access to teaching, personal wellbeing and rota design. There were two sections: section 1 was completed by both CESR and national trainees, in order to obtain a direct comparison of training experience, while section 2 was completed exclusively by CESR trainees. Section one revealed that no trainee felt that there was a notable difference in training opportunities or access to teaching, or that there was a perceived difference in clinical competency. All CESR trainees felt that they were treated equally to national trainees, with 50% rating their job satisfaction as ‘very good’, compared with 31% of national trainees. In regard to audits and quality improvement project opportunities, both cohorts felt that the geographical continuity offered with a CESR contract was advantageous, enabling CESR trainees to commit to long-term projects. Conversely, 50% of CESR trainees felt that research opportunities were limited by the 5-year timeframe of the CESR application process. ‘Geographical stability’ was the primary motivator for 50% of trainees pursuing a CESR contract and/or rejecting an NTN. That said, when applying for consultant positions, 67% felt disadvantaged by not having an NTN. This is the first CESR (portfolio pathway) satisfaction survey for dermatology trainees in this hospital. We have collated the experiences of our current CESR trainees, assessing the pros and cons of the pathway, while highlighting the beneficial role of a formalized survey to both monitor and improve their experience. We feel this is imperative, in light of rising number of trainees and the need for dermatologists to be trained via the CESR pathway.
P053 Synchronous melanoma: a single-centre 5-year retrospective study
Authors: Yong Ji Fung , O’Connell Michael , Durack Alana , Paul Lyndsey
Published: June 2024

Synchronous melanoma (SM) is defined as at least two melanomas diagnosed at the same time, or a second within 3 months of diagnosing the first melanoma. Previous studies have shown that 0.5% of all patients with cutaneous melanoma have synchronous second primaries. A retrospective study of melanoma data in one of the eight designated cancer centres in the Republic of Ireland was undertaken. We reviewed the demographics, risk factors and melanoma types in all the cases of SM discussed at the melanoma multidisciplinary team (MDT) meeting for the last 5 years. Melanoma MDT lists, and histopathology and clinical records were reviewed from July 2018 to July 2023 inclusive, for SMs. This study included invasive malignant melanoma (MM), lentigo maligna (LM) and melanoma in situ (MIS). In total, 1082 patients were diagnosed with cutaneous melanomas over the 5-year period. Of these patients, 42 (3.9%) with SMs were identified (26 male, 16 female). Ages ranged from 44 to 93 years. Thirty patients (71%) had SMs diagnosed at the same time, with the rest diagnosed within a 3-month period. Nine cases (21%) had a previous history of melanoma, with four having previous SMs. Other risk factors noted included history of immunosuppression (10%, n = 4), first-degree relatives with a history of melanoma (7%, n = 3) and history of previous nonmelanoma skin cancer (5%, n = 2). Of the 42 patients, four (10%) had more than two individual melanomas identified on the same occasion (three patients had three melanomas and one had five). Of these four patients, two had a first-degree family history of cutaneous melanomas and were referred for genetic testing, with results pending. Of the total number of SMs (n = 91), 29% (26) were invasive MM, 24% (22) were LM and 47% (43) were MIS. Most lesions (33%, n = 30) were located on the trunk, with 19 (58%) on the back. Twenty patients (48%) had synchronous in situ lesions (LM/MIS), 20 (48%) had LM/MIS with an invasive melanoma (pathological stage 1A to 4B) and two (5%) had synchronous stage 1a and 1b melanomas. In total, 93% of cases (n = 39) attended dermatology services for follow-up. To the best of our knowledge, this is the largest reported case series to date of SM. A higher incidence of SMs in our cancer centre is noted compared with the literature (3.8% vs. 0.5%). It highlights the importance of a full skin check, especially once a melanoma has been identified, to ensure further melanomas are not missed. For patients diagnosed with SMs, who also have first-degree family history of cutaneous melanoma, genetic testing should be considered to look for germline mutations and genetic variants.
P062 A multicentre retrospective observational study reviewing the management of severely dysplastic naevi
Authors: Agarwal Vani , Dave Siddarth , Bali Radhika , Khalil Nada , Bala Laksha , Carton James , Sinha Rishika , Macedo Chrystalla , P. Patel Neil , Fearfield Louise
Published: June 2024

The aim of this study was to evaluate management of severely dysplastic naevi (SDN) across two NHS trusts over 5 years. A natural language search was performed on the local histopathology system (Copath) to identify all SDN excised between 1 January 2018 and 1 January 2023. Duplicates, cases from external trusts, and histology reports mentioning ‘melanoma in situ’ (MIS) were excluded. Many cases had a spectrum of dysplastic changes mentioned in the report; in such cases the most severe diagnosis was used. Electronic medical records were reviewed to ascertain the surgical excision margin, histological margin, multidisciplinary team (MDT) discussion, management and duration of follow-up. In total, 136 cases were identified, of which 82 met the inclusion criteria. Trust A had 43 included cases and Trust B had 39 included cases. Overall, 58% (n = 25) of Trust A and 97% (n = 38) of Trust B cases were excised with a 2-mm surgical margin, while 81% (n = 35) of Trust A cases and 85% (n = 33) of Trust B cases were excised with at least a 1-mm histological margin. In total, 44% (n = 19) of Trust A and 51% (n = 20) of Trust B cases were discussed at skin cancer MDT. The diagnoses of two cases at Trust A were upgraded to MIS, and one case at Trust B was upgraded to a pT1a melanoma after MDT review. Overall, 74% (n = 32) of cases at Trust A were offered wide local excision (WLE); of these, 78% (n = 25) were to achieve 5-mm clearance. In total, 18% (n = 7) of cases at Trust B were offered WLE; of these, 29% (n = 2) were to achieve 5-mm clearance. Monitoring was recommended in 2% (n = 1) of Trust A cases and 26% (n = 10) of Trust B cases. Our results demonstrate that management of SDN can differ substantially between trusts. Trust A managed the majority (58%) of cases as MIS, whereas Trust B took a more conservative approach. There was no significant difference in outcome. There are currently no consensus guidelines on the management of SDN in the UK or internationally. Surveys from Australia, Canada, the USA and the UK have shown variation in clinical practice. Review of the current literature supports the re-excision of incompletely excised SDN and monitoring (as opposed to re-excision) of completely excised SDN, as the risk of progression to melanoma appears low. Our results highlight the need for consensus guidelines to manage these cases to provide optimal and consistent patient care, as well as minimizing unnecessary and potentially disfiguring surgery.