Trajectories Of Progression Research Articles

Developing machine learning algorithms for dynamic estimation of progression during active surveillance for prostate cancer

Active Surveillance (AS) for prostate cancer is a management option that continually monitors early disease and considers intervention if progression occurs. A robust method to incorporate “live” updates of progression risk during follow-up has hitherto been lacking. To address this, we developed a deep learning-based individualised longitudinal survival model using Dynamic-DeepHit-Lite (DDHL) that learns data-driven distribution of time-to-event outcomes. Further refining outputs, we used a reinforcement learning approach (Actor-Critic) for temporal predictive clustering (AC-TPC) to discover groups with similar time-to-event outcomes to support clinical utility. We applied these methods to data from 585 men on AS with longitudinal and comprehensive follow-up (median 4.4 years). Time-dependent C-indices and Brier scores were calculated and compared to Cox regression and landmarking methods. Both Cox and DDHL models including only baseline variables showed comparable C-indices but the DDHL model performance improved with additional follow-up data. With 3 years of data collection and 3 years follow-up the DDHL model had a C-index of 0.79 (±0.11) compared to 0.70 (±0.15) for landmarking Cox and 0.67 (±0.09) for baseline Cox only. Model calibration was good across all models tested. The AC-TPC method further discovered 4 distinct outcome-related temporal clusters with distinct progression trajectories. Those in the lowest risk cluster had negligible progression risk while those in the highest cluster had a 50% risk of progression by 5 years. In summary, we report a novel machine learning approach to inform personalised follow-up during active surveillance which improves predictive power with increasing data input over time.

Computational approach to modeling microbiome landscapes associated with chronic human disease progression.

A microbial community is a dynamic system undergoing constant change in response to internal and external stimuli. These changes can have significant implications for human health. However, due to the difficulty in obtaining longitudinal samples, the study of the dynamic relationship between the microbiome and human health remains a challenge. Here, we introduce a novel computational strategy that uses massive cross-sectional sample data to model microbiome landscapes associated with chronic disease development. The strategy is based on the rationale that each static sample provides a snapshot of the disease process, and if the number of samples is sufficiently large, the footprints of individual samples populate progression trajectories, which enables us to recover disease progression paths along a microbiome landscape by using computational approaches. To demonstrate the validity of the proposed strategy, we developed a bioinformatics pipeline and applied it to a gut microbiome dataset available from a Crohn’s disease study. Our analysis resulted in one of the first working models of microbial progression for Crohn’s disease. We performed a series of interrogations to validate the constructed model. Our analysis suggested that the model recapitulated the longitudinal progression of microbial dysbiosis during the known clinical trajectory of Crohn’s disease. By overcoming restrictions associated with complex longitudinal sampling, the proposed strategy can provide valuable insights into the role of the microbiome in the pathogenesis of chronic disease and facilitate the shift of the field from descriptive research to mechanistic studies.

MACHINE LEARNING FOR THE IDENTIFICATION AND CLASSIFICATION OF CLINICAL PHENOTYPES IN PATIENTS WITH AN IMPLANTABLE CARDIOVERTER-DEFIBRILLATOR: TOWARDS PERSONALISED ICD PATIENT CARE

Current care for patients with an implantable cardioverter defibrillator (ICD) does not take into account heterogeneity among ICD patients. Data-driven, unsupervised cluster analysis enables identification of clinical phenotypes that may exhibit distinct trajectories of disease progression, benefit from the ICD and risk for adverse events. Such clinical phenotyping could facilitate personalised care tailored to individual risk of ICD patients.

Integrative genomics important to understand host-pathogen interactions.

Infectious diseases are the leading cause of morbidity and mortality worldwide. Causative pathogenic microbes readily mutate their genome and lead to outbreaks, challenging the healthcare and the medical support. Understanding how certain symptoms manifest clinically is integral for therapeutic decisions and vaccination efficacy/protection. Notably, the interaction between infecting pathogens, host response and co-presence of microbes influence the trajectories of disease progression and clinical outcome. The spectrum of observed symptomatic patients (mild, moderate and severe) and the asymptomatic infections highlight the challenges and the potential for understanding the factors driving protection/susceptibility. With the increasing repertoire of high-throughput tools, such as cutting-edge multi-omics profiling and next-generation sequencing, genetic drivers of factors linked to heterogeneous disease presentations can be investigated in tandem. However, such strategies are not without limits in terms of effectively integrating host-pathogen interactions. Nonetheless, an integrative genomics method (for example, RNA sequencing data) for exploring multiple layers of complexity in host-pathogen interactions could be another way to incorporate findings from high-throughput data. We further propose that a Holo-transcriptome-based technique to capture transcriptionally active microbial units can be used to elucidate functional microbiomes. Thus, we provide holistic perspective on investigative methodologies that can harness the same genomic data to investigate multiple seemingly independent but deeply interconnected functional domains of host-pathogen interaction that modulate disease severity and clinical outcomes.

Echoes and Antibodies: Legal Veridiction and the Emergence of the Perpetual Hepatitis C Subject

New drugs with the potential to cure hepatitis C have emerged. There is great optimism within medicine about the transformative potential of cure, but this overlooks the entrenched discrimination and stigma associated with both hepatitis C and injecting drug use and the role of law in re/producing it. Drawing on interviews with key stakeholders such as policymakers, lawyers, and representatives from peer organisations (N = 30), Latour’s (2013) work on legal veridiction, Fraser and Seear’s (2011) conceptualisation of hepatitis C as a ‘gathering’, and Mol’s (2021) work on being, this paper explores the possibility that legal processes complicate the linear trajectory of progress and transformation cure promises. Our participants’ identify various legal processes that allow hepatitis C to echo or linger in people’s lives after treatment. These processes are remaking hepatitis C, and making perpetual hepatitis C subjects. We argue that we must grapple with these forces in the era of cure.

Cell Trajectory-Related Genes of Lung Adenocarcinoma Predict Tumor Immune Microenvironment and Prognosis of Patients

BackgroundLung adenocarcinoma (LUAD) is the most common subtype of lung cancer which typically exhibits a diverse progression trajectory. Our study sought to explore the cell differentiation trajectory of LUAD and its clinical relevance.MethodsUtilizing a single-cell RNA-sequencing dataset (GSE117570), we identified LUAD cells of distinct differential status along with differentiation-related genes (DRGs). DRGs were applied to the analysis of bulk-tissue RNA-sequencing dataset (GSE72094) to classify tumors into different subtypes, whose clinical relevance was further analyzed. DRGs were also applied to gene co-expression network analysis (WGCNA) using another bulk-tissue RNA-sequencing dataset (TCGA-LUAD). Genes from modules that demonstrated a significant correlation with clinical traits and were differentially expressed between normal tissue and tumors were identified. Among these, genes with significant prognostic relevance were used for the development of a prognostic nomogram, which was tested on TCGA-LUAD dataset and validated in GSE72094. Finally, CCK-8, EdU, cell apoptosis, cell colony formation, and Transwell assays were used to verify the functions of the identified genes.ResultsFour clusters of cells with distinct differentiation status were characterized, whose DRGs were predominantly correlated with pathways of immune regulation. Based on DRGs, tumors could be clustered into four subtypes associated with distinct immune microenvironment and clinical outcomes. DRGs were categorized into four modules. A total of nine DRGs (SFTPB, WFDC2, HLA-DPA1, TIMP1, MS4A7, HLA-DQA1, VCAN, KRT8, and FABP5) with most significant survival-predicting power were integrated to develop a prognostic model, which outperformed the traditional parameters in predicting clinical outcomes. Finally, we verified that knockdown of WFDC2 inhibited proliferation, migration, and invasion but promoted the apoptosis of A549 cells in vitro.ConclusionThe cellular composition and cellular differentiation status of tumor mass can predict the clinical outcomes of LUAD patients. It also plays an important role in shaping the tumor immune microenvironment.

Beyond homonationalism: queer temporality in contemporary Israeli gay and lesbian cinema

ABSTRACT The article explores the homonormative and homonational politics and the discourse of LGBT rights in contemporary Israeli cinema. Some Israeli films such as Yossi and Jagger, The Bubble, and The Secrets promote homonormative and homonational discourse that is guided by a progressive trajectory from traditional societies to modernity, from shame to pride, from homophobia and the secrecy of the closet to gay sexual liberation and identity that follows Western standards. In contrast, films like Joe + Belle, Blush and the Arisa Mizrahi party line videos critique logical temporal conventions of development and progress, maturity, adulthood, and responsibility, and the social scenarios which regulate identities into national, sexual, and ethnic normativity. They offer new ways of being in the world, life and relationships based on queer temporality that are not necessarily founded on past lessons or future expectations, but rather derive from the desire for the “here and now.”

Cluster phenotypes in a non-idiopathic pulmonary fibrosis fibrotic interstitial lung diseases cohort in Singapore.

BackgroundNon-idiopathic pulmonary fibrosis fibrosing interstitial lung diseases (F-ILDs) may demonstrate a progressive disease trajectory similar to idiopathic pulmonary fibrosis (IPF). We aimed to identify novel F-ILD phenotypes in a multi-ethnic South-East Asian population.MethodsF-ILD subjects (n=201) were analysed using unsupervised hierarchical cluster analysis and their outcomes compared against IPF (n=86).ResultsFour clusters were identified. Cluster 1 (n=53, 26.4%) comprised older Chinese males with high body mass index (BMI) and comorbidity burden, higher baseline forced vital capacity (FVC) percentage predicted and lower diffusing capacity of the lung for carbon monoxide (DLCO) percentage predicted. They had similar mortality to IPF. Cluster 2 (n=67, 33.3%) had younger female non-smokers with low comorbidity burden, groundglass changes on high-resolution chest computed tomography (HRCT) and a positive anti-nuclear antibody (ANA) titre ≥1:160. They had lower baseline FVC and higher DLCO, low mortality and slower lung function decline. Cluster 3 (n=42, 20.9%) consisted male smokers with low comorbidity burden, emphysema on HRCT and high baseline lung function. They had low mortality and slow lung function decline. Cluster 4 (n=39, 19.4%) was the highest risk and comprised of mainly Indians with high BMI. They had the highest proportion of ischemic heart disease (IHD) and previous pulmonary tuberculosis. Subjects had the lowest baseline lung function, highest mortality, and fastest lung function decline. Survival differences across clusters remained significant following adjustment for treatment.ConclusionsWe identified four distinct F-ILD clinical phenotypes with varying disease trajectories. This demonstrates heterogeneity in F-ILD and the need for complementary approaches for classification and prognostication beyond ATS/ERS guideline diagnosis.

Was Joseph Smith a Monarchotheist? An Engagement with Blake Ostler's Theological Position on the Nature of God

Was Joseph Smith a Monarchotheist? An Engagement with Blake Ostler's Theological Position on the Nature of God

Exploring Longitudinal Cough, Breath, and Voice Data for COVID-19 Progression Prediction via Sequential Deep Learning: Model Development and Validation.

BackgroundRecent work has shown the potential of using audio data (eg, cough, breathing, and voice) in the screening for COVID-19. However, these approaches only focus on one-off detection and detect the infection, given the current audio sample, but do not monitor disease progression in COVID-19. Limited exploration has been put forward to continuously monitor COVID-19 progression, especially recovery, through longitudinal audio data. Tracking disease progression characteristics and patterns of recovery could bring insights and lead to more timely treatment or treatment adjustment, as well as better resource management in health care systems.ObjectiveThe primary objective of this study is to explore the potential of longitudinal audio samples over time for COVID-19 progression prediction and, especially, recovery trend prediction using sequential deep learning techniques.MethodsCrowdsourced respiratory audio data, including breathing, cough, and voice samples, from 212 individuals over 5-385 days were analyzed, alongside their self-reported COVID-19 test results. We developed and validated a deep learning–enabled tracking tool using gated recurrent units (GRUs) to detect COVID-19 progression by exploring the audio dynamics of the individuals’ historical audio biomarkers. The investigation comprised 2 parts: (1) COVID-19 detection in terms of positive and negative (healthy) tests using sequential audio signals, which was primarily assessed in terms of the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity, with 95% CIs, and (2) longitudinal disease progression prediction over time in terms of probability of positive tests, which was evaluated using the correlation between the predicted probability trajectory and self-reported labels.ResultsWe first explored the benefits of capturing longitudinal dynamics of audio biomarkers for COVID-19 detection. The strong performance, yielding an AUROC of 0.79, a sensitivity of 0.75, and a specificity of 0.71 supported the effectiveness of the approach compared to methods that do not leverage longitudinal dynamics. We further examined the predicted disease progression trajectory, which displayed high consistency with longitudinal test results with a correlation of 0.75 in the test cohort and 0.86 in a subset of the test cohort with 12 (57.1%) of 21 COVID-19–positive participants who reported disease recovery. Our findings suggest that monitoring COVID-19 evolution via longitudinal audio data has potential in the tracking of individuals’ disease progression and recovery.ConclusionsAn audio-based COVID-19 progression monitoring system was developed using deep learning techniques, with strong performance showing high consistency between the predicted trajectory and the test results over time, especially for recovery trend predictions. This has good potential in the postpeak and postpandemic era that can help guide medical treatment and optimize hospital resource allocations. The changes in longitudinal audio samples, referred to as audio dynamics, are associated with COVID-19 progression; thus, modeling the audio dynamics can potentially capture the underlying disease progression process and further aid COVID-19 progression prediction. This framework provides a flexible, affordable, and timely tool for COVID-19 tracking, and more importantly, it also provides a proof of concept of how telemonitoring could be applicable to respiratory diseases monitoring, in general.

Disease Progression in Multiple System Atrophy-Novel Modeling Framework and Predictive Factors.

BackgroundMultiple system atrophy (MSA) is a rare and aggressive neurodegenerative disease that typically leads to death 6 to 10 years after symptom onset. The rapid evolution renders it crucial to understand the general disease progression and factors affecting the disease course.ObjectivesThe aims of this study were to develop a novel disease‐progression model to estimate a population‐level MSA progression trajectory and predict patient‐specific continuous disease stages describing the degree of progress into the disease.MethodsThe disease‐progression model estimated a population‐level progression trajectory of subscales of the Unified MSA Rating Scale and the Unified Parkinson's Disease Rating Scale using patients in the European MSA natural history study. The predicted disease continuum was validated via multiple analyses based on reported anchor points, and the effect of MSA subtype on the rate of disease progression was evaluated.ResultsThe predicted disease continuum spanned approximately 6 years, with an estimated average duration of 51 months for a patient with global disability score 0 to reach the highest level of 4. The predicted continuous disease stages were shown to be correlated with time of symptom onset and predictive of survival time. MSA motor subtype was found to significantly affect disease progression, with MSA‐parkinsonian (MSA‐P) type patients having an accelerated rate of progression.ConclusionsThe proposed modeling framework introduces a new method of analyzing and interpreting the progression of MSA. It can provide new insights and opportunities for investigating covariate effects on the rate of progression and provide well‐founded predictions of patient‐level future progressions. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

Trajectory of pulmonary function parameters and radiology progress among patients with Idiopathic pulmonary fibrosis treated with Doxycycline

Idiopathic pulmonary fibrosis is the most common and lethal form of interstitial lung diseases, with an associated median survival of only 2 to 3 years. Doxycycline through its matrix metalloproteinase inhibition property may be useful in slowing the disease progression among patients with ILD. However, there are only anecdotal reports in the literature in demonstrating long term trajectory in lung function parameters and radiology progress among IPF patients treated with doxycycline. In this report we describe the clinical course of two patients with IPF treated with doxycycline. We also compared the decline in the pulmonary function parameters in our patients with previously published predicted values for normal ageing (non-IPF), placebo, nintedanib and pirfenidone treated patients.

Moving forward, waiting or standing still? Service users’ experiences from a Norwegian labour activation programme

ABSTRACT Labour activation policy relies on a notion of temporal homogeneity, with the assumption that the service user, once enrolled in a labour activation programme, moves forward on a linear and progressive trajectory into the labour market. However, for many, labour activation is experienced as a discontinuous and interrupted trajectory, through which one occasionally moves forward, but which also contains periods of immobility and standstill. The literature has illuminated the complex nature of labour activation trajectories, including transitions over time. We introduce a focus on how servicer users in a Norwegian labour activation programme construct understandings of time itself, as ‘waiting’ or ‘moving forward’, in connection with their labour activation experiences. Applying a rhythm analysis that attends to biography and change over time, we explore how service users’ life course rhythms meet with and diverge from the institutionally defined labour activation rhythms, and how dissonance may shape the way that users understand their labour activation experiences. The study is based on fieldwork in The Norwegian labour and welfare services (NAV). Our findings point to how participants experience the time spent in labour activation differently and how seemingly similar experiences and activities may mean ‘waiting’ for some and ‘moving forward’ for others.

A GAN based approach for inferring progression trajectories of costal cartilage calcification from cross-sectional data at image level

BackgroundCostal cartilage calcification (CCC) increases with age and presents differently for men and women. In individuals, however, the cross-sectional studies that show such trends do not reveal the geometric trajectories through which calcification might accumulate across a lifetime. Generative adversarial networks have the potential to reveal such trajectories from cross-sectional data by learning population trends and synthesizing individualized images at progressive levels of calcification. MethodsChest wall mid-surface CT images with normalized cartilage morphologies were produced for 379 subjects aged 6 to 90, and labeled by sex and calcification severity. A conditional GAN with added loss terms to favor one-way accumulation of CCC was trained using organized image batches. GAN performance was assessed by comparing the distributions of images between the training and synthetic groups. ResultsSynthetic images generated from a common seed for a given sex and at successive calcification severity levels showed incremental and regional growth of calcification sites. CCC patterns for synthetic male and female images matched known sex-based differences, and individual CCC growth in synthetic images was consistent with previously observed population trends. These trends in the synthetic images were also quantified by structural similarity scores. Synthetic images generated from different input seeds further showed individual variance in specific regions and trajectories of CCC accumulation. ConclusionThis study inferred individual progression of CCC accumulation from uncalcified to severely calcified using cross-sectional image data. This information can inform computational models of the changing chest wall biomechanics with age, and the GAN-based technique shows potential for inferring longitudinal data from population trends in other clinical areas.

Evolution of Prodromal REM Sleep Behavior Disorder to Neurodegeneration: A Retrospective Longitudinal Case-Control Study.

Individuals with a history of recurrent dream-enactment behaviors, but with subthreshold REM sleep without atonia levels for REM sleep behavior disorder (RBD) diagnosis, are currently classified to have prodromal RBD (pRBD). However, the REM sleep-elevated EMG diagnostic cutoff, progression trajectory, and long-term neurodegenerative outcome of pRBD are not well understood. This study aimed to delineate the evolution of REM sleep EMG levels, determine the EMG cutoff score for diagnosing pRBD, and examine the risk for neurodegenerative diseases of pRBD. This retrospective longitudinal case-control study recruited patients with pRBD and age-matched, sex-matched, and follow-up duration-matched controls who were free of neurodegenerative disease at baseline in the Sleep Assessment Unit, the Chinese University of Hong Kong from 1997 to 2018. Patients and controls underwent clinical and video-polysomnography (PSG) assessments at baseline and follow-up(s). REM sleep EMG activity level on mentalis and anterior tibialis (AT) muscles on video-PSG at each visit was scored. The diagnosis of neurodegenerative diseases was confirmed by a neurologist. A total of 44 patients (age 67.4 ± 8.2 years, 6 females) and 44 controls were recruited. The combined REM sleep EMG level on mentalis and AT muscles of patients with pRBD significantly increased during 8.2 ± 3.3 years of follow-up (from 19.3% ± 9.7% to 47.3% ± 27.4% with estimated annual increase of 3.9%), yielding 29 patients with pRBD (66%) meeting the full-blown RBD diagnostic criteria. Baseline REM sleep mentalis and AT muscles EMG activity of patients who developed full-blown RBD could favorably differentiate pRBD from controls (6.3% for mentalis "any" and 9.1% for combination of mentalis any and bilateral AT muscles phasic EMG with an area under the curve of 0.88 [0.78-0.98] and 0.97 [0.92-1.00], respectively). Patients with pRBD had a higher risk for neurodegenerative diseases (9 developed Parkinson disease and 3 developed dementia with Lewy bodies) when compared with controls (5 developed Alzheimer disease, adjusted hazard ratio = 2.95, 95% CI = 1.02-8.54). pRBD has a predictive progression in both pathophysiology and neurodegenerative outcome. This finding has significant implications to the nosological status of pRBD, the current REM sleep-related EMG diagnostic criteria, spectrum concept of RBD, and future neuroprotective intervention. This study provides Class III evidence that EMG activity during REM sleep predicts the development of pRBD.

PRESENTATION, EPIDEMIOLOGY, and COGNITIVE ACCOUNTS of CHILDHOOD ANXIETY: THEORY and EMPIRICAL INVESTIGATIONS

Anxiety disorders and trait anxiety are one of the most common and impairing psychological conditions in childhood and adolescence. They cause impairments in various developmental domains and create a heavy social and economic burden. To present an extensive overall perspective to current issues childhood anxiety, the first aim of this review is to outline the identifying hallmarks of diverse anxiety disorders, prevalence rates, manifestation, and prognosis in the light of recent research. With the growing interest on cognitive accounts of anxiety, the second aim is to present the trajectory of progress in our understanding of information processing and the related information processing biases en route to youth anxiety. Specifically, theories on distortions in children’s anxiogenic cognitions, the cognitive stages to process threat-related information, and the cognitive biases (i.e., attention, interpretation, and memory) pertaining to each stage were addressed. Finally, because attention bias is the first stage of information processing and there is a potential mediational association between these biases, the importance of eliminating attention bias for threat to further reduce the other biases as well as anxiety was highlighted.

Predictive models in SMA II natural history trajectories using machine learning: A proof of concept study.

It is known from previous literature that type II Spinal Muscular Atrophy (SMA) patients generally, after the age of 5 years, presents a steep deterioration until puberty followed by a relative stability, as most abilities have been lost. Although it is possible to identify points of slope indicating early improvement, steep decline and relative stabilizations, there is still a lot of variability within each age group and it’s not always possible to predict individual trajectories of progression from age only. The aim of the study was to develop a predictive model based on machine learning using an XGBoost algorithm for regression and report, explore and quantify, in a single centre longitudinal natural history study, the influence of clinical variables on the 6/12-months Hammersmith Motor Functional Scale Expanded score prediction (HFMSE). This study represents the first approach to artificial intelligence and trained models for the prediction of individualized trajectories of HFMSE disease progression using individual characteristics of the patient. The application of this method to larger cohorts may allow to identify different classes of progression, a crucial information at the time of the new commercially available therapies.

Single-cell transcriptomics provides insights into hypertrophic cardiomyopathy.

Hypertrophic cardiomyopathy (HCM) is a genetic heart disease that is characterized by unexplained segmental hypertrophy that is usually most pronounced in the septum. While sarcomeric gene mutations are often the genetic basis for HCM, the mechanistic origin for the heterogeneous remodeling remains largely unknown. A better understanding of the gene networks driving the cardiomyocyte (CM) hypertrophy is required to improve therapeutic strategies. Patients suffering from HCM often receive a septal myectomy surgery to relieve outflow tract obstruction due to hypertrophy. Using single-cell RNA sequencing (scRNA-seq) on septal myectomy samples from patients with HCM, we identify functional links between genes, transcription factors, and cell size relevant for HCM. The data show the utility of using scRNA-seq on the human hypertrophic heart, highlight CM heterogeneity, and provide a wealth of insights into molecular events involved in HCM that can eventually contribute to the development of enhanced therapies.

Integrative analysis of genomic and transcriptomic data of normal, tumour, and co-occurring leukoplakia tissue triads drawn from patients with gingivobuccal oral cancer identifies signatures of tumour initiation and progression.

A thickened, white patch — leukoplakia — in the oral cavity is usually benign, but sometimes (in ~9% of individuals) it progresses to malignant tumour. Because the genomic basis of this progression is poorly understood, we undertook this study and collected samples of four tissues — leukoplakia, tumour, adjacent normal, and blood — from each of 28 patients suffering from gingivobuccal oral cancer. We performed multiomics analysis of the 112 collected tissues (four tissues per patient from 28 patients) and integrated information on progressive changes in the mutational and transcriptional profiles of each patient to create this genomic narrative. Additionally, we generated and analysed whole‐exome sequence data from leukoplakia tissues collected from 11 individuals not suffering from oral cancer. Nonsynonymous somatic mutations in the CASP8 gene were identified as the likely events to initiate malignant transformation, since these were frequently shared between tumour and co‐occurring leukoplakia. CASP8 alterations were also shown to enhance expressions of genes that favour lateral spread of mutant cells. During malignant transformation, additional pathogenic mutations are acquired in key genes (TP53, NOTCH1, HRAS) (41% of patients); chromosomal‐instability (arm‐level deletions of 19p and q, focal‐deletion of DNA‐repair pathway genes and NOTCH1, amplification of EGFR) (77%), and increased APOBEC‐activity (23%) are also observed. These additional alterations were present singly (18% of patients) or in combination (68%). Some of these alterations likely impact immune‐dynamics of the evolving transformed tissue; progression to malignancy is associated with immune suppression through infiltration of regulatory T‐cells (56%), depletion of cytotoxic T‐cells (68%), and antigen‐presenting dendritic cells (72%), with a concomitant increase in inflammation (92%). Patients can be grouped into three clusters by the estimated time to development of cancer from precancer by acquiring additional mutations (range: 4–10 years). Our findings provide deep molecular insights into the evolutionary processes and trajectories of oral cancer initiation and progression. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

The association of neurodevelopmental abnormalities, congenital heart and renal defects in a tuberous sclerosis complex patient cohort

BackgroundTuberous sclerosis complex (TSC) is a rare multi-system genetic disorder characterised by the presence of benign tumours throughout multiple organs including the brain, kidneys, heart, liver, eyes, lungs and skin, in addition to neurological and neuropsychiatric complications. Intracardiac tumour (rhabdomyoma), neurodevelopmental disorders (NDDs) and kidney disorders (KD) are common manifestations of TSC and have been linked with TSC1 and TSC2 loss-of-function mutations independently, but the dynamic relationship between these organ manifestations remains unexplored. Therefore, this study aims to characterise the nature of the relationship specifically between these three organs’ manifestations in TSC1 and TSC2 mutation patients.MethodsClinical data gathered from TSC patients across South Wales registered with Cardiff and Vale University Health Board (CAV UHB) between 1990 and 2020 were analysed retrospectively to evaluate abnormalities in the heart, brain and kidney development. TSC-related abnormalities such as tumour prevalence, location and size were analysed for each organ in addition to neuropsychiatric involvement and were compared between TSC1 and TSC2 mutant genotypes. Lastly, statistical co-occurrence between organ manifestations co-morbidity was quantified, and trajectories of disease progression throughout organs were modelled.ResultsThis study found a significantly greater mutational frequency at the TSC2 locus in the cohort in comparison to TSC1. An equal proportion of male and female patients were observed in this group and by meta-analysis of previous studies. No significant difference in characterisation of heart involvement was observed between TSC1 and TSC2 patients. Brain involvement was seen with increased severity in TSC2 patients, characterised by a greater prevalence of cortical tubers and communication disorders. Renal pathology was further enhanced in TSC2 patients, marked by increased bilateral angiomyolipoma prevalence. Furthermore, co-occurrence of NDDs and KDs was the most positively correlated out of investigated manifestations, regardless of genotype. Analysis of disease trajectories revealed a more diverse clinical outcome for TSC2 patients: however, a chronological association of rhabdomyoma, NDD and KD was most frequently observed for TSC1 patients.ConclusionsThis study marks the first empirical investigation of the co-morbidity between congenital heart defects (CHD), NDDs, and KDs in TSC1 and TSC2 patients. This remains a unique first step towards the characterisation of the dynamic role between genetics, heart function, brain function and kidney function during the early development in the context of TSC.