Phenotypic Clusters Research Articles

Clustering of clinical and echocardiographic phenotypes of covid-19 patients

We sought to divide COVID-19 patients into distinct phenotypical subgroups using echocardiography and clinical markers to elucidate the pathogenesis of the disease and its heterogeneous cardiac involvement. A total of 506 consecutive patients hospitalized with COVID-19 infection underwent complete evaluation, including echocardiography, at admission. A k-prototypes algorithm applied to patients' clinical and imaging data at admission partitioned the patients into four phenotypical clusters: Clusters 0 and 1 were younger and healthier, 2 and 3 were older with worse cardiac indexes, and clusters 1 and 3 had a stronger inflammatory response. The clusters manifested very distinct survival patterns (C-index for the Cox proportional hazard model 0.77), with survival best for cluster 0, intermediate for 1–2 and worst for 3. Interestingly, cluster 1 showed a harsher disease course than cluster 2 but with similar survival. Clusters obtained with echocardiography were more predictive of mortality than clusters obtained without echocardiography. Additionally, several echocardiography variables (E′ lat, E′ sept, E/e average) showed high discriminative power among the clusters. The results suggested that older infected males have a higher chance to deteriorate than older infected females. In conclusion, COVID-19 manifests differently for distinctive clusters of patients. These clusters reflect different disease manifestations and prognoses. Although including echocardiography improved the predictive power, its marginal contribution over clustering using clinical parameters only does not justify the burden of echocardiography data collection.

Combined immune and DDR pathway classifier: A novel pathway-based classification aimed at tailoring personalized therapies for acute myeloid leukemia patients

Acute myeloid leukemia (AML) constitutes a group of lethal hematological malignancies with high heterogeneity, resulting in widely variable outcomes of targeted therapy and immunotherapy. A better basic understanding of the molecular pathways of AML would help greatly in tailoring treatments to patients. Here, we propose a novel subtyping protocol for AML combination therapy. Three datasets, namely, the TCGA-LAML, BeatAML and Leucegene datasets, were used in this study. Single-sample GSEA (ssGSEA) was performed to calculate the expression scores of 15 pathways, including immune-related, stromal-related, DNA damage repair (DDR)-related and oncogenic pathways. The consensus clustering was used to classify AML based on pathway score data. We identified four phenotypic clusters—IM+DDR−, IM−DDR-, IM−DDR+ and IM+DDR+—representing distinct pathway expression profiles. The IM+DDR− subtype exhibited the most robust immune function, and patients of IM+DDR− subtype were likely to derive the greatest benefit from immunotherapy. Patients in IM+DDR+ subtype had the second highest immune scores and the highest DDR scores, suggesting that combination therapy (immune + DDR-targeted therapy) is the optimal treatment. For patients of IM−DDR- subtype, we recommend the combination of venetoclax and PHA-665752. A-674563 and dovitinib could be combined with DDR inhibitors to treat patients in IM−DDR+ subtype. Moreover, single-cell analysis revealed that there are more immune cells clustered in the IM+DDR− subtype and higher number of monocyte-like cells, which exert immunosuppressive effects, in the IM+DDR+ subtype. These findings can be applied for molecular stratification of patients and might contribute to the development of personalized targeted therapies for AML.

Functional CVIDs phenotype clusters identified by the integration of immune parameters after BNT162b2 boosters.

Assessing the response to vaccinations is one of the diagnostic criteria for Common Variable Immune Deficiencies (CVIDs). Vaccination against SARS-CoV-2 offered the unique opportunity to analyze the immune response to a novel antigen. We identify four CVIDs phenotype clusters by the integration of immune parameters after BTN162b2 boosters. We performed a longitudinal study on 47 CVIDs patients who received the 3rd and 4th vaccine dose of the BNT162b2 vaccine measuring the generation of immunological memory. We analyzed specific and neutralizing antibodies, spike-specific memory B cells, and functional T cells. We found that, depending on the readout of vaccine efficacy, the frequency of responders changes. Although 63.8% of the patients have specific antibodies in the serum, only 30% have high-affinity specific memory B cells and generate recall responses. Thanks to the integration of our data, we identified four functional groups of CVIDs patients with different B cell phenotypes, T cell functions, and clinical diseases. The presence of antibodies alone is not sufficient to demonstrate the establishment of immune memory and the measurement of the in-vivo response to vaccination distinguishes patients with different immunological defects and clinical diseases.

Multimodal Phenomap of Stargardt Disease Integrating Structural, Psychophysical, and Electrophysiologic Measures of Retinal Degeneration

To cluster the diverse phenotypic features of Stargardt disease (STGD) using unsupervised clustering of multimodal retinal structure and function data. Retrospective cross-sectional study. Eyes of subjects with STGD and fundus autofluorescence (FAF), OCT, electroretinography (ERG), and microperimetry (MP) data available within 1year of the baseline evaluation. A total of 46 variables from FAF, OCT, ERG, and MP results were recorded for subjects with STGD as defined per published criteria. Factor analysis of mixed data identified the most informative variables. Unsupervised hierarchical clustering and silhouette analysis identified the optimal number of clusters to classify multimodal phenotypes. Phenotypic clusters of STGD subjects and the corresponding cluster features. We included 52 subjects and 102 eyes with a mean visual acuity (VA) at the time of multimodal testing of 0.69±0.494 logarithm of minimum angle of resolution (20/63 Snellen). We identified 4 clusters of eyes. Compared to the other clusters, cluster 1 (n=16) included younger subjects, VA greater than that of clusters 2 and 3, normal or moderately low total macular volume (TMV), greater preservation of scotopic and photopic ERG responses and fixation stability, less atrophy, and fewer flecks. Cluster 2 (n=49) differed from cluster 1 mainly with less atrophy and relatively stable fixation. Cluster 3 (n=10) included older subjects than clusters 1 and 2 and showed the lowest VA, TMV, ERG responses, and fixation stability, with extensive atrophy. Cluster 4 (n=27) showed better VA, TMV similar to clusters 1 and 2, moderate ERG activity, stable fixation, and moderate-high atrophy and flecks. Reflecting the phenotypic complexity of STGD, an unsupervised clustering approach incorporating phenotypic measures can be used to categorize STGD eyes into distinct clusters. The clusters exhibit differences in structural and functional measures including quantity of flecks, extent of retinal atrophy, visual fixation accuracy, and ERG responses, among other features. If novel pharmacologic, gene, or cell therapy modalities become available in the future, the multimodal phenomap approach may be useful to individualize treatment decisions, and its utility in aiding prognostication requires further evaluation. Proprietary or commercial disclosure may be found after the references.

Three-dimensional phenotype characteristics of skeletal class III malocclusion in adult Chinese: a principal component analysis-based cluster analysis.

Skeletal class III malocclusion has a diverse and complicated aetiology involving environmental and genetic factors. It is critical to correctly classify and define this malocclusion to be diagnosed and treated on a clinically sound basis. Thus, this study aimed to provide reliable and detailed measurements in a large ethnically homogeneous sample of Chinese adults to generate an adequate phenotypic clustering model to identify and describe the skeletal variation present in skeletal class III malocclusion. This is a retrospective cross-sectional study in which 500 pre-treatments cone-beam computed tomography (CBCT) scans of patients with skeletal class III malocclusion (250 males and 250 females) were selected following specific selection criteria. Seventy-six linear, angular, and ratios measurements were three-dimensionally analysed using InVivo 6.0.3 software. These measurements were categorised into 47 skeletal, 18 dentoalveolar, and 11 soft tissue variables. Multivariate reduction methods: principal component analyses and cluster analyses were used to present the most common phenotypic groupings of skeletal class III malocclusion in Han ethnic group of Chinese adults. The principal component analysis revealed eight principal components accounted for 72.9% of the overall variation of the data produced from the seventy-six variables. The first four principal components accounted for 53.37% of the total variations. They explained the most variation in data and consisted mainly of anteroposterior and vertical skeletal relationships. The cluster analysis identified four phenotypes of skeletal class III malocclusion: C1, 34%; C2, 11.4%; C3, 26.4%; and C4, 28.2%. Based on three-dimensional analyses, four skeletal class III malocclusion distinct phenotypic variations were defined in a large sample of the adult Chinese population, showing the occurrence of phenotypic variation between identified clusters in the same ethnic group. These findings might serve as a foundation for accurate diagnosis and treatment planning of each cluster and future genetic studies to determine the causative gene(s) of each cluster.

The Rare and Atypical Diabetes Network (RADIANT) Study: Design and Early Results.

The Rare and Atypical Diabetes Network (RADIANT) will perform a study of individuals and, if deemed informative, a study of their family members with uncharacterized forms of diabetes. The protocol includes genomic (whole-genome [WGS], RNA, and mitochondrial sequencing), phenotypic (vital signs, biometric measurements, questionnaires, and photography), metabolomics, and metabolic assessments. Among 122 with WGS results of 878 enrolled individuals, a likely pathogenic variant in a known diabetes monogenic gene was found in 3 (2.5%), and six new monogenic variants have been identified in the SMAD5, PTPMT1, INS, NFKB1, IGF1R, and PAX6 genes. Frequent phenotypic clusters are lean type 2 diabetes, autoantibody-negative and insulin-deficient diabetes, lipodystrophic diabetes, and new forms of possible monogenic or oligogenic diabetes. The analyses will lead to improved means of atypical diabetes identification. Genetic sequencing can identify new variants, and metabolomics and transcriptomics analysis can identify novel mechanisms and biomarkers for atypical disease.

Data-driven phenotypic clustering of Parkinson’s disease patients seeking deep brain stimulation (P6-11.008)

<h3>Objective:</h3> To identify unique phenotypic clusters amongst participants in RAD-PD (Registry for the Advancement of Deep Brain Stimulation in Parkinson’s Disease). <h3>Background:</h3> RAD-PD is a longitudinal quality improvement registry that systematically characterizes participants through patient reported outcome measures and clinician administered scales, with the overarching goal of improving outcomes from deep brain stimulation (DBS) in PD. Multiple investigations demonstrate that DBS improves motor function and quality of life (QoL). Less is understood about non-motor outcomes and determinants. <h3>Design/Methods:</h3> A variety of demographic/social, disease-related, motor, non-motor, quality of life, and treatment-related datapoints are captured in RAD-PD. Continuous data variables from participants with complete pre-operative assessments were analyzed using R software. A pairs plot correlation analysis, principal component analysis (PCA) and hierarchical clustering analysis (HCA) with normalized data were conducted. <h3>Results:</h3> Data from 133 subjects were included. Amongst 32 categories of RAD-PD data covering demographic/disease (n=5), non-motor (n=18), motor (n=3), cognitive (n=3) and QoL (n=3) variables, all 3 QoL scale scores were strongly correlated with each other (r=0.47–0.64, p&lt;0.0001), and were otherwise most highly correlated (r&gt;0.4) with measures of depression, anxiety, sleep/fatigue, NMSS total, mood/cognition, IADLs, and MDS-UPDRS part 2. Motor and disease-related variables did not strongly correlate with other variables. PCA revealed two dimensions that together explain 38.7% of the variance in the data, driven mainly by quality of life, NMSS domain/total scores, and QUIP-RS subscores/total scores. HCA classified individuals into two groups that were differentiated based on non-motor (NMSS and ICD scores), cognitive, and QoL features. <h3>Conclusions:</h3> While recommendations for DBS in PD are often driven by motor disease features, QoL and non-motor symptoms are prominent features that are closely related and can be combined to classify phenotypic clusters. Such data-driven classifications can be used in the RAD-PD cohort for novel outcomes analyses and predictive modeling. <b>Disclosure:</b> Dr. Jimenez Shahed has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant for Bracket Global LLC. Dr. Jimenez Shahed has received personal compensation in the range of $500-$4,999 for serving as a Consultant for St. Jude Medical, Inc. Dr. Jimenez Shahed has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Teva. Dr. Jimenez Shahed has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Revance. Dr. Jimenez Shahed has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Medtronic. Dr. Jimenez Shahed has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Spark Therapeutics. Dr. Jimenez Shahed has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Abbvie. Dr. Jimenez Shahed has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Alpha Omega. Dr. Jimenez Shahed has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for BlueRock Therapeutics. Dr. Jimenez Shahed has received personal compensation in the range of $10,000-$49,999 for serving on a Scientific Advisory or Data Safety Monitoring board for PhotoPharmics. The institution of Dr. Jimenez Shahed has received research support from Michael J Fox Foundation. The institution of Dr. Jimenez Shahed has received research support from Amneal. Dr. Jimenez Shahed has received personal compensation in the range of $500-$4,999 for serving as a Faculty with Movement Disorders Society. Dr. Jimenez Shahed has received personal compensation in the range of $500-$4,999 for serving as a Faculty with American Academy of Neurology. Dr. Jimenez Shahed has received personal compensation in the range of $500-$4,999 for serving as a Grant Reviewer with Parkinson Foundation. Dr. Jimenez Shahed has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant on Research Grant with Florida Atlantic University. Dr. Jimenez Shahed has received personal compensation in the range of $0-$499 for serving as a Speaker with Tourette Association of America. Dr. Jimenez Shahed has received personal compensation in the range of $10,000-$49,999 for serving as a Speaker with NACCME. The institution of Arthur Berg has received research support from U.S. Army Medical Research and Development Command

P044 Prevalence and incidence of Behcets Syndrome (BS) in the Hospital Episode Statistics (HES) in England between 2011 and 2021; a retrospective validation study nested within the Birmingham Centre of Excellence for Behcets

Abstract Background/Aims Behçet’s Syndrome is considered rare in the UK. Prevalence estimates range from 0.64 to14.61 per 100 000 population. Here we explore validity of HES ICD-10 codes for BS using Birmingham Centre of excellence (BCoE) data as reference. Prevalence, incidence, clinical characteristics and mortality were compared for attendees at the three national Centres of Excellence (CoE) versus non attendees Methods We extracted all patients aged &amp;gt;18 who had a diagnosis for Behçet's Disease in HES between April 2011 and March 2019. A retrospective cross-sectional analysis of medical records (2012 to 2019) was undertaken at BCoE for MDT confirmed BS for validation. A multivariable logistic regression model examined mortality within 3 years following BS diagnosis. Kaplan Meier survival analysis was undertaken comparing CoE attenders versus non-attenders. Chi squared tests assessed significance of the relationship between CoE attendance, demography, symptom categories, infliximab use and mortality within 3 years. To determine the incidence and prevalence rates, we obtained ONS mid-year estimates of England’s adult population for each year from 2011 to 2019. Point prevalence of BS per 100,000 population was calculated per year of study while Incidence was calculated per 100,000 person years for each year of the study period Results 4452 cases of BS including 515 BCoE attendees were identified in HES between 2011 and 2019. 559 BS cases were confirmed between 2012 and 2020 in the BCoE database. BS prevalence and incidence increased from 2.2 to 3.8 and 0.7 to 0.9 per 100,000 respectively between 2011 and 2019. 65.5% of BS cases were female, 84.9% were Caucasian. BS prevalence peaked at ages 40-49 years. HES data only identified 1157/4452 patients attending one of the three national COEs. Male patients with BS were more likely to have ocular, vascular and cardiac characteristics of BS (p &amp;lt; 0.001). Caucasian ethnicity was associated with highest prevalence of genital ulceration, ocular, vascular and neurological BS. 3-year all-cause mortality was higher in the non-COE cohort (6.4%) versus COE attenders (2.6%) (p &amp;lt; 0.001). Overall BS associated mortality at 3 years follow-up was 0.83%, with higher rate noted in males (1.3%). In logistic regression analysis, mortality significantly associated with age &amp;gt;40 years, IMD and Comorbidity. Infliximab reduced risk of mortality (OR 0.51; p 0.037) Conclusion Our study strength includes use of unselected national HES data with potential coding for inpatient and outpatient episodes. Increasing prevalence of BS may be multifactorial (misclassification, improved recognition, migration effects, improved survival). Our study confirms differences in phenotype and mortality between those diagnosed with BS in the England versus endemic regions. Future work will examine primary care datasets (CPRD and linked HES) to evaluate the national prevalence, incidence, phenotype clusters and risk factors for BS and will ultimately facilitate the creation of a National BS Register Disclosure P. Chandratre: None. R. Situnayake: None. B. Coupland: None.

Characterising biological mechanisms underlying ethnicity-associated outcomes in COVID-19 through biomarker trajectories: a multicentre registry analysis

BackgroundDifferences in routinely collected biomarkers between ethnic groups could reflect dysregulated host responses to disease and to treatments, and be associated with excess morbidity and mortality in COVID-19. MethodsA multicentre registry analysis from patients aged ≥16 yr with SARS-CoV-2 infection and emergency admission to Barts Health NHS Trust hospitals during January 1, 2020 to May 13, 2020 (wave 1) and September 1, 2020 to February 17, 2021 (wave 2) was subjected to unsupervised longitudinal clustering techniques to identify distinct phenotypic patient clusters based on trajectories of routine blood results over the first 15 days of hospital admission. Distribution of trajectory clusters across ethnic categories was determined, and associations between ethnicity, trajectory clusters, and 30-day survival were assessed using multivariable Cox proportional hazards modelling. Secondary outcomes were ICU admission, survival to hospital discharge, and long-term survival to 640 days. ResultsWe included 3237 patients with hospital length of stay ≥7 days. In patients who died, there was greater representation of Black and Asian ethnicity in trajectory clusters for C-reactive protein and urea-to-creatinine ratio associated with increased risk of death. Inclusion of trajectory clusters in survival analyses attenuated or abrogated the higher risk of death in Asian and Black patients. Inclusion of C-reactive protein went from hazard ratio (HR) 1.36 [0.95–1.94] to HR 0.97 [0.59–1.59] (wave 1), and from HR 1.42 [1.15–1.75]) to HR 1.04 [0.78–1.39] (wave 2) in Asian patients. Trajectory clusters associated with reduced 30-day survival were similarly associated with worse secondary outcomes. ConclusionsClinical biochemical monitoring of COVID-19 and progression and treatment response in SARS-CoV-2 infection should be interpreted in the context of ethnic background.

Machine learning identifies clusters of longitudinal autoantibody profiles predictive of systemic lupus erythematosus disease outcomes

ObjectivesA novel longitudinal clustering technique was applied to comprehensive autoantibody data from a large, well-characterised, multinational inception systemic lupus erythematosus (SLE) cohort to determine profiles predictive of clinical outcomes.MethodsDemographic, clinical...

Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging

PurposePatients with known coronary artery disease (CAD) comprise a heterogenous population with varied clinical and imaging characteristics. Unsupervised machine learning can identify new risk phenotypes in an unbiased fashion. We use cluster analysis to risk-stratify patients with known CAD undergoing single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI).MethodsFrom 37,298 patients in the REFINE SPECT registry, we identified 9221 patients with known coronary artery disease. Unsupervised machine learning was performed using clinical (23), acquisition (17), and image analysis (24) parameters from 4774 patients (internal cohort) and validated with 4447 patients (external cohort). Risk stratification for all-cause mortality was compared to stress total perfusion deficit (< 5%, 5–10%, ≥10%).ResultsThree clusters were identified, with patients in Cluster 3 having a higher body mass index, more diabetes mellitus and hypertension, and less likely to be male, have dyslipidemia, or undergo exercise stress imaging (p < 0.001 for all). In the external cohort, during median follow-up of 2.6 [0.14, 3.3] years, all-cause mortality occurred in 312 patients (7%). Cluster analysis provided better risk stratification for all-cause mortality (Cluster 3: hazard ratio (HR) 5.9, 95% confidence interval (CI) 4.0, 8.6, p < 0.001; Cluster 2: HR 3.3, 95% CI 2.5, 4.5, p < 0.001; Cluster 1, reference) compared to stress total perfusion deficit (≥10%: HR 1.9, 95% CI 1.5, 2.5 p < 0.001; < 5%: reference).ConclusionsOur unsupervised cluster analysis in patients with known CAD undergoing SPECT MPI identified three distinct phenotypic clusters and predicted all-cause mortality better than ischemia alone.

Unsupervised clustering identifies sub-phenotypes and reveals novel outcome predictors in patients with dialysis-requiring sepsis-associated acute kidney injury

Introduction Heterogeneity exists in sepsis-associated acute kidney injury (SA-AKI). This study aimed to perform unsupervised consensus clustering in critically ill patients with dialysis-requiring SA-AKI. Patients and Methods This prospective observational cohort study included all septic patients, defined by the Sepsis-3 criteria, with dialysis-requiring SA-AKI in surgical intensive care units in Taiwan between 2009 and 2018. We employed unsupervised consensus clustering based on 23 clinical variables upon initializing renal replacement therapy. Multivariate-adjusted Cox regression models and Fine–Gray sub-distribution hazard models were built to test associations between cluster memberships with mortality and being free of dialysis at 90 days after hospital discharge, respectively. Results Consensus clustering among 999 enrolled patients identified three sub-phenotypes characterized with distinct clinical manifestations upon renal replacement therapy initiation (n = 352, 396 and 251 in cluster 1, 2 and 3, respectively). They were followed for a median of 48 (interquartile range 9.5–128.5) days. Phenotypic cluster 1, featured by younger age, lower Charlson Comorbidity Index, higher baseline estimated glomerular filtration rate but with higher severity of acute illness was associated with an increased risk of death (adjusted hazard ratio of 3.05 [95% CI, 2.35–3.97]) and less probability to become free of dialysis (adjusted sub-distribution hazard ratio of 0.55 [95% CI, 0.38–0.8]) than cluster 3. By examining distinct features of the sub-phenotypes, we discovered that pre-dialysis hyperlactatemia ≥3.3 mmol/L was an independent outcome predictor. A clinical model developed to determine high-risk sub-phenotype 1 in this cohort (C-static 0.99) can identify a sub-phenotype with high in-hospital mortality risk (adjusted hazard ratio of 1.48 [95% CI, 1.25–1.74]) in another independent multi-centre SA-AKI cohort. Conclusions Our data-driven approach suggests sub-phenotypes with clinical relevance in dialysis-requiring SA-AKI and serves an outcome predictor. This strategy represents further development toward precision medicine in the definition of high-risk sub-phenotype in patients with SA-AKI.

Radiomics-based survival risk stratification of glioblastoma is associated with different genome alteration

BackgroundGlioblastoma (GBM) is a remarkable heterogeneous tumor with few non-invasive, repeatable, and cost-effective prognostic biomarkers reported. In this study, we aim to explore the association between radiomic features and prognosis and genomic alterations in GBM. MethodsA total of 180 GBM patients (training cohort: n = 119; validation cohort 1: n = 37; validation cohort 2: n = 24) were enrolled and underwent preoperative MRI scans. From the multiparametric (T1, T1-Gd, T2, and T2-FLAIR) MR images, the radscore was developed to predict overall survival (OS) in a multistep postprocessing workflow and validated in two external validation cohorts. The prognostic accuracy of the radscore was assessed with concordance index (C-index) and Brier scores. Furthermore, we used hierarchical clustering and enrichment analysis to explore the association between image features and genomic alterations. ResultsThe MRI-based radscore was significantly correlated with OS in the training cohort (C-index: 0.70), validation cohort 1 (C-index: 0.66), and validation cohort 2 (C-index: 0.74). Multivariate analysis revealed that the radscore was an independent prognostic factor. Cluster analysis and enrichment analysis revealed that two distinct phenotypic clusters involved in distinct biological processes and pathways, including the VEGFA−VEGFR2 signaling pathway (q-value = 0.033), JAK−STAT signaling pathway (q-value = 0.049), and regulation of MAPK cascade (q-value = 0.0015/0.025). ConclusionsRadiomic features and radiomics-derived radscores provided important phenotypic and prognostic information with great potential for risk stratification in GBM.

Abstract 4657: Topological data analysis reveals pan-cancer immune phenotypes with immune-related survival differences

Abstract Cancer immune phenotypes present a wide range of heterogeneity across cases, with individual tumors displaying unique patterns of infiltrating immune cell types. Deconvolutional methods allow for scoring of various immune cell types in bulk tumor RNA as a quantification of immune phenotype. Understanding how immune phenotype relates to clinical outcome remains limited. Here, we demonstrate an approach applying topological data analysis to investigate differences of immune phenotype in a pan-cancer cohort (TCGA; n=11,373 tumors). We first define an Immune Activation Score based on relative abundance of activator and suppressor immune cell types and find this score depends on cancer type and distinguishes overall survival outcomes. We then implement a robust Mapper-based algorithm to delineate clusters of immune phenotypes of tumor samples across pan-cancer and within cancer types. Our method identifies immune-activated and immune-suppressed phenotypes with distinct survival outcomes and molecular features. Citation Format: Kevin A. Murgas, Jung H. Oh, Joseph O. Deasy, Allen R. Tannenbaum. Topological data analysis reveals pan-cancer immune phenotypes with immune-related survival differences. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4657.

PD05-01 DISTINCT PHENOTYPES OF BLADDER PAIN IDENTIFIED IN THE MULTIDISCIPLINARY APPROACH TO THE STUDY OF CHRONIC PELVIC PAIN RESEARCH NETWORK DATASET

You have accessJournal of UrologyCME1 Apr 2023PD05-01 DISTINCT PHENOTYPES OF BLADDER PAIN IDENTIFIED IN THE MULTIDISCIPLINARY APPROACH TO THE STUDY OF CHRONIC PELVIC PAIN RESEARCH NETWORK DATASET Oluwarotimi Nettey, Nicholas Jackson, and A. Lenore Ackerman Oluwarotimi NetteyOluwarotimi Nettey More articles by this author , Nicholas JacksonNicholas Jackson More articles by this author , and A. Lenore AckermanA. Lenore Ackerman More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003229.01AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Patients with chronic urologic pelvic pain present a unique challenge as their underlying condition is largely a diagnosis of exclusion. In the present analysis, we extend symptom cluster definitions from a pilot study evaluating bladder pain among ambulatory females to a multicenter prospectively recruited dataset using unsupervised machine learning techniques to assess the stability and reproducibility of these bladder pain phenotypes. METHODS: Unsupervised machine learning techniques were applied to patient reported symptoms extracted from the female Genitourinary Pain Index and Interstitial Cystitis Symptom and Problems Indices. Responses from 130 premenopausal female subjects without current opioid use enrolled in the Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) research network were analyzed. Symptom clusters generated from the MAPP multisite cohort (MC) were compared to previously published pilot data results to assess for alignment and concordance. RESULTS: Symptom clusters included a nonurological pelvic pain group with localized pain unrelated to the micturition cycle (NUPP-MC), a myofascial pelvic pain group (MFP-MC) with stranguria, sensation of incomplete bladder emptying, urinary urgency and high symptom bother/severity, and a bladder specific-pain group (BPS-MC) with pain worsened by bladder filling and relieved by bladder emptying.Cluster comparisons demonstrated concordance by subtype between local and multisite cohorts on various domains of bladder pain (Figure 1). MFP–MC patients had more pain severity (p<0.001), diffuse body pain involvement (p=0.02), pelvic floor tenderness (p=0.01), and increased number of flares (p=0.009). MFP-MC cluster assignation was associated with severe sexual dysfunction, impaired relationship scores as well as sleep and anxiety disturbances. CONCLUSIONS: Patient reported symptoms can be utilized to perform deep phenotyping and identify distinct subgroups of perceived bladder pain. Phenotypic clusters appear stable both in a single institutional pilot cohort and a larger multi-site study. Further validation of these phenotypes and their associations with response to therapy will clarify how to target treatment of bladder pain. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e148 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Oluwarotimi Nettey More articles by this author Nicholas Jackson More articles by this author A. Lenore Ackerman More articles by this author Expand All Advertisement PDF downloadLoading ...

492 Phagocyte heterogeneity and T cell dependence of cellular host defense mechanisms in tuberculosis

OBJECTIVES/GOALS: Phagocytes, diverse cells that ingest material, are the primary cell type infected by Mycobacterium tuberculosis (Mtb) and the executors of protective mechanisms. T cells play a critical role by helping phagocytes control the infection. Understanding the precise T cell-dependent mechanisms by which phagocytic cell types contain Mtb is critical. METHODS/STUDY POPULATION: To determine the impact T cells have on different phagocyte cell populations’ host defense mechanisms, groups of wild–type and T cell deficient TCRa-/- mice were infected with an Mtb strain expressing fluorescent mScarlet protein. At four weeks post-infection, a time when T cell help contributes to control of Mtb, lungs were homogenized and cells sorted based on detection of mScarlet, indicating Mtb-infected cells. Cell suspensions from each mouse background were underwent single-cell RNA sequencing analysis to reveal the heterogenous cellular transcriptional response of different phagocyte populations. RESULTS/ANTICIPATED RESULTS: We found that Mtb-infected phagocytes from wild-type and TCRa-/- mouse lungs contain the same dominant cell phenotypic clusters, but these have different patterns of gene expression. Without T cells, phagocytes are prone to a more inflammatory phenotype. DISCUSSION/SIGNIFICANCE: This will translate fundamental biological data to test the hypothesis that Mtb encounters different environmental stresses exerted by different phagocytic cell types. This work could reveal host intracellular niches that enable bacterial persistence and elucidate new pathways that could be targeted for traditional antibiotic therapies for TB.

MLKL and other necroptosis-related genes promote the tumor immune cell infiltration, guiding for the administration of immunotherapy in bladder urothelial carcinoma

The involvement of necroptosis in the immunosuppressive tumor microenvironment has been established and has been shown to contribute to the growth of pancreatic ductal adenocarcinoma, indicating its role in promoting tumor development. However, the relationship between necroptosis and bladder urothelial carcinoma (BUC) has yet to be fully understood. To shed light on this issue, our study aimed to uncover the impact of necroptosis on immune cell infiltration and immunotherapy response in BUC patients. We conducted an analysis of 67 necroptosis genes to assess their expression and genomic changes across pan-cancer and identified 12 necroptosis genes that are prognostically relevant and associated with immune subtypes and tumor stemness in BUC. Using a public database of 1841 BUC samples, we then performed Unsupervised Cluster Analysis and discovered two distinct necroptotic phenotypes in BUC. These phenotypes showed significant differences in molecular subtypes, immune infiltration patterns, and gene mutation profiles. We confirmed this discovery in BUC through qPCR and WB experiments. To evaluate the impact of necroptosis on prognosis, chemotherapy sensitivity, and immunotherapy response (such as anti-PD-L1), we developed a principal component analysis model called NecroScore. Finally, we validated the effects of RIPK3 and MLKL through a nude mouse transplantation model for BUC. Our study has uncovered that necroptosis plays a role in shaping the tumor immune microenvironment in BUC. The high necroptosis phenotype (Cluster B) was characterized by a higher abundance of tumor immunosuppressive cells and more key biological processes driving tumor progression, while the low necroptosis group (Cluster A) had higher FGFR3 mutations. We found that the infiltration levels of immune cells, including CD8+ T cells, were significantly different between FGFR3 mutated and wild-type (WT) samples. Our results confirmed the reliability of NecroScore as a comprehensive assessment tool for evaluating the immunotherapeutic effect and prognosis of BUC patients, with high NecroScore values favoring basal-like differentiation and lower FGFR3 alterations. We also observed that high expression of MLKL had a significant inhibitory effect on tumor growth and increased neutrophil infiltration in vivo. In our study, we uncovered the regulation pattern of necroptosis in the tumor immune microenvironment of BUC. Additionally, we developed a scoring tool called NecroScore that can be utilized to predict the most suitable chemotherapy and immunotherapy strategy for bladder urothelial carcinoma patients. This tool can effectively guide the chemotherapy and immunotherapy regimens for patients with advanced BUC.

Improving species delimitation for effective conservation: a case study in the endemic maple-leaf oak (Quercus acerifolia).

Species delimitation is challenging in lineages that exhibit both high plasticity and introgression. This challenge can be compounded by collection biases, which may downweight specimens morphologically intermediate between traditional species. Additionally, mismatch between named species and observable phenotypes can compromise species conservation. We studied the species boundaries of Quercus acerifolia, a tree endemic to Arkansas, U.S. We performed morphometric analyses of leaves and acorns from 527 field and 138 herbarium samples of Q. acerifolia and its close relatives, Q. shumardii and Q. rubra. We employed two novel approaches: sampling ex situ collections to detect phenotypic plasticity caused by environmental variation and comparing random field samples with historical herbarium samples to identify collection biases that might undermine species delimitation. To provide genetic evidence, we also performed molecular analyses on genome-wide SNPs. Quercus acerifolia shows distinctive morphological, ecological, and genomic characteristics, rejecting the hypothesis that Q. acerifolia is a phenotypic variant of Q. shumardii. We found mismatches between traditional taxonomy and phenotypic clusters. We detected underrepresentation of morphological intermediates in herbarium collections, which may bias species discovery and recognition. Rare species conservation requires considering and addressing taxonomic problems related to phenotypic plasticity, mismatch between taxonomy and morphological clusters, and collection biases.

Abstract GS4-06: Radiomic phenotypes of breast texture and association with breast cancer risk and masking

Abstract Breast parenchymal patterns on radiologic images are associated with breast cancer risk. Radiomic features have been proposed as quantitative measures of parenchymal patterns. We defined intrinsic imaging phenotypes of breast parenchymal patterns based on radiomic features extracted from full field digital mammography (FFDM) in breast screening populations and assessed whether these phenotypes are associated with breast cancer risk and masking. We selected 30,000 women with 4-view FFDM exams from Hologic machines from three institutions (Hospital of the University of Pennsylvania, Mayo Clinic, and San Francisco Mammography Registry), randomly split into a training (20,000 women) and test set (10,000 women). In total, 390 radiomic features were automatically extracted from each image using a validated software pipeline, standardized, and adjusted for site differences using ComBat. We used two methods, hierarchical clustering and Principal Components (PCs) analysis, to classify significant variation among the features in the training set and replicate among the test set. Next, we applied the replicated clusters and PCs to an independent nested case-control set [1082 invasive breast cancer (BC) cases (of which 151 were Black and 893 White women, 38 other race) matched to 2837 controls (411 Black and 2345 White women, 81 other race) on age, race, timing of images, and site]. We examined associations of the clusters and PCs with invasive breast cancer risk, as well as masking [defined as a false-negative (FN) screen (124 cases and 319 matched controls) and additionally the subset with symptomatic interval cancer (IC) within 12 months of negative screen (88 cases and 223 matched controls)] using conditional logistic regression. We evaluated their association with breast cancer alone, and with adjustment for age, body mass index (BMI) and breast density assessed by Breast Imaging Reporting and Data System (BI-RADS) using likelihood ratio tests. We estimated discrimination using area under the curve (AUC) and compared AUCs for models that included the radiomic clusters and PCs with the model that included only age, BMI and density. We also stratified analyses by race (Black/White). From hierarchical clustering, we defined six statistically significant phenotype clusters (each of at least 1000 women) in the training set which were replicated in the test set. For PC Analysis, we identified six PCs in the training set, explaining 85% of the variation in texture features and reproduced these in the test set. The six radiomic phenotype clusters (P&amp;lt; 0.001) and six PCs (P&amp;lt; 0.001) were both associated with invasive BC, including after adjusting for age, BMI, and density (cluster P=0.004; PCs P&amp;lt; 0.001). Improvements in discrimination of invasive BC with inclusion of PCs or clusters were more pronounced among Black women (Table). Further, the PCs (P&amp;lt; 0.001) and clusters (P&amp;lt; 0.001) were significantly associated with FN overall and for symptomatic IC (PCs P&amp;lt; 0.001; clusters P=0.001), but only PCs remained significant after adjusting for age, BMI and density (PCs P=0.004 for FN; PCs P=0.007 for symptomatic IC). Discrimination of masking also improved with inclusion of both clusters and PCs (Table). We identified reproducible radiomic phenotypes that are associated with invasive BC risk, above and beyond breast density with the strongest associations for invasive BC among Black women and symptomatic interval cancers. Citation Format: Stacey Winham, Anne Marie McCarthy, Aimilia Gastounioti, Christopher Scott, Aaron Norman, Walter C. Mankowski, Lauren Pantalone, Matthew Jensen, Eric A. Cohen, Hannah Horng, Kathleen Brandt, Emily F. Conant, Karla Kerlikowske, Despina Kontos, Celine Vachon. Radiomic phenotypes of breast texture and association with breast cancer risk and masking [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr GS4-06.

Multiomic analyses implicate a neurodevelopmental program in the pathogenesis of cerebral arachnoid cysts.

Cerebral arachnoid cysts (ACs) are one of the most common and poorly understood types of developmental brain lesion. To begin to elucidate AC pathogenesis, we performed an integrated analysis of 617 patient-parent (trio) exomes, 152,898 human brain and mouse meningeal single-cell RNA sequencing transcriptomes and natural language processing data of patient medical records. We found that damaging de novo variants (DNVs) were highly enriched in patients with ACs compared with healthy individuals (P = 1.57 × 10-33). Seven genes harbored an exome-wide significant DNV burden. AC-associated genes were enriched for chromatin modifiers and converged in midgestational transcription networks essential for neural and meningeal development. Unsupervised clustering of patient phenotypes identified four AC subtypes and clinical severity correlated with the presence of a damaging DNV. These data provide insights into the coordinated regulation of brain and meningeal development and implicate epigenomic dysregulation due to DNVs in AC pathogenesis. Our results provide a preliminary indication that, in the appropriate clinical context, ACs may be considered radiographic harbingers of neurodevelopmental pathology warranting genetic testing and neurobehavioral follow-up. These data highlight the utility of a systems-level, multiomics approach to elucidate sporadic structural brain disease.