Reliable Subtypes Research Articles

Development and Verification of Diagnosis Model for Papillary Thyroid Cancer Based on Pyroptosis-Related Genes: A Bioinformatic and in vitro Investigation.

The incidence of papillary thyroid cancer (PTC) has been increasing annually; however, early diagnosis can improve patient outcomes. Pyroptosis is a programmed cell death modality that has received considerable attention recently. However, no studies have reported using pyroptosis-related genes in PTC diagnosis. Analyzed 33 pyroptosis-related genes in PTC transcriptome data from the Gene Expression Omnibus database. Subsequently, used the Least Absolute Shrinkage and Selection Operator (LASSO) model to construct a PTC molecular diagnostic model. Furthermore, confirmed differences in the expression of five genes between PTC and non-tumor tissues using immunohistochemistry. Collected 338 PTC and control samples to construct a five-gene PTC diagnostic model, which was then validated using a training set and underwent correlation analysis with immune cell infiltration. Additionally, validated the biological functions of the core gene NOD1 in vitro. The five-gene PTC diagnostic model demonstrated good diagnostic value for PTC. Moreover, identified three reliable subtypes of pyroptosis and found that NOD1 is involved in tumor-suppressive microenvironment formation. Notably, patients with high NOD1 expression had lower Progression-Free Survival (PFS). Additionally, NOD1 expression was positively correlated with immune markers such as CD47, CD68, CD3, and CD8. Lastly, inhibiting NOD1 showed significant anti-PTC activity in vitro. Our results suggest that pyroptosis-related genes can be used for PTC diagnosis, and NOD1 could be a promising therapeutic target.

Exon-Skipping–Based Subtyping of Colorectal Cancers

The identification of colorectal cancer (CRC) molecular subtypes has prognostic and potentially diagnostic value for patients, yet reliable subtyping remains unavailable in the clinic. The current consensus molecular subtype (CMS) classification in CRCs is based on complex RNA expression patterns quantified at the gene level. The clinical application of these methods, however, is challenging due to high uncertainty of single-sample classification and associated costs. Alternative splicing, which strongly contributes to transcriptome diversity, has rarely been used for tissue type classification. Here, we present an AS-based CRC subtyping framework sensitive to differential exon use that can be adapted for clinical application. Unsupervised clustering was used to measure the strength of association between different categories of alternative splicing and CMS. To build a classifier, the ground truth for CMS labels was derived from expression data quantified at the gene level. Feature selection was achieved through bootstrapping and L1-penalized estimation. The resulting feature space was used to construct a subtype prediction framework applicable to single and multiple samples. The performance of the models was evaluated on unseen CRCs from 2 independent sources (Indivumed, n= 129; The Cancer Genome Atlas, n= 99). We developed a CRC subtype identifier based on 29 exon-skipping events that accurately classifies unseen tumors and enables more precise differentiation of subtypes characterized by distinct biological and prognostic features as compared to classifiers based on gene expression. Here, we demonstrate that a small number of exon-skipping events can reliably classify CRC subtypes using individual patient specimens in a manner suitable to clinical application.

Tumor immune dysfunction and exclusion subtypes in bladder cancer and pan-cancer: a novel molecular subtyping strategy and immunotherapeutic prediction model

BackgroundMolecular subtyping is expected to enable precise treatment. However, reliable subtyping strategies for clinical application remains defective and controversial. Given the significance of tumor immune dysfunction and exclusion (TIDE), we aimed to develop a novel TIDE-based subtyping strategy to guide personalized immunotherapy in the bladder cancer (BC).MethodsTranscriptome data of BC was used to evaluate the heterogeneity and the status of TIDE patterns. Subsequently, consensus clustering was applied to classify BC patients based on TIDE marker-genes. Patients’ clinicopathological, molecular features and signaling pathways of the different TIDE subtypes were well characterized. We also utilize the deconvolution algorithms to analyze the tumor microenvironment, and further explore the sensitivity and mechanisms of each subtype to immunotherapy. Furthermore, BC patient clinical information, real-world BC samples and urine samples were collected for the validation of our findings, which were used for RNA-seq analysis, H&E staining, immunohistochemistry and immunofluorescence staining, and enzyme-linked immunosorbent assay. Finally, we also explored the conservation of our novel TIDE subtypes in pan-cancers.ResultsWe identified 69 TIDE biomarker genes and classified BC samples into three subtypes using consensus clustering. Subtype I showed the lowest TIDE status and malignancy with the best prognosis and highest sensitivity to immune checkpoint blockade (ICB) treatment, which was enriched of metabolic related signaling pathways. Subtype III represented the highest TIDE status and malignancy with the poorest prognosis and resistance to ICB treatment, resulting from its inhibitory immune microenvironment and T cell terminal exhaustion. Subtype II was in a transitional state with intermediate TIDE level, malignancy, and prognosis. We further confirmed the existence and characteristics of our novel TIDE subtypes using real-world BC samples and collected patient clinical data. This subtyping method was proved to be more efficient than previous known methods in identifying non-responders to immunotherapy. We also propose that combining our TIDE subtypes with known biomarkers can potentially improve the sensitivity and specificity of these biomarkers. Moreover, besides guiding ICB treatment, this classification approach can assist in selecting the frontline or recommended drugs. Finally, we confirmed that the TIDE subtypes are conserved across the pan-tumors.ConclusionsOur novel TIDE-based subtyping method can serve as a powerful clinical tool for BC and pan-cancer patients, and potentially guiding personalized therapy decisions for selecting potential beneficiaries and excluding resistant patients of ICB therapy.

A reliable subtyping of de novo Parkinson disease: biomarkers, medication effects and longitudinal progression.

Divergent clinical symptoms and pathological progression suggest multiple subtypes of Parkinson disease (PD). Here, we proposed a reliable PD subtyping approach that quantifies the disturbance of an individual patient to the reference structural covariance networks derived from healthy controls. We revealed two subtypes of de novo PD patients by using longitudinal data from the PPMI dataset. Compared to the conventional clinical TD/PIGD phenotypes, our subtyping was highly stable in 5 years' visits. The two subtypes of PD showed significant differences in motor symptoms, medication effects, CSF biomarkers, and longitudinal progression. Moreover, patients of subtype 2 showed widespread lower cortical-to-dorsal raphe nucleus (DRN) connections and higher medication effects on motor symptoms which was regulated by 5-HT neurons in DRN. Our results suggest distinct neuropathological pathways underlying the two subtypes, such that, in contrast to the typical PD subtype, patients of subtype 2 may be affected by serotonergic modulation on dopaminergic neurons in striatum. Our study opens new avenue to precision medicine and personalized treatments in PD and may be applicable to other neurodegenerative diseases.

Distribution of Blastocystis subtypes isolated from various animal hosts in Thailand

Blastocystis is a parasitic protist of a variety of hosts, including humans. Mapping the distribution of Blastocystis and its genetic variants across different host species can help us understand the epidemiology of this organism and its role in health and disease. This study aimed to identify subtypes of Blastocystis detected in different animal hosts in Thailand. A total of 825 fecal samples belonging to 18 vertebrate orders, 36 families, 68 genera, and 80 species were collected. Of these, 111 specimens were Blastocystis-positive by culture. Seventy-nine samples were subjected to small subunit (SSU) ribosomal DNA amplification by PCR, and reliable subtype data were obtained for 61 specimens. At least 14 subtypes (ST), namely ST1 to ST10, ST14/ST24/ST25 complex, ST23, ST26, and ST29 were detected. In addition, Blastocystis was found in tortoises. ST1 (3.2%) and ST5 (11.5%) were found in pigs, ST2 (1.6%) and ST3 (3.2%) in non-human primates, ST4 (14.7%) in rodents and ruminants, ST6 (4.9%), ST7 (30%), ST9 (1.6%), and ST29 (1.6%) in birds, ST8 (6.6%) in Green peafowl and East Asian Porcupine, and ST10 (4.9%), ST14/ST24/ST25 (9.8%), ST23 (1.6%) and ST26 (1.6%) in ruminants. The sequence recovered from the elongated tortoises (Indotestudo elongata) (3.2%) was phylogenetically placed within the reptilian cluster of Blastocystis, for which no subtype system is available yet. Of note, we did not obtain Blastocystis sequences from any of the many canids and felids sampled in the study, and our data are in support of host specificity of Blastocystis, according to both colonization and subtype distribution.

Transferability of Alzheimer's disease progression subtypes to an independent population cohort

In the past, methods to subtype or biotype patients using brain imaging data have been developed. However, it is unclear whether and how these trained machine learning models can be successfully applied to population cohorts to study the genetic and lifestyle factors underpinning these subtypes. This work, using the Subtype and Stage Inference (SuStaIn) algorithm, examines the generalisability of data-driven Alzheimer's disease (AD) progression models.We first compared SuStaIn models trained separately on Alzheimer's disease neuroimaging initiative (ADNI) data and an AD-at-risk population constructed from the UK Biobank dataset. We further applied data harmonization techniques to remove cohort effects. Next, we built SuStaIn models on the harmonized datasets, which were then used to subtype and stage subjects in the other harmonized dataset.The first key finding is that three consistent atrophy subtypes were found in both datasets, which match the previously identified subtype progression patterns in AD: ‘typical’, ‘cortical’ and ‘subcortical’. Next, the subtype agreement was further supported by high consistency in individuals’ subtypes and stage assignment based on the different models: more than 92% of the subjects, with reliable subtype assignment in both ADNI and UK Biobank dataset, were assigned to an identical subtype under the model built on the different datasets. The successful transferability of AD atrophy progression subtypes across cohorts capturing different phases of disease development enabled further investigations of associations between AD atrophy subtypes and risk factors. Our study showed that (1) the average age is highest in the typical subtype and lowest in the subcortical subtype; (2) the typical subtype is associated with statistically more-AD-like cerebrospinal fluid biomarkers values in comparison to the other two subtypes; and (3) in comparison to the subcortical subtype, the cortical subtype subjects are more likely to associate with prescription of cholesterol and high blood pressure medications.In summary, we presented cross-cohort consistent recovery of AD atrophy subtypes, showing how the same subtypes arise even in cohorts capturing substantially different disease phases. Our study opened opportunities for future detailed investigations of atrophy subtypes with a broad range of early risk factors, which will potentially lead to a better understanding of the disease aetiology and the role of lifestyle and behaviour on AD.

OR20-2 Bilateral Primary Aldosteronism Is Not Excluded by High Adrenal Vein Sampling Lateralization Indices

Abstract Background Primary aldosteronism (PA) is broadly divided into unilateral and bilateral forms, and the most reliable subtyping tool available is adrenal vein sampling (AVS). Unilateral PA is generally diagnosed based on AVS lateralization indices (LI=[aldosterone/cortisol] ratio between the dominant and contralateral adrenal vein) ≥4, or even ≥2 when AVS is performed without cosyntopin stimulation. Long-term follow-up data from patients with PA treated with unilateral adrenalectomy have been minimal. Objective To assess the rates of residual and recurrent PA after AVS-guided unilateral adrenalectomy. Methods We enrolled patients with PA who underwent unilateral adrenalectomy between 09/2017 and 08/2021 in a single tertiary referral center and followed them longitudinally. Demographics, laboratory, imaging, and pathology data were collected. Aldosterone synthase (CYP11B2) immunohistochemistry and next generation sequencing were performed on available formalin-fixed paraffin-embedded adrenal tissue blocks. Results During the study period, 80 patients (52 men) with median age 53 (range, 19-76) years underwent unilateral adenectomy, and were followed for a median 80 days (range, 6-653 days). In total 14 (17.5%) patients had evidence of residual PA following surgery, including 3 who had complete biochemical failure, and 1 who had apparent cure of PA immediately post-operatively. All 14 patients had severe PA, with baseline plasma aldosterone concentrations between 23.4 and 496 ng/dL and suppressed renin. Baseline and post-cosyntropin stimulation LIs ranged from 2.7- 187.5 and 1.3-53.7. The LI was >4 only at baseline in 3 cases, and only after cosyntropin stimulation in 2 cases. Histology showed single adenomas, ranging from 0.7-5.3 cm in 7 (50%) patients, and multiple adenomas and/or nodular hyperplasia in 7 (50%) patients. Of the 7 cases analyzed for aldosterone-driver mutations so far, 4 harbored CACNA1D mutations, 2 ATP1A1 mutations, and 1 a KCNJ5 mutation. Conclusions Patients with severe PA and frank AVS aldosterone lateralization might have multifocal, bilateral disease. Long-term follow up and targeted medical therapy should be pursued after unilateral adrenalectomy for presumed unilateral PA. Presentation: Monday, June 13, 2022 11:15 a.m. - 11:30 a.m.

Abstract 3473: Use of DNA methylation from tumor and plasma to identify four major small cell lung cancer subtypes with distinct biology and therapeutic vulnerabilities

Abstract Small cell lung cancer (SCLC) is a highly aggressive cancer with limited treatment options and generally poor prognosis. Treatment of SCLC has not considerably changed over the last decades with therapeutic options focusing on unselected populations. Although in the past SCLC was thought to be a relatively homogenous malignancy, recent reports from our group and others identified four major distinct subgroups of SCLC, each with different therapeutic vulnerabilities. Three of the subtypes are defined by the expression of a specific transcription factor, ASCL1 (SCLC-A), NEUROD1 (SCLC-N) and POU2F3 (SCLC-P) while the fourth subtype is defined by an inflamed phenotype (SCLC-I). While our initial subtyping of SCLC is based on a gene expression signature comprised of ~1300 genes, which makes routine implementation challenging, we hypothesized that DNA methylation as a proxy to gene expression might be a more suitable approach for biomarker development in SCLC. We assembled a cohort of 105 SCLC formalin-fixed paraffin embedded (FFPE) samples (82/105 Stage > IIIb) and performed matched RNA-Sequencing (RNAseq) and methylation profiling using reduced-representation bisulfite sequencing (RRBS). To validate our findings and expand our analysis across different sample types, we profiled a panel of 59 fully characterized SCLC cell lines as well as 68 patient-derived xenograft models. We found that methylation levels differ markedly between the four subtypes, with the SCLC-N presenting with a hypermethylated phenotype and the SCLC-P with a hypomethylated phenotype across the genome, highlighting the profound differences in the underlying epigenetic regulation among the SCLC subtypes and supporting DNA methylation analysis as a potential readout for identifying SCLC subtypes. Furthermore, in order to subtype the clinical SCLC samples, we developed a predictive model using an extreme gradient boost model using RNA expression and DNA methylation, respectively, to allow the classification with 94.5% accuracy in the tissue testing cohort. Using a cohort of matched plasma samples, we further demonstrated that the DNA methylation differences were indeed preserved in cell-free DNA (cfDNA) allowing subtype classification with an accuracy of 87.5%. These data indicate that DNA methylation can be used for reliable subtyping of SCLC in tissue and in liquid biopsy samples. In summary, using a large cohort of predominantly extensive stage SCLC clinical samples, we were able to identify profound differences in DNA methylation that can be exploited as a novel biomarker for the classification of SCLC into four distinct subtypes with both tissue biopsy and non-invasive using plasma. Considering the previously shown therapeutic vulnerabilities of the four subtypes, these findings will enable the rapid initiation of personalized clinical trials in SCLC. Citation Format: Simon Heeke, Carl M. Gay, Marcos R. Estecio, Allison Stewart, Hai Tran, Bingnan Zhang, Ximing Tang, Gabriela Raso, Kyle Concannon, Luana Guimaraes De Sousa, Whitney E. Lewis, Monique Nilsson, Yuanxin Xi, Lixia Diao, Qi Wang, Jianjun Zhang, Jing Wang, Ignacio I. Wistuba, Lauren A. Byers, John V. Heymach. Use of DNA methylation from tumor and plasma to identify four major small cell lung cancer subtypes with distinct biology and therapeutic vulnerabilities [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3473.

Subtype-WESLR: identifying cancer subtype with weighted ensemble sparse latent representation of multi-view data

The discovery of cancer subtypes has become much-researched topic in oncology. Dividing cancer patients into subtypes can provide personalized treatments for heterogeneous patients. High-throughput technologies provide multiple omics data for cancer subtyping. Integration of multi-view data is used to identify cancer subtypes in many computational methods, which obtain different subtypes for the same cancer, even using the same multi-omics data. To a certain extent, these subtypes from distinct methods are related, which may have certain guiding significance for cancer subtyping. It is a challenge to effectively utilize the valuable information of distinct subtypes to produce more accurate and reliable subtypes. A weighted ensemble sparse latent representation (subtype-WESLR) is proposed to detect cancer subtypes on heterogeneous omics data. Using a weighted ensemble strategy to fuse base clustering obtained by distinct methods as prior knowledge, subtype-WESLR projects each sample feature profile from each data type to a common latent subspace while maintaining the local structure of the original sample feature space and consistency with the weighted ensemble and optimizes the common subspace by an iterative method to identify cancer subtypes. We conduct experiments on various synthetic datasets and eight public multi-view datasets from The Cancer Genome Atlas. The results demonstrate that subtype-WESLR is better than competing methods by utilizing the integration of base clustering of exist methods for more precise subtypes.

Subtyping bovine viral diarrhea virus (BVDV): Which viral gene to choose?

Bovine viral diarrhea virus-1 (BVDV-1, Pestivirus A) and BVDV-2 (Pestivirus B) have been clustered into 21 and 4 subtypes, respectively. This genetic diversity, in addition to the lack of consensus on which genomic region to use for BVDV subtyping, has resulted in conflicting classifications depending on the target analyzed. Here, we investigated which genes or UTRs would reproduce the phylogeny obtained by complete genome (CG) analyses. The study was carried out with 91 (BVDV-1) and 85 (BVDV-2) CG available on GenBank database. The viruses were subtyped by analyzing their CG, as well as their individual genes and UTRs (complete 3' and 5'UTRs, and partial 5'UTR); and the phylogeny results were compared to each other. The sequences were aligned using the ClustalW multiple method (BioEdit Alignment Editor software, v.7.0.5.3) and the phylogenetic analyses were performed by the Maximum Likelihood method (MEGA-X software, v.10.2.4), with 1000 bootstrap replicates. The best analysis model for each gene/UTR was defined using the jModelTest software. The geodesic distance between the CG (reference) and individual genes/UTRs trees was also calculated (TreeCmp software, v.2.0). In general, 3'UTR-based analyses, followed by 5'UTR, presented the least reliable subtyping results. Regarding BVDV-1, phylogeny based on C, Erns, E1, E2, p7, NS2, NS3, NS4B, NS5A and NS5B was consistent with that of CG. In contrast, analyses performed with individual BVDV-2 genes showed at least one different clustering from the phylogeny based on the CG. After analyzing the geodesic distance between the CG and genes/UTRs trees, we observed that NS4B (for BVDV-1) and NS5A (BVDV-2) presented the closest topology and edge length to the CG analyses. Finally, comparing the phylogeny performed with the CG and the genes/UTRs, as well as the geodesic distance between them, we understand that NS4B and NS5A represent the most suitable targets for BVDV-1 and -2 subtyping, respectively, and may be considered in future phylogenetic studies.

Phylogenomic analysis for Campylobacter fetus ocurring in Argentina.

Background and Aim:Campylobacter fetus is one of the most important pathogens that severely affects livestock industry worldwide. C. fetus mediated bovine genital campylobacteriosis infection in cattle has been associated with significant economic losses in livestock production in the Pampas region, the most productive area of Argentina. The present study aimed to establish the genomic relationships between C. fetus strains, isolated from the Pampas region, at local and global levels. The study also explored the utility of multi-locus sequence typing (MLST) as a typing technique for C. fetus.Materials and Methods:For pangenome and phylogenetic analysis, whole genome sequences for 34 C. fetus strains, isolated from cattle in Argentina were downloaded from GenBank. A local maximum likelihood (ML) tree was constructed and linked to a Microreact project. In silico analysis based on MLST was used to obtain information regarding sequence type (ST) for each strain. For global phylogenetic analysis, a core genome ML-tree was constructed using genomic dataset for 265 C. fetus strains, isolated from various sources obtained from 20 countries.Results:The local core genome phylogenetic tree analysis described the presence of two major clusters (A and B) and one minor cluster (C). The occurrence of 82% of the strains in these three clusters suggested a clonal population structure for C. fetus. The MLST analysis for the local strains revealed that 31 strains were ST4 type and one strain was ST5 type. In addition, a new variant was identified that was assigned a novel ST, ST70. In the present case, ST4 was homogenously distributed across all the regions and clusters. The global analysis showed that most of the local strains clustered in the phylogenetic groups that comprised exclusively of the strains isolated from Argentina. Interestingly, three strains showed a close genetic relationship with bovine strains obtained from Uruguay and Brazil. The ST5 strain grouped in a distant cluster, with strains obtained from different sources from various geographic locations worldwide. Two local strains clustered in a phylogenetic group comprising intercontinental Campylobacter fetus venerealis strains.Conclusion:The results of the study suggested active movement of animals, probably due to economic trade between different regions of the country as well as with neighboring countries. MLST results were partially concordant with phylogenetic analysis. Thus, this method did not qualify as a reliable subtyping method to assess C. fetus diversity in Argentina. The present study provided a basic platform to conduct future research on C. fetus, both at local and international levels.

Automatic Subtyping of Individuals with Primary Progressive Aphasia

Background:The classification of patients with primary progressive aphasia (PPA) into variants is time-consuming, costly, and requires combined expertise by clinical neurologists, neuropsychologists, speech pathologists, and radiologists.Objective:The aim of the present study is to determine whether acoustic and linguistic variables provide accurate classification of PPA patients into one of three variants: nonfluent PPA, semantic PPA, and logopenic PPA.Methods:In this paper, we present a machine learning model based on deep neural networks (DNN) for the subtyping of patients with PPA into three main variants, using combined acoustic and linguistic information elicited automatically via acoustic and linguistic analysis. The performance of the DNN was compared to the classification accuracy of Random Forests, Support Vector Machines, and Decision Trees, as well as to expert clinicians’ classifications.Results:The DNN model outperformed the other machine learning models as well as expert clinicians’ classifications with 80% classification accuracy. Importantly, 90% of patients with nfvPPA and 95% of patients with lvPPA was identified correctly, providing reliable subtyping of these patients into their corresponding PPA variants.Conclusion:We show that the combined speech and language markers from connected speech productions can inform variant subtyping in patients with PPA. The end-to-end automated machine learning approach we present can enable clinicians and researchers to provide an easy, quick, and inexpensive classification of patients with PPA.

Phenotypic profile clustering pragmatically identifies diagnostically and mechanistically informative subgroups of chronic pain patients.

Traditional classification and prognostic approaches for chronic pain conditions focus primarily on anatomically based clinical characteristics not based on underlying biopsychosocial factors contributing to perception of clinical pain and future pain trajectories. Using a supervised clustering approach in a cohort of temporomandibular disorder cases and controls from the Orofacial Pain: Prospective Evaluation and Risk Assessment study, we recently developed and validated a rapid algorithm (ROPA) to pragmatically classify chronic pain patients into 3 groups that differed in clinical pain report, biopsychosocial profiles, functional limitations, and comorbid conditions. The present aim was to examine the generalizability of this clustering procedure in 2 additional cohorts: a cohort of patients with chronic overlapping pain conditions (Complex Persistent Pain Conditions study) and a real-world clinical population of patients seeking treatment at duke innovative pain therapies. In each cohort, we applied a ROPA for cluster prediction, which requires only 4 input variables: pressure pain threshold and anxiety, depression, and somatization scales. In both complex persistent pain condition and duke innovative pain therapies, we distinguished 3 clusters, including one with more severe clinical characteristics and psychological distress. We observed strong concordance with observed cluster solutions, indicating the ROPA method allows for reliable subtyping of clinical populations with minimal patient burden. The ROPA clustering algorithm represents a rapid and valid stratification tool independent of anatomic diagnosis. ROPA holds promise in classifying patients based on pathophysiological mechanisms rather than structural or anatomical diagnoses. As such, this method of classifying patients will facilitate personalized pain medicine for patients with chronic pain.

The psychology of "swiping": A cluster analysis of the mobile dating app Tinder.

Background and aimsThe use of the smartphone dating application Tinder is increasingly popular and has received much media attention. However, no empirical study to date has investigated the psychological characteristics driving its adaptive or problematic use. The aim of this study is to determine whether reliable subtypes of users can be identified via a cluster analysis approach.MethodsA total of 1,159 Tinder users were recruited. Survey questions investigated user characteristics, including: motives for app use, sexual desire, attachment styles, impulsivity traits, self-esteem, problematic use, depressive mood, and patterns of use.ResultsFour reliable clusters were identified: two with low levels of problematic use (“regulated” and “regulated with low sexual desire”), one with an intermediate level of problematic use (“unregulated-avoidants”), and one with a high level of problematic use (“unregulated-highly motivated”). The clusters differed on gender, marital status, depressive mood, and use patterns.ConclusionThe findings provide insight into the dynamic relationships among key use-related factors and shed light on the mechanisms underlying the self-regulation difficulties that appear to characterize problematic Tinder use.

A Catalog of OB Stars from LAMOST Spectroscopic Survey

Abstract We present 22,901 OB spectra of 16,032 stars identified from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope data release 5 data set. A larger sample of OB candidates are first selected from the distributions in the spectral line indices’ space. Then, all 22,901 OB spectra are identified by manual inspection. Based on a subsample validation, we find that the completeness of the OB spectra reaches about 89 ± 22% for the stars with spectral types earlier than B7, while around 57 ± 16% B8–B9 stars are identified. The smaller completeness for late B stars will lead to the difficulty in discriminating them from A0–A1-type stars. The subclasses of the OB samples are determined using the software package MKCLASS. With a careful validation using 646 subsamples, we find that MKCLASS can give fairly reliable subtypes and luminosity classes for most of the OB stars. The uncertainty of the spectral subtype is around 1 subtype, and the uncertainty of the luminosity class is around 1 level. However, about 40% of the OB stars fail to be assigned to any class by MKCLASS, and a few spectra are significantly misclassified by MKCLASS. This is likely because the template spectra of MKCLASS are selected from nearby stars in the solar neighborhood, while the OB stars in this work are mostly located in the outer disk and may have lower metallicities. The rotation of the OB stars may also be responsible for the misclassifications. Moreover, we find that the spectral and luminosity classes of the OB stars located in the Galactic latitude larger than 20° are substantially different with those located in the latitude smaller than 20°, which may either be due to the observational selection effect or may hint a different origin of the high Galactic latitude OB stars.

Evaluating the evidence for biotypes of depression: Methodological replication and extension of.

BackgroundPsychiatric disorders are highly heterogeneous, defined based on symptoms with little connection to potential underlying biological mechanisms. A possible approach to dissect biological heterogeneity is to look for biologically meaningful subtypes. A recent study Drysdale et al. (2017) showed promising results along this line by simultaneously using resting state fMRI and clinical data and identified four distinct subtypes of depression with different clinical profiles and abnormal resting state fMRI connectivity. These subtypes were predictive of treatment response to transcranial magnetic stimulation therapy.ObjectiveHere, we attempted to replicate the procedure followed in the Drysdale et al. study and their findings in a different clinical population and a more heterogeneous sample of 187 participants with depression and anxiety. We aimed to answer the following questions: 1) Using the same procedure, can we find a statistically significant and reliable relationship between brain connectivity and clinical symptoms? 2) Is the observed relationship similar to the one found in the original study? 3) Can we identify distinct and reliable subtypes? 4) Do they have similar clinical profiles as the subtypes identified in the original study?MethodsWe followed the original procedure as closely as possible, including a canonical correlation analysis to find a low dimensional representation of clinically relevant resting state fMRI features, followed by hierarchical clustering to identify subtypes. We extended the original procedure using additional statistical tests, to test the statistical significance of the relationship between resting state fMRI and clinical data, and the existence of distinct subtypes. Furthermore, we examined the stability of the whole procedure using resampling.Results and conclusionAs in the original study, we found extremely high canonical correlations between functional connectivity and clinical symptoms, and an optimal three-cluster solution. However, neither canonical correlations nor clusters were statistically significant. On the basis of our extensive evaluations of the analysis methodology used and within the limits of comparison of our sample relative to the sample used in Drysdale et al., we argue that the evidence for the existence of the distinct resting state connectivity-based subtypes of depression should be interpreted with caution.

Subtyping of Canadian isolates of Salmonella Enteritidis using Multiple Locus Variable Number Tandem Repeat Analysis (MLVA) alone and in combination with Pulsed-Field Gel Electrophoresis (PFGE) and phage typing

Salmonella enterica subspecies enterica serovar Enteritidis (SE) is one of the most common causes of human salmonellosis and in Canada currently accounts for over 40% of human cases. Reliable subtyping of isolates is required for outbreak detection and source attribution. However, Pulsed-Field Gel Electrophoresis (PFGE), the current standard subtyping method for Salmonella spp., is compromised by the high genetic homogeneity of SE. Multiple Locus Variable Number Tandem Repeat Analysis (MLVA) was introduced to supplement PFGE, although there is a lack of data on the ability of MLVA to subtype Canadian isolates of SE. Three subtyping methods, PFGE, MLVA and phage typing were compared for their discriminatory power when applied to three panels of Canadian SE isolates: Panel 1: 70 isolates representing the diversity of phage types (PTs) and PFGE subtypes within these PTs; Panel 2: 214 apparently unrelated SE isolates of the most common PTs; and Panel 3: 27 isolates from 10 groups of epidemiologically related strains.For Panel 2 isolates, four MLVA subtypes were shared among 74% of unrelated isolates and in Panel 3 isolates, one MLVA subtype accounted for 62% of the isolates. For all panels, combining results from PFGE, MLVA and PT gave the best discrimination, except in Panel 1, where the combination of PT and PFGE was equally as high, due to the selection criteria for this panel. However, none of these methods is sufficiently discriminatory alone for reliable outbreak detection or source attribution, and must be applied together to achieve sufficient discrimination for practical purposes. Even then, some large clusters were not differentiated adequately. More discriminatory methods are required for reliable subtyping of this genetically highly homogeneous serovar. This need will likely be met by whole genome sequence analysis given the recent promising reports and as more laboratories implement this tool for outbreak response and surveillance.

Reliable Entity Subtyping in Non-small Cell Lung Cancer by Matrix-assisted Laser Desorption/Ionization Imaging Mass Spectrometry on Formalin-fixed Paraffin-embedded Tissue Specimens

Histopathological subtyping of non-small cell lung cancer (NSCLC) into adenocarcinoma (ADC), and squamous cell carcinoma (SqCC) is of utmost relevance for treatment stratification. However, current immunohistochemistry (IHC) based typing approaches on biopsies are imperfect, therefore novel analytical methods for reliable subtyping are needed. We analyzed formalin-fixed paraffin-embedded tissue cores of NSCLC by Matrix-assisted laser desorption/ionization (MALDI) imaging on tissue microarrays to identify and validate discriminating MALDI imaging profiles for NSCLC subtyping. 110 ADC and 98 SqCC were used to train a Linear Discriminant Analysis (LDA) model. Results were validated on a separate set of 58 ADC and 60 SqCC. Selected differentially expressed proteins were identified by tandem mass spectrometry and validated by IHC. The LDA classification model incorporated 339 m/z values. In the validation cohort, in 117 cases (99.1%) MALDI classification on tissue cores was in accordance with the pathological diagnosis made on resection specimen. Overall, three cases in the combined cohorts were discordant, after reevaluation two were initially misclassified by pathology whereas one was classified incorrectly by MALDI. Identification of differentially expressed peptides detected well-known IHC discriminators (CK5, CK7), but also less well known differentially expressed proteins (CK15, HSP27). In conclusion, MALDI imaging on NSCLC tissue cores as small biopsy equivalents is capable to discriminate lung ADC and SqCC with a very high accuracy. In addition, replacing multislide IHC by an one-slide MALDI approach may also save tissue for subsequent predictive molecular testing. We therefore advocate to pursue routine diagnostic implementation strategies for MALDI imaging in solid tumor typing.

Abstract T P191: Using Clinical Trial Data to Generate Causative Classification System (CCS) Ischemic Stroke Phenotypes for the NINDS Stroke Genetics Network (SiGN)

Background: Recruitment and phenotyping constitute major costs in stroke genetics research. Clinical trial datasets that include biological samples provide an opportunity to leverage richly phenotyped cases for genomic research. However, the heterogeneity of stroke and methodological differences across studies present challenges. We developed and validated a systematic method of mapping data abstracted from a multicenter randomized clinical trial to the inputs of the CCS stroke subtyping system for the SiGN study. Methods: The SiGN Phenotype Committee and the Secondary Prevention of Small Subcortical Strokes (SPS3) research team systematically reviewed SPS3 case report forms (CRF) to identify key elements required to generate CCS subtyping, and drafted and refined mapping rules using a derivation set of 30 charts. The resultant algorithm was compared to manual entry of clinical information into the CCS in a test set of 30 randomly selected charts. This identified problems due to multiple versions of CRFs (up to 7 versions) and prompted revision of the mapping rules. We assessed the revised algorithm in an independent test set. Results: None of the subtype classifications agreed in the initial testing prompting revision to allow capture information from different versions of the CRFs. The revised mapping algorithm applied to the same test set performed well; 29/30 agreed - 20 small artery occlusion (SAO) evident, 4 SAO possible, 2 SAO probable and 3 supra-aortic large artery atherosclerosis evident). The one chart that disagreed was classified as SAO evident by manual entry but as undetermined by the data mapping rules. In the 2nd independent test set, 30/30 agreed: 22 SAO evident, 4 SAO possible, 3 supra-aortic large artery atherosclerosis evident and 1 undetermined unknown - incomplete evaluation. Conclusions: We created mapping rules using CRFs from a clinical trial to allow reliable subtype classification using the CCS. Issues related to multiple iterations of CRFs presented challenges. Highly phenotyped stroke cases from clinical trials represent a cost-effective opportunity for genomic research.

Problematic involvement in online games: A cluster analytic approach

Playing online games can become problematic and engender adverse consequences. Several psychological factors have been shown to influence the development and the maintenance of this problematic behavior, including impulsivity traits, motives to play (immersion, achievement, social affiliation), and self-esteem. The aim of the current study is to determine whether reliable subtypes of problematic online gamers can be identified. A sample of 1057 online gamers was recruited. Validated questionnaires were used to measure established psychological risk factors (impulsivity, motives to play, self-esteem) and potential consequences of playing (addiction symptoms, positive and negative affect). Actual in-game behaviors were also monitored. Five reliable clusters of gamers were identified (three problematic and two nonproblematic clusters). Cluster comparison revealed that the psychological factors considered are differentially involved in problematic online gaming. At the theoretical level, the results emphasized that problem online gaming depends on a wide range of psychological factors. At the clinical level, the diversity of psychological profiles shown supports the development of personalized (custom-made) interventions targeting specific psychological mechanisms. Overall, our findings suggest that conceptualizing the problematic use of massively multiplayer online role-playing games as “behavioral addiction” is too restrictive and might result in the simplification of heterogeneous and multi-determined problematic behaviors.