Subtype Classification System Research Articles

Clinical Implication of Maumgyeol Basic Biotypes-Electroencephalography- and Photoplethysmogram-Based Bwave State Inventory.

The development of individual subtypes based on biomarkers offers a cost-effective and timely avenue to comprehending individual differences pertaining to mental health, independent from individuals' subjective insights. Incorporating 2-channel electroencephalography (EEG) and photoplethysmogram (PPG), we sought to establish a subtype classification system with clinical relevance. One hundred healthy participants and 99 patients with psychiatric disorders were recruited. Classification thresholds were determined using the EEG and PPG data from 2,278 individuals without mental disorders, serving to classify subtypes in our sample of 199 participants. Multivariate analysis of variance was applied to examine psychological distinctions among these subtypes. K-means clustering was employed to verify the classification system. The distribution of subtypes differed between healthy participants and those with psychiatric disorders. Cognitive abilities were contingent upon brain subtypes, while mind subtypes exhibited significant differences in symptom severity, overall health, and cognitive stress. K-means clustering revealed that the results of our theory-based classification and data-driven classification are comparable. The synergistic assessment of both brain and mind subtypes was also explored. Our subtype classification system offers a concise means to access individuals' mental health. The utilization of EEG and PPG signals for subtype classification offers potential for the future of digital mental healthcare.

TOAST stroke subtype classification in clinical practice: implications for the Get With The Guidelines-Stroke nationwide registry.

The TOAST (Trial of ORG 10172 in Acute Stroke Treatment) is the most commonly used ischemic stroke subtype classification system worldwide and a required field in the US National Get With The Guidelines-Stroke (GWTG-Stroke) registry. However, stroke diagnostics have advanced substantially since the TOAST classification was designed 30 years ago, potentially making it difficult to apply reliably. In this prospective diagnostic accuracy study, we analyzed consecutive ischemic stroke patients admitted to a Comprehensive Stroke Center between July-October 2021. Clinical practice TOAST classification diagnoses rendered by the stroke team in the electronic medical record (EMR) at discharge were retrieved from GWTG-Stroke registry and compared to a reference ("gold") standard diagnosis derived from agreement between two expert raters after review of the EMR and patient imaging. Among 49 patients; age was 72.3 years (±12.1), 53% female, and presenting NIHSS median 3 (IQR 1-11). Work-up included: brain imaging in 100%; cardiac rhythm assessment in 100%; cervical/cerebral vessel imaging in 98%; TTE ± TEE in 92%; and TCD emboli evaluation in 51%. Reference standard diagnoses were: LAA-6%, SVD-14%, CE-39%, OTH-10%, UND-M (more than one cause)-20%, and UND-C (cryptogenic)-10%. GWTG-Stroke TOAST diagnoses agreed with reference standard diagnoses in 30/49 (61%). Among the 6 subtype diagnoses, specificity was generally high (84.8%-97.7%), but sensitivity suboptimal for LAA (33%), OTH (60%), UND-M (10%), and UND-C (20%). Positive predictive value was suboptimal for 5 of the 6 subtypes: LAA (13%), SVD (58%), OTH (75%), UND-M (50%), and UND-C (50%). Clinical practice TOAST classification subtype diagnoses entered into the GWTG-Stroke registry were accurate in only 61% of patients, a performance rate that, if similarly present at other centers, would hamper the ability of the national registry to provide dependable insights into subtype-related care. Development of an updated ischemic stroke subtype classification system, with algorithmic logic embedded in electronic medical records, is desirable.

Molecular Subtype Classification of Postmenopausal Osteoporosis and Immune Infiltration Microenvironment Based on Bioinformatics Analysis of Osteoclast-Regulatory Genes.

Osteoporosis is common in postmenopausal women but is often asymptomatic until a fracture occurs, highlighting the importance of early screening and preventive interventions. This study aimed to develop molecular subtype risk stratification of postmenopausal osteoporosis and analyze the immune infiltration microenvironment. Microarray data for osteoporosis were downloaded and analyzed. Logistic and least absolute shrinkage and selection operator (LASSO) regression analyses were used to construct the molecular risk model. Circulating blood samples were collected from 10 enrolled participants to validate the key differentially expressed genes, and consistent clustering based on the expression profiles of candidate genes was performed to obtain molecular subtypes. Three key genes, CTNNB1, MITF, and TNFSF11, were obtained as variables and used to construct the risk model. External experimental validation showed substantial differences in the three key genes between patients with osteoporosis and the controls (p < 0.05). Three subtypes were obtained based on dimensionality reduction clustering results. Cluster 3 had significantly more patients with low bone mineral density (BMD), whereas Cluster 2 had significantly more patients with high BMD (p < 0.05). This study introduced a novel molecular risk model and subtype classification system, which is an evidence-based screening strategy that will guide the active prevention, early diagnosis, and treatment of osteoporosis in high-risk postmenopausal women.

Super-Resolution Approach to Enhance Bone Marrow Trephine Image in the Classification of Classical Myeloproliferative Neoplasms

Many diseases require histopathology images to characterise biological components or study cell and tissue architectures. The histopathology images are also essential in supporting disease classification, including myeloproliferative neoplasms (MPN). Despite significant developments to improve the diagnostic tools, morphological assessment from histopathology images obtained by bone marrow trephine (BMT) remains crucial to confirm MPN subtypes. However, the assessment outcome is challenging due to subjective characteristics that are hard to replicate due to its inter-observer variability. Apart from that, image processing may reduce the quality of the BMT images and affect the diagnosis result. This study has developed a classification system for classical MPN subtypes: polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (MF). It was done by reconstructing low-resolution images of BMT using a super-resolution approach to address the issue. Identified low-resolution images from calculating Laplacian variance were reconstructed using a super-resolution convolution neural network (SRCNN) to transform into rich information of high-resolution images. Original BMT images and reconstructed BMT images using the SRCNN dataset were fed into a CNN classifier, and the classifier’s output for both datasets was compared accordingly. Based on the result, the dataset consisting of the reconstructed images showed better output with 92% accuracy, while the control images gave 88% accuracy. In conclusion, the high quality of histopathology images substantially impacts disease process classification, and the reconstruction of low-resolution images has improved the classification output.

Consensus molecular subtypes and orphan non-coding RNAs in colorectal cancer.

e15651 Background: Colorectal cancer (CRC) is a highly heterogeneous disease with variable response to different treatment strategies. The consensus molecular subtype (CMS) classification system has been proposed as a gene expression-based framework aimed at capturing the genetic and molecular heterogeneity in CRCs and potentially stratifying patients into clinically relevant subgroups for treatment selection. We previously demonstrated that orphan non-coding RNAs (oncRNAs), a novel class of cancer enriched small non-coding RNAs (smRNA), exhibit cancer-specific expression. We also showed that oncRNAs can be used in a liquid biopsy assay for accurate early-stage detection of CRCs. We hypothesize that oncRNAs may be informative of CRC subtype-specific transcriptional signatures as defined by the CMS groups. Methods: To assess the effectiveness of oncRNAs for categorization of CMS group, we examined smRNA-seq data from The Cancer Genome Atlas (TCGA) for colon and rectal adenocarcinoma tumors ( n= 504), consisting of 77 CMS1, 216 CMS2, 71 CMS3, and 140 CMS4 tumors. CRC-specific oncRNA expression profiles were generated for all tumors in the TCGA cohort. We then trained a machine learning model on oncRNA expression profiles to predict CMS groups using a 5-fold cross validation setup. For our validation cohort, we applied our model to an independent CRC cohort ( n= 348) consisting of 56 CMS1, 101 CMS2, 59 CMS3, and 132 CMS4 CRC samples. Final CMS predictions for the validation cohort were made by averaging across the five training models. Results: Within TCGA testing folds, the model micro-averaged ROC-AUC for CMS classifications were 0.879 (95% CI: 0.86–0.90). In the validation cohort, micro-averaged AUC remained high at 0.915 (0.90–0.93). Sensitivities and AUCs for each subtype are provided in Table 1. The highest model-predicted likelihood for each CMS subtype had an accuracy of 73.6% (68.6%–78.1%), and the two most probable predictions had an accuracy of 91.1% (87.6%–93.9%). Conclusions: This study demonstrates the utility of oncRNA expression profiling to predict CMS for colorectal cancer. The generalizability of our machine learning model suggests that oncRNAs are informative of the biological differences across distinct CMS groups and may be used as a proxy for gene expression signatures to stratify colorectal cancers. Because smRNAs are more likely to be secreted into blood, we posit that an oncRNA-based liquid biopsy may open new opportunities to monitor patients and track changes in their tumor biology in real-time throughout their course of disease and treatment. [Table: see text]

Updated Parkinson's disease motor subtypes classification and correlation to CSF HVA and 5-HIAA levels (P3-11.005)

<h3>Objective:</h3> To update a motor subtypes classification using the MDS-UPDRS and determine if CSF neurotransmitter profiles (HVA and 5-HIAA) are different between MDS-UPDRS subtypes in a cohort from the Parkinson9s Progression Marker Initiative (PPMI). <h3>Background:</h3> Schiess <i>et al</i> designed a UPDRS motor classification of Parkinson9s Disease (PD) that has been widely used, but in the last decade, the field has moved towards the use of the MDS-UPDRS. <h3>Design/Methods:</h3> UPDRS and MDS-UPDRS scores were collected for 20 PD patients. Akinetic-rigid (AR), Tremor-dominant (TD), and Mixed (MX) subtypes were calculated using a UPDRS formula, and a new ratio was developed for MDS-UPDRS. This new formula was subsequently applied to 95 PD patients from the PPMI dataset, and subtyping was correlated to neurotransmitter levels. Data were analyzed using receiver operating characteristic models and ANOVA. <h3>Results:</h3> Compared to previous UPDRS classifications, the new MDS-UPDRS AR/TD ratios produced significantly different areas under the curves (AUC). The optimal sensitivity and specificity cutoff scores were ≥0.82 for TD, ≤0.71 for AR classifications and Mixed &gt;0.71 and &lt;0.82. Analysis of variance showed significant differences in 5-HIAA levels between the HC and AR group. Logistic models utilizing neurotransmitter levels and MDS-UPDRS motor scores showed significant AUCs for MDS-UPDRS classifications. <h3>Conclusions:</h3> This MDS-UPDRS motor subtype classification system provides a method to transition from the original UPDRS to the newer MDS-UPDRS and offers a reliable and quantifiable tool for monitoring disease progression that is supported by neurotransmitter levels. <b>Disclosure:</b> Dr. Suescun has nothing to disclose. Dr. Adams has stock in Medtronic. Dr. Block has nothing to disclose. Dr. Tharp has nothing to disclose. The institution of Prof. Ellmore has received research support from NIMH. Dr. Schiess has nothing to disclose.

Abstract TMP49: Accuracy Of Toast Subtype Classification In Clinical Practice: Implications For The Get With The Guidelines National Stroke Registry

Background: TOAST is the most commonly used ischemic stroke subtype classification system worldwide and a required field in the US National GWTG-Stroke registry. However, stroke diagnostics have advanced substantially since TOAST was designed 30 years ago, potentially making it difficult to now apply reliably. Methods: We analyzed consecutive ischemic stroke patients admitted to a CSC between July-Oct 2021. Clinical practice TOAST diagnoses rendered by the stroke team in the EMR at discharge were retrieved from GWTG-Stroke registry and compared to a reference (“gold”) standard diagnosis derived from agreement between 2 expert raters after review of the EMR and patient imaging. Results: Among 49 patients, age was 72.3y (±12.1), 53% female, and presenting NIHSS median 3 (IQR 1-11). Work-up included: brain imaging in 100%; cardiac rhythm assessment in 100%; cervical/cerebral vessel imaging in 98%; TTE±TEE in 92%; and TCD emboli eval in 51%. Reference standard diagnoses were: LAA-6%, SVD-14%, CE-41%, OTH-8%, UND-more than one cause-18%, and UND-cryptogenic-12%. The Figure shows a bubble plot of congruence between EMR/GWTG-Stroke and reference standard diagnoses. GWTG-Stroke TOAST diagnoses agreed with reference standard diagnoses in 29/49 (59%). Among the 6 subtype diagnoses singly, specificity was generally high (83%-100%), but sensitivity suboptimal for LAA (33%), OTH (75%), UND-MT1 (0%), and UND-CRYPT (17%). Positive predictive value was suboptimal for all: LAA (13%), SVD (58%), CE (77%), OTH (75%), SVD OTH (75%), UND-MT1 (0%), and UND-CRYPT (33%). Conclusion: Clinical practice subtype diagnoses using TOAST and entered into GWTG-Stroke were accurate in only 59% of patients, hampering the ability of the national registry to provide dependable insights into subtype-related aspects of care. Development of an updated ischemic stroke subtype classification system, with algorithmic logic directly embedded in electronic medical records, is desirable.

Molecular profiling identifies distinct subtypes across TP53 mutant tumors.

Tumor protein 53 mutation (TP53mut) is one of the most important driver events facilitating tumorigenesis, which could induce a series of chain reactions to promote tumor malignant transformation. However, the malignancy progression patterns under TP53 mutation remain less known. Clarifying the molecular landscapes of TP53mut tumors will help us understand the process of tumor development and aid precise treatment. Here, we distilled genetic and epigenetic features altered in TP53mut cancers for cluster-of-clusters analysis. Using integrated classification, we derived 5 different subtypes of TP53mut patients. These subtypes have distinct features in genomic alteration, clinical relevance, microenvironment dysregulation, and potential therapeutics. Among the 5 subtypes, COCA3 was identified as the subtype with worst prognosis, causing an immunosuppressive microenvironment and immunotherapeutic resistance. Further drug efficacy research highlighted olaparib as the most promising therapeutic agents for COCA3 tumors. Importantly, the therapeutic efficacy of olaparib in COCA3 and immunotherapy in non-COCA3 tumors was validated via in vivo experimentation. Our study explored the important molecular events and developed a subtype classification system with distinct targeted therapy strategies for different subtypes of TP53mut tumors. These multiomics classification systems provide a valuable resource that significantly expands the knowledge of TP53mut tumors and may eventually benefit in clinical practice.

A Causal Classification System for Intracerebral Hemorrhage Subtypes.

Determining the underlying causes of intracerebral hemorrhage (ICH) is of major importance, because risk factors, prognosis, and management differ by ICH subtype. We developed a new causal CLASsification system for ICH Subtypes, termed CLAS-ICH, based on recent advances in neuroimaging. CLAS-ICH defines 5 ICH subtypes: arteriolosclerosis, cerebral amyloid angiopathy, mixed small vessel disease (SVD), other rare forms of SVD (genetic SVD and others), and secondary causes (macrovascular causes, tumor, and other rare causes). Every patient is scored in each category according to the level of diagnostic evidence: (1) well-defined ICH subtype; (2) possible underlying disease; and (0) no evidence of the disease. We evaluated CLAS-ICH in a derivation cohort of 113 patients with ICH from Massachusetts General Hospital, Boston, USA, and in a derivation cohort of 203 patients from Inselspital, Bern, Switzerland. In the derivation cohort, a well-defined ICH subtype could be identified in 74 (65.5%) patients, including 24 (21.2%) with arteriolosclerosis, 23 (20.4%) with cerebral amyloid angiopathy, 18 (15.9%) with mixed SVD, and 9 (8.0%) with a secondary cause. One or more possible causes were identified in 42 (37.2%) patients. Interobserver agreement was excellent for each category (kappa value ranging from 0.86 to 1.00). Despite substantial differences in imaging modalities, we obtained similar results in the validation cohort. CLAS-ICH is a simple and reliable classification system for ICH subtyping, that captures overlap between causes and the level of diagnostic evidence. CLAS-ICH may guide clinicians to identify ICH causes, and improve ICH classification in multicenter studies. ANN NEUROL 2023;93:16-28.

Development and Evaluation of a Novel Deep-Learning-Based Framework for the Classification of Renal Histopathology Images.

Kidney cancer has several types, with renal cell carcinoma (RCC) being the most prevalent and severe type, accounting for more than 85% of adult patients. The manual analysis of whole slide images (WSI) of renal tissues is the primary tool for RCC diagnosis and prognosis. However, the manual identification of RCC is time-consuming and prone to inter-subject variability. In this paper, we aim to distinguish between benign tissue and malignant RCC tumors and identify the tumor subtypes to support medical therapy management. We propose a novel multiscale weakly-supervised deep learning approach for RCC subtyping. Our system starts by applying the RGB-histogram specification stain normalization on the whole slide images to eliminate the effect of the color variations on the system performance. Then, we follow the multiple instance learning approach by dividing the input data into multiple overlapping patches to maintain the tissue connectivity. Finally, we train three multiscale convolutional neural networks (CNNs) and apply decision fusion to their predicted results to obtain the final classification decision. Our dataset comprises four classes of renal tissues: non-RCC renal parenchyma, non-RCC fat tissues, clear cell RCC (ccRCC), and clear cell papillary RCC (ccpRCC). The developed system demonstrates a high classification accuracy and sensitivity on the RCC biopsy samples at the slide level. Following a leave-one-subject-out cross-validation approach, the developed RCC subtype classification system achieves an overall classification accuracy of 93.0% ± 4.9%, a sensitivity of 91.3% ± 10.7%, and a high classification specificity of 95.6% ± 5.2%, in distinguishing ccRCC from ccpRCC or non-RCC tissues. Furthermore, our method outperformed the state-of-the-art Resnet-50 model.

A quantitative polymerase chain reaction based method for molecular subtype classification of urinary bladder cancer-Stromal gene expressions show higher prognostic values than intrinsic tumor genes.

Transcriptome-based molecular subtypes of muscle-invasive bladder cancer (MIBC) have been shown to be both prognostic and predictive, but are not used in routine clinical practice. We aimed to develop a feasible, reverse transcription quantitative polymerase chain reaction (RT-qPCR)-based method for molecular subtyping. First, we defined a 68-gene set covering tumor intrinsic (luminal, basal, squamous, neuronal, epithelial-to-mesenchymal, in situ carcinoma) and stromal (immune, extracellular matrix, p53-like) signatures. Then, classifier methods with this 68-gene panel were developed in silico and validated on public data sets with available subtype class information (MD Anderson [MDA], The Cancer Genome Atlas [TCGA], Lund, Consensus). Finally, expression of the selected 68 genes was determined in 104 frozen tissue samples of our MIBC cohort by RT-qPCR using the TaqMan Array Card platform and samples were classified by our newly developed classifiers. The prognostic value of each subtype classification system and molecular signature scores were assessed. We found that the reduced marker set combined with the developed classifiers were able to reproduce the TCGA II, MDA, Lund and Consensus subtype classification systems with an overlap of 79%, 76%, 69% and 64%, respectively. Importantly, we could successfully classify 96% (100/104) of our MIBC samples by using RT-qPCR. Neuronal and luminal subtypes and low stromal gene expressions were associated with poor survival. In conclusion, we developed a robust and feasible method for the molecular subtyping according to the TCGA II, MDA, Lund and Consensus classifications. Our results suggest that stromal signatures have a superior prognostic value compared to tumor intrinsic signatures and therefore underline the importance of tumor-stroma interaction during the progression of MIBC.

Molecular Subtyping Combined with Biological Pathway Analyses to Study Regorafenib Response in Clinically Relevant Mouse Models of Colorectal Cancer.

Regorafenib (REG) is approved for the treatment of metastatic colorectal cancer, but has modest survival benefit and associated toxicities. Robust predictive/early response biomarkers to aid patient stratification are outstanding. We have exploited biological pathway analyses in a patient-derived xenograft (PDX) trial to study REG response mechanisms and elucidate putative biomarkers. Molecularly subtyped PDXs were annotated for REG response. Subtyping was based on gene expression (CMS, consensus molecular subtype) and copy-number alteration (CNA). Baseline tumor vascularization, apoptosis, and proliferation signatures were studied to identify predictive biomarkers within subtypes. Phospho-proteomic analysis was used to identify novel classifiers. Supervised RNA sequencing analysis was performed on PDXs that progressed, or did not progress, following REG treatment. Improved REG response was observed in CMS4, although intra-subtype response was variable. Tumor vascularity did not correlate with outcome. In CMS4 tumors, reduced proliferation and higher sensitivity to apoptosis at baseline correlated with response. Reverse phase protein array (RPPA) analysis revealed 4 phospho-proteomic clusters, one of which was enriched with non-progressor models. A classification decision tree trained on RPPA- and CMS-based assignments discriminated non-progressors from progressors with 92% overall accuracy (97% sensitivity, 67% specificity). Supervised RNA sequencing revealed that higher basal EPHA2 expression is associated with REG resistance. Subtype classification systems represent canonical "termini a quo" (starting points) to support REG biomarker identification, and provide a platform to identify resistance mechanisms and novel contexts of vulnerability. Incorporating functional characterization of biological systems may optimize the biomarker identification process for multitargeted kinase inhibitors.

Consistency and Stability of Motor Subtype Classifications in Patients With de novo Parkinson's Disease.

ObjectivePatients with Parkinson’s disease (PD) are commonly classified into subtypes based on motor symptoms. The aims of the present study were to determine the consistency between PD motor subtypes, to assess the stability of PD motor subtypes over time, and to explore the variables influencing PD motor subtype stability.MethodsThis study was part of a longitudinal study of de novo PD patients at a single center. Based on three different motor subtype classification systems proposed by Jankovic, Schiess, and Kang, patients were respectively categorized as tremor-dominant/indeterminate/postural instability and gait difficulty (TD/indeterminate/PIGD), TDS/mixedS/akinetic-rigidS (ARS), or TDK/mixedK/ARK at baseline evaluation and then re-assessed 1 month later. Demographic and clinical characteristics were recorded at each evaluation. The consistency between subtypes at baseline evaluation was assessed using Cohen’s kappa coefficient (κ). Additional variables were compared between PD subtype groups using the two-sample t-test, Mann–Whitney U-test or Chi-squared test.ResultsOf 283 newly diagnosed, untreated PD patients, 79 were followed up at 1 month. There was fair agreement between the Jankovic, Schiess, and Kang classification systems (κS = 0.383 ± 0.044, κK = 0.360 ± 0.042, κSK = 0.368 ± 0.038). Among the three classification systems, the Schiess classification was the most stable and the Jankovic classification was the most unstable. The non-motor symptoms questionnaire (NMSQuest) scores differed significantly between PD patients with stable and unstable subtypes based on the Jankovic classification (p = 0.008), and patients with a consistent subtype had more severe NMSQuest scores than patients with an inconsistent subtype.ConclusionFair consistency was observed between the Jankovic, Schiess, and Kang classification systems. For the first time, non-motor symptoms (NMSs) scores were found to influence the stability of the TD/indeterminate/PIGD classification. Our findings support combining NMSs with motor symptoms to increase the effectiveness of PD subtypes.

Nuclear size of circulating tumor cells in advanced prostate cancer to reveal a potential biomarker for clinical outcomes and androgen receptor indifference.

167 Background: Circulating tumor cells (CTCs) have arisen as contemporary noninvasive prognostic biomarkers for prostate cancer (PCa). Previously, a subgroup of PCa CTCs, with particularly small nuclei ( &lt; 8.5 μm), were found to be correlated with the presence of visceral metastases. This subgroup was named very-small-nuclear CTCs (vsnCTCs). We hypothesized vsnCTCs as a putative biomarker of a lethal subtype associated with androgen receptor (AR) indifference and nuclear shape instability in metastatic castration resistant PCa (mCRPC). Methods: CTCs in blood from 76 patients with mCRPC were analyzed using NanoVelcro CTC assay for CTC nuclear size measurement and CTC RNA profiling of AR-indifferent pathways. Overall survival (OS) and progression free survival (PFS) of androgen receptor signaling inhibitor (ARSI), taxanes and other therapy were correlated with CTC nuclear size using Kaplan-Meier analysis and Cox proportional hazards model. Emerin fluorescence intensity and localization from patients with and without vsnCTC were compared. RNA profiles of CTCs were scored using Prostate Cancer Subtype (PCS) classification system. The CTC-PCS scores from patients with and without vsnCTC were compared using Mann-Whitney test. To investigate the underlying biology of vsnCTC phenotype, the nuclear size, the nuclear sizes of ARSI-resistant and lineage plasticity PCa cell lines were measured and correlated with the expression levels of RNA related to ARSI-indifferent pathways and nuclear envelope protein Emerin. Results: Patients with vsnCTC (i.e., vsnCTC+) had significantly shortened OS and PFS compared with patients without vsnCTC (i.e., vsnCTC-). The median OS was 34 (vsnCTC+, n = 49) vs. 149 (vsnCTC-, n = 27) weeks (HR = 2.6 with 95% CI 1.5 to 4.5, p &lt; 0.001). The median PFS was 12 (vsnCTC+, n = 32) vs. 26 (vsnCTC-, n = 18) weeks (HR = 2.2 with 95% CI 1.3 to 4.0, p = 0.004). CTC nuclear sizes were significantly smaller in patients with prior ARSI therapy. CTC-RNA analysis revealed that vsnCTC+ patients had a significant higher CTC-PCS1 Z score(n = 19) compared with vsnCTC- patients(n = 26)(p = 0.01). In the cell line models, nuclear sizes were significantly smaller in cell lines with ARSI-resistance(p = 0.002) and lineage plasticity (p = 0.006). Emerin expression is significantly lower in vsnCTC+ patients(p = 0.009) and ARSI-resistant cell lines(p = 0.03). Conclusions: This study casts light on the importance of the vsnCTC in patients with mCRPC, as vsnCTC+ patients represented a group at risk for faster clinical progression who are at the highest risk for mortality. This has potential importance in optimizing therapeutic choices. We posit that the vsnCTC represents a new hallmark of an aggressive subtype of mCRPC and is related to the cellular mechanism of AR-indifference and Emerin dysregulation, which promotes lethal progression of metastatic PCa.

An Evaluation of the Performance of Current Morphea Subtype Classifications

Numerous classification systems for morphea subtypes exist, but none have been systematically evaluated for their ability to categorize patients with morphea into demographically and clinically coherent groups. Although some subtypes, such as linear morphea, are present across all the classification schemes, others list unique subtypes. This creates confusion among investigators and practitioners and impairs accurate categorization of patients for study and clinical evaluation. To evaluate how frequently the commonly used morphea classification systems categorize patients with morphea into clinically relevant subtypes using cross-sectional analysis of 2 large patient cohorts. This cross-sectional study comprised 944 adults and children from 2 prospective cohorts-the Morphea in Adults and Children at the University of Texas Southwestern Medical Center (Dallas, Texas), which enrolled participants from July 20, 2007, to September 21, 2018, and the National Registry for Childhood-Onset Scleroderma at the University of Pittsburgh (Pittsburgh, Pennsylvania), which enrolled participants from October 23, 2002, to November 13, 2018. Patient demographic characteristics, morphea subtype, quality-of-life measures, disease activity, and damage as measured by Localized Scleroderma Cutaneous Assessment Tool scores during initial visits. A total of 944 participants (444 patients with adult-onset morphea and 500 patients with pediatric-onset morphea; 741 female participants [78%]; median age at onset, 16 years [interquartile range, 8-44 years]) were included in this study. Most participants were White (723 [77%]) and had the linear (474 [50%]) or generalized subtype of morphea (244 [26%]). With the use of the previously published Padua criteria, most patients were classified to have linear morphea (474 [50%]), followed by generalized morphea (244 [26%]), plaque morphea (141 [15%]), mixed morphea (38 [4%]), and pansclerotic morphea (3 [0.3%]). Overall, the Padua criteria successfully classified 900 patients (95%) in comparison with the Peterson criteria (533 [56%]) and the European Dermatology Forum classification (487 [52%]). In this cross-sectional study of morphea subtype classification systems, the Padua criteria performed best in classifying patients into subgroups with cohesive demographic and clinical features, supporting its widespread use. However, they have ambiguities that might lead to misclassification, particularly in terms of generalized and pansclerotic morphea and descriptors such as morphea profunda. Consensus-based approaches are needed to address these ambiguities and develop a unified classification scheme.

A retrospective analysis of usefulness of impulse oscillometry system in the treatment of asthma

BackgroundBronchial asthma (BA) has different phenotypes, and it requires a clinically effective subtype classification system. The impulse oscillometry system (IOS) is an emerging technique device used in respiratory functional tests. However, its efficacy has not been validated. Therefore, this study aimed to assess the relationship between BA and the IOS parameters, and the difference in the therapeutic effects of inhaled corticosteroids (ICSs) among the subtype classifications was evaluated using the IOS.MethodsOf the 245 patients with bronchial asthma who were screened, 108 were enrolled in this study. These patients were divided based on three subtypes according to the IOS result as follows: central predominant type (n = 34), peripheral predominant type (n = 58), and resistless type (n = 16). Then, the following ICSs were randomly prescribed in daily medical care: coarse-particle ICS (fluticasone propionate [FP]), fine-particle ICS (mometasone furoate [MF]), and moderate-particle ICS (budesonide [BUD]). The treatment effects were assessed using the Asthma Health Questionnaire (AHQ) and the Asthma Control Test (ACT) and were compared among the three subtypes.ResultsIn the central predominant type, the AHQ score of the MF group was significantly higher than that of the FP group (15.4 vs. 3.6, p < 0.01) and the BUD group (15.4 vs. 8.8, p < 0.05); the ACT score of the FP group was significantly higher than that of the MF and BUD groups (24.3 vs. 21.7, 22.3, respectively, p < 0.05) at 4 weeks after treatment. In the peripheral predominant type, the AHQ score of the FP group was significantly higher than that of the MF group (14.1 vs. 3.4, p < 0.05); the ACT score of the FP group was lower than that of the MF and BUD groups (22.8 vs. 24.6, 24.4, respectively, p < 0.01) at 4 weeks after treatment.ConclusionsAn association was observed between IOS subtype classification and ICS particle size in terms of therapeutic efficacy in BA. This result indicates that the IOS could be an effective tool in the selection of ICS and the evaluation of the BA phenotype.

Investigation of colorectal cancer in accordance with consensus molecular subtype classification.

The classification of colorectal cancer (CRC) plays a pivotal role in predicting a patient's prognosis and determining treatment strategies. The consensus molecular subtype (CMS) classification system was constructed by analyzing genetic information from 18 CRC data sets, containing 4151 CRC samples. CRC was classified into four subtypes with distinct molecular and biological characteristics: CMS1 (microsatellite instability immune), CMS2 (canonical), CMS3 (metabolic), and CMS4 (mesenchymal). Since their designation in 2015, these classifications have been applied to basic and translational research of CRC, with the hope that understanding these subsets will influence a clinician's approach to therapeutic treatment and improve clinical outcomes. We reviewed CRC investigations in accordance with CMSs published in the last 5 years to further explore the clinical significance of these subtypes and identify underlying trends that may direct relevant future research. We determined that CMSs linked common features of CRC cell lines and PDX models in various studies. Furthermore, associations between prognosis and clinicopathological findings, including pathological grade and the stage of carcinogenesis, tumor budding, and tumor location, were correlated with CMS classification. Novel prognostic factors were identified, and the relationship between chemotherapeutic drug resistance and CMS has been fortified by our compilation of research; thus, indicating that this review provides advanced insight into clinical questions and treatment strategies for CRC.

Normal myeloid progenitor cell subset-associated gene signatures for acute myeloid leukaemia subtyping with prognostic impact.

Acute myeloid leukaemia (AML) is characterised by phenotypic heterogeneity, which we hypothesise is a consequence of deregulated differentiation with transcriptional reminiscence of the normal compartment or cell-of-origin. Here, we propose a classification system based on normal myeloid progenitor cell subset-associated gene signatures (MAGS) for individual assignments of AML subtypes. We generated a MAGS classifier including the progenitor compartments CD34+/CD38- for haematopoietic stem cells (HSCs), CD34+/CD38+/CD45RA- for megakaryocyte-erythroid progenitors (MEPs), and CD34+/CD38+/CD45RA+ for granulocytic-monocytic progenitors (GMPs) using regularised multinomial regression with three discrete outcomes and an elastic net penalty. The regularisation parameters were chosen by cross-validation, and MAGS assignment accuracy was validated in an independent data set (N = 38; accuracy = 0.79) of sorted normal myeloid subpopulations. The prognostic value of MAGS assignment was studied in two clinical cohorts (TCGA: N = 171; GSE6891: N = 520) and had a significant prognostic impact. Furthermore, multivariate Cox regression analysis using the MAGS subtype, FAB subtype, cytogenetics, molecular genetics, and age as explanatory variables showed independent prognostic value. Molecular characterisation of subtypes by differential gene expression analysis, gene set enrichment analysis, and mutation patterns indicated reduced proliferation and overrepresentation of RUNX1 and IDH2 mutations in the HSC subtype; increased proliferation and overrepresentation of CEBPA mutations in the MEP subtype; and innate immune activation and overrepresentation of WT1 mutations in the GMP subtype. We present a differentiation-dependent classification system for AML subtypes with distinct pathogenetic and prognostic importance that can help identify candidates poorly responding to combination chemotherapy and potentially guide alternative treatments.

Representation of concussion subtypes in common postconcussion symptom-rating scales.

Aim:Postconcussion symptom-rating scales are frequently used concussion assessment tools that do not align directly with new expert, consensus-based concussion subtype classification systems. This may result in delays in concussion diagnosis, subspecialty referral and rehabilitative strategies.Objective:To determine the representation of subtype-directed symptomatology in common postconcussion symptom-rating scales.Methods:Literature review and expert consensus were used to compile commonly used concussion symptom-rating scales. Statistics were generated to describe the degree of representation of the consensus symptom set.Results:The percentage of symptoms representing each subtype/associated condition is low overall (15–26%). The ocular-motor (11%) and vestibular subtypes (19%) and cervical strain (5%)-associated condition were the most under-represented and also had the greatest unmet needs.Conclusion:Concussion subtypes do not have equal representation on commonly used concussion symptom-rating scales. There is a need for a subtype-directed symptom assessment to allow for increased accuracy of diagnosis and to guide management.

Motor phenotype classification in moderate to advanced PD in BioFIND study

BackgroundThree motor phenotypes have been described in PD: postural instability and gait difficulty (PIGD) dominant, tremor-dominant (TD), and indeterminate (IND) subtype. These phenotypes have been associated with different cognitive trajectories, motor outcomes, and biomarkers profiles. However, whether motor subtype classifications change with treatment and disease progression is not well established. MethodsTo evaluate motor subtype ratio changes, we used the chi-square test for the off and on state motor subtypes for 115 PD participants in the BioFIND study and used repeated-measures analyses to evaluate longitudinal changes in 162 PD participants with five-year follow-up in the PPMI study. ResultsPIGD and TD subtypes in moderate to advanced PD participants change with dopaminergic agents. For those who shifted subtypes, improvement in tremor accounted for the transition of 15 (25.4%) TD participants, while the lack of tremor improvement along with minimal changes in PIGD score resulted in changes for eight (19.0%) PIGD individuals. Analyses of PPMI data revealed that all three subgroups had a significant decrease in subtype ratio with disease progression and a significant decline in subtype ratio occurred only in the TD subgroup with dopaminergic agents. The impact of dopaminergic medication effect on subtype shift for each visit was also more notable with disease advancement. ConclusionsMotor subtypes are not fixed but change with progression of the disease and with treatment. Improvement in tremor was the main contributor to motor phenotype transitions in the BioFIND cohort. A more stable classification system for subtypes based on underlying biological differences is desirable.