Identification Of Subtypes Research Articles

Identification of a novel subtype of SPP1 + macrophages expressing SIRPα: implications for tumor immune evasion and treatment response prediction

BackgroundSPP1 + macrophages are among the major phagocytic cells, yet promoting tumor immune evasion and predicting unfavorable prognosis, in various cancer types. Meanwhile, the predictive value of the abundance of SPP1 + macrophages in patients receiving immunotherapy remains debatable, indicating the potential existence of subtypes of SPP1 + macrophages with diverse biological functions.MethodsThe single cell RNA sequencing data of myeloid cells integrated from several cancers including esophageal squamous cell carcinoma was analyzed for characterizing the function and cellular interactions of SPP1 + macrophages expressing SIRPα. Multiplexed immunohistochemistry was used to quantify the quantity and spatial distribution of SPP1 + macrophages expressing SIRPα. Kaplan–Meier method was used for survival analysis. In vitro and in vivo studies investigating the function of SPP1 + macrophages were performed.ResultsSPP1 + macrophages possessed a high phagocytic signature and could engulf more tumor cells in vitro and in vivo. SIRPα expression could represent the phagocytic activity of SPP1 + macrophages and delineated subsets of SPP1 + macrophages with different functions. SPP1 + SIRPα + macrophages showed close spatial distance to tumor cells and positively correlated with PD1 + CD8 + T cells. A high abundance of SPP1 + SIRPα + macrophages at baseline corresponded to patients’ response to PD-1/PD-L1 inhibitors.ConclusionA novel subtype of SPP1 + macrophages expressing SIRPα was identified and their abundance predicted patients’ response to PD-1/PD-L1 inhibitors.

Identification of gastric cancer subtypes based on disulfidptosis-related genes: GPC3 as a novel biomarker for prognosis prediction

Gastric cancer (GC) is the fourth most common cancer type. “Disulfidptosis,” a distinct form of cell death, is initiated through aberrant intracellular disulfide metabolism. Here, we identified various GC subtypes based on disulfidptosis-related genes (DRGs) and constructed a risk score model to identify relevant genes to help predict patient prognosis and guide treatment. We downloaded RNA sequencing (RNA-seq) data from the TCGA-STAD database, performed a difference analysis, and combined the data with GSE84437 to successfully perform an unsupervised clustering analysis based on DRGs and differentially expressed genes (DEGs). Risk-scoring models were established by screening prognosis-related DEGs. The GC samples were segregated into high-risk (HR) and low-risk (LR) groups according to their risk scores. We then evaluated the genes screened with the model in terms of prognosis, tumor, and immune cell infiltration. The response of patients with GC to immunological therapy was assessed using tumor mutational burden, microsatellite instability, and tumor immune dysfunction and exclusion scores. Using unsupervised cluster analysis, we identified two DRG clusters and two gene clusters that differed in prognosis and tumor microenvironment. A six-gene model was developed for risk score assessment. The LR group demonstrated superior performance compared to the HR group in terms of immunity, exhibiting greater sensitivity to immunotherapy. Thereafter, we selected the model gene GPC3 for single-gene analysis and verified it by experimental validation. The results demonstrated that GPC3 can serve as a standalone biomarker with promising clinical applicability in the prognostic prediction and clinical management of GC.

Immunohistochemical Evaluation of Basal and Luminal Markers in Bladder Cancer: A Study from a Single Institution

Background: Bladder cancer (BC) presents significant molecular diversity, which affects both prognosis and treatment results. Immunohistochemistry (IHC) facilitates the identification of molecular subtypes and their relationships with clinicopathological features. Methods: We performed an IHC analysis on tissue samples from 107 BC patients, evaluating the expression of markers GATA3, CD44, CK5/6, and CK20. We applied two methods to classify the tumor samples into basal and luminal subtypes. The relationships between these marker expressions, molecular subtypes, clinicopathological characteristics, and TILs were explored. Results: Most samples showed the expression of GATA3 and CD44, with notable correlations found between CD44 and CK5/6 as well as GATA3 and CK20. CD44 and CD20 expression were linked to a poorer prognosis. Additionally, the luminal and basal subtypes had distinct TIL patterns, which influenced overall survival. A poor prognosis was associated with the basal subtype with low TIL infiltration and the luminal subtype with high TIL infiltration. Conclusions: Our study clarifies the molecular characteristics of BC, underlining the prognostic importance of CD44 expression and the role of TILs in influencing subtype-specific outcomes. IHC proves valuable in subtype identification and supports personalized treatment strategies.

AET-net: A framework for subtype classification based on the multi-omics data of breast cancer

Breast cancer (BC) is one of the most prevalent cancers worldwide and remains a significant global public health challenge. The biomechanical characteristics of tumor microenvironments provide critical insights into cellular interactions and mechanical stress responses that potentially influence cancer progression. The integration and analysis of multi-omics data for BC subtype classification present substantial challenges, including high-dimensional data complexity and difficulties in integrating heterogeneous omics data characteristics. To address these challenges, we propose an Autoencoder and Transformer integrated neural network (AET-net) classification framework. The experimental results demonstrate that our model achieves significant performance improvements in predicting BC subtypes based on integrated multi-omics datasets, with an Accuracy of 0.912 and an AUC of 0.9862. These results not only validate the high accuracy of our model in BC subtype classification, providing a valuable tool for diagnostic decision support, but also demonstrate the potential of integrated multi-omics data analysis in enhancing the precision and efficiency of BC subtype identification.

Endoscopic transsphenoidal surgery for infradiaphragmatic craniopharyngiomas and impact of diaphragm sellae competence on hypothalamic injury

Investigate the impact of diaphragm sellae competence on surgical outcomes and risk factors for postoperative hypothalamic injury (HI) in patients undergoing endoscopic transsphenoidal surgery (ETS) for infradiaphragmatic craniopharyngiomas (ICs). A retrospective analysis of 54 consecutive patients (2016–2023) with ICs treated by ETS was conducted. All tumors originated from the sellar region inferior to the diaphragm sellae and were classified into two subtypes in terms of diaphragm sellae competence: IC with competent diaphragm sellae (IC-CDS) and IC with incompetent diaphragm sellae (IC-IDS). Clinical features, intraoperative findings, and follow-up data were compared between subtypes. Postoperative HI was assessed using an magnetic resonance imaging-based scoring system. Fifty-four patients (29 males, 25 females) were included in this study, with 12 (22.2%) under 18 years old. Overall, 35 cases were IC-CDS, while 19 were IC-IDS. Compared with IC-CDS, patients with IC-IDS tended to have hormone hypofunction before surgery (p = 0.03). Tumor volume in IC-IDS group (9.0 ± 8.6 cm3) was also higher than that in IC-CDS group (3.3 ± 3.4 cm3, p = 0.011). Thirty-seven patients underwent standard endoscopic transsphenoidal approach (SEA) and 17 underwent an extended endoscopic transsphenoidal approach (EEA). Gross total resection (GTR) was achieved in 50 cases (92.6%). Postoperative CSF leak was observed in four patients (7.4%). Permanent diabetes insipidus (DI) occurred in 13 patients (27.7%), six in IC-CDS and seven in IC-IDS. Postoperative HI occurred in 38.9% of patients. Univariate analysis revealed that large tumor size (p = 0.014), prior hypopituitarism(p = 0.048) and IC-IDS (p < 0.001) were significantly associated with postoperative HI. Multivariate analysis revealed that IC- IDS was the sole predictor of postoperative HI. To our knowledge, this is the largest case series in the literature to describe IC resected by endoscopic surgery in a single institution. Classification based on diaphragm sellae competence highlights distinct clinical features and surgical outcomes between IC-CDS and IC-IDS subtypes. Notably, IC-IDS is an independent risk factor for postoperative HI. Preoperative identification of subtype can guide surgical strategy and potentially minimize complications.

Antibody screening-assisted multichannel nanoplasmonic sensing chip based on SERS for viral screening and variants identification

The Omicron variants of SARS-CoV-2 have been spreading globally and has never disappeared from our sight, indicating that its coexistence with humans has become a fact, and monitoring its evolution and spread remains a current task. Although polymerase chain reaction (PCR) is the most commonly used virus detection method, it requires labor-intensive and time-consuming procedures in a laboratory setting. Herein, a multichannel nanoplasmonic sensing chip based on surface enhanced Raman spectroscopy (SERS) was developed for detecting N and S proteins, as well as IgG and IgM, related to SARS-CoV-2 Omicron variants. Through a self-screening process, specific antibodies for on-site and rapid identification of important variants of concerns (VoCs) were obtained, and their binding was confirmed by protein structure analysis. The use of these S protein specific antibodies can accurately identify Omicron VoCs (BA. 5, BF.7，XBB.1.5) with the detection limit (LoD) at 0.16 pg/mL. Then, the proposed SERS array chip was integrated with a hand-held Raman spectrometer to successfully detect the Omicron subvariants in real saliva samples within only 20 minutes, greatly reducing the detection time of PCR. This sensing technology will provide a powerful and rapid point-of-care testing (POCT) method for virus diagnosis, subtype identification, and post infection antibody level monitoring.

A prediction model based on dual-layer spectral detector computed tomography for distinguishing nonluminal from luminal invasive breast cancer.

The identification of the molecular subtypes of breast cancer is critical to determining appropriate treatment strategies and assessing prognosis. This study aimed to evaluate the ability of dual-layer spectral detector computed tomography (DLCT) metrics to differentiate luminal from nonluminal invasive breast cancer. A total of 220 patients with invasive breast cancer who underwent routine DLCT examination were included in the study. The molecular subtypes of breast cancer were identified through immunohistochemical staining of biopsies or postoperative pathological specimens. DLCT quantitative parameters were compared between the luminal and nonluminal types of breast cancer. The diagnostic efficacy of these parameters was determined via receiver operating characteristic (ROC) curves. Univariate and multivariate regression analyses were conducted to identify independent predictors that could differentiate nonluminal from luminal breast cancer. A nomogram prediction model was established based on multivariate regression analysis. The performance of the nomogram model was assessed with ROC curve and calibration curve analyses. Among the DLCT quantitative values, eight were significantly lower in the luminal type than in the nonluminal type of breast cancer (P<0.001-0.011). The area under the curve (AUC) values for these significant DLCT quantitative parameters ranged from 0.604 to 0.694. Multivariate logistic regression analysis identified CT-reported lymph node metastasis status [hazard ratio (HR) =4.214; P<0.001], the Hounsfield unit (HU) value of the virtual monoenergetic image at 40 keV (HUVMI40) (HR =2.628; P=0.012), and the normalized iodine concentration (nIC) (HR =2.182; P=0.041) as independent predictors of the nonluminal type, with an AUC of 0.754 [95% confidence interval (CI): 0.688-0.820]. The nomogram based on multivariate logistic regression analysis exhibited good discrimination and calibration (Hosmer-Lemeshow test; P=0.835). An average AUC value of 0.75 was obtained for the internal validation data. DLCT quantitative parameters are valuable noninvasive indexes for differentiating between the luminal and nonluminal types of invasive breast cancer. Furthermore, the nomogram constructed in this study could guide individualized predictions of molecular subtypes in patients with invasive breast cancer.

The value of non-enhanced CT 3D visualization in differentiating stage Ⅰ invasive lung adenocarcinoma between LPA and non-LPA

ObjectiveThis study aims to analyze the quantitative parameters and morphological indices of three-dimensional (3D) visualization to differentiate lepidic predominant adenocarcinoma (LPA) from non-LPA subtypes, which include acinar predominant adenocarcinoma (APA), papillary predominant adenocarcinoma (PPA), micropapillary predominant adenocarcinoma (MPA), and solid predominant adenocarcinoma (SPA). MethodsA group of 178 individuals diagnosed with lung adenocarcinoma were chosen and categorized into two groups: the LPA group and the non-LPA group, according to their pathological results. Quantitative parameters and morphological indexes such as 3D volume, solid proportion, and vascular cluster sign were obtained using 3D visualization and reconstruction techniques. ResultsSignificant differences were observed in the vascular cluster sign, spiculation, shape, air bronchogram, bubble-like lucency, margin, pleural indentation, lobulation, maximum tumor diameter, 3D mean CT value, 3D volume, 3D mass, 3D density, and solid proportion between two groups (P<0.05). The optimal cut-off values for diagnosing non-LPA were a 3D mean CT value of -445.45 HU, a 3D density of 0.56mg·mm-3, and a solid proportion reaching 27.95%. Multivariate logistic regression analysis revealed that 3D mean CT value, lobulation, and margin characteristics independently predicted stageⅠinvasive lung adenocarcinoma. The combination of three indicators significantly improved prediction accuracy (AUC=0.881). ConclusionThe utilization of 3D visualization technology in a systematic approach enables the acquisition of 3D quantitative parameters and morphological indicators of thin-slice CT lesions. These efforts significantly contribute to the identification of histopathological subtypes for stageⅠinvasive lung adenocarcinoma. When integrated with pertinent clinical data, this offers essential guidance for developing various surgical techniques and treatment plans.

Identification of HER2-positive breast cancer molecular subtypes with potential clinical implications in the ALTTO clinical trial

In HER2-positive breast cancer, clinical outcome and sensitivity to HER2-targeted therapies are influenced by both tumor and microenvironment features. However, we are currently unable to depict the molecular heterogeneity of this disease with sufficient granularity. Here, by performing gene expression profiling in HER2-positive breast cancers from patients receiving adjuvant trastuzumab in the ALTTO clinical trial (NCT00490139), we identify and characterize five molecular subtypes associated with the risk of distant recurrence: immune-enriched, proliferative/metabolic-enriched, mesenchymal/stroma-enriched, luminal, and ERBB2-dependent. Additionally, we validate the biological profiles of the subtypes and explore their prognostic/predictive value in external cohorts, namely the NeoALTTO trial (NCT00553358), SCAN-B (NCT02306096), I-SPY2 (NCT01042379), METABRIC and TCGA. Immune-enriched tumors present better survival outcomes, in contrast to mesenchymal/stroma-enriched and proliferative/metabolic-enriched tumors, while luminal and ERBB2-dependent tumors are characterized by low and high rates of pathological complete response, respectively. Of note, these molecular subtypes provide the rationale for treatment approaches leveraging the heterogeneous biology of HER2-positive breast cancer.

New direction: identification of immunoinflammatory subtypes and potential therapeutic targets for cholangiocarcinoma

BackgroundCholangiocarcinoma (CHOL) is a rare cancer with low survival rates. Despite advances in precision medicine targeting molecular subtypes, the immune subtypes of CHOL remain poorly understood. This study aimed to identify immune subtypes of CHOL and investigate their implications in the metabolic regulation of macrophage functions in inflammation.MethodsWe conducted a comprehensive analysis of transcriptome and single-cell sequencing data from multiple databases to classify the immune subtypes of CHOL. Immune cell infiltration within the tumor microenvironment (TME) and the metabolic pathways involved in macrophage activation and polarization were also analyzed.ResultsTwo distinct immune subtypes, immune-infiltrated CS1 and immune-depleted CS2, were identified in CHOL. CS1 exhibited a highly active TME with substantial immune cell infiltration, including macrophages, and activation of immune-related signaling pathways, such as inflammatory and interferon pathways. In contrast, CS2 was characterized by a deficiency in immune components and poorer prognosis. Metabolic regulation of macrophages, particularly the downregulation of oxidative phosphorylation in CS1, suggested a shift towards glycolysis as an energy source for activated macrophages, contributing to the immune-responsive phenotype observed in CS1. Additionally, the oncogenic role of DLX5 in CHOL was revealed, with potential impacts on macrophage functions in inflammation.ConclusionThis study provides insights into immune subtype classification, novel biomarker identification, and the metabolic regulation of macrophage functions in CHOL. Understanding macrophage metabolic reprogramming within immune subtypes may contribute to the development of targeted immunotherapies for CHOL.

Identification of tumor-antigen signatures and immune subtypes for mRNA vaccine selection in muscle-invasive bladder cancer

BackgroundMuscle-invasive bladder cancer continues to lack reliable diagnostic and prognostic biomarkers. Recently, tumor vaccines targeting specific molecules have emerged as a promising treatment in inhibiting tumor progression, which was rekindled under the background of coronavirus disease-2019 pandemic. However, the application of mRNA vaccine targeting muscle-invasive bladder cancer–specific antigens remains limited, and there has been a lack of comprehensive studies or validations to identify suitable patient subgroups for vaccination. This study aims to explore novel muscle-invasive bladder cancer antigen signatures to identify patients most likely to benefit from vaccination. MethodsGene expression profiles of muscle-invasive bladder cancer samples, along with corresponding clinical data, were retrieved from the Cancer Genome Atlas Program. The least absolute shrinkage and selection operator model was applied to develop signatures for stratifying muscle-invasive bladder cancer patients. Prognostic accuracy of each factor was assessed using receiver operating characteristic analysis. Tumor Immune Estimation Resource was employed to visualize the relationship between the proportion of antigen-presenting cells and the expression of selected genes. The CIBERSORT and WGCNA R packages were used to identify differences in immune infiltration levels across muscle-invasive bladder cancer subgroups. Additionally, the STRING database and Cytoscape were used to construct the protein-protein interaction network. CCK-8 and colony formation assays were employed in invitro experiments. ResultsA total of 49 potential tumor antigens were identified. Using least absolute shrinkage and selection operator Cox regression, 14 tumor antigens were selected to develop a risk evaluation signature. The risk score signature can serve as a valuable tool for predicting the outcomes of muscle-invasive bladder cancer patients. Based on differential clinical, molecular, and immune-related gene profiles, muscle-invasive bladder cancer patients were classified into 2 subtypes: the immune “cold” subtype (immune score 1) and the immune “hot” subtype (immune score 2). The immune score signature, developed using a logistic score model, effectively distinguishes between patients more likely to belong to immune score 1 or 2. Notably, patients with a high risk score exhibited a higher proportion of immune score 2 compared to those with a low risk score. Additionally, the prognostic accuracy was significantly enhanced when the risk score and immune score were combined. Different tumor subtypes displayed distinct immune landscapes and signaling pathways. Moreover, novel tumor antigens associated with oxidative stress were identified. ConclusionThe risk score and immune score signatures based on tumor antigens have identified potential effective neo-antigens for the development of mRNA vaccines targeting muscle-invasive bladder cancer. Patients with low risk score and immune score 1 subtype are more likely to benefit from mRNA vaccination. Additionally, this study highlights the critical role of oxidative stress in modulating the efficacy of the mRNA vaccine.

Chronic eosinophilic leukaemia-Not otherwise specified: Clinical features, genomic insight and therapeutic strategies.

Chronic eosinophilia leukaemia-not otherwise specified (CEL-NOS) is a rare myeloproliferative neoplasm characterized by persistent clonal hypereosinophilia. Recent advances in genetics have refined diagnostic criteria, leading to the identification of CEL subtypes with specific cytogenetic and molecular abnormalities now classified as myeloid/lymphoid neoplasms with eosinophilia and tyrosine kinase gene fusions, which may benefit from targeted therapies. In contrast, CEL-NOS lacks specific genetic drivers and intervention points to halt leukemogenesis. Molecular techniques have also enabled the definition of clonality in a considerable percentage of cases otherwise classified as idiopathic hypereosinophilic syndrome. CEL-NOS poses a significant therapeutic challenge due to limited treatment options, poor prognosis and the risk of progression to acute leukaemia. Patients, often elderly and with comorbidities, face restricted access to transplantation, the only potentially curative treatment. Unfortunately, the prognosis remains poor even post-transplant, with a 5-year survival rate of only one-third of patients. Other therapies, including steroids, cytoreductive and immunomodulatory treatments, offer limited and temporary responses with significant side effects. This review aims to consolidate current knowledge on CEL-NOS, covering diagnostic approaches, genetic advancements and therapeutic challenges. It seeks to provide a comprehensive overview and highlight critical areas for future research.

Morphology of mitochondrial network in disseminated endometriosis cells in spontaneous pneumothorax diagnostic process.

Circulating endometrial cells (CECs) have emerged as a new biomarker of advanced disease in women with endometriosis. The identification of several subtypes of CECs (e.g., stem cell-like, epithelial, glandular, stromal) has opened the way for characterization of endometriosis-associated CECs. This study focused on the isolation and characterization of CECs and disseminated endometrial cells (DECs) in patients with spontaneous pneumothorax (SP). The primary objective was to differentiate between cancer and non-cancer cells in patients with no previous cancer diagnosis. The MetaCell® size-based separation protocol was used to enrich CECs/DECs. Evaluation of the captured cells by 3D microscopy was performed using a NANOLIVE™ microscope using a holographic approach. Based on gene expression analysis (GEA), we can conclude that mitochondria are much more active in primary tumors compared to endometriosis tissue (e.g. MT-ND1, MT-ATP6 genes). The culture of DECs is made of stromal, stem and immune cells. In vitro culture of DECs is characterized by an increase in the epithelial marker KRT18. Similarly, NFE2L2, a proerythroid factor, is also elevated. Further, a significant decrease in the amount of stem and immune cells was observed in the cell culture of DECs. The data presented here show how morphologically plastic the changes in the mitochondrial network can be and how cells can reflect them at the level of gene expression. The markers identified could help in the accompanying diagnostic process of the spontaneous pneumothorax in women of reproductive age.

Identification of a stable immunosuppressive molecular subtype in esophageal squamous cell carcinoma based on inflammation‐related genes

Identification of a stable immunosuppressive molecular subtype in esophageal squamous cell carcinoma based on inflammation‐related genes

Isolation and identification of patient-derived liver cancer stem cells and development of personalized treatment strategies

BackgroundLiver cancer stem cells (LCSCs) are thought to drive the metastasis and recurrence, however, the heterogeneity of molecular markers of LCSCs has hindered the development of effective methods to isolate them.MethodsThis study introduced an effective approach to isolate and culture LCSCs from human primary liver cancer (HPLC), leveraging mouse embryonic fibroblasts (MEFs) as feeder cells in conjunction with using defined medium. Isolated LCSCs were further characterized by multiple approaches. Transcriptome sequencing data analysis was conducted to identify highly expressed genes in LCSCs and classify different subtypes of liver cancers.ResultsTotal sixteen cell strains were directly isolated from 24 tissues of three types of HPLC without sorting, seven of which could be maintained long-term culture as colony growth on MEFs, which is unique characteristics of stem cells. Even 10 of cloned cells formed the tumors in immunodeficient mice, indicating that those cloned cells were tumorgenic. The histologies and gene expression pattern of human xenografts were very similar to those of HPLC where these cloned cells were isolated. Moreover, putative markers of LCSCs were further verified to all express in cloned cells, confirming that these cells were LCSCs. These cloned LCSCs could be cryopreserved, and still maintained the feature of colony growth on MEFs after the recovery. Compared to suspension culture as conventional approach to culture LCSCs, our approach much better maintained stemness of LCSCs for a long time. To date, these cloned cells could be cultured on MEFs over 12 passages. Moreover, bioinformatics analysis of sequencing data revealed the gene expression profiles in LCSCs, and liver cancers were classified into two subtypes C1 and C2 based on genes associated with the prognosis of LCSCs. Patients of the C2 subtype, which is closely related to the extracellular matrix, were found to be sensitive to treatments such as Cisplatin, Axitinib, JAK1 inhibitors, WNT-c59, Sorafenib, and RO-3306.ConclusionIn summary, this effective approach offers new insights into the molecular landscape of human liver cancers, and the identification of the C2 subtype and its unique response to the treatment pave the way for the creation of more effective, personalized therapeutic strategies.

A combined model integrating radiomics and deep learning based on multiparametric magnetic resonance imaging for classification of brain metastases

Background Radiomics and deep learning (DL) can individually and efficiently identify the pathological type of brain metastases (BMs). Purpose To investigate the feasibility of utilizing multi-parametric MRI-based deep transfer learning radiomics (DTLR) for the classification of lung adenocarcinoma (LUAD) and non-LUAD BMs. Material and Methods A retrospective analysis was performed on 342 patients with 1389 BMs. These instances were randomly assigned to a training set of 273 (1179 BMs) and a testing set of 69 (210 BMs) in an 8:2 ratio. Eight machine learning algorithms were employed to construct the radiomics models. A DL model was developed using four pre-trained convolutional neural networks (CNNs). The DTLR model was formulated by integrating the optimal performing radiomics model and the DL model using a classification probability averaging approach. The area under the curve (AUC), calibration curve, and decision curve analysis (DCA) were utilized to assess the performance and clinical utility of the models. Results The AUC for the optimal radiomics and DL model in the testing set were 0.824 (95% confidence interval [CI]= 0.726–0.923) and 0.775 (95% CI=0.666–0.884), respectively. The DTLR model demonstrated superior discriminatory power, achieving an AUC of 0.880 (95% CI=0.803–0.957). In addition, the DTLR model exhibited good consistency between actual and predicted probabilities based on the calibration curve and DCA analysis, indicating its significant clinical value. Conclusion Our study's DTLR model demonstrated high diagnostic accuracy in distinguishing LUAD from non-LUAD BMs. This method shows potential for the non-invasive identification of the histological subtype of BMs.

Utilizing gene expression profiling for the identification of small cell lung cancer subtypes

Abstract. Small cell lung cancer is a highly aggressive form of lung cancer associated with poor prognosis and limited treatment options. Gene expression profiling has emerged as a valuable tool for classifying SCLC into distinct molecular subtypes, each with its own unique therapeutic vulnerabilities and biomarkers. Recent studies have identified four primary subtypes: SCLC-A, SCLC-N, SCLC-P, and SCLC-Y, with additional classification into neuroendocrine (NE) high, NE-low, and non-NE tumors. Advanced understanding of the molecular characteristics of these subtypes presents new opportunities for targeted therapeutic approaches. The paper underscores the potential for a shift toward personalized therapy for SCLC, taking into account the inter- and intratumoral heterogeneity and dynamic subtype interconversion in response to treatment. Incorporating subtype-specific biomarkers in clinical trial designs could lead to the first-ever molecular biomarker-driven approach in the management of SCLC, greatly enhancing treatment effectiveness and patient outcomes.

Temporal sequence of incident mild cognitive impairment, incident parkinsonism, and risk of death in unimpaired community-dwelling older adults.

Mild cognitive impairment (MCI) and parkinsonism affect many older adults. The objective of this study was to determine the sequence of their occurrence and associated risk of death. 1,255 community-dwelling unimpaired participants from two epidemiological cohorts were examined annually. MCI was based on neuropsychological testing, and parkinsonism was based on the motor portion of the modified Unified Parkinson's Disease Rating Scale. A multi-state Cox proportional hazards model simultaneously examined incidences of MCI, parkinsonism, and death. Average age at baseline was 76.5 years (SD = 7.2) and 73% were female. Incident MCI occurred almost as commonly as incident parkinsonism, yet compared to no impairment, risk of death was higher for MCI (HR = 1.82, 95%CI=1.34, 2.47), but it was not different for parkinsonism (HR = 1.29; 95%CI = 0.95, 1.75). Risk of death for participants with incident MCI who progressed to parkinsonism (40%) was not significantly different from those with MCI alone (HR = 1.25, 95%CI = 0.93, 1.69). However, risk of death for participants with incident parkinsonism who progressed to MCI (51%) was significantly higher than those who did not progress (HR = 1.67, 95%CI = 1.27, 2.18), indicating that risk of death is highest with incidence of MCI. The varied patterns of sequential occurrence of cognitive and motor impairment and associated risk of death suggests much greater heterogeneity than previously recognized. Further work is needed to determine the biology underlying the temporal evolution of these phenotypes, and if identification of the various subtypes improves risk stratification.

Improving Deep Learning Models for Pediatric Low-Grade Glioma Tumours Molecular Subtype Identification Using MRI-based 3D Probability Distributions of Tumour Location.

Purpose: Pediatric low-grade gliomas (pLGG) are the most common brain tumour in children, and the molecular diagnosis of pLGG enables targeted treatment. We use MRI-based Convolutional Neural Networks (CNNs) for molecular subtype identification of pLGG and augment the models using tumour location probability maps. Materials and Methods: MRI FLAIR sequences of 214 patients (110 male, mean age of 8.54 years, 143 BRAF fused and 71 BRAF V600E mutated pLGG tumours) from January 2000 to December 2018 were included in this retrospective REB-approved study. Tumour segmentations (volumes of interest-VOIs) were provided by a pediatric neuroradiology fellow and verified by a pediatric neuroradiologist. Patients were randomly split into development and test sets with an 80/20 ratio. The 3D binary VOI masks for each class in the development set were combined to derive the probability density functions of tumour location. Three pipelines for molecular diagnosis of pLGG were developed: location-based, CNN-based, and hybrid. The experiment was repeated 100 times each with different model initializations and data splits, and the Areas Under the Receiver Operating Characteristic Curve (AUROC) was calculated, and Student's t-test was conducted. Results: The location-based classifier achieved an AUROC of 77.9, 95% confidence interval (CI) (76.8, 79.0). CNN-based classifiers achieved an AUROC of 86.1, 95% CI (85.0, 87.3), while the tumour-location-guided CNNs outperformed the other classifiers with an average AUROC of 88.64, 95% CI (87.6, 89.7), which was statistically significant (P-value .0018). Conclusion: Incorporating tumour location probability maps into CNN models led to significant improvements for molecular subtype identification of pLGG.

Identification of Molecular Subtypes and Prognostic Traits Based on Chromosomal Instability Phenotype-Related Genes in Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) exhibits significant molecular heterogeneity; however, previous studies have not fully explored its classification into distinct molecular subtypes. Here, we identified LUAD-significant chromosomal instability (CIN) phenotype genes (n = 24) using a TCGA-LUAD cohort (n = 592) and evaluated their ability to predict pathologic grade. Unsupervised clustering and principal component analysis revealed that LUAD patients could be classified into CIN phenotype-related subtypes (GroupLow, GroupModerate, and GroupHigh), each exhibiting distinct transcriptomic patterns. Notably, the GroupHigh showed significantly poor overall survival [OS; hazard ratio (HR) = 1.43, p-value < 10-3] and disease-free survival (DFS; HR = 1.27, p-value < 10-3). Univariate and multivariate analysis confirmed that its expression status was an independent prognostic predictor (p-value < 10-3, HR = 2.18, 95% C.I = 1.26-3.76) of the clinical outcomes, outperforming pathologic grade (p-value < 10-3, HR = 1.2, 95% C.I = 1.08-1.33). Moreover, analysis of surfactant metabolism-related genes revealed higher expression in the GroupLow, which was associated with a favorable prognosis. By integrating multiple independent cohorts (n = 779), we validated these findings and confirmed that CIN phenotype gene status serves as a critical prognostic marker in LUAD. Furthermore, genomic profiling showed that the GroupHigh exhibited frequent mutations in key genes such as KEAP1, LYST, SETD2, and TP53, with oncogenes in this group preferentially showing copy number gains. Our study highlights the significance of CIN phenotype gene status as a predictor of LUAD prognosis and its association with transcriptomic and genomic alterations, paving the way for further clinical validation and potential therapeutic interventions.