Distinct Subgroups Of Patients Research Articles

Hepatitis B-related hepatocellular carcinoma: classification and prognostic model based on programmed cell death genes.

Hepatitis B virus (HBV) infection is a major risk factor for hepatocellular carcinoma (HCC). Programmed cell death (PCD) is a critical process in suppressing tumor growth, and alterations in PCD-related genes may contribute to the progression of HBV-HCC. This study aims to develop a prognostic model that incorporates genomic and clinical information based on PCD-related genes, providing novel insights into the molecular heterogeneity of HBV-HCC through bioinformatics analysis and experimental validation. In this study, we analyzed 139 HBV-HCC samples from The Cancer Genome Atlas (TCGA) and validated them with 30 samples from the Gene Expression Omnibus (GEO) database. Various bioinformatics tools, including differential expression analysis, gene set variation analysis, and machine learning algorithms were used for comprehensive analysis of RNA sequencing data from HBV-HCC patients. Furthermore, among the PCD-related genes, we ultimately chose DLAT for further research on tissue chips and patient cohorts. Besides, immunohistochemistry, qRT-PCR and Western blot analysis were conducted. The cluster analysis identified three distinct subgroups of HBV-HCC patients. Among them, Cluster 2 demonstrated significant activation in DNA replication-related pathways and tumor-related processes. Analysis of copy number variations (CNVs) of PCD-related genes also revealed distinct patterns in the three subgroups, which may be associated with differences in pathway activation and survival outcomes. DLAT in tumor tissues of HBV-HCC patients is upregulated. Based on the PCD-related genes, we developed a prognostic model that incorporates genomic and clinical information and provided novel insights into the molecular heterogeneity of HBV-HCC. In our study, we emphasized the significance of PCD-related genes, particularly DLAT, which was examined in vitro to explore its potential clinical implications.

Identification of distinct subgroups of Sjögren's disease by cluster analysis based on clinical and biological manifestations: data from the cross-sectional Paris-Saclay and the prospective ASSESS cohorts

Sjögren's disease is a heterogenous autoimmune disease with a wide range of symptoms-including dryness, fatigue, and pain-in addition to systemic manifestations and an increased risk of lymphoma. We aimed to identify distinct subgroups of the disease, using cluster analysis based on subjective symptoms and clinical and biological manifestations, and to compare the prognoses of patients in these subgroups. This study included patients with Sjögren's disease from two independent cohorts in France: the cross-sectional Paris-Saclay cohort and the prospective Assessment of Systemic Signs and Evolution of Sjögren's Syndrome (ASSESS) cohort. We first used an unsupervised multiple correspondence analysis to identify clusters within the Paris-Saclay cohort using 26 variables comprising patient-reported symptoms and clinical and biological manifestations. Next, we validated these clusters using patients from the ASSESS cohort. Changes in disease activity (measured by the European Alliance of Associations for Rheumatology [EULAR] Sjögren's Syndrome Disease Activity Index [ESSDAI]), patient-acceptable symptom state (measured by the EULAR Sjögren's Syndrome Patient Reported Index [ESSPRI]), and lymphoma incidence during follow-up were compared between clusters. Finally, we compared our clusters with the symptom-based subgroups previously described by Tarn and colleagues. 534 patients from the Paris-Saclay cohort (502 [94%] women, 32 [6%] men, median age 54 years [IQR 43-64]), recruited between 1999 and 2022, and 395 patients from the ASSESS cohort (370 [94%] women, 25 [6%] men, median age 53 years [43-63]), recruited between 2006 and 2009, were included in this study. In both cohorts, hierarchical cluster analysis revealed three distinct subgroups of patients: those with B-cell active disease and low symptom burden (BALS), those with high systemic disease activity (HSA), and those with low systemic disease activity and high symptom burden (LSAHS). During follow-up in the ASSESS cohort, disease activity and symptom states worsened for patients in the BALS cluster (67 [36%] of 186 patients with ESSPRI score <5 at month 60 vs 92 [49%] of 186 at inclusion; p<0·0001). Lymphomas occurred in patients in the BALS cluster (five [3%] of 186 patients; diagnosed a median of 70 months [IQR 42-104] after inclusion) and the HSA cluster (six [4%] of 158 patients; diagnosed 23 months [13-83] after inclusion). All patients from the Paris-Saclay cohort with a history of lymphoma were in the BALS and HSA clusters. This unsupervised clustering classification based on symptoms and clinical and biological manifestations did not correlate with a previous classification based on symptoms only. On the basis of symptoms and clinical and biological manifestations, we identified three distinct subgroups of patients with Sjögren's disease with different prognoses. Our results suggest that these subgroups represent different heterogeneous pathophysiological disease mechanisms, stages of disease, or both. These findings could be of interest when stratifying patients in future therapeutic trials. Fondation pour la Recherche Médicale, French Ministry of Health, French Society of Rheumatology, Innovative Medicines Initiative 2 Joint Undertaking, Medical Research Council UK, and Foundation for Research in Rheumatology.

Enhancing Pulmonary Embolism Mortality Risk Stratification Using Machine Learning: The Role of the Neutrophil-to-Lymphocyte Ratio.

(1) Background: Acute pulmonary embolism (PE) is a significant public health concern that requires efficient risk estimation to optimize patient care and resource allocation. The purpose of this retrospective study was to show the correlation of NLR (neutrophil-to-lymphocyte ratio) and PESI (pulmonary embolism severity index)/sPESI (simplified PESI) in determining the risk of in-hospital mortality in patients with pulmonary thromboembolism. (2) Methods: A total of 160 patients admitted at the County Clinical Emergency Hospital of Sibiu from 2019 to 2022 were included and their hospital records were analyzed. (3) Results: Elevated NLR values were significantly correlated with increased in-hospital mortality. Furthermore, elevated NLR was associated with PESI and sPESI scores and their categories, as well as the individual components of these parameters, namely increasing age, hypotension, hypoxemia, and altered mental status. We leveraged the advantages of machine learning algorithms to integrate elevated NLR into PE risk stratification. Utilizing two-step cluster analysis and CART (classification and regression trees), several distinct patient subgroups emerged with varying in-hospital mortality rates based on combinations of previously validated score categories or their defining elements and elevated NLR, WBC (white blood cell) count, or the presence COVID-19 infection. (4) Conclusion: The findings suggest that integrating these parameters in risk stratification can aid in improving predictive accuracy of estimating the in-hospital mortality of PE patients.

Definition, Epidemiology, and Pathogenesis of Severe Community-Acquired Pneumonia.

The clinical presentation of community-acquired pneumonia (CAP) can vary widely among patients. While many individuals with mild symptoms can be managed as outpatients with excellent outcomes, there is a distinct subgroup of patients who present with severe CAP. In these cases, the mortality rate can reach approximately 25% within 30 days and even up to 50% within a year. It is crucial to focus attention on these patients who are at higher risk. Among the various definitions of severe CAP found in the literature, one commonly used criterion is the requirement for admission to intensive care unit. Notable epidemiological characteristics of these patients include the impact of acute cardiovascular diseases on clinical outcomes and the enduring, independent effect of pneumonia on long-term outcomes. Factors such as pathogen virulence, the presence of comorbidities, and the host response are important contributors to the pathogenesis of severe CAP. In these patients, the host response may be dysregulated and compartmentalized. Gaining a better understanding of the epidemiology and pathogenesis of severe CAP will provide a foundation for the development of new therapies for this condition. This manuscript aims to review the definition, epidemiology, and pathogenesis of severe CAP, shedding light on important aspects that can aid in the improvement of patient care and outcomes.

Transcriptomic-Based Microenvironment Classification Reveals Precision Medicine Strategies for Pancreatic Ductal Adenocarcinoma

Background & AimsThe complex tumor microenvironment (TME) of pancreatic ductal adenocarcinoma (PDAC) has hindered the development of reliable predictive biomarkers for targeted therapy and immunomodulatory strategies. A comprehensive characterization of the TME is necessary to advance precision therapeutics in PDAC. MethodsA transcriptomic profiling platform for TME classification based on functional gene signatures was applied to 14 publicly available PDAC datasets (n = 1657) and validated in a clinically annotated independent cohort of patients with PDAC (n = 79). Four distinct subtypes were identified using unsupervised clustering and assessed to evaluate predictive and prognostic utility. ResultsTME classification using transcriptomic profiling identified 4 biologically distinct subtypes based on their TME immune composition: immune enriched (IE); immune enriched, fibrotic (IE/F); fibrotic (F); and immune depleted (D). The IE and IE/F subtypes demonstrated a more favorable prognosis and potential for response to immunotherapy compared with the F and D subtypes. Most lung metastases and liver metastases were subtypes IE and D, respectively, indicating the role of clonal phenotype and immune milieu in developing personalized therapeutic strategies. In addition, distinct TMEs with potential therapeutic implications were identified in treatment-naive primary tumors compared with tumors that underwent neoadjuvant therapy. ConclusionsThis novel approach defines a distinct subgroup of PADC patients that may benefit from immunotherapeutic strategies based on their TME subtype and provides a framework to select patients for prospective clinical trials investigating precision immunotherapy in PDAC. Further, the predictive utility and real-world clinical applicability espoused by this transcriptomic-based TME classification approach will accelerate the advancement of precision medicine in PDAC.

DOP25 Antibodies against Neutrophil Extracellular Traps (ANETAs) are associated with more severe disease course in Ulcerative Colitis

Abstract Background Antibodies against Neutrophil Extracellular Traps (ANETAs) have emerged as markers in multiple autoimmune disorders (i.e. rheumatoid arthritis and systemic lupus) associated with disease activity. We investigated the presence of ANETAs and their influence on disease course in Inflammatory Bowel Disease (IBD). Methods Serum samples from UC (n=33), Crohn's Disease (CD; n=28), and healthy controls (HC; n=8) underwent incubation with non-lytic Neutrophil Extracellular Traps (NETs) for immunofluorescence microscopy. For clinical relevance, these patients were followed over ten years to track treatment resistance, extraintestinal manifestations, surgeries, and disease recurrence (Table 1). For functional relevance, in vitro generated NETs were exposed to ANETAs, followed by quantifying macrophage-mediated clearance of these ANETA pre-treated NETs by real-time microscopy via pHrodo for 24 hours, as well as assessing pro-inflammatory cytokine (IL-6, TNF-α, IL-8, and IL-23) genetic expression levels in these macrophages. Results Among UC patients, ANETA positivity correlated with high rates of treatment resistance, extraintestinal manifestations, colorectal surgeries, and recurrence of disease like pouchitis (93.3% vs. 6.7%, 61.5% vs. 38.5%; 71.4% vs. 28.6%; 87.5% vs. 12.5%, for ANETA+ and ANETA- UC patients, respectively). CD patients with ANETAs exhibited high rates of resistance to treatment (75% vs 25%) and lower rates of extraintestinal manifestations, colectomy, and disease recurrence (38.1% vs 61.9%, 40% vs 60%, 33.3% vs 66.7%) (Fig 1A). Macrophages displayed robust engulfment of untreated NETs and those treated with healthy control sera, while ANETA-coated NETs exhibited reduced susceptibility to macrophage phagocytosis (Fig 1B). Gene expression analysis revealed a significant upregulation of pro-inflammatory cytokines (IL-6, TNF-α, IL-8, and IL-23) when NETs were incubated with serum from ANETA-positive individuals compared to sera from healthy controls (p&amp;lt;0.05). Conclusion ANETAs may serve as markers for a distinct subgroup of UC patients characterized by aggressive disease and poorer prognosis. The potential mechanism might involve impaired NETs clearance by, and inducing a proinflammatory response in macrophages. Discrepancies observed between UC and CD suggest diverse pathophysiological mechanisms in both diseases. Further research is imperative to comprehensively understand the underlying pathophysiology, function, and potential clinical applications of ANETAs in IBD.

Heterogeneity in response to treatment across tinnitus phenotypes

The clinical heterogeneity of chronic tinnitus poses major challenges to patient management and prompts the identification of distinct patient subgroups (or phenotypes) that respond more predictable to a particular treatment. We model heterogeneity in treatment response among phenotypes of tinnitus patients concerning their change in self-reported health burden, psychological characteristics, and tinnitus characteristics. Before and after a 7-day multimodal treatment, 989 tinnitus patients completed 14 assessment questionnaires, from which 64 variables measured general tinnitus characteristics, quality of life, pain experiences, somatic expressions, affective symptoms, tinnitus-related distress, internal resources, and perceived stress. Our approach encompasses mechanisms for patient phenotyping, visualizations of the phenotypes and their change with treatment in a projected space, and the extraction of patient subgroups based on their change with treatment. On average, all four distinct phenotypes identified at the pre-intervention baseline showed improved values for nearly all the considered variables following the intervention. However, a considerable intra-phenotype heterogeneity was noted. Five clusters of change reflected variations in the observed improvements among individuals. These patterns of treatment effects were identified to be associated with baseline phenotypes. Our exploratory approach establishes a groundwork for future studies incorporating control groups to pinpoint patient subgroups that are more likely to benefit from specific treatments. This strategy not only has the potential to advance personalized medicine but can also be extended to a broader spectrum of patients with various chronic conditions.

A comparative study of clinical trial and real-world data in patients with diabetic kidney disease

A growing body of research is focusing on real-world data (RWD) to supplement or replace randomized controlled trials (RCTs). However, due to the disparities in data generation mechanisms, differences are likely and necessitate scrutiny to validate the merging of these datasets. We compared the characteristics of RCT data from 5734 diabetic kidney disease patients with corresponding RWD from electronic health records (EHRs) of 23,523 patients. Demographics, diagnoses, medications, laboratory measurements, and vital signs were analyzed using visualization, statistical comparison, and cluster analysis. RCT and RWD sets exhibited significant differences in prevalence, longitudinality, completeness, and sampling density. The cluster analysis revealed distinct patient subgroups within both RCT and RWD sets, as well as clusters containing patients from both sets. We stress the importance of validation to verify the feasibility of combining RCT and RWD, for instance, in building an external control arm. Our results highlight general differences between RCT and RWD sets, which should be considered during the planning stages of an RCT-RWD study. If they are, RWD has the potential to enrich RCT data by providing first-hand baseline data, filling in missing data or by subgrouping or matching individuals, which calls for advanced methods to mitigate the differences between datasets.

A Gluconeogenesis-Related Genes Model for Predicting Prognosis, Tumor Microenvironment Infiltration, and Drug Sensitivity in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a prevalent malignancy within the digestive system, known for its poor prognosis. Gluconeogenesis, a critical metabolic pathway, is responsible for the synthesis of glucose in the normal liver. This study aimed to examine the role of gluconeogenesis-related genes (GRGs) in HCC and evaluate their impact on the tumor microenvironment infiltration and drug sensitivity in HCC. We retrieved gene expression and clinical pathological data of HCC from The Cancer Genome Atlas (TCGA) database. This dataset was utilized to develop a prognosis model. The data from The International Cancer Genome Consortium (ICGC) served as an independent validation cohort. A least absolute shrinkage and selection operator (LASSO) regression analysis was applied to a curated panel of GRGs to construct and validate the predictive model. Furthermore, unsupervised consensus clustering, based on the expression levels of GRGs, categorized HCC patients into distinct subgroups. A four-gene prognostic model, referred to as GRGs, has been successfully developed with high accuracy and stability for the prediction of HCC patient prognosis. This model enables the stratification of patients into high or low risk groups based on individual risk scores, revealing significant differences in immune infiltration patterns and anti-tumor drug responses. Unsupervised consensus clustering analysis delineated four distinct subgroups of patients, each characterized by a unique prognosis and tumor immune microenvironment (TIME). This study is the first to develop a prognostic model incorporating 4-GRGs that effectively predicts the prognosis, tumor microenvironment infiltration, and drug sensitivity in HCC patients. The model based on 4 GRGs may contribute to predict the prognosis, immunotherapy and chemotherapy response of HCC patients.

Chemotherapy-related symptom networks in distinct subgroups of Chinese patients with gastric cancer

ObjectiveThis study aims to identify distinct subgroups among gastric cancer patients undergoing chemotherapy (CTX), delineate associated symptom networks, and ascertain the clinical and sociodemographic variables contributing to diverse symptom patterns. MethodsConducted in eastern China, our investigation involved gastric cancer patients receiving CTX. We gathered data using the M.D. Anderson Symptom Inventory Gastrointestinal Cancer Module along with clinical and sociodemographic variables. Subgroups were discerned based on symptom severity through latent profile analysis, and subsequent comparisons were made regarding the symptom networks in different subgroups. ResultsThe analysis encompassed 677 eligible gastric cancer patients, revealing three profiles: “Profile 1: low class” (n = 354, 52.3%), “Profile 2: moderate class” (n = 222, 32.8%), and “Profile 3: all high class” (n = 101, 14.9%). Nausea—vomiting exhibited robust associations in the symptom networks of all subgroups, whereas sadness—distress, and taste change—lack of appetite were notably linked with Profile 1 and Profile 2. Distress emerged as a core symptom in Profile 1, lack of appetite dominated the symptom network in Profile 2, and fatigue attained the highest strength in Profile 3. Distinct symptom profiles were influenced by variables such as education level, CTX combined with surgical or herbal treatment, psychological resilience, and social support. ConclusionsPatients within different subgroups manifest individualized patterns of symptom profiles. Analyzing demographics, disease characteristics, and psychosocial information among diverse subgroups facilitates healthcare providers in devising more personalized and targeted symptom management strategies, thereby alleviating the symptom burden on patients.

Late onset psychosis treatment with adjunctive medicines.

A number of studies have shown the feasibility of using adjunctive drugs in late onset psychosis (LOP). Testing hypothesis that among LOP people treated with antipsychotics and antidepressants, basing on certain clinical characteristics a subgroup of patients might be distinguished, for whom adjunctive therapy is advantageous. This subgroup might be identified by measurement of blood biochemical parameters. 59 in-patients with LOP, treated neuroleptics and antidepressants, were included, and followed in real clinical practice. Database containing demographic, clinical data (scores by PANSS, CDSS, CGI-S, HAMD-17), prescribed therapy, adverse effects of antipsychotic and antidepressant treatment, and blood biochemical parameters (enzymatic activities of glutamate- and glutathione metabolism enzymes in platelets and erythrocytes) at baseline and after the treatment course was created. Three groups of patients (Gr1, Gr2, and Gr3), based on the adjunctive therapy usage were identified: Gr1 (n = 16) was without adjunctive therapy, two other groups (Gr2 and Gr3) were with adjunctive medicines, such as 2-ethyl-6-methyl-3-hydroxypyridine succinate (EMHS; Gr2, n = 20), or other drugs, such as citicoline, cerebrolysin, cortexin, actovegin, gliatilin (choline alfoscerate; Gr3, n = 23). The enzymatic activities were assessed also in the matched control group (n = 38). In all three patient groups, as compared with controls, activity of erythrocyte glutathione reductase was decreased at baseline and after the treatment course. In Gr2, unlike Gr1 or Gr3, there was a significant decrease in baseline glutamate dehydrogenase and glutathione-S-transferase activities. Certain clinical criteria were also elucidated for prescription of EMHS as adjunctive therapy for patients of Gr2. Glutamate dehydrogenase and glutathione-S-transferase activities returned closer to control levels after the treatment course in Gr2, unlike Gr1, where they declined yet more after psychotropic treatment without adjunctive medicine. Different significant links between biochemical parameters and scores by clinical scales were observed in Gr1, Gr2, and Gr3, some having predictive value for evaluation of antipsychotic treatment efficacy. We demonstrate the validity of adjunctive neuroprotective medicines' usage in addition to antipsychotic and antidepressant therapy in distinct subgroups of patients suffering with LOP, especially those who have prominent side effects accompanying their psychotropic treatment. Returning of biochemical parameters to control range following the treatment course observed in patients of the subgroup treated with adjunctive EMHS is evidence for their metabolism normalization.

Central Sensitization: The Missing Link Between Psychological Distress and Poor Outcome Following Primary Total Knee Arthroplasty

BackgroundWhile preoperative psychological distress is known to predict risk for worse total knee arthroplasty (TKA) outcomes, distress may be too broad and nonspecific a predictor in isolation. We tested whether there are distinct preoperative TKA patient types based jointly on psychological status and measures of altered pain processing that predict adverse clinical outcomes. MethodsIn 112 TKA patients, we preoperatively assessed psychological status (depression, anxiety, and catastrophizing) and altered pain processing via a simple quantitative sensory testing protocol capturing peripheral and central pain sensitization. Outcomes (pain, function, opioid use) were prospectively evaluated at 6 weeks and 6 months after TKA. Cluster analyses were used to empirically identify TKA patient subgroups. ResultsThere were 3 distinct preoperative TKA patient subgroups identified from the cluster analysis. A low-risk (LR) group was characterized by low psychological distress and low peripheral and central sensitization. In addition, 2 subgroups with similarly elevated preoperative psychological distress were identified, differing by pain processing alterations observed: high-risk centralized pain and high-risk peripheral pain. Relative to LR patients, high-risk centralized pain patients displayed significantly worse function and greater opioid use at 6 months after TKA (P values <.05). The LR and high-risk peripheral pain patient subgroups had similar 6-month outcomes (P values >.05). ConclusionsAmong patients who have psychological comorbidity, only patients who have central sensitization were at elevated risk for poor functional outcomes and increased opioid use. Central sensitization may be the missing link between psychological comorbidity and poor TKA clinical outcomes. Preoperative testing for central sensitization may have clinical utility for improving risk stratification in TKA patients who have psychosocial risk factors.

A novel endoplasmic reticulum stress-related lncRNA signature for prognosis prediction and immune response evaluation in Stomach adenocarcinoma

backgroundStomach adenocarcinoma (STAD) is a significant contributor to cancer-related mortality worldwide. Although previous research has identified endoplasmic reticulum stress (ERS) as a regulator of various tumor-promoting properties of cancer cells, the impact of ERS-related long non-coding RNAs (lncRNAs) on STAD prognosis has not yet been investigated. Therefore, our study aims to develop and validate an ERS-related lncRNA signature that can accurately predict the prognosis of STAD patients.MethodsWe collected RNA expression profiles and clinical data of STAD patients from The Cancer Genome Atlas (TCGA) and identified ERS-related genes from the Molecular Signature Database (MSigDB). Co-expression analysis enabled us to identify ERS-related lncRNAs, and we applied univariate Cox, least absolute shrinkage, and selection operator (LASSO), and multivariate Cox regression analyses to construct a predictive signature comprising of 9 ERS-related lncRNAs. We assessed the prognostic accuracy of our signature using Kaplan-Meier survival analysis, and validated our predictive signature in an independent gene expression omnibus (GEO) cohort. We also performed tumor mutational burden (TMB) and tumor immune microenvironment (TIME) analyses. Enrichment analysis was used to investigate the functions and biological processes of the signature, and we identified two distinct STAD patient subgroups through consensus clustering. Finally, we performed drug sensitivity analysis and immunologic efficacy analysis to explore further insights.ResultsThe 9 ERS related-lncRNAs signature demonstrated satisfactory predictive performance as an independent prognostic marker and was significantly associated with STAD clinicopathological characteristics. Furthermore, patients in the high-risk group displayed a worse STAD prognosis than those in the low-risk group. Notably, gene set enrichment analysis (GSEA) revealed significant enrichment of extracellular matrix pathways in the high-risk group, indicating their involvement in STAD progression. Additionally, the high-risk group exhibited significantly lower TMB expression levels than the low-risk group. Consensus clustering revealed two distinct STAD patient subgroups, with Cluster 1 exhibiting higher immune cell infiltration and more active immune functions. Drug sensitivity analysis suggested that the low-risk group was more responsive to oxaliplatin, epirubicinl, and other drugs.ConclusionOur study highlights the crucial regulatory roles of ERS-related lncRNAs in STAD, with significant clinical implications. The 9-lncRNA signature we have constructed represents a reliable prognostic indicator that has the potential to inform more personalized treatment decisions for STAD patients. These findings shed new light on the pathogenesis of STAD and its underlying molecular mechanisms, offering opportunities for novel therapeutic strategies to be developed for STAD patients.

IFNγ Signaling in Acute Myeloid Leukemia Mediates Immunomodulation and Reveals Therapeutic Strategies

Background: Interferon gamma (IFNγ) is a critical cytokine known for its diverse roles in immune regulation, inflammation, and tumor surveillance. Studies have reported the dichotomous nature of IFNγ signaling in both the pathogenesis of cancer and immunotherapy response. However, its complex interplay with acute myeloid leukemia (AML) remains insufficiently understood. Methods: Three independent RNA-seq datasets were integrated to disentangle the IFNγ signaling in AML. We also performed single cell RNA sequencing (scRNA) on 20 newly diagnosed AML patients to discern relative contribution of cells and cellular communications for IFNγ signaling. Results: Single-sample gene set enrichment analysis was used to examine the transcriptional programs linked to IFNγ signaling in 672 newly diagnosed adult AML patients. We observed higher IFNγ signaling score in subgroups of patients including diploid with monocytic differentiation, inv16 core-binding-factor AML, and del7/7q. Notably, sorted CD34 + cells from 17 healthy donors had markedly lower levels of IFNγ signaling scores than did those from AML patients, making it a predominant feature in AML. Additionally, we found significant positive correlations between IFNγ signaling score and expression of its downstream targets HLA class 1 and 2 as well as T cell dysfunction score, T cell exhaustion score, and T cell senescence score, consistent with the fact that chronic IFNγ can drive T cell dysfunction. In scRNA, we observed that AML cells in patients with diploid monocytic AML had the highest expression of IFNγ signaling followed by del7/7q, while the non-monocytic, diploid AML cells had the lowest (Figure 1A). We then explored whether IFNγ signaling is differentially activated across the AML hierarchies and found the spectrum along the hierarchy with the primitive cells displaying significant higher IFNγ signaling than GMP while reaching the highest in Mono-like state. Transcription factor analysis revealed high regulon activity of interferon regulator factors (IRFs) in AML cells, with elevated levels of IRF1 and IRF5 regulons in del7/7q and del5/5q, respectively and elevated IRF8 regulon in diploid AML cells with monocytic differentiation, consistent with its role as a lineage-determinant factor promoting monocytic differentiation. Cell-cell interactions revealed IFNγ from CD8 T cells and NK cells as a top interaction to AML cells. These observations indicate that the activation levels of IFNγ signaling in AML cells are associated with distinct cellular states and hierarchies, and that disparate regulons of IFNγ signaling characterize distinct patient subgroups. The high level of IFNγ signaling in monocytic patient prompted us to assess its correlation with drug response as monocytic subclones are suggested to have inherent resistance to venetoclax. In BEAT-AML ex vivo drug test data, we found a strong positive correlation between IFNγ signaling score and venetoclax resistance, indicating that IFNγ signaling confers venetoclax resistance. This correlation was validated in an independent cohort. However, the IFNγ signaling score did not predict survival outcomes in our bulk cohort. Therefore, we defined a light weighted IFNγ signature to improve the prognostic sensitivity using the least absolute shrinkage and selection operator model. After regression, 47 genes related to survival were retained, forming a parsimonious IFNγ signature. The new parsimonious IFNγ score revealed a tight positive correlation with HLA class 1 and 2 scores. Importantly, this parsimonious IFNγ score was able to predict patient outcomes in our bulk cohort, whereby a higher score predicted worse survival (Figure 1B). Ultimately, these results suggest that the IFNγ pathway activation is associated with resistance to venetoclax-based therapy and can predict patient outcomes independently of known risk factors, making it a promising target for therapeutic intervention. Conclusions: Characterization of inflammation in AML using independent bulk and scRNA profiling led to the identification of novel drug targets and mechanisms of resistance to targeted therapy. We identified monocytic AML as having a unique microenvironment characterized by high IFNγsignaling in AML cells and immunosuppressive features. IFNγ signaling scores correlated strongly with venetoclax resistance. A parsimonious IFNγ gene signature demonstrated robust prognostic value.

Association of early surgery and absence of ataxia with full recovery after spinal intramedullary ependymoma resection.

Spinal intramedullary ependymomas (IEs) represent a well-defined tumor entity usually warranting resection. Factors that determine full long-term neurological recovery after resection are seldomly reported on in larger clinical series. In this study, the authors aimed to highlight the neurological outcome of patients with IEs after resection, with a focus on full neurological recovery, and to explore possible risk factors for the absence of neurological amelioration to an optimal function after surgical treatment. A single-center retrospective analysis of all patients undergoing surgery for IEs between 2007 and 2021 was performed. Data collection included patient demographics, symptoms, clinical findings, histopathological diagnosis, surgical procedures, complications, and neurological outcome. Patients harboring a favorable outcome (modified McCormick Scale [mMS] grade of I) were compared with patients with a less favorable outcome (mMS grade ≥ II) at the final follow-up. In total, 72 patients with a histologically diagnosed IE were included. IEs in those patients (41 males, 31 females; median age 51 [IQR 40-59] years) mostly occurred in the cervical (n = 40, 56%) or thoracic (n = 23, 32%) spine. Upon admission, motor deficits or gait deficits (mMS grade ≥ II) were present in 29 patients (40%), with a median mMS grade of II (IQR I-II). Gross-total resection was achieved in 60 patients (90%), and the rate of surgical complications was 7%. Histopathologically, 67 tumors (93%) were classified as WHO grade 2 ependymomas, 3 (4%) as WHO grade 1 subependymomas, and 2 (3%) as WHO grade 3 anaplastic ependymomas. After a mean follow-up of 863 ± 479 days, 37 patients (51%) had a fully preserved neurological function and 62 patients (86%) demonstrated an mMS grade of I or II. Comparison of favorable with unfavorable outcomes revealed an association of early surgery (within a year after symptom onset), the absence of ataxia or gait disorders, and a low mMS grade with full neurological recovery at the final follow-up. A subgroup of patients (n = 15, 21%) had nonresolving deterioration at the final follow-up, with no significant differences in relevant variables compared with the rest of the cohort. The data presented solidify the role of early surgery in the management of spinal IEs, especially in patients with mild neurological deficits. Furthermore, the presence of gait disturbance or ataxia confers a higher risk of incomplete long-term recovery after spinal ependymoma resection. Because a distinct subgroup of patients had nonresolving deterioration, even when presenting with an uneventful history, further analyses into this subgroup of patients are required.

Using real world practice patterns to identify clinical equipoise in cancer treatment: Advanced non-small cell lung cancer as an exemplar.

539 Background: Novel therapies have improved the survival for patients with advanced non-small cell lung cancer (NSCLC). Clinical scenarios with high treatment variation indicate a lack of consensus about the standard of care, and can identify instances there is less clinical equipoise, and comparative effectiveness research is urgently needed. We quantified the variation in the first-line therapy, stratified by patient’s biomarker and Eastern Cooperative Oncology Group (ECOG) performance score. Methods: Using Flatiron database, we categorized patients with advanced NSCLC diagnosed between 1/1/2016 and 6/30/2020 into groups by biomarker findings and performance status. Patients with unknown status of biomarkers or negative actionable gene alterations, or missing ECOG score were excluded. The degree of variation in first-line treatment regimens was measured using the Herfindahl-Hirschman Index (HHI), which calculates the sum of squared proportion of patients receiving different regimen categories. The HHI ranges from close to 0 to 10,000, with low values indicate heterogeneous treatment patterns whereas high values indicate more uniformity in treatment patterns. We used bootstrapping to calculate 95% confidence intervals (CIs) for the HHI scores. Results: Our cohort consisted of 12,708 patients. Among them, 327 were ALK-positive, 1,816 were EGFR-positive, and 10,565 were ALK/EGFR negative or unknown (including 3,616 patients who were PD-L1 &lt;1%, 3,139 PD-L1 1-49%, and 3,810 PD-L1 ≥50%, respectively). The HHI score for first-line therapy in our overall cohort was 2171 (95% CI: 2167, 2175). The HHI differed by biomarker status: 4403 for the EGFR+ group, 3809 for the ALK+ group, 3065 for the PD-L1 &lt;1% group, 2925 for the PD-L1 1-49% group, and 3370 for the PD- L1 ≥50% group. When we stratified patients by both biomarker and ECOG status, the HHI ranged from 2791 (95% CI: 2740, 2840) for patients with PD-L1 1-49% and ECOG 2-4 to 5364 (95% CI: 5161, 5649) for patients with EGFR+ and ECOG 2-4 (details in Table). Conclusions: Using HHI, we were able to quantify and compare variation in treatment patterns across clinically distinct subgroups of patients. Large variation in the first-line therapy for patients with PD-L1 1-49% suggests lack of strong consensus regarding the optimal treatment regimens for these patients.[Table: see text]

Squamous Cell Carcinoma of the Bladder: Analysis of Treatments and Outcomes.

Squamous cell carcinoma (SCC) of the bladder is a rare disease composing 2-5% of all bladder cancers. Currently, no consensus exists regarding treatment recommendations for this rare malignancy, although established regimens include various permutations of chemotherapy, radiation, laser treatments, immunotherapies, and surgeries, ranging from pelvic exenteration to local excision. The present study aims to analyze outcomes of the aforementioned treatments to guide clinical decision-making for patients with the disease. Patients with bladder SCC diagnosed between 2000 and 2018 were reviewed utilizing data from the Surveillance, Epidemiology, and End Results Registry (SEER) program. Data was analyzed using SPSS and Python. Examined variables included presence of metastases at diagnosis, surgery, radiotherapy, and chemotherapy reception. Five-year overall survival (OS) curves were analyzed using Kaplan-Meier probability stratified by treatment modality. A total of 5,635 patients with bladder SCC were identified; the most abundantly reported demographics were white race (86%), patient age > 70 years (61%), and male sex (63%). Median survival was 13 months and was significantly decreased in patients treated with chemotherapy or radiation (median survival of nine months or 12 months, respectively). Patients treated with radiation and surgery saw a decreased 10-year survival rate of about 10% if no chemotherapy was administered; this difference narrowed to about a 4% decrease in patients treated with chemotherapy. All radical surgical procedures (to include partial and total cystectomy, and total and posterior exenteration) resulted in a statistically significant increase in survival, except for patients that underwent radical cystectomy including anterior exenteration (p = 0.101). Patients that underwent surgery saw an increased median survival of 15 months. Heatmap analysis demonstrated a significant correlation between tumor size and distant metastases (r = 0.898). Preliminary unsupervised cluster analysis identified two distinct patient subgroups characterized most strongly by race and treatment modality used. Bladder SCC carries a high mortality burden, with a median survival time of 13 months after diagnosis. Based on this study's analysis, radical surgery may be the most effective treatment for this disease. This study also sets a precedent for future research, as more advanced machine learning methods (including cluster and binary tree analysis, as was done in this analysis) can be used to identify the most explanatory variables related to improved patient outcomes.

P-456 Survey on real world quality of life (QoL) issues relevant to distinct AL amyloidosis patient subgroups

P-456 Survey on real world quality of life (QoL) issues relevant to distinct AL amyloidosis patient subgroups

Abstract P424: Postpartum Blood Pressure Trajectories After Hypertensive Disorders Of Pregnancy

Background: Following a hypertensive disorder during pregnancy, blood pressure (BP) generally decreases rapidly in the first three weeks postpartum followed by a period of stabilization. Little is known about patterns of deviation from this trend. Methods: Patients with hypertension during pregnancy (chronic, gestational, or during delivery hospitalization) were enrolled in a remote BP monitoring program and provided with a cell-enabled cuff to take daily BP measurements during their first six weeks postpartum. Measurements were transmitted to a portal and integrated with their electronic medical record. Patients delivering between January 2021 and December 2022 with ≥2 BP measures at an urban safety net hospital were included (n=1,571). Quadratic group-based trajectory models were used to identify latent patterns of postpartum systolic BP (SBP) and diastolic BP (DBP) over time, a method that can be used to capture clinically distinct sub-groups of patients. Patients were grouped into one of four trajectories based on posterior probabilities. Demographic and reproductive characteristics of each trajectory group were examined. Results: Based on 1,571 patients with an average of 15 postpartum BP measurements per patient, the group trajectory model identified 4 trajectories of SBP and DBP (labeled as starting BP value-rate of BP decline): (1) low-steep (SBP 123, DBP 78), (2) moderate-steep (SBP 134, DBP 87), (3) high-gradual (SBP 143, DBP 93), and (4) high-flat (SBP 146, DBP 95). The percent of patients falling into each SBP trajectory group were (1)22.6 (n=346), (2)38.0 (n=611), (3)28.6 (n=451), (4)10.8 (n=163), and for DBP were (1)18.2 (n=288), (2)35.4 (n=557), (3)31.9 (n=508), and (4)14.4 (n=218). Key demographic and pregnancy characteristics varied across SBP and DBP trajectory groups. The high-flat groups were most likely to be older (≥36 years) and non-Hispanic Black. The high-flat SBP and DBP groups had the highest percentage of preterm births (26.4%, 43 out of 163; 20.6%, 45 out of 218, respectively). Conclusion: In this on-going observational study, we characterized 4 distinct group trajectories of SBP and DBP in the 6-weeks postpartum. These data may aid in the identification of patients at risk of persistent postpartum hypertension.

Membrane Trafficking-Related Genes Predict Tumor Immune Microenvironment and Prognosis in Colorectal Cancer.

Colorectal cancer (CRC) is a heterogeneous disease with varying clinical outcomes. The identification of distinct subgroups of CRC patients based on molecular profiling can aid in better understanding the disease and improving patient outcomes. This study aimed to investigate the potential of membrane trafficking-related genes (MTRGs) in sub-grouping colorectal cancer patients based on their overall survival and immune microenvironments. Consensus clustering analysis identified two distinct clusters with different expression profiles of membrane trafficking-related genes. The patients in cluster 1 had a significantly better overall survival than those in cluster 2. Furthermore, the immune microenvironments in the two clusters were also found to be significantly different, with cluster 1 having a higher immune score and more immune cells present. Functional analysis of differentially expressed genes between the two clusters revealed that MTRGs were involved in immune response and metabolic processes, and a risk signature model based on MTRGs was established to predict the prognosis of CRC patients. These findings suggest that MTRGs play a crucial role in the immune microenvironment and overall survival of CRC patients and may provide a potential target for personalized therapy.