Cluster 1 Research Articles

Acute Metabolic Response, Neuromuscular Activity, and Mechanical Performance to Different Set.

The aim of this study was to examine the acute metabolic response, neuromuscular activity, and mechanical performance of different set configurations in bench-press (BP). Twenty-two resistance-trained men performed three resistance exercise protocols consisting of 3 x 12 BP repetitions at 60% 1RM, with 4 minutes of rest between sets, but with different set configurations: (a) traditional set (TS), without rest within the set; (b) cluster-6 (CS6), with 30-second intraset rest after the sixth repetition in each set; and (c) cluster-2 (CS2), with 30-second intraset rest every two repetitions. Mean propulsive force (MPF), velocity (MPV), power (MPP), and electromyography (EMG) values were recorded for each repetition. Blood lactate, maximal voluntary isometric BP contraction, and dynamic strength in BP were assessed pre- and post-exercise. The CS2 protocol resulted in greater mechanical performance (i. e. MPF, MPV, and MPP) and lower alterations of EMG parameters (i. e. root mean square and median frequency) during the exercise compared to CS6 and TS (TS<CS6<CS2). The CS2 protocol induced smaller increases in lactate compared to TS and CS6. No significant "protocol x time" interactions were observed for the MVIC (maximal voluntary isometric BP contraction) variables. Introducing short but frequent intraset rest periods alleviates training-induced fatigue assessed by better performance maintenance.

Acetylation model predicts prognosis of patients and affects immune microenvironment infiltration in epithelial ovarian carcinoma

BackgroundEpithelial ovarian carcinoma (EOC) is a prevalent gynaecological malignancy. The prognosis of patients with EOC is related to acetylation modifications and immune responses in the tumour microenvironment (TME). However, the relationships between acetylation-related genes, patient prognosis, and the tumour immune microenvironment (TIME) are not yet understood. Our research aims to investigate the link between acetylation and the tumour microenvironment, with the goal of identifying new biomarkers for estimating survival of patients with EOC.MethodsUsing data downloaded from the tumour genome atlas (TCGA), genotypic tissue expression (GTEx), and gene expression master table (GEO), we comprehensively evaluated acetylation-related genes in 375 ovarian cancer specimens and identified molecular subtypes using unsupervised clustering. The prognosis, TIME, stem cell index and functional concentration analysis were compared among the three groups. A risk model based on differential expression of acetylation-related genes was established through minimum absolute contraction and selection operator (LASSO) regression analysis, and the predictive validity of this feature was validated using GEO data sets. A nomogram is used to predict a patient's likelihood of survival. In addition, different EOC risk groups were evaluated for timing, tumour immune dysfunction and exclusion (TIDE) score, stemness index, somatic mutation, and drug sensitivity.ResultsWe used the mRNA levels of the differentially expressed genes related to acetylation to classify them into three distinct clusters. Patients with increased immune cell infiltration and lower stemness scores in cluster 2 (C2) exhibited poorer prognosis. Immunity and tumourigenesis-related pathways were highly abundant in cluster 3 (C3). We developed a prognostic model for ten differentially expressed acetylation-related genes. Kaplan–Meier analysis demonstrated significantly worse overall survival (OS) in high-risk patients. Furthermore, the TIME, tumour immune dysfunction and exclusion (TIDE) score, stemness index, tumour mutation burden (TMB), immunotherapy response, and drug sensitivity all showed significant correlations with the risk scores.ConclusionsOur study demonstrated a complex regulatory mechanism of acetylation in EOC. The assessment of acetylation patterns could provide new therapeutic strategies for EOC immunotherapy to improve the prognosis of patients.

Unsupervised Machine Learning inCountermovement Jump andIsometric Mid-Thigh Pull Performance Produces Distinct Combat andPhysical Fitness Clusters inMale andFemale U.S. Marine Corps Recruits.

Several challenges face the U.S. Marine Corps (USMC) and other services in their efforts to design recruit training to augment warfighter mobility and resilience in both male and female recruits as part of an integrated model. Strength and power underpin many of the physical competencies required to meet the occupational demands one might face in military. As the military considers adopting force plate technology to assess indices of strength and power, an opportunity presents itself for the use of machine learning on large datasets to deduce the relevance of variables related to performance and injury risk. The primary aim of this study was to determine whether cluster analysis on baseline strength and power data derived from countermovement jump (CMJ) and isometric mid-thigh pull (IMTP) adequately partitions men and women entering recruit training into distinct performance clusters. The secondary aim of this study is then to assess the between-cluster frequencies of musculoskeletal injury (MSKI). Five hundred and sixty-five males (n = 386) and females (n = 179) at the Marine Corps Recruit Depots located at Parris Island and San Diego were enrolled in the study. Recruits performed CMJ and IMTP tests at the onset of training. Injury data were collected via medical chart review. Combat fitness test (CFT) and physical fitness test (PFT) results were provided to the study team by the USMC. A k-means cluster analysis was performed on CMJ relative peak power, IMTP relative peak force, and dynamic strength index. Independent sample t-tests and Cohen's d effect sizes assessed between-cluster differences in CFT and PFT performance. Differences in cumulative incidence of lower extremity %MSKIs were analyzed using Fisher's exact test. Relative risk and 95% confidence intervals (CIs) were also calculated. The overall effects of cluster designation on CMJ and IMTP outcomes ranged from moderate (relative peak power: d = -0.68, 95% CI, -0.85 to -0.51) to large (relative peak force: d = -1.69, 95% CI, -1.88 to -1.49; dynamic strength index: d = 1.20, 95% CI, 1.02-1.38), indicating acceptable k-means cluster partitioning. Independent sample t-tests revealed that both men and women in cluster 2 (C2) significantly outperformed those in cluster 1 (C1) in all events of the CFT and PFT (P < .05). The overall and within-gender effect of cluster designation on both CFT and PFT performance ranged from small (d > 0.2) to moderate (d > 0.5). Men in C2, the high-performing cluster, demonstrated a significantly lower incidence of ankle MSKI (P = .04, RR = 0.2, 95% CI, 0.1-1.0). No other between-cluster differences in MSKI were statistically significant. Our results indicate that strength and power metrics derived from force plate tests effectively partition USMC male and female recruits into distinct performance clusters with relevance to tactical and physical fitness using k-means clustering. These data support the potential for expanded use of force plates in assessing readiness in a cohort of men and women entering USMC recruit training. The ability to pre-emptively identify high and low performers in the CFT and PFT can aid in leadership developing frameworks for tailoring training to enhance combat and physical fitness with benchmark values of strength and power.

Stemness subtypes in lower-grade glioma with prognostic biomarkers, tumor microenvironment, and treatment response

Our research endeavors are directed towards unraveling the stem cell characteristics of lower-grade glioma patients, with the ultimate goal of formulating personalized treatment strategies. We computed enrichment stemness scores and performed consensus clustering to categorize phenotypes. Subsequently, we constructed a prognostic risk model using weighted gene correlation network analysis (WGCNA), random survival forest regression analysis as well as full subset regression analysis. To validate the expression differences of key genes, we employed experimental methods such as quantitative Polymerase Chain Reaction (qPCR) and assessed cell line proliferation, migration, and invasion. Three subtypes were assigned to patients diagnosed with LGG. Notably, Cluster 2 (C2), exhibiting the poorest survival outcomes, manifested characteristics indicative of the subtype characterized by immunosuppression. This was marked by elevated levels of M1 macrophages, activated mast cells, along with higher immune and stromal scores. Four hub genes—CDCA8, ORC1, DLGAP5, and SMC4—were identified and validated through cell experiments and qPCR. Subsequently, these validated genes were utilized to construct a stemness risk signature. Which revealed that Lower-Grade Glioma (LGG) patients with lower scores were more inclined to demonstrate favorable responses to immune therapy. Our study illuminates the stemness characteristics of gliomas, which lays the foundation for developing therapeutic approaches targeting CSCs and enhancing the efficacy of current immunotherapies. By identifying the stemness subtype and its correlation with prognosis and TME patterns in glioma patients, we aim to advance the development of personalized treatments, enhancing the ability to predict and improve overall patient prognosis.

Analysis Of Poverty Level Mapping In Riau Province Using The K-Means Method

This research aims to understand the pattern of poverty distribution in Riau Province, identify clusters that reflect similar characteristics and provide a basis for developing more targeted policies. This approach uses machine learning techniques, especially the K-Means algorithm, to form clusters based on poverty level data. The results of the analysis show cluster 0 (C0) with a high poverty level and cluster 1 (C1) with a low poverty level. K-Means proved effective in grouping areas with similar levels of poverty, and provided a strong foundation for further analysis. Evaluation results using the Adjusted Rand Index (ARI), Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index show that the quality of cluster formation is good. This analysis provides detailed insight into poverty patterns in Riau Province and provides an empirical basis for implementing more contextual policies.

Identification of Breast Cancer Immune-related Prognostic Characteristics in Tumor Microenvironment.

Accumulated evidence suggest that tumor microenvironment (TME) plays a crucial role in breast cancer (BRCA) progression and therapeutic effects. This study aimed to characterize immune-related BRCA subtypes in TME, and identify genes with prognostic value. RNA sequencing profiles with corresponding clinical data from The Cancer Genome Atlas (TCGA) database of BRCA patients were downloaded to evaluate immune infiltration using the single-sample gene set enrichment (ssGAEA) algorithm. Further, BRCA was clustered according to immune infiltration status by consensus clustering analysis. Using Venn analysis, differentially expressed genes (DEGs) were overlapped to obtain candidate genes. Kaplan-Meier (K-M) analysis was performed to identify prognostic genes, and the results were verified in the GEO and METABRIC datasets. RT-qPCR was conducted to detect the mRNA expression of prognostic genes. In the TCGA database, 3 immune-related BRCA subtypes were identified [cluster1 (C1), cluster2 (C2), and cluster3 (C2)]. The C2 subtype had better overall survival (OS) compared to the C1 subtype. Higher levels of immune markers and checkpoint protein were found in the C2 subtype than in others. By combining DEGs between BRCA and normal tissues, with the C1 and C2 subtypes associated with different OS, 25 BRCA candidate genes were identified. Among these, 8 genes were identified as prognostic genes for BRCA. RT-qPCR showed that the expressions of 2 genes were significantly elevated in BRCA tissues, while that of other genes were decreased. Three BRCA subtypes were identified with the immune index, which may help design advanced treatment of BRCA. The data code used for the analysis in this article was available on GitHub (https://github.com/tangzhn/BRCA1.git).

Cluster Analysis to Identify Clinical Subtypes of Ménière's Disease.

To identify distinct clinical subtypes of Ménière's disease by analyzing data acquired from a UK registry of patients who have been diagnosed with Ménière's disease. Observational study. Patients with Ménière's disease were identified at secondary/tertiary care clinics. Cluster analysis was performed by grouping participants sharing similar characteristics and risk factors into groups based on a defined measure of similarity. A total of 411 participants were recruited into this study. Two main clusters were identified: participants diagnosed with ear infections (OR = 0.30, p < 0.014, 95% CI: 0.11-0.78) were more likely to be allocated in Cluster 1 (C1). Participants reporting tinnitus in both ears (OR = 11.89, p < 0.001, 95% CI: 4.08-34.64), low pitched tinnitus (OR = 21.09, p < 0.001, 95% CI: 7.47-59.54), and those reporting stress as a trigger for vertigo attacks (OR = 14.94, p < 0.001, 95% CI: 4.54-49.10) were significantly more likely to be in Cluster 2 (C2). Also, participants diagnosed with Benign Paroxysmal Positional Vertigo (OR = 13.14, <0.001, 95% CI: 4.35-39.74), autoimmune disease (OR = 5.97, p < 0.007, 95% CI: 1.62-22.03), depression (OR = 4.72, p < 0.056, 95% CI: 0.96-23.24), migraines (OR = 3.13, p < 0.008, 95% CI: 1.34-7.26), drug allergy (OR = 3.25, p < 0.029, 95% CI: 1.13-9.34), and hay fever (OR = 3.12, p < 0.009, 95% CI: 1.33-7.34) were significantly more likely to be clustered in C2. This study supports the hypothesis that Ménière's disease is a heterogeneous condition with subgroups that may be identifiable by clinical features. Two main clusters were identified with differing putative etiological factors. 3 Laryngoscope, 134:3286-3292, 2024.

Assessing learning gains of pharmacy students in communications, ways of thinking, and intercultural skills through self-assessment

Background and purposeIntercultural and Global Health Issues was developed to address learning outcomes in communication, ways of thinking, intercultural personal skills, and intercultural knowledge. The aim of this study was to assess learning gains of pharmacy students through self-assessment. Educational activity and settingCourse design, learning outcomes, objectives, and activities were created to meet the expected educational outcomes. A revised rubric was created from the American Association of Colleges and Universities (AACU) Valid Assessment of Learning in Undergraduate Education rubrics on Intercultural knowledge, Information literacy, and Creative thinking. Students completed self-assessments at the beginning and end of the course. Ward hierarchical clustering, paired sample t-tests, and independent t-tests analyzed multidimensional data in two clusters. FindingsCluster 1 (C1) students reported pre-course capstone performances for cultural self-awareness, problem solving, and access and use of information ethically and legally. Post-course scores for C1 students statistically increased for all AACU domains reaching capstone performances for intercultural competence, creative thinking, and information literacy. Cluster 2 (C2) students reported capstone performance levels for all AACU domains from the beginning to the end of the course. All students reported achievement of self-efficacy, creative thinking, and cultural competency at the end of the course. There was no statistically significant difference in course learning outcome scores for C1 and C2 students. SummaryStudents achieved embedded learning outcomes of ways of thinking, communication, interpersonal skills, and intercultural knowledge as demonstrated from self-assessments. Course activities aided students' demonstration of self-efficacy, creative thinking, and intercultural knowledge.

DIA-Based Proteomics Reveals the Protective Effect of Quercetin against Cyclophosphamide-Induced Testicular Toxicity in Mice

Cyclophosphamide (CP) is a common employed immunosuppressive and anti-neoplastic alkylating antineoplastic drug with documented male reproductive harm. Quercetin, a widely distributed anti-oxidant flavonoid, has been demonstrated to have a variety of biological roles, including tissue-protective properties. Here, administration of quercetin alleviated the CP-induced damage to the testicles, as evidenced by improvements in body weight, testis and epididymis index, serum testosterone concentration, the pathomorphological of testis, and reduced apoptosis in testicular cells. Moreover, Proteomic data indicated that all DEPs were classified into five categories, Cluster 1 (C1) and C4 were regulated by cyclophosphamide and quercetin. KEGG pathway analyses showed that mTOR signaling pathways and Proteasome were the most abundant pathways in C1 and C4, respectively. In the protein-protein interaction (PPI) network analysis, quercetin inhibits the expression of GSK3B, EIF4E, and RPS6KA2 proteins in CP-induced spermatogenesis failure in mice via the mTOR signaling pathway, protecting against spermatogonia injury. On the other hand, quercetin alleviates spermatogonia injury by stimulating the production of Rps23, Rpl8, Rps11, and Mrps17 proteins in CP-induced testis injury in mice via the pathways of Proteasome. Our results have showed that quercetin has a putative protective effect via modulating the pathways of mTOR and Proteasome in CP induced testis injury

Stroma-associated FSTL3 is a factor of calcium channel-derived tumor fibrosis

Hepatocellular carcinoma (HCC) is the most widespread histological form of primary liver cancer, and it faces great diagnostic and therapeutic difficulties owing to its tumor diversity. Herein, we aim to establish a unique prognostic molecular subtype (MST) and based on this to find potential therapeutic targets to develop new immunotherapeutic strategies. Using calcium channel molecules expression-based consensus clustering, we screened 371 HCC patients from The Cancer Genome Atlas to screen for possible MSTs. We distinguished core differential gene modules between varying MSTs, and Tumor Immune Dysfunction and Exclusion scores were employed for the reliable assessment of HCC patient immunotherapeutic response rate. Immunohistochemistry and Immunofluorescence staining were used for validation of predicted immunotherapy outcomes and underlying biological mechanisms, respectively. We identified two MSTs with different clinical characteristics and prognoses. Based on the significant differences between the two MSTs, we further identified Follistatin-like 3 (FSTL3) as a potential indicator of immunotherapy resistance and validated this result in our own cohort. Finally, we found that FSTL3 is predominantly expressed in HCC stromal components and that it is a factor in enhancing fibroblast-M2 macrophage signaling crosstalk, the function of which is relevant to the pathogenesis of HCC. The presence of two MSTs associated with the calcium channel phenotype in HCC patients may provide promising directions for overcoming immunotherapy resistance in HCC, and the promotion of FSTL3 expressed in stromal components for HCC hyperfibrosis may be responsible for the poor response rate to immunotherapy in Cluster 2 (C2) patients.

Factors influencing consumer motivations for protein choice.

This study evaluated the factors that motivate US consumers (18-65 years) to choose protein products derived from specific protein sources. An online survey was conducted. Participants who purchased protein products (n=673) were shown agree/disagree questions, along with maximum difference (MaxDiff), constant sum, and Kano questions on factors surrounding protein choice. Last, follow-up qualitative interviews were conducted with 51 survey participants to further investigate consumer motivations behind protein choice. Survey participants conceptually desired a protein product or protein-fortified food that was a good source of protein, tasted great, and was healthy. Three clusters of consumers with distinct motivations for protein purchases were identified. Cluster 1 (C1) consumers (n=176) desired plant-based, environmentally friendly products and valued sustainability label claims more than flavor/taste. Cluster 2 (C2) consumers (n=271) were nutritionally conscious and desired high-protein healthy products that were also high in vitamins/minerals. Cluster 3 (C3) consumers (n=226) showed the most loyalty to the products they currently purchased and were also most willing to try new products based on the recommendations. Cluster 1 consumers placed importance on protein sources, while C2 valued price most and C3 gave the highest value to flavor. In side-by-side protein comparisons, plant-based proteins were considered superior to dairy proteins in sustainability, health, ethics, and digestibility, while both protein types were at parity for naturalness, satiety, and taste across all consumers, but differences were documented among consumer clusters. Results from this study demonstrate that there are many different motivations for consumers to purchase protein products. These motivations can be applied to consumer education as well as the strategic positioning of protein products. Practical Application: This study investigated consumer perception of different protein types and the drivers of choice for protein types among distinct consumer groups. Further application of findings from this study may help guide the development and formulation of new products with a diverse range of protein sources.

Multi-Dimensional Molecular and Tumor-Microenvironment Analysis of Classic Hodgkin Lymphoma

Introduction Classic Hodgkin Lymphoma (CHL) is currently classified into histological subtypes based on morphology and antigen marker expression. However, additional biological features might help guide treatment strategies and inform prognosis. Here, we aimed to uncover disease heterogeneity and biologic subtypes of CHL based on multi-dimensional molecular profiling ( Fig A) capturing somatic gene mutations, malignant cell expression phenotypes and altered tumor microenvironment (TME) architecture. METHODS We performed exome/targeted sequencing on flow-sorted or micro-dissected HRS cells from fresh-frozen CHL biopsies (n=116) of patients treated at BC Cancer. In addition, we constructed tissue microarrays (TMA) from this cohort on which we performed GeoMx® Whole Transcriptome Assay (WTA) to obtain gene expression profiles from CD30+ HRS cells and multiplexed imaging analyses using multi-colour immunofluorescence (mIF) to delineate the spatial TME ecosystem. Cox regression analysis was performed to identify outcome differences according to molecular features in CHL patients treated with ABVD-like chemotherapy (n=104). Results Mutational and copy number analyses identified known recurrent driver events including mutations and copy number changes in SOCS1, STAT6, TNFAIP3, B2M, REL and the PDL1/PDL2 locus. These mutational findings provided a statistically powered framework to investigate the association between genomic alterations with pathological and clinical disease parameters including outcomes. Mutations affecting BCL7A, TNFAIP3, and 2p15 (REL) amplifications were strongly enriched in EBV- CHL compared to EBV+ CHL. In line with “thymic mutations” reported in grey zone lymphoma, STAT6, GNA13, ITPKB, and TBL1XR1 mutations were significantly enriched in CHL with involvement of the anterior mediastinum. ZNF217, SPEN and CD58 mutations were significantly associated with progression-free survival (PFS) (all P &amp;lt; .01). When investigating outcome correlation according to age group, genomic alteration in STAT6 including both single nucleotide variants and STAT6 amplification, was the most significant feature associated with unfavorable PFS in patients &amp;lt;45 years of age ( P = .013). Alterations in the antigen recognition system, including loss of major histocompatibility complex (MHC) is one of the most studied mechanisms of immune escape in CHL. Mutations in CSF2RB, GNA13, TNFAIP3, and STAT6 were significantly associated with MHC-I loss. Strikingly, all cases harboring CSF2RB mutations (n=23) also showed loss of MHC-I. GeoMx analysis revealed that IFN-a and IFN-g pathways were significantly upregulated in MHC-I+ HRS cells. Mutations in HIST1H1E and BCL7A were significantly enriched in cases with loss of both MHC-I and MHC-II expression. To define mutational subtypes of CHL, we next applied non-negative matrix factorization (NMF) consensus clustering and discovered four robust subsets of tumors (clusters) using recurrent genomic events. Cluster1 (C1) is characterized by mutations in TNFAIP3, CSF2RB and GNA13, younger age, loss of MHC-I, and up-regulation of GCB and fatty acid metabolism signatures. C2 was associated with old age at initial diagnosis, EBV positivity and significant upregulation of the IFN-g pathway. C3 is characterized by REL and STAT6 amplification andexpression of a DNA repair signature. C4 includes samples from mostly younger patients with STAT6 and B2M mutations and high expression of CCL17 by HRS cells. MIF analyses further identified correlations between each mutational NMF cluster and TME composition ( Fig B), and in particular specific CD4+ T cell subsets. The TME of tumors was significantly enriched by C1: FOXP3+Tregs, C2: LAG3+CD4+ cells, and C3: PD1+CD4+ cells. C4 tumors showed no positive correlation with any of these immune cell compartments. C2 tumors were also significantly associated with the abundance of CD68+ macrophages. Conclusion Our multi-dimensional profiling approach enabled us to delineate molecular profiles of HRS cells that are linked to distinct TME patterns. These linkages have implications for current pathogenesis models, molecular subtyping of CHL, and identification of cellular vulnerabilities that might be therapeutically exploitable via targeting of HRS cell phenotypes and/or immune escape mechanisms.

Harnessing Btki Therapy By CDK4/6i Control of T Effector Memory Cells and T Cell Surveillance in Mantle Cell Lymphoma

Drug resistance remains a formidable challenge in mantle cell lymphoma (MCL). Cell cycle dysregulation driven by aberrant Cyclin D1 and CDK4 expression is a hallmark for MCL, providing a rationale for targeting the cell cycle in MCL therapy. We have demonstrated in preclinical studies that inhibition of CDK4/6 not only induces early G1 cell cycle arrest, but also reprograms MCL cells for cytotoxic killing. On this basis, we investigated if inhibition of cyclin D1/CDK4 with palbociclib (CDK4/6 inhibitor) could reprogram recurrent MCL to deepen and prolong the ibrutinib (BTK inhibitor) response in a phase I clinical trial, and the resistance mechanism by longitudinal genomic analysis of sequential samples from individual patients in the context of the clinical response. Palbociclib was administered to MCL patients on days 1-21 of a 28-day cycle; ibrutinib was given continuously. For longitudinal genomic analysis, sequential tissue and blood specimens from 27 evaluable MCL patients before and during therapy, and on progression were collected. Single cell RNA-seq (scRNA-seq) of PBMC or the monocytic fractions from bone marrow and lymph node (53 samples) was performed using a unique in house MCL cell-specific library. PBMC from normal donors (n=4) and untreated MCL patients (n=4) served as controls. The data were then subject to multiplex analysis with whole transcriptome sequencing and whole exome sequencing of MCL cells isolated from the same specimens, as well as flowcytometry in conjunction with IHC. Total numbers of MCL and immune cells in each specimen were deduced by integrating complete blood counts (CBC) with differential with scRNA-seq or flowcytometry. CDK4/6 Inhibition appears to deepen and prolong the BTKi response, with a CR rate of 42% and 5 patients (2 CR and 3 PR) remained on therapy for ~ 9 years. Longitudinal scRNA-seq (210,000 cells) revealed that MCL cells comprise 4 major transcriptomically distinct clusters. Cluster 1 (C1) resembles quiescent normal B cells; C2 mimics hyper-activated B cells that are enriched for signatures of BCR and cytokine signaling; C3 represents non-proliferating, long-lived MCL cells; and C4 is highly proliferative. RNA velocity analysis further indicates that having progressed to late G1 after evading CDK4/6 inhibition, MCL cells in C2 are in transit to C4. By contrast, C3 is a terminal node of MCL cells arrested in late G1 with elevated BCL2 expression Primary resistance and progression on therapy were associated with a marked expansion of either long-live non-proliferating C3 MCL cells, or C2 MCL cells that fuel the proliferating C4 MCL cells. Resistance was also associated with a profound reduction in MHC I and MHC II expression on MCL cells in some patients. Moreover, concurrent with disease progression, CD8+ T and CD+T cells were rapidly depleted by different mechanisms ( Figure 1). CD8+ T cells were proliferating and eliminated by exhaustion, as evidenced by the marked expression of LAG3 TIGIT and TIM3, and a failure to maintain CD8+ T effector memory cells. By contrast, CD4+ T cells were not proliferating and appeared die from glucose starvation. Collectively, these data suggest that T cell maintenance and surveillance is pivotal in sustaining a durable clinical response to combined inhibition of CDK4/6 and BTK. Guided by these discoveries, we have now restored BTKi sensitivity ex vivo in MCL cells from resistant patientsby targeting patient-specific expansion of C2 or C3 in vivo. In one resistant patient, this led to the maintenance of CD4+ and CD8+ T effector memory cells and a CR in response to venetoclax plus BTKi for nearly 3 years and continuing. In summary, by integrated longitudinal scRNA-seq analysis of a hypothesis-driven therapy, we have provided the first evidence that 1) MCL cells comprise 4 major transcriptomically distinct clusters; 2) resistance to CDK4/6i is associated with expansion of C2 that fuels the proliferating C4 MCL cells, or the long-lived C3 MCL cells with elevated BCL2 expression; 3) CDK4/6i harnesses BTKi response by promoting the maintenance of effector memory T cells; 4) combined inhibition of BCL2 and BTK overrides resistance to CDK4/6i and BTKi, leading to a durable clinical response; and 5) T cell surveillance is pivotal for a durable response to CDK4/6, BTK or BCL2 inhibition; implicating genome-guided combination therapy to overcome therapy resistance in MCL.

Molecular-level chemical composition of aerosol and its potential source tracking at Antarctic Peninsula

Marine organic aerosols play crucial roles in global climatic systems. However, their chemical properties and relationships with various potential organic sources still need clarification. This study employed high-resolution mass spectrometry to investigate the identity, origin, and transportation of organic aerosols in pristine Antarctic environments (King Sejong Station; 62.2°S, 58.8°W), where complex ocean-cryosphere-atmosphere interactions occur. First, we classified the aerosol samples into three clusters based on their air mass transport history. Next, we investigated the relationship between organic aerosols and their potential sources, including organic matter dissolved in the open ocean, coastal waters, and runoff waters. Cluster 1 (C1), in which the aerosols mainly originated from the open ocean area (i.e., pelagic zone-influenced), exhibited a higher abundance of lipid-like and protein-like organic aerosols than cluster 3 (C3), with ratios 1.8- and 1.6-times higher, respectively. In contrast, C3, characterized by longer air mass retention over sea ice and land areas (i.e., inshore-influenced), had higher lignin- and condensed aromatic structures (CAS)-like organic aerosols by 2.2- and 3.4-times compared to C1. Cluster 2 (C2) has intermediate characteristics between C1 and C3 concerning the chemical properties of the aerosols and air mass travel history. Notably, the chemical properties of the aerosols assigned to C1 are closely related to those of phytoplankton-derived organics enriched in the open ocean. In contrast, those of C3 are comparable to those of terrestrial plant-derived organics enriched in coastal and runoff waters. These findings help evaluate the source-dependent properties of organic aerosols in changing Antarctic environment.

Comprehensive profiling of enhancer RNA in stage II/III colorectal cancer defines two prognostic subtypes with implications for immunotherapy.

Recently, enhancer RNAs (eRNAs) have garnered attention as pivotal biomarkers for the onset and progression of cancer. However, the landscape of eRNAs and the implications of eRNA-based molecular subtypes in stage II/III colorectal cancer (CRC) remain largely unexplored. Comprehensive profiling of eRNAs was conducted on a public stage II/III CRC cohort with total RNA-seq data. We used unsupervised clustering of prognostic eRNAs to establish an eRNA-based subtyping system. Further evaluations included molecular characteristics, immune infiltration, clinical outcomes, and drug responses. Finally, we validated the eRNA-based subtyping system in The Cancer Genome Atlas (TCGA) CRC cohort. We identified a total of 6453 expressed eRNAs, among which 237 were prognostic. A global upregulation of eRNAs was observed in microsatellite-stable (MSS) CRCs when compared to microsatellite instability-high (MSI-H) CRCs. Through consensus clustering, two novel molecular subtypes, termed Cluster 1(C1) and Cluster 2(C2), were further identified. C1, associated with the activation of epithelial-mesenchymal transition (EMT), hypoxia, and KRAS signaling pathways, showed poorer prognosis. C2, correlated with the canonical CRC subtype, exhibited superior survival outcomes. In addition, C1 showed enrichment with immune infiltration and more sensitivity to immune checkpoint inhibitors. Our study unravels the molecular heterogeneity of stage II/III CRC at the eRNA level and highlights the potential applications of the novel eRNA-based subtyping system in predicting prognosis and guiding immunotherapy.

Proteomic analysis identifies subgroups of patients with active systemic lupus erythematosus

ObjectiveSystemic lupus erythematosus (SLE) is a clinically and biologically heterogenous autoimmune disease. We aimed to investigate the plasma proteome of patients with active SLE to identify novel subgroups, or endotypes, of patients.MethodPlasma was collected from patients with active SLE who were enrolled in the British Isles Lupus Assessment Group Biologics Registry (BILAG-BR). The plasma proteome was analysed using a data-independent acquisition method, Sequential Window Acquisition of All theoretical mass spectra mass spectrometry (SWATH-MS). Unsupervised, data-driven clustering algorithms were used to delineate groups of patients with a shared proteomic profile.ResultsIn 223 patients, six clusters were identified based on quantification of 581 proteins. Between the clusters, there were significant differences in age (p = 0.012) and ethnicity (p = 0.003). There was increased musculoskeletal disease activity in cluster 1 (C1), 19/27 (70.4%) (p = 0.002) and renal activity in cluster 6 (C6) 15/24 (62.5%) (p = 0.051). Anti-SSa/Ro was the only autoantibody that significantly differed between clusters (p = 0.017). C1 was associated with p21-activated kinases (PAK) and Phospholipase C (PLC) signalling. Within C1 there were two sub-clusters (C1A and C1B) defined by 49 proteins related to cytoskeletal protein binding. C2 and C6 demonstrated opposite Rho family GTPase and Rho GDI signalling. Three proteins (MZB1, SND1 and AGL) identified in C6 increased the classification of active renal disease although this did not reach statistical significance (p = 0.0617).ConclusionsUnsupervised proteomic analysis identifies clusters of patients with active SLE, that are associated with clinical and serological features, which may facilitate biomarker discovery. The observed proteomic heterogeneity further supports the need for a personalised approach to treatment in SLE.

Identification of STEAP3-based molecular subtype and risk model in ovarian cancer

BackgroundOvarian cancer (OC) is one of the most common malignancies in women. It has a poor prognosis owing to its recurrence and metastasis. Unfortunately, reliable markers for early diagnosis and prognosis of OC are lacking. Our research aimed to investigate the value of the six-transmembrane epithelial antigen of prostate family member 3 (STEAP3) as a prognostic predictor and therapeutic target in OC using bioinformatics analysis.MethodsSTEAP3 expression and clinical data were acquired from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO). Unsupervised clustering was used to identify molecular subtypes. Prognosis, tumor immune microenvironment (TIME), stemness indexes, and functional enrichment analysis were compared between two definite clusters. Through the least absolute shrinkage and selection operator (LASSO) regression analysis, a STEAP3-based risk model was developed, and the predictive effectiveness of this signature was confirmed using GEO datasets. A nomogram was used to predict the survival possibility of patients. Additionally, TIME, tumor immune dysfunction and exclusion (TIDE), stemness indexes, somatic mutations, and drug sensitivity were evaluated in different risk groups with OC. STEAP3 protein expression was detected using immunohistochemistry (IHC).ResultsSTEAP3 displayed marked overexpression in OC. STEAP3 is an independent risk factor for OC. Based on the mRNA levels of STEAP3-related genes (SRGs), two distinct clusters were identified. Patients in the cluster 2 (C2) subgroup had a considerably worse prognosis, higher immune cell infiltration, and lower stemness scores. Pathways involved in tumorigenesis and immunity were highly enriched in the C2 subgroup. A prognostic model based on 13 SRGs was further developed. Kaplan-Meier analysis indicated that the overall survival (OS) of high-risk patients was poor. The risk score was significantly associated with TIME, TIDE, stemness indexes, tumor mutation burden (TMB), immunotherapy response, and drug sensitivity. Finally, IHC revealed that STEAP3 protein expression was noticeably elevated in OC, and overexpression of STEAP3 predicted poor OS and relapse-free survival (RFS) of patients.ConclusionIn summary, this study revealed that STEAP3 reliably predicts patient prognosis and provides novel ideas for OC immunotherapy.

Clustering analysis and prognostic model based on PI3K/AKT-related genes in pancreatic cancer.

Pancreatic cancer is one of most aggressive malignancies with a dismal prognosis. Activation of PI3K/AKT signaling is instrumental in pancreatic cancer tumorigenesis. The aims of this study were to identify the molecular clustering, prognostic value, relationship with tumor immunity and targeting of PI3K/AKT-related genes (PARGs) in pancreatic cancer using bioinformatics. The GSEA website was searched for PARGs, and pancreatic cancer-related mRNA data and clinical profiles were obtained through TCGA downloads. Prognosis-related genes were identified by univariate Cox regression analysis, and samples were further clustered by unsupervised methods to identify significant differences in survival, clinical information and immune infiltration between categories. Next, a prognostic model was constructed using Lasso regression analysis. The model was well validated by univariate and multivariate Cox regression analyses, Kaplan-Meier survival analysis and ROC curves, and correlations between risk scores and patient pathological characteristics were identified. Finally, GSEA, drug prediction and immune checkpoint protein analyses were performed. Pancreatic cancers were divided into Cluster 1 (C1) and Cluster 2 (C1) according to PARG mRNA expression. C1 exhibited longer overall survival (OS) and higher immune scores and CTLA4 expression, whereas C2 exhibited more abundant PD-L1. A 6-PARG-based prognostic model was constructed to divide pancreatic cancer patients into a high-risk score (HRS) group and a low-risk score (LRS) group, where the HRS group exhibited worse OS. The risk score was defined as an independent predictor of OS. The HRS group was significantly associated with pancreatic cancer metastasis, aggregation and immune score. Furthermore, the HRS group exhibited immunosuppression and was sensitive to radiotherapy and guitarbine chemotherapy. Multidrug sensitivity prediction analysis indicated that the HRS group may be sensitive to PI3K/AKT signaling inhibitors (PIK-93, GSK2126458, CAL-101 and rapamycin) and ATP concentration regulators (Thapsigargin). In addition, we confirmed the oncogenic effect of protein phosphatase 2 regulatory subunit B'' subunit alpha (PPP2R3A) in pancreatic cancer in vitro and in vivo. PARGs predict prognosis, tumor immune profile, radiotherapy and chemotherapy drug sensitivity and are potential predictive markers for pancreatic cancer treatment that can help clinicians make decisions and personalize treatment.

Abstract 5470: Identification of gene amplification based signature as predictors for chemotherapy in squamous cell carcinoma of the lung

Abstract Background: Lung squamous cell carcinoma (LUSC) is the second most popular histologic subtype accounting for 25%~30% of lung cancer. Due to the lack of targeted therapies for LUSC, the prognosis is poor and chemotherapy still plays a full role in the treatment of LUSC patients currently. However, a significant proportion of patients showed a bad response to chemotherapy. The development of predictive biomarkers is urgent. We aimed to identify new chemotherapy biomarkers based on genomic alterations. Methods: We retrospectively analyzed DNA sequencing data of 317 LUSC patients (pts) at West China hospital. We collected the clinical features and progression-free survival (PFS) of 35 pts with chemotherapy only. Pts were assigned to different subgroups based on genomic alterations using non-negative matrix factorization (NMF) clustering. Based on the characteristics of the cluster, we then explored clear gene signatures to predict the prognosis of chemotherapy. The clinical information and sequencing data of TCGA LUSC with chemotherapy were used to validate the prognostic value of the signature. Results: The 317 LUSC pts were grouped into 4 clusters characterized by different genomic alterations. Cluster 1 (C1) had 129 pts and was characterized by TP53 alterations. There were 116 pts in cluster 2 (C2) characterized by PIK3CA amplification (amp). Cluster 3 (C3) had 33 pts and was characterized by gene amp of CCND1, FGF3, FGF4, and FGF19. Cluster 4 (C4) was characterized by gene amp of FGFR1, KDR, KIT, and PDGFRA. The 35 pts treated with chemotherapy only were also classified into 4 clusters. C1 was associated with the shortest PFS (hazard ratio (HR), 2.87; 95% confidence interval (CI), 1.15-7.13; p = 0.018) independently of the clinical stage, but C2, C3, and C4 showed no significant difference. In addition, we also found that pts with TP53 loss of function (LOF) alteration had significantly shorter PFS than those without TP53 LOF (wildtype or not LOF, p = 0.029). So we further modified the clustering by TP53 alteration from C1 and gene amp from C2, C3, and C4. Therefore, 35 pts were then grouped into 3 subtypes based on TP53 LOF and gene amp characteristics, and the PFS was significantly different. Pts (n = 8, 23%) with at least one amp of 9 genes (PIK3CA, CCND1, FGF3, FGF4, FGF19, FGFR1, KDR, KIT, and PDGFRA) but no TP53 LOF had the longest PFS (HR, 0.12; 95% CI, 0.02-0.66; p = 0.011). Pts (n = 6) with TP53 LOF but no 9-gene amp had poor survival compared with pts in other subtypes. This gene signature was validated in TCGA pts. The signature of 9-gene amp without TP53 LOF also indicated the best PFS and TP53 LOF without 9-gene amp indicated the worst PFS (p = 0.081). Conclusion: We develop a biomarker signature that consists of 9-gene amp and TP53 LOF to indicate the prognosis of chemotherapy. Our results suggest that 9-gene amp without TP53 LOF in LUSC is a favorable prognostic marker for patients taking chemotherapy. Citation Format: Yuan Tang, Yuli Li, Ting Hou, Lili Jiang, Hongjie Liu, Chunxiao Pan, Weiya Wang, Li Qiu, Yajing Zhang, Guiping Zhang, Ke Zheng. Identification of gene amplification based signature as predictors for chemotherapy in squamous cell carcinoma of the lung. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5470.

Bioinformatics analysis based on DNA methylation data identified in lung adenocarcinoma subgroups with different immune characteristics and clinical outcomes.

DNA methylation can be used to predict clinical outcomes and improve the classification of tumors. The present study aimed to develop a new lung adenocarcinoma (LUAD) classification system according to the immune cell gene-related methylation sites and to reveal the survival outcomes, clinical characteristics, immune cell infiltration, stem cell characteristics, and genomic variations of each molecular subgroup. The DNA methylation sites of LUAD samples collected from The Cancer Genome Atlas (TCGA) database were analyzed, and the prognosis-related differential methylation sites (DMS) were screened. Consistent clustering of the samples was conducted using ConsensusClusterPlus, and the classification results were verified by principal component analysis (PCA). The survival and clinical results, immune cell infiltration, stemness, DNA mutation, and copy number variation (CNV) of each molecular subgroup were analyzed. A total of 40 DMS were obtained by difference and univariate COX analyses, and the TCGA LUAD samples were divided into three subgroups: cluster 1 (C1), cluster 2 (C2), and cluster 3 (C3). Among these subgroups, the overall survival (OS) of C3 was significantly higher than that of C1 and C2. Compared with C1 and C3, C2 had the lowest innate immune cell and adaptive immune cell infiltration scores; the lowest stromal score, immune score, and iconic immune checkpoint expression; and the highest expression of messenger RNA (mRNA) expression-based stemness indices (mRNAsi), DNA methylation-based stemness index (mDNAsi), and tumor mutational burden (TMB). In this study, we proposed a LUAD typing system based on DMS, which was closely related to the survival, clinical features, immune characteristics, and genomic variations of LUAD, and may contribute to the development of personalized therapy for new specific subtypes.