Cluster 1 Research Articles

Upper tract urothelial carcinoma transcriptome profiling and immune microenvironment characterization.

564 Background: Upper tract urothelial carcinoma (UTUC) is an aggressive disease that is risk-stratified by clinicopathological factors due to an incomplete understanding of its molecular features. Thus, we performed transcriptomic profiling of UTUC tumors from radical nephroureterectomy specimens and compared their molecular characterization to survival outcomes. Methods: 100 UTUC tumors from 100 patients were subject to RNA sequencing and a hybridization capture-based assay for deep sequencing of cancer-associated genes, followed by unsupervised nonnegative matrix factorization clustering based on the top 10% of variant genes. Gene Set Enrichment and immune deconvolution analyses assessed for differences in the tumor microenvironments (TME) between clusters. Results: Consensus clustering analysis identified 5 biologically distinct clusters (Cluster 1 (C1) = 17, C2 = 18, C3 = 30, C4 = 11, and C5 = 24 patients), which were associated with significant differences in disease-free (DFS) (p < 0.01) and overall survival (OS) (p = 0.03). C1 and C2 were associated with pT3/4 stages and worse DFS and OS, while C5 was associated with pTa/1 stages and better DFS and OS. In terms of somatic mutation frequency differences, C3 and C4 had overall higher tumor mutation burden and mutations in epigenetic modulators, which corresponds with the transcriptomic finding of higher microsatellite instability expression signatures in these two clusters as well. Of note, all Lynch patients (N = 4) were in C3. C3 was enriched for the presence of FGFR3 driver mutations in 93% of tumors, and TP53 mutations were frequent in C2 and C4 in 47 and 55% of tumors, respectively. Differentially expressed genes and Gene Set Enrichment analyses revealed that C1 and C2 were enriched with several Hallmark inflammation signatures, such as TNF-α signaling via NF-kB, allograft rejection, inflammatory response, IL6 JAK/STAT3 signaling, and IL2 STAT5 signaling. C1 demonstrated a particularly inflammatory phenotype enriched with INF-γ and INF-α response gene sets. Lastly, in the TME deconvolution analysis, C1 and C2 had higher expression of PDL-1, immune checkpoint, immune suppression, cancer-associated fibroblasts, and myeloid inflammation surrogate signatures. C2 and C3 were enriched with CD8 T-cells, while C1 was enriched for INF-γ and hypoxia signatures. C2 had the least hypoxic TME, which may be related to stronger stromal, EMT, and angiogenesis signature signals seen. These results were then validated using an outside institution’s published cohort. Conclusions: Several differences in transcriptomic features indicate heterogeneity among UTUC tumors. Two clusters with high rates of recurrence and worse prognosis are associated with higher immune and myeloid cell infiltration. In addition to clinicopathologic factors, tumor microenvironment immune features may have potential use for disease prognostication.

Stemness Refines the Classification of Colorectal Cancer With Stratified Prognosis, Multi-Omics Landscape, Potential Mechanisms, and Treatment Options.

BackgroundStemness refers to the capacities of self-renewal and repopulation, which contributes to the progression, relapse, and drug resistance of colorectal cancer (CRC). Mounting evidence has established the links between cancer stemness and intratumoral heterogeneity across cancer. Currently, the intertumoral heterogeneity of cancer stemness remains elusive in CRC.MethodsThis study enrolled four CRC datasets, two immunotherapy datasets, and a clinical in-house cohort. Non-negative matrix factorization (NMF) was performed to decipher the heterogeneity of cancer stemness. Multiple machine learning algorithms were applied to develop a nine-gene stemness cluster predictor. The clinical outcomes, multi-omics landscape, potential mechanisms, and immune features of the stemness clusters were further explored.ResultsBased on 26 published stemness signatures derived by alternative approaches, we decipher two heterogeneous clusters, low stemness cluster 1 (C1) and high stemness cluster 2 (C2). C2 possessed a higher proportion of advanced tumors and displayed worse overall survival and relapse-free survival compared with C1. The MSI-H and CMS1 tumors tended to enrich in C1, and the mesenchymal subtype CMS4 was the prevalent subtype of C2. Subsequently, we developed a nine-gene stemness cluster predictor, which robustly validated and reproduced our stemness clusters in three independent datasets and an in-house cohort. C1 also displayed a generally superior mutational burden, and C2 possessed a higher burden of copy number deletion. Further investigations suggested that C1 enriched numerous proliferation-related biological processes and abundant immune infiltration, while C2 was significantly associated with mesenchyme development and differentiation. Given results derived from three algorithms and two immunotherapeutic cohorts, we observed C1 could benefit more from immunotherapy. For patients with C2, we constructed a ridge regression model and further identified nine latent therapeutic agents, which might improve their clinical outcomes.ConclusionsThis study proposed two stemness clusters with stratified prognosis, multi-omics landscape, potential mechanisms, and treatment options. Current work not only provided new insights into the heterogeneity of cancer stemness, but also shed light on optimizing decision-making in immunotherapy and chemotherapy.

A Prognostic Nomogram for Hepatocellular Carcinoma Based on Wound Healing and Immune Checkpoint Genes.

Background and AimsWound healing and tumor progression share some common biological features; however, how variations in wound healing patterns affect hepatocellular carcinoma (HCC) prognosis remains unclear.MethodsWe analyzed the wound healing patterns of 594 HCC samples from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) and correlated them with immune infiltration and the expression levels of immune checkpoint genes. A risk score, which we named the “heal.immune” score, was established via stepwise Cox estimation. We constructed a nomogram based on age, sex, TNM stage, and heal.immune score and explored its predictive value for HCC prognosis. Seventy-four clinical patients were enrolled in this study, and all were from Huashan Hospital of Fudan University between 2015 and 2017 to serve as an independent validation group.ResultsWe identified two distinct wound healing patterns in HCC. The biological processes of healing cluster 1 (C1) are related to metabolism, while those of healing cluster 2 (C2) are related to the inflammatory response and immune cell accumulation. A total of 565 wound healing-related genes (based on Gene Ontology) and 25 immune checkpoint genes were considered. By analyzing differentially expressed genes and implementing a stepwise Cox estimation analysis, six genes with p values less than 0.02 in a multivariate Cox estimation were chosen as the “heal.immune” gene set (FCER1G, PLAT, ITGA5, CCNB1, CD86 and CD40). The “heal.immune” gene set, as an OS risk factor, was further validated in Fudan cohort. We constructed a nomogram to predict the 1-, 3- and 5-year overall survival (OS) in the TCGA cohort. The area under curve vales of the receiver characteristic operator curves were 0.82, 0.76 and 0.73 in the training group and 0.84, 0.76 and 0.72 in the test group.ConclusionsWe established a prognostic nomogram based on the heal.immune gene signature, which includes six wound healing- and immunity-related genes. This nomogram accurately predicts the OS of HCC patients.

Immune Landscape Refines the Classification of Colorectal Cancer With Heterogeneous Prognosis, Tumor Microenvironment and Distinct Sensitivity to Frontline Therapies.

The immune microenvironment has profound impacts on the initiation and progression of colorectal cancer (CRC). Therefore, the goal of this article is to identify two robust immune subtypes in CRC, further provide novel insights for the underlying mechanisms and clinical management. In this study, two CRC immune subtypes were identified using the consensus clustering of immune-related gene expression profiles in the meta-GEO dataset (n = 1,198), and their reproducibility was further verified in the TCGA-CRC dataset (n = 638). Subsequently, we characterized the immune escape mechanisms, gene alterations, and clinical features of two immune subtypes. Cluster 1 (C1) was defined as the “immune cold subtype” with immune cell depletion and deficiency, while cluster 2 (C2) was designed as the “immune hot subtype”, with abundant immune cell infiltration and matrix activation. We also underlined the potential immune escape mechanisms: lack of MHC molecules and defective tumor antigen presentation capacity in C1, increased immunosuppressive molecules in C2. The prognosis and sensitivity to 5-FU, Cisplatin and immunotherapy differed between two subtypes. According to the two immune subtypes, we developed a prognosis associated risk score (PARS) with the accurate performance for predicting the prognosis. Additionally, two nomograms for overall survival (OS) and disease-free survival (DFS) were further constructed to facilitate clinical management. Overall, our research provides new references and insights for understanding and refining the CRC.

Social Support and Weight Outcomes Over A Six-Month Weight Loss Intervention for Rural Older Adults

Creative arts such as acting can promote social contact and bonding among socially isolated populations. Yet the benefits of art programming among older adults in low-income urban settings remain unexplored. A professionally administered theater group comprised of older adults living in urban low-income housing met for 12 weeks to learn acting skills and perform a play. The purpose of this study was to identify: 1) why participants enroll in a residence-based acting and improvisation course, and 2) what aspects of the course contribute to group bonding. Participants (n=14) were African American. The average age was 63 years, 14% were men, 57% had a high school degree or less, 79% reported good to excellent health, and the mean ADL score was 1.45 (range: 1-2.5). A researcher was present at each class session to observe and take field notes. Pre-post interviews included closed and open-ended questions. The researchers reviewed field notes and interview transcripts for a priori themes and emergent themes through independently coding data, discussing similarities and discrepancies, and coming to consensus on themes. Results showed that participants were motivated to enroll to meet new people, come out of themselves, learn something new, and gain artistic skills. Participants indicated the course contributed to group bonding via teaching techniques, course structure, the teacher’s use of self, the expression of self, and mutually enhanced coping skills. Results from this study provide guidance for the design of theater groups in similar settings and inform recruitment efforts of older adults in creative arts programs.

Dynamics of variable dusk–dawn flow associated with magnetotail current sheet flapping

Abstract. We present Cluster spacecraft observations from 12 October 2006 of convective plasma flows in the Earth's magnetotail. Earthward flow bursts with a dawnward v⊥y component, observed by Cluster 1 (C1), are inconsistent with the duskward flow that might be expected at the pre-midnight location of the spacecraft. Previous observations have suggested that the dusk–dawn sense of the flow can be governed by the interplanetary magnetic field (IMF) By conditions, with the related “untwisting hypothesis” of magnetotail dynamics commonly invoked to explain this dependence, in terms of a large-scale magnetospheric asymmetry. In the current study, observations of the upstream solar wind conditions from OMNI, magnetic field observations by Cluster and ionospheric convection data using SuperDARN indicate a large-scale magnetospheric morphology consistent with positive IMF By penetration into the magnetotail. At the pre-midnight location of Cluster, however, the dawnward flow observed below the neutral sheet by C1 could only be explained by the untwisting hypothesis in a negative IMF By scenario. The Cluster magnetic field data also reveal a flapping of the magnetotail current sheet, a phenomenon known to influence dusk–dawn flow. Results from the curlometer analysis technique suggest that the dusk–dawn sense of the J×B force was consistent with localised kinks in the magnetic field and the flapping associated with the transient perturbations to the dusk–dawn flow observed by C1. We therefore suggest that the flapping overcame the dusk–dawn sense of the large-scale convection which we would expect to have been net duskward in this case. We conclude that invocation of the untwisting hypothesis may be inappropriate when interpreting intervals of dynamic magnetotail behaviour such as during current sheet flapping, particularly at locations where magnetotail flaring becomes dominant.

Dementia with Lewy bodies subtypes identified by cluster analysis on structural MRI

AbstractBackgroundDementia with Lewy bodies (DLB) is a neurodegenerative disorder that presents with a variety of clinical symptoms. Part of the clinical heterogeneity within DLB has been related to the atrophy/sparing of the hippocampus. We extended that previous research by investigating whether different atrophy patterns exist in DLB, beyond and above the atrophy/sparing of the hippocampus, and whether those patterns contribute to the clinical heterogeneity in DLB. We aimed to identify DLB atrophy subtypes using a hypothesis‐free data‐driven clustering approach based on regional gray matter (GM) volumes and characterize the resulting subtypes across key measures.MethodWe included data from three centers of the European DLB consortium. A high‐resolution 3D T1‐weigthed magnetization prepared rapid gradient echo (MPRAGE) sequence was acquired for 97 DLB patients. Eighty‐four cortical regions and 12 subcortical regions from Mayo Clinic Adult Lifespan Template (https://www.nitrc.org/projects/mcalt/) atlases were obtained. Clusters of DLB patients were then determined using the random forest method. Between‐group differences in key demographic, clinical, MRI volumes and cerebrospinal fluid biomarkers (CSF) (amyloid‐beta, phosphorylated‐tau) were performed for clusters characterization.ResultWe identified three subtypes of DLB patients, with distinct patterns of GM volumes and differences in several clinical and demographic measures. Cluster 1 (C1) included 32 DLB patients (33%), cluster 2 (C2) included 29 DLB patients (30%), and cluster 3 (C3) included 36 DLB patients (37%). The clusters differed in age, with C1 being significantly older (72±8.6) than C3 (66±9.5) (F=3.93, p=0.02). C1 was characterized by lower volumes in fronto‐temporal regions and had a higher frequency of visual hallucinations (68%, vs. C2 35%). C2 showed lower volumes in occipital regions. C3 had higher GM volumes overall compared with the other two clusters and presented with a higher frequency of cognitive fluctuations (91%, vs. C1 67%). There were no differences between groups in CSF biomarkers. These results were independent of cluster differences in age and global GM volume.ConclusionThere are distinct patterns of brain atrophy in patients with DLB. These patterns seem to differentially contribute to the frequencies of visual hallucinations and cognitive fluctuations, independently of differences in age and global GM volume across subtypes.

Dynamic Immune Surveillance in Durable Clinical Response to Combined BTK and BCL2 Inhibition in MCL at Longitudinal Single-Cell Resolution

Combined inhibition of BTK with ibrutinib and BCL2 with venetoclax is one of the most promising therapies for B cell malignancies, especially mantle cell lymphoma (MCL), where durable complete remission continued after therapy cessation in some patients (Tam et al, NEJM 2018, Handunnetti ASH 2019). The MCL-intrinsic and extrinsic mechanisms underlying this deep and durable clinical response are unknown, nor have resistance mechanisms been identified. Since BTK is expressed mainly in B lineage cells and venetoclax inhibits BCL2 universally, we hypothesize that BTK inhibition selectively primes MCL cells for vulnerability to BCL2 inhibition while maintaining immune cell homeostasis, leading to differential elimination of MCL cells through immune surveillance.To test this, we undertook integrative longitudinal single-cell RNA-sequencing analysis (scRNA-seq) of PBMCs from sequential tissue and blood specimens (n=32) of 8 MCL patients before and during ibrutinib-venetoclax combination therapy, after therapy cessation or progression, as well as 4 treatment-naïve MCL patients and 4 normal subjects as controls. High dimensional analysis using a unique MCL RNA reference library that we built from bulk RNA-seq data of MCL cells from 57 patients reveals that MCL cells comprise 4 transcriptomically distinct clusters. Cluster 1 (C1) is similar to quiescent normal B cells; C2 resembles hyper-activated B cells enriched for signatures of BCR and cytokine signaling and proinflammatory pathways; C3 represents non-proliferating, long-lived MCL cells that accumulate as disease progresses; and C4 is highly proliferative, expanding with disease progression in untreated patients or on therapy.Integrative analysis of scRNA-seq and CBC with differential showed that homeostasis of all immune cells was maintained throughout ibrutinib-venetoclax therapy and after therapy cessation in 6 MCL patients with a complete response (CR). CD8+T and NK cells were functional, evidenced by the expression of cytotoxic genes such as GNLY, FGFBP2, and GZMH. In contrast, these genes were profoundly suppressed in CD8+T cells that were rapidly depleted on MCL progression after transient response in 2 patients. NK cells were also depleted on progression. In one patient, this was preceded by suppression of cytotoxic genes and loss of MHC-I and MHC-II in MCL cells. Exhaustion did not appear to be the cause. Rather, TSC22D3 upregulation suggests that inhibition of TCR-induced IL2 and IL2R expression and NF-kB activation may underlie the loss of CD8+T and NK cells in ibrutinib-venetoclax resistance.To determine whether BTK inhibition primes MCL cells for subsequent venetoclax killing while sparing other immune cells in a clinical response to ibrutinib-venetoclax, we found that BCL2 expression was higher in MCL cells than in other immune cells pre-therapy. Ibrutinib selectively downregulated the anti-apoptotic MCL1 and upregulated PMAIP1 encoding the pro-apoptotic NOXA in MCL cells, concurrent with TSC22D3 induction and inhibition of the NF-kB signaling pathway. As expected from the lack of BTK expression, CD4+ and CD8+T cells and NK cells expressed MCL1 over PMAIP1 in CR patients, but greater PMAIP1 in patients who developed progressive MCL. Collectively, these data support the hypothesis that BTK inhibition selectively primes MCL cells for venetoclax sensitivity.scRNA-seq analysis further showed that B cells emerging after therapy cessation in CR patients were nearly identical to the bone marrow B cells pre-therapy, as quiescent B cells in cluster 1 and activated B cells in cluster 2. None were detected in clusters 3 or 4. The restoration of B cell immunity confirms CR at the single-cell transcriptome level, speaking to the power of dual BTK and BCL2 inhibition, and supporting the role of immune surveillance in clinical response in targeted therapy.In summary, by unbiased longitudinal scRNA-seq analysis of sequential patient specimens from the ibrutinib-venetoclax clinical trial, we have provided the first evidence that priming of MCL cells for venetoclax vulnerability by BTK inhibition cooperates with immune surveillance to determine the depth and durability in combined targeting of BTK and BCL2 in lymphoma in humans. These findings parallel our longitudinal scRNA-seq analysis of dual BTK and CDK4/6 inhibition, suggesting priming of cancer cells and cooperation with immune surveillance underpin targeted therapy. DisclosuresKoldej: CRISPR Therapeutics: Research Funding. Ritchie: CRISPR Therapeutics: Research Funding; Takeda: Research Funding; BMS: Research Funding; Novartis: Honoraria; Amgen Inc: Honoraria, Research Funding; CSL: Honoraria. Martin: ADCT: Consultancy. Elemento: One Three Biotech: Consultancy, Other: Current equity holder; Owkin: Consultancy, Other: Current equity holder; Eli Lilly: Research Funding; AstraZeneca: Research Funding; Johnson and Johnson: Research Funding; Volastra Therapeutics: Consultancy, Other: Current equity holder, Research Funding; Janssen: Research Funding; Freenome: Consultancy, Other: Current equity holder in a privately-held company; Champions Oncology: Consultancy. Tam: Beigene: Research Funding; Loxo: Honoraria; Beigene: Honoraria; Janssen: Honoraria; Abbvie: Honoraria; Abbvie: Research Funding; Janssen: Research Funding. OffLabel Disclosure:Venetoclax is a BCL2 inhibitor FDA-approved for chronic lymphocytic leukemia (CLL), small lymphocytic lymphoma (SLL), and acute myeloid leukemia (AML). It was used off-label in combination with ibrutinib in a phase II clinical trial in patients with mantle cell lymphoma.

Identification of Phenotypes Among COVID-19 Patients in the United States Using Latent Class Analysis.

BackgroundCoronavirus disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 or COVID-19) is a heterogeneous disorder with a complex pathogenesis. Recent studies from Spain and France have indicated that underlying phenotypes may exist among patients admitted to the hospital with COVID-19. Whether those same phenotypes exist in the United States (US) remains unclear. Using latent class analysis (LCA), we sought to determine whether clinical phenotypes exist among patients admitted for COVID-19.MethodsWe reviewed the charts of adult patients who were hospitalized primarily for COVID-19 at Greenwich Hospital and performed LCA using variables based on patient demographics and comorbidities. To further examine the reliability and replicability of the clustering results, we repeated LCA on the cohort of patients who died during hospitalization for COVID-19.ResultsTwo phenotypes were identified in patients admitted for COVID-19 (N = 483). According to phenotype, patients were designated as cluster 1 (C1) or cluster 2 (C2). C1 (n = 193) consisted of older individuals with more comorbidities and a higher mortality rate (25.4% vs 8.97%, p < 0.001) than patients in C2. C2 (n = 290) consisted of younger individuals who were more likely to be obese, male, and nonwhite, with higher levels of the inflammatory markers C-reactive protein and alanine aminotransferase. When we performed LCA on the cohort of patients who died during hospitalization for COVID-19 (n = 75), we found that the distribution of patient baseline characteristics and comorbidities was similar to that of the entire cohort of patients admitted for COVID-19.ConclusionUsing LCA, we identified two clinical phenotypes of patients who were admitted to our hospital for COVID-19. These findings may reflect different pathophysiologic processes that lead to moderate to severe COVID-19 and may be useful for identifying treatment targets and selecting patients with severe COVID-19 disease for future clinical trials.

Regional variation of drought parameters and long-term trends over India using standardized precipitation evapotranspiration index

Analysing historical drought pattern is vital for implementation of efficient drought adaptation and mitigation policies. In this study, we examined the meteorological drought characteristics of India during 1901–2015, using Climate Research Unit (CRU) based Standardized Precipitation Evapotranspiration Index (SPEI) at multiple timescales i.e., 1 month (SPEI01), 3 month (SPEI03), 6 month (SPEI06), 12 month (SPEI12). Here, we applied K-means clustering algorithm on SPEI12 (December) to find out different clusters with distinct drought characteristics. The six different homogeneous regions, i.e., cluster1 (C1), cluster2 (C2), cluster3 (C3), cluster4 (C4), cluster5 (C5), and cluster6 (C6) identified by K mean clustering largely resemble with the clusters mentioned in previous researches. Different drought parameters (duration, frequency, intensity) have been also analysed for each cluster on a monthly, seasonal and interannual basis. The study indicates that northern part of India (C6, C3) experienced frequent droughts at shorter timescales whereas the western (C2) and north eastern (C4) part of the country encountered frequent drought occurrences at longer timescale. It is worthy to mention that the C2 region comprising the semi-arid and arid western part of the country including the great Indian desert, is vulnerable to frequent, prolonged and severe droughts at longer timescale (SPEI12). The study revealed a significant regional variation in drought trends identified by Modified Mann-Kendall (MMK) trend test. The annual trend analysis shows statistically significant (p < 0.05) increasing drought trend over C3 and C4 regions comprising the fertile Gangetic and Brahmaputra plains. The seasonal MMK trend analysis reveals significant increase (p < 0.05) in droughts over C3 (−0.006) and C4 (−0.005) during monsoon. The increasing drought trend over the Gangetic plain (C3) is prominent especially in the months of July (p < 0.05, slope = −0.005) and August (p < 0.001, slope = −0.006). The study provides a region-specific understanding of drought characteristics and long-term trends crucial for preparing adaptive strategies to minimize the cumulative impacts of droughts.

Prospective Evaluation of Clinical Outcomes Using a Multiplex Liquid Biopsy Targeting Diverse Resistance Mechanisms in Metastatic Prostate Cancer.

PURPOSENearly all men with prostate cancer treated with androgen receptor (AR) signaling inhibitors (ARSIs) develop resistance via diverse mechanisms including constitutive activation of the AR pathway, driven by AR genomic structural alterations, expression of AR splice variants (AR-Vs), or loss of AR dependence and lineage plasticity termed neuroendocrine prostate cancer. Understanding these de novo acquired ARSI resistance mechanisms is critical for optimizing therapy.MATERIALS AND METHODSA novel liquid biopsy technology was used to collect mRNA from circulating tumor cells (CTCs) to measure expression of AR-Vs, AR targets, and neuroendocrine prostate cancer markers. An institutional review board–approved prospective cohort (N = 99) was used to identify patterns of gene expression. Two prospective multicenter phase II clinical trials of ARSIs for men with castration-resistant prostate cancer (ClinicalTrials.gov: NCT01942837 [enzalutamide, N = 21] and NCT02025010 [abiraterone, N = 27]) were used to further validate these findings.RESULTSHierarchical clustering of CTC transcripts identified two distinct clusters. Cluster 2 (C2) exhibited increased expression of AR-regulated genes and was associated with worse overall survival (median 8.6 v 22.4 months; P < .01; hazard ratio [HR] = 3.45 [1.9 to 6.14]). In multivariable analysis, C2 was prognostic independent of other clinicopathologic variables. AR-V status was not significant when accounting for C2. Upon further validation in pooled multicenter phase II trials, C2 was associated with worse overall survival (15.2 months v not reached; P < .01; HR = 8.43 [2.74 to 25.92]), prostate-specific antigen progression-free survival (3.6 v 12 months; P < .01; HR = 4.64 [1.53 to 14.11]), and radiographic progression-free survival (2.7 v 40.6 months; P < .01; HR = 4.64 [1.82 to 17.41]).CONCLUSIONWe demonstrate that a transcriptional profile detectable in CTCs obtained from liquid biopsies can serve as an independent prognostic marker beyond AR-V7 in patients with metastatic prostate cancer and can be used to identify the emergence of multiple ARSI resistance mechanisms. This is currently being investigated in additional prospective trials.

Abstract 1612: Tumor intrinsic changes determine immune-checkpoint response in Richter's transformation

Abstract Richter's transformation (RT) is a clinically challenging and aggressive transformation of CLL (Chronic lymphocytic leukemia) to lymphoma. Immune-checkpoint therapy has improved outcomes in selective lymphoma types. Nearly 40% of RT patients respond to anti-PD1, while the underlying CLL do not show any response, and 10% of CLL patients progress post-anti-PD1 treatment. Previous studies mostly focused on T-cell responses elicited by anti-PD1, however, intrinsic changes in CLL and RT B-cells induced by the treatment remains elusive. We hypothesized that pre-existing active transcriptional circuitry and distinct B-cell subsets in the CLL and RT B-cells may determine the response to anti-PD1 treatment. Peripheral blood mononuclear cells (PBMCs) from 3 patients who received pembrolizumab(anti-PD1) as a single agent. Samples were obtained at baseline and 3 months post-treatment initiation. Among these patients, 2 are progressors while 1 is a responder. We performed single-cell RNAseq (10X Genomics) on B-cells enriched from PBMCs derived from 3 samples (1 Responder: RS2, 2 progressors: CLL14 and CLL17) before and after treatment using chromium single cell 3' gene expression library. A total of ~10000 cells were captured with an average detection of 1000 genes/sample. To delineate the transcriptional changes upon the anti-PD1 treatment, we first integrated the single-cell data from 6 samples (before and after Pembro) and performed unsupervised clustering analysis using UMAP (Uniform Manifold Approximation and projection). We identified 8 different clusters based on highly expressed marker genes. Cluster 2(C2) was increased in the responder as compared to the progressors post-pem treatment. To determine the activated pathways in each cluster, we performed gene-set enrichment analysis (GSEA) using the Enrichr method. This analysis revealed upregulation of distinct pathways related to IFNG/MYC(C1), IFNG(C2), ER stress(C3), B-cell dependent T cell activation related pathway(C4), MYC targets/TNFα(C5,6,7) indicating intra-tumor transcriptional heterogeneity. To decipher the intratumor cell differentiation changes, we performed pseudotime trajectory analysis using Monocle 2 coupled with B cell developmental stage markers. We identified 7 different states, with distinct changes of state in responder sample RS2 (State 4,5, 6, and 7 were increased in RS2 post-treatment) while CLL samples(progressors) did not have distinct changes. Integration with B cell development revealed that aggressive B cells with CD38 expression were highly induced upon anti-PD1 in responder RS2 while regulatory (CD1d) and memory B cells (CD27) remained the same, suggesting PD1 induced intrinsic changes contributing to the disease response. We identified distinct transcriptional states in B cells within responders and progressors which may govern the intra-tumor and inter-tumor heterogeneity to the anti-PD1 response. Citation Format: Prajish Sundaram Iyer, Lu Yang, Yang Zhi Zhang, Hyo Jin Kim, Charla Secreto, Sutapa Sinha, Neil Kay, Stephen Ansell, Wei Ding, Lili Wang. Tumor intrinsic changes determine immune-checkpoint response in Richter's transformation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1612.

K-Means and C4.5 Decision Tree Based Prediction of Long-Term Precipitation Variability in the Poyang Lake Basin, China

The machine learning algorithms application in atmospheric sciences along the Earth System Models has the potential of improving prediction, forecast, and reconstruction of missing data. In the current study, a combination of two machine learning techniques namely K-means, and decision tree (C4.5) algorithms, are used to separate observed precipitation into clusters and classified the associated large-scale circulation indices. Observed precipitation from the Chinese Meteorological Agency (CMA) during 1961–2016 for 83 stations in the Poyang Lake basin (PLB) is used. The results from K-Means clusters show two precipitation clusters splitting the PLB precipitation into a northern and southern cluster, with a silhouette coefficient ~0.5. The PLB precipitation leading cluster (C1) contains 48 stations accounting for 58% of the regional station density, while Cluster 2 (C2) covers 35, accounting for 42% of the stations. The interannual variability in precipitation exhibited significant differences for both clusters. The decision tree (C4.5) is employed to explore the large-scale atmospheric indices from National Climate Center (NCC) associated with each cluster during the preceding spring season as a predictor. The C1 precipitation was linked with the location and intensity of subtropical ridgeline position over Northern Africa, whereas the C2 precipitation was suggested to be associated with the Atlantic-European Polar Vortex Area Index. The precipitation anomalies further validated the results of both algorithms. The findings are in accordance with previous studies conducted globally and hence recommend the applications of machine learning techniques in atmospheric science on a sub-regional and sub-seasonal scale. Future studies should explore the dynamics of the K-Means, and C4.5 derived indicators for a better assessment on a regional scale. This research based on machine learning methods may bring a new solution to climate forecast.

Three distinct atmospheric circulation patterns associated with high temperature extremes in South Korea

The negative impact of extreme high-temperature days (EHDs) on people’s livelihood has increased over the past decades. Therefore, an improved understanding of the fundamental mechanisms of EHDs is imperative to mitigate this impact. Herein, we classify the large-scale atmospheric circulation patterns associated with EHDs that occurred in South Korea from 1982 to 2018 using a self-organizing map (SOM) and investigate the dynamic mechanism for each cluster pattern through composite analysis. A common feature of all SOM clusters is the positive geopotential height (GPH) anomaly over the Korean Peninsula, which provides favorable conditions for EHDs through adiabatic warming caused by anomalous downward motion. Results show that Cluster 1 (C1) is related to the eastward-propagating wave train in the mid-latitude Northern Hemisphere, while Cluster 2 (C2) and 3 (C3) are influenced by a northward-propagating wave train forced by enhanced convection in the subtropical western North Pacific (WNP). Compared to C2, C3 exhibits strong and eastward-extended enhanced convection over the subtropical WNP, which generates an anomalous high-pressure system over the southern part of the Kamchatka Peninsula, reinforcing EHDs via atmospheric blocking. Our results can contribute to the understanding of East Asia climate variability because wave trains influence the climate dynamics of this region.

Bioinformatics Analysis of GFAP as a Potential Key Regulator in Different Immune Phenotypes of Prostate Cancer.

Tumor immune escape plays an essential role in both cancer progression and immunotherapy responses. For prostate cancer (PC), however, the molecular mechanisms that drive its different immune phenotypes have yet to be fully elucidated. Patient gene expression data were analyzed from The Cancer Genome Atlas-prostate adenocarcinoma (TCGA-PRAD) and the International Cancer Genome Consortium (ICGC) databases. We used a Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) analysis and an unsupervised clustering analysis to identify patient subgroups with distinct immune phenotypes. These distinct phenotypes were then explored for associations for differentially expressed genes (DEGs) and both epigenetic and genetic landscapes. Finally, we used a protein-protein interaction analysis to identify key hub genes. We identified two patient subgroups with independent immune phenotypes associated with the expression of Programmed death-ligand 1 (PD-L1). Patient samples in Cluster 1 (C1) had higher scores for immune-cell subsets compared to Cluster 2 (C2), and C2 samples had higher specific somatic mutations, MHC mutations, and genomic copy number variations compared to C1. We also found additional cluster phenotype differences for DNA methylation, microRNA (miRNA) expression, and long noncoding RNA (lncRNA) expression. Furthermore, we established a 4-gene model to distinguish between clusters by integrating analyses for DEGs, lncRNAs, miRNAs, and methylation. Notably, we found that glial fibrillary acidic protein (GFAP) might serve as a key hub gene within the genetic and epigenetic regulatory networks. These results improve our understanding of the molecular mechanisms underlying tumor immune phenotypes that are associated with tumor immune escape. In addition, GFAP may be a potential biomarker for both PC diagnosis and prognosis.

Immune-desert tumor microenvironment in SMARCA4-deficient thoracic sarcomas with limited efficacy of immune checkpoint inhibitors.

11552 Background: SMARCA4-deficient thoracic sarcomas (SDS) are rare and aggressive sarcomas characterized by inactivating SMARCA4 mutations, with no approved treatment to date. Previous data linking SWI/SNF deficiency with tumor immune microenvironment (TME) are contradictory. While an immunogenic microenvironment and efficacy of immune checkpoint inhibitors (ICI) were described in SMARCA4-deficient small cell carcinoma of the ovary, the TME phenotype of SDS is unknown; in addition, response of patients to ICI is lacking. Methods: All consecutive patients diagnosed with SDS between 2016 and 2019 in Strasbourg University Hospital were included and clinical outcomes collected. Immunostainings for immune cell markers, immune checkpoints and tertiary lymphoid structures (TLS) were assessed on available samples. Validation was performed using an independent transcriptomes dataset of SDS (n = 12), not otherwise specified (NOS) non-small cell lung cancer (NSCLC) with/without SMARCA4 mutations (n = 14) and undifferentiated thoracic sarcoma (n = 5). Finally, chemokines (CXL9 and CXCL10) and PD-L1 expressions were assessed in NSCLC and thoracic fibroblast cell lines, treated with/without interferon gamma (IFNG). Results: Nine patients were identified and had all metastatic disease at presentation, with a median overall survival of 1.8 months (0.3-NR). Among them, 4 received ICI as part of their treatment. Out of 11 evaluated tumors samples, all but one case showed no TLS, consistent with an immune desert TME phenotype charted by low densities of CD3+ T-cells, CD8+ T-cells, CD20+ B-cells from one side and high density of CD68+ macrophage-cells from the other side. Conversely, the unique tumor with TLS aggregate showed an immune-rich TME phenotype associated with high mutational tumor burden. While the patient with TLS harboring tumor showed an exceptional long-lasting response, the 3 remaining patients without TLS had progressive disease at best response to ICI. Using an independent cohort, unsupervised clustering using immune cell scores identified two clusters tightly associated with cell ontogeny of cancer subtypes and immunity; while cluster 1 (C1) was enriched for NOS NSCLC independently from SMARCA4 status (n = 9/10; 90%) (p = 0.001), C2 was enriched for SDS (n = 11/12; 91.7%) (p = 0.005) and undifferentiated thoracic sarcomas (n = 5/5; 100%) (p = 0.0005). Finally, SMARCA4 loss of function experiments revealed upregulation of chemokines (CXL9 and CXCL10) and PD-L1 expression in the NSCLC cell line with no effect on thoracic fibroblast cell line. Conclusions: SDS harbor an immune desert TME phenotype with limited efficacy to ICI, similar to other sarcomas. Our data suggest that TME of SMARCA4-driven tumors might vary according to the cell of origin. Further studies are needed to understand the interplay between SWI/SNF mutations, cell ontogeny and immunity.

Needs of older persons living in long-term care institutions: on the usefulness of cluster approach

BackgroundLong-term care units’ residents do not constitute a homogeneous population. Providing effective care, tailored to individual needs, is crucial in this context. It can be facilitated by suitable tools and methods, which include needs assessment along with the physical, psychological and social aspects of care. We thus applied a cluster approach to identify their putative groupings to enable the provision of tailored care.MethodsThe needs of 242 residents of care homes in four Polish cities (Poznan, Wroclaw, Bialystok and Lublin), aged 75–102 years (184 females), with the Mini-Mental State Examination (MMSE) score ≥ 15 points, were assessed with the CANE (Camberwell Assessment of Need for the Elderly) questionnaire. Their independence in activities of daily living was evaluated by the Barthel Index (BI), and symptoms of depression by the Geriatric Depression Scale (GDS). The results of MMSE, BI and GDS were selected as variables for K-means cluster analysis.ResultsCluster 1 (C1), n = 83, included subjects without dementia according to MMSE (23.7 ± 4.4), with no dependency (BI = 85.8 ± 14.4) and no symptoms of depression (GDS = 3.3 ± 2.0). All subjects of cluster 2 (C2), n = 87, had symptoms of depression (GDS = 8.9 ± 2.1), and their MMSE (21.0 ± 4.0) and BI (79.8 ± 15.1) were lower than those in C1 (p = 0.006 and p = 0.046, respectively). Subjects of cluster 3 (C3), n = 72, had the lowest MMSE (18.3 ± 3.1) and BI (30.6 ± 18,8, p < 0.001 vs. C1 & C2). Their GDS (7.6 ± 2.3) were higher than C1 (p < 0.001) but lower than C2 (p < 0.001). The number of met needs was higher in C2 than in C1 (10.0 ± 3.2 vs 8.2 ± 2.7, p < 0.001), and in C3 (12.1 ± 3.1) than in both C1 and C2 (p < 0.001). The number of unmet needs was higher in C3 than in C1 (1.2 ± 1.5 vs 0.7 ± 1.0, p = 0.015). There were also differences in the patterns of needs between the clusters.ConclusionsClustering seems to be a promising approach for use in long-term care, allowing for more appropriate and optimized care delivery. External validation studies are necessary for generalized recommendations regarding care optimization in various regional perspectives.

Phenomenology of psychiatric stigma: A factor of patients’ motivation to treatment

IntroductionPsychiatric patients often are self-stigmatized and hardly involve in the treatment.ObjectivesAssociations of self-stigmatizing beliefs in psychiatric inpatients and their treatment motivation.Methods63 inpatients; ICD-10: F2–65%, F3–13%, F4+F6–14%, F06–8%; mean age 34±13, illness duration 12±11 years. Treatment Motivation Assessment Questionnaire (TMAQ), Internalized Stigma of Mental Illness scale (ISMI); K-mean cluster analysis; dispersion analyses; p≤0.05.Results18 patients of cluster 1 (C1) demonstrated explicit self-stigmatization. In comparison with 25 subjects from cluster 3 (C3) stigmatized patients (C1) had higher levels of overall ISMI scores (2.9±0.3) caused by alienation (3.1±0.5), stereotype endorsement (2.5±0.5), social withdrawal (2.7±0.4), and discrimination experience (2.7±0.4). 20 patients of cluster 2 (C2) had an implicit stigma. They were more self-stigmatized (ISMI score 2.7±0.3) in contrast with subjects from cluster 3 (1.9±0.2) due to a lower level of stigma resistance (C2: 3.8±0.5 and C3 3.1±0.6 – reverse scores). Patients with implicit self-stigma (C2) had the lowest intensity of treatment motivation (Z-scores -1.2±0.6) compering with others (C1 and C3) due to the lowest TMAQ factor 1 (reliance on own knowledge and skills to cope with the disorder: -1.0±0.6) and factor 4 (willingness to cooperate with doctor: -0.9±1.0). Differences between explicitly and implicitly stigmatized patients manifested also in lower TMAQ factor 3 for the second group (awareness of the psychological mechanism of maladaptation: -0.5±0.9).ConclusionsDespite alienation, stereotype endorsement, social withdrawal, discrimination experience some patients could sustain stigma due to cooperation with doctors and reliance on their own knowledge and skills to cope with illness.

The functional GRHL3-filaggrin axis maintains a tumor differentiation potential and influences drug sensitivity

Current therapies for treating heterogeneous cancers such as head and neck squamous cell carcinoma (HNSCC) are non-selective and are administered independent of response biomarkers. Therapy resistance subsequently emerges, resulting in increased cellular proliferation that is associated with loss of differentiation. Whether a cancer cell differentiation potential can dictate therapy responsiveness is still currently unknown. A multi-omic approach integrating whole-genome and whole-transcriptome sequencing with drug sensitivity was employed in a HNSCC mouse model, primary patients’ data, and human cell lines to assess the potential of functional differentiation in predicting therapy response. Interestingly, a subset of HNSCC with effective GRHL3-dependent differentiation was the most sensitive to inhibitors of PI3K/mTOR, c-Myc, and STAT3 signaling. Furthermore, we identified the GRHL3-differentiation target gene Filaggrin (FLG) as a response biomarker and more importantly, stratified HNSCC subsets as treatment resistant based on their FLG mutational profile. The loss of FLG in sensitive HNSCC resulted in a dramatic resistance to targeted therapies while the GRHL3-FLG signature predicted a favorable patient prognosis. This study provides evidence for a functional GRHL3-FLG tumor-specific differentiation axis that regulates targeted therapy response in HNSCC and establishes a rationale for clinical investigation of differentiation-paired targeted therapy in heterogeneous cancers.

Pengelompokkan Sumber Air Minum Dari Air Sungai Menggunakan Metode K-Means

River water is one of the most frequently used water by the community and has a multipurpose function for life, one of which is a source of drinking water. However, now we know that the population of river water pollution is very high and it is used as a waste disposal site which causes a lot of river water to be polluted, it can make people susceptible to disease because they consume unhealthy river water. Judging from the data obtained by province, many use river water as a source of drinking water, for this reason the authors conducted a study that aims to classify drinking water sources from river water by province using the K-means Clustering algorithm and will test it with the Rapidminer application, so that Data from 34 provinces will be divided into 3 clusters in which cluster 1 (C1) is a high group, cluster 2 (C2) is a medium group, and cluster 3 (C3) is a low group. The results obtained from this study are C1 with a total of 2 provinces, C2 with a total of 9 provinces, C3 with a total of 23 provinces and the value of the results carried out with the Rapidminer application has the same value. With this research, it is hoped that this can provide information for the government about the data on the grouping of drinking water sources and used as a consideration for overcoming polluted rivers.