Integrative spatial omics reveals distinct tumor-promoting multicellular niches and immunosuppressive mechanisms in African American and European American patients with TNBC.

Racial disparities in triple-negative breast cancer (TNBC) outcomes have been reported. However, the biological mechanisms underlying these disparities remain unclear. We integrated imaging mass cytometry and spatial transcriptomics, to characterize the tumor microenvironment (TME) of African American (AA) and European American (EA) patients with TNBC. The TME in AA patients was characterized by interactions between endothelial cells, macrophages, and mesenchymal-like cells, which were associated with poor patient survival. In contrast, the EA TNBC-associated niche is enriched in T-cells and neutrophils suggestive of an exhaustion and suppression of otherwise active T cell responses. Ligand-receptor and pathway analyses of race-associated niches found AA TNBC to be immune cold and hence immunotherapy resistant tumors, and EA TNBC as inflamed tumors that evolved a distinctive immunosuppressive mechanism. Our study revealed the presence of racially distinct tumor-promoting and immunosuppressive microenvironments in AA and EA patients with TNBC, which may explain the poor clinical outcomes.

Matrin3 mediates differentiation through stabilizing chromatin loop-domain interactions and YY1 mediated enhancer-promoter interactions.

Although emerging evidence indicates that alterations in proteins within nuclear compartments elicit changes in chromosomal architecture and differentiation, the underlying mechanisms are not well understood. Here we investigate the direct role of the abundant nuclear complex protein Matrin3 (Matr3) in chromatin architecture and development in the context of myogenesis. Using an acute targeted protein degradation platform (dTAG-Matr3), we reveal the dynamics of development-related chromatin reorganization. High-throughput chromosome conformation capture (Hi-C) experiments revealed substantial chromatin loop rearrangements soon after Matr3 depletion. Notably, YY1 binding was detected, accompanied by the emergence of novel YY1-mediated enhancer-promoter loops, which occurred concurrently with changes in histone modifications and chromatin-level binding patterns. Changes in chromatin occupancy by Matr3 also correlated with these alterations. Overall, our results suggest that Matr3 mediates differentiation through stabilizing chromatin accessibility and chromatin loop-domain interactions, and highlight a conserved and direct role for Matr3 in maintenance of chromosomal architecture.

Insulin attenuates apoptosis in neuronal cells by an integrin-linked kinase-dependent mechanism.

Insulin promotes neuronal survival by activating a phosphatidylinositol 3-kinase (PI 3-kinase)/AKT-dependent signaling pathway and reducing caspase activation. We investigated a role for integrin-linked kinase (ILK) in insulin-mediated cell survival in cultured neurons and differentiated R28 cells. We used a serum and depolarization withdrawal model to induce apoptosis in cerebellar granule neurons and a serum withdrawal model to induce apoptosis in differentiated R28 cells. ILK knock-out decreased insulin-mediated protection as did the addition of pharmacological inhibitors of ILK, KP-392 or QLT-0267. Prosurvival effects of insulin were rescued by Boc-Asp (O-methyl)-CH2F (BAF), a pancaspase inhibitor, in the presence of KP-392. Insulin and IGF-1 decreased caspase-3 activation, an effect that was inhibited by KP-392 and QLT-0267. Western blot analysis indicates that insulin-induced stimulation of AKT Ser-473 phosphorylation was decreased after the ILK gene was conditionally knocked-out, following overexpression of AKT-DN or in the presence of QLT-0267. Insulin and IGF-1 stimulated ILK kinase activity in primary neurons and this was inhibited following ILK-DN overexpression. Western blot analysis indicates that insulin exposure upregulated the expression of the cellular inhibitor of apoptosis protein c-IAP2 in an extracellular matrix-dependent manner, an effect blocked by KP-392. These results indicate that ILK is an important effector in insulin-mediated neuroprotection.

SCINA: A Semi-Supervised Subtyping Algorithm of Single Cells and Bulk Samples.

Advances in single-cell RNA sequencing (scRNA-Seq) have allowed for comprehensive analyses of single cell data. However, current analyses of scRNA-Seq data usually start from unsupervised clustering or visualization. These methods ignore prior knowledge of transcriptomes and the probable structures of the data. Moreover, cell identification heavily relies on subjective and possibly inaccurate human inspection afterwards. To address these analytical challenges, we developed SCINA (Semi-supervised Category Identification and Assignment), a semi-supervised model that exploits previously established gene signatures using an expectation-maximization (EM) algorithm. SCINA is applicable to scRNA-Seq and flow cytometry/CyTOF data, as well as other data of similar format. We applied SCINA to a wide range of datasets, and showed its accuracy, stability and efficiency, which exceeded most popular unsupervised approaches. SCINA discovered an intermediate stage of oligodendrocytes from mouse brain scRNA-Seq data. SCINA also detected immune cell population changes in cytometry data in a genetically-engineered mouse model. Furthermore, SCINA performed well with bulk gene expression data. Specifically, we identified a new kidney tumor clade with similarity to FH-deficient tumors (FHD), which we refer to as FHD-like tumors (FHDL). Overall, SCINA provides both methodological advances and biological insights from perspectives different from traditional analytical methods.

Development and Validation of a Nomogram Prognostic Model for Patients With Advanced Non-Small-Cell Lung Cancer.

IMPORTANCE: Nomogram prognostic models can facilitate cancer patient treatment plans and patient enrollment in clinical trials. OBJECTIVE: The primary objective is to provide an updated and accurate prognostic model for predicting the survival of advanced non-small-cell lung cancer (NSCLC) patients, and the secondary objective is to validate a published nomogram prognostic model for NSCLC using an independent patient cohort. DESIGN: 1817 patients with advanced NSCLC from the control arms of 4 Phase III randomized clinical trials were included in this study. Data from 524 NSCLC patients from one of these trials were used to validate a previously published nomogram and then used to develop an updated nomogram. Patients from the other 3 trials were used as independent validation cohorts of the new nomogram. The prognostic performances were comprehensively evaluated using hazard ratios, integrated area under the curve (AUC), concordance index, and calibration plots. SETTING: General community. MAIN OUTCOME: A nomogram model was developed to predict overall survival in NSCLC patients. RESULTS: We demonstrated the prognostic power of the previously published model in an independent cohort. The updated prognostic model contains the following variables: sex, histology, performance status, liver metastasis, hemoglobin level, white blood cell counts, peritoneal metastasis, skin metastasis, and lymphocyte percentage. This model was validated using various evaluation criteria on the 3 independent cohorts with heterogeneous NSCLC populations. In the SUN1087 patient cohort, the continuous risk score output by the nomogram achieved an integrated area under the receiver operating characteristics (ROC) curve of 0.83, a log-rank P-value of 3.87e-11, and a concordance index of 0.717. In the SAVEONCO patient cohort, the integrated area under the ROC curve was 0.755, the log-rank P-value was 4.94e-6 and the concordance index was 0.678. In the VITAL patient cohort, the integrated area under the ROC curve was 0.723, the log-rank P-value was 1.36e-11, and the concordance index was 0.654. We implemented the proposed nomogram and several previously published prognostic models on an online Web server for easy user access. CONCLUSIONS: This nomogram model based on basic clinical features and routine lab testing predicts individual survival probabilities for advanced NSCLC and exhibits cross-study robustness.

Cost and Utilization of Retail Clinics vs. Other Providers for Treatment of Pediatric Acute Otitis Media.

A common acute condition seen by providers in retails clinics is the evaluation and treatment of acute otitis media (AOM) in children younger than age 20. Annual direct treatment costs for AOM were US $5.3 billion in 1998 dollars. Based on the experience of a large retail pharmacy employer, the authors compared AOM episodes in covered dependents younger than age 20 in retail clinic states to those in states without retail clinic access. Relative costs as well as frequency of visits and antibiotic prescriptions were analyzed for both retail clinic-based, and non-retail clinic-based episodes. Rates of AOM episodes were lower in retail clinic than in non-retail clinic states (62.5 vs. 76.9 per 1000 members per year; P < .0001). The average number of visits per episode was similar between retail clinic and non-retail clinic states (1.417 vs. 1.430, respectively; P = 0.657), suggesting that retail clinics do not result in an increase in overall utilization. On a risk-adjusted basis, retail clinic episodes cost approximately $30-$130 less than community episodes, depending on year. In retail clinic states, the antibiotic prescription fill rate was 95.4% for retail clinic episodes and 82.8% for community episodes, consistent with rates in the literature. This study confirms results of earlier studies that retail clinics are a less costly setting than the community for the treatment of episodes of otitis media There also is little evidence that retail clinics lead to duplication of services (patients receiving follow-up care in other settings).