Comparing the antiepileptic effects of atorvastatin, celecoxib, ashwagandha, clove oil, and Sodium valproate on chemo-shock induced seizures in male Wistar albino rats

Background: Antiepileptic potential of statins, COX inhibitors and other herbal medications are to be evaluated in experimental animals so that the most efficacious can be translated for human use as an adjunct to the commonly used anti-epileptic drugs. Methods: This experimental animal study grouped 30 male Wistar albino rats into 6 groups with each containing 5 rats of which one group was control, one was the standard drug and the other 4 were treatment groups which received Atorvastatin, Celecoxib, Ashwagandha and Clove oil. These drugs were administered 30 minutes prior to administering Pentylene-tetrazole which induced convulsions and the various seizure parameters were analysed. The blood samples of the animals were also assessed for anti-oxidant activity by measuring superoxide dismutase and catalase levels in the blood. Results: The onset of seizure was significantly delayed by Ashwagandha (2.55±0.94), similar to the latency shown by the standard drug (2.09±1.21). The duration of convulsions was very significantly reduced in all the 5 drug groups in comparison to the control (p<0.001). The clonic jerk duration was not reduced as effectively as the standard drug. The duration of recovery time amongst the various groups was also significant (p<0.05). The SOD and Catalase levels of no groups showed any possible association between the anti-epileptic efficacy of these drugs and the anti-oxidant enzyme levels. Conclusions: Ashwagandha has good anti-epileptic efficacy not less than the standard drug when the various drug groups were compared

MultiK: an automated tool to determine optimal cluster numbers in single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) provides new opportunities to characterize cell populations, typically accomplished through some type of clustering analysis. Estimation of the optimal cluster number (K) is a crucial step but often ignored. Our approach improves most current scRNA-seq cluster methods by providing an objective estimation of the number of groups using a multi-resolution perspective. MultiK is a tool for objective selection of insightful Ks and achieves high robustness through a consensus clustering approach. We demonstrate that MultiK identifies reproducible groups in scRNA-seq data, thus providing an objective means to estimating the number of possible groups or cell-type populations present

Genomic characterization of rare molecular subclasses of hepatocellular carcinoma

Primary liver cancer, consisting of both cholangiocarcinoma (CCA) and hepatocellular carcinoma (HCC), is the second leading cause of cancer deaths worldwide. Our goal is to genomically characterize rare HCC subclasses to provide insight into disease biology. Leveraging The Cancer Genome Atlas (TCGA) to perform a combined analysis of CCA (n = 36) and HCC (n = 275), we integrated multiple genomic platforms, to assess transcriptional profiles, mutational signatures, and copy number patterns to uncover underlying etiology and linage specific patterns. We identified two molecular classes distinct from prototypical HCC tumors. The first, CCA-Like, although histologically indistinguishable from HCC, had enrichment of CCA mutations (IDH1, BAP1), mutational signatures, and transcriptional patterns (SOX9, KRT19). CCA-Like, however, retained a copy number landscape similar to HCC, suggesting a hepatocellular linage. The second, Blast-Like, is enriched in TP53 mutations, HBV infection, exposure related mutational signatures and transcriptionally similar to hepatoblasts. Although these subclasses are molecularly distinct, they both have a worse progression-free survival compared to classical HCC tumors, yet are clinically treated the same. The identification of and characterization of CCA-Like and Blast-Like subclasses advance our knowledge of HCC as well as represents an urgent need for the identification of class specific biomarkers and targeted therapy

SCDC: Bulk gene expression deconvolution by multiple single-cell RNA sequencing references

Recent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of transcriptomic profiles with single-cell resolution and circumvent averaging artifacts associated with traditional bulk RNA sequencing (RNA-seq) data. Here, we propose SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets. SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the problem of batch-effect confounding. SCDC is benchmarked against existing methods using both in silico generated pseudo-bulk samples and experimentally mixed cell lines, whose known cell-type compositions serve as ground truths. We show that SCDC outperforms existing methods with improved accuracy of cell-type decomposition under both settings. To illustrate how the ENSEMBLE framework performs in complex tissues under different scenarios, we further apply our method to a human pancreatic islet dataset and a mouse mammary gland dataset. SCDC returns results that are more consistent with experimental designs and that reproduce more significant associations between cell-type proportions and measured phenotypes

B Cells and T Follicular Helper Cells Mediate Response to Checkpoint Inhibitors in High Mutation Burden Mouse Models of Breast Cancer

This study identifies mechanisms mediating responses to immune checkpoint inhibitors using mouse models of triple-negative breast cancer. By creating new mammary tumor models, we find that tumor mutation burden and specific immune cells are associated with response. Further, we developed a rich resource of single-cell RNA-seq and bulk mRNA-seq data of immunotherapy-treated and non-treated tumors from sensitive and resistant murine models. Using this, we uncover that immune checkpoint therapy induces T follicular helper cell activation of B cells to facilitate the anti-tumor response in these models. We also show that B cell activation of T cells and the generation of antibody are key to immunotherapy response and propose a new biomarker for immune checkpoint therapy. In total, this work presents resources of new preclinical models of breast cancer with large mRNA-seq and single-cell RNA-seq datasets annotated for sensitivity to therapy and uncovers new components of response to immune checkpoint inhibitors

FGFR4 regulates tumor subtype differentiation in luminal breast cancer and metastatic disease

Mechanisms driving tumor progression from less aggressive subtypes to more aggressive states represent key targets for therapy. We identified a subset of luminal A primary breast tumors that give rise to HER2-enriched (HER2E) subtype metastases, but remain clinically HER2 negative (cHER2-). By testing the unique genetic and transcriptomic features of these cases, we developed the hypothesis that FGFR4 likely participates in this subtype switching. To evaluate this, we developed 2 FGFR4 genomic signatures using a patient-derived xenograft (PDX) model treated with an FGFR4 inhibitor, which inhibited PDX growth in vivo. Bulk tumor gene expression analysis and single-cell RNA sequencing demonstrated that the inhibition of FGFR4 signaling caused molecular switching. In the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) breast cancer cohort, FGFR4-induced and FGFR4-repressed signatures each predicted overall survival. Additionally, the FGFR4-induced signature was an independent prognostic factor beyond subtype and stage. Supervised analysis of 77 primary tumors with paired metastases revealed that the FGFR4-induced signature was significantly higher in luminal/ER+ tumor metastases compared with their primaries. Finally, multivariate analysis demonstrated that the FGFR4- induced signature also predicted site-specific metastasis for lung, liver, and brain, but not for bone or lymph nodes. These data identify a link between FGFR4-regulated genes and metastasis, suggesting treatment options for FGFR4-positive patients, whose high expression is not caused by mutation or amplification

Molecular analysis of TCGA breast cancer histologic types

Breast cancer is classified into multiple distinct histologic types, and many of the rarer types have limited characterization. Here, we extend The Cancer Genome Atlas Breast Cancer (TCGA-BRCA) dataset with additional histologic type annotations in a total of 1,063 breast cancers. We analyze this extended dataset to define transcriptomic and genomic profiles of six rare, special histologic types: cribriform, micropapillary, mucinous, papillary, metaplastic, and invasive carcinoma with medullary pattern. We show the broader applicability of our constructed special histologic type gene signatures in the TCGA Pan-Cancer Atlas dataset with a predictive model that detects mucinous histologic type across cancers of other organ systems. Using a normal mammary cell differentiation score analysis, we order histologic types into a continuum from stem cell-like to luminal progenitor-like to mature luminal-like. Finally, we classify TCGA-BRCA into 12 consensus groups based on integrated genomic and histological features. We present a rich, openly accessible resource of genomic, molecular, and histologic characterization of TCGA-BRCA to enable studies across the range of breast cancers

A multi-omic single-cell landscape of human gynecologic malignancies

Deconvolution of regulatory mechanisms that drive transcriptional programs in cancer cells is key to understanding tumor biology. Herein, we present matched transcriptome (scRNA-seq) and chromatin accessibility (scATAC-seq) profiles at single-cell resolution from human ovarian and endometrial tumors processed immediately following surgical resection. This dataset reveals the complex cellular heterogeneity of these tumors and enabled us to quantitatively link variation in chromatin accessibility to gene expression. We show that malignant cells acquire previously unannotated regulatory elements to drive hallmark cancer pathways. Moreover, malignant cells from within the same patients show substantial variation in chromatin accessibility linked to transcriptional output, highlighting the importance of intratumoral heterogeneity. Finally, we infer the malignant cell type-specific activity of transcription factors. By defining the regulatory logic of cancer cells, this work reveals an important reliance on oncogenic regulatory elements and highlights the ability of matched scRNA-seq/scATAC-seq to uncover clinically relevant mechanisms of tumorigenesis in gynecologic cancers

Serial single-cell profiling analysis of metastatic TNBC during Nab-paclitaxel and pembrolizumab treatment

Purpose: Immunotherapy has recently been shown to improve outcomes for advanced PD-L1-positive triple-negative breast cancer (TNBC) in the Impassion130 trial, leading to FDA approval of the first immune checkpoint inhibitor in combination with taxane chemotherapy. To further develop predictive biomarkers and improve therapeutic efficacy of the combination, interrogation of the tumor immune microenvironment before therapy as well as during each component of treatment is crucial. Here we use single-cell RNA sequencing (scRNA-seq) on tumor biopsies to assess immune cell changes from two patients with advanced TNBC treated in a prospective trial at predefined serial time points, before treatment, on taxane chemotherapy and on chemo-immunotherapy. Methods: Both patients (one responder and one progressor) received the trial therapy, in cycle 1 nab-paclitaxel given as single agent, in cycle 2 nab-paclitaxel in combination with pembrolizumab. Tumor core biopsies were obtained at baseline, 3 weeks (after cycle 1, chemotherapy alone) and 6 weeks (after cycle 2, chemo-immunotherapy). Single-cell RNA sequencing (scRNA-seq) of both cancer cells and infiltrating immune cells isolated were performed from fresh tumor core biopsy specimens by 10 × chromium sequencing. Results: ScRNA-seq analysis showed significant baseline heterogeneity of tumor-infiltrating immune cell populations between the two patients as well as modulation of the tumor microenvironment by chemotherapy and immunotherapy. In the responding patient there was a population of PD-1high-expressing T cells which significantly decreased after nab-paclitaxel plus pembrolizumab treatment as well as a presence of tissue-resident memory T cells (TRM). In contrast, tumors from the patient with rapid disease progression showed a prevalent and persistent myeloid compartment. Conclusions: Our study provides a deep cellular analysis of on-treatment changes during chemo-immunotherapy for advanced TNBC, demonstrating not only feasibility of single-cell analyses on serial tumor biopsies but also the heterogeneity of TNBC and differences in on-treatment changes in responder versus progressor

A spatially resolved single-cell genomic atlas of the adult human breast

The adult human breast is comprised of an intricate network of epithelial ducts and lobules that are embedded in connective and adipose tissue1-3. Although most previous studies have focused on the breast epithelial system4-6, many of the non-epithelial cell types remain understudied. Here we constructed the comprehensive Human Breast Cell Atlas (HBCA) at single-cell and spatial resolution. Our single-cell transcriptomics study profiled 714,331 cells from 126 women, and 117,346 nuclei from 20 women, identifying 12 major cell types and 58 biological cell states. These data reveal abundant perivascular, endothelial and immune cell populations, and highly diverse luminal epithelial cell states. Spatial mapping using four different technologies revealed an unexpectedly rich ecosystem of tissue-resident immune cells, as well as distinct molecular differences between ductal and lobular regions. Collectively, these data provide a reference of the adult normal breast tissue for studying mammary biology and diseases such as breast cancer