Pancreatic metastases from renal cell carcinoma. Postoperative outcome after surgical treatment in a Spanish multicenter study (PANMEKID)

Background: Renal Cell Carcinoma (RCC) occasionally spreads to the pancreas. The purpose of our study is to evaluate the short and long-term results of a multicenter series in order to determine the effect of surgical treatment on the prognosis of these patients. Methods: Multicenter retrospective study of patients undergoing surgery for RCC pancreatic metastases, from January 2010 to May 2020. Variables related to the primary tumor, demographics, clinical characteristics of metastasis, location in the pancreas, type of pancreatic resection performed and data on short and long-term evolution after pancreatic resection were collected. Results: The study included 116 patients. The mean time between nephrectomy and pancreatic metastases' resection was 87.35 months (ICR: 1.51-332.55). Distal pancreatectomy was the most performed technique employed (50 %). Postoperative morbidity was observed in 60.9 % of cases (Clavien-Dindo greater than IIIa in 14 %). The median follow-up time was 43 months (13-78). Overall survival (OS) rates at 1, 3, and 5 years were 96 %, 88 %, and 83 %, respectively. The disease-free survival (DFS) rate at 1, 3, and 5 years was 73 %, 49 %, and 35 %, respectively. Significant prognostic factors of relapse were a disease free interval of less than 10 years (2.05 [1.13-3.72], p 0.02) and a history of previous extrapancreatic metastasis (2.44 [1.22-4.86], p 0.01). Conclusions: Pancreatic resection if metastatic RCC is found in the pancreas is warranted to achieve higher overall survival and disease-free survival, even if extrapancreatic metastases were previously removed. The existence of intrapancreatic multifocal compromise does not always warrant the performance of a total pancreatectomy in order to improve survival. (C) 2021 The Authors. Published by Elsevier Ltd

Repeated pancreatic resection for pancreatic metastases from renal cell Carcinoma: A Spanish multicenter study (PANMEKID)

Background and objectives: Recurrent isolated pancreatic metastasis from Renal Cell Carcinoma (RCC) after pancreatic resection is rare. The purpose of our study is to describe a series of cases of relapse of pancreatic metastasis from renal cancer in the pancreatic remnant and its surgical treatment with a repeated pancreatic resection, and to analyse the results of both overall and disease -free survival. Methods: Multicenter retrospective study of patients undergoing pancreatic resection for RCC pancreatic metastases, from January 2010 to May 2020. Patients were grouped into two groups depending on whether they received a single pancreatic resection (SPS) or iterative pancreatic resection. Data on short and long-term outcome after pancreatic resection were collected. Results: The study included 131 pancreatic resections performed in 116 patients. Thus, iterative pancreatic surgery (IPS) was performed in 15 patients. The mean length of time between the first pancreatic surgery and the second was 48.9 months (95 % CI: 22.2-56.9). There were no differences in the rate of postoperative complications. The DFS rates at 1, 3 and 5 years were 86 %, 78 % and 78 % vs 75 %, 50 % and 37 % in the IPS and SPS group respectively (p = 0.179). OS rates at 1, 3, 5 and 7 years were 100 %, 100 %, 100 % and 75 % in the IPS group vs 95 %, 85 %, 80 % and 68 % in the SPS group (p = 0.895). Conclusion: Repeated pancreatic resection in case of relapse of pancreatic metastasis of RCC in the pancreatic remnant is justified, since it achieves OS results similar to those obtained after the first resection

Healthcare workers hospitalized due to COVID-19 have no higher risk of death than general population. Data from the Spanish SEMI-COVID-19 Registry

Aim To determine whether healthcare workers (HCW) hospitalized in Spain due to COVID-19 have a worse prognosis than non-healthcare workers (NHCW). Methods Observational cohort study based on the SEMI-COVID-19 Registry, a nationwide registry that collects sociodemographic, clinical, laboratory, and treatment data on patients hospitalised with COVID-19 in Spain. Patients aged 20-65 years were selected. A multivariate logistic regression model was performed to identify factors associated with mortality. Results As of 22 May 2020, 4393 patients were included, of whom 419 (9.5%) were HCW. Median (interquartile range) age of HCW was 52 (15) years and 62.4% were women. Prevalence of comorbidities and severe radiological findings upon admission were less frequent in HCW. There were no difference in need of respiratory support and admission to intensive care unit, but occurrence of sepsis and in-hospital mortality was lower in HCW (1.7% vs. 3.9%; p = 0.024 and 0.7% vs. 4.8%; p<0.001 respectively). Age, male sex and comorbidity, were independently associated with higher in-hospital mortality and healthcare working with lower mortality (OR 0.211, 95%CI 0.067-0.667, p = 0.008). 30-days survival was higher in HCW (0.968 vs. 0.851 p<0.001). Conclusions Hospitalized COVID-19 HCW had fewer comorbidities and a better prognosis than NHCW. Our results suggest that professional exposure to COVID-19 in HCW does not carry more clinical severity nor mortality

GRNBoost2 and Arboreto: efficient and scalable inference of gene regulatory networks

SUMMARY: Inferring a Gene Regulatory Network (GRN) from gene expression data is a computationally expensive task, exacerbated by increasing data sizes due to advances in high-throughput gene profiling technology, such as single-cell RNA-seq. To equip researchers with a toolset to infer GRNs from large expression datasets, we propose GRNBoost2 and the Arboreto framework. GRNBoost2 is an efficient algorithm for regulatory network inference using gradient boosting, based on the GENIE3 architecture. Arboreto is a computational framework that scales up GRN inference algorithms complying with this architecture. Arboreto includes both GRNBoost2 and an improved implementation of GENIE3, as a user-friendly open source Python package. AVAILABILITY AND IMPLEMENTATION: Arboreto is available under the 3-Clause BSD license at http://arboreto.readthedocs.io. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.status: Published onlin

Identification of genomic enhancers through spatial integration of single‐cell transcriptomics and epigenomics

Abstract Single‐cell technologies allow measuring chromatin accessibility and gene expression in each cell, but jointly utilizing both layers to map bona fide gene regulatory networks and enhancers remains challenging. Here, we generate independent single‐cell RNA‐seq and single‐cell ATAC‐seq atlases of the Drosophila eye‐antennal disc and spatially integrate the data into a virtual latent space that mimics the organization of the 2D tissue using ScoMAP (Single‐Cell Omics Mapping into spatial Axes using Pseudotime ordering). To validate spatially predicted enhancers, we use a large collection of enhancer–reporter lines and identify ~ 85% of enhancers in which chromatin accessibility and enhancer activity are coupled. Next, we infer enhancer‐to‐gene relationships in the virtual space, finding that genes are mostly regulated by multiple, often redundant, enhancers. Exploiting cell type‐specific enhancers, we deconvolute cell type‐specific effects of bulk‐derived chromatin accessibility QTLs. Finally, we discover that Prospero drives neuronal differentiation through the binding of a GGG motif. In summary, we provide a comprehensive spatial characterization of gene regulation in a 2D tissue

Decoding gene regulation in the fly brain

The Drosophila brain is a frequently used model in neuroscience. Single-cell transcriptome analysis1-6, three-dimensional morphological classification7 and electron microscopy mapping of the connectome8,9 have revealed an immense diversity of neuronal and glial cell types that underlie an array of functional and behavioural traits in the fly. The identities of these cell types are controlled by gene regulatory networks (GRNs), involving combinations of transcription factors that bind to genomic enhancers to regulate their target genes. Here, to characterize GRNs at the cell-type level in the fly brain, we profiled the chromatin accessibility of 240,919 single cells spanning 9 developmental timepoints and integrated these data with single-cell transcriptomes. We identify more than 95,000 regulatory regions that are used in different neuronal cell types, of which 70,000 are linked to developmental trajectories involving neurogenesis, reprogramming and maturation. For 40 cell types, uniquely accessible regions were associated with their expressed transcription factors and downstream target genes through a combination of motif discovery, network inference and deep learning, creating enhancer GRNs. The enhancer architectures revealed by DeepFlyBrain lead to a better understanding of neuronal regulatory diversity and can be used to design genetic driver lines for cell types at specific timepoints, facilitating their characterization and manipulation

Identification of genomic enhancers through spatial integration of single-cell transcriptomics and epigenomics

Abstract Single-cell technologies allow measuring chromatin accessibility and gene expression in each cell, but jointly utilizing both layers to map bona fide gene regulatory networks and enhancers remains challenging. Here, we generate independent single-cell RNA-seq and single-cell ATAC-seq atlases of the Drosophila eye-antennal disc and spatially integrate the data using a virtual latent space that mimics the organization of the 2D tissue. To validate spatially predicted enhancers, we use a large collection of enhancer-reporter lines and identify ∼85% of enhancers in which chromatin accessibility and enhancer activity are coupled. Next, we infer enhancer-to-gene relationships in the virtual space, finding that genes are regulated by multiple redundant enhancers. Exploiting cell-type specific enhancers, we deconvolute cell-type specific effects of bulk-derived chromatin accessibility QTLs. Finally, we discover that Prospero drives neuronal differentiation through the binding of a GGG motif. In summary, we provide a comprehensive spatial characterization of gene regulation in a 2D tissue.status: publishe

Hydrop enables droplet-based single-cell ATAC-seq and single-cell RNA-seq using dissolvable hydrogel beads

Single-cell RNA-seq and single-cell assay for transposase-accessible chromatin (ATAC-seq) technologies are used extensively to create cell type atlases for a wide range of organisms, tissues, and disease processes. To increase the scale of these atlases, lower the cost and pave the way for more specialized multiome assays, custom droplet microfluidics may provide solutions complementary to commercial setups. We developed HyDrop, a flexible and open-source droplet microfluidic platform encompassing three protocols. The first protocol involves creating dissolvable hydrogel beads with custom oligos that can be released in the droplets. In the second protocol, we demonstrate the use of these beads for HyDrop-ATAC, a low-cost noncommercial scATAC-seq protocol in droplets. After validating HyDrop-ATAC, we applied it to flash-frozen mouse cortex and generated 7996 high-quality single-cell chromatin accessibility profiles in a single run. In the third protocol, we adapt both the reaction chemistry and the capture sequence of the barcoded hydrogel bead to capture mRNA, and demonstrate a significant improvement in throughput and sensitivity compared to previous open-source droplet-based scRNA-seq assays (Drop-seq and inDrop). Similarly, we applied HyDrop-RNA to flash-frozen mouse cortex and generated 9508 single-cell transcriptomes closely matching reference single-cell gene expression data. Finally, we leveraged HyDrop-RNA’s high capture rate to analyze a small population of fluorescence-activated cell sorted neurons from the Drosophila brain, confirming the protocol’s applicability to low input samples and small cells. HyDrop is currently capable of generating single-cell data in high throughput and at a reduced cost compared to commercial methods, and we envision that HyDrop can be further developed to be compatible with novel (multi) omics protocols

Shared enhancer gene regulatory networks between wound and oncogenic programs

Wound response programs are often activated during neoplastic growth in tumors. In both wound repair and tumor growth, cells respond to acute stress and balance the activation of multiple programs, including apoptosis, proliferation, and cell migration. Central to those responses are the activation of the JNK/MAPK and JAK/STAT signaling pathways. Yet, to what extent these signaling cascades interact at the cis-regulatory level and how they orchestrate different regulatory and phenotypic responses is still unclear. Here, we aim to characterize the regulatory states that emerge and cooperate in the wound response, using the Drosophila melanogaster wing disc as a model system, and compare these with cancer cell states induced by rasV12scrib-/- in the eye disc. We used single-cell multiome profiling to derive enhancer gene regulatory networks (eGRNs) by integrating chromatin accessibility and gene expression signals. We identify a ‘proliferative’ eGRN, active in the majority of wounded cells and controlled by AP-1 and STAT. In a smaller, but distinct population of wound cells, a ‘senescent’ eGRN is activated and driven by C/EBP-like transcription factors (Irbp18, Xrp1, Slow border, and Vrille) and Scalloped. These two eGRN signatures are found to be active in tumor cells at both gene expression and chromatin accessibility levels. Our single-cell multiome and eGRNs resource offers an in-depth characterization of the senescence markers, together with a new perspective on the shared gene regulatory programs acting during wound response and oncogenesis

Single-cell gene regulatory network analysis reveals new melanoma cell states and transition trajectories during phenotype switching

Abstract Melanoma is notorious for its cellular heterogeneity, which is at least partly due to its ability to transition between alternate cell states. Similarly to EMT, melanoma cells with a melanocytic phenotype can switch to a mesenchymal-like phenotype. However, scattered emerging evidence indicates that additional, intermediate state(s) may exist. In order to search for such new melanoma states and decipher their underlying gene regulatory network (GRN), we extensively studied ten patient-derived melanoma cultures by single-cell RNA-seq of >39,000 cells. Although each culture exhibited a unique transcriptome, we identified shared gene regulatory networks that underlie the extreme melanocytic and mesenchymal cell states, as well as one (stable) intermediate state. The intermediate state was corroborated by a distinct open chromatin landscape and governed by the transcription factors EGR3, NFATC2, and RXRG. Single-cell migration assays established that this “transition” state exhibits an intermediate migratory phenotype. Through a dense time-series sampling of single cells and dynamic GRN inference, we unraveled the sequential and recurrent arrangement of transcriptional programs at play during phenotype switching that ultimately lead to the mesenchymal cell state. We provide the scRNA-Seq data with 39,263 melanoma cells on our SCope platform and the ATAC-seq data on a UCSC hub to jointly serve as a resource for the melanoma field. Together, this exhaustive analysis of melanoma cell state diversity indicates that additional states exists between the two extreme melanocytic and mesenchymal-like states. The GRN we identified may serve as a new putative target to prevent the switch to mesenchymal cell state and thereby, acquisition of metastatic and drug resistant potential.status: publishe