Stroke network performance during the first COVID-19 pandemic stage: A meta-analysis based on stroke network models.

BACKGROUND: The effect of the COVID pandemic on stroke network performance is unclear, particularly with consideration of drip&ship vs. mothership models. AIMS: We systematically reviewed and meta-analyzed variations in stroke admissions, rate and timing of reperfusion treatments during the first wave COVID pandemic vs. the pre-pandemic timeframe depending on stroke network model adopted. SUMMARY OF FINDINGS: The systematic review followed registered protocol (PROSPERO-CRD42020211535), PRISMA and MOOSE guidelines. We searched MEDLINE, EMBASE, and CENTRAL until 9 October 2020 for studies reporting variations in ischemic stroke admissions, treatment rates, and timing in COVID (first wave) vs. control-period. Primary outcome was the weekly admission incidence rate ratio (IRR = admissions during COVID-period/admissions during control-period). Secondary outcomes were (i) changes in rate of reperfusion treatments and (ii) time metrics for pre- and in-hospital phase. Data were pooled using random-effects models, comparing mothership vs. drip&ship model. Overall, 29 studies were included in quantitative synthesis (n = 212,960). COVID-period was associated with a significant reduction in stroke admission rates (IRR = 0.69, 95%CI = 0.61-0.79), with higher relative presentation of large vessel occlusion (risk ratio (RR) = 1.62, 95% confidence interval (CI) = 1.24-2.12). Proportions of patients treated with endovascular treatment increased (RR = 1.14, 95%CI = 1.02-1.28). Intravenous thrombolysis decreased overall (IRR = 0.72, 95%CI = 0.54-0.96) but not in the mothership model (IRR = 0.81, 95%CI = 0.43-1.52). Onset-to-door time was longer for the drip&ship in COVID-period compared to the control-period (+32 min, 95%CI = 0-64). Door-to-scan was longer in COVID-period (+5 min, 95%CI = 2-7). Door-to-needle and door-to-groin were similar in COVID-period and control-period. CONCLUSIONS: Despite a 35% drop in stroke admissions during the first pandemic wave, proportions of patients receiving reperfusion and time-metrics were not inferior to control-period. Mothership preserved the weekly rate of intravenous thrombolysis and the onset-to-door timing to pre-pandemic standards.

Effect of COVID-19 on Emergent Stroke Care: A Regional Experience.

BACKGROUND AND PURPOSE: Anecdotal evidence suggests that the coronavirus disease 2019 (COVID-19) pandemic mitigation efforts may inadvertently discourage patients from seeking treatment for stroke with resultant increased morbidity and mortality. Analysis of regional data, while hospital capacities for acute stroke care remained fully available, offers an opportunity to assess this. We report regional Stroke Team acute activations and reperfusion treatments during COVID-19 mitigation activities. METHODS: Using case log data prospectively collected by a Stroke Team exclusively serving ≈2 million inhabitants and 30 healthcare facilities, we retrospectively reviewed volumes of consultations and reperfusion treatments for acute ischemic stroke. We compared volumes before and after announcements of COVID-19 mitigation measures and the prior calendar year. RESULTS: Compared with the 10 weeks prior, stroke consultations declined by 39% (95% CI, 32%-46%) in the 5 weeks after announcement of statewide school and restaurant closures in Ohio, Kentucky, and Indiana. Results compared with the prior year and time trend analyses were consistent. Reperfusion treatments also appeared to decline by 31% (95% CI, 3%-51%), and specifically thrombolysis by 33% (95% CI, 4%-55%), but this finding had less precision. CONCLUSIONS: Upon the announcement of measures to mitigate COVID-19, regional acute stroke consultations declined significantly. Reperfusion treatment rates, particularly thrombolysis, also appeared to decline qualitatively, and this finding requires further study. Urgent public education is necessary to mitigate a possible crisis of avoiding essential emergency care due to COVID-19.

Defining a Cancer Dependency Map.

Most human epithelial tumors harbor numerous alterations, making it difficult to predict which genes are required for tumor survival. To systematically identify cancer dependencies, we analyzed 501 genome-scale loss-of-function screens performed in diverse human cancer cell lines. We developed DEMETER, an analytical framework that segregates on- from off-target effects of RNAi. 769 genes were differentially required in subsets of these cell lines at a threshold of six SDs from the mean. We found predictive models for 426 dependencies (55%) by nonlinear regression modeling considering 66,646 molecular features. Many dependencies fall into a limited number of classes, and unexpectedly, in 82% of models, the top biomarkers were expression based. We demonstrated the basis behind one such predictive model linking hypermethylation of the UBB ubiquitin gene to a dependency on UBC. Together, these observations provide a foundation for a cancer dependency map that facilitates the prioritization of therapeutic targets.

Parallel genome-scale loss of function screens in 216 cancer cell lines for the identification of context-specific genetic dependencies.

Using a genome-scale, lentivirally delivered shRNA library, we performed massively parallel pooled shRNA screens in 216 cancer cell lines to identify genes that are required for cell proliferation and/or viability. Cell line dependencies on 11,000 genes were interrogated by 5 shRNAs per gene. The proliferation effect of each shRNA in each cell line was assessed by transducing a population of 11M cells with one shRNA-virus per cell and determining the relative enrichment or depletion of each of the 54,000 shRNAs after 16 population doublings using Next Generation Sequencing. All the cell lines were screened using standardized conditions to best assess differential genetic dependencies across cell lines. When combined with genomic characterization of these cell lines, this dataset facilitates the linkage of genetic dependencies with specific cellular contexts (e.g., gene mutations or cell lineage). To enable such comparisons, we developed and provided a bioinformatics tool to identify linear and nonlinear correlations between these features.

A genome-scale RNA interference screen implicates NF1 loss in resistance to RAF inhibition.

RAF inhibitors such as vemurafenib and dabrafenib block BRAF-mediated cell proliferation and achieve meaningful clinical benefit in the vast majority of patients with BRAF(V600E)-mutant melanoma. However, some patients do not respond to this regimen, and nearly all progress to therapeutic resistance. We used a pooled RNA interference screen targeting more than 16,500 genes to discover loss-of-function events that could drive resistance to RAF inhibition. The highest ranking gene was NF1, which encodes neurofibromin, a tumor suppressor that inhibits RAS activity. NF1 loss mediates resistance to RAF and mitogen-activated protein kinase (MAPK) kinase kinase (MEK) inhibitors through sustained MAPK pathway activation. However, cells lacking NF1 retained sensitivity to the irreversible RAF inhibitor AZ628 and an ERK inhibitor. NF1 mutations were observed in BRAF-mutant tumor cells that are intrinsically resistant to RAF inhibition and in melanoma tumors obtained from patients exhibiting resistance to vemurafenib, thus showing the clinical potential for NF1-driven resistance to RAF/MEK-targeted therapies.

Systematic investigation of genetic vulnerabilities across cancer cell lines reveals lineage-specific dependencies in ovarian cancer.

A comprehensive understanding of the molecular vulnerabilities of every type of cancer will provide a powerful roadmap to guide therapeutic approaches. Efforts such as The Cancer Genome Atlas Project will identify genes with aberrant copy number, sequence, or expression in various cancer types, providing a survey of the genes that may have a causal role in cancer. A complementary approach is to perform systematic loss-of-function studies to identify essential genes in particular cancer cell types. We have begun a systematic effort, termed Project Achilles, aimed at identifying genetic vulnerabilities across large numbers of cancer cell lines. Here, we report the assessment of the essentiality of 11,194 genes in 102 human cancer cell lines. We show that the integration of these functional data with information derived from surveying cancer genomes pinpoints known and previously undescribed lineage-specific dependencies across a wide spectrum of cancers. In particular, we found 54 genes that are specifically essential for the proliferation and viability of ovarian cancer cells and also amplified in primary tumors or differentially overexpressed in ovarian cancer cell lines. One such gene, PAX8, is focally amplified in 16% of high-grade serous ovarian cancers and expressed at higher levels in ovarian tumors. Suppression of PAX8 selectively induces apoptotic cell death of ovarian cancer cells. These results identify PAX8 as an ovarian lineage-specific dependency. More generally, these observations demonstrate that the integration of genome-scale functional and structural studies provides an efficient path to identify dependencies of specific cancer types on particular genes and pathways.