Search

BackgroundThe COVID-19 pandemic has impacted people’s lives at unprecedented speed and scale, including how they eat and work, what they are concerned about, how much they move, and how much they can earn. Traditional surveys in the area of public health can be expensive and time-consuming, and they can rapidly become outdated. The analysis of big data sets (such as electronic patient records and surveillance systems) is very complex. Google Trends is an alternative approach that has been used in the past to analyze health behaviors; however, most existing studies on COVID-19 using these data examine a single issue or a limited geographic area. This paper explores Google Trends as a proxy for what people are thinking, needing, and planning in real time across the United States. ObjectiveWe aimed to use Google Trends to provide both insights into and potential indicators of important changes in information-seeking patterns during pandemics such as COVID-19. We asked four questions: (1) How has information seeking changed over time? (2) How does information seeking vary between regions and states? (3) Do states have particular and distinct patterns in information seeking? (4) Do search data correlate with—or precede—real-life events? MethodsWe analyzed searches on 38 terms related to COVID-19, falling into six themes: social and travel; care seeking; government programs; health programs; news and influence; and outlook and concerns. We generated data sets at the national level (covering January 1, 2016, to April 15, 2020) and state level (covering January 1 to April 15, 2020). Methods used include trend analysis of US search data; geographic analyses of the differences in search popularity across US states from March 1 to April 15, 2020; and principal component analysis to extract search patterns across states. ResultsThe data showed high demand for information, corresponding with increasing searches for coronavirus linked to news sources regardless of the ideological leaning of the news source. Changes in information seeking often occurred well in advance of action by the federal government. The popularity of searches for unemployment claims predicted the actual spike in weekly claims. The increase in searches for information on COVID-19 care was paralleled by a decrease in searches related to other health behaviors, such as urgent care, doctor’s appointments, health insurance, Medicare, and Medicaid. Finally, concerns varied across the country; some search terms were more popular in some regions than in others. ConclusionsCOVID-19 is unlikely to be the last pandemic faced by the United States. Our research holds important lessons for both state and federal governments in a fast-evolving situation that requires a finger on the pulse of public sentiment. We suggest strategic shifts for policy makers to improve the precision and effectiveness of non-pharmaceutical interventions and recommend the development of a real-time dashboard as a decision-making tool.

Replicative immortality is a hallmark of cancer, and can be achieved through telomere lengthening and maintenance. Although the role of telomere length in cancer has been well studied, its association to genomic features is less well known. Here, we report the telomere lengths of 392 localized prostate cancer tumours and characterize their relationship to genomic, transcriptomic and proteomic features. Shorter tumour telomere lengths are associated with elevated genomic instability, including single-nucleotide variants, indels and structural variants. Genes involved in cell proliferation and signaling are correlated with tumour telomere length at all levels of the central dogma. Telomere length is also associated with multiple clinical features of a tumour. Longer telomere lengths in non-tumour samples are associated with a lower rate of biochemical relapse. In summary, we describe the multi-level integration of telomere length, genomics, transcriptomics and proteomics in localized prostate cancer.

New technologies and analysis methods are enabling genomic structural variants (SVs) to be detected with ever-increasing accuracy, resolution and comprehensiveness. To help translate these methods to routine research and clinical practice, we developed a sequence-resolved benchmark set for identification of both false-negative and false-positive germline large insertions and deletions. To create this benchmark for a broadly consented son in a Personal Genome Project trio with broadly available cells and DNA, the Genome in a Bottle Consortium integrated 19 sequence-resolved variant calling methods from diverse technologies. The final benchmark set contains 12,745 isolated, sequence-resolved insertion (7,281) and deletion (5,464) calls ≥50 base pairs (bp). The Tier 1 benchmark regions, for which any extra calls are putative false positives, cover 2.51 Gbp and 5,262 insertions and 4,095 deletions supported by ≥1 diploid assembly. We demonstrate that the benchmark set reliably identifies false negatives and false positives in high-quality SV callsets from short-, linked- and long-read sequencing and optical mapping.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Prostate cancer is the second most commonly diagnosed malignancy among men worldwide. Recurrently mutated in primary and metastatic prostate tumors, FOXA1 encodes a pioneer transcription factor involved in disease onset and progression through both androgen receptor-dependent and androgen receptor-independent mechanisms. Despite its oncogenic properties however, the regulation of FOXA1 expression remains unknown. Here, we identify a set of six cis-regulatory elements in the FOXA1 regulatory plexus harboring somatic single-nucleotide variants in primary prostate tumors. We find that deletion and repression of these cis-regulatory elements significantly decreases FOXA1 expression and prostate cancer cell growth. Six of the ten single-nucleotide variants mapping to FOXA1 regulatory plexus significantly alter the transactivation potential of cis-regulatory elements by modulating the binding of transcription factors. Collectively, our results identify cis-regulatory elements within the FOXA1 plexus mutated in primary prostate tumors as potential targets for therapeutic intervention.

Oncogenesis is driven by germline, environmental and stochastic factors. It is unknown how these interact to produce the molecular phenotypes of tumors. We therefore quantified the influence of germline polymorphisms on the somatic epigenome of 589 localized prostate tumors. Predisposition risk loci influence a tumor's epigenome, uncovering a mechanism for cancer susceptibility. We identified and validated 1,178 loci associated with altered methylation in tumoral but not nonmalignant tissue. These tumor methylation quantitative trait loci influence chromatin structure, as well as RNA and protein abundance. One prominent tumor methylation quantitative trait locus is associated with AKT1 expression and is predictive of relapse after definitive local therapy in both discovery and validation cohorts. These data reveal intricate crosstalk between the germ line and the epigenome of primary tumors, which may help identify germline biomarkers of aggressive disease to aid patient triage and optimize the use of more invasive or expensive diagnostic assays.

Multiparametric magnetic resonance imaging (mpMRI) has transformed the management of localized prostate cancer by improving identification of clinically significant disease at diagnosis. Approximately 20% of primary prostate tumors are invisible to mpMRI, and we hypothesize that this invisibility reflects fundamental molecular properties of the tumor. We therefore profiled the genomes and transcriptomes of 40 International Society of Urological Pathology grade 2 tumors: 20 mpMRI-invisible (Prostate Imaging-Reporting and Data System [PI-RADS] v2

PurposeThe third-generation EGFR inhibitor, osimertinib, is the first mutant-selective inhibitor that has received regulatory approval for the treatment of patients with EGFR-mutant lung cancer. Despite the development of highly selective third-generation inhibitors, acquired resistance remains a significant clinical challenge. Recently, we and others have identified a novel osimertinib resistance mutation, G724S, which was not predicted in in vitro screens. Here, we investigate how G724S confers resistance to osimertinib.Experimental Design: We combine structure-based predictive modeling of G724S in combination with the 2 most common EGFR-activating mutations, exon 19 deletion (Ex19Del) and L858R, with in vitro drug-response models and patient genomic profiling.ResultsOur simulations suggest that the G724S mutation selectively reduces osimertinib-binding affinity in the context of Ex19Del. Consistent with our simulations, cell lines transduced with Ex19Del/G724S demonstrate resistance to osimertinib, whereas cells transduced with L858R/G724S are sensitive to osimertinib. Subsequent clinical genomic profiling data further suggest G724S occurs with Ex19Del but not L858R. Furthermore, we demonstrate that Ex19Del/G724S retains sensitivity to afatinib, but not to erlotinib, suggesting a possible therapy for patients at the time of disease relapse.ConclusionsAltogether, these data suggest that G724S is an allele-specific resistance mutation emerging in the context of Ex19Del but not L858R. Our results fundamentally reframe the problem of targeted therapy resistance from one focused on the "drug-resistance mutation" pair to one focused on the "activating mutation-drug-resistance mutation" trio. This has broad implications across clinical oncology.

During mammalian embryogenesis, de novo hematopoiesis occurs transiently in multiple anatomical sites including the yolk sac, dorsal aorta, and heart tube. A long-unanswered question is whether these local transient hematopoietic mechanisms are essential for embryonic growth. Here, we show that endocardial hematopoiesis is critical for cardiac valve remodeling as a source of tissue macrophages. Colony formation assay from explanted heart tubes and genetic lineage tracing with the endocardial specific Nfatc1-Cre mouse revealed that hemogenic endocardium is a de novo source of tissue macrophages in the endocardial cushion, the primordium of the cardiac valves. Surface marker characterization, gene expression profiling, and ex vivo phagocytosis assay revealed that the endocardially derived cardiac tissue macrophages play a phagocytic and antigen presenting role. Indeed, genetic ablation of endocardially derived macrophages caused severe valve malformation. Together, these data suggest that transient hemogenic activity in the endocardium is indispensable for the valvular tissue remodeling in the heart.

DNA sequencing has identified recurrent mutations that drive the aggressiveness of prostate cancers. Surprisingly, the influence of genomic, epigenomic, and transcriptomic dysregulation on the tumor proteome remains poorly understood. We profiled the genomes, epigenomes, transcriptomes, and proteomes of 76 localized, intermediate-risk prostate cancers. We discovered that the genomic subtypes of prostate cancer converge on five proteomic subtypes, with distinct clinical trajectories. ETS fusions, the most common alteration in prostate tumors, affect different genes and pathways in the proteome and transcriptome. Globally, mRNA abundance changes explain only ∼10% of protein abundance variability. As a result, prognostic biomarkers combining genomic or epigenomic features with proteomic ones significantly outperform biomarkers comprised of a single data type.

BackgroundInvasive cribriform and intraductal carcinoma (CR/IDC) is associated with adverse outcome of prostate cancer patients. The aim of this study was to determine the molecular aberrations associated with CR/IDC in primary prostate cancer, focusing on genomic instability and somatic copy number alterations (CNA).MethodsWhole-slide images of The Cancer Genome Atlas Project (TCGA, N = 260) and the Canadian Prostate Cancer Genome Network (CPC-GENE, N = 199) radical prostatectomy datasets were reviewed for Gleason score (GS) and presence of CR/IDC. Genomic instability was assessed by calculating the percentage of genome altered (PGA). Somatic copy number alterations (CNA) were determined using Fisher-Boschloo tests and logistic regression. Primary analysis were performed on TCGA (N = 260) as discovery and CPC-GENE (N = 199) as validation set.ResultsCR/IDC growth was present in 80/260 (31%) TCGA and 76/199 (38%) CPC-GENE cases. Patients with CR/IDC and ≥ GS 7 had significantly higher PGA than men without this pattern in both TCGA (2.2 fold; p = 0.0003) and CPC-GENE (1.7 fold; p = 0.004) cohorts. CR/IDC growth was associated with deletions of 8p, 16q, 10q23, 13q22, 17p13, 21q22, and amplification of 8q24. CNAs comprised a total of 1299 gene deletions and 369 amplifications in the TCGA dataset, of which 474 and 328 events were independently validated, respectively. Several of the affected genes were known to be associated with aggressive prostate cancer such as loss of PTEN, CDH1, BCAR1 and gain of MYC. Point mutations in TP53, SPOP and FOXA1were also associated with CR/IDC, but occurred less frequently than CNAs.ConclusionsCR/IDC growth is associated with increased genomic instability clustering to genetic regions involved in aggressive prostate cancer. Therefore, CR/IDC is a pathologic substrate for progressive molecular tumour derangement.

BackgroundThe development of clinical -omic biomarkers for predicting patient prognosis has mostly focused on multi-gene models. However, several studies have described significant weaknesses of multi-gene biomarkers. Indeed, some high-profile reports have even indicated that multi-gene biomarkers fail to consistently outperform simple single-gene ones. Given the continual improvements in -omics technologies and the availability of larger, better-powered datasets, we revisited this "single-gene hypothesis" using new techniques and datasets.ResultsBy deeply sampling the population of available gene sets, we compare the intrinsic properties of single-gene biomarkers to multi-gene biomarkers in twelve different partitions of a large breast cancer meta-dataset. We show that simple multi-gene models consistently outperformed single-gene biomarkers in all twelve partitions. We found 270 multi-gene biomarkers (one per ~11,111 sampled) that always made better predictions than the best single-gene model.ConclusionsThe single-gene hypothesis for breast cancer does not appear to retain its validity in the face of improved statistical models, lower-noise genomic technology and better-powered patient cohorts. These results highlight that it is critical to revisit older hypotheses in the light of newer techniques and datasets.

BackgroundThe phenotypes of cancer cells are driven in part by somatic structural variants. Structural variants can initiate tumors, enhance their aggressiveness, and provide unique therapeutic opportunities. Whole-genome sequencing of tumors can allow exhaustive identification of the specific structural variants present in an individual cancer, facilitating both clinical diagnostics and the discovery of novel mutagenic mechanisms. A plethora of somatic structural variant detection algorithms have been created to enable these discoveries; however, there are no systematic benchmarks of them. Rigorous performance evaluation of somatic structural variant detection methods has been challenged by the lack of gold standards, extensive resource requirements, and difficulties arising from the need to share personal genomic information.ResultsTo facilitate structural variant detection algorithm evaluations, we create a robust simulation framework for somatic structural variants by extending the BAMSurgeon algorithm. We then organize and enable a crowdsourced benchmarking within the ICGC-TCGA DREAM Somatic Mutation Calling Challenge (SMC-DNA). We report here the results of structural variant benchmarking on three different tumors, comprising 204 submissions from 15 teams. In addition to ranking methods, we identify characteristic error profiles of individual algorithms and general trends across them. Surprisingly, we find that ensembles of analysis pipelines do not always outperform the best individual method, indicating a need for new ways to aggregate somatic structural variant detection approaches.ConclusionsThe synthetic tumors and somatic structural variant detection leaderboards remain available as a community benchmarking resource, and BAMSurgeon is available at https://github.com/adamewing/bamsurgeon .

BackgroundBiomarkers are a key component of precision medicine. However, full clinical integration of biomarkers has been met with challenges, partly attributed to analytical difficulties. It has been shown that biomarker reproducibility is susceptible to data preprocessing approaches. Here, we systematically evaluated machine-learning ensembles of preprocessing methods as a general strategy to improve biomarker performance for prediction of survival from early breast cancer.ResultsWe risk stratified breast cancer patients into either low-risk or high-risk groups based on four published hypoxia signatures (Buffa, Winter, Hu, and Sorensen), using 24 different preprocessing approaches for microarray normalization. The 24 binary risk profiles determined for each hypoxia signature were combined using a random forest to evaluate the efficacy of a preprocessing ensemble classifier. We demonstrate that the best way of merging preprocessing methods varies from signature to signature, and that there is likely no 'best' preprocessing pipeline that is universal across datasets, highlighting the need to evaluate ensembles of preprocessing algorithms. Further, we developed novel signatures for each preprocessing method and the risk classifications from each were incorporated in a meta-random forest model. Interestingly, the classification of these biomarkers and its ensemble show striking consistency, demonstrating that similar intrinsic biological information are being faithfully represented. As such, these classification patterns further confirm that there is a subset of patients whose prognosis is consistently challenging to predict.ConclusionsPerformance of different prognostic signatures varies with pre-processing method. A simple classifier by unanimous voting of classifications is a reliable way of improving on single preprocessing methods. Future signatures will likely require integration of intrinsic and extrinsic clinico-pathological variables to better predict disease-related outcomes.

Radioresistance remains a significant obstacle in the treatment of Prostate Cancer (PCa). To simulate the clinical scenario of irradiation resistance (IRR), we created DU145-IRR PCa cell lines by treatment with 2 Gy daily IR for 59 fractions. DU145-IRR cells acquired an aggressive phenotype as evidenced by increased clonogenic survival, tumorigenic potential and invasiveness. We performed transcriptome profiling to discover dysregulated genes in DU145-IRR cells and identified the long non-coding RNA (lncRNA), Urothelial carcinoma-associated 1 (UCA1). We first investigated the role of UCA1 in radiation response and found that UCA1 abundance was significantly higher in DU145-IRR cells compared to control cells. UCA1 siRNA-knockdown reversed the aggressive phenotype and significantly increased sensitivity to IR. UCA1 depletion inhibited growth, induced cell cycle arrest at the G2/M transition and decreased activation of the pro-survival Akt pathway. We then studied the clinical significance of UCA1 expression in two independent cohorts of PCa patients: MSKCC (130 patients) and CPC-GENE (209 patients). UCA1 over-expression was associated with decreased 5-year disease-free survival in MSKCC patients (HR = 2.9; p = 0.007) and a trend toward lower biochemical recurrence-free survival in CPC-GENE patients (HR = 2.7; p = 0.05). We showed for the first time that UCA1 depletion induces radiosensitivity, decreases proliferative capacity and disrupts cell cycle progression, which may occur through altered Akt signaling and induced cell cycle arrest at the G2/M transition. Our results indicate that UCA1 might have prognostic value in PCa and be a potential therapeutic target.

Germline mutations in the BRCA2 tumour suppressor are associated with both an increased lifetime risk of developing prostate cancer (PCa) and increased risk of aggressive disease. To understand this aggression, here we profile the genomes and methylomes of localized PCa from 14 carriers of deleterious germline BRCA2 mutations (BRCA2-mutant PCa). We show that BRCA2-mutant PCa harbour increased genomic instability and a mutational profile that more closely resembles metastastic than localized disease. BRCA2-mutant PCa shows genomic and epigenomic dysregulation of the MED12L/MED12 axis, which is frequently dysregulated in metastatic castration-resistant prostate cancer (mCRPC). This dysregulation is enriched in BRCA2-mutant PCa harbouring intraductal carcinoma (IDC). Microdissection and sequencing of IDC and juxtaposed adjacent non-IDC invasive carcinoma in 10 patients demonstrates a common ancestor to both histopathologies. Overall we show that localized castration-sensitive BRCA2-mutant tumours are uniquely aggressive, due to de novo aberration in genes usually associated with metastatic disease, justifying aggressive initial treatment.

Radioresistance remains a significant obstacle in the treatment of Prostate Cancer (PCa). To simulate the clinical scenario of irradiation resistance (IRR), we created DU145-IRR PCa cell lines by treatment with 2 Gy daily IR for 59 fractions. DU145-IRR cells acquired an aggressive phenotype as evidenced by increased clonogenic survival, tumorigenic potential and invasiveness. We performed transcriptome profiling to discover dysregulated genes in DU145-IRR cells and identified the long non-coding RNA (lncRNA), Urothelial carcinoma-associated 1 (UCA1). We first investigated the role of UCA1 in radiation response and found that UCA1 abundance was significantly higher in DU145-IRR cells compared to control cells. UCA1 siRNA-knockdown reversed the aggressive phenotype and significantly increased sensitivity to IR. UCA1 depletion inhibited growth, induced cell cycle arrest at the G2/M transition and decreased activation of the pro-survival Akt pathway. We then studied the clinical significance of UCA1 expression in two independent cohorts of PCa patients: MSKCC (130 patients) and CPC-GENE (209 patients). UCA1 over-expression was associated with decreased 5-year disease-free survival in MSKCC patients (HR = 2.9; p = 0.007) and a trend toward lower biochemical recurrence-free survival in CPC-GENE patients (HR = 2.7; p = 0.05). We showed for the first time that UCA1 depletion induces radiosensitivity, decreases proliferative capacity and disrupts cell cycle progression, which may occur through altered Akt signaling and induced cell cycle arrest at the G2/M transition. Our results indicate that UCA1 might have prognostic value in PCa and be a potential therapeutic target.

Based on the example of mainland China's online diffusion-proofing practices against Hong Kong's Anti-Extradition Bill Movement, we identify a platform-based tactic of diffusion-proofing. We argue that this tactic manifested the orchestration of top-down statist governance and bottom-up grassroots practices, as well as within-border and cross-border discursive dynamics. Facilitated by the technical affordances of social media platforms, a faction of netizens both from mainland China and Hong Kong followed the state and its agents online to co-construct discourses for preventing the potential spillover of the Movement to mainland China. We further suggest that this tactic was enabled by the hybridization of both technical logic and discursive logic. The platform affordances, especially different types of hashtags, allowed the state and its agents to practice digitally mediated discursive production, reacting to the momentum of the Movement. As a result, a discourse was constructed, which delegitimatized protest actions and ignited nationalistic sentiment for the purpose of diffusion-proofing. [ABSTRACT FROM AUTHOR]

Tissue clearing methods promise to provide exquisite three-dimensional imaging information; however, there is a need for simplified methods for lower resource settings and for non-fluorescence based phenotyping to enable light microscopic imaging modalities. Here we describe the simplified CLARITY method (SCM) for tissue clearing that preserves epitopes of interest. We imaged the resulting tissues using light sheet microscopy to generate rapid 3D reconstructions of entire tissues and organs. In addition, to enable clearing and 3D tissue imaging with light microscopy methods, we developed a colorimetric, non-fluorescent method for specifically labeling cleared tissues based on horseradish peroxidase conversion of diaminobenzidine to a colored insoluble product. The methods we describe here are portable and can be accomplished at low cost, and can allow light microscopic imaging of cleared tissues, thus enabling tissue clearing and imaging in a wide variety of settings.

RationaleDuring embryogenesis, hematopoietic cells appear in the myocardium prior to the initiation of coronary formation. However, their role is unknown.ObjectiveHere we investigate whether pre-existing hematopoietic cells are required for the formation of coronary vasculature.Methods and resultsAs a model of for hematopoietic cell deficient animals, we used Runx1 knockout embryos and Vav1-cre; R26-DTA embryos, latter of which genetically ablates 2/3 of CD45(+) hematopoietic cells. Both Runx1 knockout embryos and Vav1-cre; R26-DTA embryos revealed disorganized, hypoplastic microvasculature of coronary vessels on section and whole-mount stainings. Furthermore, coronary explant experiments showed that the mouse heart explants from Runx1 and Vav1-cre; R26-DTA embryos exhibited impaired coronary formation ex vivo. Interestingly, in both models it appears that epicardial to mesenchymal transition is adversely affected in the absence of hematopoietic progenitors.ConclusionHematopoietic cells are not merely passively transported via coronary vessel, but substantially involved in the induction of the coronary growth. Our findings suggest a novel mechanism of coronary growth.