Neuroblastoma is a childhood cancer that is often fatal despite intense multimodality therapy. In an effort to identify therapeutic targets for this disease, we performed a comprehensive loss-offunction screen of the protein kinome. Thirty kinases showed significant cellular cytotoxicity when depleted, with loss of the cell cycle checkpoint kinase 1 (CHK1/CHEK1) being the most potent. CHK1 mRNA expression was higher in MYC-Neuroblastoma-related (MYCN)-amplified (P < 0.0001) and high-risk (P = 0.03) tumors. Western blotting revealed that CHK1 was constitutively phosphorylated at the ataxia telangiectasia response kinase target site Ser345 and the autophosphorylation site Ser296 in neuroblastoma cell lines. This pattern was also seen in six of eight high-risk primary tumors but not in control nonneuroblastoma cell lines or in seven of eight low-risk primary tumors. Neuroblastoma cells were sensitive to the two CHK1 inhibitors SB21807 and TCS2312, with median IC 50 values of 564 nM and 548 nM, respectively. In contrast, the control lines had high micromolar IC 50 values, indicating a strong correlation between CHK1 phosphorylation and CHK1 inhibitor sensitivity (P = 0.0004). Furthermore, cell cycle analysis revealed that CHK1 inhibition in neuroblastoma cells caused apoptosis during S-phase, consistent with its role in replication fork progression. CHK1 inhibitor sensitivity correlated with total MYC(N) protein levels, and inducing MYCN in retinal pigmented epithelial cells resulted in CHK1 phosphorylation, which caused growth inhibition when inhibited. These data show the power of a functional RNAi screen to identify tractable therapeutical targets in neuroblastoma and support CHK1 inhibition strategies in this disease. N euroblastoma is an embryonal tumor of early childhood thought to arise from fetal sympathetic neuroblasts (1). Children with localized neuroblastoma can be cured with surgery and/or chemotherapy. About half of children with neuroblastoma have high-risk disease, however, characterized by widespread disease dissemination at diagnosis. For these children, current treatment consists of chemotherapy, surgery, external beam radiation therapy, myeloablative chemotherapy with stem cell rescue, and a maintenance therapy regimen combining retinoids and anti-GD2-based immunotherapy (2). Despite the intense multimodality therapy, at least half of high-risk patients will experience relapse that is almost always fatal and survivors show significant morbidity (1).To address the unmet need of identifying bona fide molecular targets for drug development in neuroblastoma, we and others have undertaken comprehensive characterization of the neuroblastoma genome, leading to the identification of mutations in the anaplastic lymphoma kinase gene (ALK) in 10% of newly diagnosed cases (3, 4). Ongoing comprehensive resequencing efforts will likely discover other potential drug targets in this disease. Although these data may identify the critical genes and pathways for drug development efforts, it is also possible that ot...

Contact tracing is critical to controlling COVID-19, but most protocols only “forward-trace” to notify people who were recently exposed. Using a stochastic branching-process model, we find that “bidirectional” tracing to identify infector individuals and their other infectees robustly improves outbreak control. In our model, bidirectional tracing more than doubles the reduction in effective reproduction number (Reff) achieved by forward-tracing alone, while dramatically increasing resilience to low case ascertainment and test sensitivity. The greatest gains are realised by expanding the manual tracing window from 2 to 6 days pre-symptom-onset or, alternatively, by implementing high-uptake smartphone-based exposure notification; however, to achieve the performance of the former approach, the latter requires nearly all smartphones to detect exposure events. With or without exposure notification, our results suggest that implementing bidirectional tracing could dramatically improve COVID-19 control.

Completely random measures (CRMs) and their normalizations are a rich source of Bayesian nonparametric priors. Examples include the beta, gamma, and Dirichlet processes. In this paper we detail two major classes of sequential CRM representations-series representations and superposition representations-within which we organize both novel and existing sequential representations that can be used for simulation and posterior inference. These two classes and their constituent representations subsume existing ones that have previously been developed in an ad hoc manner for specific processes. Since a complete infinite-dimensional CRM cannot be used explicitly for computation, sequential representations are often truncated for tractability. We provide truncation error analyses for each type of sequential representation, as well as their normalized versions, thereby generalizing and improving upon existing truncation error bounds in the literature. We analyze the computational complexity of the sequential representations, which in conjunction with our error bounds allows us to directly compare representations and discuss their relative efficiency. We include numerous applications of our theoretical results to commonly-used (normalized) CRMs, demonstrating that our results enable a straightforward representation and analysis of CRMs that has not previously been available in a Bayesian nonparametric context.

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Mutational signatures are patterns of somatic alterations in the genome caused by carcinogenic exposures or aberrant cellular processes. To provide a comprehensive workflow for preprocessing, analysis, and visualization of mutational signatures, we created the Mutational Signature Comprehensive Analysis Toolkit (musicatk) package. musicatk enables users to select different schemas for counting mutation types and to easily combine count tables from different schemas. Multiple distinct methods are available to deconvolute signatures and exposures or to predict exposures in individual samples given a pre-existing set of signatures. Additional exploratory features include the ability to compare signatures to the Catalogue Of Somatic Mutations In Cancer (COSMIC) database, embed tumors in two dimensions with uniform manifold approximation and projection, cluster tumors into subgroups based on exposure frequencies, identify differentially active exposures between tumor subgroups, and plot exposure distributions across user-defined annotations such as tumor type. Overall, musicatk will enable users to gain novel insights into the patterns of mutational signatures observed in cancer cohorts. Significance: The musicatk package empowers researchers to characterize mutational signatures and tumor heterogeneity with a comprehensive set of preprocessing utilities, discovery and prediction tools, and multiple functions for downstream analysis and visualization.

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Sequential Monte Carlo (SMC) algorithms were originally designed for estimating intractable conditional expectations within state-space models, but are now routinely used to generate approximate samples in the context of general-purpose Bayesian inference. In particular, SMC algorithms are often used as subroutines within larger Monte Carlo schemes, and in this context, the demands placed on SMC are different: control of mean-squared error is insufficient-one needs to control the divergence from the target distribution directly. Towards this goal, we introduce the conditional adaptive resampling particle filter, building on the work of Gordon, Salmond, and Smith (1993), Andrieu, Doucet, and Holenstein (2010), and Whiteley, Lee, and Heine (2016). By controlling a novel notion of effective sample size, the ∞-ESS, we establish the efficiency of the resulting SMC sampling algorithm, providing an adaptive resampling extension of the work of Andrieu, Lee, and Vihola (2013). We apply our results to arrive at new divergence bounds for SMC samplers with adaptive resampling as well as an adaptive resampling version of the Particle Gibbs algorithm with the same geometric-ergodicity guarantees as its nonadaptive counterpart.

We present fast methods for filtering voltage measurements and performing optimal inference of the location and strength of synaptic connections in large dendritic trees. Given noisy, subsampled voltage observations we develop fast l 1 -penalized regression methods for Kalman state-space models of the neuron voltage dynamics. The value of the l 1 -penalty parameter is chosen using crossvalidation or, for low signal-to-noise ratio, a Mallows' C p -like criterion. Using low-rank approximations, we reduce the inference runtime from cubic to linear in the number of dendritic compartments. We also present an alternative, fully Bayesian approach to the inference problem using a spike-andslab prior. We illustrate our results with simulations on toy and real neuronal geometries. We consider observation schemes that either scan the dendritic geometry uniformly or measure linear combinations of voltages across several locations with random coefficients. For the latter, we show how to choose the coefficients to offset the correlation between successive measurements imposed by the neuron dynamics. This results in a "compressed sensing" observation scheme, with an important reduction in the number of measurements required to infer the synaptic weights.