Multicenter evaluation of the BIOFIRE Joint Infection Panel for the detection of bacteria, yeast, and AMR genes in synovial fluid samples.

The bioMérieux BIOFIRE Joint Infection (JI) Panel is a multiplex in vitro diagnostic test for the simultaneous and rapid (~1 h) detection of 39 potential pathogens and antimicrobial resistance (AMR) genes directly from synovial fluid (SF) samples. Thirty-one species or groups of microorganisms are included in the kit, as well as several AMR genes. This study, performed to evaluate the BIOFIRE JI Panel for regulatory clearance, provides data from a multicenter evaluation of 1,544 prospectively collected residual SF samples with performance compared to standard-of-care (SOC) culture for organisms or polymerase chain reaction (PCR) and sequencing for AMR genes. The BIOFIRE JI Panel demonstrated a sensitivity of 90.9% or greater for all but six organisms and a positive percent agreement (PPA) of 100% for all AMR genes. The BIOFIRE JI Panel demonstrated a specificity of 98.5% or greater for detection of all organisms and a negative percent agreement (NPA) of 95.7% or greater for all AMR genes. The BIOFIRE JI Panel provides an improvement over SOC culture, with a substantially shorter time to result for both organisms and AMR genes with excellent sensitivity/PPA and specificity/NPA, and is anticipated to provide timely and actionable diagnostic information for joint infections in a variety of clinical scenarios.

Targeting the spliceosome through RBM39 degradation results in exceptional responses in high-risk neuroblastoma models.

Aberrant alternative pre-mRNA splicing plays a critical role in MYC-driven cancers and therefore may represent a therapeutic vulnerability. Here, we show that neuroblastoma, a MYC-driven cancer characterized by splicing dysregulation and spliceosomal dependency, requires the splicing factor RBM39 for survival. Indisulam, a "molecular glue" that selectively recruits RBM39 to the CRL4-DCAF15 E3 ubiquitin ligase for proteasomal degradation, is highly efficacious against neuroblastoma, leading to significant responses in multiple high-risk disease models, without overt toxicity. Genetic depletion or indisulam-mediated degradation of RBM39 induces significant genome-wide splicing anomalies and cell death. Mechanistically, the dependency on RBM39 and high-level expression of DCAF15 determine the exquisite sensitivity of neuroblastoma to indisulam. Our data indicate that targeting the dysregulated spliceosome by precisely inhibiting RBM39, a vulnerability in neuroblastoma, is a valid therapeutic strategy.

Genetic Modification of Sodalis Species by DNA Transduction.

Bacteriophages (phages) are ubiquitous in nature. These viruses play a number of central roles in microbial ecology and evolution by, for instance, promoting horizontal gene transfer (HGT) among bacterial species. The ability of phages to mediate HGT through transduction has been widely exploited as an experimental tool for the genetic study of bacteria. As such, bacteriophage P1 represents a prototypical generalized transducing phage with a broad host range that has been extensively employed in the genetic manipulation of Escherichia coli and a number of other model bacterial species. Here we demonstrate that P1 is capable of infecting, lysogenizing, and promoting transduction in members of the bacterial genus Sodalis, including the maternally inherited insect endosymbiont Sodalis glossinidius While establishing new tools for the genetic study of these bacterial species, our results suggest that P1 may be used to deliver DNA to many Gram-negative endosymbionts in their insect host, thereby circumventing a culturing requirement to genetically manipulate these organisms.IMPORTANCE A large number of economically important insects maintain intimate associations with maternally inherited endosymbiotic bacteria. Due to the inherent nature of these associations, insect endosymbionts cannot be usually isolated in pure culture or genetically manipulated. Here we use a broad-host-range bacteriophage to deliver exogenous DNA to an insect endosymbiont and a closely related free-living species. Our results suggest that broad-host-range bacteriophages can be used to genetically alter insect endosymbionts in their insect host and, as a result, bypass a culturing requirement to genetically alter these bacteria.

Probabilistic definition of the clinical target volume-implications for tumor control probability modeling and optimization.

Evidence has been presented that moving beyond the binary definition of clinical target volume (CTV) towards a probabilistic CTV can result in better treatment plans. The probabilistic CTV takes the likelihood of disease spread outside of the gross tumor into account. An open question is: how to optimize tumor control probability (TCP) based on the probabilistic CTV. We derive expressions for TCP under the assumptions of voxel independence and dependence. For the dependent case, we make the assumption that tumors grow outward from the gross tumor volume. We maximize the (non-convex) TCP under convex dose constraints for all models. For small numbers of voxels, and when a dose-influence matrix is not used, we use exhaustive search or Lagrange multiplier theory to compute optimal dose distributions. For larger cases we present (1) a multi-start strategy using linear programming with a random cost vector to provide random feasible starting solutions, followed by a local search, and (2) a heuristic strategy that greedily selects which subvolumes to dose, and then for each subvolume assignment runs a convex approximation of the optimization problem. The optimal dose distributions are in general different for the independent and dependent models even though the probabilities of each voxel being tumorous are set to the same in both cases. We observe phase transitions, where a subvolume is either dosed to a high level, or it gets 'sacrificed' by not dosing it at all. The greedy strategy often yields solutions indistinguishable from the multi-start solutions, but for the 2D case involving organs-at-risk and the dependent TCP model, discrepancies of around 5% (absolute) for TCP are observed. For realistic geometries, although correlated voxels is a more reasonable assumption, the correlation function is in general unknown. We demonstrate a tractable heuristic that works very well for the independent models and reasonably well for the dependent models. All data are provided.

Impact of removing ESBL designation from culture reports on the selection of antibiotics for the treatment of infections associated with ESBL-producing organisms.

We characterized the impact of removal of the ESBL designation from microbiology reports on inpatient antibiotic prescribing. Definitive prescribing of carbapenems decreased from 48.4% to 16.1% (P = .01) and ß-lactam-ß-lactamase inhibitor combination increased from 19.4% to 61.3% (P = .002). Our findings confirm the importance of collaboration between microbiology and antimicrobial stewardship programs.

Analyses of non-coding somatic drivers in 2,658 cancer whole genomes.

The discovery of drivers of cancer has traditionally focused on protein-coding genes1-4. Here we present analyses of driver point mutations and structural variants in non-coding regions across 2,658 genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium5 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). For point mutations, we developed a statistically rigorous strategy for combining significance levels from multiple methods of driver discovery that overcomes the limitations of individual methods. For structural variants, we present two methods of driver discovery, and identify regions that are significantly affected by recurrent breakpoints and recurrent somatic juxtapositions. Our analyses confirm previously reported drivers6,7, raise doubts about others and identify novel candidates, including point mutations in the 5' region of TP53, in the 3' untranslated regions of NFKBIZ and TOB1, focal deletions in BRD4 and rearrangements in the loci of AKR1C genes. We show that although point mutations and structural variants that drive cancer are less frequent in non-coding genes and regulatory sequences than in protein-coding genes, additional examples of these drivers will be found as more cancer genomes become available.

Technical Note: Improving VMAT delivery efficiency by optimizing the dynamic collimator trajectory.

PURPOSE: To provide a method for optimizing the multileaf collimator angle trajectory in volumetric modulated arc therapy (VMAT) in order to make VMAT delivery and planning more efficient. METHODS: Static fluence maps are optimized at a 10-degree spacing around the patient. Sliding window delivery time of each of these fields is computed for a large set of possible collimator orientations. An optimal trajectory, which selects a collimator angle for each field and assures that the collimator angles do not differ excessively between adjacent fields, is computed by solving a network flow model of a shortest path problem. RESULTS: For four clinical cases (two brains, an anal, and a spine), we demonstrate time reductions from 6% to 32% (average: 24%) for the optimal static angle vs the worst static angle. Further reductions from 3% to 17% (average 9%) are achievable when dynamic collimator trajectories are allowed. CONCLUSIONS: Dynamic collimator trajectories, which can be computed with an efficient linear programming formulation, can improve the efficiency of VMAT delivery.

Adaptable microfluidic system for single-cell pathogen classification and antimicrobial susceptibility testing.

Infectious diseases caused by bacterial pathogens remain one of the most common causes of morbidity and mortality worldwide. Rapid microbiological analysis is required for prompt treatment of bacterial infections and to facilitate antibiotic stewardship. This study reports an adaptable microfluidic system for rapid pathogen classification and antimicrobial susceptibility testing (AST) at the single-cell level. By incorporating tunable microfluidic valves along with real-time optical detection, bacteria can be trapped and classified according to their physical shape and size for pathogen classification. By monitoring their growth in the presence of antibiotics at the single-cell level, antimicrobial susceptibility of the bacteria can be determined in as little as 30 minutes compared with days required for standard procedures. The microfluidic system is able to detect bacterial pathogens in urine, blood cultures, and whole blood and can analyze polymicrobial samples. We pilot a study of 25 clinical urine samples to demonstrate the clinical applicability of the microfluidic system. The platform demonstrated a sensitivity of 100% and specificity of 83.33% for pathogen classification and achieved 100% concordance for AST.

Simulation-assisted machine learning.

MOTIVATION: In a predictive modeling setting, if sufficient details of the system behavior are known, one can build and use a simulation for making predictions. When sufficient system details are not known, one typically turns to machine learning, which builds a black-box model of the system using a large dataset of input sample features and outputs. We consider a setting which is between these two extremes: some details of the system mechanics are known but not enough for creating simulations that can be used to make high quality predictions. In this context we propose using approximate simulations to build a kernel for use in kernelized machine learning methods, such as support vector machines. The results of multiple simulations (under various uncertainty scenarios) are used to compute similarity measures between every pair of samples: sample pairs are given a high similarity score if they behave similarly under a wide range of simulation parameters. These similarity values, rather than the original high dimensional feature data, are used to build the kernel. RESULTS: We demonstrate and explore the simulation-based kernel (SimKern) concept using four synthetic complex systems-three biologically inspired models and one network flow optimization model. We show that, when the number of training samples is small compared to the number of features, the SimKern approach dominates over no-prior-knowledge methods. This approach should be applicable in all disciplines where predictive models are sought and informative yet approximate simulations are available. AVAILABILITY AND IMPLEMENTATION: The Python SimKern software, the demonstration models (in MATLAB, R), and the datasets are available at https://github.com/davidcraft/SimKern. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Nanotube assisted microwave electroporation for single cell pathogen identification and antimicrobial susceptibility testing.

A nanotube assisted microwave electroporation (NAME) technique is demonstrated for delivering molecular biosensors into viable bacteria for multiplex single cell pathogen identification to advance rapid diagnostics in clinical microbiology. Due to the small volume of a bacterial cell (~femtoliter), the intracellular concentration of the target molecule is high, which results in a strong signal for single cell detection without amplification. The NAME procedure can be completed in as little as 30 minutes and can achieve over 90% transformation efficiency. We demonstrate the feasibility of NAME for identifying clinical isolates of bloodborne and uropathogenic pathogens and detecting bacterial pathogens directly from patient's samples. In conjunction with a microfluidic single cell trapping technique, NAME allows single cell pathogen identification and antimicrobial susceptibility testing concurrently. Using this approach, the time for microbiological analysis reduces from days to hours, which will have a significant impact on the clinical management of bacterial infections.

Campylobacter culture fails to correctly detect Campylobacter in 30% of positive patient stool specimens compared to non-cultural methods.

Campylobacter diagnosis is hampered because many laboratories continue to use traditional stool culture, which is slow and suffers false-negative results. This large multi-site study used a composite reference method consisting of a new FDA-cleared immunoassay and four molecular techniques to compare to culture. Prospectively collected patient fecal specimens (1552) were first preliminarily categorized as positive or negative by traditional culture. All specimens were also tested by EIA, and any EIA-positive or culture-discrepant results were further characterized by 16S rRNA qPCR, eight species-specific PCR assays, bidirectional sequencing, and an FDA-cleared multiplex PCR panel. The five non-culture methods showed complete agreement on all positive and discrepant specimens which were then assigned as true-positive or true-negative specimens. Among 47 true-positive specimens, culture incorrectly identified 13 (28%) as negative, and 1 true-negative specimen as positive, for a sensitivity of 72.3%. Unexpectedly, among the true-positive specimens, 4 (8%) were the pathogenic species C. upsaliensis. Culture had a 30% false result rate compared to immunoassay and molecular methods. More accurate results lead to better diagnosis and treatment of suspected campylobacteriosis.

Establishing a Long-Term Model for Analysis and Improvement of Underfilled Blood Culture Volumes.

Objectives: Underfilling of blood culture bottles decreases the sensitivity of the culture. We attempt to increase average blood culture fill volumes (ABCFVs) through an educational program. Methods: Partnerships were established with four hospital units (surgical intensive care unit [SICU], medical intensive care unit [MICU], medical intermediate care unit [MIMCU], and hematology and oncology unit [HEME/ONC]). ABCFVs were continuously tracked and communicated to each unit monthly. Educational sessions were provided to each unit. Results: ABCFVs for the SICU, MICU, MIMCU, and HEME/ONC were 4.8, 5.0, 5.0, and 6.3 mL/bottle, respectively. After the final education session, the SICU, MICU, MIMCU, and HEME/ONC were able to maintain an ABCFV of 6.8, 8.1, 7.9, and 8.2 mL/bottle, respectively. Conclusions: Partnering with a specific unit and providing monthly volume reports with educational sessions has a direct positive correlation on increasing ABCFVs. Increasing ABCFVs has the potential to decrease false-negative blood cultures, time to detection of positive blood cultures, and time to appropriate and specific antimicrobial therapy, as well as improve patient outcomes in high-acuity patient care units.

The clinical target distribution: a probabilistic alternative to the clinical target volume.

Definition of the clinical target volume (CTV) is one of the weakest links in the radiation therapy chain. In particular, inability to account for uncertainties is a severe limitation in the traditional CTV delineation approach. Here, we introduce and test a new concept for tumor target definition, the clinical target distribution (CTD). The CTD is a continuous distribution of the probability of voxels to be tumorous. We describe an approach to incorporate the CTD in treatment plan optimization algorithms, and implement it in a commercial treatment planning system. We test the approach in two synthetic and two clinical cases, a sarcoma and a glioblastoma case. The CTD is straightforward to implement in treatment planning and comes with several advantages. It allows one to find the most suitable tradeoff between target coverage and sparing of surrounding healthy organs at the treatment planning stage, without having to modify or redraw a CTV. Owing to the variable probabilities afforded by the CTD, a more flexible and more clinically meaningful sparing of critical structure becomes possible. Finally, the CTD is expected to reduce the inter-user variability of defining the traditional CTV.

Optimizing highly noncoplanar VMAT trajectories: the NoVo method.

We introduce a new method called NoVo (Noncoplanar VMAT Optimization) to produce volumetric modulated arc therapy (VMAT) treatment plans with noncoplanar trajectories. While the use of noncoplanar beam arrangements for intensity modulated radiation therapy (IMRT), and in particular high fraction stereotactic radiosurgery (SRS), is common, noncoplanar beam trajectories for VMAT are less common as the availability of treatment machines handling these is limited. For both IMRT and VMAT, the beam angle selection problem is highly nonconvex in nature, which is why automated beam angle selection procedures have not entered mainstream clinical usage. NoVo determines a noncoplanar VMAT solution (i.e. the simultaneous trajectories of the gantry and the couch) by first computing a [Formula: see text] solution (beams from every possible direction, suitably discretized) and then eliminating beams by examing fluence contributions. Also all beam angles are scored via geometrical considerations only to find out the usefulness of the whole beam space in a very short time. A custom path finding algorithm is applied to find an optimized, continuous trajectory through the most promising beam angles using the calculated score of the beam space. Finally, using this trajectory a VMAT plan is optimized. For three clinical cases, a lung, brain, and liver case, we compare NoVo to the ideal [Formula: see text] solution, nine beam noncoplanar IMRT, coplanar VMAT, and a recently published noncoplanar VMAT algorithm. NoVo comes closest to the [Formula: see text] solution considering the lung case (brain and liver case: second), as well as improving the solution time by using geometrical considerations, followed by a time effective iterative process reducing the [Formula: see text] solution. Compared to a recently published noncoplanar VMAT algorithm, using NoVo the computation time is reduced by a factor of 2-3 (depending on the case). Compared to coplanar VMAT, NoVo reduces the objective function value by 24%, 49% and 6% for the lung, brain and liver cases, respectively.

Viable bacteria persist on antibiotic spacers following two-stage revision for periprosthetic joint infection.

Treatment in periprosthetic joint infection (PJI) remains challenging. The failure rate of two-stage revision and irrigation and debridement with component retention in PJI suggests that biofilm cells have a high tolerance to antibiotic chemotherapy. Previous work has demonstrated that biofilm cells have high antibiotic tolerance in vitro, but there is little clinical evidence to support these observations. The aim of this study was to determine if retrieved antibiotic spacers from two-stage revision total knee arthroplasty for PJI have evidence of remaining viable bacteria. Antibiotic poly (methyl methacrylate) (PMMA) spacers from two-stage revision total knee arthroplasty for PJI were prospectively collected and analyzed for bacterial 16s rRNA using polymerase chain reaction (PCR), reverse transcription (RT)-PCR, quantitative RT-PCR (qRT-PCR), and single genome analysis (SGA). PCR and RT-PCR identified bacterial species on 53.8% (7/13) of these samples. When initial culture negative cases are excluded, 68% (6/9) samples were identified with bacterial species. A more rigorous qRT-PCR analysis showed a strong positive signal for bacterial contamination in 30.7% (4/13) of cases. These patients did not show any clinical evidence of PJI recurrence after 15 months of follow-up. Because the half-life of bacterial rRNA is approximately a few days, the identification of bacteria rRNA on antibiotic PMMA spacers suggests that viable bacteria were present after conclusion of antibiotic therapy. This study provides evidence for the high tolerance of biofilm cells to antibiotics in vivo and the important role of bacterial persisters in PJI. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:452-458, 2018.

Multicriteria plan optimization in the hands of physicians: a pilot study in prostate cancer and brain tumors.

BACKGROUND: The purpose of this study was to demonstrate the feasibility of physician driven planning in intensity modulated radiotherapy (IMRT) with a multicriteria optimization (MCO) treatment planning system and template based plan optimization. Exploiting the full planning potential of MCO navigation, this alternative planning approach intends to improve planning efficiency and individual plan quality. METHODS: Planning was retrospectively performed on 12 brain tumor and 10 post-prostatectomy prostate patients previously treated with MCO-IMRT. For each patient, physicians were provided with a template-based generated Pareto surface of optimal plans to navigate, using the beam angles from the original clinical plans. We compared physician generated plans to clinically delivered plans (created by dosimetrists) in terms of dosimetric differences, physician preferences and planning times. RESULTS: Plan qualities were similar, however physician generated and clinical plans differed in the prioritization of clinical goals. Physician derived prostate plans showed significantly better sparing of the high dose rectum and bladder regions (p(D1) < 0.05; D1: dose received by 1% of the corresponding structure). Physicians' brain tumor plans indicated higher doses for targets and brainstem (p(D1) < 0.05). Within blinded plan comparisons physicians preferred the clinical plans more often (brain: 6:3 out of 12, prostate: 2:6 out of 10) (not statistically significant). While times of physician involvement were comparable for prostate planning, the new workflow reduced the average involved time for brain cases by 30%. Planner times were reduced for all cases. Subjective benefits, such as a better understanding of planning situations, were observed by clinicians through the insight into plan optimization and experiencing dosimetric trade-offs. CONCLUSIONS: We introduce physician driven planning with MCO for brain and prostate tumors as a feasible planning workflow. The proposed approach standardizes the planning process by utilizing site specific templates and integrates physicians more tightly into treatment planning. Physicians' navigated plan qualities were comparable to the clinical plans. Given the reduction of planning time of the planner and the equal or lower planning time of physicians, this approach has the potential to improve departmental efficiencies.

The value of prior knowledge in machine learning of complex network systems.

MOTIVATION: Our overall goal is to develop machine-learning approaches based on genomics and other relevant accessible information for use in predicting how a patient will respond to a given proposed drug or treatment. Given the complexity of this problem, we begin by developing, testing and analyzing learning methods using data from simulated systems, which allows us access to a known ground truth. We examine the benefits of using prior system knowledge and investigate how learning accuracy depends on various system parameters as well as the amount of training data available. RESULTS: The simulations are based on Boolean networks-directed graphs with 0/1 node states and logical node update rules-which are the simplest computational systems that can mimic the dynamic behavior of cellular systems. Boolean networks can be generated and simulated at scale, have complex yet cyclical dynamics and as such provide a useful framework for developing machine-learning algorithms for modular and hierarchical networks such as biological systems in general and cancer in particular. We demonstrate that utilizing prior knowledge (in the form of network connectivity information), without detailed state equations, greatly increases the power of machine-learning algorithms to predict network steady-state node values ('phenotypes') and perturbation responses ('drug effects'). AVAILABILITY AND IMPLEMENTATION: Links to codes and datasets here: https://gray.mgh.harvard.edu/people-directory/71-david-craft-phd. CONTACT: dcraft@broadinstitute.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Fast approximate delivery of fluence maps for IMRT and VMAT.

In this article we provide a method to generate the trade-off between delivery time and fluence map matching quality for dynamically delivered fluence maps. At the heart of our method lies a mathematical programming model that, for a given duration of delivery, optimizes leaf trajectories and dose rates such that the desired fluence map is reproduced as well as possible. We begin with the single fluence map case and then generalize the model and the solution technique to the delivery of sequential fluence maps. The resulting large-scale, non-convex optimization problem was solved using a heuristic approach. We test our method using a prostate case and a head and neck case, and present the resulting trade-off curves. Analysis of the leaf trajectories reveals that short time plans have larger leaf openings in general than longer delivery time plans. Our method allows one to explore the continuum of possibilities between coarse, large segment plans characteristic of direct aperture approaches and narrow field plans produced by sliding window approaches. Exposing this trade-off will allow for an informed choice between plan quality and solution time. Further research is required to speed up the optimization process to make this method clinically implementable.

A Multiplex Electrochemical Biosensor for Bloodstream Infection Diagnosis.

Accurate and timely detection of bacterial pathogens will improve the clinical management of infections. Herein, we demonstrate an electrochemical biosensor that directly detects bacteria in human blood samples, resulting in the rapid diagnosis of a bloodstream infection. The multiplex biosensor detects the species-specific sequences of the 16S ribosomal RNA of bacteria for pathogen identification in physiological samples without preamplification. The analytical performance characteristics of the biosensor, including the limit of detection and probe cross-reactivity, are evaluated systematically. The feasibility of the biosensor for a diagnosis of a bloodstream infection is demonstrated by identifying bacterial clinical isolates spiked in whole blood and blood culture samples that were tested positive for bacteria. The electrochemical biosensor correctly identifies all the species in the samples with 100% concordance to microbiological analysis.