Purpose/Objective(s)Radiation therapy is an important treatment option for skin cancers of the head and neck. Electron beam therapy is a common radiation modality for skin cancer due to its suitability for treating superficial tumors. CT simulation is the gold standard for modeling the 3D characteristics of a patient in electron beam treatment planning. High-resolution 3D surfacing imaging has recently become available for general use. We propose that 3D surface images can be used to generate virtual CT patient models for electron therapy treatment planning. We present the results of a clinical trial evaluating spatial and dosimetric accuracy of 3D surface scans used to produce virtual CTs for electron therapy treatment planning.Materials/MethodsTen head and neck skin cancer patients receiving electron therapy were prospectively enrolled in this study, with a diversity of tumor types and locations. For each patient, a 3D scan was acquired using a hand-held high resolution 3D scanner immediately following CT acquisition during simulation. Patients were then planned and treated according to department standard protocol using the planning CT. To evaluate spatial accuracy of the 3D scans compared to the planning CTs, the external contour from the planning CT was exported from the treatment planning system as a 3D mesh and then rigidly registered to the 3D surface scan mesh using the iterative closest point (ICP) algorithm. The ICP registration was used to calculate distances between the mesh points from the planning CT and 3D scan. The 3D scan mesh was further processed to a DICOM-compliant virtual CT. This virtual CT was imported into the treatment planning system and a copy of the clinical treatment plan was applied to the virtual CT. Absorbed dose was calculated on the virtual CT assuming all tissue being water-equivalent. Dosimetric accuracy was evaluated by dose volume histogram (DVH) metrics of the clinical target volume (CTV).ResultsFor spatial accuracy of the 3D scans, mean distance and standard deviation between points were less than 1 mm for all patients. Individual points exceeding 1 mm distance were primarily localized to regions where the 3D scan included patient hair, tape, and wire stickers. For dosimetric accuracy of the virtual CT, Wilcoxon signed rank test for the CTV D95% (p>0.5) and V100% (p>0.5) DVH metrics showed no significant difference between the dose distributions of the CTVs.ConclusionThis study shows that 3D surface scans can be used for treatment planning of skin cancers of the head and neck with no appreciable loss of accuracy in most cases. This has several potential benefits, including reducing patient appointment loading on the CT simulator. Some caveats apply to the use of 3D scans for treatment planning, including when the surface has significant hair or tape used to wire the target.

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.

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.

Background : Cyanide (CN) has been shown to be chronically present in the airways of cystic fibrosis (CF) patients, as a byproduct Pseudomonas’ metabolism. We sought to determine the concentrations of CN in the sputum of CF patients, who were admitted to the hospital with acute clinical exacerbations using 2 different approaches. Methods : Sputum was collected within 1 day (median) of admission and immediately analyzed for the presence of total and free CN. Results : We found 1) toxic levels of CN in the sputum of our patients, ranging from 27 to 401 µM, 2) the majority of the cyanide was in the form of free, thus diffusible, CN and 3) no cyanide in the blood. Conclusions : While the chronic presence of CN in patients colonized with pseudomonas is not a new concept, the present data support the view that very high levels of free cyanide are present in the airways of CF patients during an acute clinical exacerbation, i.e. levels compatible with concentrations of CN able to virtually stop the mitochondrial activity. It becomes therefore urgent to investigate whether, in CF patients, cyanide is a significant factor affecting the short and long-term outcome and whether a specific treatment of CN intoxication of the airways is warranted during an exacerbation.

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.

The discovery of drivers of cancer has traditionally focused on protein-coding genes 1 – 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) Consortium 5 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 drivers 6 , 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.

We present an open-source platform to aid medical dosimetrists in preventing collisions between gantry head and patient or couch during photon or particle beam therapy treatment planning. This generic framework uses the native scripting interface of the particular planning software to import STL files of the treatment machine elements. These are visualized in 3D together with the contoured or scanned patient surface. A graphical dialog with sliders allows the interactive rotation of the gantry and couch, with real-time feedback. To prevent a future replanning, treatment planners can assess in advance and exclude beam angles resulting in a potential risk of collision. The software platform is publicly available on GitHub and has been validated for RayStation with actual patient plans. Furthermore, the incorporation of the complete patient geometry was tested with a 3D surface scan of a full-body phantom performed with a handheld smartphone. With this study, we aim at minimizing the risk of replanning due to collisions and thus of treatment delays and unscheduled consumption of manpower. The clinical workflow can be streamlined at no cost already at the treatment planning stage. By ensuring a real-time verification of the plan feasibility, the script might boost the use of optimal couch angles that a planner might shy away from otherwise.

Within the setting of intensity modulated radiation therapy (IMRT) and the fully continuous version of IMRT called volumetric modulated radiation therapy (VMAT), we consider the problem of matching a given fluence map as well as possible in limited time by the use of a linear accelerator (linac) with a multi-leaf collimator (MLC). We introduce two modeling strategies to manage the nonconvexity and the associated local minima of this problem. The first is the use of linear splines to model the MLC leaf positions as functions of time. The second is a progressively controllable smooth model (instead of a step function) of how the leaves block the fluence radiation. We propose a two part solution: an outer loop that optimizes the dose rate pattern over time, and an inner loop that given a dose rate pattern optimizes the leaf trajectories.

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.

Radiotherapy (radiation therapy) is one of the main treatments for cancer. The aim is to deliver a prescribed radiation dose to the tumor, while keeping the unavoidable dose to the surrounding healthy organs as low as possible to minimize the probability of developing radiation induced complications. Radio-therapy treatment plan optimization strives to find machine parameters that result in desirable treatment plans. This is a large scale nonconvex multi-criteria optimization problem. In this review, we focus on the multi-criteria and decision-making aspects of radiotherapy treatment plan optimization. Shaping the 3D dose distribution within the patient involves balancing 10-30 highly correlated criteria, subject to the (in general) nonconvex mechanical machine parameters and time constraints, both in plan generation and delivery time of the treatment itself. Furthermore, each patient has a unique anatomy and unique (but unknown) radiosensitivity levels for each organ. This complicates decision-making, as the trade-offs are different for each patient, the patient-specific "safe" levels are unknown, and the interplay between different damaged organs to a physical complication is not always clear. There is no "best" plan for a patient, and decisions made are based on the insights and experience of the treating physician. In this review we describe the use of multi-criteria and decision-making methods used in modern radiotherapy. To understand the difficulties and the many levels in which multi-criteria optimization and decision-making are involved, a thorough background is given. We also provide basic treatment planning guidelines and directions to datasets for those who wish to further explore the field of radiotherapy. (C) 2018 Elsevier B.V. All rights reserved.

Personalizing drug prescriptions in cancer care based on genomic information requires associating genomic markers with treatment effects. This is an unsolved challenge requiring genomic patient data in yet unavailable volumes as well as appropriate quantitative methods. We attempt to solve this challenge for an experimental proxy for which sufficient data is available: 42 drugs tested on 1018 cancer cell lines. Our goal is to develop a method to identify the drug that is most promising based on a cell line's genomic information. For this, we need to identify for each drug the machine learning method, choice of hyperparameters and genomic features for optimal predictive performance. We extensively compare combinations of gene sets (both curated and random), genetic features, and machine learning algorithms for all 42 drugs. For each drug, the best performing combination (considering only the curated gene sets) is selected. We use these top model parameters for each drug to build and demonstrate a Drug Recommendation System (Dr.S). Insights resulting from this analysis are formulated as best practices for developing drug recommendation systems. The complete software system, called the Cell Line Analyzer, is written in Python and available on github.

Abstract 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.

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.

Setup and range uncertainties compromise radiotherapy plan robustness. We introduce a method to evaluate the clinical effect of these uncertainties on the population using tumor control probability (TCP) and normal tissue complication probability (NTCP) models. Eighteen oropharyngeal cancer patients treated with curative intent were retrospectively included. Both photon (VMAT) and proton (IMPT) plans were created using a planning target volume as planning objective. Plans were recalculated for uncertainty scenarios: two for range over/undershoot (IMPT) or CT-density scaling (VMAT), six for shifts. An average shift scenario ([Formula: see text]) was calculated to assess random errors. Dose differences between nominal and scenarios were translated to TCP (2 models) and NTCP (15 models). A weighted average (W_Avg) of the TCP\NTCP based on Gaussian distribution over the variance scenarios was calculated to assess the clinical effect of systematic errors on the population. TCP/NTCP uncertainties were larger in IMPT compared to VMAT. Although individual perturbations showed risks of plan deterioration, the [Formula: see text] scenario did not show a substantial decrease in any of the TCP endpoints suggesting evaluated plans in this cohort were robust for random errors. Evaluation of the W_Avg scenario to assess systematic errors showed in VMAT no substantial decrease in TCP endpoints and in IMPT a limited decrease. In IMPT, the W_Avg scenario had a mean TCP loss of 0%-2% depending on plan type and primary or nodal control. The W_Avg for NTCP endpoints was around 0%, except for mandible necrosis in IMPT (W_Avg: 3%). The estimated population impact of setup and range uncertainties on TCP/NTCP following VMAT or IMPT of oropharyngeal cancer patients was small for both treatment modalities. The use of TCP/NTCP models allows for clinical interpretation of the population effect and could be considered for incorporation in robust evaluation methods. Highlights: - TCP/NTCP models allow for a clinical evaluation of uncertainty scenarios. - For this cohort, in silico-PTV based IMPT plans and VMAT plans were robust for random setup errors. - Effect of systematic errors on the population was limited: mean TCP loss was 0%-2%.

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.