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The development of nanoscale electronic and photonic devices will require a combination of the high throughput of lithographic patterning and the high resolution and chemical precision afforded by self-assembly 1 , 2 , 3 , 4 . However, the incorporation of nanomaterials with dimensions of less than 10 nm into functional devices has been hindered by the disparity between their size and the 100 nm feature sizes that can be routinely generated by lithography. Biomolecules offer a bridge between the two size regimes, with sub-10 nm dimensions, synthetic flexibility and a capability for self-recognition. Here, we report the directed assembly of 5-nm gold particles into large-area, spatially ordered, two-dimensional arrays through the site-selective deposition of mesoscopic DNA origami 5 onto lithographically patterned substrates 6 and the precise binding of gold nanocrystals to each DNA structure. We show organization with registry both within an individual DNA template and between components on neighbouring DNA origami, expanding the generality of this method towards many types of patterns and sizes. Gold nanoparticles can be assembled into ordered arrays through the site-selective deposition of mesoscopic DNA origami onto lithographically patterned substrates and the precise binding of gold nanocrystals to each DNA structure.

Technologies collectively called omics enable simultaneous measurement of an enormous number of biomolecules; for example, genomics investigates thousands of DNA sequences, and proteomics examines large numbers of proteins. Scientists are using these technologies to develop innovative tests to detect disease and to predict a patient's likelihood of responding to specific drugs. Following a recent case involving premature use of omics-based tests in cancer clinical trials at Duke University, the NCI requested that the IOM establish a committee to recommend ways to strengthen omics-based test development and evaluation. This report identifies best practices to enhance development, evaluation, and translation of omics-based tests while simultaneously reinforcing steps to ensure that these tests are appropriately assessed for scientific validity before they are used to guide patient treatment in clinical trials.

Artificial DNA nanostructures 1 , 2 show promise for the organization of functional materials 3 , 4 to create nanoelectronic 5 or nano-optical devices. DNA origami, in which a long single strand of DNA is folded into a shape using shorter ‘staple strands’ 6 , can display 6-nm-resolution patterns of binding sites, in principle allowing complex arrangements of carbon nanotubes, silicon nanowires, or quantum dots. However, DNA origami are synthesized in solution and uncontrolled deposition results in random arrangements; this makes it difficult to measure the properties of attached nanodevices or to integrate them with conventionally fabricated microcircuitry. Here we describe the use of electron-beam lithography and dry oxidative etching to create DNA origami-shaped binding sites on technologically useful materials, such as SiO 2 and diamond-like carbon. In buffer with ∼100 mM MgCl 2 , DNA origami bind with high selectivity and good orientation: 70–95% of sites have individual origami aligned with an angular dispersion (±1 s.d.) as low as ±10° (on diamond-like carbon) or ±20° (on SiO 2 ). Individual DNA origami shapes can be positioned and aligned on technologically useful substrates that have been patterned using electron-beam lithography and dry oxidative etching.

Many people naturally assume that the claims made for foods and nutritional supplements have the same degree of scientific grounding as those for medication, but that is not always the case. The IOM recommends that the FDA adopt a consistent scientific framework for biomarker evaluation in order to achieve a rigorous and transparent process.

Background To truly achieve personalized medicine in oncology, it is critical to catalog and curate cancer sequence variants for their clinical relevance. The Somatic Working Group (WG) of the Clinical Genome Resource (ClinGen), in cooperation with ClinVar and multiple cancer variant curation stakeholders, has developed a consensus set of minimal variant level data (MVLD). MVLD is a framework of standardized data elements to curate cancer variants for clinical utility. With implementation of MVLD standards, and in a working partnership with ClinVar, we aim to streamline the somatic variant curation efforts in the community and reduce redundancy and time burden for the interpretation of cancer variants in clinical practice. Methods We developed MVLD through a consensus approach by i) reviewing clinical actionability interpretations from institutions participating in the WG, ii) conducting extensive literature search of clinical somatic interpretation schemas, and iii) survey of cancer variant web portals. A forthcoming guideline on cancer variant interpretation, from the Association of Molecular Pathology (AMP), can be incorporated into MVLD. Results Along with harmonizing standardized terminology for allele interpretive and descriptive fields that are collected by many databases, the MVLD includes unique fields for cancer variants such as Biomarker Class, Therapeutic Context and Effect. In addition, MVLD includes recommendations for controlled semantics and ontologies. The Somatic WG is collaborating with ClinVar to evaluate MVLD use for somatic variant submissions. ClinVar is an open and centralized repository where sequencing laboratories can report summary-level variant data with clinical significance, and ClinVar accepts cancer variant data. Conclusions We expect the use of the MVLD to streamline clinical interpretation of cancer variants, enhance interoperability among multiple redundant curation efforts, and increase submission of somatic variants to ClinVar, all of which will enhance translation to clinical oncology practice.

BackgroundImmune checkpoint inhibitors (ICI) have improved outcomes in several tumor types allowing subgroups of patients to have longer, higher quality lives. However, potential life-threatening immunotoxicities can arise in susceptible patients. Identifying patients at high risk of immunotoxicities alongside those responding well can help patients understand risk-benefit profile of the treatment, improve clinical trial cohort selection, and inform therapy selection in clinical settings. Herein, we introduce a machine learning (ML) framework that can accurately predict common immunotoxicities – hepatitis, colitis, and pneumonitis – alongside efficacy utilizing routinely collected Electronic Health Records (EHR) data.MethodsOur models rely on real-world EHR data of over 2,200 ICI-treated patients from Vanderbilt University Medical Center obtained prior to December 31, 2018. During the design of the predictive models, we set the prediction time point as the ICI initiation date for each patient. 1-year prediction time window was applied to create binary labels for the four prediction outcomes. Pneumonitis and colitis episodes were manually curated to establish the labels. The hepatitis label was defined to be 1 if any of the four liver enzymes exceeded three times the upper limit of normal. Overall survival served as a surrogate for efficacy. Structured data and clinician notes prior to ICI initiation were utilized to create features for the models. Feature engineering involved aggregating laboratory measurements over 60 and 120-day time windows. 1-year window was applied for other data types including ICD-10 codes, procedures, medication, and smoking history. In model development, patients were randomly partitioned into training (80%) and test (20%) sets for each outcome. An experiment involved a baseline and an alternative model, where the latter was selected if it demonstrated statistically significant superior performance based on outer loop results from a nested cross-validation process on the training set.ResultsA random forest classifier was developed for each outcome. (table 1) demonstrates performance results with 95% bootstrap confidence intervals on the test set. Overall, each model shows reasonably strong performance achieving an AUC between 0.72 and 0.76. (table 2) contains the features used in the models.ConclusionsTo our knowledge, this is the first ML solution that can assess the risk-benefit profile of ICI for patients, based on their medical history. As the models rely on routinely collected EHR data, their applicability does not require any changes in clinical practice. We envisage utility both in pre-screening of eligible patients for clinical trials and as clinical decision support in routine patient management.Ethics ApprovalThe Vanderbilt University Medical Center Health Sciences #3 institutional review board approved this study, tracked as #211814. The IRB determined the study poses minimal risk to participants, and a waiver of consent was granted.Abstract 1300 Table 1Model performance resultsAbstract 1300 Table 2Features of the models

Precision medicine has resulted in increasing complexity in the treatment of cancer. Web-based educational materials can help address the needs of oncology health care professionals seeking to understand up-to-date treatment strategies. This study aimed to assess learning styles of oncology health care professionals and to determine whether learning style-tailored educational materials lead to enhanced learning. In all, 21,465 oncology health care professionals were invited by email to participate in the fully automated, parallel group study. Enrollment and follow-up occurred between July 13 and September 7, 2015. Self-enrolled participants took a learning style survey and were assigned to the intervention or control arm using concealed alternating allocation. Participants in the intervention group viewed educational materials consistent with their preferences for learning (reading, listening, and/or watching); participants in the control group viewed educational materials typical of the My Cancer Genome website. Educational materials covered the topic of treatment of metastatic estrogen receptor-positive (ER+) breast cancer using cyclin-dependent kinases 4/6 (CDK4/6) inhibitors. Participant knowledge was assessed immediately before (pretest), immediately after (posttest), and 2 weeks after (follow-up test) review of the educational materials. Study statisticians were blinded to group assignment. A total of 751 participants enrolled in the study. Of these, 367 (48.9%) were allocated to the intervention arm and 384 (51.1%) were allocated to the control arm. Of those allocated to the intervention arm, 256 (69.8%) completed all assessments. Of those allocated to the control arm, 296 (77.1%) completed all assessments. An additional 12 participants were deemed ineligible and one withdrew. Of the 552 participants, 438 (79.3%) self-identified as multimodal learners. The intervention arm showed greater improvement in posttest score compared to the control group (0.4 points or 4.0% more improvement on average; P=.004) and a higher follow-up test score than the control group (0.3 points or 3.3% more improvement on average; P=.02). Although the study demonstrated more learning with learning style-tailored educational materials, the magnitude of increased learning and the largely multimodal learning styles preferred by the study participants lead us to conclude that future content-creation efforts should focus on multimodal educational materials rather than learning style-tailored content.

Background Many cancer clinical trials now specify the particular status of a genetic lesion in a patient's tumor in the inclusion or exclusion criteria for trial enrollment. To facilitate search and identification of gene-associated clinical trials by potential participants and clinicians, it is important to develop automated methods to identify genetic information from narrative trial documents. Methods We developed a two-stage classification method to identify genes and genetic lesion statuses in clinical trial documents extracted from the National Cancer Institute's (NCI's) Physician Data Query (PDQ) cancer clinical trial database. The method consists of two steps: 1) to distinguish gene entities from non-gene entities such as English words; and 2) to determine whether and which genetic lesion status is associated with an identified gene entity. We developed and evaluated the performance of the method using a manually annotated data set containing 1,143 instances of the eight most frequently mentioned genes in cancer clinical trials. In addition, we applied the classifier to a real-world task of cancer trial annotation and evaluated its performance using a larger sample size (4,013 instances from 249 distinct human gene symbols detected from 250 trials). Results Our evaluation using a manually annotated data set showed that the two-stage classifier outperformed the single-stage classifier and achieved the best average accuracy of 83.7% for the eight most frequently mentioned genes when optimized feature sets were used. It also showed better generalizability when we applied the two-stage classifier trained on one set of genes to another independent gene. When a gene-neutral, two-stage classifier was applied to the real-world task of cancer trial annotation, it achieved a highest accuracy of 89.8%, demonstrating the feasibility of developing a gene-neutral classifier for this task. Conclusions We presented a machine learning-based approach to detect gene entities and the genetic lesion statuses from clinical trial documents and demonstrated its use in cancer trial annotation. Such methods would be valuable for building information retrieval tools targeting gene-associated clinical trials.

Abstract Artificial intelligence chatbots have shown promise in medical settings, but their ability to interpret complex molecular data is not clear. Here, we assessed 50 different patient scenarios with clinical and molecular data and found that chatbots provided mostly accurate and comprehensive recommendations, although key treatment options were omitted occasionally, and non-data driven treatments were recommended in cases with multiple mutations.

One way scientists are working to overcome challenges in cancer treatment and improve cancer care is through nanotechnology. Nanotechnology, engineered materials that make use of the unique physical properties, presents a new array of medical prospects that will revolutionize cancer prevention, diagnosis, and treatment practices. Giving new hope to patients, practitioners, and researchers alike, nanotechnology has the potential to translate recent discoveries in cancer biology into clinical advances in oncology. While public investments in nanotechnology for cancer continue to increase, medical products based on nanotechnology are already on the market. The National Cancer Policy forum held a workshop July 12-13, 2010, to explore challenges in the use of nanotechnology in oncology. Nanotechnology and Oncology evaluates the ongoing discussion on the role of nanotechnology in cancer as it relates to risk management, treatment, and regulatory policy. Assessments on nanomedicine and the physical properties of nanomaterials were presented during the workshop, along with an appraisal of the current status of research and development efforts.