Christine M. Micheel

Biological applications of colloidal nanocrystals

Due to their interesting properties, research on colloidal nanocrystals has moved in the last few years from fundamental research to first applications in materials science and life sciences. In this review some recent biological applications of colloidal nanocrystals are discussed, without going into biological or chemical details. First, the properties of colloidal nanocrystals and how they can be synthesized are described. Second, the conjugation of nanocrystals with biological molecules is discussed. And third, three different biological applications are introduced: (i) the arrangement of nanocrystal–oligonucleotide conjugates using molecular scaffolds such as single-stranded DNA, (ii) the use of nanocrystal–protein conjugates as fluorescent probes for cellular imaging, and (iii) a motility assay based on the uptake of nanocrystals by living cells.

Cell motility and metastatic potential studies based on quantum dot imaging of phagokinetic tracks

A wide variety of eukaryotic cells are shown to engulf colloidal semiconductor nanocrystals, or quantum dots, when they migrate. Here we show that the uptake of the nanocrystals is directly correlated with the cell motility, by comparing in detail the motions of both cancerous and healthy human breast cancer cells, as well as several other cell types. The nanocrystals are more photochemically robust than organic dyes (which fade quickly) and do not perturb the cells. The ability to examine these behaviors in live cells over extended time periods, and to quantify changes in response to various molecular manipulations, provides a powerful tool for studying the processes of cell motility and migration – behaviors that are responsible for metastases of primary cancers.

Large-area spatially ordered arrays of gold nanoparticles directed by lithographically confined DNA origami

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. 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 onto lithographically patterned substrates 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.

Evolution of Translational Omics

Sequencing the human genome opened a new era in biomedical science. Researchers have begun to untangle the complex roles of biology and genetics in specific diseases, and now better understand why particular therapies do or do not work in individual patients. New technologies have made it feasible to measure an enormous number of molecules within a tissue or cell; for example, genomics investigates thousands of DNA sequences, and pro-teomics examines large numbers of proteins. Collectively, these technologies are referred to as omics. Patients look to the scientific community to develop innovative omics-based tests to more reliably detect disease and to predict their likelihood of responding to specific drugs. However, transforming the great promise of these new technologies into clinical laboratory tests that can help patients directly has happened more slowly than anticipated. The process to translate omics-based discoveries into clinically useful tests is much more demanding than has been widely recognized. For example , verification of the complex computational procedures used to develop omics-based tests requires adequate access to the data, computer code, and computational steps used to develop that test. Also, regulatory oversight of clinical laboratory tests differs from that of drugs. Thus far, the Food and Drug Administration (FDA) has chosen not to review most of these clinical tests. These challenges converged during a recent case involving premature use of omics-based tests in clinical trials at Duke University. Flawed gene-expression tests developed by cancer researchers at Duke were used in three clinical trials to determine which chemotherapy treatment patients with lung or breast cancer would receive. For three years, the Duke investigators rebuffed Patients look to the scientific community to develop innovative omics-based tests to more reliably detect disease and to predict their likelihood of responding to specific drugs.

Evaluation of Biomarkers and Surrogate Endpoints in Chronic Disease

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.

Enzymatic Ligation Creates Discrete Multinanoparticle Building Blocks for Self-Assembly

Enzymatic ligation of discrete nanoparticle-DNA conjugates creates nanoparticle dimer and trimer structures in which the nanoparticles are linked by single-stranded DNA, rather than by double-stranded DNA as in previous experiments. Ligation was verified by agarose gel and small-angle X-ray scattering. This capability was utilized in two ways: first, to create a new class of multiparticle building blocks for nanoscale self-assembly and, second, to develop a system that can amplify a population of discrete nanoparticle assemblies.

Beyond Histology: Translating Tumor Genotypes into Clinically Effective Targeted Therapies

Increased understanding of intertumoral heterogeneity at the genomic level has led to significant advancements in the treatment of solid tumors. Functional genomic alterations conferring sensitivity to targeted therapies can take many forms, and appropriate methods and tools are needed to detect these alterations. This review provides an update on genetic variability among solid tumors of similar histologic classification, using non–small cell lung cancer and melanoma as examples. We also discuss relevant technological platforms for discovery and diagnosis of clinically actionable variants and highlight the implications of specific genomic alterations for response to targeted therapy. Clin Cancer Res; 20(9); 2264–75. ©2014 AACR.

Somatic cancer variant curation and harmonization through consensus minimum variant level data.

BackgroundTo 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.MethodsWe 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.ResultsAlong 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.ConclusionsWe 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.

Guiding Oncology Patients Through the Maze of Precision Medicine

As the role of genomics in health care grows, patients increasingly require adequate genetic literacy to fully engage in their care. This study investigated a model for delivering consumer-friendly genetic information to improve understanding of precision medicine using health literacy and learning style principles. My Cancer Genome (MCG), a freely available cancer decision support tool, was used as a testbed. MCG content on a melanoma tumor mutation, BRAF V600E, was translated to a 6th-grade reading level, incorporating multiple learning modalities. A total of 90 patients and caregivers were recruited from a melanoma clinic at an academic medical center and randomized to 3 groups. Group A (control) received an exact copy of text from MCG. Group B was given the same content with hyperlinks to videos explaining key genetic concepts, identified and labeled by the team as knowledge pearls. Group C received the translated content with the knowledge pearls embedded. Changes in knowledge were measured through pre and post questionnaires. Group C showed the greatest improvement in knowledge. The study results demonstrate that providing information based on health literacy and learning style principles can improve patients’ understanding of genetic concepts, thus increasing their likelihood of taking an active role in any decision making concerning their health.

Identifying the status of genetic lesions in cancer clinical trial documents using machine learning

BackgroundMany cancer clinical trials now specify the particular status of a genetic lesion in a patients 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.MethodsWe developed a two-stage classification method to identify genes and genetic lesion statuses in clinical trial documents extracted from the National Cancer Institutes (NCIs) 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).ResultsOur 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.ConclusionsWe 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.