Analytical validation of NeXT Personal®, an ultra-sensitive personalized circulating tumor DNA assay.

We describe the analytical validation of NeXT Personal®, an ultra-sensitive, tumor-informed circulating tumor DNA (ctDNA) assay for detecting residual disease, monitoring therapy response, and detecting recurrence in patients diagnosed with solid tumor cancers. NeXT Personal uses whole genome sequencing of tumor and matched normal samples combined with advanced analytics to accurately identify up to ~1,800 somatic variants specific to the patient's tumor. A personalized panel is created, targeting these variants and then used to sequence cell-free DNA extracted from patient plasma samples for ultra-sensitive detection of ctDNA. The NeXT Personal analytical validation is based on panels designed from tumor and matched normal samples from two cell lines, and from 123 patients across nine cancer types. Analytical measurements demonstrated a detection threshold of 1.67 parts per million (PPM) with a limit of detection at 95% (LOD95) of 3.45 PPM. NeXT Personal showed linearity over a range of 0.8 to 300,000 PPM (Pearson correlation coefficient = 0.9998). Precision varied from a coefficient of variation of 12.8% to 3.6% over a range of 25 to 25,000 PPM. The assay targets 99.9% specificity, with this validation study measuring 100% specificity and in silico methods giving us a confidence interval of 99.92 to 100%. In summary, this study demonstrates NeXT Personal as an ultra-sensitive, highly quantitative and robust ctDNA assay that can be used to detect residual disease, monitor treatment response, and detect recurrence in patients.

Analytical Performance of an Immunoprofiling Assay Based on RNA Models.

As immuno-oncology drugs grow more popular in the treatment of cancer, better methods are needed to quantify the tumor immune cell component to determine which patients are most likely to benefit from treatment. Methods such as flow cytometry can accurately assess the composition of infiltrating immune cells; however, they show limited use in formalin-fixed, paraffin-embedded (FFPE) specimens. This article describes a novel hybrid-capture RNA sequencing assay, ImmunoPrism, that estimates the relative percentage abundance of eight immune cell types in FFPE solid tumors. Immune health expression models were generated using machine learning methods and used to uniquely identify each immune cell type using the most discriminatively expressed genes. The analytical performance of the assay was assessed using 101 libraries from 40 FFPE and 32 fresh-frozen samples. With defined samples, ImmunoPrism had a precision of ±2.72%, a total error of 2.75%, and a strong correlation (r2 = 0.81; P < 0.001) to flow cytometry. ImmunoPrism had similar performance in dissociated tumor cell samples (total error of 8.12%) and correlated strongly with immunohistochemistry (CD8: r2 = 0.83; P < 0.001) in FFPE samples. Other performance metrics were determined, including limit of detection, reportable range, and reproducibility. The approach used for analytical validation is shared here so that it may serve as a helpful framework for other laboratories when validating future complex RNA-based assays.

Author Correction: Genomic analysis of a large set of currently-and historically-important human adenovirus pathogens.

The original publication of this article [1] was missing the below author that made contributions to the research and the published article.

Genomic analysis of a large set of currently-and historically-important human adenovirus pathogens.

Human adenoviruses (HAdVs) are uniquely important "model organisms" as they have been used to elucidate fundamental biological processes, are recognized as complex pathogens, and are used as remedies for human health. As pathogens, HAdVs may effect asymptomatic or mild and severe symptomatic disease upon their infection of respiratory, ocular, gastrointestinal, and genitourinary systems. High-resolution genomic data have enhanced the understanding of HAdV epidemiology, with recombination recognized as an important and major pathway in the molecular evolution and genesis of emergent HAdV pathogens. To support this view and to actualize an algorithm for identifying, characterizing, and typing novel HAdVs, we determined the DNA sequence of 95 isolates from archives containing historically important pathogens and collections housing currently circulating strains to be sequenced. Of the 85 samples that were completely sequenced, 18 novel recombinants within species HAdV-B and D were identified. Two HAdV-D genomes were found to contain novel penton base and fiber genes with significant divergence from known molecular types. In this data set, we found additional isolates of HAdV-D53 and HAdV-D58, two novel genotypes recognized recently using genomics. This supports the thesis that novel HAdV genotypes are not limited to "one-time" appearances of the prototype but are of importance in HAdV epidemiology. These data underscore the significance of lateral genomic transfer in HAdV evolution and reinforce the potential public health impact of novel genotypes of HAdVs emerging in the population.

Visualization of nucleotide substitutions in the (micro)transcriptome.

BACKGROUND: RNA-related applications of the next-generation sequencing (NGS) technologies require context-specific interpretations: e.g., sequence mismatches may indicate sites of RNA editing, or uneven read coverage often points to mature form of microRNA. Existing visualization tools traditionally show RNA molecules in two dimensions, with their base pairing and the resulting secondary structure. However, it is not straightforward to combine a linear NGS data display with the 2-D RNA depictions. RESULTS: We present a novel approach for interactive representation of nucleotide substitutions and modifications in the transcribed genome. With the focus on RNA secondary structure in the context of NGS data, it provides intuitive visualization of genomic environment, sequence reads, nucleotide polymorphisms and editing events integrated with the structural and functional elements of both coding and non-coding RNA molecules. Using our approach we present and discuss examples and general trends of polymorphisms and editing in the context of the secondary structure of microRNAs. As expected, most of the substitutions comprised A to G and C to T events, consistent with typical RNA editing patterns. However, we did not observe prevalence of editing in double-stranded regions of the microRNA stem-loop. We describe novel prominent editing event candidates, observed across several small RNA libraries of Drosophila melanogaster. CONCLUSIONS: In contrast to the existing general tools for NGS data visualization, the power of our approach is not only in the display of read alignments and their counts, but the integration of RNA secondary structure, sequencing depth, and rates/patterns of editing or other modifications. It provides a comprehensive picture, important for large-scale studies and detailed analyses, helping to gain insight into the intricate relationships between different events in RNA biogenesis.

Distance-based classifiers as potential diagnostic and prediction tools for human diseases.

Typically, gene expression biomarkers are being discovered in course of high-throughput experiments, for example, RNAseq or microarray profiling. Analytic pipelines that extract so-called signatures suffer from the "Dimensionality curse": the number of genes expressed exceeds the number of patients we can enroll in the study and use to train the discriminator algorithm. Hence, problems with the reproducibility of gene signatures are more common than not; when the algorithm is executed using a different training set, the resulting diagnostic signature may turn out to be completely different. In this paper we propose an alternative novel approach which takes into account quantifiable expression levels of all genes assayed. In our analysis, the cumulative gene expression pattern of an individual patient is represented as a point in the multidimensional space formed by all gene expression profiles assayed in given system, where the clusters of "normal samples" and "affected samples" and defined. The degree of separation of the given sample from the space occupied by "normal samples" reflects the drift of the sample away from homeostasis in the course of development of the pathophysiological process that underly the disease. The outlined approach was validated using the publicly available glioma dataset deposited in Rembrandt and associated with survival data. Additionally, the applicability of the distance analysis to the classification of non-malignant sampled was tested using psoriatic lesions and non-lesional matched controls as a model.