Use of Treatment-Focused Tumor Sequencing to Screen for Germline Cancer Predisposition.

Next-generation sequencing assays are capable of identifying cancer patients eligible for targeted therapies and can also detect germline variants associated with increased cancer susceptibility. However, these capabilities have yet to be routinely harmonized in a single assay because of challenges with accurately identifying germline variants from tumor-only data. We have developed the Oncology and Hereditary Cancer Program targeted capture panel, which uses tumor tissue to simultaneously screen for both clinically actionable solid tumor variants and germline variants across 45 genes. Validation using 14 tumor specimens, composed of patient samples and cell lines analyzed in triplicate, demonstrated high coverage with sensitive and specific identification of single-nucleotide variants and small insertions and deletions. Average coverage across all targets remained >2000× in 198 additional patient tumor samples. Analysis of 55 formalin-fixed, paraffin-embedded tumor samples for the detection of known germline variants within a subset of cancer-predisposition genes, including one multiexon deletion, yielded a 100% detection rate, demonstrating that germline variants can be reliably detected in tumor samples using a single panel. Combining targetable somatic and actionable germline variants into a single tumor tissue assay represents a streamlined approach that can inform treatment for patients with advanced cancers as well as identify those with potential germline variants who are eligible for confirmatory testing, but would not otherwise have been identified.

Assessing Limit of Detection in Clinical Sequencing.

Clinical reporting of solid tumor sequencing requires reliable assessment of the accuracy and reproducibility of each assay. Somatic mutation variant allele fractions may be below 10% in many samples due to sample heterogeneity, tumor clonality, and/or sample degradation in fixatives such as formalin. The toolkits available to the clinical sequencing community for correlating assay design parameters with assay sensitivity remain limited, and large-scale empirical assessments are often relied upon due to the lack of clear theoretical grounding. To address this uncertainty, a theoretical model was developed for predicting the expected variant calling sensitivity for a given library complexity and sequencing depth. Binomial models were found to be appropriate when assay sensitivity was only limited by library complexity or sequencing depth, but functional scaling for library complexity was necessary when both library complexity and sequencing depth were co-limiting. This model was empirically validated with sequencing experiments by using a series of DNA input amounts and sequencing depths. Based on these findings, a workflow is proposed for determining the limiting factors to sensitivity in different assay designs, and the formulas for these scenarios are presented. The approach described here provides designers of clinical assays with the methods to theoretically predict assay design outcomes a priori, potentially reducing burden in clinical tumor assay design and validation efforts.

Sample Tracking Using Unique Sequence Controls.

Sample tracking and identity are essential when processing multiple samples in parallel. Sequencing applications often involve high sample numbers, and the data are frequently used in a clinical setting. As such, a simple and accurate intrinsic sample tracking process through a sequencing pipeline is essential. Various solutions have been implemented to verify sample identity, including variant detection at the start and end of the pipeline using arrays or genotyping, bioinformatic comparisons, and optical barcoding of samples. None of these approaches are optimal. To establish a more effective approach using genetic barcoding, we developed a panel of unique DNA sequences cloned into a common vector. A unique DNA sequence is added to the sample when it is first received and can be detected by PCR and/or sequencing at any stage of the process. The control sequences are approximately 200 bases long with low identity to any sequence in the National Center for Biotechnology Information nonredundant database (<30 bases) and contain no long homopolymer (>7) stretches. When a spiked next-generation sequencing library is sequenced, sequence reads derived from this control sequence are generated along with the standard sequencing run and are used to confirm sample identity and determine cross-contamination levels. This approach is used in our targeted clinical diagnostic whole-genome and RNA-sequencing pipelines and is an inexpensive, flexible, and platform-agnostic solution.

Ectomycorrhizal hyphae structure components of the soil bacterial community for decreased phosphatase production.

Ectomycorrhizal fungi (EMF) provide nutrients to their hosts by means of hyphae that extend beyond nutrient-depleted rhizosphere soil. Soil bacteria may compete with EMF for nutrients or may act synergistically to enhance nutrient supply to hosts. To assess the interactions between hyphae and bacteria, two types of small, sand-filled mesh bags were incubated in a Pseudotsuga menziesii/Betula papyrifera forest. The bags allowed ingrowth by EMF (35-µm mesh) or excluded hyphae (0.5-µm mesh), while allowing migration of soil bacteria. After incubation, bacteria were isolated from bags using a method to enrich for Gram-positive bacteria. Isolates were assayed for phosphatase and N-acetyl glucosaminidase (NAGase) activities to assess the potential to access organic phosphorus and nitrogen. The average phosphatase activities were higher in exclusion than ingrowth bags, while NAGase activities did not differ. Streptomyces isolates, which are expected to be strong competitors and antagonists of EMF, were more prevalent in ingrowth bags and yet had lower phosphatase activities. Furthermore, there were no indications of antagonism between fungi and Streptomyces, as there were no increases in NAGase activities in ingrowth bags. We conclude that fungal hyphae can structure components of the soil bacterial community for decreased extracellular enzyme production.