Ryan T. Koehler

Distributions of free energy, melting temperature, and hybridization propensity for genomic DNA oligomers

Many molecular biology techniques such as PCR, southern blotting, molecular beacon based assays, and DNA microarrays rely on the ability to design oligonucleotide probes possessing specific thermodynamic properties. Thermodynamic parameters for DNA duplex formation (melting temperature: Tm, free energy: /spl Delta/G/spl deg//sub /spl gamma//, and hybridization extent: Fb) are accurately predicted using the nearest-neighbor model for a range of physical conditions for oligonucleotides up to about 50 bases in length. The use of thermodynamic quantities is ubiquitous in probe design schemes, but invariably focus on achieving specific values for sequences in hand. This fails to provide general insights about how these quantities depend on sequence composition, length, and experimental conditions. Here we present Tm and Fb distributions calculated for genomic DNA samples of 10 to 50 bases.

A robust targeted sequencing approach for low input and variable quality DNA from clinical samples

Next-generation deep sequencing of gene panels is being adopted as a diagnostic test to identify actionable mutations in cancer patient samples. However, clinical samples, such as formalin-fixed, paraffin-embedded specimens, frequently provide low quantities of degraded, poor quality DNA. To overcome these issues, many sequencing assays rely on extensive PCR amplification leading to an accumulation of bias and artifacts. Thus, there is a need for a targeted sequencing assay that performs well with DNA of low quality and quantity without relying on extensive PCR amplification. We evaluate the performance of a targeted sequencing assay based on Oligonucleotide Selective Sequencing, which permits the enrichment of genes and regions of interest and the identification of sequence variants from low amounts of damaged DNA. This assay utilizes a repair process adapted to clinical FFPE samples, followed by adaptor ligation to single stranded DNA and a primer-based capture technique. Our approach generates sequence libraries of high fidelity with reduced reliance on extensive PCR amplification—this facilitates the accurate assessment of copy number alterations in addition to delivering accurate single nucleotide variant and insertion/deletion detection. We apply this method to capture and sequence the exons of a panel of 130 cancer-related genes, from which we obtain high read coverage uniformity across the targeted regions at starting input DNA amounts as low as 10 ng per sample. We demonstrate the performance using a series of reference DNA samples, and by identifying sequence variants in DNA from matched clinical samples originating from different tissue types.Cancer diagnostics: Targeted DNA sequencing for low-quality tumor samplesA new DNA sequencing technology enables comprehensive genetic analyses of poor-quality tumor samples. Hanlee Ji from Stanford University in California, USA, together with colleagues from a company he cofounded called TOMA Biosciences, tested the performance of a targeted sequencing assay known as oligonucleotide-selective sequencing (OS-Seq). They used the “in-solution” version of OS-Seq, which involves a pre-processing step to remove any damaged DNA and then sequences target regions of the genome to look for duplications, insertions or deletions of DNA segments. Using archival specimens (which often contain low quantities of degraded DNA) from patients with lung and colorectal cancer, the researchers showed they could detect sequence variants in a panel of 130 cancer-related genes. The findings suggest the OS-Seq assay could help inform treatment decisions for cancer patients, even with clinical specimens of low quality.

Abstract 2712: Joint somatic mutation and germline variant identification and scoring from tumor molecular profiling and ct-DNA monitoring of cancer patients by high-throughput sequencing

Cancer tumor profiling by targeted resequencing of actionable cancer genes is rapidly becoming the standard approach for selecting targeted therapies and clinical trials in refractory cancer patients. In this clinical scenario, a tumor sample is obtained from an FFPE block and sequenced by targeted next-generation sequencing (NGS) to uncover actionable somatic mutations in relevant cancer genes. Some of the challenges that arise in analyzing tumor-derived NGS data include distinguishing between somatic and germline variants in the absence of normal tissue data, recognizing pathogenic germline variants, and identifying sequencing errors (which occur at about 0.5% rate). Additional challenges arise when considering other clinical applications of NGS such as sequencing cell-free tumor DNA (cf-DNA) from plasma samples to monitor disease response or disease recurrence. Here we present a principled approach to identify both single-nucleotide and small insertion/deletion somatic mutations and germline variants from NGS data of tumor tissue that leverages the allelic fraction patterns in tumors and prior information from external databases through the use of a Bayesian Network algorithm. Our approach allows us to score each putative mutation or variant with respect to its probability of belonging to each variant class, versus classification as a sequencing error. The method enables the joint calling of related samples form the same patient, such as cases where a cf-DNA sample and primary tumor sample are both profiled improving sensitivity and specificity. We validated our method by analyzing data obtained with the TOMA OS-Seq targeted sequencing RUO assay for 98 cancer genes from a mixture of well-known genomes, patient case triads (where normal, tumor and cf-DNA are available), and a retrospective analysis of tumor patient data that underwent clinical tumor profiling for therapy selection. Citation Format: Francisco M. De La Vega, Ryan T. Koehler, Yannick Pouliot, Yosr Bouhlal, Austin So, Federico Goodsaid, Sean Irvine, Len Trigg, Lincoln Nadauld. Joint somatic mutation and germline variant identification and scoring from tumor molecular profiling and ct-DNA monitoring of cancer patients by high-throughput sequencing. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2712.