Low-pass Whole-genome Sequencing of Circulating Cell-free DNA Demonstrates Dynamic Changes in Genomic Copy Number in a Squamous Lung Cancer Clinical Cohort.

PURPOSE: We developed a method to monitor copy number variations (CNV) in plasma cell-free DNA (cfDNA) from patients with metastatic squamous non-small cell lung cancer (NSCLC). We aimed to explore the association between tumor-derived cfDNA and clinical outcomes, and sought CNVs that may suggest potential resistance mechanisms. EXPERIMENTAL DESIGN: Sensitivity and specificity of low-pass whole-genome sequencing (LP-WGS) were first determined using cell line DNA and cfDNA. LP-WGS was performed on baseline and longitudinal cfDNA of 152 patients with squamous NSCLC treated with chemotherapy, or in combination with pictilisib, a pan-PI3K inhibitor. cfDNA tumor fraction and detected CNVs were analyzed in association with clinical outcomes. RESULTS: LP-WGS successfully detected CNVs in cfDNA with tumor fraction ≥10%, which represented approximately 30% of the first-line NSCLC patients in this study. The most frequent CNVs were gains in chromosome 3q, which harbors the PIK3CA and SOX2 oncogenes. The CNV landscape in cfDNA with a high tumor fraction generally matched that of corresponding tumor tissue. Tumor fraction in cfDNA was dynamic during treatment, and increases in tumor fraction and corresponding CNVs could be detected before radiographic progression in 7 of 12 patients. Recurrent CNVs, such as MYC amplification, were enriched in cfDNA from posttreatment samples compared with the baseline, suggesting a potential resistance mechanism to pictilisib. CONCLUSIONS: LP-WGS offers an unbiased and high-throughput way to investigate CNVs and tumor fraction in cfDNA of patients with cancer. It may also be valuable for monitoring treatment response, detecting disease progression early, and identifying emergent clones associated with therapeutic resistance.

A resource for cell line authentication, annotation and quality control.

Cell line misidentification, contamination and poor annotation affect scientific reproducibility. Here we outline simple measures to detect or avoid cross-contamination, present a framework for cell line annotation linked to short tandem repeat and single nucleotide polymorphism profiles, and provide a catalogue of synonymous cell lines. This resource will enable our community to eradicate the use of misidentified lines and generate credible cell-based data.

Human biosample authentication using the high-throughput, cost-effective SNPtrace(TM) system.

Cell lines are the foundation for much of the fundamental research into the mechanisms underlying normal biologic processes and disease mechanisms. It is estimated that 15%-35% of human cell lines are misidentified or contaminated, resulting in a huge waste of resources and publication of false or misleading data. Here we evaluate a panel of 96 single-nucleotide polymorphism (SNP) assays utilizing Fluidigm microfluidics technology for authentication and sex determination of human cell lines. The SNPtrace Panel was tested on 907 human cell lines. Pairwise comparison of these data show the SNPtrace Panel discriminated among identical, related and unrelated pairs of samples with a high degree of confidence, equivalent to short tandem repeat (STR) profiling. We also compared annotated sex calls with those determined by the SNPtrace Panel, STR and Illumina SNP arrays, revealing a high number of male samples are identified as female due to loss of the Y chromosome. Finally we assessed the sensitivity of the SNPtrace Panel to detect intra-human cross-contamination, resulting in detection of as little as 2% contaminating cell population. In conclusion, this study has generated a database of SNP fingerprints for 907 cell lines used in biomedical research and provides a reliable, fast, and economic alternative to STR profiling which can be applied to any human cell line or tissue sample.