A verified genomic reference sample for assessing performance of cancer panels detecting small variants of low allele frequency.

BACKGROUND: Oncopanel genomic testing, which identifies important somatic variants, is increasingly common in medical practice and especially in clinical trials. Currently, there is a paucity of reliable genomic reference samples having a suitably large number of pre-identified variants for properly assessing oncopanel assay analytical quality and performance. The FDA-led Sequencing and Quality Control Phase 2 (SEQC2) consortium analyze ten diverse cancer cell lines individually and their pool, termed Sample A, to develop a reference sample with suitably large numbers of coding positions with known (variant) positives and negatives for properly evaluating oncopanel analytical performance. RESULTS: In reference Sample A, we identify more than 40,000 variants down to 1% allele frequency with more than 25,000 variants having less than 20% allele frequency with 1653 variants in COSMIC-related genes. This is 5-100× more than existing commercially available samples. We also identify an unprecedented number of negative positions in coding regions, allowing statistical rigor in assessing limit-of-detection, sensitivity, and precision. Over 300 loci are randomly selected and independently verified via droplet digital PCR with 100% concordance. Agilent normal reference Sample B can be admixed with Sample A to create new samples with a similar number of known variants at much lower allele frequency than what exists in Sample A natively, including known variants having allele frequency of 0.02%, a range suitable for assessing liquid biopsy panels. CONCLUSION: These new reference samples and their admixtures provide superior capability for performing oncopanel quality control, analytical accuracy, and validation for small to large oncopanels and liquid biopsy assays.

Reproducibility assessment of independent component analysis of expression ratios from DNA microarrays.

DNA microarrays allow the measurement of transcript abundances for thousands of genes in parallel. Most commonly, a particular sample of interest is studied next to a neutral control, examining relative changes (ratios). Independent component analysis (ICA) is a promising modern method for the analysis of such experiments. The condition of ICA algorithms can, however, depend on the characteristics of the data examined, making algorithm properties such as robustness specific to the given application domain. To address the lack of studies examining the robustness of ICA applied to microarray measurements, we report on the stability of variational Bayesian ICA in this domain. Microarray data are usually preprocessed and transformed. Hence we first examined alternative transforms and data selections for the smallest modelling reconstruction errors. Log-ratio data are reconstructed better than non-transformed ratio data by our linear model with a Gaussian error term. To compare ICA results we must allow for ICA invariance under rescaling and permutation of the extracted signatures, which hold the loadings of the original variables (gene transcript ratios) on particular latent variables. We introduced a method to optimally match corresponding signatures between sets of results. The stability of signatures was then examined after (1) repetition of the same analysis run with different random number generator seeds, and (2) repetition of the analysis with partial data sets. The effects of both dropping a proportion of the gene transcript ratios and dropping measurements for several samples have been studied. In summary, signatures with a high relative data power were very likely to be retained, resulting in an overall stability of the analyses. Our analysis of 63 yeast wildtype vs. wild-type experiments, moreover, yielded 10 reliably identified signatures, demonstrating that the variance observed is not just noise.