MAESTRO affords ‘breadth and depth’ for mutation testing

The ability to assay large numbers of low-abundance mutations is crucial in biomedicine. Yet, the technical hurdles of sequencing multiple mutations at extremely high depth and accuracy remain daunting. For sequencing low-level mutations, it’s either ‘depth or breadth’ but not both. Here, we report a simple and powerful approach to accurately track thousands of distinct mutations with minimal reads. Our technique called MAESTRO (minor allele enriched sequencing through recognition oligonucleotides) employs massively-parallel mutation enrichment to empower duplex sequencing—one of the most accurate methods—to track up to 10,000 low-frequency mutations with up to 100-fold less sequencing. In example use cases, we show that MAESTRO could enable mutation validation from cancer genome sequencing studies. We also show that it could track thousands of mutations from a patient’s tumor in cell-free DNA, which may improve detection of minimal residual disease from liquid biopsies. In all, MAESTRO improves the breadth, depth, accuracy, and efficiency of mutation testing.

A Common Methodological Phylogenomics Framework for intra-patient heteroplasmies to infer SARS-CoV-2 sublineages and tumor clones

We present a common methodological framework to infer the phylogenomics from genomic data, be it reads of SARS-CoV-2 of multiple COVID-19 patients or bulk DNAseq of the tumor of a cancer patient. The commonality is in the phylogenetic retrodiction based on the genomic reads in both scenarios. While there is evidence of heteroplasmy, i.e., multiple lineages of SARS-CoV-2 in the same COVID-19 patient; to date, there is no evidence of sublineages recombining within the same patient. The heterogeneity in a patient’s tumor is analogous to intra-patient heteroplasmy and the absence of recombination in the cells of tumor is a widely accepted assumption. Just as the different frequencies of the genomic variants in a tumor presupposes the existence of multiple tumor clones and provides a handle to computationally infer them, we postulate that so do the different variant frequencies in the viral reads, offering the means to infer the multiple co-infecting sublineages. We describe the Concerti computational framework for inferring phylogenies in each of the two scenarios. To demonstrate the accuracy of the method, we reproduce some known results in both scenarios. We also make some additional discoveries. We uncovered new potential parallel mutation in the evolution of the SARS-CoV-2 virus. In the context of cancer, we uncovered new clones harboring resistant mutations to therapy from clinically plausible phylogenetic tree in a patient.

Substitutions are boring: some arguments about parallel mutations and repetitive DNA

Extant genomes are largely shaped by the global transposition, copy number fluctuation, and rearrangement of DNA sequences, rather than by the substitutions of single nucleotides. Although many of these large-scale mutations have low probabilities and are unlikely to repeat, others are recurrent or predictable in their effects, leading to stereotyped genome architectures and genetic variation in both eukaryotes and prokaryotes. Such recurrent, parallel mutation modes can profoundly shape the paths taken by evolution, and directly undermine the Wright-Fisher model of evolutionary genetics. Similar patterns are also evident at the smaller scales of individual genes or short genomic sequences. The scale and extent of this ‘non-substitution’ variation has only recently come into focus through the advent of new genomic technologies; however, it still not widely included in genotype-phenotype association studies. In this review, we identify the common features of these disparate mutational phenomena and comment on the importance and interpretation of these mutational patterns.

Parallel mutation screening and methylation quantification improves the molecular diagnostic yield for colorectal cancer.

e13003 Background: Recurrent somatic mutations and methylation patterns have previously been identified as biomarkers for non-invasive screening of colorectal cancer (CRC). However, most clinical screening assays have focused on a limited number of genomic loci, potentially reducing sensitivity and specificity. Here, we examined the potential utility of using an expanded set of mutation and methylation markers to improve screening accuracy of CRC. Methods: Pairs of colorectal tumor and tumor-adjacent tissues were resected from 41 CRC patients in Nanfang Hospital (NFH), China. The DNA was extracted, processed, and sequenced with Singlera Genomics’ OncoAim assay, which detects mutations across 59 genes implicated in cancer and quantifies the methylation level for key CRC genes. Matched pre- or post-surgery plasma samples were also collected and processed in parallel. Results: Thirty-eight tumor samples (93%) contained somatic mutations with greater than 5% allele frequency, while twenty-nine tumor samples (71%) contained aberrant methylation patterns. Among the tumor-adjacent samples, six contained somatic mutations and eight contained epigenetic abnormalities. Out of three tumor samples without any detectable somatic mutations, one showed an aberrant methylation pattern. Importantly, aberrant methylation patterns in tumor samples were also detected in matched plasma samples. Conclusions: Parallel mutation screening and methylation analysis not only improves the diagnostic yield, but also paves the way for non-invasive monitoring on patients with no common mutations.

Seasonal influenza circulation patterns and projections for 2017-2018

This is not meant as a comprehensive report of recent influenza evolution, but is instead intended as particular observations that may be of relevance. Please also note that observed patterns reflect the GISAID database and may not be entirely representative of underlying dynamics. All analyses are based on the nextflu pipeline [1] with continual updates posted to nextflu.org. We arrive at the following results:H3N2In H3N2, clade 3c2.a has continued to diversify genetically with complicated and rapid dynamics of different subclades. This diversification is not reflected in serological data that shows only minor to moderate antigenic evolution. Nevertheless, the highly parallel mutation patterns and the rapid rise and fall of clades suggests competitive dynamics of phenotypically distinct viruses.H1N1pdmVery few H1N1pdm viruses have been observed in recent months. The dominant clade continues to be 6b.1 and there is little amino acid sequence variation within HA. The only notable subclade that has been growing recently is the clade bearing HA1:R205K/S183P. This clade is dominated by North American viruses and we see no evidence that this clade has a particular competitive advantage.B/VicClade 1A has continued to dominate and mutation 117V has all but taken over the global population. The rise of this mutation was fairly gradual and we have no evidence that it is associated with antigenic change or other benefit to the virus.B/YamClade 3 has continued to dominate. Within clade 3, a clade with mutation HA1:251V is globally at frequency of about 80% throughout 2016. Within this clade, mutation 211R is at 25% frequency. In addition, a clade without prominent amino acid mutations has been rising throughout 2016.

Biased evolutionary inferences from bulk tumor samples

ABSTRACTIt is generally agreed that tumors are composed of multiple cell clones defined by different somatic mutations. Characterizing the evolutionary mechanisms driving this intratumor genetic heterogeneity (ITH) is crucial to improve both cancer diagnosis and therapeutic strategies. For that purpose, recent ITH studies have focused on qualitative comparisons of mutational profiles derived from bulk sequencing of multiple tumor samples extracted from the same patient. Here, we show some examples where the naive use of bulk data in multiregional studies may lead to erroneous inferences of the evolutionary trajectories that underlie tumor progression, including biased timing of somatic mutations, spurious parallel mutation events, and/or incorrect chronological ordering of metastatic events. In addition, we analyze three real datasets to highlight how the use of bulk mutational profiles instead of inferred clones can lead to different conclusions about mutational recurrence and population structure.