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Molecular sampling at logarithmic rates for next-generation sequencing.

ABSTRACT: Next-generation sequencing is a cutting edge technology, but to quantify a dynamic range of abundances for different RNA or DNA species requires increasing sampling depth to levels that can be prohibitively expensive due to physical limits on molecular throughput of sequencers. To overcome this problem, we introduce a new general sampling theory which uses biophysical principles to functionally encode the abundance of a species before sampling, SeQUential depletIon and enriCHment (SQUICH). In theory and simulation, SQUICH enables sampling at a logarithmic rate to achieve the same precision as attained with conventional sequencing. A simple proof of principle experimental implementation of SQUICH in a controlled complex system of ~262,000 oligonucleotides already reduces sequencing depth by a factor of 10. SQUICH lays the groundwork for a general solution to a fundamental problem in molecular sampling and enables a new generation of efficient, precise molecular measurement at logarithmic or better sampling depth.

SUBMITTER: Horn C

PROVIDER: S-EPMC6932819 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Molecular sampling at logarithmic rates for next-generation sequencing.

Horn Caroline C Salzman Julia J

PLoS computational biology 20191212 12

Next-generation sequencing is a cutting edge technology, but to quantify a dynamic range of abundances for different RNA or DNA species requires increasing sampling depth to levels that can be prohibitively expensive due to physical limits on molecular throughput of sequencers. To overcome this problem, we introduce a new general sampling theory which uses biophysical principles to functionally encode the abundance of a species before sampling, SeQUential depletIon and enriCHment (SQUICH). In th ...[more]

PMID: 31830035

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Project description:BackgroundClinical implementation of Next-Generation Sequencing (NGS) is challenged by poor control for stochastic sampling, library preparation biases and qualitative sequencing error. To address these challenges we developed and tested two hypotheses.MethodsHypothesis 1: Analytical variation in quantification is predicted by stochastic sampling effects at input of a) amplifiable nucleic acid target molecules into the library preparation, b) amplicons from library into sequencer, or c) both. We derived equations using Monte Carlo simulation to predict assay coefficient of variation (CV) based on these three working models and tested them against NGS data from specimens with well characterized molecule inputs and sequence counts prepared using competitive multiplex-PCR amplicon-based NGS library preparation method comprising synthetic internal standards (IS). Hypothesis 2: Frequencies of technically-derived qualitative sequencing errors (i.e., base substitution, insertion and deletion) observed at each base position in each target native template (NT) are concordant with those observed in respective competitive synthetic IS present in the same reaction. We measured error frequencies at each base position within amplicons from each of 30 target NT, then tested whether they correspond to those within the 30 respective IS.ResultsFor hypothesis 1, the Monte Carlo model derived from both sampling events best predicted CV and explained 74% of observed assay variance. For hypothesis 2, observed frequency and type of sequence variation at each base position within each IS was concordant with that observed in respective NTs (R2 = 0.93).ConclusionIn targeted NGS, synthetic competitive IS control for stochastic sampling at input of both target into library preparation and of target library product into sequencer, and control for qualitative errors generated during library preparation and sequencing. These controls enable accurate clinical diagnostic reporting of confidence limits and limit of detection for copy number measurement, and of frequency for each actionable mutation.

Project description:IntroductionHistorically, high rates of actionable driver mutations have been reported in never-smokers with lung adenocarcinoma (ADC). In the era of modern, comprehensive cancer mutation sequencing, this relationship necessitates a more detailed analysis.MethodsAll Mount Sinai patients between January 1, 2015, and June 1, 2020, with a diagnosis of ADC of any stage with known smoking status who received genomic testing were included. Most patients were analyzed using the Sema4 hotspot panel or the Oncomine Comprehensive Assay version 3 next-generation sequencing (NGS) panel conducted at Sema4. Patients were considered fully genotyped if they were comprehensively analyzed for alterations in EGFR, KRAS, MET, ALK, RET, ROS1, BRAF, NTRK1-3, and ERBB2, otherwise they were considered partially genotyped.ResultsTwo hundred and thirty-six never-smokers and 671 smokers met the above criteria. Of the never-smokers, 201 (85%) had a driver mutation with 167 (71%) considered actionable (ie, those with US Food and Drug Administration-approved agents). Among smokers, 439 (65%) had an identified driver mutation with 258 (38%) actionable (P < .0001). When comprehensively sequenced, 95% (70/74) of never-smokers had a driver mutation with 78% (58/74) actionable; whereas, for smokers, 75% (135/180) had a driver with only 47% (74/180) actionable (P < .0001). Within mutations groups, EGFR G719X and KRAS G12Cs were more common to smokers. For stage IV patients harboring EGFR-mutant tumors treated with EGFR-directed therapies, never-smokers had significantly improved OS compared to smokers (hazard ratio = 2.71; P = .025). In multivariable analysis, Asian ancestry and female sex remained significant predictors of (1) OS in stage IV patients and (2) likelihood of harboring a receptor of fusion-based driver.ConclusionComprehensive NGS revealed driver alterations in 95% of never-smokers, with the majority having an associated therapy available. All efforts should be exhausted to identify or rule out the presence of an actionable driver mutation in all metastatic lung ADC.

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Molecular sampling at logarithmic rates for next-generation sequencing.

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Molecular sampling at logarithmic rates for next-generation sequencing.

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