Dataset Information

MIPP-Seq: ultra-sensitive rapid detection and validation of low-frequency mosaic mutations.

ABSTRACT:

Background

Mosaic mutations contribute to numerous human disorders. As such, the identification and precise quantification of mosaic mutations is essential for a wide range of research applications, clinical diagnoses, and early detection of cancers. Currently, the low-throughput nature of single allele assays (e.g., allele-specific ddPCR) commonly used for genotyping known mutations at very low alternate allelic fractions (AAFs) have limited the integration of low-level mosaic analyses into clinical and research applications. The growing importance of mosaic mutations requires a more rapid, low-cost solution for mutation detection and validation.

Methods

To overcome these limitations, we developed Multiple Independent Primer PCR Sequencing (MIPP-Seq) which combines the power of ultra-deep sequencing and truly independent assays. The accuracy of MIPP-seq to quantifiable detect and measure extremely low allelic fractions was assessed using a combination of SNVs, insertions, and deletions at known allelic fractions in blood and brain derived DNA samples.

Results

The Independent amplicon analyses of MIPP-Seq markedly reduce the impact of allelic dropout, amplification bias, PCR-induced, and sequencing artifacts. Using low DNA inputs of either 25 ng or 50 ng of DNA, MIPP-Seq provides sensitive and quantitative assessments of AAFs as low as 0.025% for SNVs, insertion, and deletions.

Conclusions

MIPP-Seq provides an ultra-sensitive, low-cost approach for detecting and validating known and novel mutations in a highly scalable system with broad utility spanning both research and clinical diagnostic testing applications. The scalability of MIPP-Seq allows for multiplexing mutations and samples, which dramatically reduce costs of variant validation when compared to methods like ddPCR. By leveraging the power of individual analyses of multiple unique and independent reactions, MIPP-Seq can validate and precisely quantitate extremely low AAFs across multiple tissues and mutational categories including both indels and SNVs. Furthermore, using Illumina sequencing technology, MIPP-seq provides a robust method for accurate detection of novel mutations at an extremely low AAF.

SUBMITTER: Doan RN

PROVIDER: S-EPMC7881461 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Publications

MIPP-Seq: ultra-sensitive rapid detection and validation of low-frequency mosaic mutations.

Doan Ryan N RN Miller Michael B MB Kim Sonia N SN Rodin Rachel E RE Ganz Javier J Bizzotto Sara S Morillo Katherine S KS Huang August Yue AY Digumarthy Reethika R Zemmel Zachary Z Walsh Christopher A CA

BMC medical genomics 20210212 1

<h4>Background</h4>Mosaic mutations contribute to numerous human disorders. As such, the identification and precise quantification of mosaic mutations is essential for a wide range of research applications, clinical diagnoses, and early detection of cancers. Currently, the low-throughput nature of single allele assays (e.g., allele-specific ddPCR) commonly used for genotyping known mutations at very low alternate allelic fractions (AAFs) have limited the integration of low-level mosaic analyses ...[more]

PMID: 33579278

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Project description:BACKGROUND:Sensitive detection of low-frequency single nucleotide variants carries great significance in many applications. In cancer genetics research, tumor biopsies are a mixture of normal and tumor cells from various subpopulations due to tumor heterogeneity. Thus the frequencies of somatic variants from a subpopulation tend to be low. Liquid biopsies, which monitor circulating tumor DNA in blood to detect metastatic potential, also face the challenge of detecting low-frequency variants due to the small percentage of the circulating tumor DNA in blood. Moreover, in population genetics research, although pooled sequencing of a large number of individuals is cost-effective, pooling dilutes the signals of variants from any individual. Detection of low frequency variants is difficult and can be cofounded by sequencing artifacts. Existing methods are limited in sensitivity and mainly focus on frequencies around 2 % to 5 %; most fail to consider differential sequencing artifacts. RESULTS:We aimed to push down the frequency detection limit close to the position specific sequencing error rates by modeling the observed erroneous read counts with respect to genomic sequence contexts. 4 distributions suitable for count data modeling (using generalized linear models) were extensively characterized in terms of their goodness-of-fit as well as the performances on real sequencing data benchmarks, which were specifically designed for testing detection of low-frequency variants; two sequencing technologies with significantly different chemistry mechanisms were used to explore systematic errors. We found the zero-inflated negative binomial distribution generalized linear mode is superior to the other models tested, and the advantage is most evident at 0.5 % to 1 % range. This method is also generalizable to different sequencing technologies. Under standard sequencing protocols and depth given in the testing benchmarks, 95.3 % recall and 79.9 % precision for Ion Proton data, 95.6 % recall and 97.0 % precision for Illumina MiSeq data were achieved for SNVs with frequency?>?= 1 %, while the detection limit is around 0.5 %. CONCLUSIONS:Our method enables sensitive detection of low-frequency single nucleotide variants across different sequencing platforms and will facilitate research and clinical applications such as pooled sequencing, cancer early detection, prognostic assessment, metastatic monitoring, and relapses or acquired resistance identification.

Project description:BackgroundMutations in telomerase reverse transcriptase promoter (TERTp) and isocitrate dehydrogenase 1 and 2 (IDH) offer objective markers to assist in classifying diffuse gliomas into genetic subgroups. However, traditional mutation detection techniques lack sensitivity or have long turnaround times or high costs. We developed GliomaDx, an allele-specific, locked nucleic acid-based quantitative PCR assay to overcome these limitations and sensitively detect TERTp and IDH mutations.MethodsWe evaluated the performance of GliomaDx on cell line DNA and frozen tissue diffuse glioma samples with variable tumor percentage to mimic use in clinical settings and validated low percentage variants using sensitive techniques including droplet digital PCR (ddPCR) and next-generation sequencing. We also developed GliomaDx Nest, which incorporates a high-fidelity multiplex pre-amplification step prior to allele-specific PCR for low-input formalin-fixed paraffin embedded (FFPE) samples.ResultsGliomaDx detects the TERTp and IDH1 alterations at an analytical sensitivity of 0.1% mutant allele fraction, corresponding to 0.2% tumor cellularity. GliomaDx identified TERTp/IDH1 alterations in a cohort of frozen tissue samples with variable tumor percentage of all major diffuse glioma histologic types. GliomaDx Nest is able to detect these hotspot mutations with similar sensitivity from pre-amplified samples and was successfully tested on a cohort of clinical FFPE samples. Testing of a cohort of previously identified TERTpWT-IDHWT gliomas (by Sanger sequencing) revealed that 26.3% harbored low-percentage mutations. Analysis by ddPCR and whole exome sequencing of these tumors confirmed the low mutant fraction of these alterations and overall mutation-based tumor purity.ConclusionsOur results show that GliomaDx can rapidly detect TERTp/IDH mutations with high sensitivity, identifying cases that might be missed due to the lack of sensitivity of other techniques. This approach may facilitate more objective classification of diffuse glioma samples in clinical settings such as intraoperative diagnosis or in testing cases with low tumor purity.

Dataset Information

MIPP-Seq: ultra-sensitive rapid detection and validation of low-frequency mosaic mutations.

Background

Methods

Results

Conclusions

Publications

MIPP-Seq: ultra-sensitive rapid detection and validation of low-frequency mosaic mutations.

Similar Datasets

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