Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample

Background Accurate detection of somatic mutations is challenging but critical in understanding cancer formation, progression, and treatment. We recently proposed NeuSomatic, the first deep convolutional neural network-based somatic mutation detection approach, and demonstrated performance advantages on in silico data. Results In this study, we use the first comprehensive and well-characterized somatic reference data sets from the SEQC2 consortium to investigate best practices for using a deep learning framework in cancer mutation detection. Using the high-confidence somatic mutations established for a cancer cell line by the consortium, we identify the best strategy for building robust models on multiple data sets derived from samples representing real scenarios, for example, a model trained on a combination of real and spike-in mutations had the highest average performance. Conclusions The strategy identified in our study achieved high robustness across multiple sequencing technologies for fresh and FFPE DNA input, varying tumor/normal purities, and different coverages, with significant superiority over conventional detection approaches in general, as well as in challenging situations such as low coverage, low variant allele frequency, DNA damage, and difficult genomic regions

Somatic mutation detection in plants: a critical evaluation through simulations and reanalyses

Mutation, the source of genetic diversity, is the raw material of evolution; however, it remains an understudied process in plants. Using simulations, we demonstrate that generic variant callers, commonly used to detect mutations in plants, are outperformed by methods developed for cancer research. Reanalysis of published data identified up to 7x more somatic mutations than initially reported, advocating the use of cancer research callers to boost mutation research in plants.

Oncogene Concatenated Enriched Amplicon Nanopore Sequencing for rapid, accurate, and affordable somatic mutation detection

We develop the Oncogene Concatenated Enriched Amplicon Nanopore Sequencing (OCEANS) method, in which variants with low variant allele frequency (VAFs) are amplified and subsequently concatenated for Nanopore Sequencing. OCEANS allows accurate detection of somatic mutations with VAF limits of detection between 0.05 and 1%. We construct 4 distinct multi-gene OCEANS panels targeting recurrent mutations in acute myeloid leukemia, melanoma, non-small- cell lung cancer, and hepatocellular carcinoma and validate them on clinical samples. By demonstrating detection of low VAF single nucleotide variant mutations using Nanopore Sequencing, OCEANS is poised to enable same-day clinical sequencing panels.

Personalized genome assembly for accurate cancer somatic mutation discovery using cancer-normal paired reference samples

The use of personalized genome assembly as a reference for detecting the full spectrum of somatic events from cancers has long been advocated but never been systematically investigated. Here we address the critical need of assessing the accuracy of somatic mutation detection using personalized genome assembly versus the standard human reference assembly (i.e. GRCh38). We first obtained massive whole genome sequencing data using multiple sequencing technologies, and then performed de novo assembly of the first tumor-normal paired genomes, both nuclear and mitochondrial, derived from the same donor with triple negative breast cancer. Compared to standard human reference assembly, the haplotype phased chromosomal-scale personalized genome was best demonstrated with individual specific haplotypes for some complex regions and medical relevant genes. We then used this well-assembled personalized genome as a reference for read mapping and somatic variant discovery. We showed that the personalized genome assembly results in better alignments of sequencing reads and more accurate somatic mutation calls. Direct comparison of mitochondrial genomes led to discovery of unreported nonsynonymous somatic mutations. Our findings provided a unique resource and proved the necessity of personalized genome assembly as a reference in improving somatic mutation detection at personal genome level not only for breast cancer reference samples, but also potentially for other cancers.

Oncogene Concatenated Enriched Amplicon Nanopore Sequencing for Rapid, Accurate, and Affordable Somatic Mutation Detection

Nanopore sequencing is more than 10-fold faster than sequencing-by-synthesis and provides reads that are roughly 100-fold longer. However, nanopore sequencing’s 7.5% intrinsic error rate renders it difficult to call somatic mutations with low variant allele frequencies (VAFs) without significant false positives. Here, we introduce the Oncogene Concatenated Enriched Amplicon Nanopore Sequencing (OCEANS) method, in which variants with low VAFs are selectively amplified and subsequently concatenated for nanopore sequencing. OCEANS allows accurate detection of somatic mutations with VAF limits of detection between 0.05% and ≤ 1%. We constructed 4 distinct multi-gene OCEANS panels targeting recurrent mutations in acute myeloid leukemia, melanoma, non-small-cell lung cancer, and hepatocellular carcinoma. Comparison experiments against Illumina NGS showed 99.79% to 99.99% area under the receiver-operator curve for these panels on clinical FFPE tumor samples. Furthermore, we identified a significant number of mutations below the standard NGS limit of detection in clinical tissue samples using each OCEANS panel. Comparison against digital PCR on 10 of putative mutations at ≤1% VAF showed 9 concordant positive calls with VAFs between 0.02% and 0.66%. By overcoming the primary challenge of nanopore sequencing on detecting low VAF single nucleotide variant mutations, OCEANS is poised to enable same-day clinical sequencing panels.

Somatic mutation detection efficiency in EGFR: a comparison between high resolution melting analysis and Sanger sequencing

Background High resolution melting curve analysis is a cost-effective rapid screening method for detection of somatic gene mutation. The performance characteristics of this technique has been explored previously, however, analytical parameters such as limit of detection of mutant allele fraction and total concentration of DNA, have not been addressed. The current study focuses on comparing the mutation detection efficiency of High-Resolution Melt Analysis (HRM) with Sanger Sequencing in somatic mutations of the EGFR gene in non-small cell lung cancer. Methods The minor allele fraction of somatic mutations was titrated against total DNA concentration using Sanger sequencing and HRM to determine the limit of detection. The mutant and wildtype allele fractions were validated by multiplex allele-specific real-time PCR. Somatic mutation detection efficiency, for exons 19 & 21 of the EGFR gene, was compared in 116 formalin fixed paraffin embedded tumor tissues, after screening 275 tumor tissues by Sanger sequencing. Results The limit of detection of minor allele fraction of exon 19 mutation was 1% with sequencing, and 0.25% with HRM, whereas for exon 21 mutation, 0.25% MAF was detected using both methods. Multiplex allele-specific real-time PCR revealed that the wildtype DNA did not impede the amplification of mutant allele in mixed DNA assays. All mutation positive samples detected by Sanger sequencing, were also detected by HRM. About 28% cases in exon 19 and 40% in exon 21, detected as mutated in HRM, were not detected by sequencing. Overall, sensitivity and specificity of HRM were found to be 100 and 67% respectively, and the negative predictive value was 100%, while positive predictive value was 80%. Conclusion The comparative series study suggests that HRM is a modest initial screening test for somatic mutation detection of EGFR, which must further be confirmed by Sanger sequencing. With the modification of annealing temperature of initial PCR, the limit of detection of Sanger sequencing can be improved.

Somatic mutation detection efficiency in EGFR: a comparison between High Resolution Melting Analysis and Sanger Sequencing

Background : High resolution melting curve analysis is a cost-effective rapid screening method for detection of somatic gene mutation. The performance characteristics of this technique has been explored previously, however, analytical parameters such as limit of detection of mutant allele fraction and total concentration of DNA, have not been addressed. The current study focuses on comparing the mutation detection efficiency of High-Resolution Melt Analysis (HRM) with Sanger Sequencing in somatic mutations of the EGFR gene in non-small cell lung cancer. Methods : The minor allele fraction of somatic mutations was titrated against total DNA concentration using Sanger sequencing and HRM to determine the limit of detection. The mutant and wildtype allele fractions were validated by multiplex allele-specific real-time PCR. Somatic mutation detection efficiency, for exons 19 & 21 of the EGFR gene, was compared in 116 formalin fixed paraffin embedded tumor tissues, after screening 275 tumor tissues by Sanger sequencing. Results : The limit of detection of minor allele fraction of exon 19 mutation was 1% with Sequencing, and 0.25% with HRM, whereas for exon 21 mutation, 0.25% MAF was detected using both methods. Multiplex allele-specific real-time PCR revealed that the wildtype DNA did not impede the amplification of mutant allele in mixed DNA assays. All mutation positive samples detected by Sanger sequencing, were also detected by HRM. About 28% cases in exon 19 and 40% in exon 21, detected as mutated in HRM, were not detected by sequencing. Overall, sensitivity and specificity of HRM were found to be 100% and 67% respectively, and the negative predictive value was 100%, while positive predictive value was 80%. Conclusion : The comparative series study suggests that HRM is a modest initial screening test for somatic mutation detection of EGFR , which must further be confirmed by Sanger sequencing. With the modification of annealing temperature of initial PCR, the limit of detection of Sanger sequencing can be improved.