This work is supported by Geneplus&#8211;Beijing Institute.

Tumor specimens and blood samples were obtained according to a protocol approved by the Ethics Committee of Peking University People&#39;s Hospital. Written informed consent was obtained from the patients to use their samples. Participants were screened according to the following criteria: female, advanced Non-Small Cell Lung Cancer, EGFR p.L858R mutation indicated by previous Sanger Sequencing, disease progression following two session of EGFR targeted therapy with Erlotinib, and ER-Seq which was applied to ctDNA to analyze the cause of resistance and find new target drugs.
1. DNA Extraction from Peripheral Blood for cfDNA and genomic DNA (gDNA)
<ol>
	<li>Collect 10 mL of peripheral blood in a collection tube, invert gently up and down 6-8 times to mix. Avoid cell shearing. Blood samples can be stored in the collection tube at 6-37 &#176;C for up to 72 hours.</li>
	<li>Centrifuge the collection tube at room temperature for 10 min at 1600 (&#177;150) x g.</li>
	<li>Transfer the supernatant (plasma) from the collection tube to four clean 2 mL appropriately labelled centrifuge tubes using a disposable pipet without disturbing the pellet layer (white blood cells).</li>
	<li>Transfer1 mL of the cells using a clean disposable pipet to a 2 mL appropriately labelled centrifuge tube. The cells can be stored at -20 &#176;C or colder before step 1.8.</li>
	<li>Centrifuge the plasma (from step 1.3) for 10 min at 4 &#176;C, and 16000 (&#177;150) x g.</li>
	<li>Transfer the plasma to clean centrifuge tubes properly labelled as &#34;plasma&#34;, leave ~0.1 mL of residual volume at the bottom to avoid contamination. Store the plasma at -80 &#176;C until step 1.7.</li>
	<li>Use 3 mL of plasma from step 1.6 to isolate cfDNA (contains ctDNA) using a commercially available kit, following the manufacturer&#39;s instructions.</li>
	<li>Use 200 &#181;L of white blood cells from step 1.3 to isolate gDNA using a commercially available kit, following the manufacturer&#39;s instructions. 
	NOTE: Both cfDNA and gDNA can be stored at -20 &#176;C before use.</li>
	<li>Apply different quality controls for cfDNA and gDNA (Table 2). Perform quality control by a standardized method for the bioanalyzer to determine levels of sample degradation and/or gDNA contamination. Peak analysis of the quality of cfDNA extraction should appear similar to Figure 1. 
	NOTE: The peak size of cfDNA is around 170 bp. Note that increasing the amount of DNA will result in a higher quality library for the effective detection of rare mutations in cfDNA. We recommend using at least 30 ng cfDNA (30,000 copies).</li>
</ol>
2. Library Preparation
NOTE: Regarding base library construction on fragmented DNA, cfDNA exists in fragments with a peak size ~170 bp and thus does not need to be fragmented.
<ol>
	<li>Fragment the control sample (gDNA) by sonication to obtain 200-250 bp fragments.
	<ol>
		<li>Prepare 1 &#181;g of gDNA sample in 100 &#181;L of Tris-EDTA buffer in a clean sonication tube.</li>
		<li>Set the sonication program as 30 s on and 30 s off for 12 cycles for a total of 12 min.</li>
		<li>After sonication, confirm the product (5 &#181;L) contains 200-250 bp fragments by running an analysis with 2% agarose gel.</li>
		<li>Transfer all fragmentation products to a new 1.5 mL tube and incubate with 150 &#181;L of magnetic beads for 5 min to select the correct fragments.</li>
		<li>Remove the supernatant by putting the tube on a magnetic rack for 30 s.</li>
		<li>Wash the beads with 200 &#181;L of 80% ethanol (freshly prepared) with the tube staying on the magnetic rack. Incubate for 30 s before removing the supernatant. Repeat once.</li>
		<li>Air dry the beads with the tube lid open while staying on the magnetic rack. Avoid overdried beads to make sure that the DNA target recovers well. Elute DNA fragments from the beads by adding 32 &#181;L of 10 mM Tris-HCl (pH 8) to the beads to resolve the DNA fragments followed by quantification by UV/Vis spectroscopy. 
		NOTE: At least 200 ng is needed for the following experiments.</li>
	</ol>
	</li>
	<li>End Prep Reaction 
	NOTE: Follow manufacturer&#39;s instruction and modify as needed. Use 30 ng of cfDNA; and 1 &#181;g of gDNA as the control.
	<ol>
		<li>Add 7 &#181;L of reaction buffer, 3 &#181;L of the enzyme mix, 30 ng of cfDNA in a sterile nuclease-free tube, and add ddH2O to total volume of 60 &#181;L. In a separate tube, add the above components but with 1 &#181;g of gDNA instead of cfDNA.</li>
		<li>Mix and incubate the mixture at 20 &#176;C for 30 min followed by 65 &#176;C for 30 min in a thermocycler without the lid heated. Proceed immediately to the next step.</li>
	</ol>
	</li>
	<li>Adapter Ligation
	<ol>
		<li>Add 30 &#181;L of the ligation master mix, 1 &#181;L of the ligation enhancer, and 4 &#181;L of the unique sequencing adapters to the mixture from step 2.2.</li>
		<li>Incubate at 20 &#176;C for 15 min in a thermocycler without the lid heated.</li>
		<li>Vortex to resuspend the magnetic beads and leave the beads at room temperature for at least 30 min before the next step.</li>
		<li>Add 87 &#181;L of the resuspended magnetic beads to the previously mentioned mixture; mix well by pipetting up and down and then incubate at room temperature for 5 min.</li>
		<li>Wash and air dry the beads as described in Step 2.1.</li>
		<li>Elute the DNA target from the beads by adding 20 &#181;L of 10 mM Tris-HCl (pH 8).</li>
	</ol>
	</li>
	<li>Enrichment of DNA fragments ligated with adapter
	<ol>
		<li>Add 20 &#181;L of adapter ligated DNA fragments, 5 &#181;L of Index Primer/i7 (2.5 &#181;L Primer-P7 (10 pM) and 2.5 &#181;L of Primer-P5 (10 pM), for sequencing purposes), 25&#181;L of library amplification master mix, and ddH2O to a total volume of 50 &#181;L.</li>
		<li>PCR amplify the adapter ligated DNA and thermal cycle as follows: initial denaturation at 98 &#176;C for 45 s, followed by 8 cycles (cfDNA) or 4 cycles (gDNA) of annealing &#176;C for 15 s, 65 &#176;C for 30 s, 72 &#176;C for 30 s), and a final extension at 72 &#176;C for 60 s, then holding the temperature at 4 &#176;C.</li>
		<li>Add 45 &#181;L of suspended magnetic beads to the PCR-enriched DNA to get acquired fragments.</li>
		<li>Elute DNA fragments with adapters in 30 &#181;L of 10 mM Tris-HCl (pH 8).</li>
	</ol>
	</li>
	<li>Library Quality Control
	<ol>
		<li>Follow the manufacturer&#39;s protocol for the commercial kit to measure adapter ligated DNA concentration. Use the bioanalyzer to determine the DNA quality.</li>
	</ol>
	</li>
</ol>
3. Targeted DNA capture
NOTE: Target enrichment was performed using a custom sequence capture-probe which is specifically designed for a 1021 kb target enrichment region covering known tumor-associated driver mutations from 12 different types of tumors. Modifications to the manufacturer&#39;s protocol are detailed in the following steps.
<ol>
	<li>Blocking
	<ol>
		<li>Add 1.5 &#181;g of pooling libraries (maximum number of libraries to pool for cfDNA is 6, gDNA is 20) from step 2, 8 &#181;L of P5 Block(100P), 8 &#181;L of P7 Block(100P), and 5 &#181;L of Cot-1 DNA (1 &#181;g/&#181;L) in a sterile tube. Dry the contents of the tube using a vacuum concentrator set at 60 &#176;C.</li>
	</ol>
	</li>
	<li>Hybridize the DNA capture probes with the library.
	<ol>
		<li>Add the following components to the tube from step 3.1: 8.5 &#181;L of 2X Hybridization buffer, 2.7 &#181;L of Hybridization enhancer, and 1.8 &#181;L of nuclease-free ddH2O.</li>
		<li>Mix by pipetting up and down and incubate in a thermomixer&#160;at 95 &#176;C for 10 min.</li>
		<li>Following incubation, immediately add 4 &#181;L of the Custom Probe. Incubate samples in a thermal cycler at 65 &#176;C with lid heated to 75 &#176;C for 4 h.</li>
	</ol>
	</li>
	<li>DNA fragments capture
	<ol>
		<li>Incubate the hybridized target DNA with streptavidin beads followed by washing the beads to remove unbound DNA. Use the commercial kit according to manufacturer&#39;s instructions to get a final 20 &#181;L of resuspended beads with captured DNA fragments.</li>
	</ol>
	</li>
	<li>Amplification of the captured DNA fragments
	<ol>
		<li>Perform PCR using the commercial kit with 2 &#181;M backbone oligonucleotides, according to manufacturer&#39;s instructions.</li>
		<li>When the PCR reaction is completed, add 45 &#181;L of suspended beads directly to the PCR product and enrich the amplified target DNA fragments bound to the beads by elution with 30 &#181;L of 10 mM Tris-HCl (pH 8).</li>
	</ol>
	</li>
	<li>Quantify the DNA fragments with the library quantification kit and determine the average fragment length by the Bioanalyzer (Figure 2).</li>
</ol>
4. Sequencing
<ol>
	<li>Sequence multiplexed libraries using 75 bp paired-end runs on a benchtop sequencer for 18 h. Total data produced is up to 60 Gigabytes, 15G is required for patient sample cfDNA and 1G for control sample gDNA.</li>
</ol>
5. Data analysis workflow
NOTE: Figure 3 displays the general work flow and data analysis process. Data analysis parameters and commands are shown below.
<ol>
	<li>Filter low-quality reads and correct for errors and masking of UID using NCfilter. 
	NCfilter(own) 
	NCfilter filter -l 5 -q 0.5 -n 0.1 -T 3 -G -1 fastq1 -2 fastq2 
	realSeq(own) 
	python bin/realRealSeq MaskUID -1 fq1 -2 fq2 -o output dir -p prefix -u 4 -s 7 -m 3 -i uidFile -f.</li>
	<li>Referencing genome hg19 (human genome 19), locate the sequencing results using BWA (version 0.7.12-r1039) mem and realign with Genome Analysis Toolkit (GATK) (version 3.4-46-gbc02625). 
	bwa mem -t 3 hg19 fastq1 fastq2 
	java -d64 -server -XX:+UseParallelGC -XX:ParallelGCThreads=8 -Xms2g -Xmx10g - 
	Djava.io.tmpdir=/tmp/ -jar 
	GenomeAnalysisTK.jar -T RealignerTargetCreator -R hg19 -L targetRegion -known dbsnp &#8212; 
	allowPotentiallyMisencodedQuals -nt 10 -I cancerBam -I normalBam 
	java -d64 -server -XX:+UseParallelGC -XX:ParallelGCThreads=8 -Xms2g -Xmx10g &#8212; 
	Djava.io.tmpdir=/tmp/ -jar GenomeAnalysisTK.jar -T IndelRealigner -R hg19 -known dbsnp 
	-allowPotentiallyMisencodedQuals &#8212; targetIntervals intervals -I cancerBam -I normalBam</li>
	<li>Cluster the duplicates according to the UID and position of the template fragments, which will correct the error bases introduced by PCR/sequencing and modify the base quality. 
	realSeq(own) 
	python realSeq/bin/realSeq CLUSTER -b unmarkdup_sort_merge.bam -r chipRegion -o output_dir -p 
	prefix -m 6</li>
	<li>Re-align the clustered duplicates by BWA mem. 
	bwa mem -t 3 hg19 fastq1 fastq2</li>
	<li>Perform a local realignment followed by a recalibration of base quality score using GATK (version 3.4-46-gbc02625). 
	Loacal realignment: java -d64 -server -XX:+UseParallelGC -XX:ParallelGCThreads=8 -Xms2g -Xmx10g -Djava.io.tmpdir=/tmp/ -jar GenomeAnalysisTK.jar -T RealignerTargetCreator -R hg19 -L targetRegion -known dbsnp -allowPotentiallyMisencodedQuals -nt 10 -I cancerBam -I normalBam 
	java -d64 -server -XX:+UseParallelGC -XX:ParallelGCThreads=8 -Xms2g -Xmx10g -Djava.io.tmpdir=/tmp/ -jar GenomeAnalysisTK.jar -T IndelRealigner -R hg19 -known dbsnp -allowPotentiallyMisencodedQuals -targetIntervals intervals -I cancerBam -I normalBam 
	base quality-score recalibration: java -d64 -server -XX:+UseParallelGC -XX:ParallelGCThreads=8 -Xms2g -Xmx10g -Djava.io.tmpdir=/tmp/ -jar GenomeAnalysisTK.jar -T BaseRecalibrator -R hg19 -L targetRegion -knownSites dbsnp -knownSites cosmic -allowPotentiallyMisencodedQuals -nct 10 -I bam -o grp java -d64 -server -XX:+UseParallelGC -XX:ParallelGCThreads=8 -Xms2g -Xmx10g -Djava.io.tmpdir=/tmp/ -jar GenomeAnalysisTK.jar -T PrintReads -I bam -BQSR grp -o outbam -R hg19 -nct 8 -allowPotentiallyMisencodedQuals</li>
	<li>For SNV (single-nucleotide variant) and INDEL (small insertions or deletions) calling, the accuracy of the low frequency mutations is further confirmed by GSR (Good Support Reads) with both forward and reverse strand read pairs and filtration through control group (germline mutations) and the baseline database. 
	realDcaller(own) 
	python realDcaller -f hg19 -r chipRegion -C baselineDB -O -L 2 cancerBam normalBam -p 30</li>
	<li>Use baseline samples as a control for CNV (copy number variation) calling. 
	NCcnv(own) 
	python NCcnv -o cnvdir -t tmpdir -r chipRegion cnv --normgt normalBam --tumgt cancerBam --repdb repeatMarsk</li>
	<li>Realigned unmapped, clips and discordant pair reads to call SV (Structural Variations). 
	NCsv(own) 
	python NCsv -t tmpdir -r hg19 -o outputdir -x transcript -w bwapath -n 4 normalBam cancerBam</li>
</ol>

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The authors have nothing to disclose.

The existence of circulating tumor DNA (ctDNA) was discovered more than 30 years ago, however the application of ctDNA analyses is still not routine in clinical practice. Interest in the practical application of ctDNA methods has increased with the development of technologies for ctDNA detection and analysis. Tumor monitoring with ctDNA offers a minimally-invasive approach for the assessment of microscopic residual disease, response to therapy, and tumor molecular profiles under the background of tumor evolution and intr...

Next-generation sequencing (NGS), a powerful tool to investigate the mysteries of the genome, can provide a large quantity of information, which may reveal genetic alterations. The application of NGS analysis in the clinic has become more common, especially for personalized medicine. One of the greatest limitations of NGS, however, is a high error rate. Although it is deemed suitable for studying inherited mutations, the analysis of rare mutations is greatly limited1,2, especially when analyzing DNA obtained from a &#34;liquid biopsy&#34;.
Circulating tumor DNA (ctDNA) is cell-free DNA (cfDNA) in the blood that is shed from tumor cells. In most cases, the quantity of ctDNA is extremely low, which make its detection and analysis very challenging. However, ctDNA has many attractive features: its isolation is minimally invasive, it can be detected in the early stages of tumor growth, the ctDNA level reflects therapeutic efficiency, and ctDNA contains DNA mutations found in both primary and metastatic lesions3,4,5. Therefore, given the rapid development of the NGS technique and analysis, the application of ctDNA detection has become more attractive.
Different massively parallel sequencing approaches have been utilized for ctDNA detection but none of these approaches have been accepted for routine use in clinics due to their limitations: low sensitivity, lack of versatility, and a relatively high cost6,7,8. For example, duplex sequencing, based on a unique identifier tag (UID), repeatedly corrects errors in the consensus bidirectionally, rectifying most sequencing errors. However, the feasibility of this method is lost due to its high cost and low data utilization9,10. Similarly, CAPP-Seq and its improved iteration, CAPP-IDES11,12, have greater practicality in cfDNA detection, though the accuracy and universality of these methods need improvement.
To meet the current need for accurate ctDNA detection and analysis, we developed a new strategy, Enrich Rare Mutation Sequencing (ER-Seq). This approach combines the following: unique sequencing adapters to efficiently recover ctDNA molecules, with bidirectional error correction and the ability to distinguish a single mutation out of &#62; 1 &#215; 107 wild-type nucleotides; 1021 kb probes which enrich measurement of target regions that cover over 95% of the tumor-related mutations from 12 tumors, including lung cancer, colorectal cancer, gastric cancer, breast cancer, kidney cancer, pancreatic cancer, liver cancer, thyroid cancer, cervical cancer, esophageal cancer, and endometrial carcinoma (Table 1); and baseline database screening making it efficient and easy to precisely detect rare mutations in ctDNA.
To build a baseline database, find all the gene mutations by ER-Seq from a number of the same type of samples (~1000 at the beginning). These real mutations must be verified by several other reliable detection methods and analysis. Next, summarize the pattern of false mutations and cluster all the false mutations to build the initial baseline database. Continue adding false mutations found from subsequent sequencing experiments to this database. Therefore, this baseline database becomes a dynamic expanded database, which significantly improves sequencing accuracy.
To promote progress in tumor diagnosis and monitoring, we present ER-Seq, a low cost and feasible method for the acquisition of universal data. We present a case study which underwent ER-Seq analysis, demonstrating its accuracy for detecting rare mutations and feasibility for use in the clinic.

<table><tbody><tr><td>QIAamp Circulating Nucleic Acid Kit</td><td>Qiagen</td><td>55114</td><td>DNA Extraction from Peripheral Blood for cfDNA</td></tr><tr><td>QIAamp DNA Blood Mini Kit</td><td>Qiagen</td><td>51105</td><td>DNA Extraction from Peripheral Blood for gDNA</td></tr><tr><td>Quant-iT dsDNA HS Assay Kit</td><td>Invitrogen</td><td>Q32854</td><td>Measure cfDNA concentration</td></tr><tr><td>Quant-iT dsDNA BR Assay Kit</td><td>Invitrogen</td><td>Q32853</td><td>Measure library concentration</td></tr><tr><td>Agilent DNA 1000 Reagents</td><td>Agilent</td><td>5067-1504</td><td>Measure cfDNA and library fragments</td></tr><tr><td>The NEBNext UltraII DNA Library Prep Kit for Illumina</td><td>NEB</td><td>E7645L</td><td>Library Preparation</td></tr><tr><td>Agencourt SPRIselect Reagent</td><td>Beckman</td><td>B23317</td><td>DNA fragment screening and purification</td></tr><tr><td>Tris-HCl (10 mM, pH 8.0)-100ML</td><td>Sigma</td><td>93283</td><td>Dissolution</td></tr><tr><td>xGen Lockdown Probes</td><td>IDT</td><td>&amp;mdash;&amp;mdash;</td><td>xGen Custom Probe</td></tr><tr><td>Human Cot-1 DNA</td><td>Life</td><td>15279-011</td><td>Targeted DNA capture</td></tr><tr><td>Dynabeads M-270 Streptavidin</td><td>Life</td><td>65305</td><td>Targeted DNA capture</td></tr><tr><td>xGen Lockdown Reagents</td><td>IDT</td><td>1072281</td><td>Targeted DNA capture</td></tr><tr><td>KAPA HiFi HotStart ReadyMix</td><td>KAPA</td><td>KK2602</td><td>post-capture PCR enrichment libraries</td></tr><tr><td>KAPA Library Quantification Kit</td><td>KAPA</td><td>KK4602</td><td>Measure library concentration</td></tr><tr><td>NextSeq 500 High Output Kit v2((150 cycles)</td><td>illumina</td><td>FC-404-2002</td><td>Sequence</td></tr><tr><td>Centrifuge5810</td><td>eppendorf</td><td>5810</td><td></td></tr><tr><td>Nanodrop8000</td><td>Thermo Scientific</td><td>8000</td><td>Measure gDNA concentration</td></tr><tr><td>Qubit 2.0</td><td>Invitrogen</td><td></td><td>Quantify</td></tr><tr><td>Agilent 2100 Bioanalyzer</td><td>Agilent</td><td></td><td></td></tr><tr><td>ThermoMixer C</td><td>eppendorf</td><td></td><td>Incubation</td></tr><tr><td>16-tube DynaMagTM-2 Magnet</td><td>Life</td><td>12321D</td><td></td></tr><tr><td>Concentrator plus</td><td>eppendorf</td><td></td><td></td></tr><tr><td>PCR</td><td>AB</td><td>simplyamp</td><td></td></tr><tr><td>QPCR</td><td>AB</td><td>7500Dx</td><td></td></tr><tr><td>NextSeq 500</td><td>illumina</td><td></td><td></td></tr></tbody></table>

detection of rare mutations in ctdna using next generation sequencing

The analysis of circulating tumor DNA (ctDNA) using next-generation sequencing (NGS) has become a valuable tool for the development of clinical oncology. However, the application of this method is challenging due to its low sensitivity in analyzing the trace amount of ctDNA in the blood. Furthermore, the method may generate false positive and negative results from this sequencing and subsequent analysis. To improve the feasibility and reliability of ctDNA detection in the clinic, here we present a technique which enriches rare mutations for sequencing, Enrich Rare Mutation Sequencing (ER-Seq). ER-Seq can distinguish a single mutation out of 1 x 107 wild-type nucleotides, which makes it a promising tool to detect extremely low frequency genetic alterations and thus will be very useful in studying disease heterogenicity. By virtue of the unique sequencing adapter&#39;s ligation, this method enables an efficient recovery of ctDNA molecules, while at the same time correcting for errors bidirectionally (sense and antisense). Our selection of 1021 kb probes enriches the measurement of target regions that cover over 95% of the tumor-related driver mutations in 12 tumors. This cost-effective and universal method enables a uniquely successful accumulation of genetic data. After efficiently filtering out background error, ER-seq can precisely detect rare mutations. Using a case study, we present a detailed protocol demonstrating probe design, library construction, and target DNA capture methodologies, while also including the data analysis workflow. The process to carry out this method typically takes 1-2 days.

The 1021 kb probes enriched target regions used in ER-Seq are shown in Table 1, which covers over 95% of the gene mutations in 12 common tumors. The wide range of these probes makes this process applicable to a majority of cancer patients. Additionally, our unique sequencing adapters and baseline database screening make it possible to detect rare mutations precisely.
Due to the different propert...

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Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

This manuscript describes a technique for detecting mutations of low frequency in ctDNA, ER-Seq. This method is differentiated by its unique use of two-directional error correction, a special background filter, and efficient molecular acquirement.

The analysis of circulating tumor DNA (ctDNA) using next-generation sequencing (NGS) has become a valuable tool for the development of clinical oncology. ...

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Cancer Research

16.7K Views.  Geneplus-Beijing Institute. This manuscript describes a technique for detecting mutations of low frequency in ctDNA, ER-Seq. This method is differentiated by its unique use of two-directional error correction, a special background filter, and efficient molecular acquirement. 

Video: Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors

As our understanding of the driver mutations necessary for initiation and progression of cancers improves, we gain critical information on how specific molecular profiles of a tumor may predict responsiveness to therapeutic agents or provide knowledge about prognosis. At our institution a tumor genotyping program was established as part of routine clinical care, screening both hematologic and solid tumors for a wide spectrum of mutations using two next-generation sequencing (NGS) panels: a custom, 33 gene hematological malignancies panel for use with peripheral blood and bone marrow, and a commercially produced solid tumor panel for use with formalin-fixed paraffin-embedded tissue that targets 47 genes commonly mutated in cancer. Our workflow includes a pathologist review of the biopsy to ensure there is adequate amount of tumor for the assay followed by customized DNA extraction is performed on the specimen. Quality control of the specimen includes steps for quantity, quality and integrity and only after the extracted DNA passes these metrics an amplicon library is generated and sequenced. The resulting data is analyzed through an in-house bioinformatics pipeline and the variants are reviewed and interpreted for pathogenicity. Here we provide a snapshot of the utility of each panel using two clinical cases to provide insight into how a well-designed NGS workflow can contribute to optimizing clinical outcomes.

This manuscript describes clinical protocols for two next-generation sequencing panels. One panel interrogates hematologic malignancies while the other panel targets genes commonly mutated in solid tumors. Molecular classification of driver mutations in human malignancies offers valuable prognostic and predictive information.

As our understanding of the driver mutations necessary for initiation and progression of cancers improves, we gain critical information on how specific ...

Wild-type Blocking PCR Combined with Direct Sequencing as a Highly Sensitive Method for Detection of Low-Frequency Somatic Mutations

Accurate detection and identification of low frequency mutations can be problematic when assessing residual disease after therapy, screening for emerging resistance mutations during therapy, or when patients have few circulating tumor cells. Wild-type blocking PCR followed by sequencing analysis offers high sensitivity, flexibility, and simplicity as a methodology for detecting these low frequency mutations. By adding a custom designed locked nucleic acid oligonucleotide to a new or previously established conventional PCR based sequencing assay, sensitivities of approximately 1 mutant allele in a background of 1,000 WT alleles can be achieved (1:1,000). Sequencing artifacts associated with deamination events commonly found in formalin fixed paraffin embedded tissues can be partially remedied by the use of uracil DNA glycosylase during extraction steps. The optimized protocol here is specific for detecting MYD88 mutation, but can serve as a template to design any WTB-PCR assay. Advantages of the WTB-PCR assay over other commonly utilized assays for the detection of low frequency mutations including allele specific PCR and real-time quantitative PCR include fewer occurrences of false positives, greater flexibility and ease of implementation, and the ability to detect both known and unknown mutations.

Wild-type blocking PCR followed by direct sequencing offers a highly sensitive method of detection for low frequency somatic mutations in a variety of sample types.

Accurate detection and identification of low frequency mutations can be problematic when assessing residual disease after therapy, screening for emerging ...

Genetics

Detection of a CDH1 Rare Transcript Variant in Fresh-frozen Gastric Cancer Tissues by Chip-based Digital PCR

CDH1a, a non-canonical transcript of the CDH1 gene, has been found to be expressed in some gastric cancer (GC) cell lines, whereas it is absent in normal gastric mucosa. Recently, we detected CDH1a transcript variant in fresh-frozen tumor tissues obtained from patients with GC. The expression of this variant in tissue samples was investigated by the chip-based digital PCR (dPCR) approach presented here. dPCR offers the potential for an accurate, robust, and highly sensitive measurement of nucleic acids and is increasingly utilized for many applications in different fields. dPCR is capable of detecting rare targets; in addition, dPCR offers the possibility for absolute and precise quantification of nucleic acids without the need for calibrators and standard curves. In fact, the reaction partitioning enriches the target from the background, which improves amplification efficiency and tolerance to inhibitors. Such characteristics make dPCR an optimal tool for the detection of the CDH1a rare transcript.

Detection of a CDH1 Rare Transcript Variant in Fresh-frozen Gastric Cancer Tissues by Chip-based Digital PCR

The manuscript describes a chip-based digital PCR assay to detect a rare CDH1 transcript variant (CDH1a) in fresh-frozen normal and tumor tissues obtained from patients with gastric cancer.

CDH1a, a non-canonical transcript of the CDH1 gene, has been found to be expressed in some gastric cancer (GC) cell lines, whereas it is absent in normal ...

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Next-generation sequencing (NGS) is quickly revolutionizing how research into the genetic determinants of constitutional disease is performed. The technique is highly efficient with millions of sequencing reads being produced in a short time span and at relatively low cost. Specifically, targeted NGS is able to focus investigations to genomic regions of particular interest based on the disease of study. Not only does this further reduce costs and increase the speed of the process, but it lessens the computational burden that often accompanies NGS. Although targeted NGS is restricted to certain regions of the genome, preventing identification of potential novel loci of interest, it can be an excellent technique when faced with a phenotypically and genetically heterogeneous disease, for which there are previously known genetic associations. Because of the complex nature of the sequencing technique, it is important to closely adhere to protocols and methodologies in order to achieve sequencing reads of high coverage and quality. Further, once sequencing reads are obtained, a sophisticated bioinformatics workflow is utilized to accurately map reads to a reference genome, to call variants, and to ensure the variants pass quality metrics. Variants must also be annotated and curated based on their clinical significance, which can be standardized by applying the American College of Medical Genetics and Genomics Pathogenicity Guidelines. The methods presented herein will display the steps involved in generating and analyzing NGS data from a targeted sequencing panel, using the ONDRISeq neurodegenerative disease panel as a model, to identify variants that may be of clinical significance.

Targeted next-generation sequencing is a time- and cost-efficient approach that is becoming increasingly popular in both disease research and clinical diagnostics. The protocol described here presents the complex workflow required for sequencing and the bioinformatics process used to identify genetic variants that contribute to disease.

Next-generation sequencing (NGS) is quickly revolutionizing how research into the genetic determinants of constitutional disease is performed. The ...

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been used to analyze the spectrum of clonal mutations in malignancy. Though far more efficient than traditional Sanger methods, NGS struggles with identifying rare clonal and subclonal mutations due to its high error rate of ~0.5&#8211;2.0%. Thus, standard NGS has a limit of detection for mutations that are &#62;0.02 variant allele fraction (VAF). While the clinical significance for mutations this rare in patients without known disease remains unclear, patients treated for leukemia have significantly improved outcomes when residual disease is &#60;0.0001 by flow cytometry. In order to mitigate this artefactual background of NGS, numerous methods have been developed. Here we describe a method for Error-corrected DNA and RNA Sequencing (ECS), which involves tagging individual molecules with both a 16 bp random index for error-correction and an 8 bp patient-specific index for multiplexing. Our method can detect and track clonal mutations at variant allele fractions (VAFs) two orders of magnitude lower than the detection limit of NGS and as rare as 0.0001 VAF.

Next-generation sequencing (NGS) is a powerful tool for genomic characterization that is limited by the high error rate of the platform (~0.5&#8211;2.0%). We describe our methods of error-corrected sequencing that allow us to obviate the NGS error rate and detect mutations at variant allele fractions as rare as 0.0001.

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been ...

Single Droplet Digital Polymerase Chain Reaction for Comprehensive and Simultaneous Detection of Mutations in Hotspot Regions

Droplet digital polymerase chain reaction (ddPCR) is a highly sensitive quantitative polymerase chain reaction (PCR) method based on sample fractionation into thousands of nano-sized water-in-oil individual reactions. Recently, ddPCR has become one of the most accurate and sensitive tools for circulating tumor DNA (ctDNA) detection. One of the major limitations of the standard ddPCR technique is the restricted number of mutations that can be screened per reaction, as specific hydrolysis probes recognizing each possible allelic version are required. An alternative methodology, the drop-off ddPCR, increases throughput, since it requires only a single pair of probes to detect and quantify potentially all genetic alterations in the targeted region. Drop-off ddPCR displays comparable sensitivity to conventional ddPCR assays with the advantage of detecting a greater number of mutations in a single reaction. It is cost-effective, conserves precious sample material, and can also be used as a discovery tool when mutations are not known a priori.

Here, we present a protocol to accurately quantify multiple genetic alterations of a target region in a single reaction using drop-off ddPCR and a unique pair of hydrolysis probes.

Droplet digital polymerase chain reaction (ddPCR) is a highly sensitive quantitative polymerase chain reaction (PCR) method based on sample fractionation ...

Detection and Monitoring of Tumor Associated Circulating DNA in Patient Biofluids

Complications associated with upfront and repeat surgical tissue sampling present the need for minimally invasive platforms capable of molecular sub-classification and temporal monitoring of tumor response to therapy. Here, we describe our dPCR-based method for the detection of tumor somatic mutations in cell free DNA (cfDNA), readily available in&#160;patient biofluids. Although limited in the number of mutations that can be tested for in each assay, this method provides a high level of sensitivity and specificity. Monitoring of mutation abundance, as calculated by MAF, allows for the evaluation of tumor response to therapy, thereby providing a much-needed supplement to radiographic imaging.

Here, we present a protocol to detect tumor somatic mutations in circulating DNA present in patient biological fluids (biofluids). Our droplet digital polymerase chain reaction (dPCR)-based method enables quantification of the tumor mutation allelic frequency (MAF), facilitating a minimally invasive complement to diagnosis and temporal monitoring of tumor response.

Complications associated with upfront and repeat surgical tissue sampling present the need for minimally invasive platforms capable of molecular ...

Using Next Generation Sequencing to Identify Mutations Associated with Repair of a CAS9-induced Double Strand Break Near the CD4 Promoter

Double strand breaks (DSBs) in DNA are the most cytotoxic type of DNA damage. Because a myriad of insults can result in these lesions (e.g., replication stress, ionizing radiation, unrepaired UV damage), DSBs occur in most cells each day. In addition to cell death, unrepaired DSBs reduce genome integrity and the resulting mutations can drive tumorigenesis. These risks and the prevalence of DSBs motivate investigations into the mechanisms by which cells repair these lesions. Next generation sequencing can be paired with the induction of DSBs by ionizing radiation to provide a powerful tool to precisely define the mutations associated with DSB repair defects. However, this approach requires computationally challenging and cost prohibitive whole genome sequencing to detect the repair of the randomly occurring DSBs associated with ionizing radiation. Rare cutting endonucleases, such as I-Sce1, provide the ability to generate a single DSB, but their recognition sites must be inserted into the genome of interest. As a result, the site of repair is inherently artificial. Recent advances allow guide RNA (sgRNA) to direct a Cas9 endonuclease to any genome locus of interest. This could be applied to the study of DSB repair making next generation sequencing more cost effective by allowing it to be focused on the DNA flanking the Cas9-induced DSB. The goal of the manuscript is to demonstrate the feasibility of this approach by presenting a protocol that can define mutations that stem from the repair of a DSB upstream of the CD4 gene. The protocol can be adapted to determine changes in the mutagenic potential of DSB associated with exogenous factors, such as repair inhibitors, viral protein expression, mutations, and environmental exposures with relatively limited computation requirements. Once an organism's genome has been sequenced, this method can be theoretically employed at any genomic locus and in any cell culture model of that organism that can be transfected. Similar adaptations of the approach could allow comparisons of repair fidelity between different loci in the same genetic background.

Presented here is sgRNA/CAS9 endonuclease and next-generation sequencing protocol that can be used to identify the mutations associated with double strand break repair near the CD4 promoter.

Double strand breaks (DSBs) in DNA are the most cytotoxic type of DNA damage. Because a myriad of insults can result in these lesions (e.g., replication ...

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

All next-generation sequencing (NGS) procedures include assays performed at the laboratory bench (&#34;wet bench&#34;) and data analyses conducted using bioinformatics pipelines (&#34;dry bench&#34;). Both elements are essential to produce accurate and reliable results, which are particularly critical for clinical laboratories. Targeted NGS technologies have increasingly found favor in oncology applications to help advance precision medicine objectives, yet the methods often involve disconnected and variable wet and dry bench workflows and uncoordinated reagent sets. In this report, we describe a method for sequencing challenging cancer specimens with a 21-gene panel as an example of a comprehensive targeted NGS system. The system integrates functional DNA quantification and qualification, single-tube multiplexed PCR enrichment, and library purification and normalization using analytically-verified, single-source reagents with a standalone bioinformatics suite. As a result, accurate variant calls from low-quality and low-quantity formalin-fixed, paraffin-embedded (FFPE) and fine-needle aspiration (FNA) tumor biopsies can be achieved. The method can routinely assess cancer-associated variants from an input of 400 amplifiable DNA copies, and is modular in design to accommodate new gene content. Two different types of analytically-defined controls provide quality assurance and help safeguard call accuracy with clinically-relevant samples. A flexible &#34;tag&#34; PCR step embeds platform-specific adaptors and index codes to allow sample barcoding and compatibility with common benchtop NGS instruments. Importantly, the protocol is streamlined and can produce 24 sequence-ready libraries in a single day. Finally, the approach links wet and dry bench processes by incorporating pre-analytical sample quality control results directly into the variant calling algorithms to improve mutation detection accuracy and differentiate false-negative and indeterminate calls. This targeted NGS method uses advances in both wetware and software to achieve high-depth, multiplexed sequencing and sensitive analysis of heterogeneous cancer samples for diagnostic applications.

An integrated system for targeted next-generation sequencing of oncology specimens is described. This cross-platform system is optimized for low-quality and low-quantity tumor biopsies, accommodates low DNA inputs, includes well-characterized multi-variant controls, and features a novel variant caller that is informed by quantitative pre-analytical quality control measures.

All next-generation sequencing (NGS) procedures include assays performed at the laboratory bench (&#34;wet bench&#34;) and data analyses conducted using ...

Medicine

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Efforts to detect and investigate key oncogenic mutations have proven valuable to facilitate the appropriate treatment for cancer patients. The establishment of high-throughput, massively parallel "next-generation" sequencing has aided the discovery of many such mutations. To enhance the clinical and translational utility of this technology, platforms must be high-throughput, cost-effective, and compatible with formalin-fixed paraffin embedded (FFPE) tissue samples that may yield small amounts of degraded or damaged DNA. Here, we describe the preparation of barcoded and multiplexed DNA libraries followed by hybridization-based capture of targeted exons for the detection of cancer-associated mutations in fresh frozen and FFPE tumors by massively parallel sequencing. This method enables the identification of sequence mutations, copy number alterations, and select structural rearrangements involving all targeted genes. Targeted exon sequencing offers the benefits of high throughput, low cost, and deep sequence coverage, thus conferring high sensitivity for detecting low frequency mutations.

We describe the preparation of barcoded DNA libraries and subsequent hybridization-based exon capture for detection of key cancer-associated mutations in clinical tumor specimens by massively parallel "next generation" sequencing. Targeted exon sequencing offers the benefits of high throughput, low cost, and deep sequence coverage, thus yielding high sensitivity for detecting low frequency mutations.

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Summary

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Chapters in this video

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors

Wild-type Blocking PCR Combined with Direct Sequencing as a Highly Sensitive Method for Detection of Low-Frequency Somatic Mutations

Detection of a CDH1 Rare Transcript Variant in Fresh-frozen Gastric Cancer Tissues by Chip-based Digital PCR

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Single Droplet Digital Polymerase Chain Reaction for Comprehensive and Simultaneous Detection of Mutations in Hotspot Regions

Detection and Monitoring of Tumor Associated Circulating DNA in Patient Biofluids

Using Next Generation Sequencing to Identify Mutations Associated with Repair of a CAS9-induced Double Strand Break Near the CD4 Promoter

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing