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 intratumoral heterogeneity13,14. Improvements in the sensitivity and specificity of analysis will facilitate all these applications15,16.
In this manuscript, we introduced ER-Seq, a promising method aimed at improving the sensitivity of ctDNA detection, using a NSCLC case as an example. Compared with traditional analysis, the application of our unique sequencing adapters in ER-Seq significantly improved its sensitivity and specificity, indicated by a coverage depth increase and an ability to detect low-frequency mutations (such as EGFR p.T790M (0.53%)). The existence of T790M in this patient sample may be a contributing factor to Erlotinib resistance, and gave insight into how to better treat this patient, suggesting the use of a third-generation EGFR inhibitor specific for T790M, such as AZD929117-20. Another critical point in ER-Seq that contributes to its sensitivity and specificity is the baseline database screening, which facilitates the precise detection of low-frequency mutations in ctDNA by filtering out background noise.
Although ER-Seq has many advantages that make it superior to traditional methods, there are still some challenges that need to be overcome. One of the major challenges for ER-Seq is the extremely low level of ctDNA present in the blood. For those low-frequency mutations, the acquisition of sufficient template ctDNA is critical. Our unique adapters significantly increase the recycle rate of ctDNA, though they still need to be improved. Another challenge for ER-Seq, and for all the other sequencing techniques, is the limitation of coverage of target regions. Our selected 1021 kb probe enriched regions, which can cover about 95% of tumor-related mutations, are more comprehensive compared with other small-plane methods21,22,23. However, as tumor heterogeneity has been well recognized and more and more tumor biomarkers have been discovered, the relative coverage rate of our probes enriched regions decreases. To better serve as a tool for early diagnosis and disease monitoring of tumor patients, more comprehensive sequencing and analysis techniques are needed. Currently, ER-Seq still relies on a high volume of data for detecting low-frequency mutations. Further improvement of data utilization could be favorable for the application of ER-Seq.
As discussed above, many limitations associated with ER-Seq exist. However, our unique analysis technique still has many advantages compared with other top-ranking methods in this field. The pros and cons of this technique merit further research. The potential of ER-Seq for routine clinical use will be of great importance for clinical physicians, providing them with a powerful tool to diagnose tumors and monitor tumor dynamics and response to therapy. Novel mutations associated with resistance to conventional and targeted therapy found by our analysis might offer new avenues of treatment for cancer patients with advanced disease.

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>&#8212;&#8212;</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 properties of gDNA and cfDNA extracted from peripheral blood, there is a range of data quality, which is determined by the different instruments and quality controls shown in Table 2. Successfully extracted cfDNA should display a peak size of ~170 bp, as analyzed by the bioanalyzer QC and shown in Figure 1. Large fragments indicate contamination from genomic DNA, which should not appear in the final product. DNA extracted from this patient sample was of sufficient quality and quantity for ER-Seq (cfDNA - 42.6 ng; gDNA - 3.426 &#181;g).
Target DNA capture using a custom probe was then amplified by PCR. The average fragment size was evaluated by the bioanalyzer and representative data for a patient&#39;s cfDNA in Figure 2A showed a peak around ~320 bp and a unique band on agarose gel. gDNA should appear as a smear on an agarose gel, as shown in Figure 2B. The qualities of both libraries were acceptable for the subsequent sequencing.
After sequencing, the data was analyzed in order according to the work flow in Figure 3B. To illustrate the advantages of our ER-Seq versus the traditional method, we performed both analyses using the same sample. Results from both analyses are shown in Table 3 and Table 4. We showed that ER-Seq improves coverage depth by 23% compared with the traditional method (Table 3, 3214.3 reads in ER-Seq vs 2475 reads in traditional analysis), which was due to its efficient recovery of cfDNA molecules, thus greatly enhancing the analysis of rare mutations. It is also clear that the unique sequencing adapters used in ER-Seq enable easy differentiation of natural and PCR-induced duplications (Table 3, 0 PCR induced duplicated read in ER-Seq). Additionally, we found in the calling results that analysis based on ER-Seq was 100% consistent for EGFR p.L858R detection and that the frequency of detection was a bit higher (2.7% vs 1.2%) when compared with traditional analysis (Table 4). Importantly, ER-Seq analysis enabled the detection of other relatively low-frequency mutations, including EGFR p.T790M (variation frequency 0.53%) (Table 4), and this was not recognized by traditional analysis due to high background noise.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/56342/56342fig1.jpg" /> 
Figure 1. Representative QC Result of Successfully Isolated cfDNA 
For the left graphs, the X axis shows fragment size (bp) and the Y axis indicates relative fluorescence units (FU). The primary size of cfDNA is ~170 bp and there are no large fragments of contamination. A simulated electrophoresis gel is shown on the right. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56342/56342fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/56342/56342fig2.jpg" /> 
Figure 2. Example of Libraries QC Analyzed. 
The X axis shows the fragment size (bp) and the Y axis indicates the relative fluorescence units (FU). A simulated electrophoresis gel was shown on the right of each graph. (A) Patient cfDNA sample showed a sharp peak after it was amplified with adapters. (B) Control gDNA sample showed evenly distributed fragments with no sharp peak and no bias toward one side. Both samples were acceptable for subsequent sequencing. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56342/56342fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/56342/56342fig3.jpg" /> 
Figure 3. ER-Seq Work Flow. 
(A) General work flow. (B) Data analysis work flow. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56342/56342fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td colspan="10">2735 exons from all the exon region of 70 genes</td>
		</tr>
		<tr>
			<td>ABL1</td>
			<td>ABL2</td>
			<td>AKT1</td>
			<td>AKT2</td>
			<td>AKT3</td>
			<td>ALK</td>
			<td>APC</td>
			<td>AR</td>
			<td>ARAF</td>
			<td>ATM</td>
		</tr>
		<tr>
			<td>ATR</td>
			<td>AURKA</td>
			<td>AURKB</td>
			<td>AXL</td>
			<td>BAP1</td>
			<td>BCL2</td>
			<td>BRAF</td>
			<td>BRCA1</td>
			<td>BRCA2</td>
			<td>BRD2</td>
		</tr>
		<tr>
			<td>BRD3</td>
			<td>BRD4</td>
			<td>BTK</td>
			<td>C11orf30</td>
			<td>C1QA</td>
			<td>C1S</td>
			<td>CBL</td>
			<td>CCND1</td>
			<td>CCND2</td>
			<td>CCND3</td>
		</tr>
		<tr>
			<td>CCNE1</td>
			<td>CD274</td>
			<td>CDH1</td>
			<td>CDK13</td>
			<td>CDK4</td>
			<td>CDK6</td>
			<td>CDK8</td>
			<td>CDKN1A</td>
			<td>CDKN1B</td>
			<td>CDKN2A</td>
		</tr>
		<tr>
			<td>CDKN2B</td>
			<td>CHEK1</td>
			<td>CHEK2</td>
			<td>CRKL</td>
			<td>CSF1R</td>
			<td>CTNNB1</td>
			<td>DDR1</td>
			<td>DDR2</td>
			<td>DNMT3A</td>
			<td>EGFR</td>
		</tr>
		<tr>
			<td>EPHA2</td>
			<td>EPHA3</td>
			<td>EPHA5</td>
			<td>ERBB2</td>
			<td>ERBB3</td>
			<td>ERBB4</td>
			<td>ERCC1</td>
			<td>ERG</td>
			<td>ESR1</td>
			<td>EZH2</td>
		</tr>
		<tr>
			<td>FAT1</td>
			<td>FBXW7</td>
			<td>FCGR2A</td>
			<td>FCGR2B</td>
			<td>FCGR3A</td>
			<td>FGFR1</td>
			<td>FGFR2</td>
			<td>FGFR3</td>
			<td>FGFR4</td>
			<td>FLCN</td>
		</tr>
		<tr>
			<td>FLT1</td>
			<td>FLT3</td>
			<td>FLT4</td>
			<td>FOXA1</td>
			<td>FOXL2</td>
			<td>GAB2</td>
			<td>GATA3</td>
			<td>GNA11</td>
			<td>GNAQ</td>
			<td>GNAS</td>
		</tr>
		<tr>
			<td>HDAC1</td>
			<td>HDAC4</td>
			<td>HGF</td>
			<td>HRAS</td>
			<td>IDH1</td>
			<td>IDH2</td>
			<td>IGF1R</td>
			<td>IL7R</td>
			<td>INPP4B</td>
			<td>IRS2</td>
		</tr>
		<tr>
			<td>JAK1</td>
			<td>JAK2</td>
			<td>JAK3</td>
			<td>KDR</td>
			<td>KIT</td>
			<td>KRAS</td>
			<td>MAP2K1</td>
			<td>MAP2K2</td>
			<td>MAPK1</td>
			<td>MAPK3</td>
		</tr>
		<tr>
			<td>MCL1</td>
			<td>MDM2</td>
			<td>MDM4</td>
			<td>MED12</td>
			<td>MET</td>
			<td>MITF</td>
			<td>MLH1</td>
			<td>MLH3</td>
			<td>MPL</td>
			<td>MS4A1</td>
		</tr>
		<tr>
			<td>MSH2</td>
			<td>MSH3</td>
			<td>MSH6</td>
			<td>MTOR</td>
			<td>MYC</td>
			<td>MYD88</td>
			<td>NF1</td>
			<td>NF2</td>
			<td>NOTCH1</td>
			<td>NOTCH2</td>
		</tr>
		<tr>
			<td>NOTCH3</td>
			<td>NOTCH4</td>
			<td>NRAS</td>
			<td>NTRK1</td>
			<td>NTRK3</td>
			<td>PALB2</td>
			<td>PDGFRA</td>
			<td>PDGFRB</td>
			<td>PDK1</td>
			<td>PIK3CA</td>
		</tr>
		<tr>
			<td>PIK3CB</td>
			<td>PIK3R1</td>
			<td>PIK3R2</td>
			<td>PMS1</td>
			<td>PMS2</td>
			<td>PRKAA1</td>
			<td>PSMB1</td>
			<td>PSMB5</td>
			<td>PTCH1</td>
			<td>PTCH2</td>
		</tr>
		<tr>
			<td>PTEN</td>
			<td>PTPN11</td>
			<td>RAF1</td>
			<td>RARA</td>
			<td>RB1</td>
			<td>RET</td>
			<td>RHEB</td>
			<td>RHOA</td>
			<td>RICTOR</td>
			<td>RNF43</td>
		</tr>
		<tr>
			<td>ROCK1</td>
			<td>ROS1</td>
			<td>RPS6KB1</td>
			<td>SMARCA4</td>
			<td>SMARCB1</td>
			<td>SMO</td>
			<td>SRC</td>
			<td>STAT1</td>
			<td>STAT3</td>
			<td>STK11</td>
		</tr>
		<tr>
			<td>SYK</td>
			<td>TMPRSS2</td>
			<td>TOP1</td>
			<td>TP53</td>
			<td>TSC1</td>
			<td>TSC2</td>
			<td>VEGFA</td>
			<td>VHL</td>
			<td>XPO1</td>
			<td>XRCC1</td>
		</tr>
		<tr>
			<td colspan="10">introns, promoters and breakpoints or fusion region from the following 24 genes</td>
		</tr>
		<tr>
			<td>ALK</td>
			<td>FGFR1</td>
			<td>FGFR2</td>
			<td>FGFR3</td>
			<td>NTRK1</td>
			<td>NTRK3</td>
			<td>PDGFRA</td>
			<td>PDGFRB</td>
			<td>ROS1</td>
			<td>RET</td>
		</tr>
		<tr>
			<td>MET</td>
			<td>BRAF</td>
			<td>ABL1</td>
			<td>BRD3</td>
			<td>BRD4</td>
			<td>EGFR</td>
			<td>RAF1</td>
			<td>BCR</td>
			<td>ERG</td>
			<td>TMPRSS2</td>
		</tr>
		<tr>
			<td>RARA</td>
			<td>KIF5B</td>
			<td>BCL2L11</td>
			<td>TERT</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td colspan="10">other relative genes: 1122 exons from 847 genes</td>
		</tr>
	</tbody>
</table>
Table 1. 1021 kb Probes Enriched Target Regions. 
These regions included: 2735 exons from 170 genes; introns, promoter regions and breakpoint regions from 24 genes; 1122 exons from 847 related genes. These cover over 95% of the tumor related driver mutations and target mutation sites from the 12 most common tumors (lung cancer, colorectal cancer, gastric cancer, breast cancer, kidney cancer, pancreatic cancer, liver cancer, thyroid cancer, cervical cancer, esophageal cancer, and endometrial carcinoma).
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>DNA</td>
			<td>Result</td>
			<td>Indication</td>
			<td>Ideal Range</td>
		</tr>
		<tr>
			<td rowspan="2">cfDNA</td>
			<td>Fragment Size</td>
			<td>Identification of DNA Fragment distribution</td>
			<td>168bp&#177;20(a)</td>
		</tr>
		<tr>
			<td>Concentration</td>
			<td>More accurate DNA quantification</td>
			<td>Total cfDNA &#8805;30ng(b)</td>
		</tr>
		<tr>
			<td rowspan="3">gDNA</td>
			<td>A260/A230</td>
			<td>Identification of chemical contaminants (e.g., ethanol)</td>
			<td>1.50 &#8211; 2.2</td>
		</tr>
		<tr>
			<td>A260/A280</td>
			<td>Identification of protein contaminants</td>
			<td>1.60 &#8211; 2.2</td>
		</tr>
		<tr>
			<td>Concentration</td>
			<td>DNA quantification</td>
			<td>Total gDNA &#62;3ug</td>
		</tr>
	</tbody>
</table>
Table 2. Quality Analysis of cfDNA and gDNA Extracted from Peripheral Blood. 
The peak size of cfDNA is around 170Bp. Quality control should be performed by a standardized method for the 2100 Bioanalyzer to determine levels of sample degradation and/or gDNA contamination. Peak analysis of the quality of the cfDNA extraction should appear similar to Figure 1. Note that increased DNA amount will result in a library with a higher quality for the effective detection of rare mutations in cfDNA. We recommend using at least 30 ng cfDNA (30,000 copies).
<img alt="Table 3" src="/files/ftp_upload/56342/56342table3.jpg" /> 
Table 3. The QC of ER-Seq Information Analysis and Traditional Information Analysis. 
The dark gray highlight indicates a coverage depth increase occurred in ER-Seq compared with traditional methods; a light gray highlight indicates no PCR induced duplicated read in ER-Seq. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56342/56342table3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Gene</td>
			<td>Chr</td>
			<td>Start</td>
			<td>End</td>
			<td>cHGVS</td>
			<td>pHGVS</td>
			<td>Function</td>
			<td>caseAltIsHot</td>
			<td>ER-Seq var_freq</td>
			<td>Traditional var_freq</td>
		</tr>
		<tr>
			<td>TP53</td>
			<td>17</td>
			<td>7577534</td>
			<td>7577535</td>
			<td>c.746G&#62;T</td>
			<td>p.R249M</td>
			<td>Missense</td>
			<td>HighFreq</td>
			<td>0.0400</td>
			<td>0.0378</td>
		</tr>
		<tr>
			<td>EGFR</td>
			<td>7</td>
			<td>55259514</td>
			<td>55259515</td>
			<td>c.2573T&#62;G</td>
			<td>p.L858R</td>
			<td>Missense</td>
			<td>Actionable</td>
			<td>0.0270</td>
			<td>0.0120</td>
		</tr>
		<tr>
			<td>ATM</td>
			<td>11</td>
			<td>108203618</td>
			<td>108203619</td>
			<td>c.7919delC</td>
			<td>p.T2640Ifs*6</td>
			<td>frameshift</td>
			<td>ND</td>
			<td>0.0169</td>
			<td>0.0180</td>
		</tr>
		<tr>
			<td>PTCH1</td>
			<td>9</td>
			<td>98221917</td>
			<td>98221918</td>
			<td>c.2851G&#62;T</td>
			<td>p.D951Y</td>
			<td>Missense</td>
			<td>ND</td>
			<td>0.0154</td>
			<td>0.0260</td>
		</tr>
		<tr>
			<td>EGFR</td>
			<td>7</td>
			<td>55249070</td>
			<td>55249071</td>
			<td>c.2369C&#62;T</td>
			<td>p.T790M</td>
			<td>Missense</td>
			<td>Actionable</td>
			<td>0.0053</td>
			<td>&#8212;&#8212;(0.0013)</td>
		</tr>
		<tr>
			<td>RB1</td>
			<td>13</td>
			<td>49039151</td>
			<td>49039152</td>
			<td>c.2230A&#62;T</td>
			<td>p.I744F</td>
			<td>Missense</td>
			<td>ND</td>
			<td>0. 0036</td>
			<td>&#8212;&#8212;(0.0014)</td>
		</tr>
	</tbody>
</table>
Table 4. The Calling Result of ER-Seq Information Analysis and Traditional Information Analysis.

Watch this Scientific Journal Video about Detection of Rare Mutations in CtDNA Using Next Generation Sequencing at JoVE.com

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. ...

detection-of-rare-mutations-in-ctdna-using-next-generation-sequencing

Nanyang Technological University

Research

JoVE Journal

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 ...

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Drug resistance remains a major problem in the treatment of cancer for both hematological malignancies and solid tumors. Intrinsic or acquired resistance can be caused by a range of mechanisms, including increased drug elimination, decreased drug uptake, drug inactivation and alterations of drug targets. Recent data showed that other than by well-known genetic (mutation, amplification) and epigenetic (DNA hypermethylation, histone post-translational modification) modifications, drug resistance mechanisms might also be regulated by splicing aberrations. This is a rapidly growing field of investigation that deserves future attention in order to plan more effective therapeutic approaches. The protocol described in this paper is aimed at investigating the impact of aberrant splicing on drug resistance in solid tumors and hematological malignancies. To this goal, we analyzed the transcriptomic profiles of several in vitro models through RNA-seq and established a qRT-PCR based method to validate candidate genes. In particular, we evaluated the differential splicing of DDX5 and PKM transcripts. The aberrant splicing detected by the computational tool MATS was validated in leukemic cells, showing that different DDX5 splice variants are expressed in the parental vs. resistant cells. In these cells, we also observed a higher PKM2/PKM1 ratio, which was not detected in the Panc-1 gemcitabine-resistant counterpart compared to parental Panc-1 cells, suggesting a different mechanism of drug-resistance induced by gemcitabine exposure.

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Here we describe a protocol aimed at investigating the impact of aberrant splicing on drug resistance in solid tumors and hematological malignancies. To this goal, we analyzed the transcriptomic profiles of parental and resistant in vitro models through RNA-seq and established a qRT-PCR based method to validate candidate genes.

Drug resistance remains a major problem in the treatment of cancer for both hematological malignancies and solid tumors. Intrinsic or acquired resistance ...

Development of a Human Preclinical Model of Osteoclastogenesis from Peripheral Blood Monocytes Co-cultured with Breast Cancer Cell Lines

The crosstalk between tumor cells and bone cells in the bone microenvironment is crucial to understanding the mechanism of bone metastasis formation. We developed an in vitro fully human preclinical model of a co-culture of breast cancer cells and monocytes undergoing differentiation towards osteoclasts. We optimized a model of osteoclastogenesis starting from a sample of peripheral blood collected from healthy donors. Peripheral blood mononuclear cells (PBMCs) were first separated by density gradient centrifugation, seeded at a high density and induced to differentiate by adding two growth factors (GFs): receptor activator of nuclear factor-&#954;B ligand (RANKL) and macrophage colony-stimulating factor (MCSF). The cells were left in culture for 14 days and then fixed and analyzed by downstream analysis. In osteolytic bone metastases, one of the effects of cancer cell arrival in bone is the induction of osteoclastogenesis. We thus challenged our model with co-cultures of breast cancer cells to study the differentiation power of cancer cells with respect to GFs. A straightforward way of studying cancer cell-osteoclast interaction is to perform indirect co-cultures based on the use of conditioned medium collected from breast cancer cell cultures and mixed with fresh medium. This mixture is then used to induce osteoclast differentiation. We also optimized a method of direct co-culture in which cancer cells and monocytes undergoing differentiation share the medium and exchange secreted factors. This is a significant improvement over the original indirect co-culture method as researchers can observe the reciprocal interactions of the two cell types and perform downstream analyses for both cancer cells and osteoclasts. This method enables us to study the effect of drugs on the metastatic bone microenvironment and to seed cell lines other than those derived from breast cancer. The model can also be used to study other diseases such as osteoporosis or other bone conditions.

This protocol describes development of an in vitro human preclinical model of osteoclastogenesis from peripheral blood monocytes cultured with breast cancer cell lines to mimic the cancer cell-osteoclast interaction. The model could be used to further our understanding of bone metastasis formation and improve therapeutic options.

The crosstalk between tumor cells and bone cells in the bone microenvironment is crucial to understanding the mechanism of bone metastasis formation. We ...

Serum and Plasma Copy Number Detection Using Real-time PCR

Serum and plasma cell free DNA (cfDNA) has been shown as an informative, non-invasive source of biomarkers for cancer diagnosis, prognosis, monitoring, and prediction of treatment resistance. Starting from the hypothesis that androgen receptor (AR) gene copy number (CN) gain is a frequent event in metastatic castration resistance prostate cancer (mCRPC), we propose to analyze this event in cfDNA as a potential predictive biomarker.
We evaluated AR CN in cfDNA using 2 different real-time PCR assays and 2 reference genes (RNaseP and AGO1). DNA amount of 60 ng was used for each assay combination. AR CN gain was confirmed using Digital PCR as a more accurate method. CN variation analysis has already been demonstrated to be informative for the prediction of treatment resistance in the setting of mCRPC, but it could be useful also for other purposes in different patient settings. CN analysis on cfDNA has several advantages: it is non-invasive, rapid and easy to perform, and it starts from a small volume of serum or plasma material.

This manuscript describes the copy number variation analysis performed in serum or plasma DNA using real-time PCR approach. This method is suitable for the prediction of drug resistance in castration resistant prostate cancer patients, but it could be informative also for other diseases.

Serum and plasma cell free DNA (cfDNA) has been shown as an informative, non-invasive source of biomarkers for cancer diagnosis, prognosis, monitoring, ...

An Integrated Platform for Genome-wide Mapping of Chromatin States Using High-throughput ChIP-sequencing in Tumor Tissues

Histone modifications constitute a major component of the epigenome and play important regulatory roles in determining the transcriptional status of associated loci. In addition, the presence of specific modifications has been used to determine the position and identity non-coding functional elements such as enhancers. In recent years, chromatin immunoprecipitation followed by next generation sequencing (ChIP-seq) has become a powerful tool in determining the genome-wide profiles of individual histone modifications. However, it has become increasingly clear that the combinatorial patterns of chromatin modifications, referred to as Chromatin States, determine the identity and nature of the associated genomic locus. Therefore, workflows consisting of robust high-throughput (HT) methodologies for profiling a number of histone modification marks, as well as computational analyses pipelines capable of handling myriads of ChIP-Seq profiling datasets, are needed for comprehensive determination of epigenomic states in large number of samples. The HT-ChIP-Seq workflow presented here consists of two modules: 1) an experimental protocol for profiling several histone modifications from small amounts of tumor samples and cell lines in a 96-well format; and 2) a computational data analysis pipeline that combines existing tools to compute both individual mark occupancy and combinatorial chromatin state patterns. Together, these two modules facilitate easy processing of hundreds of ChIP-Seq samples in a fast and efficient manner. The workflow presented here is used to derive chromatin state patterns from 6 histone mark profiles in melanoma tumors and cell lines. Overall, we present a comprehensive ChIP-seq workflow that can be applied to dozens of human tumor samples and cancer cell lines to determine epigenomic aberrations in various malignancies.

Here, we describe an optimized high-throughput ChIP-sequencing protocol and computational analyses pipeline for the determination of genome-wide chromatin state patterns from frozen tumor tissues and cell lines.

Histone modifications constitute a major component of the epigenome and play important regulatory roles in determining the transcriptional status of ...

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 ...

Oncogenic Gene Fusion Detection Using Anchored Multiplex Polymerase Chain Reaction Followed by Next Generation Sequencing

Gene fusions frequently contribute to the oncogenic phenotype of many different types of cancer. Additionally, the presence of certain fusions in samples from cancer patients often directly influences diagnosis, prognosis, and/or therapy selection. As a result, the accurate detection of gene fusions has become a critical component of clinical management for many disease types. Until recently, clinical gene fusion detection was predominantly accomplished through the use of single-gene assays. However, the ever-growing list of gene fusions with clinical significance has created a need for assessing fusion status of multiple genes simultaneously. Next generation sequencing (NGS)-based testing has met this demand through the ability to sequence nucleic acid in massively parallel fashion. Multiple NGS-based approaches that employ different strategies for gene target enrichment are now available for use in clinical molecular diagnostics, each with its own strengths and weaknesses. This article describes the use of anchored multiplex PCR (AMP)-based target enrichment and library preparation followed by NGS to assess for gene fusions in clinical solid tumor specimens. AMP is unique among amplicon-based enrichment approaches in that it identifies gene fusions regardless of the identity of the fusion partner. Detailed here are both the wet-bench and data analysis steps that ensure accurate gene fusion detection from clinical samples.

This article details the use of an anchored multiplex polymerase chain reaction-based library preparation kit followed by next-generation sequencing to assess for oncogenic gene fusions in clinical solid tumor samples. Both wet-bench and data analysis steps are described.

Gene fusions frequently contribute to the oncogenic phenotype of many different types of cancer. Additionally, the presence of certain fusions in samples ...

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine learning (ML) algorithms permit the integration of different features into a model to perform classification or regression tasks with an accuracy exceeding its constituents. This protocol describes the development of an ML algorithm to predict the growth of breast cancer bone macrometastases in a rat model before any abnormalities are observable with standard imaging methods. Such an algorithm can facilitate the detection of early metastatic disease (i.e., micrometastasis) that is regularly missed during staging examinations.
The applied metastasis model is site-specific, meaning that the rats develop metastases exclusively in their right hind leg. The model&#8217;s tumor-take rate is 60%&#8211;80%, with macrometastases becoming visible in magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) in a subset of animals 30 days after induction, whereas a second subset of animals exhibit no tumor growth.
Starting from image examinations acquired at an earlier time point, this protocol describes the extraction of features that indicate tissue vascularization detected by MRI, glucose metabolism by PET/CT, and the subsequent determination of the most relevant features for the prediction of macrometastatic disease. These features are then fed into a model-averaged neural network (avNNet) to classify the animals into one of two groups: one that will develop metastases and the other that will not develop any tumors. The protocol also describes the calculation of standard diagnostic parameters, such as overall accuracy, sensitivity, specificity, negative/positive predictive values, likelihood ratios, and the development of a receiver operating characteristic. An advantage of the proposed protocol is its flexibility, as it can be easily adapted to train a plethora of different ML algorithms with adjustable combinations of an unlimited number of features. Moreover, it can be used to analyze different problems in oncology, infection, and inflammation.

This protocol was designed to train a machine learning algorithm to use a combination of imaging parameters derived from magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) in a rat model of breast cancer bone metastases to detect early metastatic disease and predict subsequent progression to macrometastases.

Machine learning (ML) algorithms permit the integration of different features into a model to perform classification or regression tasks with an accuracy ...

A Rapid Screening Workflow to Identify Potential Combination Therapy for GBM using Patient-Derived Glioma Stem Cells

The glioma stem cells (GSCs) are a small fraction of cancer cells which play essential roles in tumor initiation, angiogenesis, and drug resistance in glioblastoma (GBM), the most prevalent and devastating primary brain tumor. The presence of GSCs makes the GBM very refractory to most of individual targeted agents, so high-throughput screening methods are required to identify potential effective combination therapeutics. The protocol describes a simple workflow to enable rapid screening for potential combination therapy with synergistic interaction. The general steps of this workflow consist of establishing luciferase-tagged GSCs, preparing matrigel coated plates, combination drug screening, analyzing, and validating the results.

The glioma stem cells (GSCs) are a small fraction of cancer cells which play essential roles in tumor initiation, angiogenesis, and drug resistance in ...

Automated Dissection Protocol for Tumor Enrichment in Low Tumor Content Tissues

Tumor enrichment in low tumor content tissues, those below 20% tumor content depending on the method, is required to generate quality data reproducibly with many downstream assays such as next generation sequencing. Automated tissue dissection is a new methodology that automates and improves tumor enrichment in these common, low tumor content tissues by decreasing the user-dependent imprecision of traditional macro-dissection and time, cost, and expertise limitations of laser capture microdissection by using digital image annotation overlay onto unstained slides. Here, digital hematoxylin and eosin (H&#38;E) annotations are used to target small tumor areas using a blade that is 250 &#181;m2 in diameter in unstained formalin fixed paraffin embedded (FFPE) or fresh frozen sections up to 20 &#181;m in thickness for automated tumor enrichment prior to nucleic acid extraction and whole exome sequencing (WES). Automated dissection can harvest annotated regions in low tumor content tissues from single or multiple sections for nucleic acid extraction. It also allows for capture of extensive pre- and post-harvest collection metrics while improving accuracy, reproducibility, and increasing throughput with utilization of fewer slides. The described protocol enables digital annotation with automated dissection on animal and/or human FFPE or fresh frozen tissues with low tumor content and could also be used for any region of interest enrichment to boost adequacy for downstream sequencing applications in clinical or research workflows.

Digital annotation with automated tissue dissection provides an innovative approach to enriching tumor in low tumor content cases and is adaptable to both paraffin and frozen tissue types. The described workflow improves accuracy, reproducibility and throughput and could be applied to both research and clinical settings.

Tumor enrichment in low tumor content tissues, those below 20% tumor content depending on the method, is required to generate quality data reproducibly ...