Detection of mutational patterns in cell‐free DNA of colorectal cancer by custom amplicon sequencing

Abstract

Monitoring the mutational patterns of solid tumors during cancer therapy is a major challenge in oncology. Analysis of mutations in cell‐free (cf) DNA offers a noninvasive approach to detect mutations that may be prognostic for disease survival or predictive for primary or secondary drug resistance. A main challenge for the application of cfDNA as a diagnostic tool is the diverse mutational landscape of cancer. Here, we developed a flexible end‐to‐end experimental and bioinformatic workflow to analyze mutations in cfDNA using custom amplicon sequencing. Our approach relies on open‐software tools to select primers suitable for multiplex PCR using minimal cfDNA as input. In addition, we developed a robust linear model to identify specific genetic alterations from sequencing data of cfDNA. We used our workflow to design a custom amplicon panel suitable for detection of hotspot mutations relevant for colorectal cancer and analyzed mutations in serial cfDNA samples from a pilot cohort of 34 patients with advanced colorectal cancer. Using our method, we could detect recurrent and patient‐specific mutational patterns in the majority of patients. Furthermore, we show that dynamic changes of mutant allele frequencies in cfDNA correlate well with disease progression. Finally, we demonstrate that sequencing of cfDNA can reveal mechanisms of resistance to anti‐Epidermal Growth Factor Receptor(EGFR) antibody treatment. Thus, our approach offers a simple and highly customizable method to explore genetic alterations in cfDNA.

Abbreviations

• CEA
• carcinoembryonic antigen
• cfDNA
• cell‐free DNA
• CRC
• colorectal cancer
• ctDNA
• circulating tumor DNA
• EGFR
• Epidermal Growth Factor Receptor
• LDH
• lactate dehydrogenase
• UMI
• unique molecular identifiers
• VEGF
• Vascular Endothelial Growth Factor

Introduction

Although survival rates have been increasing over the last decades, the prognosis of patients with advanced stages of gastrointestinal cancer is persistently poor (Jemal et al., ). Major challenges include tumor heterogeneity and the clonal expansion of resistant clones during the course of treatment, resulting in cancer progression or relapse (Alizadeh et al., ). Monitoring the dynamics of cancer evolution during the course of a treatment may help to early identify the development of drug resistance. However, in patients with advanced disease, obtaining serial tumor biopsies to study tissue‐based biomarkers is not feasible in the majority of cases. Most biopsy techniques bear risks due to their invasiveness and are not suitable for morbid patients (Vanderlaan et al., ). Besides, a tissue biopsy only represents a small section of a heterogenous and disseminated disease, ignoring cancer biology at other (metastatic) sites. Hence, it may result in sampling errors due to branched evolution of metastatic sites and intratumoral heterogeneity (Burrell et al., ; Gerlinger et al., ).

It is known for decades that solid tumors can release single tumor cells (Alix‐Panabières and Pantel, ; Maheswaran and Haber, ) or nucleic acids into the bloodstream, including DNA, mRNA, and miRNA (Schwarzenbach et al., ; Stroun et al., ). Advances in DNA sequencing opened up opportunities to analyze the genetic composition of this circulating tumor DNA (ctDNA) (Wan et al., ). In fact, large‐scale sequencing projects of ctDNA showed that genetic alterations present in primary tumors can also be found in ctDNA (Strickler et al., ; Zill et al., ). Furthermore, serial ctDNA analysis proves to be a powerful method to longitudinally monitor therapy response and the emergence of resistant clones (Bettegowda et al., ; Siravegna et al., ; Thierry et al., ). Several methods have been developed to detect mutations in ctDNA, including BEAMing technologies (Diaz et al., ; Haselmann et al., ; Janku et al., ), panel sequencing of cancer‐associated genes (Strickler et al., ; Zill et al., ), or targeted amplicon sequencing (Lebofsky et al., ; Tie et al., ). While every method offers advantages, a major challenge to all technologies is the divergent mutational landscape of cancer. While some mutations in key oncogenes occur at high frequency across many tumors, most genetic alterations are tissue‐specific or even highly variable between cancers of the same tissue origin (Lanman et al., ). Current FDA‐approved diagnostic tests for cell‐free DNA (cfDNA) cover mutational hotspots of large predefined panels of frequently altered genes in cancer (Lanman et al., ). While this approach is suitable for application in a broad spectrum of cancers, it is not designed to detect potentially relevant mutations that occur only in specific tumors, such as RNF43 in colorectal (CRC) and pancreatic cancer (Steinhart et al., ). Thus, complementary tests are required that can be used to explore a custom‐defined set of genetic alterations in cfDNA that are not covered by commercially available tests. These tests are valuable to correlate the occurrence of rare mutations with disease outcome or drug resistance.

Here, we present an end‐to‐end experimental and bioinformatic workflow that can be used to analyze a custom set of genetic alterations in cfDNA. We designed an amplicon panel that is tailored to the genetic landscape of CRC, covering mutations in oncogenes and tumor suppressors as well as functionally relevant mutations in genes that are less frequently altered. We performed amplicon sequencing with our custom panel in serial cfDNA samples from a pilot cohort of 34 patients with metastatic CRC. Using this approach, we detected patient‐specific mutational patterns that reoccurred in serial samples. We also show that the mutant allele frequencies of genetic alterations in cfDNA increase with disease progression.

Materials and methods

Study design and clinical databank

Patients with gastrointestinal cancers across all tumor stages were included in the study. Blood was collected before start of anticancer treatment and every 3–6 months during radiologic restaging or if patients were hospitalized. Simultaneously, the following blood markers were determined: cell counts of erythrocytes, leukocytes, and thrombocytes; AST; ALT; GGT; ALP; bilirubin; albumin; creatinine; INR; LDH; CRP; and the serum tumor markers CEA, CA19‐9, and AFP. Clinical characteristics were collected in a prospective database.

Written informed consent was obtained from all patients, and the study was approved by the local ethics board (Ethikkommission II, Medical Faculty Mannheim, Heidelberg University, identifier 2013‐640N‐MA). The study design is in accordance with the standards proposed by the Declaration of Helsinki.

Sample collection and plasma DNA extraction

Two 10‐mL K₃EDTA tubes were taken during regular blood sampling and before administration of chemotherapy. Blood samples were immediately stored at 4 °C and processed within 16 h to minimize release of genomic DNA from nontumorous sources (Norton et al., ; Risberg et al., ; Wong et al., ). For isolation of blood plasma, K₃EDTA tubes were centrifuged at 1900 g for 10 min at 4 °C. Afterwards, supernatants were centrifuged at 16 000 g for 10 min at 4 °C to remove remaining blood cells. Plasma was harvested without mobilizing the cell pellet and stored at −80 °C until needed. For cfDNA isolation, 1–4 mL of frozen plasma was used. Extraction of DNA was performed using the QIAamp DNA Blood Mini Kit (Qiagen, Hilden, Germany). DNA was dissolved in 50–70 μL nuclease‐free water and stored at −20 °C for further use. The amount of DNA was quantified with qubit 2.0 (Life Technologies, Carlsbad, CA, USA) using the high‐sensitivity assay according to the manufacturer's protocol.

Design of amplicon primers and multiplex reaction

Positions of frequently mutated hotspots in CRC‐associated genes were obtained from the COSMIC database (Forbes et al., ). In addition, frequently mutated loci in genes that regulate the metabolism of 5‐fluorouracil (DPYD, TYMS, TYMP, UPP1) and mismatch repair (MLH1, MLH3, MSH6, PMS2) were selected. Nucleotide sequences surrounding the genetic alteration of interest were obtained from the NCBI database with the genome assembly GRCH38 as a reference. Specific primers for regions that cover the selected mutations were designed with Primer3 (Untergasser et al., ) with the following settings: primer length of 19–27 bp, GC ratio of 30–63%, and melting temperature of 55–61.5 °C. The universal adapter sequence TCCCTACACGACGCTCTTCCGATCT was added to the 5′ end of each forward primer. To each reverse primer, the sequence AGTTCAGACGTGTGCTCTTCCGATC was added to the 5′ end. The amplicon length varied between 100 and 175 bp. Characteristics and sequences of all primers can be found in Table S1. To develop a multiplex PCR assay, we used the software tool multiplx 2.1 (Kaplinski and Remm, ) to identify appropriate primer combinations. To this end, we set the concentration of monovalent salts to 50 mm and the concentration of Mg²⁺ to 1.5 mm. All five possible primer interaction scores that might affect primer compatibility were calculated. To group the 43 primer pairs for multiplex PCR, we used the stringency value ‘normal’. The best combination resulted in six different pools with 5–9 primer pairs.

Nested PCR protocol and library preparation

We developed a custom, nested PCR protocol suited for multiplex reactions. For the first PCR step, at least 500 pg of cfDNA was used per multiplexed reaction. In all cases, we used Q5 Hot Start Polymerase (NEB, Ipswich, MA, USA) to minimize amplification errors. To reduce the formation of primer‐dimers, we selected a concentration of 40 nm for each primer pair. The PCR protocol was adapted from a previous study (Ståhlberg et al., ). In brief, after an initial denaturation step (3 min, 98 °C), 18 PCR cycles were run with the following settings: 10 s of denaturation at 98 °C, 6 min of annealing at 62 °C, and 30 s of elongation at 72 °C. In the end, a 10‐min elongation step at 72 °C was included. The PCR products were semi‐automatically purified with a Biomek FXP (Beckman Coulter) using AMPure XP beads (Beckman Coulter, Munich, Germany) with a bead‐to‐sample ratio of 0.76 and eluted in 25 μL of nuclease‐free water. By this step, fragments larger than 120 bp were separated from primer‐dimers. In the second PCR step, the same settings were applied except for the following modifications: First, we used 15 μL of DNA products from the first PCR step as input. Second, primers consisting of Illumina universal adapter sequence and a unique combination of the TruSeq DNA HT indexes (forward primer: AATGATACGGCGACCACCGAGATCTACAC‐(Index)‐ACACTCTTTCCCTACACGACGCTCTTCCGATCT; reverse primer: CAAGCAGAAGACGGCATACGAGAT‐(Index)‐GTGACTGGAGTTCAGACGTGTGCTCTTCCGATC) were used at a concentration of 300 nm. Third, the annealing temperature was increased to 72 °C and the annealing time was reduced to 15 s. After the second PCR step, all six multiplexed PCRs of each patient sample were pooled. Ninety micro litre of the mixed PCR products was then cleaned with AMPure XP beads using a bead‐to‐sample ratio of 0.67 and eluted in 20 μL of nuclease‐free water.

Amplicon sequencing

All patient samples were pooled to a single library with a final concentration of 4 nm. The library was sequenced on an Illumina MiSeq using the MiSeq Reagent Kit V2 (Illumina Inc., San Diego, CA, USA) (150 bp single‐end sequencing) with 5% PhiX as spike‐in. Between 30 and 50 patient samples were sequenced in one MiSeq run.

Analysis of sequencing data and variant calling

First, a quality report was generated for each sequenced sample using ‘fastqc’ (Andrew, ). Quality reports were examined manually to ensure sufficient sample quality. Subsequently, the ‘trimmomatic’ software version 0.36 (Bolger et al., ) was used to remove sequenced base pairs with a quality score of < 15 (phred33) at both ends of the reads to avoid false‐positive mutations due to sequencing errors. In addition, a sliding window trimming was performed cutting the read once the average quality within a window of size 4 bp was detected to be < 15. Reads with a length of < 70 base pairs were removed. Next, reads were mapped to the human genome GRCh38 using the ‘bwa mem’ algorithm of the ‘bwa’ alignment software version 0.7.15‐r1140 (Li and Durbin, ) with default parameters. ‘samtools’ version 1.4 (Li et al., ) was used to remove reads that could not be mapped to genome. In addition, reads mapped with a quality of < 13 were excluded. To determine variants, the ‘mpileup’ algorithm implemented in the ‘samtools’ software was applied. The resulting variant calls were summarized and quantified using ‘bam‐readcount’ (https://github.com/genome/bam-readcount), counting only variants at positions with a sequencing quality of at least 30.

Analysis of sequencing variants

The allele frequency was determined for each detected variant by dividing the number of reads containing the variant by the total number of reads mapping to that position. Subsequently, variants were mapped to the amplicon panel. To this end, the genomic coordinates were determined for each amplicon using the ‘BLASTn’ algorithm as implemented in the ‘blast+’ software package (Altschul et al., ). Here, each amplicon sequence was mapped to the human genome GRCh38 requiring 100% sequence identity. Sequencing variants were then matched to the custom amplicon panel by genomic coordinates. Correctness of the variant mapping and matching was assessed by taking advantage of prior knowledge about mutations in the NRAS, KRAS, and BRAF oncogenes that had previously been determined using Sanger sequencing. In order to distinguish true mutations from false‐positive mutations introduced by, for example, PCR or sequencing errors, a model‐based approach was applied. Assuming that (a) the majority of variants are caused by PCR or sequencing errors, that (b) PCR errors do on average occur at the same frequency at each position of an amplicon, and that (c) sequencing errors at a specific genomic position are equally likely in each sample. A robust linear model of the form$$log\left(\frac{a}{1-a}\right)={\mathrm{\beta }}_0+{\mathrm{\beta }}_1{X}_1+{\mathrm{\beta }}_2{X}_2+\mathrm{ϵ},$$was fit for each amplicon, where a is the allele frequency of a variant, X₁ is the variant's position on the amplicon, and X₂ represents the sequenced patient sample. The resulting fit represents a noise model. A variant was considered a true mutation if its model residual exceeded the median of all residuals by at least three standard deviations, indicating that its presence cannot be explained by the estimated noise present in the data. In addition, we required a minimum allele frequency of at least 0.5% for a variant to be considered a true clinically relevant mutation. Next, the remaining mutations were annotated using the COSMIC database of somatic mutations in cancer (Forbes et al., ). Mutations that were not listed in COSMIC were excluded from further analysis. In addition, COSMIC mutations marked as SNPs were excluded.

Statistics

All P‐values reported in this study were computed using a two‐sample Wilcoxon rank sum test as implemented in the r statistical programming language (R Core Team, 2016). As metrics for the relationships between quantitative variables, both the parametric Pearson correlation coefficient and the rank‐based Spearman correlation coefficient are reported in all cases.

Data and software availability

Documented computer code to reproduce analyses presented in this study is available from GitHub at https://github.com/boutroslab/Supplemental-Material/tree/master/Herrmann%26Zhan%26Betge%26Rauscher_2018.

Results

cfDNA levels correlate with clinical parameters in CRC patients

To study the value of cfDNA as a biomarker in gastrointestinal cancers, we prospectively collected serial blood samples from cancer patients at a tertiary university hospital in Germany (Mannheim Liquid Biopsy Unit—MALIBU, University Hospital Mannheim, Heidelberg University) (Fig. A). For patients undergoing palliative chemotherapy, we obtained blood samples prior to start of treatment and in parallel to each radiologic assessment of therapy response. In all cases, blood sampling was performed before administration of anticancer drugs. For patients undergoing surgical removal of the cancer, we collected blood samples prior and at multiple time points after resection. In parallel, we documented relevant clinical parameters and blood markers in a prospective database. Plasma from each blood sample was isolated within 16 h after sampling and stored at −80 °C until further use. Most samples stored in the biobank were derived from patients with CRC. In total, we collected blood samples from 104 CRC patients across all UICC stages, but predominantly from patients with metastatic CRC (Fig. B). To correlate levels of cfDNA with clinical parameters, we measured cfDNA concentrations from blood samples of all CRC patients. Comparison of cfDNA levels demonstrated significantly elevated levels in patients with metastatic compared to nonmetastatic tumors (Fig. C). In contrast, no significant difference was found between cfDNA levels of patients with UICC stage I to III cancers. Next, we analyzed the correlation of cfDNA levels with two biomarkers commonly used in gastrointestinal oncology, carcinoembryonic antigen (CEA), and lactate dehydrogenase (LDH) (Fig. D,E). We observed a weak, but significant (P = 0.005; Pearson's correlation test) correlation of cfDNA concentration with CEA levels. In contrast, no correlation was found with LDH levels. This finding suggests that the cfDNA concentration may be a biological marker that occurs independently of tumor cell necrosis.

A pipeline for the design and analysis of custom amplicon panels for cfDNA

To analyze mutational patterns in cfDNA, we developed a pipeline for the design of custom amplicon panels. We selected primers that cover mutational hotspots in oncogenes and tumor suppressors of CRC based on the COSMIC database (Forbes et al., ). In addition, we included primers that amplify frequently mutated loci in genes that affect metabolism of 5‐fluorouracil (Jennings et al., ; Ooyama et al., ; Pullarkat et al., ) or DNA mismatch repair (Li, ). These mutations occur at a lower frequency and are not commonly covered by commercial amplicon panels. An overview of all genes can be found in Fig. A. We then designed primer pairs that bind 50–75 bp up‐ and downstream of the mutations of interest and calculated optimal multiplexes (see Section 2 for details). Our custom panel contained a total of 43 primer pairs, which were distributed to six multiplex reactions containing 5–9 primer pairs each (see Table S1 for primer sequences and associated mutations). We tested different amounts of cfDNA as input and found that 500 pg per multiplex reaction was sufficient for our nested PCR protocol (see overview in Fig. B and Section 2). The purified and pooled amplicons were sequenced on an Illumina MiSeq by 150 bp single‐end sequencing. In parallel, we developed a custom bioinformatic pipeline for the analysis of sequencing results (Fig. A). This pipeline comprises four consecutive steps. First, in the quality control step, reads with low sequencing quality are discarded. In the second step, all nucleotide variants across all reads of the same amplicon are detected and quantified. To discriminate PCR or sequencing artifacts from true genetic variants, we developed a robust linear regression model that we applied to each individual amplicon. Alterations identified as true variants by the model were then matched to the COSMIC database and discarded if not covered. By this bioinformatic approach, we assured that only highly confident alterations previously found in cancer tissues are reported.

Functional validation of the amplicon sequencing assay

We tested our amplicon panel in genomic DNA of cancer tissue and cell lines, as well as cfDNA of patients with CRC. First, we evaluated the performance of individual amplicons with our panel. Mapping of sequencing reads to the amplicons showed a continuous distribution of sequencing depth between amplicons (median 7576, minimum 503, maximum 20 809) (Fig. B). We found that the number of mutations identified varied between the different samples (Fig. C). Also, we noticed a correlation between number of sequenced reads and number of mutations per amplicon (Fig. D). Next, we tested the sensitivity of our method to detect specific alterations. To this end, we mixed sheared genomic DNA (fragment size 200–300 bp) from two CRC cell lines (HT29, HCT116) in different ratios. Both cell lines have distinct, monoallelic genetic alterations that can be detected with our amplicon panel. As shown in Fig. S1A, we could reliably identify mutant alleles of HCT116 at a concentration of 1.25% in the total genomic DNA mix, which corresponds to a mutant allele frequency of approximately 0.6%.

We then used our amplicon panel on genomic DNA from formalin‐fixed paraffin‐embedded primary CRC tissues (Fig. S1B). We could detect all RAS mutations previously found by Sanger sequencing, except for two KRAS mutations in Exon 4, which were not covered by the panel. Finally, we tested the reproducibility of our experimental and analysis pipeline by performing amplicon sequencing on independent biological replicates of 19 cfDNA samples, starting from cfDNA isolation from plasma. We show that mutations that occurred at an allele frequency > 0.5% could be detected as indicated by a strong correlation between mutant allele frequencies of the same genetic alterations from independent replicates (Fig. E). In summary, our quality control experiments indicate that our custom amplicon sequencing method is functional in both genomic and cfDNA. Furthermore, we identified thresholds for mutant allele frequencies that enable the detection of genetic alterations with high confidence.

Mutational patterns in cfDNA are patient‐specific and highly recurrent

Next, we used our custom amplicon panel to analyze mutational patterns in a cohort of 34 patients with advanced CRC, for which serial plasma samples were available. The patients received a chemotherapy backbone consisting of 5‐fluorouracil with or without oxaliplatin or irinotecan. In addition, most patients also received treatment with antibodies targeting Vascular Endothelial Growth Factor (VEGF) or Epidermal Growth Factor Receptor (EGFR). Detailed patient characteristics can be found in Table . Within the cohort, 14 patients had a radiologic stable disease or partial remission while the others had disease progression between two consecutive blood sampling time points. Amplicon sequencing of cfDNA demonstrated that 2.41 alterations could be detected on average per sample (Fig. A). These alterations were either recurrent (present in serial samples) or sporadic. Alterations occurred at an allele frequency between 0.5% and 96.7% (median 2.25%). Most mutations were found in amplicons of TP53. Overall, we observed a trend toward a higher number of alterations in patients with disease progression (Fig. B). Also, we found that mutant allele frequencies were significantly higher (P < 0.001) in patients with progressive disease compared to those with stable disease or radiologic therapy response (Fig. C), consistent with previous findings (Forshew et al., ; Gray et al., ; Newman et al., ). Using APC as an example case, we observed that the distribution of mutations in cfDNA is very similar to the mutational spectrum found in primary CRC tissue (Fig. S2) (Forbes et al., ).

Patient, treatment, and tumor baseline characteristics.a. 5‐FU, 5‐fluorouracil or capecitabine; FOLFOX, 5‐FU + oxaliplatin; CAPOX, capecitabine + oxaliplatin; FOLFIRI, 5‐FU + irinotecan; FOLFIRINOX, 5‐FU + oxaliplatin + irinotecan

Parameter	All CRC, n = 104	mCRC cohort, n = 34
Gender
Male	74 (71%)	25 (74%)
Female	30 (29%)	9 (26%)
Age (years)	65 (34–88)	65 (36–88)
Stage
I	9 (9%)
II	10 (10%)
III	25 (24%)	3 (9%)b
IV	60 (58%)	31 (91%)
Tumor location
Colon cancer	45 (43%)	15 (44%)
Rectal cancer	58 (56%)	19 (56%)
Both entities	1 (1%)
Therapy setting
Surveillance	24 (23%)
Adjuvant/neoadjuvant	21 (20%)
Palliative	59 (57%)	34 (100%)
Metastases (only stage IV)
Hepatic	47 (78%)	24 (71%)
Pulmonary	28 (47%)	18 (53%)
Peritoneal carcinomatosis	14 (23%)	4 (12%)
Pleural carcinomatosis	5 (8%)	2 (6%)
Osseous	4 (7%)	1 (3%)
Other	5 (8%)	0 (0%)
Therapy regimen
5‐FU	17 (16%)	6 (18%)
FOLFOX/CAPOX	18 (17%)	4 (12%)
FOLFIRI	37 (36%)	22 (65%)
FOLFOXIRI	3 (3%)	2 (6%)
Irinotecan	2 (2%)	0 (0%)
No chemotherapy	25 (24%)	0 (0%)
With anti‐VEGF‐antibody	25 (24%)	16 (47%)
With anti‐EGFR‐antibody	27 (26%)	13 (38%)

^a n is shown for categorical variables with percentage in parentheses. For continuous variables, median is shown with range in parentheses. ^b These three patients all had an inoperable relapse of their primaries.

For a small group of patients, we were able to obtain up to four consecutive blood samples during the course of the therapy, corresponding to a treatment period of approximately 1 year. For all patients in this cohort, we detected both recurrent and sporadic mutations (Fig. D). Recurrent mutations were more frequent than sporadic mutations and showed a higher mutant allele frequency (Fig. E). Furthermore, we found the pattern of recurrent mutations to be distinct for each patient and conserved throughout multiple blood samples.

Liquid biopsy allows monitoring the clinical course of CRC patients and reveals resistance mechanisms

Next, we aimed to analyze how the spectrum of mutations found in serial cfDNA samples corresponds to the course of treatment in CRC patients. To this end, we correlated the clinical course with the mutant allele frequency of specific alterations in cfDNA (Fig. ). All four presented cases were initially treated with 5‐fluorouracil, folinic acid, irinotecan (FOLFIRI), and cetuximab and were in stable disease or had tumor remissions by the time of the first liquid biopsy. Patient 119 had achieved tumor remission and therefore received maintenance treatment with 5‐FU and cetuximab. Two novel mutations occurred in the cfDNA upon radiologic disease progression. The allele frequencies of those mutations closely matched the radiologic extent of disease during the following course of treatment, as did the CEA levels (Fig. A). Accordingly, in patients 324 (Fig. B), 24 (Fig. C), and 553 (Fig. D), allele frequencies increased upon tumor progression, while regression or stable disease was associated with stable or decreasing allele frequencies. Interestingly, liquid biopsies revealed an NRAS mutation (patient 324) and a BRAF mutation (patient 553) not identified by Sanger sequencing in the patients’ primary tumors. CEA levels were not congruent with radiologic disease progression and allele frequencies of mutations in cfDNA in patient 553. Hence, in all presented cases, we observed that an increase in allele frequency is closely associated with radiologic progression of the disease. Furthermore, and in contrast to the traditional tumor marker CEA, our amplicon sequencing assay for cfDNA analysis could also reveal biological insights into the mechanisms of therapy resistance to targeted therapies.

Discussion

In the present study, we describe a flexible experimental and bioinformatic method to analyze custom‐defined gene loci in cfDNA by multiplexed amplicon sequencing. We applied our method on serial cfDNA samples from a pilot cohort of 34 CRC patients to detect recurrent and patient‐specific mutational patterns in key cancer genes.

Over the past decade, significant progress has been made in the analysis of cfDNA from cancer patients. Two recent studies have comprehensively dissected the mutational landscape of cfDNA in large cohorts of patients with CRC (Strickler et al., ) and other advanced tumors (Zill et al., ). Both studies demonstrate that the composition of mutations is highly similar between cfDNA and the corresponding primary tumors. Therefore, detection and analysis of cfDNA have been proposed as a biomarker to monitor tumor progression, treatment response, and disease recurrence (Bettegowda et al., ; Newman et al., ; Tie et al., ). Until now, many different approaches have been developed to characterize cfDNA, including the detection of specific mutations (Haselmann et al., ) or the sequencing of a broad panel of genes (Goodall et al., ; Quigley et al., ). Technologies based on both approaches have been approved by the FDA, that is Guardant360 or cobas EGFR Mutation Test V2. A major limitation to their broad clinical application is the diversity of the genetic landscape of cancers from different tissues. Particularly, mutations that occur only in selected cancer types and at medium frequencies can represent potentially druggable conditions, but are not covered by commercial gene panels. Examples include mutations in RNF43 (Steinhart et al., ) or PMS2 (Khagi et al., ), which could mark clonal subtypes that may make the tumor responsive to specific agents, such as checkpoint inhibitors. Hence, a main challenge is to customize the analysis of cfDNA to specific tumor types or defined sets of mutations for exploratory analysis. In this regard, our study provides a methodological framework that enables the design and bioinformatic analysis of custom amplicon panels using open‐source software tools. We used our method to design a gene panel tailored for colorectal cancer, including amplicons that cover hotspot mutation sites in oncogenes and tumor suppressor genes, as well as rare mutations in genes relevant for drug metabolism.

We used our custom amplicon panel to analyze sequential cfDNA samples of a pilot cohort of patients with advanced CRC. In accordance with previous studies, we found that most genetic alterations could be found in genes that are known to be highly mutated, such as APC or TP53. Furthermore, we demonstrate that alterations in specific genes, such as BRAF or KRAS (Diaz et al., ), occur de novo in cfDNA during treatment failure, thereby revealing genetic alterations that can potentially mediate drug resistance. In addition, our data show that the mutant allele frequency correlates with disease progression. Interestingly, we observed that the occurrence of individual mutations remained stable across the course of the therapy. These distinct mutations could be part of a stem mutational signature which is conserved across cfDNA, primary tumors, and metastasis and has been described for a case of breast cancer (Murtaza et al., ). Our results indicate that such a signature can be found in many patients with CRC. This application might be of particular interest for patients who do not have elevated tumor markers in spite of a high disease burden, which in case of CRC accounts for 30% of patients (Liu et al., ). However, we are aware that our amplicon sequencing‐based method works best in gastrointestinal cancers that have a limited set of frequently mutated genetic loci, as it is the case for CRC or pancreatic adenocarcinoma. For these cancer types, compact amplicon panels can be designed that cover most hotspot positions. For other cancers, such as gastric or hepatocellular cancer, the mutational load is more equally distributed to many loci, which requires the design of larger amplicon panels for sufficient coverage. This will, however, lead to an increase in cost and technical complexity. An additional goal of our custom amplicon panel was the detection of genetic alterations in genes that are not commonly covered by commercial panels. These include genes involved in 5‐fluorouracil metabolism or regulators of DNA repair. Analysis of cfDNA in our cohort shows that mutations in these genes are rarely found in cfDNA. However, we could detect the de novo occurrence of a mutation leading to a premature stop codon in PMS2 and novel mutations in TYMP and DYPD. The functional relevance of these mutations is currently unknown. Therefore, a major challenge for the future is the functional annotation of these rare mutations, in order to understand their impact on tumor biology and drug resistance. These insights will enable the profound interpretation of sequencing results from cfDNA in cancer patients.

Although our method enables the flexible design of custom amplicon panels, it still has several limitations. First, calculation of mutant allele frequencies is biased by the presence of genomic DNA from nontumorous cells, which can be increased by unwanted cell lysis. To minimize the extent of contamination by genomic DNA, we selected storage and sample processing conditions that prevent cell lysis, based on findings from other studies (Norton et al., ; Risberg et al., ; Wong et al., ). However, since the time to sample processing varied between 1 and 16 h in our cohort, we are unable to exclude that the measured cfDNA concentrations might be slightly biased by the different storage times. Furthermore, since amplicon sequencing is based on pre‐amplification by PCR, results can be influenced by PCR artifacts and sequencing errors. An effective method to reduce these technical errors is the use of unique molecular identifiers (UMI). For the analysis of cfDNA, methods have been developed to introduce UMI during first PCR amplification cycles by using barcoded primers. These approaches, for instance Safe‐SeqS (Kinde et al., ) or SiMSen‐Seq (Ståhlberg et al., ), have been shown to suppress PCR errors and enable more precise counting of mutant allele copies. A drawback of using UMIs is that a vastly higher sequencing depth is required to discriminate between different UMI families, which will increase the costs of the method. However, as sequencing costs will further decrease in the near future, the integration of UMIs in our approach will be feasible. Another limitation of our methods is that it is currently unable to detect copy number alterations or gene fusions, which can be analyzed by other methods (Strickler et al., ; Zill et al., ) and provide valuable insights into tumor biology (Stover et al., ; Weiss et al., ). Lastly, the prediction of primer pairs that will function in multiplex assays is limited with the currently available bioinformatic tools and we had to discard several primer pairs because they did not yield a functional amplicon. In another case, we were unable to differentiate between mutations and PCR errors due to a poly C region within the MSH6 amplicon region. Therefore, in‐depth testing and experimental validation of primer pairs will be necessary when establishing novel custom amplicon panels. In spite of these limitations, we could show by multiple quality control experiments that our method can reliably detect cancer mutations in cfDNA.

Conclusions

In summary, we describe a flexible method to analyze mutations in cfDNA by custom amplicon panels. We show that this method can be used to detect recurrent mutational patterns as well as de novo mutations in cfDNA in a pilot cohort of patients with metastatic CRC, which enables the monitoring of therapy response and development of drug resistance. Finally, we show that our method can be applied to explore novel mutations at genetic loci that are not covered by commercial amplicon panels. The integration of UMI for error suppression and the validation of the method in a larger, clinically defined cohort of cancer patients will be necessary to further improve our workflow.

Acknowledgements

We thank Katharina Abkai, Thilo Miersch, Svenja Leible, and Sabine Oettinger for technical assistance. We thank the physicians of the Department of Medicine II, Mannheim University Hospital, for their assistance in sample collection. B.R. was supported by the BMBF‐funded Heidelberg Center for Human Bioinformatics (HD‐HuB) within the German Network for Bioinformatics Infrastructure (de.NBI). The work was partly funded by the ‘Stiftungen und Preise’ fund of the Medical Faculties of the University of Heidelberg.

Conflict of interest

The authors declare no conflict of interest.

Data accessibility

All sequencing data generated for this study are available from the European Genome‐phenome Archive under the accession number EGAS00001003382. Documented computer code to reproduce the figures of this manuscript is available from GitHub at https://github.com/boutroslab/Supplemental-Material/tree/master/Herrmann%26Zhan%26Betge%26Rauscher_2018.

Author contributions

SH, TZ, and JB established methods and carried out the experiments. SH, TZ, JB, SB, TG, NS, RJ, NH, TG, and R‐DH collected samples and clinical data. BR performed bioinformatic analysis. MPE and MB provided funding and supervised the project. SH, TZ, JB, and MB wrote the manuscript text.