Mutated genes on ctDNA detecting postoperative recurrence presented reduced neoantigens in primary tumors in colorectal cancer cases

Enrolled patients and plasma samples

We used WES and RNA sequencing on ten primary tumors and ten postoperative metastatic tumors (the first one of metastases in each case) from ten cases of CRC from our previous study14 and established a cancer panel in the current study (Fig. S3). Therefore, we collected and examined 47 plasma samples from six cases of CRC: CRCR1, CRCR4, CRCR5, CRCR7, CRCR8, and CRCR9 (Table 1).

Ethics statement

The study design was approved by the institutional review boards and ethics committees of the hospitals to which the patients were admitted (the Kyushu University Hospital Institutional Review Board [protocol number 609-06] and Cancer Institute Hospital Institutional Review Board [protocol number 2010-1058]). This study was conducted in accordance with the principles of the Declaration of Helsinki. Written informed consent was obtained from all study participants.

Sample collection and preparation

Genomic DNA and RNA were extracted from freshly frozen tumor samples and adjacent normal intestinal mucosa using an AllPrep DNA/RNA Mini Kit (Qiagen, Hilden, Germany), according to the manufacturer’s instructions.

Establishment of the cancer panel

We focused on the fundamental dynamics of the ctDNA fraction during the clinical course of CRC. The genome sequences of ten primary tumors and ten metastatic tumors were extracted, and exome sequencing was conducted (Table 1). According to the manufacturer’s instructions, DNA was captured using a SureSelect Human All Exon 50 Mb kit (Agilent Technologies, Santa Clara, CA, USA). Captured DNA was sequenced using a HiSeq 2500 (Illumina K.K., Tokyo, Japan) with the paired-end 75–100-bp read option.

The commonly mutated gene of MAF in the primary site and the metastatic site was selected in each case for carrying on the customized cancer panel. In terms of establishing a cancer panel, we used ten primary sites and ten metastatic sites in our previous study (Table 1). We applied 451 mutated genes for the bespoke cancer panel (Fig. S3) established from commonly mutated genes between ten primary and ten metastatic sites. However, because of the inadequate amount of blood samples, we did not conduct a target sequence of plasma samples of CRCR2, CRCR3, CRCR 6, and CRCR10.

Next-generation sequencing library construction

Indexed Illumina next-generation sequencing (NGS) libraries were prepared from plasma DNA. Plasma DNA was used for library construction without additional fragmentation. Genomic DNA was sheared before library construction using a Covaris S2 instrument (Woburn, MA, USA) to obtain 200-bp fragments. According to the manufacturer’s protocol, NGS libraries of plasma DNA were constructed using the KAPA Hyper Prep Kit (Kapa Biosystems, Wilmington, MA, USA). A sequencing library was prepared using the KAPA Hyper Prep Kit (Kapa Biosystems) and SureSelect Target Enrichment System (Agilent Technologies). End repair and A-tailing reactions were performed in 60-µL reaction volumes. The mixtures were then incubated at 20 °C and 65 °C for 30 min each. Adapter ligation was performed using 110-µL volumes, and samples were incubated at 16 °C for 16 h using a SureSelect Adapter (Agilent Technologies). After postligation cleanup, the ligated fragments were amplified in a 50-µL solution containing 2 × KAPA HiFi HotStart ReadyMix and 10 × KAPA Library Amplification Primer Mix (Kapa Biosystems). We used the following cycling protocol: 98 °C for 45 s, 14–16 cycles (depending on the input DNA mass) of 98 °C for 15 s, 65 °C for 30 s, 72 °C for 30 s, and 72 °C for 5 min (1 cycle). Library purity, library concentration, and fragment length were determined using a 2100 Bioanalyzer (Agilent Technologies).

Targeted sequencing

Plasma DNA extracted from CRC patient samples was captured using a SureSelectXT Custom 1 Kb–499 kb, 16 (Agilent Technology) according to the manufacturer’s instructions. A panel of 451 genes was designed and validated in this study. Captured DNA was sequenced using a HiSeq2000 (Illumina K.K.) to generate paired-end (75–100 bp) reads for each sample. Targeted deep sequencing was performed for all samples using a multigene panel, with a mean sequencing depth of 3810×.

Mutation calling

We used WES data from our previous study14. The sequence data were processed using an in-house pipeline (genomon-project.github.io/GenomonPagesR/). The sequencing reads were aligned to the National Center for Biotechnology Information Human Reference Genome Build 37 hg19 with BWA version 0.7.8 using the default parameters. Polymerase chain reaction duplicates were removed using the Picard method. Mutation calling was performed using the EBCall algorithm23 with the following parameters: (1) mapping quality score ≥ 20; (2) base quality score ≥ 15; (3) both the tumor and normal depths ≥ 10; (4) variant reads in tumors ≥ 4; (5) variant allele frequencies (VAFs) in tumor samples ≥ 0.02; and (6) VAFs in normal samples ≤ 0.01.

RNA sequencing

We used RNA sequencing data from our previous study14; however, we applied RNA seq data from six primary sites and six metastatic sites (black boxes in Table 1). Approximately three billion single-end reads were generated using an Illumina HiSeq 2500 system, as previously described24.

Data availability statement

Data are available at: humandbs.biosciencedbc.jp/en/hum0120-v4#target2. Our sequence data are available as NBDC Research ID; hum0120.v4. In terms of mutated ctDNA, we can obtain target sequence data of ctDNA (JGAS000549). In addition, whole exome sequences of 10 primary sites and metastatic sites (9 liver tumors and 5 lung tumors) were available at: Tumor tissues (DRA011183) and non-tumor tissue non-tumor tissues (JGAD000311).

HLA genotyping (Hayashi method)

For HLA genotyping from whole-genome sequencing data, the Bayesian ALPHLARD method was used, which was designed to perform accurate HLA genotyping from short-read data and predict the HLA sequences of the sample. The latter function enables the identification of somatic mutations by comparing the HLA sequences of the tumor and matched normal samples. The statistical formulation for the posterior probability can be described as follows:

$$ {\text{P }}\left( {{\text{R, S, I}}|{\text{ X}}} \right)\, \propto \,{\text{P }}\left( {{\text{X }}|{\text{ S, I}}} \right){\text{ P }}\left( {\text{I}} \right){\text{ P}}({\text{R, S}}) $$

where R = (R1, R2) is the pair of HLA types (reference sequences), S = (S1, S2) is the pair of sample HLA sequences, X = (× 1, × 2,…) is a set of sequence reads, and I = (I1, I2,…) is a set of variables using one or two values (jth element; Ij, indicating that the jth read xj is generated from SI j). On the right-hand side of the equation, the left term indicates the likelihood of the sequence reads when the HLA and reference sequences are fixed. The middle and right times are the priors. The parameters, HLA sequences, and HLA types were determined using the Markov Chain Monte Carlo procedure.

Prediction of potential N-acetylglucosamine peptides

Using the Neoantimon package in R, the HLA types of individual patients were obtained (Fig. S3). To identify potential N-acetylglucosamine (NAG) peptides, we used a nonrelapse-based automated pipeline, available at github.com/hase62/Neoantimon. Using WES data, this pipeline can easily and automatically construct mutated, and wild-type peptides, including the mutation position, calculation of binding affinity to MHC molecules (using netMHCpan4.0), and integration of the total and tumor-specific RNA expression data based on VAFs calculated from RNA sequence data at the mutation position.

Institutional review board statement

The study design was approved by the institutional review boards and ethics committees of the hospitals to which the patients were admitted (the Kyushu University Hospital Institutional Review Board [protocol number 609-06] and Cancer Institute Hospital Institutional Review Board [protocol number 2010-1058]). This study was conducted in accordance with the principles of the Declaration of Helsinki.

Informed consent statement

Written informed consent was obtained from all study participants.

Statistical analyses

We used the Mann–Whitney U test or Fisher’s exact tests to test the associations between variables. Data analyses were performed using JMP 14 (SAS Institute, Cary, NC, USA) and R software version 3·1·1 (R Foundation for Statistical Computing, Vienna, Austria).

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