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

Mutated genes on ctDNA detecting postoperative recurrence presented reduced neoantigens in primary tumors in colorectal cancer cases | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Mutated genes on ctDNA detecting postoperative recurrence presented reduced neoantigens in primary tumors in colorectal cancer cases Satoshi Nagayama, Yuta Kobayashi, Mitsuko Fukunaga, Shotaro Sakimura, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1819523/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Jan, 2023 Read the published version in Scientific Reports → Version 1 posted 9 You are reading this latest preprint version Abstract It is essential to comprehend the specific traits of mutated genes observed commonly not only at primary sites but recurrent sites. They were applied to be monitoring targets of circulating tumor (ct) DNA in liquid biopsy assay for the detection of postoperative recurrence. In the current retrospective study, we conducted target resequencing of ctDNA using 47 plasma samples and established a cancer panel carrying the commonly mutated genes between primary and recurrent tumors. We found that mutated genes in ctDNA indicated immune-resistance traits with respect to the impaired ability to present neoantigens by loss of expression or binding affinity to HLA in the primary tumor. Compared with the estimated neoantigens from all mutated genes in primary tumors, the neoantigen peptides from commonly mutated genes between primary and recurrent tumors showed abundant and significant expression with no binding affinity to HLA. Therefore, ctDNA mutations can be frequently and postoperatively detected to identify recurrence; however, these mutated genes were derived from immune-tolerated clones owing to the loss of neoantigen presentation in primary CRC tumors. HLA binding affinity tumor-specific mutated RNA recurrence minimum residual disease Figures Figure 1 Figure 2 Figure 3 Introduction The use of circulating tumor (ct)DNA as a liquid comprehensive genomic profile (CGP) assay is not inferior to CGP tissue analysis in gastrointestinal cancers 1, 2, 3, 4 . A clinical trial in Japan revealed better clinical outcomes in metastatic colorectal cancer (CRC) patients with actionable alterations than those of patients without alterations 5 . However, in terms of liquid biopsy using ctDNA, researchers have focused on the usefulness of sequencing mutated ctDNA in monitoring the minimum residual disease (MRD) in recurrent tumors postoperatively 6 . Considering the essential characteristics of the mutated clones that were chronologically detected from the primary site to recurrent site continuously, we assumed two possibilities. First, the mutated genes in ctDNA may be detected abundantly in tumors with high mutation allele frequency (MAF) or clonally expanded mutated genes that cover the entire primary tumor. These highly mutated or clonally mutated genes may be derived from the dominant cancer cells to promote cancer progression in primary tumors. In fact, we previously disclosed that driver mutated genes, such as canonical oncogenes and suppressor genes dominated the entire region of primary tumor as the neutral evolution manner in advanced CRC cases 7, 8 . In addition, we previously reported a case of CRC in which mutated KRAS was detected in ctDNA from primary and metastatic tumors simultaneously 9 , indicating a continuously higher MAF during longitudinal radical treatment. Another possibility is that the localized host tumor immune response in primary tumors may affect the sensitivity to detect ctDNA in the circulation system. The tumor immune response in cancer microenvironment is comprised of CD8 + cytotoxic T lymphocyte, FOXP3 + CD4 + T regulatory cells, dendritic cells, macrophages, and cytokines. The several former studies touched the association between detectability of mutated ctDNA fragments and the host immunity 10, 11, 12, 13 , however, they could not reach at any definitive conclusions. In terms of the association between the host immunity and the detectability of mutated ctDNA, the current study focuses on the presentation ability of neoantigens derived from somatic mutations in ctDNA, which is determined by the following two factors: the binding affinity of the diverse estimated neoantigens of mutated genes to human leukocyte antigen (HLA) and expression of tumor-specific RNA transcribed from mutated alleles. Both factors were indispensable for presenting neoantigens derived from mutated genes in ctDNA among all mutated genes in the primary tumor. This study conducted target sequencing of ctDNA from 47 points in the clinical course of six cases of CRC with postoperative recurrence (CRCR) using a customized cancer panel for target resequencing of commonly mutated genes between primary and recurrent sites 14 (Table 1). We calculated the binding affinity to HLA (half maximal inhibitory concentration [IC50]) and tumor-specific RNA expression of mutated genes among all mutated genes in primary tumors by in silico analysis. We elucidated the characteristics of the highly detected mutated genes in plasma ctDNA, which will contribute to the establishment of a liquid biopsy system to select the most optimal mutated genes to accurately monitor the MRD. Results Landscape of mutated ctDNA using target sequencing in six CRC cases We applied ten primary tumors and ten postoperative recurrence sites to extract genomic DNA for whole-exome sequencing (WES) analysis, which was reported in our previous study 14 . We selected 443 commonly mutated genes between 10 primary sites and 10 recurrent (metastatic) sites to establish a cancer panel for target resequencing (Fig. S1). In addition, we added 35 significant canonical mutated genes. Out of those 35 genes, twenty-seven genes were overlapped with the 443 mutated genes from the current 10 cases. Therefore, as a consequence, 451 mutated genes were on the panel (Fig. S2). Unfortunately, we could not collect an adequate amount of plasma from four cases shaded areas in Table 1, such as CRCR2, CRCR3, CRCR6, and CRCR10, therefore, we excluded them from the target sequence analysis as the liquid biopsy. The clinical courses of the six cases, CRCR1, CRCR4, CRCR5, CRCR7, CRCR8, and CRCR9 involving 47 samples from primary or metastatic tumors are presented in Figure 1. For example, in CRCR7, we detected 63 mutated genes out of 65 mutations on the panel (average AF of 63 genes: 0.146) at 4 M (1) and 63 of 65 mutations (average AF of 63 genes: 0.172) at the diagnosis of metastasis (2) (Fig. 1). Verification of ctDNA to capture commonly mutated genes between primary and recurrent tumors In this retrospective study, it was essential to verify the accuracy of the current assay for implementing the target sequence of plasma ctDNA. We found that this assay system could capture mutated ctDNA genes using a cancer panel that carrying the commonly mutated genes between primary and recurrence sites. As shown in Figure 2, CRCR1, CRCR4, CRCR7, CRCR8, and CRCR9 have candidate target genes with mutations that were detected repeatedly in ctDNA for tumor tracing throughout the postoperative clinical course. In CRCR7, the PEX5 gene 15 was clearly captured multiple times by commonly mutated genes in primary and recurrent tumors. Comparison of neoantigen presentation ability between mutated genes in ctDNA and all mutated genes in primary tumors We focused on the ability to present neoantigens derived from commonly mutated genes between primary and recurrent sites compared with whole mutated genes in primary tumors. Presenting neoantigens to activate the tumor immune response requires simultaneous estimation of the binding affinity of the mutated allele to HLA and the expression of the cancer-specific mutated allele. Major histocompatibility complex (MHC) restriction was examined by predicting the binding affinity of single nucleotide variants (SNVs) to HLA (using the analytical pipeline NetMHCpan) 16, 17 (Fig. S3). We extracted an altered read from the tumor RNA BAM file and measured the expression of tumor-specific mutated genes among all mutated genes in the primary sites as the scheme. In CRCR7P, we found that tumor-specific mutated PEX5 RNA expression was significantly higher than the expression of all genes in the primary site (Table 2), however, there were no peptide PEX5 fragments within the high range of binding affinity (IC50 <50 nM) among the estimated 6,740 peptide fragments from 104 mutated genes. Therefore, the altered PEX5 must not be presented as a neoantigen peptide. In CRCR1P, mutated OR10A6 showed a higher binding affinity (20 [5.29%] of 378 peptides) to HLA than that of other mutated genes (p<0.0001). On the other hand, the mutated OR10A6 gene was not presented as a neoantigen (Table 2), therefore, OR10A6 18 must not be presented as a neoantigen peptide. As shown in Table 2, representative mutated genes that could be chronologically traced by ctDNA showed either low binding affinities with HLA or low expression of mutated genes in a mutually exclusive manner. We plotted ctDNA mutated genes to demonstrate the minimized binding affinity to HLA and low expression of mutated transcripts in ctDNA (Figure 3). Therefore, we assumed that chronologic ctDNA-detected mutated genes were derived from immune-tolerant cancer cells rather than cytolytic activity inducing collapsed cancer cells. Comparison of neoantigen presentation ability between commonly mutated genes and all mutated genes in primary tumors We summarized the results of both factors to determine the ability to present neoantigen peptides in five cases (Table 3A and 3B). As shown in Table 2, CRCR4P, CRCR7P, CRCR8P, and CRCR9P showed significantly higher expression of ctDNA-detected mutated genes compared with other mutated genes in the primary tumor. However, these four cases showed no binding affinity to HLA (Table 3B), therefore, none of the mutated genes in the four cases were presented as neoantigens. Furthermore, highly mutated genes were observed in both the primary and recurrent sites and were expressed as transcripts, however, they could not bind to HLA. Consequently, they could not be presented as neoantigens that activate the tumor immune response. Discussion We found that the commonly mutated genes between primary and recurrent tumors indicated the expression of these transcripts, although there is no binding affinity to HLA. Therefore, these mutated genes were not induced neoantigens in the activation of the tumor-immune system. We assumed that the frequently mutated genes in recurrent tumors were derived from immune-tolerated clones in primary tumors without neoantigen presentation. Our previous study supports this finding. We compared the expression of tumor immune response-related genes, such as CD8 , CD4 , PD-1 , LAG3 , A2aR , and TIM-3 , between primary and metastatic sites using the same RNA seq data from the same sample set used in the current study 14 . We found abundant expression of an immune exhausted indicator, TIM-3 , in metastatic sites compared with that in primary sites in an in-house study as well as The Cancer Genome Atlas data 14 . Meanwhile, we conducted targeted sequencing of ctDNA using the cancer panel comprising 416 commonly mutated genes between primary and recurrent sites. As a result, several genes, such as OR10A in CRCR1P and PEX5 in CRCR7P, revealed immune-tolerated findings without presentation of the neoantigens due to the mutually exclusive findings in either the loss of expression of mutated genes or lack of binding affinity to HLA. Immune tolerance induced by the loss of neoantigen presentation may be essential for clones to form recurrences. As we described above, commonly mutated clones between primary and recurrent sites indicated immune-resistant owing to the diminished binding affinity of neoantigen to HLA. In addition, the expression of immune exhausted genes, such as TIM-3 was more abundant in the recurrent than primary sites in our previous study 14 . Wang Z. et al. reported that Tim-3 inhibited the MHC-I-restricted antigen presentation not in cancer cells but in macrophages in vitro and in vivo 19 . Regarding the cause of the reduced binding affinity to HLA in CRC, the loss of MHC class I expression plays a pivotal role in presenting processed antigens to T lymphocytes, including tumor antigens in colorectal cancer cases 20 , and LOH of HLA class I genes and B2M mutations have also been reported to be an indicator of poor prognosis 21, 22 . Therefore, we assumed that most mutated genes in primary and recurrence sites detected by ctDNA have derived from the immune-resistant clones with the loss of MHC class I expression. The limited number of target genes in each cancer panel was a limitation of the current study. We could not compare the detectability of ctDNA among the three groups, such as primary and recurrence commonly mutated genes, primary site-specific mutated genes, and recurrent site-specific mutated genes owing to the limited number of plasma samples. In addition, we did not examine the binding affinity of estimated neoantigens to MHC-class II HLAs. Further study is required to elucidate the complete significance of the mutation in the plasma ctDNA. In conclusion, recurrence required immune tolerance derived from the loss of neoantigen presentation ability, which was caused either by reduced cancer-specific mutated gene expression or by low binding affinity to HLA in CRC cases. The estimated neoantigen peptide derived from commonly mutated genes between primary and recurrent tumors showed no binding affinity to HLA compared with all mutated genes at primary sites. Materials And Methods 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 study 14 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 mins each. Adapter ligation was performed using 110-µL volumes, and samples were incubated at 16 °C for 16 hours 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 1Kb-499kb, 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 study 14 . The sequence data were processed using an in-house pipeline (https://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 algorithm 23 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 study 14 , 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 described 24 . Data Availability Statement Data are available at: https://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: P (R, S, I | X)∝P (X | S, I) P (I) P(R, S) where R = (R1, R2) is the pair of HLA types (reference sequences), S = ( S 1, S 2) is the pair of sample HLA sequences, X = ( x 1, x 2,...) is a set of sequence reads, and I = ( I 1, I 2,...) is a set of variables using one or two values (j th element, I j , indicating that the j th read x j is generated from S I 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 https://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). Declarations Acknowledgments: This research used the supercomputing resources provided by the Human Genome Center, Institute of Medical Science, University of Tokyo (http://sc.hgc.jp/shirokane.html). We thank M. Kasagi, S. Sakuma, M. Murakami, T. Fukuda, N. Mishima, and T. Kawano for their assistance. Author Contributions: Conceptualization, S.N., and K.M., Methodology Software, Y.K., Validation, S.T., Formal Analysis, S.S. and Y.K., Resources, S.N., M.F., S.S., and K.M., Data Curation, S.S.., Writing – Original Draft Preparation, S.N., Writing – Review & Editing, K.S., Supervision, K.M., M.O., Y.S., and T.S., Project Administration, K.M., Funding Acquisition, K.M. Competeing Interest: The authors declare no conflict of interest. Funding: This project was supported by AMED, P-CREATE 20 cm0106475h0001(e-Rad ID: 20317791), the Takeda Science Foundation 2020, JSPS KAKENHI (20H05039, 19H03715, 19K09220), Grant-in-Aid for Scientific Research on Innovative Areas (15H05912), Priority Issue on Post-K computer (hp170227, hp160219), Project for Cancer Research and Therapeutic Evolution (19 cm0106504h0004), and a research grant from the Princess Takamatsu Cancer Research. References Chabon JJ , et al. 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Tables Table 1 to 3 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.listof10cases.xlsx Table2.representativectDNA.pdf Table3.IC50Exp.pdf Fig.S1.pdf Fig.S2.CancerPanel.pdf Fig.S3.pdf Cite Share Download PDF Status: Published Journal Publication published 24 Jan, 2023 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Major revision 19 Sep, 2022 Reviewers agreed at journal 27 Aug, 2022 Reviews received at journal 25 Aug, 2022 Reviewers agreed at journal 15 Aug, 2022 Reviewers invited by journal 03 Aug, 2022 Editor assigned by journal 03 Aug, 2022 Editor invited by journal 02 Aug, 2022 Submission checks completed at journal 02 Aug, 2022 First submitted to journal 02 Jul, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Tokyo","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yutaka","middleName":"","lastName":"Suzuki","suffix":""},{"id":125970738,"identity":"f23c4b8b-0cd5-42ec-b68b-83c058c2f691","order_by":11,"name":"Koshi Mimori","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABQUlEQVRIie2RMUvDQBSAXzyogxeyXrB/QXghEBGDDv6RHIG6SCkI4lDCgVAX0bVSf0RLwfnkoFNqNyl0kOLaQbcOKl7SDG1s0FEk38Ed9+4+3nt3ACUlfxGSTKgHUJDGp0+tLBas3ChUoFKr2uInJSNVACrKR5lT8uxcbvGX8wa4ljV8kEAVdZ8kcc1WVEdJEeZN2NxdVTxl9p0YwbPb9UACO6beJCCh2VKniWJcDYDsibxybwsEH8dUl4T7qaLMluTd5xmCqTvT8fXKKNZHAaFuJ1Ui3k2yfBQrHsoTfSSPKG6nhZFUIeuz9B2BzLXbWuGiRtmEXzh3j4rfykpDVQfsWy+jYW8q3n3nxord1zfhH1qdULHZWcSvJelNZ00/zL1YBlssi78wxAbN4rokFmLRDy1jzJc2B79SSkpKSv4xX0RfbdH3KY/KAAAAAElFTkSuQmCC","orcid":"","institution":"Kyushu University Beppu Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Koshi","middleName":"","lastName":"Mimori","suffix":""}],"badges":[],"createdAt":"2022-07-03 01:14:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1819523/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1819523/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-023-28575-3","type":"published","date":"2023-01-24T18:28:56+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":24848876,"identity":"7a4a0cbf-f9c5-4d7e-85c3-bd62ae2cf00a","added_by":"auto","created_at":"2022-08-05 21:40:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":326329,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePlasma sampling from six cases of CRC with postoperative recurrence. \u003c/strong\u003eWhole-exome sequencing and RNA sequencing of P (primary tumor) and M (metastatic tumor) were conducted. The number of mutated genes on each panel: CRCR1, 29, CRCR4, 36, CRCR5, 26, CRCR7, 65, CRCR8, 25, and CRCR9, 36. Numbers in the red circle indicate positivity for mutated genes in the ctDNA. We included the ratio of mutated genes to all genes in each cancer panel. Pre, preoperative plasma sample.\u003c/p\u003e","description":"","filename":"Fig.1.220607.png","url":"https://assets-eu.researchsquare.com/files/rs-1819523/v1/f58d4be3e3737369e1a90676.png"},{"id":24848516,"identity":"471cee56-f591-42bf-8a52-c28f8602f22e","added_by":"auto","created_at":"2022-08-05 21:35:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":416189,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAlteration of mutation allele frequency of target genes. \u003c/strong\u003eIn CRCR1, \u003cem\u003eNCKA5L\u003c/em\u003e and \u003cem\u003eSLC20A \u003c/em\u003eshowed mutations from M1 to M2, respectively. An \u003cem\u003eOR10A6\u003c/em\u003e mutation was detected preoperatively and at M1. In CRCR4, \u003cem\u003eCTNNB1\u003c/em\u003e, \u003cem\u003eCHRNB2\u003c/em\u003e, and \u003cem\u003eCLST2\u003c/em\u003e were frequently mutated in the M1 sample. In CRCR7, the MAFs of \u003cem\u003ePEX5\u003c/em\u003e and \u003cem\u003eTPCN1\u003c/em\u003e were detected multiple times with recurrent tumors. In CRCR8, \u003cem\u003eSTAC2\u003c/em\u003e, \u003cem\u003eZNF835\u003c/em\u003e, and \u003cem\u003eFBLN2\u003c/em\u003e were altered along with M2 and M4. In CRCR9, a higher MAF of \u003cem\u003eEPHB1\u003c/em\u003e was detected in preoperative and M1 samples.\u003c/p\u003e","description":"","filename":"Fig.2.monitoringgenes.png","url":"https://assets-eu.researchsquare.com/files/rs-1819523/v1/be3f69a8ff8e1043096f6303.png"},{"id":24848877,"identity":"9d1f9a74-7ee0-4a07-99fa-f8ad56881aa7","added_by":"auto","created_at":"2022-08-05 21:40:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":695566,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmunogenicity of estimated NAG peptide in each mutated ctDNA gene. \u003c/strong\u003eIn terms of neoantigen analysis for MHC restriction of ctDNA, the binding affinity of peptide fragments to HLA-A, -B, -C was estimated using SNVs with WES data of primary sites (neBindingtMHCpan) \u003cstrong\u003e(X-axis)\u003c/strong\u003e. The binding affinity of peptides was calculated as the IC50. The estimated NAG peptide derived from an SNV within 500 nM (red line) of IC50 indicated weak binding affinity to HLA. In addition, tumor-specific RNA expression was extracted from the tumor RNA BAM file and evaluated \u003cstrong\u003e(Y-axis)\u003c/strong\u003e. Neoantigens with a high binding affinity but no expression were spotted in a red elliptic circle.\u0026nbsp;\u003c/p\u003e","description":"","filename":"Fig.3.BindingAffinitytoHLA.220530.png","url":"https://assets-eu.researchsquare.com/files/rs-1819523/v1/102ca27e48acb5a291ec6f87.png"},{"id":44717639,"identity":"043f67f3-2197-4f50-b984-59967c7d3662","added_by":"auto","created_at":"2023-10-16 18:38:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1275333,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1819523/v1/a7cc2ef2-e201-4201-969f-6ae1e4487404.pdf"},{"id":24848517,"identity":"2d15db27-2649-4181-a85c-4691c359f621","added_by":"auto","created_at":"2022-08-05 21:35:15","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":13310,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.listof10cases.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-1819523/v1/f0229fee985853783a1ac792.xlsx"},{"id":24849397,"identity":"b5cb63d8-6ebf-4750-8a37-8d26fa397146","added_by":"auto","created_at":"2022-08-05 21:45:15","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":63020,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.representativectDNA.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1819523/v1/cbc1817fc902e778bb493bfd.pdf"},{"id":24848520,"identity":"5b4e2f50-bdbd-4b33-84b2-0f688d05e8a7","added_by":"auto","created_at":"2022-08-05 21:35:15","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":71958,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.IC50Exp.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1819523/v1/179d6e870ef13173a794569e.pdf"},{"id":24848523,"identity":"8431ae8c-945f-43de-aa94-835d97c5b946","added_by":"auto","created_at":"2022-08-05 21:35:15","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":863429,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1819523/v1/f715ae1b48d5165a3e20462d.pdf"},{"id":24848874,"identity":"a7f1ac97-97f9-4221-8a03-83aac465738a","added_by":"auto","created_at":"2022-08-05 21:40:15","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":484022,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S2.CancerPanel.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1819523/v1/1412b41648ad4d0a7b619f5b.pdf"},{"id":24848878,"identity":"04e90714-8a78-4cc5-9ffe-21cce9fc1f48","added_by":"auto","created_at":"2022-08-05 21:40:15","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1016754,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1819523/v1/40c4bb07d9acdc63ac11494f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mutated genes on ctDNA detecting postoperative recurrence presented reduced neoantigens in primary tumors in colorectal cancer cases","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe use of circulating tumor (ct)DNA as a liquid comprehensive genomic profile (CGP) assay is not inferior to CGP tissue analysis in gastrointestinal cancers \u003csup\u003e1, 2, 3, 4\u003c/sup\u003e. A clinical trial in Japan revealed better clinical outcomes in metastatic colorectal cancer (CRC) patients with actionable alterations than those of patients without alterations \u003csup\u003e5\u003c/sup\u003e. However, in terms of liquid biopsy using ctDNA, researchers have focused on the usefulness of sequencing mutated ctDNA in monitoring the minimum residual disease (MRD) in recurrent tumors postoperatively \u003csup\u003e6\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eConsidering the essential characteristics of the mutated clones that were chronologically detected from the primary site to recurrent site continuously, we assumed two possibilities. First, the mutated genes in ctDNA may be detected abundantly in tumors with high mutation allele frequency (MAF) or clonally expanded mutated genes that cover the entire primary tumor. These highly mutated or clonally mutated genes may be derived from the dominant cancer cells to promote cancer progression in primary tumors. In fact, we previously disclosed that driver mutated genes, such as canonical oncogenes and suppressor genes dominated the entire region of primary tumor as the neutral evolution manner in advanced CRC cases \u003csup\u003e7, 8\u003c/sup\u003e. In addition, we previously reported a case of CRC in which mutated \u003cem\u003eKRAS\u003c/em\u003e was detected in ctDNA from primary and metastatic tumors simultaneously \u003csup\u003e9\u003c/sup\u003e, indicating a continuously higher MAF during longitudinal radical treatment.\u003c/p\u003e \u003cp\u003eAnother possibility is that the localized host tumor immune response in primary tumors may affect the sensitivity to detect ctDNA in the circulation system. The tumor immune response in cancer microenvironment is comprised of CD8\u003csup\u003e+\u003c/sup\u003e cytotoxic T lymphocyte, FOXP3\u003csup\u003e+\u003c/sup\u003e CD4\u003csup\u003e+\u003c/sup\u003e T regulatory cells, dendritic cells, macrophages, and cytokines. The several former studies touched the association between detectability of mutated ctDNA fragments and the host immunity \u003csup\u003e10, 11, 12, 13\u003c/sup\u003e, however, they could not reach at any definitive conclusions. In terms of the association between the host immunity and the detectability of mutated ctDNA, the current study focuses on the presentation ability of neoantigens derived from somatic mutations in ctDNA, which is determined by the following two factors: the binding affinity of the diverse estimated neoantigens of mutated genes to human leukocyte antigen (HLA) and expression of tumor-specific RNA transcribed from mutated alleles. Both factors were indispensable for presenting neoantigens derived from mutated genes in ctDNA among all mutated genes in the primary tumor.\u003c/p\u003e \u003cp\u003eThis study conducted target sequencing of ctDNA from 47 points in the clinical course of six cases of CRC with postoperative recurrence (CRCR) using a customized cancer panel for target resequencing of commonly mutated genes between primary and recurrent sites\u003csup\u003e14\u003c/sup\u003e (Table\u0026nbsp;1). We calculated the binding affinity to HLA (half maximal inhibitory concentration [IC50]) and tumor-specific RNA expression of mutated genes among all mutated genes in primary tumors by \u003cem\u003ein silico\u003c/em\u003e analysis. We elucidated the characteristics of the highly detected mutated genes in plasma ctDNA, which will contribute to the establishment of a liquid biopsy system to select the most optimal mutated genes to accurately monitor the MRD.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eLandscape of mutated ctDNA using target sequencing in six CRC cases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe applied ten primary tumors and ten postoperative recurrence sites to extract genomic DNA for whole-exome sequencing (WES) analysis, which was reported in our previous study\u003csup\u003e14\u003c/sup\u003e. We selected 443 commonly mutated genes between 10 primary sites and 10 recurrent (metastatic) sites to establish a cancer panel for target resequencing (Fig. S1). In addition, we added 35 significant canonical mutated genes. Out of those 35 genes, twenty-seven genes were overlapped with the 443 mutated genes from the current 10 cases. Therefore, as a consequence, 451 mutated genes were on the panel (Fig. S2). Unfortunately, we could not collect an adequate amount of plasma from four cases shaded areas in Table 1, such as CRCR2, CRCR3, CRCR6, and CRCR10, therefore, we excluded them from the target sequence analysis as the liquid biopsy. The clinical courses of the six cases, CRCR1, CRCR4, CRCR5, CRCR7, CRCR8, and CRCR9 involving 47 samples from primary or metastatic tumors are presented in Figure 1. For example, in CRCR7, we detected 63 mutated genes out of 65 mutations on the panel (average AF of 63 genes: 0.146) at 4 M (1) and 63 of 65 mutations (average AF of 63 genes: 0.172) at the diagnosis of metastasis (2) (Fig. 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVerification of ctDNA to capture commonly mutated genes between primary and recurrent tumors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this retrospective study, it was essential to verify the accuracy of the current assay for implementing the target sequence of plasma ctDNA. We found that this assay system could capture mutated ctDNA genes using a cancer panel that carrying the commonly mutated genes between primary and recurrence sites. As shown in Figure 2, CRCR1, CRCR4, CRCR7, CRCR8, and CRCR9 have candidate target genes with mutations that were detected repeatedly in ctDNA for tumor tracing throughout the postoperative clinical course. In CRCR7, the PEX5 gene\u003csup\u003e15\u003c/sup\u003e was clearly captured multiple times by commonly mutated genes in primary and recurrent tumors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison of neoantigen presentation ability between mutated genes in ctDNA and all mutated genes in primary tumors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe focused on the ability to present neoantigens derived from commonly mutated genes between primary and recurrent sites compared with whole mutated genes in primary tumors. Presenting neoantigens to activate the tumor immune response\u0026nbsp;requires simultaneous estimation of the binding affinity of the mutated allele to HLA and the expression of the cancer-specific mutated allele. Major histocompatibility complex (MHC) restriction was examined by predicting the binding affinity of single nucleotide variants (SNVs) to HLA (using the analytical pipeline NetMHCpan)\u003csup\u003e16, 17\u003c/sup\u003e (Fig. S3). We extracted an altered read from the tumor RNA BAM file and measured the expression of tumor-specific mutated genes among all mutated genes in the primary sites as the scheme.\u003c/p\u003e\n\u003cp\u003eIn CRCR7P, we found that tumor-specific mutated PEX5 RNA expression was significantly higher than the expression of all genes in the primary site (Table 2), however, there were no peptide PEX5 fragments within the high range of binding affinity (IC50 \u0026lt;50 nM) among the estimated 6,740 peptide fragments from 104 mutated genes. Therefore, the altered PEX5 must not be presented as a neoantigen peptide. In CRCR1P, mutated OR10A6 showed a higher binding affinity (20 [5.29%] of 378 peptides) to HLA than that of other mutated genes (p\u0026lt;0.0001). On the other hand, the mutated OR10A6 gene was not presented as a neoantigen (Table 2), therefore, OR10A6 \u003csup\u003e18\u003c/sup\u003emust not be presented as a neoantigen peptide. As shown in Table 2, representative mutated genes that could be chronologically traced by ctDNA showed either low binding affinities with HLA or low expression of mutated genes in a mutually exclusive manner. We plotted ctDNA mutated genes to demonstrate the minimized binding affinity to HLA and low expression of mutated transcripts in ctDNA (Figure 3). Therefore, we assumed that chronologic ctDNA-detected mutated genes were derived from immune-tolerant cancer cells rather than cytolytic activity inducing collapsed cancer cells.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison of neoantigen presentation ability between commonly mutated genes and all mutated genes in primary tumors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe summarized the results of both factors to determine the ability to present neoantigen peptides in five cases (Table 3A and 3B). As shown in Table 2, CRCR4P, CRCR7P, CRCR8P, and CRCR9P showed significantly higher expression of ctDNA-detected mutated genes compared with other mutated genes in the primary tumor. However, these four cases showed no binding affinity to HLA (Table 3B), therefore, none of the mutated genes in the four cases were presented as neoantigens. Furthermore, highly mutated genes were observed in both the primary and recurrent sites and were expressed as transcripts, however, they could not bind to HLA. Consequently, they could not be presented as neoantigens that activate the tumor immune response.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe found that the commonly mutated genes between primary and recurrent tumors indicated the expression of these transcripts, although there is no binding affinity to HLA. Therefore, these mutated genes were not induced neoantigens in the activation of the tumor-immune system. We assumed that the frequently mutated genes in recurrent tumors were derived from immune-tolerated clones in primary tumors without neoantigen presentation. Our previous study supports this finding. We compared the expression of tumor immune response-related genes, such as \u003cem\u003eCD8\u003c/em\u003e, \u003cem\u003eCD4\u003c/em\u003e, \u003cem\u003ePD-1\u003c/em\u003e, \u003cem\u003eLAG3\u003c/em\u003e, \u003cem\u003eA2aR\u003c/em\u003e, and \u003cem\u003eTIM-3\u003c/em\u003e, between primary and metastatic sites using the same RNA seq data from the same sample set used in the current study \u003csup\u003e14\u003c/sup\u003e. We found abundant expression of an immune exhausted indicator, \u003cem\u003eTIM-3\u003c/em\u003e, in metastatic sites compared with that in primary sites in an in-house study as well as The Cancer Genome Atlas data \u003csup\u003e14\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMeanwhile, we conducted targeted sequencing of ctDNA using the cancer panel comprising 416 commonly mutated genes between primary and recurrent sites. As a result, several genes, such as OR10A in CRCR1P and PEX5 in CRCR7P, revealed immune-tolerated findings without presentation of the neoantigens due to the mutually exclusive findings in either the loss of expression of mutated genes or lack of binding affinity to HLA. Immune tolerance induced by the loss of neoantigen presentation may be essential for clones to form recurrences. As we described above, commonly mutated clones between primary and recurrent sites indicated immune-resistant owing to the diminished binding affinity of neoantigen to HLA. In addition, the expression of immune exhausted genes, such as \u003cem\u003eTIM-3\u003c/em\u003e was more abundant in the recurrent than primary sites in our previous study\u003csup\u003e14\u003c/sup\u003e. Wang Z. et al. reported that Tim-3 inhibited the MHC-I-restricted antigen presentation not in cancer cells but in macrophages \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e\u003csup\u003e19\u003c/sup\u003e. Regarding the cause of the reduced binding affinity to HLA in CRC, the loss of MHC class I expression plays a pivotal role in presenting processed antigens to T lymphocytes, including tumor antigens in colorectal cancer cases\u003csup\u003e20\u003c/sup\u003e, and LOH of HLA class I genes and B2M mutations have also been reported to be an indicator of poor prognosis\u003csup\u003e21, 22\u003c/sup\u003e. Therefore, we assumed that most mutated genes in primary and recurrence sites detected by ctDNA have derived from the immune-resistant clones with the loss of MHC class I expression.\u003c/p\u003e \u003cp\u003eThe limited number of target genes in each cancer panel was a limitation of the current study. We could not compare the detectability of ctDNA among the three groups, such as primary and recurrence commonly mutated genes, primary site-specific mutated genes, and recurrent site-specific mutated genes owing to the limited number of plasma samples. In addition, we did not examine the binding affinity of estimated neoantigens to MHC-class II HLAs. Further study is required to elucidate the complete significance of the mutation in the plasma ctDNA.\u003c/p\u003e \u003cp\u003eIn conclusion, recurrence required immune tolerance derived from the loss of neoantigen presentation ability, which was caused either by reduced cancer-specific mutated gene expression or by low binding affinity to HLA in CRC cases. The estimated neoantigen peptide derived from commonly mutated genes between primary and recurrent tumors showed no binding affinity to HLA compared with all mutated genes at primary sites.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eEnrolled patients and plasma samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe 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 study \u003csup\u003e14\u003c/sup\u003e 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).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample collection and preparation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenomic 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\u0026rsquo;s instructions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEstablishment of the cancer panel\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe 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\u0026apos;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\u0026ndash;100-bp read option.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe 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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNext-generation sequencing library construction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndexed 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\u0026apos;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-\u0026micro;L reaction volumes. The mixtures were then incubated at 20 \u0026deg;C and 65 \u0026deg;C for 30 mins each. Adapter ligation was performed using 110-\u0026micro;L volumes, and samples were incubated at 16 \u0026deg;C for 16 hours using a SureSelect Adapter (Agilent Technologies). After postligation cleanup, the ligated fragments were amplified in a 50-\u0026micro;L solution containing 2\u0026times; KAPA HiFi HotStart ReadyMix and 10\u0026times; KAPA Library Amplification Primer Mix (Kapa Biosystems). We used the following cycling protocol: 98 \u0026deg;C for 45 s, 14\u0026ndash;16 cycles (depending on the input DNA mass) of 98 \u0026deg;C for 15 s, 65 \u0026deg;C for 30 s, 72 \u0026deg;C for 30 s, and 72 \u0026deg;C for 5 min (1 cycle). Library purity, library concentration, and fragment length were determined using a 2100 Bioanalyzer (Agilent Technologies).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTargeted sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlasma DNA extracted from CRC patient samples was captured using a SureSelectXT Custom 1Kb-499kb, 16 (Agilent Technology) according to the manufacturer\u0026rsquo;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\u0026ndash;100 bp) reads for each sample. Targeted deep sequencing was performed for all samples using a multigene panel,\u0026nbsp;with a mean sequencing depth of 3810\u0026times;.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMutation calling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used WES data from our previous study \u003csup\u003e14\u003c/sup\u003e. The sequence data were processed using an in-house pipeline (https://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 algorithm \u003csup\u003e23\u003c/sup\u003e with the following parameters: 1) mapping quality score \u0026ge;20, 2) base quality score \u0026ge;15, 3) both the tumor and normal depths \u0026ge;10, 4) variant reads in tumors \u0026ge;4, 5) variant allele frequencies (VAFs) in tumor samples \u0026ge;0.02, and 6) VAFs in normal samples \u0026le;0.01.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA sequencing\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used RNA sequencing data from our previous study \u003csup\u003e14\u003c/sup\u003e, 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 described \u003csup\u003e24\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available at: https://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).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHLA genotyping (Hayashi method)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor 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:\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eP (R, S, I | X)\u0026prop;P (X | S, I) P (I) P(R, S)\u003c/p\u003e\n\u003cp\u003ewhere R = (R1, R2) is the pair of HLA types (reference sequences), \u003cem\u003eS\u0026nbsp;\u003c/em\u003e= (\u003cem\u003eS\u003c/em\u003e1, \u003cem\u003eS\u003c/em\u003e2) is the pair of sample HLA sequences, \u003cem\u003eX\u0026nbsp;\u003c/em\u003e= (\u003cem\u003ex\u003c/em\u003e1, \u003cem\u003ex\u003c/em\u003e2,...) is a set of sequence reads, and \u003cem\u003eI\u0026nbsp;\u003c/em\u003e= (\u003cem\u003eI\u003c/em\u003e1, \u003cem\u003eI\u003c/em\u003e2,...) is a set of variables using one or two values (j\u003csup\u003eth\u003c/sup\u003e element, I\u003csub\u003ej\u003c/sub\u003e, indicating that the j\u003csup\u003eth\u003c/sup\u003e read x\u003csub\u003ej\u0026nbsp;\u003c/sub\u003eis generated from \u003cem\u003eS\u003csub\u003eI j\u003c/sub\u003e\u003c/em\u003e). 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\u0026nbsp;Markov Chain Monte Carlo procedure.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrediction of potential N-acetylglucosamine peptides\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing 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 https://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.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u0026nbsp;\u003c/strong\u003eThe 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.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u0026nbsp;\u003c/strong\u003eWritten informed consent was obtained from all study participants.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used the Mann-Whitney U test or Fisher\u0026rsquo;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\u0026middot;1\u0026middot;1 (R Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eThis research used the supercomputing resources provided by the Human Genome Center, Institute of Medical Science, University of Tokyo (http://sc.hgc.jp/shirokane.html). We thank M. Kasagi, S. Sakuma, M. Murakami, T. Fukuda, N. Mishima, and T. Kawano for their assistance.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eConceptualization, S.N., and K.M., Methodology Software, Y.K., Validation, S.T., Formal Analysis, S.S. and Y.K., Resources, S.N., M.F., S.S., and K.M., Data Curation, S.S.., Writing \u0026ndash; Original Draft Preparation, S.N., Writing \u0026ndash; Review \u0026amp; Editing, K.S., Supervision, K.M., M.O., Y.S., and T.S., Project Administration, K.M., Funding Acquisition, K.M.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeteing Interest:\u003c/strong\u003e The authors declare no conflict of interest.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis project was supported by AMED, P-CREATE 20 cm0106475h0001(e-Rad ID: 20317791), the Takeda Science Foundation 2020, JSPS KAKENHI (20H05039, 19H03715, 19K09220), Grant-in-Aid for Scientific Research on Innovative Areas (15H05912), Priority Issue on Post-K computer (hp170227, hp160219), Project for Cancer Research and Therapeutic Evolution (19 cm0106504h0004), and a research grant from the Princess Takamatsu Cancer Research.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChabon JJ\u003cem\u003e, et al.\u003c/em\u003e Circulating tumour DNA profiling reveals heterogeneity of EGFR inhibitor resistance mechanisms in lung cancer patients. \u003cem\u003eNat Commun\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 11815 (2016).\u003c/li\u003e\n\u003cli\u003eHuang A\u003cem\u003e, et al.\u003c/em\u003e Detecting Circulating Tumor DNA in Hepatocellular Carcinoma Patients Using Droplet Digital PCR Is Feasible and Reflects Intratumoral Heterogeneity. \u003cem\u003eJ Cancer\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 1907-1914 (2016).\u003c/li\u003e\n\u003cli\u003ePectasides E\u003cem\u003e, et al.\u003c/em\u003e Genomic Heterogeneity as a Barrier to Precision Medicine in Gastroesophageal Adenocarcinoma. \u003cem\u003eCancer Discov\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 37-48 (2018).\u003c/li\u003e\n\u003cli\u003eUeda M\u003cem\u003e, et al.\u003c/em\u003e Somatic mutations in plasma cell-free DNA are diagnostic markers for esophageal squamous cell carcinoma recurrence. \u003cem\u003eOncotarget\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 62280-62291 (2016).\u003c/li\u003e\n\u003cli\u003eNakamura Y\u003cem\u003e, et al.\u003c/em\u003e Clinical utility of circulating tumor DNA sequencing in advanced gastrointestinal cancer: SCRUM-Japan GI-SCREEN and GOZILA studies. \u003cem\u003eNat Med\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e, 1859-1864 (2020).\u003c/li\u003e\n\u003cli\u003ePantel K, Alix-Panabieres C. 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HLA class I loss in colorectal cancer: implications for immune escape and immunotherapy. \u003cem\u003eCell Mol Immunol\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 556-565 (2021).\u003c/li\u003e\n\u003cli\u003eMontesion M\u003cem\u003e, et al.\u003c/em\u003e Somatic HLA Class I Loss Is a Widespread Mechanism of Immune Evasion Which Refines the Use of Tumor Mutational Burden as a Biomarker of Checkpoint Inhibitor Response. \u003cem\u003eCancer Discov\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 282-292 (2021).\u003c/li\u003e\n\u003cli\u003eTikidzhieva A\u003cem\u003e, et al.\u003c/em\u003e Microsatellite instability and Beta2-Microglobulin mutations as prognostic markers in colon cancer: results of the FOGT-4 trial. \u003cem\u003eBr J Cancer\u003c/em\u003e \u003cstrong\u003e106\u003c/strong\u003e, 1239-1245 (2012).\u003c/li\u003e\n\u003cli\u003eVan Loo P\u003cem\u003e, et al.\u003c/em\u003e Allele-specific copy number analysis of tumors. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e \u003cstrong\u003e107\u003c/strong\u003e, 16910-16915 (2010).\u003c/li\u003e\n\u003cli\u003eMagi A\u003cem\u003e, et al.\u003c/em\u003e EXCAVATOR: detecting copy number variants from whole-exome sequencing data. \u003cem\u003eGenome Biol\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, R120 (2013).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 to 3 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"HLA binding affinity, tumor-specific mutated RNA, recurrence, minimum residual disease","lastPublishedDoi":"10.21203/rs.3.rs-1819523/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1819523/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIt is essential to comprehend the specific traits of mutated genes observed commonly not only at primary sites but recurrent sites. They were applied to be monitoring targets of circulating tumor (ct) DNA in liquid biopsy assay for the detection of postoperative recurrence. In the current retrospective study, we conducted target resequencing of ctDNA using 47 plasma samples and established a cancer panel carrying the commonly mutated genes between primary and recurrent tumors. We found that mutated genes in ctDNA indicated immune-resistance traits with respect to the impaired ability to present neoantigens by loss of expression or binding affinity to HLA in the primary tumor. Compared with the estimated neoantigens from all mutated genes in primary tumors, the neoantigen peptides from commonly mutated genes between primary and recurrent tumors showed abundant and significant expression with no binding affinity to HLA. Therefore, ctDNA mutations can be frequently and postoperatively detected to identify recurrence; however, these mutated genes were derived from immune-tolerated clones owing to the loss of neoantigen presentation in primary CRC tumors.\u003c/p\u003e","manuscriptTitle":"Mutated genes on ctDNA detecting postoperative recurrence presented reduced neoantigens in primary tumors in colorectal cancer cases","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-08-05 21:35:13","doi":"10.21203/rs.3.rs-1819523/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2022-09-19T05:15:03+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"91d2c1af-d7db-40ba-9732-85c6c0ced65c","date":"2022-08-27T23:24:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-08-26T02:52:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"821948db-e3b4-474c-b2f0-7a7c44321ab8","date":"2022-08-15T11:08:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-08-03T14:13:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-08-03T14:01:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2022-08-02T22:42:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2022-08-02T22:35:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2022-07-03T01:06:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ad28636b-03fb-485f-b072-f4b9fe914c44","owner":[],"postedDate":"August 5th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T18:34:48+00:00","versionOfRecord":{"articleIdentity":"rs-1819523","link":"https://doi.org/10.1038/s41598-023-28575-3","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2023-01-24 18:28:56","publishedOnDateReadable":"January 24th, 2023"},"versionCreatedAt":"2022-08-05 21:35:13","video":"","vorDoi":"10.1038/s41598-023-28575-3","vorDoiUrl":"https://doi.org/10.1038/s41598-023-28575-3","workflowStages":[]},"version":"v1","identity":"rs-1819523","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1819523","identity":"rs-1819523","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}