Somatic Mutations and Immune Alternation in Rectal Cancer ... · Research Article Somatic Mutations and Immune Alternation in Rectal Cancer Following Neoadjuvant Chemoradiotherapy

Research Article

Somatic Mutations and Immune Alternation inRectal Cancer Following NeoadjuvantChemoradiotherapyDengboJi1, HaizhaoYi1,2, Dakui Zhang3,TianchengZhan1, Zhaowei Li1,MingLi1, JinyingJia1,Meng Qiao1, Jinhong Xia1, Zhiwei Zhai4, Can Song5,6, and Jin Gu1,6,7

Abstract

Checkpoint blockade therapy triggers tumor-specificimmune responses in a variety of cancer types. We pre-sumed that rectal cancer patients could have become sen-sitive to immunotherapy after receiving neoadjuvant che-moradiotherapy (nCRT). In this study, we report immunealternation in post-nCRT patients compared with pretreat-ment conditions from gene-expression omnibus (GEO)data. Whole-exome sequencing of 14 locally advanced rectalcancer (LARC) patient samples showed that nCRT inducednew mutations compared with the paired pretreatmentbiopsies, evidenced by appearance of a neoantigen land-scape. An association was identified between mutationburden and enrichment of immune activation–related path-ways. Animal experiment results further demonstrated thatradiotherapy enhanced the efficacy of anti–PD-1. Mutation

burden and the neoantigens of LARC patients were associ-ated with response to nCRT. The mRNA expression profilingof 66 pretreatment biopsy samples from LARC patientsshowed that immune activation–related pathways wereenriched in response to nCRT. PD-L1 expression was neg-atively correlated with disease-free survival in the CD8-low expression patient group who received nCRT in a cohortof 296 samples. Thus, nCRT was able to alter immunefunction in LARC patients, which may be associatedwith the appearance of neoantigens. Neoantigens couldmake rectal cancer patients potential candidates to receivecheckpoint blockade immunotherapy, and mutation bur-den could be a useful biomarker to stratify patients intoresponding and nonresponding groups for immunotherapy.Cancer Immunol Res; 6(11); 1401–16. �2018 AACR.

IntroductionThe treatment guidelines published by the National Com-

prehensive Cancer Network of the United States clearly statethat preoperative neoadjuvant chemoradiotherapy is crucial forrectal cancer treatment, which can improve the rate of curativeresection and significantly reduce local recurrence (1, 2).Although local recurrence and overall survival have improved,distant recurrence rate has not decreased significantly. About30% of patients treated with a curative regimen will eventuallydevelop distant metastases (3–5). Adjuvant drug therapyhas been used to prevent distant metastases by eliminatingcirculating tumor cells and micrometastases. However, the use

of adjuvant chemotherapy for patients with rectal cancer treatedwith preoperative chemoradiotherapy and surgery is still con-troversial (6). Administration of adjuvant chemotherapyto patients with stage II or III rectal cancer was based onresults from phase III trials of adjuvant treatment for coloncancer (7–10), as well as from trials in patients with rectalcancer who were treated without preoperative chemoradiother-apy (11). Fluorouracil-based adjuvant chemotherapy did notimprove overall survival, disease-free survival, or distant recur-rences (6, 12).

Colorectal cancers with a high density of tumor-infiltratinglymphocytes (TILs), especially CD8þ T lymphocytes, are associ-ated with a better prognosis (13–16), suggesting that a cytotoxicantitumor immune response is involved in controlling cancerprogression. Checkpoint blockade immunotherapy has improvedcancer treatment. Along with radical surgery, radiotherapy, che-motherapy, and targeted oncogene treatment, checkpoint block-ade is on the top of the list of therapeutic options. Checkpointblockade therapy utilizes monoclonal antibodies (mAb) torescue suppressed T cells through activating and restoring theirantitumor activity (17). Similarly, targeting cytotoxic T lympho-cyte–associated antigen 4 (CTLA-4) and programmed cell-deathprotein-1 (PD-1) pathways in metastatic melanoma, non–small-cell lung cancer (NSCLC), and other malignancies has signifi-cantly prolonged survival (18–20).

Despite revolutionary achievements, the efficacy of checkpointblockade immunotherapy varies among different tumor types,and a few cancer types, such as colorectal cancer, appear to berefractory to this therapy (19, 21). A notable exception is thatpatients with mismatch-repair (MMR)-deficient colorectal cancer

1Key Laboratory of Carcinogenesis and Translational Research (Ministry ofEducation), Department of Gastrointestinal Surgery III, Peking UniversityCancer Hospital and Institute, Haidian District, Beijing, China. 2Department ofGeneral Surgery 1, Affiliated Hospital of Chengde Medical College, Chengde,China. 3Department of General Surgery, China–Japan Friendship Hospital,Beijing, China. 4Department of Gastrointestinal Surgery, Chaoyang Hospital,Beijing, China. 5School of Life Sciences, Tsinghua University, Beijing, China.6Peking-Tsinghua Center for Life Sciences, Beijing, China. 7Peking UniversityS.G. Hospital, Beijing, China.

Note: Supplementary data for this article are available at Cancer ImmunologyResearch Online (http://cancerimmunolres.aacrjournals.org/).

D. Ji, H. Yi, and D. Zhang contributed equally to this article.

Corresponding Author: Jin Gu, Peking University Cancer Hospital and Institute,Peking-TsinghuaCenter for Life Sciences, No. 52 FuchengRoad, HaidianDistrict,Beijing 100142, China. Phone/Fax: 86-10-88196238; E-mail: [email protected]

doi: 10.1158/2326-6066.CIR-17-0630

�2018 American Association for Cancer Research.

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lesions obtain clinical benefits from the administration of anti–PD-1 (22).

Increasing evidence indicates that antitumor effects, clinicallynotedwith checkpoint inhibitors such as ipilimumab,may rely onboosting tumor-specific immune responses that were preexistingor newly induced. High somatic mutation loads are correlatedwith responsiveness to PD-1 blockade therapy in NSCLC, mela-noma, and MMR-deficient colorectal cancers (22–24). Researchhas revealed that radiation can not only reduce tumor burden butalso enhance antitumor immune responses to tumor cells (25).The combination of radiotherapy, anti–CTLA-4, and anti–PD-L1promotes clinical responses in melanoma (26).

Therefore, we hypothesized that rectal cancer patientscould be potential candidates for checkpoint blockade immuno-therapy after receiving nCRT. To test this hypothesis, whole-exome sequencing of rectal cancer pre- and post-nCRT sampleswas performed to analyze the mutational landscape differences.We further performed an integrative analysis of mutational land-scape and gene expression using The Cancer Genome Atlas(TCGA) and gene-expression omnibus (GEO) data to evaluatethe influence of somatic mutations and their association withimmune response induced by treatment.

Materials and MethodsPatients and samples

Sample collection andusagewas approvedby the Ethics ReviewCommittees of Peking University Cancer Hospital and Instituteand was in accordance with the Declaration of Helsinki. Allpatients were informed prior to the study, and a consent formwas signed by each participant.

Tumor and paired normal adjacent tissue samples from cohort1 included14LARCpatientswho receivednCRTandwereused forwhole-exome sequencing. Nine paired LARC tumor tissues wereobtained pre- and post-nCRT, and the remaining patients onlyhad pretherapy biopsies due to complete pathologic response(pCR) or near pCR after nCRT. Pretreatment blood samples fromcohort 2 consisted of 42 LARC patients who received nCRT andwere used for circulating tumor DNA (ctDNA) extraction andtargeted ctDNA sequencing. The normal adjacent biopsies frompatients of cohort 2 were obtained for tissues DNA extraction andused as controls. All the tissue and blood samples were frompatients who received nCRT and surgical resections between 2014and 2016 at the Peking University Cancer Hospital and Institute(Beijing, China). Pretreatment tumor stagingwas performed in allpatients using endorectal ultrasonography, pelvic magnetic reso-nance imaging, or computed tomography. The radiotherapyregimen consisted of a 50.6 Gy dose delivered in 22 fractions,with concurrent capecitabine treatment at a dose of 825 mg/m2

orally twice per day for 5 weeks.Inclusion criteria were as follows: (i) diagnosis of rectal ade-

nocarcinoma by biopsy and (ii) tumor staged as T3–4 or any T,Nþ by endorectal ultrasonography, pelvic magnetic resonanceimaging, or computed tomography. Exclusion criteria were asfollows: (i) previous chemotherapy or pelvic radiation and (ii)presence of any other malignant disorders or other chronic dis-eases. For cohort 1, biopsies pre-nCRT collected by rectoscopy andtissue samples collected by surgical resection were immediatelyfrozen in liquid nitrogen and stored at�80�Cuntil use. For cohort2, 3.5 mL of venous blood was collected prior to nCRT from eachpatient and processed within 1 hour based on protocols of NCI's

Early Detection Research Networks. Plasma was harvested aftercentrifugation twice at 1,600 � g for 10 minutes immediately forctDNA extraction or stored at �80�C. In order to generate differ-ential mRNA profiling between responders and nonresponders tonRT and evaluate the influence of PD-1 and PD-L1 on prognosisafter neoadjuvant therapy, large-scale cohorts of pretherapy biop-sy and cancerous tissue samples were selected and categorizedinto cohorts 3, 4, and 5.

Cohort 3 was composed of biopsy samples from 66 patientsbefore nRT and resection. All patients were treated with interme-diate-fraction nRT (30 Gy/10 fractions) followed by a totalmesorectal excision (TME) surgery. The patients achieving grade0 and1, evaluated using the tumor regression grade (TRG) system,weredefined as responders. Thepatientswith grade 3weredefinedas nonresponders. Inclusion criteria were as follows: (i) diagnosisof rectal adenocarcinoma by biopsy; (ii) tumor staged as T3–4 orany T, Nþ by endorectal ultrasonography, pelvic magnetic reso-nance imaging, or computed tomography; and (iii) no evidence ofdistantmetastasis. Patients with the following characteristics wereexcluded: (i) previous chemotherapy or pelvic radiation and (ii)presence of any other malignant disorders or other chronic dis-eases. Biopsies were collected by rectoscopy and stored immedi-ately in RNAlater (Qiagen) and then stored at �80�C until use.

Cohort 4 consisted of 294 colorectal cancer patients who didnot receive nRT, and the samples were collected after surgicalresection. Patients with stage I–IV colorectal cancer, and withclinicopathologic characteristics and follow-up informationavailable, were included. We excluded patients if they had anyother malignant disorders or other chronic diseases, previoustreatment with any anticancer therapy, presence of any tumortype other than adenocarcinoma or mucinous carcinoma, andfamilial adenomatous polyposis colorectal cancer.

Cohort 5 included 296 samples from patients who receivednRT, and the samples were collected after resection. The radiationdosage was 30 Gy and delivered in 10 fractions over 2 weeks.Inclusion criteria were as follows: (i) diagnosis of rectal adeno-carcinoma by biopsy; (ii) tumor staged as T3–4 or any T, Nþ byendorectal ultrasonography, pelvic magnetic resonance imaging,or computed tomography; and (iii) no evidence of distant metas-tasis. Patients with the following characteristics were excluded: (i)previous chemotherapy or pelvic radiation and (ii) presence ofany other malignant disorders or other chronic diseases. Forcohorts 4 and 5, tumor tissue samples were directly collectedafter surgical resection. All samples were immediately frozen inliquid nitrogen at �80�C or fixed in 10% formalin for paraffinembedding. A summary of the clinical characteristics of thesepatients is shown in Supplementary Tables S1 to S5.

Assessment of treatment response and tumor downstagingThe 7th edition of the American Joint Committee on Cancer

TNM system was used for pathologic staging (27). Neoadjuvantradiotherapy effect was evaluated after surgery by specializedgastrointestinal pathologists using TRG system as follows: grade0, complete regression, no tumor cells; grade 1, single or smallgroups of tumor cells, moderate response; grade 2, residual canceroutgrown by fibrosis, minimal response; grade 3, minimal or notumor cells killed, poor response.

Whole-exome capture and sequencingSample preparation, library construction, exome capture,

next-generation sequencing, and bioinformatics analysis were

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performed at Shanghai Biotechnology Corporation. GenomicDNA from cohort 1 tumor samples was randomly fragmentedand used to construct an in vitro shotgun library. For eachsample to be sequenced, individual library preparations, hybri-dizations, and captures were performed following the protocolof SureSelectXT Target Enrichment System for Illumina Paired-End Sequencing Library (Agilent Technologies, Inc.). Briefly,the library fragments were flanked by index adaptors followingend repair and A-tailing addition. The library was furtherenriched with biotinylated probes (from the SureSelect bioti-nylated library mix) for sequences corresponding to exonsthrough aqueous-phase hybridization capture. The hybridizedfragments were captured by streptavidin-based magnetic beads(Dynabeads MyOne Streptavidin T1, Life Technologies, cat.#65603), followed by amplification, quality inspection, andmassive parallel sequencing of the enriched library. Quantity oflibrary was assessed with Qubit 2.0 Fluoromete. The qualityand size range was assessed using 2100 Bioanalyzer HighSensitivity DNA Assay as instructed in the reagent kit guide.BaseCalls directory, containing the binary base call files (BCLfiles), was generated by Real-Time Analysis (Illumina). Thebcl2fastq (v1.8.3, Illumina) was used to combine the per-cycleBCL files in each run and translate them into FASTQ files. TheFASTQ files were aligned to a human reference genome (hg19)by Burrows-Wheeler Aligner (BWA, v0.7.12; refs. 28, 29; SangerInstitute). The aligned files (SAM/BAM format files) were ini-tially sorted by SAM tools (ref. 30; v0.1.19, Sanger Institute).The aligned read duplicating the start position of another readwas flagged as a duplicate ("Mark duplicate") by using PicardTools (v1.107, Broad Institute). Data were processed using theGenome Analysis Toolkit (GATK, v3.1, Broad Institute). Readswere locally realigned (GATK IndelRealigner), and the basequalities were recalibrated (GATK BaseRecalibrator). Finalmapping statistics, including coverage and depth, were gener-ated from recalibrated files by BED tools (ref. 31; v2.16.1,Quinlan Laboratory at the University of Virginia) and perl/python scripts. The readings were further formatted with SAMtools, duplication was removed with Picard, local realignmentaround InDels was processed by GATK IndelRealigner, and basequality score recalibration was performed by using GATK BaseRecalibrator. Potential somatic substitutions were identifiedusing GATK Unified Genotyper followed by variant annotationthrough ANNOVAR/VEP/snpEFF.

Somatic mutations were determined using MuTect2 (ref. 32;Broad Institute) to identify mutations in matched tumor andnormal samples. A detailed MuTect2 procedure is availableat http://www.broadinstitute.org/cancer/cga/mutect/. The pairedsample data were finally filtered by ANNOVAR (ref. 33; Wednes-day, June 17, 2015, Center for Applied Genomics, Children'sHospital of Philadelphia, Department of Biostatistics and Epide-miology and Department of Pediatrics, University of Pennsylva-nia, Philadelphia, PA) for further analysis.

Targeted ctDNA sequencingSample preparation, library construction, exome capture, next-

generation sequencing, and bioinformatics analysis were per-formed at Genecast Biotechnology Co. Ltd . Plasma from cohort2 blood samples was harvested after centrifugation twice at 1,600� g for 10 minutes and aliquoted immediately for ctDNA extrac-tionor stored at�80�C. ctDNAwas extracted from2mLofplasmausing the MagMAX Cell-Free DNA isolation kit (Life Technolo-

gies) and quantified by Qubit dsDNA HS Assay kit or QubitdsDNA BR Assay kit (Life Technologies).

Genomic DNA was sheared into 150 to 200 bp fragments withCovarisM220 Focused-ultrasonicator Instrument (Covaris). Frag-mented DNA and the ctDNA library were constructed by KAPAHTP Library Preparation Kit (Illumina platforms, KAPA Biosys-tems), following the manufacturer's instructions. DNA librarieswere captured following NimbleGen SeqCap EZ Library SR(Roche) Users' Guide, with a designed 0.8M size panel (GenecastBiotech), which included 325 major tumor-related genes. Thecaptured samples were subjected to Illumina HiSeq X-Ten forpaired-end sequencing.

Paired-end reads generated from Hiseq X-Ten platform weremapped to the hg19 reference genomewith BWA v0.7.12 (defaultparameters; refs. 28, 29; Sanger Institute), then sorted, filtered,and indexed with SAM tools (ref. 30; 1.3, Sanger Institute). Inorder to identify somatic single-nucleotide polymorphisms andindel mutations, the obtained BAM files from both plasma andmatched normal tissues from each patient were processed forpairwise variant calling using VarScan (ref. 34; v2.3.8, TheGenome Institute, Washington University, St. Louis, MO). Min-imum coverage for calling somatic variants in matched normaltissues samples and plasma samples were 5 and 3, respectively.P value threshold to call a somatic site was 0.05, and variants with�90% strand bias were kept for further study. The generatedcandidate mutations were annotated using ANNOVAR software(ref. 33;Wednesday, June 17, 2015,Center for AppliedGenomics,Children's Hospital of Philadelphia, Department of Biostatisticsand Epidemiology and Department of Pediatrics, University ofPennsylvania, Philadelphia, PA), the dbNSFP and Exome Aggre-gation Consortium database were used to remove either thebenignmutations with pp2_hdiv score� 0.452 or the populationpolymorphic sites. Finally, the resulted nonsynonymous muta-tions at the exonic regionswere reserved. Tumormutation burden(TMB) was defined as the number of nonsynonymous mutationsper megabase of exonic region.

MMR status testingMMR status was assessed using the MSI Analysis System (Pro-

mega), consisting of 5 pseudomonomorphic mononucleotiderepeats (BAT-25, BAT-26, NR-21, NR-24, and MONO-27)to detect MSI and 2-pentanucleotide repeat loci (PentaC andPentaD). According to the manufacturer's guidelines, a 2-ngtemplate DNA for each sample was added into the amplificationmix, including nuclease-free water, gold ST�R buffer, MSI primerpair mix, and AmpliTaq Gold DNA polymerase (Life Technolo-gies; cat. #N8080242) and mix gently. For the positive amplifi-cation control, 2 ng of the diluted K562 high-molecular-weightDNA was added into the amplification mix. For the negativeamplification control, nuclease-fee water (instead of templateDNA) was added into amplification mix. Amplification wasperformed in a GeneAmpR PCR System 9600 (Applied Biosys-tems). Amplification conditions were as follows: 95�C for 11minutes, 96�C for 1 minute; then 94�C for 30 seconds, ramp 68seconds to 58�C, hold for 30 seconds, ramp 50 seconds to 70�C,hold for 1 minute, for 10 cycles; then 90�C for 30 seconds, ramp60 seconds to 58�C, hold for 30 seconds, ramp 50 seconds to70�C, hold for 1 minute for 20 cycles; then 60�C for 30 minutes,4�C soak. The primers used were provided in the MSI AnalysisSystem (Promega). Following amplification of DNA, the fluores-cent PCR products were analyzed with the Applied Biosystems

nCRT Enables Rectal Cancer Checkpoint Blockade Therapy

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http://www.broadinstitute.org/cancer/cga/mutect/


3130xl Gene Analyzer using GeneScan analysis software (AppliedBiosystems). The length of the sequence was determined for eachmicrosatellite locus, and the tumors were designated as high-frequency MSI (MSI-H) if two or more mononucleotide locivariations were identified in length compared with the germlineDNA. One variation was considered as low-frequency MSI(MSI-L) and none as microsatellite stable (MSS).

RNA isolation and microarray analyzesTotal RNA from cohort 3 biopsy samples was isolated using

TRIzol reagent (Life Technologies), according to the manufac-turer's instructions, and purified by using the RNeasy Mini Kit(Qiagen). RNA samples of each group were subsequently used togenerate fluorescence-labeled cRNA targets for the AffymetrixHuman U133 Plus 2 arrays (Affymetrix; cat. #900467). Thelabeled cRNA targets were then hybridized on slides. After hybrid-ization, slides were scanned by GeneChip Scanner 3000 (Affyme-trix). Data were extracted with Command Console Software 4.0(Affymetrix). Rawdatawerenormalized by theMAS5.0 algorithmand Gene Spring Software v12.6.1 (Agilent technologies). Themicroarray experimentswere performed following the protocol ofAffymetrix Inc. (Shanghai Biotechnology Corporation).

Cell linesThe MC38 cell line was purchased from the National Infra-

structure of Cell Line Resource (Beijing, China) and was main-tained in RPMI 1640 with 10% fetal bovine serum (Gibco),penicillin sodium (100 U/mL), and streptomycin sulfate (100mg/mL) in humidified 5% CO2 at 37�C. The cell line was testedand authenticated by short tandem repeat profiling. Cells wereroutinely tested for mycoplasma infection and used only whennegative. Cells were passaged a maximum of 3 to 4 times in vitrobefore they were used in in vivo experiments.

In vivo mouse studiesFour- to six-week-oldmale C57BL/6mice were purchased from

Beijing Vitalriver Experimental Animal Technology Co. Ltd. Micewere maintained in a pathogen-free facility and used in accor-dance with the institutional guidelines for animal care. All animalexperiments were performed following protocols approved by theInstitutional Animal Care and Use Committee at the PekingUniversity Cancer Hospital. MC38 (1 � 104) cells were mixedwith an equal volume of Matrigel (BD Biosciences) and subcu-taneously injected on the right leg of the mice on day 0. Thetumors were irradiated with 8 Gy on day 15. Blocking antibodies(PD-1, clone RMP1-14; PD-L1, clone 10F.9G2 and rat IgG2Bisotype, clone LTF-2; all from Bio X Cell) were given intraperito-neally on days 16, 19, and 22. All irradiationwas performed usingthe Edge linear accelerator (Varian Medical Systems, Inc.). Anti-bodies used for in vivo immune-checkpoint blockade experimentswere given intraperitoneally at a dose of 200 mg/mouse andinclude PD-1 (clone RMP1-14), PD-L1 (clone 10F.9G2), and ratIgG2B isotype (clone LTF-2; all from Bio X Cell). The tumorvolumes were measured using CT scans and MRI once a week.Volumewas calculated using the formula L�A� B� 0.52, whereL is the longest dimension and A and B are long and shortdiameters of the largest coronal section, respectively. The untreat-ed tumor volumes were determined at day 14 using CT scans andwere considered as a baseline tumor volume (Vcont). Normalizedtumor response to treatment was the measured volume (V)relative to Vcont. The tumor volumes were also measured using

calipers every 3 days. Differences in survival were determined foreach group by the Kaplan–Meier method. The overall P value wascalculated by the log-rank test. For mouse studies, an event wasdefined as death or when tumor burden reached a protocol-specified size of 1.5 cm in maximum dimension to minimizemorbidity (26).

IHC analysis on tissue microarrays (TMA)IHC was performed on TMAs using rabbit polyclonal anti-

human PD-1 (LS-B540, LifeSpan BioSciences), anti-humanPD-L1 (ab58810, Abcam), anti-human PD-L2 (HPA013411,Sigma), and mouse monoclonal anti-human CD8 (clone4B11, Novus Biologicals). All images were examined by twoexperienced pathologists independently. For PD-L1 and PD-L2,the immunoreactivity of the proteins detected was recordedthrough the intensity of staining, and the percentage of immu-noreactive cells scored as: tissues with no staining were rated as0, with a faint or moderate staining to strong staining in <25%of cells rated as 1, strong staining in 25% to 50% of cells ratedas 2, and strong staining in >50% of cells rated as 3. The slideswere further analyzed to identify PD-1 and CD8 using animage analysis workstation (Spot Browser, Alphelys). The totalnumber of PD-1þ or CD8þ cells in each tissue spot wascounted, and the density of PD-1þ or CD8þ TILs was definedas the cell number per square millimeter.

TCGA and GEO data setsLevel 3 TCGA RNA-seq and DNA exome-seq data for rectal

cancer patients with clinical information were downloaded fromthe TCGA data portal, including 341 rectal cancer samples on July18, 2015. The cases that had clear information about nCRT wereanalyzed, including 7 rectal cancers with nCRT and 41 withoutnCRT. EdgeR-normalized data of these 48 cases were used forcorrelative analysis.

Two patient data sets of rectal cancer were downloaded fromthe GEO database. GSE 15781 consisted of 22 patients withresectable adenocarcinoma of the rectum, in which 13 patientshad surgery only, and 9 patients received nCRT. GSE 45404included pretreatment biopsies of 42 rectal cancer patientswho received nCRT with conventional fractionation (�50 Gy in28 fractions, 1.8 Gy/day, 5 sessions per week) and 5-fluorouracil(5-FU)–based chemotherapy.

Mutant peptide MHC binding predictionAll nonsynonymous pointmutations identifiedwere translated

into strings of 17 amino acids, with the mutant amino acidsituated centrally according to previous research (23, 24). Ourinitial analysis was focused on HLA-A and HLA-B (referenceset of 27 alleles was assembled and covered >97% of population;ref. 35), the accession numbers for the reference alleleswere shown in Supplementary Table S6. The 17 mutant aminoacid fragments were analyzed by the epitope prediction programNetMHC v4.0 (http://www.cbs.dtu.dk/services/NetMHC/). Epi-topes with a predicted affinity of <50 nm were considered tobe strong potential binders, and epitopes with a predicted affinityof <500 nmwere considered weak potential binders, as suggestedby the NetMHC group. To further refine the total neoantigenburden, we repeated the same process for the complementarywild-type peptide for each mutant peptide. The mutant peptidesthat held strong potential binders were used to compare with thecomplementary wild-type peptide, which was predicted to have a

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http://www.cbs.dtu.dk/services/NetMHC/


weak potential binder. These mutant peptides were referred to asmutation-associated neoantigens. These mutant peptides werefurther evaluated for putative binding to the T-cell receptor (TCR)using the IEDB immunogenicity predictor with patient-specificHLA types (http://tools.immuneepitope.org/immunogenicity/)and CTLPred (http://www.imtech.res.in/raghava/ctlpred/).

Statistical analysisThe Mann–Whitney test and Student t test were used to com-

pare mutation burdens and differences in the frequency of nucle-otide changes. The log-rank test was used to compare Kaplan–Meier survival curves. Correlations between the nonsynonymousmutation burden and the neoantigen burden, frequency of theneoantigen burden/nonsynonymous mutation, and immuneresponse were calculated using the Pearson correlation formula.Amixed-effect linear model was used to determine significance ofdifferences in tumor growth. Statistical analyses were performedusing GraphPad Prism v.6.

Availability of data and materialWhole-exome sequencing data are available under the NCBI

Sequence Read Archive study accession no. SRP159539. Micro-array data are prepared according tominimum information abouta microarray gene experiment guidelines and deposited in theGEO database. The GEO accession number is GSE119409.

ResultsnCRT induces immune activation in LARC

To assess whether nCRT could affect the immune response, wecompared rectal cancer samples with and without treatment fromGEO data (GSE 15781) using GSEA. These data consisted of 22patients with resectable adenocarcinoma of the rectum. Thirteenpatients had surgery only, and 9 patients received nCRT. Theirradiated and nonirradiated tumor tissues and normal tissueswere used to perform genome-wide gene-expression analysis. Asupervised method (Significance Analysis of Microarrays; SAM)was initially used to identify a statistical significance (adjusted P <0.05) in differentially expressed genes between samples with andwithout nCRT treatment. Differentially expressed genes (3,359)were identified between these two subgroups. Of which, 1,840genes revealed significantly higher expression in posttreatmentsamples, whereas 1,519 genes showed lower expression. A hier-archical clustering analysis was subsequently performed based onthe expression values of the 3,359 genes in the 22 samples(Supplementary Fig. S1) to categorize the samples into two mainsubgroups (branches), in which the gene expressions showed theopposite trend.

To further investigate the mechanism of the immuneresponse alternation after the chemoradiotherapy, the differen-tially expressed genes induced by chemoradiotherapy with theimmune-related genes defined by Gene Ontology (1,776 genes)were compared. A total of 342 immune-related geneswere altered,with most being upregulated. Unsupervised hierarchical cluster-ing of this subset of overlapping genes resulted in a clear sepa-ration of the samples by nCRT (Fig. 1A and B).

Reactome pathway analysis showed that immune activation–related pathways were induced, including interferon signaling,antigen presentation, class I MHC-mediated antigen processingand presentation, PD-1 signaling, peptide–ligand binding recep-tors, and costimulation from the CD28 family (Table 1). Among

the altered 342 immune-related genes, 17 genes fell into theCD28costimulatory signal pathway (defined by Reactome pathwaydatabase; Fig. 1B). CD28, CD86, HLA class II molecules, PIK3CA,PTPN11, MAPKAP1, GRB2, VAV1, FYN, andMAP3K8 were upre-gulated (Fig. 1C). In contrast, PAK1, PPP2R1A, and MLST8 weredownregulated. The expression of T-cell differentiation and acti-vation markers were analyzed, and the results revealed thatdifferentiation markers (KLRG1 and PTPRC) and activation mar-kers (CD69, IL2RA, CD38; refs. 36, 37) were upregulated in LARCsamples following nCRT (Fig. 1D).

We then focused on the differentially expressed genes andperformed gene set enrichment analysis using a prior definedimmunologic signature set (signature of gene expression uponperturbation of certain immune-related genes). PosttreatmentLARC was characterized by increased expression of mostimmune-related genes and associated with a diffuse immuneinfiltrate mainly composed of TH1 cells and cytotoxic T cellsexpressing checkpoint molecules (Fig. 1E).

To further assess the effect of nCRTon the infiltration of specificimmune cell subsets, we used CIBERSORT (38) as an approach todissect infiltration of specific immune cell subsets in the tumors.The higher fractions of monocytes, activated dendritic cells, andneutrophils cells were found in rectal cancers with nCRT. Thefaction of gamma delta T cells slightly increased from 0% to0.78% following nCRT (Supplementary Fig. S2). The lists ofdifferentially expressed genes, immune genes, and CD28 costi-mulatory signals are presented in Supplementary Tables S7 to S9.

nCRT influences the mutational landscape of LARCPre- and post-nCRT tumor samples from 9 of the 14 patients of

cohort 1 were collected and sequenced, and the other 5 cases onlyhad biopsy samples prior to therapy because of pathologicallyinvisible tumor masses after achieving pCR. The tumor DNAsequencing generated a mean target coverage of 239�, and amean of 99.8% of the target sequence was identified as a depth ofminimum 10�. A median of 80 nonsynonymous mutations perpre-nCRT sample (range, 3–313) was detected. The mediannumber of exonic mutations per pre-nCRT sample was 122(range, 3–455). The microsatellite instability status of the 9 caseswas tested, and all cases were MSS. The quantity and range ofmutations were similar to published series of colorectal cancers(22). The nCRT-induced mutations appeared in posttreatmentsamples. The median number of exonic mutations induced bynCRTwas 23 comparedwith pretreatment samples (range, 8–89).The median nonsynonymous mutation induced by nCRT was 15per post-nCRT sample (range, 6–57).

nCRT influences the neoantigen landscape of LARCThe landscape of neoantigens was examined according to

previously described methods (23, 24). This approach identifiedmutant nonamers with�500 nmol/L binding affinity for patient-specific class I human lymphocyte antigen (HLA) alleles, whichwere considered candidate neoantigens. Amedianof 58 candidateneoantigens from each pretreatment tumor (range, 0–256) andthe quantity of neoantigens per tumor correlated with mutationburden were identified (Pearson r¼ 0.9491, P < 0.0001; Fig. 2A).Newmutations induced by nCRT fromposttreatment tumors alsoresulted in amedian of 11 candidate neoantigenswith a range of 3to 50. These candidate neoantigens significantly correlated withnewmutation numbers (Pearson r¼ 0.9640, P < 0.0001; Fig. 2B).





http://tools.immuneepitope.org/immunogenicity/

http://www.imtech.res.in/raghava/ctlpred/


The mutational landscape from TCGA data analysis of rectalcancer with and without nCRT showed that the median nonsy-nonymous mutation burden was 163 in tumors from patientswith nCRT comparedwith 120 in patientswithout nCRT (Studentt test, P¼ 0.0387; Fig. 2C). Themedian candidate neoantigens pertumor was 22 (range, 0–55), and the quantity of neoantigens pertumor correlated with mutation burden (Pearson r¼ 0.9988, P <0.0001; Fig. 2D). Themedian putative neoantigens binding to theTCR was 18 in with nCRT patients compared with 14 in withoutnCRT (Mann–Whitney test, P ¼ 0.01, Fig. 2E).

Immune activation is related to neoantigens arising frommutation burden

To further confirm that immune activation following nCRTwasrelated to neoantigens arising from mutation burden, the corre-lation between the number of mutations and the expression ofall genes in rectal cancer from the TCGA DNA exome-seq andRNA-seq data was studied. An ordered list of 354 significantrelated genes was generated (Pearson r > 0.8 or r <�0.8, P <0.01) for the Reactome pathway database analysis. The enrichedpathways focused on the immune system, signal transduction,

Figure 1.

nCRT induces immune activation of LARC.A, Unsupervised clustering highlighting genome-wide changes in gene expression. Rectal cancer samples were clusteredaccording to the expression of 342 differentially expressed immune genes (P < 0.05) between 13 pre- and 9 post-nCRT samples (GSE 15781). Rectal cancersamples are shown across the horizontal axis, with 1 sample expression pattern shown in each column. Gene-expression values range from green (low expression) tored (high expression). B, Venn diagram showing significant overlaps of core genes from the differentially expressed genes (red circle), immune-related genes (bluecircle), and CD28 costimulatory related genes (green circle). C, Unsupervised clustering for 17 differentially expressed immune genes for CD28 costimulatorysignals (P <0.05) between pre- and post-nCRT.D,Unsupervised clustering for T-cell differentiation and activationmarkers between pre- and post-nCRT. E, Enrichedpathways for differentially expressed immune genes after nCRT using GSEA. The 20 most significant pathways and corresponding normalized enrichmentscores (NES) are shown. P � 0.01, FDR q < 0.25 using enrichment score calculation, permutation test, and multiple-hypothesis test.

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and developmental biology. An association was identifiedbetween mutation burden and enrichment of immune activa-tion–related pathways, including alpha-defensins, IL2 familysignaling, and other several interleukin-related pathways (inter-leukin receptor SHC signaling, interleukin-19, 20, 22, 24, 26, 28,and 29 signaling, interleukin-3, 5 and GM-CSF signaling, inter-leukin-17 signaling; Table 1).

RT combined with anti–PD-1 enhances control of tumorgrowth

To understand the contribution of RT to immune-checkpointblockade, we utilized the MC38 colorectal cancer mouse mod-el. Mice with right leg tumors received irradiation (RT), anti–PD-1/PD-L1 (from now on referred to as PD1/PDL1), or bothtreatments delivered sequentially (RT þ PD1 or RT þPDL1; Fig. 3A). Tumor growth was controlled by RT, PD1, RTþ PD1, and RT þ PDL1 (P ¼ 0.0163, P ¼ 0.02, P < 0.0001, andP ¼ 0.0035, respectively), and tumor growth was well con-trolled when RT þ PD1 was administered compared with eithertreatment alone (P ¼ 0.0037and P ¼ 0.0021). No significantdifferences among PDL1, RT, and RT þ PDL1 treatments wereseen (Fig. 3B–D).

Kaplan–Meier analysis showed that PD1, RT, RTþ PD1, PDL1,and RTþ PDL1 improved the survival of mice (P¼ 0.001 and P¼0.018 for comparisons of PD1 and PDL1 treatment groups,respectively). The largest improvement in survival was observedin the RT þ PD1 group. When compared with RT or PD1 alone,RT combined with PD1 still improved the survival significantly(P¼ 0.046 and P¼ 0.042, respectively). No significant differencesamong PDL1, RT, and RT þ PDL1 groups were found (Fig. 3E).

Mutation burden and neoantigens correlate with the responseto nCRT

The analysis of whole-exome sequencing indicated that rectalcancerswith highermutation burdenweremore sensitive to nCRT(Figs. 4 and 5A). The results showed high numbers of somaticmutations in pre-nCRT biopsies, whichwere associatedwith TRG;Pearson r ¼ �0.5418, P ¼ 0.0454; Fig. 5A). The potential muta-tion-associated neoantigen numbers were negatively correlatedwith TRG but were not statistically significant (Pearson r ¼�0.3684, P ¼ 0.1949; Fig. 5B). Target-capture sequencing ofplasma ctDNA and matched normal tissue DNA (cohort 2) wasperformed to detect somatic mutations in each sample, achievingamean sequencing coverage of 2,313� (914–3,792�) for plasma

ctDNA and 1,675 � (765–2,649�) for normal tissue DNA.The correlation between the TMB of ctDNA and the TRG variedamong the patients (mean 9.76mutation/Mb; range, 4–16muta-tions/Mb; Fig. 5C). TMBwas associatedwith the TRG (Pearson r¼�0.3636, P¼ 0.0179; Fig. 5D). Themean TMBwas higher in pCRpatients than in non-pCR patients (13 vs. 9.22, respectively; P ¼0.0014; Fig. 5E). The patients were divided into two groups byassessing the percentage of viable residual tumor cells less ormorethan 30% in the resected specimens. Themean TMBwas higher inthe <30% group than in the >30% group (10.5 vs. 8.78, respec-tively; P ¼ 0.0472; Fig. 5F).

Immune activity correlates with the response to nCRTTo further investigate the correlation between gene expression

and response to nRT, mRNA expression in pretherapy biopsies ofcohort 3 was profiled into responding (n¼ 19) and nonrespond-ing (n¼ 47) groups. A supervisedmethod (SAM)was used to findstatistical significance (adjusted P < 0.05) in differentiallyexpressed genes between responding and nonresponding groups.A total of 59 geneswere differentially expressed between these twosubgroups (P < 0.05, fold change >1.5), with most genes present-ing significantly higher expression in the responding group and 4genes with lower expression (Supplementary Fig. S3). Reactomepathway analysis showed that the immune activation–relatedpathways were enriched, including interleukin-10 signaling, che-mokine receptors bind chemokines, cytokine signaling inimmune system, peptide ligand-binding receptors, immune sys-tem, PD-1 signaling, interferon signaling, and costimulationfactors within the CD28 family (Supplementary Table S10).

We also compared responding and nonresponding rectalcancer pretreatment biopsies from GEO data (GSE 45404)consisting of 42 patients who received nCRT with conventionalfractionation (�50 Gy in 28 fractions, 1.8 Gy/day, 5 sessionsper week) and 5-fluorouracil (5-FU)-based chemotherapy. Thereadings from the pretreatment patients responding (n ¼ 19)and nonresponding (n ¼ 23) to chemoradiotherapy werecompared. Reactome pathway analysis showed similar resultsto cohort 3 (Supplementary Table S10).

The correlation between infiltrating immune cells and responseto nCRT was further assessed using CIBERSORT. The results ofcohort 3 showed that the fractions of CD8þ T cells (19% vs. 12%)and activated CD4þmemory T cells (0.47% vs. 0) in the respond-ing group were higher than those in the nonresponding group. Inthe nonresponding group, the fraction of regulatory T cells (Treg)

Table 1. Reactome pathway analysis with nRT versus without nRT (GSE15781) and mutation-related pathways (TCGA)

With nRT vs. without nRT (GSE15781) Mutation-related pathways (TCGA)Pathwayname

EntitiesP value Entities FDR

Pathwayname

EntitiesP value

EntitiesFDR

Interferon gamma signaling 1.11E�16 1.21E�14 Developmental biology 5.67E�11 2.34E�08Antigen presentation: Folding, assembly,and peptide loading of class I MHC

1.11E�16 1.21E�14 Alpha-defensins 8.46E�08 2.61E�05

Immunoregulatory interactions between alymphoid and a nonlymphoid cell

1.11E�16 1.21E�14 NCAM signaling for neurite outgrowth 2.39E�06 5.89E�04

Class I MHC-mediated antigen processing andpresentation

1.11E�16 1.21E�14 Interleukin-2 family signaling 2.17E�05 0.002010602

Cytokine signaling in immune system 1.11E�16 1.21E�14 Signaling to RAS 3.58E�05 0.002010602Receptor binding chemokines 1.34E�13 1.34E�11 RAF/MAP kinase cascade 5.59E�05 0.002010602PD-1 signaling 7.64E�07 6.65E�05 Interleukin receptor SHC signaling 9.43E�05 0.002545513Peptide ligand-binding receptors 1.71E�06 1.39E�04 Interleukin-19, 20, 22, 24, 26, 28,

and 29 signaling1.47E�04 0.003831806

Costimulation by the CD28 family 2.71E�05 0.00195 Interleukin-3, 5, and GM-CSF signaling 2.05E�04 0.005137273






Figure 2.

nCRT influences the neoantigen landscape of LARC. A, The quantity of neoantigens and mutations per tumor prior to nCRT (cohort 1; n ¼ 14), using Pearsoncorrelation. B, The quantity of neoantigens andmutations per tumor post-nCRT (cohort 1, n¼ 9), using Pearson correlation. C, Themutation burden in patients post-nCRT (n ¼ 7) and patients without nCRT (n ¼ 41) from TCGA data, using Student t test. D, The candidate neoantigens and mutation burden fromTCGA data, using Pearson correlation. E, The neoantigens binding to the TCR in patients post-nCRT and patients without nCRT (from TCGA data), usingMann–Whitney test. Statistical significance was set at P < 0.05.

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was higher than that in the responding group (2.29% vs. 0.59%).GSE 45404 data also showed a significant increase in the fractionsof CD8þ T cells (1.99% vs. 0%) and activated CD4þ memory Tcells (7.67% vs. 2.97%) in the responding group compared withthe nonresponding group (Supplementary Fig. S4).

PD-L1 expression negatively correlates with prognosisThe expression pattern of PD-L1, PD-1, and PDL2 in tumors

and corresponding nontumor tissue of cohort 4was studied usingIHC. PD-L1 was expressed not only on tumor cells but also onTILs. Themajority of PD-L1 expression was observed in the tumor

Figure 3.

RT combined with anti–PD-1enhances tumor growth control in amouse model. A, Schematic of theexperimental setup. Yellow arrow:location of transplantation. Timelinestarts from original tumorimplantation (day 0). Black arrows:treatments given. B, MRI images ofcombination RT þ PD1 (RT combinedwith anti–PD-1)-treated tumors andbaseline/control tumors. C and D,Total tumor growth for MC38 tumorsafter the indicated treatment.Radiotherapy: RT (n ¼ 9); anti–PD-1:PD1 (n¼ 8); anti–PD-L1: PDL1 (n¼ 9);RT combined with anti–PD-1 or anti–PD-L1: RT þ PD1 (n ¼ 8), and RT þPDL1 (n ¼ 9), respectively. C,Normalized values; D, raw values. Fornormalization, volumes were dividedby average of untreated controls (V/Vcont) to account for differences ingrowth between different treatedgroups, using a mixed-effect linearmodel. E, Survival after RT and/oranti–PD-1 (left) or anti–PD-L1 (right).Shown are overall P values. TheP values for RT þ PD1 and RT oranti–PD-1 alone are P ¼ 0.046 andP ¼ 0.042, respectively. Control is anisotype-matched antibody. Using thelog-rank test, statistical significancewas set at P < 0.05.






cells, whereas weak expression was observed on TILs (Fig. 6A). Ahigh expression of PD-L1 was detected in epithelial cells withelevated expression in tumor tissue compared with normal tissue(Z ¼ �5.538, P < 0.0001).

Expression of PD-L1was correlatedwithM stage (Spearman r¼0.13, P¼ 0.03), differentiation (Spearman r¼ 0.131, P¼ 0.029),and histologic type (U ¼ 3.8158, P ¼ 0.000136). PD-L1 expres-sion in livermetastasis tissues was lower than expression in pairedprimary tumor tissues (Z ¼ �2.346, P ¼ 0.019; SupplementaryTable S11). Positive PD-L1 expression in colorectal cancer tissueswas associated with a shorter overall survival (OS; P¼ 0.065) anddisease-free survival (DFS; P¼ 0.06; Supplementary Fig. S5A andS5B), although the tendency was not statistically significant. Forstage I–III colorectal cancer patients, positive PD-L1 expressionwas significantly associated with poor OS (P ¼ 0.019; Fig. 6B),whereas for stage IV colorectal cancer patients, positive PD-L1

expression was significantly associated with favorable prognosisof colorectal cancer (P ¼ 0.001; Supplementary Fig. S5C).

The association between PD-L1 expression and prognosis ofrectal cancer indicated that positive PD-L1 expression wassignificantly associated with poor prognosis of rectal cancer(for DFS, P ¼ 0.031; for OS, P ¼ 0.043; Fig. 6C and D). Nosignificant correlation between PD-L1 and prognosis of coloncancer was found (Supplementary Fig. S5D and S5E). Wefurther performed subgroup analysis in different stages of colonand rectal cancers to ascertain the effect of primary tumorlocation on the correlation. For stage I–III rectal cancer patients,positive PD-L1 expression was significantly associated withpoor OS (P ¼ 0.023; Supplementary Fig. S6A). In stage IVrectal cancer, PD-L1 expression was associated with better OS(P ¼ 0.011; Supplementary Fig. S6B). However, no significantcorrelation between PD-L1 and prognosis of colon cancer,whether in stage I–III (P ¼ 0.069, Supplementary Fig. S6C) orin stage IV (P ¼ 0.169; Supplementary Fig. S6D), was found.

PD-1 was also expressed in tumor cells and TILs, with TILshaving the highest expression. PD-1 expression was negativelycorrelated with tumor cell differentiation (Spearman r¼�0.135,P¼ 0.022), but no significant correlation between PD-1 and otherclinical–pathologic findings was found (Supplementary TableS11; Supplementary Fig. S7A–S7D).

PD-L2 was expressed not only in tumor cells, but also in TILs.However, the majority of PD-L2 expression was in tumor cells(Supplementary Fig. S8A). High PD-L2 expression was signifi-cantly associated with better OS (P ¼ 0.001; SupplementaryFig. S8B) in all stage colorectal cancer patients. Specifically, forstage I–III colorectal cancer patients, high PD-L2 expression wassignificantly associatedwith betterOS (P¼ 0.042; SupplementaryFig. S8C), whereas for stage IV colorectal cancer patients, high PD-L2 expression was associated with poor prognosis (P ¼ 0.067;Supplementary Fig. S8D). We also performed subgroup analysisin different stages of colon and rectal cancers. For colon cancerpatients, a significant positive correlation between PD-L2and prognosis in all stages was seen (P < 0.0001; SupplementaryFig. S8E). However, no significant correlation between PD-L2 andprognosis was found in stage I–III or stage IV alone. For all stagerectal cancer patients, no significant correlation between PD-L2and prognosis was seen. For stage I–III rectal cancer patients, therewas no significant correlation between PD-L2 and prognosis, butin stage IV rectal cancer, PD-L2 expression was associated withpoor OS (P < 0.0001; Supplementary Fig. S8F).

PD-L1 and PD-1 after nCRT correlates with CD45RO, CD8, andoutcomes

To determine whether PD-L1 and PD-1 were associated withclinical and pathologic features in LARC patients, TMA analysisof 296 LARC patients from cohort 5 was performed. Thepatients' profile is listed in Supplementary Table S5, and thetumor-infiltrating cytotoxic lymphocytes are shown in Fig. 7Aand B. No significant correlation was found between PD-L1,PD-1, and clinical–pathologic parameters observed (Supple-mentary Table S12).

To understand the relationship between PD-1, PDL-1,CD45RO, and CD8, the correlation coefficients of PD-1, PD-L1, CD45RO, and CD8 expression among all patient sampleswere calculated. PD-L1 was significantly correlated with CD45RO(r ¼ 0.157, P ¼ 0.009, Spearman test) and CD8 (r ¼ 0.278, P ¼

Figure 4.

Mutation burden correlated with patients' response to nCRT. Paired pre- andpost-nCRT tumors from 2 LARC patients. TRG of case 4 with highermutation number is 0, complete regression, better response. TRG ofcase 3 with lower mutation number is 3, poor response. The H&E stainingshows a typical rectal adenocarcinoma. The MRI images show thepre-nCRT and post-nCRT tumors.

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0.000, Spearman test). No correlation was found between PD-1and CD8 or CD45RO (Supplementary Table S12).

Our previous work demonstrated that CD45RO expressionsignificantly correlates with prognosis of LARC patients withneoadjuvant radiotherapy (39). To determine if prognosticassociations existed between the expression of PD-L1, PD-1,CD8, and patient survival, Kaplan–Meier survival curves wereplotted. Again, no significant correlation between DFS or OSand the expression of the proteins was found (SupplementaryFig. S9A–S9F).

These results suggested that PD-1, PD-L1, and CD8 were notindependent prognostic factors for patient survival. However, ifthe patient survival was plotted against a combination of PD-L1withCD8, in theCD8-low expression group, a significant negativecorrelation was observed in DFS (P ¼ 0.042; Fig. 7C). It seemedthat CD8-high expression was not associated with DFS and OS(Supplementary Fig. S10A and S10B). With regard to the associ-ation of patient survival with the combination of PDL1 withCD45RO, in the CD45RO-low expression group, a negativecorrelation was observed in overall patient survival (P ¼

Figure 5.

Mutation burden and neoantigens of LARC correlated with the response to nCRT. A and B, Cohort 1 (n ¼ 14). A, Mutation numbers in pre-nCRT biopsies with thedifferent TRG, using Pearson correlation.B,Themutation-associatedneoantigens in pre-nCRTbiopsieswith the different TRG, using Pearson correlation.C–F,Cohort2 (n ¼ 42). C, TMB in pre-nCRT ctDNA. D, The correlation between TMB in pre-nCRT ctDNA and the TRG, using Pearson correlation. E, The TMB in pCRpatients (n ¼ 6) and non-pCR patients (n ¼ 36), using Student t test. F, The TMB in residual tumor cells in the <30% group (n ¼ 24) and in the >30% group(n ¼ 18), using Student t test. Statistical significance was set at P < 0.05.






Figure 6.

Association between PD-L1 andPD-1 expression andprognosiswith colorectal cancer using IHC (cohort 4).A,Expression of PD-1 and PD-L1 in the indicated colorectalcancer tissues using IHC analysis. Positive staining is shown in brown. B, OS. Kaplan–Meier analysis of TNM stage I–III colorectal cancer patients in cohort 4.C, Kaplan–Meier analysis of the correlation between PD-L1 expression and DFS in patients with rectal cancer in cohort 4. D, Kaplan–Meier analysis of the correlationbetween PDL1 expression and OS in patients with rectal cancer in cohort 4. The number of patients per group indicated on graphs. Using the log-rank test,statistical significance was set at P < 0.05.

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0.081) or DFS (P ¼ 0.099), although it was not statisticallysignificant (Supplementary Fig. S10C and S10D).

DiscussionWe analyzed somatic gene expression in irradiated and

nonirradiated rectal cancer samples from GEO data to assessthe influence of nCRT on the immune condition. Our studyindicated that chemoradiotherapy could be able to induce animmune response in rectal cancer patients. Posttreatment LARCis characterized by increased expression of genes involved inimmune response pathways, mainly composed of cytotoxic Tand TH cells, along with strong activation of antigen presenta-tion, peptide ligand-binding receptors, CD28 costimulatory

signals, interferon signaling, and other cytokine signaling inthe immune system.

T-cell activation requires a two-step process that includesengagement of the TCR to an antigen presented by an antigen-presenting cell (APC), anda second costimulatory signal deliveredby the engagement of CD28 to its ligands CD80 and CD86 (40).Tumor cells expressing HLA class I present tumor-associatedantigens on their cell surface and are recognized by CD8þ cyto-toxic T cells. Our analysis showed that nCRT led to a significantincrease in CD28, CD86, and HLA class I and II molecule expres-sion in rectal cancer cells. The class II molecule is expressed on thesurface of professional APCs and to some degree on cancer cells(41) and plays a central role in the immune system by presentingpeptides derived from extracellular proteins. Studies have shown

Figure 7.

Expression of PD-L1 and PD-1 in post-nRT rectal cancer tissue samples and therapeutic outcome for nRT using IHC (cohort 5). A, Representative images of PD-L1expression on tumor cells and TILs. B, Representative images of PD-1 expression on tumor cells and TILs, as well as CD8 expression on TILs. C, Kaplan–Meieranalysis of the correlation between PD-L1 expression and DFS in patients with low CD8 expression in cohort 5. PD-L1–negative n ¼ 67; PD-L1–positiven ¼ 45. Using the log-rank test, statistical significance was set at P < 0.05.






the direct requirement for competent HLA class II pathway stim-ulation in the reduction of HLA class I–mediated response for aneffective immunotherapy approach (42). HLA class II antigenexpression in colorectal cancer tumors is a favorable prognosticmarker (43). We noticed that T-cell activation in posttreatmentLARCwasmainly through IFNg signaling activation and increasedPI3K signaling. We further analyzed the expression of T-celldifferentiation and activation markers. Differentiation markers(KLRG-1 and CD45 isotypes) and activation markers (CD69,CD25, and CD38) were increased in LARC following nCRTcompared with pre-nCRT conditions. It was clear that nCRT couldinduce immune activation in LARC patients.

Themechanismbywhich radiation induces adaptive immunityremains unclear. Twyman-Saint and colleagues report majortumor regressions in a subset of patients with metastatic mela-noma treated with an anti-CTLA4 and radiation, which wasreproduced inmousemodels (26). Their study demonstrates thatradiation can diversify the TCR repertoire of TILs and shape therepertoire of expanded clones. Reits and colleagues demonstratethat radiation can enhanceMHC class I expression bymodulatingthe peptide repertoire (44).

Interest in the relationship between somaticmutational burdenand antitumor immune response motivated us to examine thedifferences in the mutational landscape between the pre- andposttreatment rectal cancers by sequencing the exomes of LARCs.Our analysis revealed that nCRT influenced the mutational land-scape of LARC and induced novel somatic mutations, and wefurther validated the results in TCGAdata sets. Themost impactfulfinding from this study was the correlation between immuneactivation and mutation burden in post-nCRT treated rectalcancers.

Although the exact mechanism of the enhanced immuneresponse needs to be further clarified, our observations clearlyshowed that neoantigens are associated with nonsynonymousmutation burden, which is consistent with the hypothesis thatrecognition of neoantigens derived from somatic mutations isimportant for the activity of the immune response, regardlessof nCRT treatment. The tumors with higher mutational loadin melanoma, lung cancer, and MMR-deficient colorectalcancers have a higher rate of response to checkpoint blockadetherapy (22, 23, 45). These data provide further insight on theidea that mutation-associated neoantigen recognition is animportant component of the endogenous antitumor immuneresponse.

Numerous studies have shown that chemoradiotherapyinduces local immune reactions that contribute to tumorregression through inflammatory infiltration. Our previouswork also demonstrates that the density of CD45ROþ TILs canpredict tumor downstaging and long-term outcomes for rectalcancer following neoadjuvant radiotherapy (39). Evidenceshows that a high rate of response to checkpoint therapy isbased on boosting tumor-specific immune activity. Therefore,we hypothesized that rectal cancer patients who responded tonCRT could be good candidates for checkpoint blockadeimmunotherapy, especially for the patients without pCR. Ourpreclinical mouse experiments demonstrated that radiotherapycould enhance the efficiency of anti–PD-1, which further sup-ported our hypothesis.

In this work, we demonstrated that a higher TMB in pretreat-ment tumors is correlated with a lower TRG. We also found thatthe immune activation–related genes andpathwayswere enriched

in the patients who were responding to the nCRT. Heterogeneousimmune cell infiltration was present in responding and nonre-sponding patients, and the responding patients displayed a higherinfiltration of CD8þ and activated CD4þmemory T cells, whereasthe nonresponding patients displayed a significantly higher Treginfiltration. Our results indicated that patients' immune activity isrelated to the somatic mutation burden and associated withnCRT. Mutation burden could be a useful biomarker to stratifypatients into sensitive and resistant categories to nCRT.

Several studies have addressed that tumor shrinkage inducedbychemoradiotherapy is not simply dependent on direct damage totumor cells but is also affected by the host immune activity.Immune infiltration could be a biomarker to predict the responseto nCRT in rectal cancer. Yasuda and colleagues previously ana-lyzed the density of CD4þ and CD8þ T lymphocytes in rectalcancer patients before nCRT and demonstrated that a higherdensity of lymphocytes was correlated with a better response tonCRT (46). Anitei and colleagues analyzed immunoscores, whichscore the presence of T lymphocytes, in rectal cancer patients andshow low immunoscores for patients who did not respond tonCRT (47). Tumorswith higher immune activity are considered tobe immunogenic and tend to further evoke antitumor immuneresponses by neoantigens due to chemoradiotherapy, resultingin a better response to CRT. Studies demonstrate that irradiationcan promote remodeling of the extracellular matrix (ECM) andtumor vasculature by increasing intratumoral oxygenation andpH and upregulating the expression of cell adhesion molecules.This leads to increased recruitment of immune effector cells intothe tumor (48).

We further investigated the prognostic value of the checkpointmolecules PD-1, PD-L1, and PD-L2 in colorectal cancer. PD-L1expressionwas correlated with poor prognosis of rectal cancer butnot colon cancer. Increasing evidence suggests that colorectalcancer should be considered as a heterogeneous disease, withcolon and rectal cancers showing multiple clinicopathologic andmolecular distinctions, including the immunemicroenvironment(49, 50). The TILs in colorectal cancer and its microenvironmentare associatedwith survival, and this prognostic correlation differsaccording to tumor location (51).

In rectal patients without nCRT, PD-L1 expression negativelycorrelated with prognosis in stage I–III rectal cancer patients andpositively correlated in stage IV rectal cancers. The expressionpattern and correlation with prognosis of PD-L2, another PD-1ligand, was different from that of PD-L1. PD-L2 expression wascorrelated with better prognosis of colon cancer but poor prog-nosis of stage IV rectal cancer, indicating that the biological rolesof PD-L1 and PD-L2 were different between colon and rectalcancers. Our results are consistent with the survival analysis ofcolorectal cancer RNA-seq data from TCGA presented in theHuman Protein Atlas (www.proteinatlas.org). However, ourresults are inconsistent with the study by Wang and colleagues(52), which shows that PD-L2 overexpression in colorectal cancertumor cells associates with poor OS of patients. It was shown thatin both early colorectal cancer (AJCC stages I–II) and advancedcolorectal cancer (stages III–IV), higher PD-L2 expression associ-ated with worse OS. However, it should be noted that their studycohort was mostly composed of colon cancer and only had tworectal cancer samples. For stages of samples, the cohort had onlyfour stage IV samples, and therefore, their resultsmainly reflect theexpression of PD-L2 in stage I–III colon cancer and its prognosticsignificance.We think that the difference between their results and

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www.proteinatlas.org


oursmight be due to the different composition of the study cohortand a different antibody used in the study.

In patients with neoadjuvant radiotherapy, no significant cor-relation between PD-L1 expression and prognosis was found.Colorectal cancer is considered as an immune cold tumor,except in high mutation colorectal cancer with MSI-H or POLEmutation (53). Multiple factors produced by tumor and stromalcells contribute to the inhibition of antitumor immune response.Interaction of PD-L1 with PD-1 inhibits T-cell activationand cytokine production (54, 55). In tumor microenvironments,elevated PD-L1 can result in T-cell exhaustion (56). T cells,thus, fail to maintain an energetic status to fight tumor cellsand are rendered tolerant to tumor antigens or exhausted. Che-moradiotherapy could reprogram the immune-suppressive TMEtoward an immune-stimulating one. Following neoadjuvantradiotherapy, T-cell activation was initiated by the tumor-specificneoantigens resulting from nCRT, which could partially counter-act the effect of PD-L1. Following neoadjuvant radiotherapy, PD-L1 was significantly correlated with CD45RO and CD8. In theCD8-low expression group, in which antitumor immune waspoorly activated, PD-L1 expression was negatively correlated withDFS. Therefore, we propose that the addition of checkpointblockade to nCRT may show significant efficacy in improvingprognosis of rectal cancer. T-cell infiltration, such as CD8þ T cells,could be a potential biomarker to further stratify post-nCRTpatients into future immunotherapy groups (CD8-high expres-sion) or surgery alone group (CD8 low expression).

It is plausible that the immune features associated with nCRTcould influence the response to immunotherapeutic strategies.The mutational landscape could provide a simple selection orstratification factor to identify populations of interest for suchtreatments, and its exploration as a predictive biomarker iswarranted.

Altogether, this work establishes the link between somaticmutations and immune activity in rectal cancer patients fol-lowing nCRT and supports the hypothesis that rectal cancerpatients with nCRT could become potential candidates forcheckpoint blockade immunotherapy. Further studies to iden-tify the specific antigenic epitopes are expected. Therefore, we

prudently assume that LARC patients' mutation burden andimmune activity are correlated with the response to nCRT. Thiswould help to develop personalized cellular adoptive immu-notherapy strategies in the clinical settings to optimally com-bine radiation and checkpoint blockade with PD-1 and CTLA-4antibodies to achieve the best therapeutic benefits.

Disclosure of Potential Conflicts of InterestNo potential conflicts of interest were disclosed.

Authors' ContributionsConception and design: D. Ji, H. Yi, D. Zhang, J. GuDevelopment of methodology: D. Ji, H. Yi, D. Zhang, T. Zhan, Z. Li, J. Jia,M. Qiao, J. Xia, Z. Zhai, C. SongAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): D. Ji, H. Yi, D. Zhang, T. Zhan, Z. Li, M. Li, J. Jia,M. Qiao, J. Xia, Z. Zhai, C. SongAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): D. Ji, H. Yi, D. Zhang, T. Zhan, Z. Li, J. Jia, M. Qiao,J. Xia, Z. Zhai, C. SongWriting, review, and/or revision of the manuscript: D. Ji, H. Yi, D. Zhang,T. Zhan, Z. Li, J. Jia, M. Qiao, J. Xia, Z. Zhai, C. SongAdministrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): D. Ji, H. Yi, Z. Li, J. Jia, M. Qiao, J. Xia, Z. Zhai,C. SongStudy supervision: J. Gu

AcknowledgmentsThe authors would like to thankDr. BinDong, theDepartment of Pathology,

Peking University Cancer Hospital and Institute, for her technical assistance.This work was supported by the National Natural Science Foundation

(81772565 to D. Ji, 81372593 to J. Gu, and 81201965 to D. Ji), the BeijingNatural Science Foundation (7132052 to J. Gu), and the National HighTechnology Research and Development Program of China (863 Program; No.2012AA02A506 to J. Gu partially, 2014AA020801 to M. Li partially).

The costs of publication of this articlewere defrayed inpart by the payment ofpage charges. This article must therefore be hereby marked advertisement inaccordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Received October 30, 2017; revised April 4, 2018; accepted September 28,2018; published first October 3, 2018.

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2018;6:1401-1416. Published OnlineFirst October 3, 2018.Cancer Immunol Res Dengbo Ji, Haizhao Yi, Dakui Zhang, et al. Following Neoadjuvant ChemoradiotherapySomatic Mutations and Immune Alternation in Rectal Cancer

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