Somatic Mutations and Immune Alternation in Rectal Cancer ......8 Department of Gastrointestinal...

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Somatic Mutations and Immune Alternation in Rectal Cancer Following 1

Neoadjuvant Chemoradiotherapy 2

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Dengbo Ji1*, Haizhao Yi1, 2*, Dakui Zhang3*, Tiancheng Zhan1, Zhaowei Li1, Ming Li1, 4

Jinying Jia1, Meng Qiao1, Jinhong Xia1, Zhiwei Zhai4, Can Song5, 6, and Jin Gu1, 6, 7§ 5

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1Key laboratory of Carcinogenesis and Translational Research（Ministry of Education), 7

Department of Gastrointestinal Surgery III, Peking University Cancer Hospital & 8

Institute, No. 52 Fucheng Rd., Haidian District, Beijing, China, 100142 9

2 Department of General Surgery 1, Affiliated Hospital of Chengde Medical College, No. 10

36 Nanyingzi Rd., Chengde, China, 067000 11

3 Department of colorectal surgery, China-Japan Friendship Hospital, 100029 12

4 Department of Gastrointestinal Surgery, Chaoyang Hospital, Beijing, China, 100020 13

5School of Life Sciences, Tsinghua University, Beijing, China, 100084 14

6Peking-Tsinghua Center for Life Sciences 15

7Peking University S.G. Hospital 16

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*These authors contributed equally to this study. 18

Running title: nCRT enables rectal cancer checkpoint blockade therapy 19

Keywords: Rectal Cancer; Neoadjuvant Chemoradiotherapy; Mutation Burden; 20

Checkpoint Blockade Therapy; Immune Activation 21

Grant Support: This work was supported by the National Natural Science 22

on April 7, 2020. © 2018 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from

Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on October 3, 2018; DOI: 10.1158/2326-6066.CIR-17-0630

http://cancerimmunolres.aacrjournals.org/

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Foundation (81772565 to D.B. J, 81372593 to J G, 81201965 to D.B. J), Beijing 23

Natural Science Foundation (7132052 to J G), and the National High Technology 24

Research and Development Program of China (863 Program) (No.2012AA02A506 25

to J G partially , 2014AA020801 to M L partially). 26

§Correspondence to: 27

Jin Gu, MD; FACS 28

Key laboratory of Carcinogenesis and Translational Research (Ministry of 29

Education), Department of Gastrointestinal Surgery III, Peking University Cancer 30

Hospital & Institute, No. 52 Fucheng Road, Haidian District, Beijing, China, 100142, 31

Tel/Fax: +86-10-88196238, e-mail: [email protected] 32

Disclosure of Potential Conflicts of Interest: 33

The authors declare no competing financial interests. 34

Word Counts: Abstract 214; Body 5,484 35

Figures: 7 Figures; 1 Table; 10 Suppl. Figures; 12 Suppl. Tables 36

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Abstract 50

Checkpoint blockade therapy triggers tumor-specific immune responses in a variety 51

of cancer types. We presumed that rectal cancer patients could have become 52

sensitive to immunotherapy after receiving neoadjuvant chemoradiotherapy (nCRT). 53

In this study, we report immune alternation in post-nCRT patients compared to 54

pretreatment conditions from GEO data. Whole exome sequencing of 14 locally 55

advanced rectal cancer (LARC) patient samples showed that nCRT induced new 56

mutations compared to the paired pre-treatment biopsies evidenced by appearance of 57

a neoantigen landscape. An association was identified between mutation burden and 58

enrichment of immune activation-related pathways. Animal experiment results 59

further demonstrated that radiotherapy enhanced the efficacy of anti–PD-1. 60

Mutation burden and the neoantigens of LARC patients were associated with 61

response to nCRT. The mRNA expression profiling of 66 pre-treatment biopsy 62

samples from LARC patients showed that immune activation-related pathways were 63

enriched in response to nCRT. PD-L1 expression was negatively correlated with 64

disease-free survival in the CD8-low expression group who received nCRT in a 65

cohort of 296 samples. Thus, nCRT was able to alter immune function in LARC 66

patients, which may be associated with the appearance of neoantigens. Neoantigens 67

could make rectal cancer patients potential candidates to receive checkpoint 68

blockade immunotherapy, and mutation burden could be a useful biomarker to 69

stratify patients into responding and non-responding groups for immunotherapy. 70

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Introduction 72

The treatment guidelines, published by the National Comprehensive Cancer Network 73

(NCCN) of the United States, clearly states that pre-operative neoadjuvant 74

chemoradiotherapy is crucial for rectal cancer treatment, which can improve the rate 75

of curative resection and significantly reduce local recurrence (1,2). Although local 76

recurrence and overall survival have improved, distant recurrence rate has not 77

decreased significantly. About 30% of patients treated with a curative regiment will 78

eventually develop distant metastases (3-5). Adjuvant drug therapy has been used to 79

prevent distant metastases by eliminating circulating tumor cells and 80

micrometastases. However, the use of adjuvant chemotherapy for patients with rectal 81

cancer treated with pre-operative chemoradiotherapy and surgery is still 82

controversial (6). Administration of adjuvant chemotherapy to patients with stage II 83

or III rectal cancer was based on results from phase 3 trials of adjuvant treatment for 84

colon cancer (7-10) , as well as from trials in patients with rectal cancer who were 85

treated without pre-operative chemoradiotherapy (11). Fluorouracil-based adjuvant 86

chemotherapy did not improve overall survival, disease-free survival, or distant 87

recurrences.(6,12) 88

Colorectal cancers (CRCs) with a high density of tumor-infiltrating lymphocytes 89

(TILs), especially CD8+ T lymphocytes, are associated with a better prognosis 90

(13-16), suggesting that a cytotoxic antitumor immune response is involved in 91

controlling cancer progression. Checkpoint blockade immunotherapy has improved 92

cancer treatment. Along with radical surgery, radiation therapy, chemotherapy, and 93




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targeted oncogene treatment, checkpoint blockade is on the top of the list of 94

therapeutic options. Checkpoint blockade therapy utilizes monoclonal antibodies 95

(mAbs) to rescue suppressed T cells through activating and restoring their antitumor 96

activity (17). Similarly, targeting cytotoxic T lymphocyte–associated antigen 4 97

(CTLA-4) and programmed cell-death protein-1 (PD-1) pathways in metastatic 98

melanoma, non–small-cell lung cancer (NSCLC), and other malignancies has 99

significantly prolonged survival (18-20). 100

Despite revolutionary achievements, the efficacy of checkpoint blockade 101

immunotherapy varies among different tumor types, and a few cancer types, such as 102

CRC, appear to be refractory to this therapy (19,21). A notable exception is that 103

patients with mismatch repair (MMR)-deficient CRC lesions obtain clinical benefits 104

from the administration of anti–PD-1 (22). 105

Increasing evidence indicates that antitumor effects, clinically noted with checkpoint 106

inhibitors such as ipilimumab, may rely on boosting tumor-specific immune 107

responses that were pre-existing or newly induced. High somatic mutation loads are 108

correlated with responsiveness to PD-1 blockade therapy in NSCLC, melanoma, and 109

MMR-deficient CRCs (22-24). Research has revealed that radiation can not only 110

reduce tumor burden but also enhance antitumor immune responses to tumor cells 111

(25). The combination of radiation therapy, anti–CTLA-4, and anti–PD-L1 promotes 112

clinical responses in melanoma (26). 113

Therefore, we hypothesized that rectal cancer patients could be potential candidates 114

for checkpoint blockade immunotherapy after receiving nCRT. To test this 115




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hypothesis, whole-exome sequencing of rectal cancer pre- and post- nCRT samples 116

was performed to analyze the mutational landscape differences. We further 117

performed an integrative analysis of mutational landscape and gene expression using 118

TCGA and GSE data to evaluate the influence of somatic mutations and its 119

association with immune response induced by treatment. 120

121

Methods and Materials 122

Patients and samples 123

Sample collection and usage was approved by the Ethics Review Committees of 124

Peking University Cancer Hospital & Institute and in accordance with the Declaration 125

of Helsinki. All patients were informed prior to the study and a consent form was 126

signed by each participant. 127

Tumor and paired normal adjacent tissue samples from cohort 1 included 14 LARC 128

patients who received nCRT and were used for whole-exome sequencing. 9 paired 129

LARC tumor tissues were obtained pre- and post-nCRT, and the remaining patients 130

only had pre-therapy biopsies due to complete pathological response (pCR) or near 131

pCR after nCRT. Pre-treatment blood samples from cohort 2 consisted of 42 LARC 132

patients who received nCRT and were used for circulating tumor DNA (ctDNA) 133

extraction and targeted ctDNA sequencing. The normal adjacent biopsies from 134

patients of cohort 2 were obtained for tissues DNA extraction and used as control. All 135

the tissue and blood samples were from patients who received nCRT and surgical 136

resections between 2014 and 2016 at the Peking University Cancer Hospital & 137




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Institute (Beijing, China). Pre-treatment tumor staging was performed in all patients 138

using endorectal ultrasonography, pelvic magnetic resonance imaging, or computed 139

tomography. The radiotherapy regimen consisted of a 50.6 Gy dose delivered in 22 140

fractions with concurrent capecitabine treatment at a dose of 825 mg/m2 orally twice 141

per day for 5 weeks. 142

Inclusion criteria were: 1. diagnosis of rectal adenocarcinoma by biopsy; 2. tumor 143

staged as T3-4 or any T, N+ by endorectal ultrasonography, pelvic magnetic 144

resonance imaging, or computed tomography. Exclusion criteria were: 1. previous 145

chemotherapy or pelvic radiation; 2. presence of any other malignant disorders or 146

other chronic diseases. For cohort 1, biopsies pre nCRT collected by rectoscopy and 147

tissue samples collected by surgical resection were stored immediately in frozen in 148

liquid nitrogen and stored at −80°C until use. For cohort 2, 3.5 mL of venous blood 149

was collected prior to nCRT from each patient and processed within 1 h based on 150

protocols of NCI’s Early Detection Research Networks (EDRN). Plasma was 151

harvested after centrifugation twice at 1,600g for 10min aliquoted immediately for 152

ctDNA extraction or stored at -80 C. 153

In order to generate differential mRNA profiling between responders and 154

non-responders to nRT and evaluate the influence of PD-1 and PD-L1 on prognosis 155

after neoadjuvant therapy, large-scale cohorts of pre-therapy biopsy and cancerous 156

tissue samples were selected and categorized into cohorts 3, 4, and 5. Cohort 3 was 157

composed of biopsy samples from 66 patients before nRT and resection. All patients 158

were treated with intermediate-fraction nRT (30 Gy/10 fractions) followed by a total 159




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mesorectal excision (TME) surgery. The patients achieving Grade 0 and 1 evaluated 160

using tumor regression grade (TRG) system were defined as responders. The patients 161

with Grade 3 were defined as non-responders. Inclusion criteria were: 1. diagnosis of 162

rectal adenocarcinoma by biopsy; 2. tumor staged as T3-4 or any T, N+ by endorectal 163

ultrasonography, pelvic magnetic resonance imaging, or computed tomography; 3. no 164

evidence of distant metastasis. Patients with the following characteristics were 165

excluded: 1. previous chemotherapy or pelvic radiation; 2. presence of any other 166

malignant disorders or other chronic diseases. Biopsies were collected by rectoscopy 167

and stored immediately in RNAlater (Qiagen) and then stored at −80°C until use. 168

Cohort 4 consisted of 294 CRC patients who did not receive nRT, and the samples 169

were collected after surgical resection. Patients with stage I-IV colorectal cancer, and 170

with clinicopathological characteristics and follow-up information available, were 171

included. We excluded patients if they had any other malignant disorders or other 172

chronic diseases, previous treatment with any anticancer therapy, presence of any 173

tumor type other than adenocarcinoma or mucinous carcinoma, and familial 174

adenomatous polyposis CRC. 175

Cohort 5 included 296 samples from patients who received nRT, and the samples 176

were collected after resection. The radiation dosage was 30 Gy and delivered in 10 177

fractions over 2 weeks. Inclusion criteria were: 1. diagnosis of rectal 178

adenocarcinoma by biopsy; 2. tumor staged as T3-4 or any T, N+ by endorectal 179

ultrasonography, pelvic magnetic resonance imaging, or computed tomography; 3. 180

no evidence of distant metastasis. Patients with the following characteristics were 181




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excluded: 1. previous chemotherapy or pelvic radiation; 2. presence of any other 182

malignant disorders or other chronic diseases. For cohort 4 and 5, tumor tissue 183

samples were directly collected after surgical resection. All samples were 184

immediately frozen in liquid nitrogen and stored at −80°C or fixed in 10% formalin 185

for paraffin embedding. 186

A summary of the clinical characteristics of these patients is shown in 187

Supplementary Table S1-5. 188

189

Assessment of treatment response and tumor downstaging 190

The 7th edition of the American Joint Committee on Cancer TNM system was used 191

for Pathological staging(27). Neoadjuvant radiotherapy effect was evaluated after 192

surgery by specialized gastrointestinal pathologists using tumor regression grade 193

(TRG) system as follows: Grade 0: complete regression, no tumor cells; Grade 1: 194

single or small groups of tumor cells, moderate response; Grade 2: residual cancer 195

outgrown by fibrosis, minimal response; Grade 3: minimal or no tumor cells killed, 196

poor response. 197

198

Whole-exome capture and sequencing 199

Sample preparation, library construction, exome capture, next generation sequencing, 200

and bioinformatics analysis were performed at Shanghai Biotechnology Corporation. 201

(Shanghai, China). Genomic DNA from cohort 1 tumor samples was randomly 202

fragmented and used to construct an in vitro shotgun library. For each sample to be 203




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sequenced, individual library preparations, hybridizations, and captures were 204

performed following the protocol of SureSelectXT Target Enrichment System for 205

Illumina Paired-End Sequencing Library (Agilent Technologies, Inc. 5301 Stevens 206

Creek Rd Santa Clara, CA95051 USA). The library fragments were flanked by index 207

adaptors following end repair and A-tailing addition. The library was further 208

enriched with biotinylated probes (SureSelect biotinylated library mix) for sequences 209

corresponding to exons through aqueous-phase hybridization capture. The 210

hybridized fragments were captured by streptavidin-based magnetic beads 211

(Dynabeads MyOne Streptavidin T1, Life Technologies, cat#65603), followed by 212

amplification, quality inspection, and massive parallel sequencing of the enriched 213

library. Quantity of library was assessed with Qubit 2.0 Fluoromete. The quality and 214

size range was assessed using 2100 Bioanalyzer High Sensitivity DNA Assay as 215

instructed in the reagent kit guide. BaseCalls directory, containing the binary base 216

call files (BCL files), was generated by Real Time Analysis (RTA, Illumina, 217

California, USA). The bcl2fastq (v1.8.3, Illumina, California, USA) was used to 218

combine the per-cycle BCL files in each run and translate them into FASTQ files. 219

The FASTQ files were aligned to a human reference genome (hg19) by 220

Burrows-Wheeler Aligner (BWA, v0.7.12,(28,29) (Sanger Institute, Cambridge, 221

UK). The aligned files (SAM/BAM format files) were initially sorted by SAM tools 222

(30)(v0.1.19, Sanger Institute, Cambridge, UK). The aligned read duplicating the 223

start position of another read was flagged as a duplicate (‘Mark duplicate’) by using 224

Picard Tools (v1.107, Broad Institute, Cambridge, MA, USA). Data was processed 225




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using the Genome Analysis Toolkit (GATK, v3.1, Broad Institute, Cambridge, MA, 226

USA). Reads were locally realigned (GATK IndelRealigner), and the base qualities 227

were recalibrated (GATK BaseRecalibrator). Final mapping statistics, including 228

coverage and depth, were generated from recalibrated files by BED tools(31) 229

(v2.16.1, Quinlan Laboratory at the University of Virginia) and perl/python scripts. 230

The readings were further formatted with SAM tools, duplication was removed with 231

Picard, local realignment around InDels was processed by GATK InDelRealigner, 232

and base quality score recalibration was performed by using GATK Base 233

Recalibrator. Potential somatic substitutions were identified using GATK Unified 234

Genotyper followed by variant annotation through Annovar/VEP/snpEFF. 235

Somatic mutations were determined using MuTect2 (32)(Broad Institute, Cambridge, 236

MA, USA ) to identify mutations in matched tumor and normal samples. A detailed 237

MuTect2 procedure is available at http://www.broadinstitute.org/cancer/cga/mutect/. 238

The paired sample data was finally filtered by ANNOVAR(33) (Wed, 17 Jun 2015, 239

Center for Applied Genomics, Children’s Hospital of Philadelphia, Department of 240

Biostatistics and Epidemiology and Department of Pediatrics, University of 241

Pennsylvania, Philadelphia, PA, USA) for further analysis. 242

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Targeted ctDNA sequencing 244

Sample preparation, library construction, exome capture, next generation sequencing, 245

and bioinformatics analysis were performed at Genecast Biotechnology Co., Ltd. 246

(Beijing, China). Plasma from cohort 2 blood samples was harvested after 247




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centrifugation twice at 1,600 x g for 10 minutes and aliquoted immediately for ctDNA 248

extraction or stored at -80°C. ctDNA was extracted from 2 mL of plasma using the 249

MagMAXTM Cell-Free DNA isolation kit (Life Technologies, California, USA) and 250

quantified by Qubit dsDNA HS Assay kit or Qubit dsDNA BR Assay kit (Life 251

Technologies, California, USA). 252

Genomic DNA was sheared into 150-200 bp fragments with Covaris M220 253

Focused-ultrasonicatorTM Instrument (Covaris, Massachusetts, USA). Fragmented 254

DNA and the ctDNA library were constructed by KAPA HTP Library Preparation Kit 255

(Illumina platforms, KAPA Biosystems, Massachusetts, USA), following 256

manufacturer’s instructions. DNA libraries were captured following NimbleGen 257

SeqCap EZ Library SR (Roche, Wisconsin, USA) Users’ Guide, with a designed 0.8M 258

size panel (Genecast Biotech, Beijing, China), which included 325 major 259

tumor-related genes. The captured samples were subjected to Illumina HiSeq X-Ten 260

for paired-end sequencing. 261

Paired-end reads generated from Hiseq X-Ten platform were mapped to the hg19 262

reference genome with BWA v0.7.12 (default parameters)(28,29)(Sanger Institute, 263

Cambridge, UK), then sorted, filtered, and indexed with SAM tools(30)(1.3, Sanger 264

Institute, Cambridge, UK). In order to identify somatic single nucleotide 265

polymorphisms (SNPs) and indel mutations, the obtained BAM files from both 266

plasma and matched normal tissues from each patient were processed for pairwise 267

variant calling using VarScan (34)(v2.3.8, The Genome Institue, Washington 268

University, St. Louis, Missouri, USA). Minimum coverage for calling somatic 269

variants in matched normal tissues samples and plasma samples were 5 and 3, 270




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respectively. P-value threshold to call a somatic site was 0.05, and variants with 271

<90% strand bias were kept for further study. The generated candidate mutations 272

were annotated using ANNOVAR software (33)(Wed, 17 Jun 2015, Center for 273

Applied Genomics, Children’s Hospital of Philadelphia, Department of Biostatistics 274

and Epidemiology and Department of Pediatrics, University of Pennsylvania, 275

Philadelphia, PA, USA), the dbNSFP and Exome Aggregation Consortum (ExAC) 276

database were used to remove either the benign mutations with pp2_hdiv score < 277

0.452 or the population polymorphic sites. Finally, the resulted nonsynonymous 278

mutations at the exonic regions were reserved. Tumor mutation burden (TMB) was 279

defined as the number of non-synonymous mutations per megabase of exonic region. 280

281

Mismatch repair status testing 282

Mismatch repair status was assessed using the MSI Analysis System (Promega, 283

Madison, WI), consisting of 5 pseudomonomorphic mononucleotide repeats (BAT-25, 284

BAT-26, NR-21, NR-24, and MONO-27) to detect MSI and 2-pentanucleotide repeat 285

loci (PentaC and PentaD). According to the manufacturer’s guidelines, 2ng template 286

DNA for each sample was added into the Amplification Mix including nuclease-free 287

water, gold ST*R buffer, MSI primer pair mix and AmpliTaq Gold DNA polymerase 288

(Life Technologies Cat.# N8080242) and mix gently. For the positive amplification 289

control, 2ng of the diluted K562 High Molecular Weight DNA was added into the 290

Amplification Mix. For the negative amplification control, nuclease-fee water (instead 291

of template DNA) was added into Amplification Mix. Amplification was performed 292

in a GeneAmpR PCR System 9600 (Applied Biosystems). Amplification conditions 293

were as follows: 95°C for 11 minutes, 96°C for 1 minute, then: 94°C for 30 seconds, 294




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ramp 68 seconds to 58°C, hold for 30 seconds, ramp 50 seconds to 70°C, hold for 1 295

minute, for 10 cycles, then: 90°C for 30 seconds, ramp 60 seconds to 58°C, hold for 296

30 seconds, ramp 50 seconds to 70°C, hold for 1 minute for 20 cycles, then: 60°C for 297

30 minutes, 4°C soak. The primers used were provided in the MSI Analysis System 298

(Promega, Madison, WI). Following amplification of DNA, the fluorescent PCR 299

products were analyzed with the Applied Biosystems 3130xl Gene Analyzer using 300

GeneScan analysis software (Applied Biosystems, Foster City, CA, USA). The length 301

of the sequence was determined for each microsatellite locus, and the tumors were 302

designated as high-frequency MSI (MSI-H) if two or more mononucleotide loci varies 303

were identified in length compared to the germline DNA. One vary was considered as 304

low-frequency MSI (MSI-L) and none as microsatellite stable (MSS). 305

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RNA isolation and microarray analyses 307

Total RNA from cohort 3 biopsy samples was isolated using TRIzol reagent (Life 308

technologies, Carlsbad, CA, US), according to the manufacturer’s instructions, and 309

purified by using the RNeasy Mini Kit (Qiagen, GmBH, Germany). RNA samples of 310

each group were subsequently used to generate fluorescence-labeled cRNA targets 311

for the Affymetrix Human U133 Plus 2 arrays (Affymetrix, Santa Clara, CA, US；312

Ca#900467). The labeled cRNA targets were then hybridized on slides. After 313

hybridization, slides were scanned by GeneChip® Scanner 3000 (Affymetrix, Santa 314

Clara, CA, US). Data were extracted with Command Console Software 4.0 315

(Affymetrix, Santa Clara, CA, US). Raw data were normalized by the MAS 5.0 316




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algorithm and Gene Spring Software v12.6.1 (Agilent technologies, Santa Clara, CA, 317

US). The microarray experiments were performed following the protocol of 318

Affymetrix Inc. (Shanghai Biotechnology Corporation, China). 319

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Cell lines 321

The MC38 cell line was purchased from National Infrastructure of Cell Line Resource 322

(Beijing, China) and was maintained in RPMI 1640 with 10% fetal bovine serum 323

(Gibco, Carlsbad, CA), penicillin sodium (100 U/mL), and streptomycin sulfate (100 324

mg/mL) in humidified 5% CO2 at 37°C. The cell line was tested and authenticated by 325

(STR) profiling. Cells were routinely tested for mycoplasma infection and used only 326

when negative. Cells were passaged a maximum of 3-4 times in vitro before they were 327

used in in vivo experiments. 328

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In vivo mouse studies 330

Four- to six-week old male C57BL/6 mice were purchased from Beijing 331

Vitalriver Experimental Animal Technology Co. Ltd. (Beijing, China). Mice were 332

maintained in a pathogen-free facility and used in accordance with the institutional 333

guidelines for animal care. All animal experiments were performed following 334

protocols approved by the Institutional Animal Care and Use Committee (IACUC) at 335

the Peking University Cancer Hospital. MC38 (1×104) cells were mixed with an equal 336

volume of Matrigel (BD Biosciences) and subcutaneously injected on the right leg of 337

the mice on day 0. The tumors were irradiated with 8 Gy on day 15. Blocking 338




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antibodies (PD-1, clone RMP1-14; PD-L1, clone 10F.9G2 and rat IgG2B isotype, 339

clone LTF-2, all from BioXCell) were given intraperitoneally on days 16, 19, and 22. 340

All irradiation was performed using the Edge linear accelerator (Varian Medical 341

Systems, Inc., Palo Alto, CA). Antibodies used for in vivo immune checkpoint 342

blockade experiments were given intraperitoneally at a dose of 200 μg/mouse and 343

include: PD-1 (clone RMP1-14), PD-L1 (clone 10F.9G2), and rat IgG2B isotype 344

(clone LTF-2) (all from BioXCell). The tumor volumes were measured using CT 345

scans and MRI once a week. Volume was calculated using the formula L × A × B × 346

0.52, where L is the longest dimension and A and B are long and short diameters of 347

the largest coronal section, respectively. The untreated tumor volumes were 348

determined at day 14 using CT scans and were considered as a baseline tumor volume 349

(Vcont). Normalized tumor response to treatment was the measured volume (V) 350

relative to Vcont. The tumor volumes were also measured using calipers every three 351

days. Differences in survival were determined for each group by the Kaplan-Meier 352

method. The overall p-value was calculated by the log-rank test. For mouse studies, 353

an event was defined as death or when tumor burden reached a protocol-specified size 354

of 1.5 cm in maximum dimension to minimize morbidity (26). 355

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Immunohistochemical analysis on tissue microarrays (TMAs) 357

Immunohistochemistry was performed on TMAs using rabbit polyclonal anti-human 358

PD-1 (LS-B540, LifeSpan BioSciences, USA), anti-human PD-L1 (ab58810, Abcam, 359

USA), anti-human PD-L2 (HPA013411, Sigma, USA), mouse monoclonal 360




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anti-human CD8 (clone 4B11, Novus Biologicals, USA). All images were examined 361

by two experienced pathologists independently. For PD-L1 and PD-L2, the 362

immunoreactivity of the proteins detected was recorded through the intensity of 363

staining, and the percentage of immunoreactive cells scored as: tissues with no 364

staining were rated as 0, with a faint or moderate staining to strong staining in <25% 365

of cells rated as 1, strong staining in 25–50% of cells rated as 2, and strong staining 366

in >50% of cells rated as 3. The slides were further analyzed to identify PD-1 and 367

CD8 using an image analysis workstation (Spot Browser, Alphelys). The total 368

number of PD-1+ or CD8+ cells in each tissue spot was counted, and the density of 369

PD-1+ or CD8+ TILs was defined as the cell number per square millimeter. 370

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TCGA and GEO datasets 372

Level 3 TCGA RNA-seq and DNA exome-seq data for rectal cancer patients with 373

clinical information was downloaded from the TCGA data portal, including 341 rectal 374

cancer samples on 18 July, 2015. The cases that had clear information about nCRT 375

were analyzed, including 7 rectal cancers with nCRT and 41 without nCRT. 376

EdgeR-normalized data of these 48 cases were used for correlative analysis. 377

Two patient datasets of rectal cancer were downloaded from the Gene Expression 378

Omnibus (GEO) database. GSE 15781 consisted of 22 patients with resectable 379

adenocarcinoma of the rectum, in which thirteen patients had surgery only, and nine 380

patients received nCRT. GSE 45404 included pre-treatment biopsies of 42 rectal 381

cancer patients who received nCRT with conventional fractionation (≥50 Gy in 28 382




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fractions, 1.8 Gy/day, 5 sessions per week) and 5-fluorouracil (5-FU)-based 383

chemotherapy. 384

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Mutant peptide MHC binding prediction 386

All non-synonymous point mutations identified were translated into strings of 17 387

amino acids, with the mutant amino acid situated centrally according to previous 388

research (23,24). Our initial analysis was focused on HLA-A and HLA-B (reference 389

set of 27 alleles was assembled and covered >97% of population (35), the accession 390

numbers for the reference alleles were shown in Supplementary Table S6). The 17 391

mutant amino acid fragments were analyzed by the epitope prediction program 392

NetMHC v4.0 (http://www.cbs.dtu.dk/services/NetMHC/). Epitopes with a predicted 393

affinity of <50 nm were considered to be strong potential binders, and epitopes with a 394

predicted affinity of <500 nm were considered weak potential binders, as suggested 395

by the NetMHC group. To further refine the total neoantigen burden, we repeated the 396

same process for the complementary wild-type peptide for each mutant peptide. The 397

mutant peptides that held strong potential binders were used to compare with the 398

complementary wild-type peptide, which was predicted to have a weak potential 399

binder. These mutant peptides were referred to as mutation-associated neoantigens. 400

These mutant peptides were further evaluated for putative binding to the T-cell 401

receptor (TCR) using the IEDB immunogenicity predictor with patient-specific HLA 402

types (http://tools.immuneepitope.org/immunogenicity/) and CTLPred 403

(http://www.imtech.res.in/raghava/ctlpred/). 404




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Statistics 406

The Mann-Whitney test and Student t test were used to compare mutation burdens and 407

differences in the frequency of nucleotide changes. The log-rank test was used to 408

compare Kaplan-Meier survival curves. Correlations between the non-synonymous 409

mutation burden and the neoantigen burden, frequency of the neoantigen 410

burden/non-synonymous mutation, and immune response were calculated using the 411

Pearson correlation formula. A mixed effect linear model was used to determine 412

significance of differences in tumor growth. Statistical analyses were performed using 413

GraphPad Prism v.6. 414

Availability of data and material 415

Whole-exome sequencing data are available under the NCBI Sequence Read 416

Archive (SRA) study accession no. SRP159539. Microarray data are prepared 417

according to minimum information about a microarray gene experiment (MIAME) 418

guidelines and deposited in the GENE Expression Omnibus (GEO) database. The 419

GEO accession number is GSE119409. 420

421

Results 422

nCRT induces immune activation in LARC 423

To assess whether nCRT could affect the immune response, we compared rectal 424

cancer samples with and without treatment from GEO data (GSE 15781) using GSEA. 425

This data consisted of 22 patients with resectable adenocarcinoma of the rectum. 426




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Thirteen patients had surgery only, and nine patients received nCRT. The irradiated 427

and non-irradiated tumor tissues and normal tissues were used to perform 428

genome-wide gene expression analysis. A supervised method (Significance Analysis 429

of Microarrays -SAM-) was initially used to identify a statistical significance 430

(adjusted p<0.05) in differentially expressed genes between samples with and without 431

nCRT treatment. 3359 differentially expressed genes were identified between these 432

two subgroups. Of which, 1840 genes revealed significantly higher expression in 433

post-treatment samples, whereas 1519 genes showed lower expression. A hierarchical 434

clustering analysis was subsequently performed based on the expression values of the 435

3359 genes in the 22 samples (Supplementary Fig. S1) to categorize the samples into 436

2 main subgroups (branches), in which the gene expressions showed the opposite 437

trend. 438

To further investigate the mechanism of the immune response alternation after the 439

chemoradiotherapy, the differentially expressed genes induced by chemoradiotherapy 440

with the immune-related genes defined by Gene Ontology (1776 genes) were 441

compared. A total of 342 immune-related genes were altered, with most being 442

upregulated. Unsupervised hierarchical clustering of this subset of overlapping genes 443

resulted in a clear separation of the samples by nCRT (Fig. 1A and B). 444

Reactome pathway analysis showed that immune activation-related pathways were 445

induced, including interferon signaling, antigen presentation, class I MHC-mediated 446

antigen processing and presentation, PD-1 signaling, peptide-ligand binding receptors, 447

and co-stimulation from the CD28 family (Table 1). Amongst the altered 342 448




21

immune-related genes, 17 genes fell into the CD28 costimulatory signal pathway 449

(defined by Reactome pathway database) (Fig. 1B). CD28, CD86, HLA class II 450

molecules, PIK3CA, PTPN11, MAPKAP1, GRB2, VAV1, FYN, and MAP3K8 were 451

upregulated (Fig 1C). In contrast, PAK1, PPP2R1A, and MLST8 were downregulated. 452

The expression of T-cell differentiation and activation markers were analyzed, and the 453

results revealed that differentiation markers (KLRG1 and PTPRC) and activation 454

markers (CD69, IL2RA, CD38) (36,37) were upregulated in LARC samples following 455

nCRT (Fig. 1D). 456

We then focused on the differentially expressed genes and performed gene set 457

enrichment analysis using a prior defined immunological signature set (signature of 458

gene expression upon perturbation of certain immune-related genes). Post-treatment 459

LARC was characterized by increased expression of most immune-related genes and 460

associated with a diffuse immune infiltrate mainly composed of TH1 cells and 461

cytotoxic T cells expressing checkpoint molecules (Fig. 1E). 462

To further assess the effect of nCRT on the infiltration of specific immune cell 463

subsets, we used CIBERSORT (38) as an approach to dissect infiltration of specific 464

immune cell subsets in the tumors. The higher fractions of monocytes, activated 465

dendritic cells, and neutrophils cells were found in rectal cancers with nCRT. The 466

faction of gamma delta T cells slightly increased from 0 to 0.78% following nCRT 467

(Supplementary Fig. S2). The list of differentially expressed genes, immune genes, 468

and CD28 co-stimulatory signals are presented in Supplementary Tables S7-9. 469




22

nCRT influences the mutational landscape of LARC 470

Pre- and post-nCRT tumor samples from 9 of the 14 patients of cohort 1 were 471

collected and sequenced, and the other 5 cases only had biopsy samples prior to 472

therapy because of pathologically invisible tumor masses after achieving pCR. The 473

tumor DNA sequencing generated a mean target coverage of 239X, and a mean of 474

99.8% of the target sequence was identified as a depth of minimum 10X. A median of 475

80 non-synonymous mutations per pre-nCRT sample (range 3 to 313) was detected. 476

The median number of exonic mutations per pre-nCRT sample was 122 (range 3 to 477

455). The microsatellite instability status of the nine cases was tested, and all cases 478

were microsatellite stable (MSS). The quantity and range of mutations were similar to 479

published series of CRCs (22). The nCRT-induced mutations appeared in 480

post-treatment samples. The median number of exonic mutations induced by nCRT 481

was 23 compared to pre-treatment samples (range 8 to 89). The median 482

non-synonymous mutation induced by nCRT was 15 per post-nCRT sample (range 6 483

to 57). 484

nCRT influences the neoantigens landscape of LARC 485

The landscape of neoantigens according to previously described methods (23,24) were 486

examined. This approach identified mutant nonamers with ≤500 nM binding affinity 487

for patient-specific class I human lymphocyte antigen (HLA) alleles, which were 488

considered candidate neoantigens. A median of 58 candidate neoantigens from each 489

pre-treatment tumor (range 0 to 256) and the quantity of neoantigens per tumor 490

correlated with mutation burden was identified (Pearson r 0.9491, p<0.0001; Fig. 2A). 491




23

New mutations induced by nCRT from post-treatment tumors also resulted in a 492

median of 11 candidate neoantigens with a range 3 to 50. These candidate neoantigens 493

significantly correlated with new mutation numbers (Pearson r=0.9640, p<0.0001; Fig. 494

2B). 495

The mutational landscape from TCGA data analysis of rectal cancer with and without 496

nCRT showed that the median non-synonymous mutation burden was 163 in tumors 497

from patients with nCRT compared to 120 in patients without nCRT (Student t test, 498

p=0.0387; Fig. 2C). The median candidate neoantigens per tumor was 22 (range 0 to 499

55), and the quantity of neoantigens per tumor correlated with mutation burden 500

(Pearson r=0.9988, p<0.0001; Fig. 2D). The median putative neoantigens binding to 501

the TCR was 18 in with nCRT patients compared to 14 in without nCRT. 502

(Mann-Whitney test, p=0.01, Fig. 2E) 503

Immune activation is related to neoantigens arising from mutation burden 504

To further confirm that immune activation following nCRT was related to 505

neoantigens arising from mutation burden, the correlation between the number of 506

mutations and the expression of all genes in rectal cancer from the TCGA DNA 507

exome-seq and RNA-seq data was studied. An ordered list of 354 significant related 508

genes were generated (Pearson r>0.8 or r<-0.8, p<0.01) for the Reactome pathway 509

database analysis. The enriched pathways focused on the immune system, signal 510

transduction, and developmental biology. An association was identified between 511

mutation burden and enrichment of immune activation-related pathways, including 512




24

alpha-defensins, IL2 family signaling, and other several interleukin-related pathways 513

(interleukin receptor SHC signaling, interleukin-19, 20, 22, 24, 26, 28, and 29 514

signaling, interleukin-3, 5 and GM-CSF signaling, interleukin-17 signaling) (Table 515

1). 516

RT combined with anti–PD-1 enhances control of tumor growth 517

To understand the contribution of RT to immune checkpoint blockade, we utilized 518

the MC38 CRC mouse model. Mice with right leg tumors received irradiation (RT), 519

anti–PD-1/PD-L1 (PD1/PDL1, anti-PD-1/PD-L1 are abbreviated as PD-1/PDL1), or 520

both treatments delivered sequentially (RT+PD1 or RT+PDL1) (Fig. 3A). Tumor 521

growth was controlled by RT, PD1, RT+PD1, and RT+PDL1 (p=0.0163, p=0.02, 522

p<0.0001, p=0.0035, respectively), and tumor growth was well-controlled when 523

RT+PD1 was administered compared to either treatment alone (p=0.0037, p=0.0021). 524

No significant differences among PDL1, RT, and RT+PDL1 treatments were seen 525

(Fig. 3B-D). 526

Kaplan-Meier analysis showed that PD1, RT, RT+PD1, PDL1, and RT+PDL1 527

improved the survival of mice (p=0.001, p=0.018 for comparisons of PD1 and PDL1 528

treatment groups, respectively). The largest improvement in survival was observed in 529

the RT+PD1 group. When compared with RT or PD1 alone, RT combined with PD1 530

still improved the survival significantly (p=0.046, p=0.042, respectively). No 531

significant difference among PDL1, RT, and RT+PDL1 groups was found (Fig. 3E). 532

Mutation burden and neoantigens correlated with the response to nCRT 533




25

The analysis of whole-exome sequencing indicated that rectal cancers with higher 534

mutation burden were more sensitive to nCRT (Fig.4 and 5A). The results showed 535

high numbers of somatic mutations in pre-nCRT biopsies, whcih were associated with 536

tumor regression grade (TRG; Pearson r=-0.5418, p=0.0454; Fig.5A). The potential 537

mutation-associated neoantigen numbers were negatively correlated with TRG but 538

was not statistically significant (Pearson r=-0.3684, p=0.1949; Fig. 5B). 539

Target-capture sequencing of plasma ctDNA and matched normal tissue DNA (cohort 540

2) was performed to detect somatic mutations in each sample, achieving a mean 541

sequencing coverage of 2313×(914~3792×) for plasma ctDNA and 1675×(765~2649×) 542

for normal tissue DNA. The correlation between the tumor mutation burden (TMB) of 543

ctDNA and the tumor regression grade varied among the patients (mean 9.76 544

mutation/Mb, range 4–16 mutations/Mb; Fig. 5C). TMB was associated with the 545

tumor regression grade (Pearson r=-0.3636, p=0.0179; Fig. 5D). The mean TMB was 546

higher in pCR patients than in non-pCR patients (13 vs 9.22, respectively; p=0.0014; 547

Fig. 5E). The patients were divided into two groups by assessing the percentage of 548

viable residual tumor cells less or more than 30% in the resected specimens. The 549

mean TMB was higher in the <30% group than in the >30% group (10.5 vs 8.78, 550

respectively; p=0.0472; Fig. 5F). 551

Immune activity correlated with the response to nCRT 552

To further investigate the correlation between gene expression and response to nRT, 553

mRNA expression in pre-therapy biopsies of cohort 3 was profiled into responding 554

(n=19) and non-responding (n=47) groups. A supervised method (Significance 555




26

Analysis of Microarrays -SAM-) was used to find statistical significance (adjusted 556

p<0.05) in differentially expressed genes between responding and non-responding 557

groups. A total of 59 genes were differentially expressed between these two 558

subgroups (p<0.05, fold change >1.5), with most genes presenting significantly 559

higher expression in the responding group and 4 genes with lower expression 560

(Supplementary Fig. S3). Reactome pathway analysis showed that the immune 561

activation-related pathways were enriched, including interleukin-10 signaling, 562

chemokine receptors bind chemokines, cytokine signaling in immune system, 563

peptide ligand-binding receptors, immune system, PD-1 signaling, interferon 564

signaling, and co-stimulation factors within the CD28 family (Supplementary Table 565

S10). 566

We also compared responding and non-responding rectal cancer pre-treatment 567

biopsies from GEO data (GSE 45404) consisting of 42 patients who received nCRT 568

with conventional fractionation (≥50 Gy in 28 fractions, 1.8 Gy/day, 5 sessions per 569

week) and 5-fluorouracil (5-FU)-based chemotherapy. The readings from the 570

pre-treatment patients responding (n=19) and non-responding (n=23) to 571

chemoradiotherapy were compared. Reactome pathway analysis showed similar 572

results to cohort 3(Supplementary Table S10). 573

The correlation between infiltrating immune cells and response to nCRT was further 574

assessed using CIBERSORT. The results of cohort 3 showed that the fractions of 575

CD8+ T cells (19% vs. 12%) and activated CD4+ memory T cells (0.47% vs. 0) in the 576

responding group were higher than the non-responding group. In the non-responding 577




27

group, the fraction of regulatory T cells (Tregs) was higher than the responding 578

group (2.29% vs. 0.59%). GSE 45404 data also showed a significant increase in the 579

fractions of CD8+ T cells (1.99% vs. 0%) and activated CD4+ memory T cells 580

(7.67% vs. 2.97%) in the responding group compared to the non-responding group 581

(Supplementary Fig. S4). 582

PD-L1 expression negatively correlates with prognosis 583

The expression pattern of PD-L1, PD-1, and PDL2 in tumors and corresponding 584

non-tumor tissue of cohort 4 was studied using IHC. PD-L1 was expressed not only 585

on tumor cells, but also on TILs. The majority of PD-L1 expression was in the tumor 586

cells, whereas weak expression was observed on TILs (Fig. 6A). A high expression 587

of PD-L1 was detected in epithelial cells with elevated expression in tumor tissue 588

compared with normal tissue (Z=-5.538, p<0.0001). 589

Expression of PD-L1 was correlated with M stage (Spearman r=0.13, p=0.03), 590

differentiation (Spearman r=0.131, p=0.029), and histological type (U=3.8158, 591

p=0.000136). PD-L1 expression in liver metastasis tissues was lower than expression 592

in paired primary tumor tissues (Z=-2.346, p=0.019, Supplementary Table S11). 593

Positive PD-L1 expression in CRC tissues was associated with a shorter overall 594

survival (OS, p=0.065) and disease-free survival (DFS, p=0.06) (Supplementary Fig. 595

S5A and B), although the tendency was not statistically significant. For stage I–III 596

CRC patients, positive PD-L1 expression was significantly associated with poor OS 597

(p=0.019) (Fig. 6B), whereas for stage IV CRC patients, positive PD-L1 expression 598




28

was significantly associated with favorable prognosis of CRC (p=0.001) 599

(Supplementary Fig. S5C). 600

The association between PD-L1 expression and prognosis of rectal cancer indicated 601

that positive PD-L1 expression was significantly associated with poor prognosis of 602

rectal cancer (for DFS, p=0.031; for OS, p=0.043) (Fig. 6C and D). No significant 603

correlation between PD-L1 and prognosis of colon cancer was found 604

(Supplementary Fig. S5D and E). We further performed subgroup analysis in 605

different stages of colon and rectal cancers to ascertain the effect of primary tumor 606

location on the correlation. For stage I–III rectal cancer patients, positive P-DL1 607

expression was significantly associated with poor OS (p=0.023; Supplementary Fig. 608

S6A). In stage IV rectal cancer, PD-L1 expression was associated with better OS 609

(p=0.011; Supplementary Fig. S6B). However, no significant correlation between 610

PD-L1 and prognosis of colon cancer, whether in stage I-III (p=0.069, 611

Supplementary Fig.6C) or stage IV (p=0.169; Supplementary Fig. S6D), was found. 612

PD-1 was also expressed in tumor cells and TILs, with TILs having the highest 613

expression. PD-1 expression was negatively correlated with tumor cell 614

differentiation (Spearman r=-0.135, p=0.022), but no significant correlation between 615

PD-1 and other clinical-pathological findings was found (Supplementary Table S11, 616

Supplementary Fig. S7A-D). 617

PD-L2 was also expressed not only in tumor cells, but also in TILs. However, the 618

majority of PD-L2 expression was in tumor cells (Supplementary Fig. S8A). High 619




29

PD-L2 expression was significantly associated with better OS (p=0.001; 620

Supplementary Fig. S8B) in all stage CRC patients. Specifically, for stage I–III CRC 621

patients, high PD-L2 expression was significantly associated with better OS (p=0.042; 622

Supplementary Fig. S8C), whereas for stage IV CRC patients, high PD-L2 expression 623

was associated with poor prognosis (p=0.067; Supplementary Fig. S8D). We also 624

performed subgroup analysis in different stages of colon and rectal cancers. For colon 625

cancer patients, a significant positive correlation between PD-L2 and prognosis in all 626

stages was seen (p<0.0001; Supplementary Fig. S8E). However, no significant 627

correlation between PD-L2 and prognosis was found in stage I-III or stage IV alone. 628

For all stage rectal cancer patients, no significant correlation between PD-L2 and 629

prognosis was seen. For stage I–III rectal cancer patients, there was no significant 630

correlation between PD-L2 and prognosis, but in stage IV rectal cancer, PD-L2 631

expression was associated with poor OS (p<0.0001, Supplementary Fig. S8F). 632

PD-L1 and PD-1 after nCRT correlated with CD45RO, CD8 and outcomes 633

To determine whether PD-L1 and PD-1 were associated with clinical and pathologic 634

features in LARC patients, tissue microarray analysis of 296 LARC patients from 635

cohort 5 was performed. The patients’ profile is listed in Supplementary Table S5 and 636

the tumor-infiltrating cytotoxic lymphocytes are shown in Fig. 7A and B. No 637

significant correlation was found between PD-L1, PD-1, and clinical-pathological 638

parameters observed (Supplementary Table S12). 639

To understand the relationship between PD-1, PDL-1, CD45RO, and CD8, the 640




30

correlation coefficients of PD-1, PD-L1, CD45RO, and CD8 expression among all 641

patient samples were calculated. PD-L1 was significantly correlated with CD45RO 642

(r=0.157, p=0.009, Spearman’s test) and CD8 (r=0.278, p=0.000, Spearman’s test). 643

No correlation was found between PD-1 and CD8 or CD45RO (Supplementary Table 644

S12). 645

Our previous work demonstrated that CD45RO expression significantly correlates 646

with prognosis of LARC patients with neoadjuvant radiotherapy (39). To determine if 647

prognostic associations existed between the expression of PD-L1, PD-1, CD8, and 648

patient survival, Kaplan–Meier survival curves were plotted. Again, no significant 649

correlation between DFS or OS and the expression of the proteins was found 650

(Supplementary Fig. S9A-F). 651

These results suggested that PD-1, PD-L1, and CD8 were not independent prognostic 652

factors for patient survival. However, if the patient survival was plotted against a 653

combination of PD-L1 with CD8, in the CD8-low expression group, a significant 654

negative correlation was observed in DFS (p=0.042; Fig. 7C). It seemed that 655

CD8-high expression was not associated with DFS and OS (Supplementary Fig. S10A 656

and B). The association of patient survival with the combination of PDL1 with 657

CD45RO, in the CD45RO-low expression group, a negative correlation was observed 658

in overall patient survival (p=0.081) or DFS (p=0.099), although it was not 659

statistically significant (Supplementary Fig. S10C and D). 660

661

Discussion 662




31

We analyzed somatic gene expression in irradiated and non-irradiated rectal cancer 663

samples from GEO data to assess the influence of nCRT on the immune condition. 664

Our study indicated that chemoradiotherapy could be able to induce an immune 665

response in rectal cancer patients. Post-treatment LARC is characterized by increased 666

expression of genes involved in immune response pathways, mainly composed of 667

cytotoxic T and TH cells, along with strong activation of antigen presentation, peptide 668

ligand-binding receptors, CD28 co-stimulatory signals, interferon signaling, and other 669

cytokine signaling in the immune system. 670

T-cell activation requires a two-step process that includes engagement of the TCR to 671

an antigen presented by an antigen-presenting cell (APC), and a second costimulatory 672

signal delivered by the engagement of CD28 to its ligands CD80 and CD86 (40). 673

Tumor cells expressing HLA class I present tumor-associated antigens on their cell 674

surface and are recognized by CD8+ cytotoxic T cells. Our analysis showed that nCRT 675

led to a significant increase in CD28, CD86, and HLA class I and II molecule 676

expression in rectal cancer cells. The class II molecule is expressed on the surface of 677

professional APCs and to some degree on cancer cells (41) and plays a central role in 678

the immune system by presenting peptides derived from extracellular proteins. Studies 679

have shown the direct requirement for competent HLA class II pathway stimulation in 680

the reduction of HLA class I-mediated response for an effective immunotherapy 681

approach (42). HLA class II antigen expression in CRC tumors is a favorable 682

prognostic marker (43). We noticed that T cell activation in post-treatment LARC was 683

mainly through IFNγ signaling activation and increased PI3K signaling. We further 684




32

analyzed the expression of T-cell differentiation and activation markers. 685

Differentiation markers (KLRG-1 and CD45 isotypes) and activation markers (CD69, 686

CD25 and CD38) were increased in LARC following nCRT compared to pre-nCRT 687

conditions. It was clear that nCRT could induce immune activation in LARC patients. 688

The mechanism by which radiation induces adaptive immunity remains unclear. 689

Twyman-Saint et al. report major tumor regressions in a subset of patients with 690

metastatic melanoma treated with an anti-CTLA4 and radiation, which was 691

reproduced in mouse models (26). Their study demonstrates that radiation can 692

diversify the TCR repertoire of TILs and shape the repertoire of expanded clones. 693

Reits et al. demonstrates that radiation can enhance MHC class I expression by 694

modulating the peptide repertoire (44). 695

Interest in the relationship between somatic mutational burden and antitumor immune 696

response motivated us to examine the differences in the mutational landscape between 697

the pre- and post-treatment rectal cancers by sequencing the exomes of LARCs. Our 698

analysis revealed that nCRT influenced the mutational landscape of LARC and 699

induced novel somatic mutations, and we further validated the results in TCGA 700

datasets. The most impactful finding from this study was the correlation between 701

immune activation and mutation burden in post-nCRT treated rectal cancers. 702

Although the exact mechanism of the enhanced immune response needs to be further 703

clarified, our observations clearly showed that neoantigens are associated with 704

non-synonymous mutation burden, which is consistent with the hypothesis that 705




33

recognition of neoantigens derived from somatic mutations is important for the 706

activity of the immune response, regardless of nCRT treatment. The tumors with 707

higher mutational load in melanoma, lung cancer, and mismatch repair–deficient 708

CRCs have a higher rate of response to checkpoint blockade therapy (22,23,45). 709

These data provide further insight for the idea that mutation-associated neoantigen 710

recognition is an important component of the endogenous antitumor immune 711

response. 712

Numerous studies have shown that chemoradiotherapy induces local immune 713

reactions that contribute to tumor regression through inflammatory infiltration. Our 714

previous work also demonstrates that the density of CD45RO+ TILs can predict 715

tumor downstaging and long-term outcomes for rectal cancer following neoadjuvant 716

radiotherapy (39). Evidence shows that a high rate of response to checkpoint therapy 717

is based on boosting tumor-specific immune activity. Therefore, we hypothesized 718

that rectal cancer patients who responded to nCRT could be good candidates for 719

checkpoint blockade immunotherapy, especially for the patients without pCR. Our 720

preclinical mouse experiments demonstrated that radiotherapy could enhance the 721

efficiency of anti–PD-1, which further supported our hypothesis. 722

In this work, we demonstrated that a higher tumor mutation burden in pre-treatment 723

tumors is correlated with a lower tumor regression grade. We also found that the 724

immune activation-related genes and pathways were enriched in the patients who 725

were responding to the nCRT. Heterogeneous immune cell infiltration was present in 726

responding and non-responding patients, and the responding patients displayed a 727




34

higher infiltration of CD8+ and activated CD4+ memory T cells, whereas the 728

non-responding patients displayed a significantly higher Treg infiltration. Our results 729

indicated that patients’ immune activity is related to the somatic mutation burden and 730

associated with nCRT. Mutation burden could be a useful biomarker to stratify 731

patients into sensitive and resistant categories to nCRT. 732

Several studies have addressed that tumor shrinkage induced by chemoradiotherapy 733

is not simply dependent on direct damage to tumor cells but is also affected by the 734

host immune activity. Immune infiltration could be a biomarker to predict the 735

response to nCRT in rectal cancer. Yasuda et al. previously analyzed the density of 736

CD4+ and CD8+ T lymphocytes in rectal cancer patients before nCRT and 737

demonstrated that a higher density of lymphocytes was correlated with a better 738

response to nCRT (46). Anitei et al. analyzed immunoscores, which score the 739

presence of T lymphocytes, in rectal cancer patients and show low immunoscores for 740

patients who did not respond to nCRT (47). Tumors with higher immune activity are 741

considered to be immunogenic and tend to further evoke antitumor immune 742

responses by neoantigens due to chemoradiotherapy, resulting in a better response to 743

CRT. Studies demonstrate that irradiation can promote remodeling of the 744

extracellular matrix (ECM) and tumor vasculature by increasing intratumoral 745

oxygenation and pH and upregulating the expression of cell adhesion molecules. 746

This leads to increased recruitment of immune effector cells into the tumor (48). 747

We further investigated the prognostic value of the checkpoint molecules PD-1, 748

PD-L1, and PD-L2 in CRC. PD-L1 expression was correlated with poor prognosis of 749




35

rectal cancer but not colon cancer. Increasing evidence suggests that CRC should be 750

considered as a heterogeneous disease, with colon and rectal cancers showing 751

multiple clinicopathological and molecular distinctions, including the immune 752

microenvironment (49,50). The TILs in CRC and its microenvironment are 753

associated with survival, and this prognostic correlation differs according to tumor 754

location (51). 755

In rectal patients without nCRT, PD-L1 expression negatively correlated with 756

prognosis in stage I-III rectal cancer patients and positively correlated in stage IV 757

rectal cancers. The expression pattern and correlation with prognosis of PD-L2, 758

another PD-1 ligand, was different from that of PD-L1. PD-L2 expression was 759

correlated with better prognosis of colon cancer but poor prognosis of stage IV rectal 760

cancer, indicating that the biological roles of PD-L1 and PD-L2 were different 761

between colon and rectal cancer. Our results are consistent with the survival analysis 762

of CRC RNA-seq data from TCGA presented in the Human Protein Atlas 763

(www.proteinatlas.org). However, our results are inconsistent with the study by 764

Wang et al (52), which shows that PD-L2 overexpression in CRC tumor cells 765

associates with poor OS of patients. It was shown that in both early CRC (AJCC 766

stage I–II) and advanced CRC (stage III–IV), higher PD-L2 expression associated 767

with worse OS. However, it should be noted that their study cohort was mostly 768

composed of colon cancer and only had two rectal cancer samples. For stages of 769

samples, the cohort only had four stage IV samples, and therefore, their results 770

mainly reflect the expression of PD-L2 in stage I-III colon cancer and its prognostic 771




36

significance. We think that the difference between their results and ours might be 772

due to the different composition of the study cohort and a different antibody used in 773

the study. 774

In patients with neoadjuvant radiotherapy, no significant correlation between PD-L1 775

expression and prognosis was found. CRC is considered as an immune cold tumor, 776

except in high mutation CRC with MSI-H or POLE mutation (53). Multiple factors 777

produced by tumor and stromal cells contribute to the inhibition of antitumor immune 778

response. Interaction of PD-L1 with PD-1 inhibits T-cell activation and cytokine 779

production (54,55). In tumor microenvironments, elevated PD-L1 can result in T-cell 780

exhaustion (56). T cells, thus, fail to maintain an energetic status to fight tumor cells 781

and are rendered tolerant to tumor antigens or exhausted. Chemoradiotherapy could 782

reprogram the immune-suppressive TME towards an immune-stimulating one. 783

Following neoadjuvant radiotherapy, T-cell activation was initiated by the 784

tumor-specific neoantigens resulting from nCRT, which could partially counteract the 785

effect of PD-L1. Following neoadjuvant radiotherapy, PD-L1 was significantly 786

correlated with CD45RO and CD8. In the CD8-low expression group, in which 787

antitumor immune was poorly activated, PD-L1 expression was negatively correlated 788

with DFS. Therefore, we propose that the addition of checkpoint blockade to nCRT 789

may show significant efficacy in improving prognosis of rectal cancer. T-cell 790

infiltration, such as CD8+ T cells, could be a potential biomarker to further stratify 791

post-nCRT patients into future immunotherapy groups (CD8-high expression) or 792

surgery alone group (CD8 low expression). 793




37

It is plausible that the immune features associated with nCRT could influence the 794

response to immunotherapeutic strategies. The mutational landscape could provide a 795

simple selection or stratification factor to identify populations of interest for such 796

treatments, and its exploration as a predictive biomarker is warranted. 797

Altogether, this work establishes the link between somatic mutations and immune 798

activity in rectal cancer patients following nCRT and supports the hypothesis that 799

rectal cancer patients with nCRT could become potential candidates for checkpoint 800

blockade immunotherapy. Further studies to identify the specific antigenic epitopes 801

are expected. Therefore, we prudently assume that LARC patients’ mutation burden 802

and immune activity are correlated with the response to nCRT. This would help to 803

develop personalized cellular adoptive immunotherapy strategies in the clinical 804

settings to optimally combine radiation and checkpoint blockade with PD-1 and 805

CTLA-4 antibodies to achieve the best therapeutic benefits. 806

807

Acknowledgments 808

The authors would like to thank Dr. Bin Dong, the Department of Pathology, Peking 809

University Cancer Hospital & Institute for her technical assistance. 810

Author contributions 811

Dengbo Ji and Haizhao Yi designed and performed the experiments, prepared the 812

figures and draft the manuscript; Dakui Zhang, Tiancheng Zhan and Ming Li assisted 813

with analysis of the whole- exome sequencing and RNA-seq data. Zhaowei Li, 814




38

Jinying Jia, Meng Qiao, Jinhong Xia, Zhiwei Zhai and Can Song contributed to the 815

performance of the experiments; Jin Gu supervised the work and wrote the 816

manuscript. 817

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981




42

Table 1. Reactome pathway analysis of with nRT vs. without nRT (GSE15781) and Mutation related pathways (TCGA) 982

983

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

Pathway name Entities pValue Entities FDR Pathway name Entities pValue Entities FDR

Interferon gamma signaling 1.11E-16

1.21E-14

Developmental Biology

5.67E-11 2.34E-08

Antigen 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

a Lymphoid and a non-Lymphoid cell

1.11E-16

1.21E-14

NCAM signaling for neurite out-growth

2.39E-06 5.89E-04

Class I MHC mediated antigen

processing & presentation

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

Signalling to RAS

3.58E-05 0.002010602

Receptor binding chemokines 1.34E-13

1.34E-11

RAF/MAP kinase cascade 5.59E-05 0.002010602

PD-1 signaling 7.64E-07

6.65E-05

Interleukin receptor SHC signaling 9.43E-05 0.002545513

Peptide ligand-binding receptors 1.71E-06

1.39E-04

Interleukin-19,20,22,24,26,28 and 29

signaling

1.47E-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

984

985

986

987

on April 7, 2020. ©

2018 Am

erican Association for C

ancer Research.

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ownloaded from

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anuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Author M

anuscript Published O

nlineFirst on O

ctober 3, 2018; DO

I: 10.1158/2326-6066.CIR

-17-0630


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Figure Legends 988

Figure 1. nCRT induces immune activation of LARC. (A) Unsupervised clustering 989

highlighting genome-wide changes in gene expression. Rectal cancer samples were 990

clustered according to the expression of 342 differentially expressed immune genes 991

(p<0.05) between 13 pre- and 9 post-nCRT samples (GSE 15781). Rectal cancer 992

samples are across the horizontal axis, with 1 sample expression pattern shown in 993

each column. Gene expression values range from green (low expression) to red (high 994

expression). (B) Venn diagram showing significant overlaps of core genes from the 995

differentially expressed genes (red circle), immune related genes (blue circle), and 996

CD28 costimulatory related genes (green circle). (C) Unsupervised clustering for 17 997

differentially expressed immune genes for CD28 co-stimulatory signals (p<0.05) 998

between pre- and post-nCRT. (D) Unsupervised clustering for T-cell differentiation 999

and activation markers between pre- and post-nCRT. (E) Enriched pathways for 1000

differentially expressed immune genes after nCRT using GSEA. The twenty most 1001

significant pathways and corresponding normalized enrichment scores (NES) are 1002

shown. p ≤ 0.01, FDR q< 0.25. using Enrichment Score Calculation, Permutation test 1003

and Multiple hypothesis test. 1004

1005

Figure 2. nCRT influences the neoantigen landscape of LARC. (A) The quantity of 1006

neoantigens and mutations per tumor prior to nCRT (Cohort 1; n=14), using Pearson 1007

correlation. (B) The quantity of neoantigens and mutations per tumor post-nCRT 1008

(Cohort 1, n=9), using Pearson correlation. (C) The mutation burden in patients post 1009

nCRT (n=7) and patients without nCRT (n=41) (from TCGA data), using Student t 1010

test. (D) The candidate neoantigens and mutation burden (from TCGA data), using 1011

Pearson correlation. (E) The neoantigens binding to the T cell receptor in patients post 1012




44

nCRT and patients without nCRT (from TCGA data), using Mann-Whitney test. 1013

Statistical significance was set at p < 0.05. 1014

1015

Figure 3. RT combined with anti–PD-1 enhances tumor growth control in a 1016

mouse model. (A). Schematic of the experimental setup. Yellow arrow: location of 1017

transplantation. Timeline starts from original tumor implantation (day 0). Black 1018

arrows: treatments given. (B) MRI images of combination RT+PD1 (RT combined 1019

with anti–PD1)-treated tumors and baseline/control tumors. (C-D) Total tumor growth 1020

for MC38 tumors after the indicated treatment. Radiotherapy: RT (n=9); Anti–PD-1: 1021

PD1 (n=8); Anti–PD-L1: PDL1 (n=9); RT combined with anti–PD-1 or anti–PD-L1: 1022

RT+PD1 (n=8) and RT+PDL1 (n=9), respectively. C: normalized values; D: raw 1023

values. For normalization, volumes were divided by average of untreated controls 1024

(V/Vcont) to account for differences in growth between different treated groups. 1025

Using A mixed effect linear model. (E) Survival after RT and/or anti–PD-1 (left) or 1026

anti–PD-L1 (right). Shown are overall p-values. The p-values for RT+PD1 and RT or 1027

anti–PD-1 alone are p=0.046 and p=0.042, respectively. Control is an 1028

isotype-matched antibody. Using the log-rank test, statistical significance was set at p 1029

< 0.05. 1030

1031

Figure 4. Mutation burden correlated with patients’ response to nCRT. Paired 1032

pre- and post-nCRT tumors from two LARC patients. Tumor regression grade (TRG) 1033

of case 4 with higher mutation number is 0, complete regression, better response. 1034

TRG of case 3 with lower mutation number is 3, poor response. The H&E staining 1035

shows a typical rectal adenocarcinoma. The MRI images show the pre-nCRT and 1036

post-nCRT tumors. 1037




45

1038

Figure 5. Mutation burden and neoantigens of LARC correlated with the 1039

response to nCRT. (A-B) Cohort 1 (n=14). (A) Mutation numbers in pre-nCRT 1040

biopsies with the different tumor regression grade, using Pearson correlation. (B) The 1041

mutation-associated neoantigens in pre-nCRT biopsies with the different tumor 1042

regression grade, using Pearson correlation. (C-F) Cohort 2 (n=42). (C) Tumor 1043

mutation burden (TMB) in pre-nCRT ctDNA. (D) The correlation between TMB in 1044

pre-nCRT ctDNA and the tumor regression grade, using Pearson correlation. (E) The 1045

TMB in pCR patients (n=6) and non-pCR patients (n=36), using Student t test. (F) 1046

The TMB in residual tumor cells <30% group (n=24) and in >30% group (n=18), 1047

using Student t test. Statistical significance was set at p < 0.05. 1048

1049

Figure 6. Association between PD-L1 and PD-1 expression and prognosis with 1050

CRC using IHC (Cohort 4). (A) Expression of PD-1 and PD-L1 in the indicated 1051

CRC tissues using IHC analysis. Positive staining shows as brown. (B) Overall 1052

survival. Kaplan–Meier analysis of TNM stage I-III CRC patients in cohort 4. 1053

Number of patients per group indicated on graph. (C) Kaplan–Meier analysis of the 1054

correlation between PD-L1 expression and disease-free survival in patients with rectal 1055

cancer in cohort 4. Number of patients per group indicated on graph. (D) 1056

Kaplan–Meier analysis of the correlation between PDL1 expression and OS in 1057

patients with rectal cancer in cohort 4. Number of patients per group indicated on 1058

graph. Using the log-rank test, statistical significance was set at p < 0.05. 1059

1060

Figure 7. Expression of PD-L1 and PD-1 in post-nRT rectal cancer tissue samples 1061

and therapeutic outcome for nRT using IHC (Cohort 5). (A) Representative 1062




46

images of PD-L1 expression on tumor cells and TILs (B) Representative images of 1063

PD-1 expression on tumor cells and TILs, as well as CD8 expression on TILs (C) 1064

Kaplan–Meier analysis of the correlation between PD-L1 expression and disease-free 1065

survival in patients with low CD8 expression in cohort 5. PD-L1–negative n=67; 1066

PD-L1–positive n=45. Using the log-rank test, statistical significance was set at p < 1067

0.05. 1068

1069

1070




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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