A Novel 4-Gene Prognostic Signature for Hypermutated Colorectal Cancer

Journal name: Cancer Management and Research Article Designation: Original Research Year: 2019 Volume: 11 Cancer Management and Research Dovepress Running head verso: Ge et al Running head recto: Ge et al open access to scientific and medical research DOI: http://dx.doi.org/10.2147/CMAR.S190963

Open Access Full Text Article Original Research A novel 4-gene prognostic signature for hypermutated colorectal cancer

Weiting Ge1 Background: Hypermutated colorectal cancer (CRC) reportedly accounts for 15%–17% of Wen Cai1,2 all cases of CRC. However, the proportion and number of patients with hypermutated CRC Rui Bai1 cannot be unappreciated. Additionally, therapy options for these patients differ from those for Wangxiong Hu1 CRC patients, with a greater potential benefit from immunotherapy. Dehao Wu1,2 Materials and methods: We sequenced the tumor mucosa of CRC patients with >24 months of follow-up data at our center and identified mutation profiles of hypermutated CRC as a train- Shu Zheng1 ing data set (Zhejiang University [ZJU]); we then collected patients from The Cancer Genome Hanguang Hu1,2 Atlas (TCGA) as a validation data set. Recurrently mutated genes were combined to calculate a 1Cancer Institute (Key Laboratory of compound score via Cox proportional hazards model. Patients with higher-than-median scores Cancer Prevention and Intervention, For personal use only. China National Ministry of Education), were segregated as the high-risk group. Outcomes were analyzed by Kaplan–Meier and Cox The Second Affiliated Hospital, regression analyses using Python (3.6.0) and R (3.4.0). School of Medicine, Zhejiang Results: We constructed a 4-gene signature (ACVR2A, APC, DOCK2, and POLE), with training University, Hangzhou, Zhejiang, China; 2Department of Medical Oncology, in 45 hypermutated patients at ZJU and validation in 24 hypermutated patients from TCGA. The Second Affiliated Hospital, School Patients in the high-risk group showed poor survival (adjusted HR =9.85, 95% CI: 2.07–46.81, of Medicine, Zhejiang University, P=0.004). Further subgroup analysis was performed for stage II and III colon cancer (HR =10.91, Hangzhou, Zhejiang, China 95% CI: 1.36–87.5, P=0.005) and high microsatellite instability (MSI-H) CRC (HR =12.57, 95% CI: 1.57–100.69, P=0.002) subgroups, which verified that our signature is universal. We then compared our prognostic signature with other risk factors (including MSI status, POLE driver mutation, BRAF-p.V600E, tumor mutational burden, and TNM staging). The results proved that our 4-gene signature is better than the other risk factor for prognosis in hypermutated CRC. Conclusion: Our 4-gene signature is a good predictor of survival for hypermutated CRC, and this

Cancer Management and Research downloaded from https://www.dovepress.com/ by 54.70.40.11 on 05-Aug-2019 signature is powerful in stage II and III colon cancer and MSI-H CRC. Future prospective studies are needed to confirm the power of the 4-gene signature in patients receiving immunotherapy. Keywords: colorectal cancer, hypermutation, gene signature, prognosis

Introduction Colorectal cancer (CRC) is one of the most commonly diagnosed cancers worldwide. Indeed, most recent data show that CRC is the second leading cause of cancer death, with over 500,000 deaths annually.1

Correspondence: Hanguang Hu Hypermutation is characterized by mismatch repair (MMR) deficiency or POLE/ Department of Medical Oncology, The POLD1 driver mutations, indicating a high mutational rate. Accumulating evidence Second Affiliated Hospital, School of 2 Medicine, Zhejiang University, 88 Jiefang confirms that hypermutation occurs in many cancer types, such as melanoma, lung Road, Hangzhou, Zhejiang, 310009, China cancer,3 and bladder cancer.4 Tel +86 0571 8778 4795 Acquisition of genomic instability is a crucial feature of CRC development, and Fax +86 0571 8776 7088 Email [email protected] the microsatellite instability (MSI) pathway, which in involved in 15%5,6 of CRC

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pathogeneses, is known to arise from inactivation of one then identified using MuTect and Strelka, respectively. These of four MMR genes: MSH2, MLH1, MSH6, and PMS2.7–10 variants were annotated with ANNOVAR (Openbioinformat- An aberrant MMR process leads to additive mutations ics, USA).14 MSI status was detected using mSINGS.15 throughout the genome and ultimately a “hypermutator” phenotype.10,11 The results of a recent large-scale sequencing Detection of hypermutated samples and analysis also suggest that hypermutation in CRC should not candidate gene screening be underestimated.12 Moreover, hypermutation is associ- The threshold of hypermutation was higher than 10 Mut/ ated with a predicted high neo-epitope load, which can be Mb, which was determined according to a recently published exploited for immunotherapy in selected patients who experi- large-scale analysis.12 As there are hundreds of mutated genes ence conventional therapy failure.13 in a hypermutated tumor, it is impractical to use a mutation Because hypermutated CRC differs from CRC with frequency ≥5% as a filtering criterion, which will include regard to tumorigenesis, prognosis, and treatment, it is unrea- most of the genes sequenced. Therefore, we employed mul- sonable to use the same prognostic biomarkers as employed tiple strategies to screen for candidate prognostic genes in for patients without hypermutations. Here, we propose a hypermutated CRC. First, we employed a univariate Cox prognostic prediction signature for patients with hypermu- proportional hazard (PH) model to evaluate the association tated CRC. This signature can determine the hypermutated between gene mutations and overall survival (OS) for each status of tumors and distinguish between patients who have gene. Dedicator of cytokinesis 2 (DOCK2) was the only gene a high or low risk of disease-related death and predict those significantly associated with OS. Second, POLE driver muta- who are likely to benefit from immunotherapy. tion and MSI statuses were selected as candidates because replication repair mutations and MSI are observed in most Materials and methods types of hypermutated cancer. We identified POLE driver Sample collection and DNA extraction mutations based on the results of Campbell et al.12 However,

For personal use only. Fresh tissues were flash frozen in liquid nitrogen after surgery POLD1 was not included because no driver mutations were and stored at −80°C until genomic DNA isolation. Only found in the present study. Third, activin A receptor type 2A samples from patients with a pathologic diagnosis of CRC (ACVR2A) and BRAF were included according to previous were evaluated, but samples were excluded if they contained studies. APC, TP53, and KRAS, which have an overall higher <40% tumor cells. Tumor- and matched normal mucosa- mutation frequency in CRC, were also included. Thus, we derived DNA was purified using the QIAamp DNA mini kit obtained eight candidate factors (DOCK2, ACVR2A, BRAF, (Qiagen NV, Venlo, The Netherlands). APC, TP53, KRAS, POLE, and MSI status) to construct a All patients signed a consent form when the frozen tissue prognostic model. We summarize the selection process in was obtained. This consent form authorized us to conduct Figure 1. scientific research and publish the results anonymously with the approval of the Institutional Review Board (IRB). All pro- Compound score calculation and high-

Cancer Management and Research downloaded from https://www.dovepress.com/ by 54.70.40.11 on 05-Aug-2019 tocols and procedures of this study were approved under The risk patient detection Second Affiliated Hospital, School of Medicine of Zhejiang The final follow-up date was October 1, 2016 for patients University IRB protocol 2013-042. This study was conducted from The Second Affiliated Hospital, School of Medicine of in accordance with the Declaration of Helsinki. Zhejiang University (the ZJU cohort) and August 20, 2015 for patients from The Cancer Genome Atlas (TCGA) cohort. DNA sequencing and MSI status Only patients with >24 months of follow-up survival data detection were used for the prognosis-related analysis. OS was mea- Panel sequencing was performed using a custom-designed sured in months from the date of surgery to the date that the panel utilizing Agilent SureSelect capture kit technology patient died. Stage was assessed according to the seventh ver- (Agilent Technologies, Santa Clara, CA, USA), targeting the sion of the American Joint Commission on Cancer guidelines. exonic region of 524 genes (Table S1), with HiSeq 2000 (Illu- The compound score was calculated based on the sum mina, San Diego, CA, USA). Sequence reads were aligned to of candidate gene mutations for each patient using a Cox the human genome (GRCh37/hg19), and unique pairs were PH model. Patients with higher-than-median scores were used for variant calling. Candidate variants and indels were segregated as the high-risk group; others were defined as the detected using GATK. Somatic mutations and indels were low-risk group. The Cox PH model was employed to evaluate

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Hypermutated (n=45)

Recurrently Univariate Cox PH mutated Recurrently analysis for each gene genes in mutated Training (ZJU, n=45) Replication previous genes of CRC Testing (TCGA, n=24) repair genes studies as a whole

DOCK2 MSI, POLE ACVR2A, BRAF APC, TP53, KRAS

Candidate factors DOCK2, MSI, POLE, ACVR2A, BRAF, APC, TP53, KRAS

Selection of gene combination by one gene increment and calculations of a compound score

Convert to high or low risk classifier, median as cutoff For personal use only.

Univariate Cox PH analysis Univariate Cox PH analysis Training (ZJU, n=45) Training and testing on subclasses Testing (TCGA, n=24) of ZJU and TCGA (n=69)

A prognostic mutation signature for hypermutated CRC

Figure 1 Flowchart of prognostic signatures generation and validation procedures. Abbreviations: TCGA, The Cancer Genome Atlas; ZJU, Zhejiang University; PH, proportional hazard; CRC, colorectal cancer. Cancer Management and Research downloaded from https://www.dovepress.com/ by 54.70.40.11 on 05-Aug-2019

the association between the mutation signature and the clini- Mann–Whitney U-test were used to determine differences in cal endpoint. To rule out over-fitting of the model, MSI data, clinicopathological variables between subgroups (high-risk somatic mutation data, and clinicopathological information vs low-risk/mutant vs wild-type). Multivariate Cox regression for the TCGA cohort were obtained from the TCGA project was performed to determine the contribution of the mutation data portal (http://www.cbioportal.org) on July 3, 2017.16 signature to survival, adjusting for age, sex, and stage; the These patients were used as an independent cohort for verifica- Wald test was employed in this analysis. Clinical subgroups tion of the significance between the panel and OS. of training and testing cohorts were also used to verify the prognostic prediction of the panel. Harrell’s concordance Statistical analyses index (C-index) was used to quantify predictive accuracies. In Kaplan–Meier survival curve analysis with the log-rank addition, multiple permutation tests were performed on both test was applied to estimate the mutation signature in cohorts. To obtain a sufficient sample size for clinical sub- relation to OS. Fisher’s exact test, Student’s t-test, and the class analysis, we pooled the two cohorts and utilized 5-fold

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cross-validation. All statistical analyses were two-sided. A and HR =2.08, P=0.001, respectively). Combining the P-value of <0.05 was considered statistically significant. All remaining selected genes, DOCK2, ACVR2A, BRAF, APC, analyses were performed using Python 3.6.0 (https://www. TP53, KRAS, and POLE as well as MSI status were chosen python.org/) and R 3.4.0 (https://www.r-project.org/). as candidate factors for constructing the prognostic signature. To determine the minimum number of genes able to Results discriminate outcomes, we began training in the ZJU cohort Patients’ basic characteristics and stopped increasing the size of the signature when we We sequenced 338 samples in our center, from which we obtained the maximal C-index. We next constructed a risk identified 45 (13.30%) hypermutated patients as the training classifier based on four genes (DOCK2, ACVR2A, APC, and data set to construct the prognostic mutation signature. The POLE), with good concordance (C-index =0.748). We further proportion of hypermutation obtained in this study coincides used multiple permutation testing to confirm the robustness with that of a previous report. Samples that met the criteria of this signature, which indicated that our signature was not (63 patients) of hypermutation were selected from the TCGA generated by coincidence (Figure 2). project data portal for an independent testing data set. The demographics of the two initial cohorts, with a total of 108 Construction of a 4-gene signature for patients, are shown in Table S2. Due to the lack of information high-risk hypermutated CRC patients on specific staging, location, and survival data in the TCGA We divided patients into two groups by calculating the data set, 24 patients from TGCA were ultimately selected compound score for four genes. The compound score was as the testing group for prognosis analysis. The baseline generated from the ZJU cohort and further calculated for characteristics of the two groups are presented in Table 1. each patient in the TCGA cohort. Patients with higher-than- median scores were segregated as the high-risk group, and Identified potential genes for constructing others were defined as the low-risk group. Patients with a

For personal use only. the signature high-risk classifier in the ZJU (HR= 10.19, 95% CI: 1.25– According to the three procedures described above, DOCK2 83.23, P=0.007) and TCGA (HR =8.62, 95% CI: 1.87–39.67, was found to be the only gene significantly associated with P=0.001) cohorts had significantly worse survival. OS in both the ZJU and TCGA cohorts (HR =1.73, P=0.007 Due to the limited number of patients exhibiting hypermutation (n=45 in ZJU and n=24 in TCGA), we pooled the two cohorts to display the results and perform further subgroup Table 1 Characteristics of included patients analysis. A high-risk classifier was significantly associated ZJU center TCGA P-value with poor survival (HR =8.83, 95% CI: 2.00–39.04, P=0.001; (n=45) (n=24) Figure 3). Age, years, mean ± SD 60.00±2.10 70.04±2.87 0.168 We also employed a multivariate Cox regression analysis Median OS, months, mean 67.97±4.30 42.32±5.76 0.410 ± SD including our 4-gene risk classifier, age, sex, and stage to

Cancer Management and Research downloaded from https://www.dovepress.com/ by 54.70.40.11 on 05-Aug-2019 Sex, female, n (%) 16 (35.56) 11 (45.83) 0.405 reveal that the 4-gene risk classifier can serve as an indepen- Mutation burden, mb, mean 73.62±12.08 43.01±7.67 0.344 dent determinant of OS in patients with hypermutated CRC ± SD (adjusted HR =9.85, 95% CI: 2.07–46.81, P=0.004) (Table 2). Stage, n (%) 0.411 I 4 (8.89) 5 (20.83) There was no statistically significant difference in the propor- II 27 (60.00) 12 (50.00) tion of patients receiving adjuvant chemotherapy between the III 13 (28.89) 5 (20.83) high-risk and low-risk groups (P=0.457, Table S3). IV 1 (2.22) 0 (0.00) Location, n (%) 0.025 Right-side colon 22 (48.89) 19 (79.17) The 4-gene signature is powerful in Left-side colon 14 (31.11) 3 (12.50) specific subgroups Rectum 9 (20.00) 1 (4.17) We first employed the 4-gene signature for subgroup analy- MSI-H, n (%) 22 (48.89) 21 (87.50) 0.002 POLE driver mutant, n (%) 9 (20.00) 1 (4.17) 0.075 sis of stage II and III colon cancer patients, accounting for ACVR2A mutant, n (%) 34 (75.55) 7 (29.17) 0.002 74% (78/105) of all included patients. Twenty-two patients APC mutant, n (%) 28 (62.22) 7 (29.17) 0.312 were in the high-risk group and twenty-eight in the low-risk DOCK2 mutant, n (%) 20 (44.44) 7 (29.17) 0.278 group according to the compound score. Because the high- Abbreviations: OS, overall survival; MSI-H, high microsatellite instability; TCGA, The Cancer Genome Atlas; ZJU, Zhejiang University. risk patients shared a higher death risk, the results suggested

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Distribution of 10,000 permutation results (TCGA hypermutated cohort) 2,500

2,000

y 1,500

Frequenc 1,000

Observed log10 (P-value) 500

0 –10 –8 –6 –4 –2 0 Log10 (P-value) of log-rank test

Figure 2 Distribution of 10,000 permutation results. Abbreviation: TCGA, The Cancer Genome Atlas.

1.0

0.8 For personal use only.

y 0.6

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HR=8.83 (95% CI: 2.00–39.02); P=0.001 0.2 High risk (n=34) Low risk (n=35) 0.0 0 20 40 60 80 100 120 Overall survival (months) No at risk: Cancer Management and Research downloaded from https://www.dovepress.com/ by 54.70.40.11 on 05-Aug-2019 High risk 34 28 17 14 9 2 1 Low risk 35 34 32 19 10 4 4

Figure 3 The pool data demonstrating effectiveness of the 4-gene signature.

Table 2 Multivariate Cox regression analysis of 4-gene signature that our 4-gene signature is a powerful prognostic tool for HR (95% CI) P-value this subgroup (HR =10.91, 95% CI: 1.36–87.50, P=0.005; 4-Gene risk model 9.85 (2.07–46.81) 0.004 Figure 4A). Age 1.02 (0.98–1.06) 0.442 As hypermutations are more likely to occur on the right Sex, female vs male 2.49 (0.82–7.56) 0.108 side of colon cancer,14 we selected right-side colon can- Stage 0.65 (0.28–1.49) 0.307 Notes: Multivariate Cox regression analysis was performed on 67 of 69 pooled cer patients and employed the 4-gene signature to predict patients who met the criteria of having >24 months follow-up survival data, as well outcomes. The results showed markedly worse survival as age, sex, and stage information. P-value was calculated by Wald test.

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A Stage II and III colon cancer, the pooled cohort B Right sided colon cancer, the pooled cohort 1.0 1.0

0.8 0.8 y y 0.6 0.6

0.4 0.4 Probabilit

Probabilit

HR=10.91 (95% CI: 1.36–87.42); P=0.005 HR=8.88 (95% CI: 1.06–74.19); P=0.015 0.2 0.2 High risk (n=22) High risk (n=19) Low risk (n=28) Low risk (n=22) 0.0 0.0 020406080100 120 020406080100 120 Overall survival (months) Overall survival (months) No at risk: No at risk: High risk 22 19 13 12 7 2 1 High risk 19 16 8 7 3 0 0 Low risk 28 27 27 16 8 3 3 Low risk 22 21 20 12 7 1 1 MSI subclass, the pooled cohort C 1.0

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y 0.6

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HR=12.57 (95% CI: 1.57–100.56); P=0.002 0.2 High risk (n=22) Low risk (n=21) 0.0 For personal use only. 020406080100 120 Overall survival (months) No at risk: High risk 22 17 8 7 5 1 0 Low risk 21 21 20 13 7 3 3

Figure 4 Analysis of different groups of patients. Abbreviation: MSI, microsatellite instability.

among patients in the high-risk group (HR =8.88, 95% CI: and we therefore conducted a performance comparison for 1.06–74.50, P=0.015; Figure 4B). these risk factors/groups using the pooled cohort. MSI status is important for both prognosis and therapy Our 4-gene risk classifier stratifies patients with hyper- choice among patients with hypermutated tumors. Thus, we mutated CRC into high-risk and low-risk groups. Worse out- Cancer Management and Research downloaded from https://www.dovepress.com/ by 54.70.40.11 on 05-Aug-2019 segregated patients into MSI and microsatellite stable (MSS) comes were significantly associated with the high-risk group. subgroups, and the results showed that our signature can well MSI/POLE status divided patients into three groups: POLE, predict outcomes in the MSI group (HR =12.57, 95% CI: MSI, and POLE/MSI. Although the MSI group accounted 1.57–100.69, P=0.002; Figure 4C), although the difference for the majority of patients, with better survival than those was not significant in the MSS group (P=0.082). of the POLE group (75% vs 40%, 5-year survival rate), the difference was not significant (Figure S1A). No death Comparison of prognostic biomarkers in occurred in two patients in the POLE/MSI group. MSI/BRAF hypermutated CRC status also classified patients into three groups, MSI, MSS/ We established a prognostic predictor based on four genes BRAF-wild-type, and MSS/BRAF-p.V600E, and there was (DOCK2, ACVR2A, APC, and POLE) for hypermutated CRC. no prognostic difference between the first two groups, with Previous studies have also proposed prognostic risk stratifica- only one patient in the last group (Figure S1B). To investigate tions based on MSI/POLE or MSI/BRAF. Hypermutation, the association between TMB and prognosis, we divided MSI status, and tumor mutational burden (TMB) are cor- patients into high-TMB and low-TMB groups based on the related with the response to immune checkpoint inhibitors, median (40 mut/Mb). High-TMB patients showed a worse,

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but not significant, outcome (Figure S1C). All patients with a for the pooled cohort showed that DOCK2 is more frequently POLE driver mutation were sorted into the high-TMB group, mutated in hypermutated CRC (38.0%) than in non-hyper- which was confirmed by the poor prognosis of the POLE mutated CRC (3.9%). Germline deficiency ofDOCK2 leads group. Lastly, consistent with the results of the multivariate to life threatening, invasive bacterial and viral infections,23,24 Cox regression analysis, TNM staging confirmed that the and transgenic mice experiments confirmed that DOCK2 is prognosis of hypermutated CRC does not comply with the required for recruitment and infiltration of immune cells into TNM staging system (Figure S1D). the colon mucosa during bacterial infection.24,25 Several recent studies have reported that the gut microbiome influences the Discussion efficacy of anti-PD-1/L1 immunotherapy.26,27 Accordingly, In our study, patients with hypermutated CRC comprised DOCK2 may participate in the immune response initiated 13.30%, which agrees with previous TCGA studies (in which by gut microbes, and its deficiency is most likely involved hypermutated CRC accounted for 15.6%,16 16.9%,17 and in an immune evasion mechanism of high-risk hypermutated 15%).18 To predict the prognosis of hypermutated CRC, we CRC. ACVR2A has two 8-bp polyadenine tracts, a hot spot constructed a prognostic mutation signature of four genes for mutation in MSI CRC.28,29 ACVR2A is a member of the (DOCK2, ACVR2A, APC, and POLE) that separated patients TGF-β superfamily, which plays a key role during CRC into two risk groups. The high-risk group was significantly progression, and a previous study showed that ACVR2A is associated with worse survival (HR =8.62, P=0.001). the most frequently mutated gene of hypermutated CRC. Overall, the proportion of hypermutated CRC suggested Further study of ACVR2A is urgently needed. that hypermutation should not be underestimated in CRC. We performed subgroup analysis to verify the universal- Furthermore, this special subgroup of CRC patients deserves ity of our 4-gene signature. Due to the different treatment greater attention because there are currently many novel strategies and clinical outcomes in colon and rectal cancer, therapeutic options. For example, Campbell et al12 recently stage II and III colon cancer patients were selected as the reported dramatically different survival rates among 217 first subgroup, and the results showed that our signature For personal use only. patients with hypermutated cancer. In addition, the abundance is sufficiently powerful in this subgroup. Nonetheless, the of immune checkpoint inhibitors that target programmed effectiveness of our 4-gene signature in rectal cancer needs cell death 1 (PD-1) or their ligands (PD-L1) brings new to be further investigated. We then focused on the subgroup hope to hypermutated tumor patients. Many studies have of high microsatellite instability (MSI-H) patients because proven that hypermutation and MSI status predict the clinical these CRC patients are considered to have different clinical benefit of immune checkpoint inhibitors, which can lead to outcomes and therapeutic choices; for example, patients durable remission in some patients with conventional therapy with MSI-H CRC usually do not respond to 5-fluorouracil failure.19,20 Thus, establishing a prognostic biomarker will chemotherapy, unlike non-hypermutated CRC patients. We help physicians screen patients who have poor prognoses further classified MSI-H patients into two risk groups using but good responses to immunotherapy. Regardless, not only our 4-gene signature and observed that the low-risk group are the clinicopathological features of hypermutated tumors showed markedly good survival. Although recent studies Cancer Management and Research downloaded from https://www.dovepress.com/ by 54.70.40.11 on 05-Aug-2019 poorly understood, but prognostic tools are also lacking due have reported that MSI-H patients have a good response to to a lack of understanding of this type of disease. Recently, immune checkpoint inhibitors, there are still some patients sequencing of individual cancer genomes has prompted for whom benefit may not actually be gained or for whom scientists to search for biomarkers based on gene mutation the response may not be translated to OS. Using our 4-gene signatures. However, most previous efforts treated hyper- signature in MSI patients to identify different risk groups is mutation and non-hypermutation as a whole or focused on promising in clinical practice that selecting patients who will non-hypermutated CRC.21 To the best of our knowledge, no actually gain benefits from immunotherapy, with an effect well-known study has been conducted on hypermutated CRC, on OS. The final subgroup analysis focused on the primary highlighting the value of our work. locations, as hypermutation is more common in right-side The genes in our signature include DOCK2, ACVR2A, colon cancer.16 This subgroup analysis further verified that APC, and POLE. APC and POLE play important roles in our 4-gene signature is a powerful tool for patients with the development and progression of CRC, and DOCK2 and hypermutated CRC. ACVR2A are worth discussing further. DOCK2 is a gene Using gene mutation status to construct a prognostic sig- frequently mutated in CRC and esophageal cancer,22 and data nature is currently a hot topic in cancer research. A powerful

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prognostic signature will identify different risk group of cancer (CRC)” as a poster presentation with interim findings. patients to receive different intensity of therapies. Our 4-gene The poster’s abstract was published in “Poster Abstracts” in signature classifies hypermutation CRC patients into two ESMO’s official journal Annals of Oncology. groups and we suggest that high-risk group patients should receive more aggressive treatments than the low-risk group Disclosure patients. A previous study stratified patients into three groups The authors report no conflicts of interest in this work. based on the MSI/BRAF status: MSI/BRAF-wild-type or mutant (best prognosis), MSS/BRAF-wild-type (intermedi- References 1. Wolf AMD, Fontham ETH, Church TR, et al. Colorectal cancer screen- 30 ate prognosis), and MSS/BRAF mutant (worst prognosis). ing for average-risk adults: 2018 guideline update from the American Although the MSI/BRAF status can as a whole serve as cancer Society. CA Cancer J Clin. 2018;68(4):250–281. 2. Akbani R, Akdemir KC, Aksoy BA, et al. Genomic classification of a prognostic biomarker for CRC, it was not suitable for cutaneous melanoma. Cell. 2015;161(7):1681–1696. hypermutated CRC according to our study. TMB has also 3. Govindan R, Ding L, Griffith M, et al. Genomic landscape of been recommended as a critical criterion correlated with non-small cell lung cancer in smokers and never-smokers. Cell. 2012;150(6):1121–1134. the objective response rate of anti-PD-1/L1 immunotherapy. 4. Cancer Genome Atlas Research Network. Comprehensive molecu- However, the threshold in different kinds of tumor remains lar characterization of urothelial bladder carcinoma. Nature. 2014;507(7492):315–322. controversial, and the power for predicting patient prognosis 5. Rehder DS, Nelson RW, Borges CR. Glycosylation status of vitamin D needs to be developed. Compared with the above biomarkers, binding protein in cancer patients. Protein Sci. 2009;18(10):2036–2042. our 4-gene risk classifier exhibits good performance for pre- 6. de La Chapelle A, Hampel H. Clinical relevance of microsatellite instability in colorectal cancer. J Clin Oncol. 2010;28(20):3380–3387. dicting clinical outcome in patients with hypermutated CRC. 7. Ionov Y, Peinado MA, Malkhosyan S, Shibata D, Perucho M. Ubiquitous However, there are some limitations to our study; the somatic mutations in simple repeated sequences reveal a new mechanism for colonic carcinogenesis. Nature. 1993;363(6429):558–561. most important one is the limited number of patients in our 8. Aaltonen LA, Peltomäki P, Leach FS, et al. Clues to the pathogenesis study group due to the small percentage of hypermutated of familial colorectal cancer. Science. 1993;260(5109):812–816. CRC. Fortunately, gene sequencing technology is gradually 9. Jiricny J. The multifaceted mismatch-repair system. Nat Rev Mol Cell For personal use only. Biol. 2006;7(5):335–346. maturing and becoming faster and less expensive. We will 10. Lee V, Murphy A, Le DT, Diaz LA. Mismatch repair defi- continue to collect cases of hypermutated CRC to further ciency and response to immune checkpoint blockade. Oncologist. 2016;21(10):1200–1211. verify our signature. Furthermore, although we believe that 11. Lee SY, Chung H, Devaraj B, et al. Microsatellite alterations at selected the 4-gene signature is promising in selecting patients who tetranucleotide repeats are associated with morphologies of colorectal will gain benefits from immunotherapy, its significant value neoplasias. Gastroenterology. 2010;139(5):1519–1525. 12. Campbell BB, Light N, Fabrizio D, et al. Comprehensive analysis of still needs to be verified in prospective studies that we are hypermutation in human cancer. Cell. 2017;171(5):1042–1056. devoting ourselves to. 13. Jover R, Nguyen TP, Pérez-Carbonell L, et al. 5-fluorouracil adjuvant chemotherapy does not increase survival in patients with CpG island methylator phenotype colorectal cancer. Gastroenterology. Conclusion 2011;140(4):1174–1181. We constructed a 4-gene signature for patients with hyper- 14. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids mutated CRC, which can classify patients into different risk Res. 2010;38(16):e164 Cancer Management and Research downloaded from https://www.dovepress.com/ by 54.70.40.11 on 05-Aug-2019 groups and predict prognosis. This signature is a powerful 15. Salipante SJ, Scroggins SM, Hampel HL, Turner EH, Pritchard CC. Microsatellite instability detection by next generation sequencing. Clin tool in stage II and III colon cancer and MSI-H CRC. Future Chem. 2014;60(9):1192–1199. prospective studies are needed to confirm the power of the 16. Cancer Genome Atlas Network. Comprehensive molecular characteriza- 4-gene signature in patients receiving immunotherapy. tion of human colon and rectal cancer. Nature. 2012;487(7407):330–337. 17. Tian S, Roepman P, Popovici V, et al. A robust genomic signature for the detection of colorectal cancer patients with microsatellite instability Acknowledgments phenotype and high mutation frequency. J Pathol. 2012;228(4):586–595. 18. Arora S, Yan H, Cho I, et al. Genetic variants that predispose to DNA This study was supported by the Fundamental Research double-strand breaks in lymphocytes from a subset of patients with famil- Funds for the Central Universities, National Human Genetic ial colorectal carcinomas. Gastroenterology. 2015;149(7):1872–1883. Resources Sharing Service Platform (2005DKA21300), 19. Dudley JC, Lin MT, Le DT, Eshleman JR. Microsatellite instability as a biomarker for PD-1 blockade. Clin Cancer Res. 2016;22(4):813–820. and National High Technology Research and Development 20. La D. PD-1 blockade in tumors with mismatch-repair deficiency. N Engl Program of China (2012AA02A204). The abstract of this J Med. 2015;373(20):1979. 21. Sho S, Court CM, Winograd P, Russell MM, Tomlinson JS. A prognostic paper was presented at the MAP Conference named “A novel mutation panel for predicting cancer recurrence in stages II and III 4-gene prognostic signature for hypermutated colorectal colorectal cancer. J Surg Oncol. 2017;116(8):996–1004.

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22. Dulak AM, Stojanov P, Peng S, et al. Exome and whole-genome sequenc- 27. Hampton T. Gut microbes may shape response to cancer immunotherapy. ing of esophageal adenocarcinoma identifies recurrent driver events and JAMA. 2018;319(5):430–431. mutational complexity. Nat Genet. 2013;45(5):478–486. 28. Hempen PM, Zhang L, Bansal RK, et al. Evidence of selection for 23. Dobbs K, Domínguez Conde C, Zhang SY, et al. Inherited DOCK2 clones having genetic inactivation of the activin A type II receptor deficiency in patients with early-onset invasive infections.N Engl J (ACVR2) gene in gastrointestinal cancers. Cancer Res. 2003;63(5): Med. 2015;372(25):2409–2422. 994–999. 24. Liu Z, Man SM, Zhu Q, et al. DOCK2 confers immunity and intestinal 29. Jung B, Doctolero RT, Tajima A, et al. Loss of activin receptor type 2 colonization resistance to Citrobacter rodentium infection. Sci Rep. protein expression in microsatellite unstable colon cancers. Gastroen- 2016;6(1):27814. terology. 2004;126(3):654–659. 25. Miao S, Zhang RY, Wang W, et al. Overexpression of dedicator of cyto- 30. Lochhead P, Kuchiba A, Imamura Y, et al. Microsatellite instability and kinesis 2 correlates with good prognosis in colorectal cancer associated BRAF mutation testing in colorectal cancer prognostication. J Natl with more prominent CD8+ lymphocytes infiltration: a colorectal cancer Cancer Inst. 2013;105(15):1151–1156. analysis. J Cell Biochem. 2018;119(11):8962–8970. 26. Routy B, Le Chatelier E, Derosa L, et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Sci- ence. 2018;359(6371):91–97. For personal use only. Cancer Management and Research downloaded from https://www.dovepress.com/ by 54.70.40.11 on 05-Aug-2019

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Supplementary materials

AB 1.0 1.0

0.8 0.8

y 0.6 y 0.6

Probabilit 0.4 Probabilit 0.4

0.2 POLE (n=8) 0.2 MSI (n=43) MSI (n=41) MSS/BRAF-wild-type (n=25) MSI/POLE (n=2) P=0.453 MSS/BRAF-V600E (n=1) P=0.876 0.0 0.0 0 20 40 60 80 100 120 020406080 100120 Overall survival (months) Overall survival (months) No at risk: No at risk: POLE 8 6 5 2 1 0 0 MSI 43 38 28 20 12 4 3 MSI 41 36 26 18 10 2 1 MSS/BRAF-wild-type 25 23 20 12 7 2 2 MSI/POLE 2 2 2 2 2 2 2 MSS/BRAF-V600E 1 1 1 1 0 0 0

C D

1.0 1.0

0.8 0.8

y 0.6

Probabilit 0.4 Probabilit 0.4

For personal use only. 0.2 Stage I (n=9) 0.2 Stage II (n=39) High TMB (n=36) Stage III (n=18) P=0.260 Low TMB (n=33) P=0.504 0.0 0.0 020406080 100120 0204060 80 100 120 Overall survival (months) Overall survival (months) No at risk: No at risk: Stage I 9 9 5 4 3 1 1 High TMB 36 31 25 16 11 5 4 Stage II 39 34 29 18 8 4 3 Low TMB 33 31 24 17 8 1 1 Stage III 18 17 14 10 7 1 1

Figure S1 Power of different prognosis biomarkers. Notes: (A) MSI/POLE status divides patients into three groups: POLE, MSI, and POLE/MSI. The difference of patients’ survival is not significant. B( ) MSI/BRAF status divides patients into three groups: MSI, MSS/BRAF-wild-type, and MSS/BRAF-V600E. The difference of patients’ survival is not significant. C( ) Patients were divided into high TMB and low TMB groups by median(as 40 mut/Mb). The difference of patients’ survival is not significant. D( ) Hypermutated patients were not comply the traditional TNM stages. Abbreviations: MSI, microsatellite instability; MSS, microsatellite stable; TMB, tumor mutational burden. Cancer Management and Research downloaded from https://www.dovepress.com/ by 54.70.40.11 on 05-Aug-2019

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Dovepress Ge et al Z N F787 Z N F804 A Z N F804B Z N F831 Z N F850 ZFP36 L 2 ZFPM1 ZFPM2 Z N F208 Z N F233 Z N F267 Z N F285 Z N F334 Z N F382 Z N F417 Z N F469 Z N F479 Z N F521 Z N F569 Z N F600 Z N F665 Z N F671 Z N F679 Z N F727 Z N F728 Z N F729 Z N F735 Z N F750 Z N F761 X I RP2 XKR9 XPC XRCC1 ZB E D9 ZDBF2 ZFC3 H 1 ZF H X4 ZFP28 TR A K2 TR I M49B TR N T1 T SGA 10 T SH Z3 T S PO2 TU S C3 U A C UB E 4 A UB N 1 UBR5 U G T2 A 2 U G T3 A 2 U N C13C U N C5C L U N C93 A U SH 2 A UTP23 UT S 2B V N 1R2 VP S 37B VT I 1 A WB S CR17 WDR11 TM E M173 TM E M184 A TMPR SS 13 T NNI 3K TOP2B TOP3B TOP A Z1 TP53 TPM3 S T8 SIA 6 S T AG 1 S TK11 S TK31 S TO N 1- G TF2 A 1 L S TP G 2 S UFU S YCP1 S Y NE 1 S YT16 TBC1D7 Table 1 XR1 TBP TCF7 L 2 TDRD3 TDRD6 T EA D2 T EL O2 T G T G FBR2 T GI F1 T L R4 T L R7 TM6 S F1 S P A RC L 1 S P A T 31D1 S PDY E 6 S P G 20 S P I DR S PP1 S PT A 1 S PZ1 SS BP1 SE C63 SE M A 3D SE RP IN F1 SH KBP1 SI SL C12 A 1 SL C13 A 1 SL C25 A 51 SL C30 A 8 SL C4 A 10 SL C5 A 10 SL CO1B1 SL CO6 A 1 SLI TRK1 SLI TRK4 SL X4 S M A D2 S M A D3 S M A D4 SN T G 2 S ORB 2 S OX2 S OX9 S C N 9 A RP A 4 RP L 22 RP S 20 R S PO2 R S PO3 RYR2 SA C S S C N 1 A R A B GG TB R A D21 R A D50 R A D51 P2 R ALGA P A 1 R AL Y R ASA 1 R ASS F2 RB1 RBM12 RBMX RBMX L 3 R E V3 L RFC1 R GS 17 R GS 22 R H C G R H O A R I M S 2 R IN 3 RM I 1 R N F43 ROBO2 RP1 Q SE R1 PT EN PTP LA PTP N 11 PTPRK PTPRT PTPRZ1 PY HIN 1 QRFPR P L X N C1 PM S 1 PM S 2 PO L D1 PO L D3 PO LE PO LE 2 PO LE 4 PO LN PO L Q PO S T N POT E C POT EE POT EH POT E J PPM1 E PRDM9 PR E X2 PRK AA 1 PRKR I R PR SS 35 P S C A P SG 5 PTC H 1 P L D5 P IE ZO2 P I K3C A P I K3R1 P I K3R3 PKD2 L 2 P L C E 1 P L C 1 P L CZ1 N OTC H 1 N OTC H 2 N P I PB5 N R AS N T HL 1 N T S R2 N UDT11 N UP107 N UP50 OPRD1 OR11 H 12 P A BPC1 P A F AH 1B2 P A RP1 P A RP4 P A X1 PBRM1 PCBP1 PCD H 9 PCD H B15 PC G F6 PC L O PC S K7 P H F2 N O L C1 For personal use only. NE B NE F H N F E 2 L NIN N KX2-1 NL RP14 N M E 8 N M I M SH 3 M SH 6 MTOR MTU S 1 MTU S 2 MUC1 MUC12 MUC16 MUC17 MUC4 MUC6 MUTY H MYCBP2 MYC N MYO16 MYO3B MYO9 A MYOCD MYPOP NAALA D2 NAL C N N B EA N BPF15 N BPF3 M SH 2 M A PK1 M A PK8 I P1 MDM2 MFRP MK I 67 M LH 1 M LH 3 M N X1 LE KR1 LG R5 LI FR LIG 1 LIG 4 L M NA L P HN 3 L R I F1 L RP1B L RP2 L RRC4C L RRD1 L RR I Q1 L RRK2 L RR N 3 L RRTM3 M AGE B3 M AGEL 2 M AL 2 M ANS C4 M A P1B M A P2K1 M A P2K4 M A P3K19 L COR K I RR EL KMT2B KR AS KRT A P10-6 KRT A P4-5 KRT A P5-5 KT N 1 L C A 5 HIS T1 H 2BC HIS T1 H 2BM HI V E P2 HLA -B H MC N 1 HN R N P L H R N HS P A 8 H YD IN I DO2 IGS F3 IL 15 I RF5 I T LN 2 I TPR1 I V L J A K2 KC N J5 KC NN 3 KC NS 2 KC N T2 KDM6 A K IAA 0408 K I F4B HE M GN G O LG B1 G P N 1 G PR112 G PR149 G R E M1 G R IA 2 G R IN 2 A H D G FRP2 FB N 2 FBXW7 FCR L 1 F E TUB F LG F LG 2 F LI 1 FOX A 1 FOX E 1 FR E M2 FR G 1 FRY F SH R F SI P2 F S T L 5 GA BR G 3 GALN T12 GG T1 GLI 3 GL T8D2 GL TPD2 GNA T2 GNG 12 G O LGA 4 F A T4 Cancer Management and Research downloaded from https://www.dovepress.com/ by 54.70.40.11 on 05-Aug-2019 F AN C A F AN CD2 F AN C E F AN C I F AN CM F A T1 F A T2 F A T3 EI F4 A 2 EL F3 EL MO1 EL MOD2 E P300 E PC A M E RBB2 E RBB4 E RCC1 E RCC5 E RCC6 E R I C H 3 E XO1 E Y S F10 F13B F5 F A M123B F A M135B F A M153B F A M184 F A M194B F A M71 E 2 F AN 1 EI F3 E DPP10 DPY19 L 2 D SEL DUX4 DY N C2 H 1 E B LN 1 E D N RB E F H B CO L 6 A CRB1 CRTC1 C S F2R A C S MD1 C S MD3 CT NN B1 CT NN D2 CT SA CUX1 DC A F6 DC HS 2 DDX6 D E FB108B D NAH 11 D NAH 14 D NAH 3 D NAH 5 D NAH 6 D NAH 7 D NAH 8 D NA JC24 DOCK10 DOCK2 CO L 6 A 3 CMY A 5 C N BD1 C N T 1 C N T 6 C N T NA P5 CO L 11 A 1 CO L 12 A 1 CO L 18 A 1 C2orf44 C9 C A 4 C A NA 1B C AS P8 CB L CCDC141 CCDC168 CCDC62 CCDC66 CCDC73 CCDC88 A CC NE 1 CD40 LG CD44 CD58 CD7 CDC42 E P1 CD H 1 CDK N 2 A CF A P58 C HE K2 C LE C18B C L U 1 C20orf54 BMPR1 A BMPR2 BR A F BRC A 1 BRC A 2 BR I P1 C17orf70 C1ORF74 A T D5 A TM A TP10 A TP1 2 A TP1B2 A TP8 2 A TR A TX N 1 A UT S 2 A X IN 1 A X IN 2 A Z G P1 B2M B3 GN T6 B3 GN T9 B AGE 3 B AI 3 B AIA P2 L 2 B A P1 B A X BC L 9 BC L 9 B I VM- E RCC5 B L M AS PM L ist of 524 genes for panel sequencing AN KRD30 A A PC A POB E C3B A POB E C3F A R HGA P15 A R HGA P32 A R HGE F12 A R I D1 A BC 6 A BC 8 A BC 9 A BCB1 A COX2 A C SL 6 A CTC1 A CVR1B A CVR2 A D M21 A D M29 A D M7 A D MT S 12 A D MT S 20 A DCY2 A DM2 AG B L 2 A KT1 AL PK2 ALS 2CR11 ALS 2CR12 AL X4 A MPD1 AN KRD12 A BC 13 Table S1

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Table S2 Baseline values of all initial selected patients Table S3 Chemotherapy of patients in the final analysis ZJU center TCGA P-value High-risk group Low-risk group P-value (n=45) (n=63) (n=33) (n=35) Age, years, mean ± SD 60.00±2.10 66.02±1.94 0.459 Chemotherapy, n (%) 18 (54.55) 14 (40.00) 0.457 Median OS, months, mean 67.97±4.30 40.73±4.73 0.413 ± SD Sex, female, n (%) 16 (35.56) 25 (39.68) 0.663 Mutation burden, mb, mean 73.62±12.08 46.40±5.14 0.000 ± SD Stage, n (%) 0.519 I 4 (8.89) 11 (17.46) II 27 (60.00) 35 (55.55) III 13 (28.89) 13 (20.63) IV 1 (2.22) 2 (3.17) Location, n (%) 0.003 Right-side colon 22 (48.89) 50 (79.37) Left-side colon 14 (31.11) 7 (11.11) Rectum 9 (20.00) 5 (7.94) MSI-H, n (%) 22 (48.89) 39 (61.90) 0.179 POLE driver mutant, n (%) 9 (20.00) 5 (7.94) 0.066 ACVR2A mutant, n (%) 34 (75.55) 18 (23.57) 0.000 APC mutant, n (%) 28 (62.22) 27 (42.86) 0.152 DOCK2 mutant, n (%) 20 (44.44) 42 (33.33) 0.284 Abbreviations: OS, overall survival; MSI-H, high microsatellite instability; TCGA, The Cancer Genome Atlas; ZJU, Zhejiang University. For personal use only. Cancer Management and Research downloaded from https://www.dovepress.com/ by 54.70.40.11 on 05-Aug-2019

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