fusion kinases identified by genomic analyses of sporadic ......1 1 title: fusion kinases identified...

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Title: Fusion kinases identified by genomic analyses of sporadic microsatellite 1

instability-high colorectal cancers 2

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Authors: Kazuhito Sato,1,2 Masahito Kawazu,3 Yoko Yamamoto,1 Toshihide Ueno,2 4

Shinya Kojima,2 Genta Nagae,4 Hiroyuki Abe,5 Manabu Soda,2 Takafumi Oga,2 Shinji 5

Kohsaka,3 Eirin Sai,3 Yoshihiro Yamashita,2 Hisae Iinuma,6 Masashi Fukayama,5 6

Hiroyuki Aburatani,4 Toshiaki Watanabe,7† and Hiroyuki Mano2,8 7

8

Author affiliations Departments of 1Surgical Oncology, 2Cellular Signaling, 3Medical 9

Genomics, 5Pathology, and 7Surgical Oncology and Vascular Surgery, Graduate School 10

of Medicine, The University of Tokyo, Tokyo 113-0033, Japan; 4Genome Science 11

Division, Research Center for Advanced Science and Technologies, The University of 12

Tokyo, Tokyo 153-8904, Japan; 6Department of Surgery, Teikyo University School of 13

Medicine, Tokyo 173-8605, Japan; 8National Cancer Center Research Institute, Tokyo 14

104-0045, Japan 15

†Deceased 16

Research. on February 24, 2020. © 2018 American Association for Cancerclincancerres.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 2, 2018; DOI: 10.1158/1078-0432.CCR-18-1574

http://clincancerres.aacrjournals.org/

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Running title: Actionable fusion kinases in MSI-H CRCs 2

Keywords: DNA mismatch repair; MLH1 methylation 3

Conflict of interest: The authors declare that they have no competing interests. 4

Correspondence to: Masahito Kawazu 5

7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan 6

Tel.: +81-3-5841-0633 7

Fax: +81-3-5841-0634 8

Email: [email protected] 9

10

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ABSTRACT 1

Purpose: Colorectal cancers (CRCs) with microsatellite instability-high (MSI-H) status, 2

due to mismatch repair deficiency, are associated with poor patient outcomes after 3

relapse. We aimed to identify novel therapeutic targets for them. 4

Experimental Design: We performed MSI analyses of over 2,800 surgically resected 5

colorectal tumors obtained from consecutive patients treated in Japan from 1998 6

through June 2016. Whole-exome sequencing, transcriptome sequencing, and 7

methylation analyses were performed on 149 of 162 tumors showing MSI in BAT25 8

and BAT26 loci. We analyzed patient survival times using Bonferroni-adjusted log-rank 9

tests. 10

Results: Sporadic MSI-H CRCs with promoter methylation of MLH1 (called MM) had 11

a clinicopathological profile that was distinct from that of CRCs of patients with 12

germline mutations (Lynch syndrome-associated) or somatic, Lynch-like mutations in 13

mismatch repair genes. MM tumors had more insertions and deletions and more 14

recurrent mutations in BRAF and RNF43 than Lynch syndrome-associated or Lynch-like 15

MSI-H tumors. Eleven fusion kinases were exclusively detected in MM MSI-H CRCs 16




4

lacking oncogenic KRAS/BRAF missense mutations and were associated with worse 1

post-relapse prognosis. We developed a simple method to identify MM tumors and 2

applied it to a validation cohort of 28 MSI-H CRCs, identifying 16 MM tumors and 2 3

fusion kinases. 4

Conclusions: We discovered that fusion kinases are frequently observed among 5

sporadic MM MSI-H CRCs. The new method to identify MM tumors enables us to 6

straightforwardly group MSI-H patients into candidates of Lynch syndrome or fusion 7

kinase carriers. 8

9

Translational Relevance 10

Microsatellite instability-high colorectal cancers (MSI-H CRCs) have been 11

conventionally divided into hereditary (Lynch syndrome, LS) and sporadic categories. 12

This report provides a rational basis for further classification of sporadic MSI-H CRCs 13

into those with somatic mutations in mismatch repair genes (Lynch-like, LL) and those 14

with promoter methylation of MLH1 (MM). There were significant differences between 15

the LS/LL and MM groups in clinicopathological properties including tumor 16




5

localization, number of insertions/deletions, and recurrent mutations of KRAS/APC and 1

BRAF/RNF43. Such a classification would enable precise management of patients with 2

MSI-H CRC. Fusion kinases were detected only in MM MSI-H CRCs lacking 3

oncogenic KRAS or BRAF mutations and were associated with worse prognosis after 4

relapse. A new, convenient method for detecting MM tumors makes it possible to 5

straightforwardly identify LS candidates or MSI-H tumors likely to carry fusion kinases 6

that are therapeutic targets of kinase inhibitors. 7

8

9




6

INTRODUCTION 1

Approximately 10% of all colorectal cancers (CRCs) exhibit a microsatellite 2

instability–high (MSI-H) status, in which the number of mono-, di-, or tri-nucleotide 3

repeats in microsatellite sequences are frequently altered (1–3). Most patients with 4

MSI-H CRCs have poor prognosis after relapse according to consensus molecular 5

subtyping (4,5), supporting the concept that MSI-H cancers are resistant to 6

5-fluorouracil (6). The considerable number of mutations resulting from DNA mismatch 7

repair (MMR) deficiency has hampered the identification of driver oncogenes that play 8

essential pathogenic roles in MSI-H CRCs. 9

MSI-H CRCs have been conventionally divided into hereditary or sporadic. To 10

identify cases potentially involving Lynch syndrome (LS), we have to rely on patients’ 11

clinical information, such as family history or age. It is well known that MSI-H CRCs 12

are tightly linked to the CpG island methylator phenotype (CIMP) (7,8), which is 13

characterized by the cancer-specific methylation of a definite set of CpG islands. 14

Rational patient stratification based on the precise recognition of the etiology is 15

essential for the appropriate management of patients with MSI-H CRCs. 16

To address these issues, we conducted an integrated molecular characterization of 17




7

149 MSI-H colorectal tumors. 1

2

Material and Methods 3

Ethics 4

Patients with CRC gave written informed consent prior to their participation in the study. 5

This project was approved by the institutional ethics committees of The University of 6

Tokyo (The Human Genome, Gene Analysis Research Ethics Committee; G10063 and 7

G3546) and Teikyo University (#14-197), and the study was conducted in accordance 8

with the Declaration of Helsinki. 9

10

Sample collection 11

Surgically resected colorectal tumors (n = ~2,800) were obtained from consecutive 12

patients treated at The University of Tokyo Hospital and Teikyo University Hospital 13

between 1998 and June 2016. To validate a fusion-detection strategy, we added 185 14

primary CRCs, collected at The University of Tokyo Hospital from July 2016 through 15

April 2017. 16




8

1

Microsatellite instability testing 2

Tumor tissues and corresponding normal mucosae were obtained from surgically 3

resected specimens and were either snap-frozen in liquid nitrogen immediately after 4

resection and stored at −80°C or immersed in RNAlater Tissue Protect Tubes (Qiagen, 5

Valencia, CA, USA) overnight at 4°C followed by storage at −20°C until use. Genomic 6

DNA was extracted from tissue samples with the DNeasy Blood & Tissue Kit or the 7

QIAamp DNA Mini Kit (Qiagen) and analyzed by polymerase chain reaction (PCR) at 8

two microsatellite loci, BAT25 and BAT26, using the labeled primers indicated in 9

Supplementary Table S1. PCR products were electrophoresed on an ABI PRISM 3100, 10

an ABI PRISM 3700, or a 3130xl Genetic Analyzer (Applied Biosystems, Foster City, 11

CA, USA), and fluorescent signals were analyzed using GeneScan 3.7, GeneScan 3.5, 12

or GeneMapper 4.0, and Genotyper 2.1, Genotyper 3.6, or PeakScanner 1.0 (all from 13

Applied Biosystems), in accordance with the manufacturer’s instructions. 14

15

Comprehensive genomic and epigenomic analyses 16




9

Genomic DNA from 149 MSI-H tumors (148 adenocarcinomas and 1 adenoma in 146 1

patients, all chemotherapy-naïve, 1 case with preoperative radiotherapy) and 2

corresponding paired-normal tissues consisting of adjacent, histologically normal 3

tissues resected at the time of surgery was subjected to whole-exome sequencing (WES) 4

with HiSeq2500 (Illumina, San Diego, CA, USA). We analyzed the genome-wide DNA 5

methylation profiles of 93 tumors with an Infinium Human MethylationEPIC BeadChip 6

(Illumina) and performed whole-transcriptome sequencing (RNA-seq) from 111 MSI-H 7

with HiSeq2500 (Illumina). 8

9

DNA methylation analyses 10

Genome-wide DNA methylation analysis 11

Infinium Human MethylationEPIC BeadChip (Illumina) was used in accordance with 12

the manufacturer’s protocol. Beta values were normalized using BMIQ function in the 13

R package wateRmelon 14

(http://www.bioconductor.org/packages/release/bioc/html/wateRmelon.html). For 15

downstream analysis, we selected probes that were designed on promoter-associated 16




10

sites of autosomes. Then, we performed consensus clustering with 3,073 probes (probes 1

with variance ranked in the top 1%) using the R package ConsensusClusterPlus 2

(http://www.bioconductor.org/packages/release/bioc/html/ConsensusClusterPlus.html). 3

4

Bisulfite sequencing of MLH1 promoter 5

Genomic DNA was subjected to bisulfite conversion using an EpiTect Bisulfite Kit 6

(Qiagen). Converted DNA fragments were amplified by PCR using a Kapa HiFi Uracil+ 7

Kit (Kapa Biosystems, Woburn, MA, USA) with the primer sets indicated in 8

Supplementary Table S1. Amplified PCR products were subjected to sequencing using 9

the MiSeq system (Illumina) to determine the proportion of methylated alleles at each 10

cytosine residue. 11

12

MLH1 promoter methylation assay with methylation-sensitive restriction enzyme 13

MLH1 promoter methylation was assessed by PCR after digestion of genomic DNA 14

with methylation-sensitive restriction enzyme. Genomic DNA (200 or 25 ng) was 15

digested in a volume of 20 or 10 µL by Anza 22 SmaI (Thermo Fisher Scientific, 16




11

Waltham, MA, USA), followed by heat inactivation of restriction enzyme, in 1

accordance with the manufacturer’s instructions. Digested DNA (20 or 5 ng) was 2

subjected to 25 or 27 cycles of multiplex PCR in a total volume of 25 µL using 3

primeSTAR GXL DNA polymerase (Takara Bio, Otsu, Japan), in accordance with the 4

manufacturer’s instructions. BRAF was used as a positive control. The primer sets are 5

indicated in Supplementary Table S1. Methylation status was determined by 2% agarose 6

gel electrophoresis of 12.5 µL of PCR products. 7

8

Whole-exome sequencing including mutation call, copy number analysis, and 9

signature analysis 10

Genomic DNA was isolated from each sample and underwent enrichment of exonic 11

fragments using SureSelect Human All Exon Kit v5 (Agilent Technologies, Santa Clara, 12

CA, USA). Massively parallel sequencing of isolated fragments was performed with a 13

HiSeq2500 (Illumina) using the paired-end option. Paired-end whole-exome sequencing 14

(WES) reads were independently aligned to the human reference genome (hg38) using 15

BWA (9), Bowtie2 (http://bowtie-bio.sourceforge.net/bowtie2/index.shtml), and 16




12

NovoAlign (http://www.novocraft.com/products/novoalign/). Somatic mutations were 1

called using MuTect (http://www.broadinstitute.org/cancer/cga/mutect), 2

SomaticIndelDetector (http://www.broadinstitute.org/cancer/cga/node/87), and VarScan 3

(http://varscan.sourceforge.net). Mutations were discarded if: (I) the read depth was < 4

20 or the variant allele frequency (VAF) was < 0.1, (II) they were supported by only one 5

strand of the genome, or (III) they were present in normal human genomes in either the 6

1000 Genomes Project dataset (http://www.internationalgenome.org/) or our in-house 7

database. Gene mutations were annotated by SnpEff (http://snpeff.sourceforge.net). 8

Copy number status was analyzed using our in-house pipeline, which determines the 9

logR ratio (LRR) as follows: (I) we selected SNP positions in the 1000 Genomes Project 10

database that were in a homozygous state (VAF ≤ 0.05 or ≥ 0.95) or a heterozygous 11

state (VAF 0.4–0.6) in the genomes of respective normal samples, (II) normal and tumor 12

read depths at the selected position were adjusted based on G+C percentage of a 100-bp 13

window flanking the position (10), (III) we calculated the LRR = , where ni and 14

ti are normal and tumor-adjusted depths at position i, and (IV) each representative LRR 15

was determined by the median of a moving window (1 Mb) centred at position i. 16




13

LRR of the copy number of both alleles, that of the major allele, and that of the 1

minor allele were determined for every region of the genome. The P values for gain or 2

loss of respective genomic regions were determined from the LRRs with the 3

permutation test (100,000 iterations) following the algorithm used in GISTIC (11,12). Q 4

values were calculated from the P values using the R package qvalue 5

(http://github.com/jdstorey/qvalue). 6

Mutational signatures were analyzed using the Wellcome Trust Sanger Institute 7

Mutational Signature Framework 8

(http://jp.mathworks.com/matlabcentral/fileexchange/38724-wtsi-mutational-signature-f9

ramework). The optimal number of signatures was determined in accordance with the 10

signature stabilities and average Frobenius reconstruction errors. 11

12

Immunohistochemical analysis of mismatch repair proteins 13

Immunohistochemistry of mismatch repair proteins (MLH1, MSH2, MSH6, and PMS2) 14

was performed on whole sections of each r. Formalin-fixed paraffin-embedded tumor 15

blocks were sliced into 3-μm-thick sections, which were then immunostained with the 16




14

Ventana Benchmark automated immunostainer (Roche, Basel, Switzerland). Primary 1

antibodies used were mouse monoclonal anti-MLH1 antibody (clone ES05, dilution 2

1:50; Leica, Wetzlar, Germany), mouse monoclonal anti-MSH2 antibody (clone FE11, 3

dilution 1:50; Dako, Glostrup, Denmark), rabbit monoclonal anti-MSH6 antibody 4

(clone EPR3945, dilution 1:200; GeneTex, Hsinchu, Taiwan), and mouse monoclonal 5

anti-PMS2 antibody (clone EPR3947, no dilution; Roche, Basel, Switzerland). 6

Immunostained slides were blindly evaluated by a board-certified pathologist 7

(Hiroyuki Abe). Nuclear staining was evaluated in each tumor. Epithelial cells in the 8

proliferative zone of non-neoplastic colonic mucosa and lymphocytes in the germinal 9

centre of lymph follicles were used as internal positive controls. 10

11

Pathway analysis 12

The Database for Annotation, Visualization and Integrated Discovery web-based tool 13

(https://david.ncifcrf.gov) was used to identify pathways. Pathways defined in Gene 14

Ontology (limited to the “biological pathway” category; http://www.geneontology.org), 15

KEGG pathway (http://www.genome.jp/kegg/), BioCarta 16




15

(https://cgap.nci.nih.gov/Pathways/BioCarta_Pathways), and the Reactome Pathway 1

Database (http://www.reactome.org) were used for the analysis. 2

3

Transcriptome sequencing, expression analysis, and detection of fusion genes 4

Total RNA was extracted with RNA Bee reagent (Tel-Test Inc., Friendswood, TX, USA) 5

and treated with DNase I (Qiagen) using an RNeasy Mini Kit (Qiagen). RNA integrity 6

was evaluated using either a Bioanalyzer (Agilent Technologies) or TapeStation 7

(Agilent Technologies). RNA with a high RNA integrity number underwent RNA-seq 8

using a NEBNext Ultra Directional RNA Library Prep Kit (New England BioLabs, 9

Ipswich, MA, USA) in which complementary DNA (cDNA) was prepared from 10

polyA-selected RNA. RNA with a low RNA integrity number was subjected to 11

RNA-seq using a TruSeq RNA Access Library Prep Kit (Illumina) in which cDNA was 12

generated with random primers. Prepared RNA-seq libraries underwent next-generation 13

sequencing of 120 bp from both ends (paired-end reads). The expression level of each 14

gene was calculated using DESeq2 15

(http://bioconductor.org/packages/release/bioc/html/DESeq2.html) with VST 16




16

transformation, and gene fusions were detected using a deFuse pipeline 1

(https://bitbucket.org/dranew/defuse) and STAR (https://github.com/alexdobin/STAR). 2

3

Cloning of fusion genes 4

Complementary DNAs for wild-type, mutant, and fusion proteins were amplified by 5

reverse transcription PCR (RT-PCR) from RNA samples and then ligated into the pMXs 6

retroviral vector (Cell Biolabs, San Diego, CA, USA). The sequences of all cDNAs 7

were verified by Sanger sequencing. Primer sequences are provided in Supplementary 8

Table S1. 9

10

3T3 cell transformation assay 11

Human embryonic kidney (HEK) 293T cells and mouse 3T3 fibroblasts were obtained 12

from the American Type Culture Collection (ATCC, Manassas, VA, USA) and 13

maintained in Dulbecco’s modified Eagle’s medium (DMEM)-F12 supplemented with 14

10% foetal bovine serum (FBS) (both from Life Technologies, Carlsbad, CA, USA). 15

Cell lines were propagated for less than 3 months after initial plating. Cultured cells 16




17

were tested for mycoplasma contamination using a MycoAlert Mycoplasma Detection 1

Kit (Lonza). To obtain infectious virus particles, recombinant vectors were introduced 2

together with an ecotropic packaging plasmid (Takara Bio) into HEK293T cells by 3

transfection. For the focus formation assay, 3T3 cells were infected with ecotropic 4

recombinant retroviruses and cultured for 12 days in DMEM-F12 supplemented with 5

5% calf serum. Cell numbers were counted using a luminometer and the CellTiter-Glo 6

Luminescent Cell Viability assay (Promega, Madison, WI, USA). Linsitinib (S1091), 7

entrectinib (S7998), regorafenib (S1178), and PLX7904 (S7964) were obtained from 8

Selleck Chemicals (Houston, TX, USA). 9

10

Animal experiment 11

All animal experimental procedures were approved by the Institutional Animal Care and 12

Use Committee of The University of Tokyo. We also adhered to the standards 13

articulated in the NC3Rs guidelines (Animal Research: Reporting of In Vivo 14

Experiments). 15

3T3 cells expressing SLC12A2-INSR and RUFY1-RET were subcutaneously 16




18

inoculated into the flank of 6–8-week-old female nude mice (BALB/c-nu/nu; Charles 1

River Laboratories Japan, Inc., Yokohama, Japan) at 2 × 106 cells/200 µL of PBS. Mice 2

carrying implanted tumors were divided randomly into two or three groups after 3

confirming tumor growth in each experiment. Kinase inhibitors, dissolved in 4

N-methyl-2-pyrrolidone (Nacalai Tesque, Kyoto, Japan) and polyethylene glycol (PEG; 5

Sigma-Aldrich, St. Louis, MO, USA), were applied orally once daily as indicated. 6

Linsitinib (LC Laboratories, Woburn, MA, USA) was applied at a dose of 25 mg/kg 7

body weight (8 tumors in 4 mice). Regorafenib (Ark Pharm, Arlington Heights, IL, 8

USA) was applied at a dose of 100 mg/kg (10 tumors in 5 mice) or 10 mg/kg (8 tumors 9

in 4 mice). Tumor diameter was measured using callipers, and tumor volume was 10

determined by calculating the volume of an ellipsoid using the following formula: 11

length × width2 × 0.5. 12

13

Statistics 14

Numerical variables were summarized by median and range. Comparisons of numerical 15

variables between groups were carried out using a nonparametric approach (Wilcoxon 16




19

rank-sum test, two-tailed). Comparisons of the distribution of categorical variables in 1

different groups were performed using Fisher’s exact test. The survival curve was 2

established by the Kaplan–Meier method and compared by the log-rank test with 3

Bonferroni adjustment. Cox proportional hazards model was also utilized to evaluate 4

the effects of multiple variables. Statistical analysis was performed using the computing 5

environment R (version 3.2.3). 6

7

Data availability 8

Raw sequencing data were deposited in the Japanese Genotype-Phenotype Archive 9

(http://trace.ddbj.nig.ac.jp/jga), which is hosted by the DNA Data Bank of Japan (13) 10

under accession number JGAS00000000113 (NBDC number: hum0094). 11

12

13

RESULTS 14

Demographic characteristics 15

In the genomes of 162 tumors (5.8%), microsatellites were unstable at both BAT25 and 16




20

BAT26 loci, which we classified as MSI-H in the strict sense (Fig. 1A and 1

Supplementary Table S2) (14). The frequency of MSI-H was lower than expected (6), 2

probably because only two mononucleotide repeat markers were used, while the 3

probability of including MSI-low tumors was low (15–17). Clinicopathological 4

characteristics of the MSI-H cases are summarized in Supplementary Table S3. 5

6

Classification of MSI-H CRC 7

To clarify the association between MSI-H status and the CpG island methylator 8

phenotype CIMP (7,8), consensus clustering was performed using data from 9

methylation profile analyses. Sixty-four tumors (69%) that were clustered in the CIMP 10

branch were also found in a subset of 65 tumors in which MLH1 expression was 11

silenced because of promoter methylation (Supplementary Fig. S1), confirming almost 12

complete overlap between these two clusters. In this report, we define CIMP based on 13

the methylation status of the MLH1 promoter in order to avoid ambiguity caused by the 14

different clustering methods. Promoter methylation of MMR genes other than MLH1, 15

namely, MSH2, MSH6, PMS1, MSH3, PMS2, and MLH3, was not detected 16




21

(Supplementary Fig. S1). 1

To complement the genome-wide methylation profile analysis, the promoter region 2

of MLH1 (chr3: 36,993,202–36,993,864) in the genome of 149 samples was analyzed 3

by bisulfite sequencing. The methylation status determined by bisulfite sequencing was 4

well correlated with that determined by genome-wide analysis (Supplementary Fig. S2). 5

We refer to the MLH1-silenced MSI-H CRCs as MLH1 promoter-methylated (MM) 6

tumors (Fig. 1B). 7

Of the 23 tumor samples from patients with Lynch syndrome (LS) in our cohort, 11 8

LS tumors underwent methylation profile analysis, and we found that none of these 9

tumors clustered in the CIMP branch (Supplementary Fig. S1). Bisulfite sequencing 10

also revealed that promoter methylation of MLH1 was absent in all 23 LS tumors. 11

Neither germline mutations in MMR genes nor promoter methylation of MLH1 was 12

observed in 27 tumor samples of MSI-H CRCs, 17 of which were subjected to 13

methylation profile analysis. Because the 17 tumors clustered together with LS tumors 14

in consensus clustering, we refer to these samples as “Lynch-like (LL)” tumors (Fig. 1B, 15

Supplementary Fig. S1). As shown in the flowchart in Fig. 1B, our cohort of patients 16




22

with MSI-H CRC was classified into three subgroups with distinct genomic/epigenomic 1

profiles: LS (n = 23), LL (n = 27), and MM (n = 99) (Fig. 1B). 2

3

Distinct clinical features of LS/LL tumors and MM tumors 4

Interestingly, although MM tumors were more likely to develop in the “right” side of 5

the large intestine (consisting of the cecum, and the ascending and transverse colon), a 6

finding that was previously reported concerning MSI-H CRCs in general (1), LS/LL 7

tumors were evenly distributed across the large intestine (Fig. 1C). This observation is 8

consistent with the hypothesis that aberrant DNA methylation may develop in the 9

specific microbiological environment of the “right” side of the large intestine (18,19). 10

Notably, and justifying our classification approach, we found that the age of the patients 11

at operation differed between the subtypes [one-way ANOVA, F(2,143) = 54, P < 2.0 × 12

10−16; Fig. 1D]. The patients with MM tumors had worse survival after relapse than 13

those with LS/LL tumors (log-rank test, P = 0.013; Fig. 1E), although the difference 14

was not statistically significant after adjusting for age and stage at operation (Cox 15

proportional hazard model, HR = 0.36; 95% CI, 0.04–3.08). Further analysis with a 16




23

larger population is required to evaluate the difference in prognosis. 1

2

Mutations in MMR genes 3

Since MSI-H CRCs often harbour disruptive somatic mutations within MMR genes that 4

can in turn be affected by MMR deficiency, it is difficult to distinguish causative 5

mutations in MMR genes from resultant mutations. Because MLH1 promoter 6

methylation is regarded as the primary cause of MMR deficiency (20), we considered 7

that somatic mutations in MMR genes detected in MLH1 promoter-methylated tumors 8

were likely resultant ones, and we excluded them from the causal MMR gene mutation 9

panel of LS/LL (Fig. 2). The excluded somatic mutations were as follows: 10

MLH1(S193fs) (n = 1), MSH2(L229fs) (n = 3), MSH6(R248fs) (n = 4), and 11

MSH6(P1087fs) (n = 41) [located on (A)6, (A)7, (A)7, and (C)8 repeats, respectively]. 12

Mutations in MMR genes that were truncated or reported to be pathogenic were 13

regarded as being causative (Supplementary Table S4). 14

Next, we investigated how the function of MMR genes with a heterozygous 15

mutation in LS was abolished (Fig. 2A, Supplementary Fig. S3). Somatic uniparental 16




24

disomy (UPD) in three MMR genes was observed in 12 LS tumors (MLH1, n = 8; 1

PMS2, n = 3; and MSH2, n = 1). Additional somatic single-nucleotide variations (SNVs) 2

or insertions/deletions (indels) in MMR genes were observed in five LS tumors (MSH6, 3

n = 2; PMS2, n = 2; and MSH2, n = 1). An LS tumor with a heterozygous germline 4

mutation in PMS2 lost the wild-type allele, and additional pathogenic mutations were 5

not identified in the remaining five LS tumors. 6

We further sought to identify the alterations of MMR genes in LL tumors. Sixteen 7

LL tumors (16/27, 59%) harbored somatic mutations within MMR genes, as previously 8

reported (21). Six tumors had somatic SNVs/indels in MLH1 and one tumor had a 9

somatic SNV in MSH2, both of which were accompanied by UPD of the respective 10

gene. Biallelic mutations in MSH2 were observed in five tumors. Heterozygous somatic 11

mutations in MLH1 and MSH2 were observed in three tumors and one tumor, 12

respectively, without additional detectable events. However, in the remaining 11 tumors, 13

we did not identify pathogenic mutations in MMR genes that would account for the 14

MSI-H status, although six variants of uncertain significance (VUS), listed in 15

Supplementary Table S4, are not included in Fig. 2A. 16




25

We validated these results by immunohistochemistry (IHC) of MMR proteins, 1

obtaining concordant results in 91% (32/35) of cases for which both the genotype data 2

and protein expression data were available (Fig. 2). In seven cases without genotype 3

data, the results of IHC revealed probable disrupted MMR genes. 4

5

Somatic SNVs and indels 6

With > 20-fold coverage of at least 85% of the target regions, whole-exome sequencing 7

(WES) identified a median of 962 (range: 103–6,973) somatic nonsynonymous SNVs 8

and a median of 130 (range: 5–308) somatic indels in coding regions. The number of 9

recurrent (> 5% frequency) indels (n = 684) was larger than that of recurrent (> 5% 10

frequency) SNVs (n = 5). The frequencies of SNVs did not differ significantly between 11

LL/LS tumors and MM tumors (P = 0.49), whereas the frequencies of indels were 12

higher in MM tumors than those found in LS/LL tumors (P = 0.0028, Wilcoxon 13

rank-sum test; Supplementary Fig. S4). 14

To identify mutations that are likely relevant to MSI-H CRC oncogenesis under 15

conditions with a large number of background mutations, the identified somatic variants 16




26

were stratified into three tiers (Supplementary Fig. S4): Tier 1 (n = 78,187 in 18,646 1

genes), consisting of all somatic variants detected; Tier 2 (n = 46,502 in 10,748 genes), 2

consisting of those Tier 1 variations that were either also detected in RNA-seq analysis 3

or present recurrently (≥ 10 times) in the Catalogue of Somatic Mutations in Cancer 4

database (http://cancer.sanger.ac.uk/cosmic/) (Supplementary Table S5); and Tier 3 (n = 5

3,346 in 458 genes), consisting of those Tier 2 variations present in genes found in the 6

Cancer Gene Census (22) or that were reported to be recurrent (≥ 30% frequency) in a 7

previous study of MSI-H CRC (23). 8

The 40 genes most frequently affected in Tier 3 are well-recognized targets in the 9

MSI-H status, including ACVR2A, SEC63, TGFBR2, and RNF43 (shown in Fig. 3A). 10

We found that there were 209 frequently altered genes (> 20% of samples) within Tier 2 11

variants (Supplementary Table S5). We also obtained a list of 860 significantly (Q < 12

0.01) mutated genes using MutSigCV (Supplementary Table S6, Supplementary Fig. 13

S4) (24). Notably, 153 (73%) of the 209 most frequently altered Tier 2 genes were 14

included in the list of significantly mutated genes, partly justifying the stratification 15

strategy used in this study. Mutations in KRAS, APC, and TCF7L2 were more prevalent 16




27

in LS/LL tumors than in MM tumors (P = 5.7 × 10−9, 6.5 × 10−8, and 1.1 × 10−3, 1

respectively), whereas mutations in BRAF and RNF43 were more prevalent in MM 2

tumors than in LS/LL tumors (P = 5.5 × 10−12 and 9.4 × 10−8, respectively; Fig. 3A, 3

Supplementary Table S7). Mutations encoding BRAF(V600E) were not observed 4

among LS/LL tumors, in accordance with the well-established notion that BRAF 5

mutations and germline mutations in MMR genes are mutually exclusive (25,26). We 6

did not observe statistically significant differences in mutation frequencies between LS 7

and LL tumors (Supplementary Table S7). 8

Through signature analysis of SNVs (27), four mutational signatures (termed 9

Signatures A, B, C, and D) were detected (Figs. 3A and B). Signatures A, B, and C 10

exhibited profiles similar to signatures associated with MMR deficiency, whereas 11

Signature D was an age-related signature (28). Signature C, which may be derived from 12

G-T mismatch, was abundant in MM tumors; the median percentage of signature C 13

mutations in LS/LL tumors was 13.9% (interquartile range: 7.04%–24.4%), while that 14

in MM tumors was 24.0% (interquartile range: 19.0%–29.8%) (Fig. 3C). Signature A, 15

which may be derived from G-T and A-C mismatches, was enriched in tumors with 16




28

PMS2 disruptions; the median percentage of signature A mutations in tumors with 1

defective PMS2 was 73.2% (interquartile range: 65.0%–87.3%), while that in the other 2

tumors was 26.4% (interquartile range: 21.5%–31.3%) (Figs. 3D). These data may be 3

useful for further exploration of the etiology of MSI-H CRCs. 4

5

Somatic copy number alterations 6

A previous study reported that somatic copy number alterations (CNAs) are infrequent 7

in MSI-H CRCs compared with those in MSS CRCs (29). In this study, allele-specific 8

copy number (CN) analysis using WES data revealed 41 recurrent (Q < 0.01) CNAs, 9 9

of which were involved in UPD (Fig. 3E, Supplementary Table S8). Notably, these 10

CNA profiles differed between LS/LL and MM tumors. UPD encompassing TGFBR2 11

(3p24.1), MLH1 (3p22.2), and CTNNB1 (3p22.1) was observed specifically in LS/LL 12

tumors, and these genes have been linked to the pathogenesis of MSI-H CRC. In 13

contrast, we found that a CN gain within the long arm of chromosome 8 was recurrent 14

in MM tumors. In addition, UPD within the short arm of chromosome 6 involving the 15

major histocompatibility complex class I and class II genes was prevalent in both LS/LL 16




29

and MM tumors, which may contribute to the avoidance of antitumor immunity by 1

tumor cells. 2

3

Affected pathways 4

To better understand genetic alterations in the MSI-H CRC genome in our cohort, we 5

performed pathway analysis using the Database for Annotation, Visualization and 6

Integrated Discovery web-based tool (https://david.ncifcrf.gov) (30,31). Of the 209 7

most frequently altered Tier 2 genes, four molecular pathways were identified, 8

specifically, DNA damage-sensing and histone H3 methylation-associated pathways, as 9

well as Wnt signaling and RAS/RAF/mitogen-activated protein kinase (MAPK) 10

pathways (Fig. 4, Supplementary Table S9). A similar result was obtained using the 860 11

significantly mutated genes identified with MutSigCV (Supplementary Table S10), 12

partly supporting our stratification of mutated genes. 13

14

Oncogenic alterations in MSI-H CRC 15

Considering that activating kinase fusion events are extremely rare in CRC and do not 16




30

seem to cluster in a particular genomic or epigenomic subtype of the disease (32), we 1

did not anticipate the existence of recurrent fusion genes; surprisingly, using RNA-seq, 2

we detected in-frame fusion transcripts encoding fusion-type kinases in 11 out of 111 3

tumors (9.9%). These 11 tumors carrying fusion kinases were all found in the MM 4

subtype, appearing at a frequency of 11% (11/99). Considering the mutual exclusivity of 5

oncogenic KRAS/BRAF missense mutations and fusion kinases, clinically actionable 6

fusion kinases including novel insulin receptor gene fusion accounted for 55% (11/20) 7

of MM tumors lacking oncogenic KRAS/BRAF missense mutations (Fig. 5A). In 8

addition, we identified oncogenic mutants of ERBB2 (n = 3), ERBB3 (n = 5), HRAS (n = 9

2), RRAS2 (n = 1), and RAC1 (n = 1) (Supplementary Fig. S5, Supplementary Table 10

S11), although these variants were not necessarily mutually exclusive regarding KRAS, 11

BRAF, and fusion kinase genes. 12

The detected fusion kinases involved INSR (n = 1), RET (n = 2), NTRK1 (n = 2), 13

NTRK3 (n = 2), and BRAF (n = 4) (Fig. 5B). The expression of these fusion transcripts 14

was confirmed by reverse-transcription polymerase chain reaction (Fig. 5C, 15

Supplementary Table S12). We further identified the genomic fusion points of ten fusion 16




31

genes including SLC12A2-INSR, all of which were confirmed to be somatic (Fig. 5D, 1

Supplementary Figs. S6 and S7). 2

Excluding AKAP9-BRAF, which was predicted to be 4,253 amino acids in length 3

and technically difficult to manipulate, and TRIM24-BRAF, which has been well 4

described previously (33), tumorigenicity of the fusion proteins identified in our study 5

was confirmed using a 3T3 transformation assay (Supplementary Fig. S5). Luciferase 6

reporter assay indicated that these fusion proteins activated the mitogen-activated 7

protein kinase pathway (Supplementary Fig. S5). In addition, small molecular inhibitors 8

that suppress the activity of the kinases identified in our fusion products significantly 9

attenuated the malignant transformation of 3T3 cells in a concentration-dependent 10

manner (Fig. 5E). Notably, linsitinib, which is usually used for the main purpose of 11

antagonizing IGF1R signaling (34), inhibited the growth of 3T3 cells expressing 12

SLC12A2-INSR. Although it may be difficult to target oncogenic BRAF fusion proteins 13

with specific BRAF inhibitors that are currently available, it is anticipated that tumors 14

with BRAF fusion proteins could be targeted with a combination of MAPK kinase and 15

phosphoinositide 3-kinase inhibitors (35–37). 16




32

It has been reported that alectinib and entrectinib inhibited the in vivo growth of the 1

Lc-2/ad lung cancer cell line carrying CCDC6-RET and the KM12 CRC cell line 2

carrying TPM3-NTRK1, respectively (38,39). Because we could not identify MSI-H 3

CRC cell lines harboring SLC12A2-INSR or RUFY1-RET, we evaluated the 4

therapeutic efficacy using an in vivo mouse model, in which 3T3 cells transformed by 5

the fusion kinases were subcutaneously inoculated. Linsitinib and regorafenib 6

substantially suppressed growth of the transformed 3T3 cells expressing 7

SLC12A2-INSR and RUFY1-RET, respectively (Supplementary Fig. S8). 8

9

Comparison with MSS 10

We analyzed the MSS CRC RNA-seq database from the Cancer Genome Atlas Network 11

(29). Twenty-six paired-end and 181 single-end RNA sequences (median 36,213,120 12

reads per sample, 76 bp) were available. Only one fusion kinase (HLA-A-RET) was 13

detected in 189 patients. To adjust the conditions of analysis, the 36 randomly selected 14

mega reads per sample from our RNA-seq data (median 206,727,876 reads per sample, 15

120 bp) were trimmed down to 76 bp and subjected to analysis, identifying all of the 16




33

fusion transcripts described above except for that encoding ARMC10-BRAF. Taken 1

together, oncogenic fusion kinases were more prevalent in MSI-H CRCs than in MSS 2

CRCs. 3

4

Effective strategy for detection of fusion kinases 5

Despite the considerable number of fusion kinases in MSI-H CRCs, it is expensive to 6

perform next-generation sequencing for all MSI-H samples in clinical practice. In 7

addition, bisulfite-treated DNA and particular devices such as pyrosequencer are 8

required for precise MLH1 promoter methylation assays. To effectively detect fusion 9

kinases, we first developed a simple method to detect MLH1 promoter methylation 10

utilizing methylation-sensitive restriction enzyme. As shown in Fig. 6, the methylation 11

status determined by our MLH1 promoter methylation assay was well correlated with 12

that determined by genome-wide analysis. Tumors in a validation cohort were subjected 13

to MSI testing, and the methylation status of MSI-H tumors was determined by our 14

MLH1 methylation assay. Subsequently, we conducted RNA-seq in MM MSI-H CRCs 15

lacking oncogenic KRAS/BRAF missense mutations. We succeeded in detecting two 16




34

fusion kinases (NCOA4-RET and CUL1-BRAF) (40) out of four MM MSI-H CRCs 1

lacking oncogenic KRAS/BRAF missense mutations (Supplementary Fig. S9). We used 2

BRAF as a control for polymerase chain reaction because we detected CNAs in neither 3

chromosome 7 including BRAF as previously reported (29) nor the SmaI site. 4

5

Prognosis 6

It has been recognized that mutation in BRAF is a poor prognostic factor in metastatic 7

or relapsed MSI-H CRC (4, 41,42,43). We analyzed the survival data in our cohort, 8

given the considerable number of fusion kinases observed in MSI-H CRCs lacking 9

oncogenic KRAS/BRAF missense mutations, as mentioned above. When VUS was 10

included in the wild-type group, patients harboring BRAF (V600E) had worse overall 11

survival than patients in the BRAF-wild-type group (P = 2.3 × 10−2; Supplementary Fig. 12

S10A) and patients in the BRAF-wild-type non-fusion group (P = 9.5 × 10–3; 13

Supplementary Fig. S10B). The survival after relapse of patients harboring fusion 14

kinases was shorter than that of patients harboring oncogenic KRAS mutations (P = 8.2 15

× 10−3; Supplementary Fig. S10C). 16




35

1

DISCUSSION 2

In this study, we performed a comprehensive analysis of genetic alterations in MSI-H 3

CRC, revealing several important pathological aspects of MSI-H CRCs that have 4

clinical implications. 5

First, we showed that MSI-H CRCs could be classified into three subtypes. These 6

classifications were validated through several observations: (i) there was a greater 7

number of indels in the MM subtype than in the LS/LL subtype; (ii) one of the 8

mutational signatures associated with MMR deficiency was enriched in the MM 9

subtype; (iii) the frequencies of mutations in several genes differed between the LS/LL 10

and MM subtypes; (iv) the LS/LL and MM subtypes exhibited distinctive CNA profiles; 11

and (v) fusion genes encoding oncogenic kinases were enriched in the MM subtype. 12

However, a caveat of our findings is that the LL subtype may contain misdiagnosed LS 13

tumors because some forms of germline mutations in MMR genes are difficult to detect 14

(44). Although it is not clear whether the LS and LL subtypes have distinctive 15

clinicopathological characteristics, further investigation is warranted. 16




36

Second, we showed that the classification described above has important clinical 1

implications. We also propose a feasible method utilizing a methylation-sensitive 2

restriction enzyme for the detection of MM tumors. Importantly, we chose the genomic 3

region, in which the methylation status is most strictly associated with the silencing of 4

MLH1 based on the genome-wide methylation analysis (Supplementary Fig. S2). 5

Among the MSI-H CRCs, MM tumors that lack KRAS or BRAF mutation are expected 6

to carry a targetable fusion kinase with high probability. Determination of the MM 7

subtype may help to identify patients who can benefit from an intensive search for 8

fusion kinases. Despite the remaining ambiguity between LS and LL subtype 9

classifications, recognition of the LL subtype is also clinically valuable in practice when 10

considering patients of whom germline mutations in MMR genes may escape detection. 11

Third, we identified oncogenic fusion genes that have transforming activity and are 12

potential therapeutic targets. Using our MLH1 methylation assay and considering their 13

mutual exclusivity, it is possible to develop a simple diagnostic sequencing strategy for 14

the efficient detection of LS and fusion kinases to provide improved personalized 15

therapeutic opportunities for patients with MSI-H CRC. Although MSI-H CRCs carry 16




37

multiple driver oncogenes, knockdown of TPM3-NTRK1 in MSI-H CRC cells reduced 1

their proliferation, further supporting the role of NTRK1 fusions as clinically actionable 2

(45). 3

Fourth, we detected those biological pathways that were affected by many 4

mutations. A unique finding of our study was the identification of the DNA 5

damage-sensing pathway based on the genetic alterations in MSI-H CRC genomes, 6

which is of particular interest given that the DNA double-strand break repair machinery 7

is closely associated with MMR machinery (46) and that chromosomal aberrations are 8

relatively infrequent in MSI-H CRCs (29). A precise understanding of the functional 9

consequences of mutations in the DNA damage-sensing pathway in MSI-H CRCs may 10

lead to the development of novel diagnostic and/or therapeutic approaches for patients 11

with MSI-H CRC. 12

Taken together, our genomic and epigenomic analyses of MSI-H CRC provide 13

clinically relevant findings that may impact on patient management and care in 14

association with cancer predisposition, molecular-targeted therapy, and cytotoxic 15

chemotherapy. 16




38

1

AUTHOR CONTRIBUTIONS 2

Conception and design: K. Sato, T. Watanabe, H. Mano 3

Development of methodology: K. Sato, Y. Yamamoto 4

Acquisition of data (provided animals, acquired and managed patients, provided 5

facilities, etc.): K. Sato, M. Kawazu, Y. Yamamoto, G. Nagae, H. Abe, M. Soda, T. Oga, 6

S. Kohsaka, E. Sai, Y. Yamashita, H. Iinuma, M. Fukayama, H. Aburatani, T. Watanabe, 7

H. Mano 8

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational 9

analysis): M. Kawazu, T. Ueno, S. Kojima 10

Writing, review, and/or revision of the manuscript: K. Sato, M. Kawazu, Y. Yamamoto, 11

T. Ueno, S. Kojima, G. Nagae, H. Abe, M. Soda, T. Oga, S. Kohsaka, E. Sai, Y. 12

Yamashita, H. Iinuma, M. Fukayama, H. Aburatani, T. Watanabe, H. Mano 13

Administrative, technical, or material support (i.e., reporting or organizing data, 14

constructing databases): M. Fukayama, H. Aburatani, T. Watanabe, H. Mano 15

16




39

ACKNOWLEDGMENTS 1

The authors thank Ms. Miki Tamura, Ms. Reina Takeyama, Ms. Akane Maruyama, Ms. 2

Junko Tamura, Ms. Riyo Kakimoto, Dr. Takamitsu Kanazawa, Dr. Yuzo Nagai, and Dr. 3

Takashi Kobunai for their technical assistance, and Dr. Yoichi Furukawa for his advice 4

and discussion. We are grateful to all of the patients and families who contributed to this 5

study. We thank all members of the Colorectal Group of The University of Tokyo and 6

Teikyo University for their support of this research. We also thank Edanz Group 7

(www.edanzediting.com/ac) for editing a draft of this manuscript. 8

This study was supported in part by Grants-in-Aid for Scientific Research 9

(KAKENHI, Grant Numbers 17J00386, 26430106, 16K07143 and 16H02672) from the 10

Japan Society for the Promotion of Science (JSPS) and by grants from Leading 11

Advanced Projects for Medical Innovation (LEAP, JP17am0001001) to HM and from 12

the Project for Cancer Research and Therapeutic Evolution (P-CREATE, 13

JP17cm0106502) to MK, MS, HA and TW from the Japan Agency for Medical 14

Research and Development. KS is a Research Fellow of JSPS. 15

16




40

REFERENCES 1

1. Thibodeau S, Bren G, Schaid D. Microsatellite instability in cancer of the 2

proximal colon. Science 1993;260:816–9. 3

2. Aaltonen L, Peltomaki P, Leach F, Sistonen P, Pylkkanen L, Mecklin J, et al. 4

Clues to the pathogenesis of familial colorectal cancer. Science 1993;260:812–6. 5

3. Boland CR, Goel A. Microsatellite instability in colorectal cancer. 6

Gastroenterology 2010;138:2073–2087.e3. 7

4. Sinicrope FA, Shi Q, Allegra CJ, Smyrk TC, Thibodeau SN, Goldberg RM, et al. 8

Association of DNA mismatch repair and mutations in BRAF and KRAS with 9

survival after recurrence in stage III colon cancers: A secondary analysis of 2 10

randomized clinical trials. JAMA Oncol 2017;3:472–80. 11

5. Guinney J, Dienstmann R, Wang X, de Reyniès A, Schlicker A, Soneson C, et al. 12

The consensus molecular subtypes of colorectal cancer. Nat Med 13

2015;21:1350–6. 14

6. Arnold CN, Goel A, Boland CR. Role of hMLH1 promoter hypermethylation in 15

drug resistance to 5-fluorouracil in colorectal cancer cell lines. Int J Cancer 16




41

2003;106:66–73. 1

7. Toyota M, Ho C, Ahuja N. Identification of differentially methylated sequences 2

in colorectal cancer by methylated CpG island amplification. Cancer Res 3

1999;59:2307–12. 4

8. Weisenberger DJ, Siegmund KD, Campan M, Young J, Long TI, Faasse M a, et 5

al. CpG island methylator phenotype underlies sporadic microsatellite instability 6

and is tightly associated with BRAF mutation in colorectal cancer. Nat Genet 7

2006;38:787–93. 8

9. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler 9

transform. Bioinformatics 2009;25:1754–60. 10

10. Yoon S, Xuan Z, Makarov V, Ye K, Sebat J. Sensitive and accurate detection of 11

copy number variants using read depth of coverage. Genome Res 12

2009;19:1586–92. 13

11. Beroukhim R, Getz G, Nghiemphu L, Barretina J, Hsueh T, Linhart D, et al. 14

Assessing the significance of chromosomal aberrations in cancer: Methodology 15

and application to glioma. Proc Natl Acad Sci U S A 2007;104:20007–12. 16




42

12. Mermel CH, Schumacher SE, Hill B, Meyerson ML, Beroukhim R, Getz G. 1

GISTIC2.0 facilitates sensitive and confident localization of the targets of focal 2

somatic copy-number alteration in human cancers. Genome Biol 2011;12:1–14. 3

13. Kodama Y, Mashima J, Kosuge T, Katayama T, Fujisawa T, Kaminuma E, et al. 4

The DDBJ Japanese genotype-phenotype archive for genetic and phenotypic 5

human data. Nucleic Acids Res 2015;43:D18–22. 6

14. Jass JR, Walsh MD, Barker M, Simms LA, Young J, Leggett BA. Distinction 7

between familial and sporadic forms of colorectal cancer showing DNA 8

microsatellite instability. Eur J Cancer 2002;38:858–66. 9

15. Wright CM, Dent OF, Newland RC, Barker M, Chapuis PH, Bokey EL, et al. 10

Low level microsatellite instability may be associated with reduced cancer 11

specific survival in sporadic stage C colorectal carcinoma. Gut 2005;54:103–8. 12

16. Lee SY, Kim D-W, Lee HS, Ihn MH, Oh H-K, Min BS, et al. Low-Level 13

Microsatellite Instability as a Potential Prognostic Factor in Sporadic Colorectal 14

Cancer. Medicine 2015;94:e2260. 15

17. Bocker T, Rãschoff J. Diagnostic Microsateffite Instability: Definition and 16




43

Correlation with Mismatch Repair Protein Expression. Cancer Res 1

1997;57:4749–56. 2

18. Tahara T, Yamamoto E, Suzuki H, Maruyama R, Chung W, Garriga J, et al. 3

Fusobacterium in colonic flora and molecular features of colorectal carcinoma. 4

Cancer Res 2014;74:1311–8. 5

19. Mima K, Cao Y, Chan AT, Qian ZR, Nowak JA, Masugi Y, et al. Fusobacterium 6

nucleatum in colorectal carcinoma tissue according to tumor location. Clin Transl 7

Gastroenterol 2016;7:e200. 8

20. Herman JG, Umar A, Polyak K, Graff JR, Ahuja N, Issa J-PJ, et al. Incidence and 9

functional consequences of hMLH1 promoter hypermethylation in colorectal 10

carcinoma. Proc Natl Acad Sci U S A 1998;95:6870–5. 11

21. Mensenkamp AR, Vogelaar IP, Van Zelst-Stams WAG, Goossens M, Ouchene H, 12

Hendriks-Cornelissen SJB, et al. Somatic mutations in MLH1 and MSH2 are a 13

frequent cause of mismatch-repair deficiency in lynch syndrome-like tumors. 14

Gastroenterology 2014;146:643–646.e8. 15

22. Futreal P, Coin L, Marshall L, Down T, Hubbard T, Wooster T, et al. A census of 16




44

human cancer genes. Nat Rev Cancer 2004;4:177–83. 1

23. Kim T, Laird PW, Park PJ. The landscape of microsatellite instability in 2

colorectal and endometrial cancer genomes. Cell 2013;155:858–68. 3

24. Lawrence MS, Stojanov P, Polak P, Kryukov G V., Cibulskis K, Sivachenko A, 4

et al. Mutational heterogeneity in cancer and the search for new 5

cancer-associated genes. Nature 2013;499:214–8. 6

25. Bessa X, Ballesté B, Andreu M, Castells A, Bellosillo B, Balaguer F, et al. A 7

Prospective, Multicenter, Population-Based Study of BRAF Mutational Analysis 8

for Lynch Syndrome Screening. Clin Gastroenterol Hepatol 2008;6:206–14. 9

26. Wang L, Cunningham JM, Winters JL, Guenther JC, French AJ, Boardman L a, 10

et al. BRAF Mutations in Colon Cancer Are Not Likely Attributable to Defective 11

DNA Mismatch Repair. Cancer Res 2003;63:5209–12. 12

27. Alexandrov LB, Stratton MR. Mutational signatures: The patterns of somatic 13

mutations hidden in cancer genomes. Curr Opin Genet Dev 2014;24:52–60. 14

28. Helleday T, Eshtad S, Nik-Zainal S. Mechanisms underlying mutational 15

signatures in human cancers. Nat Rev Genet 2014;15:585–98. 16




45

29. The Cancer Genome Atlas Network. Comprehensive molecular characterization 1

of human colon and rectal cancer. Nature 2012;487:330–7. 2

30. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of 3

large gene lists using DAVID bioinformatics resources. Nat Protoc 4

2009;4:44–57. 5

31. Huang DW, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: Paths 6

toward the comprehensive functional analysis of large gene lists. Nucleic Acids 7

Res 2009;37:1–13. 8

32. Dienstmann R, Vermeulen L, Guinney J, Kopetz S, Tejpar S, Tabernero J. 9

Consensus molecular subtypes and the evolution of precision medicine in 10

colorectal cancer. Nat Rev Cancer 2017;17:79–92. 11

33. Nakaoku T, Tsuta K, Ichikawa H, Shiraishi K, Sakamoto H, Enari M, et al. 12

Druggable oncogene fusions in invasive mucinous lung adenocarcinoma. Clin 13

Cancer Res 2014;20:3087–93. 14

34. Mulvihill MJ, Cooke A, Rosenfeld-Franklin M, Buck E, Foreman K, Landfair D, 15

et al. Discovery of OSI-906: a selective and orally efficacious dual inhibitor of 16




46

the IGF-1 receptor and insulin receptor. Future Med Chem 2009;1:1153–71. 1

35. Kim HS, Jung M, Kang HN, Kim H, Park C-W, Kim S-M, et al. Oncogenic 2

BRAF fusions in mucosal melanomas activate the MAPK pathway and are 3

sensitive to MEK/PI3K inhibition or MEK/CDK4/6 inhibition. Oncogene 4

2017;36:3334-45 5

36. Hutchinson KE, Lipson D, Stephens PJ, Otto G, Lehmann BD, Lyle PL, et al. 6

BRAF fusions define a distinct molecular subset of melanomas with potential 7

sensitivity to MEK Inhibition. Clin Cancer Res 2013;19:6696–702. 8

37. Turski ML, Vidwans SJ, Janku F, Garrido-Laguna I, Munoz J, Schwab R, et al. 9

Genomically driven tumors and actionability across histologies: BRAF-mutant 10

cancers as a paradigm. Mol Cancer Ther 2016;15:533–47. 11

38. Kodama T, Tsukaguchi T, Satoh Y, Yoshida M, Watanabe Y, Kondoh O, et al. 12

Alectinib Shows Potent Antitumor Activity against RET-Rearranged Non-Small 13

Cell Lung Cancer. Mol Cancer Ther 2014;13:2910–8. 14

39. Ardini E, Menichincheri M, Banfi P, Bosotti R, DePonti C, Pulci R, et al. 15

Entrectinib, a Pan-TRK, ROS1, and ALK Inhibitor with Activity in Multiple 16




47

Molecularly Defined Cancer Indications. Mol Cancer Ther 2016:15:628-39. 1

40. Grisham RN, Sylvester BE, Won H, McDermott G, DeLair D, Ramirez R, et al. 2

Extreme Outlier Analysis Identifies Occult Mitogen-Activated Protein Kinase 3

Pathway Mutations in Patients With Low-Grade Serous Ovarian Cancer. J Clin 4

Oncol 2015;33:4099–105. 5

41. Tran B, Kopetz S, Tie J, Gibbs P, Jiang ZQ, Lieu CH, et al. Impact of BRAF 6

mutation and microsatellite instability on the pattern of metastatic spread and 7

prognosis in metastatic colorectal cancer. Cancer 2011;117:4623–32. 8

42. Godstein J, Tran B, Ensor J, Gibbs P, Wong HL, Wong SF, et al. Multicenter 9

retrospective analysis of metastatic colorectal caner (CR) with high-level 10

microsatellite instability (MSI-H). Ann Oncol 2014;25:1032-8. 11

43. Venderbosch S, Nagtegaal ID, Maughan TS, Smith CG, Cheadle JP, Fisher D, et 12

al. Mismatch Repair Status and BRAF Mutation Status in Metastatic Colorectal 13

Cancer Patients: A Pooled Analysis of the CAIRO, CAIRO2, COIN, and FOCUS 14

Studies. Clin Cancer Res 2014;20:5322-30. 15

44. Taylor CF, Charlton RS, Burn J, Sheridan E, Taylor GR. Genomic deletions in 16




48

MSH2 or MLH1 are a frequent cause of hereditary non-polyposis colorectal 1

cancer: Identification of novel and recurrent deletions by MLPA. Hum Mutat 2

2003;22:428–33. 3

45. Vaishnavi A, Capelletti M, Le AT, Kako S, Butaney M, Ercan D, et al. 4

Oncogenic and drug-sensitive NTRK1 rearrangements in lung cancer. Nat Med 5

2013;19:1469–72. 6

46. Spies M, Fishel R. Mismatch repair during homologous and homeologous 7

recombination. Cold Spring Harb Perspect Biol 2015;7:a022657. 8

9

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Figure legends 1

Figure 1 Classification of microsatellite instability-high colorectal tumors. (A) 2

Number of samples analyzed in this study. (B) Subtype classification flowchart. 3

(C) Tumor localization in the large intestine. The numbers of Lynch 4

syndrome-associated, Lynch-like, and MLH1-methylated tumors within the 5

indicated anatomical regions are shown in the pie charts. The size of the pie 6

chart is proportional to the total number of tumors within the region. (D) 7

Age-of-operation distributions of microsatellite instability-high colorectal tumors 8

according to subtype. A two-tailed Wilcoxon rank-sum test was used for 9

statistical analysis. (E) Survival after relapse of patients with microsatellite 10

instability-high colorectal tumors, according to subtype. Survival curves were 11

estimated using the Kaplan–Meier method and compared by two-sided log-rank 12

test. WES, whole-exome sequencing; RNA-seq, transcriptome sequencing; 13

MMR, mismatch repair; NA, not available; LS, Lynch syndrome-associated; LL, 14

Lynch-like; MM, MLH1 promotor-methylated; C, cecum; A, ascending colon; T, 15

transverse colon; D, descending colon; S, sigmoid colon; R, rectum. 16

17




50

1

Figure 2 Mutations in DNA mismatch repair genes and immunohistochemical 2

analysis of DNA mismatch repair proteins. (A) Mutational status of DNA 3

mismatch repair genes is shown in association with the results of 4

immunohistochemical analysis of DNA mismatch repair proteins. The columns in 5

the table denote samples and the rows denote gene alleles (upper panel) or 6

proteins (lower panel). (B) Representative images of the immunohistochemical 7

analysis. SNV, single-nucleotide variation; indel, insertion or deletion; UPD, 8

uniparental disomy. 9

10

Figure 3 Summary of mutations in microsatellite instability-high colorectal 11

tumors. (A) The 40 most frequently mutated genes with their mutation status 12

color-coded for each patient. The frequency of synonymous or nonsynonymous 13

substitutions and insertions/deletions is shown at the top, and the percentages 14

of the mutational signatures are shown at the bottom. The frequencies of 15

mutations in each gene according to subtype are shown on the left. (B) The four 16




51

mutational signatures identified in this study. (C) Percentage of Signature C 1

according to subtype. (D) Percentage of Signature A according to PMS2 2

mutational status. (E) Allele-specific copy number alterations in microsatellite 3

instability-high colorectal tumors. The profiles of allele-specific copy number 4

alterations according to subtype are shown. Red and blue lines indicate the 5

Q-value for gains of major allele and losses of minor allele, respectively. The 6

y-axis indicates the frequency of the observed gain or loss. Representative 7

genes affected by respective copy number alteration are shown. A two-tailed 8

Wilcoxon rank-sum test was used for statistical analysis. LS, Lynch syndrome; 9

LL, Lynch-like; MM, MLH1-methylated. 10

11

Figure 4 Frequently affected pathways. Major components of the pathways 12

extracted from the top 209 Tier 2 genes using The Database for Annotation, 13

Visualization and Integrated Discovery web-based tool algorithm are shown. The 14

frequencies of single-nucleotide variations and insertions/deletions in the genes 15

are expressed as a percentage of all cases according to subtype. Warm colours 16




52

denote activated genes, and cold colours denote inactivated genes. The 1

frequencies are colour-scaled. LS, Lynch syndrome; LL, Lynch-like; MM, 2

MLH1-methylated. 3

4

Figure 5 Oncogenic fusion proteins in microsatellite instability-high colorectal 5

tumors. (A) Mutation plot of oncogenes with transforming potential. Mutations 6

with transforming capacity are shown in darker colours, whereas mutations of 7

unknown significance are shown in lighter colours. (B) Schematic diagrams of 8

the fusion proteins identified in this study. Amino acid numbering on the fusion 9

proteins refers to the sequences of the wild-type proteins. A novel fusion kinase 10

(SLC12A2-INSR) is shown in orange. A novel fusion partner (RUFY1) of RET 11

fusion kinase is shown in green. (C) The transcript fusion point of 12

SLC12A2-INSR complementary DNA determined by Sanger sequencing. (D) A 13

schematic representation of the gene rearrangement generating the 14

SLC12A2-INSR fusion gene. The sequence of the genomic fusion point 15

determined by Sanger sequencing is shown at the bottom. (E) The response to 16




53

kinase inhibitors of 3T3 cells expressing fusion proteins. Cells were treated with 1

the indicated drugs at the indicated concentrations. Cellularity was measured 9 2

days after treatment and is plotted relative to that of cultures treated with the 3

lowest concentration. Error bars indicate model-based standard errors. In the 4

graphs showing the data obtained for cells expressing TPM3-NTRK1, 5

KANK1-NTRK3, or EML4-NTRK3, the control v-Ras data are identical. A 6

two-tailed Student’s t-test was used for statistical analysis. CC, coiled coil. 7

8

Figure 6 Precise and simple detection of MLH1 methylated tumors. A 9

comparison between extracted data of the degree of methylation in the MLH1 10

promoter region of 13 tumors by genome-wide methylation array (see 11

Supplementary Fig. S1C) (upper panel) and detection of MLH1 methylated 12

tumors using our MLH1 methylation assay (lower panel). Amplicon of BRAF is a 13

loading control and used as a template of Sanger sequencing for detecting 14

BRAF(V600E) mutation. M, 50-bp DNA ladder marker; RKO, positive control; 15

DLD1, negative control; NTC, no template control. 16




Published OnlineFirst October 2, 2018.Clin Cancer Res Kazuhito Sato, Masahito Kawazu, Yoko Yamamoto, et al. microsatellite instability-high colorectal cancersFusion kinases identified by genomic analyses of sporadic

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