Whole-Genome and Transcriptome Sequencing Identified NOTCH2 and HES1 As Potential Markers of Response to Imatinib in Desmoid Tumor (Aggressive Fibromatosis): A Phase II Trial Study

Whole-Genome and Transcriptome Sequencing Identified NOTCH2 and HES1 As Potential Markers of Response to Imatinib in Desmoid Tumor (Aggressive Fibromatosis): A Phase II Trial Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Whole-Genome and Transcriptome Sequencing Identified NOTCH2 and HES1 As Potential Markers of Response to Imatinib in Desmoid Tumor (Aggressive Fibromatosis): A Phase II Trial Study Joonha Kwon, Jun Hyeong Lee, Young Han Lee, Jeeyun Lee, Jin-Hee Ahn, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-993539/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Desmoid tumor, also known as aggressive fibromatosis, is well-characterized by abnormal Wnt/β-catenin signaling. Various therapeutic options, including imatinib, are available to efficiently treat desmoid tumor. However, molecular mechanism of why imatinib works remains poorly understood. Here, we describe the potential roles of NOTCH2 and HES1 in association with clinical response to imatinib as in genome and transcriptome levels. Methods: We identified all somatic mutations in coding and non-coding regions via whole genome sequencing using desmoid tumor samples. To validate the genetic interaction with expression level in desmoid-tumor condition, we utilized large-scale Whole-genome sequencing (WGS) and transcriptome datasets from the Pan-Cancer Analysis of Whole Genomes (PCAWG) project. RNA-sequencing was performed using prospective and retrospective cohort samples to evaluate the expressional relevance with clinical response. Results: Among 20 patients, 4 (20%) had a partial response and 14 (66.7%) had stable disease, 11 of which continued for ≥1 year. With gene-wise functional analyses, we detected significant correlation between recurrent NOTCH2 noncoding mutations and clinical response to imatinib. Based on PCAWG data analyses, NOTCH2 mutations affect its expression levels particularly in the presence of CTNNB1 missense mutations. By analyzing RNA-sequencing with additional desmoid tumor samples, we found that NOTCH2 expression was significantly correlated with HES1 expression. Interestingly, NOTCH2 had no statistical power to discriminate responders and non-responders. Instead, HES1 was differentially expressed with statistical significance between responders and non-responders Conclusions: Imatinib was effective and well tolerated for advanced desmoid tumor treatment. Our results show that HES1, regulated by NOTCH2, as an indicator of sensitivity to imatinib, which provides an important therapeutic consideration for desmoid tumor. Trial Registration: ClinicalTrials.gov, NCT02495519, Registered July 13, 2015- Retrospectively registered, https://www.clinicaltrials.gov/ct2/show/NCT02495519 General Biochemistry Desmoid tumor Imatinib Phase II clinical trial NOTCH2 HES1 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background A desmoid tumor (aggressive fibromatosis) is fibroproliferative neoplasm arising from deep connective tissues. A stepwise approach including an active surveillance is established as no metastatic potential and spontaneous tumor regression are observed[ 1 ]. Excision is the mainstay of treatment, but the postsurgical recurrence rate is high [ 2 , 3 ]. For unresectable or recurrent desmoid tumors, a variety of systemic therapeutic options are available, including tamoxifen, nonsteroidal anti-inflammatory drugs (NSAIDs), interferons, and chemotherapy [ 4 – 8 ]. Recently, sorafenib induced durable response and led to its approval for the disease treatment [ 9 ]. Imatinib is a new treatment option in unresectable, progressive, or recurrent desmoid tumors [ 10 – 12 ]. In addition to its promising efficacy (6–15% response rate), the favorable toxicity profiles support its long-term use for salvage treatment. In imatinib-responsive diseases, such as gastrointestinal stromal tumors or chronic myeloid leukemia, specific mutations or chromosomal translocations have been reported [ 13 – 15 ]. However, in desmoid tumors, no molecular abnormalities in imatinib-sensitive kinases have been observed [ 10 , 11 ]. Therefore, molecular mechanisms by which this rare tumor responds to imatinib are poorly understood. In this study, we conducted a multicenter phase II trial to evaluate the efficacy of imatinib in patients with relapsing or progressive desmoid tumors. To understand the molecular basis of the clinical responses to imatinib, we performed whole-genome sequencing to identify potential markers. Owing to limited insights gained from protein coding mutations, we extended our analyses to noncoding regulatory regions. Our gene-wise recurrence model using 1,009 pan-cancer whole-genome data indicated that NOTCH2 regulatory mutations are associated with the response of desmoid tumors to imatinib. We further evaluated the significance of NOTCH2 in transcriptome analysis using RNA-sequencing data. We discovered that HES1 , a well-known downstream target of Notch signaling pathway, is directly associated with imatinib sensitivity. Methods Patients and Treatment Patients with advanced desmoid tumors, defined as patients with radiographic progression after previous treatment, were eligible for prospective phase II study. Key inclusion criteria were as follows: age ≥ 10 years, Eastern Cooperative Oncology Group performance status of 0 to 2, and adequate hematologic and renal function. Patients were treated with 400 mg of imatinib mesylate (Glima; Boryung Pharmaceutical Co., Ltd, Seoul, Korea) daily until progression or unacceptable toxicity. Toxic effects were graded according to the National Cancer Institute—Common Toxicity Criteria v. 4.03. Disease was assessed every 8 weeks for the initial 32 weeks and then every 16 weeks according to RECIST (response evaluation criteria in solid tumors) v1.1 [16]. Briefly, patients who experienced grade 3/4 toxicity or intolerable grade 2 toxicity stopped treatment and then restarted at a reduced dose (300 mg/day or 200 mg/day). The study was approved by the institutional review board (IRB No. 2013-1417-001). Retrospective cohort study included 24 additional patients with desmoid tumor and approved by the institutional review board (IRB No. 2020-2244-001). Surgically resected formalin-fixed paraffin-embedded (FFPE) tissue samples obtained prior to radiotherapy or chemotherapy were subjected to transcriptome sequencing (Fig. 1). Of those, 4 cases were treated with imatinib and remaining 20 were treatment naïve. DNA Extraction and Quality Assessment Whole genome sequencing was performed using pretreatment tumor excision samples as well as matched blood samples. Briefly, 4-mm-thick sections with a tumor content of ≥80% were obtained, and ≥2 µg of DNA was extracted using the Maxwell 16 FFPE Plus LEV DNA Purification Kit (Promega, Madison, WI, USA). For peripheral blood mononuclear cells (PBMCs), genomic DNA was extracted using the QIAamp DNA Mini Kit (Qiagen, Hilden, Germany) as per the manufacturer’s instructions. DNA Library Construction and Whole Genome Sequencing Library preparation was performed using the TruSeq Nano DNA Library Preparation Kit (Illumina, San Diego, CA, USA) following the manufacturer’s instructions. Illumina utilizes a unique "bridged" amplification reaction on the surface of the flow cell. A flow cell containing libraries was prepared using the cBot Fluidics Station and was then loaded into the HiSeq X-10 sequencer (Illumina) for automated cycles of extension and imaging. Sequencing-by-Synthesis cycles were repeated to achieve a paired-end read length of 2 × 150 bp. RNA Library Construction and Whole Transcriptome Sequencing Total RNAs were extracted and purified from frozen tumor samples with ReliaPrep FFPE Total RNA Miniprep System (Promega, Inc.) according to the manufacturer's procedures. Amount of RNA and its quality were checked on an Agilent RNA 6000 Nano Kit (Agilent Technologies). For analysis of RNA-sequencing data, we prepared mRNA sequencing libraries as paired-end reads with a length of 100 bases using the SMARTer Stranded Total RNA-seq kit v2-Pico Input Mammalian according to the manufacturers’ protocols. Briefly, mRNA molecules were purified and fragmented from 2 µg of total RNA. The libraries were sequenced as paired end reads (2 x 150 bp) using the NovaSeq 6000 (Illumina). Whole Genome Data Processing To process whole genome sequencing data of desmoid tumors, we adopted the Genome Analysis Tool Kit (GATK) v3.7 best practice provided by the Broad Institute [17]. Briefly, we mapped qualified paired-end reads to the human reference genome (hg19) with Burrows-Wheeler Aligner (BWA) 0.7.15 [18]. Subsequently, we filtered PCR duplicates using Picard tools 2.8.2 to remove potential bias that occurred during sequencing processes. Then, we performed recommended procedures, such as local realignment and base quality recalibration to extract analysis-ready reads. Somatic Variant Detection MuTect2 [19] of the GATK pipeline with default parameters was used to identify somatic single nucleotide variants (SNVs) and small insertions/deletions (indels). The processed whole-genome-sequencing data for tumor and matched normal samples (PBMCs) were used in BAM format as inputs for Mutect2 [19]. Somatic variants were annotated using ANNOVAR [20]. Some candidate variants were manually inspected using Integrative Genomics Viewer (IGV) [21]. Population-level allele frequencies of candidates were obtained using Genome Aggregation Database (gnomAD) [22]. For two samples with tumor data only, normal sample data for one of the nine other patients was used as matched normal control for variant calling. A sample that was sequenced in the same batch with a read depth of greater than 30 was used. The variants were further filtered using gnomAD to obtain putative somatic mutations in the tumor-only samples. Scoring Gene-Wise Recurrence of Functional Variants Our previously developed gene-wise recurrence model was used [23]. Conventionally, mutations are considered recurrent if and only if they occur at the same genomic location across multiple samples. Mutations are considered oncogenic when their recurrence exceeds a certain threshold [24]. However, this definition of recurrence is inappropriate for analyses of noncoding regions owing to their vast size. Thus, we consider mutations recurrent if they occur in functional regions of the same gene, even if they are not recurrent in a site-specific manner. In particular, we focused on mutational events in cis-regulatory regions of a mammalian gene dispersed across a long range in the genome [25]. Genes were defined based on the GENCODE v.19 gene set mapped to GRCh37 [26]. To identify coding and noncoding mutations with significant functional consequences, deleterious effects of each SNV were predicted using two algorithms, Combined Annotation-Dependent Depletion (CADD) [27] and Deleterious Annotation of genetic variants using Neural Networks (DANN) [28]. Both models were trained to distinguish benign variants from deleterious variants [27,28]. For multiple mutations in the same gene, the one with the highest score for deleteriousness was selected to represent the functional consequence. Reference Whole-Genome and Transcriptome Datasets To characterize the functional effects of NOTCH2 noncoding mutations, a large-scale pan-cancer dataset consisting of somatic variants from whole-genome sequencing data and transcriptome data for tumor and matched normal samples were used. VCF files for somatic variant calling and gene expression matrices containing FPKM-upper quantile values were obtained from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Project[29]. RNA-sequencing data processing and QC We generated RNA-sequencing data of 31 desmoid tumor patients. We removed adapter sequences using Cutadpt [30], and aligned the trimmed reads using STAR [31] with hg19. Gene expression was quantified using RSEM [32]. Quality control check at pre-alignment step was conducted using FASTQC and at post-alignment step using RSeQC [33]. QC results were visualized with MultiQC [34]. At post-alignment step, we noticed two patients with potential problems in read distribution, and infer experiment criteria. Thus, we excluded those samples from future analysis. Bioinformatics and Statistical Analyses The χ 2 test was used to assess correlations between marker status and clinical significance. All correlation analysis was conducted using spearman correlation. To assess statistical significance between responders and non-responders of imatinib, we calculated Mann-Whitney U test. All tests were two-sided and P < 0.05 is considered significant. Cleveland, scatter and box plots were generated by using ggplot2 R package and matplotlib python package. To conduct enrichment analysis, we adopted two approaches. First of all, we identified genes that are significantly correlated with imatinib sensitivity and used those genes as input for EnrichR [35]. As an alternative step, we conducted Gene Set Enrichment Analysis (GSEA) between responders and non-responders using C2, C5, C6 and Hallmark MSigDB gene sets [36]. C5 Notch category was defined as Notch-related terms present in C5 category. Progression-free survival (PFS) was calculated from start date of imatinib to date of progression or death and progression free rate at 16 weeks (PFR 16) was defined as proportions of patients without progression at 16 weeks, analyzed using the Kaplan–Meier method [SPSS version 18.0 (IBM, Chicago, IL, USA)]. Results Sample Set, Patient Outcomes and Toxicity Total of 21 patients were enrolled between April 2014 and October 2015. One patient withdrew, leaving 20 patients (Fig. 1 ; Additional file 1: Table S1). Three patients (7, 8, and 13) had a known diagnosis of familial adenomatous polyposis (FAP). Fifteen patients (75%) underwent one previous surgery. Most of the patients had been treated with non-surgical procedures, including radiotherapy (6, 30.0%) and/or chemotherapy (12, 60.0%). Of the 20 evaluable patients, 4 (20%) had partial responses (PR) to treatment with durations of 6.7, 26.8 (Fig. 2 A), 30.3, and 35.1 (Fig. 2 B) months. One PR case had FAP. The PR duration was longer than 1 year for three patients. Fig. 2 C provides a waterfall plot of the best response; 14 patients (66.7%) had stable disease (SD), and the clinical benefit rate was 90.0%. The median time to progression was 21.4 months (range: 2.8 to 40.7 months) and PFR 16 was 85% (Fig. 2 D). In terms of toxicity, 400 mg of imatinib was well tolerated with expected grade 3/4 toxicities: neutropenia (n = 1), anorexia (n = 1), vomiting (n = 1), and fatigue (n = 1) (Additional file 2: Table S2). Three patients had a one dose level reduction (300 mg/d), and one patient had a reduction of two levels (200 mg/d) owing to toxicity. Results of Whole-Genome Sequencing After confirming adequate DNA quantities, whole genome sequencing was performed for 11 samples. Two samples (patients 11 and 13) without matched control data were excluded from the primary analysis and used for extensional validation only (Fig. 1 ). The average read depth was greater than 21.46 (range: 21.46 to 54.05) (Additional file 3: Table S3). By implementing the GATK pipeline from the Broad Institute, we identified 832–4,110 SNVs and indels per sample and used ANNOVAR for annotation (Additional file 4: Table S4). Next, we examined mutational signatures from annotated variants to identify types of mutational processes [ 37 ]. Signature 1A was dominant, accounting for around 88% of signatures (Additional file 5: Table S5), indicating that desmoid tumor variants primarily arise due to errors in replicative polymerases in the DNA repair pathway [ 37 ]. The majority of mutations were in intronic regions. Mutations in coding regions were primarily missense and synonymous variants (Additional file 6: Fig. S1). We also investigated alterations of cancer driver genes registered in the COSMIC database [ 38 ] and detected CTNNB1 mutations in 8 out of 11 samples, while 2 patients harbored adenomatous polyposis coli ( APC ) mutations. CTNNB1 and APC mutation sites of patients are summarized in Fig. 2 C. All mutations affected phosphorylation sites necessary for the proper degradation of β-catenin [ 39 – 41 ]. Remarkably, no other mutations in COSMIC cancer driver genes were detected, emphasizing the prominent role of the Wnt/β-catenin signaling pathway in desmoid tumor progression [ 8 ]. Gene-Wise Recurrence Analysis of Potential Cancer-Associated Genes To identify potential cancer-associated mutations in desmoid tumors, we employed gene-wise recurrence analysis of mutations in noncoding regions, according to previously developed method [ 23 ]. Briefly, the model assumes that mutations in multiple patients are recurrent if they affect the same gene (Fig. 3 A). Recurrently affected genes were defined as those with mutations in at least two samples. This strategy allowed us to evaluate noncoding regions with potentially significant impacts on gene regulation with limited sample size. NOTCH2 , RGPD2 , and ARHGAP11B were identified as strong candidates (Fig. 3 B). Correlation between Gene-Wise Scores and Imatinib Sensitivity We examined the association between the change in tumor volume after imatinib treatment and the deleteriousness score of recurrently mutated genes. In case of NOTCH2 , tumor volume changes were highly correlated with both CADD ( R = -0.797, P = 0.01) and DANN scores ( R = -0.831, P = 0.006) (Fig. 3 B and C; Additional file 7: Table S6). This correlation was maintained even when two tumor-only samples (patient 11 and 13) were included (Fig. 3 C). According to the annotated information of somatic variants in NOTCH2 , three (patients 3, 5, and 8) and four (patients 9, 11, 13, and 18) variants were intergenic and intronic, respectively, while only one (patient 19) variant was nonsynonymous (Additional file 8: Table S7). Using gnomAD database, we found that the allele frequencies of the NOTCH2 variants were extremely low (0–1.13%), indicating that they were likely somatic. These results suggest that the regulation of NOTCH2 at the gene level may contribute to the desmoid tumor response to imatinib. Regulatory Role of NOTCH2 Mutations To evaluate the regulatory effects of NOTCH2 mutations, we interrogated RNA sequencing and somatic mutation profiles from PCAWG datasets (n = 1,009). For comparison across cancer types, we transformed the NOTCH2 expression levels to Z-scores within each cancer type and identified samples with NOTCH2 mutations. Considering the role of CTNNB1 missense mutations in desmoid tumors [ 8 ], we further selected both- NOTCH2 - CTNNB1 mutants in the PCAWG data (Fig. 4 A; Additional file 9: Table S8). NOTCH2 expression levels were more highly correlated with CADD scores for both- NOTCH2 - CTNNB1 mutation group ( R = 0.607, P = 0.013) than for NOTCH2 mutation-only group ( R = 0.178, P = 0.01) (Fig. 4 B). The same trends were observed when DANN score was used for variant scoring ( R = 0.467) (Fig. 4 C), although the correlation was only marginally significant ( P = 0.068). These results suggest that genetic interactions between NOTCH2 noncoding mutations and CTNNB1 missense mutations may influence the NOTCH2 expression level. Role of NOTCH family members and HES1 in imatinib sensitivity To validate significance of our finding, we analyzed RNA-sequencing data of 29 desmoid tumor patients (Fig. 1 ; Additional file 10: Table S9). We focused on Notch family members, including NOTCH2 , and HES1 , a marker of stemness [ 42 ] that has been implicated as a target of Notch signaling pathway [ 43 ] and marker of imatinib sensitivity [ 44 ]. We first investigated whether expression levels of Notch family members and HES1 are correlated. We calculated spearman correlation between the genes, and discovered that NOTCH2 expression was significantly correlated with HES1 expression (P = 0.0091568) while expressions of other Notch genes were not (Fig. 5 A). This recapitulates the association between HES1 and NOTCH2 detected in WGS analysis. We also noticed that all Notch family members are significantly correlated with each other (Additional file 11: Fig. S2). This suggests that other Notch family members can potentially participate in regulation of HES1 expression although NOTCH2 is most directly associated with HES1 . Next, we examined whether expression of Notch family members or HES1 is associated with imatinib response. We classified patients into responders (PR, n=5) or non-responders (SD / PD, n=6) on the basis of clinical implementation, and compared expression values between two groups. Strikingly, we found that none of Notch family members had statistical power to distinguish two groups. Intriguingly, HES1 was significant in discriminating patients’ response (P = 0.027617) (Fig. 5 B). This is in accordance with report that overexpression of HES1 sensitizes cells to imatinib in chronic myeloid leukemia model [ 44 ]. Furthermore, we calculated spearman correlation between response rate and expression levels. Initially, we found no correlation between HES1 or Notch family genes with tumor volume change, with marginal significance in HES1 (P = 0.07). However, after removing outlier (defined as patient with highest gene expression), we found that HES1 was significantly correlated with clinical response (P = 0.00922195) (Fig. 5 C). Still, none of Notch members, except NOTCH4 , achieved statistical significance. Overall, HES1 was the most significant marker of imatinib sensitivity. Enrichment analysis of response-associated genes To identify biological pathways that are associated with imatinib sensitivity, we first conducted gene set enrichment analysis (GSEA) between responders and non-responders (Fig. 6 A). Differentially expressed genes were enriched in diverse biological pathways including muscle cell cellular homeostasis, negative regulation of myoblast differentiation, and skeletal tissue regeneration, angiogenesis and regulation of oxidative phosphorylation. We also performed enrichment analysis using genes that are significantly correlated with imatinib sensitivity (Fig. 6 B). Similar terms, such as fibroblast growth factor binding, muscle cell migration, and oxidative phosphorylation, were enriched. Among these terms, we found that glucose metabolism and mitochondrial respiration have been closely linked to imatinib sensitivity [ 45 ] and are upregulated in naïve pluripotent stem cells [ 46 ] in previous researches. In addition, angiogenesis is a well-known feature of mesenchymal stem cells [ 47 ]. Collectively, we propose that these terms, such as oxidative phosphorylation and angiogenesis, and HES1 all point to the significance of mesenchymal stem cell population that are prone to imatinib in desmoid tumor. Discussion Using whole-genome and transcriptome sequencing, we performed integrative molecular characterization of desmoid tumor in patients receiving imatinib treatment. Our analyses suggest HES1 overexpression, potentially regulated by NOTCH2 , can serve as a predictor of the clinical response to imatinib in desmoid tumor patients. To our knowledge, this is the first integrative study to characterize molecular determinants of the response to imatinib in desmoid tumor. Our coding-region analyses recapitulated previous findings on the prevalence of CTNNB1 and APC mutations [ 48 ]. Unfortunately, these mutations were not associated with clinical responses to imatinib. However, we discovered that mutations in noncoding regulatory regions of NOTCH2 are positively correlated with the clinical response to imatinib. Moreover, our investigation of PCAWG samples revealed that noncoding mutations in NOTCH2 regions increase expression. Importantly, NOTCH2 expression was significantly correlated with HES1 , and HES1 was indicative of imatinib sensitivity in our desmoid cohort. We noticed that removal of one outlier restored statistical significance when assessing correlation between HES1 and tumor volume change. Still, our correlation analysis suggests that HES1 expression is the major determinant of imatinib sensitivity. Other minor factors of imatinib sensitivity need to be determined to fully elucidate mechanism of imatinib in desmoid tumor. Taken together, we suggest that HES1 and NOTCH2 overexpression is a predictor of the anti-cancer effects of imatinib on desmoid tumors. Our study is not the very first attempt to investigate the significance of Notch signaling in desmoid tumor [ 43 , 49 ]. Based on multiple evidences, small molecule inhibitors, such as γ-secretase inhibitor, siRNA, and monoclonal antibody against Notch pathway were designed to treat desmoid tumor [ 50 ]. Studies have confirmed that activation of NOTCH2 leads to overexpression of HES1 and are accompanied by proliferation, immature morphology and aggressiveness in acute kidney injury model [ 51 ] and hepatocellular carcinoma model [ 52 ]. Of note, Notch signaling and HES1 have been associated with response to imatinib in Chronic Myeloid Leukemia cells [ 44 , 53 ], providing robust evidence for Notch- HES1 axis in mechanism of imatinib in desmoid tumor. Surprisingly, however, no research reported its association with clinical response of imatinib in desmoid tumor until now. The mechanism underlying the tumor response to imatinib is still not fully understood. In agreement with our findings, multiple lines of evidence support their significance in desmoid tumor. In a preclinical study, imatinib inhibits Notch signaling by increasing the proteosomal degradation of intracellular Notch. Furthermore, a primary effector of Notch signaling, HES1 , decreased as the imatinib concentration increased [ 54 ]. In a neurodegenerative condition, Alzheimer’s disease, imatinib results in the dose-dependent inhibition of γ-secretase activity [ 55 ]. Similarly, NSAIDs, another standard treatment for desmoid tumors, alter γ-secretase activity [ 56 ]. Taken together, these findings support the predictive value of Notch and HES1 as therapeutic strategy. The Notch pathway exhibits crosstalk with the Wnt signaling cascade [ 57 ] and is involved in the regulation of tumor microenvironments and the maintenance of cancer stem cells [ 58 – 60 ]. With NOTCH1 activation, desmoid tumors showed high expression levels of NOTCH1 and its downstream transcription factor HES1 [ 43 ], of which transcriptional activity is dependent on NOTCH2 [ 61 ]. Thus, targeting γ-secretase to prevent Notch cleavage has been suggested as a novel therapeutic approach [ 62 , 63 ]. A phase II trial of the γ-secretase inhibitor PF-03084014 demonstrated a promising efficacy, with a response rate of 29% for patients with progressive desmoid tumors [ 63 ]. Furthermore, the efficacy of PF-03084014 is high in tumors with elevated expression of genes in the Notch and Wnt pathways [ 64 ]. We sought to explain the increased correlation between NOTCH2 mutation scores and NOTCH2 expression levels when considering the CTNNB1 mutation status. A previous study has shown that the TCF4/β-catenin complex binds to the promoters of Notch signaling pathway genes, including NOTCH2 [ 65 ]. In the absence of the TCF4/β-catenin complex, the transcription machinery cannot be assembled at regulatory regions and fails to induce NOTCH2 expression, thereby preventing NOTCH2 regulatory mutations from exerting effects. We speculate that the hyperactivity of the TCF4/β-catenin complex induced by CTNNB1 msissense mutations leads to the constitutive activation of Notch signaling. This allows NOTCH2 regulatory mutations to alter gene expression levels, leading to a high correlation between deleteriousness and transcript levels. Further investigations, including functional studies, are needed to validate the mode of action of imatinib. As mentioned earlier, mutation status of driver genes failed to discriminate patients who will respond to imatinib. We overcame this hurdle with analysis of non-coding mutations, providing rationale to investigate Notch signaling and its downstream target HES1 to interpret molecular mechanism of imatinib. Thus, significance of non-coding mutation confers huge advantage to whole-genome sequencing data over whole-exome sequencing data. Despite such significance, we acknowledge several limitations of our study. First of all, we are aware that limited sample size hinders more comprehensive study of desmoid tumor. For example, although we were initially unable to obtain statistical significance for NOTCH2 in Fig. 5 B (responder versus non-responder analysis), removal of outlier restored statistical significance (p-value from 0.2 to 0.07), proving that NOTCH2 is a biologically meaningful biomarker. Rarity of desmoid tumor obscured the statistical power, and increased sample size will endow power to rescue unrecognized candidates. Also, we admit lack of functional study. Collectively, multiple evidences point out to Notch- HES1 axis in various studies, its significance in response to imatinib, and role of HES1 in stemness. Thus, future work should validate both clinical and biological significance of HES1 in desmoid tumor to gain insight into this aggressive rare tumor. In conclusion, using whole-genome sequencing with gene-wise recurrence model and transcriptome of desmoid tumor, we propose that overexpression of NOTCH2 and HES1 is the marker of sensitivity for the anti-cancer effects of imatinib on desmoid tumors. Our results suggest that HES1 should especially be considered in clinical settings when using imatinib to treat this rare and challenging disease. Conclusions Our results show that HES1 , regulated by NOTCH2 , as an indicator of sensitivity to imatinib, which provides an important therapeutic consideration for desmoid tumor. Integrative multi-omics data of desmoid tumor with gene-wise recurrent model provided novel insight into previously inexplicable mechanism of imatinib. Thus, non-coding mutations and subsequent gene expression change must be considered in clinical setting for efficient treatment. Abbreviations APC : Adenomatous Polyposis Coli BWA : Burrows-Wheeler Aligner CADD : Combined-Annotation Dependent Depletion COSMIC : Catalogue of Somatic Mutations in Cancer DANN : Deleterious Annotation of Genetic Variants Using Neural Networks FAP : Familial Adenomatous Polyposis FFPE : Formalin-fixed paraffin-embedded FPKM : Fragments Per Kilobase of transcript per Million mapped reads GATK : Genome Analysis Toolkit gnomAD : Genome Aggregation Database GSEA : Gene Set Enrichment Analysis IGV : Integrative Genomics Viewer Indels : Insertions/deletions NSAID : Nonsteroidal anti-inflammatory drugs PBMC : Peripheral blood nuclear mononuclear cells PCAWG : Pan-cancer Analysis of Whole Genomes PD : Progressive Diseases PFR : Progression-Free Rate PFS : Progression-Free Survival PR : Partial Responses QC : Quality Control SD : Stable Diseases SNV : Somatic nucleotide variant WGS : Whole-Genome Sequencing Declarations Ethics approval and consent to participate: The study was approved by the institutional review board (IRB No. 2013-1417-001). Retrospective cohort study of 24 additional patients with desmoid tumor was approved by the institutional review board (IRB No. 2020-2244-001). Consent to publish: Written informed consent for publication was provided by the participants Availability of data and materials: Variant Call Format (VCF) of whole-genome sequenced patients, gene expression matrix of RNA-sequenced patients, and clinical information used in this study are available from the corresponding author on request. PCAWG data was obtained from https://dcc.icgc.org/pcawg/ as instructed [29]. Competing Interests: The authors declare no potential conflicts of interest. Funding: This work was supported by a grant from the National Research Foundation of Korea (NRF) funded by the Korean government (MSIT) [2018R1A2B6003707 to H.S.K., NRF-2017M3A9A7050612 to J.K.C.]; and the Severance Hospital Research Fund for Clinical Excellence (SHRC) [C-2019-0015]. Authors’ Contributions: H.S.K and J.K.C conceived and designed the experiments. J.H.K and J.H.L analyzed and interpreted the data. Y.H.L, J.Y.L, J.H.A, S.H.K, S.H.K, T.I.K, K.H.Y, Y.S.P, J.E.K and K.S.L conducted clinical trial, prepared sample and generated data used in this study. J.H.K. and J.H.L were responsible for drafting the manuscript. H.S.K and J.K.C obtained the funding. All authors read and approved the final manuscript. Author information: J.K and J.H.L contributed equally to this work. Corresponding authors : Correspondence to Hyo Song Kim and Jung Kyoon Choi. Acknowledgement: Not applicable References Colombo C, Miceli R, Le Péchoux C, Palassini E, Honoré C, Stacchiotti S, et al. Sporadic extra abdominal wall desmoid-type fibromatosis: surgical resection can be safely limited to a minority of patients. Eur J Cancer England. 2015;51:186–92. Lev D, Kotilingam D, Wei C, Ballo MT, Zagars GK, Pisters PWT, et al. Optimizing treatment of desmoid tumors. 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Messersmith WA, Shapiro GI, Cleary JM, Jimeno A, Dasari A, Huang B, et al. A Phase I, Dose-Finding Study in Patients with Advanced Solid Malignancies of the Oral γ-Secretase Inhibitor PF-03084014. Clin Cancer Res [Internet]. 2015;21:60 LP – 67. Available from: http://clincancerres.aacrjournals.org/content/21/1/60.abstract . Timbergen MJM, Smits R, Grünhagen DJ, Verhoef C, Sleijfer S, Wiemer EAC. Activated Signaling Pathways and Targeted Therapies in Desmoid-Type Fibromatosis: A Literature Review [Internet]. Front. Oncol.. 2019. p. 397. Available from: https://www.frontiersin.org/article/ 10.3389/fonc.2019.00397 . Kobayashi T, Terada Y, Kuwana H, Tanaka H, Okado T, Kuwahara M, et al. Expression and function of the Delta-1/Notch-2/Hes-1 pathway during experimental acute kidney injury. Kidney Int United States. 2008;73:1240–50. Hayashi Y, Osanai M, Lee G-H NOTCH2 signaling confers immature morphology and aggressiveness in human hepatocellular carcinoma cells. Oncol Rep [Internet]. 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A subset of NSAIDs lower amyloidogenic Abeta42 independently of cyclooxygenase activity. Nature England. 2001;414:212–6. Kim H-A, Koo B-K, Cho J-H, Kim Y-Y, Seong J, Chang HJ, et al. Notch1 counteracts WNT/beta-catenin signaling through chromatin modification in colorectal cancer. J Clin Invest United States. 2012;122:3248–59. Benedito R, Hellstrom M. Notch as a hub for signaling in angiogenesis. Exp Cell Res United States. 2013;319:1281–8. Batlle E, Clevers H. Cancer stem cells revisited. Nat Med United States. 2017;23:1124–34. Meurette O, Mehlen P. Notch Signaling in the Tumor Microenvironment. Cancer Cell. 2018. Espinosa L, Ingles-Esteve J, Aguilera C, Bigas A. Phosphorylation by glycogen synthase kinase-3 beta down-regulates Notch activity, a link for Notch and Wnt pathways. J Biol Chem United States. 2003;278:32227–35. Shang H, Braggio D, Lee Y-J, Al Sannaa GA, Creighton CJ, Bolshakov S, et al. Targeting the Notch pathway: A potential therapeutic approach for desmoid tumors. Cancer United States. 2015;121:4088–96. Kummar S, O’Sullivan Coyne G, Do KT, Turkbey B, Meltzer PS, Polley E, et al. Clinical Activity of the gamma-Secretase Inhibitor PF-03084014 in Adults With Desmoid Tumors (Aggressive Fibromatosis). J Clin Oncol United States. 2017;35:1561–9. Arcaroli JJ, Quackenbush KS, Purkey A, Powell RW, Pitts TM, Bagby S, et al. Tumours with elevated levels of the Notch and Wnt pathways exhibit efficacy to PF-03084014, a gamma-secretase inhibitor, in a preclinical colorectal explant model. Br J Cancer England. 2013;109:667–75. Ungerbäck J, Elander N, Grünberg J, Sigvardsson M, Söderkvist P. The Notch-2 Gene Is Regulated by Wnt Signaling in Cultured Colorectal Cancer Cells. PLoS One Public Library of Science. 2011;6:1–13. Supplementary Files Additionalfile1TableS1.pdf Additionalfile2TableS2.pdf Additionalfile3TableS3.pdf Additionalfile4TableS4.pdf Additionalfile5TableS5.pdf Additionalfile6FigureS1.pdf Additionalfile7TableS6.pdf Additionalfile8TableS7.pdf Additionalfile9TableS8.pdf Additionalfile10TableS9.pdf Additionalfile11FigureS2.pdf SupplementaryMaterialProtocolImatinibDesmoidTumor.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-993539","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":58484210,"identity":"1066173c-36f5-4681-85af-c9e126a4cf10","order_by":0,"name":"Joonha Kwon","email":"","orcid":"","institution":"Korea Advanced Institute of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Joonha","middleName":"","lastName":"Kwon","suffix":""},{"id":58484211,"identity":"e5a263ae-29a3-4a14-a821-e5ed70aae76b","order_by":1,"name":"Jun Hyeong Lee","email":"","orcid":"","institution":"Korea Advanced Institute of 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Kim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBACAyhdv/94AwMzSVoYG84cIFnLjQQitZhLJB/+8HFHLTPjzDdm0gV/GOT5GwhosZyRliY588xxNmbpHDPpmW0MhjMOEHLYjRwzZt62YzxsIC28DQyMGwg5DKjF+DNQiwSP5BkzaZ4/DPbEaDGQ5m2rMZCQ4AFqYWNIJKzlzDOgX9oOJBjwpBVb87ZJJBP2y3FQiLXVJRiwH954m+ePjW1/AyFrBBJA5GEg5gDFkQQh9UDAD3ZHHRCzPyBC+SgYBaNgFIxEAABfhD50+Z70DQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-0625-9828","institution":"Yonsei University College of Medicine","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Hyo","middleName":"Song","lastName":"Kim","suffix":""}],"badges":[],"createdAt":"2021-10-19 06:57:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-993539/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-993539/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":14920848,"identity":"e28f48f6-ab95-4c38-9d1f-e0a6fd1abb4b","added_by":"auto","created_at":"2021-10-26 20:16:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":87852,"visible":true,"origin":"","legend":"Consort diagram of desmoid tumor patients included in this study. Prospective phase II study with treatment of imatinib included 20 patients after excluding 2 patients with clinical concerns. Tumor samples from 11 patients, which passed stringent quality check, were whole-genome-sequenced. RNA-seq was performed for 29 cases, including 7 in phase II study and 22 in retrospective cohort, of which 11 cases were treated with imatinib. ","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-993539/v1/eb249111ed5d8a95a16793c6.png"},{"id":14920849,"identity":"d86926fb-179e-4dd2-9d59-d96cdf1b564e","added_by":"auto","created_at":"2021-10-26 20:16:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":481766,"visible":true,"origin":"","legend":"Representative pre- and post-treatment imaging scans to show antitumor activity of imatinib in desmoid tumors. Soft tissue lesion in the shoulder of patient #19 (A) and pelvis of patient #20 (B) exhibited a significant size reduction after 24 and 40 weeks of imatinib treatment (red arrow). C, Relative change in tumor volume of patients (n=20) over time. * indicates a sample with WGS data. The label at the end of the bar shows mutational information in known driver genes, CTNNB1 and APC, for desmoid tumor. D, Swimmer plot. Each lane represents a single patient’s data. X axis represents the duration of treatment for each patient.","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-993539/v1/26aa09def18221fbec211f5b.png"},{"id":14920850,"identity":"3f57587b-6ce8-41a3-8014-1e66cf9f79cb","added_by":"auto","created_at":"2021-10-26 20:16:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":233390,"visible":true,"origin":"","legend":"Gene-wise recurrence of noncoding mutations. A, Definition of functional noncoding mutations and schematic overview of the gene-wise recurrence model. Functional noncoding mutations in regulatory regions, such as enhancers, affect the expression level of the target gene. Mutations outside of functional regions were excluded from our analysis. Functional mutations were identified as recurrent if they occur in regulatory regions converging of the same gene via enhancer-promoter chromatin interactions across multiple patients. Deleteriousness of functional mutations in mutant groups were quantified using CADD and DANN. B, Cleveland plot shows correlation coefficients (R2) for the relationship between tumor volume change and deleteriousness score, and recurrence for each gene in desmoid tumor patients (n=9). Genes are ordered by the magnitude of R-squared value. Only protein-coding genes with a recurrence value of 2 or greater are shown. C, CADD scores for NOTCH2 mutations according to tumor volume changes were plotted with the regression line shown in red (R = -0.716, P = 0.013). DANN scores for NOTCH2 mutations were also plotted with the regression line shown in blue (R = -0.83, P = 0.006). Each point represents a patient’s sample.","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-993539/v1/be97b5d3df8be0f83ad7a020.png"},{"id":14921154,"identity":"3eecd598-1dd7-461a-a439-0008ff66b4cb","added_by":"auto","created_at":"2021-10-26 20:19:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":311544,"visible":true,"origin":"","legend":"Correlation between the deleteriousness of NOTCH2 noncoding-mutations and expression level of NOTCH2 in PCAWG cohort. A, Proportions of patients with NOTCH2 mutations (n=210) and with both NOTCH2 and CTNNB1 missense mutations (n=16). B, Correlation between CADD score and NOTCH2 expression level in the group with NOTCH2 mutations (left side) and in selected samples harboring both NOTCH2 and CTNNB1 missense mutations (right side). C, The correlation analysis was repeated using DANN score in the group with NOTCH2 mutations (left side) and in selected samples with both NOTCH2 and CTNNB1 missense mutations (right side).","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-993539/v1/498a2cf50b07d863ba160d30.png"},{"id":14920852,"identity":"220c4ba0-282d-4b77-bf93-2e58be129551","added_by":"auto","created_at":"2021-10-26 20:16:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":157461,"visible":true,"origin":"","legend":"Expressional association between Notch family and HES1 genes, and its clinical significance in desmoid tumor samples (n=29). A, Co-expression between HES1 and one of Notch family genes, including NOTCH1, NOTCH2, NOTCH3 and NOTH4, respectively. Correlation analysis was calculated using spearman and all expression levels are in TPM values. B, Expressional differences of HES1, NOTCH1, NOTCH2, NOTCH3 and NOTCH4, between responders (n=5) and non-responders (n=6) after treatment of imatinib. Statistical significance was determined using Mann-Whitney U test. C, Correlation between tumor volume change, indicating the imatinib-response, and gene expression level: HES1 and Notch family members. Plots depict correlation analysis after removing one outlier. All red square lines indicate statistical significance (P \u003c 0.05). ","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-993539/v1/fe963f513af195bb9e7ff81e.png"},{"id":14921156,"identity":"d66bdee8-d5bc-4987-80e3-3d5b65b8e8f1","added_by":"auto","created_at":"2021-10-26 20:19:19","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":175772,"visible":true,"origin":"","legend":"A, GSEA for differential genes with statistical importance (P \u003c 0.05) between responders (n=5) and non-responders (n=6). GO terms on y-axis indicate molecular signatures database (MSigDB), of which five gene sets are shown in legend: C2, as curated gene sets; C5 BP, as gene sets from biological process ontology; C5 NOTCH, as gene sets associated with Notch pathway; C6 BP, as oncogenic signatures of biological process; Hallmark, as gene sets to represent specific biological states or processes and show consistent expression. Most significant terms for each category are depicted. B, GSEA result by using EnrichR with genes that are significantly correlated with imatinib sensitivity. Red vertical lines indicate p-value of 0.05.","description":"","filename":"fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-993539/v1/d69ecef072dde87917c51739.png"},{"id":15675866,"identity":"038673da-96dc-4710-b14e-78302e9bdebf","added_by":"auto","created_at":"2021-11-18 14:30:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1989272,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-993539/v1/9c683656-fd2c-4a32-bdfe-33ef2731e330.pdf"},{"id":14920847,"identity":"aa1f2fa9-a857-4f73-8a92-2a9dbda206d3","added_by":"auto","created_at":"2021-10-26 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20:16:19","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":213333,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile3TableS3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-993539/v1/8eb0f6c1b3dd7eb1a3cbf15f.pdf"},{"id":14921554,"identity":"b7f3f4f8-d3fe-42a2-94a4-4957dc29e63e","added_by":"auto","created_at":"2021-10-26 20:22:19","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":212153,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile4TableS4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-993539/v1/4faad2423dcfa4b91ebba713.pdf"},{"id":14920861,"identity":"f5dda34c-d693-47db-a6ce-de11648bed7d","added_by":"auto","created_at":"2021-10-26 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20:19:20","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":278988,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile9TableS8.pdf","url":"https://assets-eu.researchsquare.com/files/rs-993539/v1/99f5ef25932981f8c09be0d9.pdf"},{"id":14921555,"identity":"822e546d-73f8-40e5-8bce-a2c50c694aa2","added_by":"auto","created_at":"2021-10-26 20:22:20","extension":"pdf","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":195006,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile10TableS9.pdf","url":"https://assets-eu.researchsquare.com/files/rs-993539/v1/c578c6c577fc7418fdff9cdd.pdf"},{"id":14920864,"identity":"60aac97d-bf24-4332-afb4-abf3ccc1b7e1","added_by":"auto","created_at":"2021-10-26 20:16:20","extension":"pdf","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":1517484,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile11FigureS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-993539/v1/6802ce498e2a353cf0511d21.pdf"},{"id":14920863,"identity":"6cb3fcf4-3e8b-4b2b-a6f9-14c45dd3ca3f","added_by":"auto","created_at":"2021-10-26 20:16:20","extension":"docx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":155382,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialProtocolImatinibDesmoidTumor.docx","url":"https://assets-eu.researchsquare.com/files/rs-993539/v1/d8dfa342875c75983f6658b5.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eWhole-Genome and Transcriptome Sequencing Identified \u003cem\u003eNOTCH2\u003c/em\u003e and \u003cem\u003eHES1\u003c/em\u003e As Potential Markers of Response to Imatinib in Desmoid Tumor (Aggressive Fibromatosis): A Phase II Trial Study\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eA desmoid tumor (aggressive fibromatosis) is fibroproliferative neoplasm arising from deep connective tissues. A stepwise approach including an active surveillance is established as no metastatic potential and spontaneous tumor regression are observed[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Excision is the mainstay of treatment, but the postsurgical recurrence rate is high [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. For unresectable or recurrent desmoid tumors, a variety of systemic therapeutic options are available, including tamoxifen, nonsteroidal anti-inflammatory drugs (NSAIDs), interferons, and chemotherapy [\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Recently, sorafenib induced durable response and led to its approval for the disease treatment [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Imatinib is a new treatment option in unresectable, progressive, or recurrent desmoid tumors [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In addition to its promising efficacy (6\u0026ndash;15% response rate), the favorable toxicity profiles support its long-term use for salvage treatment. In imatinib-responsive diseases, such as gastrointestinal stromal tumors or chronic myeloid leukemia, specific mutations or chromosomal translocations have been reported [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, in desmoid tumors, no molecular abnormalities in imatinib-sensitive kinases have been observed [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, molecular mechanisms by which this rare tumor responds to imatinib are poorly understood.\u003c/p\u003e \u003cp\u003eIn this study, we conducted a multicenter phase II trial to evaluate the efficacy of imatinib in patients with relapsing or progressive desmoid tumors. To understand the molecular basis of the clinical responses to imatinib, we performed whole-genome sequencing to identify potential markers. Owing to limited insights gained from protein coding mutations, we extended our analyses to noncoding regulatory regions. Our gene-wise recurrence model using 1,009 pan-cancer whole-genome data indicated that \u003cem\u003eNOTCH2\u003c/em\u003e regulatory mutations are associated with the response of desmoid tumors to imatinib. We further evaluated the significance of \u003cem\u003eNOTCH2\u003c/em\u003e in transcriptome analysis using RNA-sequencing data. We discovered that \u003cem\u003eHES1\u003c/em\u003e, a well-known downstream target of Notch signaling pathway, is directly associated with imatinib sensitivity.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePatients and Treatment\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients with advanced desmoid tumors, defined as patients with radiographic progression after previous treatment, were eligible for prospective phase II study. Key inclusion criteria were as follows: age \u0026ge; 10 years, Eastern Cooperative Oncology Group performance status of 0 to 2, and adequate hematologic and renal function. Patients were treated with 400 mg of imatinib mesylate (Glima; Boryung Pharmaceutical Co., Ltd, Seoul, Korea) daily until progression or unacceptable toxicity. Toxic effects were graded according to the National Cancer Institute\u0026mdash;Common Toxicity Criteria v. 4.03. Disease was assessed every 8 weeks for the initial 32 weeks and then every 16 weeks according to RECIST (response evaluation criteria in solid tumors) v1.1 [16]. Briefly, patients who experienced grade 3/4 toxicity or intolerable grade 2 toxicity stopped treatment and then restarted at a reduced dose (300 mg/day or 200 mg/day). The study was approved by the institutional review board (IRB No. 2013-1417-001).\u003c/p\u003e\n\u003cp\u003eRetrospective cohort study included 24 additional patients with desmoid tumor and approved by the institutional review board (IRB No. 2020-2244-001). Surgically resected formalin-fixed paraffin-embedded (FFPE) tissue samples obtained prior to radiotherapy or chemotherapy were subjected to transcriptome sequencing (Fig. 1). Of those, 4 cases were treated with imatinib and remaining 20 were treatment na\u0026iuml;ve.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDNA Extraction and Quality Assessment\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhole genome sequencing was performed using pretreatment tumor excision samples as well as matched blood samples. Briefly, 4-mm-thick sections with a tumor content of \u0026ge;80% were obtained, and \u0026ge;2 \u0026micro;g of DNA was extracted using the Maxwell 16 FFPE Plus LEV DNA Purification Kit (Promega, Madison, WI, USA). For peripheral blood mononuclear cells (PBMCs), genomic DNA was extracted using the QIAamp DNA Mini Kit (Qiagen, Hilden, Germany) as per the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDNA Library Construction and Whole Genome Sequencing\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLibrary preparation was performed using the TruSeq Nano DNA Library Preparation Kit (Illumina, San Diego, CA, USA) following the manufacturer\u0026rsquo;s instructions. Illumina utilizes a unique \u0026quot;bridged\u0026quot; amplification reaction on the surface of the flow cell. A flow cell containing libraries was prepared using the cBot Fluidics Station and was then loaded into the HiSeq X-10 sequencer (Illumina) for automated cycles of extension and imaging. Sequencing-by-Synthesis cycles were repeated to achieve a paired-end read length of 2 \u0026times; 150 bp.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA Library Construction and Whole Transcriptome Sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNAs were extracted and purified from frozen tumor samples with ReliaPrep FFPE Total RNA Miniprep System (Promega, Inc.) according to the manufacturer\u0026apos;s procedures. Amount of RNA and its quality were checked on an Agilent RNA 6000 Nano Kit (Agilent Technologies). For analysis of RNA-sequencing data, we prepared mRNA sequencing libraries as paired-end reads with a length of 100 bases using the SMARTer Stranded Total RNA-seq kit v2-Pico Input Mammalian according to the manufacturers\u0026rsquo; protocols. Briefly, mRNA molecules were purified and fragmented from 2 \u0026micro;g of total RNA. The libraries were sequenced as paired end reads (2 x 150 bp) using the NovaSeq 6000 (Illumina).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eWhole Genome Data Processing\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo process whole genome sequencing data of desmoid tumors, we adopted the Genome Analysis Tool Kit (GATK) v3.7 best practice provided by the Broad Institute [17]. Briefly, we mapped qualified paired-end reads to the human reference genome (hg19) with Burrows-Wheeler Aligner (BWA) 0.7.15 [18]. Subsequently, we filtered PCR duplicates using Picard tools 2.8.2 to remove potential bias that occurred during sequencing processes. Then, we performed recommended procedures, such as local realignment and base quality recalibration to extract analysis-ready reads.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSomatic Variant Detection\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMuTect2 [19] of the GATK pipeline with default parameters was used to identify somatic single nucleotide variants (SNVs) and small insertions/deletions (indels). The processed whole-genome-sequencing data for tumor and matched normal samples (PBMCs) were used in BAM format as inputs for Mutect2 [19]. Somatic variants were annotated using ANNOVAR [20]. Some candidate variants were manually inspected using Integrative Genomics Viewer (IGV) [21]. Population-level allele frequencies of candidates were obtained using Genome Aggregation Database (gnomAD) [22]. For two samples with tumor data only, normal sample data for one of the nine other patients was used as matched normal control for variant calling. A sample that was sequenced in the same batch with a read depth of greater than 30 was used. The variants were further filtered using gnomAD to obtain putative somatic mutations in the tumor-only samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eScoring Gene-Wise Recurrence of Functional Variants \u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur previously developed gene-wise recurrence model was used [23]. Conventionally, mutations are considered recurrent if and only if they occur at the same genomic location across multiple samples. Mutations are considered oncogenic when their recurrence exceeds a certain threshold [24]. However, this definition of recurrence is inappropriate for analyses of noncoding regions owing to their vast size. Thus, we consider mutations recurrent if they occur in functional regions of the same gene, even if they are not recurrent in a site-specific manner. In particular, we focused on mutational events in cis-regulatory regions of a mammalian gene dispersed across a long range in the genome [25]. Genes were defined based on the GENCODE v.19 gene set mapped to GRCh37 [26].\u003c/p\u003e\n\u003cp\u003eTo identify coding and noncoding mutations with significant functional consequences, deleterious effects of each SNV were predicted using two algorithms, Combined Annotation-Dependent Depletion (CADD) [27] and Deleterious Annotation of genetic variants using Neural Networks (DANN) [28]. Both models were trained to distinguish benign variants from deleterious variants [27,28]. For multiple mutations in the same gene, the one with the highest score for deleteriousness was selected to represent the functional consequence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eReference Whole-Genome and Transcriptome Datasets\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo characterize the functional effects of \u003cem\u003eNOTCH2\u003c/em\u003e noncoding mutations, a large-scale pan-cancer dataset consisting of somatic variants from whole-genome sequencing data and transcriptome data for tumor and matched normal samples were used. VCF files for somatic variant calling and gene expression matrices containing FPKM-upper quantile values were obtained from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Project[29].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRNA-sequencing data processing and QC\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe generated RNA-sequencing data of 31 desmoid tumor patients. We removed adapter sequences using Cutadpt [30], and aligned the trimmed reads using STAR [31] with hg19. Gene expression was quantified using RSEM [32]. Quality control check at pre-alignment step was conducted using FASTQC and at post-alignment step using RSeQC [33]. QC results were visualized with MultiQC [34]. At post-alignment step, we noticed two patients with potential problems in read distribution, and infer experiment criteria. Thus, we excluded those samples from future analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBioinformatics and Statistical Analyses\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe \u0026chi;\u003csup\u003e2\u003c/sup\u003e test was used to assess correlations between marker status and clinical significance. All correlation analysis was conducted using spearman correlation. To assess statistical significance between responders and non-responders of imatinib, we calculated Mann-Whitney U test. All tests were two-sided and P \u0026lt; 0.05 is considered significant. Cleveland, scatter and box plots were generated by using ggplot2 R package and matplotlib python package.\u003c/p\u003e\n\u003cp\u003eTo conduct enrichment analysis, we adopted two approaches. First of all, we identified genes that are significantly correlated with imatinib sensitivity and used those genes as input for EnrichR [35]. As an alternative step, we conducted Gene Set Enrichment Analysis (GSEA) between responders and non-responders using C2, C5, C6 and Hallmark MSigDB gene sets [36]. C5 Notch category was defined as Notch-related terms present in C5 category.\u003c/p\u003e\n\u003cp\u003eProgression-free survival (PFS) was calculated from start date of imatinib to date of progression or death and progression free rate at 16 weeks (PFR 16) was defined as proportions of patients without progression at 16 weeks, analyzed using the Kaplan\u0026ndash;Meier method [SPSS version 18.0 (IBM, Chicago, IL, USA)].\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv class=\"Section2\" id=\"Sec13\"\u003e\n \u003ch2\u003eSample Set, Patient Outcomes and Toxicity\u003c/h2\u003e\n \u003cp\u003eTotal of 21 patients were enrolled between April 2014 and October 2015. One patient withdrew, leaving 20 patients (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e; Additional file 1: Table S1). Three patients (7, 8, and 13) had a known diagnosis of familial adenomatous polyposis (FAP). Fifteen patients (75%) underwent one previous surgery. Most of the patients had been treated with non-surgical procedures, including radiotherapy (6, 30.0%) and/or chemotherapy (12, 60.0%).\u003c/p\u003e\n \u003cp\u003eOf the 20 evaluable patients, 4 (20%) had partial responses (PR) to treatment with durations of 6.7, 26.8 (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA), 30.3, and 35.1 (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB) months. One PR case had FAP. The PR duration was longer than 1 year for three patients. Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC provides a waterfall plot of the best response; 14 patients (66.7%) had stable disease (SD), and the clinical benefit rate was 90.0%. The median time to progression was 21.4 months (range: 2.8 to 40.7 months) and PFR 16 was 85% (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e\n \u003cp\u003eIn terms of toxicity, 400 mg of imatinib was well tolerated with expected grade 3/4 toxicities: neutropenia (n = 1), anorexia (n = 1), vomiting (n = 1), and fatigue (n = 1) (Additional file 2: Table S2). Three patients had a one dose level reduction (300 mg/d), and one patient had a reduction of two levels (200 mg/d) owing to toxicity.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec14\"\u003e\n \u003ch2\u003eResults of Whole-Genome Sequencing\u003c/h2\u003e\n \u003cp\u003eAfter confirming adequate DNA quantities, whole genome sequencing was performed for 11 samples. Two samples (patients 11 and 13) without matched control data were excluded from the primary analysis and used for extensional validation only (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The average read depth was greater than 21.46 (range: 21.46 to 54.05) (Additional file 3: Table S3). By implementing the GATK pipeline from the Broad Institute, we identified 832\u0026ndash;4,110 SNVs and indels per sample and used ANNOVAR for annotation (Additional file 4: Table S4).\u003c/p\u003e\n \u003cp\u003eNext, we examined mutational signatures from annotated variants to identify types of mutational processes [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. Signature 1A was dominant, accounting for around 88% of signatures (Additional file 5: Table S5), indicating that desmoid tumor variants primarily arise due to errors in replicative polymerases in the DNA repair pathway [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. The majority of mutations were in intronic regions. Mutations in coding regions were primarily missense and synonymous variants (Additional file 6: Fig. S1).\u003c/p\u003e\n \u003cp\u003eWe also investigated alterations of cancer driver genes registered in the COSMIC database [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e] and detected \u003cem\u003eCTNNB1\u003c/em\u003e mutations in 8 out of 11 samples, while 2 patients harbored adenomatous polyposis coli (\u003cem\u003eAPC\u003c/em\u003e) mutations. \u003cem\u003eCTNNB1\u003c/em\u003e and \u003cem\u003eAPC\u003c/em\u003e mutation sites of patients are summarized in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC. All mutations affected phosphorylation sites necessary for the proper degradation of \u0026beta;-catenin [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e]. Remarkably, no other mutations in COSMIC cancer driver genes were detected, emphasizing the prominent role of the Wnt/\u0026beta;-catenin signaling pathway in desmoid tumor progression [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec15\"\u003e\n \u003ch2\u003eGene-Wise Recurrence Analysis of Potential Cancer-Associated Genes\u003c/h2\u003e\n \u003cp\u003eTo identify potential cancer-associated mutations in desmoid tumors, we employed gene-wise recurrence analysis of mutations in noncoding regions, according to previously developed method [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. Briefly, the model assumes that mutations in multiple patients are recurrent if they affect the same gene (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). Recurrently affected genes were defined as those with mutations in at least two samples. This strategy allowed us to evaluate noncoding regions with potentially significant impacts on gene regulation with limited sample size. \u003cem\u003eNOTCH2\u003c/em\u003e, \u003cem\u003eRGPD2\u003c/em\u003e, and \u003cem\u003eARHGAP11B\u003c/em\u003e were identified as strong candidates (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec16\"\u003e\n \u003ch2\u003eCorrelation between Gene-Wise Scores and Imatinib Sensitivity\u003c/h2\u003e\n \u003cp\u003eWe examined the association between the change in tumor volume after imatinib treatment and the deleteriousness score of recurrently mutated genes. In case of \u003cem\u003eNOTCH2\u003c/em\u003e, tumor volume changes were highly correlated with both CADD (\u003cem\u003eR\u003c/em\u003e = -0.797, \u003cem\u003eP\u003c/em\u003e = 0.01) and DANN scores (\u003cem\u003eR\u003c/em\u003e = -0.831, \u003cem\u003eP\u003c/em\u003e = 0.006) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB and C; Additional file 7: Table S6). This correlation was maintained even when two tumor-only samples (patient 11 and 13) were included (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC). According to the annotated information of somatic variants in \u003cem\u003eNOTCH2\u003c/em\u003e, three (patients 3, 5, and 8) and four (patients 9, 11, 13, and 18) variants were intergenic and intronic, respectively, while only one (patient 19) variant was nonsynonymous (Additional file 8: Table S7). Using gnomAD database, we found that the allele frequencies of the \u003cem\u003eNOTCH2\u003c/em\u003e variants were extremely low (0\u0026ndash;1.13%), indicating that they were likely somatic. These results suggest that the regulation of \u003cem\u003eNOTCH2\u003c/em\u003e at the gene level may contribute to the desmoid tumor response to imatinib.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec17\"\u003e\n \u003ch2\u003eRegulatory Role of NOTCH2 Mutations\u003c/h2\u003e\n \u003cp\u003eTo evaluate the regulatory effects of \u003cem\u003eNOTCH2\u003c/em\u003e mutations, we interrogated RNA sequencing and somatic mutation profiles from PCAWG datasets (n = 1,009). For comparison across cancer types, we transformed the \u003cem\u003eNOTCH2\u003c/em\u003e expression levels to Z-scores within each cancer type and identified samples with \u003cem\u003eNOTCH2\u003c/em\u003e mutations. Considering the role of \u003cem\u003eCTNNB1\u003c/em\u003e missense mutations in desmoid tumors [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e], we further selected both-\u003cem\u003eNOTCH2\u003c/em\u003e-\u003cem\u003eCTNNB1\u003c/em\u003e mutants in the PCAWG data (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA; Additional file 9: Table S8).\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eNOTCH2\u003c/em\u003e expression levels were more highly correlated with CADD scores for both-\u003cem\u003eNOTCH2\u003c/em\u003e-\u003cem\u003eCTNNB1\u003c/em\u003e mutation group (\u003cem\u003eR\u003c/em\u003e = 0.607, \u003cem\u003eP\u003c/em\u003e = 0.013) than for \u003cem\u003eNOTCH2\u003c/em\u003e mutation-only group (\u003cem\u003eR\u003c/em\u003e = 0.178, \u003cem\u003eP\u003c/em\u003e = 0.01) (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). The same trends were observed when DANN score was used for variant scoring (\u003cem\u003eR\u003c/em\u003e = 0.467) (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC), although the correlation was only marginally significant (\u003cem\u003eP\u003c/em\u003e = 0.068). These results suggest that genetic interactions between \u003cem\u003eNOTCH2\u003c/em\u003e noncoding mutations and \u003cem\u003eCTNNB1\u003c/em\u003e missense mutations may influence the \u003cem\u003eNOTCH2\u003c/em\u003e expression level.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec18\"\u003e\n \u003ch2\u003eRole of NOTCH family members and HES1 in imatinib sensitivity\u003c/h2\u003e\n \u003cp\u003eTo validate significance of our finding, we analyzed RNA-sequencing data of 29 desmoid tumor patients (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e; Additional file 10: Table S9). We focused on Notch family members, including \u003cem\u003eNOTCH2\u003c/em\u003e, and \u003cem\u003eHES1\u003c/em\u003e, a marker of stemness [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e] that has been implicated as a target of Notch signaling pathway [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e] and marker of imatinib sensitivity [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e]. We first investigated whether expression levels of Notch family members and \u003cem\u003eHES1\u003c/em\u003e are correlated. We calculated spearman correlation between the genes, and discovered that \u003cem\u003eNOTCH2\u003c/em\u003e expression was significantly correlated with \u003cem\u003eHES1\u003c/em\u003e expression (P = 0.0091568) while expressions of other Notch genes were not (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA). This recapitulates the association between \u003cem\u003eHES1\u003c/em\u003e and \u003cem\u003eNOTCH2\u003c/em\u003e detected in WGS analysis. We also noticed that all Notch family members are significantly correlated with each other (Additional file 11: Fig. S2). This suggests that other Notch family members can potentially participate in regulation of \u003cem\u003eHES1\u003c/em\u003e expression although \u003cem\u003eNOTCH2\u003c/em\u003e is most directly associated with \u003cem\u003eHES1\u003c/em\u003e.\u003c/p\u003e\n \u003cp\u003eNext, we examined whether expression of Notch family members or \u003cem\u003eHES1\u003c/em\u003e is associated with imatinib response. We classified patients into responders (PR, n=5) or non-responders (SD / PD, n=6) on the basis of clinical implementation, and compared expression values between two groups. Strikingly, we found that none of Notch family members had statistical power to distinguish two groups. Intriguingly, \u003cem\u003eHES1\u003c/em\u003e was significant in discriminating patients\u0026rsquo; response (P = 0.027617) (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB). This is in accordance with report that overexpression of \u003cem\u003eHES1\u003c/em\u003e sensitizes cells to imatinib in chronic myeloid leukemia model [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e]. Furthermore, we calculated spearman correlation between response rate and expression levels. Initially, we found no correlation between \u003cem\u003eHES1\u003c/em\u003e or Notch family genes with tumor volume change, with marginal significance in \u003cem\u003eHES1\u003c/em\u003e (P = 0.07). However, after removing outlier (defined as patient with highest gene expression), we found that \u003cem\u003eHES1\u003c/em\u003e was significantly correlated with clinical response (P = 0.00922195) (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC). Still, none of Notch members, except \u003cem\u003eNOTCH4\u003c/em\u003e, achieved statistical significance. Overall, \u003cem\u003eHES1\u003c/em\u003e was the most significant marker of imatinib sensitivity.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec19\"\u003e\n \u003ch2\u003eEnrichment analysis of response-associated genes\u003c/h2\u003e\n \u003cp\u003eTo identify biological pathways that are associated with imatinib sensitivity, we first conducted gene set enrichment analysis (GSEA) between responders and non-responders (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA). Differentially expressed genes were enriched in diverse biological pathways including muscle cell cellular homeostasis, negative regulation of myoblast differentiation, and skeletal tissue regeneration, angiogenesis and regulation of oxidative phosphorylation. We also performed enrichment analysis using genes that are significantly correlated with imatinib sensitivity (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB). Similar terms, such as fibroblast growth factor binding, muscle cell migration, and oxidative phosphorylation, were enriched. Among these terms, we found that glucose metabolism and mitochondrial respiration have been closely linked to imatinib sensitivity [\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e] and are upregulated in na\u0026iuml;ve pluripotent stem cells [\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e] in previous researches. In addition, angiogenesis is a well-known feature of mesenchymal stem cells [\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e]. Collectively, we propose that these terms, such as oxidative phosphorylation and angiogenesis, and \u003cem\u003eHES1\u003c/em\u003e all point to the significance of mesenchymal stem cell population that are prone to imatinib in desmoid tumor.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eUsing whole-genome and transcriptome sequencing, we performed integrative molecular characterization of desmoid tumor in patients receiving imatinib treatment. Our analyses suggest \u003cem\u003eHES1\u003c/em\u003e overexpression, potentially regulated by \u003cem\u003eNOTCH2\u003c/em\u003e, can serve as a predictor of the clinical response to imatinib in desmoid tumor patients. To our knowledge, this is the first integrative study to characterize molecular determinants of the response to imatinib in desmoid tumor.\u003c/p\u003e \u003cp\u003eOur coding-region analyses recapitulated previous findings on the prevalence of \u003cem\u003eCTNNB1\u003c/em\u003e and \u003cem\u003eAPC\u003c/em\u003e mutations [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Unfortunately, these mutations were not associated with clinical responses to imatinib. However, we discovered that mutations in noncoding regulatory regions of \u003cem\u003eNOTCH2\u003c/em\u003e are positively correlated with the clinical response to imatinib. Moreover, our investigation of PCAWG samples revealed that noncoding mutations in \u003cem\u003eNOTCH2\u003c/em\u003e regions increase expression. Importantly, \u003cem\u003eNOTCH2\u003c/em\u003e expression was significantly correlated with \u003cem\u003eHES1\u003c/em\u003e, and \u003cem\u003eHES1\u003c/em\u003e was indicative of imatinib sensitivity in our desmoid cohort. We noticed that removal of one outlier restored statistical significance when assessing correlation between \u003cem\u003eHES1\u003c/em\u003e and tumor volume change. Still, our correlation analysis suggests that \u003cem\u003eHES1\u003c/em\u003e expression is the major determinant of imatinib sensitivity. Other minor factors of imatinib sensitivity need to be determined to fully elucidate mechanism of imatinib in desmoid tumor. Taken together, we suggest that \u003cem\u003eHES1\u003c/em\u003e and \u003cem\u003eNOTCH2\u003c/em\u003e overexpression is a predictor of the anti-cancer effects of imatinib on desmoid tumors.\u003c/p\u003e \u003cp\u003eOur study is not the very first attempt to investigate the significance of Notch signaling in desmoid tumor [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Based on multiple evidences, small molecule inhibitors, such as γ-secretase inhibitor, siRNA, and monoclonal antibody against Notch pathway were designed to treat desmoid tumor [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Studies have confirmed that activation of \u003cem\u003eNOTCH2\u003c/em\u003e leads to overexpression of \u003cem\u003eHES1\u003c/em\u003e and are accompanied by proliferation, immature morphology and aggressiveness in acute kidney injury model [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] and hepatocellular carcinoma model [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Of note, Notch signaling and \u003cem\u003eHES1\u003c/em\u003e have been associated with response to imatinib in Chronic Myeloid Leukemia cells [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], providing robust evidence for Notch-\u003cem\u003eHES1\u003c/em\u003e axis in mechanism of imatinib in desmoid tumor. Surprisingly, however, no research reported its association with clinical response of imatinib in desmoid tumor until now.\u003c/p\u003e \u003cp\u003eThe mechanism underlying the tumor response to imatinib is still not fully understood. In agreement with our findings, multiple lines of evidence support their significance in desmoid tumor. In a preclinical study, imatinib inhibits Notch signaling by increasing the proteosomal degradation of intracellular Notch. Furthermore, a primary effector of Notch signaling, \u003cem\u003eHES1\u003c/em\u003e, decreased as the imatinib concentration increased [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. In a neurodegenerative condition, Alzheimer\u0026rsquo;s disease, imatinib results in the dose-dependent inhibition of γ-secretase activity [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Similarly, NSAIDs, another standard treatment for desmoid tumors, alter γ-secretase activity [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Taken together, these findings support the predictive value of Notch and \u003cem\u003eHES1\u003c/em\u003e as therapeutic strategy.\u003c/p\u003e \u003cp\u003eThe Notch pathway exhibits crosstalk with the Wnt signaling cascade [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] and is involved in the regulation of tumor microenvironments and the maintenance of cancer stem cells [\u003cspan additionalcitationids=\"CR59\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. With \u003cem\u003eNOTCH1\u003c/em\u003e activation, desmoid tumors showed high expression levels of \u003cem\u003eNOTCH1\u003c/em\u003e and its downstream transcription factor \u003cem\u003eHES1\u003c/em\u003e [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], of which transcriptional activity is dependent on \u003cem\u003eNOTCH2\u003c/em\u003e [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Thus, targeting γ-secretase to prevent Notch cleavage has been suggested as a novel therapeutic approach [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. A phase II trial of the γ-secretase inhibitor PF-03084014 demonstrated a promising efficacy, with a response rate of 29% for patients with progressive desmoid tumors [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Furthermore, the efficacy of PF-03084014 is high in tumors with elevated expression of genes in the Notch and Wnt pathways [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe sought to explain the increased correlation between \u003cem\u003eNOTCH2\u003c/em\u003e mutation scores and \u003cem\u003eNOTCH2\u003c/em\u003e expression levels when considering the \u003cem\u003eCTNNB1\u003c/em\u003e mutation status. A previous study has shown that the TCF4/β-catenin complex binds to the promoters of Notch signaling pathway genes, including \u003cem\u003eNOTCH2\u003c/em\u003e [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. In the absence of the TCF4/β-catenin complex, the transcription machinery cannot be assembled at regulatory regions and fails to induce \u003cem\u003eNOTCH2\u003c/em\u003e expression, thereby preventing \u003cem\u003eNOTCH2\u003c/em\u003e regulatory mutations from exerting effects. We speculate that the hyperactivity of the TCF4/β-catenin complex induced by \u003cem\u003eCTNNB1\u003c/em\u003e msissense mutations leads to the constitutive activation of Notch signaling. This allows \u003cem\u003eNOTCH2\u003c/em\u003e regulatory mutations to alter gene expression levels, leading to a high correlation between deleteriousness and transcript levels. Further investigations, including functional studies, are needed to validate the mode of action of imatinib.\u003c/p\u003e \u003cp\u003eAs mentioned earlier, mutation status of driver genes failed to discriminate patients who will respond to imatinib. We overcame this hurdle with analysis of non-coding mutations, providing rationale to investigate Notch signaling and its downstream target \u003cem\u003eHES1\u003c/em\u003e to interpret molecular mechanism of imatinib. Thus, significance of non-coding mutation confers huge advantage to whole-genome sequencing data over whole-exome sequencing data. Despite such significance, we acknowledge several limitations of our study. First of all, we are aware that limited sample size hinders more comprehensive study of desmoid tumor. For example, although we were initially unable to obtain statistical significance for \u003cem\u003eNOTCH2\u003c/em\u003e in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB (responder versus non-responder analysis), removal of outlier restored statistical significance (p-value from 0.2 to 0.07), proving that \u003cem\u003eNOTCH2\u003c/em\u003e is a biologically meaningful biomarker. Rarity of desmoid tumor obscured the statistical power, and increased sample size will endow power to rescue unrecognized candidates. Also, we admit lack of functional study. Collectively, multiple evidences point out to Notch-\u003cem\u003eHES1\u003c/em\u003e axis in various studies, its significance in response to imatinib, and role of \u003cem\u003eHES1\u003c/em\u003e in stemness. Thus, future work should validate both clinical and biological significance of \u003cem\u003eHES1\u003c/em\u003e in desmoid tumor to gain insight into this aggressive rare tumor.\u003c/p\u003e \u003cp\u003eIn conclusion, using whole-genome sequencing with gene-wise recurrence model and transcriptome of desmoid tumor, we propose that overexpression of \u003cem\u003eNOTCH2\u003c/em\u003e and \u003cem\u003eHES1\u003c/em\u003e is the marker of sensitivity for the anti-cancer effects of imatinib on desmoid tumors. Our results suggest that \u003cem\u003eHES1\u003c/em\u003e should especially be considered in clinical settings when using imatinib to treat this rare and challenging disease.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur results show that \u003cem\u003eHES1\u003c/em\u003e, regulated by \u003cem\u003eNOTCH2\u003c/em\u003e, as an indicator of sensitivity to imatinib, which provides an important therapeutic consideration for desmoid tumor. Integrative multi-omics data of desmoid tumor with gene-wise recurrent model provided novel insight into previously inexplicable mechanism of imatinib. Thus, non-coding mutations and subsequent gene expression change must be considered in clinical setting for efficient treatment.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eAPC\u003c/strong\u003e: Adenomatous Polyposis Coli\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBWA\u003c/strong\u003e: Burrows-Wheeler Aligner\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCADD\u003c/strong\u003e: Combined-Annotation Dependent Depletion\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOSMIC\u003c/strong\u003e: Catalogue of Somatic Mutations in Cancer\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDANN\u003c/strong\u003e: Deleterious Annotation of Genetic Variants Using Neural Networks\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFAP\u003c/strong\u003e: Familial Adenomatous Polyposis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFFPE\u003c/strong\u003e: Formalin-fixed paraffin-embedded\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFPKM\u003c/strong\u003e: Fragments Per Kilobase of transcript per Million mapped reads\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGATK\u003c/strong\u003e: Genome Analysis Toolkit\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003egnomAD\u003c/strong\u003e: Genome Aggregation Database\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGSEA\u003c/strong\u003e: Gene Set Enrichment Analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIGV\u003c/strong\u003e: Integrative Genomics Viewer\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndels\u003c/strong\u003e: Insertions/deletions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNSAID\u003c/strong\u003e: Nonsteroidal anti-inflammatory drugs\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePBMC\u003c/strong\u003e: Peripheral blood nuclear mononuclear cells\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePCAWG\u003c/strong\u003e: Pan-cancer Analysis of Whole Genomes\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePD\u003c/strong\u003e: Progressive Diseases\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePFR\u003c/strong\u003e: Progression-Free Rate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePFS\u003c/strong\u003e: Progression-Free Survival\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePR\u003c/strong\u003e: Partial Responses\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQC\u003c/strong\u003e: Quality Control\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e: Stable Diseases\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSNV\u003c/strong\u003e: Somatic nucleotide variant\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWGS\u003c/strong\u003e: Whole-Genome Sequencing\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThe study was approved by the institutional review board (IRB No. 2013-1417-001). Retrospective cohort study of 24 additional patients with desmoid tumor was approved by the institutional review board (IRB No. 2020-2244-001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish:\u0026nbsp;\u003c/strong\u003eWritten informed consent for publication was provided by the participants\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eVariant Call Format (VCF) of whole-genome sequenced patients, gene expression matrix of RNA-sequenced patients, and clinical information used in this study are available from the corresponding author on request. PCAWG data was obtained from https://dcc.icgc.org/pcawg/ as instructed [29].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e The authors declare no potential conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This work was supported by a grant from the National Research Foundation of Korea (NRF) funded by the Korean government (MSIT) [2018R1A2B6003707 to H.S.K., NRF-2017M3A9A7050612 to J.K.C.]; and the Severance Hospital Research Fund for Clinical Excellence (SHRC) [C-2019-0015].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo;\u003c/strong\u003e \u003cstrong\u003eContributions:\u003c/strong\u003e H.S.K and J.K.C conceived and designed the experiments. J.H.K and J.H.L analyzed and interpreted the data. Y.H.L, J.Y.L, J.H.A, S.H.K, S.H.K, T.I.K, K.H.Y, Y.S.P, J.E.K and K.S.L conducted clinical trial, prepared sample and generated data used in this study. J.H.K. and J.H.L were responsible for drafting the manuscript. H.S.K and J.K.C obtained the funding. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information:\u0026nbsp;\u003c/strong\u003eJ.K and J.H.L contributed equally to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding authors\u003c/strong\u003e: Correspondence to Hyo Song Kim and Jung Kyoon Choi.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u003c/strong\u003e Not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eColombo C, Miceli R, Le P\u0026eacute;choux C, Palassini E, Honor\u0026eacute; C, Stacchiotti S, et al. Sporadic extra abdominal wall desmoid-type fibromatosis: surgical resection can be safely limited to a minority of patients. 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Cancer United States. 2015;121:4088\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKummar S, O\u0026rsquo;Sullivan Coyne G, Do KT, Turkbey B, Meltzer PS, Polley E, et al. Clinical Activity of the gamma-Secretase Inhibitor PF-03084014 in Adults With Desmoid Tumors (Aggressive Fibromatosis). J Clin Oncol United States. 2017;35:1561\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArcaroli JJ, Quackenbush KS, Purkey A, Powell RW, Pitts TM, Bagby S, et al. Tumours with elevated levels of the Notch and Wnt pathways exhibit efficacy to PF-03084014, a gamma-secretase inhibitor, in a preclinical colorectal explant model. Br J Cancer England. 2013;109:667\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUngerb\u0026auml;ck J, Elander N, Gr\u0026uuml;nberg J, Sigvardsson M, S\u0026ouml;derkvist P. The Notch-2 Gene Is Regulated by Wnt Signaling in Cultured Colorectal Cancer Cells. PLoS One Public Library of Science. 2011;6:1\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Desmoid tumor, Imatinib, Phase II clinical trial, NOTCH2, HES1","lastPublishedDoi":"10.21203/rs.3.rs-993539/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-993539/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Desmoid tumor, also known as aggressive fibromatosis, is well-characterized by abnormal Wnt/β-catenin signaling. Various therapeutic options, including imatinib, are available to efficiently treat desmoid tumor. However, molecular mechanism of why imatinib works remains poorly understood. Here, we describe the potential roles of \u003cem\u003eNOTCH2\u003c/em\u003e and \u003cem\u003eHES1\u003c/em\u003e in association with clinical response to imatinib as in genome and transcriptome levels.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We identified all somatic mutations in coding and non-coding regions via whole genome sequencing using desmoid tumor samples. To validate the genetic interaction with expression level in desmoid-tumor condition, we utilized large-scale Whole-genome sequencing (WGS) and transcriptome datasets from the Pan-Cancer Analysis of Whole Genomes (PCAWG) project. RNA-sequencing was performed using prospective and retrospective cohort samples to evaluate the expressional relevance with clinical response.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Among 20 patients, 4 (20%) had a partial response and 14 (66.7%) had stable disease, 11 of which continued for ≥1 year. With gene-wise functional analyses, we detected significant correlation between recurrent \u003cem\u003eNOTCH2\u003c/em\u003e noncoding mutations and clinical response to imatinib. Based on PCAWG data analyses, \u003cem\u003eNOTCH2\u003c/em\u003e mutations affect its expression levels particularly in the presence of \u003cem\u003eCTNNB1\u003c/em\u003e missense mutations. By analyzing RNA-sequencing with additional desmoid tumor samples, we found that \u003cem\u003eNOTCH2\u003c/em\u003e expression was significantly correlated with \u003cem\u003eHES1\u003c/em\u003e expression. Interestingly, \u003cem\u003eNOTCH2\u003c/em\u003e had no statistical power to discriminate responders and non-responders. Instead, \u003cem\u003eHES1\u003c/em\u003e was differentially expressed with statistical significance between responders and non-responders\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Imatinib was effective and well tolerated for advanced desmoid tumor treatment. Our results show that\u0026nbsp;\u003cem\u003eHES1, regulated by NOTCH2,\u003c/em\u003e as an indicator of sensitivity to imatinib, which provides an important therapeutic consideration for desmoid tumor.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTrial Registration:\u003c/em\u003e\u003c/strong\u003e ClinicalTrials.gov, NCT02495519, Registered July 13, 2015- Retrospectively registered, https://www.clinicaltrials.gov/ct2/show/NCT02495519\u003c/p\u003e","manuscriptTitle":"Whole-Genome and Transcriptome Sequencing Identified NOTCH2 and HES1 As Potential Markers of Response to Imatinib in Desmoid Tumor (Aggressive Fibromatosis): A Phase II Trial Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-10-26 20:16:17","doi":"10.21203/rs.3.rs-993539/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"05af4dbb-dd66-4756-9e94-d9e1a042d2fd","owner":[],"postedDate":"October 26th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":8103621,"name":"General Biochemistry"}],"tags":[],"updatedAt":"2021-10-26T20:16:19+00:00","versionOfRecord":[],"versionCreatedAt":"2021-10-26 20:16:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-993539","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-993539","identity":"rs-993539","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}