Biomarker composition for diagnosing Desmoid-type fibromatosis and use thereof

Abstract

One example of the present invention relates to a desmoid-type fibromatosis (DTF) marker composition; a diagnostic composition; a diagnostic kit; a method for providing information regarding diagnosis; and a drug screening method. It has been confirmed that various biomarkers, including TWIST2 included in the present invention, exhibit high sensitivity and specificity, and in particular, can complement the limitations of the existing standard biomarker, β-catenin. Furthermore, the composition of the present invention can effectively distinguish between DTF and reactive fibrosis (RF), thereby enabling a more reliable diagnosis. Accordingly, the present invention enables early diagnosis and accurate differential diagnosis of DTF, thereby increasing clinical utility and contributing to the establishment of treatment strategies for patients.

Description

Technology Field

[0001] One example of the present invention relates to a desmoid-type fibromatosis marker composition; a diagnostic composition; a diagnostic kit; a method for providing information regarding diagnosis; and a drug screening method. Background Technology

[0002] Desmoid-type fibromatosis (DTF) is a rare, non-metastatic myofibroblastic tumor characterized by monoclonal fibroblast proliferation. DTF is also known as desmoid tumor or aggressive fibromatosis. DTF typically occurs in the abdominal wall, chest wall, mesentery, and limbs. DTF can range from asymptomatic cases to those causing significant morbidity. This is due to the invasive growth of DTF and the high risk of recurrence after surgical resection.

[0003] DTF is generally classified into two groups. Specifically, it is divided into a group associated with germline mutations in the adenomatous polyposis coli (APC) gene and a group with somatic mutations in the CTNNB1 gene encoding β-catenin. Approximately 10–15% of DTF cases are associated with conditions such as familial adenomatous polyposis (FAP) and Gardner syndrome. The majority of cases (85–90%) are associated with abnormal nuclear accumulation of β-catenin. This leads to dysregulation of the Wnt / APC / β-catenin signaling pathway and the expression of target genes (S100A4 or collagen triple helix repeat containing-1 (CTHRC1)). This sporadic form of DTF typically develops at a median age of 40 and is more prevalent in women (male:female ratio 1:2).

[0004] Although the pathogenesis of DTF is not yet fully understood, it is known to be influenced by genetic predisposition, pregnancy, hormonal factors, and physical factors (such as surgery or injury). Indeed, the incidence of trauma prior to the onset of DTF is reported to be higher compared to other soft tissue tumors. Studies suggest that DTF may arise from abnormal fibroblast activation during wound healing. This is triggered by the excessive activation of certain key regulatory circuits involved in normal tissue repair. The elevation of β-catenin levels in fibroblasts during the proliferative phase of wound healing further supports this concept. Additionally, the fact that mesenchymal stromal cells present within DTF may play a role in the initiation and localization of the disease also supports this.

[0005] Histological similarities between DTF and keloids (fibroproliferative diseases characterized by abnormal fibroblast activation) suggest that DTF may be initiated by a similar mechanism. Keloids, which develop due to excessive fibroblast activity after skin trauma, extend beyond the original site of injury, which is similar to the invasive growth of DTF. These observations led us to hypothesize that comparing the fibroblasts in DTF with those involved in physiological wound healing could reveal the key regulatory mechanisms causing the abnormal fibroblast activation observed in DTF.

[0006] The mechanism of fibroblast activation in the normal wound healing process is relatively well understood. However, the mechanism that induces abnormal fibroblast activation in fibroproliferative diseases such as DTF is still not well elucidated.

[0007] Previous transcriptome studies on DTF provided valuable datasets. However, these studies lacked adequate controls. This limited the ability to identify specific key regulators of pathological fibroblast activation in DTF.

[0008] Although β-catenin is a key diagnostic biomarker for DTF, its clinical utility is limited due to DTF cases showing negative β-catenin immunohistochemistry results and inconsistent expression within tissues. This raises concerns about false-negative results, particularly in small needle biopsies. As active surveillance increasingly replaces surgery as the primary management strategy for DTF, there is a growing need for additional and more reliable biomarkers to improve diagnostic accuracy.

[0009] To address these issues, the inventors performed comparative RNA sequencing analysis on 29 DTF and 14 reactive fibrosis (RF) tissue samples. The inventors aimed to identify the key inducing genes involved in DTF. To this end, they sought to identify network hub genes and transcription factors (TF) that are specific to DTF and responsible for the abnormal fibroblast activation induced by it. Among these DTF-specific genes, they aim to discover a novel biomarker with superior diagnostic performance compared to β-catenin, thereby suggesting the potential for improvement in the differential diagnosis and management of DTF. Prior art literature

[0010] Korean Registered Patent No. 10-2727594 The problem to be solved

[0011] Accordingly, the present invention aims to provide a desmoid-type fibromatosis marker composition comprising the TWIST2 (Twist Family BHLH Transcription Factor 2) gene or a protein encoded by said gene.

[0012] In addition, the present invention aims to provide a composition for diagnosing desmoid-type fibromatosis comprising a preparation for measuring the level of the TWIST2 (TWIST Family BHLH Transcription Factor 2) gene or the protein encoded by said gene.

[0013] In addition, the present invention aims to provide a diagnostic kit for desmoid-type fibromatosis comprising the above diagnostic composition.

[0014] In addition, the present invention aims to provide a method for providing information regarding the diagnosis of desmoid-type fibromatosis, comprising the step of measuring the level of the TWIST2 (TWIST Family BHLH Transcription Factor 2) gene or the protein encoded by said gene in a biological sample derived from a subject.

[0015] In addition, the present invention comprises (a) a step of treating a cell with a candidate substance;

[0016] (b) a step of measuring the expression level of the gene of TWIST2 (TWIST Family BHLH Transcription Factor 2) or the protein coding from the gene in the cell; and

[0017] (c) a step of selecting a substance that reduces the expression level of the TWIST2 (TWIST Family BHLH Transcription Factor 2) compared to a candidate substance untreated group as a treatment for desmoid-type fibromatosis; comprising,

[0018] The purpose is to provide a drug screening method for the treatment of desmoid-type fibromatosis. means of solving the problem

[0019] In order to achieve the above objective,

[0020] The present invention provides a desmoid-type fibromatosis marker composition comprising the TWIST2 (Twist Family BHLH Transcription Factor 2) gene or a protein encoded by said gene.

[0021] In one embodiment of the present invention, the marker composition may further comprise one or more genes selected from the group consisting of LRRC15 (Leucine Rich Repeat Containing 15), CTHRC1 (Collagen Triple Helix Repeat Containing 1), SHOX2 (SHOX Homeobox 2) and SALL4 (Spalt Like Transcription Factor 4) or a protein encoding from said genes.

[0022] In addition, the present invention provides a composition for diagnosing desmoid-type fibromatosis comprising a preparation for measuring the level of the TWIST2 (TWIST Family BHLH Transcription Factor 2) gene or the protein encoded by said gene.

[0023] In one embodiment of the present invention, the composition may further comprise one or more genes selected from the group consisting of LRRC15 (Leucine Rich Repeat Containing 15), CTHRC1 (Collagen Triple Helix Repeat Containing 1), SHOX2 (SHOX Homeobox 2) and SALL4 (Spalt Like Transcription Factor 4) or a protein encoding from said genes.

[0024] In another embodiment of the present invention, the composition may further include a preparation for measuring the level of the β-catenin gene or the protein encoded by the gene.

[0025] In addition, the present invention provides a kit for diagnosing desmoid-type fibromatosis comprising the above diagnostic composition.

[0026] In addition, the present invention provides a method for providing information regarding the diagnosis of desmoid-type fibromatosis, comprising the step of measuring the level of the TWIST2 (TWIST Family BHLH Transcription Factor 2) gene or the protein encoded by said gene in a biological sample derived from a subject.

[0027] In one embodiment of the present invention, the method may further include the step of measuring the level of one or more genes selected from the group consisting of LRRC15 (Leucine Rich Repeat Containing 15), CTHRC1 (Collagen Triple Helix Repeat Containing 1), SHOX2 (SHOX Homeobox 2) and SALL4 (Spalt Like Transcription Factor 4) or a protein encoded from said genes.

[0028] In another embodiment of the present invention, the gene level may be measured through one or more methods selected from the group consisting of next-generation sequencing (NGS), polymerase chain reaction (PCR), reverse transcription polymerase chain reaction (RT-PCR), real-time polymerase chain reaction (Real-time PCR), RNase protection assay (RPA), microarray, and northern blotting.

[0029] In another embodiment of the present invention, the protein level may be measured by one or more methods selected from the group consisting of western blotting, radioimmunoassay (RIA), radioimmunodiffusion, enzyme immunoassay (ELISA), immunoprecipitation, flow cytometry, immunofluorescence, ouchterlony, complement fixation assay, and protein chip.

[0030] In addition, the present invention comprises (a) a step of treating a cell with a candidate substance;

[0031] (b) a step of measuring the expression level of the gene of TWIST2 (TWIST Family BHLH Transcription Factor 2) or the protein coding from the gene in the cell; and

[0032] (c) a step of selecting a substance that reduces the expression level of the TWIST2 (TWIST Family BHLH Transcription Factor 2) compared to a candidate substance untreated group as a treatment for desmoid-type fibromatosis; comprising,

[0033] A drug screening method for treating desmoid-type fibromatosis is provided.

[0034] In one embodiment of the present invention, (d) the expression level of one or more genes selected from the group consisting of LRRC15 (Leucine Rich Repeat Containing 15), CTHRC1 (Collagen Triple Helix Repeat Containing 1), SHOX2 (SHOX Homeobox 2) and SALL4 (Spalt Like Transcription Factor 4) or a protein encoded by said gene may be further included compared to a candidate substance untreated group.

[0035] In another embodiment of the present invention, the method may further include the step of selecting a substance that reduces the expression level of one or more selected from the group consisting of LRRC15, CTHRC1, SHOX2 and SALL4 as a treatment for desmoid-type fibromatosis.

[0036] In another embodiment of the present invention, the cell may be a fibroblast. Effects of the invention

[0037] The present invention provides a biomarker composition for improving the diagnostic accuracy of desmoid-type fibromatosis (DTF), thereby enabling effective diagnosis of DTF. Various biomarkers included in the present invention, including TWIST2, exhibit high sensitivity and specificity, and it has been confirmed that they can particularly complement the limitations of the existing standard biomarker, β-catenin. Furthermore, the composition of the present invention can effectively distinguish between DTF and reactive fibrosis (RF), thereby enabling a more reliable diagnosis. Accordingly, the present invention enables early diagnosis and accurate differential diagnosis of DTF, thereby increasing clinical utility and contributing to the establishment of treatment strategies for patients.

[0038] The effects of the present invention are not limited to those mentioned above, and should be understood to include all effects that can be inferred from the configuration of the invention described in the detailed description or claims. Brief explanation of the drawing

[0039] Figures 1A through 1G show the identification of differentially expressed genes (DEGs) in tissues of desmoid fibromatosis (DTF) and reactive self-limiting fibrosis (RF). Figure 1A shows microresections of DTF and RF tissues performed on samples for RNA extraction. Figure 1B is a Principal Component Analysis (PCA) plot of the first two principal components, showing the separation of RNA expression profiles between the two diagnostic groups. Figure 1C indicates that pathogenic or potentially pathogenic CTNNB1 mutations were identified in 16 out of 29 DTF patients analyzed from RNA-seq data (55%, 16 / 29). Figure 1D is a DEG heatmap visualizing DEGs along with sample clustering by diagnostic group. Figure 1E is a volcano plot showing DEGs between the two groups. Figures 1F and 1G show the results of functional pathway analysis of abundant genes in the DTF (Figure 1F) and RF (Figure 1G) groups based on abundance analysis. Figures 2A through 2G show the results of identifying co-expression networks through weighted gene co-expression network analysis (WGCNA). Figure 2A shows the soft threshold power curve of the data. Figure 2B shows the results of hierarchical dendrogram clustering of co-expressed genes within the generated modules. Figure 2C shows the results of identifying statistically significant clusters among the generated modules. Figure 2D is a box plot showing the abundance of gene sets included in modules 16, 14, and 41. Figure 2E is a network heatmap plot of the genes constituting module 16 (ME16). Figure 2F is a scatter plot showing the relationship between connectivity within the module (x-axis) and DTF disease phenotype correlation (y-axis). Figure 2G shows the results of path analysis of the ME16 gene set. Figures 3A through 3D illustrate the abundance and differential expression of the ME16 gene in pathological fibroblasts. Figure 3A is a Uniform Manifold Approximation and Projection (UMAP) plot derived from scRNA-seq data, showing distinct fibroblast populations (left). Other plots, the UMAP plot and the bubble chart, show ME16 gene expression in keloid tissue and normal scar tissue. Figure 3B shows the immunohistochemical staining results of CTHRC1 and LRRC15 in keloid tissue. In each image, brown staining indicates the expression of the corresponding gene. Figure 3C is a UMAP plot derived from scRNA-seq data, visualizing distinct fibroblast populations in lung tissues classified as normal, chronic obstructive pulmonary disease, and idiopathic pulmonary fibrosis. Figure 3D shows the immunohistochemical staining results of LRRC15 in characteristic fibrotic lesions of idiopathic pulmonary fibrosis tissue. Figures 4A through 4F show the reconstruction of transcriptional network analysis for the investigation of transcription factor induction and virtual protein activity inference (VIPER) based on enriched regulator analysis. Figure 4A is a summary table of the transcriptional regulatory network. Figure 4B is a network plot visualizing the genes regulated by TWIST2 (left) and SALL4 (right), identified through transcriptional regulatory network analysis. Figure 4C is a graph showing the results of gene set enrichment analysis (GSEA), demonstrating that the target genes of TWIST2 and SALL4 show a strong correlation with DTF. Figures 4D and 4E are msVIPER plots showing the abundance of transcription factor regulators in DTF and reactive fibrosis samples. The projections of negative (repressed, blue) and positive (activated, red) targets for each transcription factor (TF) are indicated. The gene set list on the right side of each plot shows the genes activated (Figure 4D) and repressed (Figure 4E) in DTF. Figure 4F is a diagram summarizing the potential members of the key regulatory circuit in DTF and reactive fibrosis. Figures 5A and 5B show the immunohistochemical analysis of ME16 module genes and β-catenin in desmoid fibromatosis and reactive fibroblasts. Figure 5A shows the immunohistochemical staining of five ME16 module genes (TWIST2, LRRC15, CTHRC1, SHOX2, SALL4) and β-catenin in desmoid fibromatosis (DTF). Figure 5B is a box plot showing the immunohistochemical scores for each marker in DTF and reactive fibrosis samples. Figures 6A to 6D illustrate the diagnostic potential of a marker panel for desmoid fibromatosis. Figure 6A is a graph showing the results of the Receiver Operation Characteristic (ROC) analysis for the immunohistochemical scores of each marker. Figure 6B is a table summarizing the cutoff values, specificity, and sensitivity of each marker calculated through the ROC analysis. Figure 6C is a box plot showing the expression percentages of β-catenin and Twist2 in desmoid fibromatosis. Figure 6D is a table showing the expression of β-catenin and Twist2 in DTF and reactive fibrosis, with Twist2 on the horizontal axis and β-catenin on the vertical axis. Specific details for implementing the invention

[0040] The present invention will be described in detail below.

[0041] The present invention provides a desmoid-type fibromatosis marker composition comprising the TWIST2 (Twist Family BHLH Transcription Factor 2) gene or a protein encoded by said gene.

[0042] The term "TWIST2 (Twist Family BHLH Transcription Factor 2)" used in the present invention refers to a protein belonging to the basic helix-loop-helix (bHLH) transcription factor family that performs important regulatory functions in the process of cell differentiation and development, and is a transcription factor that plays an important role in sustained fibroblast activation in desmoid fibromatosis (DTF).

[0043] In the embodiments of the present invention, TWIST2 was identified as a core member of the DTF-specific gene module (ME16) and was shown to be similarly expressed in other fibrotic diseases. Immunohistochemical (IHC) analysis revealed that TWIST2 exhibited high expression in DTF, demonstrating its potential as a diagnostic biomarker with higher sensitivity and specificity than β-catenin. TWIST2 is primarily identified by nuclear staining and shows specific expression in fibroblasts. Furthermore, TWIST2 was found to regulate multiple target genes in DTF, and these target genes show a strong correlation with DTF pathology.

[0044] The term "desmoid-type fibromatosis" as used in this invention refers to a rare, non-metastatic myofibroblastic tumor characterized by the proliferation of monoclonal fibroblasts. Also known as desmoid tumor or aggressive fibromatosis, DTF is a benign but locally aggressive tumor exhibiting sustained fibroblast activation. DTF can occur in the abdominal wall, chest wall, mesentery, limbs, etc., and can manifest in varying degrees, ranging from asymptomatic to severely morbid. This is due to the invasive growth of the tumor and the high risk of recurrence after surgical resection. DTF can be divided into a group associated with germline mutations in the APC gene and a group with somatic mutations in the CTNNB1 gene, and in most cases, is associated with the abnormal nuclear accumulation of β-catenin.

[0045] In one embodiment of the present invention, the marker composition may further comprise one or more genes selected from the group consisting of LRRC15 (Leucine Rich Repeat Containing 15), CTHRC1 (Collagen Triple Helix Repeat Containing 1), SHOX2 (SHOX Homeobox 2) and SALL4 (Spalt Like Transcription Factor 4) or a protein encoding from said genes.

[0046] The term "LRRC15 (Leucine Rich Repeat Containing 15)" used in this invention refers to a transmembrane protein containing a leucine-rich repeat sequence, which is a gene that plays an important role in pathological conditions related to fibroblast activation. LRRC15 is highly expressed in various fibrotic diseases, including keloids, idiopathic pulmonary fibrosis (IPF), and cancer-associated fibroblasts (CAF), and has been identified as one of the major genes of the ME16 module that exhibits cytoplasmic staining patterns in desmoid-type fibromatosis (DTF). Immunohistochemical (IHC) analysis results showed that LRRC15 was highly expressed in DTF and is evaluated to have potential as a diagnostic marker.

[0047] The term "CTHRC1 (Collagen Triple Helix Repeat Containing 1)" used in this invention refers to a gene encoding a protein containing a collagen triple helix repeat sequence, which is associated with fibroblast activation and the development of fibrotic diseases. CTHRC1 shows high expression in various fibrotic diseases, including keloid diseases, and has been identified as one of the major genes of the ME16 module that exhibits cytoplasmic staining patterns in DTF. Furthermore, immunohistochemical analysis results indicate that CTHRC1 shows specific expression in DTF and is evaluated as having potential as a diagnostic marker.

[0048] The term "SHOX2 (SHOX Homeobox 2)" used in this invention refers to a transcription factor containing a homeobox domain, which is a gene that performs important regulatory functions in the process of cell differentiation and development. SHOX2 has been identified as one of the major genes of the ME16 module, which exhibits high expression in DTF and displays nuclear staining patterns. Furthermore, immunohistochemical analysis results indicate that SHOX2 can be usefully employed to distinguish between DTF and reactive fibrosis and is evaluated to have potential as a diagnostic marker for DTF.

[0049] The term "SALL4 (Spalt-Like Transcription Factor 4)" used in this invention refers to a transcription factor involved in the regulation of embryonic development and cell differentiation, and signifies a gene that regulates the expression of various target genes. SALL4 has been identified as one of the major genes of the ME16 module that exhibits nuclear staining patterns in DTF and is known to play an important role in the progression of the disease. Furthermore, immunohistochemical analysis results show that SALL4 exhibits high expression in DTF, indicating potential as a diagnostic marker.

[0051] In addition, the present invention provides a composition for diagnosing desmoid-type fibromatosis comprising a preparation for measuring the level of the TWIST2 (TWIST Family BHLH Transcription Factor 2) gene or the protein encoded by said gene.

[0052] The term "diagnosis" as used in the present invention, in a broad sense, means determining the actual condition of a patient's disease in all aspects. The content of the determination includes the name of the disease, the etiology, the type of disease, the severity, the detailed aspects of the disease, and the presence or absence of complications. In the present invention, diagnosis is determining whether desmoid fibromatosis has developed and the level of its progression.

[0053] The above preparation may be one or more selected from the group consisting of antibodies, oligopeptides, ligands, PNA (peptide nucleic acid), and aptamers that specifically bind to the TWIST2 (TWIST Family BHLH Transcription Factor 2) gene or the protein encoded by the said gene.

[0054] In one embodiment of the present invention, the composition may further comprise one or more genes selected from the group consisting of LRRC15 (Leucine Rich Repeat Containing 15), CTHRC1 (Collagen Triple Helix Repeat Containing 1), SHOX2 (SHOX Homeobox 2) and SALL4 (Spalt Like Transcription Factor 4) or a protein encoding from said genes.

[0055] In another embodiment of the present invention, the composition may further include a preparation for measuring the level of the β-catenin gene or the protein encoded by the gene.

[0056] The term "β-catenin" as used in this invention refers to a protein that serves as a key regulator of the Wnt / APC / β-catenin signaling pathway and plays an important role in intercellular junctions and transcriptional regulation. β-catenin is known as a major diagnostic biomarker for desmoid-type fibromatosis (DTF), and abnormal nuclear accumulation is observed in approximately 85–90% of DTF cases. This accumulation occurs due to dysregulation of the Wnt signaling pathway and leads to changes in target gene expression.

[0058] In addition, the present invention provides a kit for diagnosing desmoid-type fibromatosis comprising the above diagnostic composition.

[0059] The predictive kit of the present invention consists of one or more different component compositions, solutions, or devices suitable for the analysis method.

[0060] For example, the kit of the present invention may be a kit comprising genomic DNA derived from a sample to be analyzed, a primer set specific to the marker gene of the present invention, an appropriate amount of DNA polymerase, a dNTP mixture, a PCR buffer solution, and water to perform PCR. The PCR buffer solution may contain KCl, Tris-HCl, and MgCl2. In addition, components necessary for performing electrophoresis to verify whether the PCR product has been amplified may be additionally included in the kit of the present invention.

[0061] In addition, the kit of the present invention may be a kit containing essential elements necessary to perform RT-PCR. In addition to each primer pair specific to a marker gene, the RT-PCR kit may include a test tube or other suitable container, reaction buffer, deoxynucleotides (dNTPs), enzymes such as Taq-polymerase and reverse transcriptase, DNase, RNase inhibitors, DEPC-water, sterile water, etc. It may also include a primer pair specific to a gene used as a quantitative control.

[0062] In addition, the kit of the present invention may be a kit comprising essential elements necessary for performing DNA chip operations. The DNA chip kit comprises a substrate to which cDNA corresponding to a gene or a fragment thereof is attached as a probe, and the substrate may comprise cDNA corresponding to a gene or a fragment thereof. In addition, the kit of the present invention may be in the form of a microarray having a substrate on which the marker gene of the present invention is immobilized.

[0063] In addition, the kit of the present invention may be a kit characterized by including essential elements necessary for performing an ELISA. The ELISA kit includes an antibody specific to a marker protein and a preparation for measuring said protein level. The ELISA kit may include a reagent capable of detecting an antibody that has formed an "antigen-antibody complex," such as a labeled secondary antibody, chromophores, an enzyme, and a substrate thereof. In addition, it may include an antibody specific to a quantitative control protein.

[0065] In addition, the present invention provides a method for providing information regarding the diagnosis of desmoid-type fibromatosis, comprising the step of measuring the level of the TWIST2 (TWIST Family BHLH Transcription Factor 2) gene or the protein encoded by said gene in a biological sample derived from a subject.

[0066] In this specification, the term "subject" refers to all living organisms, including humans, rats, mice, livestock, etc. As a specific example, it may be mammals, including humans.

[0067] The term "method for providing information regarding the diagnosis of desmoid-type fibromatosis" used in the present invention refers to providing objective basic information necessary for the diagnosis of desmoid-type fibromatosis as a preliminary step for diagnosis, and excludes the clinical judgment or opinion of a physician.

[0068] The biological sample derived from the above subject may include tissue, cells, whole blood, blood, saliva, sputum, tears, cerebrospinal fluid, and urine, and preferably may be fibrotic tissue, but is not limited thereto.

[0069] In one embodiment of the present invention, the method may further include the step of measuring the level of one or more genes selected from the group consisting of LRRC15 (Leucine Rich Repeat Containing 15), CTHRC1 (Collagen Triple Helix Repeat Containing 1), SHOX2 (SHOX Homeobox 2) and SALL4 (Spalt Like Transcription Factor 4) or a protein encoded from said genes.

[0070] In another embodiment of the present invention, the gene level may be measured through one or more methods selected from the group consisting of next-generation sequencing (NGS), polymerase chain reaction (PCR), reverse transcription polymerase chain reaction (RT-PCR), real-time polymerase chain reaction (Real-time PCR), RNase protection assay (RPA), microarray, and northern blotting.

[0071] In another embodiment of the present invention, the protein level may be measured by one or more methods selected from the group consisting of western blotting, radioimmunoassay (RIA), radioimmunodiffusion, enzyme immunoassay (ELISA), immunoprecipitation, flow cytometry, immunofluorescence, ouchterlony, complement fixation assay, and protein chip.

[0073] In addition, the present invention comprises (a) a step of treating a cell with a candidate substance;

[0074] (b) a step of measuring the expression level of the gene of TWIST2 (TWIST Family BHLH Transcription Factor 2) or the protein coding from the gene in the cell; and

[0075] (c) a step of selecting a substance that reduces the expression level of the TWIST2 (TWIST Family BHLH Transcription Factor 2) compared to a candidate substance untreated group as a treatment for desmoid-type fibromatosis; comprising,

[0076] A drug screening method for treating desmoid-type fibromatosis is provided.

[0077] In this specification, "treatment" may mean any act in which the symptoms of desmoid-type fibromatosis in an individual are improved or beneficially altered by the administration of a pharmaceutical composition according to one aspect.

[0078] In one embodiment of the present invention, (d) the expression level of one or more genes selected from the group consisting of LRRC15 (Leucine Rich Repeat Containing 15), CTHRC1 (Collagen Triple Helix Repeat Containing 1), SHOX2 (SHOX Homeobox 2) and SALL4 (Spalt Like Transcription Factor 4) or a protein encoded by said gene may be further included compared to a candidate substance untreated group.

[0079] In another embodiment of the present invention, the method may further include the step of selecting a substance that reduces the expression level of one or more selected from the group consisting of LRRC15, CTHRC1, SHOX2 and SALL4 as a treatment for desmoid-type fibromatosis.

[0080] In another embodiment of the present invention, the cell may be a fibroblast.

[0081] Examples

[0082] 1. Experimental Materials and Methods

[0083] (1) FFPE tissue and paraffin collection

[0084] A total of 43 formalin-fixed paraffin-embedded (FFPE) tissue specimens, consisting of DTF (29) and fibrotic tissue (14) samples, were collected for RNA sequencing (RNA-seq). All FFPE tissue blocks were treated with neutral buffered formalin and embedded in paraffin during routine medical procedures at the Department of Pathology, Samsung Seoul Hospital. All patients participating in the study signed informed consent forms, and the study protocol adhered to the ethical guidelines of the 1975 Declaration of Helsinki (6th edition, 2008). This study was reviewed and approved by the Institutional Review Board of Samsung Seoul Hospital (Samsung Medical Center 2022-11-100-004). The inclusion criteria for case selection are as follows: (1) DTF and RF cases with a confirmed diagnosis through histological and clinical evaluation, (2) RF cases with a clear clinical association with previous injury (internal or external) and not showing excessive growth, and (3) cases with clear boundaries and a minimum size of 0.5 cm 2 The criteria for exclusion from case selection are: (1) cases where the diagnosis is not confirmed histologically or clinically, (2) cases where the lesion is located outside the abdominal region (e.g., limbs), (3) RF cases where there is no clear association between fibrosis and prior injury, (4) cases where the FFPE block is more than 3 years old, and (5) RNA samples with a DV200 value of less than 70%. These criteria were established to ensure the accuracy and reliability of the cases selected for this study.

[0086] (2) RNA extraction and library preparation for RNA sequencing

[0087] Total RNA was extracted from samples using the RNeasy FFPE kit (73504, Qiagen) according to the manufacturer's instructions. A total of 100 ng of total RNA was used to build a sequencing library using the SureSelectXT RNA Direct kit (Agilent) according to the manufacturer's protocol. For the capture of human exon regions, the SureSelect Human All Exon V6+UTR kit (Agilent) was used according to the standard Agilent SureSelect targeted enrichment protocol.

[0089] (3) Differential expression analysis

[0090] (3-1) Data Processing and Analysis

[0091] Paired-end sequencing reads were generated on the NovaSeq sequencing platform (Illumina Inc). Before starting the analysis, adapter sequences were removed and low-quality bases were trimmed using Trimmomatic v0.38. The purified reads were aligned to Homo sapiens (GRCh37) using Hierarchical Indexing Transcriptome Splice Alignment (HISAT) v2.1.0, based on HISAT and Bowtie2 implementations. Reference genome sequences and gene annotation data were downloaded from the National Center for Biotechnology Information (NCBI) Genome Assembly and NCBI RefSeq databases, respectively. The aligned data (SAM file format) was aligned and indexed using SAMtools v1.9. After alignment, transcriptomes were assembled and quantified using StringTie v2.1.3b. Quantification at the gene and transcriptome levels was calculated as the number of raw reads, the number of transcriptome fragments per kilobase per million mapped reads, and the number of transcriptomes per million. Differential expression analysis was performed using the R programming language (version 4.3.3) and the Bioconductor package DESeq2 (version 1.42.1). The DESeqDataSet was generated from count data, and size factors were estimated and normalized using DESeq2 built-in functions. All original sequencing data are publicly available on the NCBI Gene Expression Omnibus (https: / / www.ncbi.nlm.nih.gov / geo / ). StringTie v2.1.3b was used. Quantification at the gene and transcriptome levels was calculated as raw read counts, transcriptomes per million reads, fragments per kilobase, and transcriptomes per million. Differential expression analysis was performed using the R programming language (version 4.3.3) and the Bioconductor package DESeq2 (version 1.42.1). It was generated from count data, and size factors were estimated and normalized using DESeq2 built-in functions.All original sequencing data is from the NCBI Gene Expression Omnibus. ( It is publicly available at https: / / www.ncbi.nlm.nih.gov / geo / .

[0093] (4) Weighted gene co-expression network analysis

[0094] The analysis of Module 16 (ME16) using the weighted gene co-expression network analysis (WGCNA) package (version 1.72-5) involved several key steps and utilized core R packages for data manipulation, visualization, and statistical analysis. Initially, raw RNA-seq data was loaded and preprocessed using base R and functions from the DESeq2 (version 1.42.1) package. Subsequently, the WGCNA (version 1.72-5) package was used for network construction, module identification, and analysis of module-gene relationships. Key packages used throughout the process included WGCNA (version 1.72-5) for network construction and module identification, limma (version 3.58.1) for statistical analysis, ggplot2 (version 3.5.0) for data visualization, dplyr (version 1.1.4) for data manipulation, and ComplexHeatmap (version 2.18.0) for heatmap generation. These packages enabled the comprehensive exploration and interpretation of Module ME16, helping to elucidate its biological relevance and identify key genes associated with tissue differences. All source code used to analyze RNA-seq data and generate graphs is available in the public repository on GitHub (https: / / github.com / caf-lab / desmoid_RNAseq).

[0096] (5) Network analysis of RNA-seq data: Use of transcriptional regulatory network reconstruction and virtual protein activity inference through abundance analysis

[0097] For the analysis, the R package (version 4.3.3) was utilized for Transcriptional Network Reconstruction (RTN) (version 2.26.0) and Virtual Protein Activity Inference via Enrichment Analysis (VIPER; version 1.36.0). In the RTN analysis, transcriptional network inference was performed using the "Arkouda package for large-scale graph analysis," followed by transcriptional network analysis including differential expression analysis and gene set enrichment analysis (GSEA). The VIPER analysis involved generating network files and performing standard and bootstrap analyses using msVIPER to infer protein activity and identify enrichment patterns. All source code used to analyze RNA-seq data and generate graphs is available in the public repository on GitHub (https: / / github.com / caf-lab / desmoid_RNAseq).

[0099] (6) Tissue samples and immunohistochemical staining

[0100] Tissue samples were prepared from patients with four conditions: DTF (n = 84), fibrosis (n = 57), lung cancer, and keloid. This was done with the approval of the Institutional Review Board of Samsung Medical Center (Seoul, Korea) (Samsung Medical Center 2022-11-100-004). Tissue microarrays were fabricated, and immunohistochemical staining was performed.

[0102] (7) Immunohistochemical scoring

[0103] Immunohistochemical scores for each protein expression were semi-quantitatively measured based on staining intensity and the positive rate. Intensity scores of 1, 2, and 3 were assigned to weak, medium, and strong intensities of nuclear or cytoplasmic staining, respectively. The intensity was then multiplied by the percentage of positive cells (0% to 100%). The maximum achievable score is 300 points.

[0105] (8) Statistical analysis

[0106] Immunohistochemistry (IHC) scores for six markers (β-catenin, CTHRC1, LRRC15, SALL4, SHOX2, TWIST2) were analyzed using R (version 4.3.3). Receiver Operation Characteristic (ROC) curves and Area Under the Curve (AUC) were constructed to evaluate the diagnostic value of each marker. Optimal cutoff points for each marker were identified to provide guidelines for IHC scoring in a clinical setting. The source code for all statistical analyses used to analyze IHC scores and generate graphs is available in the public repository on GitHub (https: / / github.com / caf-lab / desmoid_RNAseq).

[0108] Experimental results

[0109] (1) Identifying the pathway distinguishing desmoid fibromatosis from reactive fibrosis through gene expression and mutation analysis

[0110] To investigate the regulatory mechanisms underlying the abnormal activation of fibroblast DTF, the transcriptome profiles of RF and DTF were compared. RNA-seq was performed on 29 DTF and 14 RF tissue samples. The RF samples exhibit intra-abdominal fibrosis caused by previous surgery or hernia (Table 1). The clinical characteristics of these samples are summarized in Table 1. After isolating the DTF and RF regions using microdissection, mRNA extraction and RNA-seq analysis were performed (Figure 1A).

[0111] Principal component analysis of the RNA-seq data revealed distinct clustering of the samples. Based on gene expression profiles, DTF and RF were clearly distinguished (Fig. 1B). This clear distinction supports the validity of the dataset for further comparative analysis and confirms that DTF and RF represent fundamentally different transcriptome environments.

[0112] As a result of mutation detection from the data, CTNNB1 mutations were found in 21 of the DTF cases (21 / 29, 72.4%). All of these were missense point mutations, and one case showed two hits. In contrast, no CTNNB1 mutations were detected in the RF group (0 / 14, 0%) (Fig. 1C).

[0113] Then, the transcriptome profiles of the two groups were compared using the DESeq2 R package, and 4,267 differentially expressed genes (DEGs) were identified. Of these, 4,021 were upregulated in the DTF and 246 were downregulated. These DEGs were visually represented by a heatmap (Fig. 1D) and a volcano plot (Fig. 1E).

[0114] To understand the biological significance of DEGs between DTF and RF, over-representation analysis (ORA) and gene set enrichment analysis (GSEA) were performed. The ORA results showed that DEGs upregulated in DTF were primarily involved in processes such as striated muscle contraction, embryonic limb morphogenesis, Wnt / β signaling pathways, and chemical synaptic transmission (Fig. 1F). In contrast, RF-specific DEGs were found to be abundant in pathways related to wound healing, inflammation, and immune responses, such as macrophage chemotaxis, complement activation, and regulation of apoptotic cell removal (Fig. 1G). GSEA supported these findings and highlighted the abundance of regulatory T cell (Treg)-related pathways in DTF. These findings led the inventors to investigate the cellular composition of DTF and RF tissues in more detail using xCell and CYBERSORT deconvolution methods. Both analyses showed higher abundance of macrophages and dendritic cells in DTF. Additionally, xCell analysis suggested a potential increase in Th2 and Treg cells in DTF. However, further verification through flow cytometry is required to confirm these findings.

[0115] In summary, the inventors identified distinct gene expression patterns and CTNNB1 mutation profiles between DTF and RF, providing new insights into the molecular mechanisms underlying fibroblast activation in DTF.

[0117] (2) Identification of key regulatory gene networks driving sustained fibroblast activation in desmoid fibromatosis through weighted gene co-expression network analysis

[0118] After confirming DEGs and their functional significance in DTF compared to RF, we next aimed to elucidate the key regulatory gene networks driving sustained fibroblast activation in DTF. While DEG-based analysis provides valuable information, it may fail to identify critical genes driving the disease process because high expression levels do not always correlate with biological importance. Genes playing important regulatory roles may not exhibit dramatic changes in expression, but they can remain central by influencing the activity of other genes within the broader network.

[0119] To address this, we used WGCNA, a powerful method for clustering genes into network modules based on co-expression patterns. This approach enables the identification of highly connected network hub genes within modules, which are more likely to be central regulators of key biological processes. By analyzing gene connectivity within these networks, WGCNA provides a more comprehensive view of the regulatory mechanisms driving sustained fibroblast activation in DTF and can potentially reveal new therapeutic targets.

[0120] To begin the above analysis, a scale-free network, a key step of WGCNA, was constructed. A soft threshold power of 12 was selected to ensure network stability (Fig. 2A). Subsequent hierarchical clustering yielded 41 unique gene network modules (Fig. 2B). Then, for each module, differential expression of constituent genes between DTF and RF was evaluated, and Bayesian statistics were used to assess the expression ratios of the constituent genes and B-statistics representing the reliability of differential expression (Fig. 2C).

[0121] Among the identified gene modules, ME16 was found to be most closely associated with DTF, whereas, in contrast, Module 41 was more closely associated with RF characteristics. Box plots showing the abundance of module-specific genes in individual samples further confirmed that ME16 and ME14 were strongly associated with DTF, and Module 41 was strongly associated with RF (Fig. 2D). ME16 consisted of 120 genes, 90 of which showed significant upregulation in DTF, including FGFR2, LRRC15, and TWIST2 (Fig. 2E).

[0122] To investigate network hub genes within ME16 in more detail, the correlation with DTF phenotypes and the connectivity within the module between ME16 constituent genes were analyzed (Fig. 2F). Potential hub genes identified include TWIST2, SALL4, and FGFR2.

[0123] To better understand the functional significance of ME16, ORA was performed on ME16-positive genes, a set of 90 genes that showed a positive correlation with DTF. These genes were abundant primarily in biological processes such as embryonic organ morphogenesis, myofibroblast differentiation, and Wnt signaling pathways, as well as in pathological conditions such as idiopathic pulmonary fibrosis (IPF) and cancer-associated fibroblasts (CAF). Conversely, 30 ME16-negative genes were abundant in immune-related processes such as immune response enhancement, complement system activation, and apoptosis promotion. These findings are consistent with the initial ORA and GSEA results obtained from the overall DEG analysis between DTF and RF (Fig. 2G).

[0125] (3) ME16 Gene Module: A Universal Driver of Fibroblastic Pathology in Fibrotic Diseases – Insights from Keloids, Idiopathic Pulmonary Fibrosis, and Cancer-Related Fibroblasts

[0126] Considering the central role of sustained fibroblast activation in various fibrotic diseases, we hypothesized that ME16, a key gene network module responsible for sustained fibroblast activation in DTF, may also play an important role in other pathological fibroproliferative diseases such as keloid disease.

[0127] To verify this hypothesis, we utilized single-cell RNA-seq data (GSE163973) from Deng et al. to analyze whether ME16 genes are similarly abundant in pathological fibroblasts associated with keloid disease. This data identified several fibroblast subtypes in keloid and general scar tissues, among which the "mesenchymal fibroblast" subtype was abundant in keloid tissue. The analysis supported the hypothesis by confirming significant abundance of ME16 genes within the keloid-associated fibroblast subpopulation (Fig. 3A). Furthermore, pathway analysis revealed that fibroblasts expressing ME16 exhibited upregulation of genes related to IPF, WNT signaling, myofibroblast activation, and collagen deposition. Immunohistochemical analysis further supported these findings, showing high expression of key ME16 genes, such as LRRC15 and CTHRC1, in keloid fibroblasts (Fig. 3B).

[0128] The inventors extended the above analysis to IPF fibroblasts using single-cell RNA-seq data (GSE136831). ME16 gene expression was specific to IPF fibroblasts, but was not observed in healthy lung tissue or fibroblasts of chronic obstructive pulmonary disease (Fig. 3C). A specific subgroup of IPF fibroblasts with high ME16 expression exhibited gene expression patterns indicating activated Wnt signaling, IPF pathogenesis, and extracellular matrix deposition. Immunohistochemical staining revealed strong and highly specific expression of the major ME16 gene, LRRC15, within fibrotic lesions of IPF tissue, suggesting that these ME16-expressing fibroblasts contribute to IPF progression (Fig. 3D).

[0129] To investigate ME16 gene expression in various fibroblast populations in more detail, we utilized a comprehensive fibroblast single-cell RNA-seq database (Buechler et al.; https: / / www.fibroXplorer.com / ). In the comprehensive analysis, ME16 genes were absent in fibroblasts of healthy tissue but were notably abundant in specific fibroblast subpopulations with high LRRC15 expression. Notably, Buechler et al. identified that these LRRC15-high-expression fibroblast subpopulations are primarily associated with chronic fibrosis and CAF, which further supports the role of ME16 in pathological fibroblast activity.

[0130] Collectively, these findings emphasize that ME16 genes are important not only in DTF but also in various pathological fibroblastic conditions including keloid disease, IPF, and CAF. This suggests that ME16 may play a universal role as a driver of fibroblastic pathology in fibrotic diseases.

[0132] (4) Identification of key transcription factors driving fibroblast activation in desmoid fibromatosis through transcriptional regulatory network analysis

[0133] Although the identification of the DTF-specific gene module ME16 via WGCNA provided valuable insights into co-expressed gene networks, WGCNA does not elucidate the direct regulatory relationship between transcription factors (TFs) and their target genes. To further elucidate the mechanisms driving sustained fibroblast activation in DTFs, the inventors applied a transcriptional regulatory network (TRN) analysis focused on the reconstruction of TF-target interactions. Unlike the gene co-expression emphasis of WGCNA, TRN analysis enables the more targeted identification of transcriptional regulators that regulate pathological gene expression in DTFs.

[0134] To reconstruct TRN from RNA-seq data, the RTN R package was used. This revealed a comprehensive network containing 1,395 TFs, 12,294 target genes, and 51,170 TF-target interactions across DTF and RF samples (Fig. 4A). In particular, TWIST2 was found to positively regulate 24 targets and negatively regulate 14, while SALL4 was found to positively regulate 49 targets and negatively regulate 16 (Fig. 4B). GSEA showed that the target genes of TWIST2 and SALL4 were strongly correlated with DTF pathology (Fig. 4C), highlighting their potential regulatory role in disease progression.

[0135] Based on TF-target interaction data generated by RTN, we inferred the activity of TF proteins using the VIPER R package. This package estimates TF activity based on the expression levels of target genes. This analysis revealed that 20 TFs, including TWIST2 and SALL4, exhibited significantly higher activity in DTFs compared to RFs (P < 0.05, Fig. 4D). Conversely, TFs such as KLF4 and EBF3 showed dominant activity in RFs (Fig. 4E).

[0136] Interestingly, seven TFs—ZNF536, IRX5, TWIST2, NKD2, PAX9, SHOX2, and SALL4—showed higher TF activity in DTF and were simultaneously members of the DTF-specific ME16 module identified by WGCNA (Fig. 4F). This convergence suggests that these TFs may constitute a key transcriptional circuit important for the pathogenesis of DTF.

[0138] (5) Evaluation of Module 16 Gene as Diagnostic Biomarker in Desmoid Fibromatosis: Diagnostic Potential of TWIST2 Over β-catenin

[0139] To evaluate the potential of ME16 module genes as diagnostic biomarkers for DTF, IHC staining was performed on tissue samples from 82 DTF patients and 57 RF patients. The expression of five major ME16 genes (TWIST2, LRRC15, CTHRC1, SHOX2, SALL4) was evaluated along with β-catenin, the current gold standard biomarker for DTF diagnosis.

[0140] IHC analysis revealed that TWIST2, SHOX2, SALL4, and β-catenin were predominantly nuclear-stained, while LRRC15 and CTHRC1 exhibited cytoplasmic staining (Fig. 5A). All six proteins showed significantly higher expression in DTF samples compared to RF samples, while β-catenin was completely absent in RF samples (Fig. 5B). ScRNA-seq analysis of normal and disturbed colorectal tissues showed minimal expression of TWIST2, SHOX2, CTHRC1, LRRC15, and SALL4 in vascular and immune cells, suggesting a notable association with fibroblasts. In particular, TWIST2 and CTHRC1 showed higher specificity for fibroblasts, while CTNNB1 (β-catenin) was broadly expressed across various cell types, showing particularly high levels in vascular cells.

[0141] The diagnostic accuracy of each marker was further evaluated using receiver manipulation characteristic analysis. TWIST2 and LRRC15 demonstrated superior performance to β-catenin with AUC values ​​of 0.990 and 0.930, respectively, proving their potential as highly accurate diagnostic markers. SALL4, SHOX2, and CTHRC1 also showed strong predictive performance with AUC values ​​exceeding 0.810 (Fig. 6A). In comparison, β-catenin had an intermediate AUC of 0.741.

[0142] The optimal cutoff values ​​for these markers were calculated to maximize sensitivity and specificity. β-catenin achieved perfect specificity (1.00), but its sensitivity (0.482) was limited, highlighting the need for additional markers to complement diagnostic capabilities. TWIST2 emerged as a strong candidate with high sensitivity (0.976) and specificity (0.965) at a cutoff value of 22.5 / 300 (Fig. 6B).

[0143] A major limitation of β-catenin as a biomarker is its sporadic expression. In half of the DTF cases, less than 10% of tumor cells showed positive staining for β-catenin, increasing the risk of false-negative results, particularly in core needle biopsies (Fig. 6C). In contrast, TWIST2 staining was widespread in the majority of tumor cells in DTF, making it more reliable for needle biopsy-based diagnosis.

[0144] The combined application of β-catenin and TWIST2 staining yielded a sensitivity of 0.976 and a specificity of 1.000 in distinguishing DTF from RF (Fig. 6D). These findings strongly suggest that TWIST2 can be used as a diagnostic marker, particularly when β-catenin expression is limited.

Claims

Claim 1 A desmoid-type fibromatosis marker composition comprising the TWIST2 (Twist Family BHLH Transcription Factor 2) gene or a protein encoded by said gene. Claim 2 A composition according to claim 1, wherein the marker composition further comprises one or more genes selected from the group consisting of LRRC15 (Leucine Rich Repeat Containing 15), CTHRC1 (Collagen Triple Helix Repeat Containing 1), SHOX2 (SHOX Homeobox 2) and SALL4 (Spalt Like Transcription Factor 4) or a protein encoding said gene. Claim 3 A composition for diagnosing desmoid-type fibromatosis comprising a preparation for measuring the level of the TWIST2 (TWIST Family BHLH Transcription Factor 2) gene or the protein encoded by said gene. Claim 4 A composition according to claim 3, characterized in that the composition further comprises one or more genes selected from the group consisting of LRRC15 (Leucine Rich Repeat Containing 15), CTHRC1 (Collagen Triple Helix Repeat Containing 1), SHOX2 (SHOX Homeobox 2) and SALL4 (Spalt Like Transcription Factor 4) or a protein encoding said gene. Claim 5 A composition according to claim 3, characterized in that the composition further comprises a preparation for measuring the level of the β-catenin gene or the protein encoded by said gene. Claim 6 A kit for diagnosing desmoid-type fibromatosis comprising a composition of any one of claims 3 to 5. Claim 7 A method for providing information regarding the diagnosis of desmoid-type fibromatosis, comprising the step of measuring the level of the TWIST2 (TWIST Family BHLH Transcription Factor 2) gene or the protein encoded by said gene in a biological sample derived from a subject. Claim 8 A method for providing information according to claim 7, further comprising the step of measuring the level of one or more genes selected from the group consisting of LRRC15 (Leucine Rich Repeat Containing 15), CTHRC1 (Collagen Triple Helix Repeat Containing 1), SHOX2 (SHOX Homeobox 2) and SALL4 (Spalt Like Transcription Factor 4) or a protein encoding said gene. Claim 9 A method for providing information according to claim 7, wherein the gene level is measured through one or more methods selected from the group consisting of next-generation sequencing (NGS), polymerase chain reaction (PCR), reverse transcription polymerase chain reaction (RT-PCR), real-time polymerase chain reaction (Real-time PCR), RNase protection assay (RPA), microarray, and northern blotting. Claim 10 A method for providing information according to claim 7, wherein the protein level is measured through one or more methods selected from the group consisting of western blotting, radioimmunoassay (RIA), radioimmunodiffusion, enzyme immunoassay (ELISA), immunoprecipitation, flow cytometry, immunofluorescence, ouchterlony, complement fixation assay, and protein chip. Claim 11 A method for screening drugs for the treatment of desmoid-type fibromatosis, comprising: (a) a step of treating cells with a candidate substance; (b) a step of measuring the expression level of the gene of TWIST2 (TWIST Family BHLH Transcription Factor 2) or a protein coding from said gene in said cells; and (c) a step of selecting a substance that reduces the expression level of said TWIST2 (TWIST Family BHLH Transcription Factor 2) compared to a group not treated with the candidate substance as a treatment for desmoid-type fibromatosis. Claim 12 A drug screening method according to claim 11, further comprising the step of (d) measuring the expression level of one or more genes selected from the group consisting of LRRC15 (Leucine Rich Repeat Containing 15), CTHRC1 (Collagen Triple Helix Repeat Containing 1), SHOX2 (SHOX Homeobox 2) and SALL4 (Spalt Like Transcription Factor 4) or a protein coding from said genes compared to a candidate substance untreated group. Claim 13 A drug screening method according to claim 11, further comprising the step of screening a substance that reduces the expression level of one or more selected from the group consisting of LRRC15, CTHRC1, SHOX2 and SALL4 as a treatment for desmoid-type fibromatosis. Claim 14 A screening method according to claim 11, characterized in that the cell is a fibroblast.

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