Clinical Prognostic Factors and Integrated Multi-Omics Studies Identify Potential Novel Therapeutic Targets for Pediatric Desmoid Tumor

PDT Is Highly Recurrent and Critical Clinical Prognostic Factors Affect the Prognosis

Given the high recurrence of PDTs, we concluded the clinical factors of 98 cases, including age at presentation, gender, tumor size, tumor location, and relation with important vessels or nerves. To understand the clinical prognostic factors, we analyzed the associations between these clinical data and tumor recurrence. Table 1 shows the detailed clinical data of all patients. 32 of 98 cases are primary cases (32.6%) and 66 are recurrent cases (67.4%); of which 64 are boys and 34 are girls. In gender, there was no obviously statistically significance for the recurrence rate (p = 0.062). Kaplan-Meier method for RFS showed that male patients were of higher recurrent possibility. For the age at tumor presentation, we divided three groups with 0 to 5 years old (44, 44.9%), 6 to 10 (29, 29.6%), and over 10 years old (25, 25.5%). There was a familiar recurrence rate between two lower age groups and were of higher recurrence rates than the over 10 years of age group. RFS analysis for different age groups showed that lower age tended to tumor local recurrence. According to tumor location, we divided three groups: Buttock, Extremity and Trunk. The most frequent location is the buttock (50%) and has the highest recurrence rate (79.6%, P = 0.000). Tumor at the trunk has the lowest recurrence rate (42.1%). RFS analysis for tumor location showed the same results that the tumor at the buttock has the highest local recurrence tendency. Tumor size is a very important for tumor prognosis. The larger size of the tumor, the higher the recurrence rate observed (P = 0.000/0.002). RFS showed that the largest size group had the highest recurrence tendency. We divided them into two groups according to the connection of the tumor distance to the nerves or vessels. We found that tumors close to the nerves or vessels had a higher recurrence rate than the rest patients (P = 0.000). Moreover, cox regression analysis was used to evaluate clinical prognostic factors with univariate and multivariate analyses (Table 2). The independent prognostic factors included gender, age, tumor size and adjacent to vital nerves or vessels.

Table 2 Univariate and multivariate analysis of the effect of covariates of RFS for 66 patientsWhole Exome Sequencing (WES) Identified the Critical Mutations in PDTs

To understand the genomic alteration basis of pediatric desmoid tumors, we conducted WES for nine paired frozen pediatric tumor tissue samples. More than 10G raw data was sequenced for each tumor or matched normal samples. The efficiency (%) was more than 98% and the error (%) was less than 0.05% for all samples. Figure 1 shows the variant classification results. There was a median of around 13 variants per sample. The predominant mutation type was a missense single nucleotide polymorphism (SNP). In terms of the mutational spectrum, a predominance of C > T transversions was seen. The top ten most commonly mutated genes were CTNNB1 (100%, p.T41A and p.S45F), MUC4 (44%, p.T3775T, p.S3450S, etc.), NBPF14 (33%), PER3, MUC12, KCNJ12, FRG2C, KRTAP9–1(22%), KRT18, LOC105369274 (11%). All our samples were detected with CTNNB1 mutation at either chr3–41,266,137 C > T (66.67%) or chr3–41,266,124 A > G (33.33%). The IHC staining for β-catenin in different cases of PDTs with different magnifications also showed strong nuclear staining of β-catenin (Fig. 2G).

Fig. 1figure 1

The critical clinical prognostic factors affect the prognosis of PDTs. A-B The representative gross morphology examination of complete resection PDTs. C-D The microscopic examination of tissue sections with Hematoxylin and eosin (H&E) stains under different magnifications. E The overall recurrence survival and various critical clinical prognostic factors (age, gender, tumor location, tumor size and adjacent to nerve and/or vessel) affect the prognosis of PDTs

Fig. 2figure 2

Whole Exome Sequencing (WES) identified the critical mutations in PDTs and Immunohistochemistry (IHC) analysis of β-catenin. A A summary of the number of mutations from indicated variant classifications in nine patients. B A summary of the number of mutations from indicated variant types in nine patients. C A summary of the number of base-transition in nine patients. D Comparison of the number of variants among nine patients. Red and green bars represent missense and nonsense mutation, respectively. E Comparison of the number of variants between indicated variant classifications across nine patients. F A summary of the number of variants in indicated genes. The percentage of indicated genes observed with at least one mutation in nine patients was labeled on the right of the bar. G The representative images of IHC staining for β-catenin in different cases with different magnifications

RNA Sequencing (RNAseq) Identified the Dysregulated Genes in PDTs

To identify the significantly dysregulated genes in desmoid tumors, we conducted the RNAseq analysis in nine paired pediatric desmoid tumor tissues. More than 6G raw data was sequenced for each tumor or matched normal samples. The effective (%) was more than 98% and the error (%) was less than 0.05% for all samples. The Principal components analysis (PCA) is a common unsupervised method for the analysis of gene expression data, which clearly showed the tumor was different from the matched normal samples (Fig. 3A). We identified around 600 genes that are significantly dysregulated in tumors compared to matched normal samples with cut-off 10 fold and P less than 0.05 (Fig. 3B). Among them, 47 genes are classed as the FDA approved drug targets, which may be potential therapeutic targets for PDTs such as MMP11 and FGFR2 (Fig. 3C and D). Since wnt/β-catenin signaling pathways are critical for PDTs, we further analyzed and observed that around 46 Wnt/β-catenin signaling pathway-related genes significantly dysregulated in PDTs, including CTNNB1 and PRKCA (Fig. 3E and F). These data suggested that a panel of dysregulated genes may contribute to the development of PDT and are potential therapeutic targets.

Fig. 3figure 3

RNA sequencing (RNAseq) identified the dysregulated genes in PDTs. A The Principal components analysis (PCA) analysis showed the tumor was different from the matched normal samples. B The heat map showed around 600 genes that are significantly dysregulated in tumors compared to matched normal samples with cut-off 10 fold and P less than 0.05. C The heat map showed that 47 genes are classed as the FDA-approved drug targets. D The dot plot showed the significant expression difference between MMP11 and FGFR2 in the paired PDTs. E The heat map showed around 46 Wnt/β-catenin signaling pathway-related genes significantly dysregulated in PDTs. F The dot plot showed the significant expression difference between CTNNB1 and PRKCA in the paired PDTs

The Untargeted Metabolomics Profiling Identified the Key Metabolites in PDTs

Next, we try to understand the metabolism dysregulation and key metabolites contributing to the PDT by the untargeted metabolomics analysis of 20 paired tumor (T) /matched normal (MN) frozen tissues. The differential metabolites were obtained using univariate statistical analysis (student T-test or Mann-Whitney U test, depending on the normality of data and homogeneity of variance), especially when the multivariate OPLS-DA model fails to build a reliable discriminant model under some conditions. The OPLS-DA 2D score plot is shown in Fig. 4A and the Volcano Plot of Univariate Statistics is shown in Fig. 4B. Volcano Plot displays fold change (FC) and p-value of each metabolite. In this project, threshold value for differential metabolites selection is: (1) P < 0.05,(2)|log2FC| > 0. In the volcano plot, compared with MN, differential metabolites (points with red highlight) in the right top corner are increased in T and differential metabolites (points with blue highlight) in the left top corner are decreased in T. The Boxplot of the top 9 differential metabolites ordered by P-value is shown in Fig. 4C. Threshold value for potential biomarker selection in this project is: (1) VIP > 1 in multi-dimensional statistics, (2) P < 0.05 and |log2FC| > 0 in univariate statistics [12, 13].

Fig. 4figure 4

The untargeted metabolomics profiling identified the key metabolites in PDTs. A The OPLS-DA 2D score plot showed the distribution of the 20 paired tumor (T) /matched normal (MN) frozen tissues for the untargeted metabolomics analysis. B The Volcano Plot displays the fold change (FC) and p-value of each metabolite. The threshold value for differential metabolites selection is: (1) P < 0.05, (2)|log2FC| > 0. In the volcano plot, compared with MN, differential metabolites (points with red highlight) in the right top corner are increased in T and differential metabolites (points with blue highlight) in the left top corner are decreased in T. C The Boxplot of the top 9 differential metabolites ordered by P-value are shown

Pathway Analysis on Results Obtained from Gene Expression and Metabolomics Studies

To understand the key pathways involved the dysregulated genes and metabolites, we have investigated different pathway analyses. Figure 5A shows the graphical summary of the key pathways using the significantly overexpressed genes in PDT tumors compared to matched normal samples (cut-off 10 fold and P less than 0.05) by QIAGEN Ingenuity Pathway Analysis (QIAGEN IPA). Interestingly, we observed that TGFB1 is the key upstream regulator and control many overexpressed genes which are involved in different invasion and migration process. Pathway enrichment analysis using Pathway-associated metabolite sets (SMPDB) was also shown for the top 50 enriched pathways (Fig. 5B). Furthermore, MetaboAnalyst 5.0 Joint Pathway Analysis performs integrated pathway and connection analysis on results obtained from combined metabolomics and gene expression studies (Fig. 5C). Besides, MetaboAnalyst 5.0 Network Explorer also visually explores the relationships in different biological networks of the overexpressed genes and metabolites. Figure 6  shows the overall pathways involved in various biological networks of the overexpressed genes and metabolites. For example, the key overexpressed genes/metabolites involved in the Gap junction and ECM-receptor interaction. Gap junctions have been shown to help tumor cell invasion, and metastasis, increase nutrient supply and interact with immune cells to escape detection [14]. This may be related to the highly aggressive growth, invasion and recurrence of PDTs. The extracellular matrix has also been shown to be important in soft tissue sarcomas. Interactions between ECM ligands and their corresponding adhesion receptors are critical in driving many oncogenic processes like proliferation, invasion, altered metabolism and tumor immune microenvironment [15, 16]. These pathways and key genes/metabolites may be important for developing potential treatment of PDTs.

Fig. 5figure 5

Pathway Analysis on results obtained from gene expression and metabolomics studies. A Graphical summary of the key pathways using the significantly overexpressed genes in PDT tumors compared to matched normal samples (cut-off 10 fold and P less than 0.05) by QIAGEN Ingenuity Pathway Analysis (QIAGEN IPA). B Pathway enrichment analysis using Pathway-associated metabolite sets (SMPDB) is shown for the top 50 enriched pathways. C MetaboAnalyst 5.0 Joint Pathway Analysis for the overexpressed genes and metabolites

Fig. 6figure 6

MetaboAnalyst 5.0 Network Explorer visually explores the relationships in different biological networks of the overexpressed genes and metabolites. A The overall pathways are involved in different biological networks of the overexpressed genes and metabolites. B-C The key overexpressed genes/metabolites involved in the Gap junction. D-E The key overexpressed genes/metabolites involved in the ECM-receptor interaction

Fig. 7figure 7

Establishment of the primary PDT cell lines and investigation of the function of CTNNB1. A The representative images of the established primary PDT cell lines. B-C The representative images and statistical analysis showed that silencing CTNNB1 significantly inhibits primary PDT cell invasion but promotes cell migration

Establishment of the Primary PDT Cell Lines and Investigation of the Function of CTNNB1

There were few desmoid tumor cell lines available for further investigation, especially for PDTs. So we tried to establish the primary PDT cell lines. Finally, we successfully established six primary PDT cell lines, which have been continually passaged in many generations (Fig. 5A). All the established primary culture PDT cells were examined to ensure that most of the cells were representative of the primary PDTs by comparing them with the original tumor tissues with the CTNNB1 mutation. Next, we preliminarily investigated the function of CTNNB1 using shRNA targeting CTNNB1. Interestingly, we found that silencing CTNNB1 significantly inhibits primary PDT cell invasion but promotes cell migration, suggesting that Wnt/β-catenin signaling pathways are critical for the aggressive growth of PDTs (Fig. 7B and C). The function of different mutation of CTNNB1 in PDTs is investigating.

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