Tetraploidization-driven PRKG1 deletion reveals novel mechanisms underlying Leiomyosarcoma aggressiveness

Tetraploidization-driven PRKG1 deletion reveals novel mechanisms underlying Leiomyosarcoma aggressiveness | 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 Article Tetraploidization-driven PRKG1 deletion reveals novel mechanisms underlying Leiomyosarcoma aggressiveness Frederic Chibon, Ariadna Brito, Perot Gaelle, Natacha Roussel, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6984264/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 Leiomyosarcoma (LMS) is a malignant mesenchymal tumor with smooth muscle cell (SMC) differentiation. LMS shows high metastatic rate and highly rearranged genome, associated with whole genome doubling, which is identified in more than half of cases. We tested the hypothesis that cell fusion could be one of the mechanisms involved in the development of genome doubling and the production of multiple genomic alterations. We developed a cellular fusion model between SMCs and fibroblasts to compare the genomic alterations found in the hybrid cells vs the genomic profiles of LMS patients. This cell fusion model revealed a recurrent deletion within PRKG1 in chromosome 10, gene involved in smooth muscle contractile function and proliferation. The whole genome sequencing (WGS) analysis of a cohort of 121 LMS patients revealed that 76.9% (93/121) of patients had at least one PRKG1 altered copy, from which 18.2% (17/93) showed either a breakpoint (BP) in PRKG1 or an intra-chromosomal deletion surrounding PRKG1 . RNA sequencing (cohort 147 LMS) indicated that patients with low expression of PRKG1 had significantly worse survival. Results indicate that PRKG1 is among the most common altered genes in LMS, and its function is related to cell motility in vitro and tumor aggressiveness in vivo . Biological sciences/Cancer/Sarcoma Biological sciences/Cancer/Oncogenes Biological sciences/Cell biology/Cell migration Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Soft tissue sarcomas (STSs) represent a highly heterogeneous group of neoplasias with mesenchymal origin, classified on histologic basis according to the mature tissue they resemble the most 1 . Leiomyosarcoma (LMS) is diagnosed according to its variable smooth muscle cell (SMC) differentiation and is among the most common types of STS. LMS occurs in middle-age or older adults, it can arise in almost all anatomic sites but the most frequent are: the abdominal cavity, limbs, large blood vessels and the uterus 2 . The standard treatment remains surgery, when possible, and chemotherapy with doxorubicin. In high-risk LMS, adjuvant or neoadjuvant radiation therapy may complement the surgery, but the treatment is not universal due to the lack of both, efficient therapies and biological markers predicting response/aggressiveness 3 . Although gene signatures are currently under prospective evaluation to improve patient stratification and prognosis 4 , 5 . LMSs are highly aggressive, despite complete resection of the primary tumor, around 40% of the patients develop distant metastasis 6 . Patients with metastasis dissemination remain incurable with a median overall survival of 12 months after the dissemination of the disease. LMS does not have a unique known driver mutation, on the contrary, its characterized by its chaotic genome harboring numerous gains, losses and BP mutations. While these alterations vary across patients, there are few recurrencies, the p53 and Rb pathways alterations are present in nearly all the cases 7 , 8 and the genomic deletions of PTEN 9 , DMD 10 , ATRX 11 . None of these alterations are specific to LMS, therefore, LMSs are instead characterized by their highly rearranged genome, and frequent whole-genome doubling (WGD) 12 , which makes the identification of targeted treatments extremely difficult 13 or inefficient. It has been demonstrated that WGD is one of the mechanisms involved in genomic instability 14 , 15 . Redundant DNA content often leads to imbalances in chromosome number and structure, disrupting the normal cellular machinery and promoting tumorigenesis 16 , 17 . WGD is frequently observed in cancer, it has been reported to be present in 55% of LMS 12 and 89% of undifferentiated pleomorphic sarcoma (UPS) 18 , revealing its potential role in the oncogenesis of sarcomas. There are four possible cellular mechanisms causing WGD: endoreplication 19 , abortive cytokinesis 20 , mitotic slippage 21 and cell fusion 22 , 23 . While most mechanims driving WGD involve mitotic defects, cell fusion stands as an exception. Cell fusion is a physiological mechanism crucial for mesenchymal cell differentiation during muscle or bone formation 24 – 26 . The concept of cell-cell fusion in oncogenesis was first proposed by German physician Otto Aichel in 1911 27 . Modern studies have since validated his hypothesis, demonstrating that hybrid cell lines resulting from fusion exhibit increased aggressiveness 28 , enhanced invasion capacity 29 , contribute to tumor progression 30 , and are associated with poor prognosis 31 . Consistent with these findings, our previous research revealed spontaneous cell fusion in patient-derived sarcoma cell lines 30 . This process yield hybrid cells with merged nuclei, possessing a unique combination of parental cell information and, consequently, novel properties 30 . Furthermore, we have also shown that cell fusion model can reproduce the genomic alterations observed in UPS and pleomorphic rhabdomyosarcomas, with the specific alterations dependent on the parental cells involved in the fusion 23 , 32 . Pathologists are able to classify LMS tumors due to the smooth muscle differentiation of the tumoral cells, suggesting that the initiation cell of LMS is either a SMC or a stem mesenchymal cell capable of differentiate into SMC 33 . Our team and others have recently demonstrated that there are two distinct types of soft tissue LMS based on transcriptome and genomic alteration profiles: one with overexpression of smooth muscle related genes and another one associated with fibroblasts, adipocytes and mesenchymal stem cells gene expression 12 , 34 . Given the observed dual nature of LMS tumors, characterized by both smooth muscle and fibroblast gene expression profiles, we hypothesized that the fusion of SMCs with fibroblasts would provide a highly relevant model for studying LMS oncogenesis. This fusion aims to recapitulate the complex cellular origins of LMS, allowing us to investigate how these distinct cell lineages contribute to tumor development. In this study, we explored the role of WGD via cell fusion in leiomyosarcoma oncogenesis. To address this, we developed a cell fusion model using co-culture of immortalized smooth muscle cells (SMC, E6) and transformed fibroblasts (RST, IMR90 E6E7) to promote spontaneous cell fusion. The resulting hybrid cells were subjected to genomic analysis, and specific alterations identified were compared to those found in diverse patient cohorts. We further assessed the potential involvement of these genomic alterations in LMS oncogenesis and progression. Results 1. SMCs and fibroblasts fuse to produce proliferating hybrid cells Both diploid cell lines, RST DsRed−Puro and SMCE6 CFP − Blast (see Material and Methods for cell line specifications) (Fig. 1A) were co-cultured for 72h. Hybrid cell selection was achieved through a combined approach of dual antibiotic resistance and fluorescence-activated cell sorting (FACS), targeting cells exhibiting co-expression of DsRed+/CFP+ (Fig. 1B). Six hybrid cell lines were established from independent co-cultures. All six-cell lines showed aneuploid (near tetraploid) genomes verified by cytometry with propidium iodide (PI) and by chromosome count with chromosome spreading assay (hybrids chromosome content ranging from 126 to 74; Supplementary Figs. 1A & B). To evaluate the proliferative capacity of the hybrid cell lines, we performed 72-hour proliferation assays. Based on our prior observation that RST DsRed−Puro exhibit a higher proliferation rate than SMCE6 CFP − Blast cells, we hypothesized that all hybrids would outperform SMCE6 CFP − Blast . The results confirm this, demonstrating that all hybrid cell lines showed enhanced proliferative rate to SMCE6 CFP − Blast . Hybrid cell lines also showed higher proliferative capacities compared to RST DsRed−Puro except for H2.1 and H3.2 (Supplementary Fig. 2). 2. Shared Genomic Alterations in SMC/fibroblast hybrids and LMS Tumors. Genomic profiling using DNA microarrays demonstrated that all hybrid cell lines, in contrast to parental cells, displayed a rearranged genome (Fig. 1C, top panel vs. lower panel). Although most alterations were non-recurrent losses, among all, few were recurrent: chromosome 13 (in 4/6 hybrids), loss of chromosome 17 (in 4/6 hybrids) and chromosome 10 (2/6 hybrids). Consistent with the genomic alterations commonly found in LMS, we observed deletion in RB1 (13q), TP53 (17p) and PTEN (10q 35 , Fig. 1C, black arrows). Additionally, a highly specific deletion of the PRKG1 gene (chromosome 10) was identified in the H2.1 cell line (Fig. 1C, red arrow). Given the genomic alterations observed in the H2.1 cell line, we sought to confirm their functional significance by assessing tumorigenicity in vivo. H2.1 cells were subcutaneously injected into NSG mice, resulting in tumor formation in all five mice, with initial tumor growth detected 12 days post-injection (Fig. 1D, left panel). Histopathological evaluation by an expert sarcoma pathologist classified the tumors as grade 3, poorly differentiated LMS or UPS, characterized by incomplete smooth muscle differentiation and TAGLN expression (Fig. 1D, right panel), but lacking CALD1 expression (data not shown). 3. Recurrence of PRKG1 deletion in LMS patients To determine whether the PRKG1 deletion observed in the H2.1 hybrid cell line might be clinically relevant, we conducted a comprehensive analysis of whole-genome sequencing (WGS) data obtained from a cohort of 121 LMS tumors, previously analyzed 38 , 46 . Regarding Copy Number Variation (CNV), patients were classified based on the chromosome 10 status: 28/121 (23.1%) present no chromosome 10 alteration, 49/121 (40.5%) one copy loss of chromosome 10, 27/121 (22.3%) deletion of the chromosome 10 long arm (including PRKG1 ), 11/121 (9.1%) tumors with an intra-chromosomal deletion including PRKG1 , and 6 patients (5%) with either one or several PRKG1 BP (Figs. 2A & B). All BP were validated by DNA Sanger sequencing (Supplementary Tables 1 & 2, except for LMS55T for which one of the adjacent BP sequences could not be identified because of its repetitive nature). Among those six tumors, three BP resulted as frameshift (FS) at RNA level (Supplementary Tables 1 & 3). Consequently, WGS results show that 93/121 (76.9%) of LMS patients had at least one PRKG1 altered copy (whole chromosome deletion, arm deletion, BP or intra-chromosomal alteration). Among them, 17/93 (18%) showed an alteration specifically targeting PRKG1 either an intra-chromosomal loss encompassing PRKG1 (11/93) or at least one BP in PRKG1 (6/93), which accounts for a total of 17/121 (14%). 3. Low expression of PRKG1 predicts adverse outcome in LMS Given the observed prevalence of PRKG1 deletion in LMS patients, we aim to determine the potential clinical significance of PRKG1 alteration. To this end, a cohort of 147 LMS samples with available RNA sequencing data was stratified based on PRKG1 expression levels (low vs. high, using the mean as a cut-off), and correlated with clinical follow-up information. (Supplementary Table 4). Kaplan-Meier analyses showed that metastasis-free survival, as well as cancer-specific survival and overall survival were significantly worse in the group with the lowest PRKG1 expressions (P-values of 0.04, 0.024 and 0.00038 respectively, Fig. 3). 5. Functional impact of PRKG1 loss PRKG1 is a cGMP-dependent protein kinase belonging to the family of serine/threonine-specific protein kinases activated by cGMP 37 . PRKG1 is located in chromosome 10 38 and is alternately spliced in two isoforms; PRKG1-alpha , and PRKG1-beta 39 . Protein isoforms differ in their N-terminal sequences 40 . PRKG1-alpha is found mainly in the lung, heart, platelets and cerebellum, whereas both PRKG1-beta and PRKG1-alpha are highly expressed in smooth muscle tissue of the uterus, intestine and trachea 41 . Additionally, both isoforms are present in vascular smooth muscle cells, platelets, lung, certain endothelial cells, fibroblasts, heart and the cerebellum 37 . Proteins that are phosphorylated by PRKG1 regulate platelet activation and adhesion, smooth muscle contraction/relaxation pathway and proliferation 42 . To determine whether the deletion of PRKG1 participates in LMS oncogenesis, we tested the impact of PRKG1 re-expression on three primary LMS cell lines (OC140, OC148, and OC211) in addition to the hybrid cell line (H2.1). OC140 shows one chromosome 10 loss, having one PRKG1 WT copy left. OC211 and H2.1 show a PRKG1 intragenic deletion. OC148 has two altered copies, one deleted, one broken (Fig. 4). Frameshifts were validated by RNA Sanger sequencing (Supplementary Table 5). All the cell lines were transfected with either alpha, beta or both PRKG1 isoforms. The transgene expression was validated at RNA and protein levels (Supplementary Figs. 3 & 4). A significant decrease of proliferation was observed in OC140 when beta or alpha + beta (A + B) isoforms are expressed compared to CT cell line (Supplementary Fig. 5), but not in the other cell lines regardless of the overexpressed isoform (data not shown). Additionally to smooth muscle proliferation, PRKG1 is also directly involved in smooth muscle contractile function 40 , 43 , 44 . The Boyden chamber assay revealed that the overexpression of either alpha or beta isoforms, or both, in all LMS caused a significant reduction in cell migration compared to the CT cell line (Fig. 5 & Supplementary Fig. 6). The hybrid cell line exhibited analogous behavior to the LMS cell lines, demonstrating a pronounced decrease in cellular migration upon overexpression of either isoform in contrast to the CT. However, simultaneous overexpression of both alpha and beta isoforms within the hybrid cell line was unattainable due to antibiotic incompatibility constraints (Fig. 5, first panel & Supplementary Fig. 3D). To rule out the possibility that the effect observed would not be specific to PRKG1 re-expression, the LMS cell lines with the beta isoform (OC140, OC148 and OC211) were then transduced with a shRNA directed against PRKG1 (shRNA935). This shRNA decreased the expression of PRKG1 , validated by Taqman™ and western blot (Supplementary Figs. 3 & 4). The proliferation assay showed that OC140-beta + sh proliferated at the same rate as CT cell line and both (OC140-beta + sh and CT) showed higher proliferation rate than OC140-beta (Supplementary Fig. 7A). In Boyden chamber, we observed that all the cell lines transduced with sh935 had similar migration rates than their CT cell lines, these (shRNA and CT) being higher than cell lines transduced with the beta isoform (Supplementary Fig. 7B & 7C). This re-knock-down of PRKG1 in the beta-cell lines recovered the phenotype observed in the CT cell lines ( PRKG1 deleted), confirming that the phenotype observed was specific to the level of PRKG1 expression. 6. PRKG1 deletion in LMS impacts pathways related to physiological relaxation of SMCs To delve deeper into the biological mechanisms underlying the functional impact of PRKG1 deletion, we selected the two cell lines exhibiting the most significant functional impact from PRKG1 modulation, and performed RNAseq for OC140 and OC211, each subjected to three distinct conditions: alpha and beta overexpression, and control ( PRKG1 deletion). The initial analysis encompassed both samples (OC140 and OC211) to identify possible common abnormalities. We assessed the differential gene expression across various comparisons, including A vs. CT, B vs. CT, A vs. B, and both A and B vs. CT, examining both upregulated and downregulated genes. The greatest number of dysregulated genes was comparing A vs B, could indicate that the two isoforms don’t activate the same pathways (Fig. 6). Accordingly, only three genes were found to be upregulated in both alpha and beta compared to CT: PRKG1 , PDE3B , and PLCXD3. These 2 genes (excluding PRKG1 for which overexpression is experimentally induced) are directly involved in the cGMP and cAMP pathways implicated in the relaxation mechanism of SMCs 45 . These pathways affect the contractility and mobility of these cells 46 . To further investigate the impact of PRKG1 expression, we performed a comparative transcriptomic analysis of PRKG1 isoform expression levels within each cell line. Principal Component Analysis (PCA) revealed distinct transcriptomic profiles: in OC140, the primary variance was observed between the beta isoform (B) and control (CT), while in OC211, it was between the alpha isoform (A) and CT (Supplementary Fig. 8). Consequently, these comparisons were used for subsequent analyses (Fig. 7A & B). Gene set enrichment analysis demonstrated that genes upregulated upon PRKG1-alpha or -beta overexpression were enriched for interferon signaling pathways (interferon alpha and gamma for OC140; interferon alpha and beta for OC211). Conversely, genes upregulated in the control (CT), representing PRKG1 deletion, showed enrichment in MYC, E2F , and G2M pathways (Fig. 7A & B). These enrichments closely align with the CINSARC signature, a well-established prognostic marker for metastatic events in soft tissue sarcomas 4 , 47 – 49 . Gene Set Enrichment Analysis (GSEA) was performed, ranking all genes from most upregulated in the control (CT) condition to most downregulated in CT (correspondingly, upregulated in PRKG1-alpha, -beta, or alpha + beta conditions), we observed a significant enrichment of the CINSARC signature in the CT samples (Fig. 7C), demonstrating that PRKG1 deletion correlates with an increase in the expression of genes associated with metastatic potential. Discussion Altogether, our results show that hybrid cells, derived from the spontaneous cell fusion between SMCs and fibroblasts, induce the development of LMS tumors like those observed in patients: (1) at the histological (Fig. 1D, smooth muscle differentiation expressing TAGLN) (2) and at genomic level (Fig. 1C, rearranged genome with frequent chr 10, 13 and 17 losses, and PRKG1 deletion). In this study, we tested the hypothesis that SMCs and fibroblasts are the initiative cells involved in LMS oncogenesis. Transcriptomic analyses involving various sarcoma types have revealed that not all samples of leiomyosarcoma (LMS) clustered together 12 , 34 , 50 . Such findings suggest the existence of distinct subcategories within LMS, each potentially originating from different cellular sources 12 , 51 – 53 . Recent clustering analysis revealed two distinct categories of soft tissue LMS (excluding uterine LMS), one comprising homogeneous transcriptomes (hLMS) and the other encompassing all other LMS (oLMS) 34 . hLMS exhibit more differentiated histology, lower grades, and are predominantly located intra-abdominally. Moreover, these tumors display an overexpression of genes associated with smooth muscle differentiation, and a frequent MYOCD amplification. In contrast, oLMS exhibit poor differentiation, higher grade, tend to occur in extremities, and showed histone marks associated with fibroblasts, adipocytes, and mesenchymal stem cells 34 . The tumors formed in mice from the in vitro cell fusion model of fibroblasts and SMCs show high grade, and poor differentiation (Fig. 1D) which is closer to oLMS characteristics, making it a suitable model to study this highly aggressive disease. Based on the recurrent alterations found in LMS, murine models have been developed to study the initiation of LMS. Hernando and colleagues genetically inactivated PTEN in smooth muscle cell lineage by cross breeding mice with PTEN loxP/loxP and TAGLN -cre mice. Mice carrying the homozygous PTEN deletion developed SMC hyperplasia and abdominal LMSs with an incidence of approximately 80% 33 , but these LMS tumors did not show a rearranged genome. Rubio et al , also develop murine models of LMS, in this case mesenchymal stem cells (MSC) where transformed in vitro to evaluate the potential carcinogenic effects of p53 and/or retinoblastoma (RB) genes. Only p53 −/− and p53 −/− RB −/− MSCs were capable of tumor development and originated LMS-like tumors 54 . Similarly to Hernando’s models, they were able to develop LMS at a histological level but not at the genomic level since the murine tumors did not show the peculiar chaotic genome characteristic of LMSs. To our knowledge, the in vitro cell fusion models are the most representative models of patients’ LMS tumors. In this model, although there are oncogenes activated in the parental fibroblast cells, genomic destabilization and the onset of extensive chromosomal rearrangements, including both recurrent and unique alterations such as the deletion of PRKG1 , occur only following whole genome doubling (WGD) induced by cell fusion. Hybrid cell lines showed both non-recurrent and recurrent genomic alterations that are commonly found in LMS patients. Therefore, we show here that WGD through cell fusion is the mechanism that induces the genome reshuffling we observe in hybrids and likely in LMS patients. To establish that the chromosomal alterations and the phenotypic advantage observed in hybrid cells are indeed attributable uniquely to WGD by cell fusion, it becomes imperative to conduct experiments involving tetraploidization through endoreplication, cytokinesis failure, or mitotic slippage. Given that cell fusion merges two different genomes that don’t occur with other mechanisms based on mitotic defects, this comparative approach will help elucidate the specific consequences of each tetraploidization mechanism. We identified a novel gene commonly deleted in LMS, PRKG1 . One loss or altered copy of PRKG1 was identified in 76.9% (93/121) of the patients. In the same cohort at least one copy of the following genes is also altered: RB1 in 96% of patients (116/121), TP53 in 95% (115/121), PTEN in 83.5% (101/121), and ATRX in 43.8% (53/121) 34 , consistent results we observe in other publications 4 , 12 , 35 , 55 . These make PRKG1 (76.9%) to be among the most common lost genes in LMS. PRKG1 is a cyclic guanosine monophosphate (cGMP)-dependent protein kinase intricately linked to the nitric oxide (NO) pathway, which is key in smooth muscle relaxation 40 . In this relaxation process, there exists a potential crosstalk among three mechanisms: cGMP, cAMP, and IP3 (Fig. 8, inspired from 45 , 56 ). In the cGMP pathway, elevated levels of NO stimulate soluble guanylate cyclase (sGC), leading to the production of cGMP, which activates cGMP-dependent protein kinase G (PKG). PRKG1 , the principal form of Protein Kinase G, modulates contractility by reducing intracellular calcium ion levels, thereby regulating actin filament and myosin dynamics. Similarly, the cAMP pathway involves ATP and the activation of protein kinase A, which contributes to muscle relaxation 56 , 57 . In contrast, the IP3 pathway elevates calcium levels in the endoplasmic or sarcoplasmic reticulum, promoting cell contraction and relaxation through the phosphorylation of myosin light chain, while also facilitating cell migration in response to various stimuli 57 , 58 . PDE3B emerged as an upregulated gene when comparing cells exhibiting downregulated PRKG1 to those with upregulated PRKG1 alpha and beta isoforms. PDEs comprise a family of enzymes that catalyze the hydrolysis of cAMP and cGMP, with PDE3 showing a higher affinity for cAMP. Consequently, down regulation of PDE3B would lead to the stability of cAMP, stimulating the activation of protein kinase A and thus sustaining the reduction of calcium levels, resulting in a relaxed state of the cell 59 , 60 . Additionally, PLCXD3 , a member of the PLC family, participates in the hydrolysis of PIP2 into IP3 and DAG, thereby increasing the release of calcium ions and maintaining the cell in a contracted state 56 . These could indicate a disruption of the NO/cAMP/IP3 pathways, which are crucial for the contraction and relaxation of SMCs, in LMS patients. Specifically, the overexpression of PRKG1-alpha or PRKG1-beta in these cells results in reduced migration ability (Fig. 5). Through GSEA analysis, we observed a significant enrichment of oncogenic pathways, such as MYC , E2F , and G2M, in cells with PRKG1 deletion. In contrast, overexpression of PRKG1-alpha or PRKG1-beta led to a marked overrepresentation of genes associated with interferon responses. Consequently indicating a downregulation of cGAS-STING pathway 61 in LMS cell lines upon PRKG1 deletion. Together, these findings suggest that PRKG1 deletion may promote oncogenic signaling pathways that drive LMS progression, while overexpression could activate immune-modulatory pathways, potentially counteracting tumor development. These results underscore the dual role of PRKG1 in LMS oncogenesis, offering new insights into its mechanistic contributions. Interferon alpha and gamma have direct anti-tumor effects, including inhibition of tumor cell proliferation 62 . On the contrary MYC, E2F and G2M are pathways involved in cell cycle progression, leading to increased cell division and proliferation 63 , 64 . We identified the enrichment of CINSARC genes comparing the LMS cell lines with PRKG1 deletion vs. PRKG1 overexpression (Fig. 7C). CINSARC is a prognostic marker based on the expression levels of a combination of genes linked to mitosis and chromosome integrity 47 characteristic of these types of sarcomas. This finding correlates with our phenotypic assays where we observed reduced migration upon PRKG1 re-expression, likely decreasing the capacity of cells to metastasize (Supplementary Fig. 6A). Taken together, these results support the clinical observation that low PRKG1 expression in LMS patients correlates with worse metastatic-free survival, cancer-specific survival, and overall survival (Fig. 3). In conclusion, our study reveals two novel axes in LMS oncogenesis. Firstly, tetraploidization by cellular fusion may contribute to LMS initiation, as evidenced by the huge genome reshuffling it induces together with the specific deletions ( e.g., PRKG1 ) observed in hybrid cell lines, mirroring findings in LMS patients. Secondly, PRKG1 deletion likely disrupts the cGMP/cAMP/IP3 signaling pathway and enhances the activation of mitosis and chromosome integrity related genes, increasing cell migration leading to the characteristic tumor aggressiveness observed in LMS patients. Further analyses are now warranted to deeply understand the molecular relationship between this altered pathway and CINSARC expression. Therapies that target cGMP/cAMP/IP3 pathways 65 , 66 , enhancing PRKG1 expression, may create a new therapeutic window for the treatment of aggressive LMS tumors. Material and Methods Cell lines Sarcoma cell lines, OC140, OC148, OC211 were generated in the laboratory 67 . Fibroblast IMR90 E6 E7 HRAS G12V SV40 SmallT hTERT (RST) cell lines were kindly given by Dr. M. Teichmann 68 cell model transformation described by Hahn et al. 73 , 74 and SMC cell line was bought from ATCC (primary aortic cell line PCS-100-012). SMCE6 cell line was then generated by lentiviral transduction using pCDH-CMV-MCS-EF1a-Hygro (pCD515B-1, System Biosciences) in which E6 cDNA was cloned as described in the cloning section of the Material and Methods. Primers used for cDNA cloning can be found in Supplementary Table 6. OC140, OC148 and RST were cultured in RPMI-1640 (Gibco, Invitrogen) supplemented with 10% fetal bovine serum (FBS, Sigma). OC211 and SMCs were cultured in DMEM (Gibco, Invitrogen), supplemented with 10% FBS. All cell lines were cultured in a humidified CO 2 incubator at 37 o C. Generation and selection of hybrid cells Hybrid selection and generation was first described in 30 , DsRed and CFP parental cell lines were generated by lentiviral transduction using pLenti-puromycin-DsRed and pLenti-blasticidin-CFP plasmids (generous gift from Dr. R. Iggo). 150,000 cells of each parental cell line were seeded in co-culture (6-well plates) during 72h. Spontaneus hybrid cells were selected by the addition of antibiotics into the culture medium, blasticidin (18µg/mL, Thermofisher Scientific) and puromycin (5µg/mL Thermofisher Scientific). Cell sorting was also performed to purify the culture by the selection of double positive cells DsRed and CFP. All hybrids show a unique rearranged genome, together with new phenotypes (Supplementary Figs. 1 & 2). In vivo experimentations Animals were maintained under specific pathogen-free conditions in the animal facility CREFRE-US006 (Toulouse, France). Experiments were performed in conformity with the rules of the Institutional Animal Care and Use committee (approval number DIR13109 and DAP-APAFiS-201802161802878). All efforts were made to minimize animal suffering. 10 5 cells were injected into the dorsal flank of 6–8-week-old NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ (NSG) mice. Tumor sizes were measured twice a week using a caliper and calculated using the formula: V = length × width2/2. At the end of the experiment, mice were sacrificed by cervical dislocation under anesthesia. Tumors were then weighed and divided into three parts for cell culture, formalin fixation, and nitrogen freezing. Cell proliferation assay Cell proliferation was evaluated with IncucyteS3® Live-cell analysis system (Sartorius). Cells were seeded in 96-well plates (ImageLock, Sartorius) at a concentration of 1200 cells/well (5 wells/per cell line). One picture was acquired/well/hour for 5 days. The normalization was done based on the number of cells/area covered by cells at T0. Data plotting and statistical analysis was performed with PRISM software (v6.01, GraphPad). Invasion assay Invasion was monitored using Boyden chamber cell culture inserts with 8µm pores (Corning) in 24-well plates. 25,000 cells/well were seeded in either RPMI-1640 or DMEM depending on the cell line supplemented with 5% FBS (upper chamber). The lower chamber was filled with either RPMI-1640 or DMEM supplemented with 10% FBS to create a gradient supporting cell invasion. After 18 h of incubation, cells located at the top side of the membrane were removed using cotton-tipped swab, while invasive cells located at the other end were fixed with cold ethanol absolute for 15 min and stained with Hoechst 33,342 (1/5000, Invitrogen) for 10 min at room temperature. For each insert the entire bottom membrane was acquired using an Axio Vert.A1(ZEISS) microscope. Quantification was done using the Image J (v2.1.0–1, NIH) cell counter plugin. Data were normalized according to the number of cells at time zero and 18 h evaluated in parallel (in mirror 24-well plates) by flow cytometry and plotted using PRISM software (v6.01, GraphPad). DNA extraction For tumor samples and cell lines, genomic DNA from frozen samples was isolated using standard phenol-chloroform extraction protocol. DNA was quantified using a CLARIOstar plate reader (BMG Labtech). DNA array, comparative genome hybridization (CGH) Genomic profiling was performed using the Affymetrix CytoScan™ HD Arrays (Thermofisher Scientific) for all cell lines according to the manufacturer’s instructions. CEL files obtained by scanning the arrays were analysed using the Chromosome Analysis Suite software v3.1 (Thermofisher Scientific; http://www.affymetrix.com/support/technical/byproduct . affx?product = chas) and the annotations of the genome version GRCH38 (hg38). PCR on genomic DNA For BP validation on genomic DNA in ICGC tumors, PCR primers were designed using the Primer 3 program ( https://bioinfo.ut.ee/primer3-0.4.0/ ) and are presented in Supplementary Table 2. All PCR were performed on 50ng of DNA using AmpliTaqGold® DNA polymerase (4311820, Applied Biosystems, Foster City, CA, USA) according to the manufacturer’s instructions with the PCR program described in the Supplementary Table 2 Legend. RNA extraction RNA extraction was performed using standard TRIzol (15596026, Thermo Fisher, Waltham, MA, USA) / chloroform extraction (32211-1L, Fisher Scientific, Hampton, NH, USA) followed by 100% ethanol precipitation and RNA purification using the RNeasy Mini Kit (74104, Qiagen, Hilden, Germany) with DNase treatment (79254, RNase-Free DNase Set, Qiagen, Hilden, Germany). Total RNA was quantified using a CLARIOstar plate reader (BMG Labtech). RT-PCR Total RNA was first reverse-transcribed using random hexamers and the High Capacity cDNA Reverse Transcription Kit (4368814, Applied Biosystems, Foster City, CA, USA) according to the manufacturer’s instructions. All primers used were designed using the Primer 3 program ( https://bioinfo.ut.ee/primer3-0.4.0/ ) and are presented in Supplementary Table 3. All PCR were performed as previously described for PCR on genomic DNA. Sanger Sequencing Sanger sequencing was performed by Genoscreen (Lille, France). FinchTV software (V1.4.0) was used to visualize the sequences (Geospiza, Seattle, WA, USA). E6, PRKG1α and PRKG1β cDNA cloning Sequences of cDNA (without UTR) for E6 (HPV16, NC_001526.4:7125–7601), PRKG1α (NM_ 001098512.3) and PRKG1β (NM_ 006258.4) cloned in pEX-A258 vector were obtained from Eurofins Genomics (Ebersberg, Germany). Sequences were amplified by PCR using AmpliTaqGold® DNA polymerase (4311820, Applied Biosystems, Foster City, CA, USA), primers described in Supplementary Table 6 for TD60°C program see Supplementary Table 2 legend. PCR products were then purified using QIAquick PCR Purification Kit (28104, Qiagen, Hilden, Germany) according to manufacturer’s recommendations. The cDNA and vectors (CD510B-1, CD513B-1 and CD515B-1, System Biosciences, Palo Alto, CA, USA) were double digested by XbaI and SwaI restriction enzymes (R0145S and R0604S, New-England Biolabs, Ipswich, MA, USA) then precipitated with absolute ethanol. Ligation between 100ng of double digested vector and 500ng of double digested cDNA was performed as recommended by the manufacturer using T4 DNA ligase (M0202S, New-England Biolabs, Ipswich, MA, USA). Whole ligations were then transformed into One Shot™ TOP10 bacteria (Invitrogen, Waltham, Ma, USA). Colonies were checked by PCR using AmpliTaqGold® DNA polymerase (4311820, Applied Biosystems, Foster City, CA, USA), primers described in Supplementary Table 6 and a TD60°C program and one positive colonie for each cloning was amplified and DNA was recovered using EndoFree Plasmid Maxi Kit (12362, Qiagen, Hilden, Germany) according to furnisher’s recommendations. Sequences were verified by Sanger Sequencing performed by Genoscreen (Lille, France). E6 was cloned into CD515B-1 vector, PRKG1α was cloned into CD510B-1 and CD513B-1 vectors and PRKG1β was cloned into CD513B-1 and CD515B-1 vectors. Real-Time Quantitative Reverse Transcription-PCR Reverse transcription and real-time PCR were performed as previously described 71 . We used TaqMan Gene Expression assays (Applied Biosystems, Foster City, CA, USA): Hs00956122_m1 for PRKG1; Hs99999903_m1 for ACTB and Hs99999902_m1 for RPLP0. Specific primers and probe were designed for PRKG1α: (F) 5’-CCGCAGACGTACAGGTCCTT-3’; (R) 5’-TTGGACCTTTCGGACTTGGT-3’ and 5’-ACCTCCGACAGGCAT-3’ (Probe), PRKG1β: (F) 5’-CCAGGATCTCAGCCATGTGA-3’; (R) 5’-CCTTGGACTGTGGGCTCTTG-3’ and 5’-CCTGCCCTTCTACC-3’ (Probe) and PRKG1Total (probe designed in the sequence which is deleted in the altered cell lines): (F) 5’-GGGAATTGGCTATTCTTTACAACTG-3’; (R) 5’-GGCCCAGAGTTTTACATTTACAAGAG-3’ and 5’- ACCCGGACAGCGAC − 3’ (Probe), using Primer Express™ software (Applied Biosystems, Foster City, CA, USA). To normalize the results, we used the mean of ACTB and RPLP0 genes as reference genes. The results were calculated as described by De Preter and colleagues 72 . Ploidy Evaluation by Flow Cytometry Cell lines were resuspended in PBS 1X and fixed with absolute ethanol overnight at − 20 ◦C. Then, cells were washed and a solution of propidium iodide (Sigma Aldrich, 50 µg/mL) and RNAse A (ThermoFisher Scientific, 10 µg/mL) was added for 15 min at 37°C. DNA content was quantified with propidium iodide fluorescence intensity by flow cytometry (MACS Quant VYB, Miltenyi) and analyzed using FlowJo software (v10.8.1, BD Bioscience, Franklin Lakes, NJ, USA). Chromosome spreading Cells were incubated overnight with colchicine (0.01 µg/ mL; Gibco, Thermofisher), harvested, and resuspended in an isotonic 0.075 M KCl buffer for 25 min. Cells were finally fixed by slow dropwise addition of standard 3:1 methanol: acetic acid final fixative solution. After several washes with fixative solution, a drop of cell suspension was dropped on a humidified glass slide. Slides were mounted using Vectashield medium plus DAPI (Vector Laboratories, Burlingame, California, USA). Metaphases were analyzed using Video microscope (Zeiss) with appropriate filters. Pictures were analyzed using ZEN 2012 blue edition software. Chromosome number was determined for ten metaphases for each cell line. Data plotting was performed with PRISM software (v6.01, GraphPad). Protein extraction Cells were rinsed in PBS 1× and lysed for 20 min at 4◦C in RIPA buffer (R0278, Sigma Aldrich) supplemented by phosphatase/protease inhibitor cocktail (1×, Sigma Aldrich). Proteins were collected in supernatant after 15 min of centrifugation at 13,000g and quantified by DC protein assay kit (Bio-Rad). Western Blotting PRKG1 protein expression level was evaluated using a primary antibody directed against PKG-1 (C8A4) Rabbit mAb (3248, Ozyme). This antibody recognizes alpha and beta isoforms. 40 µg of protein were loaded on the gel and separated by SDS-page. After the transfer onto a PVDF membrane, the membrane was blocked in PBS-Tween 0.1% BSA 5% and then incubated with the primary antibody (1:1000) overnight at 4°C. After several washes, the membrane was then incubated with a horseradish-peroxidase-linked anti-rabbit antibody (7074S, Cell signaling technology, 1:5000) for 1 h at room temperature. Signal was detected using ChemiDoc device (Bio-Rad) after incubation with chemiluminescent substrate (ECL Immobilon Western, WBKLS0100, Merck, Darmstadt, Germany). GAPDH mouse (sc-51907, Santa Cruz, 1:5000, 1 h at room temperature) with an anti-mouse secondary antibody (W4021, Promega; 1:5000, 1 h at room temperature) was used as a loading control for quantification. Bulk whole-genome sequencing cohorts (WGS) was previously described in 11 Survival Analysis Survival analysis was performed using R version 4.3.0 73 and the survival package (version 3.5-5) 74 by fitting a Kaplan-Meier model in which significance was set at log-rank test p-value < 0.05. Survival curves were plotted with R packages (version 0.4.9) 75 . The classification of PRKG1 patients state was performed on two LMS RNAseq bulk cohorts (n = 110 and n = 37) separately. For each cohort, PRKG1 threshold was defined as the mean expression of PRKG1 . Patient with lower than the mean of expression was classified as “Low” and other patient as “High”. Then, the classification of the different cohort was merged to perform survival analysis. The metastasis-free survival (MFS) was defined as the time from diagnostic to the first metastatic event including metastasis at diagnosis, Overall survival (OS) was defined as the time from diagnostic to death and cancer-specific survival was defined as the time from diagnostic to death for patient specifically death by their cancer. ICGC and bulk RNAseq cohorts were previously described in 34 , 76 RNAseq “bulk” analysis of cell line model Total RNAseq libraries were prepared with the Stranded Total RNA Prep Ligation with Ribo-Zero Plus Kit (Illumina, San Diego, CA, USA) using an input of 500ng of Total RNA (DV200 > 85.8%). The libraries were profiled with the HS NGS kit for the Fragment Analyzer (Agilent Technologies, Santa Clara, CA, USA) and quantified using the KAPA library quantification kit (Roche Diagnostics). The libraries were pooled and sequenced on the Illumina NovaSeq6000 instrument using S1 flow cells. RNA reads were mapped to the hg38 genome assembly with STAR 77 version 2.6.0c. Low-quality (score < 20) and duplicated PCR paired-end reads were removed with SAMtools 78 version 1.8 and PicardTools ( http://broadinstitute.github.io/picard/ ) version 2.18.2, respectively. Then, gene expression was quantified with HTSeq 79 version 0.9.1. Differential gene expression analysis was performed using R 75 (v 4.0.3) package DESeq2 80 version 1.40.2, significance for differentially expressed genes was set at p-value < 0.05. Gene Set Enrichment Analysis performed using GSEA software (Subramanian, Tamayo, et al. (2005), PNAS 102, 15545–15550, http://www.broad.mit.edu/gsea/ ) version 4.3.2 using hallmark, curated, regulatory target, and oncogenic signature gene sets from The Molecular Signatures Database. The raw RNA sequencing data generated in this study have been deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession number PRJNA1282639. Declarations Conflicts of interest: The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Serrano, C., and George, S. (2013). Leiomyosarcoma. Hematol. Oncol. Clin. North Am. 27 , 957–974. https://doi.org/10.1016/j.hoc.2013.07.002. Singh, Z. (2018). Leiomyosarcoma: A rare soft tissue cancer arising from multiple organs. J. Cancer Res. Pract. 5 , 1–8. https://doi.org/10.1016/j.jcrpr.2017.10.002. Kasper, B., D’Ambrosio, L., Davis, E.J., Ingham, M., Broto, J.M., Trent, J.C., van Houdt, W.J., and Van Tine, B.A. (2022). What Clinical Trials Are Needed for Treatment of Leiomyosarcoma? Curr. Treat. Options Oncol. 23 , 439–449. https://doi.org/10.1007/s11864-021-00928-y. Chibon, F., Lesluyes, T., Valentin, T., and Le Guellec, S. (2019). CINSARC signature as a prognostic marker for clinical outcome in sarcomas and beyond. Genes. Chromosomes Cancer 58 , 124–129. https://doi.org/10.1002/gcc.22703. Filleron, T., Le Guellec, S., Chevreau, C., Cabarrou, B., Lesluyes, T., Lodin, S., Massoubre, A., Mounier, M., Poublanc, M., Chibon, F., et al. (2020). Value of peri-operative chemotherapy in patients with CINSARC high-risk localized grade 1 or 2 soft tissue sarcoma: study protocol of the target selection phase III CHIC-STS trial. BMC Cancer 20 , 716. https://doi.org/10.1186/s12885-020-07207-3. Coindre, J.-M., Terrier, P., Guillou, L., Le Doussal, V., Collin, F., Ranchre, D., Sastre, X., Vilain, M.-O., Bonichon, F., and N’Guyen Bui, B. (2001). Predictive value of grade for metastasis development in the main histologic types of adult soft tissue sarcomas: A study of 1240 patients from the French Federation of Cancer Centers Sarcoma Group. Cancer 91 , 1914–1926. https://doi.org/10.1002/1097-0142(20010515)91:103.0.CO;2-3. Pérot, G., Chibon, F., Montero, A., Lagarde, P., De Thé, H., Terrier, P., Guillou, L., Ranchère, D., Coindre, J.-M., and Aurias, A. (2010). Constant p53 Pathway Inactivation in a Large Series of Soft Tissue Sarcomas with Complex Genetics. Am. J. Pathol. 177 , 2080–2090. https://doi.org/10.2353/ajpath.2010.100104. Chibon, F., Mairal, A., Freneaux, P., Terrier, P., Coindre, J., Sastre, X., and Aurias, A. (2000). The RB1 gene is the target of chromosome 13 deletions in malignant fibrous histiocytoma. Cancer Res. 60 , 6339–6345. Gibault, L., Ferreira, C., Pérot, G., Audebourg, A., Chibon, F., Bonnin, S., Lagarde, P., Vacher-Lavenu, M.-C., Terrier, P., Coindre, J.-M., et al. (2012). From PTEN loss of expression to RICTOR role in smooth muscle differentiation: complex involvement of the mTOR pathway in leiomyosarcomas and pleomorphic sarcomas. Mod. Pathol. 25 , 197–211. https://doi.org/10.1038/modpathol.2011.163. Mauduit, O., Delcroix, V., Lesluyes, T., Pérot, G., Lagarde, P., Lartigue, L., Blay, J.-Y., and Chibon, F. (2019). Recurrent DMD Deletions Highlight Specific Role of Dp71 Isoform in Soft-Tissue Sarcomas. Cancers 11 , 922. https://doi.org/10.3390/cancers11070922. Darmusey, L., Pérot, G., Thébault, N., Le Guellec, S., Desplat, N., Gaston, L., Delespaul, L., Lesluyes, T., Darbo, E., Gomez-Brouchet, A., et al. (2021). ATRX Alteration Contributes to Tumor Growth and Immune Escape in Pleomorphic Sarcomas. Cancers 13 , 2151. https://doi.org/10.3390/cancers13092151. Chudasama, P., Mughal, S.S., Sanders, M.A., Hübschmann, D., Chung, I., Deeg, K.I., Wong, S.-H., Rabe, S., Hlevnjak, M., Zapatka, M., et al. (2018). Integrative genomic and transcriptomic analysis of leiomyosarcoma. Nat. Commun. 9 , 144. https://doi.org/10.1038/s41467-017-02602-0. Lacuna, K., Bose, S., Ingham, M., and Schwartz, G. (2023). Therapeutic advances in leiomyosarcoma. Front. Oncol. 13 , 1149106. https://doi.org/10.3389/fonc.2023.1149106. Dewhurst, S., and Swanton, C. (2015). Tetraploidy and CIN: a dangerous combination. Cell Cycle 14 , 3217–3217. https://doi.org/10.1080/15384101.2015.1084208. Dewhurst, S.M., McGranahan, N., Burrell, R.A., Rowan, A.J., Grönroos, E., Endesfelder, D., Joshi, T., Mouradov, D., Gibbs, P., Ward, R.L., et al. (2014). Tolerance of Whole-Genome Doubling Propagates Chromosomal Instability and Accelerates Cancer Genome Evolution. Cancer Discov. 4 , 175–185. https://doi.org/10.1158/2159-8290.CD-13-0285. Bielski, C.M., Zehir, A., Penson, A.V., Donoghue, M.T.A., Chatila, W., Armenia, J., Chang, M.T., Schram, A.M., Jonsson, P., Bandlamudi, C., et al. (2018). Genome doubling shapes the evolution and prognosis of advanced cancers. Nat. Genet. 50 , 1189–1195. https://doi.org/10.1038/s41588-018-0165-1. Gemble, S., Wardenaar, R., Keuper, K., Srivastava, N., Nano, M., Macé, A.-S., Tijhuis, A.E., Bernhard, S.V., Spierings, D.C.J., Simon, A., et al. (2022). Genetic instability from a single S phase after whole-genome duplication. Nature 604 , 146–151. https://doi.org/10.1038/s41586-022-04578-4. Steele, C.D., Tarabichi, M., Oukrif, D., Webster, A.P., Ye, H., Fittall, M., Lombard, P., Martincorena, I., Tarpey, P.S., Collord, G., et al. (2019). Undifferentiated Sarcomas Develop through Distinct Evolutionary Pathways. Cancer Cell 35 , 441-456.e8. https://doi.org/10.1016/j.ccell.2019.02.002. Lee, H.O., Davidson, J.M., and Duronio, R.J. (2009). Endoreplication: polyploidy with purpose. Genes Dev. 23 , 2461–2477. https://doi.org/10.1101/gad.1829209. Fujiwara, T., Bandi, M., Nitta, M., Ivanova, E.V., Bronson, R.T., and Pellman, D. (2005). Cytokinesis failure generating tetraploids promotes tumorigenesis in p53-null cells. Nature 437 , 1043–1047. https://doi.org/10.1038/nature04217. Archetti, M. (2022). Polyploidy as an Adaptation against Loss of Heterozygosity in Cancer. Int. J. Mol. Sci. 23 , 8528. https://doi.org/10.3390/ijms23158528. Donné, R., Nader, M.B., and Desdouets, C. (2018). Cellular and Molecular Mechanisms Controlling Ploidy. In Reference Module in Life Sciences (Elsevier), p. B9780128096338122903. https://doi.org/10.1016/B978-0-12-809633-8.12290-3. Delespaul, L., Merle, C., Lesluyes, T., Lagarde, P., Le Guellec, S., Pérot, G., Baud, J., Carlotti, M., Danet, C., Fèvre, M., et al. (2019). Fusion-mediated chromosomal instability promotes aneuploidy patterns that resemble human tumors. Oncogene 38 , 6083–6094. https://doi.org/10.1038/s41388-019-0859-6. Yin, H., Price, F., and Rudnicki, M. (2013). Satellite cells and the muscle stem cell niche. Physiol Rev 93 , 23–67. https://doi.org/10.1152/physrev.00043.2011. Quinn, M.E., Goh, Q., Kurosaka, M., Gamage, D.G., Petrany, M.J., Prasad, V., and Millay, D.P. (2017). Myomerger induces fusion of non-fusogenic cells and is required for skeletal muscle development. Nat. Commun. 8 , 15665. https://doi.org/10.1038/ncomms15665. Lacour, F., Vezin, E., Bentzinger, C.F., Sincennes, M.-C., Giordani, L., Ferry, A., Mitchell, R., Patel, K., Rudnicki, M.A., Chaboissier, M.-C., et al. (2017). R-spondin1 Controls Muscle Cell Fusion through Dual Regulation of Antagonistic Wnt Signaling Pathways. Cell Rep. 18 , 2320–2330. https://doi.org/10.1016/j.celrep.2017.02.036. Aichel, O. (1911). About cell fusion with qualitatively abnormal chromosome distribution as cause for tumor formation / ̈ Uber zellverschmelzung mit qualitativ abnormer chromosomenverteilung als ursache der geschwulstbildung. In Vortr ̈ age und aufs ̈ atze über entvickelungsmechanik der organismen. Wilhelm Engelmann Leipz., 92–111. Shabo, I., Svanvik, J., Lindström, A., Lechertier, T., Trabulo, S., Hulit, J., Sparey, T., and Pawelek, J. (2020). Roles of cell fusion, hybridization and polyploid cell formation in cancer metastasis. World J. Clin. Oncol. 11 , 121–135. https://doi.org/10.5306/wjco.v11.i3.121. Brito, A., Merle, C., Lagarde, P., Faustin, B., Devin, A., Lartigue, L., and Chibon, F. (2021). Cell fusion enhances energy metabolism of mesenchymal tumor hybrid cells to sustain their proliferation and invasion. BMC Cancer 21 , 863. https://doi.org/10.1186/s12885-021-08561-6. Lartigue, L., Merle, C., Lagarde, P., Delespaul, L., Lesluyes, T., Le Guellec, S., Pérot, G., Leroy, L., Coindre, J.-M., and Chibon, F. (2020). Genome remodeling upon mesenchymal tumor cell fusion contributes to tumor progression and metastatic spread. Oncogene. https://doi.org/10.1038/s41388-020-1276-6. Gast, C.E., Silk, A.D., Zarour, L., Riegler, L., Burkhart, J.G., Gustafson, K.T., Parappilly, M.S., Roh-Johnson, M., Goodman, J.R., Olson, B., et al. (2018). Cell fusion potentiates tumor heterogeneity and reveals circulating hybrid cells that correlate with stage and survival. Sci. Adv. 4 , eaat7828. https://doi.org/10.1126/sciadv.aat7828. Merle, C., Thébault, N., LeGuellec, S., Baud, J., Pérot, G., Lesluyes, T., Delespaul, L., Lartigue, L., and Chibon, F. (2020). Tetraploidization of Immortalized Myoblasts Induced by Cell Fusion Drives Myogenic Sarcoma Development with DMD Deletion. Cancers 12 , 1281. https://doi.org/10.3390/cancers12051281. Hernando, E., Charytonowicz, E., Dudas, M.E., Menendez, S., Matushansky, I., Mills, J., Socci, N.D., Behrendt, N., Ma, L., Maki, R.G., et al. (2007). The AKT-mTOR pathway plays a critical role in the development of leiomyosarcomas. Nat. Med. 13 , 748–753. https://doi.org/10.1038/nm1560. Darbo, E., Pérot, G., Darmusey, L., Le Guellec, S., Leroy, L., Gaston, L., Desplat, N., Thébault, N., Merle, C., Rochaix, P., et al. (2023). Distinct Cellular Origins and Differentiation Process Account for Distinct Oncogenic and Clinical Behaviors of Leiomyosarcomas. Cancers 15 , 534. https://doi.org/10.3390/cancers15020534. Lee, P.J., Yoo, N.S., Hagemann, I.S., Pfeifer, J.D., Cottrell, C.E., Abel, H.J., and Duncavage, E.J. (2017). Spectrum of mutations in leiomyosarcomas identified by clinical targeted next-generation sequencing. Exp. Mol. Pathol. 102 , 156–161. https://doi.org/10.1016/j.yexmp.2017.01.012. Benhaddou, A., Gaston, L., Pérot, G., Desplat, N., Leroy, L., Le Guellec, S., Ben Haddou, M., Rochaix, P., Valentin, T., Ferron, G., et al. (2021). Medium levels of transcription and replication related chromosomal instability are associated with poor clinical outcome. Sci. Rep. 11 , 23429. https://doi.org/10.1038/s41598-021-02787-x. Hofmann, F., and Wegener, J.W. (2013). cGMP-Dependent Protein Kinases (cGK). In Guanylate Cyclase and Cyclic GMP Methods in Molecular Biology., T. Krieg and R. Lukowski, eds. (Humana Press), pp. 17–50. https://doi.org/10.1007/978-1-62703-459-3_2. Hofmann, F. (2005). The Biology of Cyclic GMP-dependent Protein Kinases. J. Biol. Chem. 280 , 1–4. https://doi.org/10.1074/jbc.R400035200. Chan, M.H., Aminzai, S., Hu, T., Taran, A., Li, S., Kim, C., Pilz, R.B., and Casteel, D.E. (2020). A substitution in cGMP-dependent protein kinase 1 associated with aortic disease induces an active conformation in the absence of cGMP. J. Biol. Chem. 295 , 10394–10405. https://doi.org/10.1074/jbc.RA119.010984. Ørstavik, S., and Natarajan, V. Characterization of the Human Gene Encoding the Type Ia and Type Ib cGMP-Dependent Protein Kinase (PRKG1). Das, A., Xi, L., and Kukreja, R.C. (2008). Protein Kinase G-dependent Cardioprotective Mechanism of Phosphodiesterase-5 Inhibition Involves Phosphorylation of ERK and GSK3β. J. Biol. Chem. 283 , 29572–29585. https://doi.org/10.1074/jbc.M801547200. Rybalkin, S.D., Yan, C., Bornfeldt, K.E., and Beavo, J.A. (2003). Cyclic GMP Phosphodiesterases and Regulation of Smooth Muscle Function. Circ. Res. 93 , 280–291. https://doi.org/10.1161/01.RES.0000087541.15600.2B. Yoon, J., Byun, H., Ivan, C., Calin, G.A., Jung, D., and Lee, S. (2022). lncRNAs UC.145 and PRKG1-AS1 Determine the Functional Output of DKK1 in Regulating the Wnt Signaling Pathway in Gastric Cancer. Cancers 14 , 2369. https://doi.org/10.3390/cancers14102369. Benvenuto, G., Todeschini, P., Paracchini, L., Calura, E., Fruscio, R., Romani, C., Beltrame, L., Martini, P., Ravaggi, A., Ceppi, L., et al. (2020). Expression profiles of PRKG1, SDF2L1 and PPP1R12A are predictive and prognostic factors for therapy response and survival in high‐grade serous ovarian cancer. Int. J. Cancer 147 , 565–574. https://doi.org/10.1002/ijc.32935. Calamera, G., Moltzau, L.R., Levy, F.O., and Andressen, K.W. (2022). Phosphodiesterases and Compartmentation of cAMP and cGMP Signaling in Regulation of Cardiac Contractility in Normal and Failing Hearts. Int. J. Mol. Sci. 23 , 2145. https://doi.org/10.3390/ijms23042145. Carvajal, J.A., Germain, A.M., Huidobro-Toro, J.P., and Weiner, C.P. (2000). Molecular mechanism of cGMP-mediated smooth muscle relaxation. J. Cell. Physiol. 184 , 409–420. https://doi.org/10.1002/1097-4652(200009)184:33.0.CO;2-K. Chibon, F., Lagarde, P., Salas, S., Pérot, G., Brouste, V., Tirode, F., Lucchesi, C., De Reynies, A., Kauffmann, A., Bui, B., et al. (2010). Validated prediction of clinical outcome in sarcomas and multiple types of cancer on the basis of a gene expression signature related to genome complexity. Nat. Med. 16 , 781–787. https://doi.org/10.1038/nm.2174. Lesluyes, T., Delespaul, L., Coindre, J.-M., and Chibon, F. (2017). The CINSARC signature as a prognostic marker for clinical outcome in multiple neoplasms. Sci. Rep. 7 , 5480. https://doi.org/10.1038/s41598-017-05726-x. Valentin, T., Lesluyes, T., Guellec, S.L., and Chibon, F. Title: Chemotherapy in localized Soft Tissue Sarcoma: will we soon have to treat grade 1 tumours? 3. Anderson, N.D., Babichev, Y., Fuligni, F., Comitani, F., Layeghifard, M., Venier, R.E., Dentro, S.C., Maheshwari, A., Guram, S., Wunker, C., et al. (2021). Lineage-defined leiomyosarcoma subtypes emerge years before diagnosis and determine patient survival. Nat. Commun. 12 , 4496. https://doi.org/10.1038/s41467-021-24677-6. Gibault, L., Pérot, G., Chibon, F., Bonnin, S., Lagarde, P., Terrier, P., Coindre, J.-M., and Aurias, A. (2011). New insights in sarcoma oncogenesis: a comprehensive analysis of a large series of 160 soft tissue sarcomas with complex genomics: Genome and transcriptome analysis of human soft tissue sarcomas. J. Pathol. 223 , 64–71. https://doi.org/10.1002/path.2787. Italiano, A., Lagarde, P., Brulard, C., Terrier, P., Laë, M., Marques, B., Ranchere-Vince, D., Michels, J.-J., Trassard, M., Cioffi, A., et al. (2013). Genetic Profiling Identifies Two Classes of Soft-Tissue Leiomyosarcomas with Distinct Clinical Characteristics. Clin. Cancer Res. 19 , 1190–1196. https://doi.org/10.1158/1078-0432.CCR-12-2970. Hemming, M.L., Fan, C., Raut, C.P., Demetri, G.D., Armstrong, S.A., Sicinska, E., and George, S. (2020). Oncogenic Gene-Expression Programs in Leiomyosarcoma and Characterization of Conventional, Inflammatory, and Uterogenic Subtypes. Mol. Cancer Res. 18 , 1302–1314. https://doi.org/10.1158/1541-7786.MCR-20-0197. Rubio, R., García-Castro, J., Gutiérrez-Aranda, I., Paramio, J., Santos, M., Catalina, P., Leone, P.E., Menendez, P., and Rodríguez, R. (2010). Deficiency in p53 but not Retinoblastoma Induces the Transformation of Mesenchymal Stem Cells In vitro and Initiates Leiomyosarcoma In vivo . Cancer Res. 70 , 4185–4194. https://doi.org/10.1158/0008-5472.CAN-09-4640. Liu, W., Tong, H., Zhang, C., Zhuang, R., Guo, H., Lv, C., Yang, H., Lin, Q., Guo, X., Wang, Z., et al. (2020). Integrated genomic and transcriptomic analysis revealed mutation patterns of de-differentiated liposarcoma and leiomyosarcoma. BMC Cancer 20 , 1035. https://doi.org/10.1186/s12885-020-07456-2. Gambaryan, S. (2022). The Role of NO/sGC/cGMP/PKG Signaling Pathway in Regulation of Platelet Function. Cells 11 , 3704. https://doi.org/10.3390/cells11223704. Hafen, B.B., and Burns, B. (2022). Physiology, Smooth Muscle. In StatPearls [Internet] Treasure Island (FL). (tatPearls Publishing). Brozovich, F.V., Nicholson, C.J., Degen, C.V., Gao, Y.Z., Aggarwal, M., and Morgan, K.G. (2016). Mechanisms of Vascular Smooth Muscle Contraction and the Basis for Pharmacologic Treatment of Smooth Muscle Disorders. Pharmacol. Rev. 68 , 476–532. https://doi.org/10.1124/pr.115.010652. Lorigo, M., Oliveira, N., and Cairrao, E. (2021). PDE-Mediated Cyclic Nucleotide Compartmentation in Vascular Smooth Muscle Cells: From Basic to a Clinical Perspective. J. Cardiovasc. Dev. Dis. 9 , 4. https://doi.org/10.3390/jcdd9010004. Pavlaki, N., and Nikolaev, V. (2018). Imaging of PDE2- and PDE3-Mediated cGMP-to-cAMP Cross-Talk in Cardiomyocytes. J. Cardiovasc. Dev. Dis. 5 , 4. https://doi.org/10.3390/jcdd5010004. Gan, Y., Li, X., Han, S., Liang, Q., Ma, X., Rong, P., Wang, W., and Li, W. (2022). The cGAS/STING Pathway: A Novel Target for Cancer Therapy. Front. Immunol. 12 , 795401. https://doi.org/10.3389/fimmu.2021.795401. Jing, Z., Liu, G., Zhou, N., Xu, D., Feng, N., Lei, Y., Ma, L., Tang, M., Tong, G., Tang, N., et al. (2024). Interferon-γ in the tumor microenvironment promotes the expression of B7H4 in colorectal cancer cells, thereby inhibiting cytotoxic T cells. Sci. Rep. 14 , 6053. https://doi.org/10.1038/s41598-024-56681-3. Oshi, M., Takahashi, H., Tokumaru, Y., Yan, L., Rashid, O.M., Matsuyama, R., Endo, I., and Takabe, K. (2020). G2M Cell Cycle Pathway Score as a Prognostic Biomarker of Metastasis in Estrogen Receptor (ER)-Positive Breast Cancer. Int. J. Mol. Sci. 21 , 2921. https://doi.org/10.3390/ijms21082921. García-Gutiérrez, L., Delgado, M.D., and León, J. (2019). MYC Oncogene Contributions to Release of Cell Cycle Brakes. Genes 10 , 244. https://doi.org/10.3390/genes10030244. Petraina, A., Nogales, C., Krahn, T., Mucke, H., Lüscher, T.F., Fischmeister, R., Kass, D.A., Burnett, J.C., Hobbs, A.J., and Schmidt, H.H.H.W. (2022). Cyclic GMP modulating drugs in cardiovascular diseases: mechanism-based network pharmacology. Cardiovasc. Res. 118 , 2085–2102. https://doi.org/10.1093/cvr/cvab240. Ataei Ataabadi, E., Golshiri, K., Jüttner, A., Krenning, G., Danser, A.H.J., and Roks, A.J.M. (2020). Nitric Oxide-cGMP Signaling in Hypertension: Current and Future Options for Pharmacotherapy. Hypertension 76 , 1055–1068. https://doi.org/10.1161/HYPERTENSIONAHA.120.15856. Lagarde, P., Brulard, C., Perot, G., Maudiut, O., Delespaul, L., Neuville, A., Stoeckle, E., Le Guellec, S., Rochaix, P., Coindre, J., et al. (2015). Stable Instability of Sarcoma Cell Lines Genome Despite Intra-Tumoral Heterogeneity: A Genomic and Transcriptomic Study of Sarcoma Cell Lines. Austin J. Genet. Genomic Res. 2 , 1014. Haurie, V., Durrieu-Gaillard, S., Dumay-Odelot, H., Da Silva, D., Rey, C., Prochazkova, M., Roeder, R.G., Besser, D., and Teichmann, M. (2010). Two isoforms of human RNA polymerase III with specific functions in cell growth and transformation. Proc. Natl. Acad. Sci. 107 , 4176–4181. https://doi.org/10.1073/pnas.0914980107. Hahn, W.C., Counter, C.M., Lundberg, A.S., Beijersbergen, R.L., Brooks, M.W., and Weinberg, R.A. (1999). Creation of human tumourcells with de®ned genetic elements. 400 . Hahn, W.C., Dessain, S.K., Brooks, M.W., King, J.E., Elenbaas, B., Sabatini, D.M., DeCaprio, J.A., and Weinberg, R.A. (2002). Enumeration of the Simian Virus 40 Early Region Elements Necessary for Human Cell Transformation. MOL CELL BIOL 22 . Pérot, G., Derré, J., Coindre, J.-M., Tirode, F., Lucchesi, C., Mariani, O., Gibault, L., Guillou, L., Terrier, P., and Aurias, A. (2009). Strong Smooth Muscle Differentiation Is Dependent on Myocardin Gene Amplification in Most Human Retroperitoneal Leiomyosarcomas. Cancer Res. 69 , 2269–2278. https://doi.org/10.1158/0008-5472.CAN-08-1443. De Preter, K., Speleman, F., Combaret, V., Lunec, J., Laureys, G., Eussen, B.H.J., Francotte, N., Board, J., Pearson, A.D.J., De Paepe, A., et al. (2002). Quantification of MYCN, DDX1, and NAG Gene Copy Number in Neuroblastoma Using a Real-Time Quantitative PCR Assay. Mod. Pathol. 15 , 159–166. https://doi.org/10.1038/modpathol.3880508. R Core Team (2023). R: A Language and Environment for Statistical Computing_. R Foundation for Statistical Computing, Vienna, Austria. . Therneau, T.M. A Package for Survival Analysis in R 2020; CRAN Repository. 2020. Available online: https://CRAN.R-project.org/package=survival. Kassambara, A.; Kosinski, M.; Biecek, P. Survminer: Drawing Survival Curves Using “Ggplot2”; CRAN Repository. 2019. Available online: https://CRAN.R-project.org/package=survminer (accessed on 24 November 2022). Le Guellec, S., Lesluyes, T., Sarot, E., Valle, C., Filleron, T., Rochaix, P., Valentin, T., Pérot, G., Coindre, J.-M., and Chibon, F. (2018). Validation of the Complexity INdex in SARComas prognostic signature on formalin-fixed, paraffin-embedded, soft-tissue sarcomas. Ann. Oncol. 29 , 1828–1835. https://doi.org/10.1093/annonc/mdy194. Dobin, A., Davis, C., Schlesinger, F., Drenkow, J., Zaleski, C., Batut, P., Chaisson, M., and Gingeras, T. (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29(1) , 15–21. Danecek, P., Bonfield, J.K., Liddle, J., Marshall, J., Ohan, V., Pollard, M.O., Whitwham, A., Keane, T., McCarthy, S.A., Davies, R.M., et al. (2021). Twelve years of SAMtools and BCFtools. GigaScience 10 , giab008. https://doi.org/10.1093/gigascience/giab008. Putri, G.H., Anders, S., Pyl, P.T., Pimanda, J.E., and Zanini, F. (2022). Analysing high-throughput sequencing data in Python with HTSeq 2.0. Bioinformatics 38 , 2943–2945. https://doi.org/10.1093/bioinformatics/btac166. Love, M.I., Huber, W., and Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15 , 550. https://doi.org/10.1186/s13059-014-0550-8. Additional Declarations There is NO conflict of interest to disclose. Supplementary Files SupplementaryfiguresPRKG1Britoetal.docx Supplementary Figures SuppTablesPRKG1Britoetal.xlsx Supplementary Tables 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. 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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-6984264","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":486536116,"identity":"66d1f146-11d0-4b7c-aa94-90554333ca6f","order_by":0,"name":"Frederic Chibon","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIie3PMWvCQBTA8VeEy/LarE9OzFdIOLAVCv0qEcEsURztInFJl9I5nyRTh5Nb4+7gEgSdRRAFhx6iItJL1w735zjuuPzgBcBm+4c5CQCdFiAovTfZ+cU1EZR3RFxIPakmpyPU9N65fGkmXE3nm+8XD5zZVA1Hi+iLf5TL9xjo2WDwqddtZysKEhyEKitW/bRRiGCWAzXk7+QNscVRUgiEvnpMVT+lmNUnOYzJNBi6O368IRGjaH3QhMwEGYcbEjIKWw/VhIn2p6QgxdjX/6ICPZjQg1EFqZXzvRx7rlOI7XCkPC+Lys0kfzWSa+zu/iew2Ww2W0U/3IdOjQ/h6KgAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-6548-2181","institution":"Cancer Research Center of Toulouse","correspondingAuthor":true,"prefix":"","firstName":"Frederic","middleName":"","lastName":"Chibon","suffix":""},{"id":486536117,"identity":"494ba4bc-686c-4563-944a-b53b0686dde5","order_by":1,"name":"Ariadna Brito","email":"","orcid":"https://orcid.org/0009-0008-1367-5185","institution":"INSERM U1037, Cancer Research Center in Toulouse (CRCT)","correspondingAuthor":false,"prefix":"","firstName":"Ariadna","middleName":"","lastName":"Brito","suffix":""},{"id":486536118,"identity":"9829c678-fbe6-4ecd-b0f7-450460082f17","order_by":2,"name":"Perot Gaelle","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Perot","middleName":"","lastName":"Gaelle","suffix":""},{"id":486536119,"identity":"db7d5ede-0619-4d61-8c94-a2d78cb151ea","order_by":3,"name":"Natacha Roussel","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Natacha","middleName":"","lastName":"Roussel","suffix":""},{"id":486536120,"identity":"3f40fb18-0d2c-4b0a-af70-031591afad11","order_by":4,"name":"Lise Pomies","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Lise","middleName":"","lastName":"Pomies","suffix":""},{"id":486536121,"identity":"07b1ab89-1e48-4d26-8c1e-27bc7ec56ad4","order_by":5,"name":"Joanna Fourquet","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Joanna","middleName":"","lastName":"Fourquet","suffix":""},{"id":486536122,"identity":"bc1a55a1-ffa4-4fb6-b550-fd6caa4bfe47","order_by":6,"name":"Lucile Delespaul","email":"","orcid":"","institution":"INSERM U1037 CRCT","correspondingAuthor":false,"prefix":"","firstName":"Lucile","middleName":"","lastName":"Delespaul","suffix":""},{"id":486536123,"identity":"32ef6f6f-e209-4544-b14f-4d8cf3decdd1","order_by":7,"name":"Anne Gomez-Brouchet","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Anne","middleName":"","lastName":"Gomez-Brouchet","suffix":""},{"id":486536124,"identity":"633faeea-fe0a-439e-bd2b-3774ad464419","order_by":8,"name":"Gwenael Ferron","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Gwenael","middleName":"","lastName":"Ferron","suffix":""},{"id":486536125,"identity":"11b8c3d6-eda9-4c92-80ea-abc74e90fad7","order_by":9,"name":"Philippe Rochaix","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Philippe","middleName":"","lastName":"Rochaix","suffix":""}],"badges":[],"createdAt":"2025-06-26 14:20:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6984264/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6984264/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87372120,"identity":"b5c5a5ad-52de-4b96-a2e7-e2dc9403593a","added_by":"auto","created_at":"2025-07-23 07:22:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":508147,"visible":true,"origin":"","legend":"\u003cp\u003eCharacterization of hybrid cell lines. \u003cstrong\u003e(A)\u003c/strong\u003eGraphical representation of the spontaneous cell fusion generated. Indicating the fluorescent protein and the antibiotic resistance of the corresponding parental cell lines. \u003cstrong\u003e(B) \u003c/strong\u003eCell sorting by flow cytometry of the hybrid cell lines selecting both DsRed and CFP fluorescence to purify the hybrid cell culture. \u003cstrong\u003e(C)\u003c/strong\u003e DNA array of parental cell lines and six-hybrid cell lines produced. Each box corresponds to one chromosome from 1 to Y. Blue arrows point at non-recurrent alterations, black arrows point at recurrent alterations among the six-hybrid cell lines and the red arrow point at the very specific deletion in \u003cem\u003ePRKG1\u003c/em\u003e in the hybrid H2.1. \u003cstrong\u003e(D) \u003c/strong\u003eLeft panel shows the tumor growth rate of the H2.1 \u003cem\u003ein vivo\u003c/em\u003e. Right panel: Hematoxylin and eosin (HE) staining and TAGLN immunohistochemistry (IHC) analysis of the mice tumors.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6984264/v1/177bc0948fd211f319943141.png"},{"id":87370161,"identity":"d7d990b7-ed2d-42bc-86dd-54ba8f38dfc1","added_by":"auto","created_at":"2025-07-23 07:06:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":80970,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency of \u003cem\u003ePRKG1\u003c/em\u003edeletion in LMS patients. \u003cstrong\u003e(A)\u003c/strong\u003eWhole genome sequencing (WGS) data of a cohort of 121 LMS patients classified in percentage based on their chromosome 10 status. \u003cstrong\u003e(B)\u003c/strong\u003eRepresentation of the six patients with one or several BPs in \u003cem\u003ePRKG1\u003c/em\u003e. In red, the total number of reads by WGS in the \u003cem\u003ePRKG1\u003c/em\u003e locus and in black, the adjacent regions.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6984264/v1/a8e35e31f7f2828c5670860d.png"},{"id":87371322,"identity":"90c603cf-0619-494c-a619-860b0cca4dbd","added_by":"auto","created_at":"2025-07-23 07:14:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":33764,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePRKG1\u003c/em\u003e as a prognostic marker in LMS. RNAseqanalysis, cohort of 147 LMS samples, classified based on high (red line) or low (blue line) expression of\u003cem\u003ePRKG1\u003c/em\u003e. \u003cstrong\u003e(A)\u003c/strong\u003eMetastasis-free survival P-value=0.04. \u003cstrong\u003e(B)\u003c/strong\u003e Cancer-specific survival P-value= 0.024. \u003cstrong\u003e(C) \u003c/strong\u003eOverall survival P-value= 0.00038.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6984264/v1/2bbb96a735de9b41b4b6db0e.png"},{"id":87370168,"identity":"2b2a059e-e8bf-4efa-9810-16a59bac94f3","added_by":"auto","created_at":"2025-07-23 07:06:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":96893,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePRKG1\u003c/em\u003estatus on LMS and hybrid cell lines. Weighted log2 ratio (top panel) and allele difference (bottom panel) of the cell lines used to study the role of \u003cem\u003ePRKG1\u003c/em\u003e. OC140, OC148 and OC211 primary cell lines derived from LMS tumors and H2.1 hybrid cell line formed by the fusion of fibroblast and SMC.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6984264/v1/e333ed0a4d32b4a975a47916.png"},{"id":87370164,"identity":"14cfe23f-5986-49ae-a72e-c73bf9b3eb7c","added_by":"auto","created_at":"2025-07-23 07:06:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":30714,"visible":true,"origin":"","legend":"\u003cp\u003ePhenotypic analysis on LMS and hybrid cell lines. Migration of H2.1, OC140, OC148, and OC211 comparing the different forms of expression of PRKG1. Evaluated by Boyden chamber T=24h. Data normalized to the number of cells seeded at T0 determined by flow cytometry. Statistical test: non-parametric, Mann-Whitney t-test (*p\u0026lt;0.01, **p=0.0063, ***p=0.0005, ****p\u0026lt;0.0001, error bars, SD; N=3 done in quadruplicate in each independent experiment.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6984264/v1/7f835353d946bdc17ad20932.png"},{"id":87370162,"identity":"af70c07f-f9d2-45bb-b832-85bd9be5c500","added_by":"auto","created_at":"2025-07-23 07:06:12","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":23064,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential gene expression from RNA sequencing. Cell lines OC140 and OC211 are analyzed together, comparing only the different isoform expressions. A= \u003cem\u003ePRKG1-alpha\u003c/em\u003eisoform, B= \u003cem\u003ePRKG1-Beta\u003c/em\u003eisoform, CT= Control, \u003cem\u003ePRKG1\u003c/em\u003edeletion. Top panel, up regulated genes in four different comparisons: A and B vs CT, A vs B, B vs CT and A vs CT. Bottom panel, down regulated genes following the same comparisons. X-axis shows the number of genes identified in both panels.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6984264/v1/49f817fdfb192340ec9291e0.png"},{"id":87370172,"identity":"adb62889-1a25-408a-bd2a-2f65af751522","added_by":"auto","created_at":"2025-07-23 07:06:12","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":313047,"visible":true,"origin":"","legend":"\u003cp\u003eGSEA enrichment plots for top enriched categories from hallmark of OC140 \u003cstrong\u003e(A)\u003c/strong\u003e and OC211 \u003cstrong\u003e(B)\u003c/strong\u003e.\u003cstrong\u003e (A)\u003c/strong\u003e Top panel, enrichment plot of interferon alpha and interferon gamma when \u003cem\u003ePRKG1-beta\u003c/em\u003e is overexpressed. Bottom panel, enrichment plot MYC, E2F and G2M, when \u003cem\u003ePRKG1\u003c/em\u003e is deleted. \u003cstrong\u003e(B) \u003c/strong\u003eTop panel enrichment of interferon response and interferon alpha and beta signaling when PRKG1-alpha is overexpressed. Bottom panel, enrichment of MYC, E2F and G2M when PRKG1 is deleted.\u003cstrong\u003e(C) \u003c/strong\u003eGSEA enrichment plots for CINSARC genes. First and second panels, comparison between CT (\u003cem\u003ePRKG1\u003c/em\u003edeletion) and amplification of \u003cem\u003ePRKG1\u003c/em\u003eisoform alpha and beta from OC140 and OC211 cell lines respectively. Third panel, comparison between CT (\u003cem\u003ePRKG1\u003c/em\u003edeletion) and amplification of \u003cem\u003ePRKG1\u003c/em\u003eisoforms combining both cell lines\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6984264/v1/87de2511a34776cddfa76e66.png"},{"id":87370171,"identity":"cf7547f9-6c19-4021-afab-e2010ccd03af","added_by":"auto","created_at":"2025-07-23 07:06:12","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":80263,"visible":true,"origin":"","legend":"\u003cp\u003eCross-talk between cGMP, cAMP and IP3 pathways. cGMP and cAMPpathways decrease the level of intracellular calcium ions. On the contrary the IP3 pathway produces a decrease of calcium in the sarcoplasmic or endoplasmic reticulum. NO = nitric oxide, sGC = soluble guanylate cyclase, AC = adenylate cyclase, ATP = Adenosine triphosphate, GTP = Guanosine triphosphate, cGMP = Cyclic adenosine monophosphate, cAMP = Guanosine 3',5'-cyclic monophosphate, MLCK = Myosin light chain kinase, MLCP = Myosin light chain phosphatase, PIP2 =Phosphatidylinositol 4,5-biphosphate, IP3 = Inositol triphosphate, DAG = Diacylglycerol.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6984264/v1/89b54a44baf4ea01f2e2f1d1.png"},{"id":106726666,"identity":"202e16ee-d19b-4f93-bb8b-8191fe61d9fd","added_by":"auto","created_at":"2026-04-12 18:36:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2159634,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6984264/v1/15bebd8b-d77b-4618-8f29-1b29e555f004.pdf"},{"id":87371324,"identity":"c934c318-9ff9-4a2d-bd58-34b8936b11d4","added_by":"auto","created_at":"2025-07-23 07:14:12","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1722843,"visible":true,"origin":"","legend":"Supplementary Figures","description":"","filename":"SupplementaryfiguresPRKG1Britoetal.docx","url":"https://assets-eu.researchsquare.com/files/rs-6984264/v1/0913da6f5125de3b2c51f868.docx"},{"id":87370160,"identity":"6369d09c-6077-4518-a061-9cae82fb254d","added_by":"auto","created_at":"2025-07-23 07:06:11","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":25263,"visible":true,"origin":"","legend":"Supplementary Tables","description":"","filename":"SuppTablesPRKG1Britoetal.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6984264/v1/35f33ad812349f4b72cb84e0.xlsx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Tetraploidization-driven PRKG1 deletion reveals novel mechanisms underlying Leiomyosarcoma aggressiveness","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSoft tissue sarcomas (STSs) represent a highly heterogeneous group of neoplasias with mesenchymal origin, classified on histologic basis according to the mature tissue they resemble the most\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Leiomyosarcoma (LMS) is diagnosed according to its variable smooth muscle cell (SMC) differentiation and is among the most common types of STS. LMS occurs in middle-age or older adults, it can arise in almost all anatomic sites but the most frequent are: the abdominal cavity, limbs, large blood vessels and the uterus\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The standard treatment remains surgery, when possible, and chemotherapy with doxorubicin. In high-risk LMS, adjuvant or neoadjuvant radiation therapy may complement the surgery, but the treatment is not universal due to the lack of both, efficient therapies and biological markers predicting response/aggressiveness\u003csup\u003e3\u003c/sup\u003e. Although gene signatures are currently under prospective evaluation to improve patient stratification and prognosis\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. LMSs are highly aggressive, despite complete resection of the primary tumor, around 40% of the patients develop distant metastasis\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Patients with metastasis dissemination remain incurable with a median overall survival of 12 months after the dissemination of the disease.\u003c/p\u003e \u003cp\u003eLMS does not have a unique known driver mutation, on the contrary, its characterized by its chaotic genome harboring numerous gains, losses and BP mutations. While these alterations vary across patients, there are few recurrencies, the p53 and Rb pathways alterations are present in nearly all the cases\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e and the genomic deletions of \u003cem\u003ePTEN\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, \u003cem\u003eDMD\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, \u003cem\u003eATRX\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. None of these alterations are specific to LMS, therefore, LMSs are instead characterized by their highly rearranged genome, and frequent whole-genome doubling (WGD)\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, which makes the identification of targeted treatments extremely difficult\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e or inefficient. It has been demonstrated that WGD is one of the mechanisms involved in genomic instability\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Redundant DNA content often leads to imbalances in chromosome number and structure, disrupting the normal cellular machinery and promoting tumorigenesis\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. WGD is frequently observed in cancer, it has been reported to be present in 55% of LMS\u003csup\u003e12\u003c/sup\u003e and 89% of undifferentiated pleomorphic sarcoma (UPS)\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, revealing its potential role in the oncogenesis of sarcomas. There are four possible cellular mechanisms causing WGD: endoreplication\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, abortive cytokinesis\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, mitotic slippage\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e and cell fusion\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile most mechanims driving WGD involve mitotic defects, cell fusion stands as an exception. Cell fusion is a physiological mechanism crucial for mesenchymal cell differentiation during muscle or bone formation\u003csup\u003e\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. The concept of cell-cell fusion in oncogenesis was first proposed by German physician Otto Aichel in 1911\u003csup\u003e27\u003c/sup\u003e. Modern studies have since validated his hypothesis, demonstrating that hybrid cell lines resulting from fusion exhibit increased aggressiveness\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, enhanced invasion capacity\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, contribute to tumor progression\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, and are associated with poor prognosis\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Consistent with these findings, our previous research revealed spontaneous cell fusion in patient-derived sarcoma cell lines\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. This process yield hybrid cells with merged nuclei, possessing a unique combination of parental cell information and, consequently, novel properties\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Furthermore, we have also shown that cell fusion model can reproduce the genomic alterations observed in UPS and pleomorphic rhabdomyosarcomas, with the specific alterations dependent on the parental cells involved in the fusion\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePathologists are able to classify LMS tumors due to the smooth muscle differentiation of the tumoral cells, suggesting that the initiation cell of LMS is either a SMC or a stem mesenchymal cell capable of differentiate into SMC\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Our team and others have recently demonstrated that there are two distinct types of soft tissue LMS based on transcriptome and genomic alteration profiles: one with overexpression of smooth muscle related genes and another one associated with fibroblasts, adipocytes and mesenchymal stem cells gene expression\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Given the observed dual nature of LMS tumors, characterized by both smooth muscle and fibroblast gene expression profiles, we hypothesized that the fusion of SMCs with fibroblasts would provide a highly relevant model for studying LMS oncogenesis. This fusion aims to recapitulate the complex cellular origins of LMS, allowing us to investigate how these distinct cell lineages contribute to tumor development.\u003c/p\u003e \u003cp\u003eIn this study, we explored the role of WGD via cell fusion in leiomyosarcoma oncogenesis. To address this, we developed a cell fusion model using co-culture of immortalized smooth muscle cells (SMC, E6) and transformed fibroblasts (RST, IMR90 E6E7) to promote spontaneous cell fusion. The resulting hybrid cells were subjected to genomic analysis, and specific alterations identified were compared to those found in diverse patient cohorts. We further assessed the potential involvement of these genomic alterations in LMS oncogenesis and progression.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1. SMCs and fibroblasts fuse to produce proliferating hybrid cells\u003c/h2\u003e \u003cp\u003eBoth diploid cell lines, RST\u003csup\u003eDsRed\u0026minus;Puro\u003c/sup\u003e and SMCE6\u003csup\u003eCFP\u0026thinsp;\u0026minus;\u0026thinsp;Blast\u003c/sup\u003e (see Material and Methods for cell line specifications) (Fig.\u0026nbsp;1A) were co-cultured for 72h. Hybrid cell selection was achieved through a combined approach of dual antibiotic resistance and fluorescence-activated cell sorting (FACS), targeting cells exhibiting co-expression of DsRed+/CFP+ (Fig.\u0026nbsp;1B). Six hybrid cell lines were established from independent co-cultures. All six-cell lines showed aneuploid (near tetraploid) genomes verified by cytometry with propidium iodide (PI) and by chromosome count with chromosome spreading assay (hybrids chromosome content ranging from 126 to 74; Supplementary Figs.\u0026nbsp;1A \u0026amp; B). To evaluate the proliferative capacity of the hybrid cell lines, we performed 72-hour proliferation assays. Based on our prior observation that RST\u003csup\u003eDsRed\u0026minus;Puro\u003c/sup\u003e exhibit a higher proliferation rate than SMCE6\u003csup\u003eCFP\u0026thinsp;\u0026minus;\u0026thinsp;Blast\u003c/sup\u003e cells, we hypothesized that all hybrids would outperform SMCE6\u003csup\u003eCFP\u0026thinsp;\u0026minus;\u0026thinsp;Blast\u003c/sup\u003e. The results confirm this, demonstrating that all hybrid cell lines showed enhanced proliferative rate to SMCE6\u003csup\u003eCFP\u0026thinsp;\u0026minus;\u0026thinsp;Blast\u003c/sup\u003e. Hybrid cell lines also showed higher proliferative capacities compared to RST\u003csup\u003eDsRed\u0026minus;Puro\u003c/sup\u003e except for H2.1 and H3.2 (Supplementary Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003cb\u003e2. Shared Genomic Alterations in SMC/fibroblast hybrids and LMS Tumors.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eGenomic profiling using DNA microarrays demonstrated that all hybrid cell lines, in contrast to parental cells, displayed a rearranged genome (Fig.\u0026nbsp;1C, top panel vs. lower panel). Although most alterations were non-recurrent losses, among all, few were recurrent: chromosome 13 (in 4/6 hybrids), loss of chromosome 17 (in 4/6 hybrids) and chromosome 10 (2/6 hybrids). Consistent with the genomic alterations commonly found in LMS, we observed deletion in \u003cem\u003eRB1\u003c/em\u003e (13q), \u003cem\u003eTP53\u003c/em\u003e (17p) and \u003cem\u003ePTEN\u003c/em\u003e (10q\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, Fig.\u0026nbsp;1C, black arrows). Additionally, a highly specific deletion of the \u003cem\u003ePRKG1\u003c/em\u003e gene (chromosome 10) was identified in the H2.1 cell line (Fig.\u0026nbsp;1C, red arrow).\u003c/p\u003e \u003cp\u003eGiven the genomic alterations observed in the H2.1 cell line, we sought to confirm their functional significance by assessing tumorigenicity in vivo. H2.1 cells were subcutaneously injected into NSG mice, resulting in tumor formation in all five mice, with initial tumor growth detected 12 days post-injection (Fig.\u0026nbsp;1D, left panel). Histopathological evaluation by an expert sarcoma pathologist classified the tumors as grade 3, poorly differentiated LMS or UPS, characterized by incomplete smooth muscle differentiation and TAGLN expression (Fig.\u0026nbsp;1D, right panel), but lacking CALD1 expression (data not shown).\u003c/p\u003e \u003cp\u003e \u003cb\u003e3. Recurrence of\u003c/b\u003e \u003cb\u003ePRKG1\u003c/b\u003e \u003cb\u003edeletion in LMS patients\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo determine whether the \u003cem\u003ePRKG1\u003c/em\u003e deletion observed in the H2.1 hybrid cell line might be clinically relevant, we conducted a comprehensive analysis of whole-genome sequencing (WGS) data obtained from a cohort of 121 LMS tumors, previously analyzed\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Regarding Copy Number Variation (CNV), patients were classified based on the chromosome 10 status: 28/121 (23.1%) present no chromosome 10 alteration, 49/121 (40.5%) one copy loss of chromosome 10, 27/121 (22.3%) deletion of the chromosome 10 long arm (including \u003cem\u003ePRKG1\u003c/em\u003e), 11/121 (9.1%) tumors with an intra-chromosomal deletion including \u003cem\u003ePRKG1\u003c/em\u003e, and 6 patients (5%) with either one or several \u003cem\u003ePRKG1\u003c/em\u003e BP (Figs.\u0026nbsp;2A \u0026amp; B). All BP were validated by DNA Sanger sequencing (Supplementary Tables\u0026nbsp;1 \u0026amp; 2, except for LMS55T for which one of the adjacent BP sequences could not be identified because of its repetitive nature). Among those six tumors, three BP resulted as frameshift (FS) at RNA level (Supplementary Tables\u0026nbsp;1 \u0026amp; 3).\u003c/p\u003e \u003cp\u003eConsequently, WGS results show that 93/121 (76.9%) of LMS patients had at least one \u003cem\u003ePRKG1\u003c/em\u003e altered copy (whole chromosome deletion, arm deletion, BP or intra-chromosomal alteration). Among them, 17/93 (18%) showed an alteration specifically targeting \u003cem\u003ePRKG1\u003c/em\u003e either an intra-chromosomal loss encompassing \u003cem\u003ePRKG1\u003c/em\u003e (11/93) or at least one BP in \u003cem\u003ePRKG1\u003c/em\u003e (6/93), which accounts for a total of 17/121 (14%).\u003c/p\u003e \u003cp\u003e \u003cb\u003e3. Low expression of\u003c/b\u003e \u003cb\u003ePRKG1\u003c/b\u003e \u003cb\u003epredicts adverse outcome in LMS\u003c/b\u003e\u003c/p\u003e \u003cp\u003eGiven the observed prevalence of \u003cem\u003ePRKG1\u003c/em\u003e deletion in LMS patients, we aim to determine the potential clinical significance of \u003cem\u003ePRKG1\u003c/em\u003e alteration. To this end, a cohort of 147 LMS samples with available RNA sequencing data was stratified based on \u003cem\u003ePRKG1\u003c/em\u003e expression levels (low vs. high, using the mean as a cut-off), and correlated with clinical follow-up information. (Supplementary Table\u0026nbsp;4). Kaplan-Meier analyses showed that metastasis-free survival, as well as cancer-specific survival and overall survival were significantly worse in the group with the lowest \u003cem\u003ePRKG1\u003c/em\u003e expressions (P-values of 0.04, 0.024 and 0.00038 respectively, Fig.\u0026nbsp;3).\u003c/p\u003e\u003cp\u003e \u003cb\u003e5. Functional impact of\u003c/b\u003e \u003cb\u003ePRKG1\u003c/b\u003e \u003cb\u003eloss\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003ePRKG1\u003c/em\u003e is a cGMP-dependent protein kinase belonging to the family of serine/threonine-specific protein kinases activated by cGMP\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003ePRKG1\u003c/em\u003e is located in chromosome 10\u003csup\u003e38\u003c/sup\u003e and is alternately spliced in two isoforms; \u003cem\u003ePRKG1-alpha\u003c/em\u003e, and \u003cem\u003ePRKG1-beta\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Protein isoforms differ in their N-terminal sequences\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003ePRKG1-alpha\u003c/em\u003e is found mainly in the lung, heart, platelets and cerebellum, whereas both \u003cem\u003ePRKG1-beta\u003c/em\u003e and \u003cem\u003ePRKG1-alpha\u003c/em\u003e are highly expressed in smooth muscle tissue of the uterus, intestine and trachea \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Additionally, both isoforms are present in vascular smooth muscle cells, platelets, lung, certain endothelial cells, fibroblasts, heart and the cerebellum\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Proteins that are phosphorylated by \u003cem\u003ePRKG1\u003c/em\u003e regulate platelet activation and adhesion, smooth muscle contraction/relaxation pathway and proliferation\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo determine whether the deletion of \u003cem\u003ePRKG1\u003c/em\u003e participates in LMS oncogenesis, we tested the impact of \u003cem\u003ePRKG1\u003c/em\u003e re-expression on three primary LMS cell lines (OC140, OC148, and OC211) in addition to the hybrid cell line (H2.1). OC140 shows one chromosome 10 loss, having one \u003cem\u003ePRKG1\u003c/em\u003e WT copy left. OC211 and H2.1 show a \u003cem\u003ePRKG1\u003c/em\u003e intragenic deletion. OC148 has two altered copies, one deleted, one broken (Fig.\u0026nbsp;4). Frameshifts were validated by RNA Sanger sequencing (Supplementary Table\u0026nbsp;5). All the cell lines were transfected with either alpha, beta or both \u003cem\u003ePRKG1\u003c/em\u003e isoforms. The transgene expression was validated at RNA and protein levels (Supplementary Figs.\u0026nbsp;3 \u0026amp; 4). A significant decrease of proliferation was observed in OC140 when beta or alpha\u0026thinsp;+\u0026thinsp;beta (A\u0026thinsp;+\u0026thinsp;B) isoforms are expressed compared to CT cell line (Supplementary Fig.\u0026nbsp;5), but not in the other cell lines regardless of the overexpressed isoform (data not shown).\u003c/p\u003e \u003cp\u003eAdditionally to smooth muscle proliferation, \u003cem\u003ePRKG1\u003c/em\u003e is also directly involved in smooth muscle contractile function\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. The Boyden chamber assay revealed that the overexpression of either alpha or beta isoforms, or both, in all LMS caused a significant reduction in cell migration compared to the CT cell line (Fig.\u0026nbsp;5 \u0026amp; Supplementary Fig.\u0026nbsp;6). The hybrid cell line exhibited analogous behavior to the LMS cell lines, demonstrating a pronounced decrease in cellular migration upon overexpression of either isoform in contrast to the CT. However, simultaneous overexpression of both alpha and beta isoforms within the hybrid cell line was unattainable due to antibiotic incompatibility constraints (Fig.\u0026nbsp;5, first panel \u0026amp; Supplementary Fig.\u0026nbsp;3D).\u003c/p\u003e \u003cp\u003eTo rule out the possibility that the effect observed would not be specific to \u003cem\u003ePRKG1\u003c/em\u003e re-expression, the LMS cell lines with the beta isoform (OC140, OC148 and OC211) were then transduced with a shRNA directed against \u003cem\u003ePRKG1\u003c/em\u003e (shRNA935). This shRNA decreased the expression of \u003cem\u003ePRKG1\u003c/em\u003e, validated by Taqman\u0026trade; and western blot (Supplementary Figs.\u0026nbsp;3 \u0026amp; 4). The proliferation assay showed that OC140-beta\u0026thinsp;+\u0026thinsp;sh proliferated at the same rate as CT cell line and both (OC140-beta\u0026thinsp;+\u0026thinsp;sh and CT) showed higher proliferation rate than OC140-beta (Supplementary Fig.\u0026nbsp;7A). In Boyden chamber, we observed that all the cell lines transduced with sh935 had similar migration rates than their CT cell lines, these (shRNA and CT) being higher than cell lines transduced with the beta isoform (Supplementary Fig.\u0026nbsp;7B \u0026amp; 7C). This re-knock-down of \u003cem\u003ePRKG1\u003c/em\u003e in the beta-cell lines recovered the phenotype observed in the CT cell lines (\u003cem\u003ePRKG1\u003c/em\u003e deleted), confirming that the phenotype observed was specific to the level of \u003cem\u003ePRKG1\u003c/em\u003e expression.\u003c/p\u003e \u003cp\u003e \u003cb\u003e6. PRKG1\u003c/b\u003e \u003cb\u003edeletion in LMS impacts pathways related to physiological relaxation of SMCs\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo delve deeper into the biological mechanisms underlying the functional impact of \u003cem\u003ePRKG1\u003c/em\u003e deletion, we selected the two cell lines exhibiting the most significant functional impact from \u003cem\u003ePRKG1\u003c/em\u003e modulation, and performed RNAseq for OC140 and OC211, each subjected to three distinct conditions: alpha and beta overexpression, and control (\u003cem\u003ePRKG1\u003c/em\u003e deletion). The initial analysis encompassed both samples (OC140 and OC211) to identify possible common abnormalities. We assessed the differential gene expression across various comparisons, including A vs. CT, B vs. CT, A vs. B, and both A and B vs. CT, examining both upregulated and downregulated genes. The greatest number of dysregulated genes was comparing A vs B, could indicate that the two isoforms don\u0026rsquo;t activate the same pathways (Fig.\u0026nbsp;6). Accordingly, only three genes were found to be upregulated in both alpha and beta compared to CT: \u003cem\u003ePRKG1\u003c/em\u003e, \u003cem\u003ePDE3B\u003c/em\u003e, and \u003cem\u003ePLCXD3.\u003c/em\u003e These 2 genes (excluding PRKG1 for which overexpression is experimentally induced) are directly involved in the cGMP and cAMP pathways implicated in the relaxation mechanism of SMCs\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. These pathways affect the contractility and mobility of these cells\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo further investigate the impact of \u003cem\u003ePRKG1\u003c/em\u003e expression, we performed a comparative transcriptomic analysis of \u003cem\u003ePRKG1\u003c/em\u003e isoform expression levels within each cell line. Principal Component Analysis (PCA) revealed distinct transcriptomic profiles: in OC140, the primary variance was observed between the beta isoform (B) and control (CT), while in OC211, it was between the alpha isoform (A) and CT (Supplementary Fig.\u0026nbsp;8). Consequently, these comparisons were used for subsequent analyses (Fig.\u0026nbsp;7A \u0026amp; B). Gene set enrichment analysis demonstrated that genes upregulated upon \u003cem\u003ePRKG1-alpha\u003c/em\u003e or \u003cem\u003e-beta\u003c/em\u003e overexpression were enriched for interferon signaling pathways (interferon alpha and gamma for OC140; interferon alpha and beta for OC211). Conversely, genes upregulated in the control (CT), representing PRKG1 deletion, showed enrichment in \u003cem\u003eMYC, E2F\u003c/em\u003e, and G2M pathways (Fig.\u0026nbsp;7A \u0026amp; B).\u003c/p\u003e \u003cp\u003eThese enrichments closely align with the CINSARC signature, a well-established prognostic marker for metastatic events in soft tissue sarcomas\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Gene Set Enrichment Analysis (GSEA) was performed, ranking all genes from most upregulated in the control (CT) condition to most downregulated in CT (correspondingly, upregulated in PRKG1-alpha, -beta, or alpha\u0026thinsp;+\u0026thinsp;beta conditions), we observed a significant enrichment of the CINSARC signature in the CT samples (Fig.\u0026nbsp;7C), demonstrating that \u003cem\u003ePRKG1\u003c/em\u003e deletion correlates with an increase in the expression of genes associated with metastatic potential.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAltogether, our results show that hybrid cells, derived from the spontaneous cell fusion between SMCs and fibroblasts, induce the development of LMS tumors like those observed in patients: (1) at the histological (Fig.\u0026nbsp;1D, smooth muscle differentiation expressing TAGLN) (2) and at genomic level (Fig.\u0026nbsp;1C, rearranged genome with frequent chr 10, 13 and 17 losses, and \u003cem\u003ePRKG1\u003c/em\u003e deletion).\u003c/p\u003e \u003cp\u003eIn this study, we tested the hypothesis that SMCs and fibroblasts are the initiative cells involved in LMS oncogenesis. Transcriptomic analyses involving various sarcoma types have revealed that not all samples of leiomyosarcoma (LMS) clustered together\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Such findings suggest the existence of distinct subcategories within LMS, each potentially originating from different cellular sources\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Recent clustering analysis revealed two distinct categories of soft tissue LMS (excluding uterine LMS), one comprising homogeneous transcriptomes (hLMS) and the other encompassing all other LMS (oLMS)\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. hLMS exhibit more differentiated histology, lower grades, and are predominantly located intra-abdominally. Moreover, these tumors display an overexpression of genes associated with smooth muscle differentiation, and a frequent \u003cem\u003eMYOCD\u003c/em\u003e amplification. In contrast, oLMS exhibit poor differentiation, higher grade, tend to occur in extremities, and showed histone marks associated with fibroblasts, adipocytes, and mesenchymal stem cells\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. The tumors formed in mice from the \u003cem\u003ein vitro\u003c/em\u003e cell fusion model of fibroblasts and SMCs show high grade, and poor differentiation (Fig.\u0026nbsp;1D) which is closer to oLMS characteristics, making it a suitable model to study this highly aggressive disease.\u003c/p\u003e \u003cp\u003eBased on the recurrent alterations found in LMS, murine models have been developed to study the initiation of LMS. Hernando and colleagues genetically inactivated \u003cem\u003ePTEN\u003c/em\u003e in smooth muscle cell lineage by cross breeding mice with \u003cem\u003ePTEN\u003c/em\u003e\u003csup\u003eloxP/loxP\u003c/sup\u003e and \u003cem\u003eTAGLN\u003c/em\u003e-cre mice. Mice carrying the homozygous \u003cem\u003ePTEN\u003c/em\u003e deletion developed SMC hyperplasia and abdominal LMSs with an incidence of approximately 80%\u003csup\u003e33\u003c/sup\u003e, but these LMS tumors did not show a rearranged genome. Rubio \u003cem\u003eet al\u003c/em\u003e, also develop murine models of LMS, in this case mesenchymal stem cells (MSC) where transformed \u003cem\u003ein vitro\u003c/em\u003e to evaluate the potential carcinogenic effects of p53 and/or retinoblastoma (RB) genes. Only p53\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e and p53\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003eRB\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e MSCs were capable of tumor development and originated LMS-like tumors\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Similarly to Hernando\u0026rsquo;s models, they were able to develop LMS at a histological level but not at the genomic level since the murine tumors did not show the peculiar chaotic genome characteristic of LMSs. To our knowledge, the \u003cem\u003ein vitro\u003c/em\u003e cell fusion models are the most representative models of patients\u0026rsquo; LMS tumors. In this model, although there are oncogenes activated in the parental fibroblast cells, genomic destabilization and the onset of extensive chromosomal rearrangements, including both recurrent and unique alterations such as the deletion of \u003cem\u003ePRKG1\u003c/em\u003e, occur only following whole genome doubling (WGD) induced by cell fusion. Hybrid cell lines showed both non-recurrent and recurrent genomic alterations that are commonly found in LMS patients. Therefore, we show here that WGD through cell fusion is the mechanism that induces the genome reshuffling we observe in hybrids and likely in LMS patients. To establish that the chromosomal alterations and the phenotypic advantage observed in hybrid cells are indeed attributable uniquely to WGD by cell fusion, it becomes imperative to conduct experiments involving tetraploidization through endoreplication, cytokinesis failure, or mitotic slippage. Given that cell fusion merges two different genomes that don\u0026rsquo;t occur with other mechanisms based on mitotic defects, this comparative approach will help elucidate the specific consequences of each tetraploidization mechanism.\u003c/p\u003e \u003cp\u003eWe identified a novel gene commonly deleted in LMS, \u003cem\u003ePRKG1\u003c/em\u003e. One loss or altered copy of \u003cem\u003ePRKG1\u003c/em\u003e was identified in 76.9% (93/121) of the patients. In the same cohort at least one copy of the following genes is also altered: \u003cem\u003eRB1\u003c/em\u003e in 96% of patients (116/121), \u003cem\u003eTP53\u003c/em\u003e in 95% (115/121), \u003cem\u003ePTEN\u003c/em\u003e in 83.5% (101/121), and \u003cem\u003eATRX\u003c/em\u003e in 43.8% (53/121)\u003csup\u003e34\u003c/sup\u003e, consistent results we observe in other publications\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. These make \u003cem\u003ePRKG1\u003c/em\u003e (76.9%) to be among the most common lost genes in LMS.\u003c/p\u003e \u003cp\u003e \u003cem\u003ePRKG1\u003c/em\u003e is a cyclic guanosine monophosphate (cGMP)-dependent protein kinase intricately linked to the nitric oxide (NO) pathway, which is key in smooth muscle relaxation\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. In this relaxation process, there exists a potential crosstalk among three mechanisms: cGMP, cAMP, and IP3 (Fig.\u0026nbsp;8, inspired from \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e). In the cGMP pathway, elevated levels of NO stimulate soluble guanylate cyclase (sGC), leading to the production of cGMP, which activates cGMP-dependent protein kinase G (PKG). \u003cem\u003ePRKG1\u003c/em\u003e, the principal form of Protein Kinase G, modulates contractility by reducing intracellular calcium ion levels, thereby regulating actin filament and myosin dynamics. Similarly, the cAMP pathway involves ATP and the activation of protein kinase A, which contributes to muscle relaxation\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. In contrast, the IP3 pathway elevates calcium levels in the endoplasmic or sarcoplasmic reticulum, promoting cell contraction and relaxation through the phosphorylation of myosin light chain, while also facilitating cell migration in response to various stimuli\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cem\u003ePDE3B\u003c/em\u003e emerged as an upregulated gene when comparing cells exhibiting downregulated \u003cem\u003ePRKG1\u003c/em\u003e to those with upregulated \u003cem\u003ePRKG1\u003c/em\u003e alpha and beta isoforms. PDEs comprise a family of enzymes that catalyze the hydrolysis of cAMP and cGMP, with PDE3 showing a higher affinity for cAMP. Consequently, down regulation of PDE3B would lead to the stability of cAMP, stimulating the activation of protein kinase A and thus sustaining the reduction of calcium levels, resulting in a relaxed state of the cell\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Additionally, \u003cem\u003ePLCXD3\u003c/em\u003e, a member of the PLC family, participates in the hydrolysis of PIP2 into IP3 and DAG, thereby increasing the release of calcium ions and maintaining the cell in a contracted state\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThese could indicate a disruption of the NO/cAMP/IP3 pathways, which are crucial for the contraction and relaxation of SMCs, in LMS patients. Specifically, the overexpression of PRKG1-alpha or PRKG1-beta in these cells results in reduced migration ability (Fig.\u0026nbsp;5). Through GSEA analysis, we observed a significant enrichment of oncogenic pathways, such as \u003cem\u003eMYC\u003c/em\u003e, \u003cem\u003eE2F\u003c/em\u003e, and G2M, in cells with \u003cem\u003ePRKG1\u003c/em\u003e deletion. In contrast, overexpression of \u003cem\u003ePRKG1-alpha\u003c/em\u003e or \u003cem\u003ePRKG1-beta\u003c/em\u003e led to a marked overrepresentation of genes associated with interferon responses. Consequently indicating a downregulation of cGAS-STING pathway\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e in LMS cell lines upon \u003cem\u003ePRKG1\u003c/em\u003e deletion. Together, these findings suggest that PRKG1 deletion may promote oncogenic signaling pathways that drive LMS progression, while overexpression could activate immune-modulatory pathways, potentially counteracting tumor development. These results underscore the dual role of \u003cem\u003ePRKG1\u003c/em\u003e in LMS oncogenesis, offering new insights into its mechanistic contributions.\u003c/p\u003e \u003cp\u003eInterferon alpha and gamma have direct anti-tumor effects, including inhibition of tumor cell proliferation\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. On the contrary MYC, E2F and G2M are pathways involved in cell cycle progression, leading to increased cell division and proliferation\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. We identified the enrichment of CINSARC genes comparing the LMS cell lines with \u003cem\u003ePRKG1\u003c/em\u003e deletion vs. \u003cem\u003ePRKG1\u003c/em\u003e overexpression (Fig.\u0026nbsp;7C). CINSARC is a prognostic marker based on the expression levels of a combination of genes linked to mitosis and chromosome integrity\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e characteristic of these types of sarcomas. This finding correlates with our phenotypic assays where we observed reduced migration upon \u003cem\u003ePRKG1\u003c/em\u003e re-expression, likely decreasing the capacity of cells to metastasize (Supplementary Fig.\u0026nbsp;6A). Taken together, these results support the clinical observation that low \u003cem\u003ePRKG1\u003c/em\u003e expression in LMS patients correlates with worse metastatic-free survival, cancer-specific survival, and overall survival (Fig.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003eIn conclusion, our study reveals two novel axes in LMS oncogenesis. Firstly, tetraploidization by cellular fusion may contribute to LMS initiation, as evidenced by the huge genome reshuffling it induces together with the specific deletions (\u003cem\u003ee.g., PRKG1\u003c/em\u003e) observed in hybrid cell lines, mirroring findings in LMS patients. Secondly, \u003cem\u003ePRKG1\u003c/em\u003e deletion likely disrupts the cGMP/cAMP/IP3 signaling pathway and enhances the activation of mitosis and chromosome integrity related genes, increasing cell migration leading to the characteristic tumor aggressiveness observed in LMS patients. Further analyses are now warranted to deeply understand the molecular relationship between this altered pathway and CINSARC expression. Therapies that target cGMP/cAMP/IP3 pathways\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e,\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e, enhancing \u003cem\u003ePRKG1\u003c/em\u003e expression, may create a new therapeutic window for the treatment of aggressive LMS tumors.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCell lines\u003c/h2\u003e \u003cp\u003eSarcoma cell lines, OC140, OC148, OC211 were generated in the laboratory\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Fibroblast IMR90 E6 E7 HRAS\u003csub\u003eG12V\u003c/sub\u003e SV40 SmallT hTERT (RST) cell lines were kindly given by Dr. M. Teichmann \u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e cell model transformation described by Hahn et al.\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e and SMC cell line was bought from ATCC (primary aortic cell line PCS-100-012). SMCE6 cell line was then generated by lentiviral transduction using pCDH-CMV-MCS-EF1a-Hygro (pCD515B-1, System Biosciences) in which E6 cDNA was cloned as described in the cloning section of the Material and Methods. Primers used for cDNA cloning can be found in Supplementary Table\u0026nbsp;6. OC140, OC148 and RST were cultured in RPMI-1640 (Gibco, Invitrogen) supplemented with 10% fetal bovine serum (FBS, Sigma). OC211 and SMCs were cultured in DMEM (Gibco, Invitrogen), supplemented with 10% FBS. All cell lines were cultured in a humidified CO\u003csub\u003e2\u003c/sub\u003e incubator at 37\u003csup\u003eo\u003c/sup\u003eC.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGeneration and selection of hybrid cells\u003c/h3\u003e\n\u003cp\u003eHybrid selection and generation was first described in\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, DsRed and CFP parental cell lines were generated by lentiviral transduction using pLenti-puromycin-DsRed and pLenti-blasticidin-CFP plasmids (generous gift from Dr. R. Iggo). 150,000 cells of each parental cell line were seeded in co-culture (6-well plates) during 72h. Spontaneus hybrid cells were selected by the addition of antibiotics into the culture medium, blasticidin (18\u0026micro;g/mL, Thermofisher Scientific) and puromycin (5\u0026micro;g/mL Thermofisher Scientific). Cell sorting was also performed to purify the culture by the selection of double positive cells DsRed and CFP. All hybrids show a unique rearranged genome, together with new phenotypes (Supplementary Figs.\u0026nbsp;1 \u0026amp; 2).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIn vivo experimentations\u003c/h2\u003e \u003cp\u003eAnimals were maintained under specific pathogen-free conditions in the animal facility CREFRE-US006 (Toulouse, France). Experiments were performed in conformity with the rules of the Institutional Animal Care and Use committee (approval number DIR13109 and DAP-APAFiS-201802161802878). All efforts were made to minimize animal suffering. 10\u003csup\u003e5\u003c/sup\u003e cells were injected into the dorsal flank of 6\u0026ndash;8-week-old NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ (NSG) mice. Tumor sizes were measured twice a week using a caliper and calculated using the formula: V\u0026thinsp;=\u0026thinsp;length \u0026times; width2/2. At the end of the experiment, mice were sacrificed by cervical dislocation under anesthesia. Tumors were then weighed and divided into three parts for cell culture, formalin fixation, and nitrogen freezing.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCell proliferation assay\u003c/h3\u003e\n\u003cp\u003eCell proliferation was evaluated with IncucyteS3\u0026reg; Live-cell analysis system (Sartorius). Cells were seeded in 96-well plates (ImageLock, Sartorius) at a concentration of 1200 cells/well (5 wells/per cell line). One picture was acquired/well/hour for 5 days. The normalization was done based on the number of cells/area covered by cells at T0. Data plotting and statistical analysis was performed with PRISM software (v6.01, GraphPad).\u003c/p\u003e\n\u003ch3\u003eInvasion assay\u003c/h3\u003e\n\u003cp\u003eInvasion was monitored using Boyden chamber cell culture inserts with 8\u0026micro;m pores (Corning) in 24-well plates. 25,000 cells/well were seeded in either RPMI-1640 or DMEM depending on the cell line supplemented with 5% FBS (upper chamber). The lower chamber was filled with either RPMI-1640 or DMEM supplemented with 10% FBS to create a gradient supporting cell invasion. After 18 h of incubation, cells located at the top side of the membrane were removed using cotton-tipped swab, while invasive cells located at the other end were fixed with cold ethanol absolute for 15 min and stained with Hoechst 33,342 (1/5000, Invitrogen) for 10 min at room temperature. For each insert the entire bottom membrane was acquired using an Axio Vert.A1(ZEISS) microscope. Quantification was done using the Image J (v2.1.0\u0026ndash;1, NIH) cell counter plugin. Data were normalized according to the number of cells at time zero and 18 h evaluated in parallel (in mirror 24-well plates) by flow cytometry and plotted using PRISM software (v6.01, GraphPad).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDNA extraction\u003c/h2\u003e \u003cp\u003eFor tumor samples and cell lines, genomic DNA from frozen samples was isolated using standard phenol-chloroform extraction protocol. DNA was quantified using a CLARIOstar plate reader (BMG Labtech).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDNA array, comparative genome hybridization (CGH)\u003c/h2\u003e \u003cp\u003eGenomic profiling was performed using the Affymetrix CytoScan\u0026trade; HD Arrays (Thermofisher Scientific) for all cell lines according to the manufacturer\u0026rsquo;s instructions. CEL files obtained by scanning the arrays were analysed using the Chromosome Analysis Suite software v3.1 (Thermofisher Scientific; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.affymetrix.com/support/technical/byproduct\u003c/span\u003e\u003cspan address=\"http://www.affymetrix.com/support/technical/byproduct\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. affx?product\u0026thinsp;=\u0026thinsp;chas) and the annotations of the genome version GRCH38 (hg38).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePCR on genomic DNA\u003c/h2\u003e \u003cp\u003eFor BP validation on genomic DNA in ICGC tumors, PCR primers were designed using the Primer 3 program (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinfo.ut.ee/primer3-0.4.0/\u003c/span\u003e\u003cspan address=\"https://bioinfo.ut.ee/primer3-0.4.0/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and are presented in Supplementary Table\u0026nbsp;2. All PCR were performed on 50ng of DNA using AmpliTaqGold\u0026reg; DNA polymerase (4311820, Applied Biosystems, Foster City, CA, USA) according to the manufacturer\u0026rsquo;s instructions with the PCR program described in the Supplementary Table\u0026nbsp;2 Legend.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRNA extraction\u003c/h2\u003e \u003cp\u003eRNA extraction was performed using standard TRIzol (15596026, Thermo Fisher, Waltham, MA, USA) / chloroform extraction (32211-1L, Fisher Scientific, Hampton, NH, USA) followed by 100% ethanol precipitation and RNA purification using the RNeasy Mini Kit (74104, Qiagen, Hilden, Germany) with DNase treatment (79254, RNase-Free DNase Set, Qiagen, Hilden, Germany). Total RNA was quantified using a CLARIOstar plate reader (BMG Labtech).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eRT-PCR\u003c/h2\u003e \u003cp\u003eTotal RNA was first reverse-transcribed using random hexamers and the High Capacity cDNA Reverse Transcription Kit (4368814, Applied Biosystems, Foster City, CA, USA) according to the manufacturer\u0026rsquo;s instructions. All primers used were designed using the Primer 3 program (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinfo.ut.ee/primer3-0.4.0/\u003c/span\u003e\u003cspan address=\"https://bioinfo.ut.ee/primer3-0.4.0/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and are presented in Supplementary Table\u0026nbsp;3. All PCR were performed as previously described for PCR on genomic DNA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSanger Sequencing\u003c/h2\u003e \u003cp\u003eSanger sequencing was performed by Genoscreen (Lille, France). FinchTV software (V1.4.0) was used to visualize the sequences (Geospiza, Seattle, WA, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eE6, PRKG1α and PRKG1β cDNA cloning\u003c/h2\u003e \u003cp\u003eSequences of cDNA (without UTR) for E6 (HPV16, NC_001526.4:7125\u0026ndash;7601), PRKG1α (NM_ 001098512.3) and PRKG1β (NM_ 006258.4) cloned in pEX-A258 vector were obtained from Eurofins Genomics (Ebersberg, Germany). Sequences were amplified by PCR using AmpliTaqGold\u0026reg; DNA polymerase (4311820, Applied Biosystems, Foster City, CA, USA), primers described in Supplementary Table\u0026nbsp;6 for TD60\u0026deg;C program see Supplementary Table\u0026nbsp;2 legend. PCR products were then purified using QIAquick PCR Purification Kit (28104, Qiagen, Hilden, Germany) according to manufacturer\u0026rsquo;s recommendations. The cDNA and vectors (CD510B-1, CD513B-1 and CD515B-1, System Biosciences, Palo Alto, CA, USA) were double digested by XbaI and SwaI restriction enzymes (R0145S and R0604S, New-England Biolabs, Ipswich, MA, USA) then precipitated with absolute ethanol. Ligation between 100ng of double digested vector and 500ng of double digested cDNA was performed as recommended by the manufacturer using T4 DNA ligase (M0202S, New-England Biolabs, Ipswich, MA, USA). Whole ligations were then transformed into One Shot\u0026trade; TOP10 bacteria (Invitrogen, Waltham, Ma, USA). Colonies were checked by PCR using AmpliTaqGold\u0026reg; DNA polymerase (4311820, Applied Biosystems, Foster City, CA, USA), primers described in Supplementary Table\u0026nbsp;6 and a TD60\u0026deg;C program and one positive colonie for each cloning was amplified and DNA was recovered using EndoFree Plasmid Maxi Kit (12362, Qiagen, Hilden, Germany) according to furnisher\u0026rsquo;s recommendations. Sequences were verified by Sanger Sequencing performed by Genoscreen (Lille, France). E6 was cloned into CD515B-1 vector, PRKG1α was cloned into CD510B-1 and CD513B-1 vectors and PRKG1β was cloned into CD513B-1 and CD515B-1 vectors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eReal-Time Quantitative Reverse Transcription-PCR\u003c/h2\u003e \u003cp\u003eReverse transcription and real-time PCR were performed as previously described \u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. We used TaqMan Gene Expression assays (Applied Biosystems, Foster City, CA, USA): Hs00956122_m1 for PRKG1; Hs99999903_m1 for ACTB and Hs99999902_m1 for RPLP0. Specific primers and probe were designed for PRKG1α: (F) 5\u0026rsquo;-CCGCAGACGTACAGGTCCTT-3\u0026rsquo;; (R) 5\u0026rsquo;-TTGGACCTTTCGGACTTGGT-3\u0026rsquo; and 5\u0026rsquo;-ACCTCCGACAGGCAT-3\u0026rsquo; (Probe), PRKG1β: (F) 5\u0026rsquo;-CCAGGATCTCAGCCATGTGA-3\u0026rsquo;; (R) 5\u0026rsquo;-CCTTGGACTGTGGGCTCTTG-3\u0026rsquo; and 5\u0026rsquo;-CCTGCCCTTCTACC-3\u0026rsquo; (Probe) and PRKG1Total (probe designed in the sequence which is deleted in the altered cell lines): (F) 5\u0026rsquo;-GGGAATTGGCTATTCTTTACAACTG-3\u0026rsquo;; (R) 5\u0026rsquo;-GGCCCAGAGTTTTACATTTACAAGAG-3\u0026rsquo; and 5\u0026rsquo;- ACCCGGACAGCGAC \u0026minus;\u0026thinsp;3\u0026rsquo; (Probe), using Primer Express\u0026trade; software (Applied Biosystems, Foster City, CA, USA). To normalize the results, we used the mean of \u003cem\u003eACTB\u003c/em\u003e and \u003cem\u003eRPLP0\u003c/em\u003e genes as reference genes. The results were calculated as described by De Preter and colleagues\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003ePloidy Evaluation by Flow Cytometry\u003c/h2\u003e \u003cp\u003eCell lines were resuspended in PBS 1X and fixed with absolute ethanol overnight at \u0026minus;\u0026thinsp;20 ◦C. Then, cells were washed and a solution of propidium iodide (Sigma Aldrich, 50 \u0026micro;g/mL) and RNAse A (ThermoFisher Scientific, 10 \u0026micro;g/mL) was added for 15 min at 37\u0026deg;C. DNA content was quantified with propidium iodide fluorescence intensity by flow cytometry (MACS Quant VYB, Miltenyi) and analyzed using FlowJo software (v10.8.1, BD Bioscience, Franklin Lakes, NJ, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eChromosome spreading\u003c/h2\u003e \u003cp\u003eCells were incubated overnight with colchicine (0.01 \u0026micro;g/ mL; Gibco, Thermofisher), harvested, and resuspended in an isotonic 0.075 M KCl buffer for 25 min. Cells were finally fixed by slow dropwise addition of standard 3:1 methanol: acetic acid final fixative solution. After several washes with fixative solution, a drop of cell suspension was dropped on a humidified glass slide. Slides were mounted using Vectashield medium plus DAPI (Vector Laboratories, Burlingame, California, USA). Metaphases were analyzed using Video microscope (Zeiss) with appropriate filters. Pictures were analyzed using ZEN 2012 blue edition software. Chromosome number was determined for ten metaphases for each cell line. Data plotting was performed with PRISM software (v6.01, GraphPad).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eProtein extraction\u003c/h2\u003e \u003cp\u003eCells were rinsed in PBS 1\u0026times; and lysed for 20 min at 4◦C in RIPA buffer (R0278, Sigma Aldrich) supplemented by phosphatase/protease inhibitor cocktail (1\u0026times;, Sigma Aldrich). Proteins were collected in supernatant after 15 min of centrifugation at 13,000g and quantified by DC protein assay kit (Bio-Rad).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eWestern Blotting\u003c/h2\u003e \u003cp\u003ePRKG1 protein expression level was evaluated using a primary antibody directed against PKG-1 (C8A4) Rabbit mAb (3248, Ozyme). This antibody recognizes alpha and beta isoforms. 40 \u0026micro;g of protein were loaded on the gel and separated by SDS-page. After the transfer onto a PVDF membrane, the membrane was blocked in PBS-Tween 0.1% BSA 5% and then incubated with the primary antibody (1:1000) overnight at 4\u0026deg;C. After several washes, the membrane was then incubated with a horseradish-peroxidase-linked anti-rabbit antibody (7074S, Cell signaling technology, 1:5000) for 1 h at room temperature. Signal was detected using ChemiDoc device (Bio-Rad) after incubation with chemiluminescent substrate (ECL Immobilon Western, WBKLS0100, Merck, Darmstadt, Germany). GAPDH mouse (sc-51907, Santa Cruz, 1:5000, 1 h at room temperature) with an anti-mouse secondary antibody (W4021, Promega; 1:5000, 1 h at room temperature) was used as a loading control for quantification.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBulk whole-genome sequencing cohorts (WGS)\u003c/b\u003e was previously described in \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eSurvival Analysis\u003c/h2\u003e \u003cp\u003eSurvival analysis was performed using R version 4.3.0 \u003csup\u003e73\u003c/sup\u003e and the survival package (version 3.5-5)\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e by fitting a Kaplan-Meier model in which significance was set at log-rank test p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Survival curves were plotted with R packages (version 0.4.9)\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe classification of \u003cem\u003ePRKG1\u003c/em\u003e patients state was performed on two LMS RNAseq bulk cohorts (n\u0026thinsp;=\u0026thinsp;110 and n\u0026thinsp;=\u0026thinsp;37) separately. For each cohort, \u003cem\u003ePRKG1\u003c/em\u003e threshold was defined as the mean expression of \u003cem\u003ePRKG1\u003c/em\u003e. Patient with lower than the mean of expression was classified as \u0026ldquo;Low\u0026rdquo; and other patient as \u0026ldquo;High\u0026rdquo;. Then, the classification of the different cohort was merged to perform survival analysis.\u003c/p\u003e \u003cp\u003eThe metastasis-free survival (MFS) was defined as the time from diagnostic to the first metastatic event including metastasis at diagnosis, Overall survival (OS) was defined as the time from diagnostic to death and cancer-specific survival was defined as the time from diagnostic to death for patient specifically death by their cancer.\u003c/p\u003e \u003cp\u003e \u003cb\u003eICGC and bulk RNAseq cohorts\u003c/b\u003e were previously described in \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eRNAseq \u0026ldquo;bulk\u0026rdquo; analysis of cell line model\u003c/h2\u003e \u003cp\u003eTotal RNAseq libraries were prepared with the Stranded Total RNA Prep Ligation with Ribo-Zero Plus Kit (Illumina, San Diego, CA, USA) using an input of 500ng of Total RNA (DV200\u0026thinsp;\u0026gt;\u0026thinsp;85.8%). The libraries were profiled with the HS NGS kit for the Fragment Analyzer (Agilent Technologies, Santa Clara, CA, USA) and quantified using the KAPA library quantification kit (Roche Diagnostics). The libraries were pooled and sequenced on the Illumina NovaSeq6000 instrument using S1 flow cells.\u003c/p\u003e \u003cp\u003eRNA reads were mapped to the hg38 genome assembly with STAR\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e version 2.6.0c. Low-quality (score\u0026thinsp;\u0026lt;\u0026thinsp;20) and duplicated PCR paired-end reads were removed with SAMtools\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e version 1.8 and PicardTools (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://broadinstitute.github.io/picard/\u003c/span\u003e\u003cspan address=\"http://broadinstitute.github.io/picard/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) version 2.18.2, respectively. Then, gene expression was quantified with HTSeq\u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e version 0.9.1.\u003c/p\u003e \u003cp\u003eDifferential gene expression analysis was performed using R\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e (v 4.0.3) package DESeq2\u003csup\u003e80\u003c/sup\u003e version 1.40.2, significance for differentially expressed genes was set at p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Gene Set Enrichment Analysis performed using GSEA software (Subramanian, Tamayo, et al. (2005), PNAS 102, 15545\u0026ndash;15550, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.broad.mit.edu/gsea/\u003c/span\u003e\u003cspan address=\"http://www.broad.mit.edu/gsea/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) version 4.3.2 using hallmark, curated, regulatory target, and oncogenic signature gene sets from The Molecular Signatures Database. The raw RNA sequencing data generated in this study have been deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession number PRJNA1282639.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cu\u003eConflicts of interest:\u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSerrano, C., and George, S. (2013). Leiomyosarcoma. Hematol. Oncol. Clin. North Am. \u003cem\u003e27\u003c/em\u003e, 957\u0026ndash;974. https://doi.org/10.1016/j.hoc.2013.07.002.\u003c/li\u003e\n\u003cli\u003eSingh, Z. (2018). Leiomyosarcoma: A rare soft tissue cancer arising from multiple organs. J. Cancer Res. Pract. \u003cem\u003e5\u003c/em\u003e, 1\u0026ndash;8. https://doi.org/10.1016/j.jcrpr.2017.10.002.\u003c/li\u003e\n\u003cli\u003eKasper, B., D\u0026rsquo;Ambrosio, L., Davis, E.J., Ingham, M., Broto, J.M., Trent, J.C., van Houdt, W.J., and Van Tine, B.A. (2022). What Clinical Trials Are Needed for Treatment of Leiomyosarcoma? Curr. Treat. Options Oncol. \u003cem\u003e23\u003c/em\u003e, 439\u0026ndash;449. https://doi.org/10.1007/s11864-021-00928-y.\u003c/li\u003e\n\u003cli\u003eChibon, F., Lesluyes, T., Valentin, T., and Le Guellec, S. (2019). CINSARC signature as a prognostic marker for clinical outcome in sarcomas and beyond. Genes. Chromosomes Cancer \u003cem\u003e58\u003c/em\u003e, 124\u0026ndash;129. https://doi.org/10.1002/gcc.22703.\u003c/li\u003e\n\u003cli\u003eFilleron, T., Le Guellec, S., Chevreau, C., Cabarrou, B., Lesluyes, T., Lodin, S., Massoubre, A., Mounier, M., Poublanc, M., Chibon, F., et al. (2020). Value of peri-operative chemotherapy in patients with CINSARC high-risk localized grade 1 or 2 soft tissue sarcoma: study protocol of the target selection phase III CHIC-STS trial. BMC Cancer \u003cem\u003e20\u003c/em\u003e, 716. https://doi.org/10.1186/s12885-020-07207-3.\u003c/li\u003e\n\u003cli\u003eCoindre, J.-M., Terrier, P., Guillou, L., Le Doussal, V., Collin, F., Ranchre, D., Sastre, X., Vilain, M.-O., Bonichon, F., and N\u0026rsquo;Guyen Bui, B. (2001). Predictive value of grade for metastasis development in the main histologic types of adult soft tissue sarcomas: A study of 1240 patients from the French Federation of Cancer Centers Sarcoma Group. Cancer \u003cem\u003e91\u003c/em\u003e, 1914\u0026ndash;1926. https://doi.org/10.1002/1097-0142(20010515)91:10\u0026lt;1914::AID-CNCR1214\u0026gt;3.0.CO;2-3.\u003c/li\u003e\n\u003cli\u003eP\u0026eacute;rot, G., Chibon, F., Montero, A., Lagarde, P., De Th\u0026eacute;, H., Terrier, P., Guillou, L., Ranch\u0026egrave;re, D., Coindre, J.-M., and Aurias, A. (2010). Constant p53 Pathway Inactivation in a Large Series of Soft Tissue Sarcomas with Complex Genetics. Am. J. Pathol. \u003cem\u003e177\u003c/em\u003e, 2080\u0026ndash;2090. https://doi.org/10.2353/ajpath.2010.100104.\u003c/li\u003e\n\u003cli\u003eChibon, F., Mairal, A., Freneaux, P., Terrier, P., Coindre, J., Sastre, X., and Aurias, A. (2000). The RB1 gene is the target of chromosome 13 deletions in malignant fibrous histiocytoma. Cancer Res. \u003cem\u003e60\u003c/em\u003e, 6339\u0026ndash;6345.\u003c/li\u003e\n\u003cli\u003eGibault, L., Ferreira, C., P\u0026eacute;rot, G., Audebourg, A., Chibon, F., Bonnin, S., Lagarde, P., Vacher-Lavenu, M.-C., Terrier, P., Coindre, J.-M., et al. (2012). From PTEN loss of expression to RICTOR role in smooth muscle differentiation: complex involvement of the mTOR pathway in leiomyosarcomas and pleomorphic sarcomas. Mod. Pathol. \u003cem\u003e25\u003c/em\u003e, 197\u0026ndash;211. https://doi.org/10.1038/modpathol.2011.163.\u003c/li\u003e\n\u003cli\u003eMauduit, O., Delcroix, V., Lesluyes, T., P\u0026eacute;rot, G., Lagarde, P., Lartigue, L., Blay, J.-Y., and Chibon, F. (2019). Recurrent DMD Deletions Highlight Specific Role of Dp71 Isoform in Soft-Tissue Sarcomas. Cancers \u003cem\u003e11\u003c/em\u003e, 922. https://doi.org/10.3390/cancers11070922.\u003c/li\u003e\n\u003cli\u003eDarmusey, L., P\u0026eacute;rot, G., Th\u0026eacute;bault, N., Le Guellec, S., Desplat, N., Gaston, L., Delespaul, L., Lesluyes, T., Darbo, E., Gomez-Brouchet, A., et al. (2021). ATRX Alteration Contributes to Tumor Growth and Immune Escape in Pleomorphic Sarcomas. Cancers \u003cem\u003e13\u003c/em\u003e, 2151. https://doi.org/10.3390/cancers13092151.\u003c/li\u003e\n\u003cli\u003eChudasama, P., Mughal, S.S., Sanders, M.A., H\u0026uuml;bschmann, D., Chung, I., Deeg, K.I., Wong, S.-H., Rabe, S., Hlevnjak, M., Zapatka, M., et al. (2018). Integrative genomic and transcriptomic analysis of leiomyosarcoma. Nat. Commun. \u003cem\u003e9\u003c/em\u003e, 144. https://doi.org/10.1038/s41467-017-02602-0.\u003c/li\u003e\n\u003cli\u003eLacuna, K., Bose, S., Ingham, M., and Schwartz, G. (2023). Therapeutic advances in leiomyosarcoma. Front. Oncol. \u003cem\u003e13\u003c/em\u003e, 1149106. https://doi.org/10.3389/fonc.2023.1149106.\u003c/li\u003e\n\u003cli\u003eDewhurst, S., and Swanton, C. (2015). Tetraploidy and CIN: a dangerous combination. Cell Cycle \u003cem\u003e14\u003c/em\u003e, 3217\u0026ndash;3217. https://doi.org/10.1080/15384101.2015.1084208.\u003c/li\u003e\n\u003cli\u003eDewhurst, S.M., McGranahan, N., Burrell, R.A., Rowan, A.J., Gr\u0026ouml;nroos, E., Endesfelder, D., Joshi, T., Mouradov, D., Gibbs, P., Ward, R.L., et al. (2014). Tolerance of Whole-Genome Doubling Propagates Chromosomal Instability and Accelerates Cancer Genome Evolution. Cancer Discov. \u003cem\u003e4\u003c/em\u003e, 175\u0026ndash;185. https://doi.org/10.1158/2159-8290.CD-13-0285.\u003c/li\u003e\n\u003cli\u003eBielski, C.M., Zehir, A., Penson, A.V., Donoghue, M.T.A., Chatila, W., Armenia, J., Chang, M.T., Schram, A.M., Jonsson, P., Bandlamudi, C., et al. (2018). Genome doubling shapes the evolution and prognosis of advanced cancers. Nat. Genet. \u003cem\u003e50\u003c/em\u003e, 1189\u0026ndash;1195. https://doi.org/10.1038/s41588-018-0165-1.\u003c/li\u003e\n\u003cli\u003eGemble, S., Wardenaar, R., Keuper, K., Srivastava, N., Nano, M., Mac\u0026eacute;, A.-S., Tijhuis, A.E., Bernhard, S.V., Spierings, D.C.J., Simon, A., et al. (2022). Genetic instability from a single S phase after whole-genome duplication. Nature \u003cem\u003e604\u003c/em\u003e, 146\u0026ndash;151. https://doi.org/10.1038/s41586-022-04578-4.\u003c/li\u003e\n\u003cli\u003eSteele, C.D., Tarabichi, M., Oukrif, D., Webster, A.P., Ye, H., Fittall, M., Lombard, P., Martincorena, I., Tarpey, P.S., Collord, G., et al. (2019). Undifferentiated Sarcomas Develop through Distinct Evolutionary Pathways. Cancer Cell \u003cem\u003e35\u003c/em\u003e, 441-456.e8. https://doi.org/10.1016/j.ccell.2019.02.002.\u003c/li\u003e\n\u003cli\u003eLee, H.O., Davidson, J.M., and Duronio, R.J. (2009). Endoreplication: polyploidy with purpose. Genes Dev. \u003cem\u003e23\u003c/em\u003e, 2461\u0026ndash;2477. https://doi.org/10.1101/gad.1829209.\u003c/li\u003e\n\u003cli\u003eFujiwara, T., Bandi, M., Nitta, M., Ivanova, E.V., Bronson, R.T., and Pellman, D. (2005). Cytokinesis failure generating tetraploids promotes tumorigenesis in p53-null cells. Nature \u003cem\u003e437\u003c/em\u003e, 1043\u0026ndash;1047. https://doi.org/10.1038/nature04217.\u003c/li\u003e\n\u003cli\u003eArchetti, M. (2022). Polyploidy as an Adaptation against Loss of Heterozygosity in Cancer. Int. J. Mol. Sci. \u003cem\u003e23\u003c/em\u003e, 8528. https://doi.org/10.3390/ijms23158528.\u003c/li\u003e\n\u003cli\u003eDonn\u0026eacute;, R., Nader, M.B., and Desdouets, C. (2018). Cellular and Molecular Mechanisms Controlling Ploidy. In Reference Module in Life Sciences (Elsevier), p. B9780128096338122903. https://doi.org/10.1016/B978-0-12-809633-8.12290-3.\u003c/li\u003e\n\u003cli\u003eDelespaul, L., Merle, C., Lesluyes, T., Lagarde, P., Le Guellec, S., P\u0026eacute;rot, G., Baud, J., Carlotti, M., Danet, C., F\u0026egrave;vre, M., et al. (2019). Fusion-mediated chromosomal instability promotes aneuploidy patterns that resemble human tumors. Oncogene \u003cem\u003e38\u003c/em\u003e, 6083\u0026ndash;6094. https://doi.org/10.1038/s41388-019-0859-6.\u003c/li\u003e\n\u003cli\u003eYin, H., Price, F., and Rudnicki, M. (2013). Satellite cells and the muscle stem cell niche. Physiol Rev \u003cem\u003e93\u003c/em\u003e, 23\u0026ndash;67. https://doi.org/10.1152/physrev.00043.2011.\u003c/li\u003e\n\u003cli\u003eQuinn, M.E., Goh, Q., Kurosaka, M., Gamage, D.G., Petrany, M.J., Prasad, V., and Millay, D.P. (2017). Myomerger induces fusion of non-fusogenic cells and is required for skeletal muscle development. Nat. Commun. \u003cem\u003e8\u003c/em\u003e, 15665. https://doi.org/10.1038/ncomms15665.\u003c/li\u003e\n\u003cli\u003eLacour, F., Vezin, E., Bentzinger, C.F., Sincennes, M.-C., Giordani, L., Ferry, A., Mitchell, R., Patel, K., Rudnicki, M.A., Chaboissier, M.-C., et al. (2017). R-spondin1 Controls Muscle Cell Fusion through Dual Regulation of Antagonistic Wnt Signaling Pathways. Cell Rep. \u003cem\u003e18\u003c/em\u003e, 2320\u0026ndash;2330. https://doi.org/10.1016/j.celrep.2017.02.036.\u003c/li\u003e\n\u003cli\u003eAichel, O. (1911). About cell fusion with qualitatively abnormal chromosome distribution as cause for tumor formation / ̈ Uber zellverschmelzung mit qualitativ abnormer chromosomenverteilung als ursache der geschwulstbildung. In Vortr ̈ age und aufs ̈ atze \u0026uuml;ber entvickelungsmechanik der organismen. Wilhelm Engelmann Leipz., 92\u0026ndash;111.\u003c/li\u003e\n\u003cli\u003eShabo, I., Svanvik, J., Lindstr\u0026ouml;m, A., Lechertier, T., Trabulo, S., Hulit, J., Sparey, T., and Pawelek, J. (2020). Roles of cell fusion, hybridization and polyploid cell formation in cancer metastasis. World J. Clin. Oncol. \u003cem\u003e11\u003c/em\u003e, 121\u0026ndash;135. https://doi.org/10.5306/wjco.v11.i3.121.\u003c/li\u003e\n\u003cli\u003eBrito, A., Merle, C., Lagarde, P., Faustin, B., Devin, A., Lartigue, L., and Chibon, F. (2021). Cell fusion enhances energy metabolism of mesenchymal tumor hybrid cells to sustain their proliferation and invasion. BMC Cancer \u003cem\u003e21\u003c/em\u003e, 863. https://doi.org/10.1186/s12885-021-08561-6.\u003c/li\u003e\n\u003cli\u003eLartigue, L., Merle, C., Lagarde, P., Delespaul, L., Lesluyes, T., Le Guellec, S., P\u0026eacute;rot, G., Leroy, L., Coindre, J.-M., and Chibon, F. (2020). Genome remodeling upon mesenchymal tumor cell fusion contributes to tumor progression and metastatic spread. Oncogene. https://doi.org/10.1038/s41388-020-1276-6.\u003c/li\u003e\n\u003cli\u003eGast, C.E., Silk, A.D., Zarour, L., Riegler, L., Burkhart, J.G., Gustafson, K.T., Parappilly, M.S., Roh-Johnson, M., Goodman, J.R., Olson, B., et al. (2018). Cell fusion potentiates tumor heterogeneity and reveals circulating hybrid cells that correlate with stage and survival. Sci. Adv. \u003cem\u003e4\u003c/em\u003e, eaat7828. https://doi.org/10.1126/sciadv.aat7828.\u003c/li\u003e\n\u003cli\u003eMerle, C., Th\u0026eacute;bault, N., LeGuellec, S., Baud, J., P\u0026eacute;rot, G., Lesluyes, T., Delespaul, L., Lartigue, L., and Chibon, F. (2020). Tetraploidization of Immortalized Myoblasts Induced by Cell Fusion Drives Myogenic Sarcoma Development with DMD Deletion. Cancers \u003cem\u003e12\u003c/em\u003e, 1281. https://doi.org/10.3390/cancers12051281.\u003c/li\u003e\n\u003cli\u003eHernando, E., Charytonowicz, E., Dudas, M.E., Menendez, S., Matushansky, I., Mills, J., Socci, N.D., Behrendt, N., Ma, L., Maki, R.G., et al. (2007). The AKT-mTOR pathway plays a critical role in the development of leiomyosarcomas. Nat. Med. \u003cem\u003e13\u003c/em\u003e, 748\u0026ndash;753. https://doi.org/10.1038/nm1560.\u003c/li\u003e\n\u003cli\u003eDarbo, E., P\u0026eacute;rot, G., Darmusey, L., Le Guellec, S., Leroy, L., Gaston, L., Desplat, N., Th\u0026eacute;bault, N., Merle, C., Rochaix, P., et al. (2023). Distinct Cellular Origins and Differentiation Process Account for Distinct Oncogenic and Clinical Behaviors of Leiomyosarcomas. Cancers \u003cem\u003e15\u003c/em\u003e, 534. https://doi.org/10.3390/cancers15020534.\u003c/li\u003e\n\u003cli\u003eLee, P.J., Yoo, N.S., Hagemann, I.S., Pfeifer, J.D., Cottrell, C.E., Abel, H.J., and Duncavage, E.J. (2017). Spectrum of mutations in leiomyosarcomas identified by clinical targeted next-generation sequencing. Exp. Mol. Pathol. \u003cem\u003e102\u003c/em\u003e, 156\u0026ndash;161. https://doi.org/10.1016/j.yexmp.2017.01.012.\u003c/li\u003e\n\u003cli\u003eBenhaddou, A., Gaston, L., P\u0026eacute;rot, G., Desplat, N., Leroy, L., Le Guellec, S., Ben Haddou, M., Rochaix, P., Valentin, T., Ferron, G., et al. (2021). Medium levels of transcription and replication related chromosomal instability are associated with poor clinical outcome. Sci. Rep. \u003cem\u003e11\u003c/em\u003e, 23429. https://doi.org/10.1038/s41598-021-02787-x.\u003c/li\u003e\n\u003cli\u003eHofmann, F., and Wegener, J.W. (2013). cGMP-Dependent Protein Kinases (cGK). In Guanylate Cyclase and Cyclic GMP Methods in Molecular Biology., T. Krieg and R. Lukowski, eds. (Humana Press), pp. 17\u0026ndash;50. https://doi.org/10.1007/978-1-62703-459-3_2.\u003c/li\u003e\n\u003cli\u003eHofmann, F. (2005). The Biology of Cyclic GMP-dependent Protein Kinases. J. Biol. Chem. \u003cem\u003e280\u003c/em\u003e, 1\u0026ndash;4. https://doi.org/10.1074/jbc.R400035200.\u003c/li\u003e\n\u003cli\u003eChan, M.H., Aminzai, S., Hu, T., Taran, A., Li, S., Kim, C., Pilz, R.B., and Casteel, D.E. (2020). A substitution in cGMP-dependent protein kinase 1 associated with aortic disease induces an active conformation in the absence of cGMP. J. Biol. Chem. \u003cem\u003e295\u003c/em\u003e, 10394\u0026ndash;10405. https://doi.org/10.1074/jbc.RA119.010984.\u003c/li\u003e\n\u003cli\u003e\u0026Oslash;rstavik, S., and Natarajan, V. Characterization of the Human Gene Encoding the Type Ia and Type Ib cGMP-Dependent Protein Kinase (PRKG1).\u003c/li\u003e\n\u003cli\u003eDas, A., Xi, L., and Kukreja, R.C. (2008). Protein Kinase G-dependent Cardioprotective Mechanism of Phosphodiesterase-5 Inhibition Involves Phosphorylation of ERK and GSK3\u0026beta;. J. Biol. Chem. \u003cem\u003e283\u003c/em\u003e, 29572\u0026ndash;29585. https://doi.org/10.1074/jbc.M801547200.\u003c/li\u003e\n\u003cli\u003eRybalkin, S.D., Yan, C., Bornfeldt, K.E., and Beavo, J.A. (2003). Cyclic GMP Phosphodiesterases and Regulation of Smooth Muscle Function. Circ. Res. \u003cem\u003e93\u003c/em\u003e, 280\u0026ndash;291. https://doi.org/10.1161/01.RES.0000087541.15600.2B.\u003c/li\u003e\n\u003cli\u003eYoon, J., Byun, H., Ivan, C., Calin, G.A., Jung, D., and Lee, S. (2022). lncRNAs UC.145 and PRKG1-AS1 Determine the Functional Output of DKK1 in Regulating the Wnt Signaling Pathway in Gastric Cancer. Cancers \u003cem\u003e14\u003c/em\u003e, 2369. https://doi.org/10.3390/cancers14102369.\u003c/li\u003e\n\u003cli\u003eBenvenuto, G., Todeschini, P., Paracchini, L., Calura, E., Fruscio, R., Romani, C., Beltrame, L., Martini, P., Ravaggi, A., Ceppi, L., et al. (2020). Expression profiles of PRKG1, SDF2L1 and PPP1R12A are predictive and prognostic factors for therapy response and survival in high‐grade serous ovarian cancer. Int. J. Cancer \u003cem\u003e147\u003c/em\u003e, 565\u0026ndash;574. https://doi.org/10.1002/ijc.32935.\u003c/li\u003e\n\u003cli\u003eCalamera, G., Moltzau, L.R., Levy, F.O., and Andressen, K.W. (2022). Phosphodiesterases and Compartmentation of cAMP and cGMP Signaling in Regulation of Cardiac Contractility in Normal and Failing Hearts. Int. J. Mol. Sci. \u003cem\u003e23\u003c/em\u003e, 2145. https://doi.org/10.3390/ijms23042145.\u003c/li\u003e\n\u003cli\u003eCarvajal, J.A., Germain, A.M., Huidobro-Toro, J.P., and Weiner, C.P. (2000). Molecular mechanism of cGMP-mediated smooth muscle relaxation. J. Cell. Physiol. \u003cem\u003e184\u003c/em\u003e, 409\u0026ndash;420. https://doi.org/10.1002/1097-4652(200009)184:3\u0026lt;409::AID-JCP16\u0026gt;3.0.CO;2-K.\u003c/li\u003e\n\u003cli\u003eChibon, F., Lagarde, P., Salas, S., P\u0026eacute;rot, G., Brouste, V., Tirode, F., Lucchesi, C., De Reynies, A., Kauffmann, A., Bui, B., et al. (2010). Validated prediction of clinical outcome in sarcomas and multiple types of cancer on the basis of a gene expression signature related to genome complexity. Nat. Med. \u003cem\u003e16\u003c/em\u003e, 781\u0026ndash;787. https://doi.org/10.1038/nm.2174.\u003c/li\u003e\n\u003cli\u003eLesluyes, T., Delespaul, L., Coindre, J.-M., and Chibon, F. (2017). The CINSARC signature as a prognostic marker for clinical outcome in multiple neoplasms. Sci. Rep. \u003cem\u003e7\u003c/em\u003e, 5480. https://doi.org/10.1038/s41598-017-05726-x.\u003c/li\u003e\n\u003cli\u003eValentin, T., Lesluyes, T., Guellec, S.L., and Chibon, F. Title: Chemotherapy in localized Soft Tissue Sarcoma: will we soon have to treat grade 1 tumours? 3.\u003c/li\u003e\n\u003cli\u003eAnderson, N.D., Babichev, Y., Fuligni, F., Comitani, F., Layeghifard, M., Venier, R.E., Dentro, S.C., Maheshwari, A., Guram, S., Wunker, C., et al. (2021). Lineage-defined leiomyosarcoma subtypes emerge years before diagnosis and determine patient survival. Nat. Commun. \u003cem\u003e12\u003c/em\u003e, 4496. https://doi.org/10.1038/s41467-021-24677-6.\u003c/li\u003e\n\u003cli\u003eGibault, L., P\u0026eacute;rot, G., Chibon, F., Bonnin, S., Lagarde, P., Terrier, P., Coindre, J.-M., and Aurias, A. (2011). New insights in sarcoma oncogenesis: a comprehensive analysis of a large series of 160 soft tissue sarcomas with complex genomics: Genome and transcriptome analysis of human soft tissue sarcomas. J. Pathol. \u003cem\u003e223\u003c/em\u003e, 64\u0026ndash;71. https://doi.org/10.1002/path.2787.\u003c/li\u003e\n\u003cli\u003eItaliano, A., Lagarde, P., Brulard, C., Terrier, P., La\u0026euml;, M., Marques, B., Ranchere-Vince, D., Michels, J.-J., Trassard, M., Cioffi, A., et al. (2013). Genetic Profiling Identifies Two Classes of Soft-Tissue Leiomyosarcomas with Distinct Clinical Characteristics. Clin. Cancer Res. \u003cem\u003e19\u003c/em\u003e, 1190\u0026ndash;1196. https://doi.org/10.1158/1078-0432.CCR-12-2970.\u003c/li\u003e\n\u003cli\u003eHemming, M.L., Fan, C., Raut, C.P., Demetri, G.D., Armstrong, S.A., Sicinska, E., and George, S. (2020). Oncogenic Gene-Expression Programs in Leiomyosarcoma and Characterization of Conventional, Inflammatory, and Uterogenic Subtypes. Mol. Cancer Res. \u003cem\u003e18\u003c/em\u003e, 1302\u0026ndash;1314. https://doi.org/10.1158/1541-7786.MCR-20-0197.\u003c/li\u003e\n\u003cli\u003eRubio, R., Garc\u0026iacute;a-Castro, J., Guti\u0026eacute;rrez-Aranda, I., Paramio, J., Santos, M., Catalina, P., Leone, P.E., Menendez, P., and Rodr\u0026iacute;guez, R. (2010). Deficiency in p53 but not Retinoblastoma Induces the Transformation of Mesenchymal Stem Cells \u003cem\u003eIn vitro\u003c/em\u003e and Initiates Leiomyosarcoma \u003cem\u003eIn vivo\u003c/em\u003e. Cancer Res. \u003cem\u003e70\u003c/em\u003e, 4185\u0026ndash;4194. https://doi.org/10.1158/0008-5472.CAN-09-4640.\u003c/li\u003e\n\u003cli\u003eLiu, W., Tong, H., Zhang, C., Zhuang, R., Guo, H., Lv, C., Yang, H., Lin, Q., Guo, X., Wang, Z., et al. (2020). Integrated genomic and transcriptomic analysis revealed mutation patterns of de-differentiated liposarcoma and leiomyosarcoma. BMC Cancer \u003cem\u003e20\u003c/em\u003e, 1035. https://doi.org/10.1186/s12885-020-07456-2.\u003c/li\u003e\n\u003cli\u003eGambaryan, S. (2022). The Role of NO/sGC/cGMP/PKG Signaling Pathway in Regulation of Platelet Function. Cells \u003cem\u003e11\u003c/em\u003e, 3704. https://doi.org/10.3390/cells11223704.\u003c/li\u003e\n\u003cli\u003eHafen, B.B., and Burns, B. (2022). Physiology, Smooth Muscle. In StatPearls [Internet] Treasure Island (FL). (tatPearls Publishing).\u003c/li\u003e\n\u003cli\u003eBrozovich, F.V., Nicholson, C.J., Degen, C.V., Gao, Y.Z., Aggarwal, M., and Morgan, K.G. (2016). Mechanisms of Vascular Smooth Muscle Contraction and the Basis for Pharmacologic Treatment of Smooth Muscle Disorders. Pharmacol. Rev. \u003cem\u003e68\u003c/em\u003e, 476\u0026ndash;532. https://doi.org/10.1124/pr.115.010652.\u003c/li\u003e\n\u003cli\u003eLorigo, M., Oliveira, N., and Cairrao, E. (2021). PDE-Mediated Cyclic Nucleotide Compartmentation in Vascular Smooth Muscle Cells: From Basic to a Clinical Perspective. J. Cardiovasc. Dev. Dis. \u003cem\u003e9\u003c/em\u003e, 4. https://doi.org/10.3390/jcdd9010004.\u003c/li\u003e\n\u003cli\u003ePavlaki, N., and Nikolaev, V. (2018). Imaging of PDE2- and PDE3-Mediated cGMP-to-cAMP Cross-Talk in Cardiomyocytes. J. Cardiovasc. Dev. Dis. \u003cem\u003e5\u003c/em\u003e, 4. https://doi.org/10.3390/jcdd5010004.\u003c/li\u003e\n\u003cli\u003eGan, Y., Li, X., Han, S., Liang, Q., Ma, X., Rong, P., Wang, W., and Li, W. (2022). The cGAS/STING Pathway: A Novel Target for Cancer Therapy. Front. Immunol. \u003cem\u003e12\u003c/em\u003e, 795401. https://doi.org/10.3389/fimmu.2021.795401.\u003c/li\u003e\n\u003cli\u003eJing, Z., Liu, G., Zhou, N., Xu, D., Feng, N., Lei, Y., Ma, L., Tang, M., Tong, G., Tang, N., et al. (2024). Interferon-\u0026gamma; in the tumor microenvironment promotes the expression of B7H4 in colorectal cancer cells, thereby inhibiting cytotoxic T cells. Sci. Rep. \u003cem\u003e14\u003c/em\u003e, 6053. https://doi.org/10.1038/s41598-024-56681-3.\u003c/li\u003e\n\u003cli\u003eOshi, M., Takahashi, H., Tokumaru, Y., Yan, L., Rashid, O.M., Matsuyama, R., Endo, I., and Takabe, K. (2020). G2M Cell Cycle Pathway Score as a Prognostic Biomarker of Metastasis in Estrogen Receptor (ER)-Positive Breast Cancer. Int. J. Mol. Sci. \u003cem\u003e21\u003c/em\u003e, 2921. https://doi.org/10.3390/ijms21082921.\u003c/li\u003e\n\u003cli\u003eGarc\u0026iacute;a-Guti\u0026eacute;rrez, L., Delgado, M.D., and Le\u0026oacute;n, J. (2019). MYC Oncogene Contributions to Release of Cell Cycle Brakes. Genes \u003cem\u003e10\u003c/em\u003e, 244. https://doi.org/10.3390/genes10030244.\u003c/li\u003e\n\u003cli\u003ePetraina, A., Nogales, C., Krahn, T., Mucke, H., L\u0026uuml;scher, T.F., Fischmeister, R., Kass, D.A., Burnett, J.C., Hobbs, A.J., and Schmidt, H.H.H.W. (2022). Cyclic GMP modulating drugs in cardiovascular diseases: mechanism-based network pharmacology. Cardiovasc. Res. \u003cem\u003e118\u003c/em\u003e, 2085\u0026ndash;2102. https://doi.org/10.1093/cvr/cvab240.\u003c/li\u003e\n\u003cli\u003eAtaei Ataabadi, E., Golshiri, K., J\u0026uuml;ttner, A., Krenning, G., Danser, A.H.J., and Roks, A.J.M. (2020). Nitric Oxide-cGMP Signaling in Hypertension: Current and Future Options for Pharmacotherapy. Hypertension \u003cem\u003e76\u003c/em\u003e, 1055\u0026ndash;1068. https://doi.org/10.1161/HYPERTENSIONAHA.120.15856.\u003c/li\u003e\n\u003cli\u003eLagarde, P., Brulard, C., Perot, G., Maudiut, O., Delespaul, L., Neuville, A., Stoeckle, E., Le Guellec, S., Rochaix, P., Coindre, J., et al. (2015). Stable Instability of Sarcoma Cell Lines Genome Despite Intra-Tumoral Heterogeneity: A Genomic and Transcriptomic Study of Sarcoma Cell Lines. Austin J. Genet. Genomic Res. \u003cem\u003e2\u003c/em\u003e, 1014.\u003c/li\u003e\n\u003cli\u003eHaurie, V., Durrieu-Gaillard, S., Dumay-Odelot, H., Da Silva, D., Rey, C., Prochazkova, M., Roeder, R.G., Besser, D., and Teichmann, M. (2010). Two isoforms of human RNA polymerase III with specific functions in cell growth and transformation. Proc. Natl. Acad. Sci. \u003cem\u003e107\u003c/em\u003e, 4176\u0026ndash;4181. https://doi.org/10.1073/pnas.0914980107.\u003c/li\u003e\n\u003cli\u003eHahn, W.C., Counter, C.M., Lundberg, A.S., Beijersbergen, R.L., Brooks, M.W., and Weinberg, R.A. (1999). Creation of human tumourcells with de\u0026reg;ned genetic elements. \u003cem\u003e400\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eHahn, W.C., Dessain, S.K., Brooks, M.W., King, J.E., Elenbaas, B., Sabatini, D.M., DeCaprio, J.A., and Weinberg, R.A. (2002). Enumeration of the Simian Virus 40 Early Region Elements Necessary for Human Cell Transformation. MOL CELL BIOL \u003cem\u003e22\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eP\u0026eacute;rot, G., Derr\u0026eacute;, J., Coindre, J.-M., Tirode, F., Lucchesi, C., Mariani, O., Gibault, L., Guillou, L., Terrier, P., and Aurias, A. (2009). Strong Smooth Muscle Differentiation Is Dependent on Myocardin Gene Amplification in Most Human Retroperitoneal Leiomyosarcomas. Cancer Res. \u003cem\u003e69\u003c/em\u003e, 2269\u0026ndash;2278. https://doi.org/10.1158/0008-5472.CAN-08-1443.\u003c/li\u003e\n\u003cli\u003eDe Preter, K., Speleman, F., Combaret, V., Lunec, J., Laureys, G., Eussen, B.H.J., Francotte, N., Board, J., Pearson, A.D.J., De Paepe, A., et al. (2002). Quantification of MYCN, DDX1, and NAG Gene Copy Number in Neuroblastoma Using a Real-Time Quantitative PCR Assay. Mod. Pathol. \u003cem\u003e15\u003c/em\u003e, 159\u0026ndash;166. https://doi.org/10.1038/modpathol.3880508.\u003c/li\u003e\n\u003cli\u003eR Core Team (2023). R: A Language and Environment for Statistical Computing_. R Foundation for Statistical Computing, Vienna, Austria. \u0026lt;https://www.R-project.org/\u0026gt;.\u003c/li\u003e\n\u003cli\u003eTherneau, T.M. A Package for Survival Analysis in R 2020; CRAN Repository. 2020. Available online: https://CRAN.R-project.org/package=survival.\u003c/li\u003e\n\u003cli\u003eKassambara, A.; Kosinski, M.; Biecek, P. Survminer: Drawing Survival Curves Using \u0026ldquo;Ggplot2\u0026rdquo;; CRAN Repository. 2019. Available online: https://CRAN.R-project.org/package=survminer (accessed on 24 November 2022).\u003c/li\u003e\n\u003cli\u003eLe Guellec, S., Lesluyes, T., Sarot, E., Valle, C., Filleron, T., Rochaix, P., Valentin, T., P\u0026eacute;rot, G., Coindre, J.-M., and Chibon, F. (2018). Validation of the Complexity INdex in SARComas prognostic signature on formalin-fixed, paraffin-embedded, soft-tissue sarcomas. Ann. Oncol. \u003cem\u003e29\u003c/em\u003e, 1828\u0026ndash;1835. https://doi.org/10.1093/annonc/mdy194.\u003c/li\u003e\n\u003cli\u003eDobin, A., Davis, C., Schlesinger, F., Drenkow, J., Zaleski, C., Batut, P., Chaisson, M., and Gingeras, T. (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics \u003cem\u003e29(1)\u003c/em\u003e, 15\u0026ndash;21.\u003c/li\u003e\n\u003cli\u003eDanecek, P., Bonfield, J.K., Liddle, J., Marshall, J., Ohan, V., Pollard, M.O., Whitwham, A., Keane, T., McCarthy, S.A., Davies, R.M., et al. (2021). Twelve years of SAMtools and BCFtools. GigaScience \u003cem\u003e10\u003c/em\u003e, giab008. https://doi.org/10.1093/gigascience/giab008.\u003c/li\u003e\n\u003cli\u003ePutri, G.H., Anders, S., Pyl, P.T., Pimanda, J.E., and Zanini, F. (2022). Analysing high-throughput sequencing data in Python with HTSeq 2.0. Bioinformatics \u003cem\u003e38\u003c/em\u003e, 2943\u0026ndash;2945. https://doi.org/10.1093/bioinformatics/btac166.\u003c/li\u003e\n\u003cli\u003eLove, M.I., Huber, W., and Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. \u003cem\u003e15\u003c/em\u003e, 550. https://doi.org/10.1186/s13059-014-0550-8.\u003c/li\u003e\n\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":"","lastPublishedDoi":"10.21203/rs.3.rs-6984264/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6984264/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLeiomyosarcoma (LMS) is a malignant mesenchymal tumor with smooth muscle cell (SMC) differentiation. LMS shows high metastatic rate and highly rearranged genome, associated with whole genome doubling, which is identified in more than half of cases. We tested the hypothesis that cell fusion could be one of the mechanisms involved in the development of genome doubling and the production of multiple genomic alterations. We developed a cellular fusion model between SMCs and fibroblasts to compare the genomic alterations found in the hybrid cells vs the genomic profiles of LMS patients. This cell fusion model revealed a recurrent deletion within \u003cem\u003ePRKG1\u003c/em\u003e in chromosome 10, gene involved in smooth muscle contractile function and proliferation. The whole genome sequencing (WGS) analysis of a cohort of 121 LMS patients revealed that 76.9% (93/121) of patients had at least one \u003cem\u003ePRKG1\u003c/em\u003e altered copy, from which 18.2% (17/93) showed either a breakpoint (BP) in \u003cem\u003ePRKG1\u003c/em\u003e or an intra-chromosomal deletion surrounding \u003cem\u003ePRKG1\u003c/em\u003e. RNA sequencing (cohort 147 LMS) indicated that patients with low expression of \u003cem\u003ePRKG1\u003c/em\u003e had significantly worse survival. Results indicate that \u003cem\u003ePRKG1\u003c/em\u003e is among the most common altered genes in LMS, and its function is related to cell motility \u003cem\u003ein vitro\u003c/em\u003e and tumor aggressiveness \u003cem\u003ein vivo\u003c/em\u003e.\u003c/p\u003e","manuscriptTitle":"Tetraploidization-driven PRKG1 deletion reveals novel mechanisms underlying Leiomyosarcoma aggressiveness","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-23 07:06:07","doi":"10.21203/rs.3.rs-6984264/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":"56e248a2-8ee8-4312-b657-6070fff935bf","owner":[],"postedDate":"July 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":51653835,"name":"Biological sciences/Cancer/Sarcoma"},{"id":51653836,"name":"Biological sciences/Cancer/Oncogenes"},{"id":51653837,"name":"Biological sciences/Cell biology/Cell migration"}],"tags":[],"updatedAt":"2026-04-10T15:13:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-23 07:06:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6984264","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6984264","identity":"rs-6984264","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}