Multi-Layered Molecular Profiling Informs the Diagnosis and Targeted Therapy of Desmoplastic Small Round Cell Tumor

Multi-Layered Molecular Profiling Informs the Diagnosis and Targeted Therapy of Desmoplastic Small Round Cell Tumor | 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 Multi-Layered Molecular Profiling Informs the Diagnosis and Targeted Therapy of Desmoplastic Small Round Cell Tumor Stefan Fröhling, Marcus Renner, Małgorzata Oleś, Nagarajan Paramasivam, and 42 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6104125/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Apr, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Desmoplastic small round cell tumor (DSRCT) is an ultra-rare sarcoma with limited treatment options. We performed whole-genome/exome, transcriptome, and DNA methylome analysis in 30 refractory DSRCT patients, complemented by (phospho)proteomic profiling in nine, within a nationwide precision oncology program. In eight patients (27%), DSRCT was diagnosed based on molecular profiling. Although all patients had “quiet” genomes, 28 (93%) received 107 molecular-based management recommendations, including assessment of clinical trial eligibility in 17 (57%). Nearly half of recommendations (45%) were based on overexpression of tyrosine kinases, as well as SSTR3/5 and CLDN6, detected in 33% and 20% of cases, respectively. Thirteen patients (46%) received recommended therapies, yielding disease control in eight (62%; partial response, n = 5; stable disease, n = 3), including three long-lasting responses (≥ 12 months) to pazopanib and trastuzumab deruxtecan, triggered by ERBB2 overexpression in the absence of constitutive ERBB2 signaling. Thus, multi-omics profiling enables individualized DSRCT treatment. Health sciences/Oncology/Cancer/Tumour biomarkers Health sciences/Biomarkers/Diagnostic markers Health sciences/Biomarkers/Predictive markers Health sciences/Molecular medicine Health sciences/Oncology/Cancer/Cancer therapy Desmoplastic Small Round Cell Tumor Molecular Profiling Targeted Therapy Trastuzumab Deruxtecan Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Desmoplastic small round cell tumor (DSRCT) is an ultra-rare, high-grade soft-tissue sarcoma (incidence, 0.2/1,000,000 persons/year) of uncertain cellular origin, predominantly affecting male children and young adults 1 , 2 . It is characterized by a pathognomonic chromosomal translocation, t(11;22)(p13;q12) 3 , 4 , resulting in a chimeric protein that contains the N-terminal domain of Ewing sarcoma breakpoint region 1 (EWSR1) and three of four zinc finger domains of Wilms tumor 1 (WT1) 5 , 6 and is essential for the viability and proliferation of DSRCT cells in preclinical models 7 . The diagnosis of DSRCT can be challenging due to ambiguous histology and expression of neuroendocrine markers and/or cytokeratins 8 . The clinical management of DSRCT involves a multimodal approach, including chemotherapy, surgery, and radiotherapy. Complete or cytoreductive surgery is the most critical component, positively impacting overall survival 9 , 10 . However, DSRCT is typically diagnosed at an advanced stage, and the overall prognosis is poor, with most patients succumbing to the disease within three years of diagnosis 11 . Due to the lack of clinical trials for this orphan disease, no standard systemic therapy has been established. To date, the use of up to 30 different chemotherapy protocols has been reported, and most patients are treated with regimens adopted from Ewing sarcoma or other soft-tissue sarcomas. Previous molecular studies have shown that DSRCT has a low mutational burden and few druggable targets 11 – 14 . In addition, the tumors are immune-cold, with little benefit from immunotherapies, although individual responses have been reported 12 , 15 . Various multi-targeted tyrosine kinase inhibitors (TKIs), used without molecular biomarker guidance, have shown limited efficacy in individual patients and small case series 16 – 18 . Retrospective studies suggest some activity of pazopanib, although the determinants of clinical benefit remain unclear 19 , 20 . Overall, there is an urgent need for effective DSRCT drugs, and it seems warranted to explore whether more comprehensive biological profiling could uncover new therapeutic targets. A rapidly expanding area of drug development focuses on therapies targeting specific antigens on tumor cells, independent of mutations or dependence on associated signaling pathways. Key examples include chimeric antigen receptor (CAR) T cells, bispecific antibodies, and next-generation antibody-drug conjugates (ADCs). These modalities have significantly broadened treatment options for previously hard-to-target cancers. However, rare malignancies are understudied with respect to these drugs and have therefore benefited less than more common cancers. In DSRCT, in vitro studies have postulated that the signaling pathway controlled by the ERBB (also called HER) family of receptor tyrosine kinases (RTKs) might be activated, and very high doses of the EGFR (also called ERBB1)-directed antibody cetuximab and the pan-ERBB small-molecule inhibitor afatinib, as well as the ERBB2 (also called HER2)-directed ADC trastuzumab deruxtecan (T-DXd) reduced tumor growth in mouse xenografts 21 , 22 . Based on these findings and given the efficacy of T-DXd in breast cancer, even with (ultra-)low ERBB2 expression 23 , and other epithelial malignancies 24 , ERBB2 emerges as a promising target in DSRCT. However, responses to ADCs and their correlation with target expression have not been studied in DSRCT patients. The MASTER (Molecularly Aided Stratification for Tumor Eradication Research) program (ClinicalTrials.gov: NCT05852522 ), a prospective observational study conducted by the German Cancer Research Center (DKFZ), the National Center for Tumor Diseases (NCT), and the German Cancer Consortium (DKTK), leverages whole-genome/exome sequencing (WGS/WES), RNA sequencing (RNA-seq), DNA methylation profiling, proteomics, and phosphoproteomics to guide treatment in young adults with advanced malignancies and patients with incurable rare cancers 25 , 26 . In this study, we present the clinical courses and molecular target landscapes of 30 patients with advanced DSRCT enrolled in MASTER. Our findings, including the sustained activity of T-DXd in two patients, demonstrate how multi-layered molecular diagnostics beyond the current standard of care can guide the clinical management of DSRCT patients and lay the groundwork for biomarker-guided clinical trials. RESULTS Patient Characteristics and Previous Treatments Between 2013 and 2022, 30 DSRCT patients underwent multi-layered molecular profiling as part of the DKFZ/NCT/DKTK MASTER program. The median age at the time of molecular analysis was 30 years (range, 18–56); four patients (13%) were female and 26 (87%) male. The median interval between cancer diagnosis and molecular analysis was nine months (range, 1–218). Median survival from the first inter-institutional molecular tumor board (MTB) was 2.1 years (95% confidence interval [CI], 1.0–2.6 years), with a four-year survival rate of 10% (95% CI, 2.8–37%). The median follow-up duration was 17 months (range, 0–48). Patient characteristics are detailed in Table 1 . In eight patients (27%), the initial diagnosis was incorrect or incomplete (Fig. 1 a): Three tumors were classified as carcinoma of unknown primary site (CUP), one as neuroendocrine carcinoma, and one as angiomatoid fibrous histiocytoma; three patients received incomplete sarcoma diagnoses (Ewing sarcoma-like sarcoma, undifferentiated sarcoma with myogenic differentiation [rhabdomyosarcoma-like], and undifferentiated anaplastic sarcoma). Histopathologic re-evaluation was prompted in each case by the finding of an EWSR1::WT1 fusion, supported further by gene expression data and a DNA methylation-based sarcoma classifier 27 , and confirmed the diagnosis of DSRCT in all five patients with available tissue samples. The median time from initial to DSRCT diagnosis was 10.5 months (range, 1–224 months). Among the 27 patients in whom the EWSR1::WT1 fusion was detected by RNA-seq, 25 (93%) had breakpoints in exons 7–8, one in exons 9 − 8, and one in exons 10 − 8 ( Supplementary Fig. 1 ). Before enrollment in MASTER, patients had received a median of three (range, 1–10) lines of systemic treatment and a median of four (range, 1–12) lines of local therapy, including surgery, radiation, and hyperthermic intraperitoneal chemotherapy (HIPEC). In addition to systemic therapy, 17 patients (57%) had undergone at least one surgical procedure, with six (20%) also receiving radiotherapy and five (17%) undergoing HIPEC. One patient (3%) had received systemic treatment, radiotherapy, surgery, and HIPEC (Fig. 1 b, c; Supplementary Table 1 ). Due to the lack of an evidence-based standard, systemic treatments were heterogeneous. Eighteen patients (60%) had been treated with the VIDE regimen (vincristine, ifosfamide, doxorubicin, etoposide) established for Ewing sarcoma. Six patients (20%) had received targeted therapy as an individual approach. Treatment details and outcomes are summarized in Fig. 1 c, d, e. Table 1. Clinical characteristics of 30 DSRCT patients Age at enrollment (years) Median Range 30 18–56 Age at diagnosis (years) Median Range 27 18–55 Sex Male Female n = 26 (86.7%) n = 4 (13.3%) Primary tumor site Intra-abdominal Extra-abdominal n = 27 (90%) n = 3 (10%) Metastatic disease at diagnosis Yes No n = 23 (76.7%) n = 7 (23.3%) Metastatic site at diagnosis Lymph nodes Liver Lung Bone n = 18 (60%) n = 10 (33.3%) n = 7 (23.3%) n = 5 (16.7%) Clinical Decision-Making Based on Multi-Layered Molecular Profiling Overview. For clinical decision-making by the inter-institutional MTB of the MASTER program, molecular biomarkers identified through multi-omics profiling ( Supplementary Fig. 2a ) and resulting clinical management recommendations were grouped into nine intervention baskets: tyrosine kinase (TK), DNA damage repair (DDR), immunotherapy (IT), PI3K-AKT-mTOR (PAM), RAF-MEK-ERK (RME), cell cycle (CC), theranostics (THER), antibody-drug conjugate (ADC), and other (OTH). The MTB provided at least one treatment recommendation (range, 1–7) in 28 of 30 patients (93.3%) (Fig. 2 a, b). Of the total of 107 recommendations, 48 (45%) fell into the TK basket, followed by the DDR ( n = 13, 12%), IT ( n = 12, 11%), and THER ( n = 11, 10%) categories (Fig. 2 a, b; Supplementary Table 2, 3) . In four patients, sequential analysis of additional tumor samples showed increasing somatic mutation rates ( Supplementary Fig. 2b ) and led to a second ( n = 3) or second and third ( n = 2) MTB consultation with further treatment recommendations. Seventeen patients (57%) were recommended for enrollment in 14 different clinical trials (Fig. 2 b; Supplementary Table 4 ). DNA analysis. Consistent with previous studies 12 , 14 , 28 , WGS/WES revealed few somatic mutations (median of 0.68 single-nucleotide variants [SNVs] and small insertions/deletions [indels] per megabase and median of 23 non-silent SNVs and indels per sample). Two genes showed acquired mutations in three patients (10%; Supplementary Fig. 3 ): DCC , encoding the netrin 1 receptor implicated in axon guidance and various epithelial cancers 29 , 30 , which was exclusively altered in female patients, and EPB41L3 , encoding a cytoskeletal component linked to tumor and/or metastasis suppression 31 . Of a total of 107 treatment recommendations, only four (4%; Supplementary Table 5 ) were based on individual SNVs. In patient DSRCT-01, missense mutations in FLT1 and EPHA3 led us to recommend a multi-targeted TKI, e.g., pazopanib or dasatinib. In patient DSRCT-05, a missense mutation in FGFR4 provided a rationale for a multi-targeted TKI, e.g., pazopanib. In patient DSRCT-09, a likely gain-of-function mutation in the kinase domain of MTOR prompted the recommendation of an mTORC1 inhibitor, e.g., everolimus. In addition to individual SNVs, eleven of the 107 treatment recommendations were made based on single-base substitution (SBS) signatures (10%; Supplementary Table 6 ). Detection of signatures SBS3 and/or SBS8, indicating deficiencies in the homologous recombination (HR) and nucleotide excision DNA repair pathways, contributed to the recommendation of a poly(ADP-ribose) polymerase (PARP) inhibitor in 10 patients, sometimes together with alterations of HR-related genes or high SLFN11 expression. Three of these patients were recommended for enrollment in a clinical trial ( NCT03127215 ) investigating the combination of the PARP inhibitor olaparib and trabectedin in HR-deficient cancers 32 . The overall landscape of somatic DNA copy number aberrations observed in our cohort ( Supplementary Fig. 4 ) was largely consistent with previous studies 14 , 28 , 33 . Specific copy number variants (CNVs) were the basis for nine of the 107 treatment recommendations (8%; Supplementary Table 7 ). For example, deletion and low mRNA expression of PTEN in two patients led us to recommend an mTORC1 inhibitor. Additionally, three patients were recommended treatment with a PARP inhibitor due to deletions of genes involved in HR-mediated DNA repair. The systematic evaluation of rare germline alterations in 101 cancer predisposition genes ( Supplementary Table 8 ) identified one likely pathogenic variant: patient DSRCT-25 had a heterozygous SDHC p.R133X stopgain variant associated with hereditary pheochromocytoma and paraganglioma 34 . However, there was no evidence that this variant was relevant for DSRCT development, and no paragangliomas were reported in the patient or the family with a diverse tumor spectrum. Finally, uniform manifold approximation and projection (UMAP) analysis of the DNA methylation profiles of 30 samples from 25 DSRCT patients, combined with previously published profiles of 305 other sarcomas, including small blue round cell tumors with BCOR or CIC alterations 27 , 35 , showed that the DSRCT samples formed a distinct and coherent cluster ( Supplementary Fig. 5a ). All but one DSRCT sample had a sarcoma classifier score > 0.9 ( Supplementary Fig. 5b ), underlining the diagnostic utility of epigenomic analysis in this entity. Together, these data demonstrated that exhaustive DNA-based analyses enhance diagnostic accuracy in DSRCT and may identify occasional patients with pathogenic germline variants but are of limited value for identifying new therapeutic targets. RNA analysis. Seventy-eight of the 107 recommendations by the MTB (73%) were based on increased mRNA expression of potential therapeutic targets, most of which fell into the TK, DDR, IT, and OTH baskets. The largest proportion of expression-based recommendations (48 of 107, 45%) was accounted for by genes encoding components of TK pathways that can be targeted with clinically approved small-molecule inhibitors (Fig. 3 a). The implementation of these recommendations in a subset of patients and associated outcomes are described below. In 10 of 30 cases (33%), the MTB recommended somatostatin receptor 3 (SSTR3)-targeted peptide receptor radionuclide therapy (PRRT). This was based on the consistent overexpression of SSTR3 and SSTR5 but not SSTR1 , SSTR2 , and SSTR4 mRNA in DSRCT compared to other sarcomas enrolled in MASTER (Fig. 3 b; Supplementary Fig. 6 ). However, while one patient underwent DOTA-PTR-58 imaging to verify SSTR3 expression and tracer uptake, none received PRRT. In four of 30 patients (13%), androgen receptor (AR) blockade was recommended due to high AR expression. In addition, we observed extreme expression of CLDN6 , encoding a cell adhesion molecule, in six of 30 patients (20%; Fig. 3 b), which prompted the recommendation to consider enrollment in a clinical trial of CLDN6-specific CAR T cells ( NCT04503278 ) 36 . This recommendation was implemented in two patients (DSRCT-13 and DSRCT-29). Finally, the MTB recommended ERBB2-targeted treatment in seven patients due to increased ERBB2 mRNA expression (Fig. 3 b), which in two cases led to the administration of T-DXd, as described below, and to combination therapy with trastuzumab, pertuzumab, and atezolizumab within a clinical trial ( NCT04551521 ) 37 in one patient, who died shortly after treatment initiation and before the first response evaluation. Besides these recurring treatment recommendations, increased SLFN11 expression in a patient who also had signature SBS8 prompted the MTB to recommend a PARP inhibitor ( Supplementary Table 6 ), which was administered in combination with trabectedin analogous to, but outside of, the clinical trial mentioned above ( NCT03127215 ), resulting in disease stabilization (Table 2 ). Proteome and phosphoproteome analysis. We recently integrated proteomic and phosphoproteomic profiling into the clinical workflow of the MASTER program 39 . Using this newly established pipeline, we retrospectively analyzed samples from nine DSRCT patients for whom suitable tumor tissue was available. For comparison, we analyzed a heterogeneous group of 554 samples representing over 20 sarcoma subtypes (Fig. 4 a). UMAP analysis of the global expression of approximately 4,000 proteins showed that the DSRCT samples formed a distinct cluster (Fig. 4 b). In differential expression analysis, high levels of ERBB2 and CLDN6 were detected in all and two of nine DSRCT patients, respectively (Fig. 4 c, d, e). The correlation of ERBB2 mRNA and protein expression was moderate in both the DSRCT samples and the comparison cohort ( r = 0.5 and r = 0.73, respectively; Fig. 4 f). Analysis of the expression and phosphorylation landscape of 43 RTKs showed that, in addition to ERBB2, TYRO3 was overexpressed in most tumors, while high levels of KDR (also known as VEGFR2), MERTK, ALK, and INSR were detected in one or two patients (Fig. 4 g). Phosphoproteomic analysis revealed aberrant activity (indicated by a phosphoprotein score > 2) of FGFR4 in patient DSRCT-10 and of KDR and TYRO3 in patient DSRCT-18. In contrast, no evidence of constitutive ERBB2 signaling was observed (Fig. 4 g). Implementation of Molecularly Guided Treatment Recommendations Overview. Of the 107 targeted therapies recommended, 16 (15%) could be administered in 13 of 30 patients (43%; Fig. 2 a, b; Table 2 ). All were based on target gene RNA expression. Disease control was achieved in eight of 13 patients (62%; partial remission [PR], n = 5; stable disease [SD], n = 3). In addition, three patients received chemotherapy according to the VIDE regimen based on the detection of an EWSR1::WT1 fusion and histopathologic re-evaluation. Table 2 RNA expression-based targeted therapies administered Patient Treatment Basket Biomarker(s) Best response Treatment duration (months) DSRCT-03 Pazopanib Nivolumab TK IT FGFR4 , FLT4 , PDGFA PD1 PD PD 5 2 DSRCT-11 Afatinib TK NRG1 PD 1 DSRCT-12 Pazopanib TK KDR , BRAF , RAF1 , NRAS , MEK2 PR 4 DSRCT-13 Pazopanib CAR T cells TK IT KDR , PDGFA CLDN6 SD NR 6 NR DSRCT-14 Olaparib A,B DDR SLFN11 SD 4 DSRCT-15 Pazopanib TK FGFR2 , FGFR4 , VEGFA PR 11 DSRCT-18 Pazopanib TK KDR , FYN , PDGFRB PR 17 DSRCT-19 Pazopanib C TK KDR SD 6 DSRCT-20 Pazopanib TK KDR , FYN , PDGFRB PD 2 DSRCT-21 Pazopanib TK KDR , NTRK3 , FLT4 , LCK , PDGFA PD 3 DSRCT-28 Pazopanib T-DXd TK ADC FGFR4, KDR, PDGFA, MERTK ERBB2 PD PR 4 18 DSRCT-29 CAR T cells IT CLDN6 NR NR DSRCT-30 T-DXd ADC ERBB2 PR > 12 A: Also supported by the detection of signature SBS8. B: In combination with trabectedin. C: In combination with gemcitabine. PR, partial response; SD, stable disease; PD, progressive disease; NR, not reported. Tyrosine kinase inhibition. Ten patients received a multi-targeted small-molecule TKI (Fig. 2 a, b; Table 2 ). Specifically, pazopanib was administered in nine patients due to overexpression of various combinations of FGFR2 , FGFR4 , FLT4 , FYN , KDR , LCK , MERTK , NTRK3 , PDGFA , PDGFRB , and VEGFA ; in one of these cases, overexpression of NRAS , BRAF , RAF1 , and MEK2 provided further support, as it has been postulated that pazopanib also acts as a pan-RAF inhibitor 40 . Five patients (56%) achieved disease control (PR, n = 3; SD, n = 2), and four (44%) had disease progression. Of particular note is patient DSRCT-18, who showed a PR lasting 17 months and was lost to follow-up on pazopanib therapy. In addition to high KDR , FYN , and PDGFRB mRNA expression, the recommendation of pazopanib in this patient was also supported by phosphoproteome analysis, which showed increased activity of KDR and TYRO3 (Fig. 4 g). One patient (DSRCT-11) whose tumor overexpressed NRG1 received the pan-ERBB inhibitor afatinib, which has activity in NRG1 -rearranged neoplasms 41 . However, treatment was discontinued after one month due to generalized disease progression with ascites and colitis with clinically relevant bleeding. ERBB2-directed ADC treatment. Two patients with high ERBB2 mRNA and protein expression levels received off-label treatment with T-DXd (Fig. 2 a, b; Table 2 ). This selection over other small molecule- or antibody-based therapies targeting ERBB2 was supported by the results of phosphoproteomic profiling, which showed no indication of increased ERBB2 signaling (Fig. 4 g). The first patient (DSRCT-28), a 36-year-old man, was diagnosed in February 2018 with poorly differentiated CUP and presented with metastases in the liver, bones, and lymph nodes. At the time of enrollment in MASTER in March 2022, he had undergone 12 lines of therapy, with cisplatin, 5-fluorouracil, and docetaxel, given for four months, and nab-paclitaxel and carboplatin, given for 11 months, each yielding a PR (Fig. 5 a; Supplementary Table 9 ). Molecular analysis revealed an EWSR1::WT1 fusion, DNA methylation profiling 27 predicted DSRCT with a score of 0.99, RNA-seq confirmed an expression pattern typical of DSRCT, and histologic re-evaluation validated the diagnosis. The MTB provided four treatment recommendations based on increased target gene expression: (i) small-molecule inhibition of FGFR4, KDR, and MERTK, e.g., with pazopanib, (ii) participation in a clinical trial of CAR T cells against CLDN6 ( NCT04503278 ), (iii) SSTR3-targeted PRRT, and (iv) ERBB2-directed therapy (Fig. 2 b, Supplementary Table 2 ). Following the revised diagnosis, the patient was treated with pazopanib but showed disease progression after three months. Next, he received six cycles of chemotherapy according to the VIDE regimen, excluding doxorubicin due to prior disease progression. In April 2023, T-DXd therapy was initiated at 6.4 mg/kg, which was generally well tolerated, with the main adverse effects being grade 2–3 nausea, grade 2–3 loss of appetite, grade 2 fatigue, and grade 1 diarrhea. The patient achieved a partial response at the first staging in July 2023, which was ongoing in July 2024 (Fig. 5 a, c; Supplementary Tables 9, 10 ). Of note, ERBB2 was not detected by routine immunohistochemistry (IHC; Fig. 5 b). Given the robust ERRB2 expression identified by mass spectrometry in DSRCT patients (Fig. 4 d), this discrepancy was likely due to limited quality of the available formalin-fixed and paraffin-embedded (FFPE) tissue. The second patient (DSRCT-30), a 34-year-old woman, was diagnosed in July 2020 with intra-abdominal DSRCT, suspected peritoneal sarcomatosis, and mesenteric lymph node metastases. Following three lines of therapy with VAIA (vincristine, adriamycin [doxorubicin], ifosfamide, actinomycin-D), VIDE, and two cycles of trabectedin, she underwent debulking surgery (Fig. 5 d; Supplementary Table 11 ). In October 2022, she was enrolled in MASTER while on sixth-line therapy with irinotecan and temozolomide. Molecular analysis of a peritoneal metastasis resulted in three treatment recommendations: (i) T-DXd, (ii) trastuzumab, pertuzumab, and atezolizumab within a clinical trial ( NCT04551521 ) 37 , both based on increased ERBB2 expression (Fig. 5 e), and (iii) PRRT targeting SSTR3. Upon disease progression, T-DXd was initiated in August 2023. Dosing was reduced to 5.4 mg/kg, 4.4 mg/kg, and 3.2 mg/kg during cycles 3, 5, and 9, respectively, due to grade 2 nausea and grade 3 fatigue. While grade 1–2 fatigue persisted, nausea resolved with dose reduction. After SD in November 2023, the patient achieved a partial metabolic response in January 2024, which was ongoing in September 2024 (Fig. 5 f; Supplementary Tables 11, 11 ). In this case, IHC detected moderate membrane expression of ERBB2, corresponding to a score of 2+ (ERBB2-low) according to American Society of Clinical Oncology (ASCO) and College of American Pathologists (CAP) testing guidelines (Fig. 5 e). Together, these observations demonstrate for the first time the potential of ERBB2-targeted ADC treatment to achieve sustained disease control in DSRCT patients. DISCUSSION In this multi-institutional study, we explored the use of extensive biomarker profiling to inform the clinical management of DSRCT, an aggressive ultra-rare cancer primarily affecting adolescents and young adults that currently lacks established drug treatments, including molecularly targeted approaches. We observed that multi-omics analyses beyond the current standard of care have relevant implications for precision diagnostics and individualized, molecular mechanism-aware therapy. More than a quarter of our patients, who were treated at various centers across Germany, initially received incorrect or incomplete diagnoses based on standard histopathology. This aligns with the real-world experience that small round cell tumors are challenging to classify and demonstrates the potential of molecular methods to improve diagnostic accuracy, independent of the individual expertise of examiners familiar with the phenotypes of these entities. In all reclassified cases, the finding of an EWSR1::WT1 fusion was essential, supported by gene expression and DNA methylation profiles characteristic of DSRCT. While we favor an approach in which all sarcomas undergo multi-omics profiling as applied in this study, the diagnostic algorithm in cases consistent with DSRCT according to morphologic criteria should include, at minimum, a method for detecting this hallmark fusion, such as RNA-seq or fluorescence in situ hybridization. In the absence of evidence-based drug therapies, an accurate diagnosis of DSRCT currently has limited impact on patient outcomes. However, we believe this will change, and a median time from initial to DSRCT diagnosis of 10.5 months will become unacceptable, as our results demonstrate in three ways the potential of comprehensive molecular profiling to significantly impact therapeutic decision-making. First, the MTB provided recommendations for the vast majority of heavily pretreated patients (93%), demonstrating the feasibility and utility of our approach in a complex clinical setting. Nearly half of the recommendations (45%) fell into the TK basket, followed by the DDR (12%), IT (11%), and THER (10%) categories, reflecting the diverse multi-omic landscape of DSRCT, a disease usually considered “molecularly silent”, and the resulting tailored therapeutic strategies. Second, sequential molecular analyses in a subset of cases revealed the dynamic nature of DSRCT evolution and prompted additional MTB recommendations. Third, the identification of clinical trial opportunities for 57% of patients underscores the role of molecular profiling in increasing access to innovative therapies. Different molecular layers contributed variably to clinical decision-making. DNA sequencing confirmed the sparse mutational landscape of DSRCT. Consequently, individual SNVs, such as those in FLT1 , EPHA3 , FGFR4 , and MTOR , informed fewer than 4% of treatment recommendations. Similarly, somatic CNVs, e.g., deletions of PTEN or HR-related genes, guided MTB recommendations in some cases but were infrequent and often insufficient on their own to dictate therapy. Mutational signatures offered additional value by suggesting vulnerability to PARP inhibitors; however, their utility may be limited by the low overall SNV count in most cases. Finally, a cancer-predisposing germline alteration was detected in only one patient and lacked direct implications for therapy. This aligns with the broader observation that, in contrast to other sarcomas 42 , germline findings in established cancer predisposition genes have limited clinical applicability in DSRCT. Larger sample sizes would be needed to analyze possible associations of rare variants in candidate genes or polygenic risk associations with DSRCT development 43 . Overall, our results suggest that DNA-based analyses, even with comprehensive methods such as WGS/WES, are rarely sufficient for guiding clinical decisions in DSRCT. Highlighting the need to consider additional molecular layers to broaden therapeutic opportunities for DSRCT patients, RNA-based biomarkers informed 73% of MTB treatment recommendations. A large proportion of expression-based recommendations (45%) were directed at TK pathways, supporting the use of small-molecule inhibitors. Additionally, consistent overexpression of SSTR3 and SSTR5 in DSRCT compared to other sarcomas prompted recommendations for PRRT in 10 patients (33%). Although PRRT was not administered in this cohort, our findings laid the molecular groundwork for a recently initiated clinical trial investigating the somatostatin analog pasireotide as maintenance therapy in DSRCT ( NCT06456359 ; Schlenk et al. ESMO Congress 2024), which will provide first insights into the value of SSTR-targeted strategies in this disease. Furthermore, extreme CLDN6 expression in six patients (20%) led to recommendations for enrollment in a clinical trial evaluating CLDN6-specific CAR T cells ( NCT04503278 ) 36 . This strategy illustrates the potential of RNA data to identify novel immunotherapeutic opportunities whose benefit is suggested by the published report of an independent DSRCT patient in this trial in whom SD was achieved by CLDN6-targeted CAR T cells 36 . Similarly, increased ERBB2 expression prompted recommendations for ERBB2-targeted therapies in seven patients, with two receiving T-DXd and another enrolling in a clinical trial combining trastuzumab, pertuzumab, and atezolizumab ( NCT04551521 ). Additionally, RNA data occasionally enhanced DNA-based recommendations. For example, increased SLFN11 expression in a patient with mutational signature SBS8 supported the administration of a PARP inhibitor combined with trabectedin, resulting in SD. These findings underscore the potential of RNA-seq for target discovery and refinement of biomarker-driven treatment strategies and support the incorporation of transcriptomic profiling into the management of DSRCT patients. Implementing molecularly guided treatment recommendations was only feasible in just over 40% of cases. This reflects challenges common to precision oncology programs, including limited off-label access to targeted agents and a scarcity of molecularly stratified clinical trials – issues that are particularly pronounced in rare cancers like DSRCT 44 . Nonetheless, we successfully acted on several discoveries, and the disease control rate of 62% in this heavily pretreated population was encouraging, marking an important step toward a rational, biology-guided approach to this disease. Among the 30 patients in our cohort, 10 (33%) received a multi-targeted TKI. In particular, pazopanib was administered in nine patients due to overexpression of various target genes, e.g., FGFR4 , FLT4 , KDR , MERTK , PDGFA , PDGFRB , and VEGFA . Notably, we observed disease control in five of these nine cases (56%), including a PR lasting at least 17 months in a patient whose tumor showed increased KDR signaling as determined by retrospective phosphoproteomic analysis. The efficacy of agents targeting TK pathways has been investigated in several DSRCT case series. Italiano et al . reported on sunitinib treatment of eight patients, which resulted in a PR in two (25%) and SD in three (38%) cases, respectively, and a median progression-free survival (PFS) of 2.6 months 16 . Frezza et al . observed that two (22%) and five (56%) of nine patients treated with pazopanib achieved a PR and SD, respectively, which translated into a median PFS and overall survival (OS) of 9.2 and 15.4 months, respectively 20 . A study by the French registry for the analysis of off-label use of targeted therapies in sarcomas examined nine patients treated with sunitinib, sorafenib, or bevacizumab. The best response observed was a 5.5-month SD, and the median PFS was 3.1 months 45 . Menegaz et al . reviewed the data of 29 patients treated with pazopanib and observed one complete response (3%), one PR (3%), and 16 cases with SD (55%); the median PFS was 5.6 months, and the median OS was 15.7 months 19 . These converging results demonstrate that TKIs have activity in a subset of DSRCT patients. However, their use was unstratified and relied either on empirical evidence or the general observation that the DSRCT microenvironment is characterized by neoangiogenesis and expression of various angiogenic factors. In contrast, we employed patient-level biomarker profiles to individualize therapy selection. In particular, the long-lasting response to pazopanib in a patient whose transcriptomic and phosphoproteomic profiles revealed aberrant activation of several kinases targeted by this drug underscores the potential of comprehensive molecular analyses. We believe that considering these two diagnostic layers, including a recently described RNA-based efficacy predictor 46 , will help maximize the therapeutic potential of pazopanib, and potentially other TKIs, and guide patients unlikely to benefit to alternative therapies. Another TK that emerged as a therapeutic target is ERBB2, whose mRNA levels were consistently higher in DSRCT than in other sarcomas. Expression of ERBB2 has been previously observed in DSRCT patients and preclinical models, suggesting that it may be constitutively activated and can be inhibited by small molecules or antibodies whose efficacy requires dependence on the corresponding signaling pathway 21 . However, our phosphoproteomic analysis found no evidence of aberrant ERBB signaling. This finding supported the use of T-DXd, which led to long-lasting remissions in both patients treated, whose disease had progressed after 12 and five prior therapy lines, respectively. These results are consistent with recent observations in patient-derived xenografts 22 and indicate that clinical trials of T-DXd, or further-generation ERBB2-targeted ADCs, in this intractable malignancy are warranted. To translate this signal of ADC efficacy in DSRCT patients into improved routine care, developing robust biomarkers will be essential. Studies in breast cancer, a disease known for its association with ERBB2 expression in a subset of cases, have shown that patients with low or ultra-low ERBB2 levels also benefit from T-DXd 23 . Consequently, the criteria for quantifying this biomarker were quickly adapted, e.g., in the ASCO/CAP testing guidelines. However, the minimal clinically relevant ERBB2 expression remains uncertain, illustrated, e.g., by the DAISY phase 2 trial, in which even patients with ERBB2-negative tumors by standard IHC responded to T-DXd 23 . This might be attributed to T-DXd’s strong bystander effect, where the targeted payload release is mediated by ERBB2 expression below the detection limit of IHC, suggesting that alternative methods, such as RNA-seq, may become critical in identifying eligible patients. Of our two long-term responders, both of whom showed readily detectable ERBB2 mRNA levels, one had an ERBB2-low tumor, and one lacked ERBB2 expression by IHC, which, given the robust expression by mass spectrometry, was probably due to insufficient tissue quality. Based on our data, we predict that most DSRCT patients are candidates for ERBB2-targeted ADC therapy and that a negative ERBB2 IHC result does not exclude the possibility of a response to these agents. However, future studies must define the range of clinically relevant target expression and determine the optimal detection methods. Our study illustrates the power of multi-layered molecular profiling and inter-institutional collaboration, principles that hold promise for advancing research in rare cancers at large. While individually infrequent, rare cancers collectively represent a significant patient population, accounting for 20–25% of all newly diagnosed cancers in many regions worldwide, and are often associated with poor outcomes due to limited treatment options and insufficient research investment 47 . We leveraged the framework of a nationwide precision oncology platform to deliver new insights into the clinically actionable molecular landscape of DSRCT, thereby offering a blueprint for accelerating research in other ultra-rare malignancies. This synergy – pooling resources, harmonizing methodologies, and rapidly sharing novel observations – illustrates that, when institutions unite under a common goal, meaningful advances become more attainable, thereby paving the way for improved outcomes in patient groups that urgently require new therapeutic strategies. MATERIALS AND METHODS Patients Thirty patients with advanced DSRCT provided written informed consent for multi-layered molecular profiling of tumor tissue and, in the case of DNA sequencing, a matched blood sample, as well as for the longitudinal collection of clinical information as part of the DKFZ/NCT/DKTK MASTER program 26 . The study was approved by the Ethics Committee of Heidelberg University (protocol no. S-206/2011) and conducted in accordance with the Declaration of Helsinki. Biological curation and clinical annotation of molecular profiles were performed as previously described, and a multi-institutional MTB involving treating physicians provided recommendations for clinical management 48 . Sample Processing Frozen tissue sections were assessed by board-certified pathologists to determine tumor cell content, including the presence of necrosis. Suitable samples were processed further at the NCT Heidelberg Sample Processing Laboratory. DNA, RNA, and cell lysates from fresh-frozen tumor specimens and DNA from blood samples were obtained using the AllPrep DNA/RNA/miRNA Universal Kit and the QIAamp DNA Mini Kit (Qiagen). Nucleic acids from FFPE samples were extracted using the Allprep DNA/RNA FFPE Kit (Qiagen). Subsequent quality control and quantification steps were performed using a Qubit Fluorometer (Life Technologies) and a 4200 or 2200 TapeStation System (Agilent). DNA Sequencing Libraries for WGS were prepared using the Illumina TruSeq Nano DNA Library Prep Kit with 100 ng DNA as input and sequenced on an Illumina HiSeq X Ten or NovaSeq 6000 instrument at the DKFZ Next Generation Sequencing Core Facility. Libraries for WES were prepared using the Agilent SureSelect All Exon Kit v5 or v5 + UTRs with 200 ng DNA as input and sequenced on an Illumina HiSeq 2000, HiSeq 2500, HiSeq 4000, or NovaSeq 6000 instrument. Nucleotide Sequence Alignment DNA sequencing reads were mapped to an assembly of the human genome version hs37d5 (1000 Genomes Project phase 2) and the Enterobacteria phage phiX174 genome using BWA mem (version 0.7.15) with the − T0 parameter as the only one deviating from the default. BAM files were sorted with bamsort (biobambam package version 0.0.148), and duplicates were marked with markdup (Sambamba package version 0.6.5) 49 . Somatic SNV and Indel Calling Somatic SNVs were detected with an in-house pipeline based on SAMtools (version 0.1.19) mpileup and bcftools and using heuristic filtering as previously described 50 – 52 . Briefly, initial SNV calls were detected in the tumor BAM file by mpileup, which considered only reads with a minimum mapping quality of 30 (− q30), and bcftools, which reported all positions containing at least one high-quality non-reference base (− vcgN − p2.0), followed by inspection of these positions in the control sample using mpileup. SNVs were annotated with ANNOVAR (version November 2014) using GENCODE (release 19). Downstream filtering discarded variants with low support of the alternative allele, occurring in tandem repeats or other read-attracting regions, with PCR or sequencing strand bias, or with significant bias in the PV4 field of the mpileup output. Somatic SNVs annotated as missense, stopgain, stoploss, or splicing were defined as non-silent. Short indels were detected with Platypus (version 0.8.1.1) 53 , and only those that had the filter tag PASS or passed custom filters allowing for low variant frequency were retained. Annotation of indels was performed using ANNOVAR (version February 2016), and calls falling into a coding sequence or splice site were extracted. Somatic Structural Variant Calling Structural variants (SVs) were detected using SOPHIA (version 35) 54 , which uses the “supplementary alignment” feature of BWA mem to discover SVs and custom thresholds and a background panel of normals to filter out common variants and recurrent artifacts. Somatic CNV Calling DNA copy number profiles of samples subjected to WGS or WES were determined using ACEseq (version 5.0.1) ( https://doi.org/10.1101/210807 ) and CNVkit (version 0.9.3) 55 , respectively. For CNVkit data processing, the steps for inferring ploidy and tumor cell content (TCC) were adopted from the ACEseq workflow. In both workflows, segments containing at least 20 heterozygous single-nucleotide polymorphisms (SNPs) were processed to infer sample ploidy and TCC. The algorithm tested every possible combination of TCC (range, 0.15–1.0) and ploidy (range, 1.0–6.5) to find the local minima and, thus, the optimal solution. If more than one solution was possible, they were visually evaluated, and one was selected. The results for patient DSRCT-16 were inconclusive due to the high level of degradation of the matched control sample and therefore removed from further analysis. The remaining 29 samples had an estimated TCC above 30% and were included in the downstream analysis. Genomic gains and losses were annotated when a given genomic segment deviated in ploidy from the overall sample ploidy by + 0.7 or − 0.7, respectively. To determine the frequency of gains and losses across the genome, the copy number segments’ breakpoints of all samples were gathered to create non-overlapping genomic sections. For each non-overlapping genomic section, the number of samples with gain, loss, and copy number neutral state was calculated. Segments covered by fewer than 20 patients were discarded, and the frequency of gains and losses was calculated for the remainder. The results were annotated using the Cancer Gene Census ( https://cancer.sanger.ac.uk/census ). Germline Variant Calling Germline SNVs and indels were called using Platypus (version 0.8.1.1), germline CNVs were detected using the GATK gCNV module (version 4.2.4.0), and germline SVs were identified using SOPHIA (version 2.2.0). After obtaining raw SNV and indel calls, variants were filtered based on a minimum coverage of 10 x and a minimum support of 3 x for the alternate alleles. Variants with the Platypus filter tags PASS or alleleBias were considered further. After coverage-based filtering, population-based variant frequencies, i.e., the minor allele frequency from gnomAD (version 2.1) and the variant frequency from an in-house panel of normals comprising 3,910 WGS and 1,198 WES samples were added using vcfanno (version 0.3.2) 56 . Variants with a frequency above 0.0005 (minor allele frequency in gnomAD) or 0.5 (variant frequency in normal samples), respectively, were considered common or artifacts and removed from further analysis. Variants in cancer predisposition genes that are annotated in ClinVar as pathogenic or likely pathogenic were excluded from population-based filtering. Additional genomic annotations and variant consensus were added to the filtered variants using VEP (version 104) 57 , followed by classification into germline or somatic based on the variant allele frequency obtained from control and tumor samples using TiNDA, which uses an EM-based clustering method in the canopy R package (version 1.3.0) 58 . The GATK gCNV module 59 was used to detect germline CNVs from WGS and WES samples by applying a background cohort model created from 200 WGS or WES samples that were matched with the target capture kit. The workflow adhered to the best practices for detecting genomic CNVs outlined by GATK. For WGS data, one deviation was that only GENCODE (release 19) protein-coding regions were analyzed to expedite the analysis process. Next, CNV segments with a quality control score above 30 were annotated with a subset of gnomAD SV data using vcfanno. CNV segments with an 80% overlap with a common gnomAD SV (minor allele frequency > 0.1%) of the same type were excluded. Additionally, targets within CNV segments were required to have denoised ploidies within the top or bottom 5% of background denoised ploidies in the case of duplication or deletion, respectively. Germline SVs were detected along with somatic SVs using SOPHIA. Finally, germline variants identified in cancer predisposition genes ( Supplementary Table 8 ) were provided to medical geneticists for classification according to American College of Medical Genetics and Genomics/Association of Molecular Pathologists criteria 60 and further ClinGen specifications 61 . Clinically relevant variants were discussed in the MTB and integrated into clinical management recommendations. Quantification of Tumor Mutational Burden The number of mutations per megabase was calculated by dividing the sum of non-silent SNVs and coding indels by the length of the genome’s coding sequence in megabases. For WES samples, the denominator was adjusted to the respective target coverage. Mutational Signature Analysis The presence of SBS signatures from the COSMIC database (version 2) 62 was assessed using the YAPSA R package 63 . The analysis included all somatic SNVs, limited to those within target regions for WES samples. For each sample, a mutation catalog was generated and further corrected for WES samples to account for the different occurrences of triplet motifs within the target sequences, and mutational signatures were computed along with their confidence intervals. Signatures were considered present if they exceeded signature-specific thresholds with a cost factor of 6 (absolute and relative for WGS and WES samples, respectively) and the lower limit of their confidence interval was greater than zero. DNA Methylation Analysis Tumor DNA (250 ng) was analyzed using Infinium MethylationEPIC BeadChip arrays (Illumina). Raw data were used to run the sarcoma classifier (version 12.2) 27 , followed by processing using the preprocessRaw function of the minfi R package 64 . Probes that were unreliable, cross-reactive, mapping to sex chromosomes, overlapping with SNPs, or had a detection p-value ≥ 0.01 were filtered out 65 , 66 . Each sample was normalized using the BMIQ function with default settings from the wateRmelon R package 67 . Beta values were used for further analyses. Only entities with three or more samples were kept ( n = 335). For dimensionality reduction, UMAP analysis was performed using the umap function of the umap R package with default settings based on the algorithm described by McInnes and Healy ( https://doi : 10.48550/arXiv.1802.03426 ). For comparison, raw methylation data of 286 non-DSRCT sarcomas, 18 DSRCT samples (EGAS00001006939), and 19 small blue round cell tumors with BCOR or CIC alterations (GSE140686) were downloaded 27 . RNA Sequencing and Fusion Gene Detection Libraries were prepared using the Illumina TruSeq RNA Library Preparation Kit with 1,000 ng total RNA as input or the Illumina TruSeq mRNA Stranded Library Preparation Kit with 500 ng total RNA as input. Three libraries were pooled and sequenced on one lane of an Illumina HiSeq 2500, HiSeq 4000, HiSeq X Ten, or NovaSeq 6000 instrument. Reads were aligned to the same reference genome as DNA sequencing data with STAR 2.5.1b 68 . Fusion transcripts were detected with Arriba (version 2.4.0) 69 . Proteome and Phosphoproteome Analysis Sample preparation. Cell lysate protein concentrations were determined using the Pierce Bradford Protein Assay Kit (Thermo Fisher). Per tandem mass tag (TMT) batch, nine patient samples and two reference samples (200 µg per sample) were processed as previously described with minor adaptations 70 . Liquid chromatography-tandem mass spectrometry data acquisition. Multiplexed samples were measured on an Orbitrap Fusion Lumos Tribrid Mass Spectrometer (Thermo Fisher) coupled to an UltiMate 3000 Nano-Flow HPLC system (Thermo Fisher) for phosphoproteome analysis or a Vanquish Micro-Flow HPLC system (Thermo Fisher) for whole-proteome analysis. For peptide and phosphopeptide identification and quantification, raw files were searched with MaxQuant (version 1.6.12.0) against the human UniProt database, including unreviewed proteins (97,086 sequences including isoforms) and the MaxQuant common contaminants database. To improve data completeness across TMT batches, identifications were transferred with SIMSI-Transfer 71 . Data were normalized using within- and across-batch median centering normalization. For the proteome, protein groups were inferred and quantified at the gene level using the Picked Protein Group FDR package 72 . Protein phosphorylation scores were computed as the sum of all z-scores of a protein’s phosphopeptides. For the proteome, phosphopeptides, and protein phosphorylation scores, per-protein/peptide ranks, z-scores, and fold changes were calculated across the cohort. IHC IHC was performed on 3 µm tissue sections with the ready-to-use, pre-diluted PATHWAY anti-HER2/neu (4B5) rabbit monoclonal primary antibody (cat. no. 05278368001, Ventana/Roche) using a BenchMark ULTRA Autostainer VENTANA (Roche) as previously described 73 . Immunoreactivity was assessed according to ASCO/CAP criteria established for breast cancer 74 . IHC readers were blinded to clinical data. Declarations Data Availability Sequencing and DNA methylation data have been deposited in the European Genome-Phenome Archive (https://www.ebi.ac.uk/ega/datasets) under accession EGAS00001007934. ACKNOWLEDGEMENTS We thank the NCT Sample Processing Laboratory and the DKFZ Next Generation Sequencing, Microarray, and Omics IT and Data Management Core Facilities for technical support. This work was supported in part by the German Federal Ministry of Education and Research (grant 031L0305A to B.K. and grant 01KD2207A to A.S., B.B.-B., S.B., R.F.S., G.M., A.S., D.B.L., B.K., D.H., H.G., and S.F.), the European Research Council (grant 833710 to B.K.), and the German Research Foundation (grant 452419311 to B.K.). The MASTER program is supported by the NCT Molecular Precision Oncology Program and DKTK. Conflict-of-interest statement: M.A. has had an advisory role and received honoraria from AbbVie and Boehringer Ingelheim. 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Supplementary Files LandscapeoftheDSRCTMASTERCohortSupplementaryData20250221.docx LandscapeoftheDSRCTMASTERCohortSupplementaryTable120250224.xlsx Supplementary Dataset 1 LandscapeoftheDSRCTMASTERCohortSupplementaryTable220250224.xlsx Supplementary Dataset 2 Cite Share Download PDF Status: Published Journal Publication published 09 Apr, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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10:20:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6104125/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6104125/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-026-71636-0","type":"published","date":"2026-04-09T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79337046,"identity":"2967ac46-04e3-4e79-a2d7-5564b02912e9","added_by":"auto","created_at":"2025-03-27 08:05:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":481116,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePatient characteristics and previous treatments.\u003c/strong\u003e \u003cstrong\u003e(a)\u003c/strong\u003eInitial diagnoses of 30 DSRCT patients analyzed in MASTER. Eight patients were recommended for diagnostic re-evaluation due to the detection of an \u003cem\u003eEWSR1::WT1\u003c/em\u003e fusion. \u003cstrong\u003e(b)\u003c/strong\u003e Proportion of patients receiving various therapies before enrollment in MASTER. \u003cstrong\u003e(c)\u003c/strong\u003e Number of patients receiving various therapies before enrollment in MASTER. \u003cstrong\u003e(d)\u003c/strong\u003e Number of patients per treatment line. \u003cstrong\u003e(e)\u003c/strong\u003e Patient-level representation of therapies administered before enrollment in MASTER. AFH, angiomatoid fibrous histiocytoma; CTX, chemotherapy; ES, Ewing sarcoma; IC, immune checkpoint inhibition; HD/ASCT, high-dose chemotherapy/autologous stem cell transplantation; HIPEC, hyperthermic intraperitoneal chemotherapy; NEC, neuroendocrine carcinoma; PKI, protein kinase inhibition; RCHT, radiochemotherapy; RHT, regional hyperthermia therapy; RT, radiation therapy; SX, surgery; UAS, undifferentiated anaplastic sarcoma; US, undifferentiated sarcoma; VX, vaccination.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6104125/v1/bd89db3a4bac0210b56c6a81.png"},{"id":79336422,"identity":"c46805bd-a153-4cbd-99e6-bf5023ae2a76","added_by":"auto","created_at":"2025-03-27 07:57:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":401701,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClinical decision-making and personalized therapy based on molecular profiling. (a)\u003c/strong\u003eOverview of MTB recommendations, molecularly targeted treatment (MTT), and patient outcomes. Twenty-eight of 30 patients (93.3%) received one or more treatment recommendations, whose percent distribution across nine intervention baskets is shown in the pie chart. Patients in whom DSRCT had been diagnosed as part of a re-evaluation prompted by molecular profiling (\u003cem\u003en\u003c/em\u003e = 8) are indicated in green. \u003cstrong\u003e(b)\u003c/strong\u003eDetailed representation of MTB recommendations, MTT, and patient outcomes (green, yellow, or red star). Recommendations for enrollment in a clinical study (S), for counseling by human genetics (H), and for diagnostic re-evaluation based on molecular findings indicating DSRCT (D) are shown next to the number of MTB recommendations. PID, patient identifier.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-6104125/v1/7669ed4e6236b0313cdeac7b.png"},{"id":79337047,"identity":"190034c0-6f13-40d5-a4d7-48a231b37f7c","added_by":"auto","created_at":"2025-03-27 08:05:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":525008,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRNA-based clinical decision-making. (a)\u003c/strong\u003e Overview of small-molecule inhibitors recommended by the MTB based on target overexpression. Compounds were assigned to inhibitor classes according to the NCT Drug Precision Oncology Thesaurus Drugs \u003ca href=\"https://paperpile.com/c/5u9UaZ/tdPm\"\u003e\u003csup\u003e38\u003c/sup\u003e\u003c/a\u003e. \u003cstrong\u003e(b)\u003c/strong\u003e \u003cem\u003eSSTR3\u003c/em\u003e,\u003cstrong\u003e \u003c/strong\u003e\u003cem\u003eSSTR5\u003c/em\u003e,\u003cstrong\u003e \u003c/strong\u003e\u003cem\u003eCLDN6\u003c/em\u003e, and\u003cem\u003e ERBB2\u003c/em\u003e mRNA expression in DSRCT compared to other sarcomas enrolled in MASTER. The vertical lines indicate median expression. GIST, gastrointestinal stromal tumor; LMS, leiomyosarcoma; LS, liposarcoma; PNET, primitive neuroectodermal tumor; SySa, synovial sarcoma; STS, soft-tissue sarcoma; TPM, transcripts per million.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6104125/v1/ddb7dc981776a0c1bd906ed9.png"},{"id":79336425,"identity":"fc6bbbb7-c19d-48b6-9200-fbc188775d1a","added_by":"auto","created_at":"2025-03-27 07:57:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":444539,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProteomic and phosphoproteomic analysis. (a)\u003c/strong\u003e Composition of the comparison cohort of 554 non-DSRCT sarcoma samples. \u003cstrong\u003e(b) \u003c/strong\u003eUMAP analysis of the global expression of approximately 4,000 proteins detected across all patients, showing a distinct DSRCT cluster. \u003cstrong\u003e(c) \u003c/strong\u003eVolcano plot summarizing the results of differential protein expression analysis between DSRCT and non-DSRCT sarcoma samples. \u003cstrong\u003e(d)\u003c/strong\u003e ERBB2 protein expression in DSRCT and the comparison cohort. \u003cstrong\u003e(e)\u003c/strong\u003e CLDN6 protein expression in DSRCT and the comparison cohort.\u003cstrong\u003e (f) \u003c/strong\u003eCorrelation between ERBB2 mRNA and protein expression in DSRCT (red, \u003cem\u003er\u003c/em\u003e = 0.5) and the comparison cohort (gray, \u003cem\u003er\u003c/em\u003e = 0.73). \u003cstrong\u003e(g)\u003c/strong\u003e Protein expression (P) and phosphorylation (P in a circle) scores of selected RTKs.\u003cstrong\u003e \u003c/strong\u003eASPS, alveolar soft part sarcoma; CHDM, chordoma; CHS, chondrosarcoma; DDLS, dedifferentiated liposarcoma; EHAE, epithelioid hemangioendothelioma; ES, Ewing sarcoma; GIST, gastrointestinal stromal tumor; LGFMS, low-grade fibromyxoid sarcoma; LMS, leiomyosarcoma; MLS, myxoid liposarcoma; MPNST, malignant peripheral nerve sheath tumor; OS, osteosarcoma; SCSARC, spindle cell sarcoma; SARCNOS, sarcoma not otherwise specified; SFT, solitary fibrous tumor; SYSA, synovial sarcoma; ULMS, uterine leiomyosarcoma; UPS, undifferentiated pleomorphic sarcoma; FC, fold change.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6104125/v1/b0cb74c0afdf0c4d063cfe1f.png"},{"id":79337049,"identity":"286e63eb-84fc-48dc-9ef2-d16be62b968b","added_by":"auto","created_at":"2025-03-27 08:05:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1151521,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEfficacy of ERBB2-directed ADC treatment. (a)\u003c/strong\u003e Treatment history of patient DSRCT-28, illustrating 15-month disease control with T-DXd. \u003cstrong\u003e(b)\u003c/strong\u003e \u003cem\u003eERBB2\u003c/em\u003e mRNA expression, determined by RNA-seq (left), and ERBB2 protein expression, determined by IHC (right), in tumor tissue from patient DSRCT-28. The transcript per million (TPM) value measured in patient DSRCT-28 relative to those of the other patients in the cohort is indicated by a blue circle. \u003cstrong\u003e(c)\u003c/strong\u003e Assessment of T-DXd response by positron emission tomography with 2-deoxy-2-[fluorine-18]fluoro-D-glucose integrated with computed tomography (\u003csup\u003e18\u003c/sup\u003eF-FDG-PET/CT) in patient DSRCT-28. The maximum intensity projections of metabolically visible tumor manifestations over time are shown. \u003cstrong\u003e(d) \u003c/strong\u003eTreatment history of patient DSRCT-30, illustrating 12-month disease control with T-DXd. \u003cstrong\u003e(e)\u003c/strong\u003e \u003cem\u003eERBB2\u003c/em\u003e mRNA expression, determined by RNA-seq (left), and ERBB2 protein expression, determined by IHC (right), in tumor tissue from patient DSRCT-30. The TPM value measured in patient DSRCT-30 relative to those of the other patients in the cohort is indicated by a blue circle. \u003cstrong\u003e(f)\u003c/strong\u003e Assessment of T-DXd response using \u003csup\u003e18\u003c/sup\u003eF-FDG-PET/CT in patient DSRCT-30. The maximum intensity projections of metabolically visible tumor manifestations over time are shown. Scale bars, 20 µm. MPR, metabolic partial response; MSD, metabolic stable disease; MTV, metabolic tumor volume; SUV, standard uptake value; TLG, tumor lesion glycolysis.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6104125/v1/a14aa55568569e42389d76a3.png"},{"id":106583001,"identity":"c9c9422c-8192-4236-8913-a9c9f1f6a4e1","added_by":"auto","created_at":"2026-04-10 07:08:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3978840,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6104125/v1/8a07f4a3-3685-43b4-b826-b3d85de578bb.pdf"},{"id":79336428,"identity":"086f2ec0-5613-48f6-aa62-10f953b24aa5","added_by":"auto","created_at":"2025-03-27 07:57:34","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1804084,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"LandscapeoftheDSRCTMASTERCohortSupplementaryData20250221.docx","url":"https://assets-eu.researchsquare.com/files/rs-6104125/v1/9b362dbfd52d11d0ee1ff763.docx"},{"id":79338159,"identity":"06dd3fcc-ce56-4e35-93fa-dc1a443461bd","added_by":"auto","created_at":"2025-03-27 08:13:34","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18832,"visible":true,"origin":"","legend":"Supplementary Dataset 1","description":"","filename":"LandscapeoftheDSRCTMASTERCohortSupplementaryTable120250224.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6104125/v1/17584a677d1a94c5cb510029.xlsx"},{"id":79336421,"identity":"f67c8dfa-3a4d-4358-b9de-ad5eb725a061","added_by":"auto","created_at":"2025-03-27 07:57:34","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":21513,"visible":true,"origin":"","legend":"Supplementary Dataset 2","description":"","filename":"LandscapeoftheDSRCTMASTERCohortSupplementaryTable220250224.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6104125/v1/450a5423ccc3c06ca31196da.xlsx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Multi-Layered Molecular Profiling Informs the Diagnosis and Targeted Therapy of Desmoplastic Small Round Cell Tumor","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eDesmoplastic small round cell tumor (DSRCT) is an ultra-rare, high-grade soft-tissue sarcoma (incidence, 0.2/1,000,000 persons/year) of uncertain cellular origin, predominantly affecting male children and young adults\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. It is characterized by a pathognomonic chromosomal translocation, t(11;22)(p13;q12)\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, resulting in a chimeric protein that contains the N-terminal domain of Ewing sarcoma breakpoint region 1 (EWSR1) and three of four zinc finger domains of Wilms tumor 1 (WT1)\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e and is essential for the viability and proliferation of DSRCT cells in preclinical models\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The diagnosis of DSRCT can be challenging due to ambiguous histology and expression of neuroendocrine markers and/or cytokeratins\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe clinical management of DSRCT involves a multimodal approach, including chemotherapy, surgery, and radiotherapy. Complete or cytoreductive surgery is the most critical component, positively impacting overall survival\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. However, DSRCT is typically diagnosed at an advanced stage, and the overall prognosis is poor, with most patients succumbing to the disease within three years of diagnosis\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Due to the lack of clinical trials for this orphan disease, no standard systemic therapy has been established. To date, the use of up to 30 different chemotherapy protocols has been reported, and most patients are treated with regimens adopted from Ewing sarcoma or other soft-tissue sarcomas.\u003c/p\u003e \u003cp\u003ePrevious molecular studies have shown that DSRCT has a low mutational burden and few druggable targets\u003csup\u003e\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In addition, the tumors are immune-cold, with little benefit from immunotherapies, although individual responses have been reported\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Various multi-targeted tyrosine kinase inhibitors (TKIs), used without molecular biomarker guidance, have shown limited efficacy in individual patients and small case series\u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Retrospective studies suggest some activity of pazopanib, although the determinants of clinical benefit remain unclear\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Overall, there is an urgent need for effective DSRCT drugs, and it seems warranted to explore whether more comprehensive biological profiling could uncover new therapeutic targets.\u003c/p\u003e \u003cp\u003eA rapidly expanding area of drug development focuses on therapies targeting specific antigens on tumor cells, independent of mutations or dependence on associated signaling pathways. Key examples include chimeric antigen receptor (CAR) T cells, bispecific antibodies, and next-generation antibody-drug conjugates (ADCs). These modalities have significantly broadened treatment options for previously hard-to-target cancers. However, rare malignancies are understudied with respect to these drugs and have therefore benefited less than more common cancers. In DSRCT, \u003cem\u003ein vitro\u003c/em\u003e studies have postulated that the signaling pathway controlled by the ERBB (also called HER) family of receptor tyrosine kinases (RTKs) might be activated, and very high doses of the EGFR (also called ERBB1)-directed antibody cetuximab and the pan-ERBB small-molecule inhibitor afatinib, as well as the ERBB2 (also called HER2)-directed ADC trastuzumab deruxtecan (T-DXd) reduced tumor growth in mouse xenografts\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Based on these findings and given the efficacy of T-DXd in breast cancer, even with (ultra-)low ERBB2 expression\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, and other epithelial malignancies\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, ERBB2 emerges as a promising target in DSRCT. However, responses to ADCs and their correlation with target expression have not been studied in DSRCT patients.\u003c/p\u003e \u003cp\u003eThe MASTER (Molecularly Aided Stratification for Tumor Eradication Research) program (ClinicalTrials.gov: \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNCT05852522\u003c/span\u003e), a prospective observational study conducted by the German Cancer Research Center (DKFZ), the National Center for Tumor Diseases (NCT), and the German Cancer Consortium (DKTK), leverages whole-genome/exome sequencing (WGS/WES), RNA sequencing (RNA-seq), DNA methylation profiling, proteomics, and phosphoproteomics to guide treatment in young adults with advanced malignancies and patients with incurable rare cancers\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. In this study, we present the clinical courses and molecular target landscapes of 30 patients with advanced DSRCT enrolled in MASTER. Our findings, including the sustained activity of T-DXd in two patients, demonstrate how multi-layered molecular diagnostics beyond the current standard of care can guide the clinical management of DSRCT patients and lay the groundwork for biomarker-guided clinical trials.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003ePatient Characteristics and Previous Treatments\u003c/h2\u003e\n \u003cp\u003eBetween 2013 and 2022, 30 DSRCT patients underwent multi-layered molecular profiling as part of the DKFZ/NCT/DKTK MASTER program. The median age at the time of molecular analysis was 30 years (range, 18\u0026ndash;56); four patients (13%) were female and 26 (87%) male. The median interval between cancer diagnosis and molecular analysis was nine months (range, 1\u0026ndash;218). Median survival from the first inter-institutional molecular tumor board (MTB) was 2.1 years (95% confidence interval [CI], 1.0\u0026ndash;2.6 years), with a four-year survival rate of 10% (95% CI, 2.8\u0026ndash;37%). The median follow-up duration was 17 months (range, 0\u0026ndash;48).\u003c/p\u003e\n \u003cp\u003ePatient characteristics are detailed in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. In eight patients (27%), the initial diagnosis was incorrect or incomplete (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea): Three tumors were classified as carcinoma of unknown primary site (CUP), one as neuroendocrine carcinoma, and one as angiomatoid fibrous histiocytoma; three patients received incomplete sarcoma diagnoses (Ewing sarcoma-like sarcoma, undifferentiated sarcoma with myogenic differentiation [rhabdomyosarcoma-like], and undifferentiated anaplastic sarcoma). Histopathologic re-evaluation was prompted in each case by the finding of an \u003cem\u003eEWSR1::WT1\u003c/em\u003e fusion, supported further by gene expression data and a DNA methylation-based sarcoma classifier\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, and confirmed the diagnosis of DSRCT in all five patients with available tissue samples. The median time from initial to DSRCT diagnosis was 10.5 months (range, 1\u0026ndash;224 months). Among the 27 patients in whom the \u003cem\u003eEWSR1::WT1\u003c/em\u003e fusion was detected by RNA-seq, 25 (93%) had breakpoints in exons 7\u0026ndash;8, one in exons 9\u0026thinsp;\u0026minus;\u0026thinsp;8, and one in exons 10\u0026thinsp;\u0026minus;\u0026thinsp;8 (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplementary Fig.\u0026nbsp;1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eBefore enrollment in MASTER, patients had received a median of three (range, 1\u0026ndash;10) lines of systemic treatment and a median of four (range, 1\u0026ndash;12) lines of local therapy, including surgery, radiation, and hyperthermic intraperitoneal chemotherapy (HIPEC). In addition to systemic therapy, 17 patients (57%) had undergone at least one surgical procedure, with six (20%) also receiving radiotherapy and five (17%) undergoing HIPEC. One patient (3%) had received systemic treatment, radiotherapy, surgery, and HIPEC (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb, c; \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplementary Table\u0026nbsp;1\u003c/span\u003e). Due to the lack of an evidence-based standard, systemic treatments were heterogeneous. Eighteen patients (60%) had been treated with the VIDE regimen (vincristine, ifosfamide, doxorubicin, etoposide) established for Ewing sarcoma. Six patients (20%) had received targeted therapy as an individual approach. Treatment details and outcomes are summarized in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ec, d, e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Clinical characteristics of 30 DSRCT patients\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cem\u003eAge at enrollment (years)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003cp\u003e18\u0026ndash;56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cem\u003eAge at diagnosis (years)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003cp\u003e18\u0026ndash;55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cem\u003eSex\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;26 (86.7%)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4 (13.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cem\u003ePrimary tumor site\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eIntra-abdominal\u003c/p\u003e\n \u003cp\u003eExtra-abdominal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;27 (90%)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cem\u003eMetastatic disease at diagnosis\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;23 (76.7%)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7 (23.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cem\u003eMetastatic site at diagnosis\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eLymph nodes\u003c/p\u003e\n \u003cp\u003eLiver\u003c/p\u003e\n \u003cp\u003eLung\u003c/p\u003e\n \u003cp\u003eBone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;18 (60%)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10 (33.3%)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7 (23.3%)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/caption\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eClinical Decision-Making Based on Multi-Layered Molecular Profiling\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eOverview.\u003c/strong\u003e For clinical decision-making by the inter-institutional MTB of the MASTER program, molecular biomarkers identified through multi-omics profiling (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplementary Fig.\u0026nbsp;2a\u003c/span\u003e) and resulting clinical management recommendations were grouped into nine intervention baskets: tyrosine kinase (TK), DNA damage repair (DDR), immunotherapy (IT), PI3K-AKT-mTOR (PAM), RAF-MEK-ERK (RME), cell cycle (CC), theranostics (THER), antibody-drug conjugate (ADC), and other (OTH). The MTB provided at least one treatment recommendation (range, 1\u0026ndash;7) in 28 of 30 patients (93.3%) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea, b). Of the total of 107 recommendations, 48 (45%) fell into the TK basket, followed by the DDR (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;13, 12%), IT (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;12, 11%), and THER (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;11, 10%) categories (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea, b; \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplementary Table\u0026nbsp;2, 3)\u003c/span\u003e. In four patients, sequential analysis of additional tumor samples showed increasing somatic mutation rates (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplementary Fig.\u0026nbsp;2b\u003c/span\u003e) and led to a second (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3) or second and third (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2) MTB consultation with further treatment recommendations. Seventeen patients (57%) were recommended for enrollment in 14 different clinical trials (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb; \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplementary Table\u0026nbsp;4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDNA analysis.\u003c/strong\u003e Consistent with previous studies\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, WGS/WES revealed few somatic mutations (median of 0.68 single-nucleotide variants [SNVs] and small insertions/deletions [indels] per megabase and median of 23 non-silent SNVs and indels per sample). Two genes showed acquired mutations in three patients (10%; \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplementary Fig.\u0026nbsp;3\u003c/span\u003e): \u003cem\u003eDCC\u003c/em\u003e, encoding the netrin 1 receptor implicated in axon guidance and various epithelial cancers\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, which was exclusively altered in female patients, and \u003cem\u003eEPB41L3\u003c/em\u003e, encoding a cytoskeletal component linked to tumor and/or metastasis suppression\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Of a total of 107 treatment recommendations, only four (4%; \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplementary Table\u0026nbsp;5\u003c/span\u003e) were based on individual SNVs. In patient DSRCT-01, missense mutations in \u003cem\u003eFLT1\u003c/em\u003e and \u003cem\u003eEPHA3\u003c/em\u003e led us to recommend a multi-targeted TKI, e.g., pazopanib or dasatinib. In patient DSRCT-05, a missense mutation in \u003cem\u003eFGFR4\u003c/em\u003e provided a rationale for a multi-targeted TKI, e.g., pazopanib. In patient DSRCT-09, a likely gain-of-function mutation in the kinase domain of \u003cem\u003eMTOR\u003c/em\u003e prompted the recommendation of an mTORC1 inhibitor, e.g., everolimus.\u003c/p\u003e\n\u003cp\u003eIn addition to individual SNVs, eleven of the 107 treatment recommendations were made based on single-base substitution (SBS) signatures (10%; \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplementary Table\u0026nbsp;6\u003c/span\u003e). Detection of signatures SBS3 and/or SBS8, indicating deficiencies in the homologous recombination (HR) and nucleotide excision DNA repair pathways, contributed to the recommendation of a poly(ADP-ribose) polymerase (PARP) inhibitor in 10 patients, sometimes together with alterations of HR-related genes or high \u003cem\u003eSLFN11\u003c/em\u003e expression. Three of these patients were recommended for enrollment in a clinical trial (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNCT03127215\u003c/span\u003e) investigating the combination of the PARP inhibitor olaparib and trabectedin in HR-deficient cancers\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe overall landscape of somatic DNA copy number aberrations observed in our cohort (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplementary Fig.\u0026nbsp;4\u003c/span\u003e) was largely consistent with previous studies\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Specific copy number variants (CNVs) were the basis for nine of the 107 treatment recommendations (8%; \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplementary Table\u0026nbsp;7\u003c/span\u003e). For example, deletion and low mRNA expression of \u003cem\u003ePTEN\u003c/em\u003e in two patients led us to recommend an mTORC1 inhibitor. Additionally, three patients were recommended treatment with a PARP inhibitor due to deletions of genes involved in HR-mediated DNA repair.\u003c/p\u003e\n\u003cp\u003eThe systematic evaluation of rare germline alterations in 101 cancer predisposition genes (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplementary Table\u0026nbsp;8\u003c/span\u003e) identified one likely pathogenic variant: patient DSRCT-25 had a heterozygous \u003cem\u003eSDHC\u003c/em\u003e p.R133X stopgain variant associated with hereditary pheochromocytoma and paraganglioma\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. However, there was no evidence that this variant was relevant for DSRCT development, and no paragangliomas were reported in the patient or the family with a diverse tumor spectrum.\u003c/p\u003e\n\u003cp\u003eFinally, uniform manifold approximation and projection (UMAP) analysis of the DNA methylation profiles of 30 samples from 25 DSRCT patients, combined with previously published profiles of 305 other sarcomas, including small blue round cell tumors with \u003cem\u003eBCOR\u003c/em\u003e or \u003cem\u003eCIC\u003c/em\u003e alterations\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, showed that the DSRCT samples formed a distinct and coherent cluster (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplementary Fig.\u0026nbsp;5a\u003c/span\u003e). All but one DSRCT sample had a sarcoma classifier score\u0026thinsp;\u0026gt;\u0026thinsp;0.9 (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplementary Fig.\u0026nbsp;5b\u003c/span\u003e), underlining the diagnostic utility of epigenomic analysis in this entity.\u003c/p\u003e\n\u003cp\u003eTogether, these data demonstrated that exhaustive DNA-based analyses enhance diagnostic accuracy in DSRCT and may identify occasional patients with pathogenic germline variants but are of limited value for identifying new therapeutic targets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA analysis.\u003c/strong\u003e Seventy-eight of the 107 recommendations by the MTB (73%) were based on increased mRNA expression of potential therapeutic targets, most of which fell into the TK, DDR, IT, and OTH baskets. The largest proportion of expression-based recommendations (48 of 107, 45%) was accounted for by genes encoding components of TK pathways that can be targeted with clinically approved small-molecule inhibitors (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea). The implementation of these recommendations in a subset of patients and associated outcomes are described below. In 10 of 30 cases (33%), the MTB recommended somatostatin receptor 3 (SSTR3)-targeted peptide receptor radionuclide therapy (PRRT). This was based on the consistent overexpression of \u003cem\u003eSSTR3\u003c/em\u003e and \u003cem\u003eSSTR5\u003c/em\u003e but not \u003cem\u003eSSTR1\u003c/em\u003e, \u003cem\u003eSSTR2\u003c/em\u003e, and \u003cem\u003eSSTR4\u003c/em\u003e mRNA in DSRCT compared to other sarcomas enrolled in MASTER (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb; \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplementary Fig.\u0026nbsp;6\u003c/span\u003e). However, while one patient underwent DOTA-PTR-58 imaging to verify SSTR3 expression and tracer uptake, none received PRRT. In four of 30 patients (13%), androgen receptor (AR) blockade was recommended due to high \u003cem\u003eAR\u003c/em\u003e expression. In addition, we observed extreme expression of \u003cem\u003eCLDN6\u003c/em\u003e, encoding a cell adhesion molecule, in six of 30 patients (20%; Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb), which prompted the recommendation to consider enrollment in a clinical trial of CLDN6-specific CAR T cells (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNCT04503278\u003c/span\u003e)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. This recommendation was implemented in two patients (DSRCT-13 and DSRCT-29). Finally, the MTB recommended ERBB2-targeted treatment in seven patients due to increased \u003cem\u003eERBB2\u003c/em\u003e mRNA expression (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb), which in two cases led to the administration of T-DXd, as described below, and to combination therapy with trastuzumab, pertuzumab, and atezolizumab within a clinical trial (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNCT04551521\u003c/span\u003e)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e in one patient, who died shortly after treatment initiation and before the first response evaluation. Besides these recurring treatment recommendations, increased \u003cem\u003eSLFN11\u003c/em\u003e expression in a patient who also had signature SBS8 prompted the MTB to recommend a PARP inhibitor (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplementary Table\u0026nbsp;6\u003c/span\u003e), which was administered in combination with trabectedin analogous to, but outside of, the clinical trial mentioned above (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNCT03127215\u003c/span\u003e), resulting in disease stabilization (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProteome and phosphoproteome analysis.\u003c/strong\u003e We recently integrated proteomic and phosphoproteomic profiling into the clinical workflow of the MASTER program\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Using this newly established pipeline, we retrospectively analyzed samples from nine DSRCT patients for whom suitable tumor tissue was available. For comparison, we analyzed a heterogeneous group of 554 samples representing over 20 sarcoma subtypes (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea). UMAP analysis of the global expression of approximately 4,000 proteins showed that the DSRCT samples formed a distinct cluster (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb). In differential expression analysis, high levels of ERBB2 and CLDN6 were detected in all and two of nine DSRCT patients, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec, d, e). The correlation of ERBB2 mRNA and protein expression was moderate in both the DSRCT samples and the comparison cohort (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.5 and \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.73, respectively; Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ef). Analysis of the expression and phosphorylation landscape of 43 RTKs showed that, in addition to ERBB2, TYRO3 was overexpressed in most tumors, while high levels of KDR (also known as VEGFR2), MERTK, ALK, and INSR were detected in one or two patients (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eg). Phosphoproteomic analysis revealed aberrant activity (indicated by a phosphoprotein score\u0026thinsp;\u0026gt;\u0026thinsp;2) of FGFR4 in patient DSRCT-10 and of KDR and TYRO3 in patient DSRCT-18. In contrast, no evidence of constitutive ERBB2 signaling was observed (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eg).\u003c/p\u003e\n\u003ch3\u003eImplementation of Molecularly Guided Treatment Recommendations\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eOverview.\u003c/strong\u003e Of the 107 targeted therapies recommended, 16 (15%) could be administered in 13 of 30 patients (43%; Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea, b; Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). All were based on target gene RNA expression. Disease control was achieved in eight of 13 patients (62%; partial remission [PR], \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5; stable disease [SD], \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3). In addition, three patients received chemotherapy according to the VIDE regimen based on the detection of an \u003cem\u003eEWSR1::WT1\u003c/em\u003e fusion and histopathologic re-evaluation.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRNA expression-based targeted therapies administered\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePatient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBasket\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBiomarker(s)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBest response\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTreatment duration (months)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDSRCT-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePazopanib\u003c/p\u003e\n \u003cp\u003eNivolumab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTK\u003c/p\u003e\n \u003cp\u003eIT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eFGFR4\u003c/em\u003e, \u003cem\u003eFLT4\u003c/em\u003e, \u003cem\u003ePDGFA\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ePD1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD\u003c/p\u003e\n \u003cp\u003ePD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDSRCT-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAfatinib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNRG1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDSRCT-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePazopanib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eKDR\u003c/em\u003e, \u003cem\u003eBRAF\u003c/em\u003e, \u003cem\u003eRAF1\u003c/em\u003e, \u003cem\u003eNRAS\u003c/em\u003e, \u003cem\u003eMEK2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDSRCT-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePazopanib\u003c/p\u003e\n \u003cp\u003eCAR T cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTK\u003c/p\u003e\n \u003cp\u003eIT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eKDR\u003c/em\u003e, \u003cem\u003ePDGFA\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eCLDN6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDSRCT-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOlaparib\u003csup\u003eA,B\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSLFN11\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDSRCT-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePazopanib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eFGFR2\u003c/em\u003e, \u003cem\u003eFGFR4\u003c/em\u003e, \u003cem\u003eVEGFA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDSRCT-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePazopanib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eKDR\u003c/em\u003e, \u003cem\u003eFYN\u003c/em\u003e, \u003cem\u003ePDGFRB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDSRCT-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePazopanib\u003csup\u003eC\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eKDR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDSRCT-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePazopanib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eKDR\u003c/em\u003e, \u003cem\u003eFYN\u003c/em\u003e, \u003cem\u003ePDGFRB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDSRCT-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePazopanib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eKDR\u003c/em\u003e, \u003cem\u003eNTRK3\u003c/em\u003e, \u003cem\u003eFLT4\u003c/em\u003e, \u003cem\u003eLCK\u003c/em\u003e, \u003cem\u003ePDGFA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDSRCT-28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePazopanib\u003c/p\u003e\n \u003cp\u003eT-DXd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTK\u003c/p\u003e\n \u003cp\u003eADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eFGFR4, KDR, PDGFA, MERTK\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eERBB2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD\u003c/p\u003e\n \u003cp\u003ePR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDSRCT-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAR T cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCLDN6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDSRCT-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT-DXd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eERBB2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eA: Also supported by the detection of signature SBS8.\u003c/p\u003e\n\u003cp\u003eB: In combination with trabectedin.\u003c/p\u003e\n\u003cp\u003eC: In combination with gemcitabine.\u003c/p\u003e\n\u003cp\u003ePR, partial response; SD, stable disease; PD, progressive disease; NR, not reported.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTyrosine kinase inhibition.\u003c/strong\u003e Ten patients received a multi-targeted small-molecule TKI (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea, b; Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Specifically, pazopanib was administered in nine patients due to overexpression of various combinations of \u003cem\u003eFGFR2\u003c/em\u003e, \u003cem\u003eFGFR4\u003c/em\u003e, \u003cem\u003eFLT4\u003c/em\u003e, \u003cem\u003eFYN\u003c/em\u003e, \u003cem\u003eKDR\u003c/em\u003e, \u003cem\u003eLCK\u003c/em\u003e, \u003cem\u003eMERTK\u003c/em\u003e, \u003cem\u003eNTRK3\u003c/em\u003e, \u003cem\u003ePDGFA\u003c/em\u003e, \u003cem\u003ePDGFRB\u003c/em\u003e, and \u003cem\u003eVEGFA\u003c/em\u003e; in one of these cases, overexpression of \u003cem\u003eNRAS\u003c/em\u003e, \u003cem\u003eBRAF\u003c/em\u003e, \u003cem\u003eRAF1\u003c/em\u003e, and \u003cem\u003eMEK2\u003c/em\u003e provided further support, as it has been postulated that pazopanib also acts as a pan-RAF inhibitor\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Five patients (56%) achieved disease control (PR, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3; SD, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2), and four (44%) had disease progression. Of particular note is patient DSRCT-18, who showed a PR lasting 17 months and was lost to follow-up on pazopanib therapy. In addition to high \u003cem\u003eKDR\u003c/em\u003e, \u003cem\u003eFYN\u003c/em\u003e, and \u003cem\u003ePDGFRB\u003c/em\u003e mRNA expression, the recommendation of pazopanib in this patient was also supported by phosphoproteome analysis, which showed increased activity of KDR and TYRO3 (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eg). One patient (DSRCT-11) whose tumor overexpressed \u003cem\u003eNRG1\u003c/em\u003e received the pan-ERBB inhibitor afatinib, which has activity in \u003cem\u003eNRG1\u003c/em\u003e-rearranged neoplasms\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. However, treatment was discontinued after one month due to generalized disease progression with ascites and colitis with clinically relevant bleeding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eERBB2-directed ADC treatment.\u003c/strong\u003e Two patients with high ERBB2 mRNA and protein expression levels received off-label treatment with T-DXd (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea, b; Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). This selection over other small molecule- or antibody-based therapies targeting ERBB2 was supported by the results of phosphoproteomic profiling, which showed no indication of increased ERBB2 signaling (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eg). The first patient (DSRCT-28), a 36-year-old man, was diagnosed in February 2018 with poorly differentiated CUP and presented with metastases in the liver, bones, and lymph nodes. At the time of enrollment in MASTER in March 2022, he had undergone 12 lines of therapy, with cisplatin, 5-fluorouracil, and docetaxel, given for four months, and nab-paclitaxel and carboplatin, given for 11 months, each yielding a PR (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea; \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplementary Table\u0026nbsp;9\u003c/span\u003e). Molecular analysis revealed an \u003cem\u003eEWSR1::WT1\u003c/em\u003e fusion, DNA methylation profiling\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e predicted DSRCT with a score of 0.99, RNA-seq confirmed an expression pattern typical of DSRCT, and histologic re-evaluation validated the diagnosis. The MTB provided four treatment recommendations based on increased target gene expression: (i) small-molecule inhibition of FGFR4, KDR, and MERTK, e.g., with pazopanib, (ii) participation in a clinical trial of CAR T cells against CLDN6 (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNCT04503278\u003c/span\u003e), (iii) SSTR3-targeted PRRT, and (iv) ERBB2-directed therapy (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplementary Table\u0026nbsp;2\u003c/span\u003e). Following the revised diagnosis, the patient was treated with pazopanib but showed disease progression after three months. Next, he received six cycles of chemotherapy according to the VIDE regimen, excluding doxorubicin due to prior disease progression. In April 2023, T-DXd therapy was initiated at 6.4 mg/kg, which was generally well tolerated, with the main adverse effects being grade 2\u0026ndash;3 nausea, grade 2\u0026ndash;3 loss of appetite, grade 2 fatigue, and grade 1 diarrhea. The patient achieved a partial response at the first staging in July 2023, which was ongoing in July 2024 (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea, c; \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplementary Tables\u0026nbsp;9, 10\u003c/span\u003e). Of note, ERBB2 was not detected by routine immunohistochemistry (IHC; Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb). Given the robust ERRB2 expression identified by mass spectrometry in DSRCT patients (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ed), this discrepancy was likely due to limited quality of the available formalin-fixed and paraffin-embedded (FFPE) tissue.\u003c/p\u003e\n\u003cp\u003eThe second patient (DSRCT-30), a 34-year-old woman, was diagnosed in July 2020 with intra-abdominal DSRCT, suspected peritoneal sarcomatosis, and mesenteric lymph node metastases. Following three lines of therapy with VAIA (vincristine, adriamycin [doxorubicin], ifosfamide, actinomycin-D), VIDE, and two cycles of trabectedin, she underwent debulking surgery (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ed; \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplementary Table\u0026nbsp;11\u003c/span\u003e). In October 2022, she was enrolled in MASTER while on sixth-line therapy with irinotecan and temozolomide. Molecular analysis of a peritoneal metastasis resulted in three treatment recommendations: (i) T-DXd, (ii) trastuzumab, pertuzumab, and atezolizumab within a clinical trial (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNCT04551521\u003c/span\u003e)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, both based on increased \u003cem\u003eERBB2\u003c/em\u003e expression (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ee), and (iii) PRRT targeting SSTR3. Upon disease progression, T-DXd was initiated in August 2023. Dosing was reduced to 5.4 mg/kg, 4.4 mg/kg, and 3.2 mg/kg during cycles 3, 5, and 9, respectively, due to grade 2 nausea and grade 3 fatigue. While grade 1\u0026ndash;2 fatigue persisted, nausea resolved with dose reduction. After SD in November 2023, the patient achieved a partial metabolic response in January 2024, which was ongoing in September 2024 (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ef; \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplementary Tables\u0026nbsp;11, 11\u003c/span\u003e). In this case, IHC detected moderate membrane expression of ERBB2, corresponding to a score of 2+ (ERBB2-low) according to American Society of Clinical Oncology (ASCO) and College of American Pathologists (CAP) testing guidelines (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ee). Together, these observations demonstrate for the first time the potential of ERBB2-targeted ADC treatment to achieve sustained disease control in DSRCT patients.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this multi-institutional study, we explored the use of extensive biomarker profiling to inform the clinical management of DSRCT, an aggressive ultra-rare cancer primarily affecting adolescents and young adults that currently lacks established drug treatments, including molecularly targeted approaches. We observed that multi-omics analyses beyond the current standard of care have relevant implications for precision diagnostics and individualized, molecular mechanism-aware therapy.\u003c/p\u003e \u003cp\u003eMore than a quarter of our patients, who were treated at various centers across Germany, initially received incorrect or incomplete diagnoses based on standard histopathology. This aligns with the real-world experience that small round cell tumors are challenging to classify and demonstrates the potential of molecular methods to improve diagnostic accuracy, independent of the individual expertise of examiners familiar with the phenotypes of these entities. In all reclassified cases, the finding of an \u003cem\u003eEWSR1::WT1\u003c/em\u003e fusion was essential, supported by gene expression and DNA methylation profiles characteristic of DSRCT. While we favor an approach in which all sarcomas undergo multi-omics profiling as applied in this study, the diagnostic algorithm in cases consistent with DSRCT according to morphologic criteria should include, at minimum, a method for detecting this hallmark fusion, such as RNA-seq or fluorescence \u003cem\u003ein situ\u003c/em\u003e hybridization.\u003c/p\u003e \u003cp\u003eIn the absence of evidence-based drug therapies, an accurate diagnosis of DSRCT currently has limited impact on patient outcomes. However, we believe this will change, and a median time from initial to DSRCT diagnosis of 10.5 months will become unacceptable, as our results demonstrate in three ways the potential of comprehensive molecular profiling to significantly impact therapeutic decision-making. First, the MTB provided recommendations for the vast majority of heavily pretreated patients (93%), demonstrating the feasibility and utility of our approach in a complex clinical setting. Nearly half of the recommendations (45%) fell into the TK basket, followed by the DDR (12%), IT (11%), and THER (10%) categories, reflecting the diverse multi-omic landscape of DSRCT, a disease usually considered \u0026ldquo;molecularly silent\u0026rdquo;, and the resulting tailored therapeutic strategies. Second, sequential molecular analyses in a subset of cases revealed the dynamic nature of DSRCT evolution and prompted additional MTB recommendations. Third, the identification of clinical trial opportunities for 57% of patients underscores the role of molecular profiling in increasing access to innovative therapies.\u003c/p\u003e \u003cp\u003eDifferent molecular layers contributed variably to clinical decision-making. DNA sequencing confirmed the sparse mutational landscape of DSRCT. Consequently, individual SNVs, such as those in \u003cem\u003eFLT1\u003c/em\u003e, \u003cem\u003eEPHA3\u003c/em\u003e, \u003cem\u003eFGFR4\u003c/em\u003e, and \u003cem\u003eMTOR\u003c/em\u003e, informed fewer than 4% of treatment recommendations. Similarly, somatic CNVs, e.g., deletions of \u003cem\u003ePTEN\u003c/em\u003e or HR-related genes, guided MTB recommendations in some cases but were infrequent and often insufficient on their own to dictate therapy. Mutational signatures offered additional value by suggesting vulnerability to PARP inhibitors; however, their utility may be limited by the low overall SNV count in most cases. Finally, a cancer-predisposing germline alteration was detected in only one patient and lacked direct implications for therapy. This aligns with the broader observation that, in contrast to other sarcomas\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, germline findings in established cancer predisposition genes have limited clinical applicability in DSRCT. Larger sample sizes would be needed to analyze possible associations of rare variants in candidate genes or polygenic risk associations with DSRCT development\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Overall, our results suggest that DNA-based analyses, even with comprehensive methods such as WGS/WES, are rarely sufficient for guiding clinical decisions in DSRCT.\u003c/p\u003e \u003cp\u003eHighlighting the need to consider additional molecular layers to broaden therapeutic opportunities for DSRCT patients, RNA-based biomarkers informed 73% of MTB treatment recommendations. A large proportion of expression-based recommendations (45%) were directed at TK pathways, supporting the use of small-molecule inhibitors. Additionally, consistent overexpression of \u003cem\u003eSSTR3\u003c/em\u003e and \u003cem\u003eSSTR5\u003c/em\u003e in DSRCT compared to other sarcomas prompted recommendations for PRRT in 10 patients (33%). Although PRRT was not administered in this cohort, our findings laid the molecular groundwork for a recently initiated clinical trial investigating the somatostatin analog pasireotide as maintenance therapy in DSRCT (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNCT06456359\u003c/span\u003e; Schlenk et al. ESMO Congress 2024), which will provide first insights into the value of SSTR-targeted strategies in this disease. Furthermore, extreme \u003cem\u003eCLDN6\u003c/em\u003e expression in six patients (20%) led to recommendations for enrollment in a clinical trial evaluating CLDN6-specific CAR T cells (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNCT04503278\u003c/span\u003e)\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. This strategy illustrates the potential of RNA data to identify novel immunotherapeutic opportunities whose benefit is suggested by the published report of an independent DSRCT patient in this trial in whom SD was achieved by CLDN6-targeted CAR T cells\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Similarly, increased \u003cem\u003eERBB2\u003c/em\u003e expression prompted recommendations for ERBB2-targeted therapies in seven patients, with two receiving T-DXd and another enrolling in a clinical trial combining trastuzumab, pertuzumab, and atezolizumab (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNCT04551521\u003c/span\u003e). Additionally, RNA data occasionally enhanced DNA-based recommendations. For example, increased \u003cem\u003eSLFN11\u003c/em\u003e expression in a patient with mutational signature SBS8 supported the administration of a PARP inhibitor combined with trabectedin, resulting in SD. These findings underscore the potential of RNA-seq for target discovery and refinement of biomarker-driven treatment strategies and support the incorporation of transcriptomic profiling into the management of DSRCT patients.\u003c/p\u003e \u003cp\u003eImplementing molecularly guided treatment recommendations was only feasible in just over 40% of cases. This reflects challenges common to precision oncology programs, including limited off-label access to targeted agents and a scarcity of molecularly stratified clinical trials \u0026ndash; issues that are particularly pronounced in rare cancers like DSRCT\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Nonetheless, we successfully acted on several discoveries, and the disease control rate of 62% in this heavily pretreated population was encouraging, marking an important step toward a rational, biology-guided approach to this disease.\u003c/p\u003e \u003cp\u003eAmong the 30 patients in our cohort, 10 (33%) received a multi-targeted TKI. In particular, pazopanib was administered in nine patients due to overexpression of various target genes, e.g., \u003cem\u003eFGFR4\u003c/em\u003e, \u003cem\u003eFLT4\u003c/em\u003e, \u003cem\u003eKDR\u003c/em\u003e, \u003cem\u003eMERTK\u003c/em\u003e, \u003cem\u003ePDGFA\u003c/em\u003e, \u003cem\u003ePDGFRB\u003c/em\u003e, and \u003cem\u003eVEGFA\u003c/em\u003e. Notably, we observed disease control in five of these nine cases (56%), including a PR lasting at least 17 months in a patient whose tumor showed increased KDR signaling as determined by retrospective phosphoproteomic analysis.\u003c/p\u003e \u003cp\u003eThe efficacy of agents targeting TK pathways has been investigated in several DSRCT case series. Italiano \u003cem\u003eet al\u003c/em\u003e. reported on sunitinib treatment of eight patients, which resulted in a PR in two (25%) and SD in three (38%) cases, respectively, and a median progression-free survival (PFS) of 2.6 months\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Frezza \u003cem\u003eet al\u003c/em\u003e. observed that two (22%) and five (56%) of nine patients treated with pazopanib achieved a PR and SD, respectively, which translated into a median PFS and overall survival (OS) of 9.2 and 15.4 months, respectively\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. A study by the French registry for the analysis of off-label use of targeted therapies in sarcomas examined nine patients treated with sunitinib, sorafenib, or bevacizumab. The best response observed was a 5.5-month SD, and the median PFS was 3.1 months\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Menegaz \u003cem\u003eet al\u003c/em\u003e. reviewed the data of 29 patients treated with pazopanib and observed one complete response (3%), one PR (3%), and 16 cases with SD (55%); the median PFS was 5.6 months, and the median OS was 15.7 months\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. These converging results demonstrate that TKIs have activity in a subset of DSRCT patients. However, their use was unstratified and relied either on empirical evidence or the general observation that the DSRCT microenvironment is characterized by neoangiogenesis and expression of various angiogenic factors. In contrast, we employed patient-level biomarker profiles to individualize therapy selection. In particular, the long-lasting response to pazopanib in a patient whose transcriptomic and phosphoproteomic profiles revealed aberrant activation of several kinases targeted by this drug underscores the potential of comprehensive molecular analyses. We believe that considering these two diagnostic layers, including a recently described RNA-based efficacy predictor\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, will help maximize the therapeutic potential of pazopanib, and potentially other TKIs, and guide patients unlikely to benefit to alternative therapies.\u003c/p\u003e \u003cp\u003eAnother TK that emerged as a therapeutic target is ERBB2, whose mRNA levels were consistently higher in DSRCT than in other sarcomas. Expression of ERBB2 has been previously observed in DSRCT patients and preclinical models, suggesting that it may be constitutively activated and can be inhibited by small molecules or antibodies whose efficacy requires dependence on the corresponding signaling pathway\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. However, our phosphoproteomic analysis found no evidence of aberrant ERBB signaling. This finding supported the use of T-DXd, which led to long-lasting remissions in both patients treated, whose disease had progressed after 12 and five prior therapy lines, respectively. These results are consistent with recent observations in patient-derived xenografts\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e and indicate that clinical trials of T-DXd, or further-generation ERBB2-targeted ADCs, in this intractable malignancy are warranted.\u003c/p\u003e \u003cp\u003eTo translate this signal of ADC efficacy in DSRCT patients into improved routine care, developing robust biomarkers will be essential. Studies in breast cancer, a disease known for its association with ERBB2 expression in a subset of cases, have shown that patients with low or ultra-low ERBB2 levels also benefit from T-DXd\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Consequently, the criteria for quantifying this biomarker were quickly adapted, e.g., in the ASCO/CAP testing guidelines. However, the minimal clinically relevant ERBB2 expression remains uncertain, illustrated, e.g., by the DAISY phase 2 trial, in which even patients with ERBB2-negative tumors by standard IHC responded to T-DXd\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. This might be attributed to T-DXd\u0026rsquo;s strong bystander effect, where the targeted payload release is mediated by ERBB2 expression below the detection limit of IHC, suggesting that alternative methods, such as RNA-seq, may become critical in identifying eligible patients. Of our two long-term responders, both of whom showed readily detectable \u003cem\u003eERBB2\u003c/em\u003e mRNA levels, one had an ERBB2-low tumor, and one lacked ERBB2 expression by IHC, which, given the robust expression by mass spectrometry, was probably due to insufficient tissue quality. Based on our data, we predict that most DSRCT patients are candidates for ERBB2-targeted ADC therapy and that a negative ERBB2 IHC result does not exclude the possibility of a response to these agents. However, future studies must define the range of clinically relevant target expression and determine the optimal detection methods.\u003c/p\u003e \u003cp\u003eOur study illustrates the power of multi-layered molecular profiling and inter-institutional collaboration, principles that hold promise for advancing research in rare cancers at large. While individually infrequent, rare cancers collectively represent a significant patient population, accounting for 20\u0026ndash;25% of all newly diagnosed cancers in many regions worldwide, and are often associated with poor outcomes due to limited treatment options and insufficient research investment\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. We leveraged the framework of a nationwide precision oncology platform to deliver new insights into the clinically actionable molecular landscape of DSRCT, thereby offering a blueprint for accelerating research in other ultra-rare malignancies. This synergy \u0026ndash; pooling resources, harmonizing methodologies, and rapidly sharing novel observations \u0026ndash; illustrates that, when institutions unite under a common goal, meaningful advances become more attainable, thereby paving the way for improved outcomes in patient groups that urgently require new therapeutic strategies.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eThirty patients with advanced DSRCT provided written informed consent for multi-layered molecular profiling of tumor tissue and, in the case of DNA sequencing, a matched blood sample, as well as for the longitudinal collection of clinical information as part of the DKFZ/NCT/DKTK MASTER program\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. The study was approved by the Ethics Committee of Heidelberg University (protocol no. S-206/2011) and conducted in accordance with the Declaration of Helsinki. Biological curation and clinical annotation of molecular profiles were performed as previously described, and a multi-institutional MTB involving treating physicians provided recommendations for clinical management\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample Processing\u003c/h3\u003e\n\u003cp\u003eFrozen tissue sections were assessed by board-certified pathologists to determine tumor cell content, including the presence of necrosis. Suitable samples were processed further at the NCT Heidelberg Sample Processing Laboratory. DNA, RNA, and cell lysates from fresh-frozen tumor specimens and DNA from blood samples were obtained using the AllPrep DNA/RNA/miRNA Universal Kit and the QIAamp DNA Mini Kit (Qiagen). Nucleic acids from FFPE samples were extracted using the Allprep DNA/RNA FFPE Kit (Qiagen). Subsequent quality control and quantification steps were performed using a Qubit Fluorometer (Life Technologies) and a 4200 or 2200 TapeStation System (Agilent).\u003c/p\u003e\n\u003ch3\u003eDNA Sequencing\u003c/h3\u003e\n\u003cp\u003eLibraries for WGS were prepared using the Illumina TruSeq Nano DNA Library Prep Kit with 100 ng DNA as input and sequenced on an Illumina HiSeq X Ten or NovaSeq 6000 instrument at the DKFZ Next Generation Sequencing Core Facility. Libraries for WES were prepared using the Agilent SureSelect All Exon Kit v5 or v5\u0026thinsp;+\u0026thinsp;UTRs with 200 ng DNA as input and sequenced on an Illumina HiSeq 2000, HiSeq 2500, HiSeq 4000, or NovaSeq 6000 instrument.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eNucleotide Sequence Alignment\u003c/h2\u003e \u003cp\u003eDNA sequencing reads were mapped to an assembly of the human genome version hs37d5 (1000 Genomes Project phase 2) and the Enterobacteria phage phiX174 genome using BWA mem (version 0.7.15) with the \u0026minus;\u0026thinsp;T0 parameter as the only one deviating from the default. BAM files were sorted with bamsort (biobambam package version 0.0.148), and duplicates were marked with markdup (Sambamba package version 0.6.5)\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSomatic SNV and Indel Calling\u003c/h2\u003e \u003cp\u003eSomatic SNVs were detected with an in-house pipeline based on SAMtools (version 0.1.19) mpileup and bcftools and using heuristic filtering as previously described\u003csup\u003e\u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Briefly, initial SNV calls were detected in the tumor BAM file by mpileup, which considered only reads with a minimum mapping quality of 30 (\u0026minus;\u0026thinsp;q30), and bcftools, which reported all positions containing at least one high-quality non-reference base (\u0026minus;\u0026thinsp;vcgN\u0026thinsp;\u0026minus;\u0026thinsp;p2.0), followed by inspection of these positions in the control sample using mpileup. SNVs were annotated with ANNOVAR (version November 2014) using GENCODE (release 19). Downstream filtering discarded variants with low support of the alternative allele, occurring in tandem repeats or other read-attracting regions, with PCR or sequencing strand bias, or with significant bias in the PV4 field of the mpileup output. Somatic SNVs annotated as missense, stopgain, stoploss, or splicing were defined as non-silent. Short indels were detected with Platypus (version 0.8.1.1)\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, and only those that had the filter tag PASS or passed custom filters allowing for low variant frequency were retained. Annotation of indels was performed using ANNOVAR (version February 2016), and calls falling into a coding sequence or splice site were extracted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSomatic Structural Variant Calling\u003c/h2\u003e \u003cp\u003eStructural variants (SVs) were detected using SOPHIA (version 35)\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e, which uses the \u0026ldquo;supplementary alignment\u0026rdquo; feature of BWA mem to discover SVs and custom thresholds and a background panel of normals to filter out common variants and recurrent artifacts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSomatic CNV Calling\u003c/h2\u003e \u003cp\u003eDNA copy number profiles of samples subjected to WGS or WES were determined using ACEseq (version 5.0.1) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/210807\u003c/span\u003e\u003cspan address=\"10.1101/210807\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e and CNVkit (version 0.9.3)\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e, respectively. For CNVkit data processing, the steps for inferring ploidy and tumor cell content (TCC) were adopted from the ACEseq workflow. In both workflows, segments containing at least 20 heterozygous single-nucleotide polymorphisms (SNPs) were processed to infer sample ploidy and TCC. The algorithm tested every possible combination of TCC (range, 0.15\u0026ndash;1.0) and ploidy (range, 1.0\u0026ndash;6.5) to find the local minima and, thus, the optimal solution. If more than one solution was possible, they were visually evaluated, and one was selected. The results for patient DSRCT-16 were inconclusive due to the high level of degradation of the matched control sample and therefore removed from further analysis. The remaining 29 samples had an estimated TCC above 30% and were included in the downstream analysis. Genomic gains and losses were annotated when a given genomic segment deviated in ploidy from the overall sample ploidy by +\u0026thinsp;0.7 or \u0026minus;\u0026thinsp;0.7, respectively. To determine the frequency of gains and losses across the genome, the copy number segments\u0026rsquo; breakpoints of all samples were gathered to create non-overlapping genomic sections. For each non-overlapping genomic section, the number of samples with gain, loss, and copy number neutral state was calculated. Segments covered by fewer than 20 patients were discarded, and the frequency of gains and losses was calculated for the remainder. The results were annotated using the Cancer Gene Census (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cancer.sanger.ac.uk/census\u003c/span\u003e\u003cspan address=\"https://cancer.sanger.ac.uk/census\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eGermline Variant Calling\u003c/h2\u003e \u003cp\u003eGermline SNVs and indels were called using Platypus (version 0.8.1.1), germline CNVs were detected using the GATK gCNV module (version 4.2.4.0), and germline SVs were identified using SOPHIA (version 2.2.0). After obtaining raw SNV and indel calls, variants were filtered based on a minimum coverage of 10 x and a minimum support of 3 x for the alternate alleles. Variants with the Platypus filter tags PASS or alleleBias were considered further. After coverage-based filtering, population-based variant frequencies, i.e., the minor allele frequency from gnomAD (version 2.1) and the variant frequency from an in-house panel of normals comprising 3,910 WGS and 1,198 WES samples were added using vcfanno (version 0.3.2)\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Variants with a frequency above 0.0005 (minor allele frequency in gnomAD) or 0.5 (variant frequency in normal samples), respectively, were considered common or artifacts and removed from further analysis. Variants in cancer predisposition genes that are annotated in ClinVar as pathogenic or likely pathogenic were excluded from population-based filtering. Additional genomic annotations and variant consensus were added to the filtered variants using VEP (version 104)\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e, followed by classification into germline or somatic based on the variant allele frequency obtained from control and tumor samples using TiNDA, which uses an EM-based clustering method in the canopy R package (version 1.3.0)\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. The GATK gCNV module\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e was used to detect germline CNVs from WGS and WES samples by applying a background cohort model created from 200 WGS or WES samples that were matched with the target capture kit. The workflow adhered to the best practices for detecting genomic CNVs outlined by GATK. For WGS data, one deviation was that only GENCODE (release 19) protein-coding regions were analyzed to expedite the analysis process. Next, CNV segments with a quality control score above 30 were annotated with a subset of gnomAD SV data using vcfanno. CNV segments with an 80% overlap with a common gnomAD SV (minor allele frequency\u0026thinsp;\u0026gt;\u0026thinsp;0.1%) of the same type were excluded. Additionally, targets within CNV segments were required to have denoised ploidies within the top or bottom 5% of background denoised ploidies in the case of duplication or deletion, respectively. Germline SVs were detected along with somatic SVs using SOPHIA. Finally, germline variants identified in cancer predisposition genes (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplementary Table\u0026nbsp;8\u003c/span\u003e) were provided to medical geneticists for classification according to American College of Medical Genetics and Genomics/Association of Molecular Pathologists criteria\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e and further ClinGen specifications\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Clinically relevant variants were discussed in the MTB and integrated into clinical management recommendations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eQuantification of Tumor Mutational Burden\u003c/h2\u003e \u003cp\u003eThe number of mutations per megabase was calculated by dividing the sum of non-silent SNVs and coding indels by the length of the genome\u0026rsquo;s coding sequence in megabases. For WES samples, the denominator was adjusted to the respective target coverage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eMutational Signature Analysis\u003c/h2\u003e \u003cp\u003eThe presence of SBS signatures from the COSMIC database (version 2)\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e was assessed using the YAPSA R package\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. The analysis included all somatic SNVs, limited to those within target regions for WES samples. For each sample, a mutation catalog was generated and further corrected for WES samples to account for the different occurrences of triplet motifs within the target sequences, and mutational signatures were computed along with their confidence intervals. Signatures were considered present if they exceeded signature-specific thresholds with a cost factor of 6 (absolute and relative for WGS and WES samples, respectively) and the lower limit of their confidence interval was greater than zero.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eDNA Methylation Analysis\u003c/h2\u003e \u003cp\u003eTumor DNA (250 ng) was analyzed using Infinium MethylationEPIC BeadChip arrays (Illumina). Raw data were used to run the sarcoma classifier (version 12.2)\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, followed by processing using the preprocessRaw function of the minfi R package\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. Probes that were unreliable, cross-reactive, mapping to sex chromosomes, overlapping with SNPs, or had a detection p-value\u0026thinsp;\u0026ge;\u0026thinsp;0.01 were filtered out\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e,\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. Each sample was normalized using the BMIQ function with default settings from the wateRmelon R package\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Beta values were used for further analyses. Only entities with three or more samples were kept (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;335). For dimensionality reduction, UMAP analysis was performed using the umap function of the umap R package with default settings based on the algorithm described by McInnes and Healy (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi\u003c/span\u003e:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.48550/arXiv.1802.03426\u003c/span\u003e\u003cspan address=\"10.48550/arXiv.1802.03426\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e For comparison, raw methylation data of 286 non-DSRCT sarcomas, 18 DSRCT samples (EGAS00001006939), and 19 small blue round cell tumors with \u003cem\u003eBCOR\u003c/em\u003e or \u003cem\u003eCIC\u003c/em\u003e alterations (GSE140686) were downloaded\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eRNA Sequencing and Fusion Gene Detection\u003c/h2\u003e \u003cp\u003eLibraries were prepared using the Illumina TruSeq RNA Library Preparation Kit with 1,000 ng total RNA as input or the Illumina TruSeq mRNA Stranded Library Preparation Kit with 500 ng total RNA as input. Three libraries were pooled and sequenced on one lane of an Illumina HiSeq 2500, HiSeq 4000, HiSeq X Ten, or NovaSeq 6000 instrument. Reads were aligned to the same reference genome as DNA sequencing data with STAR 2.5.1b\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. Fusion transcripts were detected with Arriba (version 2.4.0)\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eProteome and Phosphoproteome Analysis\u003c/h2\u003e \u003cp\u003e \u003cb\u003eSample preparation.\u003c/b\u003e Cell lysate protein concentrations were determined using the Pierce Bradford Protein Assay Kit (Thermo Fisher). Per tandem mass tag (TMT) batch, nine patient samples and two reference samples (200 \u0026micro;g per sample) were processed as previously described with minor adaptations\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLiquid chromatography-tandem mass spectrometry data acquisition.\u003c/b\u003e Multiplexed samples were measured on an Orbitrap Fusion Lumos Tribrid Mass Spectrometer (Thermo Fisher) coupled to an UltiMate 3000 Nano-Flow HPLC system (Thermo Fisher) for phosphoproteome analysis or a Vanquish Micro-Flow HPLC system (Thermo Fisher) for whole-proteome analysis.\u003c/p\u003e \u003cp\u003eFor peptide and phosphopeptide identification and quantification, raw files were searched with MaxQuant (version 1.6.12.0) against the human UniProt database, including unreviewed proteins (97,086 sequences including isoforms) and the MaxQuant common contaminants database. To improve data completeness across TMT batches, identifications were transferred with SIMSI-Transfer\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. Data were normalized using within- and across-batch median centering normalization. For the proteome, protein groups were inferred and quantified at the gene level using the Picked Protein Group FDR package\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. Protein phosphorylation scores were computed as the sum of all z-scores of a protein\u0026rsquo;s phosphopeptides. For the proteome, phosphopeptides, and protein phosphorylation scores, per-protein/peptide ranks, z-scores, and fold changes were calculated across the cohort.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eIHC\u003c/h2\u003e \u003cp\u003eIHC was performed on 3 \u0026micro;m tissue sections with the ready-to-use, pre-diluted PATHWAY anti-HER2/neu (4B5) rabbit monoclonal primary antibody (cat. no. 05278368001, Ventana/Roche) using a BenchMark ULTRA Autostainer VENTANA (Roche) as previously described\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. Immunoreactivity was assessed according to ASCO/CAP criteria established for breast cancer\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. IHC readers were blinded to clinical data.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSequencing and DNA methylation data have been deposited in the European Genome-Phenome Archive (https://www.ebi.ac.uk/ega/datasets) under accession EGAS00001007934.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eWe thank the NCT Sample Processing Laboratory and the DKFZ Next Generation Sequencing, Microarray, and Omics IT and Data Management Core Facilities for technical support. This work was supported in part by the German Federal Ministry of Education and Research (grant 031L0305A to B.K. and grant 01KD2207A to A.S., B.B.-B., S.B., R.F.S., G.M., A.S., D.B.L., B.K., D.H., H.G., and S.F.), the European Research Council (grant 833710 to B.K.), and the German Research Foundation (grant 452419311 to B.K.). The MASTER program is supported by the NCT Molecular Precision Oncology Program and DKTK.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict-of-interest statement:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eM.A. has had an advisory role and received honoraria from AbbVie and Boehringer Ingelheim. G.M. has had an advisory role and received honoraria from Boehringer Ingelheim. D.B.L. has received honoraria from Infectopharm. B.K. is a co-founder and shareholder of OmicScouts and MSAID. He has no operational role in either company. A.J. has received honoraria from AstraZeneca. C.H. has had an advisory role and received honoraria and research funding from Boehringer Ingelheim, Novartis, and Roche. S.F. has had a consulting or advisory role and received honoraria, research funding, and/or travel/accommodation expenses funding from Amgen, AstraZeneca, Bayer, Bristol Myers Squibb, Eli Lilly, Illumina, Pfizer, PharmaMar, and Roche.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ede Pinieux G et al (2021) Nationwide incidence of sarcomas and connective tissue tumors of intermediate malignancy over four years using an expert pathology review network. PLoS ONE 16:e0246958\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStacchiotti S et al (2021) Ultra-rare sarcomas: A consensus paper from the Connective Tissue Oncology Society community of experts on the incidence threshold and the list of entities. 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J Clin Oncol 41:3867\u0026ndash;3872\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Desmoplastic Small Round Cell Tumor, Molecular Profiling, Targeted Therapy, Trastuzumab Deruxtecan","lastPublishedDoi":"10.21203/rs.3.rs-6104125/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6104125/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDesmoplastic small round cell tumor (DSRCT) is an ultra-rare sarcoma with limited treatment options. We performed whole-genome/exome, transcriptome, and DNA methylome analysis in 30 refractory DSRCT patients, complemented by (phospho)proteomic profiling in nine, within a nationwide precision oncology program. In eight patients (27%), DSRCT was diagnosed based on molecular profiling. Although all patients had \u0026ldquo;quiet\u0026rdquo; genomes, 28 (93%) received 107 molecular-based management recommendations, including assessment of clinical trial eligibility in 17 (57%). Nearly half of recommendations (45%) were based on overexpression of tyrosine kinases, as well as SSTR3/5 and CLDN6, detected in 33% and 20% of cases, respectively. Thirteen patients (46%) received recommended therapies, yielding disease control in eight (62%; partial response, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5; stable disease, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3), including three long-lasting responses (\u0026ge;\u0026thinsp;12 months) to pazopanib and trastuzumab deruxtecan, triggered by ERBB2 overexpression in the absence of constitutive ERBB2 signaling. Thus, multi-omics profiling enables individualized DSRCT treatment.\u003c/p\u003e","manuscriptTitle":"Multi-Layered Molecular Profiling Informs the Diagnosis and Targeted Therapy of Desmoplastic Small Round Cell Tumor","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-27 07:57:29","doi":"10.21203/rs.3.rs-6104125/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e6e3c416-4d3c-4713-83fc-bb6582a41739","owner":[],"postedDate":"March 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":45324633,"name":"Health sciences/Oncology/Cancer/Tumour biomarkers"},{"id":45324634,"name":"Health sciences/Biomarkers/Diagnostic markers"},{"id":45324635,"name":"Health sciences/Biomarkers/Predictive markers"},{"id":45324636,"name":"Health sciences/Molecular medicine"},{"id":45324637,"name":"Health sciences/Oncology/Cancer/Cancer therapy"}],"tags":[],"updatedAt":"2026-04-10T07:08:00+00:00","versionOfRecord":{"articleIdentity":"rs-6104125","link":"https://doi.org/10.1038/s41467-026-71636-0","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2026-04-09 04:00:00","publishedOnDateReadable":"April 9th, 2026"},"versionCreatedAt":"2025-03-27 07:57:29","video":"","vorDoi":"10.1038/s41467-026-71636-0","vorDoiUrl":"https://doi.org/10.1038/s41467-026-71636-0","workflowStages":[]},"version":"v1","identity":"rs-6104125","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6104125","identity":"rs-6104125","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}