Proteomic landscape of Ewing sarcoma primary tumors and metastases

Domain: biology
Published: 2026-03-11
Editorial outcome: major_revision

Abstract
Ewing sarcoma (EWS), a rare pediatric bone tumor, poses unique therapeutic challenges due to its distinct microenvironment and limited molecular understanding. To gain a comprehensive molecular and functional view of the tumors in their microenvironment, we perform a deep mass spectrometry-based proteomic analysis of 170 tumor samples from 74 patients from primary, relapsed, and metastatic tumors. Analysis of more than 10,000 proteins across patients reveals insights into cancer prognosis, chemo-resistance, and progression. Our analyses suggest that ferroptosis pathways may be associated with chemotherapy response in EWS, and we delineate molecular subclasses that correlate the tumor immune landscape with DNA damage repair, ubiquitin‐related proteins, and patient outcomes. Multiplexed immunofluorescence imaging indicates possible associations between neutrophils and poorer prognosis, and between macrophages/T cells and a more favorable prognosis. Altogether, this investigation provides valuable insights into the intricate biology of EWS, paving the way for developing therapeutic strategies.

Introduction
Pediatric solid tumors are typically rare tumor types, which present as embryonal tumors below the age of 10 years, and non-embryonal cancer thereafter. Their rarity, their ambiguous cellular origin, and lack of research models translate to limited molecular understanding, hindering the development of targeted therapies and prognostic biomarkers1, 2, 3. Ewing sarcoma (EWS) is a pediatric malignancy that primarily affects the bones and soft tissues in adolescents and young adults. It is characterized by a single chromosomal translocation event, resulting in the formation of chimeric fusion proteins involving the EWSR1 gene and ETS family transcription factors. The most common fusion protein observed in EWS is EWSR1::FLI1, which plays a crucial role in driving tumorigenesis by dysregulating essential cellular processes such as transcriptional control, DNA repair, and signal transduction pathways4, 5, 6, 7. Despite being the only genetic driver event, the role of the fusion protein EWSR1::FLI1 and its therapeutic potential remain enigmatic. While its expression fuels cancer cell proliferation, paradoxically, low levels were associated with higher migration and invasion8. This complexity suggests that solely targeting the genetic cause may be insufficient for effective treatment4, 5, 9, 10.

Current treatments for EWS primarily rely on chemotherapy and local treatments, including surgery and radiation. Despite these interventions, patients diagnosed at a pre-metastatic stage exhibit approximately 75% five-year survival, while those patients diagnosed with metastatic disease or relapse have a significantly reduced survival rate of less than 30%4, 5. These significant differences in outcomes highlight the urgent clinical need to identify specific drug targets that mediate the aggressive tumor phenotypes associated with EWS5, 9.

Transcriptomic analyses have been instrumental in unraveling the molecular characteristics of EWS8, 11, 12, 13, 14. Variable mRNA expression of the fusion protein has been observed in EWS cell lines and tumors, revealing the downstream effects of these proteins and their impact on tumorigenesis15, 16, 17. Deconvolution of RNA expression data has provided some insights regarding the immune landscape of EWS, associating immune profiles with patient survival18. In general, the EWS tumor microenvironment (TME) is known to be immunosuppressive. It is characterized by low abundance of tumor-infiltrating lymphocytes (TILs) and a predominant presence of immunosuppressive cells that dampen anti-tumor responses15, 16. Other studies suggested that poor prognosis is associated with increased neutrophils19 and M2 macrophages20, while a higher presence of CD8 T cells correlated with a more favorable prognosis21, 22. However, current immuno-oncological treatments for EWS have shown limited efficacy, indicating the need for a deeper understanding of cancer-immune interactions and improved patient stratification to unlock the potential of immunotherapy for EWS patients15, 16, 17.

While transcriptomics and genomics have greatly improved our understanding of EWS23, 24, 25, 26, analysis of these layers alone provides a partial snapshot of the molecular landscape, neglecting post-transcriptional regulation, a crucial driver of cellular functions. Direct protein-mRNA comparisons in clinical samples have revealed significant discrepancies in multiple biological processes and signaling pathways, leading to distinct tumor classification and functional annotation27, 28. Thus, the application of proteomics holds great potential to unravel tumor functionalities and identify actionable drug targets.

In this work, we analyze a large EWS cohort comprising primary, post-neoadjuvant chemotherapy, relapse, and metastatic tumors, and correlated their proteomic landscapes to patient clinical profiles and the tumor immune landscape. We discover proteins associated with EWS progression and drug resistance using mass spectrometry-based proteomics of patient samples. These EWS proteomic profile highlight potential therapeutic targets that may pave the way for effective therapies in the future.

Results
Proteomics of EWS clinical cohort
Aiming to identify protein regulators of EWS progression and drug resistance, we performed deep MS-based proteomic profiling of a large cohort of EWS patients. Altogether, we analyzed 170 EWS samples, originating from 74 patients. Among those, the cohort included 61 primary surgical biopsy samples used initially for tumor diagnosis, nine post-neoadjuvant chemotherapy (NACT) surgical samples with poor response (necrosis < 90%), six relapsed samples, three progression, 14 metastasis samples, and in some of cases, two samples were taken from a single tumor. All tumors were localized to the bones or adjacent soft tissue (limbs, ribs, spine, sacrum, and pelvis) except for lung metastases (Fig. 1a-c). To associate between protein profiles and clinical response, we assembled all available clinical information, including survival time, relapse, treatment regimens, pathological response, and metastatic state at diagnosis (Fig. 1d, Supplementary Data 1-3). The cohort included 17 matched samples taken from the same patients at distinct progression stages of the disease (Supplementary Fig. S1a). Altogether, the size of the cohort and the clinical information set a unique opportunity to discover EWS regulators and targets.

Formalin-fixed paraffin-embedded (FFPE) tumor sections were obtained upon ethical approval from the Sourasky Medical Center, Tel Aviv, Israel. Samples were de-paraffinized and macrodissected after pathological assessment to enrich for cancer cell regions. We ensured relatively homogeneous cancer populations by dissecting small regions ranging between 5-12 mm2, and in cases of several such regions in single tumors, we collected and analyzed separate samples from the same tumor. Proteins were trypsin digested and analyzed on the Exploris480 mass spectrometer, using gas phase fractionation and data-independent acquisition (DIA) to increase depth and robustness29, 30. Altogether, we identified 10,220 proteins, and an average of 7,370 proteins per sample, with insignificant differences in proteomic depth among the cancer stages (Supplementary Fig. S1b, Supplementary Data 4). Similarly, insignificant differences were found in the number of identified proteins from decalcified and non-decalcified samples (Supplementary Fig. S1c). Overall sample-wise Pearson correlations ranged from 0.70 to 0.96, and biological replicates from the same tumor sections presented higher correlations of 0.85-0.96 (Supplementary Fig. S1d). Matched samples from the different stages showed a high correlation, except for the correlation between the primary tumor and post-treatment samples, which presented a median correlation of 0.81, attesting to the major impact of chemotherapy (Supplementary Fig. S1e). Consistent with the precise tissue dissection, analysis of cancer tissue purity using the ESTIMATE algorithm31 provided an estimation of >80% tumor purity in almost all samples, including post-NACT samples, within the proteomic regions of interest (ROIs; Supplementary Fig. S1f).

Initial data evaluation focused on examining the expression levels of known EWS-related proteins. EWSR1 was identified in all samples; the three known EWS proteins NKX2.232, LINGO1 33and CD994, 34 were identified in 96%, 98%, and 79% of the samples, respectively (Fig. 1e). Among the fusion partners, FLI1, the most frequent fusion of EWSR1, was identified in 153 samples, while ERG, the second most common fusion, showed high expression in five samples. In seven samples, we did not quantify FLI1 or ERG, and three samples expressed low levels of ERG. Examination of the EWS-related proteins across the different progression states showed largely comparable expression profiles (Supplementary Fig. S1g). Interestingly, the expression levels of these proteins varied considerably across primary tumors, with a range exceeding 30-fold (Fig. 1e). We observed variable and weak correlation between their expression and tumor purity (Supplementary Fig. S1h), indicating that stromal admixture accounts for a minor fraction of the variance and suggesting a high degree of heterogeneity within the cancer cells. Notably, while FLI1, ERG, and EWSR1 are central components of the oncogenic fusions that drive Ewing sarcoma biology, proteomic measurements reflect both wild-type and fusion-derived protein fragments, as the mass spectrometric data could not distinguish between them in the absence of patient-specific fusion-junction peptides.

Identification of potential regulators of EWS progression and drug resistance
Comparing the proteomes of EWS cancer stages can unravel protein networks associated with disease progression and the aggressive cancer phenotype. Specifically, investigation of post-treatment samples can propose mechanisms of chemotherapy resistance, as the residual disease after treatment reflects resistant cells that may induce tumor relapse and metastasis, reducing survival rates to only 30%. Thus, identification of the changes that occur in the tumor proteome upon treatment can be the first step towards the development of better therapies, as alternatives or in combination with current regimens.

Examination of the differences between each of the EWS progression states identified 1013 significantly changing proteins (FDR < 0.05, Fig. 2a, Source Data file). The largest differences were observed between primary tumors and post-treatment tumors, representing the dramatic change induced by chemotherapy. Among the down-regulated proteins upon treatment (Cluster 4) we found a large network of RNA processing proteins, epigenetic regulators, DNA replication and repair factors, and mitotic proteins, including topoisomerases, MSH2,3, KAT6, 7,2, etc. (Fig. 2b and Supplementary Fig. S2a). Most of these proteins were elevated upon relapse and progression, suggesting that their downregulation upon treatment was directly impacted by the treatment, but insufficient for complete tumor eradication. Two additional clusters (2 and 3) of protein upregulated in advanced cancer stages highlighted the involvement of proteins related to DNA replication, ribosome biogenesis, protein translation, and degradation, following the more proliferative and aggressive phenotype of advanced cancer stages. Orthogonal validation by immunohistochemistry (IHC) showed higher expression of RRS1, a ribosome biogenesis regulator, in relapse samples (cluster 2; Fig. 2c-d, p = 0.047). Cluster 1 included Notch3 and HDAC1, proteins known to mediate EWS-ETS signaling35, 36, 37 (Fig. 2b and Supplementary Fig. S2b). Interestingly, examination of the opposite trend, of upregulated proteins upon treatment (clusters 7-8), showed a network of immune-related proteins, such as STAT1, interferon-related proteins (IFIT1, IFIT5), apoptotic proteins (CASP1 and PYCARD), and a group of iron-related and redox enzymes, including ferritin (FTH1, FTL), FDXR, GPX3, and MAOB, as well as glycolytic enzymes and hypoxia-inducible factor (HIF1a) targets, including PKM, hexokinase 3, and transaldolase (Fig. 2e).

In agreement with the identified protein clusters, enrichment analysis on the ratios between the primary tumors and each one of the other stages, showed downregulation of DNA replication processes, DNA damage response proteins, ribosome biogenesis proteins upon treatment, and elevation of most of these pathways upon relapse and progression (Fig. 2f, Supplementary Fig. S2c). Mitochondrial metabolism proteins were downregulated in all stages relative to the primary tumor. Additionally, post-NACT samples presented significant enrichments of multiple immune-related pathways including MHC class II, and type I interferon (Fig. 2f, Supplementary Fig. S2c, and Source Data file).

The proteomics data showed high expression levels of iron-related proteins in post-NACT samples. We validated the high expression by IHC of FTH1 (ferritin heavy chain 1), which showed significantly higher expression in the cancer cells post-NACT (Fig. 3a-b, p = 0.0014). Similarly, IHC analysis showed higher expression of the transferrin receptor (TFRC) in post-NACT samples (Fig. 3c-d, p = 0.0163). TFRC is a key mediator of cellular iron uptake, facilitating the transport of transferrin-bound iron into the cell. FTH1 typically acts as a counter-regulatory protein by sequestering intracellular iron, limiting ROS generation and lipid peroxidation availability. Iron-related proteins and redox proteins are known to inhibit ferroptotic cell death, which was shown to be associated with response to chemotherapy. For example, chemotherapy has been shown to elevate HIF1a, which in turn upregulates GPX3, FDXR, and ferritin38, 39. These factors might then inhibit ferroptosis and confer cell survival despite high cell stress. Additionally, previous reports suggested that iron chelation and anti-oxidant activity in tumors can increase interferon signaling and immune response40, 41, 42, 43, 44, thereby connecting ferroptosis inhibition and drug resistance to tumor immunogenicity. In accordance with the hypothesized involvement of iron metabolism in chemotherapy resistance, iron staining of selected tumor samples showed a marked increase in the non-heme bound iron in the post-treatment samples (Fig. 3e-f, p = 0.0127). These results testify that the ferroptosis-related signal does not merely reflect tissue hemorrhage in the post-treatment samples. Altogether, we propose that these proteins may confer treatment resistance via inhibition of chemotherapy-induced ferroptosis, and induction of an immune response.

To further explore the expression of ferroptotic proteins in EWS clinical samples, we focused on known ferroptosis regulators from FerrDb45 and leveraged the unique paired nature of some patient samples in our study to examine how these proteins change upon treatment within single patients. This analysis increased the number of proteins that were proposed to be involved in ferroptosis inhibition and induction of autophagy, including GPX4, BRD4, HSPB1, NFS1, and ATG16L1 (Supplementary Fig. S3a; p < 0.05). Additionally, using an independent mRNA expression dataset46, we validated the clinical relevance of these proteins. Consistent with our clinical proteomics data, high mRNA expression of GPX4, FTH, and FTL associated with shorter event-free survival of EWS patients, and a similar trend was found for overall survival (Supplementary Fig. S3b-c).

Next, we wished to investigate the functional importance of ferroptosis to chemotherapy resistance. We compared the sensitivity of EWS cells to chemotherapy with and without the induction of ferroptosis. To this end, we examined the impact of two ferroptosis inducers, RSL3, a GPX4 inhibitor, and erastin, an inhibitor of the cystine/glutamate antiporter, on the response to doxorubicin and etoposide chemotherapies. We used the cell line model of A673 cells and its chemo-resistant counterpart14, and in addition, generated a resistant cell line model from the RDES cell line.

Generation of RDES cells with reduced chemo-sensitivity was achieved by treating the cells for five cycles of combination treatments with four chemotherapeutic drugs, doxorubicin, etoposide, vincristine, and cyclophosphamide (see Methods). To evaluate chemotherapy sensitivity, we treated parental and long-term drug-exposed A673 and RDES cells with increasing concentrations of doxorubicin or etoposide in 3D spheroid models. As expected, prolonged drug exposure led to an elevation in IC₅₀ values for both drugs in A673 and RDES cells (Fig. 3g). In RDES spheroids, co-treatment with the ferroptosis inducers RSL3 or erastin reduced etoposide IC₅₀ values in both parental and adapted cells. Doxorubicin co-treatment showed that the parental cells were more sensitive to RSL3, and the adapted ones were more sensitive to erastin (Fig.3h–k). Similar experiments in A673 cells showed a stronger signal in the adapted cells, primarily upon doxorubicin treatment (Supplementary Fig. S4a-b). These findings support the notion that ferroptosis-related mechanisms may contribute to the variable drug responses observed in patient tumors, warranting further validation in future studies.

Proteomic investigation of prognostic proteins suggests immune involvement in patient prognosis
Routine clinical diagnosis examines primary tumors to identify the exact translocation and EWS molecular markers. Prognosis is primarily determined by the tumor's location and presence of metastasis. Pelvic tumors are known to present with a poorer prognosis than appendicular tumors in the limbs5. These are also often diagnosed later, as they are asymptomatic for longer periods. Tumor metastatic stage at diagnosis is the most important prognostic factor, however, even non-metastatic tumors often relapse and metastasize5. Harnessing the rich data from our cohort of primary tumors and the clinical data of each patient, we aimed to identify potential prognostic features in the proteomic profiles of the primary tumors. To this end, we directly compared the proteomic differences between primary tumors from patients with good and poor prognoses, based on three clinical parameters: metastasis at diagnosis, three-year disease-free survival (DFS), and tumor relapse (Fig. 4a, b; FDR < 0.1). We found 100 significantly changing proteins between primary tumors with or without metastasis at diagnosis. Nineteen proteins differed based on the relapse, and only one protein differed based on disease-free survival, probably due to the small number of patients with more than 3-years follow-up. Enrichment analysis on the difference between the means of the two prognostic groups revealed that patients with good prognosis (no relapse, no metastasis, and more than 3 years of disease- free survival) showed enrichment of HLA proteins and type 1 interferon signaling, as well as proteasome activity. On the other hand, patients with poor prognosis exhibited enrichment of oxidative phosphorylation, cell cycle, and DNA replication (Fig. 4c-e, FDR < 0.02, Source Data file). Among the proteins that were commonly highly expressed in patients with poor prognosis (at least 2-fold change), we found known neutrophil degranulation markers such as AZU1, MPO, PRTN3, and CAMP (Fig. 4 f-g). A similar network of neutrophil proteins was also highly expressed in non-metastatic tumors that showed relapse (Supplementary Fig. 4b), raising the possibility that neutrophil abundance in primary tumors could be an early indicator of relapse risk, independent of metastatic status. Finally, we confirmed the existence of neutrophils by immunofluorescence staining of MPO and CD15 (Fig. 4h).

Unsupervised clustering of primary tumors reveals three patient groups associated with prognosis
Molecular classification of primary tumors can be critical for treatment decision-making and the basis for drug development. We wished to investigate whether unsupervised analysis of the proteomics data of the primary tumors can highlight protein networks that associate with the patients’ clinical manifestations. Using the Consensus Clustering algorithm47, tumors were separated into three clusters, which include 34, 7, and 17 patients (Fig. 5a, Supplementary Fig. S5a,b). These clusters reflected prognostic differences between clusters, with cluster 2 having a poorer prognosis than cluster 1 and cluster 3 (Fig. 5b). Cox regression analysis also showed a significantly higher hazard ratio of cluster 2 patients, independent of other clinical parameters, such as the tumor location and surgical intervention (Fig. 5c, p = 0.044). In agreement with previous reports, the metastatic stage at diagnosis also presented a significantly higher hazard ratio (p = 0.019). Similarly, more non-metastatic patients were found in cluster 3 and more relapsing patients were found in cluster 2 (Fisher exact test, p < 0.05; Supplementary Fig. 5c-d). These results show that the unsupervised clustering of the proteomic data could reveal prognostic proteins already in the primary tumors.

Analysis of the significantly changing proteins between these clusters identified 1,263 proteins that differ between clusters (Fig. 5d, Source Data file, FDR < 0.05). A two-dimensional enrichment analysis showed that mitochondrial metabolism was lower in cluster 2 relative to clusters 1 and 3 (Fig. 5e-f, Source Data file, FDR < 0.02). MHC complex was enriched in cluster 3 relative to clusters 1 and 2, while cluster 2 was highly enriched for cell cycle proteins (e.g. MCM complex proteins) and ubiquitin-mediated proteolysis, and de-enriched for proteasome core complex (Fig. 5e-f). This conundrum prompted us to closely examine the protein expression level of the ubiquitin-proteasome system. Clustering of 72 significantly changing proteins that belong to these pathways showe d separation into two main protein clusters, and almost perfect separation of samples according to the consensus clusters (Supplementary Fig. S5e). Consistent with the enrichment results, cluster 2 samples exhibited low expression of 20S core proteasome proteins and high expression of 19S proteasomal proteins. This high expression of 19S proteins coincided with increased expression of multiple deubiquitinases, notably including ten ubiquitin-specific peptidases (USPs). Moreover, multiple ubiquitin-conjugating enzymes (UBE2s) were highly expressed in the same group of samples (consensus cluster 2). UBE2A, UBE2B, UBE2D1, UBE2D1, and UBE2H were all implicated in the positive regulation of DNA-damage response proteins and are also positively correlating with these processes in our data (Supplementary Fig. S5f). In contrast to the expression of ubiquitin-proteasome related proteins in cluster 2, cluster 3 presented high expression of the 20S proteasome, and key mediator of proteasomal degradation and antigen presentation to the immune system. We speculated that higher proteasomal degradation processes in cluster 3 may be associated with antigen presentation on MHC class I in these tumors48, 49. Assessment of antigen processing and presentation proteins showed higher levels of HLA proteins (MHC class I and MHC class II) specifically in cluster 3 (Supplementary Fig. S5g). Overall, these results show two main phenotypes; the first, in cluster 2 samples, which suggests low overall ubiquitin-proteasome mediated degradation, and the second one, in cluster 3 samples, suggests high proteasomal activity and antigen presentation.

Immunological characterization of EWS consensus clusters
Identification of networks of proteins associated with antigen presentation suggested higher immunogenicity of cluster 3 tumors and lower immunogenicity of cluster 2 tumors. The significant enrichment of immune processes prompted us to generate an immune score for each tumor, based on the average expression level of immune signature proteins from the ESTIMATE algorithm31. Interestingly, despite having significantly different prognoses, both clusters 2 and 3 presented higher immune scores than cluster 1 (Fig. 6a, p = 0.0014, p = 5.4 × 10-4, respectively). Most prominently, cluster 2, which presented a poor prognosis, and cluster 3, which presented a good prognosis, had a high involvement of immune processes (Fig. 6a). However, the proteomic data showed marked differences between the immune processes in the two clusters. While cluster 2 showed high expression of anti-inflammatory signals, primarily dominated by neutrophil and checkpoint proteins (AZU1, S100A8 and 10, CD276, etc.; Fig. 6b), the immune processes in cluster 3 included HLA proteins, implying potential activation of anti-tumor signals by the adaptive immune system. In agreement, deconvolution of the proteomic data using the xCell algorithm predicted higher numbers of neutrophils, but not M2 macrophages, in cluster 2 (Fig. 6c).

To validate the proteomic-based predictions, we profiled the main immune cell populations using multiplexed immunofluorescence imaging of neutrophils (MPO and CD15), macrophages (CD68, HLADR, and MRC1), T-cells (CD4 and CD8), and the EWS cancer cells (NKX2.2) on a subset of the primary tumors (Source Data file). In agreement with the proteomic results that showed higher MHC expression and enrichment of antigen processing and presentation in cluster 3, multiplexed imaging analysis of the proteomic ROIs showed a higher abundance of CD68+ macrophages, higher abundance of HLADR+ cells and higher abundance of CD68+HLADR+ cells in cluster 3 relative to clusters 1 and 2 (Fig. 6d-e). In addition, we found a higher abundance of CD4+ T-cells in cluster 3 (Fig. 6f-g). Per the proteomics data, we found a higher percentage of neutrophils (CD15+ MPO+ , Supplementary Fig. S6i-j) in primary tumors from cluster 2, which aligns with the poorer survival of these patients (Fig. 5b and Fig. 4h). Comparison between good and poor prognosis patients (relapse vs. non-relapse, and 3-yr disease free survival) tumors showed that elevated macrophages and HLA+ cells are associated with better prognosis (Supplementary Fig. S6).

Altogether, our results show the potential involvement of neutrophils in the more aggressive tumors and the potential involvement of macrophages and T-cells in less aggressive tumors. These results suggest that the immune system could be harnessed to increase anti-tumor activity in a subset of the patients, and the substantial proteomic differences between clusters suggest that protein-based biomarkers could predict immune involvement.

Methods
Ethical statement
This study complies with all relevant ethical regulation. The use of human specimens was approved by the Institutional Review Board and Ethics Committee of the Sourasky Medical Center (Approval No. 0122-11). All samples are archived samples from the Pathology Unit of the hospital. Due to the retrospective nature of the analysis utilizing archived pathological remnants, the IRB granted a waiver of informed consent. Consequently, no written consent from patients or legal guardians was required, and no compensation was provided.

Cohort assembly
Formalin-fixed paraffin-embedded (FFPE) Ewing sarcoma tumors were collected from 74 patients. Several samples were collected from some of these patients, reaching a total of 170 EWS samples, from primary tumors, post-neoadjuvant chemotherapy treatment (NACT) samples, relapse, progression and metastases from bone and lung. The study was approved by the Institutional Review Board of Sourasky Medical Center (Approval No. 0122-11). All samples were anonymized as defined in the study protocol. All patients in the cohort were under 24 years of age. EWSR1::FLI1/ERG translocation was validated by genetic testing in 75% of the tumors. For all samples, diagnosis was confirmed by a specialized sarcoma pathologist based on classical morphology, clinical context, and an immunohistochemical profile including CD99 and NKX2.2, with exclusion of alternative entities. The clinical and pathological characteristics include biological sex (determined based on clinical records), age, metastasis at diagnosis, relapse, pathological response (based on percent necrosis), translocation type, other treatment modalities, and 3-year disease-free survival and overall survival (when available). All patients underwent neoadjuvant chemotherapy (NAT) to reduce tumor burden with the COG AEWS0031 or EURO-E.W.I.N.G. 99 protocols. To protect patient privacy, age is reported as an aggregated summary statistics (median and range) rather than as individual-level data points. A summary of these clinicopathological features at the cohort level is provided in Supplementary Data 3. All samples analyzed by proteomics are consumed for sample preparation. Remaining FFPE blocks are archived in the Sourasky Medical Center Pathology Department.

We divided our samples into different categories based on tumor stage and origin. Primary samples were treatment-naïve surgical biopsies; post-chemotherapy samples were tumors that have undergone a NAT treatment and defined as poor responders with less than 90% tumor necrosis; local relapse refers to relapse in the area surrounding the primary tumor; progression refers to a relapse stage that does not respond to treatment; metastasis includes the spread of cancer to distant sites, such as the lungs or bones. Seventeen patients in our cohort had samples from multiple disease stages (Supplementary Fig. S1a, Supplementary Data 1-2). Tissue sections were examined by an expert pathologist specializing in bone tumors to delineate tumor areas, assess necrosis, and evaluate treatment response in post-treatment samples.

Clinical Sample preparation
Each FFPE tumor sample was deparaffinized with two xylene washes, followed by rehydration through a series of ethanol washes: twice in 100% ethanol, once in 96%, and once in 70%. The samples were then placed in distilled water (DDW) until scraping. Using hematoxylin and eosin-stained tissue samples as a template, tumors were macrodissected to contain at least 80% cancer cells. Pathological analysis of each tumor ensured that we avoid mainly fibrotic and necrotic areas, as well as regions with intense lymphocyte infiltration and adipose tissue. For each sample, we macrodissected cancer cell regions from 1-4 FFPE sections (~0.5-12 -mm2 tissue area in each section) of 6 μm thickness. To improve representativeness and minimize bias, multiple ROIs from different parts of each tumor were selected. The scraped tissues were lysed in 4% SDS in 25 mM HEPES buffer followed by de-crosslinking by heating for 1.5 h at 95°C followed by sonication for 20 cycles (30 sec on, 30 s off) in a Pico-Bioruptor sonicator (Diagenode). Protein trypsin digestion was performed following the semi-automated SP3 protocol65, 66 using the Bravo liquid handler (Agilent). Proteins were reduced with 10 mM TCEP and alkylated with 40 mM CAA followed by overnight digestion at 37°C with LysC-Trypsin mix (Promega, 1:50 enzyme: protein ratio) and sequencing grade-modified trypsin (Promega, 1:25 enzyme: protein ratio), respectively. The second day of the SP3 protocol was performed manually, and the resulting peptide concentrations were determined using fluorescamine assay by fluorescence measurement on the 3300 Nanodrop.

LC-MS-based proteomics
LC-MS analyses were performed on the Dionex UltiMate RSLC 3000 nano-HPLC system (Thermo Fisher Scientific) coupled to the Exploris480 mass spectrometer, using gas phase fractionation and data-independent acquisition (DIA). Peptides were loaded onto a PepMap precolumn, and followed by separation on EASY-spray PepMap columns (75 μm i.d., 75 cm length), using a 145-minute water-acetonitrile gradient at a flow rate of 200 nL/min. The gradient was composed of two steps, 12-28% solvent B (99.9% [v/v] acetonitrile, 0.1% [v/v] formic acid) over 120 min, followed by 28-44% solvent B over 25 min. MS analyses followed the DIA acquisition approach with a mass range of 400–1000 m/z and 24 isolation windows of 25 Th. MS1 resolution was 120K, with the standard AGC target set to 300%. MS2 scans were acquired with 30K resolution and an AGC target of 100%. Normalized HCD collision energy was 32. Gas phase fractionation was performed by high-field asymmetric waveform Ion Mobility Spectrometry (FAIMS), we performed two injections of 0.5 μg peptides from each sample. The peptides were analyzed using two different combinations of compensation voltages (CVs), specifically -40 and -60, and -50 and -65.

Spectral library and DIA-MS data processing
Raw MS files were divided into subfiles based on their CV values and transformed to .d files before data analysis using the DIA-NN software67 (version 1.8). In-silico spectral library was generated in DIA-NN based on the canonical human proteome (Uniprot Release, 2018_03; 20,612 sequences) and peptide retention. We configured the following settings for database search: Trypsin/P was specified as the protease with a maximum of 1 missed cleavage. Precursor mass ranges of 300 to 1800 m/z, fragment mass ranges of 200 to 1800 m/z, with a scan window of 15, and retention of precursors with charges +1-4 and lengths between 7 and 30 amino acids. Carbamidomethylation of cysteine residues was defined as a fixed modification, and Oxidation (M) and N-terminal Acetylation were set as variable modifications. Interfering precursor peaks were eliminated, and a robust LC (high accuracy) quantification strategy was done. Precursors were filtered based on a q-value of 0.01. Protein grouping was performed at the canonical gene level. The neural network classifier operated in double-pass mode to enhance classification performance. Downstream data analysis was performed on the protein group table from the DIA-NN output, which combined all CV sub-files of each sample into a single intensity value for each protein. Raw protein intensity values were log -transformed and normalized using Quantile normalization in R (R package preprocessCore v1.64.0). Four samples were excluded from further analysis: one BCOR sarcoma and three low-quality samples with fewer than 5,700 identified proteins. In addition, we averaged two other samples with their technical replicates from the same FFPE slide and excluded two post-neoadjuvant chemotherapy samples that didn't receive the complete chemotherapy regimen.

Data preprocessing and statistical analysis
Data analysis was performed using Perseus68, 69 software (versions 1.6.13.0 and 1.6.10.43), the R environment, and GraphPad Prism. The initial protein table included a total of 10,220 proteins. For downstream analyses, we applied a focused filtering step, excluding proteins annotated as blood-derived or extracellular matrix (ECM)-associated, to emphasize tumor cell-intrinsic proteomic features. Removal of ECM proteins was performed to minimize uncertainty arising from the mixed cellular origin of ECM proteins in bulk tissue proteomics, as their contribution could reflect either tumor or stromal compartments. This filtration does not suggest that these proteins are irrelevant to this tumor type. To confirm that ECM removal did not bias our overall conclusions, we compared principal component analysis (PCA) distributions of the dataset before and after filtering. We observed no significant changes in global sample distribution (Supplementary Fig. S7a–b). The complete, unfiltered protein dataset, including all quantified proteins and ECM proteins, is available in Supplementary Data 4 to support future studies addressing Ewing Sarcoma and its tumor microenvironment including ECM. Tumor purity was assessed using the ESTIMATE (R package ‘estimate’ v1.0.13) algorithm to evaluate whether bulk proteomic samples were suitable for downstream analyses. Given the partial gene-set coverage in our dataset, and the specificity of stromal components in distinct tumor types, these scores should be regarded as approximate indicators of purity rather than exact quantifications of tumor, stroma, or immune composition, and they were not integrated into our downstream analyses. For the progression analysis of 162 samples (including primary, post-chemotherapy, relapse, progression, and metastasis samples), we performed additional filtration to ensure valid values in at least 70% of all samples. Missing values were imputed by generating a normal distribution with a width of 0.5 of the original intensity distribution and a downshift of 1.8 standard deviations. This resulted in a dataset of 6,312 proteins, which we used to perform downstream analyses. ANOVA test to compare between stages was performed with permutation-based FDR correction of 0.05, followed by a post hoc test.

In the primary tumor consensus clustering, we averaged biological replicates from each tumor, resulting in a dataset of 58 samples. Subsequently, we filtered these data to retain proteins with at least 70% valid values, which resulted in a dataset of 6,517 proteins. Missing values were imputed as mentioned above. Student's t-tests were used for comparisons between groups of poor and good prognosis patients, with permutation-based FDR < 0.1 and S0 = 0.1. One-dimensional (1D) annotation enrichment analyses, two-dimensional (2D) annotation enrichment analyses (based on the Wilcoxon-Mann-Whitney test), and Fisher exact test enrichment analyses were performed using the Perseus software, with a Benjamini–Hochberg FDR < 0.02. Protein networks were generated with the STRING database70, 71 and then edited in Cytoscape72. Biological annotations, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), were taken from UniProt.

For the unsupervised clustering of the primary EWS tumors, we applied the Consensus Clustering algorithm using the R package ConsensusClusterPlus (Pv1.66.0)47, 73. Parameters used were: maximal number of clusters, six; number of iterations, 1,000; subsampling fraction, 0.9; clustering algorithm, hierarchical; and distance matrix, Pearson correlation. Before applying the algorithm, ratios across proteins and samples were Z-score normalized. Kaplan-Meier analysis and Cox regression were performed using R's ‘survival’ (v3.8-3), ‘ggsurvfit’ and ‘survminer’ (v0.5.1) packages to compare consensus clusters based on 3-year disease-free survival. The Cox regression model was further adjusted for additional variables, including metastasis at diagnosis, pelvic disease, and surgery following neoadjuvant chemotherapy. The proportional hazards assumption was verified using Schoenfeld residuals, and no significant violations were observed (Global test p = 0.20).

Kaplan-Meier plots based on mRNA expression data were based on the Dirksen database46 and used the R2 Genomics Analysis and Visualization platform (http://r2.amc.nl). The Kaplan Scan feature on the R2 platform was used to create Kaplan-Meier curves based on the log-rank test.

Cell Culture
A673 parental and chemotherapy-resistant cell lines (A‑673; human Ewing sarcoma; kindly provided by Jacob Scott’s Laboratory, Cleveland clinic) were cultured in DMEM high-glucose (Biological Industries, #010521A) supplemented with 10% FBS, 1 mM sodium pyruvate, and 1 mM penicillin-streptomycin at 37°C in 5% CO₂. RDES parental (RD‑ES; human Ewing sarcoma of bone; kindly provided by Yosef Yarden’s Laboratory, Weizmann Institute of Science) and resistant clones were maintained in RPMI (GIBCO, #21875034) containing 15% FBS, and 1 mM penicillin–streptomycin under identical conditions. Cultures were routinely confirmed as mycoplasma-free using a PCR-based detection kit (EZ PCR Mycoplasma Test Kit, Sartorius #20-700-20). Internal mass spectrometry-based proteomic profiling demonstrated high abundance of FLI1- and EWSR1-derived peptides, consistent with the expected Ewing sarcoma proteomic signature.

Generation of RDES chemo resistant cell lines
To model the clinical standard of care for Ewing sarcoma, RDES cells were subjected to five cycles of alternating vincristine-doxorubicin-cyclophosphamide (VDC) and etoposide-cyclophosphamide (EC), recapitulating the VDC/IE protocol. Since ifosfamide in the IE regimen requires hepatic activation and no metabolically active form is commercially available, it was substituted with 4-hydroperoxycyclophosphamide, a chemically analogous compound, as previously done14. Experimental groups received either VDC-vincristine (#2068-78-2, Cayman Chemical), doxorubicin (#225316-40-9, Cayman Chemical), and 4-hydroperoxycyclophosphamide (CAS: H714675, TRC Canada)-or EC-etoposide (#33419-42-0, Cayman Chemical) and 4-hydroperoxycyclophosphamide at concentrations approximating each drug’s IC₃₀-IC₄₀. Each treatment cycle consisted of 4 days of drug exposure followed by recovery in maintenance medium, with recovery intervals progressively reduced from 10 to 5 days across cycles. Controls were exposed to DMSO vehicle only, throughout the experiment. The resistant cell lines generated in this study are available from the corresponding author upon request.

3D spheroid generation and drug sensitivity assays
Spheroid experiments were performed following published protocols74, 75 with some modifications, as detailed below. Cultures were generated in HPLM medium (Rhenium, #A4899101) supplemented with FBS (10% for A673, 15% for RDES) and 1 mM penicillin at 37°C in a humidified 5% CO₂ incubator. Prior to spheroid generation, A673 and RDES parental and resistant cells were expanded as 2D monolayers for 2 days, not exceeding 70% confluence. Spheroids were generated in 96-well round-bottom, ultra-low attachment plates (Corning/Sigma-Aldrich, CLS7007-24EA) by seeding 1,500 A673 or 2,000 RDES cells per well in HPLM. After 24 hours of aggregation, spheroids were fed with 10 μL fresh medium and cultured for an additional 24 hours before treatment initiation.

To determine single-agent IC₅₀ values, spheroids were exposed to doxorubicin, etoposide, RSL3, or erastin across an 8-point concentration series, with DMSO as a constant vehicle control. Treatments were applied for additional 60 hours.

Drug interaction studies included vehicle, chemotherapy alone (doxorubicin or etoposide) and in combination with the ferroptosis inducers (RSL3 or erastin). Treatments were applied for 60 hours on pre-formed spheroids under identical conditions. All experiments were performed in three independent biological replicates.

Spheroid viability was quantified using the CellTiter-Glo® 3D assay (Promega, #G6982) according to manufacturer instructions. Luminescence signals were background-subtracted and normalized to vehicle controls to calculate relative viability. Dose-response curves and IC₅₀ values were derived in GraphPad Prism using nonlinear regression (log[inhibitor] vs. normalized response). Significant shifts in IC₅₀ between parental and resistant lines were assessed by extra-sum-of-squares F-test.

Immunohistochemistry staining and multiplexed immunofluorescence staining and imaging
Opal multiplex staining was performed using the BOND RXm research detection platform (Leica, DS9455). Tissue sections of 28 patients were deparaffinized and rehydrated using BOND dewaxing solution (Supplementary Data 5). Endogenous peroxide blocking was done with 3% H2O2 and 1% HCL in Methanol, followed by heat induced antigen retrieval in 10mM citric acid (PH=6). Blocking was performed with 20% NHS and 0.1-0.5% triton according to target. Primary antibodies were used as described below. In case a secondary biotinylated antibody was used, an additional step of biotin blocking kit was performed.

Primary antibodies, diluted in 2% normal horse serum (NHS) and 0.1%-0.5% triton, were incubated overnight as detailed below. Secondary HRP or biotin conjugated antibodies incubation was followed by fluorescently labelled OPAL reagents or streptavidin. NKX2.2 staining was double amplified by using biotinylated secondary antibody, followed by ABC kit and OPAL reagent. Antibody removal was performed by 10-minute microwave treatment with citric acid (PH=6), and then the protocol repeated from the blocking step. Nuclei were stained with Hoechst. Imaging was performed by whole slide scanning using the Phenoimager system (Akoya Biosciences Fusion 2.0) with 10X / 0.8 objective – pixel size of 0.5 micron and compatible Opal dedicated filter set and Auto Fluorescent background channel. Final image output of 8bit. Three staining panels were used for immunofluorescence: i) Panel I with anti-MPO (PA516672, Thermo Fisher Scientific, 1:100), anti-NKX2.2 (BSB3110, Bio SB, 1:75), anti-CD15 (MAS-11789, Invitrogen, 1:50), anti-CD68 (CST-76437S, Cell Signaling Technology, 1:200); ii) Panel II with anti-NKX2.2 (as above), anti-CD4 (AB-ab133616-100, Abcam, 1:100), anti-CD8 (MA514548, Thermo Fisher Scientific, 1:100); iii) Panel 3 with anti-NKX2.2 (as above), anti-CD68 (as above), anti-HLAII (14-9956-82, Thermo Fisher Scientific, 1:75), anti-MRC1 (HPA004114, Atlas Antibodies, 1:200), anti-PSMA3 (HPA000905, Atlas Antibodies, 1:100).

DAB staining was performed using the BOND RXm polymer refine detection system (Leica, DS9800) and standard protocol. Primary antibodies were used as follows: anti-RRS1 (HPA060937, Atlas Antibodies, 1:1000), anti-NK2.2 (as above), anti-TFRC (136800, Thermo Fisher Scientific, 1:1200), anti-FTH1 (PA5257500, Thermo Fisher Scientific, 1:2000). Imaging was performed by whole slide scanning using Pannoramic scan II (3DHistech) with a 20X objective.

Perls’ Prussian Blue staining was performed to detect ferric iron deposits in tissue sections. Briefly, slides were deparaffinized, rehydrated, and incubated in freshly prepared staining solution consisting of a 1:1 mixture of 2% (w/v) potassium ferrocyanide and 2% (v/v) hydrochloric acid for 30 minutes at room temperature. Following incubation, sections were rinsed in distilled water, counterstained with nuclear fast red, dehydrated through graded alcohol, cleared in xylene, and mounted with coverslips. Stained slides were scanned using Pannoramic scan II (3DHistech) with a 20X objective.

Image analysis
Spectral unmixing and background subtraction of multiplexed QPTIFF images were performed using unstained reference slides in InFormV2.0.2, Akoya Biosciences. Tiled images were stitched in Qupath76 v0.4.3. Nuclear segmentation was performed using the Nuclei model of Cellpose 2.077 followed by expansion (estimated diameter of 14 pixels, expansion of up to 5 pixels) as implemented in the Qupath Cellpose extension v0.9.6. A Random Trees machine learning object classifier was trained in Qupath v0.5.1 on a training panel of representative stitched regions of 500*500µm from multiple slides. Proteomic regions of interest (P-ROIs) were manually annotated on the Opal slides with guidance from consecutive H&E sections. All downstream analysis were performed with percentage of cells expressing the marker in these P-ROIs. Statistical analyses were performed using GraphPad Prism. Differences among the three proteomic clusters were evaluated by one-way ANOVA with Tukey’s post hoc test for multiple comparisons. Clinical associations, including metastasis at diagnosis, relapse status, and 3-year disease-free survival (3Y DFS), were assessed using Welch’s t-test. Data are reported as mean ± SEM.

Quantitative analysis of DAB staining was performed using the pixelwise H-score method78 in QuPath v0.5.1. Proteomic regions of interest (ROIs) were manually annotated on the DAB slides as described earlier. Quantification was performed with thresholds for 1+, 2+, and 3+ staining intensities set at 0.15, 0.5, and 1.0, respectively. Comparisons between primary and relapse samples, as well as between primary and post-NACT samples, were performed in GraphPad Prism using unpaired t-tests.

General Study Design
All in vitro experiments were conducted in at least 3 independent biological replicates (n = 3) to ensure reproducibility. Technical stability in proteomic analysis was maintained through routine performance checks using standard HeLa reference samples at regular intervals. Sample acquisition order on the mass spectrometer was randomized. Investigators were not blinded to group allocation during clinical sample selection because tumor subtyping was required. However, proteomic data acquisition was automated, and subsequent clustering analyses were performed using unsupervised algorithms to ensure unbiased interpretation of the data.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this study, the authors undertake mass spectrometry profiling of a large cohort of EWS samples. They perform descriptive analyses of the proteomic data and identify some interesting findings. The strength of of the study is the large number of cases analyses for this rare entity. The weaknesses are detailed in comments below.\nMajor comments\n1. Sample and preparation details are lacking\nA. Bone sarcoma samples are often decalcified, a process which is known to impact the quality/quantity of biomolecules. Can you authors confirm if the samples were decalcified? If so, can the authors need to provide data detailing the impact of protein yields, protein IDs and protein quantification of decalcification compared to frozen or non-decalcified matched specimens in a subset of the cohort. Without this information it is difficult to assess how much of the data reflect artefacts introduced by the decalcification process. And therefore, how much of the generated proteomic data is actually representative of the disease.\nB. In the methods, the authors state that samples were dewaxed based on \"standard protocol\" - what is this standard protocol please provide details. Also it seems there is no reversal of crosslinks step, is this the case?\nC. Authors state that samples were macrodissected for 80% tumour cell content? Was this done for the tiny biopsies? Is it possible to macrodissect from tiny biopsies? Does the 1-4 FFPE sections of 6um thickness apply to biopsies as well?\n2. In general for every single section, there is a lot of descriptive analysis comparing different groups or clusters. For each of these sections and comparisons, there is a need to undertake orthogonal validation of the findings by immunohistochemistry. This includes Figure 2 different timepoints, in particular pre and post treatment, primary vs mets, Figure 3, different proteins expressed in different consensus clusters, Figure 4 different proteins associated with clinical parameters. Without IHC validation, it is not possible to ascertain if the proteomics findings are not just artefacts, particularly in the context of decalcified tissue.\n3. Figure 1d, authors state that these proteins and markers are highly expressed. Highly expressed is a relative term. Compared to what? This should it be compared to normal bone rather than just within an EWS cohort to demonstrate that the markers are highly expressed?\n4. The ESTIMATE algorithm was used several times in the manuscript, once to estimate stroma content and the other for immune cells. Can the author comment on the coverage of their protein data and the markers used by ESTIMATE. if the overlap is low, is it appropriate to use ESTMIATE for the proteomics data?\n5. Comparisons between primary and post-chemo surgical specimens. Because there are no surgery alone specimens (without chemo treatment), the authors cannot exclude the possibility that some of the protein changes seen are the result of surgery rather than chemotherapy. It is known that surgery leads to the activation of inflammation and immune related pathways some of which are similar to those seen in the authors dataset. The authors should consider this possibility.\n6. The authors make multiple claims which is not supported by the data, particularly lack of functional data to establish causation. These statements are pure conjecture so please remove them.\n\"Altogether, we propose that these proteins may confer treatment resistance via inhibition of chemotherapy-induced ferroptosis, and induction of immune response.\"\n\"Our findings suggest that inhibiting ferroptosis contributes to EWS chemo-resistance, and suggests the potential impact of the proteasome and ubiquitin proteolysis on antigen presentation and DNA damage response (DDR).\"\n7. All survival analyses, such as figure 3 and the analysis of the RNA data needs to be subjected to multivariable analysis which includes known prognostic/confounding factors to assess if the consensus clusters or proteins are independent prognostic variables.\n8. It is surprising that there is no discussion of the limitations of this study. This is a very descriptive, hypothesis generating study with no validation of findings in an independent cohort. Single institution studies are subject to selection bias. This is just one of several limitations that need to be considered in the discussion.\n9. Can the authors make clear how missing data was dealt with in the dataset? they report >10K proteins but each condition has ~7K, so how was missing data handled?", "reviewer_2": "The manuscript by Gordon et al. entitled ‘Proteomic landscapes of Ewing sarcoma unravel immunological regulation of tumor progression’ reports on a relatively large series of Ewing sarcoma (EwS) tumor samples that have been subjected to mass-spectrometry based proteomics analyses. The authors conduct a variety of subsequent bioinformatics analyses and compared (mainly unmatched) samples from treatment-naïve primary tumors and samples from metastatic lesions or relapses that are mainly post-treatment samples. These comparative and purely correlative analyses led the authors to conclude that ferroptosis might play a role in chemo-resistance. Further, they identified by various clustering analyses subgroups of primary tumors that may be associated with diverse signaling pathways and immune profiles. Some validation studies using various immunohistological markers suggested associations with metastasis at diagnosis, which led the authors to conclude that infiltration of the tumors by neutrophils may be associated with a worse patient outcome.\nWhile this study reports on a large series of samples with proteomic data, the study remains purely descriptive in nature and most conclusions are speculative and not underpinned by any functional experiment. Some of the conclusions appear overstated and the fact that some major prognostic subgroups could be also found in a published cohort based on bulk-level mRNA expression data somewhat undermines one of the major arguments of the authors to claim novelty and importance of proteomics for advancement of EwS research.\nApart from these general concerns, there are many major concerns that limit the enthusiasm of this Reviewer for this study:\n1) It is simply unacceptable that the supplementary tables contain patient-specific identifying criteria, such as the precise date of diagnosis, date of birth, date of death etc. These data, if published in that form would make it very easy to unambiguously identify patients, which is not in the interest of our patients. It is hard to believe that the authors have an IRB approval for this and this Reviewer strongly recommends to de-identify the patients and remove such (unnecessary) data from the tables. Then, the statement in the methods part of “all samples were anonymized as defined in the study protocol” would be valid. The patient-specific data given in the current table is the more so critical since Israel is a relatively small country (population-wise) and since EwS is a very rare disease, patient-specific information regarding date of birth, date of death, and Israel as a likely country of residence and the diagnosis of EwS, each and every patient can be easily identified (not only by close relatives). It is the first time this Reviewer sees such a data leak in a submitted paper, and frankly speaking, this is NOT ok.\n2) For almost all correlative associations of proteomics subgroups with patient parameters and other phenomenological findings it is unclear whether these events are the drivers or passengers of the observed phenotypes. For instance, the presence of neutrophils could be a mere expression of more necrotic tumor tissue within a given tumor due to higher proliferation rates that overwhelm the tumor’s capacity to form new vessels, which may lead then to higher rates of metastasis. As such, neutrophils are just an epiphenomenon of biological mechanisms that are as yet not defined or discovered. Also, in this instance, multiple comparisons of immune markers with phenotypes have been done. If corrected for multiple testing, the above-mentioned associated is likely rendered non-significant. Hence, subsequent conclusions are not underpinned by solid data.\n3) Similarly, many downstream pathway analyses yield only generic pathways potentially associated with metastasis. For instance, what is the exact meaning of a downregulation of ‘DNA-dependent DNA replication initiation’ in association with disease-free survival? What does that mean? How is this validated? And what do we truly lean from these associations? Are they driving the phenotype (DFS) or just epiphenomena?\n3) The identification of 13 proteins in overlap of “metastasis”, “relapse”, and poor “DFS” would have been a good start for follow-up, but again no meaning can be drawn from this (Fig. 4h).\n4) The association of immune scores with different proteomics subclusters of which one (cluster 2) is associated with a trend for worse outcome is interesting. Yet, as the result is not nominally significant, it remains unclear whether this is a trustworthy result and whether all subsequent explorations/speculations are worth being discussed in the current extension.\n5) As shown in Figures 1a/b, the tumor samples came from mostly unmatched primaries/metastases of different individuals with very different localizations. Some sites were only represented by a very few cases (such as pelvis and bone mets). Hence, some of the findings reported in this paper could be still subject of changes once larger sample series would be available. It is also unclear to what extent the findings on immune infiltrates etc. are a mere reflection of the anatomical site and or location of the tumor (soft-tissue versus bone).\n6) The statement that ferroptosis inhibition may be an event to overcome chemo-resistance (see abstract) appears as a strong overstatement and is sole based on a correlative observation that proteins involved in ferroptosis show high expression in post-chemotherapy samples. For these samples, it should be noted that the mere finding that the samples are from a post-chemotherapy condition is insufficient to conclude that these samples were chemo-resistant. In many patients, chemotherapy needs to be interrupted (e.g. due to cumulative toxicity) and is not per se a reflection of resistance. Also, all chemotherapies applied in EwS lead to cell death in a non-ferroptosis-dependent pathway. Hence, the upregulation of ferroptosis-related genes is not per se a reflection of any biology related to relevant cell death. Again, functional analyses are completely lacking.", "reviewer_3": "This is a very exciting study looking at the proteome of over 100 Ewing sarcoma tumor samples from 72 patients. To my knowledge this is the first study doing MassSpec-based analysis on Ewing sarcoma tumor tissues and hence it deserves attention. Ewing sarcoma although is a disease with high clinical heterogeneity, Ewing sarcoma tumors are genetically homogeneous. Transcriptome/epigenome studies have not yet revealed strong biomarkers that could explain clinical differences. Examining the proteome is imperative.\nWhile I am enthusiastic about this study, I do have a few concerns outlined below (not in a particular order).\n1. The treatment protocol of Ewing sarcoma is not accurately presented. Patients undergo neo-adjuvant chemotherapy, followed by surgery (in most cases) and/or radiotherapy (in some cases), which is followed by maintenance chemotherapy. As such Figure 1b is not accurate (therapy cycles indicated with a radiotherapy icon is wrong). Metastasis also occurs at initial diagnosis. Relapse and metastasis are not independent events (relapse can be localized and metastatic). Figures 1a,b at their current version they do not add anything to the story and could be omitted as well. If kept, they should be designed with higher precision and with some informative content.\nCalling surgery samples post-chemotherapy (and in some cases post-treament) samples is misleading, given that most of these patients continued to receive chemo after surgery.\n2. Surgery samples are known to be highly necrotic. Did the authors manage to dissect live cancer cells from these samples? How do they check for live cancer cells? Is the ESTIMATE algorithm run on dissected samples (MassSpec/protein level data)? It may be important to specifically indicate the purity of surgery samples on Supplementary Figure 1e.\n3. Lines 96-98: it is not a surprise that surgery samples show the lowest correlation with other time points given that these samples are highly necrotic. Could it be that most cells they profile are non-cancer cells (i.e. hematopoietic)?\n4. According to supplemental table 1b all surgery samples come from bad responders with 40% to 90% necrosis. It may be good that this point is discussed in the manuscript.\n5. EWSR1 protein is expressed in most cell types. Perhaps not a good marker for Ewing sarcoma. Can the fusion oncogene be detected with MassSpec?\n6. Similarly to the above comment, FLI1 is not expressed in Ewing sarcoma cancer cells. Does the FLI1 protein detected correspond to the fusion protein? Could it be that FLI1 protein detection is due to non-cancer cells in the tumor tissue (i.e. hematopoietic and/or endothelial cells)?\n7. Figure 1c. Age plot – dots do not need to be connected.\n8. Figure 1d and Supplementary Figure 1f (Lines 106-111). Could it be that the variation in the expression of the selected ‘markers’ could be explained by differences in tumor tissue purity?\n9. Lines 114, 121: ‘…post-treatment samples….’ Do the authors mean surgery samples (samples collected after neo-adjuvant chemo)?\n10. Lines 125-126: Cluster 4 genes may be downregulated at surgery due to necrosis (please see comments above).\n11. The presence of the EWSR1::ETS fusion oncogene is only confirmed in 75% of the patients included in this study. Given that Ewing sarcoma tumors have been misclassified in the past, have the authors confirmed by other means that the rest 25% are Ewing sarcoma tumors? This is important especially for the clustering shown in Figure 3a. Are these cases evenly distributed across the three clusters?\n12. The authors have conducted a number of different analysis:\nFigure 2: comparing samples from different time points\nFigure 3: unsupervised clustering of primary tumors. This analysis could be of relevenace/interesting, as it promises to identify disease biomarkers beyond presence/absrence of metastasis at initial diagnosis. However, the authors do not follow this up. The number of samples is also low.\nFigure 4: Supervised analysis of prognostic biomarkers\nFigure 5: Multiplexed imagining revealing neutrophil involvement and lymphoid cell exclusion in metastatic tumors.\nEach finding could be potentially interesting if followed up. Also, how these different analysis/findings could be linked?", "reviewer_4": "The authors here present a large proteomics dataset comprising primary, relapsed, and metastatic Ewing sarcoma samples. This is an important resource for the field and the data will be of high interest and useful for many investigators. A strength of this study is the large number of samples and inclusion of multiple samples per patient, and the novel contribution of clinically annotated, protein-level datasets for Ewing sarcoma patient samples that are scarce. While the correlative data and unbiased clustering are intriguing, the lack of functional or mechanistic data make this paper useful as a resource but the purely correlative observations preclude mechanistic conclusions about progression.\n• The enrichment of ferroptosis related genes post-therapy is interesting, as is the hypothesis that this would sensitize EwS cells to chemotherapy (Line 162). Functional experiments confirming this hypothesis that would make the conclusion more compelling, e.g. ferroptosis agonists/inhibitors, overexpression or knockdown of FTH1, -/+ chemotherapy treatment. Likewise the relationship between increased proteasomal protein expression and antigen presentation (Line 204) is intriguing, but not shown functionally. But again statements like Line 264 “Our findings suggest that inhibiting ferroptosis contributes to EWS chemo-resistance, and suggests the potential impact of the proteasome and ubiquitin proteolysis on antigen presentation and DNA damage response (DDR)” seem preliminary if based solely on these data.\n• Line 175: The authors state “Interestingly, these clusters reflected prognostic differences between clusters, with cluster 2 having a poorer prognosis than cluster 1 and cluster 3 (Fig. 3b). Kaplan-Meier survival analysis also agreed with other clinical parameters, indicating a significantly higher number of patients diagnosed with non metastatic disease in cluster 3, and highest number of metastatic patients, and relapse-presenting patients in cluster 2 (Fisher enrichment analysis, p-val<0.05; Supplementary Fig. 3c-d”. They state that those findings are significant due to a P<0.1, (p=0.0768) logrank test, it is not clear why p<0.1 is used as opposed to the traditional p<0.05 and if this was determined pre- or post-testing.\no Likewise Supplementary Fig. 2D only one curve has a Bonferroni adjusted p value <0.05 for EFS, but all are referred to as significant. Overall survival should probably be shown as well, which is not significant.\n• Line 377: The authors say that extracellular matrix proteins were removed for data analysis, as well as blood proteins. Can they comment on whether this is a technical necessity based on the processing pipeline, as ECM proteins can be expressed by Ewing sarcoma cells and in fact may correlate with important features such as EWS::FLI1 “low” states?\n• The frequent use of Y axes that do not begin at 0 (Fig 2D, 3F, S1D, S1E) sometimes over-inflates differences visually.\n• Line 98: Tumor purity was measured using the ESTIMATE method, but those authors comment that its use in sarcomas is problematic: Yoshihara et al. 2013 https://doi.org/10.1038/ncomms3612– “The dependency of ESTIMATE on infiltrating stromal and immune cells resulted in some limitations, such as the inability to accurately infer tumour cellularity of hematopoietic or stromal tumours (for example, leukaemia, sarcoma and gastrointestinal stromal tumours) because of the high and tumour-intrinsic expression of stromal- or immune-related genes.” Can the authors quantify tumor purity using a more Ewings tailored approach?\n• Line 384: for primary tumor consensus clustering the authors “averaged biological replicates”. Were these replicates taken from adjacent slides of the same region, or do they represent distinct geographic regions of the tumor? If so it would be interesting to see an analysis of the spatial heterogeneity of the primary tumor samples, as opposed to collapsing all regions together.\n• Figure 5B: Were all CD15+ cells called as neutrophils during multiplex immunofluorescence? It appears that other cell types, like MDSCs, can sometimes express CD15. Additional markers for each cell type could increase confidence in cell type calling. i.e by staining an adjacent section with additional markers. How confident are the authors that CD15 staining is exclusive to neutrophils in these samples? Likewise CD68 can mark osteoclasts, if any remained in decalcified bone tumor samples.", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Gordon et al. Response to Reviewers\nPoint by point response to reviewers\nReviewer #1:\nIn this study, the authors undertake mass spectrometry profiling of a large cohort of EWS samples. They\nperform descriptive analyses of the proteomic data and identify some interesting findings. The strength of\nthe study is the large number of cases analyses for this rare entity. The weaknesses are detailed in\ncomments below.\nWe thank the reviewer for the overall positive assessment of the work and the detailed evaluation of the\nmanuscript. In the revised version we answered all the reviewer’s concerns, as detailed below.\nMajor comments\n1. Sample and preparation details are lacking-\nA. Bone sarcoma samples are often decalcified, a process which is known to impact the quality/quantity\nof biomolecules. Can you authors confirm if the samples were decalcified? If so, can the authors need to\nprovide data detailing the impact of protein yields, protein IDs and protein quantification of decalcification\ncompared to frozen or non-decalcified matched specimens in a subset of the cohort. Without this\ninformation it is difficult to assess how much of the data reflect artefacts introduced by the decalcification\nprocess. And therefore, how much of the generated proteomic data is actually representative of the\ndisease.\nWe thank the reviewer for this comment. As indicated by the reviewer, some of the tissues in our cohort\nwere decalcified in the clinic before their embedding in paraffin. The decision regarding the decalcification\ndepends on the extent of bone tissue involvement. Previous studies have shown negligible impact of\ndecalcification on the proteome (e.g. Iglesias-Gato 2018), which is also in line with the fact that this\nprocedure is routinely followed by standard IHC. Potentially other biomolecules, such as nucleic acids,\nmetabolites and lipids are more sensitive. Nevertheless, following this reviewer’s comment we investigated\nthe potential impact of decalcification using several experimental and computational analyses.\n1. To examine the calcification impact experimentally, we excised mouse sternum and took one part of\nthe tissue for a decalcification procedure (24 h incubation in 4% formalin and 18hr incubation at 37C with\na plate on stirrer in EDTA (MOLDECAL), as performed in the clinic) and another part was directly fixed. We\nfollowed with sample preparation using the SP3 protocol, as described in the manuscript, and continued\nwith MS-based proteomic analysis. Overall, we identified a similar number of proteins and peptides in all\nsamples. The quantitative comparison of the protein expression levels showed an average correlation of\n0.96 between decalcified and non-decalcified tissue. A T-test to compare these two sample groups showed\na statistically significant difference in only 148 proteins, out of a total of 8562 identified proteins, which\ncorrespond to only 1.78% of the identified proteome. Importantly, since these samples do not originate\nfrom the same tissue block (since one of them was decalcified), we cannot attribute these changes to the\ndecalcification procedure. These results are presented in Reviewer Figure 1.\n2. Re-analysis of the patient derived proteomic data showed no difference in the number of identified\nproteins between decalcified and non-decalcified tumor samples focusing exclusively on primary samples\nto eliminate confounding effects from other tumor progression stages. (Reviewer Figure 2). Panel a is also\nadded to the revised manuscript as Supplementary Figure 1c.\n3. Examination of the proteomic differences between decalcified and non-decalcified samples showed\nno separation associated with the decalcification procedure.\nBased on all these analyses we are confident that there is no risk that our results were confounded by\nthis technical difference between samples.\nReviewer Figure 1: Experimental assessment of decalcification effects. a) H&E images of decalcified (left) and non-\ndecalcified mouse sternum. b) Dot plot compares the number of proteins identified in non-decalcified and decalcified mouse\ntissues. c) Pearson correlations of mouse samples show a range from 0.93 to 0.98. d) Volcano plot shows significantly\nchanging proteins between non-decalcified and decalcified sternum mouse tissue (permutation-based FDR <0.05 s0= 0.1).\nReviewer Figure 2: Comparing\ndecalcified and non-decalcified\nsamples. a) Dot plot shows the\nnumber of proteins identified from\ndecalcified and non-decalcified\nprimary EWS samples. b) Pearson\ncorrelations of all primary samples\nare depicted in the color bar,\nrepresenting a range from 0.7 to 0.96.\nB. In the methods, the authors state that samples were dewaxed based on \"standard protocol\" - what is\nthis standard protocol please provide details. Also it seems there is no reversal of crosslinks step, is this\nthe case?\nWe apologize for the missing information. The detailed protocol is now added to the revised version. Each\nFFPE tumor sample was deparaffinized with two xylene washes, followed by rehydration through a series\nof ethanol washes: twice in 100% ethanol, once in 96%, and once in 70%. The samples were then placed in\ndistilled water (DDW) until scraping.\nIn addition, we added the de-crosslinking step which includes tissue lysate incubation for 90 min in 95°C.\nC. Authors state that samples were macrodissected for 80% tumour cell content? Was this done for the\ntiny biopsies? Is it possible to macrodissect from tiny biopsies? Does the 1-4 FFPE sections of 6um thickness\napply to biopsies as well?\nMacrodissection was performed to isolate regions of 1 mm2 or larger. In our experience, we can extract\n~0.5 µg protein from ~1 mm2 tissue (of ~6 µm thickness), and we only need 2 µg for protein digestion to\nreach the presented analytical depth. Therefore, the size of the biopsy does not limit our ability to perform\nthe analysis. Of note, EWS biopsies are typically surgical biopsies, due to their location and the fact that\nthese are pediatric patients. Finally, the main limitation is not the size of the biopsy, but its cellularity, as\nwe carefully dissected only tumor regions.\n2. In general for every single section, there is a lot of descriptive analysis comparing different groups or\nclusters. For each of these sections and comparisons, there is a need to undertake orthogonal validation\nof the findings by immunohistochemistry. This includes Figure 2 different timepoints, in particular pre and\npost treatment, primary vs mets, Figure 3, different proteins expressed in different consensus clusters,\nFigure 4 different proteins associated with clinical parameters. Without IHC validation, it is not possible to\nascertain if the proteomics findings are not just artefacts, particularly in the context of decalcified tissue.\nWe thank the reviewer for this important comment. We invested major efforts to validate our findings using\northogonal approaches, including immunohistochemistry, immunofluorescence and functional validation.\na) IHC of FTH1 shows significantly higher expression in post neoadjuvant-treatment (NACT) samples\ncompared to pre-treatment ones, in agreement with the proteomics data. Quantification of the IHC signal\nshows higher expression in the proteomic region of interest (ROI) and when quantified across all cancer\ncell (NKX2.2+) regions and across the entire slide (Reviewer Figure 3 and new Figure 3a-b).\nReviewer Figure 3: IHC of FTH1 comparing primary and post neoadjuvant chemotherapy samples. a)\nRepresentative IHC images of FTH1 in matched primary and post-neoadjuvant chemotherapy samples. Dot plots\nshow the quantification of FTH1 in primary and post-neoadjuvant chemotherapy samples. Error bars represent the\nSEM values. Quantification was based on proteomic ROI, NKX2.2 positive cells, and whole tissue.\nb) IHC of TFRC (transferrin receptor 1) shows significantly higher expression in post-NACT treatment\nsamples compared to pre-treatment ones. Quantification of the IHC signal shows higher expression in the\nproteomic ROIs, when quantified across all cancer cell regions, and across the entire slide (Reviewer Figure\n4 and new Figure 3c-d). TFRC is a key mediator of cellular iron uptake, facilitating the transport of\ntransferrin-bound iron into the cell. FTH1 acts as a counter-regulatory protein by sequestering intracellular\niron, limiting ROS generation and lipid peroxidation availability. By staining for both TFRC and FTH1, we\naimed to capture the balance between iron uptake and storage, which is critical in determining ferroptosis\nsensitivity or resistance.\nReviewer Figure 4: IHC of TFRC comparing primary and post-NACT samples. a) Representative IHC images of TFRC in\nprimary and post neoadjuvant chemotherapy samples. Dot plots present the quantification of TFRC in primary and post\nneoadjuvant chemotherapy samples. Error bars represent the SEM values. Quantification was based on the proteomic\nROI, NKX2.2 positive cells, and whole tissue.\nc) IHC of RRS1, which was found to be more highly expressed in the relapsed tumors relative to the\nprimary tumors, showed higher nuclear staining in the proteomic ROIs of relapsed tumors. These results\nare presented in Reviewer Figure 5 and new Figure 2c-d.\nReviewer Figure 5: IHC of RRS1 comparing primary and local relapse samples. a) Representative IHC images of RRS1\nin primary and local relapse samples. b) Quantification of RRS1 in primary and local relapse samples. Error bars\nrepresent the SEM values. An unpaired T-test was used, and quantification was based on proteomic ROI.\nd) Multiplexed imaging was also performed to validate specific proteins in the consensus clusters and\nin relation to patient prognosis. These validations were performed in the context of the immunological\nchanges between clusters, and included PSMA3 and HLA-DR, which showed higher expression in cluster 3,\nin addition to neutrophil markers (MPO and CD15, cluster 2), and macrophage and T-cell markers (CD68,\nMRC1, CD8 and CD4 cluster 3).\nAltogether, these experiments validated the expression changes in all tested proteins. In addition, they\nshowed the protein localization in the tissue, thereby providing additional information.\nThese results are partially presented in Reviewer Figure 6 and in the revised manuscript in Figure 4h,\nFigure 5g, Figure 6d-i.\nReviewer Figure 6: Multiplexed immunofluorescence of immune-related proteins. a) The proportion of all\nneutrophils (only MPO+ and both MPO+CD15+) in three clusters was calculated as a percentage of the total cell\npopulation. b) PSMA3 staining in the three EWS clusters of primary tumors shows highest expression in cluster3.\nImages and statistics are added in Figure 5. c) Macrophage staining with anti-CD68, anti-HLADR, and anti-MRC1.\nQuantification of cell counts is added for each cell subset based on co-staining of the markers at the single cell level.\nData are shown as mean ± SEM. d) T-cell staining in EWS consensus clusters. e) Neutrophil staining with anti-CD15\nand MPO.\na. b.\nc.\nd. e.\ne) Beyond the staining validation we also further investigated the functional importance of ferroptosis\nto chemotherapy response in EWS. To this end, we tested the impact of the ferroptosis inducer RSL3 (GPX4\ninhibitor). We used the recently published EWS chemotherapy-resistance cell line model based on A673\ncells (Scarborough et al, 2020). Initial analysis showed that A673-resistant cells are less sensitive to\ntreatment with doxorubicin (as expected) and RSL3. Next, we treated parental and chemo-resistant cells\nwith increasing concentrations of doxorubicin and increasing concentrations of RSL3. Modeling the effects\nof these drugs using SynergyFinder (Ianevski et al. 2022) showed strong synergism between these drugs,\nsuggesting the importance of ferroptosis in mediating chemo-resistance. These new results are shown in\nReviewer Figure 7 and added to the revised manuscript in new Figure 3e-h.\nReviewer Figure 7: Enhancing doxorubicin sensitivity through ferroptosis induction. a) Dose-response curve\nfor doxorubicin in A673 parental and resistant cells. IC50 values were calculated using PRISM software with\nnonlinear regression curve fitting. b) Comparison of IC50 values for doxorubicin when treated alone and with\nRSL3 in A673 parental and resistant cells. c-d) Doxorubicin and RSL3 synergy models for A673 resistant (c) and\nparental (d) cell lines.\n3. Figure 1d, authors state that these proteins and markers are highly expressed. Highly expressed is a\nrelative term. Compared to what? This should be compared to normal bone rather than just within an EWS\ncohort to demonstrate that the markers are highly expressed?\nThroughout the paper the reference to the expression level is relative to other samples in the cohort.\nSpecifically in figure 1d we refer to the proteins’ expression within the sample, relative to other proteins in\nthe same sample. The intensity itself does not provide an accurate measure of absolute abundance, but it\nprovides a good estimate. To remove any potential confusion, we removed these statements from the\nrevised version.\nNotably, in the analysis of clinical samples of EWS it is impossible to use normal control. The bone is not\ntrue control since the cancer did not initiate in the bone. The cell of origin is still controversial, but the most\naccepted assumption is that these are mesenchymal stem cells. Analyzing these cells from tissue sections\nis not possible, and a comparison to cultured MSCs is expected to show mostly technical artifacts due to\nthe sample state.\n4. The ESTIMATE algorithm was used several times in the manuscript, once to estimate stroma content\nand the other for immune cells. Can the author comment on the coverage of their protein data and the\nmarkers used by ESTIMATE. if the overlap is low, is it appropriate to use ESTMIATE for the proteomics data?\nThe proteomic data covered 105/141 and 85/141 proteins of the immune and stromal ESTIMATE\nsignatures, respectively. To ensure that ESTIMATE analyses are reliable, we also compared the immune\nscores determined by ESTIMATE to those determined by two additional algorithms, ConsensusTME and\nxCell. We found a high correlation between these algorithms (Reviewer Figure 8) and therefore retained\nthe previous results.\nReviewer Figure 8: Correlation analysis of the TME scores derived from xCell, ConsensusTME and ESTIMATE\nalgorithms. Scatter plots compare the xCell TME scores (left) and ConsensusTME scores (right) with the ESTIMATE\nscores across tumor samples.\n5. Comparisons between primary and post-chemo surgical specimens. Because there are no surgery alone\nspecimens (without chemo treatment), the authors cannot exclude the possibility that some of the protein\nchanges seen are the result of surgery rather than chemotherapy. It is known that surgery leads to the\nactivation of inflammation and immune related pathways some of which are similar to those seen in the\nauthors dataset. The authors should consider this possibility.\nWe thank the reviewer for this question. As indicated above, in the case of EWS the biopsies are also taken\nunder surgery, we therefore do not expect any such artefacts in the data.\n6. The authors make multiple claims which is not supported by the data, particularly lack of functional data\nto establish causation. These statements are pure conjecture so please remove them.\n\"Altogether, we propose that these proteins may confer treatment resistance via inhibition of\nchemotherapy-induced ferroptosis, and induction of immune response.\"\n\"Our findings suggest that inhibiting ferroptosis contributes to EWS chemo-resistance, and suggests the\npotential impact of the proteasome and ubiquitin proteolysis on antigen presentation and DNA damage\nresponse (DDR).\"\nWe apologize for some over-stated claims. These are removed from the text. We adapted the writing to\nthe addition of functional validation of the ferroptosis results.\n7. All survival analyses, such as figure 3 and the analysis of the RNA data needs to be subjected to\nmultivariable analysis which includes known prognostic/confounding factors to assess if the consensus\nclusters or proteins are independent prognostic variables.\nWe thank the reviewer for this important comment. Following this reviewer’s comment, we performed an\nadditional multivariate analysis to examine the contribution of other clinical confounders to the cluster\nseparation. These analyses included the metastatic state (metastases at diagnosis), the tumor location in\nthe pelvis, and the surgical treatment. The only significant factor was the metastatic state at diagnosis.\nThese new analyses are presented in Reviewer Figure 9 and Figure 5 of the revised manuscript.\nCox regression could not be performed with the publicly available RNA data since other clinical parameters\nwere not provided.\nReviewer Figure 9: Cox regression analysis.\n8. It is surprising that there is no discussion of the limitations of this study. This is a very descriptive,\nhypothesis generating study with no validation of findings in an independent cohort. Single institution\nstudies are subject to selection bias. This is just one of several limitations that need to be considered in\nthe discussion.\nWe added a discussion of the study limitations to the revised version.\n9. Can the authors make clear how missing data was dealt with in the dataset? they report >10K proteins\nbut each condition has ~7K, so how was missing data handled?\nThe Methods section includes a detailed description of the data processing and the imputation. From the\ndata of more than 10K proteins, we removed ECM proteins and removed proteins that did not appear in\nat least 70% of the samples. This filtration ensured that we do not over-impute the data and have a\nrelatively low number of missing values per sample. Next, we imputed these values by generating an\nartificial normal distribution with a downshift of 1.8 standard deviations from the overall distribution,\nand a width of 0.5 of the distribution. This is a very common way to impute missing values when missingness\nis a result of low expression, as in our case. This information is provided in the Methods section “Data\npreprocessing and statistical analysis”.\nReviewer #2:\nThe manuscript by Gordon et al. entitled ‘Proteomic landscapes of Ewing sarcoma unravel immunological\nregulation of tumor progression’ reports on a relatively large series of Ewing sarcoma (EwS) tumor samples\nthat have been subjected to mass-spectrometry based proteomics analyses. The authors conduct a variety\nof subsequent bioinformatics analyses and compared (mainly unmatched) samples from treatment-naïve\nprimary tumors and samples from metastatic lesions or relapses that are mainly post-treatment samples.\nThese comparative and purely correlative analyses led the authors to conclude that ferroptosis might play\na role in chemo-resistance. Further, they identified by various clustering analyses subgroups of primary\ntumors that may be associated with diverse signaling pathways and immune profiles. Some validation\nstudies using various immunohistological markers suggested associations with metastasis at diagnosis,\nwhich led the authors to conclude that infiltration of the tumors by neutrophils may be associated with a\nworse patient outcome.\nWhile this study reports on a large series of samples with proteomic data, the study remains purely\ndescriptive in nature and most conclusions are speculative and not underpinned by any functional\nexperiment. Some of the conclusions appear overstated and the fact that some major prognostic\nsubgroups could be also found in a published cohort based on bulk-level mRNA expression data somewhat\nundermines one of the major arguments of the authors to claim novelty and importance of proteomics\nfor advancement of EwS research.\nApart from these general concerns, there are many major concerns that limit the enthusiasm of this\nReviewer for this study:\nWe thank the reviewer for the thorough review of the manuscript. In the revised version we added more\nvalidations, including a functional validation of the importance of ferroptosis to chemoresistance and\naddressed all other comments.\n1) It is simply unacceptable that the supplementary tables contain patient-specific identifying criteria, such\nas the precise date of diagnosis, date of birth, date of death etc. These data, if published in that form\nwould make it very easy to unambiguously identify patients, which is not in the interest of our patients. It\nis hard to believe that the authors have an IRB approval for this and this Reviewer strongly recommends\nto de-identify the patients and remove such (unnecessary) data from the tables. Then, the statement in\nthe methods part of “all samples were anonymized as defined in the study protocol” would be valid. The\npatient-specific data given in the current table is the more so critical since Israel is a relatively small country\n(population-wise) and since EwS is a very rare disease, patient-specific information regarding date of birth,\ndate of death, and Israel as a likely country of residence and the diagnosis of EwS, each and every patient\ncan be easily identified (not only by close relatives). It is the first time this Reviewer sees such a data leak\nin a submitted paper, and frankly speaking, this is NOT ok.\nWe apologize for this mistake. All patient data is anonymized and approved by the IRB committee.\nHowever, our error was to include the dates and age in the table. These were removed from the\nsupplementary tables. We agree that in Israel this error is more critical, and therefore thank the reviewer\nfor this comment. However, we should add that not all patients are Israeli, but some of them arrive here\nfor the treatment; and not all patients were diagnosed or treated in one center throughout the entire\ncourse of their disease.\n2) For almost all correlative associations of proteomics subgroups with patient parameters and other\nphenomenological findings it is unclear whether these events are the drivers or passengers of the\nobserved phenotypes. For instance, the presence of neutrophils could be a mere expression of more\nnecrotic tumor tissue within a given tumor due to higher proliferation rates that overwhelm the tumor’s\ncapacity to form new vessels, which may lead then to higher rates of metastasis. As such, neutrophils are\njust an epiphenomenon of biological mechanisms that are as yet not defined or discovered. Also, in this\ninstance, multiple comparisons of immune markers with phenotypes have been done. If corrected for\nmultiple testing, the above-mentioned associated is likely rendered non-significant. Hence, subsequent\nconclusions are not underpinned by solid data.\nIn this manuscript we do not claim that the observed results drive the phenotype, but in many of these\ncases, we are the first to show the phenotype. We are the first to show the patient clusters, the first to\nshow their association with the immune profiles and the connection between the immune profiles and the\nproteome. Notably, this is all done in patient samples, which represent the true clinical variability.\nUnderstanding the causal relations between all these results will be the subject of many more years of\nresearch and should not be expected within one manuscript.\nTo address this reviewer’s comment about the association between neutrophils and necrosis, we performed\na pathological analysis of necrosis in the cancer regions of the tumors included in the imaging, as typically\ndone in the clinical routine. We correlated the percentage of necrosis in the tumors with the overall\nneutrophil counts by staining (Reviewer Fig. 10). Overall, we found a positive correlation, which is driven\nby one sample with very high neutrophils and very high necrosis. Based on these results we cannot\ndetermine that neutrophil infiltration is driven by tumor necrosis.\nReviewer Figure 10: Correlation between tumor\nnecrosis and neutrophil counts. Tumor necrosis was\ndetermined by pathological examination and neutrophil\ncounts were determined by tissue staining.\n3) Similarly, many downstream pathway analyses yield only generic pathways potentially associated with\nmetastasis. For instance, what is the exact meaning of a downregulation of ‘DNA-dependent DNA\nreplication initiation’ in association with disease-free survival? What does that mean? How is this\nvalidated? And what do we truly lean from these associations? Are they driving the phenotype (DFS) or\njust epiphenomena?\nEnrichment analyses typically show the overall expression of specific pathways, as defined by external,\nwell-accepted databases (e.g. gene ontology and KEGG). They are not expected to prove causal relations\nbut show that these pathways are more highly expressed and suggest that they may be more active.\nBeyond that, causal relations can be suggested based on previously published literature. To specifically\naddress this reviewer’s example, “DNA-dependent DNA replication initiation” suggests higher cell\nproliferation. Association between higher cell proliferation and low disease-free survival is a very-well\nestablished link in most cancer types. In this type of research, which provides a large data resource, it is\nimpossible to follow-up on every point. In our work we decided to emphasize the ferroptosis and immune-\nrelated results but did not follow-up on other results. Nevertheless, we believe that presenting the entire\ndataset is valuable for the community and could be a good starting point for other studies.\n3) The identification of 13 proteins in overlap of “metastasis”, “relapse”, and poor “DFS” would have been\na good start for follow-up, but again no meaning can be drawn from this (Fig. 4h).\nThe protein network presented in Figure 4 includes multiple neutrophil proteins, among them MPO, AZU1,\nLCN2, PRTN3, ITGAM, LRG1. Indeed, we follow up on the neutrophils. In the revised version we added\nstaining of neutrophils. As mentioned above, additional follow-up studies are ongoing but are beyond the\nscope of this research.\n4) The association of immune scores with different proteomics subclusters of which one (cluster 2) is\nassociated with a trend for worse outcome is interesting. Yet, as the result is not nominally significant, it\nremains unclear whether this is a trustworthy result and whether all subsequent explorations/speculations\nare worth being discussed in the current extension.\nWe appreciate the reviewer’s interest in these results. Following the reviewers’ requests we repeated these\nanalyses on additional tumors, and added staining of additional immune markers, including MPO, CD4,\nCD8, HLA-DR, in addition to CD15 and CD68 that were also included in the previous submission. The results\nshow some significant differences that reflect a clear difference between clusters. These results are also\nreinforced by Cox regression analysis that was added in the revised version. Analyzing such a rare type of\ncancer is challenging due to the limited size of available cohorts. Nevertheless, the analyses show clear\ndifferences. The neutrophil results are added to the revised manuscript in Figure 4 and Supplementary\nFigure 4. The macrophage and T-cell results are added as Figure 6 and Supplementary Figure 6. These\nresults are also presented here as Reviewer Figure 6 (above).\nBeyond the staining, we also used the xCell algorithm to deconvolute the proteomic data and predict the\nnumber of immune cells in each sample, based on the proteomics data. These results suggest higher levels\nof neutrophils in cluster 2 compared to the two other clusters (Reviewer Figure 11 and Figure 6c-d).\nReviewer Figure 6: Neutrophil and macrophage\nestimation using xCell. Dot plots of cell type xCell's\nscores for neutrophils and M2 macrophages. Each dot\nrepresents a patient. Significance was calculated with\nANOVA and Bonferroni multiple comparisons test.\n5) As shown in Figures 1a/b, the tumor samples came from mostly unmatched primaries/metastases of\ndifferent individuals with very different localizations. Some sites were only represented by a very few cases\n(such as pelvis and bone mets). Hence, some of the findings reported in this paper could be still subject of\nchanges once larger sample series would be available. It is also unclear to what extent the findings on\nimmune infiltrates etc. are a mere reflection of the anatomical site and or location of the tumor (soft-tissue versus bone).\nWe would like to emphasize that the entire study of immune infiltrates was performed on the primary\ntumors before any treatment. We agree that drawing conclusions from the different progression states\nwould be difficult, and therefore we did not do that. Overall, the tumors in the presented cohort originate\nfrom different bone locations, as detailed below (Reviewer Figure 12).\nReviewer Figure 7: Tumor locations across\nclusters. The stacked chart shows the\ndistribution of tumor locations across three\nconsensus clusters. Each cluster is divided\ninto the primary tumor anatomical\nregions: lower limb, upper limb, pelvis,\nribs, vertebra, skull, and sacrum.\n6) The statement that ferroptosis inhibition may be an event to overcome chemo-resistance (see abstract)\nappears as a strong overstatement and is sole based on a correlative observation that proteins involved in\nferroptosis show high expression in post-chemotherapy samples. For these samples, it should be noted\nthat the mere finding that the samples are from a post-chemotherapy condition is insufficient to conclude\nthat these samples were chemo-resistant. In many patients, chemotherapy needs to be interrupted (e.g.\ndue to cumulative toxicity) and is not per se a reflection of resistance. Also, all chemotherapies applied in\nEwS lead to cell death in a non-ferroptosis-dependent pathway. Hence, the upregulation of ferroptosis-\nrelated genes is not per se a reflection of any biology related to relevant cell death. Again, functional\nanalyses are completely lacking.\nWe thank the reviewer for this comment. We acknowledge that there are cases in which patients stop\nchemotherapy prematurely. However, in our cohort we included only patients who received the full\nregimen and all post-NACT tumors had a poor response to neoadjuvant chemotherapy (<90% necrosis).\nThis information is now added to the revised Methods section.\nIn the revised version we also increased the number of validations, with a specific focus on ferroptosis.\nFirst, we validated the increased expression level of ferroptosis related proteins using orthogonal IHC\nstaining. Second, we used an EWS chemo-resistance cell line model and showed that inductions of\nferroptosis with RSL3, a GPX4 inhibitor has a synergistic effect with doxorubicin (DOXO). These results show\nthe functional importance of ferroptosis in chemoresistance. The results are presented here in Reviewer\nFigure 7 (above) and presented in the manuscript in New Figure 3.\nReviewer #3 :\nThis is a very exciting study looking at the proteome of over 100 Ewing sarcoma tumor samples from 72\npatients. To my knowledge this is the first study doing MassSpec-based analysis on Ewing sarcoma tumor\ntissues and hence it deserves attention. Ewing sarcoma although is a disease with high clinical\nheterogeneity, Ewing sarcoma tumors are genetically homogeneous. Transcriptome/epigenome studies\nhave not yet revealed strong biomarkers that could explain clinical differences. Examining the proteome is\nimperative.\nWe thank the reviewer for the positive evaluation of the work. In the revised version, we answered all\nquestions and comments and added a substantial number of validations.\nWhile I am enthusiastic about this study, I do have a few concerns outlined below (not in a particular\norder).\n1. The treatment protocol of Ewing sarcoma is not accurately presented. Patients undergo neo-adjuvant\nchemotherapy, followed by surgery (in most cases) and/or radiotherapy (in some cases), which is followed\nby maintenance chemotherapy. As such Figure 1b is not accurate (therapy cycles indicated with a\nradiotherapy icon is wrong). Metastasis also occurs at initial diagnosis. Relapse and metastasis are not\nindependent events (relapse can be localized and metastatic). Figures 1a,b at their current version they do\nnot add anything to the story and could be omitted as well. If kept, they should be designed with higher\nprecision and with some informative content.\nCalling surgery samples post-chemotherapy (and in some cases post-treament) samples is misleading,\ngiven that most of these patients continued to receive chemo after surgery.\nWe appreciate this comment, and we corrected the illustration to better reflect the treatment course. The\n“post-treatment” samples are indeed surgical samples after neoadjuvant chemotherapy (NACT), and the\npatient receive additional cycles after surgery. To clarify this point, in the revised manuscript we corrected\nthe illustration in Figure 1 (Reviewer Figure 13), we added the necessary explanation in the cohort\ndescription and changed the writing throughout the manuscript from “post-treatment” to “post-NACT”.\nFigure 8: Schematic illustration of patient treatment and tumor locations.\n2. Surgery samples are known to be highly necrotic. Did the authors manage to dissect live cancer cells\nfrom these samples? How do they check for live cancer cells? Is the ESTIMATE algorithm run on dissected\nsamples (MassSpec/protein level data)? It may be important to specifically indicate the purity of surgery\nsamples on Supplementary Figure 1e.\nWe agree that necrotic regions are much more substantial in surgical samples after chemotherapy. We\ntherefore performed H&E staining of all regions and followed with a careful pathological examination.\nNext, we carefully dissected these regions. Following the reviewer suggestion, we color coded (orange) the\nESTIMATE tumor purity of post-neoadjuvant chemotherapy samples in Supplementary Figure 1f (Reviewer\nFigure 14). Tumor purity is lower in some of them, but the differences are not substantial.\nReviewer Figure 14: Tumor purity score of EWS samples. The dot plot presents the Tumor-PurityScore for each\nsample, assessing the cancer percentage in each tumor sample based on ESTIMATE. Post-neoadjuvant\nchemotherapy samples are colored in orange.\n3. Lines 96-98: it is not a surprise that surgery samples show the lowest correlation with other time points\ngiven that these samples are highly necrotic. Could it be that most cells they profile are non-cancer cells\n(i.e. hematopoietic)?\nWe agree that the marked changes upon treatment are not surprising. Following the previous comment\nabout the ESTIMATE tumor score of the surgical samples, the pathological examination of the tumors, and\nthe fact that we see similar expression level of known EWS markers (e.g. LINGO1, EWSR1, FLI1, CD99,\nNKX2.2), we have no reason to think that we do not analyze EWS cells. In the revised version we also\nincluded several IHC images that are used to compare pre-treatment samples and surgical post-treatment\nsamples (Reviewer Figures 3 and 4, above, and Figure 3a, c). We also stained for the EWS marker NKX2.2\nthe same regions in consecutive slides, to ensure that we only included EWS cancer regions in our analyses\nReviewer Figure 15 shows NKX2.2 staining of all post-neoadjuvant chemotherapy samples.\nReviewer Figure 15: IHC images show NKX2.2 expression in post-NACT samples.\n4. According to supplemental table 1b all surgery samples come from bad responders with 40% to 90%\nnecrosis. It may be good that this point is discussed in the manuscript.\nWe thank the reviewer for this comment. We added the following explanation to the results section line\n75: “Among those, the cohort included 62 primary biopsy samples used initially for tumor diagnosis, nine\npost-neoadjuvant chemotherapy surgical samples with poor response (<90% necrosis).”\n5. EWSR1 protein is expressed in most cell types. Perhaps not a good marker for Ewing sarcoma. Can the\nfusion oncogene be detected with MassSpec?\nWe agree that EWSR1 is not a specific EWS marker. Unfortunately, we could not search for the fusion\nprotein, since every patient has a slightly different translocation, and the exact site is not routinely tested\nin the clinic. Since this is not a major focus of this manuscript, we did not think it is useful to sequence all\npatients at this point. Furthermore, we used several other clinically approved markers to ensure that there\nare no classification errors.\n6. Similarly to the above comment, FLI1 is not expressed in Ewing sarcoma cancer cells. Does the FLI1\nprotein detected correspond to the fusion protein? Could it be that FLI1 protein detection is due to non-\ncancer cells in the tumor tissue (i.e. hematopoietic and/or endothelial cells)?\nFLI1 is not expressed as highly as other markers, such as NKX2.2, and the expression level also varies\nsubstantially across samples. Since we always selected the dissected regions under the microscope, we are\nconfident that we are analyzing EWS regions. We cannot exclude having some contamination of immune\ncells and blood vessels, as we also report 80% purity. Following this reviewer’s comment, we examined the\ncorrelation between FLI1 expression level and tumor purity and found no correlation (Reviewer Figure 16).\nThis result is added in Supplementary Figure 1h.\nReviewer Figure 16: Correlation between FLI1 expression and\ntumor purity. \n7. Figure 1c . Age plot – dots do not need to be connected.\nWe removed the lines from this plot.\n8. Figure 1d and Supplementary Figure 1f (Lines 106-111). Could it be that the variation in the expression\nof the selected ‘markers’ could be explained by differences in tumor tissue purity?\nFollowing this reviewer’s question, and as indicated above, we know that the tissues are ~80% pure,\naccording to the ESTIMATE results. Examination of the correlation between the expression level and the\ntumor purity score shows no correlation with purity, and in some cases very low correlation is found\n(Reviewer Figure 17). Overall, we think that the variable expression level that we see is not directly linked\nto purity but is also affected by other factors that impact their expression level. Notably, variable expression\nof these markers is also often seen by IHC staining, suggesting that there are various levels of regulation,\nwhich are not the focus of this research. These results are added in Supplementary Figure 1.\nReviewer Figure 9: Pearson correlation\nbetween EWS markers and tumor purity score.\nEach plot displays the Pearson correlation of\none marker with the ESTIMATE tumor purity\nscore. Each dot represents an EWS sample.\n9. Lines 114, 121: ‘…post-treatment samples….’ Do the authors mean surgery samples (samples collected\nafter neo-adjuvant chemo)?\nYes, post-treatment samples are surgical samples after neo-adjuvant chemotherapy. To clarify this point\nwe changed the description to post-NACT samples throughout the manuscript.\n10. Lines 125-126: Cluster 4 genes may be downregulated at surgery due to necrosis (please see comments\nabove).\nThe surgical samples post-neoadjuvant chemotherapy were dissected from cancer cell regions and not\nfrom necrotic areas. As indicated above, we carefully isolated these regions, and the proteomic data shows\nthe expression of the EWS markers and the calculated tumor purity.\n11. The presence of the EWSR1::ETS fusion oncogene is only confirmed in 75% of the patients included in\nthis study. Given that Ewing sarcoma tumors have been misclassified in the past, have the authors\nconfirmed by other means that the rest 25% are Ewing sarcoma tumors? This is important especially for\nthe clustering shown in Figure 3a. Are these cases evenly distributed across the three clusters?\nAll cases were confirmed by NKX2.2 staining. We validated all patients with missing clinical information,\nto be sure that there are no misclassifications (Reviewer Figure 18). We could not repeat genomic analyses\ndue to ethical constraints.\nReviewer Figure 18: EWS validation by NKX2.2\nvalidation. IHC images showing NKX2.2 expression in\nprimary tumor samples (first two rows) and lung\nmetastasis samples (rows 3-4) from patients with\nunknown EWSR1-FLI1 translocation status.\n12. The authors have conducted a number of different analysis:\nFigure 2: comparing samples from different time points\nFigure 3: unsupervised clustering of primary tumors. This analysis could be of relevenace/interesting, as it\npromises to identify disease biomarkers beyond presence/absence of metastasis at initial diagnosis.\nHowever, the authors do not follow this up. The number of samples is also low.\nFigure 4: Supervised analysis of prognostic biomarkers\nFigure 5: Multiplexed imagining revealing neutrophil involvement and lymphoid cell exclusion in\nmetastatic tumors.\nEach finding could be potentially interesting if followed up. Also, how these different analysis/findings\ncould be linked?\nWe appreciate this reviewer’s comment. Typically, in studies that present large amounts of data, it is\nimpossible to follow-up on every result, and it might also make the manuscript more difficult to grasp.\nHere, we decided to focus on two main aspects, the ferroptosis and the immune characterization. In the\nrevised manuscript, we added substantial validation to each part, to strengthen our claims.\n1. Ferroptosis- we validated the expression of selected proteins highly expressed in post-NACT samples\n(FTH and TFCR) using IHC staining. These results confirm our findings and show the expression in the cancer\ncell regions (Reviewer Figures 3 and 4, above). These results are also added to the revised manuscript in\nFigure 3.\n2. Ferroptosis- we tested the impact of the ferroptosis inducer RSL3 (GPX4 inhibitor) on cell viability of\nthe recently published EWS chemotherapy-resistance cell line model based on A673 cells. Initial analysis\nshowed that A673-resistant cells are less sensitive to treatment with doxorubicin (as expected) and RSL3.\nNext, we treated parental and chemo-resistant cells with increasing concentrations of doxorubicin and\nincreasing concentrations of RSL3. Modeling the effects of these drugs using SynergyFinder software\nshowed strong synergism between these drugs, showing the importance of ferroptosis in mediating\nchemo-resistance (Reviewer Figure 7, above). These results are also added to the revised manuscript in\nFigure 3.\n3. EWS immune landscape- We enlarged our immune marker panel to be more confident about the\ncell identity. To this end, we included CD4, CD8, MPO, HLA-DR beyond the previous staining of CD3,\nCD68, and CD15 (Reviewer Figure 6, above, and Manuscript Figures 4 and 6 and Supplementary Figures 4 and\n6).\nWe also changed a bit the flow of the manuscript to improve the connection between the prognostic\nmarkers and the tumor cluster.\nReviewer #4:\nThe authors here present a large proteomics dataset comprising primary, relapsed, and metastatic Ewing\nsarcoma samples. This is an important resource for the field and the data will be of high interest and useful\nfor many investigators. A strength of this study is the large number of samples and inclusion of multiple\nsamples per patient, and the novel contribution of clinically annotated, protein-level datasets for Ewing\nsarcoma patient samples that are scarce. While the correlative data and unbiased clustering are intriguing,\nthe lack of functional or mechanistic data make this paper useful as a resource but the purely correlative\nobservations preclude mechanistic conclusions about progression.\nWe thank the reviewer for the positive evaluation of the importance of our work. This manuscript does not\naim to provide a mechanism of EWS progression. Mechanistic studies in clinical samples are inherently\nchallenging due to the inability to perturb human systems directly and the limited availability of late-stage\ntumor samples in clinical settings. One of the key strengths of our study lies in its ability to capture true\nhuman variability within a physiological and immunological context, which complements insights gained\nfrom in-vitro and in-vivo models. Nevertheless, we made substantial effort to add functional validations\nand strengthen the orthogonal validations by immunohistochemistry and immunofluorescence imaging.\n• The enrichment of ferroptosis related genes post-therapy is interesting, as is the hypothesis that this\nwould sensitize EWS cells to chemotherapy (Line 162). Functional experiments confirming this hypothesis\nthat would make the conclusion more compelling, e.g. ferroptosis agonists/inhibitors, overexpression or\nknockdown of FTH1, -/+ chemotherapy treatment. Likewise, the relationship between increased\nproteasomal protein expression and antigen presentation (Line 204) is intriguing but not shown\nfunctionally. But again, statements like Line 264 “Our findings suggest that inhibiting ferroptosis\ncontributes to EWS chemo-resistance and suggests the potential impact of the proteasome and ubiquitin\nproteolysis on antigen presentation and DNA damage response (DDR)” seem preliminary if based solely\non these data.\nFollowing this important comment, in the revised manuscript we added substantial validation. Beyond the\nIHC validations of ferroptosis-related proteins, FTH1 and TFRC, we also further investigated the functional\nimportance of ferroptosis to chemotherapy response in EWS. To this end, we tested the impact of the\nferroptosis inducer RSL3 (GPX4 inhibitor) on cell viability using the recently published EWS chemotherapy-\nresistance cell line model based on A673 cells. Initial analysis showed that A673-resistant cells are less\nsensitive to treatment with doxorubicin (as expected) and RSL3. Next, we treated parental and chemo-\nresistant cells with increasing concentrations of doxorubicin and increasing concentrations of RSL3.\nModeling the effects of these drugs using SynergyFinder software showed strong synergism between these\ndrugs, showing the importance of ferroptosis in mediating chemo-resistance. These results are added to\nthe revised manuscript as new Figure 3. The results are also presented in Reviewer Figure 4,5, and 7\n(above).\nIn addition, we toned down some of the speculative statements.\n• Line 175: The authors state “Interestingly, these clusters reflected prognostic differences between\nclusters, with cluster 2 having a poorer prognosis than cluster 1 and cluster 3 (Fig. 3b). Kaplan-Meier\nsurvival analysis also agreed with other clinical parameters, indicating a significantly higher number of\npatients diagnosed with non-metastatic disease in cluster 3, and highest number of metastatic patients,\nand relapse-presenting patients in cluster 2 (Fisher enrichment analysis, p-val<0.05; Supplementary Fig.\n3c-d”. They state that those findings are significant due to a P<0.1, (p=0.0768) logrank test, it is not clear\nwhy p<0.1 is used as opposed to the traditional p<0.05 and if this was determined pre- or post-testing.\nWe apologize for this error. Reanalysis showed a significant p-value. Furthermore, we added Cox regression\nanalysis that shows significant association with survival (Reviewer Figure 19).\nReviewer Figure 19: Survival analyses of EWS consensus clusters. a) Kaplan-Meier survival curve for three clusters shows\nsignificant differences in 3 years DFS (p=0.028). Cluster 2 shows the lowest survival probability. Additionally, pairwise\ncomparisons using the Log-Rank test with BH adjustment revealed cluster 1 vs. cluster 2 (p=0.035), cluster 1 vs. cluster 3\n(p=0.927), and cluster 2 vs. cluster 3 (p=0.078). b) Cox regression results showing cluster 2 (HR: 3.9562, p=0.0437) and\nmetastasis at diagnosis (HR: 4.5863, p=0.0192) significantly predict poorer survival. Pelvic disease and surgery are not\nsignificant.\n-Likewise Supplementary Fig. 2D only one curve has a Bonferroni adjusted p value <0.05 for EFS, but all\nare referred to as significant. Overall survival should probably be shown as well, which is not significant.\nLike the previous result, we omitted the word “significant” from the cases that have a higher corrected p-\nvalue. Of note, overall survival is largely impacted by the patient follow-up time. Newer patients with short\nfollow-up often reduce the statistical significance of these analyses. Therefore, initially we did not include\nthese analyses. Following this reviewer’s request, we added this result to Supplementary Figure S3\n(Reviewer Figure 20).\nReviewer Figure 20: Clinical association of\nferroptosis-related genes using mRNA\nEWS DB. a) Kaplan Meier survival analysis\nof a publicly available mRNA expression\nanalysis of EWS (Dirksen dataset, n = 85)\nshows shorter event-free survival for\ntumors with high GPX4, FTH1, and FTL\nexpression. b) Kaplan Meier survival\nanalysis of a publicly available mRNA\nexpression analysis of EWS (Dirksen\ndataset, n = 85) shows shorter overall\nsurvival for tumors with high GPX4, FTH1,\nand FTL expression.\n• Line 377: The authors say that extracellular matrix proteins were removed for data analysis, as well as\nblood proteins. Can they comment on whether this is a technical necessity based on the processing\npipeline, as ECM proteins can be expressed by Ewing sarcoma cells and in fact may correlate with\nimportant features such as EWS: FLI1 “low” states?\nWe removed the ECM proteins and blood proteins as they often vary due to the technical variability in\ntissue dissection and often do not reflect the cancer cell phenotype itself. This allows normalization of the\nentire data to ensure more accurate sample ratio calculation. We agree that ECM proteins may have\nimportant roles, but in the case of these complex tissues, we cannot be sure of the source of ECM proteins.\n• The frequent use of Y axes that do not begin at 0 (Fig 2D, 3F, S1D, S1E) sometimes over-inflates\ndifferences visually.\nWe disagree with this comment, since the intensity values do not start at zero, but the detection limit is\naround Log2 intensity of ~9. Therefore, there is no meaning to starting the y axis at zero.\n• Line 98: Tumor purity was measured using the ESTIMATE method, but those authors comment that its\nuse in sarcomas is problematic: Yoshihara et al. 2013 https://doi.org/10.1038/ncomms3612&#x2013;\n“The dependency of ESTIMATE on infiltrating stromal and immune cells resulted in some limitations, such\nas the inability to accurately infer tumour cellularity of hematopoietic or stromal tumours (for example,\nleukaemia, sarcoma and gastrointestinal stromal tumours) because of the high and tumour-intrinsic\nexpression of stromal- or immune-related genes.” Can the authors quantify tumor purity using a more\nEwings tailored approach?\nWe thank the reviewer for this comment. Unfortunately, we did not find an EWS-specific tool. Nevertheless,\nto address the reviewer’s comment, we compared ESTIMATE to other tools- ConsensusTME and xCell, and\nobtained similar results in the assessment of immune infiltration (Reviewer Figure 8, above).\nOf note, if all these tools overestimate stromal components due to expression of stromal protein expression\nby the cancer cells, we can speculate that the actual tumor purity is higher than reported. These values are\nnot a part of any downstream analysis.\n• Line 384: for primary tumor consensus clustering the authors “averaged biological replicates”. Were\nthese replicates taken from adjacent slides of the same region, or do they represent distinct geographic\nregions of the tumor? If so it would be interesting to see an analysis of the spatial heterogeneity of the\nprimary tumor samples, as opposed to collapsing all regions together.\nOur decision to average samples from the same tumors was intended to be more strict and avoid increasing\nthe clusters with biological repeats of the same patients. This decision was also based on our observation\nthat most repeats from the same patient highly correlated. Nevertheless, following the reviewer’s\ncomment we investigated the consensus clustering including all tested primary tumor samples. Optimal\nclustering was achieved with four clusters, instead of three, but the main difference was that cluster 1 split\ninto two clusters, and the rest remained almost identical (Reviewer Figure 21).\nReviewer Figure 21: Comparing\nnon-averaged consensus clusters and averaged\nconsensus clusters. Alluvial plots illustrate the\ncorrespondence between clusters identified\nusing averaged (Cluster_Avg) and non-\naveraged (Cluster_nonAvg) of the primary\nEWS samples. Each flow represents the\ndistribution of samples between clusters. Most\nsamples maintain their original cluster, while\nCluster 1 from the averaged data splits into\ntwo subclusters (1a and 1b) in the non-\naveraged data.\n• Figure 5B: Were all CD15+ cells called as neutrophils during multiplex immunofluorescence? It appears\nthat other cell types, like MDSCs, can sometimes express CD15. Additional markers for each cell type could\nincrease confidence in cell type calling. i.e by staining an adjacent section with additional markers. How\nconfident are the authors that CD15 staining is exclusive to neutrophils in these samples? Likewise CD68\ncan mark osteoclasts, if any remained in decalcified bone tumor samples.\nWe thank the reviewer for the important comment. In response, we expanded and refined our immune\npanel to ensure greater confidence in cell identity and specificity. Neutrophils are now labeled using both\nCD15 and MPO markers and are referred to as MPO-positive, with or without CD15 expression.\nMacrophages are stained with HLA-DR, MRC1, and CD68, while T-cell staining has been separated into\nCD8+ and CD4+ populations. This largely expanded the immune panel and increased the specificity. Due to\na limited number of sections from previously stained tumor samples, we performed all staining on a new\nset of samples to ensure robust and consistent results. Using this sample panel, we found a significantly\nhigher number of CD68+ macrophages in cluster 3 to be associated with patient prognosis, and a higher\nexpression level in consensus cluster 3. We also show a higher number of MPO+CD15+ and MPO+\nneutrophils in patients with poorer prognosis and in patients from consensus cluster 2.\nAll these analyses recapitulated the trends of the results presented in the previous version but added the\nmore accurate cell identity. The results are presented in the revised manuscript in Figure 4, Figure 6 and\nSupplementary Figures 4 and 6. The results are also presented here in Reviewer Figure 6 (above).\nReviewer #5:\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is\npart of the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nWe thank the reviewer for her/his contribution to our manuscript review.\nPoint-by-Point Response to Reviewers\nReviewer #1 (Remarks to the Author):\nIn this revised version of the manuscript, the authors have addressed some of the points\npreviously raised but not others.\nPoints that have been addressed\n1. Calcified vs decalcified samples - the authors have adequately addressed this issue\n2. Details of sample prep and data analysis methodology - the authors have now provided details\nof sample prep and data analysis methods.\nWe thank the reviewer for the positive evaluation of our analyses.\nThere are some responses that are inadequate or problematic\n1. Immune and stromal deconvolution - The authors provide information in their rebuttal that\nonly half to two-thirds of the genes that comprise the immune deconvolution of ESTIMATE is\nrepresented in their dataset. They then go on to say that they are confident that the results are\nreal because the use of xCell and ConsensusTME give similar results. I'm afraid this logic is flawed.\nConsistency of the different algorithms is meaningless if they all have poor coverage, i.e. they are\nall giving similarly incorrect deconvolution estimates especially since there is high overlap in the\nmarkers used in each of the algorithms. I also agree with Reviewer 4 and question the suitability\nof these algorithms for cells of stromal origin. The authors did attempt to validate some of the\nfindings by IHC or multiplex IF, but this is problematic too, see next point.\nWe understand this reviewer’s concerns but do not fully agree about the potential impact of these\nanalyses on any of the results, since these analyses only provide a general description of the data\nand are not used for any downstream analysis. Nevertheless, we addressed this point in\nadditional analytical way that use experimental data, not only computational. The major points\nare as follows:\n1. Concordance across orthogonal methods: This reviewer is concerned that the different\nanalytical tools use the same gene sets, and therefore, their concordance does not reflect on their\ncorrectness. While we agree in principle with the reviewer, it is worth noting that the\ncomputational frameworks differ significantly. ESTIMATE utilizes ssGSEA-based enrichment of\nimmune/stromal signatures; xCell is based on enrichment of 64 immune/stromal cell types; and\nConsensusTME aggregates multiple tools and curated gene sets for consensus scoring.\nImportantly, the high concordance across these methodologies suggests that these estimates\ncapture robust trends in immune and stromal content, supporting that our bulk EWS proteomic\nsamples are sufficiently pure for downstream analyses. Moreover, due to the high purity, we did\nnot perform any data correction. Therefore, overall, these results are only intended to provide an\noverview of the data, but do not have any impact on the analyses.\n2. Applicability of ESTIMATE and related tools to proteomic data: We acknowledge that\nESTIMATE, ConsensusTME, and xCell were developed for transcriptomic data and require careful\ninterpretation when applied to proteomic datasets. Nonetheless, their use has been extended to\nmass spectrometry-based studies in sarcomas, for instance, Tang, S., Wang, Y., Luo, R., et al. Nat\nCommun 2024 (https://www.nature.com/articles/s41467-024-45306-y) applied ESTIMATE to\ninfer tumor purity and stromal/immune content in a large-scale pan-sarcoma proteomic cohort,\nsupporting the relevance of such approaches in our study context.\n3. Independent evaluation of tumor purity: To examine the ESTIMATE tumor purity derived\nfrom our proteomic data, we made use of the immunofluorescence staining data of some of the\ntumors in our cohort. We correlated the ESTIMATE scores with IHC staining in the same tumor\nROIs used for mass spectrometry. Specifically, we assessed tumor cell content by quantifying\nNKX2.2⁺ cells. We observed a positive correlation between NKX2.2⁺ cell fraction and estimated\ntumor purity (Pearson r = 0.41, p =0.034, 28 primary tumors available, Reviewer Figure 1). Some\nof the tumors show lower %NKX2.2+ cells, but these are outliers and may be related to the fact\nthat these sections are ~10 µm apart.\n4. Discussion of the ESTIMATE limitations: To avoid overinterpretation, we have now revised\nthe manuscript in the following ways: i) We explicitly state the limitations of partial gene-set\ncoverage in the Methods section. ii) We emphasize that stromal and immune scores derived from\ndeconvolution are supplementary findings, not used as the basis for core conclusions. iii) We\nclarify that these tools provide approximate estimates and do not accurately quantify purity or\ncell-type composition.\nReviewer Figure 1: Purity score correlation with NKX2.2.\nScatter plot showing the fraction of NKX2.2⁺ cells (measured by\nIF in the MS-analyzed ROI) versus the tumor purity score\ninferred from ESTIMATE. A positive correlation (Pearson r = 0.41,\np = 0.03) supports the use of the ESTIMATE method.\n2. IHC or IF \"validation\" - I want to thank the authors for taking the revisions seriously by doing a\nrange of different IHC and multiplexed IF validation. However, many of the findings are actually\nnot significant and therefore not consistent with the mass spec data. What is more concerning is\nthat the authors make several claims in the text that the findings are indeed validated even when\nthe quantitative differences are not statistically significant, e.g the PMSA quantification (Figure 5)\nand many of the immune cell quants (Figure 6 and several of the supp figures) are not statistically\ndifferent between the groups.\nWe accept the reviewer’s feedback that our validation experiments should be interpreted\ncarefully. To clarify, some of the discrepancy between the proteomics data and the staining\nanalysis is attributed to the fact that staining quantification was performed on the entire stained\nslides rather than the proteomic ROI. This was intended to provide a more comprehensive view\nof these proteins, expanding beyond the regional data. In addition, validations were performed\non consecutive slides, suggesting that some discrepancies might occur. Lastly, staining could not\nbe performed on all slides, but only on a subset of the samples, thus limiting the statistical power\nof these analyses.\nTo address the reviewer’s concern, we repeated all multiplexed imaging analyses using only the\nsame regions of interest (ROIs) used for proteomics analysis, rather than whole tumor sections\nas in the initial version. This ROI-matched strategy more accurately reflects the biological cases\nanalyzed by mass spectrometry.\nMacrophage analysis: Using one-way ANOVA followed by pairwise comparisons, we\ncharacterized CD68⁺ macrophage subpopulations. These region-wise analyses show the following\nresults: CD68⁺HLA-DR⁺ macrophages, were more abundant in Cluster 3 compared to Cluster 2\n(ANOVA p = 0.05, pairwise p = 0.06; Reviewer Figure 2a-c). All HLA-DR⁺ cells (irrespective of CD68\nexpression), showed significantly higher levels in Cluster 3 tumors than Cluster 2 (ANOVA p = 0.04,\npairwise p = 0.05; Reviewer Figure 2a-c), further supporting the enhanced immunogenic profile\nof this subgroup. Other macrophage populations, including total CD68⁺, CD68⁺MRC1⁺, and\nCD68⁺MRC1⁻HLA-DR⁻ did not reach statistical significance, but showed a consistent trend of\nincreased macrophages in Cluster 3. The results of CD68+, HLADR+, and CD68+HLADR+ are\nincluded in the revised manuscript in Figure 6d.\nT cell analyses: CD4⁺ T-cell infiltration was elevated in Cluster 3 compared to Cluster 1 and 2\n(ANOVA p = 0.015; pairwise comparisons p = 0.0152 and p = 0.085, respectively; Reviewer Figure\n2d), suggesting a more immunologically active microenvironment.\nNeutrophil analysis: Using one-way ANOVA followed by pairwise comparisons, we observed that\nMPO⁺ CD15⁺ neutrophils were significantly more abundant in Cluster 2 tumors compared to\nClusters 1 and 3 (ANOVA p = 0.013, Pairwise p = 0.01, p = 0.058, respectively; Reviewer Figure\n2e), supporting the proposed myeloid-enriched and immunosuppressive nature of this cluster.\nThese results are presented in the revised manuscript in Figure 6d-i.\nReviewer Figure 2: Multiplexed\nimmunofluorescence validation of\nimmune cell subsets in the proteomic\nROIs and their association with EWS\nconsensus clusters. a-c) The proportion of\nCD68+, HLA-DR+CD68+, and HLA-DR+ cells\nin three clusters were calculated as a\npercentage of the total cell population in\nthe pROIs. Statistical analysis was\nperformed using one-way ANOVA to\nassess overall differences, followed by\npost-hoc pairwise comparisons between\nclusters. Data are shown as mean ± SEM.\nd) same for CD4+ T-cells. e) same for\nMPO⁺CD15⁺ neutrophils.\nBeyond the analysis of the EWS clusters, we repeated the investigation of the associations with\nclinical outcome. Similarly, we focused the repeated analysis on the proteomic ROIs rather than\nthe entire slides. Clinical outcome correlations revealed that CD68⁺ macrophages and their\nsubpopulations were significantly associated with improved prognosis. Total CD68⁺ cell density\nwas significantly higher in non-relapsing tumors and in tumors from patients with favorable 3-\nyear disease free survival (Welch’s t-test, p= 0.015, p= 0.02; Reviewer Figure 3a-b). When\nevaluating all HLA-DR⁺ cells, irrespective of CD68 status, we observed a significant association\nwith both relapse status and 3-year DFS (Welch’s t-test, p= 0.054, p= 0.001; Reviewer Figure 3c-\nd). The CD68⁺HLA-DR⁺ macrophage population was also significantly higher in tumors with 3-year\nDFS, and the sample trend was also seen for the association with relapse. The CD68+MRC1+\nmacrophages population was very small and showed a similar trend (Reviewer Figure 3e-h).\nThese associations provide independent, orthogonal support for the immune-related survival\nsignatures identified in our proteomic dataset.\nThese updated analyses closely mirrored the trends observed in our prior whole-slide analyses.\nbut provide a better validation of the proteomic results. We speculate that the lower statistical\nsignificance in the whole slide analysis resulted from the limited cohort used for immunostaining.\nBut the more focused analyses provide cleaner and statistically significant signal.\nReviewer Figure 3: Multiplexed immunofluorescence\nvalidation of immune cell subsets in the proteomic\nROIs and their association with clinical outcomes. a-\nb) Analysis of total CD68⁺ macrophages in patients\nwith and without relapse, and with and without 3-year\ndisease-free survival (3Y DFS). Welch’s t-test; mean ±\nSEM. c-d) Same for all HLA-DR+ cells. e-f) Same for\nCD68+HLA-DR+ macrophages. g-h)Same for\nCD68+MRC1+ macrophages.\nTo further assess PSMA3 expression, we quantified it across the proteomics ROIs and the NKX2.2+\nregions, marking EWS tumor cells. Across all approaches, we observed a trend toward higher\nPSMA3 intensity in Cluster 3 tumors compared with Cluster 2, consistent with the proteomic\nfindings. However, statistical significance was not achieved in any individual comparison, possibly\ndue to the limited sample size. We could not validate other 20S proteasomal subunits due to the\nlack of available antibodies. Given the limited statistical significance of these analyses, we\nremoved these results from the revised manuscript.\n3. Functional validation - In response to my and other reviewers’ comments about the lack of\nfunctional work on ferroptosis and resistance, the authors have done some drug testing in a\npaired cell line experiment to demonstrate that combination with GPX4 inhibitor is able to\novercome drug resistance. These experiments are unfortunately really superficial and lacking in\ndepth. They raise more questions than answers, for instance, does the cell line pair have similar\nproteomic changes in the ferroptosis pathways as the tissues, what are GPX4 levels in the cell\nlines, biochemical analysis that ferroptosis has been inhibited was not done, and doing it in one\ncell line and one drug is really insufficient to draw conclusions in contemporary cancer research.\nWe regret that the reviewer was not satisfied with our previous functional analyses. The study\nfocused on the proteomics of the clinical cohort, and typically such studies do not include, in\naddition, very deep investigation of potential regulatory pathways. Furthermore, recapitulating\nall the ferroptosis-related proteomic results in cultured cells should not be expected as the\nconditions are markedly different (iron and oxygen levels, growth in culture, etc.), and cell lines\nnever fully mimic tumor samples. Nevertheless, we wished to address the reviewer comments\nand added the following functional analyses related to the ferroptosis pathway: (i) We included\nan additional novel chemo-resistant EWS model from the RDES cell line, recently established in\nour lab (not yet published). (ii) We analyzed chemoresistance and ferroptosis sensitivity in 3D\nspheroids. iii) We extended the analyses to include two chemotherapies and two ferroptosis\ninducers, on two cell line model (four cell lines in total). iv) We quantified expression of\nferroptosis related proteins. v) We quantified non-heme iron levels in the tissue samples.\nUnfortunately, we did not succeed in performing BODIPY analysis of ferroptosis, since we\nobtained highly variable signal depending on the exact treatment duration. Since we moved the\nfunctional analyses to 3D models, it is difficult to match the 2D (BODIPY) and 3D (sensitivity)\nconditions. We realized that these experiments would require extensive optimization, which is\nbeyond the scope of this study.\nOur key findings are summarized below:\n1. Establishment of a novel RDES chemo-resistance model: To model acquired\nchemoresistance, we adopted the previously published protocol by Scarborough et al. (2020),\nwhich generated a chemotherapy-resistant A673 line. RDES cells were treated with five full cycles\nof vincristine-doxorubicin-cyclophosphamide (VDC) and etoposide-cyclophosphamide (EC). This\nprocedure models the standard of care given to patients with EWS, which consists of VDC and IE\n(ifosfamide-etoposide) cycles. Since ifosfamide requires metabolic activation, we substituted\nifosfamide with cyclophosphamide (as in the original protocol by Scarborough et al.), since these\nare analog compounds. The cells were treated at concentrations of IC30/IC40 for each drug in the\ncombination VDC/EC for four days. Recovery time ranged from five to ten days. Control clones\nwere maintained in the vehicle control medium. The detailed description of the model is included\nin the Methods section.\n2. Establishment of 3D spheroid models based on A673 and RDES cell lines: Following the\nreviewers’ concerns about the physiological relevance of 2D cultures, we established 3D spheroid\nmodels of A673 and RDES, including parental and chemo-resistant cells. We followed previously\npublished protocols by Ceranski et al. (2025) and Li et al. (2021) with adaptations to our systems.\nSpheroids were formed in HPLM media in round-bottom ultra-low attachment microplates and\nwere allowed to grow for 48 hours prior to drug exposure. Cells were treated with etoposide or\ndoxorubicin, and after 60 hours, spheroid viability was quantified using CellTiter-Glo® 3D\naccording to the manufacturer's instructions. Luminescence values were background-subtracted\nand normalized to vehicle control to calculate percent viability. Dose-response curves and IC₅₀\nvalues were calculated in GraphPad Prism using nonlinear regression. We observed significant\nshifts in IC₅₀ values between parental and resistant cell lines. In both models, resistance indexes\n(IC₅₀ resistant/IC₅₀ parental) were higher than 3.2 for both etoposide and doxorubicin (Reviewer\nFigure 4). These results confirm the successful establishment of a chemo-resistant phenotype and\nthe 3D spheroid models. These results are presented in the revised manuscript in Figure 3g.\nReviewer Figure 4: Chemotherapy resistance\nprofiling in 3D spheroid models of Ewing\nsarcoma. Dose-response curves for doxorubicin\nand etoposide in parental and resistant A673 and\nRDES spheroids. The x-axis shows log₁₀ drug\nconcentration; the y-axis shows cell viability\nrelative to untreated controls. IC₅₀ values\ndemonstrate a greater than 3.27-fold increase in\nresistance compared to parental cells, confirming\nthe successful establishment of resistant models.\nSpheroid viability was quantified using CellTiter-\nGlo® 3D, and IC₅₀ values were calculated for each\ncondition using nonlinear regression in GraphPad\nPrism.\n3. Combination treatments of chemotherapy and ferroptosis inducers: To strengthen the\nfunctional validation and address the reviewers’ concerns, we expanded our testing to include\ntwo chemotherapeutic agents (doxorubicin and etoposide) and two ferroptosis inducers (RSL3\nand erastin). We applied fixed concentrations of RSL3 (20nm for RDES, 30nm for A673) or erastin\n(0.33um for RDES, 0.4um for A673), which were determined in preliminary experiments, in\ncombination with a dose range of chemotherapy to examine the potential of combined therapy\nin overcoming chemotherapy resistance. The spheres were grown for 48 hours, and the\ncombination treatment was applied for additional 60 hours. We determined the IC₅₀ values of\neach chemotherapeutic drug and performed a sum of squares F test to assess the statistical\ndifferences in IC₅₀ values between chemotherapy alone and each chemotherapy plus ferroptosis\ninducer combination. For A673 cells, both parental and resistant cells showed a significant\ndecrease in doxorubicin IC₅₀ when combined with either RSL3 or erastin. For RDES cells, both\nparental and resistant cells demonstrated a significant decrease in etoposide IC₅₀ when\ncombined with either RSL3 or erastin. Additionally, RDES-resistant cells showed a significant\ndecrease in doxorubicin IC₅₀ when combined with erastin. These reductions indicate a\npotentiation of chemotherapy efficacy by ferroptosis inducers, particularly in the resistant cell\nlines (Reviewer Figure 5a, Figure 3h).\nReviewer Figure 5: Ferroptosis-based\ncombination therapies reduce\nchemotherapy resistance in RDES and\nA673 cells. a) Bar plots show\nchemotherapy IC₅₀ values in A673 and\nRDES spheroids treated with\ndoxorubicin or etoposide alone, or in\ncombination with RSL3 or erastin (fixed\nconcentrations: 20/30 nM or 0.33/0.4\nμM for RDES/A673, respectively). The\nsum-of-squares F test determined\nstatistical significance; significant\ndecreases in IC₅₀ indicate enhanced\nefficacy. Data are shown for both\nparental and resistant lines. b+c)\nRepresentative images of RDES and\nA673 parental and resistant spheroids\ntreated with chemotherapy alone or in\ncombination with RSL3 or erastin.\nTo further strengthen the CellTiter-Glo® 3D findings, we included representative spheroid images\nfor both A673 and RDES parental and resistant cells. Images were taken for DMSO control and for\ntwo different concentrations of chemotherapy, either alone or in combination with RSL3 or\nerastin. This allowed for a direct visual comparison of spheroids treated with the various drug\ncombinations in both models (Reviewer Figure 5b). Consistent with the IC₅₀ results,\nchemotherapy combined with a ferroptosis inducer produced visibly smaller spheroids compared\nto chemotherapy alone. The effects were more pronounced in RDES spheroids and were added\nto Fig. 3i. Representative images from A673 spheroids are provided in the Supplementary\nFig. 4a. These expanded experiments, involving multiple agents and combinations, further\nreinforce our hypothesis that inducing ferroptosis may increase chemotherapy response.\n4. Proteomic analysis of parental and chemo-resistant RDES cells showed significant\ndifferences in several ferroptosis-related proteins (from FerrDB). Reviewer Figure 6 shows the\nintensities of four proteins that present significant differences between the parental and chemo-\nresistant cells. The resistant cells expressed lower levels of SLC7A11, the cystine/glutamate\nantiporter critical for glutathione biosynthesis, thioredoxin reductase (TXNRD1), and\nperoxiredoxin (PRDX1). All these proteins harbor critical antioxidant activities. The transferrin\nreceptor (TFRC) level increases in the resistant RDES cells, similar to our observation in post-NACT\nclinical samples, suggesting that the cells may have higher potential for iron uptake. GPX4 did not\nshow significant differences between cells. We speculate that in the 2D in-vitro conditions, the\nimpact of growth conditions with low iron and high oxygen, and the relatively short durations of\nthese treatments, might not accurately represent the real human tumor conditions. To fully\nunderstand the relationship between ferroptosis induction and the protein levels of each\nregulator, it would be necessary to run extensive research of multiple growth conditions (with\nfully optimized iron and oxygen concentrations), and study multiple experimental durations to\nlink the proteomic changes, lipid peroxidation, and cell death. We believe that full mechanistic\nelucidation is outside the scope of this manuscript, and the in-depth investigation of the cell line\nmodels does not sufficiently contribute to the understanding of the ferroptosis involvement. We\ntherefore did not include these results in the revised manuscript.\nReviewer Figure 6: Expression of ferroptosis related proteins. We\nperformed proteomic analysis of RDES cells (parental and\nresistant cells) and compared the levels of ferroptosis-related\nproteins. We crossed the proteomics data with FerrDb and\nselected the proteins that are directly associated with the\niron metabolism and the redox state. Selected results are\npresented.\n5. Quantification of non-heme iron levels in primary and post-NACT samples\nTo examine the nature and localization of iron accumulation, we performed Perls’ Prussian Blue\nstaining, a well-established histochemical technique that specifically detects non-heme ferric iron\n(Fe³⁺), typically stored within ferritin and hemosiderin complexes. This method does not detect\nheme-bound iron, such as that found in hemoglobin, and thus does not stain red blood\ncells. As a result, Perls’ staining is particularly effective at distinguishing true intracellular iron\nstorage from hemorrhage-related extracellular iron or heme-bound iron derived from red blood\ncells. Quantitative analysis of Perls’ staining within the proteomic ROIs revealed a significantly\nhigher percentage of positive area in post-NACT tumors compared to primary tumors (p = 0.0127).\nReviewer Figure 7: Quantification of non-heme iron levels in primary and post-NACT samples. a) Representative\nIHC images of Pearl Prussian blue in primary and post-NACT samples. Scale bar, 50 μm. b) Perl's Prussian blue\nquantification in primary and post-NACT samples. Error bars represent the SEM values. A welch’s T-test was used,\nand quantification was based on the proteomic ROI.\nTo support these observations, we have now included representative Perls’ Prussian Blue staining\nimages from matched primary and post-NACT tumors (Reviewer Fig. 7), with examples chosen to\nreflect staining intensities near the cohort median rather than extreme phenotypes. While we\ncannot completely exclude some contribution of intratumoral hemorrhage in post-treatment\nsamples, we note that the upregulation of both FTH1 and TFRC at the protein level was observed\nconsistently across the post-NACT cohort in our MS data and validated by quantitative IHC\nstaining. Together with Perls’ staining, these findings strengthen the conclusion that iron\nregulatory pathways are actively modulated following chemotherapy.\n4. Removal of ECM proteins - In providing more detail to the data analysis section, the authors\nrevealed that ECM proteins were removed before analysis. In response to reviewer 4, they said\n\"they often vary due to the technical variability in tissue dissection and often do not reflect the\ncancer cell phenotype itself. This allows normalization of the entire data to ensure more accurate\nsample ratio calculation\". This is very unusual, especially given that the authors have enriched for\n80% tumour cell content. It is not the norm to remove ECM proteins before analysis, can the\nauthors provide some data to show what happens to the data if you include ECM proteins?\nCould the ECM proteins be differentially impacted by decalcification. It has been shown in soft\ntissue sarcomas that ECM proteins are consistent and representative of histological subtypes and\nthere is similar evidence in proteomic data from other bone tumours like chordoma as well.\nWe thank the reviewer for raising this point. The ECM proteins were removed prior to initial\nnormalization because we aim to minimize artifacts introduced by bone and soft tissue ECM and\nfocus on cancer cell-intrinsic biology. We referred to the ECM as a potential artifact because it is\nlargely affected by the technical accuracy of the dissection of the tissue, rather than the inherent\nextent of ECM secreted by the cancer cells. In addition, we cannot know which cells deposit the\nECM, and any claim about the ECM protein expression in these tissues would be inaccurate. Of\nnote, it is very common to eliminate groups of genes/proteins in omics data. For example,\nremoval of various outliers or constant proteins is common in RNA gene expression analyses, and\nremoval of potential contaminants, such as keratins and blood proteins is common in proteomics\nresearch. Importantly, the raw, unprocessed data is freely available for additional future analyses,\nin case readers are interested specifically in the involvement of ECM in EWS. Nevertheless, we\naddressed this reviewer’s request and examined the impact of the removal of ECM proteins on\nthe overall results. We performed several comparisons between the datasets with and without\nECM.\n1. Correlation of sample-sample distances: We calculated the Euclidean distances between all\npossible sample pairs (162 samples, resulting in N(N–1)/2 = 13,041 pairs) for both the ECM-\nincluded and ECM-filtered datasets. The Pearson correlation between the two distance matrices\nwas R = 0.995, indicating that sample relationships were nearly identical (Reviewer Figure 8a).\nWhile most distances were nearly identical, a small number of sample pairs showed minor\ndeviations. This analysis confirms that the removal of ECM proteins did not alter the global data\nstructure and introduced minor, localized changes.\n2. Clustering consistency - Adjusted Rand Index (ARI): We performed k-means clustering with k\n= 3 on both datasets and compared the resulting assignments using the Adjusted Rand Index\n(ARI). Comparing the resulting cluster assignments using the Adjusted Rand Index revealed a\nhigh degree of similarity with an ARI of 0.89. This concordance is demonstrated by the fact that\nout of 162 samples, only 6 samples (3.7%) were assigned to different clusters after the removal\nof ECM proteins (Reviewer Figure 8b). This confirms that the removal of ECM proteins did not\nsignificantly alter the overall grouping pattern of the data.\n3. ANOVA comparison of key protein groups\nWe repeated the ANOVA analysis of all sample stages (FDR < 0.05) for both datasets to assess\nwhether ECM removal affected the detection of biologically relevant differences. The ECM-\nincluded dataset yielded 1,071 significantly changing proteins (145 unique), and the ECM-filtered\ndataset yielded 1,013 significantly changing proteins (88 unique). Importantly, 924 (91.3%)\nsignificantly changing proteins were shared between both groups, which represents the\nmajority of significant findings (Reviewer Figure 8c). These results indicate strong concordance\nbetween the datasets with and without ECM. Specifically, ferroptosis inhibitors, which were found\nto be higher in post-NACT samples were not altered by the filtering process.\n5. PCA pattern comparison: PCA plots (Reviewer Figure 8d-e) show that the spatial\narrangement of samples is preserved between datasets. This visual consistency supports the\nquantitative results from the correlation and ARI analyses.\nOverall, our analyses show minimal impact of ECM removal on the conclusions. Since ECM protein\nremoval aimed to minimize technical variability related to matrix contamination in the\nmacrodissected regions, we did not change these pre-processing steps.\nReviewer Figure 8: Evaluating the impact of ECM removal. a) Scatterplot showing pairwise Euclidean distances\nbetween all sample pairs in the ECM-included dataset (x-axis) and the ECM-removed dataset (y-axis). Each point\nrepresents a sample pair (N × N-1). The high Pearson correlation coefficient (R = 0.995) indicates strong preservation\nof the sample similarity structure after ECM protein removal. b) K-means (k=3) clustering consistency plot between\nECM included and ECM-filtered proteome datasets. Each point represents a single sample, with its position\ndetermined by its cluster assignment in the ECM included proteomics data (x-axis) and the ECM-filtered data (y-axis).\nThe color of each point corresponds to its cluster in the whole proteomic dataset. The Adjusted Rand Index of 0.89\nindicates a high degree of concordance, showing that the vast majority of samples retained their cluster assignment.\nThe few points located off the diagonal represent the minor cluster changes resulting from ECM removal. c) A Venn\ndiagram comparing the significantly changing proteins (FDR < 0.05) from the ANOVA analysis of the whole proteome\ndataset (1,071 proteins) and the ECM-filtered dataset (1,013 proteins). d+e) Principal Component Analysis (PCA) plots\nof the whole proteome and ECM-filtered datasets, respectively. The overall distribution of the sample points is highly\nsimilar in both plots, demonstrating that ECM removal did not cause a significant change in the global structure of\nthe data.\n5. Specificity of NKX2.2 - in response to reviewer 3, the authors said that for those cases without\nfusion status, staining for NKX2.2 was done and it was positive for all cases. Unfortunately this\nstain is not specific for EWS as shown by Jason Hornick and Chris Fletcher (see Hung et al., Modern\nPathology 2016). Therefore it is still uncertain if these cases are truly EWS.\nWe appreciate the reviewer's concern regarding NKX2.2's diagnostic specificity. We would like to\nclarify that in our study, all diagnoses were confirmed by an experienced sarcoma pathologist\nbased on a comprehensive assessment of morphology, clinical context, and\nimmunohistochemistry. While we acknowledge that NKX2.2 is not entirely specific, it remains\namong the most sensitive and widely used immunohistochemical markers for Ewing sarcoma,\nparticularly when combined with other diagnostic criteria. NKX2.2 was consistently positive in all\nfusion-unknown cases, in line with the expected EWS phenotype.\nThe pathological diagnosis in our cohort (accepted by leaders in the field) was established based\non classical morphological features of Ewing sarcoma, namely small, round blue cell morphology\nwith scant cytoplasm and uniform nuclei, in combination with a characteristic\nimmunohistochemical profile. Tumors showed strong membranous CD99 expression and nuclear\nNKX2.2 staining. In older cases, FLI1 was also used as an additional supportive marker. To ensure\nspecificity, we routinely performed negative staining for non-Ewing markers, including LCA, TdT,\nPan-Cytokeratin (PanCK), S100, and Desmin, which were consistently negative, helping to exclude\nalternative diagnoses. This immunohistochemistry profile, interpreted within the clinical and\nanatomical context of each patient, forms the diagnostic foundation for EWS in contemporary\npractice.\nImportantly, molecular testing is often not routinely performed in cases with classic\nhistopathological features and strong supportive IHC markers in current clinical practice, including\nin major sarcoma centers. This approach aligns with accepted diagnostic standards when the\nmorphology and immunohistochemistry profile are typical and there are no unusual anatomical,\nclinical, or histological features to suggest alternative diagnoses.\nWe have clarified this point in the revised Methods of the manuscript.\n6. I still cannot find an explicit limitation section in the discussion for this study.\nWe added an explicit limitations paragraph in the revised manuscript.\n“While our study provides novel insights into Ewing sarcoma biology through high-resolution\nproteomics and spatial profiling, several limitations should be acknowledged. First, although the\ncohort is relatively large and includes primary, relapse, and post-NACT samples, it was derived\nfrom a single institution. Broader, multi-center studies with larger and more diverse patient\npopulations will be required to validate and generalize our findings. Second, functional validation\nof the implicated pathways, particularly those related to mitochondrial metabolism, proteasome\nactivity, and DNA damage response, is needed to confirm mechanistic relevance. In addition, while\nour multiplexed imaging analysis provides spatial resolution of the tumor immune\nmicroenvironment, the functional assessment of immune cell states (e.g., T-cell exhaustion,\nmacrophage polarization) was not performed. Lastly, despite the enrichment of cancer cells in the\nproteomic ROIs, the analyses are still considered bulk measurements and include some non-cancer\ncells. The use of the ESTIMATE algorithm provided a rough estimate of tumor purity. However, this\nwas based on a partial gene-set coverage, and the in general, this tool was not generated\nspecifically for this cancer type. We therefore refer to these results as general assessments of\npurity, and we did not use them for downstream data correction.”\nMinor issues\n1. The authors in their intro and discussion highlight EWS as a paediatric disease. However quite\na number of their patients are in the AYA age range, so it may be good to reflect this in the text.\nIndeed, several patients in our cohort fall within the adolescent and young adult (AYA) age range\n(above 18 and below 24 years). We have revised the manuscript to acknowledge that Ewing\nsarcoma predominantly affects children and adolescents but also frequently occurs in young\nadults and may present unique clinical challenges in this population. This has been corrected in\nthe Introduction.\n2. The authors should provide a table summarising the clinicopath features of the cohort. It is\nvery difficult to ascertain cohort level features from the figures and the supplemental table of\nindividual cases.\nWe appreciate the reviewer’s suggestion and have added a summary table detailing the key\nclinicopathological features of the cohort, including age range, survival, relapse, stage at\ndiagnosis, and response to therapy. This has been included as Supplementary Table 1c and\nreferenced in the revised Results sections.\n3. Who is the pathologist for this study? Is he/she a named author. I only ask this because the\ndiagnosis as well as the assessment of necrosis should be done by a expert sarcoma pathologist.\nThe pathologist involved in this study is Dr. Osant Sher, a named author of the paper, who is a\ncertified pathologist with specific expertise in sarcomas. She assessed tumor necrosis on available\nprimary diagnostic slides and was involved in the diagnostic workup of the patients in the cohort.\nHer evaluations were integral to both the clinical diagnosis and the histopathological annotation\nof the samples used in the proteomic analysis.\nReviewer #2 (Remarks to the Author):\nThe revised manuscript by Gordon et al. entitled ‘Proteomic landscapes of Ewing sarcoma unravel\nimmunological regulation of tumor progression’ reports on a relatively large series of Ewing\nsarcoma (EwS) tumor samples that have been subjected to mass-spectrometry based proteomics\nanalyses. While the manuscript has been slightly improved, it remains a rather exploratory\nanalysis that largely stays at the phenotypic description as the authors now admit in the rebuttal\nletter, and some of the provided new data are still not convincing as outlined below:\n1) The new data on the so-called “A673-resistant” cell lines is problematic. First, A673 is a\ncomplicated model that shows a relatively high degree of genomic drift (Kasan M et al. 2025 Nat\nCommun) and that has an unusual second driver mutation (BRAF V600E). This model is alone not\nsuitable to address the research question. Second, the shown “resistance” is defined by a\nmoderate increase (not even a doubling) of the relative IC50 towards doxorubicin. The “resistant”\ncell lines still respond at lower nanomolar concentrations to this drug. Thus, the used cell line is\nnot suitable for modeling chemoresistance as they are still by and large sensitive toward this drug.\nThe authors should use more suitable and truly resistant models grown in 3D and also include\nother chemotherapeutics used in EwS. The hitherto presented data are not convincing to support\nany mechanistic conclusion. \nWe thank the reviewer for the thorough evaluation of our work but regret that the validations\nwere found insufficient. We wish to emphasize that the intention was not to initiate an\nindependent research study, but rather check the hypotheses based on the solid clinical\nproteomics data. Nevertheless, in the revised version we made substantial efforts and addressed\nall the reviewer’s requests: (i) We included an additional novel chemo-resistant EWS model from\nthe RDES cell line, recently established in our lab (not yet published). (ii) We analyzed\nchemoresistance and ferroptosis sensitivity in 3D spheroids. iii) we extended the analyses to\ninclude two chemotherapies and two ferroptosis inducers, on two cell line model (four cell lines).\nOur key findings are summarized below:\n1. Establishment of a novel RDES chemo-resistance model: To model acquired\nchemoresistance, we adopted the previously published protocol by Scarborough et al. (2020),\nwhich generated a chemotherapy-resistant A673 line. RDES cells were treated with five full cycles\nof vincristine-doxorubicin-cyclophosphamide (VDC) and etoposide-cyclophosphamide (EC). This\nprocedure models the standard of care given to patients with EWS, which consists of VDC and IE\n(ifosfamide-etoposide) cycles. Since ifosfamide requires metabolic activation, we substituted\nifosfamide with cyclophosphamide (as in the original protocol by Scarborough et al.), since these\nare analog compounds. The cells were treated at concentrations of IC30/IC40 for each drug in the\ncombination VDC/EC for four days. Recovery time ranged from five to ten days. Control clones\nwere maintained in the vehicle control medium. The detailed description of the model is included\nin the Methods section.\n2. Establishment of 3D spheroid models based on A673 and RDES cell lines: Following the\nreviewers’ concerns about the physiological relevance of 2D cultures, we established 3D spheroid\nmodels of A673 and RDES, including parental and chemo-resistant cells. Spheroids were formed\nin HPLM media in round-bottom ultra-low attachment microplates and were allowed to grow for\n48 hours prior to drug exposure. Cells were treated with etoposide or doxorubicin, and after 60\nhours, spheroid viability was quantified using CellTiter-Glo® 3D according to the manufacturer's\ninstructions. Luminescence values were background-subtracted and normalized to vehicle control\nto calculate percent viability. Dose-response curves and IC₅₀ values were calculated in GraphPad\nPrism using nonlinear regression. We observed significant shifts in IC₅₀ values between parental\nand resistant cell lines. In both models, Resistance indexes (IC₅₀ resistant/IC₅₀ parental) were\nhigher than 3.2 for both etoposide and doxorubicin (Reviewer Figure 9). These results confirm\nthe successful establishment of a chemo-resistant phenotype and the 3D spheroid models. These\nresults are presented in the revised manuscript in Figure 3g.\n–\nReviewer Figure 9: Chemotherapy resistance\nprofiling in 3D spheroid models of Ewing\nsarcoma. Dose-response curves for doxorubicin\nand etoposide in parental and resistant A673 and\nRDES spheroids. The x-axis shows log₁₀ drug\nconcentration; the y-axis shows cell viability\nrelative to untreated controls. Spheroid viability\nwas quantified using CellTiter-Glo® 3D, and IC₅₀\nvalues were calculated for each condition using\nGraphPad Prism.\n3. Combination treatments of chemotherapy and ferroptosis inducers: To strengthen the\nfunctional validation and address the reviewers’ concerns, we expanded our testing to include\ntwo chemotherapeutic agents (doxorubicin and etoposide) and two ferroptosis inducers (RSL3\nand erastin). We applied fixed concentrations of RSL3 (20nm for RDES, 30nm for A673) or erastin\n(0.33um for RDES, 0.4um for A673), which were determined in preliminary experiments, in\ncombination with a dose range of chemotherapy to examine the potential of combined therapy\nin overcoming chemotherapy resistance. The spheres were grown for 48 hours, and the\ncombination treatment was applied for additional 60 hours. We determined the IC₅₀ values of\neach chemotherapeutic drug and performed a sum of squares F test to assess the statistical\ndifferences in IC₅₀ values between chemotherapy alone and each chemotherapy plus ferroptosis\ninducer combination. For A673 cells, both parental and resistant cells showed a significant\ndecrease in doxorubicin IC₅₀ when combined with either RSL3 or erastin. For RDES cells, both\nparental and resistant cells demonstrated a significant decrease in etoposide IC₅₀ when\ncombined with either RSL3 or erastin. Additionally, RDES-resistant cells showed a significant\ndecrease in doxorubicin IC₅₀ when combined with erastin. These reductions indicate a\npotentiation of chemotherapy efficacy by ferroptosis inducers, particularly in the resistant cell\nlines (Reviewer Figure 10a, new Figure 3h).\n4. To further strengthen the CellTiter-Glo® 3D findings, we included representative spheroid\nimages for both A673 and RDES parental and resistant cells. Images were taken for DMSO control\nand for two different concentrations of chemotherapy, either alone or in combination with RSL3\nor erastin. This allowed for a direct visual comparison of spheroids treated with the various drug\ncombinations in both models (Reviewer Figure 10b). Consistent with the IC₅₀ results,\nchemotherapy combined with a ferroptosis inducer produced visibly smaller spheroids compared\nto chemotherapy alone. The effects were more pronounced in RDES spheroids and were added\nto new Fig. 3i. Representative images from A673 spheroids are provided in the Supplementary\nFig. 4a. These expanded experiments, involving multiple agents and combinations further\nreinforce our hypothesis that inducing ferroptosis may increase chemotherapy response. We\nacknowledge that there are differences between the cell lines in terms of their response to RSL3\nand erastin, but this is expected given the differences in the mechanisms of action of the two\ndrugs and the expected difference in protein expression levels of various proteins. As also\nindicated by the reviewer, the growth conditions in 2D, 3D or the in-vivo environment differ\nsubstantially in terms of iron and oxygen availability. Therefore, differences in these pathways are\nto be expected.\nThese expanded experiments, involving multiple agents and combinations, further reinforce our\nhypothesis that inducing ferroptosis may increase chemotherapy response in chemo-resistant\ncells.\nReviewer Figure 10: Ferroptosis-\nbased combination therapies reduce\nchemotherapy resistance in RDES and\nA673 cells. a) Bar plots show\nchemotherapy IC₅₀ values in A673 and\nRDES spheroids treated with\ndoxorubicin or etoposide alone, or in\ncombination with RSL3 or erastin (fixed\nconcentrations: 20/30 nM or 0.33/0.4\nμM for RDES/A673, respectively). The\nsum-of-squares F test determined\nstatistical significance; significant\ndecreases in IC₅₀ indicate enhanced\nefficacy. Data are shown for both\nparental and resistant lines. resistant lines. b+c)\nRepresentative images of RDES and\nA673 parental and resistant spheroids\ntreated with chemotherapy alone or in\ncombination with RSL3 or erastin.\n2) The authors now provide data on stromal contamination (ESTIMATE). Plausibly, the number of\nidentified immune cells correlates with the estimated amount of stroma in each sample. How can\nthe authors exclude a selection bias, that is that the samples taken from the EwS tumors that\nunderwent MS analysis did not have different amounts of stroma per se? How representative is\neach sample for a given tumor?\nIn our study, multiple regions were selected for many of the tumors, based on comprehensive\nhistopathological evaluation of the H&E slides, covering different parts of the viable tumor tissue.\nThis strategy was intentionally chosen to minimize bias from any single ROI and provide a\ncomposite representation of the tumor proteome. We believe this approach improves the\nrepresentation of the samples and reduces the likelihood of regional sampling bias. Generally, all\nROIs were selected to include highly pure cancer cell populations and exclude as much as possible\nECM proteins and immune cells. In this way, we aimed to focus on the cancer cell phenotype. Of\nnote, if we were to select random regions, we would likely see mostly ECM and blood proteins,\nresulting in low proteome coverage and providing few insights into the cancer cells themselves.\nThe ESTIMATE analysis was only aimed at examining the predicted tumor purity to ensure that\nwe work with sufficiently pure cancer regions, and was not intended to reflect on the tumor\ncellularity/stromal components altogether. Examination of the correlation between the %\nalphaSMA in the proteomic ROI and in the whole slide showed a strong correlation (Pearson r =\n0.79, p= 5.4e-005; Reviewer Figure 11), but that the stroma in the ROI is much lower.\nReviewer Figure 11: Evaluation of stromal representation in the\nproteomic ROI. Scatter plot demonstrating the correlation\nbetween αSMA⁺ cell density (as a surrogate for stromal content)\nin multiplex-imaged MS-analyzed ROIs and corresponding whole-\nslide stromal scores. Each dot represents a tumor.\n3) The authors claim that the highly significant correlation between neutrophils and necrosis is\nmerely driven by one outlier sample. How would the statistics look like if this outlier sample would\nbe removed (Reviewer Figure 10)? If it would stay significant, then the speculations on the\npossible biological relevance of neutrophils in the paper should be removed as this is likely driven\nby just catching up more necrotic tissue in the samples. From the new histological images\n(Reviewer Figures 3-5) one can actually see a variable amount of necrosis.\nIn response to the reviewer’s question, we reanalyzed the correlation between neutrophil\nabundance and histologically assessed tumor necrosis after removing one outlier sample that\nexhibited 55% necrosis, substantially higher than all other samples in the cohort. After excluding\nthis sample, the correlation was no longer statistically significant (r=0.29, p-value=0.21),\nindicating that this outlier largely drives the initial association (Reviewer Figure 12a).\nNeutrophil extracellular traps (NETs) have been implicated in modulating the immune\nmicroenvironment, promoting tumor progression, and contributing to therapy resistance. In our\npreviously published work (Shukrun et al., Cancer Sci., 2024; doi:10.1111/cas.15992), we\ndemonstrated that NET formation in EWS tumors is associated with poor prognosis. To further\nexamine the potential association between neutrophils and necrosis, we revisited a subset of 13\nEWS tumor samples in which immunofluorescence staining for NETs was performed using H3 and\nneutrophil elastase (NE). We quantified both neutrophil density and the percentage of\nneutrophils undergoing NETosis (H3⁺NE⁺ co-localization among all neutrophils) and correlated\nthese with histologically assessed necrosis scores (Reviewer Figure 12b-c). We found no\nstatistically significant correlation between neutrophil density and necrosis (r=0.35, p-\nvalue=0.126) or NETosis (%) and necrosis (r=0.325, p-value=0.28).\nTogether, these findings suggest that the neutrophils' presence and role in patient prognosis is\nnot merely a result of tumor necrosis.\nReviewer Figure 12: Neutrophils and necrosis correlation. a) Scatter plot shows the correlation between neutrophil\nabundance (proteomic data) and histologically assessed tumor necrosis after exclusion of one outlier sample with 55%\nnecrosis.b+c) Scatter plots show correlations between neutrophil density (center) and percentage of NETosis (right)\nwith tumor necrosis scores in a subset of 13 tumors. No significant associations were observed.\n4) The histological images showing FTH1 and TFRC immunoreactivity show very dramatic staining\nintensities that do not correspond to the moderately different mean or median (not clear from\nthe figure and legends) H-scores. The authors should show images that are representative for the\nmedians and not extreme phenotypes. Can the authors show a simple iron stain. It is plausible\nthat upregulation of both proteins is a mere epiphenomenon of hemorrhage and bleeding into\nthe tumor tissue post-NACT. Also, both immunostains show huge background smears.\nWe thank the reviewer for the valuable feedback regarding the interpretation and presentation\nof FTH1 and TFRC immunostaining data. We fully agree that representative images should ideally\nreflect the median H-scores. However, we specifically selected the current images because they\nrepresent unique matched primary and post-NACT tumor samples. This pairing enabled us to\nvisually illustrate the biological shift in staining intensity following therapy, which adds unique\nvalue beyond the representation of a single time point's median. The graph provides the\ninformation of the additional unmatched samples. That said, the staining patterns shown are\nbroadly representative and not extreme outliers in terms of H-score distribution. For\ntransparency, we have added additional panels in Reviewer Figure 13a showing other examples\ncloser to the cohort-wide median H-scores to capture better the typical staining range observed.\nReviewer figure 13: Quantification of non-heme iron levels in primary and post-NACT samples. a) Representative\nIHC images of TFRC and FTH1 in primary and post-NACT samples. Scale bar, 50 μm. b) Representative IHC images of\nPearl Prussian blue in primary and post-NACT samples. Scale bar, 50 μm. c) Perl's Prussian blue quantification in\nprimary and post-NACT samples. Error bars represent the SEM values. A welch T-test was used, and quantification\nwas based on the proteomic ROI.\nTo clarify the nature and localization of iron accumulation, we performed Perls’ Prussian Blue\nstaining, a well-established histochemical technique that specifically detects non-heme ferric iron\n(Fe³⁺), typically stored within ferritin and hemosiderin complexes. This method does not detect\nheme-bound iron, such as that found in hemoglobin, and thus does not stain intact red blood\ncells. As a result, Perls’ staining is particularly effective at distinguishing true intracellular iron\nstorage from hemorrhage-related extracellular iron or heme-bound iron derived from red blood\ncells. To support these observations, we have now included representative Perls’ Prussian Blue\nstaining images from both primary and post-NACT tumors (Reviewer Fig. 13b). Quantitative\nanalysis of Perls’ staining within the proteomic ROIs revealed a significantly higher % positive area\nin post-NACT tumors compared to primary tumors (p = 0.012, Reviewer Fig. 13c).\nWhile we cannot completely exclude some contribution of intratumoral hemorrhage in post-\ntreatment samples, we note that the upregulation of both FTH1 and TFRC at the protein level was\nobserved consistently across the post-NACT cohort in our MS data and validated by quantitative\nIHC scoring (H-score). Together with Perls’ staining, these findings strengthen the conclusion that\niron regulatory pathways are actively modulated following chemotherapy, and may reflect\nintrinsic tumor adaptations rather than passive iron accumulation.\n5) The applied statistical tests should be clearly stated in the Figure Legends. It is currently very\ndifficult to judge which bar graph has been assessed with which test (and post-hoc test)\nThe detailed statistical tests were added to the relevant figure legends.\nReviewer #3 (Remarks to the Author):\nThe authors have responded adequately to my comments.\nI still have a few points to be addressed:\n1. Patients with Ewing sarcoma normally undergo surgery only after neo-adjuvant therapy. At\ninitial diagnosis they are normally subjected to core needle biopsy. The authors should double\ncheck if the tumor samples used in this manuscript and correspond to initially diagnosis (primary\ntumor) they were indeed surgical samples. If this is not the case, then their response to Reviewer\n#1-Comment C (…..Of note, EWS biopsies are typically surgical biopsies,…..) is not accurate.\nWe accept the reviewer’s comment regarding the statement about surgical biopsies. Our\nstatement that 'EWS biopsies are typically surgical biopsies' reflected the historical practice at our\ninstitution during the relevant period. For the vast majority of cases included in our cohort,\nopen\nsurgical biopsy was the customary method for obtaining a diagnosis. This was often driven by\nestablished protocols and the need for sufficient diagnostic material at the time. We acknowledge\nthat core needle biopsies (CNB) are increasingly the standard according to current guidelines.\nImportantly, we confirm that all analyzed specimens represent initial diagnostic biopsies obtained\nfrom the primary tumor prior to any neoadjuvant therapy.\n2. Study S-BIAD1597 cannot be found in the BioImage Archive (http://www.ebi.ac.uk/bioimage-\narchive).\nStudy S-BIAD1597 is not public in the BioImage Archive until the manuscript is accepted. The data\ncan only be accessed via the link provided:\nhttps://www.ebi.ac.uk/biostudies/bioimages/studies/S-BIAD1597?key=ef4c9d7b-5b96-4cde-\nbc09-56123235a609\n3. Supplementary Fig. 3b-c. Why the patient numbers do not much for EFS and OS analysis?\nThe observed differences in patient numbers between OS/ EFS groups stem from using a \"Kaplan-\nMeier scan\" analysis. Unlike fixed-threshold methods (e.g., median split), the Kaplan-Meier scan\nidentifies the optimal expression cutoff that yields the most significant separation in survival\noutcomes for each endpoint. As this threshold is data-driven, it is recalculated independently for\nOS and EFS, depending on the distribution of events and the gene's prognostic relationship with\neach outcome. Therefore, the differences in sample sizes between FTH1 high vs. low in OS and\nEFS reflect the independent optimization performed for each survival endpoint.\nSupplementary Fig. 3c. FLT plot – you may want to show FLT low in red.\nWe apologize for this error. It has been corrected in the revised version.\nReviewer still finds Figure 1e misleading – FLI1, EWSR1, and ERG and not Ewing sarcoma markers.\nEwing sarcoma tumors are driven by EWSR1::FLI1/EWSR1:ERG fusion oncoproteins. CD99 is not\na very specific marker. The authors may want to provide further explanation in the figure legend.\nDO the authors assume the FLI1 protein measurements correspond to the fusion protein for\nexample? the same applies to EWSR1 and ERG.\nWe agree that FLI1, ERG, and EWSR1 are not Ewing sarcoma-specific markers when considered\nas standalone proteins. We included them in Figure 1e because they are key components of the\nEWSR1::FLI1 and EWSR1::ERG fusion oncoproteins, which drive the biology of Ewing sarcoma.\nHowever, we mass spectrometry-based proteomics cannot distinguish between the fusion\noncoprotein and the wild-type forms of these proteins without fusion-junction peptides, based\non sequencing of each patient, which is not ethically approved at this point.\nWe have revised the figure legend of Figure 1e to clarify this point and explicitly state that the\nprotein measurements are shown for their biological context, not as diagnostic biomarkers.\nThese clarifications appear in the revised manuscript and in the figure legend.\nReviewer #4 (Remarks to the Author):\nThe authors have responded to prior comments: new data have been added to address\nferroptosis resistance and conclusions tempered appropriately. Two minor corrections are\nsuggested: The EWS::FLI1 fusion should be labeled with :: per latest nomenclature standard and\nit is better called a \"fusion gene\" rather than a \"transgene\".\nWe thank the reviewer for this comment and corrected the term throughout the manuscript.\nReviewer #5 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is\npart of the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nReviewer #1:\nI thank the authors for undertaking an extensive revision of the manuscript and have largely\naddress most of my comments.\nGiven that the authors now show that the removal of ECM proteins has no impact on the\nanalysis and interpretation of the data, I would request that this step be removed from the\npre-processing in the final manuscript. As this reviewer suspected, the authors have\nconfirmed that this step is unnecessary - it is important that the sarcoma field is aware of\nthis and is not misled that this pre-processing step is required to remove such\n\"contaminants\". Furthermore, the authors should also report any ECM proteins that were\nquantified in this study as part of the final proteomic dataset as this is important for the EWS\nfield moving forward given that the TME is an increasingly key area of research as a potential\nsource of targets and biomarkers.\nWe thank the reviewer for appreciating our extensive revision. As our analyses\ndemonstrated, removal of ECM proteins did not affect the main outcomes. All quantified\nECM proteins are included in the final proteomic dataset (see Methods and Supplementary\nTable S1). We have added the following text referring to this analytical step: “For downstream\nanalyses, we applied a focused filtering step, excluding proteins annotated as blood-derived or\nextracellular matrix (ECM)-associated, to emphasize tumor cell-intrinsic proteomic features.\nRemoval of ECM proteins was performed to minimize uncertainty arising from the mixed cellular\norigin of ECM proteins in bulk tissue proteomics, as their contribution could reflect either tumor or\nstromal compartments. This filtration does not suggest that these proteins are irrelevant to this\ntumor type. To confirm that ECM removal did not bias our overall conclusions, we compared\nprincipal component analysis (PCA) distributions of the dataset before and after filtering. We\nobserved no significant changes in global sample distribution (Supplementary Fig. S7a–b). The\ncomplete, unfiltered protein dataset, including all quantified proteins and ECM proteins, is available\nin Supplementary Table S1d to support future studies addressing Ewing Sarcoma and its tumor\nmicroenvironment including ECM”. We added a new Supplementary Figure S7 showing\nPrincipal Component Analysis (PCA) plots of the whole proteome and ECM-filtered datasets.\nThe overall distribution of the sample points is highly similar in both plots, demonstrating that\nECM removal did not cause a significant change in the global structure of the data. We wish\nto avoid changing all figures at this point, as we also believe that for the intracellular cancer\nproteins, our approach is more accurate.\nThe further revised manuscript by Gordon et al. entitled ‘Proteomic landscapes of Ewing\nsarcoma unravel immunological regulation of tumor progression’ reports on a relatively large\nseries of Ewing sarcoma (EwS) tumor samples that have been subjected to mass-\nspectrometry based proteomics analyses. While this reviewer acknowledges the additional\neffort made by the authors that partially addressed significant concerns, the manuscript\nremains a rather exploratory analysis that largely stays at the phenotypic description. The\nstrength of the paper lies in the unique and large proteomics dataset with matched clinical\nannotation, which would – if (and only if) made freely available to the scientific community –\nconstitute a strong argument for publication. However, the authors still overinterpret some\nWe thank the reviewer for the thorough review of our work and the appreciation of the value\nof our proteomic work. We regret that this reviewer did not find the validations sufficient.\nNevertheless, we addressed the reviewer’s comments according to the editor’s advice, as\ndetailed below.\n1) This Reviewer thanks the authors for their efforts to generate 3D spheroid models, which\nis an improvement per se, and recognizes their efforts to generate a second model for\n“chemoresistance” in EwS (derived from RDES cells). Yet, the minimal differences in\nsensitivity towards Doxorubicin and the modest differences in sensitivity towards Etoposide\nare not convincing to declare these new models as “chemoresistant”. The situation is even\nworse for the previously reported A673 model. This whole set of experiments, while going in\nthe right direction, is an example for an overstatement. The paper would be better if these\nweak data are omitted from the otherwise strong resources paper and would better fit in a\nsolid follow-up paper with truly convincing chemoresistance models. It is simply not\nconvincing that if even the “better” model can still be killed by Doxorubicin in doses at the\nnanomolar range, to declare them as “resistance models”. Accordingly, conclusions (e.g.\nregarding ferroptosis) from these so-called “resistance” models are not valid. The observed\ndifferences in ferroptosis could be due to multiple other factors apart of the so-called\n“resistance”, e.g. by metabolic selection bias during the long-term culture (see Methods).\nSimilarly, the minimal differences in protein abundance of “ferroptosis-related” proteins in\n“resistant” versus parental cells become only visible if the Y-axis of the shown graph is\ncropped (data between 2 and 16 AU not shown). The abundance might be statistically\nsignificant but the actual difference is minimal and the biological relevance of these\ndifferences is unclear. The strong resources aspect of this paper is actually diluted by the\nattempt to “validate” potential findings from the clinical cohort in preliminary experiments.\nThis Reviewer strongly recommends to take these data out of this paper and to validate them\nthoroughly in follow-up studies.\ndisappointed to see the suggestion to remove much of the data. We also disagree about the\nuse of the term chemo-resistant. Resistance is always relevant only to a specific drug\nconcentration, and no cell is expected to be resistant to high drug doses. Therefore, calling\ncells ‘resistant’ is a common practice even when it is observed as an increase in the IC50.\nNevertheless, following this comment, rather than using the term chemo-resistance, we\nexplained that the cells are adapted after prolonged culture with the chemotherapeutic\ndrugs. We also removed the term ‘resistant’ in several places in this section. In addition, we\nmoved the A673 results to the supplementary material and only retained RDES data and\npresented these data as preliminary. Overall, we significantly toned down our claims and\nstated that future work is needed for robust mechanistic validation. The resources aspect\nand clinical proteomics remain central to the revised manuscript, as it was before.\nSimilar to point #1, the role of neutrophils and necrosis is a weakening aspect of this paper.\nIn their rebuttal letter, the authors refer to a previous paper of the same group (Shukrun et\nal., Cancer Sci., 2024; doi:10.1111/cas.15992) that postulated that neutrophil extracellular\ntraps (NETs) are associated with poor outcome. It should be noted that this previous study\nwas based on a very small cohort (n=46) of EwS patients of which only n=6 died from disease.\nSome of the main conclusions of this paper are based on graphs such as this presented in\nFigure 3b and c that show highly overlapping data points across groups and only minimal\nchanges overall.\nThese figures of the paper of Shukrun et al. (Cancer Sci., 2024) shows that patients with\nrelapse or death of disease have actually more or less the same levels of NETs. The result\nmay be statistically significant (which appears questionable given the massive overlap of\ndata points), but are they biologically and/or clinically relevant? What is the clinical meaning\nif a patient has e.g. a “NETs release (%) at diagnosis” of 50%? The patient could easily fit in\neither group (relapse vs no relapse; live vs dead). This Reviewer is not convinced that the\nabundance of neutrophils and/or NETs is of any clinical meaningful relevance for patient\nprognosis, risk-stratification, and decision-making. Hence, also the findings of the current\nstudy, which are likely based on an extension of the previous cohort, are likely not clinically\nrelevant. Again, the paper would be better off by omitting overstatements on the\nprognostic/clinical role of these findings and by presenting the current paper as a pure but\nhonest resources article. Given the prior data (Shukrun et al. 2024) and data of the current\nstudy, this Reviewer again recommends to either strongly tone down on the relevance of\nneutrophils and NETs in EwS or even better to take these data out and thoroughly validate\nthem in an independent follow-up study.\nhave moved this data to the Supplementary Information and further toned down our claims,\nnow presenting the association as a preliminary observation, clearly stating that validation\nin larger cohorts is necessary before any prognostic utility can be claimed.\nReviewer #3:\nThe authors have adequately addressed my previous comments.\nWe thank the reviewer for the thoughtful evaluation and are pleased that our revisions have\naddressed the previous concerns.\nReviewer #1 (Remarks to the Author)\nThe authors have addressed my comments in this latest version of the manuscript.\nWe thank the reviewer for the positive evaluation of our revised manuscript.\nReviewer #2 (Remarks to the Author)\nThank you for the thoughtful revisions. The main text is now appropriately tempered; however,\nthe abstract still reads more assertively than the data support.\nIn particular, the abstract retains bold claims about chemo‐resistance and includes the\nsentence:\n> “We found ferroptosis inhibition as a potential mediator of EWS chemo‐resistance and\nidentified novel subclasses of EWS that link the tumor immune landscape with DNA damage\nrepair, ubiquitin‐related proteins, and patient prognosis. Multiplexed immunofluorescence\nimaging supported the association between poor patient prognosis and tumor neutrophils, and\nthe association of macrophages and T‐cells with a favorable prognosis.”\nAs currently written, this phrasing implies causality that I do not see demonstrated in the\nmanuscript. If you wish to keep these points in the abstract, I recommend reframing them as\ncorrelations or hypotheses and aligning the strength of language with the evidence shown.\nFor example, consider wording such as:\n> “Our analyses suggest that ferroptosis pathways may be associated with chemotherapy\nresponse in EWS, and we delineate molecular subclasses that correlate the tumor immune\nlandscape with DNA damage repair, ubiquitin‐related proteins, and patient outcome.\nMultiplexed immunofluorescence indicate possible associations between neutrophils and\npoorer prognosis, and between macrophages/T cells and more favorable prognosis.”\nRelatedly, the title remains unchanged and, in my view, is potentially misleading. The title “\nimmunological regulation of tumor progression” implies a demonstrated causal role; the data\npresented support association rather than regulation. Please adjust the title to avoid implying\ncausality. A more accurate and appropriately cautious title might be:\n“Proteomic landscape of Ewing sarcoma primary tumors and metastases,”\nTo summarize: the dataset is sound and valuable, and of clear interest. My recommendation is\nto harmonize the abstract (and title) with the more measured conclusions in the main text by\navoiding causal language where only associations are shown, and by defining terms precisely.\nThis will strengthen the manuscript and ensure readers take away conclusions that are fully\nsupported by the data.\nWe thank the reviewer for the appreciation of our revision. In accordance with the reviewer’s\nrequest, we changed the title and abstract of the manuscript.\nTitle: Proteomic landscape of Ewing sarcoma primary tumors and metastases\nAbstract: Our analyses suggest that ferroptosis pathways may be associated with chemotherapy response in EWS, and we delineate molecular subclasses that correlate the tumor immune landscape with DNA damage repair, ubiquitin‐related proteins, and patient outcome. Multiplexed immunofluorescence indicate possible associations between neutrophils and poorer prognosis, and between macrophages/T cells and more favorable prognosis."}]

Decision letter
Editorial Decision Letter:
Dear Professor Geiger,
Thank you for submitting your revised manuscript "Proteomic landscape of Ewing sarcoma primary tumors and metastases".
We have now received the comments from the reviewers. As you will see, the reviewers are generally satisfied with the revisions and the tempered conclusions. 
Reviewer 2 has suggested some final adjustments to the abstract and title to ensure the language is appropriately cautious and avoids implying causality where only associations are shown. We agree with these suggestions and ask that you incorporate them into your final version.
Once these minor changes are made, we will be happy to accept your manuscript for publication in Nature Communications.