# Multi-omics profiling identified two epithelioid sarcoma molecular subtypes with distinct signaling and immune characteristics

Carine Ngo, Léo Colmet-daage, Julien Vibert, Clémence Hénon, Daniel Pissaloux, Alexander Valent, Jia Xiang Jin, Riwan Brillet, Julien Masliah-planchon, Gaëlle Pierron, et al.

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## To cite this version:

Carine Ngo, Léo Colmet-daage, Julien Vibert, Clémence Hénon, Daniel Pissaloux, et al. Multi-omics profiling identified two epithelioid sarcoma molecular subtypes with distinct signaling and immune characteristics. Cancer Communications, 2025, 45 (12), pp.1760 - 1766. 10.1002/cac2.70077. hal-05473598

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Submitted on 23 Jan 2026

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# Multi-omics profiling identified two epithelioid sarcoma molecular subtypes with distinct signaling and immune characteristics

Epithelioid sarcoma (EpS) is an aggressive soft tissue sarcoma characterized by switch/sucrose non-fermentable (SWI/SNF)-related matrix-associated actin-dependent regulator of chromatin subfamily B member 1 (SMARCB1) loss [1]. Conventionally, EpS is histologically classified as either distal or proximal subtype, each exhibiting distinct clinical behaviors; these sometimes co-exist as “hybrid” EpS [2, 3]. Differential diagnosis from other SMARCB1-deficient tumors, such as extracranial extrarenal rhabdoid tumors (EERTs), can be challenging [4]. Beyond phenotypic diversity, EpS molecular heterogeneity remains poorly understood. To address this, we aimed to build a molecular classification and explore inter- and intra-tumor heterogeneity, using multi-omics profiling.

We profiled 33 EpSs and 3 EERTs using whole-exome sequencing (WES), DNA methylation, and bulk RNA-sequencing (RNA-seq); single-cell RNA-sequencing (scRNA-seq) and 10x Genomics Visium spatial transcriptomics were performed on 8 and 4 EpSs, respectively (Figure 1A, Supplementary Materials and Methods). Cases were reviewed by two senior pathologists and classified by histology (14 distal, 14 proximal, 5 hybrid EpSs) (Supplementary Figure S1, Supplementary Tables S1-S2).

Unsupervised RNA-seq clustering identified two EpS transcriptomic subtypes (Figure 1B): (i) distal-like (n = 20) — including all distal EpS, 2 proximal and 4 hybrid EpSs — enriched in cell adhesion, circulatory system development genes and extracellular matrix; (ii) proximal-like

(n = 13) — comprising 12 proximal and 1 hybrid EpSs — enriched in synapse organization, response to wounding and macrophage activation (Supplementary Figure S2A, Supplementary Table S3). Gene set enrichment analysis revealed a significant enrichment in epithelial-mesenchymal transition (EMT) and ultraviolet response in distal-like EpS compared to proximal-like EpS (normalized enrichment score = 1.80 and 1.66, respectively; false discovery rate < 25%; *p* < 0.05) (Supplementary Figure S2B, Supplementary Table S4).

We explored mechanisms of SMARCB1 loss in 31 EpSs and 3 EERTs, using WES, targeted next-generation sequencing, single-nucleotide polymorphism array, shallow whole-genome sequencing, DNA methylation and fluorescence in situ hybridization — depending on material availability (Supplementary Figure S3A, Supplementary Table S5). Biallelic SMARCB1 inactivation was identified in 16 (51.6%) of 31 EpSs (mostly homozygous deletions), without germline SMARCB1 alteration. Heterozygous loss-of-function alterations were found in 14/31 (45.2%) EpSs. SMARCB1 alteration types were similar across EpS transcriptomic subtypes and unrelated to SMARCB1 mRNA levels (Supplementary Figure S3B-D).

Recurrent alterations, excluding SMARCB1, occurred in up to 13% of EpSs (Supplementary Figure S3A, Supplementary Tables S6-S7). Median tumor mutational burden was 0.84 (range, 0.56-15.75) and 0.87 (range, 0.34-1.56) mut/Mb in distal-like and proximal-like EpS, respectively (Supplementary Figure S4A, Supplementary Table S8). Distal-like EpS harbored a higher number of arm-level copy number alterations (CNAs) than proximal-like EpS [median = 5 (range, 0-15) vs. 1 (range, 0-11), *p* = 0.041; Supplementary Figure S4B], though prior chemotherapy or disease stage may have influenced this result [5].

Immune deconvolution of bulk RNA-seq data showed significant enrichment in cytotoxic and CD8+ T cells, natural killer cells and M1 macrophages in distal-like

**List of Abbreviations:** CNA, Copy number alteration; DSG2, Desmoglein 2; EERT, Extracranial extrarenal rhabdoid tumor; EMT, Epithelial-mesenchymal transition; EpS, Epithelioid sarcoma; IHC, Immunohistochemistry; RNA-Seq, RNA-sequencing; ScRNA-Seq, Single-cell RNA-sequencing; SMARCB1, Switch/sucrose non-fermentable-related matrix associated actin dependent regulator of chromatin subfamily B member 1; SWI/SNF, Switch/sucrose non-fermentable; TREM2, macrophages expressing triggering receptor expressed on myeloid cells 2; WES, Whole-exome sequencing.

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

© 2025 The Author(s). *Cancer Communications* published by John Wiley & Sons Australia, Ltd on behalf of Sun Yat-sen University Cancer Center.
<figure><img src="image_3.png" /><figcaption>**FIGURE 1** Multi-omics profiling identifies two molecular subtypes of EpS – distal-like and proximal-like. (A) Study design and sample workflow of the bulk multi-omics, single-cell and spatial transcriptomic analyses. Eight scRNA-seq samples were collected from 7 patients, including 2 samples from the same patient at different recurrences. (B) Unsupervised hierarchical clustering of the bulk RNA-seq dataset of</figcaption></figure>
EpS, whereas proximal-like tumors were enriched in M2 macrophages (Figure 1C, Supplementary Figure S5A). Immunohistochemistry (IHC) revealed that CD8⁺ T cells and CD163⁺ macrophages located at the tumor periphery in distal-like EpS with more frequent mature tertiary lymphoid structures, while macrophages were intratumoral in proximal-like EpS (Figure 1D, Supplementary Figure S5B-D).

ScRNA-seq of 3 distal-like and 5 proximal-like EpS samples profiled 28,912 high-quality cells assigned to six main subpopulations according to the expression-based clustering, SMARCB1 expression, the canonical mark-

ers of known cell types, and inferred CNAs (Figure 1E, Supplementary Figure S6).

Marker identification of myeloid cells, the most abundant immune subpopulation (Supplementary Figure S7A), showed predominant pro-tumoral macrophages expressing triggering receptor expressed on myeloid cells 2 (TREM2)⁺ in proximal-like EpS and inflammatory monocytes in distal-like EpS (Supplementary Figure S7B). To investigate cell-cell interactions, we independently integrated 6,542 single cells from distal-like EpS (Supplementary Figure S8) and 21,763 from proximal-like EpS (Supplementary Figure S9). CellPhoneDB analysis

33 EpS and 3 EERT, using the top 25% most variable genes. Transcriptomic subtype, histological subtype, tumor status and clinical features are indicated by color bars along the top of the heatmap, and sample IDs are displayed at the bottom of the graph. The color scale represents the similarity scores between samples based on their gene expression profiles. (C) Supervised clustering of MCP counter scores between distal-like and proximal-like EpS transcriptomic subtypes. Heatmap displaying row Z-scores of normalized enrichment values for each cell population across samples. *p* values comparing MCP-counter scores between subtypes were calculated using a two-sided Mann-Whitney *U*-test. Significant *p* values (< 0.05) are shown in bold. (D) Distribution of EpS transcriptomic subtypes according to high- and low-IHC density of CD8⁺ T cells or CD163⁺ macrophages in intratumoral or peritumoral areas. ** *p* < 0.01 and *** *p* < 0.001 (Fisher's exact test). (E) UMAP showing single-cell profiles of malignant and non-malignant cells from 8 EpSs colored by sample of origin and cell identity. P36, P16, and P34 were classified as distal-like EpS samples. P6A, P6B, P23, P35, and P12 were classified as proximal-like EpS. P6A and P6B originated from the same patient at different recurrences. (F) Dot plot inferred from the CellPhoneDB algorithm showing the mean expression levels of significant ligand-receptor pairs involved in the intercellular interactions between selected tumor cells and T cell/macrophage subclusters identified from each integrated single-cell dataset of distal-like and proximal-like EpS samples (as labeled in Supplementary Figures S8 and S9). (G) Spatial representation of the expression levels of selected ligand-receptor pairs involved in the intercellular interactions between the T cells/macrophages and tumor cells in a distal-like EpS sample using Visium Spatial transcriptomics (10x Genomics). The ligand DSC2 (up) is expressed within the infiltrating immune cells at the P36 distal-like EpS tumor's periphery, while its respective cognate receptor, DSG2 (down), colocalizes with tumor cells. (H) UMAP showing integrated single-cell profiles of EpS malignant cells colored by sample of origin and subclusters. (I) Density plot indicating single cell-derived *EMT_5* signature score in the bulk RNA-seq dataset of the EpS cohort and an external RNA-seq panel comprising 1,041 samples from 32 mesenchymal tumor entities. The vertical line represents the mean value. * *p* < 0.05 and *** *p* < 0.001 (ANOVA test). (J) Distribution of EpS transcriptomic subtypes in DSG2⁺ and DSG2⁻ IHC staining groups. (K) Kaplan-Meier overall survival analysis according to high and low *EMT_5* signature score in the bulk RNA-seq dataset of the EpS cohort. *p*-value was calculated using the Log-rank test. ACKR2, atypical chemokine receptor 2; ADGRG6, adhesion G protein-coupled receptor G6; AFH, angiomatoid histiocytofibroma; ANOVA, analysis of variance; AREG, amphiregulin; ARMS, alveolar rhabdomyosarcoma; ASPS, alveolar soft part sarcoma; BCOR, BCOR-fused sarcoma; CAF, cancer-associated fibroblast; CCL4, C-C motif chemokine ligand 4; CCL5, C-C motif chemokine ligand 5; CCS, clear cell sarcoma; CD40LG, CD40 ligand; CDH1, cadherin 1; CEACAM1, carcinoembryonic antigen-related cell adhesion molecule 1; CIC_fused, CIC-fused sarcoma; CS, chondrosarcoma; CSF1, colony stimulating factor 1; CSF1R, colony stimulating factor 1 receptor; DDLPS, dedifferentiated liposarcoma; DLL3, delta-like canonical notch ligand 3; DSC2, desmocollin 2; DSG2, desmoglein 2; DSRCT, desmoplastic small round cell tumor; EERT, extracranial extrarenal rhabdoid tumor; EFNB2, ephrin-B2; EGFR, epidermal growth factor receptor; EHCF, epithelioid histiocytofibroma; EHE, epithelioid hemangioendothelioma; EMCS, extraskeletal myxoid chondrosarcoma; EMT, epithelial-mesenchymal transition; EPHB2, ephrin type-B receptor 2; EpS, epithelioid sarcoma; ERMS, embryonal rhabdomyosarcoma; EWS, Ewing sarcoma; EWSR1::PATZ1, EWSR1::PATZ1 sarcoma; FDR, false discovery rate; FISH, fluorescence in situ hybridization; FUS::NFATC2, FUS::NFATC2 sarcoma; HLA-E, major histocompatibility complex, class I, E; IFN, interferon; ILIRAP, interleukin 1 receptor accessory protein; IMT, inflammatory myofibroblastic tumor; JAML, junctional adhesion molecule-like; KLRG1, killer cell lectin-like receptor subfamily G member 1; KLRK1, killer cell lectin-like receptor subfamily K member 1; LMS, leiomyosarcoma; LncRNA, long non-coding RNA; LPS, liposarcoma; MFS, myxofibrosarcoma; MPNST, malignant peripheral nerve sheath tumor; MRT, malignant rhabdoid tumor; NES, normalized enrichment score; NK, natural killer; NOTCH2, notch receptor 2; NRP1, neuropilin 1; ns, non-significant; OST, osteosarcoma; PLXNB1, plexin B1; PMHE, pseudomyogenic hemangioendothelioma; PODXL, podocalyxin-like; PRNP, prion protein; PTPRF, protein tyrosine phosphatase receptor type F; RMS, rhabdomyosarcoma; RNA-Seq, RNA-sequencing; SELL, selectin L; SIRPA, signal-regulatory protein alpha; SMARCB1, switch/sucrose non-fermentable-related matrix associated actin dependent regulator of chromatin subfamily B member 1; SS, synovial sarcoma; sWGS, shallow whole-genome sequencing; TNFRSF10B, tumor necrosis factor receptor superfamily member 10b; TNFSF10, tumor necrosis factor (ligand) superfamily member 10; UMAP, uniform manifold approximation and projection; UPS, undifferentiated pleomorphic sarcoma; UT, undifferentiated tumor; VEGFB, vascular endothelial growth factor B; WES, whole-exome sequencing; y, years.
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suggested distinct ligand-receptor interactions (Figure 1F): in distal-like EpS, inferred tumor-immune interactions mainly involved adhesion molecules (e.g., desmoglein 2 [DSG2]) or chemokine receptors (e.g., atypical chemokine receptor 2), consistent with spatial transcriptomics observations (Figure 1G, Supplementary Figure S10); proximal-like EpS was enriched in macrophage activation signaling (Figure 1G, Supplementary Figure S11).

We next focused on the 14,320 tumor single cells and identified ten subclusters (Figure 1H, Supplementary Figure S12, Supplementary Table S9). One subcluster (EMT_5) gathered virtually all cells from distal-like samples and was enriched in genes related to extracellular matrix and cell adhesion. By contrast, subclusters from proximal-like samples were mostly patient-specific and linked to various biological processes.

To assess the specificity of single-cell subclusters, we deconvoluted their signatures in bulk RNA-seq data (Supplementary Figure S13). EMT_5 was the most abundant signature in distal-like EpS, rare in proximal-like EpS and virtually absent from EERTs. We revalidated this in an independent dataset of 1,041 mesenchymal tumors. Again, distal-like EpS exhibited the highest EMT_5 signature score, while SMARCB1-deficient malignant rhabdoid tumors and SMARCA4-deficient undifferentiated tumors had the lowest (Figure 1I). Other sarcomas with mesenchymal-epithelial features (e.g., desmoplastic small round cell tumors) had low EMT_5 scores, highlighting the specificity of this signature towards distal-like EpS. Since DSG2 scored within the top five markers of the EMT_5 signature (Supplementary Figure S12A) and showed functional cell-cell interactions (Figure 1F), we asked whether its expression could serve as a IHC diagnostic biomarker for distal-like EpS. DSG2 was expressed in all distal-like tumor cells, while it was undetectable in all proximal-like tumors but one – the latter exhibiting a weak, focal staining – supporting its potential diagnostic value (Figure 1J, Supplementary Figure S14).

We eventually explored the prognostic value of the EMT_5 signature. It was significantly associated with better overall survival and metastasis-free survival in two independent series (Figure 1K, Supplementary Figure S15).

Epithelioid sarcoma is a rare, morphologically and clinically heterogeneous disease that lacks a molecular classification. By integrating bulk multi-omics and single-cell datasets from a large EpS cohort, combined with clinical annotation and pathological review, we propose a molecular classification comprising two transcriptomic subtypes: distal-like EpS, characterized by a prognostic, cancer cell-specific EMT signature and a T-cell-rich microenvironment; and proximal-like EpS, with distinct transcriptomic programs and an immunosuppressive, macrophage-rich microenvironment.

The EMT signature is consistent with prior transcriptomic studies [5, 6]; however, these relied on supervised clustering based on histological subtypes. By contrast, our unsupervised analysis identified molecular-based subtypes, thereby allowing to reclassify 6 cases, originally categorized as “proximal” or “hybrid” histological subtypes, as distal-like EpS, which may carry therapeutic implications. Further, single-cell and spatial analyses enabled us to make significant advances. First, we confirmed that the EMT signature was specific to tumor cells, rather than resulting from stromal contamination in bulk RNA-seq data. Second, we identified that DSG2 might serve as a diagnostic marker for distal-like EpS. Third, we found that this scRNA-seq-derived EMT signature was prognostic, highly specific to the distal-like EpS, and virtually absent from other SWI/SNF-defective sarcomas. Whether this relates to the role of SMARCB1 in favoring EMT gene expression in preclinical models deserves further exploration [7, 8].

Finally, beyond confirming quantitative differences in immune populations recently reported in a smaller series of 18 EpSs [4], we additionally identify a specific spatial distribution and distinct functional cell-cell interactions using spatial transcriptomics and in silico prediction, which may have important therapeutic implications.

Overall, this multi-omics single-cell dataset provides important molecular insights into the biology, diagnosis and prognostic of EpS, and should aid in developing new therapeutic strategies.

## AUTHOR CONTRIBUTIONS

**Conceptualization:** C.N., L.C-D., J.V. and S.P-V. **Methodology:** C.N., L.C.D., J.V., C.H., R.B., J.X.J., D.P., J. M-P., A.V., A.B., L.L., E.R., C.R-S., L.L., C.A., M.G., J-Y.S., G.P., W.H.F., C.S-F., J.J.W., T.G.P.G., F.T., F.B. and S.P-V. **Pathological review:** C.N, J-M.C. **Software:** C.N., L.C-D., J.V. and R. B. **Formal analysis:** C.N. and L.C-D. **Investigation:** C.N., L.C-D and J.V. **Resources:** C.N., D.P., F.T., J-Y.B., F.B., B.V., R.B., A.LC., M.F., Ch.H, and S.P-V. **Data curation:** C.N., L.C-D and S.P-V.; **Writing—Original draft:** C.N. **Writing—Review & editing:** C.N., L.C-D., J.V., J.X.J., C.H. T.G.P.G., J.J.W., F.B., C.S-F. and S.P-V. **Visualization:** C.N., L.C-D. and S.P-V. **Supervision:** T.G.P.G., F.B., F.T. and S.P-V. **Project administration:** C.N. and S.P-V. **Funding acquisition:** J-Y. S., T.G.P.G., and S.P-V.

## ACKNOWLEDGEMENTS

We dedicate this research to patients and their families. We thank patients and families for their donations to the Gustave Roussy Sarcoma Tumor Board and Research program, Gustave Roussy Sarcoma Donors, Association “Un élan pour Lucas” and Family L.
We also thank FIGHT KIDS CANCER (FKC) Representatives KickCancer, Imagine for Margo, Foundation Kriibskrank Kanner and CRIS Cancer Foundation for awarding the 2024 FIGHT KIDS CANCER & St. Baldrick's Foundation Arceci Innovation Award to Sophie Postel-Vinay. The independent selection process of this Innovation Award, funded by FKC, was administered by the St Baldrick's Foundation.

## CONFLICT OF INTEREST STATEMENT

Sophie Postel-Vinay is a principal investigator or sub-investigator of clinical trials from Abbvie, Agios Pharmaceuticals, Amgen, Argen-X Bvba, Arno Therapeutics, Astex Pharmaceuticals, Astra Zeneca, Aveo, Bayer Healthcare Ag, Bbb Technologies Bv, Blueprint Medicines, Boehringer Ingelheim, Bristol Myers Squibb, Celgene Corporation, Chugai Pharmaceutical Co., Clovis Oncology, Daiichi Sankyo, Debiopharm S.A., Eisai, Eli Lilly, Exelixis, Forma, Gamamabs, Genentech, Inc., GlaxoSmithKline, H3 Biomedicine, Inc, Hoffmann La Roche Ag, Innate Pharma, Iris Servier, Janssen Cilag, Kyowa Kirin Pharm. Dev., Inc., Loxo Oncology, Lytix Biopharma As, MedImmune, Menarini Ricerche, Merck Sharp & Dohme Chibret, Merrimack Pharmaceuticals, Merus, Millennium Pharmaceuticals, Nanobiotix, Nektar Therapeutics, Novartis Pharma, Octimet Oncology Nv, Oncoethix, Onyx Therapeutics, Orion Pharma, Oryzon Genomics, Pfizer, Pharma Mar, Pierre Fabre, Roche, Sanofi Aventis, Taiho Pharma, Tesaro Inc., and Xencor. SPV has participated in advisory boards for Merck KGaA and has received laboratory research funding for work unrelated to this project from AMGEN and Hoffman-LaRoche.

## FUNDING INFORMATION

This work was funded by program grants to Sophie Postel-Vinay from National Institute of Health and Medical Research Action Thématique Incitative sur Programme-Avenir Group / La Ligue Contre le Cancer 2018; Fondation Association pour la Recherche sur le Cancer (PGA1-RF20190208576), European Research Council (ERC) (TargetSWitch 101077864), Cancéropôle Ile-de-France (2017-1-EMERG-72 and 2020-1-EMRG-28-IGR-1), La Ligue contre le Cancer Val de Marne (Subvention Recherche Scientifique 2021 et 2022), Agence Nationale pour la Recherche (RHU Condor ANR-21-RHUS-0010), and supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre. This work was further funded by program grants to Gustave Roussy from Institut National du Cancer (INCa-DGOS-Inserm_12551 SIRIC2 and INCa-DGOS-Inserm-ITMO Cancer_18002 SIRIC EpiCURE), as well as the “ Programme émergent d'épigénétique” funded by The Fondation Gustave Roussy.

The laboratory of Thomas G.P. Grünewald acknowledges funding by the SMARCB1 association, the Barbara and Wilfried Mohr foundation, and the European Union (ERC, CANCER-HARAKIRI, 101122595). For Sophie Postel-Vinay and Thomas G.P. Grünewald ERC grants, views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them.

Jia Xiang Jin acknowledges support by scholarships of the Rudolf and Brigitte Zenner Foundation and the German Academic Scholarship Foundation.

Julien Vibert is supported by a fellowship from the Inserm-Foundation Bettencourt.

## DATA AVAILABILITY STATEMENT

All data generated or analyzed during this study are included in this published article and its supporting information files. The Data Access Committee (DAC) and the policies associated with that have been submitted and validated by EGA are as follows: DAC: EGAC50000000552; Policy: EGAP50000000502. Scripts for the figures are available on request at https://github.com/LeoColmet-Daage/swisarc-manuscript-figures. The RNA-seq processing pipeline is available at https://github.com/nf-core/rnaseq/tree/master. The WES processing pipeline is available at https://github.com/nf-core/sarek/tree/master. Single-cell processing pipeline is available at https://github.com/gustaveroussy/single-cell.

## ETHICS APPROVAL AND CONSENT TO PARTICIPATE

All experiments were performed in accordance with the Declaration of Helsinki. The study was approved by the Institut Gustave Roussy Review Board (IRB number: 2024-316). All the patients signed an informed consent to allow the use of tumor residual samples for scRNA-seq analysis. Archived samples were used according to the French legal framework of MR-004 reference methodology set up by the French Data Protection Authority (CNIL) (Number 114112002202020 Health Data Hub).

Carine Ngo¹,² ID

Léo Colmet-Daage¹

Julien Vibert¹,³,⁴

Clémence Hénon³,⁴

Daniel Pissaloux⁵

Alexander Valent²

Jia Xiang Jin⁶,⁷,⁸

Riwan Brillet¹

Julien Masliah-Planchon⁹

Gaëlle Pierron⁹
Ludovic Lacroix<fnref n="2" />

Etienne Rouleau<fnref n="2" />

Cyril Roussel-Simonin<fnref n="3" />

Lilian Lecorgne<fnref n="1" />

Clémence Astier<fnref n="1" />

Marlène Garrido<fnref n="1" />

Rastislav Bahleda<fnref n="3" />

Benjamin Verret<fnref n="4" />

Axel Le Cesne<fnref n="4" />

Charles Honore<fnref n="10" />

Matthieu Faron<fnref n="10" />

Wolf Herman Fridman<fnref n="11" />

Catherine Sautès-Fridman<fnref n="11" />

Jean-Michel Coindre¹²

Jean-Yves Scoazec<fnref n="2" />,¹³,¹⁴

Joshua J Waterfall¹⁵,¹⁶

Franck Bourdeaut¹⁷

Thomas G. P. Grünewald<fnref n="6" />,<fnref n="7" />,<fnref n="8" />,¹⁸

Jean-Yves Blay<fnref n="5" />

Franck Tirode<fnref n="5" />

Sophie Postel-Vinay<fnref n="1" />,<fnref n="3" />,¹⁴,¹⁹

University, The French League Against Cancer, Paris, France

¹²Department of Pathology, Institut Bergonié, Bordeaux, France

¹³Experimental and Translational Pathology Platform, Molecular Analysis, Modeling, and Imaging of Cancer, National Institute of Health and Medical Research US23/UAR3655, Institut Gustave Roussy, Villejuif, France

¹⁴Paris Saclay University, Bicêtre Faculty of Medicine, Le Kremlin-Bicêtre, France

¹⁵National Institute of Health and Medical Research U830, Integrative Functional Genomics of Cancer Lab, Paris Sciences & Lettres Research University, Institut Curie Research Center, Paris, France

¹⁶Department of Translational Research, Paris Sciences & Lettres Research University, Institut Curie Research Center, Paris, France

¹⁷Paris-Cité University & Expert Center in Childhood and Adolescent Oncology Center, National Institute of Health and Medical Research U830, Institut Curie Research Center, Paris, France

¹⁸Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany

¹⁹University College of London Cancer Institute, London, UK

**Correspondence:**

Sophie Postel-Vinay, Department of Therapeutic Innovation and Early Clinical Trials, Institut Gustave Roussy, 114 rue Edouard Vaillant, Villejuif 94800, France.
Email: sophie.postel-vinay@gustaveroussy.fr

Carine Ngo, Department of Pathology and Laboratory Medicine, Institut Gustave Roussy, 114 rue Edouard Vaillant, Villejuif 94800, France.
Email: carine.ngo@gustaveroussy.fr

**ORCID**

Carine Ngo https://orcid.org/0000-0002-8172-5876

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<footnote n="2">²Department of Pathology and Laboratory Medicine, Institut Gustave Roussy, Villejuif, France</footnote>

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## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.