Metabolic elevation in soft-tissue sarcomas (STS), as documented with 18 F-Fluorodeoxyglucose positron emission tomography ( 18 F-FDG-PET/CT) has been linked with cell proliferation, higher grade, and lower survivals. However, the recent diagnostic innovations (CINSARC gene-expression signature and tertiary lymphoid structure [TLS]) and therapeutic innovations (immune checkpoint inhibitors [ICIs]) for STS patients underscore the need to re-assess the role of 18F-FDG-PET/CT. Thus, in this correspondence, our objective was to investigate the correlations between STS metabolism as assessed by nuclear imaging, and the immune landscape as estimated by transcriptomics analysis, immunohistochemistry panels, and TLS assessment. Based on a prospective cohort of 85 adult patients with high-grade STS recruited in the NEOSARCOMICS trial (NCT02789384), we identified 3 metabolic groups according to 18 F-FDG-PET/CT metrics (metabolic-low [60%], -intermediate [15.3%] and high [24.7%]). We found that T-cells CD8 pathway was significantly enriched in metabolic-high STS. Conversely, several pathways involved in antitumor immune response, cell differentiation and cell cycle, were downregulated in extreme metabolic-low STS. Next, multiplex immunofluorescence showed that densities of CD8+, CD14+, CD45+, CD68+, and c-MAF cells were significantly higher in the metabolic-high group compared to the metabolic-low group. Lastly, no association was found between metabolic group and TLS status. Overall, these results suggest that (i) rapidly proliferating and metabolically active STS can instigate a more robust immune response, thereby attracting immune cells such as T cells and macrophages, and (ii) metabolic activity and TLS could independently influence immune responses.

Undifferentiated pleomorphic sarcoma (UPS) is the most frequent and the most aggressive sarcoma subtype for which therapeutic options are limited. The identification of new therapeutic strategies is therefore an important medical need. Epigenetic modifiers has been extensively investigated in recent years leading to the development of novel therapeutic agents. Dual BET/EP300 inhibitors have shown synergistic antitumor activity and have recently entered clinical development. To date, no data related to potential of BET/EP300 inhibition as a treatment in UPS have been reported. To investigate the therapeutic potential of BET/EP300 inhibition, we evaluated the antitumor activity of three compounds in vitro via MTT, apoptosis and cell cycle assays. The most potent inhibitor was evaluated in vivo in two animal models and the mechanisms of action were investigated by RNA sequencing, Western blotting and immunofluorescence staining. A CRISPR knockout screen was performed to identify resistance mechanisms. Among the three compounds tested, the dual inhibitor NEO2734 was the most potent, decreased the viability of UPS cells in vitro through a regulation of E2F targets and cell cycle and decreased the tumor growth in vivo. Moreover, we identified GPX4 as a gene involved in resistance and showed synergy between BET inhibition and ferroptosis induction.The present study demonstrated that dual BET/EP300 inhibitors have a relevant antitumor activity in a subgroup of UPS characterized by expression of MYC-targets pathway and identified a potent combination therapeutic strategy that deserves further investigation in the clinical setting.

Our objective was to capture subgroups of soft-tissue sarcoma (STS) using handcraft and deep radiomics approaches to understand their relationship with histopathology, gene-expression profiles, and metastatic relapse-free survival (MFS). We included all consecutive adults with newly diagnosed locally advanced STS (N = 225, 120 men, median age: 62 years) managed at our sarcoma reference center between 2008 and 2020, with contrast-enhanced baseline MRI. After MRI postprocessing, segmentation, and reproducibility assessment, 175 handcrafted radiomics features (h-RFs) were calculated. Convolutional autoencoder neural network (CAE) and half-supervised CAE (HSCAE) were trained in repeated cross-validation on representative contrast-enhanced slices to extract 1024 deep radiomics features (d-RFs). Gene-expression levels were calculated following RNA sequencing (RNAseq) of 110 untreated samples from the same cohort. Unsupervised classifications based on h-RFs, CAE, HSCAE, and RNAseq were built. The h-RFs, CAE, and HSCAE grouping were not associated with the transcriptomics groups but with prognostic radiological features known to correlate with lower survivals and higher grade and SARCULATOR groups (a validated prognostic clinical-histological nomogram). HSCAE and h-RF groups were also associated with MFS in multivariable Cox regressions. Combining HSCAE and transcriptomics groups significantly improved the prognostic performances compared to each group alone, according to the concordance index. The combined radiomic-transcriptomic group with worse MFS was characterized by the up-regulation of 707 genes and 292 genesets related to inflammation, hypoxia, apoptosis, and cell differentiation. Overall, subgroups of STS identified on pre-treatment MRI using handcrafted and deep radiomics were associated with meaningful clinical, histological, and radiological characteristics, and could strengthen the prognostic value of transcriptomics signatures.

Abstract Background: Improving the assessment of the immune landscape of soft-tissue sarcoma (STS) through imaging biomarkers could help better selecting and monitoring patients that could benefit from immunotherapy. Our aim was identify whether metabolic patterns of soft-tissue sarcoma (STS) on pre-treatment 18F-Fluorodeoxyglucose (18F-FDG) positron emission tomography (PET/CT) were associated with different immune profiles on molecular and cellular levels. Methods: This single-center prospective study included consecutive adult patients with newly-diagnosed, non-metastatic, high-grade STS treated in a curative intent with available pre-treatement 18F-FDG-PET/CT. Maximal standardized uptake value (SUVmax), SUVpeak, SUVmean, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were extracted. A cross-validated principal component analysis (PCA) was developed on the PET/CT metrics. The first two principal components (PC1 and PC2) and an unsupervised metabolic classifications were computed. Differential gene expression (DGE), oncogenesis pathways analyses, complexity index in sarcoma (CINSARC) molecular signature and immunohistochemistry panels (CD8, CD14, CD20, CD45, CD68, c-MAF) were performed. Correlations between nuclear imaging, immunohistochemistry and transcriptomics data were achieved. Results: 85 patients were included (median age: 62 years, 37 women) between 2016 and 2021. The robust PCA defined 3 metabolic groups (high [n=21], intermediate [n=15] and low [n=49]). PC1 reflected the tumor metabolism and PC2 the size and amount of necrosis. Transcriptomics and immunohistochemistry data were available in 32 and 31 patients, respectively. PC1 was significantly positively correlated with CINSARC (P=0.0029) and the cellular densities in CD8+, CD14+, CD45+, CD68+ and c-MAF (range of P-values: 0.0175-0.0499). The metabolic-high group was characterized by the upregulation of 13 immune pathways, including ICOS, CD27, IFNG, CXCL9-10/CXCL3 genes. Conclusion: Metabolic profiles on 18F-FDG-PET/CT of high-grade STS highlights distinct immune profiles, which could pave the way for potential biomarkers of STS immunophenotyping. Citation Format: Amandine Crombe, Frédéric Bertolo, Jean-Philippe Guegan, Alban Bessede, Raul Perret, Mariella Spalato-Ceruso, Maud Toulmonde, Audrey Laroche, Francois Le Loarer, Vanessa Chaire, Michèle Kind, Carlo Lucchesi, Antoine Italiano. Tumor glucose metabolism profiles detected via [18F]-FDG PET/CT correlate with the immune landascape in soft-tissue sarcomas. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5613.

Introduction: STS are mostly prognosticated through nomograms relying on age, size, histotype and grade. Radiomics approaches, complemented with deep-learning, deep-radiomics and gene-expression profiling, could help understanding the bridge between STS radiophenotypes and molecular features and provide more efficient prognostic tools. Our goals were to investigate correlations between imaging and transcriptomics patterns, and to develop supervised prognostic models for STS patients. Methods: We included all consecutive adult patients with newly-diagnosed locally-advanced STS managed at our sarcoma reference center between 2008 and 2020, with contrast-enhanced baseline MRI. After MRI dataset homogenization, we reduced the dimensions of the MRI data space by extracting 138 radiomics features (RFs) and 1024 deep-RFs with computational approach and autoencoder neural networks. Patient RNA was extracted from untreated samples. Following transcriptomic sequence analysis, gene expression levels for each patient were calculated. Complexity Index in Sarcoma (CINSARC) signature was extracted. Unsupervised classifications of patients based on radiomics, deep-radiomics and transcriptomics datasets were built using consensus hierarchical clustering. Differential Gene Expression and oncogenetic pathways analyses were performed. Associations between the 3 classifications, CINSARC, grade, histotypes and SARCULATOR were explored, as well as their prognostic value. The main outcome was the metastatic-relapse free survival (MFS). The SARCULATOR nomogram and prognostic semantic-radiological features were extracted for benchmarking and understanding models outputs. Results: 220 patients were included (111 men, median age: 62 years); 60 patients developed metastases after completing curative treatments (data are being updated with 2 additional follow-up years). Transcriptomic analysis was achieved in 54 patients and is being updated with 56 additional samples. So far, no significant associations were found between the radiomics-based classifications and the transcriptomics-based (including CINSARC). Nevertheless, the computational radiomics, deep-radiomics, and transcriptomics classifications were associated with MFS, though transcriptomic significance was dampened by the small sample size (P=0.008 [N=220], 0.006 [N=220] and 0.070 [N=54], respectively), suggesting complementary prognostic information. Supervised models using data-splitting, cross-validated algorithms training, and various combinations of input data are being elaborated to improve the MFS prediction. Conclusion: Integrating complementary multiomics datasets with computational and deep radiomics should pave the way for better performing and personalized prognostications in STS patients. Citation Format: Amandine Crombe, Carlo Lucchesi, Frédéric Bertolo, Michèle Kind, Raul Perret, Francois Le Loarer, Mariella Spalato-Ceruso, Maud Toulmonde, Audrey Laroche, Vanessa Chaire, Aurelien Bourdon, Antoine Italiano. Correlating and combining computational radiomics, deep radiomics and transcriptomics data in soft-tissue sarcomas (STS) patients highlight complementary prognostic information. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5435.

Radiomics of soft tissue sarcomas (STS) is assumed to correlate with histologic and molecular tumor features, but radiogenomics analyses are lacking. Our aim was to identify if distinct patterns of natural evolution of STS obtained from consecutive pre-treatment MRIs are associated with differential gene expression (DGE) profiling in a pathway analysis. All patients with newly diagnosed STS treated in a curative intent in our sarcoma reference center between 2008 and 2019 and with two available pre-treatment contrast-enhanced MRIs were included in this retrospective study. Radiomics features (RFs) were extracted from fat-sat contrast-enhanced T1-weighted imaging. Log ratio and relative change in RFs were calculated and used to determine grouping of samples based on a consensus hierarchical clustering. DGE and oncogenesis pathway analysis were performed in the delta-radiomics groups identified in order to detect associations between delta-radiomics patterns and transcriptomics features of STS. Secondarily, the prognostic value of the delta-radiomics groups was investigated. Sixty-three patients were included (median age: 63 years, interquartile range: 52.5–70). The consensus clustering identified 3 reliable delta-radiomics patient groups (A, B, and C). On imaging, group B patients were characterized by increase in tumor heterogeneity, necrotic signal, infiltrative margins, peritumoral edema, and peritumoral enhancement before the treatment start (p value range: 0.0019–0.0244), and, molecularly, by downregulation of natural killer cell–mediated cytotoxicity genes and upregulation of Hedgehog and Hippo signaling pathways. Group A patients were characterized by morphological stability of pre-treatment MRI traits and no local relapse (log-rank p = 0.0277). This study highlights radiomics and transcriptomics convergence in STS. Proliferation and immune response inhibition were hyper-activated in the STS that were the most evolving on consecutive imaging. • Three consensual and stable delta-radiomics clusters were identified and captured the natural patterns of morphological evolution of STS on pre-treatment MRIs. • These 3 patterns were explainable and correlated with different well-known semantic radiological features with an ascending gradient of pejorative characteristics from the A group to C group to B group. • Gene expression profiling stressed distinct patterns of up/downregulated oncogenetic pathways in STS from B group in keeping with its most aggressive radiological evolution.

Introduction: Undifferentiated pleomorphic sarcoma is the most frequent and the most aggressive sarcoma subtype. Despite adequate locoregional treatment, up to 40% will develop metastatic disease. Doxorubicin represents the standard 1st line of treatment for patient with advanced disease. However, its activity is limited with a response rate of only 10% and a progression-free survival of less than 6-months. Identification of new therapeutic strategies is therefore an important medical need. Bromodomain and extra-terminal domain (BET) proteins, cyclic adenosine monophosphate response element-binding protein (CBP), and the E1A-binding protein of p300 (EP300) are important players in histone acetylation. BET inhibitors have already shown pre-clinical activity in translocation-related sarcomas such as Ewing tumors. However, BET and dual BET/P300 inhibitors could have also relevant anti-tumor activity if MYC driven tumors. We previously found that at least a subgroup of UPS is characterized by a strong expression of MYC-targets pathway (Toulmonde et al, EBIOMEDICINE, 2020) Methods: The anti-tumor activity of three compounds which inhibits either CBP/P300 only (CPI-637) or dual inhibitors of both BET and CBP/P300 proteins (NEO1132 and NEO2437) was investigated in four UPS cell lines and two patient-derived xenografts (PDX) established at Institut Bergonié (Bordeaux, France). A CRISPR-KO screen was used to identify genes that mediate resistance to NEO2734. Results: We found that the 3 compounds act mainly by inhibiting the cell cycle with a concomitant reduction of MYC expression. NEO2734 was the most potent one with IC50 range between 0.2 to 1 µM in vitro and an antitumor activity in vivo in the two PDX models of UPS. To investigate the mechanism of action, we performed a transcriptomic analysis by RNA-sequencing. Gene set enrichment analysis revealed that NEO2734 induced a downregulation of G2M checkpoint and E2F targets hallmarks. We confirmed these results at the protein level showing a downregulation of several proteins involved in these pathways such as PLK1, AURKA or CCNB1. Interestingly, two of our four UPS cells lines were resistant to NEO2734 (IC50 27µM and 73µM). To elucidate the genetic factors involved in this resistance, we performed a CRISPR-KO screen. We identified more than 200 genes as potential candidates involved in drug resistance and decided to focus on hnRNPU which is an interactor of P300 and play a role in cell cycle regulation through the formation of the mitotic spindle with PLK1 and AURKA. Conclusion: Dual inhibition of BET and CBP/P300 proteins has promising antitumor activity in undifferentiated pleomorphic sarcoma. Suppressing hnRNPU may enhance the activity of dual BET and P300/CBP bromodomain inhibitor in sarcoma and other cancers. Citation Format: Stephanie Verbeke, Frederic Bertolo, Vanessa Chaire, Aurelien Bourdon, Amina Naït Eldjoudi, Marie-Alix Derieppe, Francis Giles, Antoine Italiano. Anti-tumor activity of a dual BET/CBP/EP300 inhibitor, NEO2734, in undifferentiated pleomorphic sarcomas and identification of genes involved in resistance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1844.

BackgroundUndifferentiated pleomorphic sarcoma (UPS) is the most frequent, aggressive and less-characterized sarcoma subtype. This study aims to assess UPS molecular characteristics and identify specific therapeutic targets.MethodsHigh-throughput technologies encompassing immunohistochemistry, RNA-sequencing, whole exome-sequencing, mass spectrometry, as well as radiomics were used to characterize three independent cohorts of 110, 25 and 41 UPS selected after histological review performed by an expert pathologist. Correlations were made with clinical outcome. Cell lines and xenografts were derived from human samples for functional experiments.FindingsCD8 positive cell density was independently associated with metastatic behavior and prognosis. RNA-sequencing identified two main groups: the group A, enriched in genes involved in development and stemness, including FGFR2, and the group B, strongly enriched in genes involved in immunity. Immune infiltrate patterns on tumor samples were highly predictive of gene expression classification, leading to call the group B 'immune-high' and the group A 'immune-low'. This molecular classification and its prognostic impact were confirmed on an independent cohort of UPS from TCGA. Copy numbers alterations were significantly more frequent in immune-low UPS. Proteomic analysis identified two main proteomic groups that highly correlated with the two main transcriptomic groups. A set of nine radiomic features from conventional MRI sequences provided the basis for a radiomics signature that could select immune-high UPS on their pre-therapeutic imaging. Finally, in vitro and in vivo anti-tumor activity of FGFR inhibitor JNJ-42756493 was selectively shown in cell lines and patient-derived xenograft models derived from immune-low UPS.InterpretationTwo main disease entities of UPS, with distinct immune phenotypes, prognosis, molecular features and MRI textures, as well as differential sensitivity to specific anticancer agents were identified. Immune-high UPS may be the best candidates for immune checkpoint inhibitors, whereas this study provides rational for assessing FGFR inhibition in immune-low UPS.FundingThis work was partly founded by a grant from La Ligue.