Miallot et al. J Hematol Oncol (2021) 14:114 https://doi.org/10.1186/s13045-021-01125-y REVIEW Open Access Metabolic landscapes in sarcomas Richard Miallot1*, Franck Galland1, Virginie Millet1, Jean‑Yves Blay2 and Philippe Naquet1* Abstract Metabolic rewiring ofers novel therapeutic opportunities in cancer. Until recently, there was scant information regard‑ ing soft tissue sarcomas, due to their heterogeneous tissue origin, histological defnition and underlying genetic his‑ tory. Novel large‑scale genomic and metabolomics approaches are now helping stratify their physiopathology. In this review, we show how various genetic alterations skew activation pathways and orient metabolic rewiring in sarcomas. We provide an update on the contribution of newly described mechanisms of metabolic regulation. We underscore mechanisms that are relevant to sarcomagenesis or shared with other cancers. We then discuss how diverse meta‑ bolic landscapes condition the tumor microenvironment, anti‑sarcoma immune responses and prognosis. Finally, we review current attempts to control sarcoma growth using metabolite‑targeting drugs. Keywords: Sarcoma, Metabolism, Microenvironment, Metabolomics, Transcriptomics, Metabolite‑targeted therapies Background improving sarcoma typing and treatment requires the Sarcomas encompass a wide variety of tumors, with more use of large-scale “omics” tools to identify the oncogenic than 170 subtypes, according to the last WHO classif- drivers, often resulting from multiple genetic alterations cation. Tey originate from the neoplastic transforma- in adult STS. Tese can include translocations, mutations tion of mesenchymal cells in connective tissues [1, 2]: or amplifcations/deletions that cripple major growth and 87% arise from soft tissue and 13% from bone [3, 4]. Soft diferentiation pathways [8–12]. tissue sarcoma (STS) presents as an indolent or aggres- Given the limits of current treatments, exploiting drugs sive disease, often only diagnosed at an advanced and/or targeting metabolic pathways may pave the way to efec- metastatic stages. Current sarcoma classifcation relies tive therapy for these largely incurable diseases. on histopathology that may lead to errors in up to a quar- Aggressive tumors must survive in a reorganized, ter of cases [5]. In terms of prevalence, they represent less stressful and metabolically competitive microenviron- than 1% of adult cancers, but up to one ffth of pediat- ment. Tis necessary adaptation exploits tumor heteroge- ric solid malignant cancers [3]. Surgery is the standard neity and cell networks in the tumor microenvironment. of care for patients supplemented with chemotherapy Furthermore, within a given cell, plasticity depends on or radiotherapy [6]. Targeted therapies remain limited interconnections between various metabolic pathways to to tumors with well-defned oncogenic drivers [1, 2]. adapt growth to the available metabolites. A major trait Clinical trials targeting immune checkpoints show low often amplifed in these tumors is the use of aerobic gly- response rates, with few responsive histotypes. Finally, colysis, known as the Warburg efect [13], that optimizes biomarkers or tertiary lymphoid structures may be be tumor cell growth through provision of building blocks predictive tools for 10% of patients [7]. Consequently, to increase biomass [14]. Since Warburg’s discovery, a debate has existed about the persistence of mitochon- drial activity in glycolytic tumors and its potential to be a *Correspondence: [email protected]‑mrs.fr; [email protected]‑mrs.fr drug target [15]. Despite the central role of mitochondria 1 Centre National de la Recherche Scientifque, Institut National de la Santé et de la Recherche Médicale, Centre d’Immunologie de Marseille not only in cell energetics, homeostasis and stress sens- Luminy, Aix Marseille Univ, Marseille, France ing [16] but also reactive oxygen species (ROS) produc- Full list of author information is available at the end of the article tion [17] their contribution to oncogenic transformation © The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. 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In some STS, germline mutations afect- interconversion (PGI) pathway, also amplifed in the Yang ing mitochondrial enzymes lead to the accumulation of Huang syndrome described in the context of traditional oncometabolites that induce a pseudo-hypoxic response Chinese pharmacology [21]. Te PGI pathway relies on and alter epigenetic marks and diferentiation [18]. In UDP-glucuronosyltransferase (UGT) enzymes that cata- the tumor microenvironment, glycolytic and oxidizing lyze the binding of d-glucuronic acid to toxic substances cells may compete or cooperate for an optimal use and or endogenous compounds such as bilirubin via glyco- exchange of energetic metabolites. Tis network involves sidic bonds, contributing to the detoxifcation of lipo- immune cells that adapt their metabolism to exert their philic compounds or glucuronides. functions in this competitive environment [19]. Te Exploration of the TCGA database allows one to iden- purpose of this review is to link recent fndings on STS tify more discrete signatures displayed by major STS sub- genetics to the alterations of intracellular pathways types versus other types of cancers. As shown in Fig. 1, afecting their tumor metabolic landscapes. Although all cancer types display abnormalities in cell cycle regu- not necessarily specifc to STS, they may represent novel lation. Most carcinomas show an enrichment in onco- therapeutic opportunities. genic pathways, glycolytic signatures and alterations of energetic, nucleotide, amino acid or macromolecule Unsupervised omics and single cell‑based analyses pathways. When considering STS as a whole, RAS, PI3K highlight metabolic signatures in cancer and HIPPO pathways light up, as in [8], coupled to a Te development of more integrated technologies dominant glycolytic/OXPHOS signature. More discrete with increased sensitivity and/or resolution has helped signals confrm the enhancement of the PGI pathway in to unravel tumor genomic and metabolic complexity STS, although this trend is not detectable when consid- in situ and to bridge the gap between mouse models and ering individually the STS subtypes. Our analysis also patients. Several recent studies documented the power indicates that distinct signatures preferentially match of integrated genomic or metabolomic strategies to deci- with STS subtypes, with UPS featuring an enrichment in pher tumors complexity. An article from Te Cancer PPAR/fatty acids and glycine/serine/threonine pathways, Genome Atlas (TCGA) Research Network [8] combined whereas LMS display an enhanced OXPHOS signature. genetic, epigenetic and transcriptomic analyses and pro- Similarly, diferences in oncogenic pathway usage are posed a novel classifcation of STS subtypes with com- apparent but it is difcult to relate these pathways to the plex genomes. In their analysis of the number and nature metabolic bias in tumors. of copy number variations (CNVs), they identifed three Te improvement of chromatographic and mass dominant profles from leiomyosarcoma (LMS), myxo- spectrometry (MS) analyses such as Ultra High Per- fbrosarcoma (MFS), undiferentiated pleomorphic sar- formance Liquid Chromatography Q-Exactive MS coma (UPS) to dediferentiated liposarcoma (DDLPS) (UHPLC-QE MS) has allowed time to be saved in sam- displaying the highest level of genomic alterations. In ple separation while preserving the detection capacity addition to these modifcations, the nature of epigenet- of a large spectrum of metabolites. In osteosarcoma ics marks, activating pathways or immune signatures (OS), studies combining state-of-the-art transcriptomic add further prognostic value. Another article exploited and metabolomics approaches highlighted the ampli- TCGA data to describe the relative contribution of 114 fcation of nucleotide and amino acid (namely alanine, metabolic pathways to cancer progression [20]. Tis anal- aspartate, glutamate, arginine, proline, methionine) ysis showed that master metabolic transcriptional regu- pathways, glycolysis and the pentose phosphate shunt lators behave as genetic drivers explaining the metabolic [26, 27]. Spatially-correlated analysis, mass spectrom- profles displayed by various tumors compared to nor- etry imaging (MALDI-MSI) can further reveal how mal tissues, and help predict responsiveness to metabo- biomolecular ions are distributed on tissue sections,