bims-tumhet Biomed News
on Tumor Heterogeneity
Issue of 2021–07–11
thirteen papers selected by
Sergio Marchini, Humanitas Research
  1. A method of sample-wise region-set enrichment analysis for DNA methylomics.
  2. Technical and biological constraints on ctDNA-based genotyping.
  3. DNA methylation biomarkers for diagnosis of primary liver cancer and distinguishing hepatocellular carcinoma from intrahepatic cholangiocarcinoma.
  4. DNA Methylation Markers in Lung Cancer.
  5. Integration of genome-level data to allow identification of subtype-specific vulnerability genes as novel therapeutic targets.
  6. The Opportunities and Challenges of Molecular Tagging Next-Generation Sequencing in Liquid Biopsy.
  7. Multimodal Treatment of Primary Advanced Ovarian Cancer.
  8. PhyliCS: a Python library to explore scCNA data and quantify spatial tumor heterogeneity.
  9. Comprehensive Landscape of Ovarian Cancer Immune Microenvironment Based on Integrated Multi-Omics Analysis.
  10. Applicability of liquid biopsies to represent the mutational profile of tumor tissue from different cancer entities.
  11. High-grade serous tubo-ovarian cancer refined with single-cell RNA sequencing: specific cell subtypes influence survival and determine molecular subtype classification.
  12. Differential expression analysis in ovarian cancer: A functional genomics and systems biology approach.
  13. Analysis of clinical factors associated with survival in patients with soft-tissue sarcoma receiving trabectedin.
  1. Epigenomics. 2021 Jul 09.
    A method of sample-wise region-set enrichment analysis for DNA methylomics.
    Ryu Minegishi, Osamu Gotoh, Norio Tanaka, Reo Maruyama, Jeffrey T Chang, Seiichi Mori.
      Aim: Gene set analysis has commonly been used to interpret DNA methylome data. However, summarizing the DNA methylation level of a gene is challenging due to variability in the number, density and methylation levels of CpG sites, and the numerous intergenic CpGs. Instead, we propose to use region sets to annotate the DNA methylome. Methods: We developed single sample region-set enrichment analysis for DNA methylome (methyl-ssRSEA) to conduct sample-wise, region-set enrichment analysis. Results: Methyl-ssRSEA can handle both microarray- and sequencing-based platforms and reproducibly recover the known biology from the methylation profiles of peripheral blood cells and breast cancers. The performance was superior to existing tools for region-set analysis in discriminating blood cell types. Conclusion: Methyl-ssRSEA offers a novel way to functionally interpret the DNA methylome in the cell.
    Keywords:  DNA methylome; functional annotation; gene set analysis; region set analysis; sample wise analysis; transcription factor binding site
    DOI:  https://doi.org/10.2217/epi-2021-0065
  2. Trends Cancer. 2021 Jul 01. pii: S2405-8033(21)00122-9. [Epub ahead of print]
    Technical and biological constraints on ctDNA-based genotyping.
    Cameron Herberts, Alexander W Wyatt.
      Circulating tumor DNA (ctDNA) enables real-time genomic profiling of cancer without the need for tissue biopsy. ctDNA-based technology is seeing rapid uptake in clinical practice due to the potential to inform patient management from diagnosis to advanced disease. In metastatic disease, ctDNA can identify somatic mutations, copy-number variants (CNVs), and structural rearrangements that are predictive of therapy response. However, the ctDNA fraction (ctDNA%) is unpredictable and confounds variant detection strategies, undermining confidence in liquid biopsy results. Assay design also influences which types of genomic alterations are identifiable. Here, we describe the relationships between ctDNA%, methodology, and sensitivity-specificity for major classes of genomic alterations in prostate cancer. We provide recommendations to navigate the technical complexities that constrain the detection of clinically relevant genomic alterations in ctDNA.
    Keywords:  biomarker; clonal hematopoiesis; ctDNA; detection sensitivity; genotyping; mCRPC
    DOI:  https://doi.org/10.1016/j.trecan.2021.06.001
  3. Aging (Albany NY). 2021 Jul 08. 13(undefined):
    DNA methylation biomarkers for diagnosis of primary liver cancer and distinguishing hepatocellular carcinoma from intrahepatic cholangiocarcinoma.
    Yi Bai, Wen Tong, Fucun Xie, Liuyang Zhu, Hao Wu, Rui Shi, Lianjiang Wang, Long Yang, Zhisong Liu, Fei Miao, Qiang Zhao, Yaming Zhang.
      Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) are the two most common pathology subtypes of primary liver cancer (PLC). Identifying DNA methylation biomarkers for diagnosis of PLC and further distinguishing HCC from ICC plays a vital role in subsequent treatment options selection. To obtain potential diagnostic DNA methylation sites for PLC, differentially methylated CpG (DMC) sites were first screened by comparing the methylation data between normal liver samples and PLC samples (ICC samples and HCC samples). A random forest algorithm was then used to select specific DMC sites with top Gini value. To avoid overfitting, another cohort was taken as an external validation for evaluating the area under curves (AUCs) of different DMC sites combination. A similar model construction strategy was applied to distinguish HCC from ICC. In addition, we identified DNA Methylation-Driven Genes in HCC and ICC via MethylMix method and performed pathway analysis by utilizing MetaCore. Finally, we not only performed methylator phenotype based on independent prognostic sites but also analyzed the correlations between methylator phenotype and clinical factors in HCC and ICC, respectively. To diagnose PLC, we developed a model based on three PLC-specific methylation sites (cg24035245, cg21072795, and cg00261162), whose sensitivity and specificity achieved 98.8%,94.8% in training set and 97.3%,81% in validation set. Then, to further divide the PLC samples into HCC and ICC, we established another mode through three methylation sites (cg17769836, cg17591574, and cg07823562), HCC accuracy and ICC accuracy achieved 95.8%, 89.8% in the training set and 96.8%,85.4% in the validation set. In HCC, the enrichment pathways were mainly related to protein folding, oxidative stress, and glutathione metabolism. While in ICC, immune response, embryonic hepatocyte maturation were the top pathways. Both in HCC and ICC, methylator phenotype correlated well with overall survival time and clinical factors involved in tumor progression. In summary, our study provides the biomarkers based on methylation sites not only for the diagnosis of PLC but also for distinguishing HCC from ICC.
    Keywords:  diagnostic biomarker; hepatocellular carcinoma; intrahepatic cholangiocarcinoma; methylation; primary liver cancer
    DOI:  https://doi.org/10.18632/aging.203249
  4. Curr Genomics. 2021 Feb;22(2): 79-87
    DNA Methylation Markers in Lung Cancer.
    Yoonki Hong, Woo Jin Kim.
      Lung cancer is the most common cancer and the leading cause of cancer-related morbidity and mortality worldwide. As early symptoms of lung cancer are minimal and non-specific, many patients are diagnosed at an advanced stage. Despite a concerted effort to diagnose lung cancer early, no biomarkers that can be used for lung cancer screening and prognosis prediction have been established so far. As global DNA demethylation and gene-specific promoter DNA methylation are present in lung cancer, DNA methylation biomarkers have become a major area of research as potential alternative diagnostic methods to detect lung cancer at an early stage. This review summarizes the emerging DNA methylation changes in lung cancer tumorigenesis, focusing on biomarkers for early detection and their potential clinical applications in lung cancer.
    Keywords:  DNA methylation; Non-small-cell lung cancer; biomarker; epigenetic; hypomethylation; tumorigenesis
    DOI:  https://doi.org/10.2174/1389202921999201013164110
  5. Oncogene. 2021 Jul 06.
    Integration of genome-level data to allow identification of subtype-specific vulnerability genes as novel therapeutic targets.
    Edward C Schwalbe, Lalchungnunga H, Fadhel Lafta, Timothy M Barrow, Gordon Strathdee.
      The identification of cancer-specific vulnerability genes is one of the most promising approaches for developing more effective and less toxic cancer treatments. Cancer genomes exhibit thousands of changes in DNA methylation and gene expression, with the vast majority likely to be passenger changes. We hypothesised that, through integration of genome-wide DNA methylation/expression data, we could exploit this inherent variability to identify cancer subtype-specific vulnerability genes that would represent novel therapeutic targets that could allow cancer-specific cell killing. We developed a bioinformatics pipeline integrating genome-wide DNA methylation/gene expression data to identify candidate subtype-specific vulnerability partner genes for the genetic drivers of individual genetic/molecular subtypes. Using acute lymphoblastic leukaemia as an initial model, 21 candidate subtype-specific vulnerability genes were identified across the five common genetic subtypes, with at least one per subtype. To confirm the approach was applicable across cancer types, we also assessed medulloblastoma, identifying 15 candidate subtype-specific vulnerability genes across three of four established subtypes. Almost all identified genes had not previously been implicated in these diseases. Functional analysis of seven candidate subtype-specific vulnerability genes across the two tumour types confirmed that siRNA-mediated knockdown induced significant inhibition of proliferation/induction of apoptosis, which was specific to the cancer subtype in which the gene was predicted to be specifically lethal. Thus, we present a novel approach that integrates genome-wide DNA methylation/expression data to identify cancer subtype-specific vulnerability genes as novel therapeutic targets. We demonstrate this approach is applicable to multiple cancer types and identifies true functional subtype-specific vulnerability genes with high efficiency.
    DOI:  https://doi.org/10.1038/s41388-021-01923-1
  6. Mol Diagn Ther. 2021 Jul 05.
    The Opportunities and Challenges of Molecular Tagging Next-Generation Sequencing in Liquid Biopsy.
    Giuseppa De Luca, Mariella Dono.
      Liquid biopsy (LB) is a promising tool that is rapidly evolving as a standard of care in early and advanced stages of cancer settings. Next-generation sequencing (NGS) methods have become essential in molecular diagnostics and clinical laboratories dealing with LB analytes, i.e., cell-free DNA and RNA. The sensitivity and high-throughput capacity of NGS enable us to overcome technical issues that are mainly attributable to low-abundance (below 1% mutated allelic frequency) tumour genetic material circulating within biological fluids. In this context, the introduction of unique molecular identifiers (UMIs), also known as molecular barcodes, applied to various NGS platforms greatly improved the characterization of rare genetic alterations, as they resulted in a drastic reduction in background noise while maintaining high levels of positive predictive value and sensitivity. Different UMI strategies have been developed, such as single (e.g., safe-sequencing system, Safe-SeqS) or double (duplex-sequencing system, Duplex-Seq) strand-based labelling, and, currently, considerable results corroborate their potential implementation in a routine laboratory. Recently, the US Food and Drug Administration approved the clinical use of two comprehensive UMI-based NGS assays (FoundationOne Liquid CDx and Guardant360 CDx) in cfDNA mutational assessment. However, to definitively translate LB into clinical practice, UMI-based NGS protocols should meet certain feasibility requirements in terms of cost-effectiveness, wet laboratory performance and easy access to web-source and bioinformatic tools for downstream molecular data.
    DOI:  https://doi.org/10.1007/s40291-021-00542-6
  7. Anticancer Res. 2021 Jul;41(7): 3253-3260
    Multimodal Treatment of Primary Advanced Ovarian Cancer.
    Michael Friedrich, Dominique Friedrich, Clayton Kraft, Christoph Rogmans.
      Epithelial ovarian cancer is the second most common malignancy of the female genital tract, with approximately 7,400 new cases annually in Germany. With 5,500 deaths per year, ovarian cancer is the leading gynecologic cause of death. Epithelial ovarian cancer is characterized by morphologic heterogeneity with 4 molecular biological subtypes (immunoreactive-like, differentiated-like, proliferative-like, mesenchymal-like) with different prognosis. Significantly improved survival is achieved by optimal debulking with no residual disease (R0). Systematic lymphonodectomy of clinical negative lymph nodes has no effect on overall survival in advanced ovarian cancer. Interval debulking in advanced ovarian cancer after three cycles of neoadjuvant chemotherapy with carboplatin/paclitaxel is controversial. Standard chemotherapy for advanced ovarian cancer consists of six cycles of carboplatin AUC5 and paclitaxel 175 mg/m2, in a three-week cycle. Intraperitoneal chemotherapy is not a standard therapy. Anti-hormonal therapy with an aromatase inhibitor plays a minor role in therapy of both low grade serous ovarian cancer (LGSOC) and high grade serous ovarian cancer (HGSOC). A major achievement in ovarian cancer therapy has been the results of the SOLO-1 trial, in which olaparib as a first line maintenance monotherapy resulted in an overall 70% lower risk of disease progression in patients with advanced Breast Cancer Gene (BRCA)-mutated ovarian cancer.
    Keywords:  Ovarian cancer; PARP inhibition; chemotherapy; review; surgery; treatment
    DOI:  https://doi.org/10.21873/anticanres.15111
  8. BMC Bioinformatics. 2021 Jul 03. 22(1): 360
    PhyliCS: a Python library to explore scCNA data and quantify spatial tumor heterogeneity.
    Marilisa Montemurro, Elena Grassi, Carmelo Gabriele Pizzino, Andrea Bertotti, Elisa Ficarra, Gianvito Urgese.
       BACKGROUND: Tumors are composed by a number of cancer cell subpopulations (subclones), characterized by a distinguishable set of mutations. This phenomenon, known as intra-tumor heterogeneity (ITH), may be studied using Copy Number Aberrations (CNAs). Nowadays ITH can be assessed at the highest possible resolution using single-cell DNA (scDNA) sequencing technology. Additionally, single-cell CNA (scCNA) profiles from multiple samples of the same tumor can in principle be exploited to study the spatial distribution of subclones within a tumor mass. However, since the technology required to generate large scDNA sequencing datasets is relatively recent, dedicated analytical approaches are still lacking.
    RESULTS: We present PhyliCS, the first tool which exploits scCNA data from multiple samples from the same tumor to estimate whether the different clones of a tumor are well mixed or spatially separated. Starting from the CNA data produced with third party instruments, it computes a score, the Spatial Heterogeneity score, aimed at distinguishing spatially intermixed cell populations from spatially segregated ones. Additionally, it provides functionalities to facilitate scDNA analysis, such as feature selection and dimensionality reduction methods, visualization tools and a flexible clustering module.
    CONCLUSIONS: PhyliCS represents a valuable instrument to explore the extent of spatial heterogeneity in multi-regional tumour sampling, exploiting the potential of scCNA data.
    Keywords:  Algorithms; Cancer evolution; Clones; DNA; Intra-tumor heterogeneity; Single-cell sequencing
    DOI:  https://doi.org/10.1186/s12859-021-04277-3
  9. Front Oncol. 2021 ;11 685065
    Comprehensive Landscape of Ovarian Cancer Immune Microenvironment Based on Integrated Multi-Omics Analysis.
    Jiacheng Shen, Tingwei Liu, Qiaoli Bei, Shaohua Xu.
      Epithelial ovarian cancer has a low response rate to immunotherapy and a complex immune microenvironment that regulates its treatment outcomes. Understanding the immune microenvironment and its molecular basis is of great clinical significance in the effort to improve immunotherapy response and outcomes. To determine the characteristics of the immune microenvironment in ovarian cancer, we stratified ovarian cancer patients into three immune subtypes (C1, C2, and C3) using immune-related genes based on gene expression data from The Cancer Genome Atlas and found that these three subtypes had significant differences in immune characteristics and prognosis. Methylation and copy number variant analysis showed that the immune checkpoint genes that influenced immune response were significantly hypermethylated and highly deleted in the immunosuppressive C3 subtype, suggesting that epigenetic therapy may be able to reverse the efficacy of immunotherapy. In addition, the mutation frequencies of BRCA2 and CDK12 were significantly higher in the C2 subtype than in the other two subtypes, suggesting that mutation of DNA repair-related genes significantly affects the prognosis of ovarian cancer patients. Our study further elucidated the molecular characteristics of the immune microenvironment of ovarian cancer, which providing an effective hierarchical method for the immunotherapy of ovarian cancer patients, and has clinical relevance to the design of new immunotherapies and a reasonable combination strategies.
    Keywords:  immune classification; immune microenvironment; immunotherapy; multi-omics; ovarian cancer
    DOI:  https://doi.org/10.3389/fonc.2021.685065
  10. Oncogene. 2021 Jul 06.
    Applicability of liquid biopsies to represent the mutational profile of tumor tissue from different cancer entities.
    Sandra Liebs, Theresa Eder, Frederick Klauschen, Moritz Schütte, Marie-Laure Yaspo, Ulrich Keilholz, Ingeborg Tinhofer, Evelyn Kidess-Sigal, Diana Braunholz.
      Genetic investigation of tumor heterogeneity and clonal evolution in solid cancers could be assisted by the analysis of liquid biopsies. However, tumors of various entities might release different quantities of circulating tumor cells (CTCs) and cell-free DNA (cfDNA) into the bloodstream, potentially limiting the diagnostic potential of liquid biopsy in distinct tumor histologies. Patients with advanced colorectal cancer (CRC), head and neck squamous cell carcinoma (HNSCC), and melanoma (MEL) were enrolled in the study, representing tumors with different metastatic patterns. Mutation profiles of cfDNA, CTCs, and tumor tissue were assessed by panel sequencing, targeting 327 cancer-related genes. In total, 30 tissue, 18 cfDNA, and 7 CTC samples from 18 patients were sequenced. Best concordance between the mutation profile of tissue and cfDNA was achieved in CRC and MEL, possibly due to the remarkable heterogeneity of HNSCC (63%, 55% and 11%, respectively). Concordance especially depended on the amount of cfDNA used for library preparation. While 21 of 27 (78%) tissue mutations were retrieved in high-input cfDNA samples (30-100 ng, N = 8), only 4 of 65 (6%) could be detected in low-input samples (<30 ng, N = 10). CTCs were detected in 13 of 18 patients (72%). However, downstream analysis was limited by poor DNA quality, allowing targeted sequencing of only seven CTC samples isolated from four patients. Only one CTC sample reflected the mutation profile of the respective tumor. Private mutations, which were detected in CTCs but not in tissue, suggested the presence of rare subclones. Our pilot study demonstrated superiority of cfDNA- compared to CTC-based mutation profiling. It was further shown that CTCs may serve as additional means to detect rare subclones possibly involved in treatment resistance. Both findings require validation in a larger patient cohort.
    DOI:  https://doi.org/10.1038/s41388-021-01928-w
  11. Genome Med. 2021 Jul 09. 13(1): 111
    High-grade serous tubo-ovarian cancer refined with single-cell RNA sequencing: specific cell subtypes influence survival and determine molecular subtype classification.
    Siel Olbrecht, Pieter Busschaert, Junbin Qian, Adriaan Vanderstichele, Liselore Loverix, Toon Van Gorp, Els Van Nieuwenhuysen, Sileny Han, Annick Van den Broeck, An Coosemans, Anne-Sophie Van Rompuy, Diether Lambrechts, Ignace Vergote.
       BACKGROUND: High-grade serous tubo-ovarian cancer (HGSTOC) is characterised by extensive inter- and intratumour heterogeneity, resulting in persistent therapeutic resistance and poor disease outcome. Molecular subtype classification based on bulk RNA sequencing facilitates a more accurate characterisation of this heterogeneity, but the lack of strong prognostic or predictive correlations with these subtypes currently hinders their clinical implementation. Stromal admixture profoundly affects the prognostic impact of the molecular subtypes, but the contribution of stromal cells to each subtype has poorly been characterised. Increasing the transcriptomic resolution of the molecular subtypes based on single-cell RNA sequencing (scRNA-seq) may provide insights in the prognostic and predictive relevance of these subtypes.
    METHODS: We performed scRNA-seq of 18,403 cells unbiasedly collected from 7 treatment-naive HGSTOC tumours. For each phenotypic cluster of tumour or stromal cells, we identified specific transcriptomic markers. We explored which phenotypic clusters correlated with overall survival based on expression of these transcriptomic markers in microarray data of 1467 tumours. By evaluating molecular subtype signatures in single cells, we assessed to what extent a phenotypic cluster of tumour or stromal cells contributes to each molecular subtype.
    RESULTS: We identified 11 cancer and 32 stromal cell phenotypes in HGSTOC tumours. Of these, the relative frequency of myofibroblasts, TGF-β-driven cancer-associated fibroblasts, mesothelial cells and lymphatic endothelial cells predicted poor outcome, while plasma cells correlated with more favourable outcome. Moreover, we identified a clear cell-like transcriptomic signature in cancer cells, which correlated with worse overall survival in HGSTOC patients. Stromal cell phenotypes differed substantially between molecular subtypes. For instance, the mesenchymal, immunoreactive and differentiated signatures were characterised by specific fibroblast, immune cell and myofibroblast/mesothelial cell phenotypes, respectively. Cell phenotypes correlating with poor outcome were enriched in molecular subtypes associated with poor outcome.
    CONCLUSIONS: We used scRNA-seq to identify stromal cell phenotypes predicting overall survival in HGSTOC patients. These stromal features explain the association of the molecular subtypes with outcome but also the latter's weakness of clinical implementation. Stratifying patients based on marker genes specific for these phenotypes represents a promising approach to predict prognosis or response to therapy.
    Keywords:  High-grade serous tubo-ovarian cancer; Molecular subtypes; Overall survival; Prognosis; Single-cell RNA sequencing; Stromal heterogeneity; Transcriptomic markers; Tumour microenvironment
    DOI:  https://doi.org/10.1186/s13073-021-00922-x
  12. Saudi J Biol Sci. 2021 Jul;28(7): 4069-4081
    Differential expression analysis in ovarian cancer: A functional genomics and systems biology approach.
    Yinbing Zhang, Sahar Qazi, Khalid Raza.
       Background: Ovarian cancer is one of the rarest lethal oncologic diseases that have hardly any specific biomarkers. The availability of high-throughput genomic data and advancement in bioinformatics tools allow us to predict gene biomarkers and apply systems biology approaches to get better diagnosis, and prognosis of the disease with a tentative drug that may be repurposed.
    Objective: To perform genome-wide association studies using microarray gene expression of ovarian cancer and identify gene biomarkers, construction and analyze networks, perform survival analysis, and drug interaction studies for better diagnosis, prognosis, and treatment of ovarian cancer.
    Method: The gene expression profiles of both healthy and serous ovarian cancer epithelial samples were considered. We applied a series of bioinformatics methods and tools, including fold-change statistics for differential expression analysis, DisGeNET and NCBI-Gene databases for gene-disease association mapping, DAVID 6.8 for GO enrichment analysis, GeneMANIA for network construction, Cytoscape 3.8 with its plugins for network visualization, analysis, and module detection, the UALCAN for patient survival analysis, and PubChem, DrugBank and DGIdb for gene-drug interaction.
    Results: We identified 8 seed genes that were subjected for drug-gene interaction studies. Because of over-expression in all the four stages of ovarian cancer, we discern that genes HMGA1 and PSAT1 are potential therapeutic biomarkers for its diagnosis at an early stage (stage I). Our analysis suggests that there are 11 drugs common in the seed genes. However, hypermethylated seed genes HMGA1 and PSAT1 showcased a good interaction affinity with drugs cisplatin, cyclosporin, bisphenol A, progesterone, and sunitinib, and are crucial in the proliferation of ovarian cancer.
    Conclusion: Our study reveals that HMGA1 and PSAT1 can be deployed for initial screening of ovarian cancer and drugs cisplatin, bisphenol A, cyclosporin, progesterone, and sunitinib are effective in curbing the epigenetic alteration.
    Keywords:  Drug repurposing; Epithelial ovarian cancer; Gene biomarker; Microarray; Network analysis; Systems biology
    DOI:  https://doi.org/10.1016/j.sjbs.2021.04.022
  13. Anticancer Drugs. 2021 Jul 04.
    Analysis of clinical factors associated with survival in patients with soft-tissue sarcoma receiving trabectedin.
    Luka Simetić, Krešimir Blažičević, Tatjana Ladenhauser, Marin Golčić, Tea Majnarić, Davorin Herceg.
       OBJECTIVES: Soft-tissue sarcomas (STSs) are a heterogeneous group of rare malignancies. Treatment for advanced STS usually starts with anthracycline-based therapies, with no clear sequence for further treatment. A preferred option is trabectedin, especially for liposarcoma and leiomyosarcoma (L-sarcoma). However, due to severe side effects and few clinical trials, further research of the parameters affecting survival is necessary for the optimal selection of patients.
    METHODS: We retrospectively analyzed 73 consecutive patients with STS treated with trabectedin at the University Hospital Centers at Zagreb and Osijek from 2014 to 2021. Our primary goals were evaluating factors affecting progression-free survival (PFS) and overall survival (OS).
    RESULTS: The median PFS and OS for trabectedin were 3.6 months and 13.7 months, respectively. Patients with L-sarcoma exhibited longer PFS and a trend towards longer OS compared to those with non-L-sarcoma. However, these effects were primarily a result of the myxoid liposarcoma subtype, which exhibited a median PFS of 21.1 months and a median OS of 33.3 months, both significantly longer compared to non-myxoid L-sarcoma. Additionally, patients with three or more sites of metastases exhibited shorter median PFS (3.1 months vs. 3.6 months) and OS (5.7 months vs. 23.8 months) compared to only one metastatic site. There was no correlation between the PFS values of trabectedin and pazopanib and no difference in survival, regardless of the treatment sequence.
    CONCLUSIONS: Trabectedin treatment yields the greatest survival benefit in patients with myxoid liposarcoma and low metastatic burden, whereas the additional use of pazopanib provides further clinical benefit, regardless of treatment sequence.
    DOI:  https://doi.org/10.1097/CAD.0000000000001101
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