bims-myxlip Biomed News
on Myxoid liposarcoma
Issue of 2022‒07‒24
two papers selected by
Laura Mannarino
Humanitas Research

  1. Cancer Med. 2022 Jul 18.
      BACKGROUND: The Complexity INdex in SARComas (CINSARC) is a transcriptional signature derived from the expression of 67 genes involved in mitosis control and chromosome integrity. This study aims to assess CINSARC value of in an independent series of high-risk patients with localized soft tissue sarcoma (STS) treated with preoperative chemotherapy within a prospective, randomized, phase III study (ISG-STS 1001).PATIENTS AND METHODS: Patients with available pre-treatment samples, treated with 3 cycles of either standard (ST) preoperative or histotype-tailored (HT) chemotherapy, were scored according to CINSARC (low-risk, C1; high-risk, C2). The 10-year overall survival probability (pr-OS) according to SARCULATOR was calculated, and patients were classified accordingly (low-risk, Sarc-LR, 10-year pr-OS>60%; high-risk, Sarc-HR, 10-year pr-OS<60%). Survival functions were estimated using the Kaplan-Meier method and compared using log-rank test.
    RESULTS: Eighty-six patients were included, 30 C1 and 56 C2, 49 Sarc-LR and 37 Sarc-HR. A low level of agreement between CINSARC and SARCULATOR was observed (Cohen's Kappa = 0.174). The 5-year relapse-free survival in C1 and C2 were 0.57 and 0.55 (p = 0.481); 5-year metastases-free survival 0.63 and 0.64 (p = 0.740); 5-year OS 0.80 and 0.72 (p = 0.460). The 5-year OS in C1 treated with ST and HT chemotherapy was 0.84 and 0.76 (p = 0.251) respectively; in C2 treated it was 0.72 and 0.70 (p = 0.349). The 5-year OS in Sarc-LR treated with S and HT chemotherapy was 0.80 and 0.82 (p = 0.502) respectively; in Sarc-HR it was 0.70 and 0.61 (p = 0.233).
    CONCLUSIONS: Our results, although constrained by the small size of the series, suggest that CINSARC has weak prognostic power in high-risk, localized STS treated with neoadjuvant chemotherapy.
    Keywords:  CINSARC; chemotherapy; outcome; prognostication; sarcoma
  2. Cell Mol Life Sci. 2022 Jul 16. 79(8): 427
      The epithelial-to-mesenchymal transition (EMT) is a reversible process that may interact with tumour immunity through multiple approaches. There is increasing evidence demonstrating the interconnections among EMT-related processes, the tumour microenvironment, and immune activity, as well as its potential influence on the immunotherapy response. Long non-coding RNAs (lncRNAs) are emerging as critical modulators of gene expression. They play fundamental roles in tumour immunity and act as promising biomarkers of immunotherapy response. However, the potential roles of lncRNA in the crosstalk of EMT and tumour immunity are still unclear in sarcoma. We obtained multi-omics profiling of 1440 pan-sarcoma patients from 19 datasets. Through an unsupervised consensus clustering approach, we categorised EMT molecular subtypes. We subsequently identified 26 EMT molecular subtype and tumour immune-related lncRNAs (EILncRNA) across pan-sarcoma types and developed an EILncRNA signature-based weighted scoring model (EILncSig). The EILncSig exhibited favourable performance in predicting the prognosis of sarcoma, and a high-EILncSig was associated with exclusive tumour microenvironment (TME) characteristics with desert-like infiltration of immune cells. Multiple altered pathways, somatically-mutated genes and recurrent CNV regions associated with EILncSig were identified. Notably, the EILncSig was associated with the efficacy of immune checkpoint inhibition (ICI) therapy. Using a computational drug-genomic approach, we identified compounds, such as Irinotecan that may have the potential to convert the EILncSig phenotype. By integrative analysis on multi-omics profiling, our findings provide a comprehensive resource for understanding the functional role of lncRNA-mediated immune regulation in sarcomas, which may advance the understanding of tumour immune response and the development of lncRNA-based immunotherapeutic strategies for sarcoma.
    Keywords:  Epithelial-to-mesenchymal transition; LncRNA; Machine learning; Prognostic risk model; Sarcoma; Tumour immunity