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Overview

Soft tissue sarcomas (STS) represent a large group of rare and ultra-rare tumors distinguished by unique morphological, molecular and clinical features. Patients with such rare cancers are generally underrepresented in clinical trials which has limited the introduction of new treatment options and subsequent improvement of patient outcomes. Preclinical models of STS that recapitulate the human disease can aid progress in identifying new effective treatments. However, due to the rarity of these tumors there are limited STS models available. Here we review the existing preclinical models of STS, including patient-derived cell lines and organoids, patient-derived xenografts and genetically engineered mouse models. We discuss the advantages and disadvantages of the different models and describe to what extent they have aided clinical translation. Finally, we consider what can be done in the future to enhance their predictivity in the preclinical setting.

01 Aug 2024·eBioMedicine

Spatial distribution of tumour immune infiltrate predicts outcomes of patients with high-risk soft tissue sarcomas after neoadjuvant chemotherapy

Article

Author: Coindre, Jean Michel ; Pasquali, Sandro ; Rivoltini, Licia ; Romagosa, Cleofe ; Barisella, Marta ; Grignani, Giovanni ; Bague, Silvia ; Casiraghi, Elena ; Bergamaschi, Laura ; Beveridge, Robert Diaz ; Gronchi, Alessandro ; Lalli, Luca ; Collini, Paola ; Lopez-Pousa, Antonio ; Blay, Jean-Yves ; Sbaraglia, Marta ; Quagliuolo, Vittorio ; Vallacchi, Viviana ; Vergani, Barbara ; Stacchiotti, Silvia ; Dei Tos, Angelo Paolo ; Renne, Salvatore Lorenzo ; Brich, Silvia ; Martin-Broto, Javier ; Palmerini, Emanuela ; Casali, Paolo Giovanni

BACKGROUNDAnthracycline-based neoadjuvant chemotherapy (NAC) may modify tumour immune infiltrate. This study characterized immune infiltrate spatial distribution after NAC in primary high-risk soft tissue sarcomas (STS) and investigate association with prognosis.METHODSThe ISG-STS 1001 trial randomized STS patients to anthracycline plus ifosfamide (AI) or a histology-tailored (HT) NAC. Four areas of tumour specimens were sampled: the area showing the highest lymphocyte infiltrate (HI) at H&E; the area with lack of post-treatment changes (highest grade, HG); the area with post-treatment changes (lowest grade, LG); and the tumour edge (TE). CD3, CD8, PD-1, CD20, FOXP3, and CD163 were analyzed at immunohistochemistry and digital pathology. A machine learning method was used to generate sarcoma immune index scores (SIS) that predict patient disease-free and overall survival (DFS and OS).FINDINGSTumour infiltrating lymphocytes and PD-1+ cells together with CD163+ cells were more represented in STS histologies with complex compared to simple karyotype, while CD20+ B-cells were detected in both these histology groups. PD-1+ cells exerted a negative prognostic value irrespectively of their spatial distribution. Enrichment in CD20+ B-cells at HI and TE areas was associated with better patient outcomes. We generated a prognostic SIS for each tumour area, having the HI-SIS the best performance. Such prognostic value was driven by treatment with AI.INTERPRETATIONThe different spatial distribution of immune populations and their different association with prognosis support NAC as a modifier of tumour immune infiltrate in STS.FUNDINGPharmamar; Italian Ministry of Health [RF-2019-12370923; GR-2016-02362609]; 5 × 1000 Funds-2016, Italian Ministry of Health; AIRC Grant [ID#28546].

01 Jul 2024·Cancer Medicine

Proteomic profiling improves prognostic risk stratification of the Sarculator nomogram in soft tissue sarcomas of the extremities and trunk wall

Article

Author: Chadha, Madhumeeta ; Strauss, Dirk C. ; Chen, Liang ; Pasquali, Sandro ; Callegaro, Dario ; Thway, Khin ; Tam, Yuen Bun ; Gronchi, Alessandro ; Iadecola, Sara ; Huang, Paul H. ; Jones, Robin L. ; Miceli, Rosalba ; Burns, Jessica ; Arthur, Amani ; Jenks, Andrew ; Chudasama, Priya ; Pankova, Valeriya ; Wilding, Christopher P.

AbstractBackgroundHigh‐risk soft tissue sarcomas of the extremities and trunk wall (eSTS), as defined by the Sarculator nomogram, are more likely to benefit from (neo)adjuvant anthracycline‐based therapy compared to low/intermediate‐risk patients. The biology underpinning these differential treatment outcomes remain unknown.MethodsWe analysed proteomic profiles and clinical outcomes of 123 eSTS patients. A Cox model for overall survival including the Sarculator was fitted to individual data to define four risk groups. A DNA replication protein signature‐Sarcoma Proteomic Module 6 (SPM6) was evaluated for association with clinicopathological factors and risk groups. SPM6 was added as a covariate together with Sarculator in a multivariable Cox model to assess improvement in prognostic risk stratification.ResultsDNA replication and cell cycle proteins were upregulated in high‐risk versus very low‐risk patients. Evaluation of the functional effects of CRISPR‐Cas9 gene knockdown of proteins enriched in high‐risk patients using the cancer cell line encyclopaedia database identified candidate drug targets. SPM6 was significantly associated with tumour malignancy grade (p = 1.6e‐06), histology (p = 1.4e‐05) and risk groups (p = 2.6e‐06). Cox model analysis showed that SPM6 substantially contributed to a better calibration of the Sarculator nomogram (Index of Prediction Accuracy = 0.109 for Sarculator alone versus 0.165 for Sarculator + SPM6).ConclusionsRisk stratification of patient with STS is defined by distinct biological pathways across a range of cancer hallmarks. Incorporation of SPM6 protein signature improves prognostic risk stratification of the Sarculator nomogram. This study highlights the utility of integrating protein signatures for the development of next‐generation nomograms.

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Pipeline

Pipeline Snapshot as of 08 Dec 2025

The statistics for drugs in the Pipeline is the current organization and its subsidiaries are counted as organizations,Early Phase 1 is incorporated into Phase 1, Phase 1/2 is incorporated into phase 2, and phase 2/3 is incorporated into phase 3

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