Molecular Predictors of Radiotherapy Response in Sarcoma

Molecular mechanism of radiation tolerance in lung adenocarcinoma cells using single-cell RNA sequencing

The efficacy of radiotherapy, a cornerstone in the treatment of lung adenocarcinoma (LUAD), is profoundly undermined by radiotolerance. This resistance not only poses a significant clinical challenge but also compromises patient survival rates. Therefore, it is important to explore this mechanism for the treatment of LUAD. Multiple public databases were used for single-cell RNA sequencing (scRNA-seq) data. We filtered, normalized and downscaled scRNA-seq data based on the Seurat package to obtain different cell subpopulations. Subsequently, the ssGSEA algorithm was used to assess the enrichment scores of the different cell subpopulations, and thus screen the cell subpopulations that are most relevant to radiotherapy tolerance based on the Pearson method. Finally, pseudotime analysis was performed, and a preliminary exploration of gene mutations in different cell subpopulations was performed. We identified HIST1H1D+ A549 and PIF1+ A549 as the cell subpopulations related to radiotolerance. The expression levels of cell cycle-related genes and pathway enrichment scores of these two cell subpopulations increased gradually with the extension of radiation treatment time. Finally, we found that the proportion of TP53 mutations in patients who had received radiotherapy was significantly higher than that in patients who had not received radiotherapy. We identified two cellular subpopulations associated with radiotherapy tolerance, which may shed light on the molecular mechanisms of radiotherapy tolerance in LUAD and provide new clinical perspectives.

Opportunity in Complexity: Harnessing Molecular Biomarkers and Liquid Biopsies for Personalized Sarcoma Care

Due to their rarity and complexity, sarcomas represent a substantial therapeutic challenge. However, the incredible diversity within and across sarcoma subtypes presents an opportunity for personalized care to maximize efficacy and limit toxicity. A deeper understanding of the molecular alterations that drive sarcoma development and treatment response has paved the way for molecular biomarkers to shape sarcoma treatment. Genetic, transcriptomic, and protein biomarkers have become critical tools for diagnosis, prognostication, and treatment selection in patients with sarcomas. In the future, emerging biomarkers like circulating tumor DNA analysis offer the potential to improve early detection, monitoring response to treatment, and identifying mechanisms of resistance to personalize sarcoma treatment. Here, we review the current state of molecular biomarkers for sarcomas and highlight opportunities and challenges for the implementation of new technologies in the future.

Predictive Biomarkers of Pathological Response to Neoadjuvant Chemoradiotherapy for Locally Advanced Soft Tissue Sarcomas

BACKGROUND

Marginally resectable and unresectable soft tissue sarcomas (STS) remain a therapy challenge due to the lack of highly active treatment. The aim of the study was to identify a biomarker to predict the pathological response (PR) to preplanned treatment of these STSs.

METHODS

In the phase II clinical trial (NCT03651375), locally advanced STS patients received preoperative treatment with a combination of doxorubicin-ifosfamide chemotherapy and 5 × 5 Gy radiotherapy. PR to the treatment was classified using the European Organization for Research and Treatment of Cancer-Soft Tissue and Bone Sarcoma Group recommendations. We have chosen HIF-1α, CD163, CD68, CD34, CD105, and γH2AFX proteins, rendering different biological phenomena, for biomarker study.

RESULTS

Nineteen patients were enrolled and in four cases a good PR was reported. The high expression of HIF-1α before surgery showed a negative correlation with PR, which means a poor response to therapy. Furthermore, the samples after surgery had decreased expression of HIF-1α, which confirmed the correlation with PR. However, high expression of γH2AFX positively correlated with PR, which provides better PR. The high number of positive-staining TAMs and the high IMVD did not correlate with PR.

CONCLUSIONS

HIF1α and γH2AFX could be potential biomarkers for PR prediction after neoadjuvant treatment in STS.

Silencing KIF18B enhances radiosensitivity: identification of a promising therapeutic target in sarcoma

Background

Sarcomas are rare heterogeneous tumours, derived from primitive mesenchymal stem cells, with more than 100 distinct subtypes. Radioresistance remains a major clinical challenge for sarcomas, demanding urgent for effective biomarkers of radiosensitivity.

Methods

The radiosensitive gene Kinesin family member 18B (KIF18B) was mined through bioinformatics with integrating of 15 Gene Expression Omnibus (GEO) datasets and The Cancer Genome Atlas (TCGA) database. We used radiotherapy-sh-KIF18B combination to observe the anti-tumour effect in sarcoma cells and subcutaneous or orthotopic xenograft models. The KIF18B-sensitive drug T0901317 (T09) was further mined to act as radiosensitizer using the Genomics of Drug Sensitivity in Cancer (GDSC) database.

Findings

KIF18B mRNA was significantly up-regulated in most of the subtypes of bone and soft tissue sarcoma. Multivariate Cox regression analysis showed that KIF18B high expression was an independent risk factor for prognosis in sarcoma patients with radiotherapy. Silencing KIF18B or using T09 significantly improved the radiosensitivity of sarcoma cells, delayed tumour growth in subcutaneous and orthotopic xenograft model, and elongated mice survival time. Furthermore, we predicted that T09 might bind to the structural region of KIF18B to exert radiosensitization.

Interpretation

These results indicated that sarcomas with low expression of KIF18B may benefit from radiotherapy. Moreover, the radiosensitivity of sarcomas with overexpressed KIF18B could be effectively improved by silencing KIF18B or using T09, which may provide promising strategies for radiotherapy treatment of sarcoma.

Fundings

A full list of funding can be found in the Funding Sources section.