bims-mesote 2024-12-15 papers

Issue of 2024–12–15
three papers selected by
Laura Mannarino, Humanitas Research

J Med Imaging (Bellingham). 2024 Nov;11(6): 064501

Radiomics for differentiation of somatic BAP1 mutation on CT scans of patients with pleural mesothelioma.

Mena Shenouda, Abbas Shaikh, Ilana Deutsch, Owen Mitchell, Hedy L Kindler, Samuel G Armato.

Purpose: The BRCA1-associated protein 1 (BAP1) gene is of great interest because somatic (BAP1) mutations are the most common alteration associated with pleural mesothelioma (PM). Further, germline mutation of the BAP1 gene has been linked to the development of PM. This study aimed to explore the potential of radiomics on computed tomography scans to identify somatic BAP1 gene mutations and assess the feasibility of radiomics in future research in identifying germline mutations.
Approach: A cohort of 149 patients with PM and known somatic BAP1 mutation status was collected, and a previously published deep learning model was used to first automatically segment the tumor, followed by radiologist modifications. Image preprocessing was performed, and texture features were extracted from the segmented tumor regions. The top features were selected and used to train 18 separate machine learning models using leave-one-out cross-validation (LOOCV). The performance of the models in distinguishing between BAP1-mutated (BAP1+) and BAP1 wild-type (BAP1-) tumors was evaluated using the receiver operating characteristic area under the curve (ROC AUC).
Results: A decision tree classifier achieved the highest overall AUC value of 0.69 (95% confidence interval: 0.60 and 0.77). The features selected most frequently through the LOOCV were all second-order (gray-level co-occurrence or gray-level size zone matrices) and were extracted from images with an applied transformation.
Conclusions: This proof-of-concept work demonstrated the potential of radiomics to differentiate among BAP1+/- in patients with PM. Future work will extend these methods to the assessment of germline BAP1 mutation status through image analysis for improved patient prognostication.

Keywords: BRCA1-associated protein 1; U-Net; classification; computed tomography scan; deep learning; mesothelioma; somatic mutation

DOI: https://doi.org/10.1117/1.JMI.11.6.064501

Cancer Treat Res Commun. 2024 Dec 02. pii: S2468-2942(24)00068-6. [Epub ahead of print]42 100856

The prevalence and prognostic significance of Sarcopenia and Adipopenia in Pleural Mesothelioma.

Andrew C Kidd, Gordon W Cowell, Geoffrey A Martin, Jenny Ferguson, Dean A Fennell, Matt Evison, Kevin G Blyth.

INTRODUCTION: Altered body composition is associated with adverse survival in multiple cancers. We determined the prevalence, prognostic significance and clinicopathological correlates of sarcopenia and adipopenia in Pleural Mesothelioma (PM) patients receiving chemotherapy.
METHODS: We performed a multi-centre retrospective cohort study. Clinical data and CT images were retrieved for 111 patients from 4 UK centres. Skeletal muscle (at L3 and T4) and fat tissue areas (at L3 only) were measured on pre- and post-chemotherapy CT scans (ImageJ software) and normalised for height. Pre-chemotherapy sarcopenia and adipopenia were defined using validated thresholds, where available or indices <25th percentile. Muscle/fat loss were defined by < 0 % change (%∆) between CT scans. Extreme muscle/fat loss were defined by <25th percentile of %∆. Overall survival associations were evaluated using Kaplan-Meier methodology ± Cox proportional hazards models.
RESULTS: T4 and L3 measurements were possible in 111/111 and 91/111 (82 %). L3 sarcopenia was observed at baseline in 35 % (32/91); all other features were observed in 25 % at baseline, as defined a priori. Body composition changes during chemotherapy were heterogeneous. Overall, 61.5 % and 53.1 % patients lost muscle at L3 and T4. 60.4 % lost fat (at L3 only). Extreme T4 muscle loss and total fat loss were independently prognostic (HR 2.99, p < 0.001; HR 1.92, p = 0.014). Pre-chemotherapy T4 muscle indices were inversely associated with age. No associations were observed with tumour volume, histology, weight, inflammatory markers.
CONCLUSION: T4 muscle indices were feasible in all cases and outperformed L3 values in prognostication. Extreme T4 muscle and total fat loss were independently prognostic.

Keywords: Adipopenia; Cachexia; Mesothelioma; Sarcopenia; Survival

DOI: https://doi.org/10.1016/j.ctarc.2024.100856

J Cancer. 2024 ;15(20): 6505-6520

Establishment and bioinformatics analysis of a four-miRNA prognostic signature for pleural mesothelioma.

Xi Yang, Yaru Lin, Baoru Dong, Bin Li, Ruai Liu, Xinmeng Wang, Jinsong Li, Xue Cheng, Zhengliang Li, Wei Xiong.

Objective: Pleural mesothelioma (PM), an uncommon yet highly aggressive malignant neoplasm, has a very poor prognosis with a median survival of less than one year after diagnosis, morbidity and mortality due to PM are on the rise year by year worldwide. Our research aims to utilize molecular characteristics and microRNAs (miRNAs) as a breakthrough in predicting the survival of PM patients, hoping to find a molecular mechanism that can predict the survival of PM patients. Methods: The miRNA expression profiles and corresponding clinical information of patients with PM were obtained from The Cancer Genome Atlas (TCGA) database, a miRNA-based prognostic signature was developed using Cox regression analysis in the training cohort, which was validated in the testing cohort and complete cohort. The association between miRNA levels and survival outcomes was determined, the miRNAs in prognostic model were experimentally validated by quantitative real-time PCR (qRT-PCR) in cell lines. Target genes of prognostic miRNAs were identified using TargetScan, miRDB, and miRTarBase databases, biological function prediction of which was accomplished by GO and KEGG analysis. Gene Expression Omnibus (GEO) database was utilized for core targets recognition, immune infiltration and survival analysis were conducted to investigate the relationship between core targets and immune cells by bioinformatics analysis. Results: This miRNA-related prognostic risk model can effectively stratify patients into high-risk and low-risk groups, and have good sensitivity and specificity to assess the prognosis of patients with PM, which can also be used as an independent prognostic factor for overall survival (OS) prediction in patients with PM, the OS for patients in high-risk group was significantly poorer compared with patients in low-risk group. Moreover, all four miRNAs (hsa-miR-181a-2-3p, hsa-miR-491-5P, hsa-miR-503-5p, and hsa-miR-3934-5p) were found to be differentially expressed in PM cell lines as compared with normal cell line, GO and KEGG analysis revealed that target genes of miRNAs in prognostic model were involved in multiple tumor-associated signaling pathways and functions in PM, core miRNA targets also correlated with immune cell infiltration, indicating their potential role in PM initiation and progression. Conclusions: A robust four-miRNA prognostic signature with great performances in prediction of the OS for PM patients was developed in our study, providing new avenues for the prognostic predication of PM.

Keywords: Bioinformatics; Biomarker; Gene Expression Omnibus (GEO); Pleural mesothelioma (PM); Prognostic signature; The Cancer Genome Atlas (TCGA)

DOI: https://doi.org/10.7150/jca.101914