MedComm (2020). 2026 Mar 2;7(3):e70656. doi: 10.1002/mco2.70656. eCollection 2026 Mar.

ABSTRACT

Chemotherapy is the mainstay in the treatment of advanced gastric cancer (GC); yet, GC showed diverse responses to first-line chemotherapy regimens and the underlying molecular basis is still not clear. Here, we established a system that combined organoid-based chemotherapy regimen screening and transcriptome-based evaluation to identify underlying molecular signatures of different responses to chemotherapy. We generated 19 GC patient-derived organoids (PDOs) from surgically resected specimens with corresponding histological characteristics of parent tumors and tested all of the five most commonly used first-line chemotherapy regimens. Based on the treatment responses, PDOs were classified into double-sensitive, single-sensitive, and not-sensitive groups. PDOs that responded well to chemotherapy presented high expression levels of the P53 pathway genes and low expression levels of cell proliferative activity genes. Furthermore, the chemotherapy-based tumor classification of GC was established. The GC tumor classification was verified by multi-omics features from the TCGA dataset and public drug response datasets. In conclusion, this study systematically evaluated clinical chemotherapy regimens for GC and identified chemotherapy response-associated molecular signatures based on human GC organoids, which are beneficial to the precise treatments of GC.

PMID:41782964 | PMC:PMC12954136 | DOI:10.1002/mco2.70656

Clin Transl Med. 2025 Feb;15(2):e70225. doi: 10.1002/ctm2.70225.

ABSTRACT

BACKGROUND: Lung cancer is a leading cause of cancer mortality, highlighting the need for innovative non-invasive early detection methods. Although cell-free DNA (cfDNA) analysis shows promise, its sensitivity in early-stage lung cancer patients remains a challenge. This study aimed to integrate insights from epigenetic modifications and fragmentomic features of cfDNA using machine learning to develop a more accurate lung cancer detection model.

METHODS: To address this issue, a multi-centre prospective cohort study was conducted, with participants harbouring suspicious malignant lung nodules and healthy volunteers recruited from two clinical centres. Plasma cfDNA was analysed for its epigenetic and fragmentomic profiles using chromatin immunoprecipitation sequencing, reduced representation bisulphite sequencing and low-pass whole-genome sequencing. Machine learning algorithms were then employed to integrate the multi-omics data, aiding in the development of a precise lung cancer detection model.

RESULTS: Cancer-related changes in cfDNA fragmentomics were significantly enriched in specific genes marked by cell-free epigenomes. A total of 609 genes were identified, and the corresponding cfDNA fragmentomic features were utilised to construct the ensemble model. This model achieved a sensitivity of 90.4% and a specificity of 83.1%, with an AUC of 0.94 in the independent validation set. Notably, the model demonstrated exceptional sensitivity for stage I lung cancer cases, achieving 95.1%. It also showed remarkable performance in detecting minimally invasive adenocarcinoma, with a sensitivity of 96.2%, highlighting its potential for early detection in clinical settings.

CONCLUSIONS: With feature selection guided by multiple epigenetic sequencing approaches, the cfDNA fragmentomics-based machine learning model demonstrated outstanding performance in the independent validation cohort. These findings highlight its potential as an effective non-invasive strategy for the early detection of lung cancer.

KEYPOINTS: Our study elucidated the regulatory relationships between epigenetic modifications and their effects on fragmentomic features. Identifying epigenetically regulated genes provided a critical foundation for developing the cfDNA fragmentomics-based machine learning model. The model demonstrated exceptional clinical performance, highlighting its substantial potential for translational application in clinical practice.

PMID:39909829 | PMC:PMC11798665 | DOI:10.1002/ctm2.70225