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Machine-learning detects non-invasive 5-hydroxymeythylcytosine biomarkers for predicting response to chemotherapy in patients with metastatic colorectal cancer

ABSTRACT: Background: For the majority of patients of metastatic colorectal cancer (mCRC), systematic chemotherapy has been a choice for palliative treatment. When first-line chemotherapies do not work, targeted therapies are administered. Because of inter-individual genetic and epigenetic variations, patients with mCRC who are treated with these therapies may respond differently. It is of great clinical interest to elucidate response mechanisms that could be utilized in predicting individual response to these treatments. Methods: A highly sensitive technique, the 5hmC-Seal was applied to profile genome-wide 5-hydroxymethylcytosines (5hmC) in 155 cfDNA samples from patients with mCRC recruited for two clinical trials at Zhongshan Hospital, Fudan University in China. Response to systematic chemotherapy and targeted therapy (becacizumab or cetuximab) (CR, complete response; PR, partial response; SD, stable disease; PD, progressive disease) was measured and a 5hmC-based predictive model in cfDNA was developed using a machine learning approach in the PROVE Study (NCT03679039) (n = 100), followed by validation in the METHOD Study (NCT03599947) (n = 55). Results: Genome-wide mapping of 5hmC in patient-derived cfDNA suggested a distinct 5hmC landscape showing tissue origin and gene regulatory relevance. A eleven-gene model involving relevant pathways such as Rap1 signaling pathway, cAMP signaling pathway, Phospholipase D signaling pathway and cGMP-PKG signaling pathway, distinguished patients with mCRC who responded to chemotherapy, regardless of targeted therapy, resectability, or mutation status in the validation samples (AUC=0.77; 95% CI, 0.64-0.90), outperforming RAS mutation status (AUC=0.70; 95% CI, 0.57-0.83). In addition, the 5hmC model also showed a trend of capacity for predicting progression-free survival (PFS) for mCRC. Conclusions: Genome-wide mapping reflected relevant pathways and functional interaction networks implicated in the determination of treatment response for patients with mCRC. The 5hmC-Seal model in cfDNA provided a promising non-invasive method for predicting response to systematic chemotherapy in patients with mCRC, who will be essential for personalized medicine.

ORGANISM(S): Homo sapiens

PROVIDER: GSE203071 | GEO | 2025/01/01

REPOSITORIES: GEO

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Project description:Background: Gastric cancer (GC) remains the third leading cause of cancer death worldwide due to the absence of sensitive and specific biomarkers for its early detection. 5-hydroxymethylcytosine (5hmC)-enriched gene profiles and regions show tissue-specific and tumor specific, and cell-free DNA (cfDNA) 5hmC modification feature sequencing provides a robust tool for gastric cancer detection.Methods: A matched case‒control study design with 50 gastric cancer patients and 50 controls was performed to sequence the 5hmC modification of cfDNA. Significantly differential 5hmC modification genes were identified to construct a gastric cancer diagnostic model. The data set from Gene Expression Omnibus consisted of 61 gastric cancer patients, 90 healthy individuals and 22 benign gastric disease controls was used as an external testing set to test the robustness of the diagnostic model. Results: The vast majority of 5hmC peaks were distributed in the gene body, accounting for more than 90% of the total 5hmC peaks in both the GC and control groups. The diagnostic model was developed based on five different 5hmC modification genes, FBXL7, PDE3A, TPO, SNTG2 and SXBP5. The model could clearly distinguish gastric cancer patients from controls in the training (AUC=0.95, sensitivity=88.6%, specificity=94.3%), validation (AUC=0.87, sensitivity=73.3%, specificity=93.3%) and testing (AUC=0.90, sensitivity=81.9%, specificity=90.2%) sets. The predicted risk scores of the controls were significantly lower than those of GC patients in our own data (P<0.001) or GEO external testing data (P<0.001), but no significant difference was observed between different TNM stage groups (P=0.09 and 0.66). Furthermore, the risk scores of healthy and benign gastric disease controls in the testing set were also not different (P=0.10). Conclusions: The characteristics of 5hmC in cfDNA are specific to GC patients, and the diagnostic model constructed by the 5hmC features of five genes could effectively identify GC patients.

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Machine-learning detects non-invasive 5-hydroxymeythylcytosine biomarkers for predicting response to chemotherapy in patients with metastatic colorectal cancer

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