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

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

PROVIDER: PRJNA838485 | ENA |

REPOSITORIES: ENA

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Project description: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.

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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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