Metadata for ELIFE paper about integrating TCR sequencing data to select and prioritize neoantigen.Processed data used in this paper.article : DOI: https://doi.org/10.7554/eLife.94658.2

Metadata for ELIFE paper about integrating TCR sequencing data to select and prioritize neoantigen.Processed data used in this paper.article : DOI: https://doi.org/10.7554/eLife.94658.2

Aims: Early detection of colorectal cancer (CRC) provides substantially better survival rates. This study aimed to develop a blood-based screening assay named SPOT-MAS (‘screen for the presence of tumor by DNA methylation and size’) for early CRC detection with high accuracy. Methods: Plasma cell-free DNA samples from 159 patients with nonmetastatic CRC and 158 healthy controls were simultaneously analyzed for fragment length and methylation profiles. We then employed a deep neural network with fragment length and methylation signatures to build a classification model. Results: The model achieved an area under the curve of 0.989 and a sensitivity of 96.8% at 97% specificity in detecting CRC. External validation of our model showed comparable performance, with an area under the curve of 0.96. Conclusion: SPOT-MAS based on integration of cancer-specific methylation and fragmentomic signatures could provide high accuracy for early-stage CRC detection. A novel blood test for early detection of colorectal cancer. Colorectal cancer is a cancer of the colon or rectum, located at the lower end of the digestive tract. The early detection of colorectal cancer can help people with the disease have a higher chance of survival and a better quality of life. Current screening methods can be invasive, cause discomfort or have low accuracy; therefore newer screening methods are needed. In this study we developed a new screening method, called SPOT-MAS, which works by measuring the signals of cancer DNA in the blood. By combining different characteristics of cancer DNA, SPOT-MAS could distinguish blood samples of people with colorectal cancer from those of healthy individuals with high accuracy. SPOT-MAS technology combines methylation and fragmentomic signatures of blood-based circulating tumor DNA in a multimodal deep-learning analysis to enable early detection of colorectal cancer with high accuracy.

Aims: Early detection of colorectal cancer (CRC) provides substantially better survival rates. This study aimed to develop a blood-based screening assay named SPOT-MAS (‘screen for the presence of tumor by DNA methylation and size’) for early CRC detection with high accuracy. Methods: Plasma cell-free DNA samples from 159 patients with nonmetastatic CRC and 158 healthy controls were simultaneously analyzed for fragment length and methylation profiles. We then employed a deep neural network with fragment length and methylation signatures to build a classification model. Results: The model achieved an area under the curve of 0.989 and a sensitivity of 96.8% at 97% specificity in detecting CRC. External validation of our model showed comparable performance, with an area under the curve of 0.96. Conclusion: SPOT-MAS based on integration of cancer-specific methylation and fragmentomic signatures could provide high accuracy for early-stage CRC detection. A novel blood test for early detection of colorectal cancer. Colorectal cancer is a cancer of the colon or rectum, located at the lower end of the digestive tract. The early detection of colorectal cancer can help people with the disease have a higher chance of survival and a better quality of life. Current screening methods can be invasive, cause discomfort or have low accuracy; therefore newer screening methods are needed. In this study we developed a new screening method, called SPOT-MAS, which works by measuring the signals of cancer DNA in the blood. By combining different characteristics of cancer DNA, SPOT-MAS could distinguish blood samples of people with colorectal cancer from those of healthy individuals with high accuracy. SPOT-MAS technology combines methylation and fragmentomic signatures of blood-based circulating tumor DNA in a multimodal deep-learning analysis to enable early detection of colorectal cancer with high accuracy.