Aim: Cancers lacking standard screening (LSS) options account for approximately 70% of cancer-related deaths due to late-stage diagnosis. Circulating tumor DNA (ctDNA) is a promising biomarker for multi-cancer early detection. We previously developed SPOT-MAS, a multimodal ctDNA-based assay analyzing methylation and fragmentomic profiles, effective in detecting common cancers (breast, colorectal, liver, lung and gastric). This study extends the analysis to five LSS cancers: endometrial, esophageal, head and neck, ovarian and pancreatic. Methods: SPOT-MAS was applied to profile cfDNA methylation and fragmentomic patterns in 739 healthy individuals and 135 LSS cancer patients. Results: We identified 347 differentially methylated regions and observed genome-wide hypomethylation across all five LSS cancers. Esophageal and head and neck cancers showed an enrichment of short cfDNA fragments (<150 bp). Eleven 4-mer end motifs were consistently altered in cfDNA fragments across all LSS cancers. Many significant signatures were consistent with previous observations in common cancers. Notably, SPOT-MAS achieved 96.2% specificity and 74.8% overall sensitivity, with a lower sensitivity of 60.7% in early-stage cancers. Conclusion: This proof-of-concept study demonstrates that SPOT-MAS a non-invasive test trained on five common cancer types, could detect a number of LSS cancer cases, potentially complementing existing screening programs. Many cancers do not have standard tests, so they are often found too late, which leads to about 70% of cancer deaths. We've created a blood test that can help find cancer early. This test has already worked well for common cancers like breast and lung cancer, and now we're testing it on five harder-to-detect cancers: endometrial, esophageal, head and neck, ovarian and pancreatic cancers. In our study, we tested our blood test on 739 healthy people and 135 patients with these difficult cancers. Our method correctly identified healthy people 96.2% of the time and found cancer cases 74.8% of the time. This new test could help with screening for types of cancer that do not have good tests right now. Approximately 70% of cancer deaths result from late-stage diagnoses in cancers lacking standard-of-care screening (LSS). The SPOT-MAS test is a ctDNA-based assay designed to detect five common cancers (breast, lung, colorectal, gastric and liver) and has been extended to five additional LSS cancers: endometrial, esophageal, head and neck, ovarian and pancreatic. The assay analyzed cfDNA methylation and fragmentomic patterns in 135 LSS cancer patients and 739 healthy individuals. It identified 347 differentially methylated regions and specific fragmentomic alterations. SPOT-MAS achieved 96.2% specificity and 74.8% overall sensitivity, with 60.7% sensitivity for early-stage detection. This study suggests that SPOT-MAS could serve as a non-invasive method for detecting LSS cancers.

Aim: Cancers lacking standard screening (LSS) options account for approximately 70% of cancer-related deaths due to late-stage diagnosis. Circulating tumor DNA (ctDNA) is a promising biomarker for multi-cancer early detection. We previously developed SPOT-MAS, a multimodal ctDNA-based assay analyzing methylation and fragmentomic profiles, effective in detecting common cancers (breast, colorectal, liver, lung and gastric). This study extends the analysis to five LSS cancers: endometrial, esophageal, head and neck, ovarian and pancreatic. Methods: SPOT-MAS was applied to profile cfDNA methylation and fragmentomic patterns in 739 healthy individuals and 135 LSS cancer patients. Results: We identified 347 differentially methylated regions and observed genome-wide hypomethylation across all five LSS cancers. Esophageal and head and neck cancers showed an enrichment of short cfDNA fragments (<150 bp). Eleven 4-mer end motifs were consistently altered in cfDNA fragments across all LSS cancers. Many significant signatures were consistent with previous observations in common cancers. Notably, SPOT-MAS achieved 96.2% specificity and 74.8% overall sensitivity, with a lower sensitivity of 60.7% in early-stage cancers. Conclusion: This proof-of-concept study demonstrates that SPOT-MAS a non-invasive test trained on five common cancer types, could detect a number of LSS cancer cases, potentially complementing existing screening programs. Many cancers do not have standard tests, so they are often found too late, which leads to about 70% of cancer deaths. We've created a blood test that can help find cancer early. This test has already worked well for common cancers like breast and lung cancer, and now we're testing it on five harder-to-detect cancers: endometrial, esophageal, head and neck, ovarian and pancreatic cancers. In our study, we tested our blood test on 739 healthy people and 135 patients with these difficult cancers. Our method correctly identified healthy people 96.2% of the time and found cancer cases 74.8% of the time. This new test could help with screening for types of cancer that do not have good tests right now. Approximately 70% of cancer deaths result from late-stage diagnoses in cancers lacking standard-of-care screening (LSS). The SPOT-MAS test is a ctDNA-based assay designed to detect five common cancers (breast, lung, colorectal, gastric and liver) and has been extended to five additional LSS cancers: endometrial, esophageal, head and neck, ovarian and pancreatic. The assay analyzed cfDNA methylation and fragmentomic patterns in 135 LSS cancer patients and 739 healthy individuals. It identified 347 differentially methylated regions and specific fragmentomic alterations. SPOT-MAS achieved 96.2% specificity and 74.8% overall sensitivity, with 60.7% sensitivity for early-stage detection. This study suggests that SPOT-MAS could serve as a non-invasive method for detecting LSS cancers.

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.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.