Abstract: The present disclosure provides methods and systems for screening or detecting a tumor or following disease progression that may be applied to cell-free nucleic acids, such as cell-free DNA. The method may use detection of methylation signals within a single sequencing read in identified genomic regions as input features to train a machine learning model and generate a classifier useful for stratifying populations of individuals. The method may comprise extracting DNA from a cell-free sample obtained from a subject, converting the DNA for methylation sequencing, generating sequencing reads, detecting proliferative cell disorder-associated signals in the sequencing information, and training a machine learning model to provide a discriminator capable of distinguishing groups in a subject population such as healthy, cancer, or distinguishing disease subtype or stage. The method may be used for, e.g., predicting, prognosticating, and/or monitoring response to treatment, tumor load, relapse, or cancer development.

Abstract: Systems, media, compositions, methods, and kits disclosed herein relate to a panel of autoantibody biomarkers for the early detection of colon cell proliferative disorders, including colorectal cancer. The presence or levels of the autoantibodies in a biological sample for the autoantibody panels described herein may be used for classifier generation, and as inputs in machine learning models useful to classify subjects in a population for the detection of colon cell proliferative disorders.

Abstract: The present disclosure provides methods and systems for screening or detecting a tumor or following disease progression that may be applied to cell-free nucleic acids, such as cell-free DNA. The method may use detection of methylation signals within a single sequencing read in identified genomic regions as input features to train a machine learning model and generate a classifier useful for stratifying populations of individuals. The method may comprise extracting DNA from a cell-free sample obtained from a subject, converting the DNA for methylation sequencing, generating sequencing reads, detecting proliferative cell disorder-associated signals in the sequencing information, and training a machine learning model to provide a discriminator capable of distinguishing groups in a subject population such as healthy, cancer, or distinguishing disease subtype or stage. The method may be used for, e.g., predicting, prognosticating, and/or monitoring response to treatment, tumor load, relapse, or cancer development.

Abstract: The present disclosure pertains to miRNAs that are differentially expressed in samples of an individual having colon cell proliferative disorder, or having a high risk of developing colon cell proliferative disorder, as compared to the corresponding sample of an individual not having colon cell proliferative disorder, or having low risk of developing colon cell proliferative disorder, respectively. In some embodiments, the miRNAs are differentially expressed in a tissue sample or blood plasma sample of an individual having a colorectal lesion and having a high risk of developing colon cell proliferative disorder as compared to the corresponding tissue sample or blood sample of an individual having the colorectal lesion and having no risk or low risk of developing colon cell proliferative disorder. These differentially expressed miRNAs can be used as biomarkers for diagnosis, treatment, and/or prevention of a colon cell proliferative disorder, particularly, in a subject having a colorectal lesion.