Abstract: Methods and systems are disclosed for component deconvolution by a mixture model based on methylation information. A mixture model may be trained agnostic of labels or known component contributions. A system generates a methylation signature for each of a plurality of training samples. The methylation signature may be based on a count or a percentage of a methylation variant(s) expressed in the methylation sequence reads of a training sample at each genomic region of a plurality of genomic regions. The system may train the mixture model using maximum likelihood estimation to deconvolve the component contributions. The mixture model may comprise component submodels and a deconvolution submodel. The component submodels predict a component likelihood based on the methylation signature. The deconvolution submodel predicts the component contributions based on the component likelihoods.

Abstract: A processing system uses a Bayesian inference based model for targeted sequencing or variant calling. In an embodiment, the processing system generates candidate variants of a cell free nucleic acid sample. The processing system determines likelihoods of true alternate frequencies for each of the candidate variants in the cell free nucleic acid sample and in a corresponding genomic nucleic acid sample. The processing system filters or scores the candidate variants by the model using at least the likelihoods of true alternate frequencies. The processing system outputs the filtered candidate variants, which may be used to generate features for a predictive cancer or disease model.

Abstract: Systems and methods for predicting survival outcomes in patients diagnosed with Myelodysplastic Syndrome (MDS) are disclosed. One method may include: receiving DNA sequencing data derived from a methylation assay performed on a biological sample associated with the at least one patient; computing methylation beta-values for one or more CpG-sites identified in the sequencing data; identifying one or more differentially methylated regions (DMRs) based on statistical analysis of the methylation beta-values for the one or more CpG-sites; selecting, via a feature selection process, a subset of the one or more DMRs to utilize as training data; and training, using the training data, the classifier to predict the survival outcome of the at least one patient. Other aspects are described and claimed.

Abstract: The present description provides a cancer assay panel for targeted detection of cancer-specific methylation patterns. Further provided herein are methods of designing, making, and using the cancer assay panel for the diagnosis of cancer.

Abstract: Systems and methods for detecting a subtype of a disease state and for determining the development of a resistance mechanism in a disease are disclosed. One method may include: receiving, at an input component of the system, a set of sequence reads associated with a nucleic acid sample; generating, using a processor of the system and via analysis of the set of sequence reads, methylation data; and analyzing, using the processor, the methylation data to identify the subtype of the disease state. Another method may include: obtaining methylation data from a targeted methylation sequencing assay, applying the methylation data to a trained machine learning model, and receiving an output indicating whether MRD is present in a test subject and/or whether a resistance mechanism has been developed by a disease. Other aspects are described and claimed.

Abstract: The present description provides a cancer assay panel for targeted detection of cancer-specific methylation patterns. Further provided herein are methods of designing, making, and using the cancer assay panel for the diagnosis of cancer.

Abstract: A computer-implemented method for generating a tumor fraction estimate from a DNA sample of a subject is disclosed. The method may include receiving a dataset of methylation sequence reads from the sample of the subject. The method may also include dividing the dataset into a plurality of variants. The method may further include determining methylation states of the plurality of variants. The method may further include filtering the plurality of variants based on a bank of reference sequence reads to generate a filtered subset of variants. The bank may include reads generated from non-cancer samples and biopsy samples of a plurality of tissues of reference individuals. The counts of the methylation states of variants in the filtered subset are determined and input to a model that is trained based on recurrence rates of the variants in the reference sequence reads. The tumor fraction estimate may be generated by the model.

Abstract: The present description provides a cancer assay panel for targeted detection of cancer-specific methylation patterns. Further provided herein are methods of designing, making, and using the cancer assay panel for the diagnosis of cancer.

Abstract: The present description provides a cancer assay panel for targeted detection of cancer-specific methylation patterns. Further provided herein are methods of designing, making, and using the cancer assay panel for the diagnosis of cancer.

Abstract: Technical solutions for classifying patients with respect to multiple cancer classes are provided. The classification can be done using cell-free whole genome sequencing information from subjects. A reference set of subjects is used to train classifiers to recognize genomic markers that distinguish such cancer classes. The classifier training includes dividing the reference genome into a set of non-overlapping bins, applying a dimensionality reduction method to obtain a feature set, and using the feature set to train classifiers. For subjects with unknown cancer class, the trained classifiers provide probabilities or likelihoods that the subject has a respective cancer class for each cancer in a set of cancer classes. The present disclosure thus describes methods to improve the screening and detection of cancer class from among several cancer classes. This serves to facilitate early and appropriate treatment for subjects afflicted with cancer.

Abstract: The present description provides a cancer assay panel for targeted detection of cancer-specific methylation patterns. Further provided herein are methods of designing, making, and using the cancer assay panel for the diagnosis of cancer.

Abstract: Systems and methods for identifying variant alleles as somatic or germline are provided. Reference and variant alleles for a genomic position are identified. Methylation states and sequences of nucleic acid fragment sequences that map to the genomic position are obtained from a sample of a subject. Using the sequences of nucleic acid fragment sequences, each nucleic acid fragment sequence that has the reference allele is assigned to a reference subset, and each nucleic acid fragment sequence that has the variant allele is assigned to a variant subset. One or more indications of the methylation states across the nucleic acid fragment sequences in the variant subset and an indication of the number of nucleic acid fragment sequences in the reference subset versus the variant subset are applied to a trained binary classifier. An identification of the variant allele at the genomic position as somatic or germline is obtained from the classifier.

Abstract: Technical solutions for classifying patients with respect to multiple cancer classes are provided. The classification can be done using cell-free whole genome sequencing information from subjects. A reference set of subjects is used to train classifiers to recognize genomic markers that distinguish such cancer classes. The classifier training includes dividing the reference genome into a set of non-overlapping bins, applying a dimensionality reduction method to obtain a feature set, and using the feature set to train classifiers. For subjects with unknown cancer class, the trained classifiers provide probabilities or likelihoods that the subject has a respective cancer class for each cancer in a set of cancer classes. The present disclosure thus describes methods to improve the screening and detection of cancer class from among several cancer classes. This serves to facilitate early and appropriate treatment for subjects afflicted with cancer.

Abstract: The present description provides a cancer assay panel for targeted detection of cancer-specific methylation patterns. Further provided herein are methods of designing, making, and using the cancer assay panel for the diagnosis of cancer.

Abstract: Aspects of the invention include methods and systems for identifying somatic mutational signatures for detecting, diagnosing, monitoring and/or classifying cancer in a patient known to have, or suspected of having cancer. In various embodiments, the methods of the invention use a non-negative matrix factorization (NMF) approach to construct a signature matrix that can be used to identify latent signatures in a patient sample for detection and classification of cancer. In some embodiments, the methods of the invention may use principal components analysis (PCA) or vector quantization (VQ) approaches to construct a signature matrix.

Abstract: Disclosed herein are systems and methods for localization of a disease state (e.g., tissue of origin of cancer) using nucleic acid samples. In an embodiment, a method comprises receiving a plurality of cancer signals of a sample, each cancer signal indicating a probability that the sample is associated with a different disease state of a plurality of disease states. The method determines a first cancer signal having a greatest probability among the plurality of cancer signals. In accordance with a determination that the first cancer signal satisfies a criterion, the method associates the sample with a first disease state. In accordance with a determination that the first cancer signal does not satisfy the criterion, the method determines a second cancer signal having a second greatest probability among the plurality of cancer signals, and associates the sample with the first disease state and a second disease state.

Abstract: The present description provides a cancer assay panel for targeted detection of cancer-specific methylation patterns. Further provided herein are methods of designing, making, and using the cancer assay panel for the diagnosis of cancer.

Abstract: The present description provides a cancer assay panel for targeted detection of cancer-specific methylation patterns. Further provided herein includes methods of designing, making, and using the cancer assay panel to detect cancer and particular types of cancer.

Abstract: The present description provides a cancer assay panel for targeted detection of cancer-specific methylation patterns. Further provided herein includes methods of designing, making, and using the cancer assay panel for detection of cancer tissue of origin (e.g., types of cancer).

Abstract: The present description provides a cancer assay panel for targeted detection of cancer-specific methylation patterns. Further provided herein includes methods of designing, making, and using the cancer assay panel to detect cancer and particular types of cancer.