Abstract: Systems and methods for high fidelity sequencing and identification of rare mutations at dilute concentrations in a sample are described herein. In various aspects, the use of specialized library preparation techniques including adapter ligation conditions and hybrid capture enrichment panels are used along with controls to increase yield of sequence-ready molecules and identify and minimize contamination and errors. Systems and methods also relate to analyzing sequencing data to differentiate true variants from false positives using ensembles and a quasi-maximum likelihood model.

Abstract: A predictive cancer model generates a cancer prediction for an individual of interest by analyzing values of one or more types of features that are derived from cfDNA obtained from the individual. Specifically, cfDNA from the individual is sequenced to generate sequence reads using one or more physical assays, examples of which include a small variant sequencing assay, whole genome sequencing assay, and methylation sequencing assay. The sequence reads of the physical assays are processed through corresponding computational analyses to generate each of small variant features, whole genome features, and methylation features. The values of features can be provided to a predictive cancer model that generates a cancer prediction. In some embodiments, the values of different types of features can be separately provided into different predictive models. Each separate predictive model can output a score that can serve as input into an overall model that outputs the cancer prediction.

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: 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: Aspects of the invention relate to methods for tracking patient health by longitudinally tracking genetic variants in patients, such that it is possible to provide a tumor, or mutation, classification signature. Longitudinal tracking improves the ability to detect minimal residual disease (MRD; the small number of cells that remain in the patient after treatment and/or during remission) and/or treatment response at an early stage, both of which can help guide treatment decisions and guard against missing different intra-/inter-tumor responses in a patient.