GRAIL, Inc.

Provided herein are methods, systems, and computer readable medium for detecting nucleic acid from a pathogen, e.g., virus, e.g., Epstein-Barr virus (EBV), in a cell-free nucleic sample from an individual at risk of a condition, e.g., nasopharyngeal carcinoma (NPC). The methods, systems, and computer readable medium can be used to screen for the presence of the condition, e.g., NPC, using thresholds adjusted based on sample attributes.

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 diagnosis of cancer.

A method and system for determining one or more sources of a cell free deoxyribonucleic acid (cfDNA) test sample from a test subject. The cfDNA test sample contains a plurality of deoxyribonucleic acid (DNA) molecules with numerous CpG sites that may be methylated or unmethylated. A trained deconvolution model comprises a plurality of methylation parameters, including a methylation level at each CpG site for each source, and a function relating a sample vector as input and a source of origin prediction as output. The method generates a test sample vector comprising a site methylation metric relating to DNA molecules from the test sample that are methylated at that CpG site. The method inputs the test sample vector into the trained deconvolution model to generate a source of origin prediction indicating a predicted DNA molecule contribution of each source.

Detecting cross-contamination between test samples used for determining cancer in a subject is beneficial. To detect cross-contamination, test sequences including at least one single nucleotide polymorphism are prepared using genome sequencing techniques. Some of the test sequences can be filtered to improve accuracy and precision. A prior contamination probability for each test sequence is determined based on a minor allele frequency. A contamination model including a likelihood test is applied to a test sequence. The likelihood test obtains a current contamination probability representing the likelihood that the test sample is contaminated. The contamination model can also determine a likelihood that the sample includes loss of heterozygosity representing the likelihood that the test sequence is contaminated. Test samples that are contaminated are removed. A source for the contaminated test sample can be found by comparing contaminated test sequences to other test sequences.

A method for training a convolutional neural net for contamination analysis is provided. A training dataset is obtained comprising, for each respective training subject in a plurality of subjects, a variant allele frequency of each respective single nucleotide variant in a respective plurality of single nucleotide variants, and a respective contamination indication. First and second subsets of the plurality of training subjects have first and second contamination indication values, respectively. A corresponding first channel comprising a first plurality of parameters that include a respective parameter for a single nucleotide variant allele frequency of each respective single nucleotide variant in a set of single nucleotide variants in a reference genome is constructed for each respective training subject. An untrained or partially trained convolutional neural net is trained using, for each respective training subject, at least the corresponding first channel of the respective training subject as input against the respective contamination indication.

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.

In an automated liquid processing system, a first centrifuge performs a centrifuging cycle on a plurality of first inserts, a pairing robot to places first tubes, including pairs of first tubes, from the first inserts on a pairing platform, an excising station excises labels on the first tubes, captures an image of the excised first tubes, and determines, using an image analysis process, a height of a portion of the liquid within the first tubes. A first liquid handling station combines a portion of the liquid within first tubes, including pairs of first tubes, into a second tube. A second centrifuge performs a second centrifuging cycle, and a second liquid handling station aspirates liquid from the second tube and dispenses the liquid into an output container.

Methods for preparing enriched sequencing libraries from test samples that contain double-stranded deoxyribonucleic acid (dsDNA) are provided.

Classification of cancer condition, in a plurality of different cancer conditions, for a species, is provided in which, for each training subject in a plurality of training subjects, there is obtained a cancer condition and a genotypic data construct including genotypic information for the respective training subject. Genotypic constructs are formatted into corresponding vector sets comprising one or more vectors. Vector sets are provided to a network architecture including a convolutional neural network path comprising at least a first convolutional layer associated with a first filter that comprise a first set of filter weights and a scorer. Scores, corresponding to the input of vector sets into the network architecture, are obtained from the scorer. Comparison of respective scores to the corresponding cancer condition of the corresponding training subjects is used to adjust the filter weights thereby training the network architecture to classify cancer condition.

Described herein are methods of preparing dual-indexed nucleic acid libraries for methylation profiling using bisulfite conversion sequencing. In various embodiments, the methods use a two-step indexing process to tag bisulfite-treated DNA with unique molecular identifiers (UMIs).