GRAIL, Inc.

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.

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.

A method for discriminating a cancer state is provided. A first dataset is obtained for a plurality of subjects having a first cancer state. Each subject has a plurality of nucleic acid methylation fragments with methylation patterns comprising CpG site methylation states. An autoencoder including an encoder and decoder is trained by evaluating the error in the autoencoder reconstruction of the methylation pattern and nucleic acid sequence of each nucleic acid methylation fragment in the first dataset. A second dataset is obtained for a plurality of subjects having a second cancer state. A plurality of features is identified by inputting the methylation pattern and nucleic acid sequence of each nucleic acid methylation fragment in the second dataset into the trained autoencoder and computing a score determined by the autoencoder reconstruction of the methylation pattern. The plurality of features is used to train a supervised model that discriminates a cancer state.

A system, method and computer program product for analyzing data of high dimensionality (e.g., sequence reads of nucleic acid samples in connection with a disease condition) are provided.

Systems and methods of the disclosure may include a computer-implemented method, the computer-implemented method including: receiving, at a computer system, nucleic acid sequencing data derived from a methylation assay performed on a biological sample associated with at least one subject; computing, using a processor associated with the computer system, a beta value matrix based on the nucleic acid sequencing data, wherein the beta value matrix comprises one or more missing beta values; addressing, using the processor, the one or more missing beta values in the beta value matrix using a missing beta value completion approach; identifying, using the processor, one or more principal components in the completed beta value matrix; and training, using the one or more principal components in combination with a predetermined set of clinical variables, a classifier to predict a survival outcome for a target subject associated with a disease type.

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.

Systems and methods described herein include detecting a presence or absence of HPV in a biological sample having cell-free nucleic acids from a subject and potentially cell-free nucleic acids from an HPV strain. In response to detecting HPV viral nucleic acids in the biological sample, an HPV-based multiclass classifier that predicts a score for each HPV-associated cancer type is applied. The HPV-based multiclass classifier is trained on a training set consisting of HPV-positive cancer samples. An HPV-associated cancer associated with the biological sample is determined based on the scores predicted by the HPV multiclass classifier.

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.

The ends of cell-free DNA fragments may be used for analysis of a biological sample. In some embodiments, DNA from a urine sample may be analyzed. Cell-free DNA fragments often include jagged ends, where one end of one strand of double-stranded DNA extends beyond the other end of the other strand. The length and amount of these jagged ends may be used to determine a level of a condition of an individual. The density of ends of fragments in certain regions may also be used in classifying the level of a condition. Additionally, DNA fragments may show a periodic pattern with the amount of DNA fragments corresponding to a length of the overhang. The periodicity may be analyzed to determine properties of a biological sample. Jagged ends may also be analyzed with a technique that avoids trimming overhanging 3′ ends of a double-stranded DNA.

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.