Grail, Inc.

M Cyrus Maher

Joerg Bredno

Onur Sakarya

Robert Abe Paine Calef

Darya Filippova

Anton Valouev

Virgil Nicula

Samuel S Gross

Oliver Claude Venn

Matthew H Larson

Monica Portela Dos Santos Pimentel

Arash Jamshidi

Srinka Ghosh

Seyedmehdi Shojaee

John Beausang

Hamed Amini

Qinwen Liu

Alexander P Fields

Collin A Melton

Rongsu Qi

Jan Schellenberger

Karthik Jagadeesh

Collin Melton

John F Beausang

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.

Methylation markers and targeted methylation probe panel

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.

Convolutional neural network systems and methods for data classification

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 for classifying patients with respect to multiple cancer classes

Systems and methods for determining a cancer class of a subject are provided in which a plurality of sequence reads, in electronic form, are obtained from a biological sample of the subject. The sample comprises a plurality of cell-free DNA molecules including respective DNA molecules longer than a threshold length of less than 160 nucleotides. The plurality of sequence reads excludes sequence reads of cell-free DNA molecules in the plurality of cell-free DNA molecules longer than the threshold length. The plurality of sequence reads is used to identify a relative copy number at each respective genomic location in a plurality of genomic locations in the genome of the subject. The genetic information about the subject obtained from the sample and the genetic information consisting of the identification of the relative copy number at each respective genomic location, is applied to a classifier that determines the cancer class of the subject.

Systems and methods for enriching for cancer-derived fragments using fragment size

Systems and methods for classifier training are provided. A first dataset is obtained that comprises, for each first subject, a corresponding plurality of bin values, each for a bin in a plurality of bins, and subject cancer condition. A feature extraction technique is applied to the first dataset thereby obtaining feature extraction functions, each of which is an independent linear or nonlinear function of bin values of the bins. A second dataset is obtained comprising, for each second subject, a corresponding plurality of bin values, each for a bin in the plurality of bins and subject cancer condition. The plurality of bin values of each corresponding subject in the second plurality are projected onto the respective feature extraction functions, thereby forming a transformed second dataset comprising feature values for each subject. The transformed second dataset and subject cancer condition serves to train a classifier on the cancer condition set.

Systems and methods for determining whether a subject has a cancer condition using transfer learning

Diagnostic devices and methods are provided for screening for a disease condition, including a cancer condition or a mendelian disease. The diagnostic devices allow for in vivo contact of cell-free nucleic acids or circulating tumor cells. The diagnostic device has a needle with a body and a detection reaction module attached to the body.

Needle-based devices and methods for in vivo diagnostics of disease conditions

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San Mateo, CA, United States of America

M Cyrus Maher