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公开(公告)号：US20240312561A1
公开(公告)日：2024-09-19
申请号：US18603727
申请日：2024-03-13
Applicant: GRAIL, LLC
Inventor： Robert Abe Paine Calef , Alexander P. Fields , Samuel S. Gross
IPC: G16B20/20 , C12Q1/6809 , C12Q1/6886
CPC classification number: G16B20/20 , C12Q1/6809 , C12Q1/6886
Abstract: The present disclosure relates to a method for improving sequencing panel assignments for samples from two or more individual. The system is configured to generate a sequencing panel assignment having an optimized set of samples for each panel that reduces sequencing costs but does not compromise Limit of Detection of the assay.
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公开(公告)号：US20240161867A1
公开(公告)日：2024-05-16
申请号：US18511450
申请日：2023-11-16
Applicant: Grail, LLC
Inventor： Alexander P. Fields , John F. Beausang , Oliver Claude Venn , Arash Jamshidi , M. Cyrus Maher , Qinwen Liu , Jan Schellenberger , Joshua Newman , Robert Abe Paine Calef , Samuel S. Gross , Frank Chu , Earl Hubbell
Abstract: One or more techniques for optimizing cancer classification based on covariate characteristics is disclosed. In a first approach, an analytics system may determine separate cutoff thresholds for positively detecting disease signal for different labels for a covariate characteristic. The system may subdivide training samples based on their labels for the covariate characteristic, to separately determine the cutoff thresholds. In other approaches, the system may train disparate classifiers for each population. The system separates the training samples based on their labels for the covariate characteristic, and separately trains classifiers to generate a signal vector representing an amount of disease signal detected in a sample. The classifiers may be trained on different feature sets as determined based on mutual information gain, genomic region coverage, and healthy activation fraction.
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公开(公告)号：US20240249798A1
公开(公告)日：2024-07-25
申请号：US18428793
申请日：2024-01-31
Applicant: Grail, LLC
Inventor： Darya Filippova , Matthew H. Larson , M. Cyrus Maher , Monica Portela dos Santos Pimentel , Robert Abe Paine Calef
IPC: G16B30/00 , C12Q1/6886 , G06N20/00 , G16B20/10 , G16H10/40 , G16H10/60 , G16H50/20 , G16H50/50 , G16H50/70
CPC classification number: G16B30/00 , C12Q1/6886 , G06N20/00 , G16B20/10 , G16H10/40 , G16H10/60 , G16H50/20 , G16H50/50 , G16H50/70 , C12Q2600/112
Abstract: 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.
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公开(公告)号：US11783915B2
公开(公告)日：2023-10-10
申请号：US17936529
申请日：2022-09-29
Applicant: GRAIL, LLC
Inventor： Virgil Nicula , Anton Valouev , Darya Filippova , Matthew H. Larson , M. Cyrus Maher , Monica Portela dos Santos Pimentel , Robert Abe Paine Calef , Collin Melton
Abstract: 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.
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公开(公告)号：US20230045925A1
公开(公告)日：2023-02-16
申请号：US17936529
申请日：2022-09-29
Applicant: GRAIL, LLC
Inventor： Virgil Nicula , Anton Valouev , Darya Filippova , Matthew H. Larson , M. Cyrus Maher , Monica Portela dos Santos Pimentel , Robert Abe Paine Calef , Collin Melton
Abstract: 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.
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Patent Agency Ranking
公开(公告)号：US20220333209A1
公开(公告)日：2022-10-20
申请号：US17714062
申请日：2022-04-05
Applicant: Grail, LLC
Inventor： Oliver Claude Venn , Peter D. Freese , Samuel S. Gross , Robert Abe Paine Calef , Arash Jamshidi
IPC: C12Q1/6886 , G16B30/00 , G16H50/20
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.
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公开(公告)号：US12191000B2
公开(公告)日：2025-01-07
申请号：US18151197
申请日：2023-01-06
Applicant: Grail, LLC
Inventor： M. Cyrus Maher , Anton Valouev , Darya Filippova , Virgil Nicula , Karthik Jagadeesh , Oliver Claude Venn , Samuel S. Gross , John F. Beausang , Robert Abe Paine Calef
IPC: G16B30/00 , G06N5/04 , G06N20/00 , G16B20/20 , G16B40/00 , G16H10/40 , G16H10/60 , G16H50/20 , G16H50/70 , G16H70/60
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.
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公开(公告)号：US11929148B2
公开(公告)日：2024-03-12
申请号：US16816918
申请日：2020-03-12
Applicant: GRAIL, LLC
Inventor： Darya Filippova , Matthew H. Larson , M. Cyrus Maher , Monica Portela dos Santos Pimentel , Robert Abe Paine Calef
IPC: G16B30/00 , C12Q1/6886 , G06N20/00 , G16B20/10 , G16H10/40 , G16H10/60 , G16H50/20 , G16H50/50 , G16H50/70
CPC classification number: G16B30/00 , C12Q1/6886 , G06N20/00 , G16B20/10 , G16H10/40 , G16H10/60 , G16H50/20 , G16H50/50 , G16H50/70 , C12Q2600/112
Abstract: 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.
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公开(公告)号：US11581062B2
公开(公告)日：2023-02-14
申请号：US16709537
申请日：2019-12-10
Applicant: GRAIL, LLC
Inventor： M. Cyrus Maher , Anton Valouev , Darya Filippova , Virgil Nicula , Karthik Jagadeesh , Oliver Claude Venn , Samuel S. Gross , John F. Beausang , Robert Abe Paine Calef
IPC: G16B30/00 , G16B20/20 , G16B40/00 , G16H70/60 , G06N5/04 , G16H10/60 , G16H50/70 , G16H50/20 , G06N20/00 , G16H10/40
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.
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公开(公告)号：US11482303B2
公开(公告)日：2022-10-25
申请号：US16428575
申请日：2019-05-31
Applicant: GRAIL, LLC
Inventor： Virgil Nicula , Anton Valouev , Darya Filippova , Matthew H. Larson , M. Cyrus Maher , Monica Portela dos Santos Pimentel , Robert Abe Paine Calef , Collin Melton
Abstract: 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.
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