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公开(公告)号：US20200004927A1
公开(公告)日：2020-01-02
申请号：US16022075
申请日：2018-06-28
摘要： A computer-implemented method incudes calculating, by a processor, based on sequence data for a tumor from a subject at a plurality of time points, a mutation frequency for each of a plurality of SSVs at each of the time points to provide a plurality of time-resolved mutation frequencies (between 0 and 1) for each of the plurality of SSVs, the sequence data including a plurality of simple somatic variations (SSVs) at each of the time points; binning, by the processor, the plurality of time-resolved mutation frequencies for each SSV at each of the time points to provide a matrix of SSVs and time points; converting, by the processor, the matrix cells to pseudo-clones; and constructing, by the processor, a time-series tumor evolution tree from the pseudo-clones, wherein each time point in the time-series evolution tree represents an event in the subject's cancer treatment.
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公开(公告)号：US20210005282A1
公开(公告)日：2021-01-07
申请号：US16459948
申请日：2019-07-02
IPC分类号： G16B25/10 , G16B20/00 , G16B30/00 , G16B25/20 , C12Q1/6869
摘要： A computer-implemented method includes to determine a cell, tissue or a lesion representation in cell-free DNA comprises inputting, to a processor, cell-free DNA (cfDNA) genomic profiles from one or more fluid biopsy samples from a patient and one or more genomic profiles from one or more cells, tissues or lesions from the patient; constructing, by the processor, a plurality of synthetic fluid hypotheses (SFs); comparing, by the processor, each of the plurality of SFs to the cfDNA genomic profiles to determine goodness of fit, of each of the plurality of SFs; selecting, by the processor, a subset of the plurality of SFs, wherein each SF of the subset of SFs has a minimum distance in goodness of fit compared to the cfDNA genomic profile; and outputting, by the processor, based on the subset of SFs, a cell, tissue or a lesion representation in the cfDNA of the patient.
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公开(公告)号：US11211148B2
公开(公告)日：2021-12-28
申请号：US16022075
申请日：2018-06-28
摘要： A computer-implemented method incudes calculating, by a processor, based on sequence data for a tumor from a subject at a plurality of time points, a mutation frequency for each of a plurality of SSVs at each of the time points to provide a plurality of time-resolved mutation frequencies (between 0 and 1) for each of the plurality of SSVs, the sequence data including a plurality of simple somatic variations (SSVs) at each of the time points; binning, by the processor, the plurality of time-resolved mutation frequencies for each SSV at each of the time points to provide a matrix of SSVs and time points; converting, by the processor, the matrix cells to pseudo-clones; and constructing, by the processor, a time-series tumor evolution tree from the pseudo-clones, wherein each time point in the time-series evolution tree represents an event in the subject's cancer treatment.
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公开(公告)号：US20200279614A1
公开(公告)日：2020-09-03
申请号：US16288371
申请日：2019-02-28
摘要： A computer-implemented method includes inputting, to a processor, genomic data from a plurality of subjects, the genomic data including first sample genomic data prior to a treatment, and second sample genomic data after the treatment; determining, by the processor, a plurality of δ's for the plurality of subjects, wherein each δ is a genetic change in the second sample compared to the first sample genomic data; creating, by the processor, a matrix of the plurality of subjects and their features which features are the genetic changes or clusters of genetic changes in the plurality of δ's of the subjects; biclustering, by the processor, the matrix of the plurality of subjects and their features, to provide clumps of subjects sharing a common feature such as a shared genetic change or shared cluster of genetic changes; and outputting, by the processor, the clumps of subjects, the common features, and the treatment.
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公开(公告)号：US11238955B2
公开(公告)日：2022-02-01
申请号：US15900048
申请日：2018-02-20
发明人： Filippo Utro , Aldo Guzman Saenz , Chaya Levovitz , Laxmi Parida
摘要： A computer-implemented method includes generating, by a processor, a set of training data for each phenotype in a database including a set of subjects. The set of training data is generated by dividing genomic information of N subjects selected with or without repetition into windows, computing a distribution of genomic events in the windows for each of N subjects, and extracting, for each window, a tensor that represents the distribution of genomic events for each of N subjects. A set of test data is generated for each phenotype in the database, a distribution of genomic events in windows for each phenotype is computed, and a tensor is extracted for each window that represents a distribution of genomic events for each phenotype. The method includes classifying each phenotype of the test data with a classifier, and assigning a phenotype to a patient.
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公开(公告)号：US11189361B2
公开(公告)日：2021-11-30
申请号：US16022088
申请日：2018-06-28
摘要： A computer-implemented method includes determining, by a processor, from a time-series evolution tree comprising one or more clones at each of the plurality of time points, that the one or more clones are sensitive clones or resistant clones, wherein the time-series evolution tree is based on sequence data for a tumor from a subject at a plurality of time points, wherein each time point in the time-series evolution tree represents an event in the subject's cancer treatment, and wherein a clone is a collection of gene alterations; based at least in part on determining that the one or more clones that are the sensitive or resistant clones, determining, by the processor, a geneset composition of the one or more clones that are the sensitive or resistant clones; and based at least in part on determining the geneset composition, determining by the processor, a further treatment for the subject.
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公开(公告)号：US11139046B2
公开(公告)日：2021-10-05
申请号：US15828747
申请日：2017-12-01
摘要： Embodiments include methods, systems, and computer program products for analyzing genomic data. Aspects include receiving genomic data for an organism, sample phenotypes, and a plurality of gene sets. Aspects include, for each of the gene sets, determining a set of genes G corresponding to genes in the gene set and a set of genes G′ corresponding to genes outside the gene set for the phenotypes R and R′. Aspects also include determining a set of mutated genes M and a set of non-mutated genes M′ for R and R′ and a mutation enrichment score. Aspects also include determining a set of differentiated genes D a set of non-differentiated genes D′ for R and R′. Aspects also include identifying an enriched gene set GE based at least in part upon the mutation enrichment score and the differentiation enrichment score.
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公开(公告)号：US20240296929A1
公开(公告)日：2024-09-05
申请号：US18116114
申请日：2023-03-01
摘要： An AI platform is used for developing a combination therapy for a patient afflicted with a tumor that has produced clones. The combination therapy, which includes at least two perturbations, is capable of targeting clones (including subclones) that have escaped therapeutic intervention due to resistance and/or evolution. The AI platform is trained with perturbation data obtained from at least one cell line that has similar characteristics to a clone of interest. The trained AI platform predicts how the clone of interest will respond to perturbations and ranks the perturbation responses from highest to lowest. The at least one cell line may be an existing cell line from a well-established database or a synthetic cell line generated by the AI platform. The AI platform may include one or more of a machine learning platform, a deep learning platform, an artificial neural network (ANN), a convolution neural network (CNN), and a generative adversarial network (GAN).
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公开(公告)号：US20240038336A1
公开(公告)日：2024-02-01
申请号：US17814850
申请日：2022-07-26
摘要： A method is provided for training a predicting cfDNA shedding model using a plurality of lesion and cfDNA datasets. A new cfDNA shedding sample and the plurality of lesion and cfDNA datasets are clustered to predict a shedding pattern. A diagnostic type is determined for a subsequent cfDNA shedding sample based on the predicted shedding pattern.
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公开(公告)号：US20210012861A1
公开(公告)日：2021-01-14
申请号：US16507811
申请日：2019-07-10
摘要： A computer-implemented method is disclosed which includes receiving biological sample information from one or more subjects at a first time period. The method further includes receiving biological sample information from the one or more subjects at a second time period. The method further includes comparing the biological sample information at the second time period with the biological sample information at the first time period. The method further includes generating a precedence graph based on results of the comparison. The method further includes determining one or more actions based on the precedence graph.
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