公开(公告)号：US20240185952A1

公开(公告)日：2024-06-06

申请号：US18556143

申请日：2022-04-21

申请人： Personalis, Inc.

发明人： Rachel Marty PYKE ,  Dattatreya MELLACHERUVU ,  Steven DEA ,  Charles Wilbur ABBOTT ,  Simo V. ZHANG ,  Eric LEVY ,  John WEST ,  Richard CHEN ,  Sean Michael BOYLE

IPC分类号： G16B20/20 ,  G16B20/10 ,  G16B40/20

CPC分类号： G16B20/20 ,  G16B20/10 ,  G16B40/20

摘要： A method of detecting loss of heterozygosity in HLA alleles is provided. The method can include accessing a trained machine-learning model, which was trained using a training data set that included at least a training data set that includes an adjusted B allele frequency that represents a ratio between a first B allele frequency of heterozygous alleles in the tumor sample that correspond to the genomic region and a second B allele frequency of heterozygous alleles in the genomic region and associated with one or more control samples. The method can also include using the machine-learning model to generate a result corresponding to a probability of whether a loss of heterozygosity exists in an HLA allele identified in the biological sample of the particular subject by processing the sequence data using the machine-learning model.

公开(公告)号：US20230115039A1

公开(公告)日：2023-04-13

申请号：US18065410

申请日：2022-12-13

申请人： Personalis, Inc.

发明人： Charles Wilbur ABBOTT, III ,  Sean Michael BOYLE ,  Rachel Marty PYKE ,  Eric LEVY ,  Dattatreya MELLACHERUVU ,  Rena MCCLORY ,  Richard CHEN ,  Robert POWER ,  Gabor BARTHA ,  Jason HARRIS ,  Pamela MILANI ,  Prateek TANDON ,  Paul MCNITT ,  Massimo MORRA ,  Sejal DESAI ,  Juan-Sebastian SALVIDAR ,  Michael CLARK ,  Christian HAUDENSCHILD ,  John WEST ,  Nick PHILLIPS ,  Simo V. ZHANG

IPC分类号： G16B40/20 ,  G16B25/10 ,  G06N20/20

摘要： The disclosure provides methods for predicting surface-presenting peptides using binding and surface-presentation characteristics. The method can include accessing a trained machine-learning model that is configured to generate an output that indicates an extent to which the one or more expression levels and the one or more peptide-presentation metrics are related in accordance with a population-level relationship between expression and presentation. For each peptide of the set of peptides for a tissue sample, a score can be determined using the machine-learning model and genomic and transcriptomic data corresponding to the peptide. The score is predictive of whether a corresponding peptide is a surface-presenting peptide that binds to an MHC molecule and is presented on a cell surface.

公开(公告)号：US20230050395A1

公开(公告)日：2023-02-16

申请号：US17965719

申请日：2022-10-13

申请人： Personalis, Inc.

发明人： Charles Wilbur ABBOTT, III ,  Sean Michael BOYLE ,  Rachel Marty PYKE ,  Eric LEVY ,  Dattatreya MELLACHERUVU ,  Rena MCCLORY ,  Richard CHEN ,  Robert POWER ,  Gabor BARTHA ,  Jason HARRIS ,  Pamela MILANI ,  Prateek TANDON ,  Paul MCNITT ,  Massimo MORRA ,  Sejal DESAI ,  Juan-Sebastian SALVIDAR ,  Michael CLARK ,  Christian HAUDENSCHILD ,  John WEST ,  Nick PHILLIPS ,  Simo V. ZHANG

IPC分类号： G16B20/00 ,  G16B5/00 ,  G16B40/20

摘要： Methods for generating a composite biomarker that identifies a predicted level of responsiveness of a subject to a particular type of an immunotherapy treatment is provided. The method can include generating genomic metrics that represent one or more characteristics corresponding to one or more DNA sequences. The method can also include generating transcriptomic metrics represent one or more characteristics corresponding to a set of peptides that are translated from a corresponding RNA sequence of the one or more RNA sequences. The method can also include generating a composite biomarker score derived from the set of genomic metrics and the set of transcriptomic metrics. The method can also include determining, based on the composite biomarker score, a predicted level of responsiveness of the subject to a particular type of an immunotherapy treatment.