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US 2023/0420134 A1Published application
Cancer diagnosis and classification by non-human metagenomic pathway analysis
AssigneeMicronoma, Inc.
InventorsStephen WANDRO, Eddie ADAMS, Sandrine MILLER-MONTGOMERY
Life of this patent
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Abstract
Provided are methods for the diagnosis and classification of cancer by non-human metagenomic pathway analysis.
Claims114
A method of determining the presence or lack thereof cancer of a subject, the method comprising:
- (a) providing one or more sequencing reads of a subject's biological sample;
- (b) filtering the sequencing reads with a genome database to produce a set of filtered non-human sequencing reads;
- (c) translating the non-human sequencing reads to non-human proteins;
- (d) mapping the non-human proteins to a protein database, thereby producing a set of protein database associations;
- and
- (e) determining the presence or lack thereof cancer of the subject as an output to the trained model when the trained model is provided an input of the set of protein database associations.
3 more elements
A method of providing a determination of the presence or lack thereof cancer of a subject, the method comprising:
- (a) sequencing a nucleic acid compositions of a subject's biological sample thereby generating sequencing reads;
- (b) filtering the sequencing reads with a genome database to produce a set of filtered non-human sequencing reads;
- (c) translating the non-human sequencing reads to non-human proteins;
- (d) mapping the non-human proteins to a protein database, thereby producing a set of protein database associations;
- and
- (e) providing a determination of the presence or lack thereof cancer of the subject as an output of a trained model when the trained model is provided an input of the set protein database associations.
3 more elements
A method of training a model configured to determine the presence or lack thereof cancer of a subject, the method comprising:
- (a) providing a dataset comprising nucleic acid sequencing reads of a first set of one or more subjects' nucleic acid compositions and a corresponding one or more cancers of the first set of one or more subjects;
- (b) filtering the nucleic acid sequencing reads with a build of a genome database to generate non-human sequencing reads;
- (c) translating the non-human sequencing reads to non-human proteins;
- (d) mapping the non-human proteins to a protein database, thereby producing a set of protein database associations;
- and
- (e) training a model with the set of protein database associations and the corresponding one or more cancer states of the first set of one or more subjects, thereby generating a trained model configured to determine the presence or lack thereof cancer of a second set of one or more subjects.
3 more elements
A computer-implemented method for utilizing a trained predictive model to provide a therapeutic treatment prediction for one or more subjects, the method comprising:
- (f) receiving a first set of one or more subjects' nucleic acid sequencing reads of a biological sample and corresponding cancer classification;
- (g) filtering the nucleic acid sequencing reads with a build of a genome database to generate non-human sequencing reads;
- (h) translating the non-human sequencing reads to non-human proteins;
- (i) mapping the non-human proteins to a protein database, thereby producing a set of protein database associations;
- and
- (j) utilizing a trained predictive model to provide a treatment prediction for the first set of one or more subjects when the set of protein database associations are provided as an input to the trained predictive model.
3 more elements
A method of changing a subject's cancer treatment with a trained predictive model, the method comprising:
- (a) providing one or more sequencing reads of a subject's biological sample with cancer, cancer type, and treatment administered to treat the cancer;
- (b) filtering the sequencing reads with a genome database to produce a set of filtered non-human sequencing reads;
- (c) translating the non-human sequencing reads to non-human proteins;
- (d) mapping the non-human proteins to a protein database, thereby producing a set of protein database associations;
- and
- (e) changing the subject's cancer treatment when the treatment administered differs from a treatment recommendation outputted by a trained predictive model when inputted with the set of protein database associations.
3 more elements
Dependencies are inferred from the claim text.