Multi-Assay Prediction Model for Cancer Detection

A system for determining a cancer prediction for a subject, the system comprising:

• a processor;
• and
• a non-transitory computer-readable storage medium with encoded instructions that, when executed by the processor, cause the processor to accomplish steps of:
• accessing a dataset associated with cell-free nucleic acids in a test sample obtained from the subject, the dataset comprising sequence reads generated from one or more sequencing assays on the cell-free nucleic acids;
• performing or having performed a computational analysis on the sequence reads to generate values for one or more features derived from the sequence reads;
• applying a cancer prediction model to the values for the one or more features to generate a cancer prediction for the subject, the cancer prediction model comprising a function that computes the cancer prediction using learned weights;
• providing the cancer prediction for the subject.

A non-transitory computer readable storage medium storing executable instructions for determining a cancer prediction for a subject that, when executed by a hardware processor, causes the hardware processor to perform steps comprising:

• accessing a dataset associated with cell-free nucleic acids in a test sample obtained from the subject, the dataset comprising sequence reads generated from one or more sequencing assays on the cell-free nucleic acids;
• performing or having performed a computational analysis on the sequence reads to generate values for one or more features derived from the sequence reads;
• applying a cancer prediction model to the values for the one or more features to generate a cancer prediction for the subject, the cancer prediction model comprising a function that computes the cancer prediction using learned weights;
• providing the cancer prediction for the subject.

A method for determining a cancer prediction for a subject, the method comprising:

• obtaining a dataset associated with cell-free nucleic acids in a test sample obtained from the subject, the dataset comprising sequence reads generated from one or more sequencing assays on the cell-free nucleic acids;
• performing or having performed a computational analysis on the sequence reads to generate a first set and a second set of values from a first set and a second set of features derived from the sequence reads;
• applying a first model to the first set of values from the first set of features to generate a first score,
• applying a second model to the second set of values from the second set of features to generate a second score,
• the first model comprising a first function that computes the first score and the second model comprising a second function that computes the second score such that each of the first score and the second score are computed based on different features;
• applying a cancer prediction model to the first score and the second score to generate a cancer prediction;
• and
• providing the cancer prediction for the subject.

A system for determining a cancer prediction for a subject, the system comprising:

• a processor;
• and
• a non-transitory computer-readable storage medium with encoded instructions that, when executed by the processor, cause the processor to accomplish steps of
• accessing a dataset associated with cell-free nucleic acids in a test sample obtained from the subject, the dataset comprising sequence reads generated from one or more sequencing assays on the cell-free nucleic acids;
• performing or having performed a computational analysis on the sequence reads to generate a first set and a second set of values from a first set and a second set of features derived from the sequence reads;
• applying a first model to the first set of values from the first set of features to generate a first score,
• applying a second model to the second set of values from the second set of features to generate a second score,
• the first model comprising a first function that computes the first score and the second model comprising a second function that computes the second score such that each of the first score and the second score are computed based on different features;
• applying a cancer prediction model to the first score and the second score to generate a cancer prediction;
• and
• providing the cancer prediction for the subject.

A non-transitory computer readable storage medium storing executable instructions for determining a cancer prediction for a subject that, when executed by a hardware processor, cause the hardware processor to perform steps comprising:

• accessing a dataset associated with cell-free nucleic acids in a test sample obtained from the subject, the dataset comprising sequence reads generated from one or more sequencing assays on the cell-free nucleic acids;
• performing or having performed a computational analysis on the sequence reads to generate a first set and a second set of values from a first set and a second set of features derived from the sequence reads;
• applying a first model to the first set of values from the first set of features to generate a first score,
• applying a second model to the second set of values from the second set of features to generate a second score,
• the first model comprising a first function that computes the first score and the second model comprising a second function that computes the second score such that each of the first score and the second score are computed based on different features;
• applying a cancer prediction model to the first score and the second score to generate a cancer prediction;
• and
• providing the cancer prediction for the subject.

A method for determining a cancer prediction for a subject, the method comprising:

• obtaining a dataset associated with cell-free nucleic acids in a test sample obtained from the subject, the dataset comprising sequence reads generated from one or more sequencing assays on the cell-free nucleic acids;
• performing or having performed a computational analysis on the dataset to generate values for two or more features describing the cell-free nucleic acids in the test sample, the features including:
• a first set of methylation features derived from sequence reads from a methylation sequencing assay on nucleic acids in the test sample, and
• a second set of non-methylation features derived from sequence reads from a sequencing assay on nucleic acids in the test sample;
• applying a cancer prediction model to the values from the first set of methylation features and the values from the second set of non-methylation features to generate a cancer prediction for the subject, the cancer prediction model comprising a first function that computes the cancer prediction using learned weights;
• and
• providing the cancer prediction for the subject.

A system for determining a cancer prediction for a subject, the system comprising:

• a processor;
• and
• a non-transitory computer-readable storage medium with encoded instructions that, when executed by the processor, cause the processor to accomplish steps of
• accessing a dataset associated with cell-free nucleic acids in a test sample obtained from the subject, the dataset comprising sequence reads generated from one or more sequencing assays on the cell-free nucleic acids;
• performing or having performed a computational analysis on the dataset to generate values for two or more features describing the cell-free nucleic acids in the test sample, the features including:
• a first set of methylation features derived from sequence reads from a methylation sequencing assay on nucleic acids in the test sample, and
• a second set of non-methylation features derived from sequence reads from a sequencing assay on nucleic acids in the test sample;
• applying a cancer prediction model to the values from the first set of methylation features and the values from the second set of non-methylation features to generate a cancer prediction for the subject, the cancer prediction model comprising a first function that computes the cancer prediction using learned weights;
• and
• providing the cancer prediction for the subject.

A non-transitory computer readable storage medium storing executable instructions for determining a cancer prediction for a subject that, when executed by a hardware processor, cause the hardware processor to perform steps comprising:

• obtaining a dataset associated with cell-free nucleic acids in a test sample obtained from the subject, the dataset comprising sequence reads generated from one or more sequencing assays on the cell-free nucleic acids;
• performing or having performed a computational analysis on the dataset to generate values for two or more features describing the cell-free nucleic acids in the test sample, the features including:
• a first set of methylation features derived from sequence reads from a methylation sequencing assay on nucleic acids in the test sample, and
• a second set of non-methylation features derived from sequence reads from a sequencing assay on nucleic acids in the test sample;
• applying a cancer prediction model to the values from the first set of methylation features and the values from the second set of non-methylation features to generate a cancer prediction for the subject, the cancer prediction model comprising a first function that computes the cancer prediction using learned weights;
• and
• providing the cancer prediction for the subject.