Population based treatment recommender using cell free DNA
Systems and methods are disclosed for generating a therapeutic response predict or detecting a disease, by: using a genetic analyzer to generate genetic information; receiving into computer memory a training dataset comprising, for each of a plurality of individuals having a disease, (1) genetic information from the individual generated at first time point and (2) treatment response of the individual to one or more therapeutic interventions determined at a second, later, time point; and implementing a machine learning algorithm using the dataset to generate at least one computer implemented classification algorithm, wherein the classification algorithm, based on genetic information from a subject, predicts therapeutic response of the subject to a therapeutic intervention.
1 . A method, comprising:
obtaining or having obtained a cell-free biological sample comprising cell-free DNA (cfDNA) molecules from a subject from at least two different time points;
generating a plurality of sequencing reads from the cfDNA molecules;
identifying, in the plurality of sequencing reads, genetic variants comprising sequence variants and copy number variants;
quantifying the frequency of genetic variants;
generating an initial diagnostic confidence indication based on the genetic variants and frequencies of the genetic variants from the first of the at least two different time points;
generating an adjusted diagnostic confidence indication based on conflict or corroboration of the genetic variants and frequencies of the genetic variants from the second of the at least two different time points;
storing genetic information comprising the genetic variants and frequencies of the genetic variants and clinical information comprising initial and adjusted diagnostic confidence indications of the subject in at least one database;
obtaining genetic information from each of a plurality of subjects in the at least one database;
operably connecting at least one extractor to the at least one database, wherein the extractor is configured to extract one or more features from the genetic and clinical information stored in the at least one database; and,
operably connecting at least one treatment efficacy monitor to the at least one extractor, operably connecting at least one classifier to the extractor and to the recommender, wherein the classifier is configured to classify the one or more features extracted from the genetic and clinical information, and wherein classifier training comprises:
(a) providing a plurality of different classes, wherein each class represents a set of subjects with a shared characteristic;
(b) providing a multi-parametric model representative of the cfNAs from each of a plurality of samples belonging to each of the classes, thereby providing a training data set; and
(c) training a learning algorithm on the training data set to create one or more trained classifiers, wherein each trained classifier comprises one or more random forests, linear classifiers, support vector machines, and/or Hidden Markov models (HMMs), and classifies a test sample into one or more of the plurality of classes, thereby generating the system;
wherein the at least one treatment efficacy monitor is configured to:
implement the one or more trained classifiers to determine treatment efficacy for at least one treatment from among a plurality of treatment options for a cancer of at least one test subject of the plurality of subjects, based at least in part on an analysis of the one or more features at a first time point of the two or more time points; and
implement the one or more trained classifiers to determine an effective treatment from among a plurality of treatment options for a cancer of at least one test subject of the plurality of subjects, wherein selection of the treatment, based on at least an analysis of the:
one or more features comprising genetic information for the given subject from a second time point of the two or more time points; and
amount of time between at least two or more time points.
2 . The method of claim 1 , wherein the plurality of subjects comprises subjects having cancer.
3 . The method of claim 1 , wherein the plurality of subjects comprises cancer-free subjects.
4 . The method of claim 1 , further comprising operably connecting at least one genetic analyzer to the system, wherein the genetic analyzer is configured to the genetic information.
5 . The method of claim 1 , further comprising operably connecting at least one report generator to the recommender.
6 . The method of claim 1 , wherein the genetic information comprises unstructured text data.
7 . The method of claim 1 , comprising storing the clinical information in at least one database array.
8 . The method of claim 1 , wherein the clinical information comprises patient information from physicians and test labs.
9 . The method of claim 1 , wherein the clinical information comprises CT scans, MRI scans, ultrasound scans, bone scans, PET scans, bone marrow tests, X-rays, endoscopies, lymphangiograms, intravenous urograms (IVU), IV pyelograms (IVP), lumbar punctures, cystoscopies, immunological tests, histology reports, and/or cancer marker tests.
10 . The method of claim 1 , further comprising using the system to predict a course of treatment for at least one test subject having cancer.
11 . The system generated by the method of claim 1 .
12 . The method of claim 1 , wherein the plurality of different classes is selected from the group consisting of: cancer-free, breast cancer, colon cancer, lung cancer, pancreatic cancer, prostate cancer, ovarian cancer, melanoma, and liver cancer.
13 . The method of claim 1 , wherein the one or more features comprise treatment responses to therapeutic interventions to generate one or more treatment classifications.
14 . The method of claim 13 , wherein the one or more treatment classifications comprise responsive to treatment, nonresponsive to treatment, or a level of responsiveness to treatment.
15 . The method of claim 1 , further comprising operably connecting at least one inference unit to the classifier and to the recommender.
16 . The method of claim 15 , wherein output of the classifier is provided to the inference unit.
17 . The method of claim 1 , wherein a diagnostic confidence indication can be increased in the subsequent characterization if the information from a first time point corroborates information from the second time point.
18 . The method of claim 1 , wherein diagnostic confidence indication can be decreased in the subsequent characterization if the information from a first time point conflicts with information from the second time point.
19 . The method of claim 1 , comprising detecting genetic variants in the noise range of a machine configured to generate the plurality of sequencing reads.