Population based treatment recommender using cell free dna

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;
• ligating molecular barcodes to a plurality of the cfDNA molecules, wherein the molecular barcodes uniquely tag polynucleotides in the cfDNA molecules to generate tagged cfDNA molecules;
• generating a plurality of sequencing reads from the tagged cfDNA molecules;
• grouping sequence reads into families, each family generated from a uniquely tagged parent polynucleotide;
• identifying, in the plurality of sequencing reads, genetic variants comprising sequence variants and copy number variants;
• quantifying the frequency of genetic variants;
• storing genetic information comprising the genetic variants and frequencies of the genetic variants and clinical information 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 information and the clinical information stored in the at least one database;
• performing a training process of one or more classifiers that are included in at least one recommender, wherein the training process includes:
• determining a plurality of classes of subjects by identifying individual sets of subjects having a shared characteristic;
• generating a training data set using a multi-parametric model, the training data set indicating characteristics of polynucleotides of samples of subjects that belong to each class of the plurality of classes;
• and
• training a machine learning algorithm using the training data set to generate one or more trained classifiers, wherein individual trained classifiers of the one or more trained classifiers determines one or more classes of the plurality of classes for a test sample and
• operably connecting the at least one recommender to the at least one extractor, thereby generating the system;
• wherein the at least one recommender is configured to:
• implement the one or more trained classifiers to select a 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 select a later 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.