Abstract: This disclosure describes example techniques and systems for identifying the presence and/or composition of nucleic acids in the blood of a host organism of a model species harboring tissue of a donor organism of another species. For example, the technique may involve identifying the presence and composition of nucleic acids in the blood of a mouse harboring tissue of a human or another companion animal. These cell-free nucleic acids that are identified can be used as biomarkers to determine the presence of a disease, its biological behavior, its rate of progression, and/or the response of the disease to one or more unique therapies. In other examples, the cell-free nucleic acids may be used as biomarkers to determine a response of the host species to the tissue of the donor organism or a response of tissue derived from the second organism to transplantation within the first organism of the first species.

Abstract: This disclosure describes example techniques and systems for identifying the presence and/or composition of nucleic acids in the blood of a host organism of a model species harboring tissue of a donor organism of another species. For example, the technique may involve identifying the presence and composition of nucleic acids in the blood of a mouse harboring tissue of a human or another companion animal. These cell-free nucleic acids that are identified can be used as biomarkers to determine the presence of a disease, its biological behavior, its rate of progression, and/or the response of the disease to one or more unique therapies. In other examples, the cell-free nucleic acids may be used as biomarkers to determine a response of the host species to the tissue of the donor organism or a response of tissue derived from the second organism to transplantation within the first organism of the first species.

Abstract: The techniques and systems described herein relate to using machine learning models to associate a known biological state of an organism with patterns of expression exhibited by the organism of genes of a gene signature associated with a disease state, such as to train the machine learning models to determine unknown biological states associated with the patterns of expression. Some techniques include determining an unknown biological status of an organism based on an expression pattern of genes of a gene signature in the organism, which the machine learning model may compare to known expression patients learned during the training technique. The expression patterns may be determined based on sequences of exosomal RNAs isolated from exosomes from a sample of bodily fluid from the organism and an approximate number of times each RNA sequence that substantially aligns with a gene of the gene signature occurs in the sample of bodily fluid.

Abstract: A method for detecting aggressive tumor behavior and/or increased risk for tumor metastasis generally includes analyzing a tumor sample from the subject for expression of transcripts from coding regions of the cell cycle gene cluster, the immune-1 gene cluster, or the immune-2 gene cluster; computing a sum of log2-transformed mean-centered expression values, thereby generating a Gene Cluster Expression Summary Score (GCESS) for the sample; and detecting a tumor with aggressive behavior and/or increased risk for tumor metastasis. An aggressive tumor and/or increased risk for tumor metastasis may be indicated where the cell cycle gene cluster is analyzed and the sample GCESS is greater than 0, or the immune-1 gene cluster or the immune-2 gene cluster is analyzed and the sample GCESS is less than 0.

Abstract: This disclosure describes example techniques and systems for identifying the presence and/or composition of nucleic acids in the blood of a host organism of a model species harboring tissue of a donor organism of another species. For example, the technique may involve identifying the presence and composition of nucleic acids in the blood of a mouse harboring tissue of a human or another companion animal. These cell-free nucleic acids that are identified can be used as biomarkers to determine the presence of a disease, its biological behavior, its rate of progression, and/or the response of the disease to one or more unique therapies. In other examples, the cell-free nucleic acids may be used as biomarkers to determine a response of the host species to the tissue of the donor organism or a response of tissue derived from the second organism to transplantation within the first organism of the first species.