The invention provides a method of validating a predicted pathway activity of a pathway in a solid tumor of a subject, with a digital computer, the method including: a) obtaining a tumor associated sample from the subject; b) obtaining omics data from the tumor associated sample obtained in (a); c) applying the omics data obtained in (b) to a digital computer programmed with a pathway analysis engine configured to generate predicted tumor cell pathway activities in silico, to provide a prediction of one or more pharmaceutically active anticancer compounds effective for treating the subject’s tumor; and d) obtaining enriched viable tumor cells from the subject, and interrogating the enriched viable tumor cells with at least one pharmaceutically active compound known to interact with a pathway element of one or more of the pathways predicted by (c), to measure anticancer activity of the at least one pharmaceutically active compound with respect to the subject’s enriched viable tumor cells.

The invention provides oncogenomic methods for detecting tumors by identifying circulating tumor DNA. A patient-specific reference directed acyclic graph (DAG) represents known human genomic sequences and non-tumor DNA from the patient as well as known tumor-associated mutations. Sequence reads from cell-free plasma DNA from the patient are mapped to the patient-specific genomic reference graph. Any of the known tumor-associated mutations found in the reads and any de novo mutations found in the reads are reported as the patient's tumor mutation burden.

The invention provides oncogenomic methods for detecting tumors by identifying circulating tumor DNA. A patient-specific reference directed acyclic graph (DAG) represents known human genomic sequences and non-tumor DNA from the patient as well as known tumor-associated mutations. Sequence reads from cell-free plasma DNA from the patient are mapped to the patient-specific genomic reference graph. Any of the known tumor-associated mutations found in the reads and any de novo mutations found in the reads are reported as the patient's tumor mutation burden.

Techniques for generating therapy biomarker scores and visualizing same. The techniques include determining, using a patient's sequence data and distributions of biomarker values across one or more reference populations, a first set of normalized scores for a first set of biomarkers associated with a first therapy, and a second set of normalized scores for a second set of biomarkers associated with a second therapy, generating a graphical user interface (GUI) including a first portion associated with the first therapy and having at least one visual characteristic determined based on a normalized score of the respective biomarker in the first set of normalized scores; and a second portion associated with a second therapy and having at least one visual characteristic determined based on a normalized score of the respective biomarker in the second set of normalized scores; and displaying the generated GUI.

Techniques for generating therapy biomarker scores and visualizing same. The techniques include determining, using a patient's sequence data and distributions of biomarker values across one or more reference populations, a first set of normalized scores for a first set of biomarkers associated with a first therapy, and a second set of normalized scores for a second set of biomarkers associated with a second therapy, generating a graphical user interface (GUI) including a first portion associated with the first therapy and having at least one visual characteristic determined based on a normalized score of the respective biomarker in the first set of normalized scores; and a second portion associated with a second therapy and having at least one visual characteristic determined based on a normalized score of the respective biomarker in the second set of normalized scores; and displaying the generated GUI.

Methods of isolating cell free RNA from individual's bodily fluid and reliably obtain cell free RNA data are presented, preferably by use of high-stability portions and/or use of targeted small amplicons on the cell free RNA.

Methods of isolating cell free RNA from individual's bodily fluid and reliably obtain cell free RNA data are presented, preferably by use of high-stability portions and/or use of targeted small amplicons on the cell free RNA.

Technologies are disclosed for determining or predicting the occurrence of a macrophage activation syndrome, such as hemophagocytic lymphohistiocytosis (HLH). A detection of the emergence of and/or a reliable estimation of the likelihood of future significant macrophage activation syndromes, such as HLH, may be determined or predicted from a time series of laboratory and physiologic values to be measured in a patient. Root mean square of successive deviations (RMSSD) is utilized as a surrogate non-parametric measure of the high-frequency power spectral density (PSD) to identify strong statistical associations with the presence and/or near-term future emergence of macrophage activation syndromes. Utilizing these input variables, a model having satisfactory predictive accuracy is constructed using linear discriminant analysis (LDA), gradient boosting, random forest (RF), neural network, logistic regression, or the like, and may be used for the prediction.

Technologies are disclosed for determining or predicting the occurrence of a macrophage activation syndrome, such as hemophagocytic lymphohistiocytosis (HLH). A detection of the emergence of and/or a reliable estimation of the likelihood of future significant macrophage activation syndromes, such as HLH, may be determined or predicted from a time series of laboratory and physiologic values to be measured in a patient. Root mean square of successive deviations (RMSSD) is utilized as a surrogate non-parametric measure of the high-frequency power spectral density (PSD) to identify strong statistical associations with the presence and/or near-term future emergence of macrophage activation syndromes. Utilizing these input variables, a model having satisfactory predictive accuracy is constructed using linear discriminant analysis (LDA), gradient boosting, random forest (RF), neural network, logistic regression, or the like, and may be used for the prediction.

A computer-implemented method of prioritising biological targets is disclosed. The method comprises: receiving a selection of classes of one or more categories; and, for each of a plurality of biological targets, determining an extent of alignment of the biological target to each selected class. The method also comprises prioritising the biological targets based on the extents of alignment; and outputting a representation of one or more prioritised biological targets.