Abstract: Described herein are techniques for predicting whether a subject will experience an immune-related adverse event (irAE) in response to administration of an immune checkpoint inhibitor (ICI) therapy. In some embodiments, the techniques include: determining a likelihood that the subject will experience the irAE in response to administration of the ICI therapy, the determining comprising: performing: (a) processing clinical data using a first machine learning (ML) model to output a first likelihood that the subject will experience the irAE, (b) processing RNA sequencing data using a second ML model to output a second likelihood that the subject will experience the irAE, and/or (c) processing immune receptor data using a third ML model to output a third likelihood that the subject will experience the irAE; and processing the first, second, and/or third likelihoods using a fourth ML model trained to predict the likelihood that the subject will experience the irAE.

Abstract: Aspects of the disclosure relate to methods, systems, and computer-readable storage media that are useful for characterizing subjects having certain cancers, for example cancers typified by solid tumors. The disclosure is based, in part, on methods for determining a tumor microenvironment (TME) type of a cancer subject and determining the subject's prognosis and/or likelihood of responding to one or more therapies based upon the TME type determination.

Abstract: Aspects of the disclosure relate to methods, systems, and computer-readable storage media that are useful for characterizing subjects having certain cancers, for example cancers typified by solid tumors. The disclosure is based, in part, on methods for determining a tumor microenvironment (TME) type of a cancer subject and determining the subject's prognosis and/or likelihood of responding to one or more therapies based upon the TME type determination.

Abstract: Aspects of the disclosure relate to methods, systems, computer-readable storage media, and graphical user interfaces (GUIs) that are useful for characterizing subjects having certain cancers, for example solid tumor cancers or blood cancers. The disclosure is based, in part, on methods for determining a cytokine signature of a subject and the subject's prognosis and/or likelihood of responding to a therapy based upon the cytokine signature determination.

Abstract: Aspects of the disclosure relate to methods, systems, and computer-readable storage media that are useful for characterizing subjects having certain cancers, for example cancers typified by solid tumors. The disclosure is based, in part, on methods for determining a tumor microenvironment (TME) type of a cancer subject and determining the subject's prognosis and/or likelihood of responding to one or more therapies based upon the TME type determination.