Abstract: Techniques for identifying malignant cell populations. The techniques include: obtaining sequencing data previously obtained from a biological sample from a subject; processing the sequencing data to identify: a plurality of cell population estimates for a cell of a first type, the plurality of cell population estimates including a first cell population estimate and a second cell population estimate associated respectively with largest and second largest cell population estimates from among the identified plurality of cell population estimates; and features associated with the plurality of cell population estimates, the features including: a first feature indicative of a size of the first cell population estimate; and a second feature indicative of a ratio between sizes of the first cell population estimate and the second cell population estimate; and determining, using the features and a trained machine learning model, whether the first cell population estimate includes malignant cells of the first type.

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 lymphomas. The disclosure is based, in part, on methods for determining the tumor microenvironment (TME) type of a lymphoma (e.g., follicular lymphoma) subject and identifying the subject's prognosis based upon the TME type determination.