Figure 2.

The challenge of integrating data from heterogeneous molecular assays and experimental sources. Prior and ongoing consortia have provided a wealth of data for multiple immune cell populations in isolation and in interaction with each other and tumor cells. Integrative data analysis is a major challenge in the field, and leveraging publicly available immunological data may be a valuable approach for systems immunology model development. The use of unlabeled immune cell data may be framed under an unsupervised transfer learning framework, in which the target task may be dimensionality reduction or clustering of immune cell populations. The use of labeled source data - for example, transcriptomic profiles paired with clinical outcome of another immunotherapy modality – may follow a framework of inductive transfer learning, in which the target task is the prediction of CAR T-cell clinical outcome.