Evaluating the transcriptional fidelity of cancer models.

ABSTRACT:

Background

Cancer researchers use cell lines, patient-derived xenografts, engineered mice, and tumoroids as models to investigate tumor biology and to identify therapies. The generalizability and power of a model derive from the fidelity with which it represents the tumor type under investigation; however, the extent to which this is true is often unclear. The preponderance of models and the ability to readily generate new ones has created a demand for tools that can measure the extent and ways in which cancer models resemble or diverge from native tumors.

Methods

We developed a machine learning-based computational tool, CancerCellNet, that measures the similarity of cancer models to 22 naturally occurring tumor types and 36 subtypes, in a platform and species agnostic manne

SUBMITTER: Peng D 

PROVIDER: S-EPMC8086312 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature