Erratum: Iyer, A., et al. Integrative Analysis and Machine Learning Based Characterization of Single Circulating Tumor Cells. 2020, , 1206.

In the published manuscript [...

Author Correction: Identifying inhibitors of epithelial-mesenchymal plasticity using a network topology-based approach.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Identifying inhibitors of epithelial-mesenchymal plasticity using a network topology-based approach.

Metastasis is the cause of over 90% of cancer-related deaths. Cancer cells undergoing metastasis can switch dynamically between different phenotypes, enabling them to adapt to harsh challenges, such as overcoming anoikis and evading immune response. This ability, known as phenotypic plasticity, is crucial for the survival of cancer cells during metastasis, as well as acquiring therapy resistance.

Integrative Analysis and Machine Learning based Characterization of Single Circulating Tumor Cells.

We collated publicly available single-cell expression profiles of circulating tumor cells (CTCs) and showed that CTCs across cancers lie on a near-perfect continuum of epithelial to mesenchymal (EMT) transition. Integrative analysis of CTC transcriptomes also highlighted the inverse gene expression pattern between PD-L1 and MHC, which is implicated in cancer immunotherapy. We used the CTCs expression profiles in tandem with publicly available peripheral blood mononuclear cell (PBMC) transcriptomes to train a classifier that accurately recognizes CTCs of diverse phenotype.