Christopher E. Lietz

A Shared Adenocarcinoma Transcriptomic Program Enables Prediction of Therapeutics Applicable Across Tissues

This paper identifies a shared transcriptomic module across adenocarcinomas and uses it to predict FDA-approved drugs applicable to multiple cancer types.

A dynamic microRNA profile that tracks a chemotherapy resistance phenotype in osteosarcoma. Implications for novel therapeutics.

The paper identifies a dynamic microRNA profile associated with chemotherapy resistance in osteosarcoma and predicts novel therapeutic targets that could reverse resistance.

Genome-wide DNA methylation patterns reveal clinically relevant predictive and prognostic subtypes in osteosarcoma.

This study identifies DNA methylation subtypes in osteosarcoma that predict clinical outcomes and response to chemotherapy.

MicroRNA-mRNA networks define translatable molecular outcome phenotypes in osteosarcoma.

The paper identifies microRNA-mRNA networks associated with prognosis in osteosarcoma and validates their predictive value across independent datasets.

Machine learning-based prediction of response to PARP inhibition across cancer types.

The paper develops a multi-gene signature predicting PARP inhibitor response across cancer types using machine learning and validates it in vitro and in clinical datasets.