Scott H. Kaufmann

FANCM restrains structural genome evolution and defines a synthetic lethal dependency in BRCA1-deficient breast cancer

This study identifies FANCM as a synthetic lethal target in BRCA1-deficient breast cancer, showing it restricts structural genome instability and links replication stress to transcriptional changes.

Nuclear Cathepsin L Remodels the Replication Machinery to Create a Therapeutic Vulnerability in Ovarian Cancer

This paper identifies nuclear cathepsin L as a regulator of replication stress in ovarian cancer, showing that its activation creates a therapeutic vulnerability through replication machinery remodeling and DNA repair inhibition.

Drug-induced nuclear cathepsin L (nCTSL) defines a targetable DNA damage response axis and PARP inhibitor sensitivity in ovarian cancer

This study identifies nuclear cathepsin L (nCTSL) as a biomarker for PARP inhibitor sensitivity in ovarian cancer and shows that combining clofarabine with PARP inhibitors enhances therapeutic response.

A Rare Multipotent Peg-like Epithelial Cell is a Candidate Cell-of-Origin for High-Grade Serous Ovarian Cancer

The paper identifies a rare multipotent epithelial cell in the fallopian tube as a potential cell-of-origin for high-grade serous ovarian cancer using multi-omic profiling and organoid models.

Lestaurtinib's antineoplastic activity converges on JAK/STAT signaling to inhibit advanced forms of therapy resistant ovarian cancer

This study identifies lestaurtinib as a potent inhibitor of ovarian cancer, effective against resistant models, by targeting JAK/STAT signaling and synergizing with standard therapies.

Single cell resolution of an epigenetic signature of persister tumor cell

The paper identifies an epigenetic signature associated with persister tumor cells in ovarian cancer that correlates with chemotherapy resistance and defines the persister state.

DAGBagM: Learning directed acyclic graphs of mixed variables with an application to identify prognostic protein biomarkers in ovarian cancer

DAGBagM learns DAGs with mixed continuous and binary variables to identify prognostic protein biomarkers in ovarian cancer.