Putzker, K. | Search bioRxiv

bioRxiv2024

Gemcitabine and ATR inhibitors synergize to kill PDAC cells by blocking DNA damage response

The DNA-damaging agent gemcitabine (GEM) is a first-line treatment for pancreatic cancer but chemoresistance is frequently observed. Several clinical trials investigate the efficacy of GEM in combination with targeted drugs including kinase inhibitors but the experimental evidence for such rational is often unclear. Here, we phenotypically screened 13 human pancreatic adenocarcinoma (PDAC) cell lines against GEM in combination with 140 clinical kinase inhibitors and observed strong synergy for the ATR inhibitor Elimusertib in most cell lines. Dose-dependent phosphoproteome profiling of four ATR inhibitors following DNA damage induction by GEM revealed a strong block of the DNA damage response pathway including phosphorylated pS468 of CHEK1 as the underlying mechanism of drug synergy. The current work provides a strong rationale for why the combination of GEM and ATR inhibition may be useful for the treatment of PDAC patients and constitutes a rich phenotypic and molecular resource for further investigating effective drug combinations.

cancer biology↗

bioRxiv2022

Inferring tumor-specific cancer dependencies through integrating ex-vivo drug response assays and drug-protein profiling

The development of cancer therapies may be improved by the discovery of tumor-specific molecular dependencies. The requisite tools include genetic and chemical perturbations, each with its strengths and limitations. Drug perturbations can be readily applied to primary cancer samples at a large scale, but mechanistic understanding of hits and further pharmaceutical development is often complicated by the fact that a small compound has a range of affinities to multiple proteins. To computationally infer the molecular dependencies of individual cancers from their ex-vivo drug sensitivity profiles, we developed a mathematical model that deconvolutes these data using measurements of protein-drug affinity profiles. Our method, DepInfeR, correctly identified known dependencies, including EGFR dependence in Her2+ breast cancer cell line, FLT3 dependence in AML tumors with FLT3-ITD mutations and the differential dependencies on the B-cell receptor pathway in two major subtypes of chronic lymphocytic leukemia (CLL). Furthermore, our method uncovered new subgroup-specific dependencies, including a previously unreported dependence of high-risk CLL on Checkpoint kinase 1 (CHEK1). The method also produced a more accurate map of the molecular dependencies in a heterogeneous set of 117 CLL samples. The ability to deconvolute polypharmacological phenotypes into underlying causal molecular dependencies should increase the utility of high-throughput drug response assays for functional precision oncology.

bioinformatics↗