ABSTRACT
BACKGROUND: Tumor heterogeneity is increasingly being recognized as a major source of variability in the histopathological assessment of drug responses. Quantitative analysis of immunohistochemistry (IHC) and immunofluorescence (IF) images using biomarkers that capture spatialpatterns of distinct tumor biology and drug concentration in tumors is of high interest to the field. METHODS: We have developed an image analysis pipeline to measure drug response using IF and IHC images along spatial gradients of local drug release from a tumor-implantable drug delivery microdevice. The pipeline utilizes a series of user-interactive python scripts and CellProfiler pipelines with custom modules to perform image and spatial analysis of regions of interest within whole-slide images. RESULTS: Worked examples demonstrate that intratumor measurements such as apoptosis, cell proliferation, and immune cell population density can be quantitated in a spatially and drug concentration-dependent manner, establishing in vivo profiles of pharmacodynamics and pharmacokinetics in tumors. CONCLUSIONS: Spatial image analysis of tumor response along gradients of local drug release is achievable in high throughput. The major advantage of this approach is the use of spatially aware annotation tools to correlate drug gradients with drug effects in tumors in vivo.
ABSTRACT
There is an unmet need for new antimitotic drug combinations that target cancer-specific vulnerabilities. Based on our finding of elevated biomolecule oxidation in mitotically arrested cancer cells, we combined Plk1 inhibitors with TH588, an MTH1 inhibitor that prevents detoxification of oxidized nucleotide triphosphates. This combination showed robust synergistic killing of cancer, but not normal, cells that, surprisingly, was MTH1-independent. To dissect the underlying synergistic mechanism, we developed VISAGE, a strategy integrating experimental synergy quantification with computational-pathway-based gene expression analysis. VISAGE predicted, and we experimentally confirmed, that this synergistic combination treatment targeted the mitotic spindle. Specifically, TH588 binding to ß-tubulin impaired microtubule assembly, which when combined with Plk1 blockade, synergistically disrupted mitotic chromosome positioning to the spindle midzone. These findings identify a cancer-specific mitotic vulnerability that is targetable using Plk1 inhibitors with microtubule-destabilizing agents and highlight the general utility of the VISAGE approach to elucidate molecular mechanisms of drug synergy.
Subject(s)
Antineoplastic Agents/therapeutic use , Growth Inhibitors/therapeutic use , Neoplasms/drug therapy , Pyrimidines/therapeutic use , Spindle Apparatus/drug effects , Cell Cycle Proteins/antagonists & inhibitors , Cell Line, Tumor , Computational Biology , DNA Repair Enzymes/antagonists & inhibitors , Drug Synergism , Gene Expression Profiling , Humans , Molecular Targeted Therapy , Phosphoric Monoester Hydrolases/antagonists & inhibitors , Protein Binding , Protein Serine-Threonine Kinases/antagonists & inhibitors , Proto-Oncogene Proteins/antagonists & inhibitors , Spindle Apparatus/physiology , Tubulin/metabolism , Polo-Like Kinase 1ABSTRACT
Growing evidence suggests that microbes can influence the efficacy of cancer therapies. By studying colon cancer models, we found that bacteria can metabolize the chemotherapeutic drug gemcitabine (2',2'-difluorodeoxycytidine) into its inactive form, 2',2'-difluorodeoxyuridine. Metabolism was dependent on the expression of a long isoform of the bacterial enzyme cytidine deaminase (CDDL), seen primarily in Gammaproteobacteria. In a colon cancer mouse model, gemcitabine resistance was induced by intratumor Gammaproteobacteria, dependent on bacterial CDDL expression, and abrogated by cotreatment with the antibiotic ciprofloxacin. Gemcitabine is commonly used to treat pancreatic ductal adenocarcinoma (PDAC), and we hypothesized that intratumor bacteria might contribute to drug resistance of these tumors. Consistent with this possibility, we found that of the 113 human PDACs that were tested, 86 (76%) were positive for bacteria, mainly Gammaproteobacteria.
