Identification of pharmacodynamic biomarkers and common molecular mechanisms of response to genotoxic agents in cancer cell lines

Synthesis, Characterization and Biological Investigation of the Platinum(IV) Tolfenamato Prodrug–Resolving Cisplatin-Resistance in Ovarian Carcinoma Cell Lines

The research on the anticancer potential of platinum(IV) complexes represents one strategy to circumvent the deficits of approved platinum(II) drugs. Regarding the role of inflammation during carcinogenesis, the effects of non-steroidal anti-inflammatory drug (NSAID) ligands on the cytotoxicity of platinum(IV) complexes is of special interest. The synthesis of cisplatin- and oxaliplatin-based platinum(IV) complexes with four different NSAID ligands is presented in this work. Nine platinum(IV) complexes were synthesized and characterized by use of nuclear magnetic resonance (NMR) spectroscopy (1H, 13C, 195Pt, 19F), high-resolution mass spectrometry, and elemental analysis. The cytotoxic activity of eight compounds was evaluated for two isogenic pairs of cisplatin-sensitive and -resistant ovarian carcinoma cell lines. Platinum(IV) fenamato complexes with a cisplatin core showed especially high in vitro cytotoxicity against the tested cell lines. The most promising complex, 7, was further analyzed for its stability in different buffer solutions and behavior in cell cycle and cell death experiments. Compound 7 induces a strong cytostatic effect and cell line-dependent early apoptotic or late necrotic cell death processes. Gene expression analysis suggests that compound 7 acts through a stress-response pathway integrating p21, CHOP, and ATF3.

TPWshiny: an interactive R/Shiny app to explore cell line transcriptional responses to anti-cancer drugs

SUMMARY

The NCI Transcriptional Pharmacodynamics Workbench (NCI TPW) is an extensive compilation of directly measured transcriptional responses to anti-cancer agents across the well-characterized NCI-60 cancer cell lines. The NCI TPW data are publicly available through a web interface that allows limited user interaction with the data. We developed 'TPWshiny' as a standalone, easy to install, R application to facilitate more interactive data exploration.With no programming skills required, TPWshiny provides an intuitive and comprehensive graphical interface to help researchers understand the response of tumor cell lines to 15 therapeutic agents. The data are presented in interactive scatter plots, heatmaps, time series and Venn diagrams. Data can be queried by drug concentration, time point, gene and tissue type. Researchers can download the data for further analysis.

AVAILABILITY AND IMPLEMENTATION

Users can download the ready-to-use, self-extracting package for Windows or macOS, and R source code from the project website (https://brb.nci.nih.gov/TPWshiny/). TPWshiny documentation and additional information can be found on the project website.

Transcriptomics-Based Phenotypic Screening Supports Drug Discovery in Human Glioblastoma Cells

Simple Summary

Glioblastoma (GBM) remains a particularly challenging cancer, with an aggressive phenotype and few promising treatment options. Future therapy will rely heavily on diagnosing and targeting aggressive GBM cellular phenotypes, both before and after drug treatment, as part of personalized therapy programs. Here, we use a genome-wide drug-induced gene expression (DIGEX) approach to define the cellular drug response phenotypes associated with two clinical drug candidates, the phosphodiesterase 10A inhibitor Mardepodect and the multi-kinase inhibitor Regorafenib. We identify genes encoding specific drug targets, some of which we validate as effective antiproliferative agents and combination therapies in human GBM cell models, including HMGCoA reductase (HMGCR), salt-inducible kinase 1 (SIK1), bradykinin receptor subtype B2 (BDKRB2), and Janus kinase isoform 2 (JAK2). Individual, personalized treatments will be essential if we are to address and overcome the pharmacological plasticity that GBM exhibits, and DIGEX will play a central role in validating future drugs, diagnostics, and possibly vaccine candidates for this challenging cancer.

Abstract

We have used three established human glioblastoma (GBM) cell lines—U87MG, A172, and T98G—as cellular systems to examine the plasticity of the drug-induced GBM cell phenotype, focusing on two clinical drugs, the phosphodiesterase PDE10A inhibitor Mardepodect and the multi-kinase inhibitor Regorafenib, using genome-wide drug-induced gene expression (DIGEX) to examine the drug response. Both drugs upregulate genes encoding specific growth factors, transcription factors, cellular signaling molecules, and cell surface proteins, while downregulating a broad range of targetable cell cycle and apoptosis-associated genes. A few upregulated genes encode therapeutic targets already addressed by FDA approved drugs, but the majority encode targets for which there are no approved drugs. Amongst the latter, we identify many novel druggable targets that could qualify for chemistry-led drug discovery campaigns. We also observe several highly upregulated transmembrane proteins suitable for combined drug, immunotherapy, and RNA vaccine approaches. DIGEX is a powerful way of visualizing the complex drug response networks emerging during GBM drug treatment, defining a phenotypic landscape which offers many new diagnostic and therapeutic opportunities. Nevertheless, the extreme heterogeneity we observe within drug-treated cells using this technique suggests that effective pan-GBM drug treatment will remain a significant challenge for many years to come.

Gene Panel of Persister Cells as a Prognostic Indicator for Tumor Repopulation After Radiation

Tumor repopulation during cycles of radiotherapy limits the radio-response in ensuing cycles and causes failure of treatment. It is thus of vital importance to unveil the mechanisms underlying tumor repopulating cells. Increasing evidence suggests that a subpopulation of drug-tolerant persister cancer cells (DTPs) could survive the cytotoxic treatment and resume to propagate. Whether these persister cells contribute to development of radio-resistance remains elusive. Based on the genetic profiling of DTPs by integrating datasets from Gene Expression Omnibus database, this study aimed to provide novel insights into tumor-repopulation mediated radio-resistance and identify predictive biomarkers for radio-response in clinic. A prognostic risk index, grounded on four persister genes (LYNX1, SYNPO, GADD45B, and PDLIM1), was constructed in non-small-cell lung cancer patients from The Cancer Genome Atlas Program (TCGA) using stepwise Cox regression analysis. Weighted gene co-expression network analysis further confirmed the interaction among persister-gene based risk score, radio-response and overall survival time. In addition, the predictive role of risk index was validated in vitro and in other types of TCGA patients. Gene set enrichment analysis was performed to decipher the possible biological signaling, which indicated that two forces behind persister cells, stress response and survival adaptation, might fuel the tumor repopulation after radiation. Targeting these persister cells may represent a new prognostic and therapeutic approach to enhance radio-response and prevent radio-resistance induced by tumor repopulation.