The signal transducer and activator of transcription 3 (STAT3) protein is a master regulator of most key hallmarks and enablers of cancer, including cell proliferation and the response to DNA damage. G-Quadruplex (G4) structures are four-stranded noncanonical DNA structures enriched at telomeres and oncogenes’ promoters. In cancer cells, stabilization of G4 DNAs leads to replication stress and DNA damage accumulation and is therefore considered a promising target for oncotherapy. Here, we designed and synthesized novel quinazoline-based compounds that simultaneously and selectively affect these two well-recognized cancer targets, G4 DNA structures and the STAT3 protein. Using a combination of in vitro assays, NMR, and molecular dynamics simulations, we show that these small, uncharged compounds not only bind to the STAT3 protein but also stabilize G4 structures. In human cultured cells, the compounds inhibit phosphorylation-dependent activation of STAT3 without affecting the antiapoptotic factor STAT1 and cause increased formation of G4 structures, as revealed by the use of a G4 DNA-specific antibody. As a result, treated cells show slower DNA replication, DNA damage checkpoint activation, and an increased apoptotic rate. Importantly, cancer cells are more sensitive to these molecules compared to noncancerous cell lines. This is the first report of a promising class of compounds that not only targets the DNA damage cancer response machinery but also simultaneously inhibits the STAT3-induced cancer cell proliferation, demonstrating a novel approach in cancer therapy.

Pancreatic ductal adenocarcinoma (PDAC) has one of the worst outcomes among cancers with a 5-years survival rate of below 10%. This is a result of late diagnosis and the lack of effective treatments. The tumor is characterized by a highly fibrotic stroma containing distinct cellular components, embedded within an extracellular matrix (ECM). This ECM-abundant tumor microenvironment (TME) in PDAC plays a pivotal role in tumor progression and resistance to treatment. Cancer-associated fibroblasts (CAFs), being a dominant cell type of the stroma, are in fact functionally heterogeneous populations of cells within the TME. Certain subtypes of CAFs are the main producer of the ECM components of the stroma, with the most abundant one being the collagen family of proteins. Collagens are large macromolecules that upon deposition into the ECM form supramolecular fibrillar structures which provide a mechanical framework to the TME. They not only bring structure to the tissue by being the main structural proteins but also contain binding domains that interact with surface receptors on the cancer cells. These interactions can induce various responses in the cancer cells and activate signaling pathways leading to epithelial-to-mesenchymal transition (EMT) and ultimately metastasis. In addition, collagens are one of the main contributors to building up mechanical forces in the tumor. These forces influence the signaling pathways that are involved in cell motility and tumor progression and affect tumor microstructure and tissue stiffness by exerting solid stress and interstitial fluid pressure on the cells. Taken together, the TME is subjected to various types of mechanical forces and interactions that affect tumor progression, metastasis, and drug response. In this review article, we aim to summarize and contextualize the recent knowledge of components of the PDAC stroma, especially the role of different collagens and mechanical traits on tumor progression. We furthermore discuss different experimental models available for studying tumor-stromal interactions and finally discuss potential therapeutic targets within the stroma.

The tumor microenvironment is crucial in the initiation and progression of cancers. The interplay between cancer cells and the surrounding stroma shapes the tumor biology and dictates the response to cancer therapies. Consequently, a better understanding of the interactions between cancer cells and different components of the tumor microenvironment will drive progress in developing novel, effective, treatment strategies. Co-cultures can be used to study various aspects of these interactions in detail. This includes studies of paracrine relationships between cancer cells and stromal cells such as fibroblasts, endothelial cells, and immune cells, as well as the influence of physical and mechanical interactions with the extracellular matrix of the tumor microenvironment. The development of novel co-culture models to study the tumor microenvironment has progressed rapidly over recent years. Many of these models have already been shown to be powerful tools for further understanding of the pathophysiological role of the stroma and provide mechanistic insights into tumor-stromal interactions. Here we give a structured overview of different co-culture models that have been established to study tumor-stromal interactions and what we have learnt from these models. We also introduce a set of guidelines for generating and reporting co-culture experiments to facilitate experimental robustness and reproducibility.

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Pancreatic ductal adenocarcinoma (PDAC) is a major cause of cancer death that typically presents at an advanced stage. No reliable markers for early detection presently exist. The prominent tumor stroma represents a source of circulating biomarkers for use together with cancer cell-derived biomarkers for earlier PDAC diagnosis. CA19-9 and CEA (cancer cell-derived biomarkers), together with endostatin and collagen IV (stroma-derived) were examined alone, or together, by multivariable modelling, using pre-diagnostic plasma samples (n = 259 samples) from the Northern Sweden Health and Disease Study biobank. Serial samples were available for a subgroup of future patients. Marker efficacy for future PDAC case prediction (n = 154 future cases) was examined by both cross-sectional (ROC analysis) and longitudinal analyses. CA19-9 performed well at, and within, six months to diagnosis and multivariable modelling was not superior to CA19-9 alone in cross-sectional analysis. Within six months to diagnosis, CA19-9 (AUC = 0.92) outperformed the multivariable model (AUC = 0.81) at a cross-sectional level. At diagnosis, CA19-9 (AUC = 0.995) and the model (AUC = 0.977) performed similarly. Longitudinal analysis revealed increases in CA19-9 up to two years to diagnosis which indicates a window of opportunity for early detection of PDAC.