Purpose: Publicly available databases, for example, The Cancer Genome Atlas (TCGA), containing clinical and molecular data from many patients are useful in validating the contribution of particular genes to disease mechanisms and in forming novel hypotheses relating to clinical outcomes. Experimental Design: The impact of key drivers of cancer progression can be assessed by segregating a patient cohort by certain molecular features and constructing survival plots using the associated clinical data. However, conclusions drawn from this straightforward analysis are highly dependent on the quality and source of tissue samples, as demonstrated through the pancreatic ductal adenocarcinoma (PDAC) subset of TCGA. Results: Analyses of the PDAC-TCGA database, which contains mainly resectable cancer samples from patients in stage IIB, reveal a difference from widely known historic median and 5-year survival rates of PDAC. A similar discrepancy was observed in lung, stomach, and liver cancer subsets of TCGA. The whole transcriptome expression patterns of PDAC-TCGA revealed a cluster of samples derived from neuroendocrine tumors, which have a distinctive biology and better disease prognosis than PDAC. Furthermore, PDAC-TCGA contains numerous pseudo-normal samples, as well as those that arose from tumors not classified as PDAC. Conclusions: Inclusion of misclassified samples in the bioinformatic analyses distorts the association of molecular biomarkers with clinical outcomes, altering multiple published conclusions used to support and motivate experimental research. Hence, the stringent scrutiny of type and origin of samples included in the bioinformatic analyses by researchers, databases, and web-tool developers is of crucial importance for generating accurate conclusions. Clin Cancer Res; 24(16); 3813–9. ©2018 AACR.

BOLD-100, a ruthenium-based complex, sodium trans-[tetrachloridobis (1H-indazole) ruthenate (III)] (also known as IT-139, NKP1339 or KP1339), is a novel small molecule drug that demonstrated a manageable safety profile at the maximum tolerated dose and modest antitumor activity in a phase I clinical trial. BOLD-100 has been reported to inhibit the upregulation of the endoplasmic reticulum stress sensing protein GRP78. However, response to BOLD-100 varies in different cancer models and the precise mechanism of action in high-response versus low-response cancer cells remains unclear. In vitro studies have indicated that BOLD-100 induces cytostatic rather than cytotoxic effects as a monotherapy. To understand BOLD-100-mediated signaling mechanism in breast cancer cells, we used estrogen receptor positive (ER+) MCF7 breast cancer cells to obtain gene-metabolite integrated models. At 100 μM, BOLD-100 significantly reduced cell proliferation and expression of genes involved in the DNA repair pathway. BOLD-100 also induced reactive oxygen species (ROS) and phosphorylation of histone H2AX, gamma-H2AX (Ser139), suggesting disruption of proper DNA surveillance. In estrogen receptor negative (ER−) breast cancer cells, combination of BOLD-100 with a PARP inhibitor, olaparib, induced significant inhibition of cell growth and xenografts and increased gamma-H2AX. Thus, BOLD-100 is a novel DNA repair pathway targeting agent and can be used with other chemotherapies in ER− breast cancer.

Pancreatic ductal adenocarcinoma (PDAC) is a lethal aggressive cancer, in part due to elements of the microenvironment (hypoxia, hypoglycemia) that cause metabolic network alterations. The FDA-approved antihelminthic pyrvinium pamoate (PP) has previously been shown to cause PDAC cell death, although the mechanism has not been fully determined. We demonstrated that PP effectively inhibited PDAC cell viability with nanomolar IC50 values (9–93 nmol/L) against a panel of PDAC, patient-derived, and murine organoid cell lines. In vivo, we demonstrated that PP inhibited PDAC xenograft tumor growth with both intraperitoneal (IP; P < 0.0001) and oral administration (PO; P = 0.0023) of human-grade drug. Metabolomic and phosphoproteomic data identified that PP potently inhibited PDAC mitochondrial pathways including oxidative phosphorylation and fatty acid metabolism. As PP treatment reduced oxidative phosphorylation (P < 0.001), leading to an increase in glycolysis (P < 0.001), PP was 16.2-fold more effective in hypoglycemic conditions similar to those seen in PDAC tumors. RNA sequencing demonstrated that PP caused a decrease in mitochondrial RNA expression, an effect that was not observed with established mitochondrial inhibitors rotenone and oligomycin. Mechanistically, we determined that PP selectively bound mitochondrial G-quadruplexes and inhibited mitochondrial RNA transcription in a G-quadruplex–dependent manner. This subsequently led to a 90% reduction in mitochondrial encoded gene expression. We are preparing to evaluate the efficacy of PP in PDAC in an IRB-approved window-of-opportunity trial (IND:144822).

Digital twins are made of a real-world component where data is measured and a virtual component where those measurements are used to parameterize computational models. There is growing interest in applying digital twins-based approaches to optimize personalized treatment plans and improve health outcomes. The integration of artificial intelligence is critical in this process, as it enables the development of sophisticated disease models that can accurately predict patient response to therapeutic interventions. There is a unique and equally important application of AI to the real-world component of a digital twin when it is applied to medical interventions. The patient can only be treated once, and therefore, we must turn to the experience and outcomes of previously treated patients for validation and optimization of the computational predictions. The physical component of a digital twins instead must utilize a compilation of available data from previously treated cancer patients whose characteristics (genetics, tumor type, lifestyle, etc.) closely parallel those of a newly diagnosed cancer patient for the purpose of predicting outcomes, stratifying treatment options, predicting responses to treatment and/or adverse events. These tasks include the development of robust data collection methods, ensuring data availability, creating precise and dependable models, and establishing ethical guidelines for the use and sharing of data. To successfully implement digital twin technology in clinical care, it is crucial to gather data that accurately reflects the variety of diseases and the diversity of the population.