Mitochondrial electron transport is the cellular target of the oncology drug elesclomol.

Elesclomol is a first-in-class investigational drug currently undergoing clinical evaluation as a novel cancer therapeutic. The potent antitumor activity of the compound results from the elevation of reactive oxygen species (ROS) and oxidative stress to levels incompatible with cellular survival. However, the molecular target(s) and mechanism by which elesclomol generates ROS and subsequent cell death were previously undefined. The cellular cytotoxicity of elesclomol in the yeast S. cerevisiae appears to occur by a mechanism similar, if not identical, to that in cancer cells. Accordingly, here we used a powerful and validated technology only available in yeast that provides critical insights into the mechanism of action, targets and processes that are disrupted by drug treatment. Using this approach we show that elesclomol does not work through a specific cellular protein target. Instead, it targets a biologically coherent set of processes occurring in the mitochondrion. Specifically, the results indicate that elesclomol, driven by its redox chemistry, interacts with the electron transport chain (ETC) to generate high levels of ROS within the organelle and consequently cell death. Additional experiments in melanoma cells involving drug treatments or cells lacking ETC function confirm that the drug works similarly in human cancer cells. This deeper understanding of elesclomol's mode of action has important implications for the therapeutic application of the drug, including providing a rationale for biomarker-based stratification of patients likely to respond in the clinical setting.

Elesclomol induces cancer cell apoptosis through oxidative stress.

Elesclomol (formerly STA-4783) is a novel small molecule undergoing clinical evaluation in a pivotal phase III melanoma trial (SYMMETRY). In a phase II randomized, double-blinded, controlled, multi-center trial in 81 patients with stage IV metastatic melanoma, treatment with elesclomol plus paclitaxel showed a statistically significant doubling of progression-free survival time compared with treatment with paclitaxel alone. Although elesclomol displays significant therapeutic activity in the clinic, the mechanism underlying its anticancer activity has not been defined previously. Here, we show that elesclomol induces apoptosis in cancer cells through the induction of oxidative stress. Treatment of cancer cells in vitro with elesclomol resulted in the rapid generation of reactive oxygen species (ROS) and the induction of a transcriptional gene profile characteristic of an oxidative stress response. Inhibition of oxidative stress by the antioxidant N-acetylcysteine blocked the induction of gene transcription by elesclomol. In addition, N-acetylcysteine blocked drug-induced apoptosis, indicating that ROS generation is the primary mechanism responsible for the proapoptotic activity of elesclomol. Excessive ROS production and elevated levels of oxidative stress are critical biochemical alterations that contribute to cancer cell growth. Thus, the induction of oxidative stress by elesclomol exploits this unique characteristic of cancer cells by increasing ROS levels beyond a threshold that triggers cell death.

A rare-cell detector for cancer.

Although a reliable method for detection of cancer cells in blood would be an important tool for diagnosis and monitoring of solid tumors in early stages, current technologies cannot reliably detect the extremely low concentrations of these rare cells. The preferred method of detection, automated digital microscopy (ADM), is too slow to scan the large substrate areas. Here we report an approach that uses fiber-optic array scanning technology (FAST), which applies laser-printing techniques to the rare-cell detection problem. With FAST cytometry, laser-printing optics are used to excite 300,000 cells per sec, and emission is collected in an extremely wide field of view, enabling a 500-fold speed-up over ADM with comparable sensitivity and superior specificity. The combination of FAST enrichment and ADM imaging has the performance required for reliable detection of early-stage cancer in blood.

Automated detection of immunofluorescently labeled cytomegalovirus-infected cells in isolated peripheral blood leukocytes using decision tree analysis.

BACKGROUND: Cytomegalovirus (CMV) infection continues to be a major problem for immunocompromised patients. Detection of viral antigens in leukocytes (antigenemia assay) is widely used for the diagnosis of CMV infection and for guiding antiviral therapy. The antigenemia technique, contingent upon the manual microscopic analysis of rare cells, is a laborious task that is subject to human error. In this study, we combine automated microscopy with artificial intelligence for reliable detection of fluorescently labeled CMV-infected cells. METHODS: Cytospin preparations of peripheral blood leukocytes were immunofluorescently labeled for the CMV lower matrix phosphoprotein (pp65) and scanned in the Rare Event Imaging System (REIS), a fully automated image cytometer. The REIS detected potential positive objects and digitally recorded 49 measured cellular features for each identified case. The measurement data of these objects were analyzed by the See5 decision tree (DT) algorithm to ascertain whether they were true-positive detections. RESULTS: The DT was built from the measurement data of 2,047 true- and 2,028 false-positive detections, collected from 32 patient samples. By designating misclassifications of false-negatives three times more costly, the 10-fold cross-validation sensitivity, specificity, and misclassification error of the assay was 94.3%, 56.2%, and 25%, respectively. The method was also validated using an independent test set of 21 patient samples, in which similar results were obtained. CONCLUSIONS: To our knowledge, this study represents the first attempt to improve the accuracy of rare event image cytometry through the implementation of artificial intelligence methodology. Results suggest that cost-sensitive decision tree analysis of digitally measured cellular features vastly improves the performance of rare event image cytometry.