Chloroquine eliminates cancer stem cells through deregulation of Jak2 and DNMT1.

Triple negative breast cancer (TNBC) is known to contain a high percentage of CD44(+) /CD24(-/low) cancer stem cells (CSCs), corresponding with a poor prognosis despite systemic chemotherapy. Chloroquine (CQ), an antimalarial drug, is a lysotropic reagent which inhibits autophagy. CQ was identified as a potential CSC inhibitor through in silico gene expression signature analysis of the CD44(+) /CD24(-/low) CSC population. Autophagy plays a critical role in adaptation to stress conditions in cancer cells, and is related with drug resistance and CSC maintenance. Thus, the objectives of this study were to examine the potential enhanced efficacy arising from addition of CQ to standard chemotherapy (paclitaxel) in TNBC and to identify the mechanism by which CQ eliminates CSCs in TNBCs. Herein, we report that CQ sensitizes TNBC cells to paclitaxel through inhibition of autophagy and reduces the CD44(+) /CD24(-/low) CSC population in both preclinical and clinical settings. Also, we are the first to report a mechanism by which CQ regulates the CSCs in TNBC through inhibition of the Janus-activated kinase 2 (Jak2)-signal transducer and activator of transcription 3 signaling pathway by reducing the expression of Jak2 and DNA methyltransferase 1.

Modeling of nanotherapeutics delivery based on tumor perfusion.

Heterogeneities in the perfusion of solid tumors prevent optimal delivery of nanotherapeutics. Clinical imaging protocols to obtain patient-specific data have proven difficult to implement. It is challenging to determine which perfusion features hold greater prognostic value and to relate measurements to vessel structure and function. With the advent of systemically administered nanotherapeutics, whose delivery is dependent on overcoming diffusive and convective barriers to transport, such knowledge is increasingly important. We describe a framework for the automated evaluation of vascular perfusion curves measured at the single vessel level. Primary tumor fragments, collected from triple-negative breast cancer patients and grown as xenografts in mice, were injected with fluorescence contrast and monitored using intravital microscopy. The time to arterial peak and venous delay, two features whose probability distributions were measured directly from time-series curves, were analyzed using a Fuzzy C-mean (FCM) supervised classifier in order to rank individual tumors according to their perfusion characteristics. The resulting rankings correlated inversely with experimental nanoparticle accumulation measurements, enabling modeling of nanotherapeutics delivery without requiring any underlying assumptions about tissue structure or function, or heterogeneities contained within. With additional calibration, these methodologies may enable the study of nanotherapeutics delivery strategies in a variety of tumor models.