Spironolactone inhibits the growth of cancer stem cells by impairing DNA damage response.

The cancer stem cell (CSC) model suggests that a subpopulation of cells within the tumor, the CSCs, is responsible for cancer relapse and metastasis formation. CSCs hold unique characteristics, such as self-renewal, differentiation abilities, and resistance to chemotherapy, raising the need for discovering drugs that target CSCs. Previously we have found that the antihypertensive drug spironolactone impairs DNA damage response in cancer cells. Here we show that spironolactone, apart from inhibiting cancerous cell growth, is also highly toxic to CSCs. Notably, we demonstrate that CSCs have high basal levels of DNA double-strand breaks (DSBs). Mechanistically, we reveal that spironolactone does not damage the DNA but impairs DSB repair and induces apoptosis in cancer cells and CSCs while sparing healthy cells. In vivo, spironolactone treatment reduced the size and CSC content of tumors. Overall, we suggest spironolactone as an anticancer reagent, toxic to both cancer cells and, particularly to, CSCs.

Analyzing movement trajectories using a Markov bi-clustering method.

In this study we treat scribbling motion as a compositional system in which a limited set of elementary strokes are capable of concatenating amongst themselves in an endless number of combinations, thus producing an unlimited repertoire of complex constructs. We broke the continuous scribblings into small units and then calculated the Markovian transition matrix between the trajectory clusters. The Markov states are grouped in a way that minimizes the loss of mutual information between adjacent strokes. The grouping algorithm is based on a novel markov-state bi-clustering algorithm derived from the Information-Bottleneck principle. This approach hierarchically decomposes scribblings into increasingly finer elements. We illustrate the usefulness of this approach by applying it to human scribbling.