Publications

Cell cycle checkpoints ensure genome integrity and are frequently compromised in human cancers. A therapeutic strategy being explored takes advantage of checkpoint defects in p53-deficient tumors in order to sensitize them to DNA-damaging agents by eliminating Chk1-mediated checkpoint responses. Using mouse models, we demonstrated that p21 is a key determinant of how cells respond to the combination of DNA damage and Chk1 inhibition (combination therapy) in normal cells as well as in tumors. Loss of p21 sensitized normal cells to the combination therapy much more than did p53 loss and the enhanced lethality was partially blocked by CDK inhibition. In addition, basal pools of p21 (p53 independent) provided p53 null cells with protection from the combination therapy. Our results uncover a novel p53-independent function for p21 in protecting cells from the lethal effects of DNA damage followed by Chk1 inhibition. As p21 levels are low in a significant fraction of colorectal tumors, they are predicted to be particularly sensitive to the combination therapy. Results reported in this study support this prediction.

Effective clinical text processing requires accurate extraction and representation of temporal expressions. Multiple temporal information extraction models were developed but a similar need for extracting temporal expressions in eligibility criteria (e.g., for eligibility determination) remains. We identified the temporal knowledge representation requirements of eligibility criteria by reviewing 100 temporal criteria. We developed EliXR-TIME, a frame-based representation designed to support semantic annotation for temporal expressions in eligibility criteria by reusing applicable classes from well-known clinical temporal knowledge representations. We used EliXR-TIME to analyze a training set of 50 new temporal eligibility criteria. We evaluated EliXR-TIME using an additional random sample of 20 eligibility criteria with temporal expressions that have no overlap with the training data, yielding 92.7% (76 / 82) inter-coder agreement on sentence chunking and 72% (72 / 100) agreement on semantic annotation. We conclude that this knowledge representation can facilitate semantic annotation of the temporal expressions in eligibility criteria.