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Background Current diagnostic tools for prostate cancer lack specificity and sensitivity for detecting very early lesions. DNA methylation is a stable genomic modification that is detectable in peripheral patient fluids such as urine and blood plasma that could serve as a non-invasive diagnostic biomarker for prostate cancer. Methods We measured genome-wide DNA methylation patterns in 73 clinically annotated fresh-frozen prostate cancers and 63 benign-adjacent prostate tissues using the Illumina Infinium HumanMethylation450 BeadChip array. We overlaid the most significantly differentially methylated sites in the genome with transcription factor binding sites measured by the Encyclopedia of DNA Elements consortium. We used logistic regression and receiver operating characteristic curves to assess the performance of candidate diagnostic models. Results We identified methylation patterns that have a high predictive power for distinguishing malignant prostate tissue from benign-adjacent prostate tissue, and these methylation signatures were validated using data from The Cancer Genome Atlas Project. Furthermore, by overlaying ENCODE transcription factor binding data, we observed an enrichment of enhancer of zeste homolog 2 binding in gene regulatory regions with higher DNA methylation in malignant prostate tissues. Conclusions DNA methylation patterns are greatly altered in prostate cancer tissue in comparison to benign-adjacent tissue. We have discovered patterns of DNA methylation marks that can distinguish prostate cancers with high specificity and sensitivity in multiple patient tissue cohorts, and we have identified transcription factors binding in these differentially methylated regions that may play important roles in prostate cancer development. Electronic supplementary material The online version of this article (doi:10.1186/s12885-017-3252-2) contains supplementary material, which is available to authorized users.

Background Inhibition of kinases involved in the DNA damage response sensitizes cells to genotoxic agents by abrogating checkpoint-induced cell cycle arrest. CHK1 and WEE1 act in a pathway upstream of CDK1 to inhibit cell cycle progression in response to damaged DNA. Therapeutic targeting of either CHK1 or WEE1, in combination with chemotherapy, is under clinical evaluation. These studies examine the overlap and potential for synergy when CHK1 and WEE1 are inhibited in cancer cell models. Methods Small molecules MK-8776 and MK-1775 were used to selectively and potently inhibit CHK1 and WEE1, respectively. Results In vitro, the combination of MK-8776 and MK-1775 induces up to 50-fold more DNA damage than either MK-8776 or MK-1775 alone at a fixed concentration. This requires aberrant cyclin-dependent kinase activity but does not appear to be dependent on p53 status alone. Furthermore, DNA damage takes place primarily in S-phase cells, implying disrupted DNA replication. When dosed together, the combination of MK-8776 and MK-1775 induced more intense and more durable DNA damage as well as anti-tumor efficacy than either MK-8776 or MK-1775 dosed alone. DNA damage induced by the combination was detected in up to 40% of cells in a treated xenograft tumor model. Conclusions These results highlight the roles of WEE1 and CHK1 in maintaining genomic integrity. Importantly, the strong synergy observed upon inhibition of both kinases suggests unique yet complimentary anti-tumor effects of WEE1 and CHK1 inhibition. This demonstration of DNA double strand breaks in the absence of a DNA damaging chemotherapeutic provides preclinical rationale for combining WEE1 and CHK1 inhibitors as a cancer treatment regimen.

Background Hyperactivation of the Ras signaling pathway is a driver of many cancers, and RAS pathway activation can predict response to targeted therapies. Therefore, optimal methods for measuring Ras pathway activation are critical. The main focus of our work was to develop a gene expression signature that is predictive of RAS pathway dependence. Methods We used the coherent expression of RAS pathway-related genes across multiple datasets to derive a RAS pathway gene expression signature and generate RAS pathway activation scores in pre-clinical cancer models and human tumors. We then related this signature to KRAS mutation status and drug response data in pre-clinical and clinical datasets. Results The RAS signature score is predictive of KRAS mutation status in lung tumors and cell lines with high (> 90%) sensitivity but relatively low (50%) specificity due to samples that have apparent RAS pathway activation in the absence of a KRAS mutation. In lung and breast cancer cell line panels, the RAS pathway signature score correlates with pMEK and pERK expression, and predicts resistance to AKT inhibition and sensitivity to MEK inhibition within both KRAS mutant and KRAS wild-type groups. The RAS pathway signature is upregulated in breast cancer cell lines that have acquired resistance to AKT inhibition, and is downregulated by inhibition of MEK. In lung cancer cell lines knockdown of KRAS using siRNA demonstrates that the RAS pathway signature is a better measure of dependence on RAS compared to KRAS mutation status. In human tumors, the RAS pathway signature is elevated in ER negative breast tumors and lung adenocarcinomas, and predicts resistance to cetuximab in metastatic colorectal cancer. Conclusions These data demonstrate that the RAS pathway signature is superior to KRAS mutation status for the prediction of dependence on RAS signaling, can predict response to PI3K and RAS pathway inhibitors, and is likely to have the most clinical utility in lung and breast tumors.