Biomarkers for patient selection are essential for the successful and rapid development of emerging targeted anti-cancer therapeutics. In this study, we report the discovery of a novel patient selection strategy for the p53–HDM2 inhibitor NVP-CGM097, currently under evaluation in clinical trials. By intersecting high-throughput cell line sensitivity data with genomic data, we have identified a gene expression signature consisting of 13 up-regulated genes that predicts for sensitivity to NVP-CGM097 in both cell lines and in patient-derived tumor xenograft models. Interestingly, these 13 genes are known p53 downstream target genes, suggesting that the identified gene signature reflects the presence of at least a partially activated p53 pathway in NVP-CGM097-sensitive tumors. Together, our findings provide evidence for the use of this newly identified predictive gene signature to refine the selection of patients with wild-type p53 tumors and increase the likelihood of response to treatment with p53–HDM2 inhibitors, such as NVP-CGM097.
Stress from daily activities and exposure to chemicals or UV radiation can all damage cells. Damaged cells may develop into cancerous tumors if unchecked. Normally, a protein called p53 helps to repair or eliminate damaged cells and prevent tumors from forming. The p53 protein does this by switching on or off genes that control DNA repair, cell division, and cell death. But half of all cancerous tumors have mutations that prevent p53 from doing its job.
Another protein called HDM2 keeps p53 in check by binding to p53 and preventing it from switching on and off genes after the stress passes. In cancers that have normal p53, sometimes HDM2 is overly active and prevents p53 from suppressing tumor formation and growth. Scientists are developing anticancer drugs that work by targeting HDM2; this frees p53 and allows it to wipe out cancerous cells. However, it is not always clear which patients with cancer are the most likely to benefit from anti-HDM2 therapy.
Jeay et al. screened hundreds of cancer cells to determine which ones are sensitive to HDM2-targeting drugs. As expected, the screen revealed that cancer cells that have mutations in the gene encoding p53 are insensitive to the anti-HDM2 drug because there is no working p53 to free up. But about 60% of the cancer cells that have normal p53 proteins also did not respond to the anti-HDM2 therapy. This finding indicates that the presence of normal p53 protein is necessary but not sufficient for tumor cells to respond to anti-HDM2 therapy.
Next, Jeay et al. compared the patterns of gene expression in the cancer cells that responded to an anti-HDM2 drug with those in cells that didn't respond. The analysis showed that a group of 13 genes are expressed more in the cells that responded to the drug. All 13 genes are unexpectedly direct targets of p53, suggesting that p53 remains active in these tumor cells, even if it is not working optimally. To verify these results, Jeay et al. grew human tumors in mice and found that tumors with high expression of the 13 genes are sensitive to the anti-HDM2 drug (called NVP-CGM097). The experiments strongly suggest that this 13-gene signature can be used to determine if a patient with cancer will respond to anti-HDM2 therapy. Following on from this work, researchers have already launched an early clinical trial with the anti-HDM2 drug and will test whether this gene signature is indeed useful in a real clinical setting.