We describe the design of a set of inhibitors to investigate the relationship between cyclin G associated kinase (GAK) and epidermal growth factor receptor (EGFR) in chordoma bone cancers. These compounds were characterized both in vitro and using in cell target engagement assays. The most potent chordoma inhibitors were further characterized in a kinome-wide screen demonstrating narrow spectrum profiles..

Aims Individualization of cancer chemotherapy based on the patient’s genetic makeup holds promise for reducing side effects and improving efficacy. However, the relative contribution of genetics to drug response is unknown. Materials & methods In this study, we investigated the cytotoxic effect of 29 commonly prescribed chemotherapeutic agents from diverse drug classes on 125 lymphoblastoid cell lines derived from 14 extended families. Results The results of this systematic study highlight the variable role that genetics plays in response to cytotoxic drugs, ranging from a heritability of <0.15 for gemcitabine to >0.60 for epirubicin. Conclusion Putative quantitative trait loci for cytotoxic response were identified, as well as drug class-specific signatures, which could indicate possible shared genetic mechanisms. In addition to the identification of putative quantitative trait locis, the results of this study inform the prioritization of chemotherapeutic drugs with a sizable genetic response component for future investigation.

Aims Association mapping with lymphoblastoid cell lines (LCLs) is a promising approach in pharmacogenomics research, and in the current study we utilize this model to perform association mapping for 29 chemotherapy drugs. Materials and Methods Currently, we use LCLs to perform genome-wide association mapping of the cytotoxic response of 520 European Americans to 29 different anticancer drugs, the largest LCL study to date. A novel association approach using a multivariate analysis of covariance design was employed with the software program MAGWAS, testing for differences in the dose-response profiles between genotypes without making assumptions about the response curve or the biological mode of association. Additionally, by classifying 25 of the 29 drugs into 8 families according to structural and mechanistic relationships, MAGWAS was used to test for associations that were shared across each drug family. Finally, a unique algorithm using multivariate responses and multiple linear regressions across pairs of response curves was used for unsupervised clustering of drugs. Results Among the single drug studies, suggestive associations were obtained for 18 loci, 12 within/near genes. Three of these, MED12L, CHN2 and MGMT, have been previously implicated in cancer pharmacogenomics. The drug family associations resulted in 4 additional suggestive loci (3 contained within/near genes). One of these genes, HDAC4, associated with the DNA alkylating agents, shows possible clinical interactions with temozolomide. For the drug clustering analysis, 18 of 25 drugs clustered into the appropriate family. Conclusions This study demonstrates the utility of LCLs for identifying genes having clinical importance in drug response, for assigning unclassified agents to specific drug families, and proposes new candidate genes for follow-up in a large number of chemotherapy drugs.

Metformin is the first-line treatment for type 2 diabetes (T2D). Although widely prescribed, the glucose-lowering mechanism for metformin is incompletely understood. Here, we used a genome-wide association approach in a diverse group of individuals with T2D from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) clinical trial to identify common and rare variants associated with HbA response to metformin treatment and followed up these findings in four replication cohorts. Common variants in and were associated with worse and better metformin response, respectively ( < 5 × 10), and meta-analysis in independent cohorts displayed similar associations with metformin response ( = 1.2 × 10 and = 0.005, respectively). Previous studies have shown that(+/-) knockout mice have increased total body fat ( = 1.78 × 10) and increased fasted circulating glucose ( = 5.73 × 10). Furthermore, rare variants in associated with worse metformin response ( <0.1). STAT3 is a ubiquitously expressed pleiotropic transcriptional activator that participates in the regulation of metabolism and feeding behavior. Here, we provide novel evidence for associations of common and rare variants in and with metformin response that may provide insight into mechanisms important for metformin efficacy in T2D.