In an effort to understand molecular mechanisms of human disease and to determine genes responsible, we systematically examine relationships between 3,949 genes, 62,663 mutations and 3,453 associated disorders within the framework of a three-dimensional structurally resolved human interactome, consisting of 4,222 high-quality binary protein-protein interactions with their atomic-resolution interfaces. We find that in-frame mutations (missense point mutations and in-frame insertions and deletions) are enriched on the interaction interfaces of proteins associated with the corresponding disorders, indicating that alteration of specific interactions by in-frame disease mutations is critical in understanding the pathogenesis of many genes. Furthermore, locations of mutations on proteins with regard to interaction interfaces are significantly associated with underlying pathogenic processes and the disease specificity for different mutations of the same gene. Based on these findings, we generate 292 new gene candidates for 694 unknown disease-to-gene associations with proposed molecular mechanism hypotheses, readily expanding our understanding of human genetic diseases and corresponding therapeutic possibilities.

Mutations of the retinoblastoma tumor suppressor gene (RB1) or components regulating the CDK-RB-E2F pathway have been identified in nearly every human malignancy. Re-establishing cell cycle control through CDK inhibition has therefore emerged as an attractive option in the development of targeted cancer therapy. The most successful example of this today is the use of the CDK4/6 inhibitor palbociclib combined with aromatase inhibitors for the treatment of estrogen receptor-positive breast cancers. Multiple studies have demonstrated that the CDK-RB-E2F pathway is critical for the control of cell proliferation. More recently, studies have highlighted additional roles of this pathway, especially E2F transcription factors themselves, in tumor progression, angiogenesis and metastasis. Specific E2Fs also have prognostic value in breast cancer, independent of clinical parameters. We discuss here recent advances in understanding of the RB-E2F pathway in breast cancer. We also discuss the application of genome-wide genetic screening efforts to gain insight into synthetic lethal interactions of CDK4/6 inhibitors in breast cancer for the development of more effective combination therapies.

Handling of data below the lower limit of quantification (LLOQ), below the limit of quantification (BLOQ) in population pharmacokinetic (PopPK) analyses is important for reducing bias and imprecision in parameter estimation. We aimed to evaluate whether using the concentration data below the LLOQ has superior performance over several established methods. The performance of this approach (“All data”) was evaluated and compared to other methods: “Discard,” “LLOQ/2,” and “LIKE” (likelihood-based). An analytical and residual error model was constructed on the basis of in-house analytical method validations and analyses from literature, with additional included variability to account for model misspecification. Simulation analyses were performed for various levels of BLOQ, several structural PopPK models, and additional influences. Performance was evaluated by relative root mean squared error (RMSE), and run success for the various BLOQ approaches. Performance was also evaluated for a real PopPK data set. For all PopPK models and levels of censoring, RMSE values were lowest using “All data.” Performance of the “LIKE” method was better than the “LLOQ/2” or “Discard” method. Differences between all methods were small at the lowest level of BLOQ censoring. “LIKE” method resulted in low successful minimization (<50%) and covariance step success (<30%), although estimates were obtained in most runs (∼90%). For the real PK data set (7.4% BLOQ), similar parameter estimates were obtained using all methods. Incorporation of BLOQ concentrations showed superior performance in terms of bias and precision over established BLOQ methods, and shown to be feasible in a real PopPK analysis.