Large-scale pathogenicity prediction analysis of cancer-associated kinase mutations reveals variability in sensitivity and specificity of computational methods.

BACKGROUND: Mutations in kinases are the most frequent genetic alterations in cancer; however, experimental evidence establishing their cancerous nature is available only for a small fraction of these mutants. AIMS: Predicition analysis of kinome mutations is the primary aim of this study. Further objective is to compare the performance of various softwares in pathogenicity prediction of kinase mutations. MATERIALS AND METHODS: We employed a set of computational tools to predict the pathogenicity of over forty-two thousand mutations and deposited the kinase-wise data in Mendeley database (Estimated Pathogenicity of Kinase Mutants [EPKiMu]). RESULTS: Mutations are more likely to be drivers when being present in the kinase domain (vs. non-kinase domain) and belonging to hotspot residues (vs. non-hotspot residues). We identified that, while predictive tools have low specificity in general, PolyPhen-2 had the best accuracy. Further efforts to combine all four tools by consensus, voting, or other simple methods did not significantly improve accuracy. DISCUSSION: The study provides a large dataset of kinase mutations along with their predicted pathogenicity that can be used as a training set for future studies. Furthermore, a comparative sensitivity and selectivity of commonly used computational tools is presented. CONCLUSION: Primary-structure-based in silico tools identified more cancerous/deleterious mutations in the kinase domains and at the hot spot residues while having higher sensitivity than specificity in detecting deleterious mutations.

Applications of promiscuity of FDA-approved kinase inhibitors in drug repositioning and toxicity.

Promiscuity of therapeutics has important implications in treatment and toxicity. So far, a comprehensive understanding of promiscuity related to kinase inhibitors is lacking and such an analysis may offer potential opportunities for drug repurposing. In the present study, profiling of inhibitor-specific kinases based on the available biochemical IC50s was performed, fold-change of IC50 values for additional targets were calculated by taking the primary target as the reference kinase, and finally the promiscuity degree (PD) for FDA-approved kinase inhibitors was calculated. Surprisingly, class II inhibitors showed more PD than that of the class I inhibitors. We further identified cancer types and sub-types in which additional kinase targets or off-targets of inhibitors were overexpressed for potential drug repurposing. In addition, the expression of these kinases in normal human tissues were also profiled to predict toxicity following drug repositioning. Taken together, the study offers opportunities for cancer treatment in a kinase-specific manner.

Molecular docking analysis reveals differential binding affinities of multiple classes of selective inhibitors towards cancer-associated KRAS mutants.

KRAS is the most frequently mutated oncogene in solid cancers, and inhibitors that specifically target the KRAS-G12C mutant were recently approved for clinical use. The limited availability of experimental data pertaining to the sensitivity of KRAS-non-G12C mutants towards RAS inhibitors made it difficult to predict the response of KRAS-mutated cancers towards RAS-targeted therapies. The current study aims at evaluating sensitivity profiles of KRAS-non-G12C mutations towards clinically approved sotorasib and adagrasib, and experimental RAS inhibitors based on binding energies derived through molecular docking analysis. Computationally predicted sensitivities of KRAS mutants conformed with the available but limited experimental data, thus validating the usefulness of molecular docking approach in predicting clinical response towards RAS inhibitor treatment. Our results indicate differential sensitivity of KRAS mutants towards both clinical and experimental therapeutics; while certain mutants exhibited broad cross-resistance to most inhibitors, some mutants showed resistance towards specific inhibitors. These results thus suggest the potential of emergence of more resistance mutations in future towards RAS-targeted therapy and points to an urgent need to develop novel classes of inhibitors that are able to overcome both primary and secondary drug resistance. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-022-03407-9.

Estimated sensitivity profiles of lung cancer specific uncommon BRAF mutants towards experimental and clinically approved kinase inhibitors.

Current experimental and clinical data are inadequate to conclusively predict the oncogenicity of uncommon BRAF mutants and their sensitivity towards kinase inhibitors. Therefore, the present study aims at estimating sensitivity profiles of uncommon lung cancer specific BRAF mutations towards clinically approved as well as experimental therapeutics based on computationally derived direct binding energies. Based on the data derived from cBioportal, BRAF mutants displayed significant mutual exclusivity with KRAS and EGFR mutants indicating them as potential drivers in lung cancer. Predicted sensitivity of BRAF-V600E conformed to published experimental and clinical data thus validating the usefulness of computational approach. The BRAF-V600K displayed higher sensitivity to most inhibitors as compared to that of the BRAF-V600E. All the uncommon mutants displayed higher sensitivity than both the wild type and BRAF-V600E towards PLX 8394 and LSN3074753. While V600K, G469R and N581S displayed favorable sensitivity profiles to most inhibitors, V600L/M, G466A/E/V and G469A/V displayed resistance profiles to a variable degree. Notably, molecular dynamic (MD) simulation revealed that increased number of interactions caused enhanced sensitivity of G469R and N581S towards sorafenib. RAF kinase inhibitors were further classified into two groups as per their selectivity (Group I: BRAF-V600E-selective and Group II: CRAF-selective) based on which potential mutation-wise combinations of RAF kinase inhibitors were proposed to overcome resistance. Based on computational inhibitor sensitivity profiles, appropriate treatment strategies may be devised to prevent or overcome secondary drug resistance in lung cancer patients with uncommon mutations.

Elucidation of conformational diversity of druggable enzymes and classification of chemical modulators based on inhibitor-bound structures.

Communicated by Ramaswamy H. Sarma.

Consensus anticancer activity profiles derived from the meta-analysis of reference compounds for widely used cell lines.

AIM: To establish a standard reference for bioactivity of widely used anticancer compounds that might be useful for meaningful interpretation of the cell viability data generated for novel synthetic derivatives. MATERIALS & METHODS: Meta-analysis of published IC50 values was carried out for commonly used anticancer compounds and cell viability experiments were performed to validate the role of certain factors in drug activity. RESULTS & CONCLUSION: Variability in the published IC50 values was demonstrated. Based on median IC50 values derived from pooled published data, cell lines were classified as either sensitive or resistant. Further, factors that influence IC50 values were discussed, thus encouraging researchers to devise more rational experimental approaches to enhance the success rate in later stages of drug development.

Computational Analysis of Epidermal Growth Factor Receptor Mutations Predicts Differential Drug Sensitivity Profiles toward Kinase Inhibitors.

INTRODUCTION: A significant proportion of patients with lung cancer carry mutations in the EGFR kinase domain. The presence of a deletion mutation in exon 19 or L858R point mutation in the EGFR kinase domain has been shown to cause enhanced efficacy of inhibitor treatment in patients with NSCLC. Several less frequent (uncommon) mutations in the EGFR kinase domain with potential implications in treatment response have also been reported. The role of a limited number of uncommon mutations in drug sensitivity was experimentally verified. However, a huge number of these mutations remain uncharacterized for inhibitor sensitivity or resistance. METHODS: A large-scale computational analysis of clinically reported 298 point mutants of EGFR kinase domain has been performed, and drug sensitivity profiles for each mutant toward seven kinase inhibitors has been determined by molecular docking. In addition, the relative inhibitor binding affinity toward each drug as compared with that of adenosine triphosphate was calculated for each mutant. RESULTS: The inhibitor sensitivity profiles predicted in this study for a set of previously characterized mutants correlated well with the published clinical, experimental, and computational data. Both the single and compound mutations displayed differential inhibitor sensitivity toward first- and next-generation kinase inhibitors. CONCLUSIONS: The present study provides predicted drug sensitivity profiles for a large panel of uncommon EGFR mutations toward multiple inhibitors, which may help clinicians in deciding mutant-specific treatment strategies.

Computational analysis of ABL kinase mutations allows predicting drug sensitivity against selective kinase inhibitors.

The ABL kinase inhibitor imatinib has been used as front-line therapy for Philadelphia-positive chronic myeloid leukemia. However, a significant proportion of imatinib-treated patients relapse due to occurrence of mutations in the ABL kinase domain. Although inhibitor sensitivity for a set of mutations was reported, the role of less frequent ABL kinase mutations in drug sensitivity/resistance is not known. Moreover, recent reports indicate distinct resistance profiles for second-generation ABL inhibitors. We thus employed a computational approach to predict drug sensitivity of 234 point mutations that were reported in chronic myeloid leukemia patients. Initial validation analysis of our approach using a panel of previously studied frequent mutations indicated that the computational data generated in this study correlated well with the published experimental/clinical data. In addition, we present drug sensitivity profiles for remaining point mutations by computational docking analysis using imatinib as well as next generation ABL inhibitors nilotinib, dasatinib, bosutinib, axitinib, and ponatinib. Our results indicate distinct drug sensitivity profiles for ABL mutants toward kinase inhibitors. In addition, drug sensitivity profiles of a set of compound mutations in ABL kinase were also presented in this study. Thus, our large scale computational study provides comprehensive sensitivity/resistance profiles of ABL mutations toward specific kinase inhibitors.