Computational algorithms for in silico profiling of activating mutations in cancer

Coevolution-based computational approach to detect resistance mechanism of epidermal growth factor receptor

Tyrosine kinase epidermal growth factor receptor (EGFR) correlates the neoplastic cell metastasis, angiogenesis, neoplastic incursion, and apoptosis. Due to the involvement of EGFR in these biological processes, it becomes a most potent target for treating non-small cell lung cancer (NSCLC). The tyrosine kinase inhibitors (TKI) have endorsed high efficacy and anticipation to patients but unfortunately, within a year of treatment, drug targets develop resistance due to mutations. The present study detected the compensatory mutations in EGFR to know the evolutionary mechanism of drug resistance. The results of this study demonstrate that compensatory mutations enlarge the drug-binding pocket which may lead to the altered orientation of the ligand (gefitinib and erlotinib) causing drug resistance. This indicates that coevolutionary forces play a significant role in fine-tuning the structure of EGFR protein against the drugs. The analysis provides insight into the evolution-induced structural aspects of drug resistance changes in EGFR which in turn be useful in designing drugs with better efficacy.

Activating mutations drive human MEK1 kinase using a gear-shifting mechanism

There is an unmet need to classify cancer-promoting kinase mutations in a mechanistically cognizant way. The challenge is to understand how mutations stabilize different kinase configurations to alter function, and how this influences pathogenic potential of the kinase and its responses to therapeutic inhibitors. This goal is made more challenging by the complexity of the mutational landscape of diseases, and is further compounded by the conformational plasticity of each variant where multiple conformations coexist. We focus here on the human MEK1 kinase, a vital component of the RAS/MAPK pathway in which mutations cause cancers and developmental disorders called RASopathies. We sought to explore how these mutations alter the human MEK1 kinase at atomic resolution by utilizing enhanced sampling simulations and free energy calculations. We computationally mapped the different conformational stabilities of individual mutated systems by delineating the free energy landscapes, and showed how this relates directly to experimentally quantified developmental transformation potentials of the mutations. We conclude that mutations leverage variations in the hydrogen bonding network associated with the conformational plasticity to progressively stabilize the active-like conformational state of the kinase while destabilizing the inactive-like state. The mutations alter residue-level internal molecular correlations by differentially prioritizing different conformational states, delineating the various modes of MEK1 activation reminiscent of a gear-shifting mechanism. We define the molecular basis of conversion of this kinase from its inactive to its active state, connecting structure, dynamics, and function by delineating the energy landscape and conformational plasticity, thus augmenting our understanding of MEK1 regulation.

A multiscale orchestrated computational framework to reveal emergent phenomena in neuroblastoma

Neuroblastoma is a complex and aggressive type of cancer that affects children. Current treatments involve a combination of surgery, chemotherapy, radiotherapy, and stem cell transplantation. However, treatment outcomes vary due to the heterogeneous nature of the disease. Computational models have been used to analyse data, simulate biological processes, and predict disease progression and treatment outcomes. While continuum cancer models capture the overall behaviour of tumours, and agent-based models represent the complex behaviour of individual cells, multiscale models represent interactions at different organisational levels, providing a more comprehensive understanding of the system. In 2018, the PRIMAGE consortium was formed to build a cloud-based decision support system for neuroblastoma, including a multi-scale model for patient-specific simulations of disease progression. In this work we have developed this multi-scale model that includes data such as patient's tumour geometry, cellularity, vascularization, genetics and type of chemotherapy treatment, and integrated it into an online platform that runs the simulations on a high-performance computation cluster using Onedata and Kubernetes technologies. This infrastructure will allow clinicians to optimise treatment regimens and reduce the number of costly and time-consuming clinical trials. This manuscript outlines the challenging framework's model architecture, data workflow, hypothesis, and resources employed in its development.

The Yin and Yang of ERBB4: Tumor Suppressor and Oncoprotein

ERBB4 (HER4) is a member of the ERBB family of receptor tyrosine kinases, a family that includes the epidermal growth factor receptor (EGFR/ERBB1/HER1), ERBB2 (Neu/HER2), and ERBB3 (HER3). EGFR and ERBB2 are oncoproteins and validated targets for therapeutic intervention in a variety of solid tumors. In contrast, the role that ERBB4 plays in human malignancies is ambiguous. Thus, here we review the literature regarding ERBB4 function in human malignancies. We review the mechanisms of ERBB4 signaling with an emphasis on mechanisms of signaling specificity. In the context of this signaling specificity, we discuss the hypothesis that ERBB4 appears to function as a tumor suppressor protein and as an oncoprotein. Next, we review the literature that describes the role of ERBB4 in tumors of the bladder, liver, prostate, brain, colon, stomach, lung, bone, ovary, thyroid, hematopoietic tissues, pancreas, breast, skin, head, and neck. Whenever possible, we discuss the possibility that ERBB4 mutants function as biomarkers in these tumors. Finally, we discuss the potential roles of ERBB4 mutants in the staging of human tumors and how ERBB4 function may dictate the treatment of human tumors. SIGNIFICANCE STATEMENT: This articles reviews ERBB4 function in the context of the mechanistic model that ERBB4 homodimers function as tumor suppressors, whereas ERBB4-EGFR or ERBB4-ERBB2 heterodimers act as oncogenes. Thus, this review serves as a mechanistic framework for clinicians and scientists to consider the role of ERBB4 and ERBB4 mutants in staging and treating human tumors.

Molecular-based precision oncology clinical decision making augmented by artificial intelligence

The rapid growth and decreasing cost of Next-generation sequencing (NGS) technologies have made it possible to conduct routine large panel genomic sequencing in many disease settings, especially in the oncology domain. Furthermore, it is now known that optimal disease management of patients depends on individualized cancer treatment guided by comprehensive molecular testing. However, translating results from molecular sequencing reports into actionable clinical insights remains a challenge to most clinicians. In this review, we discuss about some representative systems that leverage artificial intelligence (AI) to facilitate some processes of clinicians' decision making based upon molecular data, focusing on their application in precision oncology. Some limitations and pitfalls of the current application of AI in clinical decision making are also discussed.

Mechanism of activation and the rewired network: New drug design concepts

Precision oncology benefits from effective early phase drug discovery decisions. Recently, drugging inactive protein conformations has shown impressive successes, raising the cardinal questions of which targets can profit and what are the principles of the active/inactive protein pharmacology. Cancer driver mutations have been established to mimic the protein activation mechanism. We suggest that the decision whether to target an inactive (or active) conformation should largely rest on the protein mechanism of activation. We next discuss the recent identification of double (multiple) same-allele driver mutations and their impact on cell proliferation and suggest that like single driver mutations, double drivers also mimic the mechanism of activation. We further suggest that the structural perturbations of double (multiple) in cis mutations may reveal new surfaces/pockets for drug design. Finally, we underscore the preeminent role of the cellular network which is deregulated in cancer. Our structure-based review and outlook updates the traditional Mechanism of Action, informs decisions, and calls attention to the intrinsic activation mechanism of the target protein and the rewired tumor-specific network, ushering innovative considerations in precision medicine.

In-silico identification of frequently mutated genes and their co-enriched metabolic pathways associated with Prostate cancer progression

Prostate cancer (PCa) has emerged as a significant health burden in men globally. Several genetic anomalies such as mutations and also epigenetic aberrations are responsible for the heterogeneity of this disease. This study identified the 20 most frequently mutated genes reported in PCa based on literature and database survey. Further gene ontology and functional enrichment analysis were conducted to determine their co-modulated molecular and biological pathways. A protein-protein interaction network was used for the identification of hub genes. These hub genes identified were then subjected to survival analysis. The prognostic values of these identified genes were investigated using GEPIA and HPA. Gene Ontology analysis of the identified genes depicted that these genes significantly contributed to the cell cycle, apoptosis, angiogenesis and TGF-β receptor signalling. Further, the research showed that high expressions of identified mutated genes led to a reduction in the long-term survival of PCa patients, which was supported by immunohistochemical and mRNA expression level data. Our results suggest that identified panel of mutated genes viz., CTNNB1, TP53, ATM, AR and KMT2D play crucial roles in the onset and progression of PCa, thereby providing candidate diagnostic markers for PCa for individualised treatment in the future.

Computational studies of anaplastic lymphoma kinase mutations reveal common mechanisms of oncogenic activation

High-risk tumors are genomically heterogeneous, harboring gene amplifications and mutations. The activation status of mutated proteins in cancer can profoundly impact disease progression, patient response, and drug sensitivity. Yet, outside of a few hotspot mutations, functional studies of clinically observed mutations are not commonly pursued. We report a combined experimental profiling and computational analysis of the effects of clinically observed and “test” mutations in the kinase domain of anaplastic lymphoma kinase (ALK), a known oncogenic driver in pediatric neuroblastoma. We find that the activation status of the mutated protein is a good indicator of the transforming ability in NIH 3T3 cells. We also report biophysical as well as data-driven models with predictive power to profile these mutant kinases in silico.

Kinases play important roles in diverse cellular processes, including signaling, differentiation, proliferation, and metabolism. They are frequently mutated in cancer and are the targets of a large number of specific inhibitors. Surveys of cancer genome atlases reveal that kinase domains, which consist of 300 amino acids, can harbor numerous (150 to 200) single-point mutations across different patients in the same disease. This preponderance of mutations—some activating, some silent—in a known target protein make clinical decisions for enrolling patients in drug trials challenging since the relevance of the target and its drug sensitivity often depend on the mutational status in a given patient. We show through computational studies using molecular dynamics (MD) as well as enhanced sampling simulations that the experimentally determined activation status of a mutated kinase can be predicted effectively by identifying a hydrogen bonding fingerprint in the activation loop and the αC-helix regions, despite the fact that mutations in cancer patients occur throughout the kinase domain. In our study, we find that the predictive power of MD is superior to a purely data-driven machine learning model involving biochemical features that we implemented, even though MD utilized far fewer features (in fact, just one) in an unsupervised setting. Moreover, the MD results provide key insights into convergent mechanisms of activation, primarily involving differential stabilization of a hydrogen bond network that engages residues of the activation loop and αC-helix in the active-like conformation (in >70% of the mutations studied, regardless of the location of the mutation).

An interdomain helix in IRE1α mediates the conformational change required for the sensor's activation

The unfolded protein response plays an evolutionarily conserved role in homeostasis, and its dysregulation often leads to human disease, including diabetes and cancer. IRE1α is a major transducer that conveys endoplasmic reticulum stress via biochemical signals, yet major gaps persist in our understanding of how the detection of stress is converted to one of several molecular outcomes. It is known that, upon sensing unfolded proteins via its endoplasmic reticulum luminal domain, IRE1α dimerizes and then oligomerizes (often visualized as clustering). Once assembled, the kinase domain trans-autophosphorylates a neighboring IRE1α, inducing a conformational change that activates the RNase effector domain. However, the full details of how the signal is transmitted are not known. Here, we describe a previously unrecognized role for helix αK, located between the kinase and RNase domains of IRE1α, in conveying this critical conformational change. Using constructs containing mutations within this interdomain helix, we show that distinct substitutions affect oligomerization, kinase activity, and the RNase activity of IRE1α differentially. Furthermore, using both biochemical and computational methods, we found that different residues at position 827 specify distinct conformations at distal sites of the protein, such as in the RNase domain. Of importance, an RNase-inactive mutant, L827P, can still dimerize with wildtype monomers, but this mutation inactivates the wildtype molecule and renders leukemic cells more susceptible to stress. We surmise that helix αK is a conduit for the activation of IRE1α in response to stress.

A survey of multiscale modeling: Foundations, historical milestones, current status, and future prospects

Research problems in the domains of physical, engineering, biological sciences often span multiple time and length scales, owing to the complexity of information transfer underlying mechanisms. Multiscale modeling (MSM) and high-performance computing (HPC) have emerged as indispensable tools for tackling such complex problems. We review the foundations, historical developments, and current paradigms in MSM. A paradigm shift in MSM implementations is being fueled by the rapid advances and emerging paradigms in HPC at the dawn of exascale computing. Moreover, amidst the explosion of data science, engineering, and medicine, machine learning (ML) integrated with MSM is poised to enhance the capabilities of standard MSM approaches significantly, particularly in the face of increasing problem complexity. The potential to blend MSM, HPC, and ML presents opportunities for unbound innovation and promises to represent the future of MSM and explainable ML that will likely define the fields in the 21st century.

Associations between WTAP gene polymorphisms and neuroblastoma susceptibility in Chinese children

BACKGROUND

Previous studies have revealed that WTAP is related to multiple types of cancer. Recently, WTAP has been reported as an independent prognostic factor in patients with neuroblastoma.

METHODS

To explore the association between three WTAP polymorphisms (rs9457712 G>A, rs1853259 A>G and rs7766006 G>T) and neuroblastoma susceptibility in Chinese populations, we performed this case-control study including 898 neuroblastoma cases and 1,734 controls. We genotyped these potentially functional single nucleotide polymorphisms (SNPs) by TaqMan assays. The odds ratios (ORs) and 95% confidence intervals (CIs) by logistic regression models were used to assess the relationship between WTAP SNPs and the risk of neuroblastoma.

RESULTS

No significant associations were observed in the overall analysis between any of the three WTAP polymorphisms and the risk of neuroblastoma. However, in the age ≤18 months subgroup, we found that the rs1853259 AG/GG genotype exerted protective effects against neuroblastoma (adjusted OR =0.77, 95% CI: 0.59-0.998, P=0.048), whereas the presence of 1-2 combined risk genotypes significantly increased the risk of neuroblastoma (adjusted OR =1.32, 95% CI: 1.02-1.71, P=0.036).

CONCLUSIONS

WTAP gene polymorphisms only have a weak impact on the risk of neuroblastoma in the Chinese children. Further case-control studies, preferable on larger sample sizes, are needed to validate our results.