Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers

Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development

Understanding enzyme mechanisms is essential for unraveling the complex molecular machinery of life. In this review, we survey the field of computational enzymology, highlighting key principles governing enzyme mechanisms and discussing ongoing challenges and promising advances. Over the years, computer simulations have become indispensable in the study of enzyme mechanisms, with the integration of experimental and computational exploration now established as a holistic approach to gain deep insights into enzymatic catalysis. Numerous studies have demonstrated the power of computer simulations in characterizing reaction pathways, transition states, substrate selectivity, product distribution, and dynamic conformational changes for various enzymes. Nevertheless, significant challenges remain in investigating the mechanisms of complex multistep reactions, large-scale conformational changes, and allosteric regulation. Beyond mechanistic studies, computational enzyme modeling has emerged as an essential tool for computer-aided enzyme design and the rational discovery of covalent drugs for targeted therapies. Overall, enzyme design/engineering and covalent drug development can greatly benefit from our understanding of the detailed mechanisms of enzymes, such as protein dynamics, entropy contributions, and allostery, as revealed by computational studies. Such a convergence of different research approaches is expected to continue, creating synergies in enzyme research. This review, by outlining the ever-expanding field of enzyme research, aims to provide guidance for future research directions and facilitate new developments in this important and evolving field.

Direct K-Ras Inhibitors to Treat Cancers: Progress, New Insights, and Approaches to Treat Resistance

Here we discuss approaches to K-Ras inhibition and drug resistance scenarios. A breakthrough offered a covalent drug against K-RasG12C. Subsequent innovations harnessed same-allele drug combinations, as well as cotargeting K-RasG12C with a companion drug to upstream regulators or downstream kinases. However, primary, adaptive, and acquired resistance inevitably emerge. The preexisting mutation load can explain how even exceedingly rare mutations with unobservable effects can promote drug resistance, seeding growth of insensitive cell clones, and proliferation. Statistics confirm the expectation that most resistance-related mutations are in cis, pointing to the high probability of cooperative, same-allele effects. In addition to targeted Ras inhibitors and drug combinations, bifunctional molecules and innovative tri-complex inhibitors to target Ras mutants are also under development. Since the identities and potential contributions of preexisting and evolving mutations are unknown, selecting a pharmacologic combination is taxing. Collectively, our broad review outlines considerations and provides new insights into pharmacology and resistance.

Methods for Estimating Personal Disease Risk and Phylogenetic Diversity of Hematopoietic Stem Cells

An individual's chronological age does not always correspond to the health of different tissues in their body, especially in cases of disease. Therefore, estimating and contrasting the physiological age of tissues with an individual's chronological age may be a useful tool to diagnose disease and its progression. In this study, we present novel metrics to quantify the loss of phylogenetic diversity in hematopoietic stem cells (HSCs), which are precursors to most blood cell types and are associated with many blood-related diseases. These metrics showed an excellent correspondence with an age-related increase in blood cancer incidence, enabling a model to estimate the phylogeny-derived age (phyloAge) of HSCs present in an individual. The HSC phyloAge was generally older than the chronological age of patients suffering from myeloproliferative neoplasms (MPNs). We present a model that relates excess HSC aging with increased MPN risk. It predicted an over 200 times greater risk based on the HSC phylogenies of the youngest MPN patients analyzed. Our new metrics are designed to be robust to sampling biases and do not rely on prior knowledge of driver mutations or physiological assessments. Consequently, they complement conventional biomarker-based methods to estimate physiological age and disease risk.

SCING: Inference of robust, interpretable gene regulatory networks from single cell and spatial transcriptomics

Gene regulatory network (GRN) inference is an integral part of understanding physiology and disease. Single cell/nuclei RNA-seq (scRNA-seq/snRNA-seq) data has been used to elucidate cell-type GRNs; however, the accuracy and speed of current scRNAseq-based GRN approaches are suboptimal. Here, we present Single Cell INtegrative Gene regulatory network inference (SCING), a gradient boosting and mutual information-based approach for identifying robust GRNs from scRNA-seq, snRNA-seq, and spatial transcriptomics data. Performance evaluation using Perturb-seq datasets, held-out data, and the mouse cell atlas combined with the DisGeNET database demonstrates the improved accuracy and biological interpretability of SCING compared to existing methods. We applied SCING to the entire mouse single cell atlas, human Alzheimer's disease (AD), and mouse AD spatial transcriptomics. SCING GRNs reveal unique disease subnetwork modeling capabilities, have intrinsic capacity to correct for batch effects, retrieve disease relevant genes and pathways, and are informative on spatial specificity of disease pathogenesis.

CEP55 as a promising biomarker and therapeutic target on gallbladder cancer

Introduction

Gallbladder cancer (GBC) is a highly malignant biliary tumor with a poor prognosis. As existing therapies for advanced metastatic GBC are rarely effective, there is an urgent need to identify more effective targets for treatment.

Methods

Hub genes of GBC were identified by bioinformatics analysis and their expression in GBC was analyzed by tissue validation. The biological role of CEP55 in GBC cell and the underlying mechanism of the anticancer effect of CEP55 knockdown were evaluated via CCK8, colony formation assay, EDU staining, flow cytometry, western blot, immunofluorescence, and an alkaline comet assay.

Results

We screened out five hub genes of GBC, namely PLK1, CEP55, FANCI, NEK2 and PTTG1. CEP55 is not only overexpressed in the GBC but also correlated with advanced TNM stage, differentiation grade and poorer survival. After CEP55 knockdown, the proliferation of GBC cells was inhibited with cell cycle arrest in G2/M phase and DNA damage. There was a marked increase in the apoptosis of GBC cells in the siCEP55 group. Besides, in vivo, CEP55 inhibition attenuated the growth and promoted apoptosis of GBC cells. Mechanically, the tumor suppressor effect of CEP55 knockdown is associated with dysregulation of the AKT and ERK signaling networks.

Discussion

These data not only demonstrate that CEP55 is identified as a potential independent predictor crucial to the diagnosis and prognosis of gallbladder cancer but also reveal the possibility for CEP55 to be used as a promising target in the treatment of GBC.

RASAL2 Deficiency Attenuates Hepatic Steatosis by Promoting Hepatic VLDL Secretion via the AKT/TET1/MTTP Axis

BACKGROUND AND AIMS

RAS protein activator like 2 (RASAL2) is a newly discovered metabolic regulator involved in energy homeostasis and adipogenesis. However, whether RASAL2 is involved in hepatic lipid metabolism remains undetermined. This study explored the function of RASAL2 and elucidated its potential mechanisms in nonalcoholic fatty liver disease (NAFLD).

METHODS

NAFLD models were established either by feeding mice a high-fat diet or by incubation of hepatocytes with 1 mM free fatty acids (oleic acid:palmitic acid=2:1). Pathological changes were observed by hematoxylin and eosin staining. Lipid accumulation was assessed by Oil Red O staining, BODIPY493/503 staining, and triglyceride quantification. The in vivo secretion rate of very low-density lipoprotein was determined by intravenous injection of tyloxapol. Gene regulation was analyzed by chromatin immunoprecipitation assays and hydroxymethylated DNA immunoprecipitation combined with real-time polymerase chain reaction.

RESULTS

RASAL2 deficiency ameliorated hepatic steatosis both in vivo and in vitro. Mechanistically, RASAL2 deficiency upregulated hepatic TET1 expression by activating the AKT signaling pathway and thereby promoted MTTP expression by DNA hydroxymethylation, leading to increased production and secretion of very low-density lipoprotein, which is the major carrier of triglycerides exported from the liver to distal tissues.

CONCLUSIONS

Our study reports the first evidence that RASAL2 deficiency ameliorates hepatic steatosis by regulating lipid metabolism through the AKT/TET1/MTTP axis. These findings will help understand the pathogenesis of NAFLD and highlight the potency of RASAL2 as a new molecular target for NAFLD.

Pan-cancer clinical impact of latent drivers from double mutations

Here, we discover potential 'latent driver' mutations in cancer genomes. Latent drivers have low frequencies and minor observable translational potential. As such, to date they have escaped identification. Their discovery is important, since when paired in cis, latent driver mutations can drive cancer. Our comprehensive statistical analysis of the pan-cancer mutation profiles of ~60,000 tumor sequences from the TCGA and AACR-GENIE cohorts identifies significantly co-occurring potential latent drivers. We observe 155 same gene double mutations of which 140 individual components are cataloged as latent drivers. Evaluation of cell lines and patient-derived xenograft response data to drug treatment indicate that in certain genes double mutations may have a prominent role in increasing oncogenic activity, hence obtaining a better drug response, as in PIK3CA. Taken together, our comprehensive analyses indicate that same-gene double mutations are exceedingly rare phenomena but are a signature for some cancer types, e.g., breast, and lung cancers. The relative rarity of doublets can be explained by the likelihood of strong signals resulting in oncogene-induced senescence, and by doublets consisting of non-identical single residue components populating the background mutational load, thus not identified.

Exploration of Computational Aids for Effective Drug Designing and Management of Viral Diseases: A Comprehensive Review

BACKGROUND

Microbial diseases, specifically originating from viruses are the major cause of human mortality all over the world. The current COVID-19 pandemic is a case in point, where the dynamics of the viral-human interactions are still not completely understood, making its treatment a case of trial and error. Scientists are struggling to devise a strategy to contain the pandemic for over a year and this brings to light the lack of understanding of how the virus grows and multiplies in the human body.

METHODS

This paper presents the perspective of the authors on the applicability of computational tools for deep learning and understanding of host-microbe interaction, disease progression and management, drug resistance and immune modulation through in silico methodologies which can aid in effective and selective drug development. The paper has summarized advances in the last five years. The studies published and indexed in leading databases have been included in the review.

RESULTS

Computational systems biology works on an interface of biology and mathematics and intends to unravel the complex mechanisms between the biological systems and the inter and intra species dynamics using computational tools, and high-throughput technologies developed on algorithms, networks and complex connections to simulate cellular biological processes.

CONCLUSION

Computational strategies and modelling integrate and prioritize microbial-host interactions and may predict the conditions in which the fine-tuning attenuates. These microbial-host interactions and working mechanisms are important from the aspect of effective drug designing and fine- tuning the therapeutic interventions.

Clinical management of molecular alterations identified by high throughput sequencing in patients with advanced solid tumors in treatment failure: Real-world data from a French hospital

Background

In the context of personalized medicine, screening patients to identify targetable molecular alterations is essential for therapeutic decisions such as inclusion in clinical trials, early access to therapies, or compassionate treatment. The objective of this study was to determine the real-world impact of routine incorporation of FoundationOne analysis in cancers with a poor prognosis and limited treatment options, or in those progressing after at least one course of standard therapy.

Methods

A FoundationOneCDx panel for solid tumor or liquid biopsy samples was offered to 204 eligible patients.

Results

Samples from 150 patients were processed for genomic testing, with a data acquisition success rate of 93%. The analysis identified 2419 gene alterations, with a median of 11 alterations per tumor (range, 0-86). The most common or likely pathogenic variants were on TP53, TERT, PI3KCA, CDKN2A/B, KRAS, CCDN1, FGF19, FGF3, and SMAD4. The median tumor mutation burden was three mutations/Mb (range, 0-117) in 143 patients with available data. Of 150 patients with known or likely pathogenic actionable alterations, 13 (8.6%) received matched targeted therapy. Sixty-nine patients underwent Molecular Tumor Board, which resulted in recommendations in 60 cases. Treatment with genotype-directed therapy had no impact on overall survival (13 months vs. 14 months; p = 0.95; hazard ratio = 1.04 (95% confidence interval, 0.48-2.26)].

Conclusions

This study highlights that an organized center with a Multidisciplinary Molecular Tumor Board and an NGS screening system can obtain satisfactory results comparable with those of large centers for including patients in clinical trials.

A New View of Activating Mutations in Cancer

A vast effort has been invested in the identification of driver mutations of cancer. However, recent studies and observations call into question whether the activating mutations or the signal strength are the major determinant of tumor development. The data argue that signal strength determines cell fate, not the mutation that initiated it. In addition to activating mutations, factors that can impact signaling strength include (i) homeostatic mechanisms that can block or enhance the signal, (ii) the types and locations of additional mutations, and (iii) the expression levels of specific isoforms of genes and regulators of proteins in the pathway. Because signal levels are largely decided by chromatin structure, they vary across cell types, states, and time windows. A strong activating mutation can be restricted by low expression, whereas a weaker mutation can be strengthened by high expression. Strong signals can be associated with cell proliferation, but too strong a signal may result in oncogene-induced senescence. Beyond cancer, moderate signal strength in embryonic neural cells may be associated with neurodevelopmental disorders, and moderate signals in aging may be associated with neurodegenerative diseases, like Alzheimer's disease. The challenge for improving patient outcomes therefore lies in determining signaling thresholds and predicting signal strength.

AlphaFold, Artificial Intelligence (AI), and Allostery

AlphaFold has burst into our lives. A powerful algorithm that underscores the strength of biological sequence data and artificial intelligence (AI). AlphaFold has appended projects and research directions. The database it has been creating promises an untold number of applications with vast potential impacts that are still difficult to surmise. AI approaches can revolutionize personalized treatments and usher in better-informed clinical trials. They promise to make giant leaps toward reshaping and revamping drug discovery strategies, selecting and prioritizing combinations of drug targets. Here, we briefly overview AI in structural biology, including in molecular dynamics simulations and prediction of microbiota–human protein–protein interactions. We highlight the advancements accomplished by the deep-learning-powered AlphaFold in protein structure prediction and their powerful impact on the life sciences. At the same time, AlphaFold does not resolve the decades-long protein folding challenge, nor does it identify the folding pathways. The models that AlphaFold provides do not capture conformational mechanisms like frustration and allostery, which are rooted in ensembles, and controlled by their dynamic distributions. Allostery and signaling are properties of populations. AlphaFold also does not generate ensembles of intrinsically disordered proteins and regions, instead describing them by their low structural probabilities. Since AlphaFold generates single ranked structures, rather than conformational ensembles, it cannot elucidate the mechanisms of allosteric activating driver hotspot mutations nor of allosteric drug resistance. However, by capturing key features, deep learning techniques can use the single predicted conformation as the basis for generating a diverse ensemble.

Widespread redundancy in -omics profiles of cancer mutation states

BACKGROUND

In studies of cellular function in cancer, researchers are increasingly able to choose from many -omics assays as functional readouts. Choosing the correct readout for a given study can be difficult, and which layer of cellular function is most suitable to capture the relevant signal remains unclear.

RESULTS

We consider prediction of cancer mutation status (presence or absence) from functional -omics data as a representative problem that presents an opportunity to quantify and compare the ability of different -omics readouts to capture signals of dysregulation in cancer. From the TCGA Pan-Cancer Atlas that contains genetic alteration data, we focus on RNA sequencing, DNA methylation arrays, reverse phase protein arrays (RPPA), microRNA, and somatic mutational signatures as -omics readouts. Across a collection of genes recurrently mutated in cancer, RNA sequencing tends to be the most effective predictor of mutation state. We find that one or more other data types for many of the genes are approximately equally effective predictors. Performance is more variable between mutations than that between data types for the same mutation, and there is little difference between the top data types. We also find that combining data types into a single multi-omics model provides little or no improvement in predictive ability over the best individual data type.

CONCLUSIONS

Based on our results, for the design of studies focused on the functional outcomes of cancer mutations, there are often multiple -omics types that can serve as effective readouts, although gene expression seems to be a reasonable default option.

A Histone Deacetylase Inhibitor Induces Acetyl-CoA Depletion Leading to Lethal Metabolic Stress in RAS-Pathway Activated Cells

BACKGROUND

The mechanism of action of romidepsin and other histone deacetylase inhibitors is still not fully explained. Our goal was to gain a mechanistic understanding of the RAS-linked phenotype associated with romidepsin sensitivity.

METHODS

The NCI60 dataset was screened for molecular clues to romidepsin sensitivity. Histone acetylation, DNA damage, ROS production, metabolic state (real-time measurement and metabolomics), and gene expression alterations (transcriptomics) were determined in KRAS-WT versus KRAS-mutant cell groups. The search for biomarkers in response to HDACi was implemented by supervised machine learning analysis on a 608-cell transcriptomic dataset and validated in a clinical dataset.

RESULTS

Romidepsin treatment induced depletion in acetyl-CoA in all tested cell lines, which led to oxidative stress, metabolic stress, and increased death-particularly in KRAS-mutant cell lines. Romidepsin-induced stresses and death were rescued by acetyl-CoA replenishment. Two acetyl-CoA gene expression signatures associated with HDACi sensitivity were derived from machine learning analysis in the CCLE (Cancer Cell Line Encyclopedia) cell panel. Signatures were then validated in the training cohort for seven HDACi, and in an independent 13-patient cohort treated with belinostat.

CONCLUSIONS

Our study reveals the importance of acetyl-CoA metabolism in HDAC sensitivity, and it highlights acetyl-CoA generation pathways as potential targets to combine with HDACi.

Combination Strategies Involving Immune Checkpoint Inhibitors and Tyrosine Kinase or BRAF Inhibitors in Aggressive Thyroid Cancer

Thyroid cancer is the most common (~90%) type of endocrine-system tumor, accounting for 70% of the deaths from endocrine cancers. In the last years, the high-throughput genomics has been able to identify pathways/molecular targets involved in survival and tumor progression. Targeted therapy and immunotherapy individually have many limitations. Regarding the first one, although it greatly reduces the size of the cancer, clinical responses are generally transient and often lead to cancer relapse after initial treatment. For the second one, although it induces longer-lasting responses in cancer patients than targeted therapy, its response rate is lower. The individual limitations of these two different types of therapies can be overcome by combining them. Here, we discuss MAPK pathway inhibitors, i.e., BRAF and MEK inhibitors, combined with checkpoint inhibitors targeting PD-1, PD-L1, and CTLA-4. Several mutations make tumors resistant to treatments. Therefore, more studies are needed to investigate the patient's individual tumor mutation burden in order to overcome the problem of resistance to therapy and to develop new combination therapies.

Application of Patient-Derived Cancer Organoids to Personalized Medicine

Cell models are indispensable for the research and development of cancer therapies. Cancer medications have evolved with the establishment of various cell models. Patient-derived cell lines are very useful for identifying characteristic phenotypes and susceptibilities to anticancer drugs as well as molecularly targeted therapies for tumors. However, conventional 2-dimensional (2D) cell cultures have several drawbacks in terms of engraftment rate and phenotypic changes during culture. The organoid is a recently developed in vitro model with cultured cells that form a three-dimensional structure in the extracellular matrix. Organoids have the capacity to self-renew and can organize themselves to resemble the original organ or tumor in terms of both structure and function. Patient-derived cancer organoids are more suitable for the investigation of cancer biology and clinical medicine than conventional 2D cell lines or patient-derived xenografts. With recent advances in genetic analysis technology, the genetic information of various tumors has been clarified, and personalized medicine based on genetic information has become clinically available. Here, we have reviewed the recent advances in the development and application of patient-derived cancer organoids in cancer biology studies and personalized medicine. We have focused on the potential of organoids as a platform for the identification and development of novel targeted medicines for pancreatobiliary cancer, which is the most intractable cancer.

Open Structural Data in Precision Medicine

Three-dimensional protein structural data at the molecular level are pivotal for successful precision medicine. Such data are crucial not only for discovering drugs that act to block the active site of the target mutant protein but also for clarifying to the patient and the clinician how the mutations harbored by the patient work. The relative paucity of structural data reflects their cost, challenges in their interpretation, and lack of clinical guidelines for their utilization. Rapid technological advancements in experimental high-resolution structural determination increasingly generate structures. Computationally, modeling algorithms, including molecular dynamics simulations, are becoming more powerful, as are compute-intensive hardware, particularly graphics processing units, overlapping with the inception of the exascale era. Accessible, freely available, and detailed structural and dynamical data can be merged with big data to powerfully transform personalized pharmacology. Here we review protein and emerging genome high-resolution data, along with means, applications, and examples underscoring their usefulness in precision medicine. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

DrugCVar: a platform for evidence-based drug annotation for genetic variants in cancer

Motivation

Targeted therapy for cancer-related genetic variants is critical for precision medicine. Although several databases including The Clinical Interpretation of Variants in Cancer (CIViC), The Oncology Knowledge Base (OncoKB), The Cancer Genome Interpreter (CGI), My Cancer Genome (MCG) provide clinical interpretations of variants in cancer, the clinical evidence was limited and miscellaneous. In this study, we developed the DrugCVar database, which integrated our manually curated cancer variant-drug targeting evidence from literature and the interpretations from the public resources.

Results

In total, 7,830 clinical evidences for cancer variant-drug targeting were integrated and classified into ten evidence tiers. Searching and browsing functions were provided for quick queries of cancer variant-drug targeting evidence. Also, batch annotation module was developed for user-provided massive genetic variants in various formats. Details such as the mutation function, location of the variants in gene and protein structures, and mutation statistics of queried genes in various tumor types were also provided for further investigations. Thus, DrugCVar could serve as a comprehensive annotation tool to interpret potential drugs for cancer variants especially the massive ones from clinical cancer genomics studies.

Availability and implementation

The database is available at http://drugcvar.omicsbio.info.

Supplementary information

Supplementary data are available at Bioinformatics online.

All on size-coded single bead set: a modular enrich-amplify-amplify strategy for attomolar level multi-immunoassay

Ultrasensitive protein analysis is of great significance for early diagnosis and biological studies. The core challenge is that many critical protein markers at extremely low aM to fM levels are difficult to accurately quantify because the target-induced weak signal may be easily masked by the surrounding background. Hence, we propose herein an ultrasensitive immunoassay based on a modular Single Bead Enrich-Amplify-Amplify (SBEAA) strategy. The highly efficient enrichment of targets on only a single bead (enrich) could confine the target-responsive signal output within a limited tiny space. Furthermore, a cascade tyramide signal amplification design enables remarkable in situ signal enhancement just affixed to the target. As a result, the efficient but space-confined fluorescence deposition on a single bead will significantly exceed the background and provide a wide dynamic range. Importantly, the SBEAA system can be modularly combined to meet different levels of clinical need regarding the detection sensitivity from aM to nM. Finally, a size-coded SBEAA set (SC-SBEAA) is also designed that allows ultrasensitive multi-immunoassay for rare samples in a single tube.

Predicting the conformational variability of oncogenic GTP-bound G12D mutated KRas-4B proteins at zwitterionic model cell membranes

KRas proteins are the largest family of mutated Ras isoforms, participating in a wide variety of cancers. Due to their importance, large effort is being carried out on drug development by small-molecule inhibitors. However, understanding protein conformational variability remains a challenge in drug discovery. In the case of the Ras family, their multiple conformational states can affect the binding of potential drug inhibitors. To overcome this challenge, we propose a computational framework based on combined all-atom Molecular Dynamics and Metadynamics simulations in order to accurately access conformational variants of the target protein. We tested the methodology using a G12D mutated GTP bound oncogenic KRas-4B protein located at the interface of a DOPC/DOPS/cholesterol model anionic cell membrane. Two main orientations of KRas-4B at the anionic membrane have been determined. The corresponding torsional angles are taken as reliable reaction coordinates so that free-energy landscapes are obtained by well-tempered metadynamics simulations, revealing local and global minima of the free-energy hypersurface and unveiling reactive paths of the system between the two preferential orientations. We have observed that GTP-binding to KRas-4B has huge influence on the stabilisation of the protein and it can potentially help to open Switch I/II druggable pockets, lowering energy barriers between stable states and resulting in cumulative conformers of KRas-4B. This may highlight new opportunities for targeting the unique meta-stable states through the design of new efficient drugs.

Great future or greedy venture: Precision medicine needs philosophy

INTRODUCTION

Over the past decade, we have witnessed the initiation and implementation of precision medicine (PM), a discipline that promises to individualize and personalize medical management and treatment, rendering them ultimately more precise and effective. Despite of the continuing advances and numerous clinical applications, the potential of PM remains highly controversial, sparking heated debates about its future.

METHOD

The present article reviews the philosophical issues and practical challenges that are critical to the feasibility and implementation of PM.

OUTCOME

The explanation and argument about the relations between PM and computability, uncertainty as well as complexity, show that key foundational assumptions of PM might not be fully validated.

CONCLUSION

The present analysis suggests that our current understanding of PM is probably oversimplified and too superficial. More efforts are needed to realize the hope that PM has elicited, rather than make the term just as a hype.

Single Nucleotide Polymorphism Induces Divergent Dynamic Patterns in CYP3A5: A Microsecond Scale Biomolecular Simulation of Variants Identified in Sub-Saharan African Populations

Pharmacogenomics aims to reveal variants associated with drug response phenotypes. Genes whose roles involve the absorption, distribution, metabolism, and excretion of drugs, are highly polymorphic between populations. High coverage whole genome sequencing showed that a large proportion of the variants for these genes are rare in African populations. This study investigated the impact of such variants on protein structure to assess their functional importance. We used genetic data of CYP3A5 from 458 individuals from sub-Saharan Africa to conduct a structural bioinformatics analysis. Five missense variants were modeled and microsecond scale molecular dynamics simulations were conducted for each, as well as for the CYP3A5 wildtype and the Y53C variant, which has a known deleterious impact on enzyme activity. The binding of ritonavir and artemether to CYP3A5 variant structures was also evaluated. Our results showed different conformational characteristics between all the variants. No significant structural changes were noticed. However, the genetic variability seemed to act on the plasticity of the protein. The impact on drug binding might be drug dependant. We concluded that rare variants hold relevance in determining the pharmacogenomics properties of populations. This could have a significant impact on precision medicine applications in sub-Saharan Africa.

Mechnetor: a web server for exploring protein mechanism and the functional context of genetic variants

Advances in DNA sequencing and proteomics mean that researchers must now regularly interrogate thousands of positional gene/protein changes in order to find those relevant for potential clinical application or biological insights. The abundance of already known information on protein interactions, mechanism, and tertiary structure provides the possible means to understand these changes rapidly, though a careful and systematic integration of these diverse datasets is first needed. For this purpose, we developed Mechnetor, a tool that allows users to quickly explore and visualize integrated mechanistic data for proteins or interactions of interest. Central to the system is a careful cataloguing of diverse sources of protein interaction mechanism, and an efficient means to visualize interactions between relevant and/or known protein regions. The result is a finer resolution interaction network that provides more immediate clues as to points of intervention or mechanistic understanding. Users can import protein, interactions, genetic variants or post-translational modifications and see these data in the best known mechanistic context. We demonstrate the tool with topical examples in human genetic diseases and cancer genomics. The tool is freely available at: mechnetor.russelllab.org.

Identification of combinations of somatic mutations that predict cancer survival and immunotherapy benefit

Cancer evolves through the accumulation of somatic mutations over time. Although several methods have been developed to characterize mutational processes in cancers, these have not been specifically designed to identify mutational patterns that predict patient prognosis. Here we present CLICnet, a method that utilizes mutational data to cluster patients by survival rate. CLICnet employs Restricted Boltzmann Machines, a type of generative neural network, which allows for the capture of complex mutational patterns associated with patient survival in different cancer types. For some cancer types, clustering produced by CLICnet also predicts benefit from anti-PD1 immune checkpoint blockade therapy, whereas for other cancer types, the mutational processes associated with survival are different from those associated with the improved anti-PD1 survival benefit. Thus, CLICnet has the ability to systematically identify and catalogue combinations of mutations that predict cancer survival, unveiling intricate associations between mutations, survival, and immunotherapy benefit.

Making Sense of Genetic Information: The Promising Evolution of Clinical Stratification and Precision Oncology Using Machine Learning

Precision medicine is a medical approach to administer patients with a tailored dose of treatment by taking into consideration a person's variability in genes, environment, and lifestyles. The accumulation of omics big sequence data led to the development of various genetic databases on which clinical stratification of high-risk populations may be conducted. In addition, because cancers are generally caused by tumor-specific mutations, large-scale systematic identification of single nucleotide polymorphisms (SNPs) in various tumors has propelled significant progress of tailored treatments of tumors (i.e., precision oncology). Machine learning (ML), a subfield of artificial intelligence in which computers learn through experience, has a great potential to be used in precision oncology chiefly to help physicians make diagnostic decisions based on tumor images. A promising venue of ML in precision oncology is the integration of all available data from images to multi-omics big data for the holistic care of patients and high-risk healthy subjects. In this review, we provide a focused overview of precision oncology and ML with attention to breast cancer and glioma as well as the Bayesian networks that have the flexibility and the ability to work with incomplete information. We also introduce some state-of-the-art attempts to use and incorporate ML and genetic information in precision oncology.

CanDriS: posterior profiling of cancer-driving sites based on two-component evolutionary model

Current cancer genomics databases have accumulated millions of somatic mutations that remain to be further explored. Due to the over-excess mutations unrelated to cancer, the great challenge is to identify somatic mutations that are cancer-driven. Under the notion that carcinogenesis is a form of somatic-cell evolution, we developed a two-component mixture model: while the ground component corresponds to passenger mutations, the rapidly evolving component corresponds to driver mutations. Then, we implemented an empirical Bayesian procedure to calculate the posterior probability of a site being cancer-driven. Based on these, we developed a software CanDriS (Cancer Driver Sites) to profile the potential cancer-driving sites for thousands of tumor samples from the Cancer Genome Atlas and International Cancer Genome Consortium across tumor types and pan-cancer level. As a result, we identified that approximately 1% of the sites have posterior probabilities larger than 0.90 and listed potential cancer-wide and cancer-specific driver mutations. By comprehensively profiling all potential cancer-driving sites, CanDriS greatly enhances our ability to refine our knowledge of the genetic basis of cancer and might guide clinical medication in the upcoming era of precision medicine. The results were displayed in a database CandrisDB (http://biopharm.zju.edu.cn/candrisdb/).

Exploring the Allosteric Territory of Protein Function

While the pervasiveness of allostery in proteins is commonly accepted, we further show the generic nature of allosteric mechanisms by analyzing here transmembrane ion-channel viroporin 3a and RNA-dependent RNA polymerase (RdRp) from SARS-CoV-2 along with metabolic enzymes isocitrate dehydrogenase 1 (IDH1) and fumarate hydratase (FH) implicated in cancers. Using the previously developed structure-based statistical mechanical model of allostery (SBSMMA), we share our experience in analyzing the allosteric signaling, predicting latent allosteric sites, inducing and tuning targeted allosteric response, and exploring the allosteric effects of mutations. This, yet incomplete list of phenomenology, forms a complex and unique allosteric territory of protein function, which should be thoroughly explored. We propose a generic computational framework, which not only allows one to obtain a comprehensive allosteric control over proteins but also provides an opportunity to approach the fragment-based design of allosteric effectors and drug candidates. The advantages of allosteric drugs over traditional orthosteric compounds, complemented by the emerging role of the allosteric effects of mutations in the expansion of the cancer mutational landscape and in the increased mutability of viral proteins, leave no choice besides further extensive studies of allosteric mechanisms and their biomedical implications.

Computational methods for cancer driver discovery: A survey

Identifying the genes responsible for driving cancer is of critical importance for directing treatment. Accordingly, multiple computational tools have been developed to facilitate this task. Due to the different methods employed by these tools, different data considered by the tools, and the rapidly evolving nature of the field, the selection of an appropriate tool for cancer driver discovery is not straightforward. This survey seeks to provide a comprehensive review of the different computational methods for discovering cancer drivers. We categorise the methods into three groups; methods for single driver identification, methods for driver module identification, and methods for identifying personalised cancer drivers. In addition to providing a “one-stop” reference of these methods, by evaluating and comparing their performance, we also provide readers the information about the different capabilities of the methods in identifying biologically significant cancer drivers. The biologically relevant information identified by these tools can be seen through the enrichment of discovered cancer drivers in GO biological processes and KEGG pathways and through our identification of a small cancer-driver cohort that is capable of stratifying patient survival.

Mutations of Intrinsically Disordered Protein Regions Can Drive Cancer but Lack Therapeutic Strategies

Many proteins contain intrinsically disordered regions (IDRs) which carry out important functions without relying on a single well-defined conformation. IDRs are increasingly recognized as critical elements of regulatory networks and have been also associated with cancer. However, it is unknown whether mutations targeting IDRs represent a distinct class of driver events associated with specific molecular and system-level properties, cancer types and treatment options. Here, we used an integrative computational approach to explore the direct role of intrinsically disordered protein regions driving cancer. We showed that around 20% of cancer drivers are primarily targeted through a disordered region. These IDRs can function in multiple ways which are distinct from the functional mechanisms of ordered drivers. Disordered drivers play a central role in context-dependent interaction networks and are enriched in specific biological processes such as transcription, gene expression regulation and protein degradation. Furthermore, their modulation represents an alternative mechanism for the emergence of all known cancer hallmarks. Importantly, in certain cancer patients, mutations of disordered drivers represent key driving events. However, treatment options for such patients are currently severely limited. The presented study highlights a largely overlooked class of cancer drivers associated with specific cancer types that need novel therapeutic options.

Patient-Specific Network for Personalized Breast Cancer Therapy with Multi-Omics Data

The development of new computational approaches that are able to design the correct personalized drugs is the crucial therapeutic issue in cancer research. However, tumor heterogeneity is the main obstacle to developing patient-specific single drugs or combinations of drugs that already exist in clinics. In this study, we developed a computational approach that integrates copy number alteration, gene expression, and a protein interaction network of 73 basal breast cancer samples. 2509 prognostic genes harboring a copy number alteration were identified using survival analysis, and a protein–protein interaction network considering the direct interactions was created. Each patient was described by a specific combination of seven altered hub proteins that fully characterize the 73 basal breast cancer patients. We suggested the optimal combination therapy for each patient considering drug–protein interactions. Our approach is able to confirm well-known cancer related genes and suggest novel potential drug target genes. In conclusion, we presented a new computational approach in breast cancer to deal with the intra-tumor heterogeneity towards personalized cancer therapy.

Current Achievements and Applications of Transcriptomics in Personalized Cancer Medicine

Over the last decades, transcriptome profiling emerged as one of the most powerful approaches in oncology, providing prognostic and predictive utility for cancer management. The development of novel technologies, such as revolutionary next-generation sequencing, enables the identification of cancer biomarkers, gene signatures, and their aberrant expression affecting oncogenesis, as well as the discovery of molecular targets for anticancer therapies. Transcriptomics contribute to a change in the holistic understanding of cancer, from histopathological and organic to molecular classifications, opening a more personalized perspective for tumor diagnostics and therapy. The further advancement on transcriptome profiling may allow standardization and cost reduction of its analysis, which will be the next step for transcriptomics to become a canon of contemporary cancer medicine.

My personal mutanome: a computational genomic medicine platform for searching network perturbing alleles linking genotype to phenotype

Massive genome sequencing data have inspired new challenges in personalized treatments and facilitated oncological drug discovery. We present a comprehensive database, My Personal Mutanome (MPM), for accelerating the development of precision cancer medicine protocols. MPM contains 490,245 mutations from over 10,800 tumor exomes across 33 cancer types in The Cancer Genome Atlas mapped to 94,563 structure-resolved/predicted protein-protein interaction interfaces ("edgetic") and 311,022 functional sites ("nodetic"), including ligand-protein binding sites and 8 types of protein posttranslational modifications. In total, 8884 survival results and 1,271,132 drug responses are obtained for these mapped interactions. MPM is available at https://mutanome.lerner.ccf.org .

Use of signals of positive and negative selection to distinguish cancer genes and passenger genes

A major goal of cancer genomics is to identify all genes that play critical roles in carcinogenesis. Most approaches focused on genes positively selected for mutations that drive carcinogenesis and neglected the role of negative selection. Some studies have actually concluded that negative selection has no role in cancer evolution. We have re-examined the role of negative selection in tumor evolution through the analysis of the patterns of somatic mutations affecting the coding sequences of human genes. Our analyses have confirmed that tumor suppressor genes are positively selected for inactivating mutations, oncogenes, however, were found to display signals of both negative selection for inactivating mutations and positive selection for activating mutations. Significantly, we have identified numerous human genes that show signs of strong negative selection during tumor evolution, suggesting that their functional integrity is essential for the growth and survival of tumor cells.

A new precision medicine initiative at the dawn of exascale computing

Which signaling pathway and protein to select to mitigate the patient's expected drug resistance? The number of possibilities facing the physician is massive, and the drug combination should fit the patient status. Here, we briefly review current approaches and data and map an innovative patient-specific strategy to forecast drug resistance targets that centers on parallel (or redundant) proliferation pathways in specialized cells. It considers the availability of each protein in each pathway in the specific cell, its activating mutations, and the chromatin accessibility of its encoding gene. The construction of the resulting Proliferation Pathway Network Atlas will harness the emerging exascale computing and advanced artificial intelligence (AI) methods for therapeutic development. Merging the resulting set of targets, pathways, and proteins, with current strategies will augment the choice for the attending physicians to thwart resistance.

Molecular dynamics of the immune checkpoint programmed cell death protein I, PD-1: conformational changes of the BC-loop upon binding of the ligand PD-L1 and the monoclonal antibody nivolumab

BACKGROUND

The immune checkpoint receptor programmed cell death protein I (PD-1) has been identified as a key target in immunotherapy. PD-1 reduces the risk of autoimmunity by inducing apoptosis in antigen-specific T cells upon interaction with programmed cell death protein ligand I (PD-L1). Various cancer types overexpress PD-L1 to evade the immune system by inducing apoptosis in tumor-specific CD8+ T cells. The clinically used blocking antibody nivolumab binds to PD-1 and inhibits the immunosuppressive interaction with PD-L1. Even though PD-1 is already used as a drug target, the exact mechanism of the receptor is still a matter of debate. For instance, it is hypothesized that the signal transduction is based on an active conformation of PD-1.

RESULTS

Here we present the results of the first molecular dynamics simulations of PD-1 with a complete extracellular domain with a focus on the role of the BC-loop of PD-1 upon binding PD-L1 or nivolumab. We could demonstrate that the BC-loop can form three conformations. Nivolumab binds to the BC-loop according to the conformational selection model whereas PD-L1 induces allosterically a conformational change of the BC-loop.

CONCLUSION

Due to the structural differences of the BC-loop, a signal transduction based on active conformation cannot be ruled out. These findings will have an impact on drug design and will help to refine immunotherapy blocking antibodies.

Long-lasting Salt Bridges Provide the Anchoring Mechanism of Oncogenic Kirsten Rat Sarcoma Proteins at Cell Membranes

RAS proteins work as GDP-GTP binary switches and regulate cytoplasmic signaling networks that are able to control several cellular processes, playing an essential role in signal transduction pathways involved in cell growth, differentiation, and survival, so that overacting RAS signaling can lead to cancer. One of the hardest challenges to face is the design of mutation-selective therapeutic strategies. In this work, a G12D-mutated farnesylated GTP-bound Kirsten RAt sarcoma (KRAS) protein has been simulated at the interface of a DOPC/DOPS/cholesterol model anionic cell membrane. A specific long-lasting salt bridge connection between farnesyl and the hypervariable region of the protein has been identified as the main mechanism responsible for the binding of oncogenic farnesylated KRAS-4B to the cell membrane. Free-energy landscapes allowed us to characterize local and global minima of KRAS-4B binding to the cell membrane, revealing the main pathways between anchored and released states.

Why All the Fuss about Oxidative Phosphorylation (OXPHOS)?

Certain subtypes of cancer cells require oxidative phosphorylation (OXPHOS) to survive. Increased OXPHOS dependency is frequently a hallmark of cancer stem cells and cells resistant to chemotherapy and targeted therapies. Suppressing the OXPHOS function might also influence the tumor microenvironment by alleviating hypoxia and improving the antitumor immune response. Thus, targeting OXPHOS is a promising strategy to treat various cancers. A growing arsenal of therapeutic agents is under development to inhibit this biological process. This Perspective provides an overview of the structure and function of OXPHOS complexes, their biological functions in cancer, relevant research tools and models, as well as the limitations of OXPHOS as drug targets. We also focus on the current development status of OXPHOS inhibitors and potential therapeutic strategies to strengthen their clinical applications.

Identification of the Sixth Complement Component as Potential Key Genes in Hepatocellular Carcinoma via Bioinformatics Analysis

The present study is designed to determine potential target genes involved in hepatocellular carcinoma (HCC) and provide possible underlying mechanisms of action. Several studies (GSE112790, GSE87630, and GSE56140) from the GEO database looking at molecular characteristics in HCC were screened and analyzed by GEO2R, which led to the identification of a total of 93 differentially expressed genes (DEGs). From the protein–protein interaction (PPI) network, we selected 13 key genes with high degree of variability in expression in HCC. Expression of three key genes (NQO1, CYP2C9, and C6) presented with poor overall survival (OS) in HCC patients by UALCAN. C6, which is a complement component, was found by ONCOMINE and TIMER to have low expression in many solid cancers including HCC. Besides, Kaplan-Meier plotter and UALCAN database analysis to access diseases prognosis suggested that low expression of C6 is significantly related to worse OS in LIHC patients, especially in advanced HCC patients. Finally, the TIMER analysis suggested that the C6 expression showed significant negative correlation with infiltrating levels of six immune cells. The somatic copy number alterations (SCNAs) of C6 were associated with CD4+ T cell infiltration in HCC. Taken together, these results together identified C6 as a potential key gene in the diagnosis and prognosis of HCC.

AlloSigMA 2: paving the way to designing allosteric effectors and to exploring allosteric effects of mutations

The AlloSigMA 2 server provides an interactive platform for exploring the allosteric signaling caused by ligand binding and/or mutations, for analyzing the allosteric effects of mutations and for detecting potential cancer drivers and pathogenic nsSNPs. It can also be used for searching latent allosteric sites and for computationally designing allosteric effectors for these sites with required agonist/antagonist activity. The server is based on the implementation of the Structure-Based Statistical Mechanical Model of Allostery (SBSMMA), which allows one to evaluate the allosteric free energy as a result of the perturbation at per-residue resolution. The Allosteric Signaling Map (ASM) providing a comprehensive residue-by-residue allosteric control over the protein activity can be obtained for any structure of interest. The Allosteric Probing Map (APM), in turn, allows one to perform the fragment-based-like computational design experiment aimed at finding leads for potential allosteric effectors. The server can be instrumental in elucidating of allosteric mechanisms and actions of allosteric mutations, and in the efforts on design of new elements of allosteric control. The server is freely available at: http://allosigma.bii.a-star.edu.sg.

Unsupervised Learning and Multipartite Network Models: A Promising Approach for Understanding Traditional Medicine

The ultimate goal of precision medicine is to determine right treatment for right patients based on precise diagnosis. To achieve this goal, correct stratification of patients using molecular features and clinical phenotypes is crucial. During the long history of medical science, our understanding on disease classification has been improved greatly by chemistry and molecular biology. Nowadays, we gain access to large scale patient-derived data by high-throughput technologies, generating a greater need for data science including unsupervised learning and network modeling. Unsupervised learning methods such as clustering could be a better solution to stratify patients when there is a lack of predefined classifiers. In network modularity analysis, clustering methods can be also applied to elucidate the complex structure of biological and disease networks at the systems level. In this review, we went over the main points of clustering analysis and network modeling, particularly in the context of Traditional Chinese medicine (TCM). We showed that this approach can provide novel insights on the rationale of classification for TCM herbs. In a case study, using a modularity analysis of multipartite networks, we illustrated that the TCM classifications are associated with the chemical properties of the herb ingredients. We concluded that multipartite network modeling may become a suitable data integration tool for understanding the mechanisms of actions of traditional medicine.

Limitations and challenges in protein stability prediction upon genome variations: towards future applications in precision medicine

Protein stability predictions are becoming essential in medicine to develop novel immunotherapeutic agents and for drug discovery. Despite the large number of computational approaches for predicting the protein stability upon mutation, there are still critical unsolved problems: 1) the limited number of thermodynamic measurements for proteins provided by current databases; 2) the large intrinsic variability of ΔΔG values due to different experimental conditions; 3) biases in the development of predictive methods caused by ignoring the anti-symmetry of ΔΔG values between mutant and native protein forms; 4) over-optimistic prediction performance, due to sequence similarity between proteins used in training and test datasets. Here, we review these issues, highlighting new challenges required to improve current tools and to achieve more reliable predictions. In addition, we provide a perspective of how these methods will be beneficial for designing novel precision medicine approaches for several genetic disorders caused by mutations, such as cancer and neurodegenerative diseases.

Online informatics resources to facilitate cancer target and chemical probe discovery

The advances in cancer genomics, chemical biology, high-throughput screening technologies, and synthetic medicinal chemistry have tremendously expanded the biological space of cancer targets and chemical space of bioactive small molecules to interrogate oncogenic signaling. To explore and leverage these exponentially growing cancer-associated data, a great number of computational tools, databases, and algorithms have been developed. This review summarizes recent cancer-related web resources that allow researchers working at the interface of chemical, biological, and cancer genomics fields to integrate clinical and genomics data for specific actionable targets and selective chemical compounds to facilitate cancer therapeutic discovery.

Prioritizing Cancer Genes Based on an Improved Random Walk Method

Identifying driver genes that contribute to cancer progression from numerous passenger genes, although a central goal, is a major challenge. The protein-protein interaction network provides convenient and reasonable assistance for driver gene discovery. Random walk-based methods have been widely used to prioritize nodes in social or biological networks. However, most studies select the next arriving node uniformly from the random walker's neighbors. Few consider transiting preference according to the degree of random walker's neighbors. In this study, based on the random walk method, we propose a novel approach named Driver_IRW (Driver genes discovery with Improved Random Walk method), to prioritize cancer genes in cancer-related network. The key idea of Driver_IRW is to assign different transition probabilities for different edges of a constructed cancer-related network in accordance with the degree of the nodes' neighbors. Furthermore, the global centrality (here is betweenness centrality) and Katz feedback centrality are incorporated into the framework to evaluate the probability to walk to the seed nodes. Experimental results on four cancer types indicate that Driver_IRW performs more efficiently than some previously published methods for uncovering known cancer-related genes. In conclusion, our method can aid in prioritizing cancer-related genes and complement traditional frequency and network-based methods.

Current Insights into Combination Therapies with MAPK Inhibitors and Immune Checkpoint Blockade

The recent development of high-throughput genomics has revolutionized personalized medicine by identifying key pathways and molecular targets controlling tumor progression and survival. Mitogen-activated protein kinase (MAPK) pathways are examples of such targets, and inhibitors against these pathways have shown promising clinical responses in patients with melanoma, non-small-cell lung cancer, colorectal cancer, pancreatic cancer, and thyroid cancer. Although MAPK pathway-targeted therapies have resulted in significant clinical responses in a large proportion of cancer patients, the rate of tumor recurrence is high due to the development of resistance. Conversely, immunotherapies have shown limited clinical responses, but have led to durable tumor regression in patients, and complete responses. Recent evidence indicates that MAPK-targeted therapies may synergize with immune cells, thus providing rationale for the development of combination therapies. Here, we review the current status of ongoing clinical trials investigating MAPK pathway inhibitors, such as BRAF and MAPK/ERK kinase (MEK) inhibitors, in combination with checkpoint inhibitors targeting programmed death protein 1 (PD-1), programmed death-ligand 1 (PD-L1), and cytotoxic T cell associated antigen-4 (CTLA-4). A better understanding of an individual drug's mechanism of action, patterns of acquired resistance, and the influence on immune cells will be critical for the development of novel combination therapies.

Overall survival in patients with lung adenocarcinoma harboring "niche" mutations: an observational study

Objective: In addition to the most common somatic lung cancer mutations (i. e., KRAS and EGFR mutations), other genes may harbor mutations that could be relevant for lung cancer. We defined BRAF, c-MET, DDR2, HER2, MAP2K1, NRAS, PIK3CA, and RET mutations as “niche” mutations and analyzed. The aim of this retrospective cohort study was to assess the differences in the overall survival (OS) of patients with lung adenocarcinoma harboring niche somatic mutations.

Results: Data were gathered for 252 patients. Mutations were observed in all genes studied, except c-MET, DDR2, MAP2K1, and RET. The multivariable analysis showed that 1) niche mutations had a higher mortality than EGFR mutations (HR = 2.3; 95% CI = 1.2–4.4; p = 0.009); 2) KRAS mutations had a higher mortality than EGFR mutations (HR = 2.5; 95% CI = 1.4–4.5; p = 0.003); 3) niche mutations presented a similar mortality to KRAS mutations (HR = 0.9; 95% CI = 0.6–1.5; p = 0.797).

Methods: Three cohorts of mutations were selected from patients with lung adenocarcinoma and their OS was compared. Mutations that were searched for, were 1) BRAF, c-MET, DDR2, HER2, MAP2K1, NRAS, PIK3CA, and RET; 2) K-RAS; and 3) EGFR. Differences in OS between these three cohorts were assessed by means of a multivariable Cox model that adjusted for age, sex, smoking habits, clinical stages, and treatments.

Conclusions: Niche mutations exhibited an increased risk of death when compared with EGFR mutations and a similar risk of death when compared with KRAS mutations.

Integrated Computational Approaches and Tools forAllosteric Drug Discovery

Understanding molecular mechanisms underlying the complexity of allosteric regulationin proteins has attracted considerable attention in drug discovery due to the benefits and versatilityof allosteric modulators in providing desirable selectivity against protein targets while minimizingtoxicity and other side effects. The proliferation of novel computational approaches for predictingligand-protein interactions and binding using dynamic and network-centric perspectives has ledto new insights into allosteric mechanisms and facilitated computer-based discovery of allostericdrugs. Although no absolute method of experimental and in silico allosteric drug/site discoveryexists, current methods are still being improved. As such, the critical analysis and integration ofestablished approaches into robust, reproducible, and customizable computational pipelines withexperimental feedback could make allosteric drug discovery more efficient and reliable. In this article,we review computational approaches for allosteric drug discovery and discuss how these tools can beutilized to develop consensus workflows for in silico identification of allosteric sites and modulatorswith some applications to pathogen resistance and precision medicine. The emerging realization thatallosteric modulators can exploit distinct regulatory mechanisms and can provide access to targetedmodulation of protein activities could open opportunities for probing biological processes and insilico design of drug combinations with improved therapeutic indices and a broad range of activities.

Molecular profiling for precision cancer therapies

The number of druggable tumor-specific molecular aberrations has grown substantially in the past decade, with a significant survival benefit obtained from biomarker matching therapies in several cancer types. Molecular pathology has therefore become fundamental not only to inform on tumor diagnosis and prognosis but also to drive therapeutic decisions in daily practice. The introduction of next-generation sequencing technologies and the rising number of large-scale tumor molecular profiling programs across institutions worldwide have revolutionized the field of precision oncology. As comprehensive genomic analyses become increasingly available in both clinical and research settings, healthcare professionals are faced with the complex tasks of result interpretation and translation. This review summarizes the current and upcoming approaches to implement precision cancer medicine, highlighting the challenges and potential solutions to facilitate the interpretation and to maximize the clinical utility of molecular profiling results. We describe novel molecular characterization strategies beyond tumor DNA sequencing, such as transcriptomics, immunophenotyping, epigenetic profiling, and single-cell analyses. We also review current and potential applications of liquid biopsies to evaluate blood-based biomarkers, such as circulating tumor cells and circulating nucleic acids. Last, lessons learned from the existing limitations of genotype-derived therapies provide insights into ways to expand precision medicine beyond genomics.

Assessing the Concordance of Genomic Alterations between Circulating-Free DNA and Tumour Tissue in Cancer Patients

Somatic alterations to the genomes of solid tumours, which in some cases represent actionable drivers, provide diagnostic and prognostic insight into these complex diseases. Spatial and longitudinal tracking of somatic genomic alterations (SGAs) in patient tumours has emerged as a new avenue of investigation, not only as a disease monitoring strategy, but also to improve our understanding of heterogeneity and clonal evolution from diagnosis through disease progression. Furthermore, analysis of circulating-free DNA (cfDNA) in the so-called "liquid biopsy" has emerged as a non-invasive method to identify genomic information to inform targeted therapy and may also capture the heterogeneity of the primary and metastatic tumours. Considering the potential of cfDNA analysis as a translational laboratory tool in clinical practice, establishing the extent to which cfDNA represents the SGAs of tumours, particularly actionable driver alterations, becomes a matter of importance, warranting standardisation of methods and practices. Here, we assess the utilisation of cfDNA for molecular profiling of SGAs in tumour tissue across a broad range of solid tumours. Moreover, we examine the underlying factors contributing to discordance of detected SGAs between cfDNA and tumour tissue.

Alterations in ZnT1 expression and function lead to impaired intracellular zinc homeostasis in cancer

Zinc is vital for the structure and function of ~3000 human proteins and hence plays key physiological roles. Consequently, impaired zinc homeostasis is associated with various human diseases including cancer. Intracellular zinc levels are tightly regulated by two families of zinc transporters: ZIPs and ZnTs; ZIPs import zinc into the cytosol from the extracellular milieu, or from the lumen of organelles into the cytoplasm. In contrast, the vast majority of ZnTs compartmentalize zinc within organelles, whereas the ubiquitously expressed ZnT1 is the sole zinc exporter. Herein, we explored the hypothesis that qualitative and quantitative alterations in ZnT1 activity impair cellular zinc homeostasis in cancer. Towards this end, we first used bioinformatics to analyze inactivating mutations in ZIPs and ZNTs, catalogued in the COSMIC and gnomAD databases, representing tumor specimens and healthy population controls, respectively. ZnT1, ZnT10, ZIP8, and ZIP10 showed extremely high rates of loss of function mutations in cancer as compared to healthy controls. Analysis of the putative functional impact of missense mutations in ZnT1-ZnT10 and ZIP1-ZIP14, using homologous protein alignment and structural predictions, revealed that ZnT1 displays a markedly increased frequency of predicted functionally deleterious mutations in malignant tumors, as compared to a healthy population. Furthermore, examination of ZnT1 expression in 30 cancer types in the TCGA database revealed five tumor types with significant ZnT1 overexpression, which predicted dismal prognosis for cancer patient survival. Novel functional zinc transport assays, which allowed for the indirect measurement of cytosolic zinc levels, established that wild type ZnT1 overexpression results in low intracellular zinc levels. In contrast, overexpression of predicted deleterious ZnT1 missense mutations did not reduce intracellular zinc levels, validating eight missense mutations as loss of function (LoF) mutations. Thus, alterations in ZnT1 expression and LoF mutations in ZnT1 provide a molecular mechanism for impaired zinc homeostasis in cancer formation and/or progression.