A new precision medicine initiative at the dawn of exascale computing

Slower CDK4 and faster CDK2 activation in the cell cycle

Dysregulation of cyclin-dependent kinases (CDKs) impacts cell proliferation, driving cancer. Here, we ask why the cyclin-D/CDK4 complex governs cell cycle progression through the longer G1 phase, whereas cyclin-E/CDK2 regulates the shorter G1/S phase transition. We consider available experimental cellular and structural data including cyclin-E's high-level burst, sustained duration of elevated cyclin-D expression, and explicit solvent molecular dynamics simulations of the inactive monomeric and complexed states, to establish the conformational tendencies along the landscape of the distinct activation scenarios of cyclin-D/CDK4 and cyclin-E/CDK2 in the G1 phase and G1/S transition of the cell cycle, respectively. These lead us to propose slower activation of cyclin-D/CDK4 and rapid activation of cyclin-E/CDK2. We provide the mechanisms through which this occurs, offering innovative CDK4 drug design considerations. Our insightful mechanistic work addresses a compelling cell cycle regulation question and illuminates the distinct activation speeds between the G1 and the G1/S phases, which are crucial for function.

Global DNA and RNA Methylation Signature in Response to Antipsychotic Treatment in First-Episode Schizophrenia Patients

Background

Schizophrenia is a heterogeneous chronic psychiatric disorder influenced by genetic and environmental factors. Environmental factors can alter epigenetic marks, which regulate gene expression and cause an array of systemic changes. Several studies have demonstrated the association of epigenetic modulations in schizophrenia, which can influence clinical course, symptoms, and even treatment. Based on this, we have examined the global DNA methylation patterns, namely the 5-methylcytosine (5mC), 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC); and the global RNA modification N6-methyladenosine (m6A) RNA methylation status in peripheral blood cells. First-Episode Psychosis (FEP) patients who were diagnosed with Schizophrenia (SCZ) and undergoing treatment were stratified as Treatment-Responsive (TR) and Treatment-Non-Responsive (TNR). Age- and sex-matched healthy subjects served as controls.

Results

The methylation pattern of 5mC and 5hmC showed significant increases in patients in comparison to controls. Further, when patients were classified based on their response to treatment, there was a statistically significant increase in methylation patterns in the treatment non-responder group. 5fC and m6A levels did not show any statistical significance across the groups. Further, gender-based stratification did not yield any significant difference for the markers.

Conclusion

The study highlights the increased global methylation pattern in SCZ patients and a significant difference between the TR versus TNR groups. Global 5mC and 5hmC epigenetic marks suggest their potential roles in schizophrenia pathology, and also in the treatment response to antipsychotics. Since not many studies were available on the treatment response, further validation and the use of more sensitive techniques to study methylation status could unravel the potential of these epigenetic modifications as biomarkers for SCZ as well as distinguishing the antipsychotic treatment response in patients.

Mitogen signaling strength and duration can control cell cycle decisions

Decades ago, mitogen-promoted signaling duration and strength were observed to be sensed by the cell and to be critical for its decisions: to proliferate or differentiate. Landmark publications established the importance of mitogen signaling not only in the G1 cell cycle phase but also through the S and the G2/M transition. Despite these early milestones, how mitogen signal duration and strength, short and strong or weaker and sustained, control cell fate has been largely unheeded. Here, we center on cardinal signaling-related questions, including (i) how fluctuating mitogenic signals are converted into cell proliferation-differentiation decisions and (ii) why extended duration of weak signaling is associated with differentiation, while bursts of strong and short induce proliferation but, if too strong and long, induce irreversible senescence. Our innovative broad outlook harnesses cell biology and protein conformational ensembles, helping us to define signaling strength, clarify cell cycle decisions, and thus cell fate.

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.

Protein conformational ensembles in function: roles and mechanisms

The sequence-structure-function paradigm has dominated twentieth century molecular biology. The paradigm tacitly stipulated that for each sequence there exists a single, well-organized protein structure. Yet, to sustain cell life, function requires (i) that there be more than a single structure, (ii) that there be switching between the structures, and (iii) that the structures be incompletely organized. These fundamental tenets called for an updated sequence-conformational ensemble-function paradigm. The powerful energy landscape idea, which is the foundation of modernized molecular biology, imported the conformational ensemble framework from physics and chemistry. This framework embraces the recognition that proteins are dynamic and are always interconverting between conformational states with varying energies. The more stable the conformation the more populated it is. The changes in the populations of the states are required for cell life. As an example, in vivo, under physiological conditions, wild type kinases commonly populate their more stable "closed", inactive, conformations. However, there are minor populations of the "open", ligand-free states. Upon their stabilization, e.g., by high affinity interactions or mutations, their ensembles shift to occupy the active states. Here we discuss the role of conformational propensities in function. We provide multiple examples of diverse systems, including protein kinases, lipid kinases, and Ras GTPases, discuss diverse conformational mechanisms, and provide a broad outlook on protein ensembles in the cell. We propose that the number of molecules in the active state (inactive for repressors), determine protein function, and that the dynamic, relative conformational propensities, rather than the rigid structures, are the hallmark of cell life.

Cell cycle progression mechanisms: slower cyclin-D/CDK4 activation and faster cyclin-E/CDK2

Dysregulation of cyclin-dependent kinases (CDKs) impacts cell proliferation, driving cancer. Here, we ask why the cyclin-D/CDK4 complex governs cell cycle progression through the longer G1 phase, whereas cyclin-E/CDK2 regulates the short G1/S phase transition. We consider the experimentally established high-level bursting of cyclin-E, and sustained duration of elevated cyclin-D expression in the cell, available experimental cellular and structural data, and comprehensive explicit solvent molecular dynamics simulations to provide the mechanistic foundation of the distinct activation scenarios of cyclin-D/CDK4 and cyclin-E/CDK2 in the G1 phase and G1/S transition of the cell cycle, respectively. These lead us to propose slower activation of cyclin-D/CDK4 and rapid activation of cyclin-E/CDK2. Importantly, we determine the mechanisms through which this occurs, offering innovative CDK4 drug design considerations. Our insightful mechanistic work addresses the compelling cell cycle regulation question and illuminates the distinct activation speeds in the G1 versus G1/S phases, which are crucial for cell function.

A personalized pharmaco-epistatic network model of precision medicine

Precision medicine, the utilization of targeted treatments to address an individual's disease, relies on knowledge about the genetic cause of that individual's drug response. Here, we present a functional graph (FunGraph) theory to chart comprehensive pharmacogenetic architecture for each and every patient. FunGraph is the combination of functional mapping - a dynamic model for genetic mapping and evolutionary game theory guiding interactive strategies. It coalesces all pharmacogenetic factors into multilayer and multiplex networks that fully capture bidirectional, signed and weighted epistasis. It can visualize and interrogate how epistasis moves in the cell and how this movement leads to patient- and context-specific genetic architecture in response to organismic physiology. We discuss the future implementation of FunGraph to achieve precision medicine. Teaser: We present a functional graph (FunGraph) theory to draw a complete picture of pharmacogenetic architecture underlying interindividual variability in drug response. FunGraph can characterize how each gene acts and interacts with every other gene to mediate therapeutic response.

Genomic Landscape and Potential Regulation of RNA Editing in Drug Resistance

Adenosine-to-inosine RNA editing critically affects the response of cancer therapies. However, comprehensive identification of drug resistance-related RNA editing events and systematic understanding of how RNA editing mediates anticancer drug resistance remain unclear. Here, 7157 differential editing sites (DESs) are identified from 98 127 informative RNA editing sites in tumor tissues, many of which are validated in cancer cell lines. Diverse editing patterns of DESs are discovered in resistant samples, which could not be fully explained by adenosine deaminase acting on RNA enzymes. Some RNA-binding proteins are identified that potentially regulate these editing events. Notably, the DESs are significantly enriched in 3'-untranslated regions (3'-UTRs). The impact of DESs in 3'-UTR on the microRNA (miRNA) regulations is explored, and some triplets (DES, miRNA, and gene) that may contribute to drug resistance are identified. In addition, it is determined that the functions of genes enriched with DESs are associated with drug resistance, such as apoptosis, drug metabolism, and DNA synthesis involved in DNA repair. An online resource (http://www.jianglab.cn/REDR/) to support convenient retrieval of DESs is also built. The findings reveal the landscape and potential regulatory mechanism of RNA editing in drug resistance, providing new therapeutic targets for reversing drug resistance.

Neurodevelopmental disorders, like cancer, are connected to impaired chromatin remodelers, PI3K/mTOR, and PAK1-regulated MAPK

Neurodevelopmental disorders (NDDs) and cancer share proteins, pathways, and mutations. Their clinical symptoms are different. However, individuals with NDDs have higher probabilities of eventually developing cancer. Here, we review the literature and ask how the shared features can lead to different medical conditions and why having an NDD first can increase the chances of malignancy. To explore these vital questions, we focus on dysregulated PI3K/mTOR, a major brain cell growth pathway in differentiation, and MAPK, a critical pathway in proliferation, a hallmark of cancer. Differentiation is governed by chromatin organization, making aberrant chromatin remodelers highly likely agents in NDDs. Dysregulated chromatin organization and accessibility influence the lineage of specific cell brain types at specific embryonic development stages. PAK1, with pivotal roles in brain development and in cancer, also regulates MAPK. We review, clarify, and connect dysregulated pathways with dysregulated proliferation and differentiation in cancer and NDDs and highlight PAK1 role in brain development and MAPK regulation. Exactly how PAK1 activation controls brain development, and why specific chromatin remodeler components, e.g., BAF170 encoded by SMARCC2 in autism, await clarification.

Conformational ensemble of the NSP1 CTD in SARS-CoV-2: Perspectives from the free energy landscape

The nonstructural protein-1 (NSP1) of the severe acute respiratory syndrome-associated coronavirus 2 plays a crucial role in the translational shutdown and immune evasion inside host cells. Despite its known intrinsic disorder, the C-terminal domain (CTD) of NSP1 has been reported to form a double α-helical structure and block the 40S-ribosomal channel for mRNA translation. Experimental studies indicate that NSP1 CTD functions independently from the globular N-terminal region separated with a long linker domain, underscoring the necessity of exploring the standalone conformational ensemble. In this contribution, we utilize exascale computing resources to yield unbiased molecular dynamics simulation of NSP1 CTD in all-atom resolution starting from multiple initial seed structures. A data-driven approach elicits collective variables (CVs) that are significantly superior to conventional descriptors in capturing the conformational heterogeneity. The free energy landscape as a function of the CV space is estimated using the modified expectation maximized molecular dynamics. Originally developed by us for small peptides, here, we establish the efficacy of expectation maximized molecular dynamics in conjunction with data-driven CV space for a more complex and relevant biomolecular system. The results reveal the existence of two disordered metastable populations in the free energy landscape that are separated from the conformation resembling ribosomal subunit bound state by high kinetic barriers. Chemical shift correlation and secondary structure analysis capture significant differences among key structures of the ensemble. Altogether, these insights can underpin drug development studies and mutational experiments that help induce population shifts to alter translational blocking and understand its molecular basis in further detail.

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.

Deep generative molecular design reshapes drug discovery

Recent advances and accomplishments of artificial intelligence (AI) and deep generative models have established their usefulness in medicinal applications, especially in drug discovery and development. To correctly apply AI, the developer and user face questions such as which protocols to consider, which factors to scrutinize, and how the deep generative models can integrate the relevant disciplines. This review summarizes classical and newly developed AI approaches, providing an updated and accessible guide to the broad computational drug discovery and development community. We introduce deep generative models from different standpoints and describe the theoretical frameworks for representing chemical and biological structures and their applications. We discuss the data and technical challenges and highlight future directions of multimodal deep generative models for accelerating drug discovery.

Monoclonal antibodies and Fc-fusion protein biologic medicines: A multinational cross-sectional investigation of accessibility and affordability in Asia Pacific regions between 2010 and 2020

BACKGROUND

Monoclonal antibody (mAb) and Fc-fusion protein (FcP) are highly effective therapeutic biologics. We aimed to analyse consumption and expenditure trends in 14 Asia-Pacific countries/regions (APAC) and three benchmark countries (the UK, Canada, and the US).

METHODS

We analysed 440 mAb and FcP biological products using the IQVIA-MIDAS global sales database. For each year between 2010 and 2020 inclusive, we used standard units (SU) sold per 1000 population and manufacture level price (standardised in 2019 US dollars) to evaluate consumption (accessibility) and expenditure (affordability). Changes of consumption and expenditure were estimated using compound annual growth rate (CAGR). Correlations between consumption, country's economic and health performance indicators were measured using Spearman correlation coefficient.

FINDINGS

Between 2010 and 2020, CAGRs of consumption in each region ranged from 7% to 34% and the CAGRs of expenditure ranged from 9% to 31%. The median consumption of biologics was extremely low in lower-middle-income economies (0·29 SU/1000 population) compared with upper-middle-income economies (1·20), high-income economies (40·94) and benchmark countries (109·55), although the median CAGRs of biologics consumption in lower-middle-income economies (31%) was greater than upper-middle-income (14%), high-income economies (13%) and benchmark countries (9%). Consumption was correlated with GDP per capita [Spearman's rank correlation coefficient (r) = 0·75, p < 0·001], health expenditure as a percentage of total (r = 0·83, p < 0·001) and medical doctors' density (r = 0·85, p < 0·001).

INTERPRETATION

There have been significant increases in mAb and FcP biologics consumption and expenditure, however accessibility of biological medicines remains unequal and is largely correlated with country's income level.

FUNDING

This research was funded by NHMRC Project Grant GNT1157506 and GNT1196900; Enhanced Start-up Fund for new academic staff and Internal Research Fund, Department of Medicine, LKS Faculty of Medicine, University of Hong Kong.

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.

Validation of a Liquid Biopsy Protocol for Canine BRAFV595E Variant Detection in Dog Urine and Its Evaluation as a Diagnostic Test Complementary to Cytology

A significant proportion of canine urothelial carcinomas carry the driver valine to glutamic acid variation (V595E) in BRAF kinase. The detection of V595E may prove suitable to guide molecularly targeted therapies and support non-invasive diagnosis of the urogenital system by means of a liquid biopsy approach using urine. Three cohorts and a control group were included in this multi-step validation study which included setting up a digital PCR assay. This was followed by investigation of preanalytical factors and two alternative PCR techniques on a liquid biopsy protocol. Finally, a blind study using urine as diagnostic sample has been carried out to verify its suitability as diagnostic test to complement cytology. The digital PCR (dPCR) assay proved consistently specific, sensitive, and linear. Using the dPCR assay, the prevalence of V595E in 22 urothelial carcinomas was 90.9%. When compared with histopathology as gold standard in the blind-label cases, the diagnostic accuracy of using the canine BRAF (cBRAF) variation as a surrogate assay against the histologic diagnosis was 85.7% with 92.3% positive predictive value and 80.0% negative predictive value. In all the cases, in which both biopsy tissue and the associated urine were assayed, the findings matched completely. Finally, when combined with urine sediment cytology examination in blind-label cases with clinical suspicion of malignancy, the dPCR assay significantly improved the overall diagnostic accuracy. A liquid biopsy approach on urine using the digital PCR may be a valuable breakthrough in the diagnostic of urothelial carcinomas in dogs.

Neurodevelopmental disorders, immunity, and cancer are connected

Immunity could be viewed as the common factor in neurodevelopmental disorders and cancer. The immune and nervous systems coevolve as the embryo develops. Immunity can release cytokines that activate MAPK signaling in neural cells. In specific embryonic brain cell types, dysregulated signaling that results from germline or embryonic mutations can promote changes in chromatin organization and gene accessibility, and thus expression levels of essential genes in neurodevelopment. In cancer, dysregulated signaling can emerge from sporadic somatic mutations during human life. Neurodevelopmental disorders and cancer share similarities. In neurodevelopmental disorders, immunity, and cancer, there appears an almost invariable involvement of small GTPases (e.g., Ras, RhoA, and Rac) and their pathways. TLRs, IL-1, GIT1, and FGFR signaling pathways, all can be dysregulated in neurodevelopmental disorders and cancer. Although there are signaling similarities, decisive differentiating factors are timing windows, and cell type specific perturbation levels, pointing to chromatin reorganization. Finally, we discuss drug discovery.

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.

Allostery, and how to define and measure signal transduction

Here we ask: What is productive signaling? How to define it, how to measure it, and most of all, what are the parameters that determine it? Further, what determines the strength of signaling from an upstream to a downstream node in a specific cell? These questions have either not been considered or not entirely resolved. The requirements for the signal to propagate downstream to activate (repress) transcription have not been considered either. Yet, the questions are pivotal to clarify, especially in diseases such as cancer where determination of signal propagation can point to cell proliferation and to emerging drug resistance, and to neurodevelopmental disorders, such as RASopathy, autism, attention-deficit/hyperactivity disorder (ADHD), and cerebral palsy. Here we propose a framework for signal transduction from an upstream to a downstream node addressing these questions. Defining cellular processes, experimentally measuring them, and devising powerful computational AI-powered algorithms that exploit the measurements, are essential for quantitative science.

In-Cell Structural Biology by NMR: The Benefits of the Atomic Scale

In-cell structural biology aims at extracting structural information about proteins or nucleic acids in their native, cellular environment. This emerging field holds great promise and is already providing new facts and outlooks of interest at both fundamental and applied levels. NMR spectroscopy has important contributions on this stage: It brings information on a broad variety of nuclei at the atomic scale, which ensures its great versatility and uniqueness. Here, we detail the methods, the fundamental knowledge, and the applications in biomedical engineering related to in-cell structural biology by NMR. We finally propose a brief overview of the main other techniques in the field (EPR, smFRET, cryo-ET, etc.) to draw some advisable developments for in-cell NMR. In the era of large-scale screenings and deep learning, both accurate and qualitative experimental evidence are as essential as ever to understand the interior life of cells. In-cell structural biology by NMR spectroscopy can generate such a knowledge, and it does so at the atomic scale. This review is meant to deliver comprehensive but accessible information, with advanced technical details and reflections on the methods, the nature of the results, and the future of the field.

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.

How can same-gene mutations promote both cancer and developmental disorders?

The question of how same-gene mutations can drive both cancer and neurodevelopmental disorders has been puzzling. It has also been puzzling why those with neurodevelopmental disorders have a high risk of cancer. Ras, MEK, PI3K, PTEN, and SHP2 are among the oncogenic proteins that can harbor mutations that encode diseases other than cancer. Understanding why some of their mutations can promote cancer, whereas others promote neurodevelopmental diseases, and why even the same mutations may promote both phenotypes, has important clinical ramifications. Here, we review the literature and address these tantalizing questions. We propose that cell type–specific expression of the mutant protein, and of other proteins in the respective pathway, timing of activation (during embryonic development or sporadic emergence), and the absolute number of molecules that the mutations activate, alone or in combination, are pivotal in determining the pathological phenotypes—cancer and (or) developmental disorders.

Smart Health

In recent decades, healthcare organizations around the world have increasingly appreciated the value of information technologies for a variety of applications. Three of the new technological advancements that are impacting smart health are metaverse, artificial intelligence (AI), and data science. The metaverse is the intersection of three major technologies — AI, augmented reality (AR), and virtual reality (VR). Metaverse provides new possibilities and potential that are still emerging. The increased work efficiency enabled by artificial intelligence and data science in hospitals not only improves patient care but also cuts costs and workload for healthcare providers. Artificial intelligence, coupled with machine learning, is transforming the healthcare industry. The availability of big data enables data scientists to use the data for descriptive, predictive, and prescriptive analytics. This article reviews multiple case studies and the literature on AI and data science applications in hospital administration. The article also presents unresolved research questions and challenges in the applications of the metaverse, AI, and data science in the smart health context. For researchers, in addition to providing a good synopsis of the development and applications of the metaverse, AI, and data science in the healthcare area, this article identifies possible future research directions and discusses the possibilities of the metaverse, artificial intelligence, and data science in smart health. For practitioners, this article provides both hospital decision-makers and healthcare workers with practical guidelines and a smart health management model.

Anticancer drug resistance: An update and perspective

Driver mutations promote initiation and progression of cancer. Pharmacological treatment can inhibit the action of the mutant protein; however, drug resistance almost invariably emerges. Multiple studies revealed that cancer drug resistance is based upon a plethora of distinct mechanisms. Drug resistance mutations can occur in the same protein or in different proteins; as well as in the same pathway or in parallel pathways, bypassing the intercepted signaling. The dilemma that the clinical oncologist is facing is that not all the genomic alterations as well as alterations in the tumor microenvironment that facilitate cancer cell proliferation are known, and neither are the alterations that are likely to promote metastasis. For example, the common KRasG12C driver mutation emerges in different cancers. Most occur in NSCLC, but some occur, albeit to a lower extent, in colorectal cancer and pancreatic ductal carcinoma. The responses to KRasG12C inhibitors are variable and fall into three categories, (i) new point mutations in KRas, or multiple copies of KRAS G12C which lead to higher expression level of the mutant protein; (ii) mutations in genes other than KRAS; (iii) original cancer transitioning to other cancer(s). Resistance to adagrasib, an experimental antitumor agent exerting its cytotoxic effect as a covalent inhibitor of the G12C KRas, indicated that half of the cases present multiple KRas mutations as well as allele amplification. Redundant or parallel pathways included MET amplification; emerging driver mutations in NRAS, BRAF, MAP2K1, and RET; gene fusion events in ALK, RET, BRAF, RAF1, and FGFR3; and loss-of-function mutations in NF1 and PTEN tumor suppressors. In the current review we discuss the molecular mechanisms underlying drug resistance while focusing on those emerging to common targeted cancer drivers. We also address questions of why cancers with a common driver mutation are unlikely to evolve a common drug resistance mechanism, and whether one can predict the likely mechanisms that the tumor cell may develop. These vastly important and tantalizing questions in drug discovery, and broadly in precision medicine, are the focus of our present review. We end with our perspective, which calls for target combinations to be selected and prioritized with the help of the emerging massive compute power which enables artificial intelligence, and the increased gathering of data to overcome its insatiable needs.

Ras isoform-specific expression, chromatin accessibility, and signaling

The anchorage of Ras isoforms in the membrane and their nanocluster formations have been studied extensively, including their detailed interactions, sizes, preferred membrane environments, chemistry, and geometry. However, the staggering challenge of their epigenetics and chromatin accessibility in distinct cell states and types, which we propose is a major factor determining their specific expression, still awaits unraveling. Ras isoforms are distinguished by their C-terminal hypervariable region (HVR) which acts in intracellular transport, regulation, and membrane anchorage. Here, we review some isoform-specific activities at the plasma membrane from a structural dynamic standpoint. Inspired by physics and chemistry, we recognize that understanding functional specificity requires insight into how biomolecules can organize themselves in different cellular environments. Within this framework, we suggest that isoform-specific expression may largely be controlled by the chromatin density and physical compaction, which allow (or curb) access to "chromatinized DNA." Genes are preferentially expressed in tissues: proteins expressed in pancreatic cells may not be equally expressed in lung cells. It is the rule-not an exception, and it can be at least partly understood in terms of chromatin organization and accessibility state. Genes are expressed when they can be sufficiently exposed to the transcription machinery, and they are less so when they are persistently buried in dense chromatin. Notably, chromatin accessibility can similarly determine expression of drug resistance genes.

FRL: An Integrative Feature Selection Algorithm Based on the Fisher Score, Recursive Feature Elimination, and Logistic Regression to Identify Potential Genomic Biomarkers

Accurate screening on cancer biomarkers contributes to health assessment, drug screening, and targeted therapy for precision medicine. The rapid development of high-throughput sequencing technology has identified abundant genomic biomarkers, but most of them are limited to single-cancer analysis. Based on the combination of Fisher score, Recursive feature elimination, and Logistic regression (FRL), this paper proposes an integrative feature selection algorithm named FRL to explore potential cancer genomic biomarkers on cancer subsets. Fisher score is initially used to calculate the weights of genes to rapidly reduce the dimension. Recursive feature elimination and Logistic regression are then jointly employed to extract the optimal subset. Compared to the current differential expression analysis tool GEO2R based on the Limma algorithm, FRL has greater classification precision than Limma. Compared with five traditional feature selection algorithms, FRL exhibits excellent performance on accuracy (ACC) and F1-score and greatly improves computational efficiency. On high-noise datasets such as esophageal cancer, the ACC of FRL is 30% superior to the average ACC achieved with other traditional algorithms. As biomarkers found in multiple studies are more reliable and reproducible, and reveal stronger association on potential clinical value than single analysis, through literature review and spatial analyses of gene functional enrichment and functional pathways, we conduct cluster analysis on 10 diverse cancers with high mortality and form a potential biomarker module comprising 19 genes. All genes in this module can serve as potential biomarkers to provide more information on the overall oncogenesis mechanism for the detection of diverse early cancers and assist in targeted anticancer therapies for further developments in precision medicine.

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.