Neurodevelopmental disorders and cancer networks share pathways, but differ in mechanisms, signaling strength, and outcome.

Epidemiological studies suggest that individuals with neurodevelopmental disorders (NDDs) are more prone to develop certain types of cancer. Notably, however, the case statistics can be impacted by late discovery of cancer in individuals afflicted with NDDs, such as intellectual disorders, autism, and schizophrenia, which may bias the numbers. As to NDD-associated mutations, in most cases, they are germline while cancer mutations are sporadic, emerging during life. However, somatic mosaicism can spur NDDs, and cancer-related mutations can be germline. NDDs and cancer share proteins, pathways, and mutations. Here we ask (i) exactly which features they share, and (ii) how, despite their commonalities, they differ in clinical outcomes. To tackle these questions, we employed a statistical framework followed by network analysis. Our thorough exploration of the mutations, reconstructed disease-specific networks, pathways, and transcriptome levels and profiles of autism spectrum disorder (ASD) and cancers, point to signaling strength as the key factor: strong signaling promotes cell proliferation in cancer, and weaker (moderate) signaling impacts differentiation in ASD. Thus, we suggest that signaling strength, not activating mutations, can decide clinical outcome.

Dietary risk assessment of benzophenone derivatives using bread consumption estimates in a Taiwanese population.

Listed as endocrine-disrupting chemicals, benzophenone (BP) and its nine analogues (BPs) are an emerging group of contaminants. The migration of BPs from ultraviolet inks to food has been investigated in many studies; however, few studies have investigated BPs in foods and the risks of human exposure to BPs. We validated a trace and multi-residue method for simultaneously determining 10 BPs, including BP, BP-1, BP-2, BP-3, BP-8, 4-MBP, 2-OHBP, 4-OHBP, M2BB, and PBZ. Eighty-one bread samples were analyzed using stable isotope labeling and ultrahigh-performance liquid chromatography-electrospray ionization tandem mass spectrometry with solid-liquid extraction. We determined the estimated daily intake of BPs, non-cancer risks, and lifetime cancer risks (LTCRs) from daily bread consumption for seven age groups using a Monte Carlo simulation. The method demonstrated robust linearity (R2 ≥ 0.991), low limits of detection (0.04-2 ng/g), and satisfactory precision. The intra- and interday relative standard deviation ranges were 0.6%-9% and 3%-20%, respectively. BP, 4-MBP, 2-OHBP, BP-1, and BP-3 were detected in 97%, 67%, 59%, 24%, and 23% of the samples, respectively. 2-OHBP had the highest mean (range) value of 18.3 (

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.

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.

HMI-PRED 2.0: a biologist-oriented web application for prediction of host-microbe protein-protein interaction by interface mimicry.

SUMMARY: HMI-PRED 2.0 is a publicly available web service for the prediction of host-microbe protein-protein interaction by interface mimicry that is intended to be used without extensive computational experience. A microbial protein structure is screened against a database covering the entire available structural space of complexes of known human proteins. AVAILABILITY AND IMPLEMENTATION: HMI-PRED 2.0 provides user-friendly graphic interfaces for predicting, visualizing and analyzing host-microbe interactions. HMI-PRED 2.0 is available at https://hmipred.org/.

Allosteric regulation of autoinhibition and activation of c-Abl.

c-Abl, a non-receptor tyrosine kinase, regulates cell growth and survival in healthy cells and causes chronic myeloid leukemia (CML) when fused by Bcr. Its activity is blocked in the assembled inactive state, where the SH3 and SH2 domains dock into the kinase domain, reducing its conformational flexibility, resulting in the autoinhibited state. It is active in an extended 'open' conformation. Allostery governs the transitions between the autoinhibited and active states. Even though experiments revealed the structural hallmarks of the two states, a detailed grasp of the determinants of c-Abl autoinhibition and activation at the atomic level, which may help innovative drug discovery, is still lacking. Here, using extensive molecular dynamics simulations, we decipher exactly how these determinants regulate it. Our simulations confirm and extend experimental data that the myristoyl group serves as the switch for c-Abl inhibition/activation. Its dissociation from the kinase domain promotes the SH2-SH3 release, initiating c-Abl activation. We show that the precise SH2/N-lobe interaction is required for full activation of c-Abl. It stabilizes a catalysis-favored conformation, priming it for catalytic action. Bcr-Abl allosteric drugs elegantly mimic the endogenous myristoyl-mediated autoinhibition state of c-Abl 1b. Allosteric activating mutations shift the ensemble to the active state, blocking ATP-competitive drugs. Allosteric drugs alter the active-site conformation, shifting the ensemble to re-favor ATP-competitive drugs. Our work provides a complete mechanism of c-Abl activation and insights into critical parameters controlling at the atomic level c-Abl inactivation, leading us to propose possible strategies to counter reemergence of drug resistance.

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.

The structural basis of BCR-ABL recruitment of GRB2 in chronic myelogenous leukemia.

BCR-ABL drives chronic myeloid leukemia (CML). BCR binding to GRB2 transduces signaling via the Ras/MAPK pathway. Despite considerable data confirming the binding, molecular-level understanding of exactly how the two proteins interact, and, especially, what are the determinants of the specificity of the SH2GRB2 domain-phosphorylated BCR (pBCR) recognition are still open questions. Yet, this is vastly important for understanding binding selectivity, and for predicting the phosphorylated receptors, or peptides, that are likely to bind. Here, we uncover these determinants and ascertain to what extent they relate to the affinity of the interaction. Toward this end, we modeled the complexes of the pBCR and SH2GRB2 and other pY/Y-peptide-SH2 complexes and compared their specificity and affinity. We observed that pBCR's 176FpYVNV180 motif is favorable and specific to SH2GRB2, similar to pEGFR, but not other complexes. SH2GRB2 contains two binding pockets: pY-binding recognition pocket triggers binding, and the specificity pocket whose interaction is governed by N179 in pBCR and W121 in SH2GRB2. Our proposed motif with optimal affinity to SH2GRB2 is E/D-pY-E/V-N-I/L. Collectively, we provide the structural basis of BCR-ABL recruitment of GRB2, outline its specificity hallmarks, and delineate a blueprint for prediction of BCR-binding scaffolds and for therapeutic peptide design.

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.

Allostery: Allosteric Cancer Drivers and Innovative Allosteric Drugs.

Here, we discuss the principles of allosteric activating mutations, propagation downstream of the signals that they prompt, and allosteric drugs, with examples from the Ras signaling network. We focus on Abl kinase where mutations shift the landscape toward the active, imatinib binding-incompetent conformation, likely resulting in the high affinity ATP outcompeting drug binding. Recent pharmacological innovation extends to allosteric inhibitor (GNF-5)-linked PROTAC, targeting Bcr-Abl1 myristoylation site, and broadly, allosteric heterobifunctional degraders that destroy targets, rather than inhibiting them. Designed chemical linkers in bifunctional degraders can connect the allosteric ligand that binds the target protein and the E3 ubiquitin ligase warhead anchor. The physical properties and favored conformational state of the engineered linker can precisely coordinate the distance and orientation between the target and the recruited E3. Allosteric PROTACs, noncompetitive molecular glues, and bitopic ligands, with covalent links of allosteric ligands and orthosteric warheads, increase the effective local concentration of productively oriented and placed ligands. Through covalent chemical or peptide linkers, allosteric drugs can collaborate with competitive drugs, degrader anchors, or other molecules of choice, driving innovative 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 personalizedpharmacology. Here we review protein and emerging genome high-resolution data, along with means, applications, and examples underscoring their usefulness in precision medicine.

Artificial intelligence approaches to human-microbiome protein-protein interactions.

Host-microbiome interactions play significant roles in human health and disease. Artificial intelligence approaches have been developed to better understand and predict the molecular interplay between the host and its microbiome. Here, we review recent advancements in computational methods to predict microbial effects on human cells with a special focus on protein-protein interactions. We categorize recent methods from traditional ones to more recent deep learning methods, followed by several challenges and potential solutions in structure-based approaches. This review serves as a brief guide to the current status and future directions in the field.

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.

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 type­specific 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 phenotypes­cancer and (or) developmental disorders.

SARS-CoV-2 Interactome 3D: A Web interface for 3D visualization and analysis of SARS-CoV-2-human mimicry and interactions.

SUMMARY: We present a web-based server for navigating and visualizing possible interactions between SARS-CoV-2 and human host proteins. The interactions are obtained from HMI_Pred which relies on the rationale that virus proteins mimic host proteins. The structural alignment of the viral protein with one side of the human protein-protein interface determines the mimicry. The mimicked human proteins and predicted interactions, and the binding sites are presented. The user can choose one of the 18 SARS-CoV-2 protein structures and visualize the potential 3D complexes it forms with human proteins. The mimicked interface is also provided. The user can superimpose two interacting human proteins in order to see whether they bind to the same site or different sites on the viral protein. The server also tabulates all available mimicked interactions together with their match scores and number of aligned residues. This is the first server listing and cataloging all interactions between SARS-CoV-2 and human protein structures, enabled by our innovative interface mimicry strategy. AVAILABILITY AND IMPLEMENTATION: The server is available at https://interactome.ku.edu.tr/sars/.

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.

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.

Signaling in the crowded cell.

High-resolution technologies have clarified some of the principles underlying cellular actions. However, understanding how cells receive, communicate, and respond to signals is still challenging. Questions include how efficient regulation of assemblies, which execute cell actions at the nanoscales, transmits productively at micrometer scales, especially considering the crowded environment, and how the cell organization makes it happen. Here, we describe how cells can navigate long-range diffusion-controlled signaling via association/dissociation of spatially proximal entities. Dynamic clusters can span the cell, engaging in most signaling steps. Effective local concentration, allostery, scaffolding, affinities, and the chemical and mechanical properties of the macromolecules and the environment play key roles. Signaling strength and duration matter, for example, deciding if a mutation promotes cancer or developmental syndromes.

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.

Phosphorylation and Driver Mutations in PI3Kα and PTEN Autoinhibition.

PI3K and PTEN are the second and third most highly mutated proteins in cancer following only p53. Their actions oppose each other. PI3K phosphorylates signaling lipid PIP2 to PIP3 PTEN dephosphorylates it back. Driver mutations in both proteins accrue PIP3 PIP3 recruits AKT and PDK1 to the membrane, promoting cell-cycle progression. Here we review phosphorylation events and mutations in autoinhibition in PI3K and PTEN from the structural standpoint. Our purpose is to clarify how they control the autoinhibited states. In autoinhibition, a segment or a subunit of the protein occludes its functional site. Protein-protein interfaces are often only marginally stable, making them sensitive to changes in conditions in living cells. Phosphorylation can stabilize or destabilize the interfaces. Driver mutations commonly destabilize them. In analogy to "passenger mutations," we coin "passenger phosphorylation" to emphasize that the presence of a phosphorylation recognition sequence logo does not necessarily imply function. Rather, it may simply reflect a statistical occurrence. In both PI3K and PTEN, autoinhibiting phosphorylation events are observed in the occluding "piece." In PI3Kα, the "piece" is the p85α subunit. In PTEN, it is the C-terminal segment. In both enzymes the stabilized interface covers the domain that attaches to the membrane. Driver mutations that trigger rotation of the occluding piece or its deletion prompt activation. To date, both enzymes lack specific, potent drugs. We discuss the implications of detailed structural and mechanistic insight into oncogenic activation and how it can advance allosteric precision oncology.