Phenotypic Characterization of a Comprehensive Set of MAPK1/ERK2 Missense Mutants

DeMaSk: a deep mutational scanning substitution matrix and its use for variant impact prediction

Motivation

Accurately predicting the quantitative impact of a substitution on a protein’s molecular function would be a great aid in understanding the effects of observed genetic variants across populations. While this remains a challenging task, new approaches can leverage data from the increasing numbers of comprehensive deep mutational scanning (DMS) studies that systematically mutate proteins and measure fitness.

Results

We introduce DeMaSk, an intuitive and interpretable method based only upon DMS datasets and sequence homologs that predicts the impact of missense mutations within any protein. DeMaSk first infers a directional amino acid substitution matrix from DMS datasets and then fits a linear model that combines these substitution scores with measures of per-position evolutionary conservation and variant frequency across homologs. Despite its simplicity, DeMaSk has state-of-the-art performance in predicting the impact of amino acid substitutions, and can easily and rapidly be applied to any protein sequence.

Availability and implementation

https://demask.princeton.edu generates fitness impact predictions and visualizations for any user-submitted protein sequence.

Supplementary information

Supplementary data are available at Bioinformatics online.

Understanding oncogenicity of cancer driver genes and mutations in the cancer genomics era

One of the key challenges of cancer biology is to catalogue and understand the somatic genomic alterations leading to cancer. Although alternative definitions and search methods have been developed to identify cancer driver genes and mutations, analyses of thousands of cancer genomes return a remarkably similar catalogue of around 300 genes that are mutated in at least one cancer type. Yet, many features of these genes and their role in cancer remain unclear, first and foremost when a somatic mutation is truly oncogenic. In this review, we first summarize some of the recent efforts in completing the catalogue of cancer driver genes. Then, we give an overview of different aspects that influence the oncogenicity of somatic mutations in the core cancer driver genes, including their interactions with the germline genome, other cancer driver mutations, the immune system, or their potential role in healthy tissues. In the coming years, this research holds promise to illuminate how, when, and why cancer driver genes and mutations are really drivers, and thereby move personalized cancer medicine and targeted therapies forward.

Toward Developing Intuitive Rules for Protein Variant Effect Prediction Using Deep Mutational Scanning Data

Protein structure and function can be severely altered by even a single amino acid mutation. Predictions of mutational effects using extensive artificial intelligence (AI)-based models, although accurate, remain as enigmatic as the experimental observations in terms of improving intuitions about the contributions of various factors. Inspired by Lipinski's rules for drug-likeness, we devise simple thresholding criteria on five different descriptors such as conservation, which have so far been limited to qualitative interpretations such as high conservation implies high mutational effect. We analyze systematic deep mutational scanning data of all possible single amino acid substitutions on seven proteins (25153 mutations) to first define these thresholds and then to evaluate the scope and limits of the predictions. At this stage, the approach allows us to comment easily and with a low error rate on the subset of mutations classified as neutral or deleterious by all of the descriptors. We hope that complementary to the accurate AI predictions, these thresholding rules or their subsequent modifications will serve the purpose of codifying the knowledge about the effects of mutations.

Leveraging Systematic Functional Analysis to Benchmark an In Silico Framework Distinguishes Driver from Passenger MEK Mutants in Cancer

Despite significant advances in cancer precision medicine, a significant hurdle to its broader adoption remains the multitude of variants of unknown significance identified by clinical tumor sequencing and the lack of biologically validated methods to distinguish between functional and benign variants. Here we used functional data on MAP2K1 and MAP2K2 mutations generated in real-time within a co-clinical trial framework to benchmark the predictive value of a three-part in silico methodology. Our computational approach to variant classification incorporated hotspot analysis, three-dimensional molecular dynamics simulation, and sequence paralogy. In silico prediction accurately distinguished functional from benign MAP2K1 and MAP2K2 mutants, yet drug sensitivity varied widely among activating mutant alleles. These results suggest that multifaceted in silico modeling can inform patient accrual to MEK/ERK inhibitor clinical trials, but computational methods need to be paired with laboratory- and clinic-based efforts designed to unravel variabilities in drug response. SIGNIFICANCE: Leveraging prospective functional characterization of MEK1/2 mutants, it was found that hotspot analysis, molecular dynamics simulation, and sequence paralogy are complementary tools that can robustly prioritize variants for biologic, therapeutic, and clinical validation.See related commentary by Whitehead and Sebolt-Leopold, p. 4042.

Enhanced MAPK1 Function Causes a Neurodevelopmental Disorder within the RASopathy Clinical Spectrum

Signal transduction through the RAF-MEK-ERK pathway, the first described mitogen-associated protein kinase (MAPK) cascade, mediates multiple cellular processes and participates in early and late developmental programs. Aberrant signaling through this cascade contributes to oncogenesis and underlies the RASopathies, a family of cancer-prone disorders. Here, we report that de novo missense variants in MAPK1, encoding the mitogen-activated protein kinase 1 (i.e., extracellular signal-regulated protein kinase 2, ERK2), cause a neurodevelopmental disease within the RASopathy phenotypic spectrum, reminiscent of Noonan syndrome in some subjects. Pathogenic variants promote increased phosphorylation of the kinase, which enhances translocation to the nucleus and boosts MAPK signaling in vitro and in vivo. Two variant classes are identified, one of which directly disrupts binding to MKP3, a dual-specificity protein phosphatase negatively regulating ERK function. Importantly, signal dysregulation driven by pathogenic MAPK1 variants is stimulus reliant and retains dependence on MEK activity. Our data support a model in which the identified pathogenic variants operate with counteracting effects on MAPK1 function by differentially impacting the ability of the kinase to interact with regulators and substrates, which likely explains the minor role of these variants as driver events contributing to oncogenesis. After nearly 20 years from the discovery of the first gene implicated in Noonan syndrome, PTPN11, the last tier of the MAPK cascade joins the group of genes mutated in RASopathies.

Discovery through clinical sequencing in oncology

The molecular characterization of tumors now informs clinical cancer care for many patients. This advent of molecular oncology is driven by the expanding number of therapeutic biomarkers that can predict sensitivity to both approved and investigational agents. Beyond its role in driving clinical trial enrollments and guiding therapy in individual patients, large-scale clinical genomics in oncology also represents a rapidly expanding research resource for translational scientific discovery. Here, we review the progress, opportunities, and challenges of scientific and translational discovery from prospective clinical genomic screening programs now routinely conducted in cancer patients.

A dominant-negative effect drives selection of TP53 missense mutations in myeloid malignancies

TP53, which encodes the tumor suppressor p53, is the most frequently mutated gene in human cancer. The selective pressures shaping its mutational spectrum, dominated by missense mutations, are enigmatic, and neomorphic gain-of-function (GOF) activities have been implicated. We used CRISPR-Cas9 to generate isogenic human leukemia cell lines of the most common TP53 missense mutations. Functional, DNA-binding, and transcriptional analyses revealed loss of function but no GOF effects. Comprehensive mutational scanning of p53 single-amino acid variants demonstrated that missense variants in the DNA-binding domain exert a dominant-negative effect (DNE). In mice, the DNE of p53 missense variants confers a selective advantage to hematopoietic cells on DNA damage. Analysis of clinical outcomes in patients with acute myeloid leukemia showed no evidence of GOF for TP53 missense mutations. Thus, a DNE is the primary unit of selection for TP53 missense mutations in myeloid malignancies.

Variant effect predictions capture some aspects of deep mutational scanning experiments

BACKGROUND

Deep mutational scanning (DMS) studies exploit the mutational landscape of sequence variation by systematically and comprehensively assaying the effect of single amino acid variants (SAVs; also referred to as missense mutations, or non-synonymous Single Nucleotide Variants - missense SNVs or nsSNVs) for particular proteins. We assembled SAV annotations from 22 different DMS experiments and normalized the effect scores to evaluate variant effect prediction methods. Three trained on traditional variant effect data (PolyPhen-2, SIFT, SNAP2), a regression method optimized on DMS data (Envision), and a naïve prediction using conservation information from homologs.

RESULTS

On a set of 32,981 SAVs, all methods captured some aspects of the experimental effect scores, albeit not the same. Traditional methods such as SNAP2 correlated slightly more with measurements and better classified binary states (effect or neutral). Envision appeared to better estimate the precise degree of effect. Most surprising was that the simple naïve conservation approach using PSI-BLAST in many cases outperformed other methods. All methods captured beneficial effects (gain-of-function) significantly worse than deleterious (loss-of-function). For the few proteins with multiple independent experimental measurements, experiments differed substantially, but agreed more with each other than with predictions.

CONCLUSIONS

DMS provides a new powerful experimental means of understanding the dynamics of the protein sequence space. As always, promising new beginnings have to overcome challenges. While our results demonstrated that DMS will be crucial to improve variant effect prediction methods, data diversity hindered simplification and generalization.

Emerging roles of the αC-β4 loop in protein kinase structure, function, evolution, and disease

The faithful propagation of cellular signals in most organisms relies on the coordinated functions of a large family of protein kinases that share a conserved catalytic domain. The catalytic domain is a dynamic scaffold that undergoes large conformational changes upon activation. Most of these conformational changes, such as movement of the regulatory αC-helix from an "out" to "in" conformation, hinge on a conserved, but understudied, loop termed the αC-β4 loop, which mediates conserved interactions to tether flexible structural elements to the kinase core. We previously showed that the αC-β4 loop is a unique feature of eukaryotic protein kinases. Here, we review the emerging roles of this loop in kinase structure, function, regulation, and diseases. Through a kinome-wide analysis, we define the boundaries of the loop for the first time and show that sequence and structural variation in the loop correlate with conformational and regulatory variation. Many recurrent disease mutations map to the αC-β4 loop and contribute to drug resistance and abnormal kinase activation by relieving key auto-inhibitory interactions associated with αC-helix and inter-lobe movement. The αC-β4 loop is a hotspot for post-translational modifications, protein-protein interaction, and Hsp90 mediated folding. Our kinome-wide analysis provides insights for hypothesis-driven characterization of understudied kinases and the development of allosteric protein kinase inhibitors.

Targeting Aberrant RAS/RAF/MEK/ERK Signaling for Cancer Therapy

The RAS/RAF/MEK/ERK (MAPK) signaling cascade is essential for cell inter- and intra-cellular communication, which regulates fundamental cell functions such as growth, survival, and differentiation. The MAPK pathway also integrates signals from complex intracellular networks in performing cellular functions. Despite the initial discovery of the core elements of the MAPK pathways nearly four decades ago, additional findings continue to make a thorough understanding of the molecular mechanisms involved in the regulation of this pathway challenging. Considerable effort has been focused on the regulation of RAF, especially after the discovery of drug resistance and paradoxical activation upon inhibitor binding to the kinase. RAF activity is regulated by phosphorylation and conformation-dependent regulation, including auto-inhibition and dimerization. In this review, we summarize the recent major findings in the study of the RAS/RAF/MEK/ERK signaling cascade, particularly with respect to the impact on clinical cancer therapy.

Defining the landscape of ATP-competitive inhibitor resistance residues in protein kinases

Kinases are involved in disease development and modulation of their activity can be therapeutically beneficial. Drug-resistant mutant kinases are valuable tools in drug discovery efforts, but the prediction of mutants across the kinome is challenging. Here, we generate deep mutational scanning data to identify mutant mammalian kinases that drive resistance to clinically relevant inhibitors. We aggregate these data with subsaturation mutagenesis data and use it to develop, test and validate a framework to prospectively identify residues that mediate kinase activity and drug resistance across the kinome. We validate predicted resistance mutations in CDK4, CDK6, ERK2, EGFR and HER2. Capitalizing on a highly predictable residue, we generate resistance mutations in TBK1, CSNK2A1 and BRAF. Unexpectedly, we uncover a potentially generalizable activation site that mediates drug resistance and confirm its impact in BRAF, EGFR, HER2 and MEK1. We anticipate that the identification of these residues will enable the broad interrogation of the kinome and its inhibitors.

Deep2Full: Evaluating strategies for selecting the minimal mutational experiments for optimal computational predictions of deep mutational scan outcomes

Performing a complete deep mutational scan with all single point mutations may not be practical, and may not even be required, especially if predictive computational models can be developed. Computational models are however naive to cellular response in the myriads of assay-conditions. In a realistic paradigm of assay context-aware predictive hybrid models that combine minimal experimental data from deep mutational scans with structure, sequence information and computational models, we define and evaluate different strategies for choosing this minimal set. We evaluated the trivial strategy of a systematic reduction in the number of mutational studies from 85% to 15%, along with several others about the choice of the types of mutations such as random versus site-directed with the same 15% data completeness. Interestingly, the predictive capabilities by training on a random set of mutations and using a systematic substitution of all amino acids to alanine, asparagine and histidine (ANH) were comparable. Another strategy we explored, augmenting the training data with measurements of the same mutants at multiple assay conditions, did not improve the prediction quality. For the six proteins we analyzed, the bin-wise error in prediction is optimal when 50-100 mutations per bin are used in training the computational model, suggesting that good prediction quality may be achieved with a library of 500-1000 mutations.

Mutations That Confer Drug-Resistance, Oncogenicity and Intrinsic Activity on the ERK MAP Kinases-Current State of the Art

Unique characteristics distinguish extracellular signal-regulated kinases (Erks) from other eukaryotic protein kinases (ePKs). Unlike most ePKs, Erks do not autoactivate and they manifest no basal activity; they become catalysts only when dually phosphorylated on neighboring Thr and Tyr residues and they possess unique structural motifs. Erks function as the sole targets of the receptor tyrosine kinases (RTKs)-Ras-Raf-MEK signaling cascade, which controls numerous physiological processes and is mutated in most cancers. Erks are therefore the executers of the pathway’s biology and pathology. As oncogenic mutations have not been identified in Erks themselves, combined with the tight regulation of their activity, Erks have been considered immune against mutations that would render them intrinsically active. Nevertheless, several such mutations have been generated on the basis of structure-function analysis, understanding of ePK evolution and, mostly, via genetic screens in lower eukaryotes. One of the mutations conferred oncogenic properties on Erk1. The number of interesting mutations in Erks has dramatically increased following the development of Erk-specific pharmacological inhibitors and identification of mutations that cause resistance to these compounds. Several mutations have been recently identified in cancer patients. Here we summarize the mutations identified in Erks so far, describe their properties and discuss their possible mechanism of action.

An Activating Mutation in ERK Causes Hyperplastic Tumors in a scribble Mutant Tissue in Drosophila

Receptor tyrosine kinase signaling plays prominent roles in tumorigenesis, and activating oncogenic point mutations in the core pathway components Ras, Raf, or MEK are prevalent in many types of cancer. Intriguingly, however, analogous oncogenic mutations in the downstream effector kinase ERK have not been described or validated in vivo To determine if a point mutation could render ERK intrinsically active and oncogenic, we have assayed in Drosophila the effects of a mutation that confers constitutive activity upon a yeast ERK ortholog and has also been identified in a few human tumors. Our analyses indicate that a fly ERK ortholog harboring this mutation alone (RolledR80S), and more so in conjunction with the known sevenmaker mutation (RolledR80S+D334N), suppresses multiple phenotypes caused by loss of Ras-Raf-MEK pathway activity, consistent with an intrinsic activity that is independent of upstream signaling. Moreover, expression of RolledR80S and RolledR80S+D334N induces tissue overgrowth in an established Drosophila cancer model. Our findings thus demonstrate that activating mutations can bestow ERK with pro-proliferative, tumorigenic capabilities and suggest that Drosophila represents an effective experimental system for determining the oncogenicity of ERK mutants and their response to therapy.

PASTMUS: mapping functional elements at single amino acid resolution in human cells

Identification of functional elements for a protein of interest is important for achieving a mechanistic understanding. However, it remains cumbersome to assess each and every amino acid of a given protein in relevance to its functional significance. Here, we report a strategy, PArsing fragmented DNA Sequences from CRISPR Tiling MUtagenesis Screening (PASTMUS), which provides a streamlined workflow and a bioinformatics pipeline to identify critical amino acids of proteins in their native biological contexts. Using this approach, we map six proteins-three bacterial toxin receptors and three cancer drug targets, and acquire their corresponding functional maps at amino acid resolution.

MaveDB: an open-source platform to distribute and interpret data from multiplexed assays of variant effect

Multiplex assays of variant effect (MAVEs), such as deep mutational scans and massively parallel reporter assays, test thousands of sequence variants in a single experiment. Despite the importance of MAVE data for basic and clinical research, there is no standard resource for their discovery and distribution. Here, we present MaveDB ( https://www.mavedb.org ), a public repository for large-scale measurements of sequence variant impact, designed for interoperability with applications to interpret these datasets. We also describe the first such application, MaveVis, which retrieves, visualizes, and contextualizes variant effect maps. Together, the database and applications will empower the community to mine these powerful datasets.

Target discovery using biobanks and human genetics

Large-scale biobanks can yield unprecedented insights into our health and provide discoveries of new and potentially targetable biomarkers. Several protective loss-of-function alleles have been identified, including variants that protect against cardiovascular disease, obesity, type 2 diabetes, and asthma and allergic diseases. These alleles serve as indicators of efficacy, mimicking the effects of drugs and suggesting that inhibiting these genes could provide therapeutic benefit, as has been observed for PCSK9. We provide a context for these findings through a multifaceted review covering the use of genetics in drug discovery efforts through genome-wide and phenome-wide association studies, linking deep mutation scanning data to molecular function and highlighting some additional tools that might help in the interpretation of newly discovered variants.

Characterizing variants of unknown significance in rhodopsin: A functional genomics approach

Characterizing the pathogenicity of DNA sequence variants of unknown significance (VUS) is a major bottleneck in human genetics, and is increasingly important in determining which patients with inherited retinal diseases could benefit from gene therapy. A library of 210 rhodopsin (RHO) variants from literature and in‐house genetic diagnostic testing were created to efficiently detect pathogenic RHO variants that fail to express on the cell surface. This study, while focused on RHO, demonstrates a streamlined, generalizable method for detecting pathogenic VUS. A relatively simple next‐generation sequencing‐based readout was developed so that a flow cytometry‐based assay could be performed simultaneously on all variants in a pooled format, without the need for barcodes or viral transduction. The resulting dataset characterized the surface expression of every RHO library variant with a high degree of reproducibility (r² = 0.92–0.95), recategorizing 37 variants. For example, three retinitis pigmentosa pedigrees were solved by identifying VUS which showed low expression levels (p.G18D, p.G101V, and p.P180T). Results were validated across multiple assays and correlated with clinical disease severity. This study presents a parallelized, higher‐throughput cell‐based assay for the functional characterization of VUS in RHO, and can be applied more broadly to other inherited retinal disease genes and other disorders.

A Novel Class of Common Docking Domain Inhibitors That Prevent ERK2 Activation and Substrate Phosphorylation

Extracellular signal-regulated kinases (ERK1/2) are mitogen-activated protein kinases (MAPKs) that play a pro-tumorigenic role in numerous cancers. ERK1/2 possess two protein-docking sites that are distinct from the active site: the D-recruitment site (DRS) and the F-recruitment site. These docking sites facilitate substrate recognition, intracellular localization, signaling specificity, and protein complex assembly. Targeting these sites on ERK in a therapeutic context may overcome many problems associated with traditional ATP-competitive inhibitors. Here, we identified a new class of inhibitors that target the ERK DRS by screening a synthetic combinatorial library of more than 30 million compounds. The screen detects the competitive displacement of a fluorescent peptide from the DRS of ERK2. The top molecular scaffold from the screen was optimized for structure-activity relationship by positional scanning of different functional groups. This resulted in 10 compounds with similar binding affinities and a shared core structure consisting of a tertiary amine hub with three functionalized cyclic guanidino branches. Compound 2507-1 inhibited ERK2 from phosphorylating a DRS-targeting substrate and prevented the phosphorylation of ERK2 by a constitutively active MEK1 (MAPK/ERK kinase 1) mutant. Interaction between an analogue, 2507-8, and the ERK2 DRS was confirmed by nuclear magnetic resonance and X-ray crystallography. 2507-8 forms critical interactions at the common docking domain residue Asp319 via an arginine-like moiety that is shared by all 10 hits, suggesting a common binding mode. The structural and biochemical insights reported here provide the basis for developing new ERK inhibitors that are not ATP-competitive but instead function by disrupting critical protein-protein interactions.

Cancer Treatment in the Genomic Era

The complexity of human cancer underlies its devastating clinical consequences. Drugs designed to target the genetic alterations that drive cancer have improved the outcome for many patients, but not the majority of them. Here, we review the genomic landscape of cancer, how genomic data can provide much more than a sum of its parts, and the approaches developed to identify and validate genomic alterations with potential therapeutic value. We highlight notable successes and pitfalls in predicting the value of potential therapeutic targets and discuss the use of multi-omic data to better understand cancer dependencies and drug sensitivity. We discuss how integrated approaches to collecting, curating, and sharing these large data sets might improve the identification and prioritization of cancer vulnerabilities as well as patient stratification within clinical trials. Finally, we outline how future approaches might improve the efficiency and speed of translating genomic data into clinically effective therapies and how the use of unbiased genome-wide information can identify novel predictive biomarkers that can be either simple or complex.

A driving test for oncogenic mutations

Activating mutations in protein kinases are a frequent cause of cancer, and selecting drugs that act on these oncogenic kinases can lead to effective therapies. Targeted or whole-genome sequencing of tumor samples can readily reveal the presence of mutations, but discerning previously uncharacterized activating "driver" mutations that will respond to drug treatment from much more abundant but inconsequential "passenger" mutations is problematic. Chakroborty et al. apply a screening approach that leverages error-prone PCR and a proliferating cell model to identify such gain-of-function mutants in the epidermal growth factor receptor (EGFR) kinase. The screen is validated by the identification of known cancer-promoting mutations and reveals a previously unappreciated oncogenic EGFR mutation, A702V, demonstrating its power for discovery of driver mutations.

Targeting ERK beyond the boundaries of the kinase active site in melanoma

Extracellular signal-regulated kinase 1/2 (ERK1/2) constitute a point of convergence for complex signaling events that regulate essential cellular processes, including proliferation and survival. As such, dysregulation of the ERK signaling pathway is prevalent in many cancers. In the case of BRAF-V600E mutant melanoma, ERK inhibition has emerged as a viable clinical approach to abrogate signaling through the ERK pathway, even in cases where MEK and Raf inhibitor treatments fail to induce tumor regression due to resistance mechanisms. Several ERK inhibitors that target the active site of ERK have reached clinical trials, however, many critical ERK interactions occur at other potentially druggable sites on the protein. Here we discuss the role of ERK signaling in cell fate, in driving melanoma, and in resistance mechanisms to current BRAF-V600E melanoma treatments. We explore targeting ERK via a distinct site of protein-protein interaction, known as the D-recruitment site (DRS), as an alternative or supplementary mode of ERK pathway inhibition in BRAF-V600E melanoma. Targeting the DRS with inhibitors in melanoma has the potential to not only disrupt the catalytic apparatus of ERK but also its noncatalytic functions, which have significant impacts on spatiotemporal signaling dynamics and cell fate.

Biological Rationale for Targeting MEK/ERK Pathways in Anti-Cancer Therapy and to Potentiate Tumour Responses to Radiation

ERK1 and ERK2 (ERKs), two extracellular regulated kinases (ERK1/2), are evolutionary-conserved and ubiquitous serine-threonine kinases involved in regulating cell signalling in normal and pathological tissues. The expression levels of these kinases are almost always different, with ERK2 being the more prominent. ERK1/2 activation is fundamental for the development and progression of cancer. Since their discovery, much research has been dedicated to their role in mitogen-activated protein kinases (MAPK) pathway signalling and in their activation by mitogens and mutated RAF or RAS in cancer cells. In order to gain a better understanding of the role of ERK1/2 in MAPK pathway signalling, many studies have been aimed at characterizing ERK1/2 splicing isoforms, mutants, substrates and partners. In this review, we highlight the differences between ERK1 and ERK2 without completely discarding the hypothesis that ERK1 and ERK2 exhibit functional redundancy. The main goal of this review is to shed light on the role of ERK1/2 in targeted therapy and radiotherapy and highlight the importance of identifying ERK inhibitors that may overcome acquired resistance. This is a highly relevant therapeutic issue that needs to be addressed to combat tumours that rely on constitutively active RAF and RAS mutants and the MAPK pathway.

Therapeutic strategies to target RAS-mutant cancers

RAS genes are the most commonly mutated oncogenes in cancer, but effective therapeutic strategies to target RAS-mutant cancers have proved elusive. A key aspect of this challenge is the fact that direct inhibition of RAS proteins has proved difficult, leading researchers to test numerous alternative strategies aimed at exploiting RAS-related vulnerabilities or targeting RAS effectors. In the past few years, we have witnessed renewed efforts to target RAS directly, with several promising strategies being tested in clinical trials at different stages of completion. Important advances have also been made in approaches designed to indirectly target RAS by improving inhibition of RAS effectors, exploiting synthetic lethal interactions or metabolic dependencies, using therapeutic combination strategies or harnessing the immune system. In this Review, we describe historical and ongoing efforts to target RAS-mutant cancers and outline the current therapeutic landscape in the collective quest to overcome the effects of this crucial oncogene.

Functional assays for transcription mechanisms in high-throughput

Dramatic increases in the scale of programmed synthesis of nucleic acid libraries coupled with deep sequencing have powered advances in understanding nucleic acid and protein biology. Biological systems centering on nucleic acids or encoded proteins greatly benefit from such high-throughput studies, given that large DNA variant pools can be synthesized and DNA, or RNA products of transcription, can be easily analyzed by deep sequencing. Here we review the scope of various high-throughput functional assays for studies of nucleic acids and proteins in general, followed by discussion of how these types of study have yielded insights into the RNA Polymerase II (Pol II) active site as an example. We discuss methodological considerations in the design and execution of these experiments that should be valuable to studies in any system.

Functional characterization of 3D protein structures informed by human genetic diversity

Sequence variation data of the human proteome can be used to analyze 3D protein structures to derive functional insights. We used genetic variant data from nearly 140,000 individuals to analyze 3D positional conservation in 4,715 proteins and 3,951 homology models using 860,292 missense and 465,886 synonymous variants. Sixty percent of protein structures harbor at least one intolerant 3D site as defined by significant depletion of observed over expected missense variation. Structural intolerance data correlated with deep mutational scanning functional readouts for PPARG, MAPK1/ERK2, UBE2I, SUMO1, PTEN, CALM1, CALM2, and TPK1 and with shallow mutagenesis data for 1,026 proteins. The 3D structural intolerance analysis revealed different features for ligand binding pockets and orthosteric and allosteric sites. Large-scale data on human genetic variation support a definition of functional 3D sites proteome-wide.

Exploiting rare driver mutations for precision cancer medicine

Catalyzed by the ability to develop precision therapies targeting the unique genetic changes that drive individual tumors, sequencing patients' tumor genomes is an increasingly common practice in oncology. In most cancer types, however, a limited number of common mutations are accompanied by a plethora of low-frequency variants whose functional consequences and clinical actionability are often unknown. We here illustrate that this 'long tail' of infrequent molecular alterations includes oncogenic drivers of biological significance that can be the genetic basis of extraordinary responses to systemic cancer therapies. Furthermore, we review current strategies to identify, prioritize, and experimentally validate novel long-tail driver mutations, efforts that will likely provide new insights into the clinically actionable genome and improve outcomes for patients.

A Systematic p53 Mutation Library Links Differential Functional Impact to Cancer Mutation Pattern and Evolutionary Conservation

The TP53 gene is frequently mutated in human cancer. Research has focused predominantly on six major "hotspot" codons, which account for only ∼30% of cancer-associated p53 mutations. To comprehensively characterize the consequences of the p53 mutation spectrum, we created a synthetically designed library and measured the functional impact of ∼10,000 DNA-binding domain (DBD) p53 variants in human cells in culture and in vivo. Our results highlight the differential outcome of distinct p53 mutations in human patients and elucidate the selective pressure driving p53 conservation throughout evolution. Furthermore, while loss of anti-proliferative functionality largely correlates with the occurrence of cancer-associated p53 mutations, we observe that selective gain-of-function may further favor particular mutants in vivo. Finally, when combined with additional acquired p53 mutations, seemingly neutral TP53 SNPs may modulate phenotypic outcome and, presumably, tumor progression.

The Nuclear Translocation of Mitogen-Activated Protein Kinases: Molecular Mechanisms and Use as Novel Therapeutic Target

The mitogen-activated protein kinase (MAPK) cascades are central signaling pathways that play a central role in the regulation of most stimulated cellular processes including proliferation, differentiation, stress response and apoptosis. Currently 4 such cascades are known, each termed by its downstream MAPK components: the extracellular signal-regulated kinase 1/2 (ERK1/2), cJun-N-terminal kinase (JNK), p38 and ERK5. One of the hallmarks of these cascades is the stimulated nuclear translocation of their MAPK components using distinct mechanisms. ERK1/2 are shuttled into the nucleus by importin7, JNK and p38 by a dimer of importin3 with either importin9 or importin7, and ERK5 by importin-α/β. Dysregulation of these cascades often results in diseases, including cancer and inflammation, as well as developmental and neurological disorders. Much effort has been invested over the years in developing inhibitors to the MAPK cascades to combat these diseases. Although some inhibitors are already in clinical use or clinical trials, their effects are hampered by development of resistance or adverse side-effects. Recently, our group developed 2 myristoylated peptides: EPE peptide, which inhibits the interaction of ERK1/2 with importin7, and PERY peptide, which prevents JNK/p38 interaction with either importin7 or importin9. These peptides block the nuclear translocation of their corresponding kinases, resulting in prevention of several cancers, while the PERY peptide also inhibits inflammation-induced diseases. These peptides provide a proof of concept for the use of the nuclear translocation of MAPKs as therapeutic targets for cancer and/or inflammation.

Combining Mutational Signatures, Clonal Fitness, and Drug Affinity to Define Drug-Specific Resistance Mutations in Cancer

The emergence of mutations that confer resistance to molecularly targeted therapeutics is dependent upon the effect of each mutation on drug affinity for the target protein, the clonal fitness of cells harboring the mutation, and the probability that each variant can be generated by DNA codon base mutation. We present a computational workflow that combines these three factors to identify mutations likely to arise upon drug treatment in a particular tumor type. The Osprey-based workflow is validated using a comprehensive dataset of ERK2 mutations and is applied to small-molecule drugs and/or therapeutic antibodies targeting KIT, EGFR, Abl, and ALK. We identify major clinically observed drug-resistant mutations for drug-target pairs and highlight the potential to prospectively identify probable drug resistance mutations.

Engineering allosteric regulation in protein kinases

Phosphoregulation, in which the addition of a negatively charged phosphate group modulates protein activity, enables dynamic cellular responses. To understand how new phosphoregulation might be acquired, we mutationally scanned the surface of a prototypical yeast kinase (Kss1) to identify potential regulatory sites. The data revealed a set of spatially distributed "hotspots" that might have coevolved with the active site and preferentially modulated kinase activity. By engineering simple consensus phosphorylation sites at these hotspots, we rewired cell signaling in yeast. Using the same approach with a homolog yeast mitogen-activated protein kinase, Hog1, we introduced new phosphoregulation that modified its localization and signaling dynamics. Beyond revealing potential use in synthetic biology, our findings suggest that the identified hotspots contribute to the diversity of natural allosteric regulatory mechanisms in the eukaryotic kinome and, given that some are mutated in cancers, understanding these hotspots may have clinical relevance to human disease.

CRISPRO: identification of functional protein coding sequences based on genome editing dense mutagenesis

CRISPR/Cas9 pooled screening permits parallel evaluation of comprehensive guide RNA libraries to systematically perturb protein coding sequences in situ and correlate with functional readouts. For the analysis and visualization of the resulting datasets, we develop CRISPRO, a computational pipeline that maps functional scores associated with guide RNAs to genomes, transcripts, and protein coordinates and structures. No currently available tool has similar functionality. The ensuing genotype-phenotype linear and three-dimensional maps raise hypotheses about structure-function relationships at discrete protein regions. Machine learning based on CRISPRO features improves prediction of guide RNA efficacy. The CRISPRO tool is freely available at gitlab.com/bauerlab/crispro .

Deep generative models of genetic variation capture the effects of mutations

The functions of proteins and RNAs are defined by the collective interactions of many residues, and yet most statistical models of biological sequences consider sites nearly independently. Recent approaches have demonstrated benefits of including interactions to capture pairwise covariation, but leave higher-order dependencies out of reach. Here we show how it is possible to capture higher-order, context-dependent constraints in biological sequences via latent variable models with nonlinear dependencies. We found that DeepSequence ( https://github.com/debbiemarkslab/DeepSequence ), a probabilistic model for sequence families, predicted the effects of mutations across a variety of deep mutational scanning experiments substantially better than existing methods based on the same evolutionary data. The model, learned in an unsupervised manner solely on the basis of sequence information, is grounded with biologically motivated priors, reveals the latent organization of sequence families, and can be used to explore new parts of sequence space.

Mutational processes shape the landscape of TP53 mutations in human cancer

Unlike most tumor suppressor genes, the most common genetic alterations in TP53 are missense mutations^1,2. Mutant p53 protein is often abundantly expressed in cancers, and specific allelic variants exhibit dominant-negative or gain-of-function activities in experimental models^3–8. To gain a systematic view of p53 function, we interrogated loss-of-function screens conducted in hundreds of human cancer cell lines and performed TP53 saturation mutagenesis screens in an isogenic pair of TP53-wild-type and -null cell lines. We found that loss or dominant-negative inhibition of p53 function reliably enhanced cellular fitness. By integrating these data with the COSMIC mutational signatures database^9,10, we developed a statistical model that describes the TP53 mutational spectrum as a function of the baseline probability of acquiring each mutation and the fitness advantage conferred by attenuation of p53 activity. Collectively, these observations show that widely-acting and tissue-specific mutational processes combine with phenotypic selection to dictate the frequencies of recurrent TP53 mutations.

Intrinsically active MEK variants are differentially regulated by proteinases and phosphatases

MAPK/ERK kinase (MEK) 1/2 are central signaling proteins that serve as specificity determinants of the MAPK/ERK cascade. More than twenty activating mutations have been reported for MEK1/2, and many of them are known to cause diseases such as cancers, arteriovenous malformation and RASopathies. Changes in their intrinsic activity do not seem to correlate with the severity of the diseases. Here we studied four MEK1/2 mutations using biochemical and molecular dynamic methods. Although the studied mutants elevated the activating phosphorylation of MEK they had no effect on the stimulated ERK1/2 phosphorylation. Studying the regulatory mechanism that may explain this lack of effect, we found that one type of mutation affects MEK stability and two types of mutations demonstrate a reduced sensitivity to PP2A. Together, our results indicate that some MEK mutations exert their function not only by their elevated intrinsic activity, but also by modulation of regulatory elements such as protein stability or dephosphorylation.

Multiplexed assays of variant effects contribute to a growing genotype-phenotype atlas

Given the constantly improving cost and speed of genome sequencing, it is reasonable to expect that personal genomes will soon be known for many millions of humans. This stands in stark contrast with our limited ability to interpret the sequence variants which we find. Although it is, perhaps, easiest to interpret variants in coding regions, knowledge of functional impact is unknown for the vast majority of missense variants. While many computational approaches can predict the impact of coding variants, they are given a little weight in the current guidelines for interpreting clinical variants. Laboratory assays produce comparatively more trustworthy results, but until recently did not scale to the space of all possible mutations. The development of deep mutational scanning and other multiplexed assays of variant effect has now brought feasibility of this endeavour within view. Here, we review progress in this field over the last decade, break down the different approaches into their components, and compare methodological differences.

ERK Mutations and Amplification Confer Resistance to ERK-Inhibitor Therapy

Purpose: MAPK pathway inhibitors targeting BRAF and MEK have shown clinical efficacy in patients with RAF- and/or RAS-mutated tumors. However, acquired resistance to these agents has been an impediment to improved long-term survival in the clinic. In such cases, targeting ERK downstream of BRAF/MEK has been proposed as a potential strategy for overcoming acquired resistance. Preclinical studies suggest that ERK inhibitors are effective at inhibiting BRAF/RAS-mutated tumor growth and overcome BRAF or/and MEK inhibitor resistance. However, as observed with other MAPK pathway inhibitors, treatment with ERK inhibitors is likely to cause resistance in the clinic. Here, we aimed to model the mechanism of resistance to ERK inhibitors.Experimental Design: We tested five structurally different ATP-competitive ERK inhibitors representing three different scaffolds on BRAF/RAS-mutant cancer cell lines of different tissue types to generate resistant lines. We have used in vitro modeling, structural biology, and genomic analysis to understand the development of resistance to ERK inhibitors and the mechanisms leading to it.Results: We have identified mutations in ERK1/2, amplification and overexpression of ERK2, and overexpression of EGFR/ERBB2 as mechanisms of acquired resistance. Structural analysis of ERK showed that specific compounds that induced on-target ERK mutations were impaired in their ability to bind mutant ERK. We show that in addition to MEK inhibitors, ERBB receptor and PI3K/mTOR pathway inhibitors are effective in overcoming ERK-inhibitor resistance.Conclusions: These findings suggest that combination therapy with MEK or ERBB receptor or PI3K/mTOR and ERK inhibitors may be an effective strategy for managing the emergence of resistance in the clinic. Clin Cancer Res; 24(16); 4044-55. ©2018 AACR.

A Saturation Mutagenesis Approach to Understanding PTEN Lipid Phosphatase Activity and Genotype-Phenotype Relationships

Phosphatase and tensin homolog (PTEN) is a tumor suppressor frequently mutated in diverse cancers. Germline PTEN mutations are also associated with a range of clinical outcomes, including PTEN hamartoma tumor syndrome (PHTS) and autism spectrum disorder (ASD). To empower new insights into PTEN function and clinically relevant genotype-phenotype relationships, we systematically evaluated the effect of PTEN mutations on lipid phosphatase activity in vivo. Using a massively parallel approach that leverages an artificial humanized yeast model, we derived high-confidence estimates of functional impact for 7,244 single amino acid PTEN variants (86% of possible). We identified 2,273 mutations with reduced cellular lipid phosphatase activity, which includes 1,789 missense mutations. These data recapitulated known functional findings but also uncovered new insights into PTEN protein structure, biochemistry, and mutation tolerance. Several residues in the catalytic pocket showed surprising mutational tolerance. We identified that the solvent exposure of wild-type residues is a critical determinant of mutational tolerance. Further, we created a comprehensive functional map by leveraging correlations between amino acid substitutions to impute functional scores for all variants, including those not present in the assay. Variant functional scores can reliably discriminate likely pathogenic from benign alleles. Further, 32% of ClinVar unclassified missense variants are phosphatase deficient in our assay, supporting their reclassification. ASD-associated mutations generally had less severe fitness scores relative to PHTS-associated mutations (p = 7.16 × 10-5) and a higher fraction of hypomorphic mutations, arguing for continued genotype-phenotype studies in larger clinical datasets that can further leverage these rich functional data.

Comprehensive Characterization of Cancer Driver Genes and Mutations

Identifying molecular cancer drivers is critical for precision oncology. Multiple advanced algorithms to identify drivers now exist, but systematic attempts to combine and optimize them on large datasets are few. We report a PanCancer and PanSoftware analysis spanning 9,423 tumor exomes (comprising all 33 of The Cancer Genome Atlas projects) and using 26 computational tools to catalog driver genes and mutations. We identify 299 driver genes with implications regarding their anatomical sites and cancer/cell types. Sequence- and structure-based analyses identified >3,400 putative missense driver mutations supported by multiple lines of evidence. Experimental validation confirmed 60%-85% of predicted mutations as likely drivers. We found that >300 MSI tumors are associated with high PD-1/PD-L1, and 57% of tumors analyzed harbor putative clinically actionable events. Our study represents the most comprehensive discovery of cancer genes and mutations to date and will serve as a blueprint for future biological and clinical endeavors.

CRISPRi and CRISPRa Screens in Mammalian Cells for Precision Biology and Medicine

Next-generation DNA sequencing technologies have led to a massive accumulation of genomic and transcriptomic data from patients and healthy individuals. The major challenge ahead is to understand the functional significance of the elements of the human genome and transcriptome, and implications for diagnosis and treatment. Genetic screens in mammalian cells are a powerful approach to systematically elucidating gene function in health and disease states. In particular, recently developed CRISPR/Cas9-based screening approaches have enormous potential to uncover mechanisms and therapeutic strategies for human diseases. The focus of this review is the use of CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa) for genetic screens in mammalian cells. We introduce the underlying technology and present different types of CRISPRi/a screens, including those based on cell survival/proliferation, sensitivity to drugs or toxins, fluorescent reporters, and single-cell transcriptomes. Combinatorial screens, in which large numbers of gene pairs are targeted to construct genetic interaction maps, reveal pathway relationships and protein complexes. We compare and contrast CRISPRi and CRISPRa with alternative technologies, including RNA interference (RNAi) and CRISPR nuclease-based screens. Finally, we highlight challenges and opportunities ahead.

Combined inhibition of MEK and nuclear ERK translocation has synergistic antitumor activity in melanoma cells

Genetic alterations in BRAF, NRAS and NF1 that activate the ERK cascade, account for over 80% of metastatic melanomas. However, ERK cascade inhibitors have been proven beneficial almost exclusively for BRAF mutant melanomas. One of the hallmarks of the ERK cascade is the nuclear translocation of ERK1/2, which is important mainly for the induction of proliferation. This translocation can be inhibited by the NTS-derived peptide (EPE) that blocks the ERK1/2-importin7 interaction, inhibits the nuclear translocation of ERK1/2, and arrests active ERK1/2 in the cytoplasm. In this study, we found that the EPE peptide significantly reduced the viability of not only BRAF, but also several NRAS and NF1 mutant melanomas. Importantly, combination of the EPE peptide and trametinib showed synergy in reducing the viability of some NRAS mutant melanomas, an effect driven by the partial preservation of negative feedback loops. The same combination significantly reduced the viability of other melanoma cells, including those resistant to mono-treatment with EPE peptide and ERK cascade inhibitors. Our study indicates that targeting the nuclear translocation of ERK1/2, in combination with MEK inhibitors can be used for the treatment of different mutant melanomas.

A platform for functional assessment of large variant libraries in mammalian cells

Sequencing-based, massively parallel genetic assays have revolutionized our ability to quantify the relationship between many genotypes and a phenotype of interest. Unfortunately, variant library expression platforms in mammalian cells are far from ideal, hindering the study of human gene variants in their physiologically relevant cellular contexts. Here, we describe a platform for phenotyping variant libraries in transfectable mammalian cell lines in two steps. First, a landing pad cell line with a genomically integrated, Tet-inducible cassette containing a Bxb1 recombination site is created. Second, a single variant from a library of transfected, promoter-less plasmids is recombined into the landing pad in each cell. Thus, every cell in the recombined pool expresses a single variant, allowing for parallel, sequencing-based assessment of variant effect. We describe a method for incorporating a single landing pad into a defined site of a cell line of interest, and show that our approach can be used generate more than 20 000 recombinant cells in a single experiment. Finally, we use our platform in combination with a sequencing-based assay to explore the N-end rule by simultaneously measuring the effects of all possible N-terminal amino acids on protein expression.

phydms: software for phylogenetic analyses informed by deep mutational scanning

It has recently become possible to experimentally measure the effects of all amino-acid point mutations to proteins using deep mutational scanning. These experimental measurements can inform site-specific phylogenetic substitution models of gene evolution in nature. Here we describe software that efficiently performs analyses with such substitution models. This software, phydms, can be used to compare the results of deep mutational scanning experiments to the selection on genes in nature. Given a phylogenetic tree topology inferred with another program, phydms enables rigorous comparison of how well different experiments on the same gene capture actual natural selection. It also enables re-scaling of deep mutational scanning data to account for differences in the stringency of selection in the lab and nature. Finally, phydms can identify sites that are evolving differently in nature than expected from experiments in the lab. As data from deep mutational scanning experiments become increasingly widespread, phydms will facilitate quantitative comparison of the experimental results to the actual selection pressures shaping evolution in nature.

Analysis of Large-Scale Mutagenesis Data To Assess the Impact of Single Amino Acid Substitutions

Mutagenesis is a widely used method for identifying protein positions that are important for function or ligand binding. Advances in high-throughput DNA sequencing and mutagenesis techniques have enabled measurement of the effects of nearly all possible amino acid substitutions in many proteins. The resulting large-scale mutagenesis data sets offer a unique opportunity to draw general conclusions about the effects of different amino acid substitutions. Thus, we analyzed 34,373 mutations in 14 proteins whose effects were measured using large-scale mutagenesis approaches. Methionine was the most tolerated substitution, while proline was the least tolerated. We found that several substitutions, including histidine and asparagine, best recapitulated the effects of other substitutions, even when the identity of the wild-type amino acid was considered. The effects of histidine and asparagine substitutions also correlated best with the effects of other substitutions in different structural contexts. Furthermore, highly disruptive substitutions like aspartic and glutamic acid had the most discriminatory power for detecting ligand interface positions. Our work highlights the utility of large-scale mutagenesis data, and our conclusions can help guide future single substitution mutational scans.

Comparison of algorithms for the detection of cancer drivers at subgene resolution

Understanding genetic events that lead to cancer initiation and progression remains one of the biggest challenges in cancer biology. Traditionally, most algorithms for cancer-driver identification look for genes that have more mutations than expected from the average background mutation rate. However, there is now a wide variety of methods that look for nonrandom distribution of mutations within proteins as a signal for the driving role of mutations in cancer. Here we classify and review such subgene-resolution algorithms, compare their findings on four distinct cancer data sets from The Cancer Genome Atlas and discuss how predictions from these algorithms can be interpreted in the emerging paradigms that challenge the simple dichotomy between driver and passenger genes.

Implementing Genome-Driven Oncology

Early successes in identifying and targeting individual oncogenic drivers, together with the increasing feasibility of sequencing tumor genomes, have brought forth the promise of genome-driven oncology care. As we expand the breadth and depth of genomic analyses, the biological and clinical complexity of its implementation will be unparalleled. Challenges include target credentialing and validation, implementing drug combinations, clinical trial designs, targeting tumor heterogeneity, and deploying technologies beyond DNA sequencing, among others. We review how contemporary approaches are tackling these challenges and will ultimately serve as an engine for biological discovery and increase our insight into cancer and its treatment.

ERK2-ZEB1-miR-101-1 axis contributes to epithelial-mesenchymal transition and cell migration in cancer

Regulation of metastasis continues to remain enigmatic despite our improved understanding of cancer. Identification of microRNAs associated with metastasis in the recent past has provided a new hope. Here, we show how microRNA-101 (miR-101) regulates two independent processes of cellular metastasis by targeting pro-metastatic upstream regulatory transcription factors, ZEB1 and ZEB2, and downstream effector-actin modulators, RHOA and RAC1, providing a single target for therapeutic intervention. Further, we depict how down-regulation of miR-101 by extracellular signal-regulated kinase-2 (ERK2) is vital for MAP kinase pathway induced cellular migration and mesenchymal transition. Importantly, EKR2 induced expression of ZEB1 seems essential for down-regulation of miR-101-1 and induction of EMT. Given the role of EMT in metastasis, we also observe a significant correlation between miR-101 expression and lymph node metastasis; and identify the ERK2-ZEB1-miR-101-1 pathway active in breast cancer tissues, with an apparent clinicopathological implication.