Massively parallel variant characterization identifies NUDT15 alleles associated with thiopurine toxicity

SSEmb: A joint embedding of protein sequence and structure enables robust variant effect predictions

The ability to predict how amino acid changes affect proteins has a wide range of applications including in disease variant classification and protein engineering. Many existing methods focus on learning from patterns found in either protein sequences or protein structures. Here, we present a method for integrating information from sequence and structure in a single model that we term SSEmb (Sequence Structure Embedding). SSEmb combines a graph representation for the protein structure with a transformer model for processing multiple sequence alignments. We show that by integrating both types of information we obtain a variant effect prediction model that is robust when sequence information is scarce. We also show that SSEmb learns embeddings of the sequence and structure that are useful for other downstream tasks such as to predict protein-protein binding sites. We envisage that SSEmb may be useful both for variant effect predictions and as a representation for learning to predict protein properties that depend on sequence and structure.

Deep mutational scanning of CYP2C19 in human cells reveals a substrate specificity-abundance tradeoff

The cytochrome P450s enzyme family metabolizes ∼80% of small molecule drugs. Variants in cytochrome P450s can substantially alter drug metabolism, leading to improper dosing and severe adverse drug reactions. Due to low sequence conservation, predicting variant effects across cytochrome P450s is challenging. Even closely related cytochrome P450s like CYP2C9 and CYP2C19, which share 92% amino acid sequence identity, display distinct phenotypic properties. Using variant abundance by massively parallel sequencing, we measured the steady-state protein abundance of 7,660 single amino acid variants in CYP2C19 expressed in cultured human cells. Our findings confirmed critical positions and structural features essential for cytochrome P450 function, and revealed how variants at conserved positions influence abundance. We jointly analyzed 4,670 variants whose abundance was measured in both CYP2C19 and CYP2C9, finding that the homologs have different variant abundances in substrate recognition sites within the hydrophobic core. We also measured the abundance of all single and some multiple wild type amino acid exchanges between CYP2C19 and CYP2C9. While most exchanges had no effect, substitutions in substrate recognition site 4 reduced abundance in CYP2C19. Double and triple mutants showed distinct interactions, highlighting a region that points to differing thermodynamic properties between the 2 homologs. These positions are known contributors to substrate specificity, suggesting an evolutionary tradeoff between stability and enzymatic function. Finally, we analyzed 368 previously unannotated human variants, finding that 43% had decreased abundance. By comparing variant effects between these homologs, we uncovered regions underlying their functional differences, advancing our understanding of this versatile family of enzymes.

FUSE: Improving the estimation and imputation of variant impacts in functional screening

Deep mutational scanning enables high-throughput functional assessment of genetic variants. While phenotypic measurements from screening assays generally align with clinical outcomes, experimental noise may affect the accuracy of individual variant estimates. We developed the FUSE (functional substitution estimation) pipeline, which leverages measurements collectively within screening assays to improve the estimation of variant impacts. Drawing data from 115 published functional assays, FUSE assesses the mean functional effect per amino acid position and makes estimates for individual allelic variants. It enhances the correlation of variant functional effects from different assay platforms and increases the classification accuracy of missense variants in ClinVar across 29 genes (area under the receiver operating characteristic [ROC] curve [AUC] from 0.83 to 0.90). In UK Biobank patients with rare missense variants in BRCA1, LDLR, or TP53, FUSE improves the classification accuracy of associated phenotypes. FUSE can also impute variant effects for substitutions not experimentally screened. This approach improves accuracy and broadens the utility of data from functional screening.

Influence of TPMT and NUDT15 Genetic Polymorphisms on Mercaptopurine Pharmacokinetics in Healthy Volunteers

Aims: This study aimed to investigate the impact of genetic polymorphisms of thiopurine methyltransferase (TPMT) and NUDT15 on pharmacokinetics profile of mercaptopurine in healthy adults in China. Methods: Blood samples were obtained from 45 healthy adult volunteers who were administered azathioprine. Genomic DNA was extracted and sequenced for TPMT and NUDT15. The plasma concentrations of 6-mercaptopurine (6-MP) were determined by ultra-performance liquid chromatography-tandem mass spectrometry. Finally, pharmacokinetic parameters were calculated based on the time-concentration curve. Results: Among the 45 healthy adult volunteers enrolled in the study, two TPMT allelic variants and three NUDT15 allelic variants were detected. In total, six genotypes were identified, including TPMT*1/*1&NUDT15*1/*1, TPMT*1/*1&NUDT15*1/*2, TPMT*1/*1&NUDT15*1/*9, TPMT*1/*1&NUDT15*2/*5, TPMT*1/*6&NUDT15*1/*2, and TPMT*1/*3&NUDT15*1/*2. The results indicated that Area Under Curve (AUC) of 6-MP in volunteers with TPMT*1/*3&NUDT15*1/*2 and TPMT*1/*6&NUDT15*1/*2 were 1.57-1.62-fold higher than in individuals carrying the wild type (TPMT*1/*1&NUDT15*1/*1). Compared with wild type, the half-life (T1/2) of TPMT*1/*6&NUDT15*1/*2 was extended by 1.98 times, whereas T1/2 of TPMT*1/*3&NUDT15*1/*2 decreased by 67%. The maximum concentration (Cmax) of TPMT*1/*3&NUDT15*1/*2 increased significantly by 2.15-fold, whereas the corresponding clearance (CL/F) decreased significantly by 58.75%. Conclusion: The findings of this study corroborate the notion that various genotypes of TPMT and NUDT15 can impact the pharmacokinetics of mercaptopurine, potentially offering foundational insights for personalized mercaptopurine therapy.

Germ line genetic NBN variation and predisposition to B-cell acute lymphoblastic leukemia in children

ABSTRACT

Biallelic mutation in the DNA-damage repair gene NBN is the genetic cause of Nijmegen breakage syndrome, which is associated with predisposition to lymphoid malignancies. Heterozygous carriers of germ line NBN variants may also be at risk for leukemia development, although this is much less characterized. By sequencing 4325 pediatric patients with B-cell acute lymphoblastic leukemia (B-ALL), we systematically examined the frequency of germ line NBN variants and identified 25 unique, putatively damaging NBN coding variants in 50 patients. Compared with the frequency of NBN variants in gnomAD noncancer controls (189 unique, putatively damaging NBN coding variants in 472 of 118 479 individuals), we found significant overrepresentation in pediatric B-ALL (P = .004; odds ratio, 1.8). Most B-ALL-risk variants were missense and cluster within the NBN N-terminal domains. Using 2 functional assays, we verified 14 of 25 variants with severe loss-of-function phenotypes and thus classified these as nonfunctional or partially functional. Finally, we found that germ line NBN variant carriers, all of whom were identified as heterozygous genotypes, showed similar survival outcomes relative to those with wild type status. Taken together, our findings provide novel insights into the genetic predisposition to B-ALL, and the impact of NBN variants on protein function and suggest that heterozygous NBN variant carriers may safely receive B-ALL therapy. These trials were registered at www.clinicaltrials.gov as #NCT01225874, NCT00075725, NCT00103285, NCI-T93-0101D, and NCT00137111.

APF2: an improved ensemble method for pharmacogenomic variant effect prediction

Lack of efficacy or adverse drug response are common phenomena in pharmacological therapy causing considerable morbidity and mortality. It is estimated that 20-30% of this variability in drug response stems from variations in genes encoding drug targets or factors involved in drug disposition. Leveraging such pharmacogenomic information for the preemptive identification of patients who would benefit from dose adjustments or alternative medications thus constitutes an important frontier of precision medicine. Computational methods can be used to predict the functional effects of variant of unknown significance. However, their performance on pharmacogenomic variant data has been lackluster. To overcome this limitation, we previously developed an ensemble classifier, termed APF, specifically designed for pharmacogenomic variant prediction. Here, we aimed to further improve predictions by leveraging recent key advances in the prediction of protein folding based on deep neural networks. Benchmarking of 28 variant effect predictors on 530 pharmacogenetic missense variants revealed that structural predictions using AlphaMissense were most specific, whereas APF exhibited the most balanced performance. We then developed a new tool, APF2, by optimizing algorithm parametrization of the top performing algorithms for pharmacogenomic variations and aggregating their predictions into a unified ensemble score. Importantly, APF2 provides quantitative variant effect estimates that correlate well with experimental results (R2 = 0.91, p = 0.003) and predicts the functional impact of pharmacogenomic variants with higher accuracy than previous methods, particularly for clinically relevant variations with actionable pharmacogenomic guidelines. We furthermore demonstrate better performance (92% accuracy) on an independent test set of 146 variants across 61 pharmacogenes not used for model training or validation. Application of APF2 to population-scale sequencing data from over 800,000 individuals revealed drastic ethnogeographic differences with important implications for pharmacotherapy. We thus think that APF2 holds the potential to improve the translation of genetic information into pharmacogenetic recommendations, thereby facilitating the use of Next-Generation Sequencing data for stratified medicine.

PreMode predicts mode-of-action of missense variants by deep graph representation learning of protein sequence and structural context

Accurate prediction of the functional impact of missense variants is important for disease gene discovery, clinical genetic diagnostics, therapeutic strategies, and protein engineering. Previous efforts have focused on predicting a binary pathogenicity classification, but the functional impact of missense variants is multi-dimensional. Pathogenic missense variants in the same gene may act through different modes of action (i.e., gain/loss-of-function) by affecting different aspects of protein function. They may result in distinct clinical conditions that require different treatments. We developed a new method, PreMode, to perform gene-specific mode-of-action predictions. PreMode models effects of coding sequence variants using SE(3)-equivariant graph neural networks on protein sequences and structures. Using the largest-to-date set of missense variants with known modes of action, we showed that PreMode reached state-of-the-art performance in multiple types of mode-of-action predictions by efficient transfer-learning. Additionally, PreMode's prediction of G/LoF variants in a kinase is consistent with inactive-active conformation transition energy changes. Finally, we show that PreMode enables efficient study design of deep mutational scans and optimization in protein engineering.

Integrated multiplexed assays of variant effect reveal determinants of catechol-O-methyltransferase gene expression

Multiplexed assays of variant effect are powerful methods to profile the consequences of rare variants on gene expression and organismal fitness. Yet, few studies have integrated several multiplexed assays to map variant effects on gene expression in coding sequences. Here, we pioneered a multiplexed assay based on polysome profiling to measure variant effects on translation at scale, uncovering single-nucleotide variants that increase or decrease ribosome load. By combining high-throughput ribosome load data with multiplexed mRNA and protein abundance readouts, we mapped the cis-regulatory landscape of thousands of catechol-O-methyltransferase (COMT) variants from RNA to protein and found numerous coding variants that alter COMT expression. Finally, we trained machine learning models to map signatures of variant effects on COMT gene expression and uncovered both directional and divergent impacts across expression layers. Our analyses reveal expression phenotypes for thousands of variants in COMT and highlight variant effects on both single and multiple layers of expression. Our findings prompt future studies that integrate several multiplexed assays for the readout of gene expression.

Characterizing glucokinase variant mechanisms using a multiplexed abundance assay

BACKGROUND

Amino acid substitutions can perturb protein activity in multiple ways. Understanding their mechanistic basis may pinpoint how residues contribute to protein function. Here, we characterize the mechanisms underlying variant effects in human glucokinase (GCK) variants, building on our previous comprehensive study on GCK variant activity.

RESULTS

Using a yeast growth-based assay, we score the abundance of 95% of GCK missense and nonsense variants. When combining the abundance scores with our previously determined activity scores, we find that 43% of hypoactive variants also decrease cellular protein abundance. The low-abundance variants are enriched in the large domain, while residues in the small domain are tolerant to mutations with respect to abundance. Instead, many variants in the small domain perturb GCK conformational dynamics which are essential for appropriate activity.

CONCLUSIONS

In this study, we identify residues important for GCK metabolic stability and conformational dynamics. These residues could be targeted to modulate GCK activity, and thereby affect glucose homeostasis.

Collateral damage of NUDT15 deficiency in cancer provides a cancer pharmacogenetic therapeutic window with thiopurines

Genome instability is a hallmark of cancer and are driven by mutations in oncogenes and tumor suppressor genes. Despite successes seen with select targeted therapeutics, this type of personalized medicine is only beneficial for a small subpopulation of cancer patients who have one of a few actionable genetic changes. Most tumors also contain hundreds of passenger mutations that offered no fitness advantage or disadvantage during tumor evolution. Mutations in known pharmacogenetic (PGx) loci for which germline variants encode variability in drug response can cause somatically acquired drug sensitivity. The NUDT15 gene is a known PGx locus that participates in the rate-limiting metabolism of thiopurines. People with two defective germline alleles of NUDT15 are hypersensitive to the toxic effects of thiopurines. NUDT15 is located adjacent to the Retinoblastoma ( RB1 ) tumor suppressor gene, which often undergoes homozygous deletion in retinoblastomas and other epithelial cancers. We observed that RB1 undergoes homozygous deletions in 9.4% of prostate adenocarcinomas and 2.5% of ovarian cancers, and in nearly all of these cases NUDT15 is also lost. Moreover, 44% of prostate adenocarcinomas and over 60% of ovarian cancers have lost one allele of NUDT15, which predicts that a majority of all prostate and ovarian cancers have somatically acquired hypersensitivity to thiopurine treatment. We performed a retrospective analysis of >16,000 patients in the US Veterans Administration health care system and found concurrent xanthine oxidase inhibition (XOi) and thiopurine usage for non-cancer indications is significantly associated with reduced incidence of prostate cancer. The hazard ratio for the development of prostate cancer in patients treated with thiopurines and XOi was 0.562 (0.301-1.051) for the unmatched cohort and 0.389 (0.185-0.819) for the propensity score matched cohort. We experimentally depleted NUDT15 from ovarian and prostate cancer cell lines and observed a dramatic sensitization to thiopurine-induced and DNA damage-dependent toxicity. These results indicate that somatic loss of NUDT15 predicts therapeutic sensitivity to a low cost and well tolerated drug with a broad therapeutic window.

Rheostats, toggles, and neutrals, Oh my! A new framework for understanding how amino acid changes modulate protein function

Advances in personalized medicine and protein engineering require accurately predicting outcomes of amino acid substitutions. Many algorithms correctly predict that evolutionarily-conserved positions show "toggle" substitution phenotypes, which is defined when a few substitutions at that position retain function. In contrast, predictions often fail for substitutions at the less-studied "rheostat" positions, which are defined when different amino acid substitutions at a position sample at least half of the possible functional range. This review describes efforts to understand the impact and significance of rheostat positions: (1) They have been observed in globular soluble, integral membrane, and intrinsically disordered proteins; within single proteins, their prevalence can be up to 40%. (2) Substitutions at rheostat positions can have biological consequences and ∼10% of substitutions gain function. (3) Although both rheostat and "neutral" (defined when all substitutions exhibit wild-type function) positions are nonconserved, the two classes have different evolutionary signatures. (4) Some rheostat positions have pleiotropic effects on function, simultaneously modulating multiple parameters (e.g., altering both affinity and allosteric coupling). (5) In structural studies, substitutions at rheostat positions appear to cause only local perturbations; the overall conformations appear unchanged. (6) Measured functional changes show promising correlations with predicted changes in protein dynamics; the emergent properties of predicted, dynamically coupled amino acid networks might explain some of the complex functional outcomes observed when substituting rheostat positions. Overall, rheostat positions provide unique opportunities for using single substitutions to tune protein function. Future studies of these positions will yield important insights into the protein sequence/function relationship.

Multiplex Functional Characterization of Protein Variant Libraries in Mammalian Cells with Single-Copy Genomic Integration and High-Throughput DNA Sequencing

Sequencing-based, massively parallel genetic assays have enabled simultaneous characterization of the genotype-phenotype relationships for libraries encoding thousands of unique protein variants. Since plasmid transfection and lentiviral transduction have characteristics that limit multiplexing with pooled libraries, we developed a mammalian synthetic biology platform that harnesses the Bxb1 bacteriophage DNA recombinase to insert single promoterless plasmids encoding a transgene of interest into a pre-engineered "landing pad" site within the cell genome. The transgene is expressed behind a genomically integrated promoter, ensuring only one transgene is expressed per cell, preserving a strict genotype-phenotype link. Upon selecting cells based on a desired phenotype, the transgene can be sequenced to ascribe each variant a phenotypic score. We describe how to create and utilize landing pad cells for large-scale, library-based genetic experiments. Using the provided examples, the experimental template can be adapted to explore protein variants in diverse biological problems within mammalian cells.

A probabilistic graphical model for estimating selection coefficient of missense variants from human population sequence data

Accurately predicting the effect of missense variants is a central problem in interpretation of genomic variation. Commonly used computational methods does not capture the quantitative impact on fitness in populations. We developed MisFit to estimate missense fitness effect using biobank-scale human population genome data. MisFit jointly models the effect at molecular level ( d ) and population level (selection coefficient, s ), assuming that in the same gene, missense variants with similar d have similar s . MisFit is a probabilistic graphical model that integrates deep neural network components and population genetics models efficiently with inductive bias based on biological causality of variant effect. We trained it by maximizing probability of observed allele counts in 236,017 European individuals. We show that s is informative in predicting frequency across ancestries and consistent with the fraction of de novo mutations given s . Finally, MisFit outperforms previous methods in prioritizing missense variants in individuals with neurodevelopmental disorders.

ProteinGym: Large-Scale Benchmarks for Protein Design and Fitness Prediction

Predicting the effects of mutations in proteins is critical to many applications, from understanding genetic disease to designing novel proteins that can address our most pressing challenges in climate, agriculture and healthcare. Despite a surge in machine learning-based protein models to tackle these questions, an assessment of their respective benefits is challenging due to the use of distinct, often contrived, experimental datasets, and the variable performance of models across different protein families. Addressing these challenges requires scale. To that end we introduce ProteinGym, a large-scale and holistic set of benchmarks specifically designed for protein fitness prediction and design. It encompasses both a broad collection of over 250 standardized deep mutational scanning assays, spanning millions of mutated sequences, as well as curated clinical datasets providing high-quality expert annotations about mutation effects. We devise a robust evaluation framework that combines metrics for both fitness prediction and design, factors in known limitations of the underlying experimental methods, and covers both zero-shot and supervised settings. We report the performance of a diverse set of over 70 high-performing models from various subfields (eg., alignment-based, inverse folding) into a unified benchmark suite. We open source the corresponding codebase, datasets, MSAs, structures, model predictions and develop a user-friendly website that facilitates data access and analysis.

Deep mutational scanning of proteins in mammalian cells

Protein mutagenesis is essential for unveiling the molecular mechanisms underlying protein function in health, disease, and evolution. In the past decade, deep mutational scanning methods have evolved to support the functional analysis of nearly all possible single-amino acid changes in a protein of interest. While historically these methods were developed in lower organisms such as E. coli and yeast, recent technological advancements have resulted in the increased use of mammalian cells, particularly for studying proteins involved in human disease. These advancements will aid significantly in the classification and interpretation of variants of unknown significance, which are being discovered at large scale due to the current surge in the use of whole-genome sequencing in clinical contexts. Here, we explore the experimental aspects of deep mutational scanning studies in mammalian cells and report the different methods used in each step of the workflow, ultimately providing a useful guide toward the design of such studies.

Integrated multiplexed assays of variant effect reveal cis-regulatory determinants of catechol-O-methyltransferase gene expression

Multiplexed assays of variant effect are powerful methods to profile the consequences of rare variants on gene expression and organismal fitness. Yet, few studies have integrated several multiplexed assays to map variant effects on gene expression in coding sequences. Here, we pioneered a multiplexed assay based on polysome profiling to measure variant effects on translation at scale, uncovering single-nucleotide variants that increase and decrease ribosome load. By combining high-throughput ribosome load data with multiplexed mRNA and protein abundance readouts, we mapped the cis-regulatory landscape of thousands of catechol-O-methyltransferase (COMT) variants from RNA to protein and found numerous coding variants that alter COMT expression. Finally, we trained machine learning models to map signatures of variant effects on COMT gene expression and uncovered both directional and divergent impacts across expression layers. Our analyses reveal expression phenotypes for thousands of variants in COMT and highlight variant effects on both single and multiple layers of expression. Our findings prompt future studies that integrate several multiplexed assays for the readout of gene expression.

Mammalian Nudt15 hydrolytic and binding activity on methylated guanosine mononucleotides

The Nudt15 enzyme of the NUDIX protein family is the subject of extensive study due to its action on thiopurine drugs used in the treatment of cancer and inflammatory diseases. In addition to thiopurines, Nudt15 is enzymatically active in vitro on several nucleotide substrates. It has also been suggested that this enzyme may play a role in 5'RNA turnover by hydrolyzing m7GDP, a product of mRNA decapping. However, no detailed studies on this substrate with Nudt15 are available. Here, we analyzed the enzymatic activity of Nudt15 with m7GDP, its triphosphate form m7GTP, and the trimethylated counterparts (m32,2,7GDP and m32,2,7GTP). Kinetic data revealed a moderate activity of Nudt15 toward these methylated mononucleotides compared to the dGTP substrate. However m7GDP and m32,2,7GDP showed a distinct stabilization of Nudt15 upon ligand binding, in the same range as dGTP, and thus these two mononucleotides may be used as leading structures in the design of small molecule binders of Nudt15.

Translating pharmacogenomic sequencing data into drug response predictions-How to interpret variants of unknown significance

The rapid development of sequencing technologies during the past 20 years has provided a variety of methods and tools to interrogate human genomic variations at the population level. Pharmacogenes are well known to be highly polymorphic and a plethora of pharmacogenomic variants has been identified in population sequencing data. However, so far only a small number of these variants have been functionally characterized regarding their impact on drug efficacy and toxicity and the significance of the vast majority remains unknown. It is therefore of high importance to develop tools and frameworks to accurately infer the effects of pharmacogenomic variants and, eventually, aggregate the effect of individual variations into personalized drug response predictions. To address this challenge, we here first describe the technological advances, including sequencing methods and accompanying bioinformatic processing pipelines that have enabled reliable variant identification. Subsequently, we highlight advances in computational algorithms for pharmacogenomic variant interpretation and discuss the added value of emerging strategies, such as machine learning and the integrative use of omics techniques that have the potential to further contribute to the refinement of personalized pharmacological response predictions. Lastly, we provide an overview of experimental and clinical approaches to validate in silico predictions. We conclude that the iterative feedback between computational predictions and experimental validations is likely to rapidly improve the accuracy of pharmacogenomic prediction models, which might soon allow for an incorporation of the entire pharmacogenetic profile into personalized response predictions.

Protein engineering via sequence-performance mapping

Discovery and evolution of new and improved proteins has empowered molecular therapeutics, diagnostics, and industrial biotechnology. Discovery and evolution both require efficient screens and effective libraries, although they differ in their challenges because of the absence or presence, respectively, of an initial protein variant with the desired function. A host of high-throughput technologies-experimental and computational-enable efficient screens to identify performant protein variants. In partnership, an informed search of sequence space is needed to overcome the immensity, sparsity, and complexity of the sequence-performance landscape. Early in the historical trajectory of protein engineering, these elements aligned with distinct approaches to identify the most performant sequence: selection from large, randomized combinatorial libraries versus rational computational design. Substantial advances have now emerged from the synergy of these perspectives. Rational design of combinatorial libraries aids the experimental search of sequence space, and high-throughput, high-integrity experimental data inform computational design. At the core of the collaborative interface, efficient protein characterization (rather than mere selection of optimal variants) maps sequence-performance landscapes. Such quantitative maps elucidate the complex relationships between protein sequence and performance-e.g., binding, catalytic efficiency, biological activity, and developability-thereby advancing fundamental protein science and facilitating protein discovery and evolution.

Updated benchmarking of variant effect predictors using deep mutational scanning

The assessment of variant effect predictor (VEP) performance is fraught with biases introduced by benchmarking against clinical observations. In this study, building on our previous work, we use independently generated measurements of protein function from deep mutational scanning (DMS) experiments for 26 human proteins to benchmark 55 different VEPs, while introducing minimal data circularity. Many top-performing VEPs are unsupervised methods including EVE, DeepSequence and ESM-1v, a protein language model that ranked first overall. However, the strong performance of recent supervised VEPs, in particular VARITY, shows that developers are taking data circularity and bias issues seriously. We also assess the performance of DMS and unsupervised VEPs for discriminating between known pathogenic and putatively benign missense variants. Our findings are mixed, demonstrating that some DMS datasets perform exceptionally at variant classification, while others are poor. Notably, we observe a striking correlation between VEP agreement with DMS data and performance in identifying clinically relevant variants, strongly supporting the validity of our rankings and the utility of DMS for independent benchmarking.

Germline Genetic NBN Variation and Predisposition to B-cell Acute Lymphoblastic Leukemia in Children

Biallelic mutation in the DNA-damage repair gene NBN is the genetic cause of Nijmegen Breakage Syndrome, which is associated with predisposition to lymphoid malignancies. Heterozygous carriers of germline NBN variants may also be at risk for leukemia development, although this is much less characterized. We systematically examined the frequency of germline NBN variants in pediatric B-ALL and identified 25 putatively damaging NBN coding variants in 50 of 4,183 B-ALL patients. Compared with the frequency of NBN variants in 118,479 gnomAD non-cancer controls we found significant overrepresentation in pediatric B-ALL (p=0.004, OR=1.77). Most B-ALL-risk variants were missense and cluster within the NBN N-terminal domains. Using two functional assays, we verified 14 of 25 variants with severe loss-of-function phenotypes and thus classified these as pathogenic or likely pathogenic. Finally, we found that heterozygous germline NBN variant carriers showed similar survival outcomes relative to those with WT status. Taken together, our findings provide novel insights into the genetic predisposition to B-ALL, the impact of NBN variants on protein function and suggest that heterozygous NBN variant carriers may safely receive B-ALL therapy.

Discovering functionally important sites in proteins

Proteins play important roles in biology, biotechnology and pharmacology, and missense variants are a common cause of disease. Discovering functionally important sites in proteins is a central but difficult problem because of the lack of large, systematic data sets. Sequence conservation can highlight residues that are functionally important but is often convoluted with a signal for preserving structural stability. We here present a machine learning method to predict functional sites by combining statistical models for protein sequences with biophysical models of stability. We train the model using multiplexed experimental data on variant effects and validate it broadly. We show how the model can be used to discover active sites, as well as regulatory and binding sites. We illustrate the utility of the model by prospective prediction and subsequent experimental validation on the functional consequences of missense variants in HPRT1 which may cause Lesch-Nyhan syndrome, and pinpoint the molecular mechanisms by which they cause disease.

An Atlas of Variant Effects to understand the genome at nucleotide resolution

Sequencing has revealed hundreds of millions of human genetic variants, and continued efforts will only add to this variant avalanche. Insufficient information exists to interpret the effects of most variants, limiting opportunities for precision medicine and comprehension of genome function. A solution lies in experimental assessment of the functional effect of variants, which can reveal their biological and clinical impact. However, variant effect assays have generally been undertaken reactively for individual variants only after and, in most cases long after, their first observation. Now, multiplexed assays of variant effect can characterise massive numbers of variants simultaneously, yielding variant effect maps that reveal the function of every possible single nucleotide change in a gene or regulatory element. Generating maps for every protein encoding gene and regulatory element in the human genome would create an 'Atlas' of variant effect maps and transform our understanding of genetics and usher in a new era of nucleotide-resolution functional knowledge of the genome. An Atlas would reveal the fundamental biology of the human genome, inform human evolution, empower the development and use of therapeutics and maximize the utility of genomics for diagnosing and treating disease. The Atlas of Variant Effects Alliance is an international collaborative group comprising hundreds of researchers, technologists and clinicians dedicated to realising an Atlas of Variant Effects to help deliver on the promise of genomics.

Correspondence between functional scores from deep mutational scans and predicted effects on protein stability

Many methodologically diverse computational methods have been applied to the growing challenge of predicting and interpreting the effects of protein variants. As many pathogenic mutations have a perturbing effect on protein stability or intermolecular interactions, one highly interpretable approach is to use protein structural information to model the physical impacts of variants and predict their likely effects on protein stability and interactions. Previous efforts have assessed the accuracy of stability predictors in reproducing thermodynamically accurate values and evaluated their ability to distinguish between known pathogenic and benign mutations. Here, we take an alternate approach, and explore how well stability predictor scores correlate with functional impacts derived from deep mutational scanning (DMS) experiments. In this work, we compare the predictions of 9 protein stability-based tools against mutant protein fitness values from 49 independent DMS datasets, covering 170,940 unique single amino acid variants. We find that FoldX and Rosetta show the strongest correlations with DMS-based functional scores, similar to their previous top performance in distinguishing between pathogenic and benign variants. For both methods, performance is considerably improved when considering intermolecular interactions from protein complex structures, when available. Furthermore, using these two predictors, we derive a "Foldetta" consensus score, which improves upon the performance of both, and manages to match dedicated variant effect predictors in reflecting variant functional impacts. Finally, we also highlight that predicted stability effects show consistently higher correlations with certain DMS experimental phenotypes, particularly those based upon protein abundance, and, in certain cases, can significantly outcompete sequence-based variant effect prediction methodologies for predicting functional scores from DMS experiments.

Characterizing glucokinase variant mechanisms using a multiplexed abundance assay

Amino acid substitutions can perturb protein activity in multiple ways. Understanding their mechanistic basis may pinpoint how residues contribute to protein function. Here, we characterize the mechanisms of human glucokinase (GCK) variants, building on our previous comprehensive study on GCK variant activity. We assayed the abundance of 95% of GCK missense and nonsense variants, and found that 43% of hypoactive variants have a decreased cellular abundance. By combining our abundance scores with predictions of protein thermodynamic stability, we identify residues important for GCK metabolic stability and conformational dynamics. These residues could be targeted to modulate GCK activity, and thereby affect glucose homeostasis.

Deep Mutational Scanning of an Oxygen-Independent Fluorescent Protein CreiLOV for Comprehensive Profiling of Mutational and Epistatic Effects

Oxygen-independent, flavin mononucleotide-based fluorescent proteins (FbFPs) are promising alternatives to green fluorescent protein in anaerobic contexts. Deep mutational scanning performs systematic profiling of protein sequence-function relationships but has not been applied to FbFPs. Focusing on CreiLOV from Chlamydomonas reinhardtii, we created and analyzed two comprehensive mutant collections: (1) single-residue, site-saturation mutagenesis libraries covering all 118 residues; and (2) a full combinatorial metagenesis library among 20 mutations at 15 residues, where mutation and residue selection was based on single-site mutagenesis results. Notably, the second type of library is indispensable to study higher-order epistasis but underrepresented in the literature. Using optimized FACS-seq assays, 2,185 (>92.5%) out of 2,360 possible single-site mutants and 165,428 (>89.7%) out of 184,320 possible combinatorial mutants were reliably assigned with fitness values. We constructed statistical and machine-learning models to analyze the CreiLOV data set, enabling accurate fitness prediction of higher-order mutants using lower-order mutagenesis data. In addition, we successfully isolated CreiLOV variants with improved fluorescence quantum yield and thermostability. This work provides new empirical data and design rules to engineer combinatorial protein variants.

Rapid protein stability prediction using deep learning representations

Predicting the thermodynamic stability of proteins is a common and widely used step in protein engineering, and when elucidating the molecular mechanisms behind evolution and disease. Here, we present RaSP, a method for making rapid and accurate predictions of changes in protein stability by leveraging deep learning representations. RaSP performs on-par with biophysics-based methods and enables saturation mutagenesis stability predictions in less than a second per residue. We use RaSP to calculate ∼ 230 million stability changes for nearly all single amino acid changes in the human proteome, and examine variants observed in the human population. We find that variants that are common in the population are substantially depleted for severe destabilization, and that there are substantial differences between benign and pathogenic variants, highlighting the role of protein stability in genetic diseases. RaSP is freely available-including via a Web interface-and enables large-scale analyses of stability in experimental and predicted protein structures.

satmut_utils: a simulation and variant calling package for multiplexed assays of variant effect

The impact of millions of individual genetic variants on molecular phenotypes in coding sequences remains unknown. Multiplexed assays of variant effect (MAVEs) are scalable methods to annotate relevant variants, but existing software lacks standardization, requires cumbersome configuration, and does not scale to large targets. We present satmut_utils as a flexible solution for simulation and variant quantification. We then benchmark MAVE software using simulated and real MAVE data. We finally determine mRNA abundance for thousands of cystathionine beta-synthase variants using two experimental methods. The satmut_utils package enables high-performance analysis of MAVEs and reveals the capability of variants to alter mRNA abundance.

High-throughput identification of prefusion-stabilizing mutations in SARS-CoV-2 spike

Designing prefusion-stabilized SARS-CoV-2 spike is critical for the effectiveness of COVID-19 vaccines. All COVID-19 vaccines in the US encode spike with K986P/V987P mutations to stabilize its prefusion conformation. However, contemporary methods on engineering prefusion-stabilized spike immunogens involve tedious experimental work and heavily rely on structural information. Here, we establish a systematic and unbiased method of identifying mutations that concomitantly improve expression and stabilize the prefusion conformation of the SARS-CoV-2 spike. Our method integrates a fluorescence-based fusion assay, mammalian cell display technology, and deep mutational scanning. As a proof-of-concept, we apply this method to a region in the S2 domain that includes the first heptad repeat and central helix. Our results reveal that besides K986P and V987P, several mutations simultaneously improve expression and significantly lower the fusogenicity of the spike. As prefusion stabilization is a common challenge for viral immunogen design, this work will help accelerate vaccine development against different viruses.

Molecular Mechanisms and Clinical Phenotypes of GJB2 Missense Variants

The GJB2 gene is the most common gene responsible for hearing loss (HL) worldwide, and missense variants are the most abundant type. GJB2 pathogenic missense variants cause nonsyndromic HL (autosomal recessive and dominant) and syndromic HL combined with skin diseases. However, the mechanism by which these different missense variants cause the different phenotypes is unknown. Over 2/3 of the GJB2 missense variants have yet to be functionally studied and are currently classified as variants of uncertain significance (VUS). Based on these functionally determined missense variants, we reviewed the clinical phenotypes and investigated the molecular mechanisms that affected hemichannel and gap junction functions, including connexin biosynthesis, trafficking, oligomerization into connexons, permeability, and interactions between other coexpressed connexins. We predict that all possible GJB2 missense variants will be described in the future by deep mutational scanning technology and optimizing computational models. Therefore, the mechanisms by which different missense variants cause different phenotypes will be fully elucidated.

Molecular dynamics simulations reveal the impact of NUDT15 R139C and R139H variants in structural conformation and dynamics

NUDT15, also known as MTH2, is a member of the NUDIX protein family that catalyzes the hydrolysis of nucleotides and deoxynucleotides, as well as thioguanine analogues. NUDT15 has been reported as a DNA sanitizer in humans, and more recent studies have shown that some genetic variants are related to a poor prognosis in neoplastic and immunologic diseases treated with thioguanine drugs. Despite this, the role of NUDT15 in physiology and molecular biology is quite unclear, as is the mechanism of action of this enzyme. The existence of clinically relevant variants has prompted the study of these enzymes, whose capacity to bind and hydrolyze thioguanine nucleotides is still poorly understood. By using a combination of biomolecular modeling techniques and molecular dynamics, we have studied the monomeric wild type NUDT15 as well as two important variants, R139C and R139H. Our findings reveal not only how nucleotide binding stabilizes the enzyme but also how two loops are responsible for keeping the enzyme in a packed, close conformation. Mutations in α2 helix affect a network of hydrophobic and π-interactions that enclose the active site. This knowledge contributes to the understanding of NUDT15 structural dynamics and will be valuable for the design of new chemical probes and drugs targeting this protein.Communicated by Ramaswamy H. Sarma.

Functional E3 ligase hotspots and resistance mechanisms to small-molecule degraders

Targeted protein degradation is a novel pharmacology established by drugs that recruit target proteins to E3 ubiquitin ligases. Based on the structure of the degrader and the target, different E3 interfaces are critically involved, thus forming defined 'functional hotspots'. Understanding disruptive mutations in functional hotspots informs on the architecture of the assembly, and highlights residues susceptible to acquire resistance phenotypes. Here we employ haploid genetics to show that hotspot mutations cluster in substrate receptors of hijacked ligases, where mutation type and frequency correlate with gene essentiality. Intersection with deep mutational scanning revealed hotspots that are conserved or specific for chemically distinct degraders and targets. Biophysical and structural validation suggests that hotspot mutations frequently converge on altered ternary complex assembly. Moreover, we validated hotspots mutated in patients that relapse from degrader treatment. In sum, we present a fast and widely accessible methodology to characterize small-molecule degraders and associated resistance mechanisms.

Joint estimation and imputation of variant functional effects using high throughput assay data

Deep mutational scanning assays enable the functional assessment of variants in high throughput. Phenotypic measurements from these assays are broadly concordant with clinical outcomes but are prone to noise at the individual variant level. We develop a framework to exploit related measurements within and across experimental assays to jointly estimate variant impact. Drawing from a large corpus of deep mutational scanning data, we collectively estimate the mean functional effect per AA residue position within each gene, normalize observed functional effects by substitution type, and make estimates for individual allelic variants with a pipeline called FUSE (Functional Substitution Estimation). FUSE improves the correlation of functional screening datasets covering the same variants, better separates estimated functional impacts for known pathogenic and benign variants (ClinVar BRCA1, p=2.24×10-51), and increases the number of variants for which predictions can be made (2,741 to 10,347) by inferring additional variant effects for substitutions not experimentally screened. For UK Biobank patients who carry a rare variant in TP53, FUSE significantly improves the separation of patients who develop cancer syndromes from those without cancer (p=1.77×10-6). These approaches promise to improve estimates of variant impact and broaden the utility of screening data generated from functional assays.

Challenges Related to the Use of Next-Generation Sequencing for the Optimization of Drug Therapy

Over the last decade, next-generation sequencing (NGS) methods have become increasingly used in various areas of human genomics. In routine clinical care, their use is already implemented in oncology to profile the mutational landscape of a tumor, as well as in rare disease diagnostics. However, its utilization in pharmacogenomics is largely lacking behind. Recent population-scale genome data has revealed that human pharmacogenes carry a plethora of rare genetic variations that are not interrogated by conventional array-based profiling methods and it is estimated that these variants could explain around 30% of the genetically encoded functional pharmacogenetic variability.To interpret the impact of such variants on drug response a multitude of computational tools have been developed, but, while there have been major advancements, it remains to be shown whether their accuracy is sufficient to improve personalized pharmacogenetic recommendations in robust trials. In addition, conventional short-read sequencing methods face difficulties in the interrogation of complex pharmacogenes and high NGS test costs require stringent evaluations of cost-effectiveness to decide about reimbursement by national healthcare programs. Here, we illustrate current challenges and discuss future directions toward the clinical implementation of NGS to inform genotype-guided decision-making.

Integrating deep mutational scanning and low-throughput mutagenesis data to predict the impact of amino acid variants

BACKGROUND

Evaluating the impact of amino acid variants has been a critical challenge for studying protein function and interpreting genomic data. High-throughput experimental methods like deep mutational scanning (DMS) can measure the effect of large numbers of variants in a target protein, but because DMS studies have not been performed on all proteins, researchers also model DMS data computationally to estimate variant impacts by predictors.

RESULTS

In this study, we extended a linear regression-based predictor to explore whether incorporating data from alanine scanning (AS), a widely used low-throughput mutagenesis method, would improve prediction results. To evaluate our model, we collected 146 AS datasets, mapping to 54 DMS datasets across 22 distinct proteins.

CONCLUSIONS

We show that improved model performance depends on the compatibility of the DMS and AS assays, and the scale of improvement is closely related to the correlation between DMS and AS results.

DNA-Thioguanine Nucleotides as a Marker for Thiopurine Induced Late Leukopenia after Dose Optimizing by NUDT15 C415T in Chinese Patients with IBD

Thiopurine dose optimization by thiopurine-S-methyltransferase (TPMT) or nudix hydrolase-15 (NUDT15) significantly reduced early leucopenia in Asia. However, it fails to avoid the late incidence (> 2 months). Although laboratory monitoring of 6-thioguanine nucleotides (6TGN) to optimize thiopurine dose was suggested in White patients the exact association between leucopenia and 6TGN was controversial in Asian patients. In the present study, we aimed to explore whether DNA-thioguanine nucleotides (DNA-TGs) in leukocytes, compared with 6TGN in erythrocytes, can be a better biomarker for late leucopenia. This was a prospective, observational study. Patients with inflammatory bowel disease (IBD) prescribed thiopurine from February 2019 to December 2019 were recruited. Thiopurine dose was optimized by NUDT15 C415T (rs116855232). DNA-TG and 6TGN levels were determined at the time of late leucopenia or 2 months after the stable dose was obtained. A total of 308 patients were included. Thiopurine induced late leucopenia (white blood cells < 3.5 × 10⁹ /L) were observed in 43 patients (14.0%), who had significantly higher DNA-TG concentration than those without leucopenia (P = 4.1 × 10^-9 , 423.3 (~ 342.2 to 565.7) vs. 270.5 (~ 188.1 to 394.3) fmol/μg DNA). No difference in 6TGN concentrations between leucopenia and non-leucopenia was found. With a DNA-TG threshold of 340.1 fmol/μg DNA, 83.7% of leucopenia cases could be identified. Multivariate analysis showed that DNA-TG was an independent risk factor for late leucopenia. Quantification of DNA-TG, rather than 6TGN, can be applied to gauge thiopurine therapy after NUDT15 screening in Chinese patients with IBD.

Scalable Functional Assays for the Interpretation of Human Genetic Variation

Scalable sequence-function studies have enabled the systematic analysis and cataloging of hundreds of thousands of coding and noncoding genetic variants in the human genome. This has improved clinical variant interpretation and provided insights into the molecular, biophysical, and cellular effects of genetic variants at an astonishing scale and resolution across the spectrum of allele frequencies. In this review, we explore current applications and prospects for the field and outline the principles underlying scalable functional assay design, with a focus on the study of single-nucleotide coding and noncoding variants.

Defining the sensitivity landscape of EGFR variants to tyrosine kinase inhibitors

Tyrosine kinase inhibitor (TKI) is a standard treatment for patients with NSCLC harboring constitutively active epidermal growth factor receptor (EGFR) mutations. However, most rare EGFR mutations lack treatment regimens except for the well-studied ones. We constructed two EGFR variant libraries containing substitutions, deletions, or insertions using the saturation mutagenesis method. All the variants were located in the EGFR mutation hotspot (exons 18-21). The sensitivity of these variants to afatinib, erlotinib, gefitinib, icotinib, and osimertinib was systematically studied by determining their enrichment in massively parallel cytotoxicity assays using an endogenous EGFR-depleted cell line. A total of 3914 and 70,475 variants were detected in the constructed EGFR Substitution-Deletion (Sub-Del) and exon 20 Insertion (Ins) libraries. Of the 3914 Sub-Del variants, 221 proliferated fast in the control assay and were sensitive to EGFR-TKIs. For the 70,475 Ins variants, insertions at amino acid positions 770-774 were highly enriched in all 5 TKI cytotoxicity assays. Moreover, the top 5% of the enriched insertion variants included a glycine or serine insertion at high frequency. We present a comprehensive reference for the sensitivity of EGFR variants to five commonly used TKIs. The approach used here should be applicable to other genes and targeted drugs. BACKGROUND: Tyrosine kinase inhibitors (TKIs) therapy is a standard treatment for patients with advanced non-small-cell lung carcinoma (NSCLC) when activating epidermal growth factor receptor (EGFR) mutations are detected. However, except for the well-studied EGFR mutations, most EGFR mutations lack treatment regimens. TRANSLATIONAL SIGNIFICANCE: The results demonstrated that patients with rare EGFR mutations were most likely to benefit from osimertinib therapy compared to afatinib, erlotinib, gefitinib, or icotinib therapy. This study provides a case of deep mutational scanning that simultaneously assayed substitution, deletion, and insertion variants. This approach is applicable for other oncogenes and targeted drugs.

Long-read HiFi sequencing of NUDT15: Phased full-gene haplotyping and pharmacogenomic allele discovery

To determine the phase of NUDT15 sequence variants for more comprehensive star (*) allele diplotyping, we developed a novel long-read single-molecule real-time HiFi amplicon sequencing method. A 10.5 kb NUDT15 amplicon assay was validated using reference material positive controls and additional samples for specimen type and blinded accuracy assessment. Triplicate NUDT15 HiFi sequencing of two reference material samples had nonreference genotype concordances of >99.9%, indicating that the assay is robust. Notably, short-read genome sequencing of a subset of samples was unable to determine the phase of star (*) allele-defining NUDT15 variants, resulting in ambiguous diplotype results. In contrast, long-read HiFi sequencing phased all variants across the NUDT15 amplicons, including a *2/*9 diplotype that previously was characterized as *1/*2 in the 1000 Genomes Project v3 data set. Assay throughput was also tested using 8.5 kb amplicons from 100 Ashkenazi Jewish individuals, which identified a novel NUDT15 *1 suballele (c.-121G>A) and a rare likely deleterious coding variant (p.Pro129Arg). Both novel alleles were Sanger confirmed and assigned as *1.007 and *20, respectively, by the PharmVar Consortium. Taken together, NUDT15 HiFi amplicon sequencing is an innovative method for phased full-gene characterization and novel allele discovery, which could improve NUDT15 pharmacogenomic testing and subsequent phenotype prediction.

High-throughput identification of prefusion-stabilizing mutations in SARS-CoV-2 spike

Designing prefusion-stabilized SARS-CoV-2 spike is critical for the effectiveness of COVID-19 vaccines. All COVID-19 vaccines in the US encode spike with K986P/V987P mutations to stabilize its prefusion conformation. However, contemporary methods on engineering prefusion-stabilized spike immunogens involve tedious experimental work and heavily rely on structural information. Here, we established a systematic and unbiased method of identifying mutations that concomitantly improve expression and stabilize the prefusion conformation of the SARS-CoV-2 spike. Our method integrated a fluorescence-based fusion assay, mammalian cell display technology, and deep mutational scanning. As a proof-of-concept, this method was applied to a region in the S2 domain that includes the first heptad repeat and central helix. Our results revealed that besides K986P and V987P, several mutations simultaneously improved expression and significantly lowered the fusogenicity of the spike. As prefusion stabilization is a common challenge for viral immunogen design, this work will help accelerate vaccine development against different viruses.

Challenges and opportunities associated with rare-variant pharmacogenomics

Recent advances in next-generation sequencing (NGS) have resulted in the identification of tens of thousands of rare pharmacogenetic variations with unknown functional effects. However, although such pharmacogenetic variations have been estimated to account for a considerable amount of the heritable variability in drug response and toxicity, accurate interpretation at the level of the individual patient remains challenging. We discuss emerging strategies and concepts to close this translational gap. We illustrate how massively parallel experimental assays, artificial intelligence (AI), and machine learning can synergize with population-scale biobank projects to facilitate the interpretation of NGS data to individualize clinical decision-making and personalized medicine.

An Active Learning Framework Improves Tumor Variant Interpretation

SIGNIFICANCE

A novel machine learning approach predicts the impact of tumor mutations on cellular phenotypes, overcomes limited training data, minimizes costly functional validation, and advances efforts to implement cancer precision medicine.

ACE2 and TMPRSS2 SARS-CoV-2 infectivity genes: deep mutational scanning and characterization of missense variants

The human angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) proteins play key roles in the cellular internalization of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the coronavirus responsible for the coronavirus disease of 2019 (COVID-19) pandemic. We set out to functionally characterize the ACE2 and TMPRSS2 protein abundance for variant alleles encoding these proteins that contained non-synonymous single-nucleotide polymorphisms (nsSNPs) in their open reading frames (ORFs). Specifically, a high-throughput assay, deep mutational scanning (DMS), was employed to test the functional implications of nsSNPs, which are variants of uncertain significance in these two genes. Specifically, we used a 'landing pad' system designed to quantify the protein expression for 433 nsSNPs that have been observed in the ACE2 and TMPRSS2 ORFs and found that 8 of 127 ACE2, 19 of 157 TMPRSS2 isoform 1 and 13 of 149 TMPRSS2 isoform 2 variant proteins displayed less than ~25% of the wild-type protein expression, whereas 4 ACE2 variants displayed 25% or greater increases in protein expression. As a result, we concluded that nsSNPs in genes encoding ACE2 and TMPRSS2 might potentially influence SARS-CoV-2 infectivity. These results can now be applied to DNA sequence data for patients infected with SARS-CoV-2 to determine the possible impact of patient-based DNA sequence variation on the clinical course of SARS-CoV-2 infection.

Maintenance therapy for acute lymphoblastic leukemia: basic science and clinical translations

Maintenance therapy (MT) with oral methotrexate (MTX) and 6-mercaptopurine (6-MP) is essential for the cure of acute lymphoblastic leukemia (ALL). MTX and 6-MP interfere with nucleotide synthesis and salvage pathways. The primary cytotoxic mechanism involves the incorporation of thioguanine nucleotides (TGNs) into DNA (as DNA-TG), which may be enhanced by the inhibition of de novo purine synthesis by other MTX/6-MP metabolites. Co-medication during MT is common. Although Pneumocystis jirovecii prophylaxis appears safe, the benefit of glucocorticosteroid/vincristine pulses in improving survival and of allopurinol to moderate 6-MP pharmacokinetics remains uncertain. Numerous genetic polymorphisms influence the pharmacology, efficacy, and toxicity (mainly myelosuppression and hepatotoxicity) of MTX and thiopurines. Thiopurine S-methyltransferase (encoded by TPMT) decreases TGNs but increases methylated 6-MP metabolites (MeMPs); similarly, nudix hydrolase 15 (encoded by NUDT15) also decreases TGNs available for DNA incorporation. Loss-of-function variants in both genes are currently used to guide MT, but do not fully explain the inter-patient variability in thiopurine toxicity. Because of the large inter-individual variations in MTX/6-MP bioavailability and metabolism, dose adjustments are traditionally guided by the degree of myelosuppression, but this does not accurately reflect treatment intensity. DNA-TG is a common downstream metabolite of MTX/6-MP combination chemotherapy, and a higher level of DNA-TG has been associated with a lower relapse hazard, leading to the development of the Thiopurine Enhanced ALL Maintenance (TEAM) strategy-the addition of low-dose (2.5-12.5 mg/m²/day) 6-thioguanine to the 6-MP/MTX backbone-that is currently being tested in a randomized ALLTogether1 trial (EudraCT: 2018-001795-38). Mutations in the thiopurine and MTX metabolism pathways, and in the mismatch repair genes have been identified in early ALL relapses, providing valuable insights to assist the development of strategies to detect imminent relapse, to facilitate relapse salvage therapy, and even to bring about changes in frontline ALL therapy to mitigate this relapse risk.

Keeping pace with CYP2D6 haplotype discovery: innovative methods to assign function

The discovery of haplotypes with unknown or uncertain function in the CYP2D6 pharmacogene is outpacing the capabilities of traditional in vitro and in vivo approaches to characterize their function. This challenge will undoubtedly grow as pharmacogenomic research becomes more inclusive of globally diverse populations. As accurate phenotypic assignment is paramount to the utility of pharmacogenomics, high-throughput technologies are needed for this complex pharmacogene. We describe the evolving landscape of innovative approaches to assign function to CYP2D6 haplotypes and possibilities for adopting these technologies into cohesive processes. Promising approaches include ADME-optimized prediction frameworks, machine learning algorithms, deep mutational scanning and phenoconversion predictions. Implementing these approaches will lead to improved personalization of treatment for patients.

Comprehensive characterization of pharmacogenetic variants in TPMT and NUDT15 in children with acute lymphoblastic leukemia

Thiopurines [e.g. 6-mercaptopurine (6MP)] are essential for the cure of acute lymphoblastic leukemia (ALL) but can cause dose-limiting hematopoietic toxicity. Germline variants in drug-metabolizing enzyme genes TPMT and NUDT15 have been linked to the risk of thiopurine toxicity. However, the full spectrum of genetic polymorphism in these genes and their impact on the pharmacological effects of thiopurines remain unclear. Herein, we comprehensively sequenced the TPMT and NUDT15 genes in 685 children with ALL from the Children's Oncology Group AALL03N1 trial and evaluated their association with 6MP dose intensity. We identified 6 and 5 coding variants in TPMT and NUDT15 respectively, confirming the association at known pharmacogenetic variants. Importantly, we discovered a novel gain-of-function noncoding variants in TPMT associated with increased 6MP tolerance (rs12199316), with independent validation in 380 patients from the St. Jude Total Therapy XV protocol. Located adjacent to a regulatory DNA element, this intergenic variant was strongly associated TPMT transcription, with the variant allele linked to higher expression (P = 2.6 × 10-9). For NUDT15, one noncoding common variant, rs73189762, was identified as potentially related to 6MP intolerance. Collectively, we described pharmacogenetic variants in TPMT and NUDT15 associated with thiopurine sensitivity, providing further insights for implementing pharmacogenetics-based thiopurine individualization.

Predicting and interpreting large-scale mutagenesis data using analyses of protein stability and conservation

Understanding and predicting the functional consequences of single amino acid changes is central in many areas of protein science. Here, we collect and analyze experimental measurements of effects of >150,000 variants in 29 proteins. We use biophysical calculations to predict changes in stability for each variant and assess them in light of sequence conservation. We find that the sequence analyses give more accurate prediction of variant effects than predictions of stability and that about half of the variants that show loss of function do so due to stability effects. We construct a machine learning model to predict variant effects from protein structure and sequence alignments and show how the two sources of information support one another and enable mechanistic interpretations. Together, our results show how one can leverage large-scale experimental assessments of variant effects to gain deeper and general insights into the mechanisms that cause loss of function.

NUDT15 Genetic Variants in Chinese Han, Uighur, Kirghiz, and Dai Nationalities

BACKGROUND

Thiopurines are widely used as anti-cancer and immunosuppressant agents, but have a narrow therapeutic index owing to frequent toxicity and life-threatening bone marrow suppression. The nudix hydrolase 15 (NUDT15) genetic polymorphism is strongly associated with the tolerance and myelosuppressive effect of mercaptopurine administration, but the frequency of NUDT15 variants is known to vary among different ethnic groups or nationalities. At present, the NUDT15 gene polymorphism in ethnic minorities such as the Uighur, Kirghiz, and Dai nationalities in China is unclear.

PROCEDURE

DNA samples were isolated from 1,071 Chinese children, including 675 Han children with acute lymphoblastic leukemia and 396 healthy minority children, including 118 Uighur, 126 Kirghiz, and 152 Dai participants. The coding regions of NUDT15 exons 1 to 3 were amplified by polymerase chain reaction. NUDT15 genotypes were identified by Sanger sequencing.

RESULTS

Five NUDT15 genetic variants of coding regions including rs746071566 (c.55_56insGAGTCG), rs186364861 (c.52G > A), c.137C > G, and c.138T > G in exon 1, and the variant rs116855232 (c.415C > T) in exon 3 were found among the participants. The frequency of NUDT15 rs746071566 variants was lower in the Uighur and Kirghiz populations than in the Han population and in other East Asian nationalities, while the frequency of c.415C > T variants was lower in the Dai population. The c.52G > A variant was relatively uncommon in children of the Han, Uighur, Kirghiz, and Dai ethnic groups. Notably, the rare variants c.137C > G and c.138T > G in a Uighur child were predicted to be disruptive sites.

CONCLUSION

In summary, our results illustrate the NUDT15 polymorphisms in Chinese children of Han, Uighur, Kirghiz, and Dai nationalities, and provide the most effective detection recommendations for different ethnic groups to predict thiopurine-related toxicity, which could be used to guide future clinical thiopurine dose adjustment.