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

Role of Tumor Suppressor PTEN and Its Regulation in Malignant Transformation of Endometrium

Tumor-suppressive effects of PTEN are well-known, but modern evidence suggest that they are not limited to its ability to inhibit pro-oncogenic PI3K/AKT signaling pathway. Features of PTEN structure facilitate its interaction with substrates of different nature and display its activity in various ways both in the cytoplasm and in cell nuclei, which makes it possible to take a broader look at its ability to suppress tumor growth. The possible mechanisms of the loss of PTEN effects are also diverse - PTEN can be regulated at many levels, leading to change in the protein activity or its amount in the cell, while their significance for the development of malignant tumors has yet to be studied. Here we summarize the current data on the PTEN structure, its functions and changes in its regulatory mechanisms during malignant transformation of the cells, focusing on one of the most sensitive to the loss of PTEN types of malignant tumors - endometrial cancer.

Predicting functional effect of missense variants using graph attention neural networks

Accurate prediction of damaging missense variants is critically important for interpreting a genome sequence. Although many methods have been developed, their performance has been limited. Recent advances in machine learning and the availability of large-scale population genomic sequencing data provide new opportunities to considerably improve computational predictions. Here we describe the graphical missense variant pathogenicity predictor (gMVP), a new method based on graph attention neural networks. Its main component is a graph with nodes that capture predictive features of amino acids and edges weighted by co-evolution strength, enabling effective pooling of information from the local protein context and functionally correlated distal positions. Evaluation of deep mutational scan data shows that gMVP outperforms other published methods in identifying damaging variants in TP53, PTEN, BRCA1 and MSH2. Furthermore, it achieves the best separation of de novo missense variants in neuro developmental disorder cases from those in controls. Finally, the model supports transfer learning to optimize gain- and loss-of-function predictions in sodium and calcium channels. In summary, we demonstrate that gMVP can improve interpretation of missense variants in clinical testing and genetic studies.

Signaling pathways and targeted therapies in lung squamous cell carcinoma: mechanisms and clinical trials

Lung cancer is the leading cause of cancer-related death across the world. Unlike lung adenocarcinoma, patients with lung squamous cell carcinoma (LSCC) have not benefitted from targeted therapies. Although immunotherapy has significantly improved cancer patients' outcomes, the relatively low response rate and severe adverse events hinder the clinical application of this promising treatment in LSCC. Therefore, it is of vital importance to have a better understanding of the mechanisms underlying the pathogenesis of LSCC as well as the inner connection among different signaling pathways, which will surely provide opportunities for more effective therapeutic interventions for LSCC. In this review, new insights were given about classical signaling pathways which have been proved in other cancer types but not in LSCC, including PI3K signaling pathway, VEGF/VEGFR signaling, and CDK4/6 pathway. Other signaling pathways which may have therapeutic potentials in LSCC were also discussed, including the FGFR1 pathway, EGFR pathway, and KEAP1/NRF2 pathway. Next, chromosome 3q, which harbors two key squamous differentiation markers SOX2 and TP63 is discussed as well as its related potential therapeutic targets. We also provided some progress of LSCC in epigenetic therapies and immune checkpoints blockade (ICB) therapies. Subsequently, we outlined some combination strategies of ICB therapies and other targeted therapies. Finally, prospects and challenges were given related to the exploration and application of novel therapeutic strategies for LSCC.

The trilateral interactions between mammalian target of rapamycin (mTOR) signaling, the circadian clock, and psychiatric disorders: an emerging model

Circadian (~24 h) rhythms in physiology and behavior are evolutionarily conserved and found in almost all living organisms. The rhythms are endogenously driven by daily oscillatory activities of so-called "clock genes/proteins", which are widely distributed throughout the mammalian brain. Mammalian (mechanistic) target of rapamycin (mTOR) signaling is a fundamental intracellular signal transduction cascade that controls important neuronal processes including neurodevelopment, synaptic plasticity, metabolism, and aging. Dysregulation of the mTOR pathway is associated with psychiatric disorders including autism spectrum disorders (ASD) and mood disorders (MD), in which patients often exhibit disrupted daily physiological rhythms and abnormal circadian gene expression in the brain. Recent work has found that the activities of mTOR signaling are temporally controlled by the circadian clock and exhibit robust circadian oscillations in multiple systems. In the meantime, mTOR signaling regulates fundamental properties of the central and peripheral circadian clocks, including period length, entrainment, and synchronization. Whereas the underlying mechanisms remain to be fully elucidated, increasing clinical and preclinical evidence support significant crosstalk between mTOR signaling, the circadian clock, and psychiatric disorders. Here, we review recent progress in understanding the trilateral interactions and propose an "interaction triangle" model between mTOR signaling, the circadian clock, and psychiatric disorders (focusing on ASD and MD).

SMARCB1-Deficient Cancers: Novel Molecular Insights and Therapeutic Vulnerabilities

Simple Summary

Loss of SMARCB1 has been identified as the sole mutation in a number of rare pediatric and adult cancers, most of which have a poor prognosis despite intensive therapies including surgery, radiation, and chemotherapy. Thus, a more robust understanding of the mechanisms driving this set of cancers is vital to improving patient treatment and outcomes. This review outlines recent advances made in our understanding of the function of SMARCB1 and how these advances have been used to discover putative therapeutic vulnerabilities.

Abstract

SMARCB1 is a critical component of the BAF complex that is responsible for global chromatin remodeling. Loss of SMARCB1 has been implicated in the initiation of cancers such as malignant rhabdoid tumor (MRT), atypical teratoid rhabdoid tumor (ATRT), and, more recently, renal medullary carcinoma (RMC). These SMARCB1-deficient tumors have remarkably stable genomes, offering unique insights into the epigenetic mechanisms in cancer biology. Given the lack of druggable targets and the high mortality associated with SMARCB1-deficient tumors, a significant research effort has been directed toward understanding the mechanisms of tumor transformation and proliferation. Accumulating evidence suggests that tumorigenicity arises from aberrant enhancer and promoter regulation followed by dysfunctional transcriptional control. In this review, we outline key mechanisms by which loss of SMARCB1 may lead to tumor formation and cover how these mechanisms have been used for the design of targeted therapy.

Molecular Landscape of LncRNAs in Prostate Cancer: A focus on pathways and therapeutic targets for intervention

Background

One of the most malignant tumors in men is prostate cancer that is still incurable due to its heterogenous and progressive natures. Genetic and epigenetic changes play significant roles in its development. The RNA molecules with more than 200 nucleotides in length are known as lncRNAs and these epigenetic factors do not encode protein. They regulate gene expression at transcriptional, post-transcriptional and epigenetic levels. LncRNAs play vital biological functions in cells and in pathological events, hence their expression undergoes dysregulation.

Aim of review

The role of epigenetic alterations in prostate cancer development are emphasized here. Therefore, lncRNAs were chosen for this purpose and their expression level and interaction with other signaling networks in prostate cancer progression were examined.

Key scientific concepts of review

The aberrant expression of lncRNAs in prostate cancer has been well-documented and progression rate of tumor cells are regulated via affecting STAT3, NF-κB, Wnt, PI3K/Akt and PTEN, among other molecular pathways. Furthermore, lncRNAs regulate radio-resistance and chemo-resistance features of prostate tumor cells. Overexpression of tumor-promoting lncRNAs such as HOXD-AS1 and CCAT1 can result in drug resistance. Besides, lncRNAs can induce immune evasion of prostate cancer via upregulating PD-1. Pharmacological compounds such as quercetin and curcumin have been applied for targeting lncRNAs. Furthermore, siRNA tool can reduce expression of lncRNAs thereby suppressing prostate cancer progression. Prognosis and diagnosis of prostate tumor at clinical course can be evaluated by lncRNAs. The expression level of exosomal lncRNAs such as lncRNA-p21 can be investigated in serum of prostate cancer patients as a reliable biomarker.

Interpreting protein variant effects with computational predictors and deep mutational scanning

Computational predictors of genetic variant effect have advanced rapidly in recent years. These programs provide clinical and research laboratories with a rapid and scalable method to assess the likely impacts of novel variants. However, it can be difficult to know to what extent we can trust their results. To benchmark their performance, predictors are often tested against large datasets of known pathogenic and benign variants. These benchmarking data may overlap with the data used to train some supervised predictors, which leads to data re-use or circularity, resulting in inflated performance estimates for those predictors. Furthermore, new predictors are usually found by their authors to be superior to all previous predictors, which suggests some degree of computational bias in their benchmarking. Large-scale functional assays known as deep mutational scans provide one possible solution to this problem, providing independent datasets of variant effect measurements. In this Review, we discuss some of the key advances in predictor methodology, current benchmarking strategies and how data derived from deep mutational scans can be used to overcome the issue of data circularity. We also discuss the ability of such functional assays to directly predict clinical impacts of mutations and how this might affect the future need for variant effect predictors.

Shape shifting: The multiple conformational substates of the PTEN N-terminal PIP2 -binding domain

The Phosphatase and TENsin homolog deleted on chromosome 10 (PTEN) is a chief regulator of a variety of cellular processes including cell proliferation, migration, growth, and death. It is also a major tumor suppressor gene that is frequently mutated or lost under cancerous conditions. PTEN encodes a dual-specificity (lipid and protein) phosphatase that negatively regulates the PI3K/AKT/mTOR signaling pathway where the PIP2 -binding domain (PBD) regulates the lipid phosphatase function. Unfortunately, despite two decades of research, a full-length structure of PTEN remains elusive, leaving open questions regarding PTEN's disordered regions that mediate protein stability, post-translational modifications, protein-protein interactions, while also hindering the design of small molecules that can regulate PTEN's function. Here, we utilized a combination of crosslinking mass spectrometry, in silico predicted structural modeling (including AlphaFold2), molecular docking, molecular dynamics simulations, and residue interaction network modeling to obtain structural details and molecular insight into the behavior of the PBD of PTEN. Our study shows that the PBD exists in multiple conformations which suggests its ability to regulate PTEN's variety of functions. Studying how these specific conformational substates contribute to PTEN function is imperative to defining its function in disease pathogenesis, and to delineate ways to modulate its tumor suppressor activity.

Computational and experimental methods for classifying variants of unknown clinical significance

The increase in sequencing capacity, reduction in costs, and national and international coordinated efforts have led to the widespread introduction of next-generation sequencing (NGS) technologies in patient care. More generally, human genetics and genomic medicine are gaining importance for more and more patients. Some communities are already discussing the prospect of sequencing each individual's genome at time of birth. Together with digital health records, this shall enable individualized treatments and preventive measures, so-called precision medicine. A central step in this process is the identification of disease causal mutations or variant combinations that make us more susceptible for diseases. Although various technological advances have improved the identification of genetic alterations, the interpretation and ranking of the identified variants remains a major challenge. Based on our knowledge of molecular processes or previously identified disease variants, we can identify potentially functional genetic variants and, using different lines of evidence, we are sometimes able to demonstrate their pathogenicity directly. However, the vast majority of variants are classified as variants of uncertain clinical significance (VUSs) with not enough experimental evidence to determine their pathogenicity. In these cases, computational methods may be used to improve the prioritization and an increasing toolbox of experimental methods is emerging that can be used to assay the molecular effects of VUSs. Here, we discuss how computational and experimental methods can be used to create catalogs of variant effects for a variety of molecular and cellular phenotypes. We discuss the prospects of integrating large-scale functional data with machine learning and clinical knowledge for the development of accurate pathogenicity predictions for clinical applications.

Comprehensive characterization of PTEN mutational profile in a series of 34,129 colorectal cancers

Loss of expression or activity of the tumor suppressor PTEN acts similarly to an activating mutation in the oncogene PIK3CA in elevating intracellular levels of phosphatidylinositol (3,4,5)-trisphosphate (PIP3), inducing signaling by AKT and other pro-tumorigenic signaling proteins. Here, we analyze sequence data for 34,129 colorectal cancer (CRC) patients, capturing 3,434 PTEN mutations. We identify specific patterns of PTEN mutation associated with microsatellite stability/instability (MSS/MSI), tumor mutational burden (TMB), patient age, and tumor location. Within groups separated by MSS/MSI status, this identifies distinct profiles of nucleotide hotspots, and suggests differing profiles of protein-damaging effects of mutations. Moreover, discrete categories of PTEN mutations display non-identical patterns of co-occurrence with mutations in other genes important in CRC pathogenesis, including KRAS, APC, TP53, and PIK3CA. These data provide context for clinical targeting of proteins upstream and downstream of PTEN in distinct CRC cohorts.

Linking protein structural and functional change to mutation using amino acid networks

The function of a protein is strongly dependent on its structure. During evolution, proteins acquire new functions through mutations in the amino-acid sequence. Given the advance in deep mutational scanning, recent findings have found functional change to be position dependent, notwithstanding the chemical properties of mutant and mutated amino acids. This could indicate that structural properties of a given position are potentially responsible for the functional relevance of a mutation. Here, we looked at the relation between structure and function of positions using five proteins with experimental data of functional change available. In order to measure structural change, we modeled mutated proteins via amino-acid networks and quantified the perturbation of each mutation. We found that structural change is position dependent, and strongly related to functional change. Strong changes in protein structure correlate with functional loss, and positions with functional gain due to mutations tend to be structurally robust. Finally, we constructed a computational method to predict functionally sensitive positions to mutations using structural change that performs well on all five proteins with a mean precision of 74.7% and recall of 69.3% of all functional positions.

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.

LYRUS: a machine learning model for predicting the pathogenicity of missense variants

SUMMARY

Single amino acid variations (SAVs) are a primary contributor to variations in the human genome. Identifying pathogenic SAVs can provide insights to the genetic architecture of complex diseases. Most approaches for predicting the functional effects or pathogenicity of SAVs rely on either sequence or structural information. This study presents 〈Lai Yang Rubenstein Uzun Sarkar〉 (LYRUS), a machine learning method that uses an XGBoost classifier to predict the pathogenicity of SAVs. LYRUS incorporates five sequence-based, six structure-based and four dynamics-based features. Uniquely, LYRUS includes a newly proposed sequence co-evolution feature called the variation number. LYRUS was trained using a dataset that contains 4363 protein structures corresponding to 22 639 SAVs from the ClinVar database, and tested using the VariBench testing dataset. Performance analysis showed that LYRUS achieved comparable performance to current variant effect predictors. LYRUS's performance was also benchmarked against six Deep Mutational Scanning datasets for PTEN and TP53.

AVAILABILITY AND IMPLEMENTATION

LYRUS is freely available and the source code can be found at https://github.com/jiaying2508/LYRUS.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics Advances online.

The Novel Phosphatase Domain Mutations Q171R and Y65S Switch PTEN from Tumor Suppressor to Oncogene

Phosphatase and tensin homolog deleted on chromosome 10, or PTEN, is a well-characterized tumor suppressor with both lipid and protein phosphatase activities. PTEN is often downregulated by epigenetic mechanisms such as hypermethylation, which leads to constitutive activation of the PI3K-Akt pathway. Large datasets from next-generation sequencing, however, revealed that mutations in PTEN may not only hamper protein function but may also affect interactions with downstream effectors, leading to variable oncogenic readouts. Here, two novel PTEN mutations, Q171R and Y65S, identified in Filipino colorectal cancer patients, were phenotypically characterized in NIH3T3 and HCT116 cells, alongside the C124S canonical mutant and wild-type controls. The novel mutants increased cellular proliferation, resistance to apoptosis and migratory capacity. They induced gross morphological changes including cytoplasmic shrinkage, increased cellular protrusions and extensive cytoskeletal reorganization. The mutants also induced a modest increase in Akt phosphorylation. Further mechanistic studies will help determine the differential oncogenic potencies of these mutants, and resolve whether the structural constraints imposed by the mutations may have altered associations with downstream effectors.

Closing the gap: Systematic integration of multiplexed functional data resolves variants of uncertain significance in BRCA1, TP53, and PTEN

Clinical interpretation of missense variants is challenging because the majority identified by genetic testing are rare and their functional effects are unknown. Consequently, most variants are of uncertain significance and cannot be used for clinical diagnosis or management. Although not much can be done to ameliorate variant rarity, multiplexed assays of variant effect (MAVEs), where thousands of single-nucleotide variant effects are simultaneously measured experimentally, provide functional evidence that can help resolve variants of unknown significance (VUSs). However, a rigorous assessment of the clinical value of multiplexed functional data for variant interpretation is lacking. Thus, we systematically combined previously published BRCA1, TP53, and PTEN multiplexed functional data with phenotype and family history data for 324 VUSs identified by a single diagnostic testing laboratory. We curated 49,281 variant functional scores from MAVEs for these three genes and integrated four different TP53 multiplexed functional datasets into a single functional prediction for each variant by using machine learning. We then determined the strength of evidence provided by each multiplexed functional dataset and reevaluated 324 VUSs. Multiplexed functional data were effective in driving variant reclassification when combined with clinical data, eliminating 49% of VUSs for BRCA1, 69% for TP53, and 15% for PTEN. Thus, multiplexed functional data, which are being generated for numerous genes, are poised to have a major impact on clinical variant interpretation.

Disease variant prediction with deep generative models of evolutionary data

Quantifying the pathogenicity of protein variants in human disease-related genes would have a marked effect on clinical decisions, yet the overwhelming majority (over 98%) of these variants still have unknown consequences^1-3. In principle, computational methods could support the large-scale interpretation of genetic variants. However, state-of-the-art methods^4-10 have relied on training machine learning models on known disease labels. As these labels are sparse, biased and of variable quality, the resulting models have been considered insufficiently reliable¹¹. Here we propose an approach that leverages deep generative models to predict variant pathogenicity without relying on labels. By modelling the distribution of sequence variation across organisms, we implicitly capture constraints on the protein sequences that maintain fitness. Our model EVE (evolutionary model of variant effect) not only outperforms computational approaches that rely on labelled data but also performs on par with, if not better than, predictions from high-throughput experiments, which are increasingly used as evidence for variant classification^12-16. We predict the pathogenicity of more than 36 million variants across 3,219 disease genes and provide evidence for the classification of more than 256,000 variants of unknown significance. Our work suggests that models of evolutionary information can provide valuable independent evidence for variant interpretation that will be widely useful in research and clinical settings.

Integrating thousands of PTEN variant activity and abundance measurements reveals variant subgroups and new dominant negatives in cancers

Background

PTEN is a multi-functional tumor suppressor protein regulating cell growth, immune signaling, neuronal function, and genome stability. Experimental characterization can help guide the clinical interpretation of the thousands of germline or somatic PTEN variants observed in patients. Two large-scale mutational datasets, one for PTEN variant intracellular abundance encompassing 4112 missense variants and one for lipid phosphatase activity encompassing 7244 variants, were recently published. The combined information from these datasets can reveal variant-specific phenotypes that may underlie various clinical presentations, but this has not been comprehensively examined, particularly for somatic PTEN variants observed in cancers.

Methods

Here, we add to these efforts by measuring the intracellular abundance of 764 new PTEN variants and refining abundance measurements for 3351 previously studied variants. We use this expanded and refined PTEN abundance dataset to explore the mutational patterns governing PTEN intracellular abundance, and then incorporate the phosphatase activity data to subdivide PTEN variants into four functionally distinct groups.

Results

This analysis revealed a set of highly abundant but lipid phosphatase defective variants that could act in a dominant-negative fashion to suppress PTEN activity. Two of these variants were, indeed, capable of dysregulating Akt signaling in cells harboring a WT PTEN allele. Both variants were observed in multiple breast or uterine tumors, demonstrating the disease relevance of these high abundance, inactive variants.

Conclusions

We show that multidimensional, large-scale variant functional data, when paired with public cancer genomics datasets and follow-up assays, can improve understanding of uncharacterized cancer-associated variants, and provide better insights into how they contribute to oncogenesis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-021-00984-x.

The structural basis of PTEN regulation by multi-site phosphorylation

Phosphatase and tensin homolog (PTEN) is a phosphatidylinositol-3,4,5-triphosphate (PIP₃) phospholipid phosphatase that is commonly mutated or silenced in cancer. PTEN's catalytic activity, cellular membrane localization and stability are orchestrated by a cluster of C-terminal phosphorylation (phospho-C-tail) events on Ser380, Thr382, Thr383 and Ser385, but the molecular details of this multi-faceted regulation have remained uncertain. Here we use a combination of protein semisynthesis, biochemical analysis, NMR, X-ray crystallography and computational simulations on human PTEN and its sea squirt homolog, VSP, to obtain a detailed picture of how the phospho-C-tail forms a belt around the C2 and phosphatase domains of PTEN. We also visualize a previously proposed dynamic N-terminal α-helix and show that it is key for PTEN catalysis but disordered upon phospho-C-tail interaction. This structural model provides a comprehensive framework for how C-tail phosphorylation can impact PTEN's cellular functions.

Linking genome variants to disease: scalable approaches to test the functional impact of human mutations

The application of genomics to medicine has accelerated the discovery of mutations underlying disease and has enhanced our knowledge of the molecular underpinnings of diverse pathologies. As the amount of human genetic material queried via sequencing has grown exponentially in recent years, so too has the number of rare variants observed. Despite progress, our ability to distinguish which rare variants have clinical significance remains limited. Over the last decade, however, powerful experimental approaches have emerged to characterize variant effects orders of magnitude faster than before. Fueled by improved DNA synthesis and sequencing and, more recently, by CRISPR/Cas9 genome editing, multiplex functional assays provide a means of generating variant effect data in wide-ranging experimental systems. Here, I review recent applications of multiplex assays that link human variants to disease phenotypes and I describe emerging strategies that will enhance their clinical utility in coming years.

A scalable Drosophila assay for clinical interpretation of human PTEN variants in suppression of PI3K/AKT induced cellular proliferation

Gene variant discovery is becoming routine, but it remains difficult to usefully interpret the functional consequence or disease relevance of most variants. To fill this interpretation gap, experimental assays of variant function are becoming common place. Yet, it remains challenging to make these assays reproducible, scalable to high numbers of variants, and capable of assessing defined gene-disease mechanism for clinical interpretation aligned to the ClinGen Sequence Variant Interpretation (SVI) Working Group guidelines for 'well-established assays'. Drosophila melanogaster offers great potential as an assay platform, but was untested for high numbers of human variants adherent to these guidelines. Here, we wished to test the utility of Drosophila as a platform for scalable well-established assays. We took a genetic interaction approach to test the function of ~100 human PTEN variants in cancer-relevant suppression of PI3K/AKT signaling in cellular growth and proliferation. We validated the assay using biochemically characterized PTEN mutants as well as 23 total known pathogenic and benign PTEN variants, all of which the assay correctly assigned into predicted functional categories. Additionally, function calls for these variants correlated very well with our recent published data from a human cell line. Finally, using these pathogenic and benign variants to calibrate the assay, we could set readout thresholds for clinical interpretation of the pathogenicity of 70 other PTEN variants. Overall, we demonstrate that Drosophila offers a powerful assay platform for clinical variant interpretation, that can be used in conjunction with other well-established assays, to increase confidence in the accurate assessment of variant function and pathogenicity.

Classification of MSH6 Variants of Uncertain Significance Using Functional Assays

Lynch syndrome (LS) is one of the most common hereditary cancer predisposition syndromes worldwide. Individuals with LS have a high risk of developing colorectal or endometrial cancer, as well as several other cancers. LS is caused by autosomal dominant pathogenic variants in one of the DNA mismatch repair (MMR) genes MLH1, MSH2, PMS2 or MSH6, and typically include truncating variants, such as frameshift, nonsense or splicing variants. However, a significant number of missense, intronic, or silent variants, or small in-frame insertions/deletions, are detected during genetic screening of the MMR genes. The clinical effects of these variants are often more difficult to predict, and a large fraction of these variants are classified as variants of uncertain significance (VUS). It is pivotal for the clinical management of LS patients to have a clear genetic diagnosis, since patients benefit widely from screening, preventive and personal therapeutic measures. Moreover, in families where a pathogenic variant is identified, testing can be offered to family members, where non-carriers can be spared frequent surveillance, while carriers can be included in cancer surveillance programs. It is therefore important to reclassify VUSs, and, in this regard, functional assays can provide insight into the effect of a variant on the protein or mRNA level. Here, we briefly describe the disorders that are related to MMR deficiency, as well as the structure and function of MSH6. Moreover, we review the functional assays that are used to examine VUS identified in MSH6 and discuss the results obtained in relation to the ACMG/AMP PS3/BS3 criterion. We also provide a compiled list of the MSH6 variants examined by these assays. Finally, we provide a future perspective on high-throughput functional analyses with specific emphasis on the MMR genes.

Exploring absent protein function in yeast: assaying post translational modification and human genetic variation

Yeast is a valuable eukaryotic model organism that has evolved many processes conserved up to humans, yet many protein functions, including certain DNA and protein modifications, are absent. It is this absence of protein function that is fundamental to approaches using yeast as an in vivo test system to investigate human proteins. Functionality of the heterologous expressed proteins is connected to a quantitative, selectable phenotype, enabling the systematic analyses of mechanisms and specificity of DNA modification, post-translational protein modifications as well as the impact of annotated cancer mutations and coding variation on protein activity and interaction. Through continuous improvements of yeast screening systems, this is increasingly carried out on a global scale using deep mutational scanning approaches. Here we discuss the applicability of yeast systems to investigate absent human protein function with a specific focus on the impact of protein variation on protein-protein interaction modulation.

In silico saturation mutagenesis of cancer genes

Despite the existence of good catalogues of cancer genes^1,2, identifying the specific mutations of those genes that drive tumorigenesis across tumour types is still a largely unsolved problem. As a result, most mutations identified in cancer genes across tumours are of unknown significance to tumorigenesis³. We propose that the mutations observed in thousands of tumours-natural experiments testing their oncogenic potential replicated across individuals and tissues-can be exploited to solve this problem. From these mutations, features that describe the mechanism of tumorigenesis of each cancer gene and tissue may be computed and used to build machine learning models that encapsulate these mechanisms. Here we demonstrate the feasibility of this solution by building and validating 185 gene-tissue-specific machine learning models that outperform experimental saturation mutagenesis in the identification of  driver and passenger mutations. The models and their assessment of each mutation are designed to be interpretable, thus avoiding a black-box prediction device. Using these models, we outline the blueprints of potential driver mutations in cancer genes, and demonstrate the role of mutation probability in shaping the landscape of observed driver mutations. These blueprints will support the interpretation of newly sequenced tumours in patients and the study of the mechanisms of tumorigenesis of cancer genes across tissues.

Prevalence and clinical/molecular characteristics of PTEN mutations in Turkish children with autism spectrum disorders and macrocephaly

Background

Phosphatase and tensin homolog (PTEN) germline mutations are associated with cancer syndromes (PTEN hamartoma tumor syndrome; PHTS) and in pediatric patients with autism spectrum disorder (ASD) and macrocephaly. The exact prevalence of PTEN mutations in patients with ASD and macrocephaly is uncertain; with prevalence rates ranging from 1% to 17%. Most studies are retrospective and contain more adult than pediatric patients, there is a need for more prospective pediatric studies.

Methods

We recruited 131 patients (108 males, 23 females) with ASD and macrocephaly between the ages of 3 and 18 from five child and adolescent psychiatry clinics in Turkey from July 2018 to December 2019. We defined macrocephaly as occipito‐frontal HC size at or greater than 2 standard deviations (SD) above the mean for age and sex on standard growth charts. PTEN gene sequence analysis was performed using a MiSeq next generation sequencing (NGS) platform, (Illumina).

Conclusion

PTEN gene sequence analyses identified three pathogenic/likely pathogenic mutations [NM_000314.6; p.(Pro204Leu), (p.Arg233*) and novel (p.Tyr176Cys*8)] and two variants of uncertain significance (VUS) [NM_000314.6; p.(Ala79Thr) and c.*10del]. We also report that patient with (p.Tyr176Cys*8) mutation has Grade 1 hepatosteatosis, a phenotype not previously described. This is the first PTEN prevalence study of patients with ASD and macrocephaly in Turkey and South Eastern Europe region with a largest homogenous cohort. The prevalence of PTEN mutations was found 3.8% (VUS included) or 2.29% (VUS omitted). We recommend testing for PTEN mutations in all patients with ASD and macrocephaly.

Functional and structural analyses of novel Smith-Kingsmore Syndrome-Associated MTOR variants reveal potential new mechanisms and predictors of pathogenicity

Smith-Kingsmore syndrome (SKS) is a rare neurodevelopmental disorder characterized by macrocephaly/megalencephaly, developmental delay, intellectual disability, hypotonia, and seizures. It is caused by dominant missense mutations in MTOR. The pathogenicity of novel variants in MTOR in patients with neurodevelopmental disorders can be difficult to determine and the mechanism by which variants cause disease remains poorly understood. We report 7 patients with SKS with 4 novel MTOR variants and describe their phenotypes. We perform in vitro functional analyses to confirm MTOR activation and interrogate disease mechanisms. We complete structural analyses to understand the 3D properties of pathogenic variants. We examine the accuracy of relative accessible surface area, a quantitative measure of amino acid side-chain accessibility, as a predictor of MTOR variant pathogenicity.

We describe novel clinical features of patients with SKS. We confirm MTOR Complex 1 activation and identify MTOR Complex 2 activation as a new potential mechanism of disease in SKS. We find that pathogenic MTOR variants disproportionately cluster in hotspots in the core of the protein, where they disrupt alpha helix packing due to the insertion of bulky amino acid side chains. We find that relative accessible surface area is significantly lower for SKS-associated variants compared to benign variants. We expand the phenotype of SKS and demonstrate that additional pathways of activation may contribute to disease. Incorporating 3D properties of MTOR variants may help in pathogenicity classification. We hope these findings may contribute to improving the precision of care and therapeutic development for individuals with SKS.

Smith-Kingsmore Syndrome is a rare disease caused by damage in a gene named MTOR that is associated with excessive growth of the head and brain, delays in development and deficits in intellectual functioning. We report 7 patients who have changes in MTOR that have never been reported before. We describe new medical findings in these patients that may be common in Smith-Kingsmore Syndrome more broadly. We then identify how these new gene changes impact the function of the MTOR protein and thus cell function downstream. Lastly, we show that changes in the gene that lie deep inside the 3D structure of the MTOR protein are more likely to cause disease than those changes that lie on the surface of the protein. We may be able to use the 3D properties of MTOR gene changes to predict if future changes we see are likely to cause disease or not.

Germline nuclear-predominant Pten murine model exhibits impaired social and perseverative behavior, microglial activation, and increased oxytocinergic activity

BACKGROUND

Autism spectrum disorder (ASD) has a strong genetic etiology. Germline mutation in the tumor suppressor gene PTEN is one of the best described monogenic risk cases for ASD. Animal modeling of cell-specific Pten loss or mutation has provided insight into how disruptions to the function of PTEN affect neurodevelopment, neurobiology, and social behavior. As such, there is a growing need to understand more about how various aspects of PTEN activity and cell-compartment-specific functions, contribute to certain neurological or behavior phenotypes.

METHODS

To understand more about the relationship between Pten localization and downstream effects on neurophenotypes, we generated the nuclear-predominant PtenY68H/+ mouse, which is identical to the genotype of some PTEN-ASD individuals. We subjected the PtenY68H/+ mouse to morphological and behavioral phenotyping, including the three-chamber sociability, open field, rotarod, and marble burying tests. We subsequently performed in vivo and in vitro cellular phenotyping and concluded the work with a transcriptomic survey of the PtenY68H/+ cortex, which profiled gene expression.

RESULTS

We observe a significant increase in P-Akt downstream of canonical Pten signaling, macrocephaly, decreased sociability, decreased preference for novel social stimuli, increased repetitive behavior, and increased thigmotaxis in PtenY68H/+ six-week-old (P40) mice. In addition, we found significant microglial activation with increased expression of complement and neuroinflammatory proteins in vivo and in vitro accompanied by enhanced phagocytosis. These observations were subsequently validated with RNA-seq and qRT-PCR, which revealed overexpression of many genes involved in neuroinflammation and neuronal function, including oxytocin. Oxytocin transcript was fivefold overexpressed (P = 0.0018), and oxytocin protein was strongly overexpressed in the PtenY68H/+ hypothalamus.

CONCLUSIONS

The nuclear-predominant PtenY68H/+ model has clarified that Pten dysfunction links to microglial pathology and this associates with increased Akt signaling. We also demonstrate that Pten dysfunction associates with changes in the oxytocin system, an important connection between a prominent ASD risk gene and a potent neuroendocrine regulator of social behavior. These cellular and molecular pathologies may related to the observed changes in social behavior. Ultimately, the findings from this work may reveal important biomarkers and/or novel therapeutic modalities that could be explored in individuals with germline mutations in PTEN with ASD.

Distinguishing between PTEN clinical phenotypes through mutation analysis

Phosphate and tensin homolog on chromosome ten (PTEN) germline mutations are associated with an overarching condition known as PTEN hamartoma tumor syndrome. Clinical phenotypes associated with this syndrome range from macrocephaly and autism spectrum disorder to Cowden syndrome, which manifests as multiple noncancerous tumor-like growths (hamartomas), and an increased predisposition to certain cancers. It is unclear, however, the basis by which mutations might lead to these very diverse phenotypic outcomes. Here we show that, by considering the molecular consequences of mutations in PTEN on protein structure and function, we can accurately distinguish PTEN mutations exhibiting different phenotypes. Changes in phosphatase activity, protein stability, and intramolecular interactions appeared to be major drivers of clinical phenotype, with cancer-associated variants leading to the most drastic changes, while ASD and non-pathogenic variants associated with more mild and neutral changes, respectively. Importantly, we show via saturation mutagenesis that more than half of variants of unknown significance could be associated with disease phenotypes, while over half of Cowden syndrome mutations likely lead to cancer. These insights can assist in exploring potentially important clinical outcomes delineated by PTEN variation.

Prioritizing genes for systematic variant effect mapping

MOTIVATION

When rare missense variants are clinically interpreted as to their pathogenicity, most are classified as variants of uncertain significance (VUS). Although functional assays can provide strong evidence for variant classification, such results are generally unavailable. Multiplexed assays of variant effect can generate experimental 'variant effect maps' that score nearly all possible missense variants in selected protein targets for their impact on protein function. However, these efforts have not always prioritized proteins for which variant effect maps would have the greatest impact on clinical variant interpretation.

RESULTS

Here, we mined databases of clinically interpreted variants and applied three strategies, each building on the previous, to prioritize genes for systematic functional testing of missense variation. The strategies ranked genes (i) by the number of unique missense VUS that had been reported to ClinVar; (ii) by movability- and reappearance-weighted impact scores, to give extra weight to reappearing, movable VUS and (iii) by difficulty-adjusted impact scores, to account for the more resource-intensive nature of generating variant effect maps for longer genes. Our results could be used to guide systematic functional testing of missense variation toward greater impact on clinical variant interpretation.

AVAILABILITY AND IMPLEMENTATION

Source code available at: https://github.com/rothlab/mave-gene-prioritization.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Protein design and variant prediction using autoregressive generative models

The ability to design functional sequences and predict effects of variation is central to protein engineering and biotherapeutics. State-of-art computational methods rely on models that leverage evolutionary information but are inadequate for important applications where multiple sequence alignments are not robust. Such applications include the prediction of variant effects of indels, disordered proteins, and the design of proteins such as antibodies due to the highly variable complementarity determining regions. We introduce a deep generative model adapted from natural language processing for prediction and design of diverse functional sequences without the need for alignments. The model performs state-of-art prediction of missense and indel effects and we successfully design and test a diverse 105-nanobody library that shows better expression than a 1000-fold larger synthetic library. Our results demonstrate the power of the alignment-free autoregressive model in generalizing to regions of sequence space traditionally considered beyond the reach of prediction and design.

Understanding the Origins of Loss of Protein Function by Analyzing the Effects of Thousands of Variants on Activity and Abundance

Understanding and predicting how amino acid substitutions affect proteins are keys to our basic understanding of protein function and evolution. Amino acid changes may affect protein function in a number of ways including direct perturbations of activity or indirect effects on protein folding and stability. We have analyzed 6,749 experimentally determined variant effects from multiplexed assays on abundance and activity in two proteins (NUDT15 and PTEN) to quantify these effects and find that a third of the variants cause loss of function, and about half of loss-of-function variants also have low cellular abundance. We analyze the structural and mechanistic origins of loss of function and use the experimental data to find residues important for enzymatic activity. We performed computational analyses of protein stability and evolutionary conservation and show how we may predict positions where variants cause loss of activity or abundance. In this way, our results link thermodynamic stability and evolutionary conservation to experimental studies of different properties of protein fitness landscapes.

The Roles of Protein Structure, Taxon Sampling, and Model Complexity in Phylogenomics: A Case Study Focused on Early Animal Divergences

Despite the long history of using protein sequences to infer the tree of life, the potential for different parts of protein structures to retain historical signal remains unclear. We propose that it might be possible to improve analyses of phylogenomic datasets by incorporating information about protein structure. We test this idea using the position of the root of Metazoa (animals) as a model system. We examined the distribution of “strongly decisive” sites (alignment positions that support a specific tree topology) in a dataset comprising >1500 proteins and almost 100 taxa. The proportion of each class of strongly decisive sites in different structural environments was very sensitive to the model used to analyze the data when a limited number of taxa were used but they were stable when taxa were added. As long as enough taxa were analyzed, sites in all structural environments supported the same topology regardless of whether standard tree searches or decisive sites were used to select the optimal tree. However, the use of decisive sites revealed a difference between the support for minority topologies for sites in different structural environments: buried sites and sites in sheet and coil environments exhibited equal support for the minority topologies, whereas solvent-exposed and helix sites had unequal numbers of sites, supporting the minority topologies. This suggests that the relatively slowly evolving buried, sheet, and coil sites are giving an accurate picture of the true species tree and the amount of conflict among gene trees. Taken as a whole, this study indicates that phylogenetic analyses using sites in different structural environments can yield different topologies for the deepest branches in the animal tree of life and that analyzing larger numbers of taxa eliminates this conflict. More broadly, our results highlight the desirability of incorporating information about protein structure into phylogenomic analyses.

A global analysis of the reconstitution of PTEN function by translational readthrough of PTEN pathogenic premature termination codons

The PTEN tumor suppressor gene is mutated with high incidence in tumors and in the germline of patients with cancer predisposition or with macrocephaly associated with autism. PTEN nonsense mutations generating premature termination codons (PTC) and producing nonfunctional truncated PTEN proteins are frequent in association with human disease. However, there are no studies addressing the restoration of full-length PTEN proteins from the PTC-mutated PTEN gene by translational readthrough. Here, we have performed a global translational and functional readthrough analysis of the complete collection of PTEN PTC somatic or hereditary mutations found in tumors or in the germline of patients (disease-associated PTEN PTCome), and we set standards for the analysis of the potential of readthrough functional reconstitution in disease-relevant genes. Our analysis indicates that prevalent pathogenic PTEN PTC mutations are susceptible to PTEN functional restoration in response to readthrough-inducing compounds. Comprehensive readthrough analyses of disease-associated PTComes will be valuable tools for the implementation of readthrough-based precision interventions in specific groups of patients.

Cross-level analysis of molecular and neurobehavioral function in a prospective series of patients with germline heterozygous PTEN mutations with and without autism

Background

PTEN is a well-established risk gene for autism spectrum disorder (ASD). Yet, little is known about how PTEN mutations and associated molecular processes influence neurobehavioral function in mutation carriers with (PTEN-ASD) and without ASD (PTEN no-ASD). The primary aim of the present study was to examine group differences in peripheral blood-derived PTEN pathway protein levels between PTEN-ASD, PTEN no-ASD, and idiopathic macrocephalic ASD patients (macro-ASD). Secondarily, associations between protein levels and neurobehavioral functions were examined in the full cohort.

Methods

Patients were recruited at four tertiary medical centers. Peripheral blood-derived protein levels from canonical PTEN pathways (PI3K/AKT and MAPK/ERK) were analyzed using Western blot analyses blinded to genotype and ASD status. Neurobehavioral measures included standardized assessments of global cognitive ability and multiple neurobehavioral domains. Analysis of variance models examined group differences in demographic, neurobehavioral, and protein measures. Bivariate correlations, structural models, and statistical learning procedures estimated associations between molecular and neurobehavioral variables. To complement patient data, Western blots for downstream proteins were generated to evaluate canonical PTEN pathways in the PTEN-m3m4 mouse model.

Results

Participants included 61 patients (25 PTEN-ASD, 16 PTEN no-ASD, and 20 macro-ASD). Decreased PTEN and S6 were observed in both PTEN mutation groups. Reductions in MnSOD and increases in P-S6 were observed in ASD groups. Elevated neural P-AKT/AKT and P-S6/S6 from PTEN murine models parallel our patient observations. Patient PTEN and AKT levels were independently associated with global cognitive ability, and p27 expression was associated with frontal sub-cortical functions. As a group, molecular measures added significant predictive value to several neurobehavioral domains over and above PTEN mutation status.

Limitations

Sample sizes were small, precluding within-group analyses. Protein and neurobehavioral data were limited to a single evaluation. A small number of patients were excluded with invalid protein data, and cognitively impaired patients had missing data on some assessments.

Conclusions

Several canonical PTEN pathway molecules appear to influence the presence of ASD and modify neurobehavioral function in PTEN mutation patients. Protein assays of the PTEN pathway may be useful for predicting neurobehavioral outcomes in PTEN patients. Future longitudinal analyses are needed to replicate these findings and evaluate within-group relationships between protein and neurobehavioral measures.

Trial registration

ClinicalTrials.gov Identifier NCT02461446

Multi-parametric analysis of 57 SYNGAP1 variants reveal impacts on GTPase signaling, localization, and protein stability

SYNGAP1 is a neuronal Ras and Rap GTPase-activating protein with important roles in regulating excitatory synaptic plasticity. While many SYNGAP1 missense and nonsense mutations have been associated with intellectual disability, epilepsy, schizophrenia, and autism spectrum disorder (ASD), whether and how they contribute to individual disease phenotypes is often unknown. Here, we characterize 57 variants in seven assays that examine multiple aspects of SYNGAP1 function. Specifically, we used multiplex phospho-flow cytometry to measure variant impact on protein stability, pERK, pGSK3β, pp38, pCREB, and high-content imaging to examine subcellular localization. We find variants ranging from complete loss-of-function (LoF) to wild-type (WT)-like in their regulation of pERK and pGSK3β, while all variants retain at least partial ability to dephosphorylate pCREB. Interestingly, our assays reveal that a larger proportion of variants located within the disordered domain of unknown function (DUF) comprising the C-terminal half of SYNGAP1 exhibited higher LoF, compared to variants within the better studied catalytic domain. Moreover, we find protein instability to be a major contributor to dysfunction for only two missense variants, both located within the catalytic domain. Using high-content imaging, we find variants located within the C2 domain known to mediate membrane lipid interactions exhibit significantly larger cytoplasmic speckles than WT SYNGAP1. Moreover, this subcellular phenotype shows both correlation with altered catalytic activity and unique deviation from signaling assay results, highlighting multiple independent molecular mechanisms underlying variant dysfunction. Our multidimensional dataset allows clustering of variants based on functional phenotypes and provides high-confidence, multi-functional measures for making pathogenicity predictions.

Cytoplasmic-predominant Pten increases microglial activation and synaptic pruning in a murine model with autism-like phenotype

Germline mutations in PTEN account for ~10% of cases of autism spectrum disorder (ASD) with coincident macrocephaly. To explore the importance of nuclear PTEN in the development of ASD and macrocephaly, we previously generated a mouse model with predominantly cytoplasmic localization of Pten (Pten^m3m4/m3m4).Cytoplasmic predominant Pten localization results in a phenotype of extreme macrocephaly and autistic-like traits. Transcriptomic analysis of the Pten^m3m4/m3m4 cortex found upregulated gene pathways related to myeloid cell activation, myeloid cell migration, and phagocytosis. These transcriptomic findings were used to direct in vitro assays on Pten wild-type and Pten^m3m4/m3m4 microglia. We found increased Iba1 and C1q expression with enhanced phagocytic capacity in Pten^m3m4/m3m4 microglia, indicating microglial activation. Moreover, through a series of neuron-microglia co-culture experiments, we found Pten^m3m4/m3m4 microglia are more efficient at synaptic pruning compared with wild-type controls. In addition, we found evidence for neuron-microglia cross-talk, where Pten^m3m4/m3m4 neurons elicit enhanced pruning from innately activated microglia. Subsequent in vivo studies validated our in vitro findings. We observed a concurrent decline in the expression of Pten and synaptic markers in the Pten^m3m4/m3m4 cortex. At ~3 weeks of age, with a 50% drop in Pten expression compared with wild-type levels, we observed enhanced activation of microglia in the Pten^m3m4/m3m4 brain. Collectively, our data provide evidence that dysregulated Pten in microglia has an etiological role in microglial activation, phagocytosis, and synaptic pruning, creating avenues for future studies on the importance of PTEN in maintaining microglia homeostasis.

Multiplexed Functional Assessment of Genetic Variants in CARD11

Genetic testing has increased the number of variants identified in disease genes, but the diagnostic utility is limited by lack of understanding variant function. CARD11 encodes an adaptor protein that expresses dominant-negative and gain-of-function variants associated with distinct immunodeficiencies. Here, we used a "cloning-free" saturation genome editing approach in a diploid cell line to simultaneously score 2,542 variants for decreased or increased function in the region of CARD11 associated with immunodeficiency. We also described an exon-skipping mechanism for CARD11 dominant-negative activity. The classification of reported clinical variants was sensitive (94.6%) and specific (88.9%), which rendered the data immediately useful for interpretation of seven coding and splicing variants implicated in immunodeficiency found in our clinic. This approach is generalizable for variant interpretation in many other clinically actionable genes, in any relevant cell type.

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.

Comprehensive characterization of amino acid positions in protein structures reveals molecular effect of missense variants

Interpretation of the colossal number of genetic variants identified from sequencing applications is one of the major bottlenecks in clinical genetics, with the inference of the effect of amino acid-substituting missense variations on protein structure and function being especially challenging. Here we characterize the three-dimensional (3D) amino acid positions affected in pathogenic and population variants from 1,330 disease-associated genes using over 14,000 experimentally solved human protein structures. By measuring the statistical burden of variations (i.e., point mutations) from all genes on 40 3D protein features, accounting for the structural, chemical, and functional context of the variations' positions, we identify features that are generally associated with pathogenic and population missense variants. We then perform the same amino acid-level analysis individually for 24 protein functional classes, which reveals unique characteristics of the positions of the altered amino acids: We observe up to 46% divergence of the class-specific features from the general characteristics obtained by the analysis on all genes, which is consistent with the structural diversity of essential regions across different protein classes. We demonstrate that the function-specific 3D features of the variants match the readouts of mutagenesis experiments for BRCA1 and PTEN, and positively correlate with an independent set of clinically interpreted pathogenic and benign missense variants. Finally, we make our results available through a web server to foster accessibility and downstream research. Our findings represent a crucial step toward translational genetics, from highlighting the impact of mutations on protein structure to rationalizing the variants' pathogenicity in terms of the perturbed molecular mechanisms.

Comprehensive in silico mutational-sensitivity analysis of PTEN establishes signature regions implicated in pathogenesis of Autism Spectrum Disorders

An extensively studied cancer and Autism Spectrum Disorders (ASD) gene like PTEN provided an exclusive opportunity to map its mutational-landscape, compare and establish plausible genotypic predictors of ASD-associated phenotypic outcomes. Our exhaustive in silico analysis on 4252 SNPs using >30 tools identified increased mutational-density in exon7. Phosphatase domain, although evolutionarily conserved, had the most nsSNPs localised within signature regions. The evolutionarily variable C-terminal side contained the highest truncating-SNPs outside signature regions of C2 domain and most PTMs within C-tail site which displayed maximum intolerance to polymorphisms, and permitted benign but destabilising nsSNPs that enhanced its intrinsically-disordered nature. ASD-associated SNPs localised within ATP-binding motifs and Nuclear-Localising-Sequences were the most potent triggers of ASD manifestation. These, along with variations within P, WPD and TI loops, M1 within phosphatase domain, M2 and MoRFs of C2 domain, caused severe long-range conformational fluctuations altering PTEN's dynamic stability- not observed in variations outside signature regions. 3'UTR-SNPs affected 44 strong miRNA brain-specific targets; several 5' UTR-SNPs targeted transcription-factor POLR2A and 10 pathogenic Splice-Affecting-Variants were identified.

Neurogenetic fetal akinesia and arthrogryposis: genetics, expanding genotype-phenotypes and functional genomics

BACKGROUND

Fetal akinesia and arthrogryposis are clinically and genetically heterogeneous and have traditionally been refractive to genetic diagnosis. The widespread availability of affordable genome-wide sequencing has facilitated accurate genetic diagnosis and gene discovery in these conditions.

METHODS

We performed next generation sequencing (NGS) in 190 probands with a diagnosis of arthrogryposis multiplex congenita, distal arthrogryposis, fetal akinesia deformation sequence or multiple pterygium syndrome. This sequencing was a combination of bespoke neurogenetic disease gene panels and whole exome sequencing. Only class 4 and 5 variants were reported, except for two cases where the identified variants of unknown significance (VUS) are most likely to be causative for the observed phenotype. Co-segregation studies and confirmation of variants identified by NGS were performed where possible. Functional genomics was performed as required.

RESULTS

Of the 190 probands, 81 received an accurate genetic diagnosis. All except two of these cases harboured class 4 and/or 5 variants based on the American College of Medical Genetics and Genomics guidelines. We identified phenotypic expansions associated with CACNA1S, CHRNB1, GMPPB and STAC3. We describe a total of 50 novel variants, including a novel missense variant in the recently identified gene for arthrogryposis with brain malformations-SMPD4.

CONCLUSIONS

Comprehensive gene panels give a diagnosis for a substantial proportion (42%) of fetal akinesia and arthrogryposis cases, even in an unselected cohort. Recently identified genes account for a relatively large proportion, 32%, of the diagnoses. Diagnostic-research collaboration was critical to the diagnosis and variant interpretation in many cases, facilitated genotype-phenotype expansions and reclassified VUS through functional genomics.

Biophysical ambiguities prevent accurate genetic prediction

A goal of biology is to predict how mutations combine to alter phenotypes, fitness and disease. It is often assumed that mutations combine additively or with interactions that can be predicted. Here, we show using simulations that, even for the simple example of the lambda phage transcription factor CI repressing a gene, this assumption is incorrect and that perfect measurements of the effects of mutations on a trait and mechanistic understanding can be insufficient to predict what happens when two mutations are combined. This apparent paradox arises because mutations can have different biophysical effects to cause the same change in a phenotype and the outcome in a double mutant depends upon what these hidden biophysical changes actually are. Pleiotropy and non-monotonic functions further confound prediction of how mutations interact. Accurate prediction of phenotypes and disease will sometimes not be possible unless these biophysical ambiguities can be resolved using additional measurements.

The Tumor Suppressor PTEN as Molecular Switch Node Regulating Cell Metabolism and Autophagy: Implications in Immune System and Tumor Microenvironment

Recent studies conducted over the past 10 years evidence the intriguing role of the tumor suppressor gene Phosphatase and Tensin Homolog deleted on Chromosome 10 PTEN in the regulation of cellular energy expenditure, together with its capability to modulate proliferation and survival, thus expanding our knowledge of its physiological functions. Transgenic PTEN mice models are resistant to oncogenic transformation, present decreased adiposity and reduced cellular glucose and glutamine uptake, together with increased mitochondrial oxidative phosphorylation. These acquisitions led to a novel understanding regarding the role of PTEN to counteract cancer cell metabolic reprogramming. Particularly, PTEN drives an “anti-Warburg state” in which less glucose is taken up, but it is more efficiently directed to the mitochondrial Krebs cycle. The maintenance of cellular homeostasis together with reduction of metabolic stress are controlled by specific pathways among which autophagy, a catabolic process strictly governed by mTOR and PTEN. Besides, a role of PTEN in metabolic reprogramming and tumor/stroma interactions in cancer models, has recently been established. The genetic inactivation of PTEN in stromal fibroblasts of mouse mammary glands, accelerates breast cancer initiation and progression. This review will discuss our novel understanding in the molecular connection between cell metabolism and autophagy by PTEN, highlighting novel implications regarding tumor/stroma/immune system interplay. The newly discovered action of PTEN opens innovative avenues for investigations relevant to counteract cancer development and progression.

Sentinel interaction mapping - a generic approach for the functional analysis of human disease gene variants using yeast

Advances in sequencing technology have led to an explosion in the number of known genetic variants of human genes. A major challenge is to now determine which of these variants contribute to diseases as a result of their effect on gene function. Here, we describe a generic approach using the yeast Saccharomyces cerevisiae to quickly develop gene-specific in vivo assays that can be used to quantify the level of function of a genetic variant. Using synthetic dosage lethality screening, 'sentinel' yeast strains are identified that are sensitive to overexpression of a human disease gene. Variants of the gene can then be functionalized in a high-throughput fashion through simple growth assays using solid or liquid media. Sentinel interaction mapping (SIM) has the potential to create functional assays for the large majority of human disease genes that do not have a yeast orthologue. Using the tumour suppressor gene PTEN as an example, we show that SIM assays can provide a fast and economical means to screen a large number of genetic variants.

Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations

To deal with the huge number of novel protein-coding variants identified by genome and exome sequencing studies, many computational variant effect predictors (VEPs) have been developed. Such predictors are often trained and evaluated using different variant data sets, making a direct comparison between VEPs difficult. In this study, we use 31 previously published deep mutational scanning (DMS) experiments, which provide quantitative, independent phenotypic measurements for large numbers of single amino acid substitutions, in order to benchmark and compare 46 different VEPs. We also evaluate the ability of DMS measurements and VEPs to discriminate between pathogenic and benign missense variants. We find that DMS experiments tend to be superior to the top-ranking predictors, demonstrating the tremendous potential of DMS for identifying novel human disease mutations. Among the VEPs, DeepSequence clearly stood out, showing both the strongest correlations with DMS data and having the best ability to predict pathogenic mutations, which is especially remarkable given that it is an unsupervised method. We further recommend SNAP2, DEOGEN2, SNPs&GO, SuSPect and REVEL based upon their performance in these analyses.

An Integrated Deep-Mutational-Scanning Approach Provides Clinical Insights on PTEN Genotype-Phenotype Relationships

Germline variation in PTEN results in variable clinical presentations, including benign and malignant neoplasia and neurodevelopmental disorders. Despite decades of research, it remains unclear how the PTEN genotype is related to clinical outcomes. In this study, we combined two recent deep mutational scanning (DMS) datasets probing the effects of single amino acid variation on enzyme activity and steady-state cellular abundance with a large, well-curated clinical cohort of PTEN-variant carriers. We sought to connect variant-specific molecular phenotypes to the clinical outcomes of individuals with PTEN variants. We found that DMS data partially explain quantitative clinical traits, including head circumference and Cleveland Clinic (CC) score, which is a semiquantitative surrogate of disease burden. We built logistic regression models that use DMS and CADD scores to separate clinical PTEN variation from gnomAD control-only variation with high accuracy. By using a survival-like analysis, we identified molecular phenotype groups with differential risk of early cancer onset as well as lifetime risk of cancer. Finally, we identified classes of DMS-defined variants with significantly different risk levels for classical hamartoma-related features (odds ratio [OR] range of 4.1-102.9). In stark contrast, the risk for developing autism or developmental delay does not significantly change across variant classes (OR range of 5.4-12.4). Together, these findings highlight the potential impact of combining DMS datasets with rich clinical data and provide new insights that might guide personalized clinical decisions for PTEN-variant carriers.

DNA Repair Gene Mutations as Predictors of Immune Checkpoint Inhibitor Response beyond Tumor Mutation Burden

Immune checkpoint inhibitors (ICIs) have revolutionized cancer therapy, but prediction of their benefit is challenging. Neoantigens generated through impaired non-mismatch DNA repair may result in greater ICI activity. By analyzing 1,661 ICI-treated patients, we show that deletions and mutations in nucleotide excision repair (NER) and homologous repair (HR) pathways are predictors of ICI benefit independent of tumor mutation burden and tumor type. NER and HR mutations are also associated with objective response rates to ICIs in esophagogastric and non-small-cell lung cancers. In a cohort of 40,181 unique patients, NER and HR mutations are present in 3.4% and 13.9% of cancers, respectively. These results indicate that NER and HR gene mutations occur in a subpopulation of cancer patients and may aid patient selection for ICI therapy. Assessing NER and HR mutations in the context of other biomarkers may yield powerful predictors of ICI activity across different cancer types.

Yeast-based assays for the functional characterization of cancer-associated variants of human DNA repair genes

Technological advances are continuously revealing new genetic variants that are often difficult to interpret. As one of the most genetically tractable model organisms, yeast can have a central role in determining the consequences of human genetic variation. DNA repair gene mutations are associated with many types of cancers, therefore the evaluation of the functional impact of these mutations is crucial for risk assessment and for determining therapeutic strategies. Owing to the evolutionary conservation of DNA repair pathways between human cells and the yeast Saccharomyces cerevisiae, several functional assays have been developed. Here, we describe assays for variants of human genes belonging to the major DNA repair pathways divided in functional assays for human genes with yeast orthologues and human genes lacking a yeast orthologue. Human genes with orthologues can be studied by introducing the correspondent human mutations directly in the yeast gene or expressing the human gene carrying the mutations; while the only possible approach for human genes without a yeast orthologue is the heterologous expression. The common principle of these approaches is that the mutated gene determines a phenotypic alteration that can vary according to the gene studied and the domain of the protein. Here, we show how the versatility of yeast can help in classifying cancer-associated variants.

A Premalignant Cell-Based Model for Functionalization and Classification of PTEN Variants

As sequencing becomes more economical, we are identifying sequence variations in the population faster than ever. For disease-associated genes, it is imperative that we differentiate a sequence variant as either benign or pathogenic, such that the appropriate therapeutic interventions or surveillance can be implemented. PTEN is a frequently mutated tumor suppressor that has been linked to the PTEN hamartoma tumor syndrome. Although the domain structure of PTEN and the functional impact of a number of its most common tumor-linked mutations have been characterized, there is a lack of information about many recently identified clinical variants. To address this challenge, we developed a cell-based assay that utilizes a premalignant phenotype of normal mammary epithelial cells lacking PTEN. We measured the ability of PTEN variants to rescue the spheroid formation phenotype of PTEN^-/- MCF10A cells maintained in suspension. As proof of concept, we functionalized 47 missense variants using this assay, only 19 of which have clear classifications in ClinVar. We utilized a machine learning model trained with annotated genotypic data to classify variants as benign or pathogenic based on our functional scores. Our model predicted with high accuracy that loss of PTEN function was indicative of pathogenicity. We also determined that the pathogenicity of certain variants may have arisen from reduced stability of the protein product. Overall, this assay outperformed computational predictions, was scalable, and had a short run time, serving as an ideal alternative for annotating the clinical significance of cancer-associated PTEN variants. SIGNIFICANCE: Combined three-dimensional tumor spheroid modeling and machine learning classifies PTEN missense variants, over 70% of which are currently listed as variants of uncertain significance. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/80/13/2775/F1.large.jpg.

Multi-model functionalization of disease-associated PTEN missense mutations identifies multiple molecular mechanisms underlying protein dysfunction

Functional variomics provides the foundation for personalized medicine by linking genetic variation to disease expression, outcome and treatment, yet its utility is dependent on appropriate assays to evaluate mutation impact on protein function. To fully assess the effects of 106 missense and nonsense variants of PTEN associated with autism spectrum disorder, somatic cancer and PTEN hamartoma syndrome (PHTS), we take a deep phenotypic profiling approach using 18 assays in 5 model systems spanning diverse cellular environments ranging from molecular function to neuronal morphogenesis and behavior. Variants inducing instability occur across the protein, resulting in partial-to-complete loss-of-function (LoF), which is well correlated across models. However, assays are selectively sensitive to variants located in substrate binding and catalytic domains, which exhibit complete LoF or dominant negativity independent of effects on stability. Our results indicate that full characterization of variant impact requires assays sensitive to instability and a range of protein functions.

Mutations in PTEN have been associated with various human disease, including autism spectrum disorder (ASD) and cancer. Here, the authors assess the function of 106 PTEN variants in yeast, invertebrate models and cell culture and report that PTEN variants generally decrease protein stability.