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Buckley M, Terwagne C, Ganner A, Cubitt L, Brewer R, Kim DK, Kajba CM, Forrester N, Dace P, De Jonghe J, Shepherd STC, Sawyer C, McEwen M, Diederichs S, Neumann-Haefelin E, Turajlic S, Ivakine EA, Findlay GM. Saturation genome editing maps the functional spectrum of pathogenic VHL alleles. Nat Genet 2024; 56:1446-1455. [PMID: 38969834 PMCID: PMC11250436 DOI: 10.1038/s41588-024-01800-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 05/13/2024] [Indexed: 07/07/2024]
Abstract
To maximize the impact of precision medicine approaches, it is critical to identify genetic variants underlying disease and to accurately quantify their functional effects. A gene exemplifying the challenge of variant interpretation is the von Hippel-Lindautumor suppressor (VHL). VHL encodes an E3 ubiquitin ligase that regulates the cellular response to hypoxia. Germline pathogenic variants in VHL predispose patients to tumors including clear cell renal cell carcinoma (ccRCC) and pheochromocytoma, and somatic VHL mutations are frequently observed in sporadic renal cancer. Here we optimize and apply saturation genome editing to assay nearly all possible single-nucleotide variants (SNVs) across VHL's coding sequence. To delineate mechanisms, we quantify mRNA dosage effects and compare functional effects in isogenic cell lines. Function scores for 2,268 VHL SNVs identify a core set of pathogenic alleles driving ccRCC with perfect accuracy, inform differential risk across tumor types and reveal new mechanisms by which variants impact function. These results have immediate utility for classifying VHL variants encountered clinically and illustrate how precise functional measurements can resolve pleiotropic and dosage-dependent genotype-phenotype relationships across complete genes.
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Affiliation(s)
- Megan Buckley
- The Genome Function Laboratory, The Francis Crick Institute, London, UK
| | - Chloé Terwagne
- The Genome Function Laboratory, The Francis Crick Institute, London, UK
| | - Athina Ganner
- Renal Division, Department of Medicine, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Laura Cubitt
- The Genome Function Laboratory, The Francis Crick Institute, London, UK
| | - Reid Brewer
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Dong-Kyu Kim
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Christina M Kajba
- The Genome Function Laboratory, The Francis Crick Institute, London, UK
| | - Nicole Forrester
- The Genome Function Laboratory, The Francis Crick Institute, London, UK
| | - Phoebe Dace
- The Genome Function Laboratory, The Francis Crick Institute, London, UK
| | - Joachim De Jonghe
- The Genome Function Laboratory, The Francis Crick Institute, London, UK
| | - Scott T C Shepherd
- The Cancer Dynamics Laboratory, The Francis Crick Institute, London, UK
- Renal and Skin Units, The Royal Marsden Hospital, London, UK
- Melanoma and Kidney Cancer Team, The Institute of Cancer Research, London, UK
| | - Chelsea Sawyer
- Scientific Computing, The Francis Crick Institute, London, UK
| | - Mairead McEwen
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Sven Diederichs
- Division of Cancer Research, Department of Thoracic Surgery, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany
- German Cancer Consortium (DKTK), Partner Site Freiburg, A Partnership Between DKFZ and University Medical Center Freiburg, Freiburg, Germany
| | - Elke Neumann-Haefelin
- Renal Division, Department of Medicine, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Samra Turajlic
- The Cancer Dynamics Laboratory, The Francis Crick Institute, London, UK
- Renal and Skin Units, The Royal Marsden Hospital, London, UK
- Melanoma and Kidney Cancer Team, The Institute of Cancer Research, London, UK
| | - Evgueni A Ivakine
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Department of Physiology, University of Toronto, Toronto, Ontario, Canada
| | - Gregory M Findlay
- The Genome Function Laboratory, The Francis Crick Institute, London, UK.
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Biar CG, Pfeifer C, Carvill GL, Calhoun JD. Multimodal framework to resolve variants of uncertain significance in TSC2. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.07.597916. [PMID: 38895336 PMCID: PMC11185720 DOI: 10.1101/2024.06.07.597916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Efforts to resolve the functional impact of variants of uncertain significance (VUS) have lagged behind the identification of new VUS; as such, there is a critical need for scalable VUS resolution technologies. Computational variant effect predictors (VEPs), once trained, can predict pathogenicity for all missense variants in a gene, set of genes, or the exome. Existing tools have employed information on known pathogenic and benign variants throughout the genome to predict pathogenicity of VUS. We hypothesize that taking a gene-specific approach will improve pathogenicity prediction over globally-trained VEPs. We tested this hypothesis using the gene TSC2, whose loss of function results in tuberous sclerosis, a multisystem mTORopathy affecting about 1 in 6,000 individuals born in the United States. TSC2 has been identified as a high-priority target for VUS resolution, with (1) well-characterized molecular and patient phenotypes associated with loss-of-function variants, and (2) more than 2,700 VUS already documented in ClinVar. We developed Tuberous sclerosis classifier to Resolve variants of Uncertain Significance in T SC2 (TRUST), a machine learning model to predict pathogenicity of TSC2 missense VUS. To test whether these predictions are accurate, we further introduce curated loci prime editing (cliPE) as an accessible strategy for performing scalable multiplexed assays of variant effect (MAVEs). Using cliPE, we tested the effects of more than 200 TSC2 variants, including 106 VUS. It is highly likely this functional data alone would be sufficient to reclassify 92 VUS with most being reclassified as likely benign. We found that TRUST's classifications were correlated with the functional data, providing additional validation for the in silico predictions. We provide our pathogenicity predictions and MAVE data to aid with VUS resolution. In the near future, we plan to host these data on a public website and deposit into relevant databases such as MAVEdb as a community resource. Ultimately, this study provides a framework to complete variant effect maps of TSC1 and TSC2 and adapt this approach to other mTORopathy genes.
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Affiliation(s)
- Carina G Biar
- Ken and Ruth Davee Department of Neurology, Northwestern Feinberg School of Medicine, Chicago, Illinois
| | - Cole Pfeifer
- Ken and Ruth Davee Department of Neurology, Northwestern Feinberg School of Medicine, Chicago, Illinois
| | - Gemma L Carvill
- Ken and Ruth Davee Department of Neurology, Northwestern Feinberg School of Medicine, Chicago, Illinois
| | - Jeffrey D Calhoun
- Ken and Ruth Davee Department of Neurology, Northwestern Feinberg School of Medicine, Chicago, Illinois
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Cheng J, Novati G, Pan J, Bycroft C, Žemgulytė A, Applebaum T, Pritzel A, Wong LH, Zielinski M, Sargeant T, Schneider RG, Senior AW, Jumper J, Hassabis D, Kohli P, Avsec Ž. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science 2023; 381:eadg7492. [PMID: 37733863 DOI: 10.1126/science.adg7492] [Citation(s) in RCA: 287] [Impact Index Per Article: 287.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 08/23/2023] [Indexed: 09/23/2023]
Abstract
The vast majority of missense variants observed in the human genome are of unknown clinical significance. We present AlphaMissense, an adaptation of AlphaFold fine-tuned on human and primate variant population frequency databases to predict missense variant pathogenicity. By combining structural context and evolutionary conservation, our model achieves state-of-the-art results across a wide range of genetic and experimental benchmarks, all without explicitly training on such data. The average pathogenicity score of genes is also predictive for their cell essentiality, capable of identifying short essential genes that existing statistical approaches are underpowered to detect. As a resource to the community, we provide a database of predictions for all possible human single amino acid substitutions and classify 89% of missense variants as either likely benign or likely pathogenic.
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Derbel H, Giacoletto CJ, Benjamin R, Chen G, Schiller MR, Liu Q. Accurate Prediction of Transcriptional Activity of Single Missense Variants in HIV Tat with Deep Learning. Int J Mol Sci 2023; 24:6138. [PMID: 37047108 PMCID: PMC10093788 DOI: 10.3390/ijms24076138] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 03/23/2023] [Accepted: 03/23/2023] [Indexed: 04/14/2023] Open
Abstract
Tat is an essential gene for increasing the transcription of all HIV genes, and affects HIV replication, HIV exit from latency, and AIDS progression. The Tat gene frequently mutates in vivo and produces variants with diverse activities, contributing to HIV viral heterogeneity as well as drug-resistant clones. Thus, identifying the transcriptional activities of Tat variants will help to better understand AIDS pathology and treatment. We recently reported the missense mutation landscape of all single amino acid Tat variants. In these experiments, a fraction of double missense alleles exhibited intragenic epistasis. However, it is too time-consuming and costly to determine the effect of the variants for all double mutant alleles through experiments. Therefore, we propose a combined GigaAssay/deep learning approach. As a first step to determine activity landscapes for complex variants, we evaluated a deep learning framework using previously reported GigaAssay experiments to predict how transcription activity is affected by Tat variants with single missense substitutions. Our approach achieved a 0.94 Pearson correlation coefficient when comparing the predicted to experimental activities. This hybrid approach can be extensible to more complex Tat alleles for a better understanding of the genetic control of HIV genome transcription.
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Affiliation(s)
- Houssemeddine Derbel
- Nevada Institute of Personalized Medicine, University of Nevada Las Vegas, 4505 S Maryland Pkwy, Las Vegas, NV 89154, USA
| | - Christopher J. Giacoletto
- School of Life Sciences, College of Sciences, University of Nevada Las Vegas, 4505 S Maryland Pkwy, Las Vegas, NV 89154, USA
| | - Ronald Benjamin
- Nevada Institute of Personalized Medicine, University of Nevada Las Vegas, 4505 S Maryland Pkwy, Las Vegas, NV 89154, USA
- School of Life Sciences, College of Sciences, University of Nevada Las Vegas, 4505 S Maryland Pkwy, Las Vegas, NV 89154, USA
| | - Gordon Chen
- Nevada Institute of Personalized Medicine, University of Nevada Las Vegas, 4505 S Maryland Pkwy, Las Vegas, NV 89154, USA
| | - Martin R. Schiller
- Nevada Institute of Personalized Medicine, University of Nevada Las Vegas, 4505 S Maryland Pkwy, Las Vegas, NV 89154, USA
- School of Life Sciences, College of Sciences, University of Nevada Las Vegas, 4505 S Maryland Pkwy, Las Vegas, NV 89154, USA
| | - Qian Liu
- Nevada Institute of Personalized Medicine, University of Nevada Las Vegas, 4505 S Maryland Pkwy, Las Vegas, NV 89154, USA
- School of Life Sciences, College of Sciences, University of Nevada Las Vegas, 4505 S Maryland Pkwy, Las Vegas, NV 89154, USA
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5
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Abildgaard AB, Nielsen SV, Bernstein I, Stein A, Lindorff-Larsen K, Hartmann-Petersen R. Lynch syndrome, molecular mechanisms and variant classification. Br J Cancer 2023; 128:726-734. [PMID: 36434153 PMCID: PMC9978028 DOI: 10.1038/s41416-022-02059-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 10/31/2022] [Accepted: 11/02/2022] [Indexed: 11/27/2022] Open
Abstract
Patients with the heritable cancer disease, Lynch syndrome, carry germline variants in the MLH1, MSH2, MSH6 and PMS2 genes, encoding the central components of the DNA mismatch repair system. Loss-of-function variants disrupt the DNA mismatch repair system and give rise to a detrimental increase in the cellular mutational burden and cancer development. The treatment prospects for Lynch syndrome rely heavily on early diagnosis; however, accurate diagnosis is inextricably linked to correct clinical interpretation of individual variants. Protein variant classification traditionally relies on cumulative information from occurrence in patients, as well as experimental testing of the individual variants. The complexity of variant classification is due to (1) that variants of unknown significance are rare in the population and phenotypic information on the specific variants is missing, and (2) that individual variant testing is challenging, costly and slow. Here, we summarise recent developments in high-throughput technologies and computational prediction tools for the assessment of variants of unknown significance in Lynch syndrome. These approaches may vastly increase the number of interpretable variants and could also provide important mechanistic insights into the disease. These insights may in turn pave the road towards developing personalised treatment approaches for Lynch syndrome.
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Affiliation(s)
- Amanda B Abildgaard
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Sofie V Nielsen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
| | - Inge Bernstein
- Department of Surgical Gastroenterology, Aalborg University Hospital, Aalborg, Denmark
- Institute of Clinical Medicine, Aalborg University Hospital, Aalborg University, Aalborg, Denmark
| | - Amelie Stein
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Kresten Lindorff-Larsen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
| | - Rasmus Hartmann-Petersen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
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Tabet D, Parikh V, Mali P, Roth FP, Claussnitzer M. Scalable Functional Assays for the Interpretation of Human Genetic Variation. Annu Rev Genet 2022; 56:441-465. [PMID: 36055970 DOI: 10.1146/annurev-genet-072920-032107] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
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.
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Affiliation(s)
- Daniel Tabet
- Donnelly Centre, Department of Molecular Genetics, and Department of Computer Science, University of Toronto, Toronto, Ontario, Canada;
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada
| | - Victoria Parikh
- Center for Inherited Cardiovascular Disease, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Prashant Mali
- Department of Bioengineering, University of California, San Diego, California, USA
| | - Frederick P Roth
- Donnelly Centre, Department of Molecular Genetics, and Department of Computer Science, University of Toronto, Toronto, Ontario, Canada;
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada
| | - Melina Claussnitzer
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Center for Genomic Medicine and Endocrine Division, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Harvard University, Boston, Massachusetts, USA;
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7
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Mighton C, Lerner‐Ellis J. Principles of molecular testing for hereditary cancer. Genes Chromosomes Cancer 2022; 61:356-381. [DOI: 10.1002/gcc.23048] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 04/03/2022] [Accepted: 04/06/2022] [Indexed: 11/10/2022] Open
Affiliation(s)
- Chloe Mighton
- Laboratory Medicine and Pathology, Mount Sinai Hospital, Sinai Health Toronto ON Canada
- Lunenfeld Tanenbaum Research Institute, Sinai Health Toronto ON Canada
- Genomics Health Services Research Program Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto Toronto ON Canada
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health University of Toronto Toronto ON Canada
| | - Jordan Lerner‐Ellis
- Laboratory Medicine and Pathology, Mount Sinai Hospital, Sinai Health Toronto ON Canada
- Lunenfeld Tanenbaum Research Institute, Sinai Health Toronto ON Canada
- Department of Laboratory Medicine and Pathobiology University of Toronto Toronto ON Canada
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