دورية أكاديمية
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Massively parallel phenotyping of coding variants in cancer with Perturb-seq.

التفاصيل البيبلوغرافية
العنوان: Massively parallel phenotyping of coding variants in cancer with Perturb-seq.
المؤلفون: Ursu O; Broad Institute of Harvard and MIT, Cambridge, MA, USA.; Genentech, South San Francisco, CA, USA., Neal JT; Broad Institute of Harvard and MIT, Cambridge, MA, USA., Shea E; Broad Institute of Harvard and MIT, Cambridge, MA, USA.; Department of Cancer Biology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA., Thakore PI; Broad Institute of Harvard and MIT, Cambridge, MA, USA.; Genentech, South San Francisco, CA, USA., Jerby-Arnon L; Broad Institute of Harvard and MIT, Cambridge, MA, USA.; Chan Zuckerberg Biohub, San Francisco, CA, USA.; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA., Nguyen L; Broad Institute of Harvard and MIT, Cambridge, MA, USA., Dionne D; Broad Institute of Harvard and MIT, Cambridge, MA, USA., Diaz C; Broad Institute of Harvard and MIT, Cambridge, MA, USA.; Department of Cancer Biology, Stanford University School of Medicine, Stanford, CA, USA., Bauman J; Broad Institute of Harvard and MIT, Cambridge, MA, USA.; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA., Mosaad MM; Broad Institute of Harvard and MIT, Cambridge, MA, USA., Fagre C; Broad Institute of Harvard and MIT, Cambridge, MA, USA., Lo A; Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA., McSharry M; Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA., Giacomelli AO; Broad Institute of Harvard and MIT, Cambridge, MA, USA.; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.; Princess Margaret Cancer Centre, Toronto, ON, Canada., Ly SH; Broad Institute of Harvard and MIT, Cambridge, MA, USA.; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.; Duke-NUS Medical School, Singapore, Singapore., Rozenblatt-Rosen O; Broad Institute of Harvard and MIT, Cambridge, MA, USA.; Genentech, South San Francisco, CA, USA., Hahn WC; Broad Institute of Harvard and MIT, Cambridge, MA, USA.; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.; Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA., Aguirre AJ; Broad Institute of Harvard and MIT, Cambridge, MA, USA.; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.; Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA., Berger AH; Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA., Regev A; Broad Institute of Harvard and MIT, Cambridge, MA, USA. aviv.regev.sc@gmail.com.; Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. aviv.regev.sc@gmail.com.; Howard Hughes Medical Institute, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA. aviv.regev.sc@gmail.com.; Genentech, South San Francisco, CA, USA. aviv.regev.sc@gmail.com., Boehm JS; Broad Institute of Harvard and MIT, Cambridge, MA, USA. boehm@broadinstitute.org.
المصدر: Nature biotechnology [Nat Biotechnol] 2022 Jun; Vol. 40 (6), pp. 896-905. Date of Electronic Publication: 2022 Jan 20.
نوع المنشور: Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: Nature America Publishing Country of Publication: United States NLM ID: 9604648 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1546-1696 (Electronic) Linking ISSN: 10870156 NLM ISO Abbreviation: Nat Biotechnol Subsets: MEDLINE
أسماء مطبوعة: Publication: New York Ny : Nature America Publishing
Original Publication: New York, NY : Nature Pub. Co., [1996-
مواضيع طبية MeSH: Lung Neoplasms*/genetics , Proto-Oncogene Proteins p21(ras)*/genetics, Chromosome Mapping ; Humans ; Phenotype
مستخلص: Genome sequencing studies have identified millions of somatic variants in cancer, but it remains challenging to predict the phenotypic impact of most. Experimental approaches to distinguish impactful variants often use phenotypic assays that report on predefined gene-specific functional effects in bulk cell populations. Here, we develop an approach to functionally assess variant impact in single cells by pooled Perturb-seq. We measured the impact of 200 TP53 and KRAS variants on RNA profiles in over 300,000 single lung cancer cells, and used the profiles to categorize variants into phenotypic subsets to distinguish gain-of-function, loss-of-function and dominant negative variants, which we validated by comparison with orthogonal assays. We discovered that KRAS variants did not merely fit into discrete functional categories, but spanned a continuum of gain-of-function phenotypes, and that their functional impact could not have been predicted solely by their frequency in patient cohorts. Our work provides a scalable, gene-agnostic method for coding variant impact phenotyping, with potential applications in multiple disease settings.
(© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)
التعليقات: Erratum in: Nat Biotechnol. 2022 Nov;40(11):1691. (PMID: 36258042)
References: Rehm, H. L. & Fowler, D. M. Keeping up with the genomes: scaling genomic variant interpretation. Genome Med. 12, 5 (2019). (PMID: 318923666938604)
Lawrence, M. S. et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505, 495–501 (2014). (PMID: 243903504048962)
Zehir, A. et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat. Med. 23, 703–713 (2017). (PMID: 284813595461196)
Bailey, M. H. et al. Comprehensive characterization of cancer driver genes and mutations. Cell 174, 1034–1035 (2018). (PMID: 300963028045146)
Tate, J. G. et al. COSMIC: the catalogue of somatic mutations in cancer. Nucleic Acids Res. 47, D941–D947 (2019). (PMID: 30371878)
Hess, J. M. et al. Passenger hotspot mutations in cancer. Cancer Cell 36, 288–301.e14 (2019). (PMID: 315267597371346)
Muiños, F. et al. In silico saturation mutagenesis of cancer genes. Nature 596, 428–432 (2021). (PMID: 34321661)
Chang, M. T. et al. Identifying recurrent mutations in cancer reveals widespread lineage diversity and mutational specificity. Nat. Biotechnol. 34, 155–163 (2016). (PMID: 26619011)
Kamburov, A. et al. Comprehensive assessment of cancer missense mutation clustering in protein structures. Proc. Natl Acad. Sci. USA 112, E5486–E5495 (2015). (PMID: 263925354603469)
Hopf, T. A. et al. Mutation effects predicted from sequence co-variation. Nat. Biotechnol. 35, 128–135 (2017). (PMID: 280926585383098)
Figliuzzi, M., Jacquier, H., Schug, A., Tenaillon, O. & Weigt, M. Coevolutionary landscape inference and the context-dependence of mutations in beta-lactamase TEM-1. Mol. Biol. Evol. 33, 268–280 (2016). (PMID: 26446903)
Giacomelli, A. O. et al. Mutational processes shape the landscape of TP53 mutations in human cancer. Nat. Genet. 50, 1381–1387 (2018). (PMID: 302246446168352)
Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013). (PMID: 239455923776390)
Lawrence, M. S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013). (PMID: 237705673919509)
Alexandrov, L. B. et al. Clock-like mutational processes in human somatic cells. Nat. Genet. 47, 1402–1407 (2015). (PMID: 265516694783858)
Brenan, L. et al. Phenotypic characterization of a comprehensive set of MAPK1/ERK2 missense mutants. Cell Rep. 17, 1171–1183 (2016). (PMID: 277603195120861)
Findlay, G. M. et al. Accurate classification of BRCA1 variants with saturation genome editing. Nature 562, 217–222 (2018). (PMID: 302093996181777)
Dogruluk, T. et al. Identification of variant-specific functions of PIK3CA by rapid phenotyping of rare mutations. Cancer Res. 75, 5341–5354 (2015). (PMID: 266270074681596)
Yu, K. et al. PIK3CA variants selectively initiate brain hyperactivity during gliomagenesis. Nature 578, 166–171 (2020). (PMID: 319968457577741)
Gao, Y. et al. Allele-specific mechanisms of activation of MEK1 mutants determine their properties. Cancer Discov. 8, 648–661 (2018). (PMID: 294831356112572)
Boettcher, S. et al. A dominant-negative effect drives selection of TP53 missense mutations in myeloid malignancies. Science 365, 599–604 (2019). (PMID: 313957857327437)
Kotler, E. et al. A systematic p53 mutation library links differential functional impact to cancer mutation pattern and evolutionary conservation. Mol. Cell 71, 873 (2018). (PMID: 301931026127029)
Hamza, A. et al. Complementation of yeast genes with human genes as an experimental platform for functional testing of human genetic variants. Genetics 201, 1263–1274 (2015). (PMID: 263547694649650)
Sun, S. et al. An extended set of yeast-based functional assays accurately identifies human disease mutations. Genome Res. 26, 670–680 (2016). (PMID: 269757784864455)
Weile, J. et al. A framework for exhaustively mapping functional missense variants. Mol. Syst. Biol. 13, 957 (2017). (PMID: 292693825740498)
Lee, M. G. & Nurse, P. Complementation used to clone a human homologue of the fission yeast cell cycle control gene cdc2. Nature 327, 31–35 (1987). (PMID: 3553962)
Osborn, M. J. & Miller, J. R. Rescuing yeast mutants with human genes. Brief. Funct. Genom. Proteomic. 6, 104–111 (2007).
Gerasimavicius, L., Liu, X. & Marsh, J. A. Identification of pathogenic missense mutations using protein stability predictors. Sci. Rep. 10, 15387 (2020). (PMID: 329588057506547)
Sahni, N. et al. Widespread macromolecular interaction perturbations in human genetic disorders. Cell 161, 647–660 (2015). (PMID: 259102124441215)
Moal, I. H. & Fernández-Recio, J. SKEMPI: a structural kinetic and energetic database of mutant protein interactions and its use in empirical models. Bioinformatics 28, 2600–2607 (2012). (PMID: 22859501)
Leung, I., Dekel, A., Shifman, J. M. & Sidhu, S. S. Saturation scanning of ubiquitin variants reveals a common hot spot for binding to USP2 and USP21. Proc. Natl Acad. Sci. USA 113, 8705–8710 (2016). (PMID: 274368994978272)
Heyne, M., Papo, N. & Shifman, J. M. Generating quantitative binding landscapes through fractional binding selections combined with deep sequencing and data normalization. Nat. Commun. 11, 297 (2020). (PMID: 319418826962383)
Yang, M., Wu, Z. & Fields, S. Protein-peptide interactions analyzed with the yeast two-hybrid system. Nucleic Acids Res. 23, 1152–1156 (1995). (PMID: 7739893306824)
Kim, E. et al. Systematic functional interrogation of rare cancer variants identifies oncogenic alleles. Cancer Discov. 6, 714–726 (2016). (PMID: 271475994930723)
Berger, A. H. et al. High-throughput phenotyping of lung cancer somatic mutations. Cancer Cell 30, 214–228 (2016). (PMID: 274780405003022)
Rohban, M. H. et al. Systematic morphological profiling of human gene and allele function via Cell Painting. eLife 6, e24060 (2017). (PMID: 283155215386591)
Dixit, A. et al. Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA Profiling of pooled genetic screens. Cell 167, 1853–1866.e17 (2016). (PMID: 279847325181115)
Adamson, B. et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167, 1867–1882.e21 (2016). (PMID: 279847335315571)
FoundationOne CDx. https://www.foundationmedicine.com/test/foundationone-cdx.
AACR Project GENIE Consortium. AACR Project GENIE: powering precision medicine through an international consortium. Cancer Discov. 7, 818–831 (2017).
Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016). (PMID: 275355335018207)
Hotelling, H. The generalization of Student’s ratio. Ann. Math. Stat. 2, 360–378 (1931).
Fischer, M. Census and evaluation of p53 target genes. Oncogene 36, 3943–3956 (2017). (PMID: 282881325511239)
Jeay, S. et al. A distinct p53 target gene set predicts for response to the selective p53–HDM2 inhibitor NVP-CGM097. eLife 4, e06498 (2015). (PMID: 259651774468608)
Hong, D. S. et al. KRASG12C inhibition with sotorasib in advanced solid tumors. N. Engl. J. Med. 383, 1207–1217 (2020). (PMID: 329551767571518)
Singh, A. et al. A gene expression signature associated with ‘K-Ras addiction’ reveals regulators of EMT and tumor cell survival. Cancer Cell 15, 489–500 (2009). (PMID: 194774282743093)
Meyers, R. M. et al. Computational correction of copy number effect improves specificity of CRISPR–Cas9 essentiality screens in cancer cells. Nat. Genet. 49, 1779–1784 (2017). (PMID: 290834095709193)
Rotem, A. et al. Alternative to the soft-agar assay that permits high-throughput drug and genetic screens for cellular transformation. Proc. Natl Acad. Sci. USA 112, 5708–5713 (2015). (PMID: 259024954426412)
Ly, S. H. Investigation of KRAS Dependency Bypass and Functional Characterization of All Possible KRAS Missense Variants. PhD thesis, Harvard Univ. http://nrs.harvard.edu/urn-3:HUL.InstRepos:40050098 (2018).
UniProt Consortium. UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res. 47, D506–D515 (2019).
Lu, J., Bera, A. K., Gondi, S. & Westover, K. D. KRAS switch mutants D33E and A59G crystallize in the state 1 conformation. Biochemistry 57, 324–333 (2018). (PMID: 29235861)
Akagi, K. et al. Characterization of a novel oncogenic K-ras mutation in colon cancer. Biochem. Biophys. Res. Commun. 352, 728–732 (2007). (PMID: 17150185)
Serrano, M., Lin, A. W., McCurrach, M. E., Beach, D. & Lowe, S. W. Oncogenic ras provokes premature cell senescence associated with accumulation of p53 and p16INK4a. Cell 88, 593–602 (1997). (PMID: 9054499)
Bouaoun, L. et al. TP53 variations in human cancers: new lessons from the IARC TP53 database and genomics data. Hum. Mutat. 37, 865–876 (2016). (PMID: 27328919)
Datlinger, P. et al. Ultra-high-throughput single-cell RNA sequencing and perturbation screening with combinatorial fluidic indexing. Nat. Methods 18, 635–642 (2021). (PMID: 340598277612019)
Ma, S. et al. Chromatin potential identified by shared single-cell profiling of RNA and chromatin. Cell https://doi.org/10.1016/j.cell.2020.09.056 (2020). (PMID: 10.1016/j.cell.2020.09.056332967027770098)
Sidore, A. M. et al. DropSynth 2.0: high-fidelity multiplexed gene synthesis in emulsions. Nucleic Acids Res. 48, e95 (2020). (PMID: 326923497498354)
Kinker, G. S. et al. Pan-cancer single cell RNA-seq uncovers recurring programs of cellular heterogeneity. Preprint at bioRxiv https://doi.org/10.1101/807552 (2019).
McFarland, J. M. et al. Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action. Nat. Commun. 11, 4296 (2020). (PMID: 328553877453022)
Gaidukov, L. et al. A multi-landing pad DNA integration platform for mammalian cell engineering. Nucleic Acids Res. 46, 4072–4086 (2018). (PMID: 296178735934685)
Gaudelli, N. M. et al. Programmable base editing of A•T to G•C in genomic DNA without DNA cleavage. Nature 551, 464–471 (2017). (PMID: 291603085726555)
Komor, A. C., Kim, Y. B., Packer, M. S., Zuris, J. A. & Liu, D. R. Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage. Nature 533, 420–424 (2016). (PMID: 270963654873371)
Anzalone, A. V. et al. Search-and-replace genome editing without double-strand breaks or donor DNA. Nature 576, 149–157 (2019). (PMID: 316349026907074)
Lebrigand, K., Magnone, V., Barbry, P. & Waldmann, R. High throughput error corrected Nanopore single cell transcriptome sequencing. Nat. Commun. 11, 4025 (2020). (PMID: 327886677423900)
Volden, R. & Vollmers, C. Highly multiplexed single-cell full-length cDNA sequencing of human immune cells with 10X Genomics and R2C2. Preprint at bioRxiv https://doi.org/10.1101/2020.01.10.902361 (2020).
Cao, J. et al. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357, 661–667 (2017). (PMID: 288189385894354)
Rosenberg, A. B. et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360, 176–182 (2018). (PMID: 295455117643870)
Cleary, B., Cong, L., Cheung, A., Lander, E. S. & Regev, A. Efficient generation of transcriptomic profiles by random composite measurements. Cell 171, 1424–1436.e18 (2017). (PMID: 291538355726792)
Cleary, B. & Regev, A. The necessity and power of random, under-sampled experiments in biology. Preprint at https://arxiv.org/abs/2012.12961 (2020).
Frangieh, C. J. et al. Multimodal pooled Perturb-CITE-seq screens in patient models define mechanisms of cancer immune evasion. Nat. Genet. 53, 332–341 (2021). (PMID: 336495928376399)
Buschmann, T. & Bystrykh, L. V. Levenshtein error-correcting barcodes for multiplexed DNA sequencing. BMC Bioinf. 14, 272 (2013).
Jaitin, D. A. et al. Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-seq. Cell 167, 1883–1896.e15 (2016). (PMID: 27984734)
Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017). (PMID: 280916015241818)
Benson, D. A. et al. GenBank. Nucleic Acids Res. 41, D36–D42 (2013). (PMID: 23193287)
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018). (PMID: 294095325802054)
Blondel, V. D. et al. Fast unfolding of communities in large networks. J. Stat. Mech. 2008, P10008 (2008).
Levine, J. H. et al. Data-Driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015). (PMID: 260952514508757)
Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009). (PMID: 192611742690996)
Dixit, A. Correcting chimeric crosstalk in single cell RNA-seq experiments. Preprint at bioRxiv https://doi.org/10.1101/093237 (2016).
Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA 100, 9440–9445 (2003). (PMID: 12883005170937)
Rodriguez, J. M. et al. APPRIS 2017: principal isoforms for multiple gene sets. Nucleic Acids Res. 46, D213–D217 (2018). (PMID: 29069475)
Grau, J., Grosse, I. & Keilwagen, J. PRROC: computing and visualizing precision-recall and receiver operating characteristic curves in R. Bioinformatics 31, 2595–2597 (2015). (PMID: 258104284514923)
Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
معلومات مُعتمدة: F32 AI138458 United States AI NIAID NIH HHS; U01 CA176058 United States CA NCI NIH HHS; R00 CA197762 United States CA NCI NIH HHS; R37 CA252050 United States CA NCI NIH HHS; United States HHMI Howard Hughes Medical Institute
المشرفين على المادة: EC 3.6.5.2 (Proto-Oncogene Proteins p21(ras))
تواريخ الأحداث: Date Created: 20220121 Date Completed: 20220617 Latest Revision: 20221019
رمز التحديث: 20231215
DOI: 10.1038/s41587-021-01160-7
PMID: 35058622
قاعدة البيانات: MEDLINE
الوصف
تدمد:1546-1696
DOI:10.1038/s41587-021-01160-7