High-throughput functional analysis of human cancer-associated mutations the utilize of frightful editors

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April 12, 2022
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Summary

Comprehensive phenotypic characterization of the varied mutations learned in cancer tissues is one in every of the supreme challenges in cancer genomics. On this scrutinize, we evaluated the functional outcomes of 29,060 cancer-connected transition mutations that outcome in protein variants on the survival and proliferation of non-tumorigenic lung cells the utilize of cytosine and adenine frightful editors and single files RNA (sgRNA) libraries. By monitoring frightful enhancing efficiencies and outcomes the utilize of surrogate plan sequences paired with sgRNA-encoding sequences on the lentiviral provide effect, we identified sgRNAs that prompted a single predominant protein variant per sgRNA, enabling linking these mutations to the mobile phenotypes brought about by frightful enhancing. The capabilities of the excellent majority of the protein variants (28,458 variants, 98%) had been classified as neutral or most likely neutral; easiest 18 (0.06%) and 157 (0.5%) variants brought on outgrowing and most likely outgrowing phenotypes, respectively. We rely on that our plan will also be prolonged to extra variants of unknown significance and other tumor sorts.

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Info availability

We beget submitted the deep sequencing files from this scrutinize to the National Heart of Biotechnology Info’s Sequence Learn Archive below accession amount PRJNA667758. We beget provided the datasets aged on this scrutinize as Supplementary Tables 2–4 and deepcrispr.data/BEvariants.

Code availability

The custom Python scripts aged for the era of the MAGeCK enter file the utilize of UMIs are on hand on GitHub (https://github.com/oreolic/CancerLibrary).

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Accumulate references

Acknowledgements

We thank J. W. Choi for aiding with computational diagnosis. This work used to be supported, partly, by the National Learn Foundation of Korea (grants 2017R1A2B3004198 (H.H.Okay.), 2017M3A9B4062403 (H.H.Okay.) and 2018R1A5A2025079 (H.H.Okay)); the Brain Korea 21 Plus Project (Yonsei College College of Remedy); the Yonsei Signature Learn Cluster Program of 2021-22-0014 (H.H.Okay.); a grant of the MD-PhD/Scientific Scientist Practising Program (S.L.) thru the Korea Neatly being Commerce Vogue Institute (KHIDI), funded by the Ministry of Neatly being & Welfare, Republic of Korea; Lung Most cancers SPORE P50 (CA070907; J.D.M.); and the Korean Neatly being Skills R&D Project, Ministry of Neatly being and Welfare, Republic of Korea (grant HI21C1314 (H.H.Okay.)).

Author files

Author notes

These authors contributed equally: Younggwang Kim, Seungho Lee.

Affiliations

Department of Pharmacology, Yonsei College College of Remedy, Seoul, Republic of Korea

Younggwang Kim, Seungho Lee, Soohyuk Cho, Jinman Park, Dongwoo Chae & Hyongbum Henry Kim
Graduate College of Scientific Science, Brain Korea 21 Plus Project for Scientific Sciences, Yonsei College College of Remedy, Seoul, Republic of Korea

Younggwang Kim, Soohyuk Cho, Jinman Park & Hyongbum Henry Kim
Department of Utilized Statistics, Yonsei College, Seoul, Republic of Korea

Taeyoung Park
Hamon Heart for Therapeutic Oncology Learn, College of Texas Southwestern Scientific Heart, Dallas, TX, USA

John D. Minna
Severance Biomedical Science Institute, Yonsei College College of Remedy, Seoul, Republic of Korea

Hyongbum Henry Kim
Heart for Nanomedicine, Institute for Traditional Science (IBS), Seoul, Republic of Korea

Hyongbum Henry Kim
Yonsei-IBS Institute, Yonsei College, Seoul, Republic of Korea

Hyongbum Henry Kim
Institute for Immunology and Immunological Ailments, Yonsei College College of Remedy, Seoul, Republic of Korea

Hyongbum Henry Kim

Contributions

Y.Okay., S.L. and H.H.Okay. conceived and designed the scrutinize. Y.Okay. and S.L. performed a entire lot of the experiments. J.P. seriously contributed to computational diagnosis. S.C. seriously assisted within the moist experiments. Y.Okay. and S.L. analyzed the files per comments of H.H.Okay. J.D.M. generated and provided HBEC30KT-shTP53 cells (P cells). D.C. and T.P. contributed to the mathematical diagnosis (Supplementary Point to 2). Y.Okay. and H.H.Okay. wrote the manuscript with enter from all authors.

Corresponding author

Correspondence to
Hyongbum Henry Kim.

Ethics declarations

Competing interests

Yonsei College has filed a patent utility per this work, by which Y.Okay., S.L. and H.H.Okay. are listed as inventors. J.D.M. receives licensing prices from the National Institutes of Neatly being and the College of Texas Southwestern Scientific Heart for distributing human cell lines. Your entire other authors repeat no competing interests.

Gape analysis

Gape analysis files

Nature Biotechnology thanks the nameless reviewers for their contribution to the gawk analysis of this work.

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Prolonged files

Prolonged Info Fig. 1 Exon transcript profiles of P cells.

a, Expression of TP53 mRNA in P cells and HBEC30KT cells. FPKM, fragments per kilobase of transcript per million. Boxplots are represented for n = 3 biologically self reliant samples as follows: heart line of box indicating the median, box limits indicating the upper and lower quartile; whiskers point to the 1.5 cases interquartile vary. b, Gene position enrichment diagnosis (GSEA) of exon transcript profiles of HBEC30KT, P cells, and HCC4017, a lung cancer cell line. The single sample GSEA rating (ssGSEA rating) represents the level to which the genes in a particular gene position are up- or down-regulated interior the sample. RNA expression files had been retrieved from Kim et al³⁴.

Prolonged Info Fig. 2 Skills of libraries C and A.

a, The system of deciding on sgRNA-plan pairs for the era of libraries C and A. SNVs, single nucleotide variants; sgRNA, single files RNA. b, Skills of lentiviral libraries of sgRNA-encoding and plan sequence pairs with odd molecular identifiers (UMIs). Oligonucleotides containing a 20-nt files sequence, and the corresponding plan sequence had been synthesized and cloned into the pLenti-gRNA-puro vector to produce plasmid library 1. The plasmids had been then digested with BsmBI restriction enzyme and ligated with fragments containing the sgRNA scaffold sequences and UMIs to produce plasmid library 2. Lentiviral libraries generated from plasmid library 2 had been then transduced into cells expressing cytosine frightful editor (CBE) or adenine frightful editor (ABE) in a doxycycline-inducible system.

Prolonged Info Fig. 3 Snide enhancing efficiencies and indel frequencies at built-in plan sequences.

Snide enhancing efficiencies measured at each space within the indicated space for plan nucleotide Cs (a) or As (b) in built-in surrogate plan sequences. Place 1 is the 5’ kill of the plan sequence and space 20 is straight away upstream of the NGG PAM. The numbers of analyzed plan sequences (n) are as follows: n = 5,865 (space −4), 5,393 (space −3), 5,782 (space -2), 5,815 (space -1), 5,292 (space 1), 5,614 (space 2), 5,697, 6,394, 10,586, 9,382, 8,837, 5,421, 6,130, 5,339, 5,541, 5,796, 5,058, 5,723, 5,955, 5,348, 5,779, 5,437, 4,884, 5,502 (space 20) for ABE (a); n = 19,475 (space -4), 20,753 (space -3), 20,110 (space -2), 19,425 (space -1), 19,984 (space 1), 20,004 (space 2), 17,873, 24,870, 35,421, 33,186, 32,807, 19,895, 19,195, 20,227, 19,549, 18,986, 20,367, 18,793, 18,361, 20,478, 19,605, 20,975, 21,542, 22,952 (space 20) for CBE (b). Boxplots are represented as follows: heart line of box indicating the median, box limits indicating the upper and lower quartile; whiskers point to the 10th and 90th percentiles. Outliers are shown the utilize of dots. c, Indel frequencies measured 10 days after the transduction of sgRNA plan pairs. The different of analyzed plan sequence is indicated on the kill of every dataset. (n = 62,000 (Library C, Replicate 1), 77,201 (Library C, Replicate 2), 21,617 (Library A, Replicate 1) and 20,913 (Library A, Replicate 2). Boxplots are represented as follows: heart white dot of box indicating the median, box limits indicating the upper and lower quartile; the distributions of indel frequencies are represented with kernel densities. d, Nonsynonymous frightful enhancing efficiencies on the built-in plan sequences of synonymous get rid of watch over sgRNAs and other sgRNAs within the given datasets. The different of synonymous and other sgRNAs are as follows; 431 and 21,055 (Library A, Replicate 1), 413 and 20,372 (Library A, Replicate 2), 2,272 and 59,390 (Library C, Replicate 1), and a pair of,795 and 73,691 (Library C, replicate 2), respectively. Boxplots are represented as follows: heart line of box indicating the median, box limits indicating the upper and lower quartile; whiskers point to the 1.5 cases interquartile vary.

Prolonged Info Fig. 4 Efficiency of high-throughput reports.

a, Distribution of median normalized log fold changes (LFCs) of 338 sgRNAs focusing on the biggest genes relying on the nonsynonymous frightful enhancing efficiencies sure on the built-in plan sequences in library C2. NT, nontargeting sgRNAs. The different of sgRNAs n = 359 (NT), 5 (60%). Boxplots are represented as follows: heart line of box indicating the median, box limits indicating the upper and lower quartile; whiskers point to the 1.5 cases interquartile vary. (in contrast with NT, student’s t-check; NS, now now not considerable, *P = 1.5 × 10⁻⁴, P = 2.2 × 10⁻³²). b,c, Receiver working attribute-space below the curve (ROC-AUC) diagnosis of LFCs for sgRNAs predicted to induce halt codons in well-liked the biggest genes versus nontargeting sgRNAs in library C2 (b) and library C (c) at rising thresholds of nonsynonymous frightful enhancing efficiencies. AUC values are indicated in parentheses. d, ROC-AUC diagnosis of LFCs for sgRNAs predicted to induce halt codons in well-liked the biggest genes versus nontargeting controls at rising thresholds of the different of UMIs in each sgRNA in library C. An space below curve for each UMI cutoff is shown within the parenthesis. e, Correlations between median LFCs of UMIs for sgRNAs and LFCs of UMI CPM (counts per million) for the identical sgRNAs in library C2. Crimson dots expose sgRNAs predicted to induce nonsense mutations in selected well-liked the biggest genes. The different of sgRNAs n = 3,229 (merged), 2,913 (other sgRNAs, blue dots), 217 (sgRNAs focusing on the biggest genes, purple dots), 99 (nontargeting sgRNAs, dark dots). Pearson correlation coefficients (r) are shown.

Prolonged Info Fig. 5 Invent of little libraries and reproducibility of frightful enhancing efficiencies the utilize of these libraries.

a-b, Invent of little libraries C1, C2, and A1 (a) and C3 and A2 (b). UMIs, odd molecular identifiers. c, Correlations between nonsynonymous frightful enhancing efficiencies on the built-in plan sequences of natural replicates. The coloration of every dot used to be resolute by the different of neighboring dots (that is, dots interior a distance that is three cases the radius of the dot). The frightful enhancing efficiencies had been sure ten days after the initial transduction of every library into P-C or P-A cells. Handiest sgRNAs with extra than 100 raw learn counts in each replicate had been included. Pearson correlation coefficients (r) are shown. The different of sgRNAs n = 3,181 (library C1), 3,063 (library C2), and 1,520 (library A1).

Prolonged Info Fig. 6 The different of protein variants generated by an sgRNA.

a, The proportion of sgRNAs that induce a predominant protein variant. The numbers of sgRNAs are indicated in parentheses. b, The different of considerable (frequency > 10%) protein variants generated by sgRNAs that induce extra than one protein variants without a predominant protein variant. The numbers of protein variants are indicated in parentheses.

Prolonged Info Fig. 7 Affiliation between computationally predicted capabilities of variants and measured capabilities of variants.

a, The ratings from driver detection algorithms (CTAT-cancer and CHASM) for 4,143 protein variants. The different of variants n = 15 (depleting), 39 (most likely depleting), 864 (perchance depleting), 2,141 (neutral), 1,056 (perchance outgrowing), 25 (most likely outgrowing), and 3 (outgrowing). b, The ratings from algorithms that predict the functional outcomes of variants (SIFT and PolyPhen-2) for 3,899 protein variants. The different of variants n = 12 (depleting), 38 (most likely depleting), 807 (perchance depleting), 2,009 (neutral), 1,008 (perchance outgrowing), 22 (most likely outgrowing), and 3 (outgrowing). c,d, Distribution of SIFT ratings (c) and PolyPhen-2 ratings (d) for missense variants in well-liked the biggest genes per the LFC in library C. The different of variants n = 10 (

Prolonged Info Fig. 8 Allele frequency monitoring after transduction of sgRNA-encoding sequences into P-C or P-A cells.

sgRNA-encoding lentivirus used to be transduced into P-C and P-A cells at day 0 and doxycycline used to be added to induce expression of CBE and ABE, respectively, and maintained except day 10, after which doxycycline used to be eliminated. The functional classification outcomes obtained from the high-throughput experiments and these from these person experiments are shown in purple and green, respectively, on the kill of every graph. The mean values of two self reliant samples are indicated.

Prolonged Info Fig. 9 The outcomes of competitive proliferation assays.

a, An example for scoot cytometry gating method aged within the competitive proliferation assays. b, Mean relative enrichment values ± abnormal deviation of three replicates. Pupil’s t check used to be performed below the null hypothesis that the proportions of sgRNA-transduced and nontargeting sgRNA-transduced cells will most likely be the identical. Two nontargeting sgRNAs had been aged because the get rid of watch over and the mean values of relative enrichment had been aged because the get rid of watch over.

Prolonged Info Fig. 10 Notable gene groups associated with outgrowing/most likely outgrowing and depleting/most likely depleting sgRNAs and variants.

a, (Left panel) The part of functionally classified sgRNAs (prime) focusing on cancer gene census (CGC)⁵ genes and predominant protein variants (backside) encoded by CGC genes within the outgrowing and most likely outgrowing groups. Outcomes from all libraries excluding library eC had been combined. P-values from two-sided Fisher’s accurate check are shown. The different of sgRNAs or variants both focusing on or encoded by CGC genes amongst all sgRNAs or variants in each team are shown on the x-axes. (Horny panel) Detailed distribution of sgRNAs predicted to introduce mutations in CGC genes (prime) and variants generated in CGC genes (backside). The different of sgRNAs or variants equivalent to each gene is specified in parentheses. b, The part of functionally classified sgRNAs (left) focusing on Depmap well-liked the biggest genes (CEGs) and protein variants (lawful) encoded by CEGs within the depleting and most likely depleting groups. Outcomes from all libraries excluding library eC had been combined. P-values from two-sided Fisher’s accurate check are shown. The numbers of sgRNAs or variants both focusing on or encoded by CEG genes amongst all sgRNAs or variants in each team are shown on the x-axes.

Supplementary files

Supplementary Info

Supplementary Notes 1 and a pair of and Supplementary Figs. 1–7

Supplementary Desk

Supplementary Desk 1. Composition of sgRNA-encoding libraries C, A, C1, C2, C3, A1, A2, dA and eC. Barcode sequences aged for sorting, sgRNA sequences and plan sequences, at the side of neighboring sequences (5′-neighboring sequence (4 bp) + plan sequence (20 bp + 3-bp PAM = 23 bp) + 3′-neighboring sequence (3 bp) = 30 bp of genomic DNA sequence). Info about intended mutations and DeepCBE or DeepABE effectivity ratings are moreover included (provided as a separate Excel file). Supplementary Desk 2. The outcomes of MAGeCK analyses. RPM of four replicate^UMI, LFCs, median LFCs (mLFCs), clear or detrimental MAGeCK RRA P values and LFCs of UMI CPM are shown for each sgRNA (provided as a separate Excel file). Supplementary Desk 3. Helpful classifications of sgRNAs and protein variants. a, Helpful classification of sgRNAs per the proliferation and survival (sheet 1). b, Helpful classification of sgRNAs in library eC (sheet 2). c, Snide enhancing outcomes and allele frequencies on the built-in plan sequences (dependency on EGF signaling) (sheet 3). d, Doable classification of sgRNAs with low frightful enhancing efficiencies (sheet 4) (provided as a separate Excel file). Supplementary Desk 4. Outcomes of allele frequency monitoring after provide of an person sgRNA for 20 selected sgRNAs. After lentiviral transduction of the required person sgRNA, protein variant frequencies had been calculated from DNA sequence diagnosis. Endogenous DNA sequence variants encoding the identical amino acid change had been combined into one protein variant (provided as a separate Excel file). Supplementary Desk 5. Oligonucleotides aged on this scrutinize (provided in a separate Excel file).

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Kim, Y., Lee, S., Cho, S. et al. High-throughput functional analysis of human cancer-associated mutations the utilize of frightful editors.
Nat Biotechnol (2022). https://doi.org/10.1038/s41587-022-01276-4

Accumulate citation

Bought: 18 Could perchance moreover merely 2021
Authorized: 10 March 2022
Revealed: 11 April 2022
DOI: https://doi.org/10.1038/s41587-022-01276-4