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High-throughput functional evaluation of human cancer-associated mutations using base editors

Nature Biotechnology volume 40, pages 874–884 (2022)Cite this article

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Abstract

Comprehensive phenotypic characterization of the many mutations found in cancer tissues is one of the biggest challenges in cancer genomics. In this study, we evaluated the functional effects of 29,060 cancer-related transition mutations that result in protein variants on the survival and proliferation of non-tumorigenic lung cells using cytosine and adenine base editors and single guide RNA (sgRNA) libraries. By monitoring base editing efficiencies and outcomes using surrogate target sequences paired with sgRNA-encoding sequences on the lentiviral delivery construct, we identified sgRNAs that induced a single primary protein variant per sgRNA, enabling linking those mutations to the cellular phenotypes caused by base editing. The functions of the vast majority of the protein variants (28,458 variants, 98%) were classified as neutral or likely neutral; only 18 (0.06%) and 157 (0.5%) variants caused outgrowing and likely outgrowing phenotypes, respectively. We expect that our approach can be extended to more variants of unknown significance and other tumor types.

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Fig. 1: Base editor-directed generation of cancer-associated transition mutations.
Fig. 2: Functional classification of cancer-associated transition mutations.
Fig. 3: High-throughput classifications are reproducible at different scales.
Fig. 4: Individual validation of sgRNAs and their associated base-edited variants supports the high accuracy of high-throughput functional classifications.
Fig. 5: Base editor-directed investigation of mutations related to resistance to an EGFR tyrosine kinase inhibitor.

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Data availability

We have submitted the deep sequencing data from this study to the National Center of Biotechnology Information’s Sequence Read Archive under accession number PRJNA667758. We have provided the datasets used in this study as Supplementary Tables 2–4 and deepcrispr.info/BEvariants.

Code availability

The custom Python scripts used for the generation of the MAGeCK input file using UMIs are available on GitHub (https://github.com/oreolic/CancerLibrary).

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Acknowledgements

We thank J. W. Choi for assisting with computational analysis. This work was supported, in part, by the National Research Foundation of Korea (grants 2017R1A2B3004198 (H.H.K.), 2017M3A9B4062403 (H.H.K.) and 2018R1A5A2025079 (H.H.K)); the Brain Korea 21 Plus Project (Yonsei University College of Medicine); the Yonsei Signature Research Cluster Program of 2021-22-0014 (H.H.K.); a grant of the MD-PhD/Medical Scientist Training Program (S.L.) through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea; Lung Cancer SPORE P50 (CA070907; J.D.M.); and the Korean Health Technology R&D Project, Ministry of Health and Welfare, Republic of Korea (grant HI21C1314 (H.H.K.)).

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Author notes

  1. These authors contributed equally: Younggwang Kim, Seungho Lee.

Authors and Affiliations

  1. Department of Pharmacology, Yonsei University College of Medicine, Seoul, Republic of Korea

    Younggwang Kim, Seungho Lee, Soohyuk Cho, Jinman Park, Dongwoo Chae & Hyongbum Henry Kim

  2. Graduate School of Medical Science, Brain Korea 21 Plus Project for Medical Sciences, Yonsei University College of Medicine, Seoul, Republic of Korea

    Younggwang Kim, Soohyuk Cho, Jinman Park & Hyongbum Henry Kim

  3. Department of Applied Statistics, Yonsei University, Seoul, Republic of Korea

    Taeyoung Park

  4. Hamon Center for Therapeutic Oncology Research, University of Texas Southwestern Medical Center, Dallas, TX, USA

    John D. Minna

  5. Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Republic of Korea

    Hyongbum Henry Kim

  6. Center for Nanomedicine, Institute for Basic Science (IBS), Seoul, Republic of Korea

    Hyongbum Henry Kim

  7. Yonsei-IBS Institute, Yonsei University, Seoul, Republic of Korea

    Hyongbum Henry Kim

  8. Institute for Immunology and Immunological Diseases, Yonsei University College of Medicine, Seoul, Republic of Korea

    Hyongbum Henry Kim

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Contributions

Y.K., S.L. and H.H.K. conceived and designed the study. Y.K. and S.L. performed most of the experiments. J.P. critically contributed to computational analysis. S.C. critically assisted in the wet experiments. Y.K. and S.L. analyzed the data based on comments of H.H.K. J.D.M. generated and provided HBEC30KT-shTP53 cells (P cells). D.C. and T.P. contributed to the mathematical analysis (Supplementary Note 2). Y.K. and H.H.K. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Hyongbum Henry Kim.

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Competing interests

Yonsei University has filed a patent application based on this work, in which Y.K., S.L. and H.H.K. are listed as inventors. J.D.M. receives licensing fees from the National Institutes of Health and the University of Texas Southwestern Medical Center for distributing human cell lines. All the other authors declare no competing interests.

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Extended data

Extended Data 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 independent samples as follows: center line of box indicating the median, box limits indicating the upper and lower quartile; whiskers show the 1.5 times interquartile range. b, Gene set enrichment analysis (GSEA) of exon transcript profiles of HBEC30KT, P cells, and HCC4017, a lung cancer cell line. The single sample GSEA score (ssGSEA score) represents the degree to which the genes in a particular gene set are up- or down-regulated within the sample. RNA expression data were retrieved from Kim et al34.

Extended Data Fig. 2 Generation of libraries C and A.

a, The process of selecting sgRNA-target pairs for the generation of libraries C and A. SNVs, single nucleotide variants; sgRNA, single guide RNA. b, Generation of lentiviral libraries of sgRNA-encoding and target sequence pairs with unique molecular identifiers (UMIs). Oligonucleotides containing a 20-nt guide sequence, and the corresponding target sequence were synthesized and cloned into the pLenti-gRNA-puro vector to create plasmid library 1. The plasmids were then digested with BsmBI restriction enzyme and ligated with fragments containing the sgRNA scaffold sequences and UMIs to create plasmid library 2. Lentiviral libraries generated from plasmid library 2 were then transduced into cells expressing cytosine base editor (CBE) or adenine base editor (ABE) in a doxycycline-inducible manner.

Extended Data Fig. 3 Base editing efficiencies and indel frequencies at integrated target sequences.

Base editing efficiencies measured at each position in the indicated region for target nucleotide Cs (a) or As (b) in integrated surrogate target sequences. Position 1 is the 5’ end of the target sequence and position 20 is immediately upstream of the NGG PAM. The numbers of analyzed target sequences (n) are as follows: n = 5,865 (position −4), 5,393 (position −3), 5,782 (position -2), 5,815 (position -1), 5,292 (position 1), 5,614 (position 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 (position 20) for ABE (a); n = 19,475 (position -4), 20,753 (position -3), 20,110 (position -2), 19,425 (position -1), 19,984 (position 1), 20,004 (position 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 (position 20) for CBE (b). Boxplots are represented as follows: center line of box indicating the median, box limits indicating the upper and lower quartile; whiskers show the 10th and 90th percentiles. Outliers are shown using dots. c, Indel frequencies measured 10 days after the transduction of sgRNA target pairs. The number of analyzed target sequence is indicated at the top of each 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: center 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 base editing efficiencies at the integrated target sequences of synonymous control sgRNAs and other sgRNAs in the given datasets. The number 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 2,795 and 73,691 (Library C, replicate 2), respectively. Boxplots are represented as follows: center line of box indicating the median, box limits indicating the upper and lower quartile; whiskers show the 1.5 times interquartile range.

Extended Data Fig. 4 Performance of high-throughput evaluations.

a, Distribution of median normalized log fold changes (LFCs) of 338 sgRNAs targeting essential genes depending on the nonsynonymous base editing efficiencies determined at the integrated target sequences in library C2. NT, nontargeting sgRNAs. The number of sgRNAs n = 359 (NT), 5 (<20%), 10 (20%~40%), 55 (40%~60%), 268 (>60%). Boxplots are represented as follows: center line of box indicating the median, box limits indicating the upper and lower quartile; whiskers show the 1.5 times interquartile range. (in comparison with NT, student’s t-test; NS, not significant, *P = 1.5 × 10−4, **P = 2.2 × 10−32). b,c, Receiver operating characteristic-area under the curve (ROC-AUC) analysis of LFCs for sgRNAs predicted to induce stop codons in common essential genes versus nontargeting sgRNAs in library C2 (b) and library C (c) at increasing thresholds of nonsynonymous base editing efficiencies. AUC values are indicated in parentheses. d, ROC-AUC analysis of LFCs for sgRNAs predicted to induce stop codons in common essential genes versus nontargeting controls at increasing thresholds of the number of UMIs in each sgRNA in library C. An area under curve for each UMI cutoff is shown in the parenthesis. e, Correlations between median LFCs of UMIs for sgRNAs and LFCs of UMI CPM (counts per million) for the same sgRNAs in library C2. Red dots indicate sgRNAs predicted to induce nonsense mutations in selected common essential genes. The number of sgRNAs n = 3,229 (merged), 2,913 (other sgRNAs, blue dots), 217 (sgRNAs targeting essential genes, red dots), 99 (nontargeting sgRNAs, black dots). Pearson correlation coefficients (r) are shown.

Extended Data Fig. 5 Design of small libraries and reproducibility of base editing efficiencies using these libraries.

a-b, Design of small libraries C1, C2, and A1 (a) and C3 and A2 (b). UMIs, unique molecular identifiers. c, Correlations between nonsynonymous base editing efficiencies at the integrated target sequences of biological replicates. The color of each dot was determined by the number of neighboring dots (that is, dots within a distance that is three times the radius of the dot). The base editing efficiencies were determined ten days after the initial transduction of each library into P-C or P-A cells. Only sgRNAs with more than 100 raw read counts in each replicate were included. Pearson correlation coefficients (r) are shown. The number of sgRNAs n = 3,181 (library C1), 3,063 (library C2), and 1,520 (library A1).

Extended Data Fig. 6 The number of protein variants generated by an sgRNA.

a, The proportion of sgRNAs that induce a primary protein variant. The numbers of sgRNAs are indicated in parentheses. b, The number of significant (frequency > 10%) protein variants generated by sgRNAs that induce multiple protein variants without a primary protein variant. The numbers of protein variants are indicated in parentheses.

Extended Data Fig. 7 Association between computationally predicted functions of variants and measured functions of variants.

a, The scores from driver detection algorithms (CTAT-cancer and CHASM) for 4,143 protein variants. The number of variants n = 15 (depleting), 39 (likely depleting), 864 (possibly depleting), 2,141 (neutral), 1,056 (possibly outgrowing), 25 (likely outgrowing), and 3 (outgrowing). b, The scores from algorithms that predict the functional effects of variants (SIFT and PolyPhen-2) for 3,899 protein variants. The number of variants n = 12 (depleting), 38 (likely depleting), 807 (possibly depleting), 2,009 (neutral), 1,008 (possibly outgrowing), 22 (likely outgrowing), and 3 (outgrowing). c,d, Distribution of SIFT scores (c) and PolyPhen-2 scores (d) for missense variants in common essential genes according to the LFC in library C. The number of variants n = 10 (<−0.4), 65 (−0.4~0), 82 (0~0.4). Boxplots are represented as follows: center line of box indicating the median, box limits indicating the upper and lower quartile; whiskers show the 1.5 times interquartile range.

Extended Data Fig. 8 Allele frequency tracking after transduction of sgRNA-encoding sequences into P-C or P-A cells.

sgRNA-encoding lentivirus was transduced into P-C and P-A cells at day 0 and doxycycline was added to induce expression of CBE and ABE, respectively, and maintained until day 10, after which doxycycline was removed. The functional classification results obtained from the high-throughput experiments and those from these individual experiments are shown in red and green, respectively, on the top of each graph. The mean values of two independent samples are indicated.

Extended Data Fig. 9 The results of competitive proliferation assays.

a, An example for flow cytometry gating strategy used in the competitive proliferation assays. b, Mean relative enrichment values ± standard deviation of three replicates. Student’s t test was performed under the null hypothesis that the proportions of sgRNA-transduced and nontargeting sgRNA-transduced cells would be the same. Two nontargeting sgRNAs were used as the control and the mean values of relative enrichment were used as the control.

Extended Data Fig. 10 Notable gene groups associated with outgrowing/likely outgrowing and depleting/likely depleting sgRNAs and variants.

a, (Left panel) The fraction of functionally classified sgRNAs (top) targeting cancer gene census (CGC)5 genes and primary protein variants (bottom) encoded by CGC genes in the outgrowing and likely outgrowing groups. Results from all libraries except library eC were combined. P-values from two-sided Fisher’s exact test are shown. The number of sgRNAs or variants either targeting or encoded by CGC genes among all sgRNAs or variants in each group are shown on the x-axes. (Right panel) Detailed distribution of sgRNAs predicted to introduce mutations in CGC genes (top) and variants generated in CGC genes (bottom). The number of sgRNAs or variants corresponding to each gene is specified in parentheses. b, The fraction of functionally classified sgRNAs (left) targeting Depmap common essential genes (CEGs) and protein variants (right) encoded by CEGs in the depleting and likely depleting groups. Results from all libraries except library eC were combined. P-values from two-sided Fisher’s exact test are shown. The numbers of sgRNAs or variants either targeting or encoded by CEG genes among all sgRNAs or variants in each group are shown on the x-axes.

Supplementary information

Supplementary Information

Supplementary Notes 1 and 2 and Supplementary Figs. 1–7

Reporting Summary

Supplementary Table

Supplementary Table 1. Composition of sgRNA-encoding libraries C, A, C1, C2, C3, A1, A2, dA and eC. Barcode sequences used for sorting, sgRNA sequences and target sequences, including neighboring sequences (5′-neighboring sequence (4 bp) + target sequence (20 bp + 3-bp PAM = 23 bp) + 3′-neighboring sequence (3 bp) = 30 bp of genomic DNA sequence). Information about intended mutations and DeepCBE or DeepABE efficiency scores are also included (provided as a separate Excel file). Supplementary Table 2. The results of MAGeCK analyses. RPM of four replicateUMI, LFCs, median LFCs (mLFCs), positive or negative MAGeCK RRA P values and LFCs of UMI CPM are shown for each sgRNA (provided as a separate Excel file). Supplementary Table 3. Functional classifications of sgRNAs and protein variants. a, Functional classification of sgRNAs based on the proliferation and survival (sheet 1). b, Functional classification of sgRNAs in library eC (sheet 2). c, Base editing outcomes and allele frequencies at the integrated target sequences (dependency on EGF signaling) (sheet 3). d, Potential classification of sgRNAs with low base editing efficiencies (sheet 4) (provided as a separate Excel file). Supplementary Table 4. Results of allele frequency tracking after delivery of an individual sgRNA for 20 selected sgRNAs. After lentiviral transduction of the specified individual sgRNA, protein variant frequencies were calculated from DNA sequence analysis. Endogenous DNA sequence variants encoding the same amino acid change were combined into one protein variant (provided as a separate Excel file). Supplementary Table 5. Oligonucleotides used in this study (provided in a separate Excel file).

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Kim, Y., Lee, S., Cho, S. et al. High-throughput functional evaluation of human cancer-associated mutations using base editors. Nat Biotechnol 40, 874–884 (2022). https://doi.org/10.1038/s41587-022-01276-4

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