Abstract
Identification of optimal target antigens that distinguish cancer cells from normal surrounding tissue cells remains a key challenge in chimeric antigen receptor (CAR) cell therapy for tumors with intratumoral heterogeneity. In this study, we dissected tissue complexity to the level of individual cells through the construction of a single-cell expression atlas that integrates ~1.4 million tumor, tumor-infiltrating normal and reference normal cells from 412 tumors and 12 normal organs. We used a two-step screening method using random forest and convolutional neural networks to select gene pairs that contribute most to discrimination between individual malignant and normal cells. Tumor coverage and specificity are evaluated for the AND, OR and NOT logic gates based on the combinatorial expression pattern of the pairing genes across individual single cells. Single-cell transcriptome-coupled epitope profiling validates the AND, OR and NOT switch targets identified in ovarian cancer and colorectal cancer.
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Data availability
The list of cell surface proteins was obtained from the in silico human surfaceome database at http://wlab.ethz.ch/surfaceome/ (ref. 23). The tumor-normal single-cell meta-atlas is available at https://cellatlas.kaist.ac.kr/cart. Raw and processed datasets used in this study are deposited at the Zenodo repository (https://doi.org/10.5281/zenodo.7416669)50. Ovarian cancer single-cell transcriptome data generated in this work are available on the Gene Expression Omnibus with accession number GSE192898 (ref. 51). Source data are provided with this paper.
Code availability
Source codes for CAR target antigen identification, named PCASA (prioritization of combinatorial cancer-associated surface antigens), are available on GitHub at https://github.com/kaistomics/PCASA (ref. 52).
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Acknowledgements
This work was supported by the Bio & Medical Technology Development Program of the National Research Foundation, funded by the Ministry of Science & ICT (NRF-2017M3A9A7050612, NRF-2019M3A9B6064688, NRF-2019M3A9B6064691 and NRF-2021M3A9I402444711), and also supported by the Korea Health Technology R&D Project of the Korea Health Industry Development Institute, funded by the Ministry of Health & Welfare (HI16C1559).
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Contributions
Conceptualization: J. Kwon, J. Kang, J.E.P. and J.K.C. Methodology: J. Kwon, J. Kang, A.J., K.S., D.A., M.Y.B., J.H.L., N.K., H.H.E., J.M.L. and W.Y.P. Investigation: J. Kwon, J. Kang, A.J., K.S., D.A., N.K. and H.H.E. Visualization: J. Kwon, J. Kang and M.Y.B. Funding acquisition: J.K.C. Project administration: H.J.A., J.E.P. and J.K.C. Supervision: S.H., H.J.A., H.O.L., J.E.P. and J.K.C. Writing—original draft: J. Kwon, J. Kang and J.H.L. Writing—review and editing: J. Kwon, J. Kang, J.E.P. and J.K.C.
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Extended data
Extended Data Fig. 2 Tumor versus normal differences in ECFs and expression levels.
Differences of ECFs (a) and expression levels (b) between tumor and normal cellsfor surfaceome genes ordered by the FI values from the RF model in selected cancer types.
Extended Data Fig. 3 Comparison of cancer types with expression level and FI for surfaceome genes.
a, Clustering heatmap displaying the expression level of surface antigens according to the cancer type. Five major clusters (C1 ~ C5) of genes were denoted. b, Comparison of the different cancer types based on the correlation of the FI values from the cancer type-specific RF model.
Extended Data Fig. 4 Tumor-specific ECF values of subtype marker genes in BRCA samples for which clinical subtype information was available.
The boxplot shows the ECF status of three markers, containing ERBB2, ESR1, and PGR, for all subtypes (ERpositive: n = 11, HER2-positive: n = 8, HER2 and ER double positive: n = 4, TNBC: n = 12): TNBC, Triple-negative breast cancer; ‘+‘ means ‘positive’. Average values of ECFs for ERBB2, ESR1, and PGR, were 0.33 ± 0.15 (s.d.), 0.68 ± 0.15 (s.d), and 0.29 ± 0.18 (s.d.) in ER-positive subtype while HER2-positive subtype showed 0.86 ± 0.19 (s.d.), 0.18 ± 0.3 (s.d.), and 0.01 ± 0.01 (s.d.), respectively. In the case of HER2+ ER+, average values were 0.47 ± 0.19 (s.d.), 0.25 ± 0.23 (s.d.), and 0.06 ± 0.05 (s.d.), and the TNBC subtype showed 0.24 ± 0.14 (s.d), 0.05 ± 0.12 (s.d.), and 0.05 ± 0.15 (s.d.), for ERBB2, ESR1, and PGR, respectively. Data in the box plot represent the first quartile (25%), median (center), and third quartile (75%) with minimum and maximum values. Black points indicate the outliers.
Extended Data Fig. 5 Correlation between the CNN weight and ECF of each antigen combination in PAAD, LIHC, NSCLC, CRC, and BRCA.
For each of the pairs, the GradCAM weight returned by the CNN model was mapped to the ECF among tumor cells (blue) and normal cells (red). The Pearson correlation coefficient and two-sided P values are shown at the top. Translucent bands around the regression lines represent the confidence interval (CI) of 95%.
Extended Data Fig. 6 Comparison of tumor versus normal ECFs according to the CNN weight in PAAD, LIHC, NSCLC, CRC, and BRCA.
Tumor and normal ECFs of the combinations were ordered by their CNN weight. The combinations meeting the ECF criteria (> 70% tumor and < 10% normal) were highlighted in color.
Extended Data Fig. 7 Sample-wise co-expression of selected combinations in OV.
Expression correlations of the OV gene pairs were computed by using pan-cancer TCGA samples (n = 11,768) and GTEx normal samples (n = 17,382). The Pearson correlation coefficient was used.
Extended Data Fig. 8 Across-sample expression patterns of selected logical CAR switches in PAAD, LIHC, NSCLC, CRC, and BRCA.
Shown are the top 10 gene combinations with the largest CNN weight for each cancer type and each logic switch (AND, OR, and NOT). The heatmap intensity scales with the combinatorial tumor (left) and normal (right) ECF values.
Extended Data Fig. 9 Additional single-cell epitope analysis results for EPCAM and CD24 in ovarian cancer.
a, Distribution of EPCAM and CD24 protein expression levels broken down by annotated cell type. Log2 fold changes comparing CITE-seq ADT signals from targeting antibodies versus isotype controls were computed and are displayed in each cell type. b, Clustering and cell-type mapping of tumor and tumor-resident normal cells based on the CITE-seq transcriptome. c, Single-cell maps, as in (b), overlaid with mRNA expression levels retrieved from CITEseq. d, Single-cell maps, as in (b), overlaid with protein expression levels retrieved from CITE-seq. e, Single-cell maps, as in (b), overlaid with the mRNA expression status indicating cells with both EPCAM and CD24 (brown), only EPCAM (light blue), only CD24 (light red), or none (gray). f, Single-cell maps, as in (b), overlaid with the epitope expression status indicating cells with both EPCAM and CD24 (brown), only EPCAM (light blue), only CD24 (light red), or none (gray).
Extended Data Fig. 10 Single-cell epitope analysis of validation targets in colorectal cancer.
a, Clustering and cell-type mapping of the tumor and tumor-infiltrating normal cells based on the CITE-seq transcriptome of our pooled CRC samples. b, Single-cell maps, as in (a), overlaid with mRNA expression levels retrieved from the CITE-seq experiments. c, Single-cell maps, as in (b), overlaid with protein expression levels retrieved from the CITE-seq experiments. d, Distribution of protein expression levels broken down by annotated cell type. Log2 fold changes comparing CITE-seq ADT signals from targeting antibodies versus isotype controls were computed and are displayed for each protein in each cell type. e, Cell-by-cell protein expression patterns in the tumor and tumor-resident normal cells for CEACAM5 versus EPCAM, CEACAM5 versus CA4, CEACAM5 versus CPM, and CEACAM5 versus VSIG2. Cells enclosed in the marked zones indicate the expression of both proteins for the AND gating and only one protein (CEACAM5) for the NOT gating.
Supplementary information
Supplementary Tables 1–9
Supplementary Table 1: Summary of our cancer cell atlas. Supplementary Table 2: Status of cell types for tumor datasets integrated with normal cell atlas. Supplementary Table 3: Summary of selected tumor datasets of six cancer types. Supplementary Table 4: Performance of RF cell classifier for each cancer type. Supplementary Table 5: RF FI for top 100 genes for each cancer type. Supplementary Table 6: CNN weights calculated by Grad-CAM for each cancer type. Supplementary Table 7: Performance of CNN cell classifier for each cancer type. Supplementary Table 8: Sample-wise co-expression for OV target combinations. Supplementary Table 9: Normal ECF distribution by cell types for OV target combinations.
Source data
Source Data Fig. 2
Fig 2a: Sample-wise ECF and pseudo bulk expression level. Fig 2c,e and Extended Data Figs 2 and 3: ECF and expression level for each gene. Fig 2d: Sample-wise differentially expressed gene analysis result.
Source Data Fig. 3
Fig 3e,f: ECF of gene combinations for OV.
Source Data Fig. 4
Fig 4c: ECF for each gene combination of AND and NOT gates depending on the cell type in OV. Fig 4e: Sample-wise ECF status for each gene combination in OV.
Source Data Extended Data Fig./Table 4
ECF status of BRCA subtype-specific markers.
Source Data Extended Data Fig./Table 5
ECF of gene combinations for PAAD, LIHC, NSCLC, CRC and BRCA.
Source Data Extended Data Fig./Table 7
Co-expression for each gene combination for OV in GTEx and TCGA data.
Source Data Extended Data Fig./Table 8
Extended Data Fig 8: Sample-wise ECF status for each gene combination in PAAD, LIHC, NSCLC, CRC and BRCA.
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Kwon, J., Kang, J., Jo, A. et al. Single-cell mapping of combinatorial target antigens for CAR switches using logic gates. Nat Biotechnol 41, 1593–1605 (2023). https://doi.org/10.1038/s41587-023-01686-y
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DOI: https://doi.org/10.1038/s41587-023-01686-y
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