Research ArticleCancer

Noncoding regions are the main source of targetable tumor-specific antigens

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Science Translational Medicine  05 Dec 2018:
Vol. 10, Issue 470, eaau5516
DOI: 10.1126/scitranslmed.aau5516
  • Fig. 1 Proteogenomic workflow for the identification of TSAs.

    (A and B) Schematic detailing how the canonical cancer proteome (A) and cancer-specific proteome (B) were built for each analyzed sample. In (A), “quality” refers to the Phred score; a score of >20 means that the accuracy of the nucleobase call is at least 99%. (C) The combination of the above two proteomes, termed the global cancer database, was then used to identify MHC peptides, and more specifically TSAs, sequenced by liquid chromatography–MS/MS (LC-MS/MS). We analyzed two well-characterized murine cell lines, CT26 and EL4, and seven human primary samples, namely, four B-ALLs and three lung tumor biopsies (n = 2 to 4 per sample). Statistics regarding each part of the global cancer database can be found in table S1, and implementation details of building the cancer-specific proteome by k-mer profiling are presented in fig. S2. aa, amino acids; nts, nucleotides; th, sample-specific threshold for k-mer occurrence; tpm, transcripts per million.

  • Fig. 2 Most TSAs derive from the translation of noncoding regions.

    (A) Flowcharts indicating key steps involved in TSA discovery [see fig. S3 (A to C) for details]. I/L, isoleucine/leucine. (B) Barplot showing the number of mTSAs (m) and aeTSA candidates (ae) in CT26 and EL4 cells. (C) Heatmap showing the average expression of peptide-coding sequences, in reads per hundred million reads sequenced (rphm), for aeTSA candidates and EL4 tumor-associated antigens (41, 42) in 22 tissues/organs (see table S5). For each peptide-coding sequence, the expression fold change and the number of positive tissues (rphm > 0, bold squares) are presented on the left-hand side of the heatmap. For fold changes, N/A indicates that the corresponding peptide-coding sequence was not expressed in syngeneic mTEChi. Adip. tissue, adipose tissue; mam. gland, mammary gland; s.c. adip. tissue, subcutaneous adipose tissue. (D) Barplots depicting the number of TSAs derived from the translation of noncoding regions (noncoding) and of coding exons in-frame (coding–in) or out-of-frame (coding–out). The number of aeTSAs/mTSAs is reported within bars. The proportion of TSAs derived from atypical translation events is shown above bars. Features of CT26 and EL4 TSAs can be found in table S6 (A and B, respectively).

  • Fig. 3 Immunization against individual TSAs confers different degrees of protection against EL4 cells.

    C57BL/6 mice were immunized twice with DCs pulsed with individual TSAs: (A) two aeTSAs, (B) two ERE TSAs (one aeTSA or one mTSA), or (C) one mTSA. Mice were injected intravenously with 5 × 105 live EL4 cells (arrowheads) on day 0, and all surviving mice were rechallenged on day 150. Control groups were immunized with unpulsed DCs (solid black line). Embedded Image represents the median survival. Statistical significance of immunized group versus control group was calculated using a log-rank test, where ns stands for not significant (P > 0.05). n = 10 mice per group for peptide-specific immunization, n = 19 mice for control group.

  • Fig. 4 Frequency of and IFN-γ secretion by TSA-responsive T cells in naïve and immunized mice.

    (A) Number of tetramer+ CD8+ T cells per 106 CD8+ T cells in naïve mice. Circles, one mouse (n = 5 to 9 mice per group); dotted line, frequency of 1 tetramer+ T cell per 106 CD8+ T cells. (B) Fold enrichment of tetramer+ CD8+ T cells after immunization with relevant (white bars) or irrelevant (gray bars) peptides Embedded Image. (C) The number of spot-forming cells (SFCs), measured by an IFN-γ ELISpot assay, averaged across technical replicates (circles) after being converted to SFCs per 106 CD8+ T cells: Embedded Image. (D) The functional avidity of T cells recognizing specific TSAs and two previously reported positive controls [H7a and H13a (42)] was estimated by calculating a half maximal effective concentration (EC50), corresponding to the peptide concentration where half of plated antigen-specific T cells secreted IFN-γ. (B to D) Three independent experiments. On relevant panels, full horizontal lines and numbers above each condition represent mean values. Viral peptides used as control are highlighted in gray. *P ≤ 0.05 and **P ≤ 0.01 (two-sided Wilcoxon rank sum test with the Benjamini-Hochberg correction).

  • Fig. 5 High expression of EL4 TSAs is necessary but not sufficient to induce antileukemic responses.

    (A and B) Analysis of TSA expression at the RNA and peptide levels was performed on EL4 cells injected into mice at day 0 or day 150, respectively. (A) The number of RNA-seq reads fully overlapping the RNA sequences encoding each TSA. (B) TSA copy number per cell was estimated by PRM MS using 13C-synthetic peptide analogs of the TSAs (three replicates). Black lines represent the mean TSA copy number per cell (also indicated on the left-hand side of the graph). N.D., not detected. (C) Fold enrichment for tetramer+ CD8+ T cells after injection with live EL4 cells without prior immunization Embedded Image. Fold enrichment for T cells recognizing viral peptides is shown as negative controls and is highlighted in gray. Three independent experiments were performed. (D) Overall survival of C57BL/6 female mice immunized twice with irradiated (10,000 cGy) EL4 cells (blue line, n = 10 mice) or unpulsed DCs (black line, n = 19 mice) and then injected intravenously with 5 × 105 live EL4 cells. Embedded Image represents the median survival. Statistical significance of immunized group versus control group was calculated using a log-rank test.

  • Fig. 6 Most TSAs detected in human primary tumors derive from the translation of noncoding regions.

    (A) Barplot showing the number of aeTSAs candidates (ae) and mTSAs (m) in each primary sample analyzed. (B) Heatmap showing the average expression of peptide-coding sequences, in rphm, for aeTSAs and overexpressed tumor-associated antigens obtained from the Cancer Immunity Peptide database (49) across a panel of 28 tissues (see table S16). For each peptide-coding sequence, the expression fold change (tumor/TEC and mTEC) and the number of positive tissues (rphm > 15, bold squares) are shown on the left-hand side of the heatmap. For fold changes, N/A, and---indicate that the corresponding peptide-coding sequence was not detected in TEC/mTEC samples or not computed, respectively. Adip. s.c., adipose subcutaneous. (C) Barplots depicting the number of TSAs derived from the translation of noncoding regions (noncoding) or from coding exons translated in-frame (coding–in) or out-of-frame (coding–out). The number of aeTSAs/mTSAs is shown within bars. Features of human TSAs identified in each sample can be found in tables S17 and S18.

Supplementary Materials

  • www.sciencetranslationalmedicine.org/cgi/content/full/10/470/eaau5516/DC1

    Materials and Methods

    Fig. S1. Gating strategies for cells isolated by fluorescence-activated cell sorting.

    Fig. S2. Architecture of the codes used for our k-mer profiling workflow.

    Fig. S3. TSA validation process.

    Fig. S4. MS validation of CT26 and EL4 TSA candidates using synthetic analogs.

    Fig. S5. Detection of antigen-specific CD8+ T cells in naïve and immunized mice.

    Fig. S6. Frequencies of antigen-specific T cells.

    Fig. S7. Correlation between antigen-specific T cell frequencies in naïve and immunized mice.

    Fig. S8. Purity of the 10H080 B-ALL sample after expansion in NSG mice.

    Fig. S9. Overview of the human TEC and mTEC transcriptomic landscapes.

    Fig. S10. MS validation of B-ALL TSA candidates using synthetic analogs.

    Fig. S11. MS validation of lung cancer TSA candidates using synthetic analogs.

    Table S1. Statistics related to the generation of the global cancer databases.

    Table S2. Information about samples used in this study.

    Table S3. List of CT26 MHC class I–associated peptides.

    Table S4. List of EL4 MHC class I–associated peptides.

    Table S5. Accession numbers of the ENCODE datasets used in this study.

    Table S6. Features of murine TSAs.

    Table S7. Experimental values obtained in analyses of mouse TSA immunogenicity.

    Table S8. List of 07H103 MHC class I–associated peptides.

    Table S9. List of 10H080 MHC class I–associated peptides obtained by mild acid elution.

    Table S10. List of 10H080 MHC class I–associated peptides obtained by immunoprecipitation.

    Table S11. List of 10H118 MHC class I–associated peptides.

    Table S12. List of 12H018 MHC class I–associated peptides.

    Table S13. List of lc2 MHC class I–associated peptides.

    Table S14. List of lc4 MHC class I–associated peptides.

    Table S15. List of lc6 MHC class I–associated peptides.

    Table S16. Accession numbers of the Genotype-Tissue Expression (GTEx) datasets used in this study.

    Table S17. Features of human TSAs detected in B-ALL specimens.

    Table S18. Features of human TSAs detected in lung tumor biopsies.

    References (6068)

  • The PDF file includes:

    • Materials and Methods
    • Fig. S1. Gating strategies for cells isolated by fluorescence-activated cell sorting.
    • Fig. S2. Architecture of the codes used for our k-mer profiling workflow.
    • Fig. S3. TSA validation process.
    • Fig. S4. MS validation of CT26 and EL4 TSA candidates using synthetic analogs.
    • Fig. S5. Detection of antigen-specific CD8+ T cells in naïve and immunized mice.
    • Fig. S6. Frequencies of antigen-specific T cells.
    • Fig. S7. Correlation between antigen-specific T cell frequencies in naïve and immunized mice.
    • Fig. S8. Purity of the 10H080 B-ALL sample after expansion in NSG mice.
    • Fig. S9. Overview of the human TEC and mTEC transcriptomic landscapes.
    • Fig. S10. MS validation of B-ALL TSA candidates using synthetic analogs.
    • Fig. S11. MS validation of lung cancer TSA candidates using synthetic analogs.
    • References (6068)

    [Download PDF]

    Other Supplementary Material for this manuscript includes the following:

    • Table S1. Statistics related to the generation of the global cancer databases.
    • Table S2. Information about samples used in this study.
    • Table S3. List of CT26 MHC class I–associated peptides.
    • Table S4. List of EL4 MHC class I–associated peptides.
    • Table S5. Accession numbers of the ENCODE datasets used in this study.
    • Table S6. Features of murine TSAs.
    • Table S7. Experimental values obtained in analyses of mouse TSA immunogenicity.
    • Table S8. List of 07H103 MHC class I–associated peptides.
    • Table S9. List of 10H080 MHC class I–associated peptides obtained by mild acid elution.
    • Table S10. List of 10H080 MHC class I–associated peptides obtained by immunoprecipitation.
    • Table S11. List of 10H118 MHC class I–associated peptides.
    • Table S12. List of 12H018 MHC class I–associated peptides.
    • Table S13. List of lc2 MHC class I–associated peptides.
    • Table S14. List of lc4 MHC class I–associated peptides.
    • Table S15. List of lc6 MHC class I–associated peptides.
    • Table S16. Accession numbers of the Genotype-Tissue Expression (GTEx) datasets used in this study.
    • Table S17. Features of human TSAs detected in B-ALL specimens.
    • Table S18. Features of human TSAs detected in lung tumor biopsies.

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