Abstract
A variety of species of bacteria are known to colonize human tumours1,2,3,4,5,6,7,8,9,10,11, proliferate within them and modulate immune function, which ultimately affects the survival of patients with cancer and their responses to treatment12,13,14. However, it is not known whether antigens derived from intracellular bacteria are presented by the human leukocyte antigen class I and II (HLA-I and HLA-II, respectively) molecules of tumour cells, or whether such antigens elicit a tumour-infiltrating T cell immune response. Here we used 16S rRNA gene sequencing and HLA peptidomics to identify a peptide repertoire derived from intracellular bacteria that was presented on HLA-I and HLA-II molecules in melanoma tumours. Our analysis of 17 melanoma metastases (derived from 9 patients) revealed 248 and 35 unique HLA-I and HLA-II peptides, respectively, that were derived from 41 species of bacteria. We identified recurrent bacterial peptides in tumours from different patients, as well as in different tumours from the same patient. Our study reveals that peptides derived from intracellular bacteria can be presented by tumour cells and elicit immune reactivity, and thus provides insight into a mechanism by which bacteria influence activation of the immune system and responses to therapy.
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Data availability
All raw mass spectrometry files, as well as human and bacteria proteomes and the MaxQuant version used for analysing the data, have been deposited in the ProteomeXchange Consortium via the PRIDE partner repository, with the dataset identifier PXD022150. Raw sequence data of the whole-genome sequencing of isolated bacteria have been deposited in the NCBI Sequence Read Archive (SRA), under BioProject accession number PRJNA669827. Source data are provided with this paper.
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Acknowledgements
We thank the UT MDACC clinical TIL laboratory for processing the tumour specimens; E. Elinav and. J. Suez for providing bacteria cultures; S. Jung and S. Trzebanski for access to their flow cyometer; and S. Cheriyamundath for his help with confocal imaging. This work was supported by the Intramural Research Programs of the National Cancer Institute. Y.S. is supported by the Israel Science Foundation grant no. 696/17, the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 770854), MRA (no. 622106), Rising Tide Foundation, Henry Chanoch Krenter Institute for Biomedical Imaging and Genomics, Estate of Alice Schwarz-Gardos, Estate of John Hunter, Knell Family, Peter and Patricia Gruber Award and the Hamburger Family. J.A.W is supported by generous philanthropic contributions to the University of Texas MD Anderson Moon Shots Program for support of tumor-line generation, from the Estate of Mady Dukler, the Joel and Mady Dukler Fund for Cancer Research, the Estate of Judith Safirstein, the Estate of Elaine S. Scheye, the Estate of David Levinson, the Hadar Impact Fund, the Fannie Sherr Fund, the Erica Drake Fund, the Estate of Bernard Berkowitz, the Bernard and Audrey Jaffe Foundation and the Jacques Asseoff Trust. K.V. and T.D.L. are supported by the Wellcome Sanger core funding (WT098051). The CLEM studies were conducted at the Irving and Cherna Moskowitz Center for Nano and Bio-Nano Imaging at the Weizmann Institute of Science. J.R.G.-P. is funded by (and whole-genome sequencing was paid for using) NIH NIAID K01AI143881. Whole-genome sequencing of bacteria isolates was performed by the MD Anderson Advanced Technology Genomics Core grant, funded by the CCSG NIH NCI grant CA016672. A.A. is supported by the Israel Science Foundation grant no. 1435/16. D.J.A., K.V. and T.D.L. are supported by the Wellcome Sanger core funding (WT098051).
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Contributions
S.K., A.N. and Y.S. designed the study and wrote the paper. S.K. and A.N. analysed the data, and performed and supervised experiments. S.K., A.N., M.A., E.B. and P.G. performed the HLA peptidomics experiments. D.N., N.S., G.F. and R.S. analyzsed the bacteria composition of tumours. T.D. and S.L.-Z. performed the CLEM experiments. L.T.G., N.B., N.G.-Z., L.R., O.G. and R.E. helped with the bacteria staining techniques, imaging and imaging analysis. A.N., P.G., G.Y. and A.P. grew the cell lines for HLA peptidomics. K.V., T.D.L. and D.J.A. performed the taxonomic analysis of whole-genome sequencing data from melanoma. Y.B., W.C.S., A.W., M.L.-P. and E.S. performed whole-genome sequencing and analysis of bacteria isolates. J.R.G.-P. and S.N.P. performed bacteria isolation and sequencing. C. Barbolin, K.C., R.L., K.W., L.H. and E.R. performed computational analysis. R.R. helped with the statistical analysis. A.N. and S.T. performed the immunofluorescence experiments. M.L. helped with the TIL rapid expansion protocols. A.A. and Y.L. helped with the HLA peptidomics and mass spectrometry analyses. A.R., S.B.J., C. Bernatchez, C.H., R.A., M.P.L., M.J.B., J.S. and J.A.W. provided patient materials and information. S.L.C.K. and P.K. helped with TIL reactivity assay. G.B. performed HLA typing. All authors contributed to the final version of the paper.
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J.A.W. is an inventor on a US patent application (PCT/US17/53.717) submitted by the University of Texas MD Anderson Cancer Center that covers methods to enhance immune checkpoint blockade responses by modulating the microbiome. J.A.W. reports compensation for speaker’s bureau and honoraria from Imedex, Dava Oncology, Omniprex, Illumina, Gilead, PeerView, Physician Education Resource, MedImmune and Bristol–Myers Squibb. J.A.W. serves as a consultant/advisory board member for Roche/Genentech, Novartis, AstraZeneca, GlaxoSmithKline, Bristol–Myers Squibb, Merck, Biothera Pharmaceuticals and Microbiome DX. J.A.W. also receives research support from GlaxoSmithKline, Roche/Genentech, Bristol–Myers Squibb and Novartis.
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Extended data figures and tables
Extended Data Fig. 1 Similarity of bacterial composition between metastases.
A Jaccard index was calculated to determine the similarity between the bacterial composition of the different metastases on the species level. Colour code indicates the Jaccard index. The highest similarity was observed between metastases from the same patient, but metastases of different patients also showed similarity. Black boxes indicate tumour samples taken from the same patient.
Extended Data Fig. 2 Visualization of bacterial 16S rRNA in tissue sections from melanoma tumours.
a, 16S rRNA fluorescence in situ hybridization (FISH) staining of tissue sections from melanoma tumours using pan-bacteria EUB338 probe (red) and DAPI (blue). b, 16S rRNA FISH staining of tissue microarray sections of melanoma tumours (red) and DAPI (blue). Slice name indicates the position in the tissue microarray. Images are presented at 20× magnification. Scale bars, 100 μm. c, Representative control using 16S FISH nonspecific control probe. Asterisks mark the region that was selected for higher magnification. Figures are representative of at least three independent experiments.
Extended Data Fig. 3 Taxonomic analysis of 108 whole-genome-sequenced melanoma samples identify a bacterial composition similar to that found in our tumour cohort.
a, Alpha diversity, measured as the number of observed species in a tumour (green) or blood (purple) sample. P values from paired two-tailed Wilcoxon test between tumour and blood taxonomic diversity. b, Microbiome similarity within and between groups. Bray–Curtis dissimilarity measured between each pair of samples, then stratified into four groups. P values from two-tailed Wilcoxon test. c, Comparison of the relative abundance between tumour samples and associated blood samples. P values from paired two-tailed Wilcoxon test between tumour and blood taxonomic abundance. ***P < 0.001, **P < 0.01, *P < 0.05. d, List of groups of bacteria that are more abundant in the tumour samples plotted in c. P values from paired two-tailed Wilcoxon test between tumour and blood taxonomic abundance. P values with asterisks survived multiple hypothesis correction (false-discovery rate of 5%). In the box plots, the centre lines represent the medians, the boxes represent the range between the 25th and 75th percentile, and the whiskers represent the range between the smallest and largest data point.
Extended Data Fig. 4 Pipeline for the identification of bacteria-derived HLA peptides.
Tumour samples were 16S-sequenced to identify their bacterial composition and analysed using HLA peptidomics. Searching the data according to the bacteria resulted in the identification of bacteria-derived peptides. Peptides were filtered according to their identification quality and ability to bind to the HLA alleles of the patient. Selected bacterial peptides were then tested for reactivity. Identification of bacterial peptide presentation was validated by a HLA peptidomics analysis of cell lines cocultured with bacteria.
Extended Data Fig. 5 Length distribution of bacteria-derived peptides.
The length distribution of bacteria-derived peptides is similar to the expected length of HLA-I and HLA-II peptides.
Extended Data Fig. 6 Bacterial proteomes contain a higher amount of hydrophobic amino acids compared to the human proteome.
a, For each metastasis, the percentages of bacterial and human peptides that match each HLA-A, HLA-B and HLA-C allele of the patient is indicated. The allele with the best per cent rank binding prediction by NetMHCpan was assigned to each peptide. b, Kyte–Doolittle hydrophobicity index was calculated for bacterial and human peptides. The hydrophobicity of HLA-I bacterial peptides is higher than that of human-derived peptides (indicated P value is from an unpaired two-sample Wilcoxon test). c, The percentage of hydrophobic and nonhydrophobic amino acids was calculated for bacterial proteomes and the human proteome. Two groupings were used for selecting hydrophobic amino acids: L, I, V, F and M, or L, I, V, F, M, W, Y and A. The percentage of hydrophobic and nonhydrophobic amino acids from bacterial proteomes is plotted in the box plot. In the box plots, the centre lines represent the medians, the boxes represent the range between the 25th and 75th percentile, and the whiskers represent the range between the smallest and largest data point. The percentages representing the human proteome are marked by a red dashed line. d, Two-sided Student’s t-test comparing the percentage of hydrophobic and non-hydrophobic amino acids between bacterial proteomes and the human proteome. The P values and false-discovery rates are indicated in the table.
Extended Data Fig. 7 Hydrophobicity of bacterial and human peptides per allele.
Kyte–Doolittle hydrophobicity index was calculated for bacterial and human peptides and plotted in a box plot for each HLA allele. In the box plots, the centre lines represent the medians, the boxes represent the range between the 25th and 75th percentile, and the whiskers represent the range between the smallest and largest data point. a, The hydrophobicity of the bacterial peptides that bind to the HLA-C*03:04, HLA-C*03:03 and—to a lesser extent—HLA-A*02:01 was higher compared to the hydrophobicity of other alleles (marked in red). Additional alleles also show this trend, but they were derived from a lower number of tumour samples and therefore are not indicated. b, Bacterial peptides are marked in red and human peptides are marked in grey.
Extended Data Fig. 8 Gentamicin assay demonstrating the entry of bacteria into melanoma cells.
a, b, Colony-forming units (CFU) of A. odontolyticus and S. caprae after coculture with 51AL and 55A3 melanoma cells. Less-invasive bacteria (L. animalis and L. plantarum) were used as a control, and show lower CFU. c, d, CFU of S. capitis (c) (isolated from tumour 58) after coculture with 58A melanoma cells, or S. succinus (d) (isolated from tumour Mel261) after coculture with 51AL or 55A3 cells. Cells were cultured with the indicated bacteria for 4 and 8 h. ‘Supernatant’ refers to the CFU of medium taken from samples incubated with gentamicin after coculture with the bacteria (grey). ‘No gentamicin’ refers to samples not treated with gentamicin after the coculture (red). ‘With gentamicin’ refers to samples treated with gentamicin for 1 h after the coculture (blue). Bars represent the average of s.e. between biological replicates (n = 3). P values from Student’s t-test between the supernatant sample and the without gentamicin or with gentamicin samples; P values between S. caprae or A. odontolyticus with gentamicin to L. animalis or L. plantarum with gentamicin control samples are from a one‐way analysis of variance followed by Tukey’s test, and are presented in Supplementary Table 4.
Extended Data Fig. 9 Immunofluorescence staining of melanoma cells cocultured with aerobically grown bacteria, demonstrating the ability of the bacteria to enter the cells.
a, Melanoma cells expressing GFP (green) were cocultured with the aerobically grown bacteria S. caprae, S. capitis or S. succinus stained with antibacterial antibody lipoteichoic acid (LTA) (red); cell nuclei were stained with DAPI (blue). White arrows indicate the location of bacteria that entered the melanoma cells. b, A representative image of 51AL cells expressing GFP (green) cocultured with S. caprae and stained without a primary LTA antibody (red), to exclude nonspecific staining. Images are presented at 63× magnification. Scale bars, 10 μm. Figures are representative of at least three independent experiments.
Extended Data Fig. 10 Immunofluorescence staining of melanoma cells cocultured with anaerobically grown bacteria, demonstrating the ability of the bacteria to enter the cells.
a, Melanoma cells stained an anti-HLA antibody (red) were cocultured with anaerobically grown bacteria F. nucleatum or A. odontolyticus. These bacteria were labelled with click chemistry (green). Cell nuclei were stained with DAPI (blue). White arrows indicate the location of bacteria that entered the melanoma cells. b, A representative image of 51AL cells stained with the anti-HLA antibody (red) cocultured with F. nucleatum that were not grown with D-GalNAz and labelled with Alexa Fluor F488 (green), to exclude nonspecific staining. Images are presented at 63× magnification. Scale bars, 10 μm. Figures are representative of at least three independent experiments.
Extended Data Fig. 11 CLEM images showing entry of bacteria into melanoma cells.
Fusobacterium nucleatum was grown with D-GalNAz and then labelled with DIBO–Alexa Fluor 488. Actinomyces odontolyticus and S. caprae were incubated with an anti-LTA antibody, and then with an anti-mouse secondary antibody labelled with Alexa Fluor 488. The 51AL and 55A3 cell lines were coincubated with the bacteria for 8 h. Ultra-thin sections were analysed by fluorescence microscopy to identify the bacteria (green and blue labelling are for bacteria and nucleus, respectively), followed by transmission electron microscopy (TEM) of the same cells for high-resolution morphology. Left and middle panels show CLEM and TEM images, respectively. Scale bars, 5 μm. The right panel shows high-magnification TEM image of the area in the black box in the corresponding middle panel. Scale bars, 1 μm. Bacteria that entered the melanoma cell are indicated with a white arrow. N, nucleus; M, mitochondrion; ER, endoplasmic reticulum. Figures are representative of at least three independent experiments.
Extended Data Fig. 12 TIL reactivity towards bacteria-derived antigens.
Flow cytometry analysis of IFNγ-secreting TILs after coculture of 51AL and 55A3 TILs with B cells loaded with the indicated bacterial peptide. Each peptide was loaded on a different B cell that exhibited the HLA alleles to which the peptide was predicted to bind. TILs were stained with anti-IFNγ and anti-CD45 bifunctional antibody, which binds secreted IFNγ. The value indicates the ratio of IFNγ-secreting cells with the peptides to those with the DMSO control. Grey dots indicate the results of n = 3 biological replicates, and blue dots represent the average of replicates. Bars represent s.e. between replicates.
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Kalaora, S., Nagler, A., Nejman, D. et al. Identification of bacteria-derived HLA-bound peptides in melanoma. Nature 592, 138–143 (2021). https://doi.org/10.1038/s41586-021-03368-8
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DOI: https://doi.org/10.1038/s41586-021-03368-8
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