Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 May 29;368(6494):973-980.
doi: 10.1126/science.aay9189.

The human tumor microbiome is composed of tumor type-specific intracellular bacteria

Deborah Nejman #  1 , Ilana Livyatan #  1   2 , Garold Fuks #  3 , Nancy Gavert  1 , Yaara Zwang  1 , Leore T Geller  1 , Aviva Rotter-Maskowitz  1 , Roi Weiser  4   5 , Giuseppe Mallel  1 , Elinor Gigi  1 , Arnon Meltser  1 , Gavin M Douglas  6 , Iris Kamer  7 , Vancheswaran Gopalakrishnan  8 , Tali Dadosh  9 , Smadar Levin-Zaidman  9 , Sofia Avnet  10 , Tehila Atlan  11 , Zachary A Cooper  12 , Reetakshi Arora  8 , Alexandria P Cogdill  13 , Md Abdul Wadud Khan  8 , Gabriel Ologun  8 , Yuval Bussi  1   2   14 , Adina Weinberger  1   2 , Maya Lotan-Pompan  1   2 , Ofra Golani  15 , Gili Perry  16 , Merav Rokah  17 , Keren Bahar-Shany  16 , Elisa A Rozeman  18 , Christian U Blank  18 , Anat Ronai  19 , Ron Shaoul  19 , Amnon Amit  20   21 , Tatiana Dorfman  22   23 , Ran Kremer  24 , Zvi R Cohen  5   25 , Sagi Harnof  5   26 , Tali Siegal  27 , Einav Yehuda-Shnaidman  28 , Einav Nili Gal-Yam  29 , Hagit Shapira  28 , Nicola Baldini  10   30 , Morgan G I Langille  6   31 , Alon Ben-Nun  5   17 , Bella Kaufman  5   7 , Aviram Nissan  32 , Talia Golan  5   7 , Maya Dadiani  16 , Keren Levanon  5   16 , Jair Bar  5   7 , Shlomit Yust-Katz  5   27 , Iris Barshack  5   33 , Daniel S Peeper  34 , Dan J Raz  35 , Eran Segal  1   2 , Jennifer A Wargo  8   13 , Judith Sandbank  28 , Noam Shental #  36 , Ravid Straussman #  37
Affiliations

The human tumor microbiome is composed of tumor type-specific intracellular bacteria

Deborah Nejman et al. Science. .

Abstract

Bacteria were first detected in human tumors more than 100 years ago, but the characterization of the tumor microbiome has remained challenging because of its low biomass. We undertook a comprehensive analysis of the tumor microbiome, studying 1526 tumors and their adjacent normal tissues across seven cancer types, including breast, lung, ovary, pancreas, melanoma, bone, and brain tumors. We found that each tumor type has a distinct microbiome composition and that breast cancer has a particularly rich and diverse microbiome. The intratumor bacteria are mostly intracellular and are present in both cancer and immune cells. We also noted correlations between intratumor bacteria or their predicted functions with tumor types and subtypes, patients' smoking status, and the response to immunotherapy.

PubMed Disclaimer

Figures

Fig. 1
Fig. 1. Bacterial components are detected in human tumors.
(A) Number of human samples analyzed in the study. Normal samples include both normal tissue samples and normal adjacent tissue (NAT) to tumor samples, as detailed in table S1. Dashes indicate data not available. GBM, glioblastoma multiforme. (B) The presence of bacterial DNA in human tumors was assessed by bacterial 16S rDNA qPCR. A calibration curve, generated by spiking bacterial DNA into human DNA, was used to estimate bacterial load, which was then normalized against batch-specific qPCR NTCs. Values were floored to 0.1. Red bars represent the median. The proportion of samples of each cancer type that had more bacteria than the 99th percentile of the DNA extraction control samples (black bar) is depicted above each cancer type. (C) Heatmap representing the proportion of tumors that stained positively for 16S rRNA, LPS, or LTA. n = 40 to 101 tissue cores per tumor type. (D) Consecutive slices from four human tumor types were stained with H&E, anti-LPS antibody (LPS), or with FISH probes against bacterial 16S rRNA. Scale bars, 200 μm. The letter (T) indicates samples originating from tumors.
Fig. 2
Fig. 2. Intratumor bacteria are found inside both cancer and immune cells.
(A) Summary of the staining patterns of LPS, LTA, and bacterial 16SrRNA in different cell types across 459, 427, and 354 tumor cores, respectively. CD45+/CD68+ cells are referred to as macrophages; CD45+/CD68− cells are referred to as other immune cells. (B to D) Representative cores are shown demonstrating the different staining patterns in human tumors. Asterisks mark the region that was selected for higher magnification. (B) Bacterial LPS and 16S rRNA are demonstrated in breast cancer cells. (C) Bacterial LPS and 16S rRNA are demonstrated in CD45+/CD68− cells of a highly inflamed breast tumor. (D) A melanoma tumor demonstrating typical staining of macrophage-associated bacteria (M), with positive LPS and LTA staining but no 16S rRNA staining. Nearby tumor cells (T) show the typical LPS and 16S rRNA staining, with negative LTA staining. Each inset demonstrates a low magnification of the entire core. Scale bars in high-magnification images, 20 μm. (E) CLEM demonstrates intracellular bacteria in human breast cancer. IF image shows DAPI in blue and LPS in red. Two bacteria are marked with arrows. TEM images of the same cell are shown in grayscale. High-magnification image of the boxed area is shown on the right. The letter N marks the cell nucleus.
Fig. 3
Fig. 3. The microbiome of breast tumors is richer and more diverse than that of other tumor types.
(A) Graphic representation of the bacterial 16S rRNA gene with its conserved (blue) and variable (yellow) regions. The sequence from Escherichia coli K-12 substrain MG1655 was used as a reference sequence. The five amplicons of the multiplexed 5R PCR method are depicted in gray. (B) Schematic representation of the analysis pipeline applied to 16S rDNA sequencing data. (C) Rarefaction plots showing the number of bacterial genera that passed all filters in the different tumor types per number of tumor samples that were selected for the analysis. Light color shading represents confidence intervals based on 100 random subsamplings for each number of tumor samples that was analyzed. (D) Box blot of Shannon diversity indexes of all samples, segregated by tumor type. Neg., negative. (E) Box blot of the numbers of bacterial species present in each tumor. For (D) and (E), values were calculated on rarefied data of 40 samples per tumor type, with 10 iterations. For each iteration, only bacteria that passed all filters in any of the tumor types were included in the analysis. (F) Rarefaction plots for the number of bacterial genera that passed all filters in breast tumor, breast NAT, and breast normal samples. Light color shading represents confidence intervals based on 100 random subsamplings for each number of samples that was analyzed. (G) Fluorescent images from four human breast tumors that were cultured ex vivo with fluorescently labeled D-alanine for 2 hours (blue). Nuclei were stained with DRAQ5 (orange). Scale bars, 10 μm.
Fig. 4
Fig. 4. Different tumor types have distinct microbial compositions.
(A) Jaccard similarity indexes were computed on the basis of profiles of bacterial species that passed all filters in tumors (n = 528) between all possible pairs of samples. The heatmap presents the average of all indexes between sample pairs from any two cancer types. (B) Distribution of order-level phylotypes across different tumor types. Relative abundances were calculated by summing up the reads of species that passed all filters in the different tumor types and belong to the same order. Orders are colored according to their associated phylum. (C) Unsupervised hierarchical clustering of the prevalence of 137 bacteria species that were hits in one of the tumor types and are present in 10% or more of the samples in at least one of the tumor types. (D) The prevalence of 19 bacteria from (C), displayed across the different tumor types. Only bacteria that are a hit in a given tumor type are represented with colored circles. Circle size indicates the prevalence level in samples. US, unknown species. (E) Bacterial taxa with a significant differential prevalence between different breast tumor subtypes are presented in a bar plot. P values were calculated using a two-sample proportion z test to compare between HER2+ (n = 61) and HER2− (n = 247), ER+ (n = 270) and ER− (n = 49), or triple negative (TNG) (n = 36) and non-TNG (n = 284) breast tumors. The direction of the bars indicates the enrichment direction. All bacteria presented had a false discovery rate (FDR)–corrected Q value <0.25. US, unknown species; UG, unknown genus; UF, unknown family; (s), species; (g), genus; (f), family; (c), class. (F) Principal coordinate analysis (PCoA) biplot on the Jaccard similarity indexes between bacterial species profiles of the different tissue types. Only bacteria that passed all filters for the specific tissue type were considered. Tumor types and their normal tissue are grouped. (G) Volcano plot demonstrating the differential prevalence of bacteria between tumors (T) and their NAT in breast, lung, and ovary samples. A two-sample proportion z test was used to calculate the P values. Sizes of dots reflect phylotype levels, gradually increasing from species to phylum. Bacteria are colored according to the tumor type (breast, pink; lung, green; and ovary, purple) if they passed significance thresholds (effect size >5%, P value <0.05, and FDR-corrected Q value <0.25).
Fig. 5
Fig. 5. Predicted bacterial metabolic functions are associated with clinical features.
(A) Heatmap demonstrating unsupervised hierarchical clustering of the frequencies of 287 MetaCyc pathways across the different tumor types. Only pathways that are abundant (frequency >10% in at least one tumor type) and variable (standard deviation divided by the average of frequencies >0.4) were included (table S10). (B and C) Volcano plots demonstrating bacterial MetaCyc pathways (B) and taxa (C) that are enriched in lung tumors from smokers (n = 100) versus never-smokers (n = 43). Effect size represents the difference in the proportion between the groups. A two-sample proportion z test was used to calculate the P values. Green filled circles indicate pathways with FDR-corrected Q values <0.25. Degrading pathways of smoke chemicals are indicated by blue circles in (B); plant-related metabolic pathways are indicated by red circles in (B). (o), order. (D) Taxonomy wheel plot of bacterial species that contributed to degradation of cigarette smoke metabolites (blue ring) and to the biosynthesis of plant metabolites functions (red ring) are indicated on the phylogenetic tree, along with all bacteria that are hits in lung tumors (green ring). (E) Volcano plot demonstrating enriched bacterial MetaCyc functions in ER+ versus ER− breast tumors. A two-sample proportion z test was used to calculate the P values. Colored circles indicate pathways with FDR-corrected Q values <0.25. (F) Volcano plot demonstrating the bacterial taxa enriched in melanoma patients who responded (R) to immune checkpoint inhibitors (ICI) versus nonresponders (NR). A binomial test was used to calculate the Pvalues for the enrichment or depletion of bacterial taxa in the responder cohort versus the nonresponder cohort. The size of dots reflects phylotype levels, gradually increasing from species to phylum. Colored circles indicate taxa with FDR-corrected Q values <0.25.
See this image and copyright information in PMC

Comment in

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. deMartel C, Ferlay J, Franceschi S, Vignat J, Bray F, Forman D, Plummer M, Global burden of cancers attributable to infections in 2008: A review and synthetic analysis. Lancet Oncol. 13, 607–615(2012). doi:10.1016/S1470-2045(12)70137-7 pmid:22575588 - DOI - PubMed
    1. Xuan C, Shamonki JM, Chung A, Dinome ML, Chung M, Sieling PA, Lee DJ, Microbial dysbiosis is associated with human breast cancer. PLOS ONE 9, e83744 (2014).doi:10.1371/journal.pone.0083744pmid:24421902 - DOI - PMC - PubMed
    1. Thompson KJ, Ingle JN, Tang X, Chia N, Jeraldo PR, Walther-Antonio MR, Kandimalla KK, Johnson S, Yao JZ, Harrington SC, Suman VJ, Wang L, Weinshilboum RL, Boughey JC, Kocher J-P, Nelson H, Goetz MP, Kalari KR, A comprehensive analysis of breast cancer microbiota and host gene expression. PLOS ONE 12, e0188873 (2017). doi:10.1371/journal.pone.0188873pmid:29190829 - DOI - PMC - PubMed
    1. Banerjee S, Tian T, Wei Z, Shih N, Feldman MD, Peck KN, DeMichele AM, Alwine JC, Robertson ES, Distinct Microbial Signatures Associated With Different Breast Cancer Types. Front. Microbiol 9, 951 (2018). doi:10.3389/fmicb.2018.00951pmid:29867857 - DOI - PMC - PubMed
    1. Costantini L, Magno S, Albanese D, Donati C, Molinari R, Filippone A, Masetti R, Merendino N, Characterization of human breast tissue microbiota from core needle biopsies through the analysis of multi hypervariable 16S-rRNA gene regions. Sci. Rep 8, 16893 (2018). doi:10.1038/s41598-018-35329-zpmid:30442969 - DOI - PMC - PubMed

Publication types

Substances