Systematic characterization of the cancer microbiome provides the opportunity to develop techniques that exploit non-human, microorganism-derived molecules in the diagnosis of a major human disease. Following recent demonstrations that some types of cancer show substantial microbial contributions1,2,3,4,5,6,7,8,9,10, we re-examined whole-genome and whole-transcriptome sequencing studies in The Cancer Genome Atlas11 (TCGA) of 33 types of cancer from treatment-naive patients (a total of 18,116 samples) for microbial reads, and found unique microbial signatures in tissue and blood within and between most major types of cancer. These TCGA blood signatures remained predictive when applied to patients with stage Ia–IIc cancer and cancers lacking any genomic alterations currently measured on two commercial-grade cell-free tumour DNA platforms, despite the use of very stringent decontamination analyses that discarded up to 92.3% of total sequence data. In addition, we could discriminate among samples from healthy, cancer-free individuals (n = 69) and those from patients with multiple types of cancer (prostate, lung, and melanoma; 100 samples in total) solely using plasma-derived, cell-free microbial nucleic acids. This potential microbiome-based oncology diagnostic tool warrants further exploration.
Subscribe to Journal
Get full journal access for 1 year
only $3.90 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Pre-processed cancer microbiome data generated and analysed in this study (that is, summarized read counts at the genus taxonomic level) as well as the metadata are available at ftp://ftp.microbio.me/pub/cancer_microbiome_analysis/. Raw outputs of Kraken- or SHOGUN-processed TCGA sequencing data comprise hundreds of terabytes of files and are not directly available unless otherwise coordinated with the corresponding author. However, all raw TCGA data and the bioinformatics pipeline necessary to generate such raw outputs from Kraken can be accessed through SevenBridge’s CGC. Each of the hundreds of ML models in this work generated a list of ranked features used to make predictions, and we provide the code to generate these lists, in addition to showing them on our website. Raw data for the plasma validation study are available through the European Nucleotide Archive (accession IDs ERP119598 (HIV-free); ERP119596 (PC); ERP119597 (LC and SKCM)); these data and the SHOGUN-processed data for the plasma validation study are available in Qiita (https://qiita.ucsd.edu/)79 under study IDs (12667 (HIV-free); 12691 (PC); 12692 (LC and SKCM)).
All programming scripts used to access, manage, and run data on the CGC as well as development of the supervised normalization, decontamination, ML pipelines, and so forth can be found at our GitHub repository link: https://github.com/biocore/tcga. These can be applied directly to the summarized, genus-level count data given above. Our CGC pipeline is also publicly shareable and available upon reasonable request from the corresponding author.
Bullman, S. et al. Analysis of Fusobacterium persistence and antibiotic response in colorectal cancer. Science 358, 1443–1448 (2017).
Dejea, C. M. et al. Patients with familial adenomatous polyposis harbor colonic biofilms containing tumorigenic bacteria. Science 359, 592–597 (2018).
Geller, L. T. et al. Potential role of intratumor bacteria in mediating tumor resistance to the chemotherapeutic drug gemcitabine. Science 357, 1156–1160 (2017).
Gopalakrishnan, V. et al. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 359, 97–103 (2018).
Jin, C. et al. Commensal microbiota promote lung cancer development via γδ T cells. Cell 176, 998–1013.e16 (2019).
Ma, C. et al. Gut microbiome-mediated bile acid metabolism regulates liver cancer via NKT cells. Science 360, eaan5931 (2018).
Matson, V. et al. The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients. Science 359, 104–108 (2018).
Meisel, M. et al. Microbial signals drive pre-leukaemic myeloproliferation in a Tet2-deficient host. Nature 557, 580–584 (2018).
Routy, B. et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 359, 91–97 (2018).
Ye, H. et al. Subversion of systemic glucose metabolism as a mechanism to support the growth of leukemia cells. Cancer Cell 34, 659–673.e6 (2018).
The Cancer Genome Atlas Research Network et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113–1120 (2013).
Hanahan, D. & Weinberg, R. A. The hallmarks of cancer. Cell 100, 57–70 (2000).
Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).
Salter, S. J. et al. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol. 12, 87 (2014).
Glassing, A., Dowd, S. E., Galandiuk, S., Davis, B. & Chiodini, R. J. Inherent bacterial DNA contamination of extraction and sequencing reagents may affect interpretation of microbiota in low bacterial biomass samples. Gut Pathog. 8, 24 (2016).
Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6, 226 (2018).
Robinson, K. M., Crabtree, J., Mattick, J. S. A., Anderson, K. E. & Dunning Hotopp, J. C. Distinguishing potential bacteria-tumor associations from contamination in a secondary data analysis of public cancer genome sequence data. Microbiome 5, 9 (2017).
Eisenhofer, R. et al. Contamination in low microbial biomass microbiome studies: issues and recommendations. Trends Microbiol. 27, 105–117 (2019).
The Cancer Genome Atlas Research Network. Comprehensive molecular characterization of gastric adenocarcinoma. Nature 513, 202–209 (2014).
The Cancer Genome Atlas Research Network. Integrated genomic and molecular characterization of cervical cancer. Nature 543, 378–384 (2017).
Tang, K.-W., Alaei-Mahabadi, B., Samuelsson, T., Lindh, M. & Larsson, E. The landscape of viral expression and host gene fusion and adaptation in human cancer. Nat. Commun. 4, 2513 (2013).
Minich, J. J. et al. KatharoSeq enables high-throughput microbiome analysis from low-biomass samples. mSystems 3, e00218-17 (2018).
Wood, D. E. & Salzberg, S. L. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol. 15, R46 (2014).
Zhang, H. et al. Integrated proteogenomic characterization of human high-grade serous ovarian cancer. Cell 166, 755–765 (2016).
Choi, J.-H., Hong, S.-E. & Woo, H. G. Pan-cancer analysis of systematic batch effects on somatic sequence variations. BMC Bioinformatics 18, 211 (2017).
Lauss, M. et al. Monitoring of technical variation in quantitative high-throughput datasets. Cancer Inform. 12, 193–201 (2013).
Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).
Mecham, B. H., Nelson, P. S. & Storey, J. D. Supervised normalization of microarrays. Bioinformatics 26, 1308–1315 (2010).
Boedigheimer, M. J. et al. Sources of variation in baseline gene expression levels from toxicogenomics study control animals across multiple laboratories. BMC Genomics 9, 285 (2008).
Scherer, A. Batch Effects and Noise in Microarray Experiments: Sources and Solutions (Wiley, 2009).
Hillmann, B. et al. Evaluating the information content of shallow shotgun metagenomics. mSystems 3, e00069-18 (2018).
Knights, D. et al. Bayesian community-wide culture-independent microbial source tracking. Nat. Methods 8, 761–763 (2011).
Integrative HMP (iHMP) Research Network Consortium. The Integrative Human Microbiome Project: dynamic analysis of microbiome-host omics profiles during periods of human health and disease. Cell Host Microbe 16, 276–289 (2014).
Yamamura, K. et al. Human microbiome Fusobacterium nucleatum in esophageal cancer tissue is associated with prognosis. Clin. Cancer Res. 22, 5574–5581 (2016).
Hsieh, Y.-Y. et al. Increased abundance of Clostridium and Fusobacterium in gastric microbiota of patients with gastric cancer in Taiwan. Sci. Rep. 8, 158 (2018).
Kostic, A. D. et al. PathSeq: software to identify or discover microbes by deep sequencing of human tissue. Nat. Biotechnol. 29, 393–396 (2011).
Svircev, Z. et al. Molecular aspects of microcystin-induced hepatotoxicity and hepatocarcinogenesis. J. Environ. Sci. Health C Environ. Carcinog. Ecotoxicol. Rev. 28, 39–59 (2010).
Jervis-Bardy, J. et al. Deriving accurate microbiota profiles from human samples with low bacterial content through post-sequencing processing of Illumina MiSeq data. Microbiome 3, 19 (2015).
Kwong, T. N. Y. et al. Association between bacteremia from specific microbes and subsequent diagnosis of colorectal cancer. Gastroenterology 155, 383–390.e8 (2018).
Blauwkamp, T. A. et al. Analytical and clinical validation of a microbial cell-free DNA sequencing test for infectious disease. Nat. Microbiol. 4, 663–674 (2019).
Hong, D. K. et al. Liquid biopsy for infectious diseases: sequencing of cell-free plasma to detect pathogen DNA in patients with invasive fungal disease. Diagn. Microbiol. Infect. Dis. 92, 210–213 (2018).
Burnham, P. et al. Urinary cell-free DNA is a versatile analyte for monitoring infections of the urinary tract. Nat. Commun. 9, 2412 (2018).
De Vlaminck, I. et al. Temporal response of the human virome to immunosuppression and antiviral therapy. Cell 155, 1178–1187 (2013).
Huang, Y.-F. et al. Analysis of microbial sequences in plasma cell-free DNA for early-onset breast cancer patients and healthy females. BMC Med. Genomics 11 (Suppl. 1), 16 (2018).
Bettegowda, C. et al. Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci. Transl. Med. 6, 224ra24 (2014).
Clark, T. A. et al. Analytical validation of a hybrid capture-based next-generation sequencing clinical assay for genomic profiling of cell-free circulating tumor DNA. J. Mol. Diagn. 20, 686–702 (2018).
Sanders, J. G. et al. Optimizing sequencing protocols for leaderboard metagenomics by combining long and short reads. Genome Biol. 20, 226 (2019).
Huang S. et al. Human skin, oral, and gut microbiomes predict chronological age. mSystems 5, e00630-19 (2020).
Zhu, Q. et al. Phylogenomics of 10,575 genomes reveals evolutionary proximity between domains Bacteria and Archaea. Nat. Commun. 10, 5477 (2019).
Chiu, K.-P. & Yu, A. L. Application of cell-free DNA sequencing in characterization of bloodborne microbes and the study of microbe-disease interactions. PeerJ 7, e7426 (2019).
Lau, J. W. et al. The Cancer Genomics Cloud: collaborative, reproducible, and democratized—a new paradigm in large-scale computational research. Cancer Res. 77, e3–e6 (2017).
Hoadley, K. A. et al. Cell-of-origin patterns dominate the molecular classification of 10,000 tumors from 33 types of cancer. Cell 173, 291–304.e6 (2018).
Reynolds, S. M. et al. The ISB Cancer Genomics Cloud: a flexible cloud-based platform for cancer genomics research. Cancer Res. 77, e7–e10 (2017).
Ellrott, K. et al. Scalable open science approach for mutation calling of tumor exomes using multiple genomic pipelines. Cell Syst. 6, 271–281.e7 (2018).
The Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 490, 61–70 (2012).
Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012).
Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 6, pl1 (2013).
Land, M. L. et al. Quality scores for 32,000 genomes. Stand. Genomic Sci. 9, 20 (2014).
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
Greathouse, K. L. et al. Interaction between the microbiome and TP53 in human lung cancer. Genome Biol. 19, 123 (2018).
Shanmughapriya, S. et al. Viral and bacterial aetiologies of epithelial ovarian cancer. Eur. J. Clin. Microbiol. Infect. Dis. 31, 2311–2317 (2012).
Banerjee, S. et al. The ovarian cancer oncobiome. Oncotarget 8, 36225–36245 (2017).
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
McDonald, D. et al. The Biological Observation Matrix (BIOM) format or: how I learned to stop worrying and love the ome-ome. 1, 2047-217X-1-7 (2012).
Friedman, J. H. Stochastic gradient boosting. Comput. Stat. Data Anal. 38, 367–378 (2002).
Friedman, J. H. Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001).
Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28, 1–26 (2008).
Grau, J., Grosse, I. & Keilwagen, J. PRROC: computing and visualizing precision-recall and receiver operating characteristic curves in R. Bioinformatics 31, 2595–2597 (2015).
Gire, S. K. et al. Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak. Science 345, 1369–1372 (2014).
Matranga, C. B. et al. Enhanced methods for unbiased deep sequencing of Lassa and Ebola RNA viruses from clinical and biological samples. Genome Biol. 15, 519 (2014).
Gonzalez, A. et al. Avoiding pandemic fears in the subway and conquering the platypus. mSystems 1, e00050-16 (2016).
Didion, J. P., Martin, M. & Collins, F. S. Atropos: specific, sensitive, and speedy trimming of sequencing reads. PeerJ 5, e3720 (2017).
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).
Magoč, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).
Gonzalez, A. et al. Qiita: rapid, web-enabled microbiome meta-analysis. Nat. Methods 15, 796-798 (2018).
We acknowledge conversations with C. Sepich, C. Martino, R. Bejar, and H. Carter. G.D.P. has been supported by training grants from the National Institutes of Health during the course of this work (5T32GM007198-42; 5T32GM007198-43). S.F. is partially funded through trainee support from Merck KGaA in partnership with the Center for Microbiome Innovation at UC San Diego. Samples acquired for the validation cohort were collected under the following grants: R00 AA020235, R01 DA026334, P30 MH062513, P01 DA012065, and P50 DA026306. The Seven Bridges Cancer Genomics Cloud was used during the course of this work and has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, Contract No. HHSN261201400008C, and ID/IQ Agreement No. 17X146 under Contract No. HHSN261201500003I. This work was supported in part by the Chancellor’s Initiative in the Microbiome and Microbial Sciences (R.K., A.D.S., S.M.-M.) and by Illumina, Inc. through reagent donation in partnership with the Center for Microbiome Innovation at UC San Diego. We thank G. Humphrey and K. Sanders for sample processing, and G. Ackermann, A. Gonzalez, and J. DeReus for assistance with metadata curation and data handling.
Clarity Genomics, the employer of E.K., did not provide funding for this study. G.D.P. and R.K. have jointly filed U.S. Provisional Patent Application Serial No. 62/754,696 and International Application No. PCT/US19/59647 on the basis of this work. G.D.P., R.K., and S.M.-M. have started a company to commercialize the intellectual property. R.K. is a member of the scientific advisory board for GenCirq, holds an equity interest in GenCirq, and can receive reimbursements for expenses up to US$5,000 per year. R.K., A.D.S., and S.M.-M. are directors at the Center for Microbiome Innovation at UC San Diego, which receives industry research funding for various microbiome initiatives, but no industry funding was provided for this cancer microbiome project.
Peer review information Nature thanks Eran Elinav, Victor Velculescu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
a, TCGA study abbreviations. b, PCA of Voom-normalized data, where colours represent sequencing platform of the sample and each dot denotes a cancer microbiome sample. c, PCA of the data following consecutive Voom-SNM supervised normalization, as labelled by sequencing platform. d, PCA of Voom-normalized data, where colours represent experimental strategy of the sample and each dot denotes a cancer microbiome sample. e, PCA of the data following consecutive Voom-SNM supervised normalization, as labelled by experimental strategy. f, g, Microbial reads counts as normalized by the quantity of samples within a given sample type across all types of cancer in TCGA after metadata quality control (Fig. 1b), including the three major sample types analysed in the paper (f) and the remaining sample types (g). ANP, additional, new primary; AM, additional metastatic; MM, metastatic; RT, recurrent tumour. For PCAs of raw and normalized data, n = 17,625; the number of samples per cancer type and per tissue type are shown in Supplementary Table 4. Source data
Extended Data Fig. 2 Performance metrics details discriminating between and within TCGA types of cancer using microbial abundances.
a–f, Expanded examples from the heatmaps in Fig. 1f–h. A colour gradient (top) denotes the probability threshold at any point along the ROC and PR curves. An inset confusion matrix is shown using a 50% probability threshold cutoff, which can be used to calculate sensitivity, specificity, precision, recall, positive predictive value, negative predictive values, and so forth at the corresponding point on the ROC and PR curves. g, h, Linear regressions of model performance, specifically AUROC (g) and AUPR (h), for discriminating between types of cancer in a one-cancer-type-versus-all-others manner, as a function of minority class size. Performances are shown for models using microorganisms detected in primary tumours, for which we had the greatest number of samples (n = 13,883) and types of cancer (n = 32) to compare. As AUROC and AUPR have domains of [0,1] and the minority class size varied from 20 to 1,238 samples, the latter is regressed on a log10 scale. Inset hypothesis tests and associated P values are based on the null hypothesis of there being no relationship between the dependent and independent variables (two-sided hypothesis test of slope). The number of samples included to evaluate performance of each comparison can be found in the data browser confusion matrices at http://cancermicrobiome.ucsd.edu/CancerMicrobiome_DataBrowser. Source data
a, Two independent halves of TCGA raw microbial count data were normalized and used for model training to predict one cancer type versus all others using tumour microbial DNA and RNA; each model was then applied to the other half’s normalized data. This heatmap compares the performances of these models compared to training and testing on 50–50% splits of the full data set (split 1: n = 8,814 samples; split 2: n = 8,811 samples; total samples: n = 17,625). b, c, Model performance comparison when subsetting the full Voom-SNM data by primary tumour RNA samples (n = 11,741) across multiple sequencing centres to predict one cancer type versus all others (b, AUROC; c, AUPR). d, e, Model performance comparison when subsetting the full Voom-SNM data by primary tumour DNA samples (n = 2,142) across multiple sequencing centres to predict one cancer type versus all others (d, AUROC; e, AUPR). f, g, Model performance comparison when subsetting the full Voom-SNM data by samples from the UNC (n = 9,726), which only did RNA-seq, to predict one cancer type versus all others using primary tumour RNA samples (f, AUROC; g, AUPR). h, i, Model performance comparison when subsetting the full Voom-SNM data by samples from HMS (n = 898), which only did WGS, to predict one cancer type versus all others using primary tumour DNA samples (h, AUROC; i, AUPR). b–i, Generalized linear models with s.e. are shown in grey; dotted diagonal line denotes a perfect linear relationship; for sample size comparison, the full Voom-SNM data set contained 13,883 primary tumour samples. Source data
Extended Data Fig. 4 Orthogonal validation of Kraken-derived TCGA cancer microbiome profiles and their ML performances.
a–h, Four TCGA types of cancer (CESC, n = 142 (DNA) and n = 309 (RNA); STAD, n = 322 (DNA) and n = 770 (RNA); LUAD, n = 351 (DNA) and n = 600 (RNA); and OV, n = 189 (DNA) and n = 850 (RNA)) underwent additional filtering after Kraken-based taxonomy assignments via direct genome alignments (BWA59) using tumour microbial DNA and RNA. ML performances are compared between the normalized, BWA filtered data and matched, independently normalized Kraken data for one cancer type versus all others using primary tumour microorganisms (a, AUROC; b, AUPR), tumour-versus-normal discriminations (c, AUROC; d, AUPR), stage I versus stage IV tumour discriminations using primary tumour microorganisms (e, AUROC; f, AUPR), and one cancer type versus all others using blood-derived microorganisms (g, AUROC; h, AUPR) (see Methods). i, Venn diagram of the taxon count between the BWA filtered data and the Kraken full data. j–t, An orthogonal microbial-detection pipeline called SHOGUN31 and a separate database49 were run on a subset of TCGA samples (n = 13,517 total samples), normalized via Voom-SNM, analogous to its Kraken counterpart, and used for downstream ML analyses. j, Venn diagram of the SHOGUN-derived microbial taxa (S) and the Kraken-derived microbial taxa (K). Note that SHOGUN’s database49 does not include viruses whereas the Kraken database does. k, l, PCA of Voom (k) and Voom-SNM (l) normalized SHOGUN data, coloured by sequencing centre. m–t, ML performance comparisons between models trained and tested on SHOGUN data and matched Kraken data, using the same 70%–30% splits, for one cancer type versus all others using primary tumour microorganisms (m, AUROC; n, AUPR), tumour-versus-normal discriminations (o, AUROC; p, AUPR), stage I versus stage IV tumour discriminations using primary tumour microorganisms (q, AUROC; r, AUPR), and one cancer type versus all others using blood-derived microorganisms (s, AUROC; t, AUPR). For fair comparison, matched Kraken data were derived by removing all virus assignments in the raw Kraken count data and subsetting to the same 13,517 TCGA samples analysed by SHOGUN; these matched Kraken data were then normalized independently via Voom-SNM in the same way as the SHOGUN data (see Methods) and fed into downstream ML pipelines. For all ML performances, ≥ 20 samples in each class was required to be eligible. For regression subfigures, the dotted diagonal line denotes perfect performance correspondence; generalized linear models with s.e. ribbons are shown. Source data
Extended Data Fig. 5 Pan-cancer microbial abundances and an interactive website for TCGA cancer microbiome profiling and ML model inspection.
a, Pan-cancer normalized abundances of Fusobacterium with a one-way ANOVA (Kruskal–Wallis) test for microbial abundances across types of cancer for each sample type. Sample sizes are inset in blue and box plots show median (line), 25th and 75th percentiles (box), and 1.5 × IQR (whiskers); TCGA study names are listed below. b, SourceTracker2 results for faecal contribution, as based on HMP2 data, for TCGA-COAD solid-tissue normal samples (n = 70) and TCGA-SKCM primary tumour samples (n = 122). Only one solid tissue normal sample was available for TCGA-SKCM (Supplementary Table 4), so primary tumours were used instead as the best proxy of expected skin flora. It is expected that colon samples should have higher faecal contribution than skin, so a one-sided Mann–Whitney U-test was used. As SourceTracker2 outputs the mean fractional contributions of each source (that is, HMP2) to each sink (that is, COAD, SKCM samples), the centre value of each bar plot is the mean of these values and the error bars denote the s.e.m. The sample sizes are shown below in blue. c, Pan-cancer normalized abundances of Alphapapillomavirus with a one-way ANOVA (Kruskal–Wallis) test for microbial abundances across types of cancer for each sample type. Sample sizes are inset in blue, and box plots show median (line), 25th and 75th percentiles (box), and 1.5 × IQR (whiskers); TCGA study names are listed below. TCGA studies that clinically tested patients for HPV infection are divided into negative and positive groups. d, Screenshot of interactive website showing plotting of Alphapapillomavirus normalized microbial abundances using Kraken-derived data. Plotting using SHOGUN-derived normalized microbial abundances is available on another tab of the website (left-hand side). e, Screenshot of interactive website of ML model inspection. Selecting the data type (for example, all likely contaminants removed), cancer type (for example, invasive breast carcinoma), and comparison of interest (for example, tumour versus normal) will automatically update the ROC and PR curves, as well as the confusion matrix (using a probability cutoff threshold of 50%) and the ranked model feature list. Website is accessible at http://cancermicrobiome.ucsd.edu/CancerMicrobiome_DataBrowser. Source data
Extended Data Fig. 6 The decontamination approach along with its results, benefits, and limitations on cancer microbiome data.
a, Various approaches used to evaluate, mitigate, remove and/or simulate sources of contamination. b, The proportion of remaining taxa or microbial reads in TCGA after varying levels of decontamination. Decontamination by sequencing centre removed all taxa identified as a contaminant at any one sequencing centre (n = 8 batches); decontamination by plate–centre combinations removed all taxa identified as a contaminant on any single sequencing plate with more than ten TCGA samples on it (n = 351 batches). c–f, Body-site attribution prediction on the likely contaminants removed data set (c), the plate–centre decontaminated data set (d), the all putative contaminants removed data set (e), and the most stringent filtering data set (f). g–l, All of the models and concomitant performance values (AUROC and AUPR) were re-generated using the four decontaminated data sets described above (each labelled with a different colour as shown above). The AUROC and AUPR values obtained from models trained and tested on the decontaminated data sets are plotted against the AUROC or AUPR values from the full data set (Fig. 1f–h). The dashed diagonal line denotes a perfect linear relationship. Generalized linear models have been fitted to the AUROC and AUPR values of the corresponding data sets; s.e. of the linear fits are shown by the associated shaded regions. COAD (n = 1,006 total samples; Supplementary Table 4) model performances are identified throughout the Figures. Source data
The total read count (DNA and RNA) of each major sample type (primary tumour (a), solid-tissue normal (b), blood-derived normal (c)) was summed and divided by the total number of samples within each sample type. This normalized read count (per sample type) was then divided by the summed normalized read count across all sample types for each cancer type, thereby providing an estimate of the proportion of average reads per sample type per cancer type. This was repeated for all five data sets, as shown by the legend, to assess whether decontamination differentially impacted certain types of sample and/or cancer; relative stability in the percentages shown would suggest a lack of differential contamination. Minor sample types that were not further analysed in this paper by decontamination or ML (for example, additional metastatic lesions; n = 4 sample types; Extended Data Fig. 1g) are not shown and comprised only 3.80% of total TCGA samples. Note, in the special case that only one sample type existed for a given cancer type (primary tumour in ACC, MESO, UCS), then all bars will show that 100% of the normalized reads came from that one sample type. The number of samples examined for each cancer type and sample type are shown in Supplementary Table 4. Source data
Extended Data Fig. 8 Measuring spiked pseudo-contaminant contribution in downstream ML models and theoretical sensitivities of commercially available, host-based, ctDNA assays in patients from TCGA.
a, b, Feature importance scores were calculated for all taxa used in models trained to discriminate one cancer type versus all others in all four decontaminated data sets (Extended Data Fig. 6b) using primary tumour microbial DNA or RNA (a), or using blood-derived mbDNA (b). These decontaminated data sets were spiked with pseudo-contaminants before the decontamination and normalization pipelines to evaluate their performance (see Methods), and the test set performances of the models shown are given in Extended Data Fig. 6g, h and Fig. 3a, respectively. Any spiked pseudo-contaminant(s) used by a model had their feature importance score(s) divided by the sum total of all feature importance scores in that model to estimate their percentage contribution towards making accurate predictions; the higher the score (out of 100), the less biologically reliable the model is. Note, zero means that no spiked pseudo-contaminants were used for making predictions by the model; none of the models generated on the plate–centre decontaminated data included spiked pseudo-contaminants as features. The number of samples included to evaluate performance of each comparison can be found in the data browser confusion matrices at http://cancermicrobiome.ucsd.edu/CancerMicrobiome_DataBrowser. c, d, Percentage distribution among TCGA studies of patients with one or more genomic alterations on FoundationOne Liquid ctDNA coding genes (c) or on Guardant360 ctDNA coding genes (d). The number of samples examined and raw data are available at https://www.cbioportal.org/. e, The specific list of coding genes for the FoundationOne and Guardant360 ctDNA assays and their examined alterations (source listed in the Methods). Source data
Extended Data Fig. 9 Supporting analysis for real-world, plasma-derived, cell-free microbial DNA analysis between and among healthy individuals and multiple types of cancer.
a, Discriminatory simulations in TCGA used to empirically power the real-world validation study (Fig. 4; see Methods). Centre values for each stratified sample size are the means of the performances across ten iterations; error bars denote s.e.m. b, Evaluation of Aliivibrio genus abundance values (raw read counts) among positive control bacterial (Aliivibrio) monocultures, negative control blanks, and human sample types using Kraken and SHOGUN-derived data. c, Aliivibrio genus abundance (raw read counts) across bacterial monoculture dilutions. d, Age distribution among cancer-free healthy control individuals (Ctrl) and grouped patients with lung cancer (LC), prostate cancer (PC), or melanoma (SKCM). e, Gender distribution among patients with inset Pearson’s χ2 test (one-sided critical region). f, Venn diagram of taxon assignments between Kraken and SHOGUN, which used different databases. g, Iterative LOO ML regression of host age using Kraken (pink) or SHOGUN (aqua) raw microbial count data in healthy cancer-free individuals. Mean absolute errors (MAE) evaluated across all samples are shown. h–j, The effects of permuted age (h), sex (i), and age and sex (j) before Voom-SNM on ML performance to discriminate healthy individuals versus grouped patients with cancer using cell-free microbial DNA. One hundred permutations were used for each comparison (see Methods). k, Iterative subsampling of PC, LC, SKCM, and control groups to match SKCM cohort size (n = 16 samples), followed by LOO pairwise ML of each subsampled cancer type against subsampled healthy controls. One hundred permuted iterations were used to estimate discriminatory performance distributions and standard errors (see Methods). b, c, Note the log10 scale and 0.5 pseudo-count lower limit (dotted line). b–d, h–k, All hypothesis tests are two-sided Mann–Whitney U-tests with multiple testing correction when testing more than two comparisons; box plots show median (line), 25th and 75th percentiles (box), and 1.5 × IQR (whiskers). For all box plots and bar plots, sample sizes are shown in blue below. Source data
Extended Data Fig. 10 SHOGUN-derived ML performances to discriminate between types of cancer and healthy, cancer-free individuals using cell-free microbial DNA.
a, Bootstrapped performance estimates for distinguishing grouped patients with cancer (n = 100) from cancer-free healthy control individuals (n = 69). ROC and PR curve data from 500 iterations with different training–testing splits (70%–30%) are shown on the rasterized density plot; mean values and 95% CI estimates are shown. b–g, LOO iterative ML performance between two classes: PC versus control (b), LC versus control (c), SKCM versus control (d), PC versus LC (e), LC versus SKCM (f), and PC versus SKCM (g). h–j, Multi-class (n = 3 or 4), LOO iterative ML performances to distinguish between types of cancer, as well as between patients with cancer and healthy cancer-free control individuals. Mean AUROC and AUPR, as calculated from one-versus-all-others AUROC and AUPR values, are shown below confusion matrices. h, LOO ML performance between the three types of cancer under study. i, LOO ML performance between the three sample types with at least 20 samples in the minority class (that is, the cutoff used in the TCGA analysis, Fig. 1f–h). j, LOO ML performance between all four sample types under study. For all subfigures with confusion matrix plots: LOO ML was used instead of single or bootstrapped training–testing splits because of small sample sizes; these confusion matrices also reflect the number of samples used for each comparison. Source data
About this article
Cite this article
Poore, G.D., Kopylova, E., Zhu, Q. et al. Microbiome analyses of blood and tissues suggest cancer diagnostic approach. Nature 579, 567–574 (2020). https://doi.org/10.1038/s41586-020-2095-1
Allergy, Asthma & Immunology Research (2021)
Plasma of Acute Lymphoblastic Leukemia Patients React to the Culture of a Mycovirus Containing Aspergillus flavus
Journal of Pediatric Hematology/Oncology (2020)
Computational and Structural Biotechnology Journal (2020)
Molecular Cell (2020)
Current Opinion in Microbiology (2020)