Changes of the Commensal Microbiome during Treatment are Associated with Clinical

Response in Nasopharyngeal Carcinoma Patients

Supplementary Materials:

Supplementary Methods

Method S1. Admission criteria of nasopharyngeal carcinoma (NPC) patients

Method S2. Routine of clinical practice

Method S3. Sample collection

Method S4. Samples processing

Method S5. Data denoising, pre-processing and diversity calculation

Supplementary Figures

Figure S1. Procrustes analysis

Figure S2. Overview of nasopharyngeal microbiome over treatment

Figure S3. Abundant ASVs change during treatment

Figure S4. Workflow of patient recruitment and sample collection

Figure S5. Workflow of data process and statistical analysis

Supplementary Tables

Table S1. Demographic and clinical characteristics of NPC patients

Table S2. Linear mixed effect models were employed to ∆-wUFs

Table S3. Abundant ASVs with significant change during treatment

Table S4. Smoothing-spline ANOVA

Supplementary References

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Supplementary Methods

Method S1. Admission criteria of nasopharyngeal carcinoma (NPC) patients

1. Inclusion criteria:

a. Newly diagnosed and histopathologically confirmed NPC patients;

b. For NPC staging, any T, or any N, and M0;

c. Place of birth located in Guangxi province, China;

d. Received and completed the IMRT regimen;

e. Written informed consent.

2. Exclusion criteria:

a. Diagnosed with psychiatric disorders;

b. Had ever received prior head-and-neck radiation before enrollment;

c. Had taken antibiotics within 3 months before enrollment;

d. Had been administered antibiotics intravenously during radiotherapy;

e. Epistaxis ever occurred during radiotherapy.

Method S2. Routine of clinical practice

The cancer stages of NPC patients were evaluated according to the criteria of the 7th edition of the American Joint Committee on Cancer/Union for International Cancer Control TNM Staging System. Treatment protocols were determined by a panel of oncologists based on the cancer stage and overall health of the patients following the National Comprehensive Cancer Network (NCCN) clinical practice guideline for NPC (https://www.nccn.org/professionals/physician_gls/default.aspx#head-and-neck). All of the patients received a comprehensive oral evaluation, including tooth extraction, endodontic therapy, and dental scaling/polishing if necessary, at least 2-4 weeks before radiation began. Intensity-modulated radiation therapy (IMRT) was administered with a total dose of 68-73 Gy of high energy 6MV-X-ray radiation within 7-8 weeks, with the prescription of 5 times per week at 2-2.2 Gy per fraction. Systematic therapy including chemotherapy was provided as part of the treatment protocols.

All patients have received the first clinical check-up at 3 months after the completion of radiotherapy and followed up for 24 months. The clinical responses of NPC patients were assessed by 3 oncologists independently, according to the images of magnetic resonance imaging or computed tomography, following the criteria of RECIST (Response Evaluation Criteria in Solid Tumors) guideline (1).

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Method S3. Sample collection

At the end of 2015, in total 870 nasopharyngeal swabs were collected from 62 NPC patients (Supplementary Figure 1). A sampling of nasopharyngeal swabs was conducted as previously described (2, 3). Briefly, the sterile swab with a protection tube was inserted into the nasopharynx without touching nasal mucosa until reaching the posterior nasopharyngeal wall; the tube was withdrawn 1.5-2cm while the swab was firmly protruded against the posterior wall and swept over the surface several times. Then the swab was withdrawn until fully inside the tube (Figure S1). The swab together with the tube was removed. The flocked tip was cut off and stored in a 2ml sterile DNAase/RNase-free cryovial filled with 400µl Phosphate-buffered saline. All samples were collected by well-trained clinicians. The swab samples were stored at -20 within 24h, prior to being frozen at -80 in the laboratory of Guangxi ℃ ℃Medical University before DNA extraction.

Method S4. Samples processing

A two-step cell lysis procedure including enzymatic lysis and bead-beating was employed for DNA extraction of nasopharyngeal swabs. First, the tube with swab was vortexed thoroughly and centrifuged at 4000 rpm for 2 min in room temperature. The swab was discarded using a sterile forceps. A total of 180 ml lysozyme (20 mg/ml, Sigma-Aldrich, cat. no. L-6876, St. Louis, MO, USA; decontaminated using Millipore Amicon Ultra-4 Centrifugal Filter, cat. no.UFC803024, Fisher Scientific) was added and mixed thoroughly with cell suspension, then incubated for 30 minutes at 37 . A 300 mg mixture of 0.1-mm-℃diameter, 0.5-mm-diameter and 1-mm-diameter glass beads (BioSpec, Bartlesville, OK) was added to the lysate and the microbial cells were disrupted mechanically using Cell Tissue Homogenizer (WISBIOMED, San Mateo, CA) at max speed for 5 minutes. Further isolation and purification of the total genomic DNA from lysates were completed using QIAamp DNA mini kits (Qiagen, cat. no. 51304, Hilden, Germany). DNA was eluted using 50 ul buffer AE according to the manufacturer’s instructions. The DNA concentration was detected and quantitated by Qubit fluorescence-based quantitation system (Qubit® 3.0 Fluorometer, Thermo Fisher Scientific, Wilmington, DE, USA) according to the manufacturer’s instruction. All beads, tubes, and non-enzymatic reagents were disinfected by ultraviolet light for 60 min prior to use (4). Two reagent-controls were added in each run of DNA extraction for 22 swab samples. The elution of controls was confirmed by 16S polymerase chain reaction (PCR) to be absent from contaminating bacteria.

Four different standard bacteria DNA and their mixture DNA were used as quality controls in our study: Corynebacterium diphtheria and Staphylococcus aureus from the Department of Microbiology in Guangxi Medical University (China); Burkholderia cepacian and Escherichia coli ordered from China General Microbiological Culture Collection Center.

The indexed libraries were generated using the primers targeted V3-V4 hypervariable regions of the prokaryotic 16S ribosomal RNA gene: V3F-CCTACGGGNGGCWGCAG and V4R-GACTACHVGGGTATCTAATCC (5, 6). Primer-based amplification was performed using PCR with the following program: 98 °C for 3 min, followed by 35 cycles of 98 °C for

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10 s , 66 °C for 30 s, 72 °C for 30 s, and an extension of 72 °C for 5 min. The 16S V3-V4 amplicon was purified away from free primers and primer-dimer species using Agencourt AMPure XP (Beckman Coulter, item. no. A63880, Brea, CA). Afterward, dual indices as previously described (5) and Illumina sequencing adapters were attached to the cleaned 16S amplicon in index PCR step with the conditions: 98 °C for 3 min, followed by 10 cycles of 98 °C for 10 s , 66 °C for 30 s, 72 °C for 30 s, and an extension of 72 °C for 5 min. The indexed amplicon was purified as before. A mix of bacterial reference standards mentioned above which was used as a mock community and 3 reagent-controls were included in each run of library preparation for around 90 to 120 samples. The PCR products were electrophoresed on an agarose gel and visualized by UV illumination, of which dsDNA concentration was measured by Qubit® 3.0.

The quality and quantity validation of all the libraries was performed at the Beijing Genomics Institute (BGI, Wuhan, China) by Agilent 2100 Bioanalyzer system (Agilent Technologies, Santa Clara, CA, USA) and qPCR. Overall, 526 out of 648 libraries and 85 quality controls were eligible both in quality and quantity (criteria: a. concentration > 5 nM; b. the peak of the fragments shall be located in the range from 500-700 bp; c. undesirable fragments: primer-dimer < 10% in concentration, large fragments < 10% in concentration). All of the libraries were sequenced using a 300-bp paired-end strategy on Illumina Miseq according to the manufacturer’s instructions. Runs with Q30 lower than 70% or more than 10% of the libraries with < 20,000 qualified reads were re-sequenced. The raw sequence data was transferred to and stored at the server at the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Sweden.

Method S5. Data denoising, pre-processing and diversity calculation

Raw sequences of 526 nasopharyngeal samples referred to 43 NPC patients were demultiplexed using an in-house script and imported into the QIIME 2 2019.10 for performing microbiome-bioinformatics (7). Adaptors were trimmed and pair-end sequences were joined using VSEARCH (8). Sequences were quality filtered (q2-quality-filter) (9) and followed by denoising with deblur (10) using the default parameters on 420 bp amplicons for generating amplicon sequence variants (ASVs). All ASVs were used to build a fragment insertion tree with the August 2013 Greengenes 99% identity reference tree backbone (q2-fragment-insertion) (11, 12) and to make a taxonomic assignment with a naïve Bayesian classifier trained against the same reference (q2-feature-classifier) (13, 14). In total, 2,951,957 high quality reads corresponding to 10,452 ASVs were retained.

Forty-seven samples corresponding to four NPC patients who were lost follow-up and nine samples not collected within the radiotherapy period were excluded from downstream analysis. ASVs which had not been assigned to any bacterial “Phylum” and those assigned as “Rickettsiales” and “Chloroplast” were excluded (15, 16). Twenty-five samples with fewer than 1500 reads were also excluded, leaving 445 samples as the main dataset for further analyses (from 39 NPCs, with 9,320 ASVs; Figure S2).

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Alpha-diversity metrics (Faith’s Phylogenetic Diversity, results were not shown and can be achieved by contacting authors) (17) and beta-diversity metrics (weighted UniFrac (18), unweighted UniFrac (19), and Bray-Curtis dissimilarity) were estimated using q2-diversity after samples were rarefied to 1500 sequences per sample (20).

Feature-based analyses were performed using a representative subset of ASVs with at least 0.1% relative abundance in at least 10% of samples, as abundant ASV-table (n=73). Two samples were further removed due to the absence of abundant ASVs, retaining 443 samples. A Procrustean randomization test and Mantel test were applied to test the degree of concordance between main ASV-table and the abundant ASV-table using Bray-Curtis distances and PCoA with Cailiez correction as ordination (21).

The phyloseq R package (version 1.24.2) (22) was used to organize and synthesize information of the ASV-table, metadata, phylogenetic tree and taxonomic assignments as a single data object in R 3.5.1 for further analyses in R.

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Supplementary Figures

Figure S1. Workflow of patient recruitment and sample collection

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Figure S2. Workflow of data process and statistical analysis

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Figure S3. Overview of nasopharyngeal microbiome over treatment

The unweighted (panel a and b) and weighted UniFrac distance matrices (panel c and d) of 445 samples are visualized in PCoA projections. Brown spheres represent the samples derived from the early responders, and green stars the late responders. Panels show different angles of the same 3D plot. In the unweighted PCoA projection, the first axis explains 13.23% of the variance, while the second and third axis 5.88% and 3.59% of the variance respectively. In the weighted PCoA projection, the first axis explains 29.50% of the variance, while the second and third axis 12.93% and 7.84% of the variance respectively.

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Figure S4. Procrustes analysis

A Procrustes analysis was employed for 443 samples (2 samples removed due to absence of abundant ASVs) to access the degree of concordance between the full set of 9,308 ASVs and the condensed set of 73 abundant ASVs using Bray-Curtis distances (rarefied to 1500 sequences/sample) and PCoA with Cailiez correction as ordination (panel A). It is noticed that the large residuals appeared and clustered in groups, suggesting that few subjects have microbiota patterns that are harder to approximate via the 73 abundant ASVs than others. Panel B shows the residuals when approximating the full data set with the 73 abundant ASVs only. The set of abundant ASVs is well concordant with the full set. PROTEST (999 within-subject permutations), correlation = 0.9669 (based on symmetric Procrustes transformation of the PCoA with Cailliez correction), with p-value = 0.001 (range of permuted null: 0.77 - 0.79); Mantel test (999 within-subject permutations), Spearman correlation between full / reduced Bray-Curtis distances = 0.9506, with p-value = 0.001 (range of permuted null: 0.24 - 0.37).

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Figure S5. Abundant ASVs change during treatment

A volcano plot of the spearman rank correlation was compared to normalized ANCOM W statistic (A) identified 7 ASVs with a correlation to time and ANCOM W of more than 0.8. The temporal trajectories of the normalized abundance with log transform of Corynebacterium (gCoryn.0582), Corynebacterium (gCoryn.1650), and Corynebacterium (gCoryn.ff32) with the highest ANCOM W are plot in panel B, C, and D. The trend lines were calculated with a simple linear regression of sampling points from time 0 to time 17 (0 = before radiotherapy). We also looked at the overall relative abundance of abundant ASVs at the family level (E), which suggests some stability.

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Supplementary Tables

Table S1. Demographic and clinical characteristics of NPC patients

Variable NPC patients (n=39) NPC patients (n=36)(for NMITa)

Age (years) 41.7 ± 11.5 41.6 ± 11.9 Age groups: < 30 8 (20.5 %) 8 (22.2 %) 30 - 39 7 (17.9 %) 6 (16.7 %) 40 - 49 15 (38.5 %) 13 (36.1 %) 50 - 59 8 (20.5 %) 8 (22.2 %) ≥ 60 1 (2.6 %) 1 (2.8 %)Gender Male 27 (69.2 %) 25 (69.4 %) Female 12 (30.8 %) 11 (30.6 %)TNM stage (overall) I - II (Early stage) 3 (7.7 %) 3 (8.3 %) III 12 (30.8 %) 12 (33.3 %) IV 24 (61.5 %) 21 (58.3 %)Treatment Radiation only 4 (10.3 %) 4 (11.1 %) Radiation + CTb 10 (25.6%) 9 (25.0 %) Radiation + CT + ICc/ACd 23 (59.0 %) 21 (58.3 %) Radiation + CT + IC/AC + Oe 2 (5.1 %) 2 (5.6 %)RECISTf

Early responderg 27 (69.2 %) 25 (69.4 %) Late responderh 12 (30.8 %) 11 (30.6 %)

a: Non-parametric microbial interdependence test.

b: Concurrent chemotherapy.

c: Induction chemotherapy.

d: Adjuvant chemotherapy.

e: Other treatments, surgery or brachytherapy after radiotherapy.

f: Response Evaluation Criteria in Solid Tumors 1.1.

g: NPC patients who achieved complete response within 3 months after radiotherapy.

h: NPC patients who achieved complete response in 3 to 24 months after radiotherapy.

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Table S2. Linear mixed effect models were employed to ∆-wUFs

Model Formulaa dfb AICc Ref. modeld P-valuee

1 1 + (1 | Pat_no) 3 -276.5 NA NA2 1 + (time | Pat_no) 5 -287.6 1 5.00E-043 time + (1 | Pat_no) 4 -276.6 NA NA4 time + (time | Pat_no) 6 -286.1 2 0.46095 Response + (time | Pat_no) 6 -291.5 2 0.01526 Response * time + (time | Pat_no) 8 -289.1 5 0.44097 Response + (time | Pat_no) + (1 | Response) 7 -289.5 5 18 Response + (time | Pat_no) + (time | Response) 9 -285.5 5 1

a: Random effects in parentheses, with grouping variable indicated after vertical line; intercept-only terms specified by 1;

b: Model degrees of freedom;

c: Akaike’s information criterion;

d: Reference model for likelihood ratio test comparison;

e: Likelihood ratio test P-value.

The table is to show the results from the linear mixed effect models with and without grouping. It is noticed that Model 2 (constant intercept-only fixed effect for ∆-wUFs, with random intercept and slope of time in individuals) has the smallest Akaike’s information criterion (AIC) among overall models not accounting for the clinical response (Model 1 to 4). Furthermore, the Model 5 (constant fixed effect of clinical response, with random intercept and slope of time in individuals) has the smallest AIC among all Models (1 to 8).

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Table S3. Abundant ASVs with significant change during treatment

ASV-ID W Phylum Family GenusgCoryn.0582 0.931 p__Actinobacteria f__Corynebacteriaceae g__CorynebacteriumgCoryn.1650 0.917 p__Actinobacteria f__Corynebacteriaceae g__CorynebacteriumgCoryn.ff32 0.903 p__Actinobacteria f__Corynebacteriaceae g__CorynebacteriumgCoryn.0838 0.875 p__Actinobacteria f__Corynebacteriaceae g__CorynebacteriumgCoryn.6860 0.875 p__Actinobacteria f__Corynebacteriaceae g__CorynebacteriumgCoryn.c059 0.847 p__Actinobacteria f__Corynebacteriaceae g__CorynebacteriumgCoryn.5ba9 0.833 p__Actinobacteria f__Corynebacteriaceae g__Corynebacterium

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Table S4. Smoothing-spline ANOVA

By applying SS-ANOVA, seventy-three abundant ASVs were examined whether they were different between the early and late responders over treatment. Twenty-eight ASVs highlighted with red color were detected with significant differences between groups. The time intervals of significant differences were also determined together with the starting and ending time points (sampling time points). The taxonomic assignment of each ASV is presented. Three clusters (marked as A - C) were grouped using Ward’s D2 (refer to the Figure 4 in main text). The list of ASVs was grouped by clusters and ordered by P-value.

ASV-Label ASV-ID TaxonomyaInterval

sb Start End P_valuec

Clusterd

gBurkh.6364 6364b02051554310f40adc1dd6bdc35a g__Burkholderia 1 7 20 0.005 AgBurkh.39c8 39c8f901b8d7db5f9217a1649eebd6bf g__Burkholderia 1 8 20 0.007 AgCandi.9a10 9a101bcde4adbe160d2468fa68e49b68 g__Candidatus Rhodoluna 1 14 20 0.009 AoBacil.0094 0094f583ae99cef19fef1f2e17f1ceab o__Bacillales 1 8 20 0.011 AgAcine.32b4 32b4ff57a26aa1062e690112b40de218 g__Acinetobacter 1 7 20 0.011 AgAcine.efe8 efe88f29876ffb96365e2c90d7aa4637 g__Acinetobacter 1 18 20 0.011 AgEnhyd.0ef3 0ef34a14d7d965b18df2a50ae32615c5 g__Enhydrobacter 1 10 20 0.018 AgStrep.3ed3 3ed3b08e3032f76a89c4a725d29bcb01 g__Streptococcus 2 1 17 0.020 AgBrevu.1285 1285f59768fba63747241be2cfb5dedf g__Brevundimonas 1 1 19 0.024 AgCoryn.ca4d ca4dfd5eb79e5b5f89a61f5c07902a75 g__Corynebacterium 1 8 20 0.029 AgBurkh.f802 f802eae364170acb4b944fecb8482bb6 g__Burkholderia 1 14 20 0.044 AgBurkh.ee2f ee2fe4a62e9b05590a5151ffc76c453c g__Burkholderia 1 7 20 0.049 AgBrevu.db25 db254626ba8bfd70a03f6a253b32f5b2 g__Brevundimonas 1 6 20 0.051 AgAcine.1372 1372f5039bfc43cd2717490f1e294ab4 g__Acinetobacter 1 14 20 0.057 A

gBrevu.2620 2620d602d2299e719ae32520ec559767 g__Brevundimonas 1 6 20 0.063 A

fCaulo.0db1 0db15d8d60ec5fea03c447e40fcad4de f__Caulobacteraceae 1 5 20 0.077 AgAcine.8e2b 8e2b88a1651ee8eee3a085d51d880f8e g__Acinetobacter 1 9 16 0.085 AgCoryn.f313 f313863593de352cbec9e146d2ee4835 g__Corynebacterium 0 NA NA NA A

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gCoryn.a40f a40fd5a6d686cb962165350b5515f48c g__Corynebacterium 0 NA NA NA AfEnter.2de3 2de3fd9d6b9d67c74f603160b241a2fb f__Enterobacteriaceae 0 NA NA NA AgCoryn.0c08 0c08d04f3a4dcd092425e9a99b510ec3 g__Corynebacterium 0 NA NA NA AgStaph.49ac 49ac07d876cf50c964dfdce574cb1f12 g__Staphylococcus 0 NA NA NA AfMethy.cbae cbae0d8058e74a26697bf9a8f0691b4c f__Methylobacteriaceae 1 5 11 0.017 BgCoryn.1650 16504ce9b6db71a496680fde46749724 g__Corynebacterium 1 1 12 0.026 BfBrady.ebba ebba98e8c703a570f9f8505ae5757d2e f__Bradyrhizobiaceae 1 6 20 0.029 BgCoryn.5ba9 5ba9aeb03243cfc3e04efa143cc45b55 g__Corynebacterium 1 1 13 0.043 BgStaph.f22f f22ff874aadf1eb08ac0da99f48b20e3 g__Staphylococcus 1 6 14 0.061 BgCoryn.f6e9 f6e9c7ede84a5a618dbeddd68b0ce195 g__Corynebacterium 1 1 9 0.151 BgVibri.dee4 dee40db5a970e4c2e2d9de288e6cf0e1 g__Vibrio 1 1 5 0.155 BgCoryn.6860 6860daedc5510d971aa63491819fdd5c g__Corynebacterium 1 1 7 0.157 BgStaph.8595 8595a583f5c9e3c250a85c1bebdf5bc2 g__Staphylococcus 1 15 20 0.205 BgCoryn.c92d c92d2296ea3f4ddff78dc8bc818b545d g__Corynebacterium 1 1 8 0.211 BgStaph.01ec 01ecbe477f450af48f19556a88c39845 g__Staphylococcus 1 15 20 0.227 BgAcine.36d9 36d99fccb3d055306a23ab63170158a6 g__Acinetobacter 1 6 15 0.24 BgAlloi.6eb6 6eb6716e9d3e2ede006ce5b158cffc62 g__Alloiococcus 0 NA NA NA BgBrevu.a2ac a2ac53192c802fd24cf824bc557a3aa3 g__Brevundimonas 0 NA NA NA BgStrep.4d14 4d14c0164953278d9f557e77ac17cafc g__Streptococcus 0 NA NA NA BgCoryn.a292 a292a965e900fd38976d3a79267fddc2 g__Corynebacterium 0 NA NA NA B

gAlloi.5184 51848e903c3556482ebaa5deb6ca04b9 g__Alloiococcus 0 NA NA NA B

fEnter.aee3 aee32ee923287642ec0e817728e981ba f__Enterobacteriaceae 0 NA NA NA B

gVibri.bc93 bc93d9361bbf124d5017358d641f64b9 g__Vibrio 0 NA NA NA BgEnhyd.2552 2552b4e62bdbe33a45593cb710352ffc g__Enhydrobacter 0 NA NA NA BgCoryn.966d 966d4765ce41ef79f9a7da6904551097 g__Corynebacterium 0 NA NA NA B

gStaph.43c0 43c0a3babd8ad1207eb994a30779986e g__Staphylococcus 0 NA NA NA B

fXanth.fb62 fb62f49e78d21bb18eb4b214cd9d8a3c f__Xanthomonadaceae 0 NA NA NA B

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gAcine.0358 0358b83177f901227050ca1e413edef6 g__Acinetobacter 0 NA NA NA BgStaph.b689 b689f8f0477ce802f8fb53154a446935 g__Staphylococcus 0 NA NA NA BgStrep.82ef 82ef2c150322026009bf4bf2dd504e24 g__Streptococcus 0 NA NA NA BfCaulo.8177 8177fda47df52c9c6620ad5f2fc2ea19 f__Caulobacteraceae 0 NA NA NA BgCoryn.c059 c059bd3297c99e5fa6621089d50c6922 g__Corynebacterium 0 NA NA NA BgAlloi.6956 695635c58ad8ece1224a3f4dc251bd8b g__Alloiococcus 0 NA NA NA B

gCoryn.619d 619d77a801054bb11addec5aad08158e g__Corynebacterium 0 NA NA NA B

gCoryn.febd febd1c370aed228a5b9e8db46a240db9 g__Corynebacterium 0 NA NA NA BgPseud.1b55 1b55f2025c5bafa7456d619d53b1fdd7 g__Pseudomonas 0 NA NA NA BfXanth.d0e9 d0e989f77094a4e014486d5958ecfaa0 f__Xanthomonadaceae 0 NA NA NA BgAlloi.3e84 3e84e5bbf4f6dd76606000a2c29111d5 g__Alloiococcus 0 NA NA NA BgCoryn.d37f d37f6c86c66a921a7e07d4a25e7136ef g__Corynebacterium 0 NA NA NA B

gSphin.a688 a68801ed8d527ea66165990148637769 g__Sphingomonas 0 NA NA NA B

gStrep.ee77 ee7749eaac22a6524bac2d6a2e3a22eb g__Streptococcus 0 NA NA NA BgTherm.b26c b26c8cb8857033626e3ebd166cb98bf8 g__Thermus 1 1 20 0.003 CfCaulo.a200 a200be849becf02a6f492bba95c148fc f__Caulobacteraceae 1 2 20 0.003 CgTherm.94db 94db2b6bfb347e1dc51342fcec869b42 g__Thermus 1 1 19 0.003 C

gTherm.087b 087bf729b69b74a2971b842e04712611 g__Thermus 1 1 20 0.004 C

gAcine.4f58 4f589827a939204124a34c5a45afe1de g__Acinetobacter 1 1 20 0.005 CgRalst.edc7 edc7e6adfb888f89451df136992aecdc g__Ralstonia 1 1 20 0.006 CgAcine.4c95 4c956ca5f4acf3a7562315c60249359a g__Acinetobacter 1 1 20 0.009 CgAcine.dfae dfaebe2954a4d80fd29f274597b4aa5c g__Acinetobacter 1 1 20 0.013 CgCoryn.ff32 ff320926f2a0b4874440da77e1017710 g__Corynebacterium 1 1 12 0.016 CgStaph.ab0b ab0b6c7356f8c0db5dbea1f947b73410 g__Staphylococcus 1 1 14 0.021 C

gCoryn.0838 0838f8a1760ee31794ae38d3b1d25709 g__Corynebacterium 1 1 15 0.025 C

gRalst.a529 a529b9c9f3e80f120c81f63aeac31ee9 g__Ralstonia 1 4 20 0.041 CgAcine.1bca 1bcae0eef553fb05137051752e2d6c54 g__Acinetobacter 1 1 20 0.059 C

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gCoryn.0582 058240ad65ec9842f5870fc335d1ed8a g__Corynebacterium 1 1 14 0.088 Ca: Taxonomy assignment. “g__” refers to genus; “f__” refers to family; “o__” refers to order.

b: Interval = number of distanct significant intervals found; Start = first time point of interval, End = last time point of interval, number refers to sampling time (1 = before radiotherapy, 20 = end of radiotherapy).

c: P_value = permutative p-value for interval, with 1500 permutations.

d: Cluster = putative clusters were grouped using Ward’s D2.

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