Reduced microbial diversity in adult survivors of childhood acute lymphoblastic leukemia and microbial associations with increased immune activation
- Ling Ling Chua†1,
- Reena Rajasuriar†2, 3, 4Email author,
- Mohamad Shafiq Azanan1, 5,
- Noor Kamila Abdullah3,
- Mei San Tang6,
- Soo Ching Lee3, 7,
- Yin Ling Woo1, 3, 8,
- Yvonne Ai Lian Lim3, 7,
- Hany Ariffin1, 5Email author and
- P’ng Loke6Email author
© The Author(s). 2017
Received: 22 September 2016
Accepted: 2 March 2017
Published: 20 March 2017
Adult survivors of childhood cancers such as acute lymphoblastic leukemia (ALL) have health problems that persist or develop years after cessation of therapy. These late effects include chronic inflammation-related comorbidities such as obesity and type 2 diabetes, but the underlying cause is poorly understood.
We compared the anal microbiota composition of adult survivors of childhood ALL (N = 73) with healthy control subjects (N = 61). We identified an altered community with reduced microbial diversity in cancer survivors, who also exhibit signs of immune dysregulation including increased T cell activation and chronic inflammation. The bacterial community among cancer survivors was enriched for Actinobacteria (e.g. genus Corynebacterium) and depleted of Faecalibacterium, correlating with plasma concentrations of IL-6 and CRP and HLA-DR+CD4+ and HLA-DR+CD8+ T cells, which are established markers of inflammation and immune activation.
We demonstrated a relationship between microbial dysbiosis and immune dysregulation in adult ALL survivors. These observations suggest that interventions that could restore microbial diversity may ameliorate chronic inflammation and, consequently, development of late effects of childhood cancer survivors.
KeywordsAdult survivors of childhood cancer Acute lymphoblastic leukemia Microbiome Alpha diversity Microbiota dysbiosis Immune activation Inflammation
Acute lymphoblastic leukemia (ALL) is the most common childhood cancer, which now has 5-year survival rates exceeding 80% as a result of improved therapy . Adult survivors of childhood cancer have a higher risk of chronic comorbidities [2–4] and early mortality compared to their age-matched controls . These comorbidities include diabetes mellitus [3, 6], metabolic syndrome, cardiovascular disease , renal insufficiency  and frailty . The development of these conditions could be attributed to radiotherapy [9, 10] and chemotherapy , though the exact mechanisms remain unclear.
Chemotherapeutic agents have a broad impact on the gastrointestinal (GI) system including structural damage to the gut, dysregulation of the gut-associated lymphoid tissue  and alteration of the gut microbiota [13, 14]. Adult survivors of childhood cancer who received abdominal radiation, as well as exposure to anthracyclines and alkylating agents, report a higher incidence of GI tract complications . In childhood myeloid leukemia, chemotherapy has been associated with a reduction in anaerobic bacteria and increased enterococci, persisting up to at least 6 weeks post treatment . Multiple courses of broad-spectrum antibiotics for febrile neutropenia and antimicrobial prophylaxis [16, 17] may also have long-term effects on the gut microbiota.
The relationship between the gut microbiota and human health is now well-documented [18–20]. The imbalance or dysbiosis of microbial communities is known to be associated with the development of many different diseases, including diabetes mellitus, metabolic syndrome , atherosclerosis [22, 23] and frailty [24–26], all of which are also prevalent among childhood cancer survivors [3, 4]. It is difficult to determine whether dysbiosis is the cause or result of human disease , and causality has only been demonstrated in mouse models. In murine studies, dysbiosis can trigger systemic immune dysregulation through its local effects on T cells in the gut, as well as in the circulation [28–30]. The mechanisms by which microbes impact immune responses during homeostasis and disease is an area of intense investigation. Alterations in bacterial metabolites [31, 32] and increased intestinal translocation [33, 34] are mechanisms that have been proposed to link microbial dysbiosis with immune dysregulation. Specific alterations to the microbial communities are associated with markers of immune activation and chronic inflammation. Hence, monitoring gut microbial communities and inflammation status of ALL survivors may be a potential surveillance strategy for targeting health counseling. In addition, data on microbial communities may also be crucial for the design and testing of therapeutic interventions to restore microbial diversity thereby potentially reducing systemic inflammation.
We recently observed that a cohort of adult survivors of childhood leukemia in Malaysia exhibited signs of increased immune activation and chronic inflammation . We hypothesized that this inflammatory phenotype could be associated with persistent changes in microbial communities. Using subjects from this cohort, we investigated whether adult survivors of childhood ALL have reduced microbial diversity compared to controls without history of cancers.
Participants for this study were selected from a larger study originally to explore the presence of a phenotype of aging on young adult leukemia survivors . Adult survivors of childhood ALL who attended a late-effect surveillance clinic at the University of Malaya Medical Centre (UMMC), Malaysia, for their annual follow-up were recruited. Inclusion criteria were (1) individuals aged between 18 and 35 years, (2) at least 5 years since completion of leukemia treatment, (3) no history of bone marrow transplant and (4) no acute illness and not pregnant at the point of recruitment. Healthy controls who also fulfilled the inclusion criteria but without history of cancers were recruited among healthcare workers, siblings and volunteers. The study protocol was approved by the institutional ethical committees (reference number: MEC 2014/1093.65), and signed informed consent was obtained from all the participants for sample collection and data analysis.
DNA extraction and 16S ribosomal RNA genes sequencing
Fecal microbiome sample was collected using sterile anal swabs and stored at −80 °C prior to processing. Anal swabs were more practical for collection in the clinic setting as opposed to obtaining a fecal sample. We assumed the microbiota obtained from anal swabs would resemble that of fecal samples as has been demonstrated in previous studies [36, 37].
DNA from anal swabs were extracted using the NucleoSpin® Tissue kit according to the manufacturer’s protocol (Macherey-Nagel, Germany). DNA was eluted with 50 μl of Buffer BE and stored at −20 °C. Subsequently, DNA library preparation and sequencing were performed in New York University Genomic Centre. Briefly, DNA samples were amplified for the variable 4 region (V4) of 16S rRNA gene using the method and primer constructs as previously described . The forward primer construct contained the 5’ Illumina adapter, the forward primer pad, a two-base linker (‘GT’) and the 515F primer (5′-AAT GAT ACG GCG ACC ACC GAG ATC TAC ACT ATG GTA ATT GTG TGC CAG CMG CCG CGG TAA-3′). The reverse primer construct contained the 3’ Illumina adapter, a unique 12-base error-correcting Golay barcode, the reverse primer pad, a two-base linker sequence (‘CC’) and the 806R primer (5′-CAA GCA GAA GAC GGC ATA CGA GAT NNN NNN NNN NNN AGT CAG TCA GCC GGA CTA CHV GGG TWT CTA AT-3′). 16S rRNA V4-targeted polymerase chain reaction (PCR) was carried out in triplicate with thermal cycling of 94 °C for 3 min, followed by 35 cycles of 94 °C for 45 s, 50 °C for 60 s, 72 °C for 90 s, and lastly, 10 min at 72 °C to confirm full amplification. Three replicate amplicons were measured using TapeStation for DNA concentrations and pooled at equimolar ratio. Sequencing on the amplicons was conducted on the Illumina MiSeq system (Illumina, San Diego CA, USA).
16S rRNA gene sequences analysis
The paired-end sequencing reads were joined using the fastq-join function from EA-utils with default parameters followed by demultiplexing using the 12-base Golay barcodes. Demultiplexing was done with the default quality-filtering parameters: minimum quality score of 25, minimum/maximum sequence length of 200/1000, no ambiguous bases and no mismatches in the primer sequence . Sequences were then analyzed with QIIME version 1.8.0 . Using pick_open_reference_otus.py workflow, operational taxonomic unit (OTU) picking was performed first, with a closed reference method by aligning the sequences to reference in Greengenes 13.8 database. Unaligned sequences were clustered by de novo method using the UCLUST consensus taxonomy assigner, at the minimum of 97% sequence similarity.
After picking for OTUs, samples with low reads (<1000) were removed using filter_samples_from_otu_table.py script. We had on average 7313 reads per sample for 134 samples (a total of 980,062 reads, ranging from 1127 to 28,299 reads). After that, OTUs were grouped at different levels of taxonomy classification (phylum, class, order, family and genus) and normalized at each level to get the relative abundance of each taxonomy using summarize_taxa_through_plots.py script.
Microbial diversity analysis
OTU-based alpha diversity was estimated by calculating three diversity matrices, phylogenetic distance, observed OTUs and Chao1 using QIIME workflow (alpha_rarefaction.py). First, rarefaction analysis was iterated over 10 depths up to the 1000 reads depth (to match the minimum sampling depth) with 10 times subsampling at each depth. Rarefaction curves were generated for each matrix. Non-parametric test was used to compare the statistical significance of the rarefraction curves between the survivor and control groups implemented in QIIME function (compare_alpha_diversity.py) as the data distribution was not normal. Chao1 index, phylogenetic distance and number of observed OTUs at the rarefaction of 1000 reads were compared.
Inferred metagenomics and functions using PICRUSt
Closed-reference OTU table was generated using QIIME script pick_closed_reference_otus.py for community metagenome functions inference. First, the original 16S rRNA sequencing data was quality-filtered and demultiplexed, followed by taxonomy assignment of the representative sequences to the reference sequences from Greengenes 13.8 database at 97% similarity. The resulting closed-reference OTU table was passing through the PICRUSt workflow on the online Galaxy interface from the Huttenhower Lab (https://huttenhower.sph.harvard.edu/galaxy/). Each OTU was first normalized for 16S rRNA copy number, followed by metagenome prediction. Functional annotation and pathway inference of the metagenome was done using Kyoto Encyclopedia of Genes and Genomes (KEGG) database at pathways of hierarchy levels 2 and 3 . Linear discriminant analysis (LDA) Effect Size (LEfSe) was used to identify the differentially abundant KEGG pathways in the survivor and control groups.
LDA Effect Size analysis
To identify the bacterial taxa and the predicted KEGG functional pathways that are differentially abundant in the survivors’ and controls’ microbiome, LEfSe analysis was performed using the online Galaxy interface (http://huttenhower.sph.harvard.edu/galaxy/). LEfSe uses the non-parametric factorial Kruskal-Wallis rank-sum test to detect features that are significantly different in abundance between the survivor and control groups. The effect sizes of the identified features were then estimated with linear discriminant analysis model . False discovery rates of the resulting p values were corrected using the p.adjust() function with the Benjamini-Hochberg algorithm implemented in R, and reported as q values (also summarized in Additional file 1: Table S1).
Group associated with OTU selection using sparse partial linear square discriminant analysis (sPLS-DA)
sPLS-DA  was conducted to select the most discriminative OTUs associated with survivor or control groups. OTU data was added with a single pseudocount prior to compositionally normalization using total sum scaling (TSS) and centered log-ratio (CLR) transformation. sPLS-DA analysis was then performed using the splsda() function to select 10 features (OTUs) each on the first and second principal components respectively to best discriminate survivor group or control group. Individual samples were presented on a PCA plot based on the selected OTUs and are distinguished by group with color and 95% confidence eclipses using the plotIndiv() function. The contribution of the OTUs that were associated to each group on the first and second components are presented with contribution plots using the plotLoadings() function. The abundance of each selected OTUs are presented on clustering heatmaps using the cim() function. The taxa hierarchy of each OTU is summarized in Additional file 1: Table S2. All the analyses were conducted using mixOmics package version 6.1 implemented in R software .
Cytokine and inflammatory markers
Peripheral blood was collected from participants in EDTA vacutainer and processed within 4 h of sampling as previously described . Plasma IL-6 was measured by enzyme-linked immunosorbent assay (ELISA) using the Quantikine HS IL-6 kit (R&D Systems, Minneapolis, USA). All assays were conducted in duplicate and according to manufacturer’s instructions. Plasma CRP was measured by the hospital central diagnostic laboratory.
T cells activation markers
T cells activation was assessed with the expression of HLA-DR using flow cytometry and reported as %HLA-DR+ CD4 or CD8 T cells. Immunophenotyping was performed on peripheral whole blood as previously described . Briefly, 100 μl of blood was stained for surface markers CD3-PerCp-Cy5.5 (clone SK7), CD4-PE-Cy7 (clone SK3), CD8-APC-H7 (clone SK1) and HLA-DR-BV421 (clone G46-6) using antibody cocktails for 15 min in the dark. Red blood cells were then lysed using a 1:10 dilution of BD lysis buffer (all antibodies and reagents are from BD Pharmigen, San Jose, CA) for 10 min, and the cells were washed once with phosphate-buffered saline (PBS). Samples were acquired on a BD FACSCanto II (BD Biosciences, San Jose, CA) for 100,000 events and analyzed using the FACS Diva software (version 6.0). The gating strategy used is shown in Additional file 1: Figure S1.
Statistical analysis was performed using SPSS software (version 21, IBM) and R software (version 3.3.0). Data was checked with Shapiro-Wilk test for distribution prior to significance tests. Unpaired two-tailed t tests (Student’s t test for normally distributed data and Mann-Whitney test for skewed data) were used to evaluate differences between two groups. Correlation of each taxa feature identified from LEfSe analysis with immunological markers were estimated with Spearman rank partial correlation test adjusted for age, gender and BMI using the pcor.test() function in the ppcor package implemented in R .Correlation analysis was performed on the combined data (survivors and controls), survivor only group and control only group, to show if the correlation for all of the other taxa of interest is observed when data from both or either survivors or controls are used separately. Correlation coefficient (rho) and p value are presented in Additional file 1: Table S3.
Adult survivors of childhood ALL have reduced anal microbial diversity
Participant clinical and demographic characteristics
Number in each group
N = 73
N = 61
Sex, n (%)
Age at recruitment, years (IQR)
Age at diagnosis, years (IQR)
Diagnosis, n (%)
Duration of cancer therapy, years (IQR)
Cancer therapy history
Anthracyclines, n (%)
Alkylating agents, n (%)
Anthracylines and alkylating agents, n (%)
Radiotherapy received, n (%)
Cancer relapse, n (%)
Second neoplasms, n (%)
Diabetes mellitus or hypertension, n (%)
BMI, kg/m2 (IQR)
Mode of birth (caesarean/vaginal), n/n
aAntibiotic intake, n (%)
A different microbial signature in adult survivors of childhood ALL
T cell activation and systemic inflammation markers are associated with dysregulated microbial taxa
Functional differences in bacterial communities among adult survivors of childhood ALL
Our results provide the first evidence that asymptomatic adult survivors of childhood ALL have indications of microbial dysbiosis that share characteristics with alterations previously associated with the risk of immunological and metabolic diseases, including obesity. Additionally, there are specific relationships between the altered bacterial taxa and biomarkers of T cell activation and systemic inflammation, raising the hypothesis that differences in the bacterial communities may be associated with dysregulated immune activation. There is increasing evidence that environmentally induced alterations to the microbiota can be irreversible [57, 58]. Here, we also found that dysbiosis can be detected and probably persist for years after successful completion of cancer therapy.
Of particular significance is the reduction in microbial diversity among the adult survivors of childhood ALL compared to controls. Such clear differences in alpha diversity between groups (with a relatively small sample size) are rarely observed between healthy, or even diseased individuals (e.g. ), unless they are suffering from intestinal diseases such as inflammatory bowel disease [60, 61]. Microbial diversity increases and stabilizes over the first 3 years of life ; hence, dysbiosis during childhood is more likely to persist. It is now well documented that birth by caesarean section , obesity and antibiotic use are correlated with reduced microbial diversity, and reduced diversity during early life can be associated with inflammatory diseases such as allergic diseases , and children at high risk of type 1 diabetes have decreased microbial diversity over time .
Although there are significant differences in age and BMI (Table 1 and Additional file 1: Figure S4) between the survivors and controls, we corrected for potential confounding effects of age, BMI and gender with partial correlation analysis (as described in methodology section), and the relationship between bacterial taxa and immune activation markers remain significant. Colonic transit time is another potential confounder  that we did not measure in our study participants. We also did not assess incidences of GI complications, which has been reported to be greater in the adult survivors of childhood cancer  and could be associated with microbiome diversity and composition. Prolonged treatment with antibiotics during cancer therapy may contribute to microbial dysbiosis. All the childhood ALL survivors in this cohort received cotrimoxazole throughout the 2-year course of cancer therapy as prophylaxis against Pneumocystis jerovici; however, gut decontamination with quinolones was not practiced. Future studies with longitudinal samples from childhood ALL survivors are crucial to determine if this reduction in microbial diversity is a result of radiation and chemotherapy and persists into adulthood. Data from a few ALL survivors (N = 19) that are of a younger age (9–17 years old) indicates this reduced microbial diversity is present prior to adulthood (Additional file 1: Figure S5).
We also noted with interest that relative abundance values of the genus Faecalibacterium were reduced among the adult survivors of childhood ALL and negatively associated with the inflammatory biomarkers IL-6 and CRP. F. prausnitzii is one of the most abundant components of the gut microbiota . For inflammatory bowel disease patients in particular, there are multiple studies showing differences in abundance for Faecalibacterium relative to healthy controls [50, 51, 55]. Additionally, a reduced abundance of F. pausnitzii has also been associated with the frailty index in a recent large population-based study , a phenotype previously described to be prevalent among young adult survivors of childhood cancer . As a producer of short-chain fatty acids (SCFA) such as butyrate , it may be linked to regulation of intestinal inflammation through increasing regulatory T cells . Future studies, including longitudinal studies, should monitor the abundance of F. prausnitzii in the feces of young adult survivors of childhood ALL as an indicator of intestinal health. A reduction in F. prausnitzii abundance was also observed in children with acute myeloid leukemia during treatment and 6 weeks post treatment , indicating that the dysbiosis we find here may have persisted years after cessation of chemotherapy.
Because of the limited quantities of microbial DNA available, it was not possible to perform complete metagenomic analyses of the participant samples. We instead used an inferred metagenomic approach to generate a preliminary overview of differences in microbiome functionality between the survivors of childhood ALL and controls. We noticed that tryptophan metabolism is higher among the survivors. Increase in tryptophan metabolism has also been previously reported in HIV-infected patients, and the higher level of tryptophan catabolism correlates with markers of chronic immune activation and inflammation . While we have not measured production of kynurenine in this study, circulating levels of the inflammatory cytokine IL-6 have been shown to correlate with kynurenine production in HIV patients. The interferon-inducible enzyme indoleamine 2,3-dioxygenase 1 (IDO1) is associated with intestinal lymphoid tissue disruption, depletion of TH17 cells and chronic inflammation . Consistent with the HIV study, we find here that bacterial taxa enriched among the survivors of childhood ALL may encode for enzymes that performs the same catabolic function as human IDO1, raising the hypothesis that the survivors may bear similarities in the mechanism of intestinal disruption that was previously described in HIV-infected patients [69, 70]. While interesting, these observations are based on inferred function with 16S sequences only and hence highly speculative. The microbially derived tryptophan catabolites and their effects on the host are considerably diverse [71, 72]. Future studies quantifying trytophan catabolites (such as kynurenine) will help determine the relationship between microbial changes in the childhood cancer survivors and tryptophan metabolism.
While the mechanisms underlying our observed associations here are still poorly understood, the basic observation of dysbiosis and reduced microbial diversity in adult survivors of childhood ALL raises several crucial hypotheses. The composition of certain members of the microbial communities are associated with biomarkers of immune activation, namely, the higher levels of T cell activation and circulating levels of IL-6 and CRP. Hence, community dysbiosis may be driving low-grade inflammation, although, to establish causation, additional functional studies (e.g. transfer of microbiota samples into germ-free animals) would be required. More importantly, our findings here raise the possibility that therapeutic interventions that could restore microbial diversity and reverse dysbiosis to the ALL survivors may help to mitigate long-term effects. While we still do not know how to restore a healthy microbial community in people, multiple approaches (e.g. probiotics ), fecal transplantation  and helminth colonization [75, 76] are being investigated, which may be applicable to ALL survivors in the future. It is also important to note that in this study, we have not demonstrated a causal relationship between the altered microbial taxa and chronic inflammation. Future work to establish causality would involve isolation of specific taxa and transferring of anaerobically cultured clones into germ-free animals and demonstrating an increased inflammatory response in the recipient animals.
Acute lymphoblastic leukemia
False discovery rate
Kyoto Encyclopedia of Genes and Genomes
Linear Discriminant Analysis
Linear discriminant analysis (LDA) Effect Size
Operational taxonomic unit
Sparse partial least squares discriminate analysis
Total sum scaling
This work was supported by the following grants: UM.C/HIR/MOHE/MED/12 (HA and LLC), UM.C/625/HIR/MOE/MED/23 (YL and PL), Merdeka Award (LLC) and UMRG RP029-14HTM (RR and WYL). The funders had no role in the study design, data collection, analysis and interpretation, decision to publish or preparation of the manuscript.
Availability of data and materials
16S rRNA sequence files and corresponding mapping files for all samples used in this study have been deposited in NCBI BioSample repository (Accession Number: SRR4447191).
LLC recruited the patients, performed the experiments, analyzed the data and wrote the manuscript; RR conceptualized and designed the study and performed the experiment; MSA recruited the patients, performed the experiments and analyzed the data; NKA performed the experiments; MST and SCL analyzed the data; YLW designed the cohort and conceptualized the study; YALL obtained the funds for microbiome analysis; HA designed the cohort study, wrote the IRB protocol, enrolled the patients and obtained the funds for the cohort study; PL obtained the funds for the microbiome analysis, analyzed the data and wrote the manuscript. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
The institutional ethical committees of the University of Malaya Medical Centre approved the study protocol (Reference No: MEC 2014/1093.65). Signed informed consent was obtained from all the participants for sample collection and data analysis according to the Declaration of Helsinki.
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