Clearance of Circulating Bacteria and Recovery of Microbiota Diversity Following Adoptive Cellular Therapy in Advanced Colorectal Cancer Patients

Objective: There is increasing evidence of a human blood-microbiome that may have an immune modulatory effect. Anticancer therapies including adoptive cellular therapy (ACT) may inuence this blood microbiome. The purpose of this study was to investigate impacts of biological ACT infusions on the blood microbiota. Materials and Methods: Plasma was collected from colorectal cancer patients (CRC) treated with either chemotherapy alone (oxaliplatin and capecitabine) (XELOX alone group, CT, n=19), or a mixed dendritic cell/cytokine induced killer cell product (DC-CIK) + XELOX (group, ICT, n=20). Circulating microbiota analysis was performed by amplifying and sequencing variable regions V3~V4 of bacterial 16S rRNA genes. The association of microbiota with clinical immunotherapeutic response was evaluated. Results: The plasma bacterial DNA copy number decreased and diversity of plasma microbiota signicantly increased after administration of the DC-CIK product. The plasma bacterial DNA copy number correlated with the CD3 - /CD16 + /CD56 + NK cells in circulation. The diversity index was associated with CD3 - /CD16 + /CD56 + and CD8 + /CD28 + cells infused. A more diverse blood microbiome including bidobacterium, lactobacillus and enterococcus was identied among responders to immunotherapy compared with non-responders. Conclusions: Cellular immunotherapy can affect the plasma microbiota’s diversity in a manner favorable to clinical responses. Sustainable bacteria diversity was associated with benet from immune therapy in CRC patients.

the microbiome of cancer patients. We therefore wished to study the interaction of the microbiome and immune response of CRC patients undergoing ACT with DC-CIK.
Although the gut microbiome would be attractive to study, we reasoned that because the adoptively transferred cells tra c intravenously to sites of malignancy, they would interact with the microbiome more directly in the bloodstream. Although the bloodstream is often thought to be "sterile", recent data suggests the presence of a bloodstream microbiome as well (13). The blood microbiome may also modulate clinical activity of immunotherapies (14). Therefore, we investigated whether there was an association between the bloodstream microbial composition and the e cacy of DC-CIK therapy in colorectal cancer patients.

Setting and participants
This was a retrospective cohort study based on Clinical Trial NCT01906632. The study was approved by the Ethics Committee of Beijing Shijitan Hospital (2019KYLS(51)). All patients gave written informed consent for participation. From January 1, 2014 to January 31, 2019, 39 colorectal cancer patients hospitalized in Beijing Shijitan Hospital, Capital Medical University Cancer Center, who met the following criteria were enrolled in this study: treatment with 2 cycles of standard capecitabine/oxaliplatin chemotherapy (CT group, n=19) or 2 cycles of capecitabine/oxaliplatin chemotherapy combined with 2 cycles of DC-CIK therapy (Immunochemotherapy (ICT) group, n=20). (as previously described (14).
Patients who had received prior immunotherapy or chemotherapy within 8 weeks before the rst cycle of XELOX were not included.
Clinical and laboratory data were extracted from the hospital electronic medical record and included: age; sex; colorectal cancer stage; diabetes and hyperlipidemia (which was de ned as cholesterol and/or triglycerides higher than normal level before the rst cycle of XELOX); results of CEA, CA19-9, CA-153, and CA-125 before therapy; phenotypes of lymphocytes before therapy and after therapy; results of clinical assessment after 2 cycles of XELOX or XELOX combined with 2 cycles of DC-CIK. Responses were assessed by RECIST1.1. Patients were deemed to have clinical bene t if they had a CR (complete response), PR (partial response), or SD (stable disease) and these patients were considered responders (R). They were considered non-responders (NR) if they had progressive disease (PD). Characteristics of all patients before therapy are detailed in Table 1. Study ow is presented in Figure S1).

Sample collecting and preservation
Time points of sample collection are shown in Figure S1. Fasting venous blood was collected in K2 EDTA tubes (BD Vacutainer) and centrifuged at 1500rpm for 20 minutes to isolate the plasma fraction, which was placed in DNA-free, sterile collecting tubes and stored frozen at -80°C until further analysis.
DNA extraction and microbiota analysis DNA extraction All microbial DNA isolation procedures were performed in a biological ow cabinet and all consumables used were sterile. QIAamp UCP Pathogen Mini Kit (Cat No. 50214.Qiagen Inc, Venlo, Netherlands) was used to isolate microbial DNA from plasma following the manufacturer's instructions. DNA was quanti ed with a Qubit Fluorometer by using the Qubit® dsDNA BR Assay kit (Invitrogen, USA) and the quality was checked by running aliquots on 1% agarose gels.
Quanti cation of total bacterial 16S DNA gene copy number Total bacterial 16S DNA gene copy number in plasma was quanti ed using TaqMan qPCR as described previously (15) . Extraction blanks (DNA-free water) served as environmental DNA contamination controls.

Bacterial 16S rRNA sequencing
The plasma microbial composition and diversity were assessed by real-time full-length 16S rRNA gene sequencing (BGI Genomics, Shenzhen, China). All samples were coded before they were sent to BGI so the technicians performing the assays would be blinded to the study group with which a particular sample was associated. .
(1) Library construction Variable regions V3~V4 of bacterial 16S rRNA gene were ampli ed with degenerate PCR primers, 341F (5' -ACTCCTACGGGAGGCAGCAG-3') and 806R (5' -GGACTACHVGGGTWTCTAAT-3 '). Forward and reverse primers were both tagged with Illumina pad, adapter and linker sequences. PCR enrichment was performed in 50 μ L reaction that contained fusion PCR primer, PCR master mix and 30ng template. PCR cycling conditions were: 94 ℃ for 3 minutes, 30 cycles of 94 ℃ for 30 seconds, 56 ℃ for 45 seconds, 72 ℃ for 45 seconds and nal extension for 10 minutes at 72 ℃ for 10 minutes. AmpureXP beads were used for PCR product puri cation and puri ed product was eluted in Elution buffer. Libraries were quali ed with an Agilent 2100 bioanalyzer (Agilent, USA). The validated libraries were sequenced on an Illumina MiSeq platform (BGI, Shenzhen, China) following standard pipelines of Illumina, and generating 2 × 300 bp paired-end reads.
(2) Sequencing and bioinformatics analysis Raw reads were ltered to remove adaptors, low-quality and ambiguous bases. Then paired-end reads were added to tags using Fast Length Adjustment of Short reads program (FLASH, v1.2.11) (16) to get the tags. With a cutoff value of 97%, the tags were clustered into OTUs using UPARSE software (v7 .0.1090) (17). The chimera sequences were detected by comparing with the Gold database using UCHIME (v4.2.40) (18) . Using Ribosomal Database Project (RDP) Classi er v.2.2 with a minimum con dence threshold of 0.6, the OTU representative sequences were taxonomically classi ed. Then they were trained on the Greengenes database v201305 by QIIME v1.8.0 (19). All the Tags were compared back to OTU by USEARCH_global (20) to get the OTU abundance statistics table of each sample. Alpha and beta diversity were estimated by QIIME (v1.8.0) (19) and MOTHUR (v1.31.2) (21) at the OTU level, respectively.
GraPhlan map of species composition was created using GraPhlAn. Linear discriminant analysis effect size (LEfSe) cluster analysis was conducted by LEfSe. Barplot and heatmap and different classi cation levels was plotted with R package v3.4.1 and Morpheus (https://software.broadinstitute.org/morpheus/), respectively.

Statistical analysis of patient characters
The non-normal continuous variables were expressed as median (range) and compared using Kruskal-Wallis tests. Categorical variables were compared using chi-square tests.

Statistical analysis for bacteria-related results
Bacterial 16S DNA copy number was compared between different groups using Kruskal-Wallis tests. Alpha diversity (including Chao, Shannon and Simpson) was compared between different groups using the Mann-Whitney test. Pairwise comparisons of taxonomic abundances based on the response to therapy were also conducted using the Mann-Whitney test. The spearman's correlation analysis was used for correlation analysis. The ability of signi cant bacterial variables in predicting response to ICT was assessed by computing receiver operating characteristic (ROC) curves. All statistical evaluations were carried out using SPSS software (Statistical Package for the Social Science, version 17.0, SPSS Inc.) and GraphPad Prism 5 (Version 5.01, GraphPad Software, Inc.). A value of p<0.05 was considered to be statistically signi cant.

Results
Bacterial DNA quantity was reduced following immunochemotherapy and negatively correlated with the CD3 − /CD16 + /CD56 + NK cell proportion in peripheral blood.
All the pre-and post-therapy samples were tested for bacterial 16S gene copy number. Before therapy, the bacterial DNA copies in the plasma of the CT and ICT groups were not signi cantly different (Fig. 1A).
After one and two cycles of standard chemotherapy, compared with pre-CT, bacterial DNA copies in the samples of post-CT-1 (after 1 cycle of CT) and post-CT-2 (after two cycles of CT) were not signi cantly different, respectively (Fig. 1B. There were only 18 samples in the post-CT-2 group that were positive for 16S DNA). For the patients who were treated with two cycles of standard chemotherapy and 2 cycles of DC-CIK therapy, signi cantly lower bacterial DNA copies were found in the peripheral blood samples of the group post-ICT-2 (after two cycles of CT + DC-CIK: n = 16) compared with pre-ICT (p = 0.002) and post-ICT-1 (after one cycle of CT + DC-CIK; p = 0.020. Figure 1C).
Lower amounts of plasma bacterial DNA were associated with higher proportions of CD3 − /CD16 + /CD56 + cells in peripheral blood as shown by a negative Spearman's correlation both in the CT group (r=-0.544, p = 0.0005) and the ICT group (r=-0.783, p < 0.0001). The CD3 − /CD16 + /CD56 + T cell subset of peripheral blood signi cantly decreased in post-CT-2 compared with pre-CT (p = 0.045) but it did not decrease statistically after therapy in the ICT group ( Fig. 1D-G). These data suggest that peripheral blood CD3 − /CD16 + /CD56 + cells, which are maintained by DC-CIK therapy, may reduce bacteria in the bloodstream.
Enhanced bacterial diversity associated with the number of CIK cells infused.
After 1 cycle of therapy, pre-ICT and post-ICT-1 showed signi cant differences for Shannon (p = 0.025) and Simpson (p = 0.014) at OTU level ( Fig. 2E-F). The Shannon and Simpson index of post-ICT-1 increased compared with that of pre-ICT. In contrast, there was no statistical difference for bacterial diversity between pre-CT and post-CT-1 ( Fig. 2A-C).
At the operational taxonomic unit (OTU) level, the Chao, Shannon and Simpson diversity were not signi cantly different between the CT group and ICT before therapy, respectively, but the Chao (p = 0.027), Shannon (p = 0.001) and the Simpson (p = 0.003) indices were statistically different respectively between the CT group and ICT after therapy ( Figure S4).
LEfSe cluster analysis identi ed that the relative abundance of bacteroidetes at phylum level was different between pre-and post ICT. The relative abundance of bacteroides at the genus level was increased after ICT compared with pre-ICT (Fig. 2G-H), but it was decreased after CT compared with pre-CT ( Figure S5).
Baseline highly rich and diverse plasma microbiome identi ed in responders and higher abundance of bi dobacterium, lactobacillus and enterococcus in pre-ICT-R plasma than in pre-ICT-NR.
To better understand the impact of the plasma microbiota on e cacy of immune therapies, the microbial composition of sub-group pre-R and pre-NR was analyzed.
At the OTU level, the Chao and Shannon result of pre-ICT-R and pre-ICT-NR were statistically different (Chao: p = 0.011; Shannon: p = 0.039), respectively (Fig. 3A-C) while the bacterial richness and diversity between pre-CT-R and pre-CT-NR was not different signi cantly ( Figure S7A-C).
At the genus level, relative abundance showed distinctive variation in microbial composition based on clinical assessment in the ICT group (Fig. 3D, S6), while in the CT group, there was no clear distinctive pro le based on clinical assessment ( Figure S7D). We found that the relative abundance of bi dobacterium, lactobacillus and enterococcus at the genus level was signi cantly higher in pre-ICT-R than in pre-ICT-NR (p = 0.016, 0.016 and 0.029, respectively) and relative abundance of pseudomonas (p = 0.036) was statistically lower in pre-ICT-R than in pre-ICT-NR (Fig. 3E). There was no signi cant difference in the abundant of these 4 bacterial subgroups between pre-CT-R and pre-CT-NR ( Figure S7D-F). These data suggest that the relative abundance of the different bacteria affects clinical response when ACT is used but this relative abundance does not seem to have an impact on clinical response when only chemotherapy is used.

Discussion
Immunotherapy is now the fourth pillar of cancer therapy and has been increasingly combined with other standard anti-cancer treatments (22,23). Although single agent immune checkpoint blockade may affect a single aspect of the immune response to malignancy, adoptive cellular immunotherapy has the capability for direct cytotoxicity against cancer cells, for modulation of immunosuppressive T cell populations, and for enhancing recovery of exhausted T cells in vivo (24)(25)(26). We have previously reported the signi cant anti-tumor activity of CIK therapy (3,5) alone and in conjunction with chemotherapy. A number of host and tumor factors govern the e cacy of immunotherapy and in many cases limit e cacy. The gut microbiome, for example, affects responsiveness to immune checkpoint blockade (10). Little is known about the interactions of peripheral blood bacteria with ACT that may affect ACT e cacy. In the present study, the bacterial richness, diversity and composition (at OTU level) in the peripheral blood of colorectal cancer patients was analyzed before and after initiating immunochemotherapy (ICT) to explore the relationship between the blood microbiota and the clinical e cacy of ICT. Bacteria 16S DNA copy number in plasma was also measured pre-and post-therapy in order to understand the effect of ICT on permeability of the gut barrier and bacteremia.
We found that the diversity of plasma bacteria in CRC patients was affected by ICT such that greater diversity of plasma microbiota was found after ICT therapy compared with that before therapy. Greater diversity of microbiota is associated with improved immune responses (27). Less diversity in microbiota can lead to impaired local and systemic immune responses with breakdown of mucosal barriers and incitement of a profound in ammatory state both locally and systemically (28). Further, studies indicate that the anti-tumor immune response may be impaired by disrupting the gut microbiota (29). Thus, the diversity of the microbiome may be positively affected by ICT therapy and may in turn, enhance the e cacy of local and systemic anti-tumor immunity.
The DC-CIK product contains multiple cell types. We observed that the Shannon index in post-ICT was directly related to the number of CIK cells infused and the CD3 − /CD16 + /CD56 + and CD8 + /CD28 + proportion within the infused CIK. CD3 − /CD16 + /CD56 + cells play a critical role in combating transformed and malignant cells (30,31). Increased CD8 + /CD28 + T cells predict better early response to tumor therapy (32) and are associated with progression-free survival (PFS) and overall survival (OS) time (33). Our results suggest that modi cations to increase the number of CIK cells infused and the proportion of these two T cell subtype within the infused cellular product might have a positive effect on the diversity of microbiota.
We also found that the difference of bacterial composition before and after ICT therapy was mainly manifest within the Bacteroides subgroup. The relative abundance of bacteroides in plasma of CRC patients increased after ICT compared with pre-ICT while it decreased after CT. Studies have provided evidence that bacteroides are bene cial to human health and play key roles in interacting with host immunity (34). The relative abundance of bacteroides may be increased by ICT and this might contribute to a greater anti-tumor response.
Studies have indicated that gut barrier would be broken by chemotherapy (35)(36)(37). In this study, we found plasma bacteria copy numbers increased slightly after 1 cycle of CT or ICT, but after 2 cycles of therapy, plasma bacterial copy numbers were statistically decreased in the ICT group while they were not decreased signi cantly in the CT group.
Peripheral blood nature killer (NK) cells can localize to the gut and are essential for gut mucosal epithelial cell survival and barrier remodeling (38). We analyzed immune cell subsets in the peripheral blood before and after ICT and found that, after 2 cycles of therapy, the proportion of CD3 − /CD16 + /CD56 + NK cells signi cantly decreased in the CT group while it did not decrease statistically in the ICT group. Correlation analysis indicated that after therapy, the CD3 − /CD16 + /CD56 + cell subset in peripheral blood was negatively correlated with bacteria copy number, both in the CT and the ICT groups. These data suggest that the CD3 − /CD16 + /CD56 + cells might be increase by ICT and they may have a role in repairing the gut barrier.
It has been con rmed that gut microbiota are associated with outcome following ICI (7,10,39,40). Greater richness and diversity of gut microbiota are found in ICI responders. Pre-clinical mouse models suggest that gut microbiota may modulate tumor response to adoptive T cell therapy (11,12). In this study, we found plasma microbiota before therapy in ICT responders was signi cantly different from that in ICT non-responders. A highly rich and diverse plasma microbiota was found in responders to ICT before therapy. In the CT group, the richness and diversity of plasma microbiota was not signi cantly different between responders and non-responders before therapy. We infer from these results that richness and diversity of microbiota is related to outcome of DC-CIK therapy in CRC patients.
When we analyzed the composition of plasma microbiota according to response to ICT, we found that before therapy, the relative abundance of bi dobacterium, lactobacillus and enterococcus in responders was signi cantly higher than that in non-responders, respectively, but the relative abundance of these speci c bacteria was not statistically different between responders and non-responders in the CT group. It has been demonstrated that speci c probiotic strains, including lactobacillus rhamnosus, bi dobacterium longum and enterococcus faecium may in uence the patient's response to ICI (7,10,39,40). Indeed the current study demonstrated a relationship between probiotics and outcome of DC-CIK therapy in CRC patients.
One limitation of this study is that there is no group of DC-CIK therapy alone. After retrospectively analyzing CRC patients hospitalized during the study period, we found only 4 patients treated with DC-CIK alone who were without any anti-tumor therapy within 8 weeks before DC-CIK therapy. Of these, only two had pre-therapy samples satisfactory for microbiota analysis. Therefore, the group of DC-CIK alone was abandoned in subsequent analyses because of the limited number of samples. Nevertheless, we believe that a comparison of patients treated with chemotherapy alone and immunochemotherapy would still provide insight on the impact of the immunotherapy alone.
Mapping and modulating the microbiota to predict and improve therapeutic outcomes are areas of intense study in cancer immunotherapy (43,44). In this study, we estimated receiver operating characteristic curves (ROC) for Bi dobacterium (p = 0.016), Lactobacillus (p = 0.013) and Enterococcus (p = 0.013) in predicting response to ICT. Follow-up clinical trials are needed to con rm the predictive effect of probiotics on ICT outcomes. Further, the in uence on outcomes of DC-CIK by probiotics need to be veri ed by animal models and large clinical trials.

Conclusion
Recent clinical studies revealed the presence of a bloodstream microbiome and this may modulate clinical activity of immunotherapies (14,45). This study suggested that cellular immunotherapy might affect the plasma microbiota's diversity in a manner favorable to clinical responses and sustainable plasma bacteria diversity was associated with bene t from immune therapy in CRC patients. We expect this result to facilitate adoptive cellular immunotherapy for cancer.

Declarations
Ethics approval and consent to participate This was a retrospective cohort study based on Clinical Trial NCT01906632. The study was approved by the Ethics Committee of Beijing Shijitan Hospital (2019KYLS(51)). All patients gave written informed consent for participation. Microbial analysis of pre-ICT samples according to clinical assessment. Chao (A), Shannon (B) and Simpson (C) at operational taxonomic unit (OTU) level according to clinical assessment of group pre-ICT were displayed. Median for relative abundance of OTUs at genus level (D) is shown in R-and NR subgroups by bar plots. Median for relative abundance of Bi dobacterium, Lactobacillus, enterococcus, pseudomonas at genus level was displayed by R and NR sub-groups (E). Statistical comparisons between sub-groups were made using Kruskal-Wallis test or Mann-Whitney test with *p<0.05 and **p<0.01. ICT, Immunotherapy combined with chemotherapy; R, response; NR, no-response; pre-, before therapy.

Supplementary Files
This is a list of supplementary les associated with this preprint. Click to download.