Integrated multiomics reveals gut microbiota-protein-metabolite alterations that regulate adverse events and responses to radio-chemo-immunotherapy in microsatellite stable rectal cancer: a prospective longitudinal study

Integrated multiomics reveals gut microbiota-protein-metabolite alterations that regulate adverse events and responses to radio-chemo-immunotherapy in microsatellite stable rectal cancer: a prospective longitudinal study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrated multiomics reveals gut microbiota-protein-metabolite alterations that regulate adverse events and responses to radio-chemo-immunotherapy in microsatellite stable rectal cancer: a prospective longitudinal study Yang Lv, ZheHui Zhu, LeChi Ye, Peng Zheng, HongYu Zhang, Qi Lin, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6133863/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The potential role of fecal samples from patients with microsatellite stable (MSS) rectal cancer (RC) receiving immunotherapy was lack for investigation. By leveraging multiomics approaches, including metagenomics, metabolomics, and metaproteomics, we aimed to identify signatures that could predict treatment efficacy and adverse events and elucidate the molecular changes associated with different responses. Methods: Fecal samples were collected from patients before and after chemotherapy and immunotherapy. Metagenomic sequencing was performed to characterize microbial composition. Metaproteomic analysis was conducted to assess the protein expression profiles, and metabolomic profiling was utilized to identify metabolic changes. Data integration and differential analysis were performed. A multiomics model was constructed using machine learning algorithms to predict treatment outcomes on the basis of these signatures. Results: Our results revealed significant alterations in the fecal microbiome, proteome, and metabolome of patients after neoadjuvant immunotherapy. Differential analysis identified a set of biomarkers, including 6 bacteria, 4 meta-proteins, and 4 metabolites, that were predictive of treatment response. The multiomics model demonstrated high accuracy in predicting treatment efficacy, with a significant correlation between the model's predictions and actual clinical outcomes. Conclusion: This study demonstrated the feasibility of using fecal multi-omics data to predict the efficacy of neoadjuvant immunotherapy in RC patients. The identified biomarkers and the multiomics model provide a novel approach for personalized medicine, potentially improving treatment strategies and patient outcomes. Further validation in larger cohorts is warranted to refine the predictive model and explore its clinical applicability. Rectal cancer microsatellite stable neoadjuvant chemoradiotherapy PDL1 multiomics analysis biomarker personalized medicine Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Colorectal cancer (CRC) is a common disease worldwide[ 1 ] and is the third leading cause of cancer-related mortality in China[ 2 ], and rectal cancer accounts for one-third of these cases[ 3 ]. For rectal cancer (RC), especially locally advanced rectal cancer (LARC), the ideal treatment plan is a multifaceted process that requires the consideration of the complex anatomical structures involved, local recurrence, and distant metastasis[ 4 ]. Since 2004, neoadjuvant chemoradiotherapy (nCRT) has demonstrated significant superiority regarding survival outcomes in LARC patients[ 5 ]. With the initiation of the era of immune checkpoint inhibitors (ICIs), a robust association has emerged between the advantageous effects of programmed cell death protein 1 (PD-L1/PD1) blockade and radiotherapy in solid tumors, including CRC[ 6 ]. Pelvic radiotherapy has prominent immunostimulatory effects, including the upregulation of neoantigen-encoding genes[ 7 ], the induction of immunogenic cell death (ICD) coupled with the release of damage-associated molecular patterns (DAMPs) and immunostimulatory cytokines[ 8 ], and the increase in neoantigen presentation on MHC class I molecules, which enhances cancer cell recognition by cytotoxic T lymphocytes (CTLs)[ 9 ]. Currently, several clinical trials focusing on nCRT and PD1 (or PDL1) in patients with LARC are underway, and numerous investigations have demonstrated that these combinations can elicit synergistic antitumor effects[ 10 , 11 ], with a reported pathological complete response rate of nearly 40%[ 12 , 13 ]. Good responses to nCRT and ICIs improve local control, and these treatments can even be used as alternatives to clinical strategies such as radical resection or “watch and wait”(WW) strategies [ 14 ]. However, responses to nCRT vary among patients. In this context, a reliable tool that can predict the therapeutic response to nCRT and ICIs is critical but is not yet available. Intriguingly, many clinical and preclinical studies have demonstrated that the gut microbiome actively participates in various biochemical and pathophysiologic reactions in cancers, especially those related to immunomodulation[ 15 , 16 ]. To this end, we initiated this prospective, longitudinal trial in 85 patients with MSS type RC treated with nCRT and ICIs. After evaluating the feasibility of the gut microbiome in predicting the response to nCRT, we established a random forest classifier for its clinical application. In addition, our research shed light on how the gut microbiome affects responses to radiotherapy, which could be clinically transformative. Materials and Methods Study Subjects and Sample Collection This prospective study was performed at Zhongshan Hospital of Fudan University (Shanghai, P.R. China), in accordance with the Declaration of Helsinki. Ethical approval was obtained from the Institutional Review Board of Zhongshan Hospital of Fudan University (Shanghai, P.R. China), and written informed consent was provided by all subjects before sampling. Treatment regimen A total of 85 patients were enrolled. The patients entered the screening period after being fully informed and signed the informed consent. The enrolled patients will receive concurrent chemotherapy and radiotherapy. Radiotherapy will endure a total of 25 days and chemotherapy with capecitabine plus Oxaliplatin (CAPOX regimen). 2 weeks after the last radiotherapy, patients will start to receive PDL1 combined with CAPOX for 2 cycles. After completing the treatment 2 weeks (± 7 days), the distance from the end of the last radiotherapy should not exceed 12 weeks. Then Total mesorectal excision (TME) is the recommended surgical principle, and the specific operation method can be selected by the surgeon according to the clinical routine and the situation of the patients. Response evaluation The response evaluation criteria were based on pathological examination. Clinical response evaluation was performed according to MRI images. Further independent central reviews were reported and issued by two professional radiologists using standardized radiological reports before and after neoadjuvant therapy. After nCRT plus PDL1 inhibitor, resection of primary lesion was performed by the same surgeon team. Additionally, these surgeons were also required with experience of at least 30 surgical procedures. The primary endpoint was pCR rate, namely pathological complete response rate, defined as the proportion of patients with pCR (ypT0N0). The pathological tumor regression was assessed according to the standards in the 9th Edition of the American Joint Committee on Cancer Guidelines (AJCC)[ 17 ]. Tumor regression grade (TRG) 0, 1, 2, and 3 indicated no residual tumor cells, single or small groups of cells, residual cancer with a desmoplastic response, and minimal evidence of tumor response, correspondingly[ 18 ]. The resected specimen was collected and evaluated by surgeons and pathologists together to identify the tumor site and to confirm pCR. All lymph nodes were examined, and a minimum of 12 lymph nodes were acquired for adequate assessment of the N stage. Data extraction and Statistical analysis For metagenomics and proteomic data, based on the taxonomic and functional profiles of non-redundant genes, LEfSe (Linear discriminant analysis effect size) was performed to detect differentially abundant taxa and functions across groups using the default parameters. Beta diversity analysis was performed to investigate the compositional and functional variation of microbial communities across samples using Bray-Curtis distance metrics and visualized via principal coordinate analysis (PCoA), nonmetric multidimensional scaling (NMDS) and unweighted pair-group method with arithmetic means (UPGMA) hierarchical clustering (Ramette 2007). For metabolomics data, Our proprietary software can perform a collection of data processing, interpretation, and visualization. For many metabolomics studies, two types of statistical analysis are extensively performed: 1) multivariate statistical analyses such as principal component analysis (PCA), partial least square discriminant analysis (PLS-DA), orthogonal partial least square discriminant analysis (OPLS-DA), random forest, support vector machine learning and so on; 2) univariate statistical analyses including student t-test, Mann-Whitney-Wilcoxon (U-test), ANOVA, correlation analysis, etc. Statistical algorithms are adapted from the widely used statistical analysis software packages in R studio ( http://cran.r-project.org/ ). The optimal choice of statistical methods is often driven by the data and the project goals. Results Clinical Information of the Participants A total of 406 fecal samples were collected for analysis in this study. The clinical information of the participants in the RC metaproteomic, metagenomic, and metabolomic studies is summarized in Table 1 . Nineteen of 50 (38.0%) patients were pathologically Shown to have a pathological complete response (pCR). A total of 46% of the patients were over 60.0 years of age (range, 17–90 years), and most patients were men (36 patients, 72.0%). The median distance to the anal verge was 5.4 ± 2.3 cm in the NonpCR (Nonresponse) cohort and 5.2 ± 1.8 cm in the pCR (Response) cohort ( P = 0.701 ). According to the AJCC TNM stage, this study included 22.0% N0 patients, 70.0% N1 patients, 8.0% N2 patients and 6.0% stage IV patients. The proportion of RAS-mutant tumors was essentially similar to that of the wild-type tumors (48.0% vs. 52.0%). No statistically significant differences in age, sex, or body mass index were observed among the different response cohorts with respect to the integrated omics cohort. A control cohort of 35 patients receiving nCRT was also included in this study, as detailed in Supplementary Table 1 . The specific study design is shown in Fig. 1 A. Table 1 Baseline characteristics of responder and non-responder patients Characteristics Non-pCR (n = 31) pCR (n = 19) P-value Gender 0.659 Male 23 (74.2%) 13 (68.4%) Female 8 (25.8%) 6 (31.6%) Age 0.879 ≥ 60 14 (45.2%) 9 (47.4%) <60 17 (54.8%) 10 (52.6%) cT stage 0.429 T3 30 (96.8%) 19 (100%) T4 1 (3.2%) 0 (0%) cN stage 0.836 N0 7 (22.6%) 4 (21.1%) N1 21 (67.7%) 14 (73.7%) N2 3 (9.7%) 1 (5.3%) RAS/RAF status 0.608 Mutant 14 (45.2%) 10 (52.6%) Wide type 17 (54.8%) 9 (47.4%) TRG score 0.000 0 0 (0%) 19 (100%) 1 17 (54.8%) 0 (0%) 2 10 (32.3%) 0 (0%) 3 4 (12.9%) 0 (0%) Distance to anal verge 5.4 ± 2.3cm 5.2 ± 1.8cm 0.701 Gut microbiome and metabolic signatures of patients with RC receiving nCRT plus PDL1 inhibitors We first surveyed the gut microbiota and metabolic structure in the fecal samples of RC patients in the Response, Nonresponse, and Control cohorts. Figures S1 A to S1C present the quality control results of the metabolomics sequencing. Significant differences in metabolites among the different cohorts are shown in Fig. 1 B, and the types of metabolites that exhibited the greatest variation were amino acids (48.96%), SCFAs (31.69%), carbohydrates (7.77%), and bile acids (3.25%) (Fig. 1 C and Figure S1 D ). Additionally, differences in the results of principal component analysis (PCA) are presented in Fig. 1 D ( p = 0.006 ). For the gut microbiota structure in the different cohorts, there were significant differences in diversity at the baseline, postradiotherapy, and postimmunotherapy levels (PLS-DA test, P < 0.05; Figure S2 A–I ). Using the GraphLain visualization tool [ 19 ], a taxonomic hierarchy tree was constructed for the species at each taxonomic level ( Figure S3 A ). Species accumulation curves, rank abundance curves, and taxonomies were generated ( Figure S3 B-S3D ). More than 20 microbiota species, such as Bacteroides uniformis, Anaerostipeshadrus, and Ruminococcus torque , were observed to vary among the cohorts (Fig. 1 E and 1 F). In addition, several taxa, including Parabacteroides distasonis , Leuconostoc gelidum , Aggregatibacter aphrophilus , and Limosilactobacillus mucosae , were significantly enriched in the Response group compared with the control group (LEfSe: p < 0.05 , q 2.0 ) (Fig. 1 G). Metaproteomic characterization of the gut microbiome of RC patients with various response levels At the protein group level, 36,414 protein groups were identified, including 24,089 (66.2%) from the HMP database and 12,325 (33.8%) from the NCBI nr database ( Figure S4 ). Most of the identified protein groups were from microbes, indicating good sample pretreatment by differential centrifugation ( Figure S4 A-D ). The alpha diversity values of the Simpson index and Shannon index were compared at the species level (Fig. 2 A) and genus level (Fig. 2 B), which revealed the richness and evenness of the dominant species in the community. PCA was used to understand the effects of different batches between labeled samples at different levels ( Figure S5 ) within a stratified atlas ( Figure S6 ). At the species and genus levels, heatmap analysis between the Response and Nonresponse cohorts was performed (Fig. 2 C to 2 D, Figure S7 ). The density of bacteria in the large intestine is very high[ 20 ]. In this study, we utilized the quantitative metaproteomics of all the identified peptides to display the taxonomic abundances of the gut microbiome of the response cohort compared to that of the nonresponse cohort. Lefse analysis revealed that many taxa, including Bianquea_renquensis and Ruthenibacterum_lactatiformans , were significantly enriched in the response cohort, whereas Collirsella_aerofaciens and Lachnospira_eligens were more abundant in the nonresponse cohort than in the Response cohort (Fig. 2 E). We annotated the functions of the differentially expressed proteins via GO[ 21 ] and KEGG[ 22 ] analyses. The peptide catabolic process, disaccharide metabolic process, and oligosaccharide catabolic process of the differentially expressed proteins were annotated in the GO database (shown in Fig. 2 F). For most of the KEGG categories, more proteins were more highly abundant in the Response group than in the nonresponse group. However, we found that there were more gut microbial proteins related to starch and sucrose metabolism, the renin − angiotensin system, galactose metabolism and carbohydrate digestion and absorption that were more abundant in the response cohort than in the nonresponse cohort (Fig. 2 G). Next, volcano analysis of the response and nonresponse cohorts was performed on the basis of the COG database, human-derived protein groups, and whole protein groups (Fig. 2 H). Longitudinal analysis of metagenomic, metaproteomic and metabolic alterations and functions between groups with responses to treatment We attempted to evaluate RC treatment–specific microbiota, metabolic and proteomic patterns. To this end, the patients were divided into Baseline, postradiotherapy and postimmunotherapy cohorts. With respect to metabolic alterations, although there were no differences between the baseline and postradiotherapy cohorts, as revealed by principal coordinate analysis (ANOSIM test, P > .05; Figure S8 ), PCA revealed significant differences between the baseline and postimmunotherapy cohorts (P > .05; Figure S8 ) . We subsequently investigated the differentially abundant metabolites between the response-baseline, response-postradiotherapy, response-postimmunotherapy, nonresponse-baseline, nonresponse-postradiotherapy and nonresponse-postimmunotherapy groups ( Figure S9 ). Most of the differentially abundant metabolites were distinct between the baseline group and posttreatment groups ( Figure S9 A-D ). Within this analysis, metabolites that were especially different at baseline compared with the postimmunotherapy stage included enriched dodecanedioic acid and bile acid and depleted THCA (t test, p < 0.05 ) in the response cohort. In the nonresponsive cohort, increased UDCA and depleted dodecanedioic acid levels were observed (Fig. 3 A). Closely related to fatty acid metabolism, beta-oxidation of very long-chain fatty acids and fatty acid biosynthesis were observed in the post immunotherapy cohort of patients ( Figure S10 A-D ). Considering the multiomics shift in the gut microbiome and metabolome, we hypothesized that the differences in metabolites might reflect the differences in microbial enzyme-encoding gene expression among patients with RC under different treatment stresses. To further determine the microbial alterations, we annotated the microbial genes and constructed a heatmap to show the differentially expressed genes grouped in the related pathways (Fig. 3 B and Figure S11 to Figure S14 ). Species, including Turicibacter, Dorea formicigenerans , and Lachnospira eligens , and genera, including Roseburia, Adlercreutzia and Simiaoa , were enriched between the Baseline and postimmunotherapy cohorts. Pathways involved in lipid metabolism and glutamate metabolism were significantly altered after immunotherapy ( Figure S15 to Figure S18 ). Paired analysis at the metaproteomic level was also performed. Among the differentially expressed proteins, 110 were more abundant in the postimmunotherapy RC cohort (Fig. 3 C and Figure S19 to Figure S20 ). In our paired metagenomics and metaproteomics analyses, the baseline samples demonstrated significantly greater diversity and a trend toward greater unevenness than the postradiotherapy and postimmunotherapy samples did ( Figure S21 ); this variation trend was also observed in the subgroup analysis stratified by response status. Taxonomic analyses of the response cohort revealed that, at the genus level, Turicibacter and Fusobacterium were significantly increased following immunotherapy, whereas Peptostreptococcus and Parvimonas were decreased after immunotherapy (Fig. 3 D). At the species level, the samples from the Response cohort presented decreased P. stomatis and Isourolithinifaciens and increased Sanguinis abundances after immunotherapy. In the nonresponse cohort, increased Pneumosintes , and Lactatifermentans, micrain abundances and decreased Gemella, Peptostreptococcus , and Fusobacteriumaddition abundances were detected after immunotherapy (Fig. 3 E). Next, functional analysis was performed to detect pathway alterations after treatment. Negative regulation of endopeptidase activity, peptidase activity, antimicrobial humoral response and defense response to fungi were separately enriched in the response and nonresponse cohorts according to the GO analysis (Fig. 3 F). According to the KEGG analysis, neutrophil extracellular trap formation and complement and coagulation cascades were enriched after immunotherapy (Fig. 3 G). Notably, even though the gut microbiome in patients with RC was strongly altered by nCRT and immunotherapy, it was still significantly different depending on the response status. Integrated multiomics analysis revealed microbiota-metabolic derived phenotypes associated with stratified response status Our multiomics data enabled us to identify the dynamic interactions among differential taxonomic, metabolic and gene signatures. The results of the Mantel test revealed that metabolism-related microbes at the species level were significantly correlated with lipid and bile acid metabolites (Mantel test in Fig. 4 A and microbiota and gene signatures in Figure S22 to Figure S23 ). Additionally, the Sankey diagram provided evidence of the potential correlation between key bacteria and key metabolites (Fig. 4 B). The genus and species abundances determined via metagenomics and meta-proteomics were significantly consistent (Fig. 4 C, 4 D and Figure S21) . To dissect interactions between the host and microbiota that might underlie features in the Response and Nonresponse cohorts, we assessed the correlations between all the differentially abundant taxa and metabolites and combined the associations of representative taxa, metabolites and KO genes to draw network diagrams representing the multiomics signatures (Fig. 4 E and Figure S24 ). Next, a two-way orthogonal partial least squares (O2PLS) model for multiomics was constructed (Fig. 4 F). The model loading diagram and the evaluation parameters of the model indicate that the model construction was relatively satisfactory. Collectively, these data demonstrated that alterations in microbial KO genes and metabolites were associated with changes in the microbiota in RC feces, which may partially explain the specific pathogenesis of RC in the postimmunotherapy population within response status. Microbial and metabolic signatures associated with nCRT plus immunotherapy-related side effects in RC patients We identified abundant microbiota and metabolic differences between adverse events (AEs) and nonadverse events (NAEs), and we identified more differential species or metabolites associated with radiotherapy-related AEs and immunotherapy-related AEs, which were highly biologically distinct. Differentially abundant signatures comprising bacterial genera and species are shown in Fig. 5 A. A broad overview of the taxonomic heatmap at the baseline, postradiotherapy and postimmunotherapy levels is also displayed in Figure S25 . Lefse analysis for different levels is shown in Fig. 5 B and Fig. 5 C, which revealed that various kinds of bacteria, including Streptococcus thermophilus , Bifidobacterium longum , and Ruminococcus gauvreauii , were statistically correlated with AEs. In addition, metabolite alterations between the AE and NAE cohorts, including alterations in bHDCA ( p = 1e − 03 ), aMCA ( p = 1.3e − 03 ), GCA ( p = 7.7e − 03 ) and ortho − hydroxyphenylacetic acid ( p = 1.4e − 02 ), are shown in Fig. 5 D and Fig. 5 E. Next, we first identified the common changes that varied consistently in LARC patients between the AE and NAE groups at the microbial and metabolic levels. Notably, Streptococcus mutans was enriched in samples from patients with radiotherapy-related AEs, whereas Aggregatibacter was enriched in samples from patients with immunotherapy-related AEs (Fig. 5 F). With respect to metabolites, isovaleric acid and aMCA tended to increase across the different AE groups (Fig. 5 G). Collectively, our results revealed unique microbial and metabolic profiles in different AE groups, which may be associated with the mechanisms underlying immunotherapy-related AEs in RC patients. A multiomics model explained the response to immunotherapy and the mechanisms underlying the enhanced response due to radiotherapy Our data revealed that the differential species, metabolites and key genes varied among patients in different response groups, leading to our hypothesis that for RC patients receiving presurgical immunotherapy, an independent diagnostic model should be used to screen for responsive patients more precisely. The separate metabolites at the baseline, postradiotherapy, and postimmunotherapy stages are shown in Figure S26 , and the results revealed that fatty acid and bile acid levels were significantly altered in different response cohorts. Moreover, differences in the metagenome and metaproteome at different treatment stages were also revealed ( Figure S27 to S34 ). To further investigate the potential to use microbial and metabolic profiles as diagnostic markers, we built random forest (RF), logistic regression (LR), and gradient boosting (GB) models to discriminate patients in the Response and Nonresponse groups, especially patients at baseline. Boruta feature selection was performed in the response cohort to select key discriminatory bacterial, metabolic and proteomic taxa. The selected multiomics data were different among the different response groups, which supported our hypothesis that RC patients with different responses to ICIs need different biomarkers and diagnostic models (the analysis flow chart is shown in Figure S35 ). Overlapping the separate significant markers, Fig. 6 A shows the heatmap expression analysis of selected markers between the baseline and postimmunotherapy stages. Moreover, the areas under the receiver operating characteristic curves (AUROCs) of the diagnostic classifiers constructed with metabolic, microbial and metaproteomic markers were 0.945, 0.892, and 0.999 in the baseline, postradiotherapy and postimmunotherapy groups, respectively (the GRs are shown in Fig. 6 B, and the LRs are shown in Fig. 6 C). To examine the robustness and efficacy of the diagnostic strategy, we conducted repeated group-to-group validation analyses of the Baseline cohort and calculated a model predictive ability of 0.854; in contrast, postimmunotherapy validation revealed 0.999 predictive ability. Overall, the features selected from each group showed excellent potential for identifying RC patients who will exhibit pathological responses to neoadjuvant immunotherapy, indicating the great potential of this screening strategy in people at pretreatment stages. Discussion Immune checkpoint inhibitors (ICIs) have revolutionized the field of oncology, and their application in neoadjuvant therapy is currently under intense investigation[ 23 ]. The advancement of the use of neoadjuvant immunotherapy for treating RC has shown promising results, offering new treatment options for patients with high risk of recurrence [ 10 , 24 , 25 ]. Many reports have demonstrated a consistency of 40% pCR rates after treatment with nCRT and ICIs. However, precise biomarkers for predicting the response of individuals are needed. Recent studies have highlighted the significant impact of the gut microbiota on the efficacy of cancer immunotherapies, such as ICIs and CAR-T cells[ 26 ]. For example, the presence of specific bacterial strains within the microbiota has been associated with better responses to ICIs in patients with epithelial tumors[ 27 ]. Bifidobacterium species have been shown to promote antitumor immunity and enhance the efficacy of anti-PDL1 treatment[ 28 ]. Thus, our study first highlights the potential signature of using fecal samples—including metagenomics, metaproteomics, and metabolomics—to understand the changes in RC after neoadjuvant immunotherapy[ 29 ]. The integration of metagenomic, proteomic, and metabolomic data provides a comprehensive view of the tumor microenvironment and systemic changes induced by immunotherapy[ 30 ]. Some consistently altered differential taxa and metabolites have been identified across ICI response groups, especially P. stomatis and Isourolithinifaciens , whose abundances are decreased after immunotherapy. P. stomatis has been reported to be an oncogenic bacterium and to contribute to nonresponsiveness to RTK inhibitors in RC patients [ 31 ]. The increased abundance of P. stomatis in patient with RC treated with ICIs may partly explain the oncogenic role of promoting CRC cell viability. Additionally, samples from ICI responders have unique metabolic characteristics. In this study, we discovered a prominent feature characterized by downregulated 3-methyl-2-oxopentanoic acid, oxoglutaric acid, azelaic acid, and HDCA and upregulated UCA, UDCA, HCA and CDCA. A higher level of UDCA was reported in primary biliary cholangitis patients [ 32 , 33 ], indicating that patients with an adequate response to UDCA remain at risk of poor outcomes. Collectively, these findings emphasize the multifaceted impact of the gut microbiota and metabolic signatures on cancer immunotherapy. Notably, in our dataset, fecal cholesterol levels were positively correlated with response status at different stages, which has rarely been investigated in previous reports[ 34 ]. Considering the complex hepatic and intestinal circulation of bile acids, this finding may be the result of disrupted gut bacteria due to ICI treatment. To more thoroughly understand the characteristics of alterations in RC patients receiving immunotherapy, we first conducted a comparative analysis of metaproteomic data from the Zhongshan Hospital cohort. Surprisingly, despite the diverse geographical origins of these cohorts, we observed a high degree of diversity similarity in terms of peptides, proteins, COGs, and KOs. Additionally, when we compared fecal protein characteristics between samples from the Response and Nonresponse groups, as well as among samples from patients at different treatment stages, we again found substantial similarities. Protein functional analysis revealed that human-derived proteins are predominantly associated with pathways such as “starch and sucrose metabolism”, “galactose metabolism” and “thiamine metabolism”. The activation of these pathways may be associated with external pressure on the rectal mucosa. On the other hand, microbial-derived proteins are predominantly associated with pathways such as “protein digestion and absorption” and the “renin − angiotensin system”, which play crucial roles in microbial growth and metabolism. Our study is the first to explore the correlation between fecal multiomics data and therapy-related side effects. We identified bHDCA, aMCA, GCA and ortho − hydroxyphenylacetic acid as significant indicators for the early detection of AEs. Moreover, we identified Streptococcus mutans and Aggregatibacter as members of the AE-related microbiota. A previous study revealed that LTA D-alanylation plays an important role in Streptococcus mutans acidogenesis by altering glycolytic activity[ 35 ], whereas Aggregatibacter was reported to lead to gut dysbiosis and to be correlated with treatment-related gastrointestinal dysfunction[ 36 ]. Specifically, our predictive models, which are based on multiomics data, offer promising tools for forecasting treatment responses. This could allow clinicians to tailor immunotherapy protocols more precisely, thereby increasing the likelihood of achieving a complete clinical response or minimizing adverse effects in nonresponders. Through integrated analysis and screening, we identified a panel including 6 microbiota members, 4 meta proteins and 4 metabolites as a remarkable model to predict response early in the course of treatment to optimize therapeutic strategies and improve patient prognosis. Microbial, metabolic and metaproteomic signatures selected from each treatment stage showed promising efficacy in distinguishing patients in the response and nonresponse cohorts. Our diagnostic model, which was constructed for individuals, especially those in the baseline stages, could increase diagnostic efficacy, suggesting a reasonable and powerful screening strategy for detecting RC stratified by final response. Therefore, we suggest that personalized screening strategies may help improve the identification of individuals with a greater risk of ICI resistance and reduce the incidence of related AEs in RC patients. In conclusion, our findings suggest several directions for future research. First, longitudinal studies with larger patient cohorts are necessary to validate the predictive models and assess their robustness across diverse populations. Additionally, exploring the mechanisms underlying the observed omics changes could provide deeper insights into how immunotherapy exerts its effects and help identify novel therapeutic targets. Moreover, the integration of these multiomics approaches could further refine our understanding of tumor biology and treatment response. This integrated approach may ultimately lead to more effective and personalized treatment strategies for RC and other cancers. Abbreviations MSS, microsatellite stable; RC, rectal cancer; ICI, immune checkpoint inhibitors; LARC, locally advanced rectal cancer; nCRT, neoadjuvant chemoradiotherapy; PDL1, programmed cell death protein 1; ICD, immunogenic cell death; DAMPs, damage-associated molecular patterns; CTLs, cytotoxic T lymphocytes; TME, total mesorectal excision; AJCC, American Joint Committee on Cancer Guidelines; TRG, Tumor regression grade; LEfSe, Linear discriminant analysis effect size; PCoA, principal coordinate analysis; NMDS, nonmetric multidimensional scaling; UPGMA, unweighted pair-group method with arithmetic means; PCA, principal component analysis, PLS-DA, partial least square discriminant analysis, OPLS-DA, orthogonal partial least square discriminant analysis; pCR, pathological complete response; O2PLS, two-way orthogonal partial least squares; Declarations Acknowledgement This manuscript has not been submitted to any other journal and is not currently being considered for publication by another journal. We Thank the doctors and nurses who performed the treatment. Patient and Public Involvement Patients or the public WERE NOT involved in the design, or conduct, or reporting, or dissemination plans of our research Ethics approval and consent to participate Written informed consent was obtained from all patients. The study protocol followed the ethical guidelines of the Declaration of Helsinki and was approved by the Ethics Committee of Zhongshan Hospital of Fudan University. The ethical number was 2022-019. Consent for publication We have obtained consent to publish from the participant to report individual patient data. Availability of data and material The datasets used and/or analyzed during the current study are available from the corresponding authors upon reasonable request. Conflicts of interest The authors declare that they have no competing interests. Authors' contributions Prof. Xu JianMin and Prof. Ye LeChi contributed to the design of this study. Dr. Lv Yang, Tang WenTao, Ji MeiLing, Zhang HongYu and Zhu ZheHui performed the research, and Dr. Lv Yang, Tang Wentao, Lin Qi, Niu ZhengChuan and Ji Meiling analyzed and interpreted the patient data. Pro. Zhang Xian and Pro. Wang Jian provided radiotherapy for rectal cancer patietns. Dr. Lv Yang was a major contributor to the writing of the manuscript. Dr. Ji MeiLing provided research background and perspectives. Prof. Xu JianMin, Dr. Tang WenTao were the corresponding authors and approved the final version of this manuscript for publication. Funding This work was supported by the Natural Science Foundation of China (82303889), Clinical research project of Health Professions of Shanghai Municipal Health Commission (20214Y0277), Beijing Bethune Charitable Foundation (1017), the Natural Science Foundation of Shanghai (23ZR1410400 and 23ZR1410600) and Shanghai Sailing Program (23YF1406100). The funding bodies had no role in the design of the study; collection, analysis, and interpretation of data; or in the writing of the manuscript. References Siegel RL, Giaquinto AN, Jemal A: Cancer statistics, 2024 . CA Cancer J Clin 2024, 74 (1):12-49. Wei W, Zeng H, Zheng R, Zhang S, An L, Chen R, Wang S, Sun K, Matsuda T, Bray F et al : Cancer registration in China and its role in cancer prevention and control . Lancet Oncol 2020, 21 (7):e342-e349. Keller DS, Berho M, Perez RO, Wexner SD, Chand M: The multidisciplinary management of rectal cancer . Nat Rev Gastroenterol Hepatol 2020, 17 (7):414-429. Feeney G, Sehgal R, Sheehan M, Hogan A, Regan M, Joyce M, Kerin M: Neoadjuvant radiotherapy for rectal cancer management . World J Gastroenterol 2019, 25 (33):4850-4869. Sauer R, Becker H, Hohenberger W, Rodel C, Wittekind C, Fietkau R, Martus P, Tschmelitsch J, Hager E, Hess CF et al : Preoperative versus postoperative chemoradiotherapy for rectal cancer . N Engl J Med 2004, 351 (17):1731-1740. Galluzzi L, Aryankalayil MJ, Coleman CN, Formenti SC: Emerging evidence for adapting radiotherapy to immunotherapy . Nat Rev Clin Oncol 2023, 20 (8):543-557. McLaughlin M, Patin EC, Pedersen M, Wilkins A, Dillon MT, Melcher AA, Harrington KJ: Inflammatory microenvironment remodelling by tumour cells after radiotherapy . Nat Rev Cancer 2020, 20 (4):203-217. Vaes RDW, Hendriks LEL, Vooijs M, De Ruysscher D: Biomarkers of Radiotherapy-Induced Immunogenic Cell Death . Cells 2021, 10 (4). Li C, Wang B, Tu J, Liu C, Wang Y, Chen J, Huang Y, Liu B, Yuan X: ATM inhibition enhance immunotherapy by activating STING signaling and augmenting MHC Class I . Cell Death Dis 2024, 15 (7):519. Xia F, Wang Y, Wang H, Shen L, Xiang Z, Zhao Y, Zhang H, Wan J, Zhang H, Wang Y et al : Randomized Phase II Trial of Immunotherapy-Based Total Neoadjuvant Therapy for Proficient Mismatch Repair or Microsatellite Stable Locally Advanced Rectal Cancer (TORCH) . J Clin Oncol 2024:JCO2302261. Lin ZY, Zhang P, Chi P, Xiao Y, Xu XM, Zhang AM, Qiu XF, Wu JX, Yuan Y, Wang ZN et al : Neoadjuvant short-course radiotherapy followed by camrelizumab and chemotherapy in locally advanced rectal cancer (UNION): early outcomes of a multicenter randomized phase III trial . Ann Oncol 2024. Bando H, Tsukada Y, Inamori K, Togashi Y, Koyama S, Kotani D, Fukuoka S, Yuki S, Komatsu Y, Homma S et al : Preoperative Chemoradiotherapy plus Nivolumab before Surgery in Patients with Microsatellite Stable and Microsatellite Instability-High Locally Advanced Rectal Cancer . Clin Cancer Res 2022, 28 (6):1136-1146. Lin Z, Cai M, Zhang P, Li G, Liu T, Li X, Cai K, Nie X, Wang J, Liu J et al : Phase II, single-arm trial of preoperative short-course radiotherapy followed by chemotherapy and camrelizumab in locally advanced rectal cancer . J Immunother Cancer 2021, 9 (11). Dossa F, Chesney TR, Acuna SA, Baxter NN: A watch-and-wait approach for locally advanced rectal cancer after a clinical complete response following neoadjuvant chemoradiation: a systematic review and meta-analysis . Lancet Gastroenterol Hepatol 2017, 2 (7):501-513. Gopalakrishnan V, Helmink BA, Spencer CN, Reuben A, Wargo JA: The Influence of the Gut Microbiome on Cancer, Immunity, and Cancer Immunotherapy . Cancer Cell 2018, 33 (4):570-580. Routy B, Le Chatelier E, Derosa L, Duong CPM, Alou MT, Daillere R, Fluckiger A, Messaoudene M, Rauber C, Roberti MP et al : Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors . Science 2018, 359 (6371):91-97. Janczewski LM, Faski J, Nelson H, Gollub MJ, Eng C, Brierley JD, Palefsky JM, Goldberg RM, Washington MK, Asare EA et al : Survival outcomes used to generate version 9 American Joint Committee on Cancer staging system for anal cancer . CA Cancer J Clin 2023, 73 (5):516-523. Gogenur M, Balsevicius L, Bulut M, Colak N, Justesen TF, Fiehn AK, Jensen MB, Host-Rasmussen K, Cappelen B, Gaggar S et al : Neoadjuvant intratumoral influenza vaccine treatment in patients with proficient mismatch repair colorectal cancer leads to increased tumor infiltration of CD8+ T cells and upregulation of PD-L1: a phase 1/2 clinical trial . J Immunother Cancer 2023, 11 (5). Asnicar F, Weingart G, Tickle TL, Huttenhower C, Segata N: Compact graphical representation of phylogenetic data and metadata with GraPhlAn . PeerJ 2015, 3 :e1029. Sobhani I, Amiot A, Le Baleur Y, Levy M, Auriault ML, Van Nhieu JT, Delchier JC: Microbial dysbiosis and colon carcinogenesis: could colon cancer be considered a bacteria-related disease? Therap Adv Gastroenterol 2013, 6 (3):215-229. Hosomi K, Saito M, Park J, Murakami H, Shibata N, Ando M, Nagatake T, Konishi K, Ohno H, Tanisawa K et al : Oral administration of Blautia wexlerae ameliorates obesity and type 2 diabetes via metabolic remodeling of the gut microbiota . Nat Commun 2022, 13 (1):4477. Kong C, Liang L, Liu G, Du L, Yang Y, Liu J, Shi D, Li X, Ma Y: Integrated metagenomic and metabolomic analysis reveals distinct gut-microbiome-derived phenotypes in early-onset colorectal cancer . Gut 2023, 72 (6):1129-1142. Chong X, Madeti Y, Cai J, Li W, Cong L, Lu J, Mo L, Liu H, He S, Yu C et al : Recent developments in immunotherapy for gastrointestinal tract cancers . J Hematol Oncol 2024, 17 (1):65. Yang Z, Gao J, Zheng J, Han J, Li A, Liu G, Sun Y, Zhang J, Chen G, Xu R et al : Efficacy and safety of PD-1 blockade plus long-course chemoradiotherapy in locally advanced rectal cancer (NECTAR): a multi-center phase 2 study . Signal Transduct Target Ther 2024, 9 (1):56. Xiao WW, Chen G, Gao YH, Lin JZ, Wu XJ, Luo HL, Lu ZH, Wang QX, Sun R, Cai PQ et al : Effect of neoadjuvant chemoradiotherapy with or without PD-1 antibody sintilimab in pMMR locally advanced rectal cancer: A randomized clinical trial . Cancer Cell 2024. Chambers L, Grencewicz D, Spakowicz D: From poo to promise: Fecal microbiota transplants support immunotherapy re-sensitization in solid tumors . Cell Host Microbe 2024, 32 (8):1217-1218. Qiu J, Jiang Y, Ye N, Jin G, Shi H, Qian D: Leveraging the intratumoral microbiota to treat human cancer: are engineered exosomes an effective strategy? J Transl Med 2024, 22 (1):728. Derosa L, Routy B, Thomas AM, Iebba V, Zalcman G, Friard S, Mazieres J, Audigier-Valette C, Moro-Sibilot D, Goldwasser F et al : Intestinal Akkermansia muciniphila predicts clinical response to PD-1 blockade in patients with advanced non-small-cell lung cancer . Nat Med 2022, 28 (2):315-324. Kruk ME, Mehta S, Murray K, Higgins L, Do K, Johnson JE, Wagner R, Wendt CH, O'Connor JB, Harris JK et al : An integrated metaproteomics workflow for studying host-microbe dynamics in bronchoalveolar lavage samples applied to cystic fibrosis disease . mSystems 2024, 9 (7):e0092923. Li J, Chen Z, Wang Q, Du L, Yang Y, Guo F, Li X, Chao Y, Ma Y: Microbial and metabolic profiles unveil mutualistic microbe-microbe interaction in obesity-related colorectal cancer . Cell Rep Med 2024, 5 (3):101429. Huang P, Ji F, Cheung AH, Fu K, Zhou Q, Ding X, Chen D, Lin Y, Wang L, Jiao Y et al : Peptostreptococcus stomatis promotes colonic tumorigenesis and receptor tyrosine kinase inhibitor resistance by activating ERBB2-MAPK . Cell Host Microbe 2024, 32 (8):1365-1379 e1310. Corpechot C, Lemoinne S, Soret PA, Hansen B, Hirschfield G, Gulamhusein A, Montano-Loza AJ, Lytvyak E, Pares A, Olivas I et al : Adequate versus deep response to ursodeoxycholic acid in primary biliary cholangitis: To what extent and under what conditions is normal alkaline phosphatase level associated with complication-free survival gain? Hepatology 2024, 79 (1):39-48. Guo Z, He K, Pang K, Yang D, Lyu C, Xu H, Wu D: Exploring Advanced Therapies for Primary Biliary Cholangitis: Insights from the Gut Microbiota-Bile Acid-Immunity Network . Int J Mol Sci 2024, 25 (8). Xu L, Zhang Y, Lin Z, Deng X, Ren X, Huang M, Li S, Zhou Q, Fang F, Yang Q et al : FASN-mediated fatty acid biosynthesis remodels immune environment in Clonorchis sinensis infection-related intrahepatic cholangiocarcinoma . J Hepatol 2024, 81 (2):265-277. Wu M, Huang S, Du J, Li Y, Jiang S, Zhan L, Huang X: D-alanylation of lipoteichoic acid contributes to biofilm formation and acidogenesis capacity of Streptococcusmutans . Microb Pathog 2022, 169 :105666. Mukherjee S, Chopra A, Karmakar S, Bhat SG: Periodontitis increases the risk of gastrointestinal dysfunction: an update on the plausible pathogenic molecular mechanisms . Crit Rev Microbiol 2024:1-31. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigures.docx metaproteomicsprotocol.docx metabolismprotocol.docx metagenomicsprotocol.docx Supplementarydata1wholeproteinNumberTable.csv Supplementarydata2HostproteinNumberTable.csv Supplementarydata3microproteinNumberTable.csv Supplementarydata4heatmapTopSD500.csv Supplementarytable1.xlsx visualabstract.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6133863","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":423324655,"identity":"b48dd6b9-7392-44cc-8aed-64fbe9ea018a","order_by":0,"name":"Yang Lv","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Lv","suffix":""},{"id":423324656,"identity":"c45b5fef-c847-427e-9873-bc4be518e7d8","order_by":1,"name":"ZheHui Zhu","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"ZheHui","middleName":"","lastName":"Zhu","suffix":""},{"id":423324659,"identity":"6d79352d-71be-4f25-8620-2cbe9b1abce3","order_by":2,"name":"LeChi Ye","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"LeChi","middleName":"","lastName":"Ye","suffix":""},{"id":423324665,"identity":"e37a56f7-75bb-4cee-ab08-c5cfe91add95","order_by":3,"name":"Peng Zheng","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Zheng","suffix":""},{"id":423324667,"identity":"5745737b-149a-4760-b2b4-164ebc8a2453","order_by":4,"name":"HongYu Zhang","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"HongYu","middleName":"","lastName":"Zhang","suffix":""},{"id":423324669,"identity":"109481db-93c1-4961-b455-f66d4a606d11","order_by":5,"name":"Qi Lin","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Lin","suffix":""},{"id":423324670,"identity":"2b582e32-4fe9-497a-98f7-44588c817bba","order_by":6,"name":"MeiLing Ji","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"MeiLing","middleName":"","lastName":"Ji","suffix":""},{"id":423324671,"identity":"2b18230d-4b39-40f4-b6a9-436a90328a25","order_by":7,"name":"ZhengChuan Niu","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"ZhengChuan","middleName":"","lastName":"Niu","suffix":""},{"id":423324672,"identity":"83b86d89-a125-4da4-aff8-cbf5886a591e","order_by":8,"name":"Xian Zhang","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Xian","middleName":"","lastName":"Zhang","suffix":""},{"id":423324673,"identity":"d6aca912-1a50-491d-ba06-b4e36548ae3e","order_by":9,"name":"Jian Wang","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Wang","suffix":""},{"id":423324674,"identity":"ad10fe22-0e2a-4cb8-bad3-cb74be4e6389","order_by":10,"name":"WenTao Tang","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"WenTao","middleName":"","lastName":"Tang","suffix":""},{"id":423324675,"identity":"acaeed0b-e74d-4b3b-a3de-f9c13d65b140","order_by":11,"name":"JianMin Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtElEQVRIiWNgGAWjYHCCBCC0YeBjJlFLGgMbKVpA4DADG9FqDW4kPN3wcMd5eTZ2HjPpAgY7Od0GAlokZySk3Ug8c9uwjRmoZQZDsrHZAQJa+CVAWtpuM4K18DAcSNxGSAsbRMs5e+K1QG05kEi8FsmeByAtycltzGzF1jwGRPjF4HhO2s2fbXa2/fyHN97mqbCTI6iFgYEnAcrgMACaQFA5CLDDTGV/QJT6UTAKRsEoGHkAAEKYOvUvSf+CAAAAAElFTkSuQmCC","orcid":"","institution":"Fudan University","correspondingAuthor":true,"prefix":"","firstName":"JianMin","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2025-03-01 09:38:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6133863/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6133863/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78224047,"identity":"8ed9b45c-56a0-41fa-8339-7458065a80e6","added_by":"auto","created_at":"2025-03-11 06:41:50","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2344622,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow diagram and microbiota landscape of LARC receiving neoadjuvant radio-chemotherapy plus PDL1 blockade.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Study design was shown. In this study, a total of 85 patients and 406 fecal specimens were included; accordingly, metagenomics, metabolomics and metaproteomic analysis were performed. Combined analysis and predictive model were constructed and validated; \u003cstrong\u003e(B) \u003c/strong\u003eExpression landscape of fecal Metabolites in response, non-response and control cohorts revealed; \u003cstrong\u003e(C)\u003c/strong\u003e Type of altered metabolite was exhibited and the greatest variation were as follows: amino acids (48.96%), SCFAs (31.69%), Carbohydrates (7.77%) and Bile Acids (3.25%); \u003cstrong\u003e(D) \u003c/strong\u003ePCA analysis of metabolic data demonstrated statistical differences\u003cstrong\u003e (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ep=0.006\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e); (E) \u003c/strong\u003eand \u003cstrong\u003e(F) \u003c/strong\u003eFor microbiota signature,\u003cstrong\u003e \u003c/strong\u003eSpecies accumulation curves, rank abundance curve and taxonomies were revealed. Comparing response cohorts to non-response cohorts, over 20 microbiota species were observed and revealed with statistical significance; \u003cstrong\u003e(G)\u003c/strong\u003e Lefse analysis revealed several different taxa, including \u003cem\u003eParabacteroides_distasonis\u003c/em\u003e, \u003cem\u003eLeuconostoc_gelidum\u003c/em\u003e, \u003cem\u003eAggregatibacter_aphrophilus\u003c/em\u003eand \u003cem\u003eLimosilactobacillus_mucosae\u003c/em\u003e (\u003cem\u003e\u003cstrong\u003ep\u0026lt;0.05, q\u0026lt;0.1, LDA\u0026gt;2.0\u003c/strong\u003e\u003c/em\u003e). \u003cstrong\u003eAbbreviation:\u003c/strong\u003e PDL1, Programmed cell death 1 ligand 1; SCFAs, Short-chain fatty acids; LARC, locally advanced rectal cancer; PCA, Principal Component Analysis; Lefse, Linear discriminant analysis Effect Size.\u003c/p\u003e","description":"","filename":"Binder21.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6133863/v1/b46acb9901b718b97c853940.jpg"},{"id":78224434,"identity":"70f96480-4f9d-44b1-a793-3e9657edecb5","added_by":"auto","created_at":"2025-03-11 06:49:50","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5665762,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetaproteomic alterations between response and non-response cohorts after nCRT plus PDL1 blockade.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e and \u003cstrong\u003e(B)\u003c/strong\u003eAlpha-diversity of Simpson index and Shannon index were compared at the species level and genus level, these data demonstrating the richness and evenness of dominant species in the community; \u003cstrong\u003e(C)\u003c/strong\u003eAt species level, heatmap analysis between Response and Non-Response cohorts was shown, data related to\u003cstrong\u003e Figure S7\u003c/strong\u003e; \u003cstrong\u003e(D)\u003c/strong\u003e At genus level, heatmap analysis between Response and Non-Response cohorts was shown; \u003cstrong\u003e(E)\u003c/strong\u003e Lefse analysis represented that many taxa, including \u003cem\u003eBianquea_renquensis and Ruthenibacterum_lactatiformans\u003c/em\u003e were significantly enriched in response cohort, whereas \u003cem\u003eCollirsella_aerofaciens\u003c/em\u003e and \u003cem\u003eLachnospira_eligens\u003c/em\u003e showed higher abundance in non-response cohort than Response cohort; \u003cstrong\u003e(F)\u003c/strong\u003eFunctional annotation of differential expressed proteins were performed based on GO and Peptide catabolic process, disaccharide metabolic process and oligosaccharide catabolic process of differential proteins were annotated; \u003cstrong\u003e(G)\u003c/strong\u003eFor most of the KEGG categories, we found that there were more gut microbial proteins related to starch and sucrose metabolism, renin−angiotensin system, Galactose metabolism and carbohydrate digestion and absorption that were more abundant in the response cohort patient compared to non-response group; \u003cstrong\u003e(H) \u003c/strong\u003evolcano analysis between response and non-response cohorts were performed based on COG database, Human derived protein and whole protein groups. \u003cstrong\u003eAbbreviation:\u003c/strong\u003e PDL1, Programmed cell death 1 ligand 1; nCRT, neoadjuvant chemoradiotherapy; LARC, locally advanced rectal cancer; PCA, Principal Component Analysis; Lefse, Linear discriminant analysis Effect Size; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; COG, Clusters of Orthologous Groups.\u003c/p\u003e","description":"","filename":"Binder22.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6133863/v1/8bc2da8c484c3cdfaabf7959.jpg"},{"id":78224095,"identity":"1139f778-1a66-4cea-bf9a-a6a01634c1fe","added_by":"auto","created_at":"2025-03-11 06:41:53","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":10221578,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLongitude analysis of metagenomic, metaproteomic and metabolic alterations and function between treatment responses. (A) \u003c/strong\u003eFor metabolic alterations,\u003cstrong\u003e \u003c/strong\u003ecompared to non-response cohorts,\u003cstrong\u003e \u003c/strong\u003emetabolites that especially varied in baseline compared to post-immunotherapy included enriched dodecanedioic acid and bile acid and depleted THCA (t-test, \u003cem\u003e\u003cstrong\u003ep\u0026lt;0.05\u003c/strong\u003e\u003c/em\u003e) in response cohort; \u003cstrong\u003e(B) \u003c/strong\u003emetagenomics analysis\u003cstrong\u003e \u003c/strong\u003ewas performed and significant microbiota was annotated and data related to \u003cstrong\u003eFigure S11 \u003c/strong\u003eto\u003cstrong\u003eFigure S14\u003c/strong\u003e. Species including \u003cem\u003eTuricibacter, Dorea formicigenerans, Lachnospira eligens\u003c/em\u003e and genus including \u003cem\u003eRoseburia, Adlercreutzia and Simiaoa\u003c/em\u003e were enriched between Baseline and post-immunotherapy cohort; \u003cstrong\u003e(C) \u003c/strong\u003ePaired analysis for metaproteomic level and 110 proteins were more abundant in post-immunotherapy LARC cohort and data were related to\u003cstrong\u003e Figure S19 \u003c/strong\u003eto\u003cstrong\u003eFigure S20; (D) \u003c/strong\u003etaxonomic analyses for response cohort revealed significant alteration of microbiota at genus and species level; \u003cstrong\u003e(E)\u003c/strong\u003e In nonresponse cohort, increased \u003cem\u003epneumosintes, lactatifermentans, micrain\u003c/em\u003e and decreased \u003cem\u003eGemella, Peptostreptococcus, Fusobacteriumaddition\u003c/em\u003e were found after immunotherapy; \u003cstrong\u003e(F)\u003c/strong\u003e Functional analysis were performed to detect pathway alteration after nCRT plus PDL1 blockade. Negative regulation of endopeptidase activity, peptidase activity and antimicrobial humoral response, defense response to fungus were separately enriched in response and nonresponse cohorts by GO; \u003cstrong\u003e(G)\u003c/strong\u003e For KEGG analysis, Neutrophil extracellular trap formation, Complement and coagulation cascades were enriched after immunotherapy. \u003cstrong\u003eAbbreviation:\u003c/strong\u003ePDL1, Programmed cell death 1 ligand 1; nCRT, neoadjuvant chemoradiotherapy; LARC, locally advanced rectal cancer; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.\u003c/p\u003e","description":"","filename":"Binder23.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6133863/v1/53da249a0621bf6605f3d10a.jpg"},{"id":78224052,"identity":"ff3fd874-1fc5-408d-b552-c9b160a831eb","added_by":"auto","created_at":"2025-03-11 06:41:50","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":6592110,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntegrated multi-omics analysis demonstrated microbiota-metabolic derived phenotypes of stratified response status. (A)\u003c/strong\u003e Mantel test revealed the metabolism-related microbes at the species level were significantly correlated with lipid and bile acid metabolites; data related to \u003cstrong\u003eFigure S22 \u003c/strong\u003eand\u003cstrong\u003eFigure S23\u003c/strong\u003e; \u003cstrong\u003e(B)\u003c/strong\u003e Sankey diagram was performed and provided evidence of the potential correlation between key bacteria and metabolites; \u003cstrong\u003e(C)\u003c/strong\u003eand \u003cstrong\u003e(D)\u003c/strong\u003e Genus and Species abundance between metagenomics and meta-proteomics were calculated with significant consistency, data related to \u003cstrong\u003eFigure S21;\u003c/strong\u003e \u003cstrong\u003e(E)\u003c/strong\u003e correlations analysis between all differentially abundant taxa and metabolites were drawn, data related to \u003cstrong\u003eFigure S24\u003c/strong\u003e; \u003cstrong\u003e(F)\u003c/strong\u003e Next, O2PLS model among multi-omics were constructed and different microbiota, metabolites and proteins was screened out.\u003cstrong\u003e Abbreviation:\u003c/strong\u003e O2PLS, Two-way Orthogonal Partial Least Squares.\u003c/p\u003e","description":"","filename":"Binder24.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6133863/v1/a54788f1047291c440f33cff.jpg"},{"id":78226928,"identity":"60551e05-11f3-4fb5-87e0-8a6516f408a5","added_by":"auto","created_at":"2025-03-11 07:05:50","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":5858517,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrobial and metabolic signature for nCRT plus immunotherapy-related side effects in LARC patients. (A) \u003c/strong\u003eDifferentially abundant signatures comprised of bacteria genus and species were shown between AE and NAE cohort and data related to \u003cstrong\u003eFigure S25\u003c/strong\u003e; \u003cstrong\u003e(B) \u003c/strong\u003eand\u003cstrong\u003e (C)\u003c/strong\u003e, Lefse analysis for different levels was shown, and \u003cem\u003estreptococcus thermophilus\u003c/em\u003e, \u003cem\u003eBifidobacterium longum \u003c/em\u003eand \u003cem\u003eRuminococcus gauvreauii\u003c/em\u003e, were statistically correlated with treatment induced AEs; \u003cstrong\u003e(D)\u003c/strong\u003e and \u003cstrong\u003e(E)\u003c/strong\u003e, Besides, metabolites alterations of immunotherapy related AE and no-immunotherapy related AEs\u003cstrong\u003e, \u003c/strong\u003eincluding bHDCA (\u003cem\u003e\u003cstrong\u003ep=1e−03\u003c/strong\u003e\u003c/em\u003e), aMCA (\u003cem\u003e\u003cstrong\u003ep=1.3e−03\u003c/strong\u003e\u003c/em\u003e), GCA (\u003cem\u003e\u003cstrong\u003ep=7.7e−03\u003c/strong\u003e\u003c/em\u003e) and ortho−Hydroxyphenylacetic acid (\u003cem\u003e\u003cstrong\u003ep=1.4e−02\u003c/strong\u003e\u003c/em\u003e); \u003cstrong\u003e(F)\u003c/strong\u003e we first identified the common alterations that varied consistently in LARC among the AEs and NAEs groups at microbial and metabolic levels. \u003cem\u003eStreptococcusmutans\u003c/em\u003e was enriched in radiotherapy-related AEs specimen, and \u003cem\u003eAggregatibacter\u003c/em\u003e was upregulated in immunotherapy-related AEs specimen; \u003cstrong\u003e(G)\u003c/strong\u003e For metabolites, Isovaleric acid and aMCA showed an increasing trend across separate AEs groups. \u003cstrong\u003eAbbreviation:\u003c/strong\u003ePDL1, Programmed cell death 1 ligand 1; nCRT, neoadjuvant chemoradiotherapy; LARC, locally advanced rectal cancer; AE, adverse events.\u003c/p\u003e","description":"","filename":"Binder25.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6133863/v1/6d11ec1463234ce2708aee13.jpg"},{"id":78224076,"identity":"452cf826-11e1-4e7e-a57a-85756902bbfc","added_by":"auto","created_at":"2025-03-11 06:41:51","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":8241515,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMulti-omics model explained response to immunotherapy response in LARC. (A) \u003c/strong\u003ewe built RF, LR, GB to discriminate Responses and Non-response cases especially the patients at baseline stages. Overlapping the separate significant markers, we demonstrated the heatmap expression analysis of selected markers between baseline and post-immunotherapy and data related to \u003cstrong\u003eFigure S26\u003c/strong\u003e; \u003cstrong\u003e(B)\u003c/strong\u003e and \u003cstrong\u003e(C)\u003c/strong\u003eAUROC of the diagnostic classifier constructed by metabolic, microbial and metaproteomic markers were 0.945, 0.892, and 0.999 at the baseline, postradiotherapy and post-immunotherapy stages; \u003cstrong\u003e(D)\u003c/strong\u003e and \u003cstrong\u003e(E)\u003c/strong\u003e Repeated group-to-group validation analyses of Baseline cohort and calculated Model predictive ability of 0.854, in contrast, the post-immunotherapy validation shown 0.999 predictive ability. \u003cstrong\u003eAbbreviation:\u003c/strong\u003e PDL1, Programmed cell death 1 ligand 1; nCRT, neoadjuvant chemoradiotherapy; LARC, locally advanced rectal cancer; RF, Random Forest; LR, logistic regression; GB, Gradient Boosting; AUROC, area under the operating receiver characteristics curve.\u003c/p\u003e","description":"","filename":"Binder26.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6133863/v1/319ab0d4d200e4b4e65919cc.jpg"},{"id":79056522,"identity":"0223e6c3-6394-4437-bde5-67f43e715487","added_by":"auto","created_at":"2025-03-23 23:01:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":42432922,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6133863/v1/02bc657f-0b1e-48bf-8006-7cc415aa7e6a.pdf"},{"id":78224446,"identity":"fc41c499-045e-4eda-a555-cda719461e5c","added_by":"auto","created_at":"2025-03-11 06:49:53","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14541420,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-6133863/v1/1afd832dfe3da1aff96773a4.docx"},{"id":78224049,"identity":"a5ba3ee9-ec7d-440a-86e9-2ad3b86da311","added_by":"auto","created_at":"2025-03-11 06:41:50","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":21948,"visible":true,"origin":"","legend":"","description":"","filename":"metaproteomicsprotocol.docx","url":"https://assets-eu.researchsquare.com/files/rs-6133863/v1/8fcd7cfd0f520aa8e3f1cba0.docx"},{"id":78224048,"identity":"54280fa2-584c-4a46-b12f-7bcd5c8f715a","added_by":"auto","created_at":"2025-03-11 06:41:50","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":36106,"visible":true,"origin":"","legend":"","description":"","filename":"metabolismprotocol.docx","url":"https://assets-eu.researchsquare.com/files/rs-6133863/v1/45e8a2212fac397284375aea.docx"},{"id":78224097,"identity":"9bbd113b-be64-45ce-b1aa-8788556957a5","added_by":"auto","created_at":"2025-03-11 06:41:54","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":21084,"visible":true,"origin":"","legend":"","description":"","filename":"metagenomicsprotocol.docx","url":"https://assets-eu.researchsquare.com/files/rs-6133863/v1/ff9d1aedf20a4ea85d681486.docx"},{"id":78224094,"identity":"3c14e0d2-1478-434a-8da5-babcae5de0b6","added_by":"auto","created_at":"2025-03-11 06:41:52","extension":"csv","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1938,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata1wholeproteinNumberTable.csv","url":"https://assets-eu.researchsquare.com/files/rs-6133863/v1/be680bc3909433553a8412fe.csv"},{"id":78224435,"identity":"abff2cda-5361-4499-bb84-e2c7560dfba8","added_by":"auto","created_at":"2025-03-11 06:49:50","extension":"csv","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1732,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata2HostproteinNumberTable.csv","url":"https://assets-eu.researchsquare.com/files/rs-6133863/v1/52dda7a75e29372106118075.csv"},{"id":78226927,"identity":"36449bb9-603f-4122-bac8-6b2b60d885f4","added_by":"auto","created_at":"2025-03-11 07:05:50","extension":"csv","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":1938,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata3microproteinNumberTable.csv","url":"https://assets-eu.researchsquare.com/files/rs-6133863/v1/0d24707e8cdde3607607c0b6.csv"},{"id":78224077,"identity":"ad90f753-54fd-4269-a818-bc9aa4029ec0","added_by":"auto","created_at":"2025-03-11 06:41:51","extension":"csv","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":18830988,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata4heatmapTopSD500.csv","url":"https://assets-eu.researchsquare.com/files/rs-6133863/v1/00c88b9e7a6a73fc2284cffa.csv"},{"id":78224059,"identity":"73b8a0dd-faa1-4de3-9ac6-2eda0eae6207","added_by":"auto","created_at":"2025-03-11 06:41:50","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":9464,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6133863/v1/c765efcb00f05012f68aa781.xlsx"},{"id":78224063,"identity":"adb780ac-87fb-49d7-8c72-70dc5e7c3b9c","added_by":"auto","created_at":"2025-03-11 06:41:50","extension":"pdf","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":327326,"visible":true,"origin":"","legend":"","description":"","filename":"visualabstract.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6133863/v1/ae7c71e6aa32d5a116ddd040.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated multiomics reveals gut microbiota-protein-metabolite alterations that regulate adverse events and responses to radio-chemo-immunotherapy in microsatellite stable rectal cancer: a prospective longitudinal study","fulltext":[{"header":"Background","content":"\u003cp\u003eColorectal cancer (CRC) is a common disease worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and is the third leading cause of cancer-related mortality in China[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], and rectal cancer accounts for one-third of these cases[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. For rectal cancer (RC), especially locally advanced rectal cancer (LARC), the ideal treatment plan is a multifaceted process that requires the consideration of the complex anatomical structures involved, local recurrence, and distant metastasis[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Since 2004, neoadjuvant chemoradiotherapy (nCRT) has demonstrated significant superiority regarding survival outcomes in LARC patients[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWith the initiation of the era of immune checkpoint inhibitors (ICIs), a robust association has emerged between the advantageous effects of programmed cell death protein 1 (PD-L1/PD1) blockade and radiotherapy in solid tumors, including CRC[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Pelvic radiotherapy has prominent immunostimulatory effects, including the upregulation of neoantigen-encoding genes[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], the induction of immunogenic cell death (ICD) coupled with the release of damage-associated molecular patterns (DAMPs) and immunostimulatory cytokines[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and the increase in neoantigen presentation on MHC class I molecules, which enhances cancer cell recognition by cytotoxic T lymphocytes (CTLs)[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Currently, several clinical trials focusing on nCRT and PD1 (or PDL1) in patients with LARC are underway, and numerous investigations have demonstrated that these combinations can elicit synergistic antitumor effects[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], with a reported pathological complete response rate of nearly 40%[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGood responses to nCRT and ICIs improve local control, and these treatments can even be used as alternatives to clinical strategies such as radical resection or \u0026ldquo;watch and wait\u0026rdquo;(WW) strategies [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, responses to nCRT vary among patients. In this context, a reliable tool that can predict the therapeutic response to nCRT and ICIs is critical but is not yet available. Intriguingly, many clinical and preclinical studies have demonstrated that the gut microbiome actively participates in various biochemical and pathophysiologic reactions in cancers, especially those related to immunomodulation[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo this end, we initiated this prospective, longitudinal trial in 85 patients with MSS type RC treated with nCRT and ICIs. After evaluating the feasibility of the gut microbiome in predicting the response to nCRT, we established a random forest classifier for its clinical application. In addition, our research shed light on how the gut microbiome affects responses to radiotherapy, which could be clinically transformative.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Subjects and Sample Collection\u003c/h2\u003e \u003cp\u003eThis prospective study was performed at Zhongshan Hospital of Fudan University (Shanghai, P.R. China), in accordance with the Declaration of Helsinki. Ethical approval was obtained from the Institutional Review Board of Zhongshan Hospital of Fudan University (Shanghai, P.R. China), and written informed consent was provided by all subjects before sampling.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTreatment regimen\u003c/h3\u003e\n\u003cp\u003eA total of 85 patients were enrolled. The patients entered the screening period after being fully informed and signed the informed consent. The enrolled patients will receive concurrent chemotherapy and radiotherapy. Radiotherapy will endure a total of 25 days and chemotherapy with capecitabine plus Oxaliplatin (CAPOX regimen). 2 weeks after the last radiotherapy, patients will start to receive PDL1 combined with CAPOX for 2 cycles. After completing the treatment 2 weeks (\u0026plusmn;\u0026thinsp;7 days), the distance from the end of the last radiotherapy should not exceed 12 weeks. Then Total mesorectal excision (TME) is the recommended surgical principle, and the specific operation method can be selected by the surgeon according to the clinical routine and the situation of the patients.\u003c/p\u003e\n\u003ch3\u003eResponse evaluation\u003c/h3\u003e\n\u003cp\u003eThe response evaluation criteria were based on pathological examination. Clinical response evaluation was performed according to MRI images. Further independent central reviews were reported and issued by two professional radiologists using standardized radiological reports before and after neoadjuvant therapy. After nCRT plus PDL1 inhibitor, resection of primary lesion was performed by the same surgeon team. Additionally, these surgeons were also required with experience of at least 30 surgical procedures. The primary endpoint was pCR rate, namely pathological complete response rate, defined as the proportion of patients with pCR (ypT0N0). The pathological tumor regression was assessed according to the standards in the 9th Edition of the American Joint Committee on Cancer Guidelines (AJCC)[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Tumor regression grade (TRG) 0, 1, 2, and 3 indicated no residual tumor cells, single or small groups of cells, residual cancer with a desmoplastic response, and minimal evidence of tumor response, correspondingly[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The resected specimen was collected and evaluated by surgeons and pathologists together to identify the tumor site and to confirm pCR. All lymph nodes were examined, and a minimum of 12 lymph nodes were acquired for adequate assessment of the N stage.\u003c/p\u003e\n\u003ch3\u003eData extraction and Statistical analysis\u003c/h3\u003e\n\u003cp\u003eFor metagenomics and proteomic data, based on the taxonomic and functional profiles of non-redundant genes, LEfSe (Linear discriminant analysis effect size) was performed to detect differentially abundant taxa and functions across groups using the default parameters. Beta diversity analysis was performed to investigate the compositional and functional variation of microbial communities across samples using Bray-Curtis distance metrics and visualized via principal coordinate analysis (PCoA), nonmetric multidimensional scaling (NMDS) and unweighted pair-group method with arithmetic means (UPGMA) hierarchical clustering (Ramette 2007).\u003c/p\u003e \u003cp\u003eFor metabolomics data, Our proprietary software can perform a collection of data processing, interpretation, and visualization. For many metabolomics studies, two types of statistical analysis are extensively performed: 1) multivariate statistical analyses such as principal component analysis (PCA), partial least square discriminant analysis (PLS-DA), orthogonal partial least square discriminant analysis (OPLS-DA), random forest, support vector machine learning and so on; 2) univariate statistical analyses including student t-test, Mann-Whitney-Wilcoxon (U-test), ANOVA, correlation analysis, etc. Statistical algorithms are adapted from the widely used statistical analysis software packages in R studio (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cran.r-project.org/\u003c/span\u003e\u003cspan address=\"http://cran.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The optimal choice of statistical methods is often driven by the data and the project goals.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eClinical Information of the Participants\u003c/h2\u003e \u003cp\u003eA total of 406 fecal samples were collected for analysis in this study. The clinical information of the participants in the RC metaproteomic, metagenomic, and metabolomic studies is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Nineteen of 50 (38.0%) patients were pathologically Shown to have a pathological complete response (pCR). A total of 46% of the patients were over 60.0 years of age (range, 17\u0026ndash;90 years), and most patients were men (36 patients, 72.0%). The median distance to the anal verge was 5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3 cm in the NonpCR (Nonresponse) cohort and 5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8 cm in the pCR (Response) cohort (\u003cb\u003eP\u0026thinsp;=\u0026thinsp;0.701\u003c/b\u003e). According to the AJCC TNM stage, this study included 22.0% N0 patients, 70.0% N1 patients, 8.0% N2 patients and 6.0% stage IV patients. The proportion of RAS-mutant tumors was essentially similar to that of the wild-type tumors (48.0% vs. 52.0%). No statistically significant differences in age, sex, or body mass index were observed among the different response cohorts with respect to the integrated omics cohort. A control cohort of 35 patients receiving nCRT was also included in this study, as detailed in \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e. The specific study design is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of responder and non-responder patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-pCR (n\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epCR (n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (74.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (68.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (31.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (45.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (47.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (54.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (52.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ecT stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (96.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ecN stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.836\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (22.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (21.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (67.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (73.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (9.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRAS/RAF status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.608\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (45.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (52.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWide type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (54.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (47.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTRG score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (54.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (32.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (12.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDistance to anal verge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGut microbiome and metabolic signatures of patients with RC receiving nCRT plus PDL1 inhibitors\u003c/h3\u003e\n\u003cp\u003eWe first surveyed the gut microbiota and metabolic structure in the fecal samples of RC patients in the Response, Nonresponse, and Control cohorts. \u003cb\u003eFigures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA\u003c/b\u003e to \u003cb\u003eS1C\u003c/b\u003e present the quality control results of the metabolomics sequencing. Significant differences in metabolites among the different cohorts are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, and the types of metabolites that exhibited the greatest variation were amino acids (48.96%), SCFAs (31.69%), carbohydrates (7.77%), and bile acids (3.25%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and \u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eD\u003c/b\u003e). Additionally, differences in the results of principal component analysis (PCA) are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD (\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.006\u003c/b\u003e). For the gut microbiota structure in the different cohorts, there were significant differences in diversity at the baseline, postradiotherapy, and postimmunotherapy levels (PLS-DA test, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; \u003cb\u003eFigure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA\u0026ndash;I\u003c/b\u003e). Using the GraphLain visualization tool [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], a taxonomic hierarchy tree was constructed for the species at each taxonomic level (\u003cb\u003eFigure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eA\u003c/b\u003e). Species accumulation curves, rank abundance curves, and taxonomies were generated (\u003cb\u003eFigure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eB-S3D\u003c/b\u003e). More than 20 microbiota species, such as \u003cem\u003eBacteroides uniformis, Anaerostipeshadrus, and Ruminococcus torque\u003c/em\u003e, were observed to vary among the cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). In addition, several taxa, including \u003cem\u003eParabacteroides distasonis\u003c/em\u003e, \u003cem\u003eLeuconostoc gelidum\u003c/em\u003e, \u003cem\u003eAggregatibacter aphrophilus\u003c/em\u003e, and \u003cem\u003eLimosilactobacillus mucosae\u003c/em\u003e, were significantly enriched in the Response group compared with the control group (LEfSe: \u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/b\u003e, \u003cb\u003eq\u0026thinsp;\u0026lt;\u0026thinsp;0.1, LDA\u0026thinsp;\u0026gt;\u0026thinsp;2.0\u003c/b\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG).\u003c/p\u003e\n\u003ch3\u003eMetaproteomic characterization of the gut microbiome of RC patients with various response levels\u003c/h3\u003e\n\u003cp\u003eAt the protein group level, 36,414 protein groups were identified, including 24,089 (66.2%) from the HMP database and 12,325 (33.8%) from the NCBI nr database (\u003cb\u003eFigure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e\u003c/b\u003e). Most of the identified protein groups were from microbes, indicating good sample pretreatment by differential centrifugation (\u003cb\u003eFigure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eA-D\u003c/b\u003e). The alpha diversity values of the Simpson index and Shannon index were compared at the species level (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) and genus level (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), which revealed the richness and evenness of the dominant species in the community. PCA was used to understand the effects of different batches between labeled samples at different levels (\u003cb\u003eFigure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e\u003c/b\u003e) within a stratified atlas (\u003cb\u003eFigure \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e\u003c/b\u003e). At the species and genus levels, heatmap analysis between the Response and Nonresponse cohorts was performed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC to \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, \u003cb\u003eFigure \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e\u003c/b\u003e). The density of bacteria in the large intestine is very high[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In this study, we utilized the quantitative metaproteomics of all the identified peptides to display the taxonomic abundances of the gut microbiome of the response cohort compared to that of the nonresponse cohort. Lefse analysis revealed that many taxa, including \u003cem\u003eBianquea_renquensis\u003c/em\u003e and \u003cem\u003eRuthenibacterum_lactatiformans\u003c/em\u003e, were significantly enriched in the response cohort, whereas \u003cem\u003eCollirsella_aerofaciens\u003c/em\u003e and \u003cem\u003eLachnospira_eligens\u003c/em\u003e were more abundant in the nonresponse cohort than in the Response cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe annotated the functions of the differentially expressed proteins via GO[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and KEGG[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] analyses. The peptide catabolic process, disaccharide metabolic process, and oligosaccharide catabolic process of the differentially expressed proteins were annotated in the GO database (shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). For most of the KEGG categories, more proteins were more highly abundant in the Response group than in the nonresponse group. However, we found that there were more gut microbial proteins related to starch and sucrose metabolism, the renin\u0026thinsp;\u0026minus;\u0026thinsp;angiotensin system, galactose metabolism and carbohydrate digestion and absorption that were more abundant in the response cohort than in the nonresponse cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). Next, volcano analysis of the response and nonresponse cohorts was performed on the basis of the COG database, human-derived protein groups, and whole protein groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH).\u003c/p\u003e \u003cp\u003e \u003cb\u003eLongitudinal analysis of metagenomic, metaproteomic and metabolic alterations and functions between groups with responses to treatment\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe attempted to evaluate RC treatment\u0026ndash;specific microbiota, metabolic and proteomic patterns. To this end, the patients were divided into Baseline, postradiotherapy and postimmunotherapy cohorts. With respect to metabolic alterations, although there were no differences between the baseline and postradiotherapy cohorts, as revealed by principal coordinate analysis (ANOSIM test, P\u0026thinsp;\u0026gt;\u0026thinsp;.05; \u003cb\u003eFigure \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003e\u003c/b\u003e), PCA revealed significant differences between the baseline and postimmunotherapy cohorts (P\u0026thinsp;\u0026gt;\u0026thinsp;.05; \u003cb\u003eFigure \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003e)\u003c/b\u003e. We subsequently investigated the differentially abundant metabolites between the response-baseline, response-postradiotherapy, response-postimmunotherapy, nonresponse-baseline, nonresponse-postradiotherapy and nonresponse-postimmunotherapy groups (\u003cb\u003eFigure \u003cspan refid=\"MOESM9\" class=\"InternalRef\"\u003eS9\u003c/span\u003e\u003c/b\u003e). Most of the differentially abundant metabolites were distinct between the baseline group and posttreatment groups (\u003cb\u003eFigure \u003cspan refid=\"MOESM9\" class=\"InternalRef\"\u003eS9\u003c/span\u003eA-D\u003c/b\u003e). Within this analysis, metabolites that were especially different at baseline compared with the postimmunotherapy stage included enriched dodecanedioic acid and bile acid and depleted THCA (t test, \u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/b\u003e) in the response cohort. In the nonresponsive cohort, increased UDCA and depleted dodecanedioic acid levels were observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Closely related to fatty acid metabolism, beta-oxidation of very long-chain fatty acids and fatty acid biosynthesis were observed in the post immunotherapy cohort of patients (\u003cb\u003eFigure \u003cspan refid=\"MOESM10\" class=\"InternalRef\"\u003eS10\u003c/span\u003e A-D\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eConsidering the multiomics shift in the gut microbiome and metabolome, we hypothesized that the differences in metabolites might reflect the differences in microbial enzyme-encoding gene expression among patients with RC under different treatment stresses. To further determine the microbial alterations, we annotated the microbial genes and constructed a heatmap to show the differentially expressed genes grouped in the related pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and \u003cb\u003eFigure S11 to Figure S14\u003c/b\u003e). Species, including \u003cem\u003eTuricibacter, Dorea formicigenerans\u003c/em\u003e, and \u003cem\u003eLachnospira eligens\u003c/em\u003e, and genera, including \u003cem\u003eRoseburia, Adlercreutzia and Simiaoa\u003c/em\u003e, were enriched between the Baseline and postimmunotherapy cohorts. Pathways involved in lipid metabolism and glutamate metabolism were significantly altered after immunotherapy (\u003cb\u003eFigure S15 to Figure S18\u003c/b\u003e).\u003c/p\u003e \u003cp\u003ePaired analysis at the metaproteomic level was also performed. Among the differentially expressed proteins, 110 were more abundant in the postimmunotherapy RC cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC and \u003cb\u003eFigure S19 to Figure S20\u003c/b\u003e). In our paired metagenomics and metaproteomics analyses, the baseline samples demonstrated significantly greater diversity and a trend toward greater unevenness than the postradiotherapy and postimmunotherapy samples did (\u003cb\u003eFigure S21\u003c/b\u003e); this variation trend was also observed in the subgroup analysis stratified by response status. Taxonomic analyses of the response cohort revealed that, at the genus level, \u003cem\u003eTuricibacter\u003c/em\u003e and \u003cem\u003eFusobacterium\u003c/em\u003e were significantly increased following immunotherapy, whereas \u003cem\u003ePeptostreptococcus\u003c/em\u003e and \u003cem\u003eParvimonas\u003c/em\u003e were decreased after immunotherapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). At the species level, the samples from the Response cohort presented decreased \u003cem\u003eP. stomatis\u003c/em\u003e and \u003cem\u003eIsourolithinifaciens\u003c/em\u003e and increased \u003cem\u003eSanguinis\u003c/em\u003e abundances after immunotherapy. In the nonresponse cohort, increased \u003cem\u003ePneumosintes\u003c/em\u003e, and \u003cem\u003eLactatifermentans, micrain\u003c/em\u003e abundances and decreased \u003cem\u003eGemella, Peptostreptococcus\u003c/em\u003e, and \u003cem\u003eFusobacteriumaddition\u003c/em\u003e abundances were detected after immunotherapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Next, functional analysis was performed to detect pathway alterations after treatment. Negative regulation of endopeptidase activity, peptidase activity,\u003c/p\u003e \u003cp\u003e antimicrobial humoral response and defense response to fungi were separately enriched in the response and nonresponse cohorts according to the GO analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). According to the KEGG analysis, neutrophil extracellular trap formation and complement and coagulation cascades were enriched after immunotherapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). Notably, even though the gut microbiome in patients with RC was strongly altered by nCRT and immunotherapy, it was still significantly different depending on the response status.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIntegrated multiomics analysis revealed microbiota-metabolic derived phenotypes associated with stratified response status\u003c/h2\u003e \u003cp\u003eOur multiomics data enabled us to identify the dynamic interactions among differential taxonomic, metabolic and gene signatures. The results of the Mantel test revealed that metabolism-related microbes at the species level were significantly correlated with lipid and bile acid metabolites (Mantel test in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and microbiota and gene signatures in \u003cb\u003eFigure S22 to Figure S23\u003c/b\u003e). Additionally, the Sankey diagram provided evidence of the potential correlation between key bacteria and key metabolites (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The genus and species abundances determined via metagenomics and meta-proteomics were significantly consistent (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD and \u003cb\u003eFigure S21)\u003c/b\u003e. To dissect interactions between the host and microbiota that might underlie features in the Response and Nonresponse cohorts, we assessed the correlations between all the differentially abundant taxa and metabolites and combined the associations of representative taxa, metabolites and KO genes to draw network diagrams representing the multiomics signatures (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE and \u003cb\u003eFigure S24\u003c/b\u003e). Next, a two-way orthogonal partial least squares (O2PLS) model for multiomics was constructed (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). The model loading diagram and the evaluation parameters of the model indicate that the model construction was relatively satisfactory. Collectively, these data demonstrated that alterations in microbial KO genes and metabolites were associated with changes in the microbiota in RC feces, which may partially explain the specific pathogenesis of RC in the postimmunotherapy population within response status.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMicrobial and metabolic signatures associated with nCRT plus immunotherapy-related side effects in RC patients\u003c/h2\u003e \u003cp\u003eWe identified abundant microbiota and metabolic differences between adverse events (AEs) and nonadverse events (NAEs), and we identified more differential species or metabolites associated with radiotherapy-related AEs and immunotherapy-related AEs, which were highly biologically distinct. Differentially abundant signatures comprising bacterial genera and species are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA. A broad overview of the taxonomic heatmap at the baseline, postradiotherapy and postimmunotherapy levels is also displayed in \u003cb\u003eFigure S25\u003c/b\u003e. Lefse analysis for different levels is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, which revealed that various kinds of bacteria, including \u003cem\u003eStreptococcus thermophilus\u003c/em\u003e, \u003cem\u003eBifidobacterium longum\u003c/em\u003e, and \u003cem\u003eRuminococcus gauvreauii\u003c/em\u003e, were statistically correlated with AEs. In addition, metabolite alterations between the AE and NAE cohorts, including alterations in bHDCA (\u003cb\u003ep\u0026thinsp;=\u0026thinsp;1e\u0026thinsp;\u0026minus;\u0026thinsp;03\u003c/b\u003e), aMCA (\u003cb\u003ep\u0026thinsp;=\u0026thinsp;1.3e\u0026thinsp;\u0026minus;\u0026thinsp;03\u003c/b\u003e), GCA (\u003cb\u003ep\u0026thinsp;=\u0026thinsp;7.7e\u0026thinsp;\u0026minus;\u0026thinsp;03\u003c/b\u003e) and ortho\u0026thinsp;\u0026minus;\u0026thinsp;hydroxyphenylacetic acid (\u003cb\u003ep\u0026thinsp;=\u0026thinsp;1.4e\u0026thinsp;\u0026minus;\u0026thinsp;02\u003c/b\u003e), are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, we first identified the common changes that varied consistently in LARC patients between the AE and NAE groups at the microbial and metabolic levels. Notably, \u003cem\u003eStreptococcus mutans\u003c/em\u003e was enriched in samples from patients with radiotherapy-related AEs, whereas \u003cem\u003eAggregatibacter\u003c/em\u003e was enriched in samples from patients with immunotherapy-related AEs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). With respect to metabolites, isovaleric acid and aMCA tended to increase across the different AE groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG). Collectively, our results revealed unique microbial and metabolic profiles in different AE groups, which may be associated with the mechanisms underlying immunotherapy-related AEs in RC patients.\u003c/p\u003e \u003cp\u003e \u003cb\u003eA multiomics model explained the response to immunotherapy and the mechanisms underlying the enhanced response due to radiotherapy\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOur data revealed that the differential species, metabolites and key genes varied among patients in different response groups, leading to our hypothesis that for RC patients receiving presurgical immunotherapy, an independent diagnostic model should be used to screen for responsive patients more precisely. The separate metabolites at the baseline, postradiotherapy, and postimmunotherapy stages are shown in \u003cb\u003eFigure S26\u003c/b\u003e, and the results revealed that fatty acid and bile acid levels were significantly altered in different response cohorts. Moreover, differences in the metagenome and metaproteome at different treatment stages were also revealed (\u003cb\u003eFigure S27 to S34\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eTo further investigate the potential to use microbial and metabolic profiles as diagnostic markers, we built random forest (RF), logistic regression (LR), and gradient boosting (GB) models to discriminate patients in the Response and Nonresponse groups, especially patients at baseline. Boruta feature selection was performed in the response cohort to select key discriminatory bacterial, metabolic and proteomic taxa. The selected multiomics data were different among the different response groups, which supported our hypothesis that RC patients with different responses to ICIs need different biomarkers and diagnostic models (the analysis flow chart is shown in \u003cb\u003eFigure S35\u003c/b\u003e). Overlapping the separate significant markers, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA shows the heatmap expression analysis of selected markers between the baseline and postimmunotherapy stages. Moreover, the areas under the receiver operating characteristic curves (AUROCs) of the diagnostic classifiers constructed with metabolic, microbial and metaproteomic markers were 0.945, 0.892, and 0.999 in the baseline, postradiotherapy and postimmunotherapy groups, respectively (the GRs are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, and the LRs are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). To examine the robustness and efficacy of the diagnostic strategy, we conducted repeated group-to-group validation analyses of the Baseline cohort and calculated a model predictive ability of 0.854; in contrast, postimmunotherapy validation revealed 0.999 predictive ability. Overall, the features selected from each group showed excellent potential for identifying RC patients who will exhibit pathological responses to neoadjuvant immunotherapy, indicating the great potential of this screening strategy in people at pretreatment stages.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eImmune checkpoint inhibitors (ICIs) have revolutionized the field of oncology, and their application in neoadjuvant therapy is currently under intense investigation[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The advancement of the use of neoadjuvant immunotherapy for treating RC has shown promising results, offering new treatment options for patients with high risk of recurrence [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Many reports have demonstrated a consistency of 40% pCR rates after treatment with nCRT and ICIs. However, precise biomarkers for predicting the response of individuals are needed. Recent studies have highlighted the significant impact of the gut microbiota on the efficacy of cancer immunotherapies, such as ICIs and CAR-T cells[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. For example, the presence of specific bacterial strains within the microbiota has been associated with better responses to ICIs in patients with epithelial tumors[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. \u003cem\u003eBifidobacterium\u003c/em\u003e species have been shown to promote antitumor immunity and enhance the efficacy of anti-PDL1 treatment[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThus, our study first highlights the potential signature of using fecal samples\u0026mdash;including metagenomics, metaproteomics, and metabolomics\u0026mdash;to understand the changes in RC after neoadjuvant immunotherapy[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The integration of metagenomic, proteomic, and metabolomic data provides a comprehensive view of the tumor microenvironment and systemic changes induced by immunotherapy[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Some consistently altered differential taxa and metabolites have been identified across ICI response groups, especially \u003cem\u003eP. stomatis\u003c/em\u003e and \u003cem\u003eIsourolithinifaciens\u003c/em\u003e, whose abundances are decreased after immunotherapy. \u003cem\u003eP. stomatis\u003c/em\u003e has been reported to be an oncogenic bacterium and to contribute to nonresponsiveness to RTK inhibitors in RC patients [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The increased abundance of \u003cem\u003eP. stomatis\u003c/em\u003e in patient with RC treated with ICIs may partly explain the oncogenic role of promoting CRC cell viability. Additionally, samples from ICI responders have unique metabolic characteristics. In this study, we discovered a prominent feature characterized by downregulated 3-methyl-2-oxopentanoic acid, oxoglutaric acid, azelaic acid, and HDCA and upregulated UCA, UDCA, HCA and CDCA. A higher level of UDCA was reported in primary biliary cholangitis patients [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], indicating that patients with an adequate response to UDCA remain at risk of poor outcomes. Collectively, these findings emphasize the multifaceted impact of the gut microbiota and metabolic signatures on cancer immunotherapy. Notably, in our dataset, fecal cholesterol levels were positively correlated with response status at different stages, which has rarely been investigated in previous reports[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Considering the complex hepatic and intestinal circulation of bile acids, this finding may be the result of disrupted gut bacteria due to ICI treatment.\u003c/p\u003e \u003cp\u003eTo more thoroughly understand the characteristics of alterations in RC patients receiving immunotherapy, we first conducted a comparative analysis of metaproteomic data from the Zhongshan Hospital cohort. Surprisingly, despite the diverse geographical origins of these cohorts, we observed a high degree of diversity similarity in terms of peptides, proteins, COGs, and KOs. Additionally, when we compared fecal protein characteristics between samples from the Response and Nonresponse groups, as well as among samples from patients at different treatment stages, we again found substantial similarities. Protein functional analysis revealed that human-derived proteins are predominantly associated with pathways such as \u0026ldquo;starch and sucrose metabolism\u0026rdquo;, \u0026ldquo;galactose metabolism\u0026rdquo; and \u0026ldquo;thiamine metabolism\u0026rdquo;. The activation of these pathways may be associated with external pressure on the rectal mucosa. On the other hand, microbial-derived proteins are predominantly associated with pathways such as \u0026ldquo;protein digestion and absorption\u0026rdquo; and the \u0026ldquo;renin\u0026thinsp;\u0026minus;\u0026thinsp;angiotensin system\u0026rdquo;, which play crucial roles in microbial growth and metabolism.\u003c/p\u003e \u003cp\u003eOur study is the first to explore the correlation between fecal multiomics data and therapy-related side effects. We identified bHDCA, aMCA, GCA and ortho\u0026thinsp;\u0026minus;\u0026thinsp;hydroxyphenylacetic acid as significant indicators for the early detection of AEs. Moreover, we identified \u003cem\u003eStreptococcus mutans\u003c/em\u003e and \u003cem\u003eAggregatibacter\u003c/em\u003e as members of the AE-related microbiota. A previous study revealed that LTA D-alanylation plays an important role in \u003cem\u003eStreptococcus mutans\u003c/em\u003e acidogenesis by altering glycolytic activity[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], whereas \u003cem\u003eAggregatibacter\u003c/em\u003e was reported to lead to gut dysbiosis and to be correlated with treatment-related gastrointestinal dysfunction[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSpecifically, our predictive models, which are based on multiomics data, offer promising tools for forecasting treatment responses. This could allow clinicians to tailor immunotherapy protocols more precisely, thereby increasing the likelihood of achieving a complete clinical response or minimizing adverse effects in nonresponders. Through integrated analysis and screening, we identified a panel including 6 microbiota members, 4 meta proteins and 4 metabolites as a remarkable model to predict response early in the course of treatment to optimize therapeutic strategies and improve patient prognosis. Microbial, metabolic and metaproteomic signatures selected from each treatment stage showed promising efficacy in distinguishing patients in the response and nonresponse cohorts. Our diagnostic model, which was constructed for individuals, especially those in the baseline stages, could increase diagnostic efficacy, suggesting a reasonable and powerful screening strategy for detecting RC stratified by final response. Therefore, we suggest that personalized screening strategies may help improve the identification of individuals with a greater risk of ICI resistance and reduce the incidence of related AEs in RC patients.\u003c/p\u003e \u003cp\u003eIn conclusion, our findings suggest several directions for future research. First, longitudinal studies with larger patient cohorts are necessary to validate the predictive models and assess their robustness across diverse populations. Additionally, exploring the mechanisms underlying the observed omics changes could provide deeper insights into how immunotherapy exerts its effects and help identify novel therapeutic targets. Moreover, the integration of these multiomics approaches could further refine our understanding of tumor biology and treatment response. This integrated approach may ultimately lead to more effective and personalized treatment strategies for RC and other cancers.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMSS, microsatellite stable; RC, rectal cancer; ICI, immune checkpoint inhibitors; LARC, locally advanced rectal cancer; nCRT, neoadjuvant chemoradiotherapy; PDL1, programmed cell death protein 1; ICD, immunogenic cell death; DAMPs, damage-associated molecular patterns; CTLs, cytotoxic T lymphocytes; TME, total mesorectal excision; AJCC, American Joint Committee on Cancer Guidelines; TRG, Tumor regression grade; LEfSe, Linear discriminant analysis effect size; PCoA, principal coordinate analysis; NMDS, nonmetric multidimensional scaling; UPGMA, unweighted pair-group method with arithmetic means; PCA, principal component analysis, PLS-DA, partial least square discriminant analysis, OPLS-DA, orthogonal partial least square discriminant analysis; pCR, pathological complete response; O2PLS, two-way orthogonal partial least squares;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis manuscript has not been submitted to any other journal and is not currently being considered for publication by another journal. We Thank the doctors and nurses who performed the treatment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient and Public Involvement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients or the public WERE NOT involved in the design, or conduct, or reporting, or dissemination plans of our research\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all patients. The study protocol followed the ethical guidelines of the Declaration of Helsinki and was approved by the Ethics Committee of Zhongshan Hospital of Fudan University. The ethical number was 2022-019.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe have obtained consent to publish from the participant to report individual patient data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProf. Xu JianMin and Prof. Ye LeChi contributed to the design of this study. Dr. Lv Yang, Tang WenTao, Ji MeiLing, Zhang HongYu and Zhu ZheHui performed the research, and Dr. Lv Yang, Tang Wentao, Lin Qi, Niu ZhengChuan and Ji Meiling analyzed and interpreted the patient data. Pro. Zhang Xian and Pro. Wang Jian provided radiotherapy for rectal cancer patietns. Dr. Lv Yang was a major contributor to the writing of the manuscript. Dr. Ji MeiLing provided research background and perspectives. Prof. Xu JianMin, Dr. Tang WenTao were the corresponding authors and approved the final version of this manuscript for publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Natural Science Foundation of China (82303889), Clinical research project of Health Professions of Shanghai Municipal Health Commission (20214Y0277), Beijing Bethune Charitable Foundation (1017), the Natural Science Foundation of Shanghai (23ZR1410400 and 23ZR1410600) and Shanghai Sailing Program (23YF1406100). The funding bodies had no role in the design of the study; collection, analysis, and interpretation of data; or in the writing of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Giaquinto AN, Jemal A: \u003cstrong\u003eCancer statistics, 2024\u003c/strong\u003e. \u003cem\u003eCA Cancer J Clin \u003c/em\u003e2024, \u003cstrong\u003e74\u003c/strong\u003e(1):12-49.\u003c/li\u003e\n\u003cli\u003eWei W, Zeng H, Zheng R, Zhang S, An L, Chen R, Wang S, Sun K, Matsuda T, Bray F\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eCancer registration in China and its role in cancer prevention and control\u003c/strong\u003e. \u003cem\u003eLancet Oncol \u003c/em\u003e2020, \u003cstrong\u003e21\u003c/strong\u003e(7):e342-e349.\u003c/li\u003e\n\u003cli\u003eKeller DS, Berho M, Perez RO, Wexner SD, Chand M: \u003cstrong\u003eThe multidisciplinary management of rectal cancer\u003c/strong\u003e. \u003cem\u003eNat Rev Gastroenterol Hepatol \u003c/em\u003e2020, \u003cstrong\u003e17\u003c/strong\u003e(7):414-429.\u003c/li\u003e\n\u003cli\u003eFeeney G, Sehgal R, Sheehan M, Hogan A, Regan M, Joyce M, Kerin M: \u003cstrong\u003eNeoadjuvant radiotherapy for rectal cancer management\u003c/strong\u003e. \u003cem\u003eWorld J Gastroenterol \u003c/em\u003e2019, \u003cstrong\u003e25\u003c/strong\u003e(33):4850-4869.\u003c/li\u003e\n\u003cli\u003eSauer R, Becker H, Hohenberger W, Rodel C, Wittekind C, Fietkau R, Martus P, Tschmelitsch J, Hager E, Hess CF\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003ePreoperative versus postoperative chemoradiotherapy for rectal cancer\u003c/strong\u003e. \u003cem\u003eN Engl J Med \u003c/em\u003e2004, \u003cstrong\u003e351\u003c/strong\u003e(17):1731-1740.\u003c/li\u003e\n\u003cli\u003eGalluzzi L, Aryankalayil MJ, Coleman CN, Formenti SC: \u003cstrong\u003eEmerging evidence for adapting radiotherapy to immunotherapy\u003c/strong\u003e. \u003cem\u003eNat Rev Clin Oncol \u003c/em\u003e2023, \u003cstrong\u003e20\u003c/strong\u003e(8):543-557.\u003c/li\u003e\n\u003cli\u003eMcLaughlin M, Patin EC, Pedersen M, Wilkins A, Dillon MT, Melcher AA, Harrington KJ: \u003cstrong\u003eInflammatory microenvironment remodelling by tumour cells after radiotherapy\u003c/strong\u003e. \u003cem\u003eNat Rev Cancer \u003c/em\u003e2020, \u003cstrong\u003e20\u003c/strong\u003e(4):203-217.\u003c/li\u003e\n\u003cli\u003eVaes RDW, Hendriks LEL, Vooijs M, De Ruysscher D: \u003cstrong\u003eBiomarkers of Radiotherapy-Induced Immunogenic Cell Death\u003c/strong\u003e. \u003cem\u003eCells \u003c/em\u003e2021, \u003cstrong\u003e10\u003c/strong\u003e(4).\u003c/li\u003e\n\u003cli\u003eLi C, Wang B, Tu J, Liu C, Wang Y, Chen J, Huang Y, Liu B, Yuan X: \u003cstrong\u003eATM inhibition enhance immunotherapy by activating STING signaling and augmenting MHC Class I\u003c/strong\u003e. \u003cem\u003eCell Death Dis \u003c/em\u003e2024, \u003cstrong\u003e15\u003c/strong\u003e(7):519.\u003c/li\u003e\n\u003cli\u003eXia F, Wang Y, Wang H, Shen L, Xiang Z, Zhao Y, Zhang H, Wan J, Zhang H, Wang Y\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eRandomized Phase II Trial of Immunotherapy-Based Total Neoadjuvant Therapy for Proficient Mismatch Repair or Microsatellite Stable Locally Advanced Rectal Cancer (TORCH)\u003c/strong\u003e. \u003cem\u003eJ Clin Oncol \u003c/em\u003e2024:JCO2302261.\u003c/li\u003e\n\u003cli\u003eLin ZY, Zhang P, Chi P, Xiao Y, Xu XM, Zhang AM, Qiu XF, Wu JX, Yuan Y, Wang ZN\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eNeoadjuvant short-course radiotherapy followed by camrelizumab and chemotherapy in locally advanced rectal cancer (UNION): early outcomes of a multicenter randomized phase III trial\u003c/strong\u003e. \u003cem\u003eAnn Oncol \u003c/em\u003e2024.\u003c/li\u003e\n\u003cli\u003eBando H, Tsukada Y, Inamori K, Togashi Y, Koyama S, Kotani D, Fukuoka S, Yuki S, Komatsu Y, Homma S\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003ePreoperative Chemoradiotherapy plus Nivolumab before Surgery in Patients with Microsatellite Stable and Microsatellite Instability-High Locally Advanced Rectal Cancer\u003c/strong\u003e. \u003cem\u003eClin Cancer Res \u003c/em\u003e2022, \u003cstrong\u003e28\u003c/strong\u003e(6):1136-1146.\u003c/li\u003e\n\u003cli\u003eLin Z, Cai M, Zhang P, Li G, Liu T, Li X, Cai K, Nie X, Wang J, Liu J\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003ePhase II, single-arm trial of preoperative short-course radiotherapy followed by chemotherapy and camrelizumab in locally advanced rectal cancer\u003c/strong\u003e. \u003cem\u003eJ Immunother Cancer \u003c/em\u003e2021, \u003cstrong\u003e9\u003c/strong\u003e(11).\u003c/li\u003e\n\u003cli\u003eDossa F, Chesney TR, Acuna SA, Baxter NN: \u003cstrong\u003eA watch-and-wait approach for locally advanced rectal cancer after a clinical complete response following neoadjuvant chemoradiation: a systematic review and meta-analysis\u003c/strong\u003e. \u003cem\u003eLancet Gastroenterol Hepatol \u003c/em\u003e2017, \u003cstrong\u003e2\u003c/strong\u003e(7):501-513.\u003c/li\u003e\n\u003cli\u003eGopalakrishnan V, Helmink BA, Spencer CN, Reuben A, Wargo JA: \u003cstrong\u003eThe Influence of the Gut Microbiome on Cancer, Immunity, and Cancer Immunotherapy\u003c/strong\u003e. \u003cem\u003eCancer Cell \u003c/em\u003e2018, \u003cstrong\u003e33\u003c/strong\u003e(4):570-580.\u003c/li\u003e\n\u003cli\u003eRouty B, Le Chatelier E, Derosa L, Duong CPM, Alou MT, Daillere R, Fluckiger A, Messaoudene M, Rauber C, Roberti MP\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eGut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors\u003c/strong\u003e. \u003cem\u003eScience \u003c/em\u003e2018, \u003cstrong\u003e359\u003c/strong\u003e(6371):91-97.\u003c/li\u003e\n\u003cli\u003eJanczewski LM, Faski J, Nelson H, Gollub MJ, Eng C, Brierley JD, Palefsky JM, Goldberg RM, Washington MK, Asare EA\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eSurvival outcomes used to generate version 9 American Joint Committee on Cancer staging system for anal cancer\u003c/strong\u003e. \u003cem\u003eCA Cancer J Clin \u003c/em\u003e2023, \u003cstrong\u003e73\u003c/strong\u003e(5):516-523.\u003c/li\u003e\n\u003cli\u003eGogenur M, Balsevicius L, Bulut M, Colak N, Justesen TF, Fiehn AK, Jensen MB, Host-Rasmussen K, Cappelen B, Gaggar S\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eNeoadjuvant intratumoral influenza vaccine treatment in patients with proficient mismatch repair colorectal cancer leads to increased tumor infiltration of CD8+ T cells and upregulation of PD-L1: a phase 1/2 clinical trial\u003c/strong\u003e. \u003cem\u003eJ Immunother Cancer \u003c/em\u003e2023, \u003cstrong\u003e11\u003c/strong\u003e(5).\u003c/li\u003e\n\u003cli\u003eAsnicar F, Weingart G, Tickle TL, Huttenhower C, Segata N: \u003cstrong\u003eCompact graphical representation of phylogenetic data and metadata with GraPhlAn\u003c/strong\u003e. \u003cem\u003ePeerJ \u003c/em\u003e2015, \u003cstrong\u003e3\u003c/strong\u003e:e1029.\u003c/li\u003e\n\u003cli\u003eSobhani I, Amiot A, Le Baleur Y, Levy M, Auriault ML, Van Nhieu JT, Delchier JC: \u003cstrong\u003eMicrobial dysbiosis and colon carcinogenesis: could colon cancer be considered a bacteria-related disease?\u003c/strong\u003e \u003cem\u003eTherap Adv Gastroenterol \u003c/em\u003e2013, \u003cstrong\u003e6\u003c/strong\u003e(3):215-229.\u003c/li\u003e\n\u003cli\u003eHosomi K, Saito M, Park J, Murakami H, Shibata N, Ando M, Nagatake T, Konishi K, Ohno H, Tanisawa K\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eOral administration of Blautia wexlerae ameliorates obesity and type 2 diabetes via metabolic remodeling of the gut microbiota\u003c/strong\u003e. \u003cem\u003eNat Commun \u003c/em\u003e2022, \u003cstrong\u003e13\u003c/strong\u003e(1):4477.\u003c/li\u003e\n\u003cli\u003eKong C, Liang L, Liu G, Du L, Yang Y, Liu J, Shi D, Li X, Ma Y: \u003cstrong\u003eIntegrated metagenomic and metabolomic analysis reveals distinct gut-microbiome-derived phenotypes in early-onset colorectal cancer\u003c/strong\u003e. \u003cem\u003eGut \u003c/em\u003e2023, \u003cstrong\u003e72\u003c/strong\u003e(6):1129-1142.\u003c/li\u003e\n\u003cli\u003eChong X, Madeti Y, Cai J, Li W, Cong L, Lu J, Mo L, Liu H, He S, Yu C\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eRecent developments in immunotherapy for gastrointestinal tract cancers\u003c/strong\u003e. \u003cem\u003eJ Hematol Oncol \u003c/em\u003e2024, \u003cstrong\u003e17\u003c/strong\u003e(1):65.\u003c/li\u003e\n\u003cli\u003eYang Z, Gao J, Zheng J, Han J, Li A, Liu G, Sun Y, Zhang J, Chen G, Xu R\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eEfficacy and safety of PD-1 blockade plus long-course chemoradiotherapy in locally advanced rectal cancer (NECTAR): a multi-center phase 2 study\u003c/strong\u003e. \u003cem\u003eSignal Transduct Target Ther \u003c/em\u003e2024, \u003cstrong\u003e9\u003c/strong\u003e(1):56.\u003c/li\u003e\n\u003cli\u003eXiao WW, Chen G, Gao YH, Lin JZ, Wu XJ, Luo HL, Lu ZH, Wang QX, Sun R, Cai PQ\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eEffect of neoadjuvant chemoradiotherapy with or without PD-1 antibody sintilimab in pMMR locally advanced rectal cancer: A randomized clinical trial\u003c/strong\u003e. \u003cem\u003eCancer Cell \u003c/em\u003e2024.\u003c/li\u003e\n\u003cli\u003eChambers L, Grencewicz D, Spakowicz D: \u003cstrong\u003eFrom poo to promise: Fecal microbiota transplants support immunotherapy re-sensitization in solid tumors\u003c/strong\u003e. \u003cem\u003eCell Host Microbe \u003c/em\u003e2024, \u003cstrong\u003e32\u003c/strong\u003e(8):1217-1218.\u003c/li\u003e\n\u003cli\u003eQiu J, Jiang Y, Ye N, Jin G, Shi H, Qian D: \u003cstrong\u003eLeveraging the intratumoral microbiota to treat human cancer: are engineered exosomes an effective strategy?\u003c/strong\u003e \u003cem\u003eJ Transl Med \u003c/em\u003e2024, \u003cstrong\u003e22\u003c/strong\u003e(1):728.\u003c/li\u003e\n\u003cli\u003eDerosa L, Routy B, Thomas AM, Iebba V, Zalcman G, Friard S, Mazieres J, Audigier-Valette C, Moro-Sibilot D, Goldwasser F\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eIntestinal Akkermansia muciniphila predicts clinical response to PD-1 blockade in patients with advanced non-small-cell lung cancer\u003c/strong\u003e. \u003cem\u003eNat Med \u003c/em\u003e2022, \u003cstrong\u003e28\u003c/strong\u003e(2):315-324.\u003c/li\u003e\n\u003cli\u003eKruk ME, Mehta S, Murray K, Higgins L, Do K, Johnson JE, Wagner R, Wendt CH, O\u0026apos;Connor JB, Harris JK\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eAn integrated metaproteomics workflow for studying host-microbe dynamics in bronchoalveolar lavage samples applied to cystic fibrosis disease\u003c/strong\u003e. \u003cem\u003emSystems \u003c/em\u003e2024, \u003cstrong\u003e9\u003c/strong\u003e(7):e0092923.\u003c/li\u003e\n\u003cli\u003eLi J, Chen Z, Wang Q, Du L, Yang Y, Guo F, Li X, Chao Y, Ma Y: \u003cstrong\u003eMicrobial and metabolic profiles unveil mutualistic microbe-microbe interaction in obesity-related colorectal cancer\u003c/strong\u003e. \u003cem\u003eCell Rep Med \u003c/em\u003e2024, \u003cstrong\u003e5\u003c/strong\u003e(3):101429.\u003c/li\u003e\n\u003cli\u003eHuang P, Ji F, Cheung AH, Fu K, Zhou Q, Ding X, Chen D, Lin Y, Wang L, Jiao Y\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003ePeptostreptococcus stomatis promotes colonic tumorigenesis and receptor tyrosine kinase inhibitor resistance by activating ERBB2-MAPK\u003c/strong\u003e. \u003cem\u003eCell Host Microbe \u003c/em\u003e2024, \u003cstrong\u003e32\u003c/strong\u003e(8):1365-1379 e1310.\u003c/li\u003e\n\u003cli\u003eCorpechot C, Lemoinne S, Soret PA, Hansen B, Hirschfield G, Gulamhusein A, Montano-Loza AJ, Lytvyak E, Pares A, Olivas I\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eAdequate versus deep response to ursodeoxycholic acid in primary biliary cholangitis: To what extent and under what conditions is normal alkaline phosphatase level associated with complication-free survival gain?\u003c/strong\u003e \u003cem\u003eHepatology \u003c/em\u003e2024, \u003cstrong\u003e79\u003c/strong\u003e(1):39-48.\u003c/li\u003e\n\u003cli\u003eGuo Z, He K, Pang K, Yang D, Lyu C, Xu H, Wu D: \u003cstrong\u003eExploring Advanced Therapies for Primary Biliary Cholangitis: Insights from the Gut Microbiota-Bile Acid-Immunity Network\u003c/strong\u003e. \u003cem\u003eInt J Mol Sci \u003c/em\u003e2024, \u003cstrong\u003e25\u003c/strong\u003e(8).\u003c/li\u003e\n\u003cli\u003eXu L, Zhang Y, Lin Z, Deng X, Ren X, Huang M, Li S, Zhou Q, Fang F, Yang Q\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eFASN-mediated fatty acid biosynthesis remodels immune environment in Clonorchis sinensis infection-related intrahepatic cholangiocarcinoma\u003c/strong\u003e. \u003cem\u003eJ Hepatol \u003c/em\u003e2024, \u003cstrong\u003e81\u003c/strong\u003e(2):265-277.\u003c/li\u003e\n\u003cli\u003eWu M, Huang S, Du J, Li Y, Jiang S, Zhan L, Huang X: \u003cstrong\u003eD-alanylation of lipoteichoic acid contributes to biofilm formation and acidogenesis capacity of Streptococcusmutans\u003c/strong\u003e. \u003cem\u003eMicrob Pathog \u003c/em\u003e2022, \u003cstrong\u003e169\u003c/strong\u003e:105666.\u003c/li\u003e\n\u003cli\u003eMukherjee S, Chopra A, Karmakar S, Bhat SG: \u003cstrong\u003ePeriodontitis increases the risk of gastrointestinal dysfunction: an update on the plausible pathogenic molecular mechanisms\u003c/strong\u003e. \u003cem\u003eCrit Rev Microbiol \u003c/em\u003e2024:1-31.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Rectal cancer, microsatellite stable, neoadjuvant chemoradiotherapy, PDL1, multiomics analysis, biomarker, personalized medicine","lastPublishedDoi":"10.21203/rs.3.rs-6133863/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6133863/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe potential role of fecal samples from patients with microsatellite stable (MSS) rectal cancer (RC) receiving immunotherapy was lack for investigation. By leveraging multiomics approaches, including metagenomics, metabolomics, and metaproteomics, we aimed to identify signatures that could predict treatment efficacy and adverse events and elucidate the molecular changes associated with different responses.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eFecal samples were collected from patients before and after chemotherapy and immunotherapy. Metagenomic sequencing was performed to characterize microbial composition. Metaproteomic analysis was conducted to assess the protein expression profiles, and metabolomic profiling was utilized to identify metabolic changes. Data integration and differential analysis were performed. A multiomics model was constructed using machine learning algorithms to predict treatment outcomes on the basis of these signatures.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eOur results revealed significant alterations in the fecal microbiome, proteome, and metabolome of patients after neoadjuvant immunotherapy. Differential analysis identified a set of biomarkers, including 6 bacteria, 4 meta-proteins, and 4 metabolites, that were predictive of treatment response. The multiomics model demonstrated high accuracy in predicting treatment efficacy, with a significant correlation between the model's predictions and actual clinical outcomes.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eThis study demonstrated the feasibility of using fecal multi-omics data to predict the efficacy of neoadjuvant immunotherapy in RC patients. The identified biomarkers and the multiomics model provide a novel approach for personalized medicine, potentially improving treatment strategies and patient outcomes. Further validation in larger cohorts is warranted to refine the predictive model and explore its clinical applicability.\u003c/p\u003e","manuscriptTitle":"Integrated multiomics reveals gut microbiota-protein-metabolite alterations that regulate adverse events and responses to radio-chemo-immunotherapy in microsatellite stable rectal cancer: a prospective longitudinal study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-11 06:41:43","doi":"10.21203/rs.3.rs-6133863/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ad14b2d9-b7b6-4087-a7be-16946387f9c3","owner":[],"postedDate":"March 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-23T22:53:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-11 06:41:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6133863","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6133863","identity":"rs-6133863","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}