Prospective, longitudinal analysis of the gut microbiome in patients with locally advanced rectal cancer predicts response to neoadjuvant concurrent chemoradiotherapy

Journal of Translational Medicine - 2023
Yi Sun1,2, Xiang Zhang1, Chuandi Jin3,4, Kaile Yue4,3, Dashuang Sheng4,3, Tao Zhang3, Xue Dou1, Jing Liu1, Hongbiao Jing5, Lei Zhang4,3,6, Jinbo Yue1
1Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
2The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
3Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
4Microbiome-X, National Institute of Health Data Science of China & Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
5Department of Pathology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
6State Key Laboratory of Microbial Technology, Shandong University, Qingdao, China

Tóm tắt

Neoadjuvant concurrent chemoradiotherapy (nCCRT) is a standard treatment for locally advanced rectal cancer (LARC). The gut microbiome may be reshaped by radiotherapy through its effects on microbial composition, mucosal immunity, and the systemic immune system. We sought to clarify dynamic, longitudinal changes in the gut microbiome and blood immunomodulators throughout nCCRT and to explore the relationship of such changes with outcomes after nCCRT. A total of 39 patients with LARC were recruited for this study. Fecal samples and peripheral blood samples were collected from all 39 patients before nCCRT, during nCCRT (at week 3), and after nCCRT (at week 5). The gut microbiota and the microbial community structure were analyzed by 16S rRNA sequencing of the V3–V4 region. Levels of blood immunomodulatory proteins were measured with a Millipore HCKPMAG-11 K kit and Luminex 200 platform (Luminex, USA). Cross-sectional and longitudinal analyses revealed that the gut microbiome profile and enterotype exhibited characteristic variations that could distinguish patients with good response (AJCC TRG classification 0–1) vs poor response (TRG 2–3) to nCCRT. Sparse partial least squares regression and canonical correspondence analyses showed multivariate associations between specific microbial taxa, host immunomodulatory proteins, immune cells, and outcomes after nCCRT. An integrated model consisting of baseline Clostridium sensu stricto 1 levels, fold changes in Intestinimonas, blood levels of the herpesvirus entry mediator (HVEM/CD270), and lymphocyte counts could predict good vs poor outcome after nCCRT [area under the receiver-operating characteristics curve (AUC)= 0.821; area under the precision-recall curve [AUPR] = 0.911]. Our results showed that longitudinal variations in specific gut taxa, associated host immune cells, and immunomodulatory proteins before and during nCCRT could be useful for early predictions of the efficacy of nCCRT, which could guide the choice of individualized treatment for patients with LARC.

Từ khóa


Tài liệu tham khảo

Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, Jemal A, Yu XQ, He J. Cancer statistics in China, 2015. CA Cancer J Clin. 2016;66(2):115–32.

Janjan NA, Khoo VS, Abbruzzese J, Pazdur R, Dubrow R, Cleary KR, Allen PK, Lynch PM, Glober G, Wolff R, Rich TA, Skibber J. Tumor downstaging and sphincter preservation with preoperative chemoradiation in locally advanced rectal cancer: the M. D. Anderson cancer center experience. Int J Radiat Oncol Biol Phys. 1999;44(5):1027–38.

Hoda, Syed, A., AJCC Cancer Staging Manua, 8th edition, Advances in Anatomic Pathology (2017).

MB Amin, FL Greene, SB Edge, CC Compton, JE Gershenwald, RK Brookland, L Meyer, DM Gress, DR Byrd, DP Winchester. 2017. The Eighth Edition AJCC Cancer Staging Manual Continuing to build a bridge from a population-based to a more "personalized" approach to cancer staging. CA Cancer J Clin. 67(2):93–99

van der Valk MJM, Hilling DE, Bastiaannet E, Meershoek-Klein Kranenbarg E, Beets GL, Figueiredo NL, Habr-Gama A, Perez RO, Renehan AG, van de Velde CJH. Long-term outcomes of clinical complete responders after neoadjuvant treatment for rectal cancer in the international watch & wait database (IWWD): an international multicentre registry study. Lancet. 2018;391(10139):2537–45.

Roy S, Trinchieri G. Microbiota: a key orchestrator of cancer therapy. Nat Rev Cancer. 2017;17(5):271–85.

Hanahan D. Hallmarks of cancer: new dimensions. Cancer Discov. 2022;12(1):31–46.

Cho I, Blaser MJ. The human microbiome: at the interface of health and disease. Nat Rev Genet. 2012;13(4):260–70.

Bultman SJ. Emerging roles of the microbiome in cancer. Carcinogenesis. 2014;35(2):249–55.

Schwabe RF, Jobin C. The microbiome and cancer. Nat Rev Cancer. 2013;13(11):800–12.

Helmink BA, Khan MAW, Hermann A, Gopalakrishnan V, Wargo JA. The microbiome, cancer, and cancer therapy. Nat Med. 2019;25(3):377–88.

Dizman N, Meza L, Bergerot P, Alcantara M, Dorff T, Lyou Y, Frankel P, Cui Y, Mira V, Llamas M, Hsu J, Zengin Z, Salgia N, Salgia S, Malhotra J, Chawla N, Chehrazi-Raffle A, Muddasani R, Gillece J, Reining L, Trent J, Takahashi M, Oka K, Higashi S, Kortylewski M, Highlander SK, Pal SK. Nivolumab plus ipilimumab with or without live bacterial supplementation in metastatic renal cell carcinoma: a randomized phase 1 trial. Nature Med. 2022;4:704.

Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, Alexander H, Alm EJ, Arumugam M, Asnicar F. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37(8):852–7.

Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13(7):581–3.

Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glöckner FO. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41(Database issue):D590–6.

McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8(4):e61217.

Sun Y, Zuo T, Cheung CP, Gu W, Wan Y, Zhang F, Chen N, Zhan H, Yeoh YK, Niu J, Du Y, Zhang F, Wen Y, Yu J, Sung JJY, Chan PKS, Chan FKL, Wang K, Ng SC, Miao Y. Population-level configurations of gut Mycobiome across 6 ethnicities in urban and rural China. Gastroenterology. 2021. https://doi.org/10.1053/j.gastro.2020.09.014.

Chen L, Zhai Y, Wang Y, Fearon ER, Núñez G, Inohara N, Cho KR. Altering the microbiome inhibits tumorigenesis in a mouse model of Oviductal high-grade serous carcinoma. Can Res. 2021;81(12):3309–18.

Xiao L, Wang J, Zheng J, Li X, Zhao F. Deterministic transition of enterotypes shapes the infant gut microbiome at an early age. Genome Biol. 2021;22(1):243.

Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8(1):118–27.

Gao Y, Zhu Z, Sun F. Increasing prediction performance of colorectal cancer disease status using random forests classification based on metagenomic shotgun sequencing data. Synth Syst Biotechnol. 2022;7(1):574–85.

Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012;28(6):882–3.

Plantinga AM, Chen J, Jenq RR, Wu MC. pldist: ecological dissimilarities for paired and longitudinal microbiome association analysis. Bioinformatics. 2019;35(19):3567–75.

Mitchell CM, Ma N, Mitchell AJ, Wu MC, Valint DJ, Proll S, Reed SD, Guthrie KA, Lacroix AZ, Larson JC, Pepin R, Raftery D, Fredricks DN, Srinivasan S. Association between postmenopausal vulvovaginal discomfort, vaginal microbiota, and mucosal inflammation. Am J Obstet Gynecol. 2021. https://doi.org/10.1016/j.ajog.2021.02.034.

von Schwartzenberg RJ, Bisanz JE, Lyalina S, Spanogiannopoulos P, Ang QY, Cai J, Dickmann S, Friedrich M, Liu SY, Collins SL, Ingebrigtsen D, Miller S, Turnbaugh JA, Patterson AD, Pollard KS, Mai K, Spranger J, Turnbaugh PJ. Caloric restriction disrupts the microbiota and colonization resistance. Nature. 2021;595(7866):272–7.

Oluwagbemigun K, O’Donovan AN, Berding K, Lyons K, Alexy U, Schmid M, Clarke G, Stanton C, Cryan J, Nöthlings U. Long-term dietary intake from infancy to late adolescence is associated with gut microbiota composition in young adulthood. Am J Clin Nutr. 2021;113(3):647–56.

Chen EZ, Li H. A two-part mixed-effects model for analyzing longitudinal microbiome compositional data. Bioinformatics. 2016;32(17):2611–7.

Brunson JC. ggalluvial: Layered grammar for alluvial plots. J Open Source Softw. 2020;5(49):2017. https://doi.org/10.21105/joss.02017.

M Maechler, P Rousseeuw, A Struyf, M Hubert, K Hornik. Cluster: Cluster Analysis Basics and Extensions. 2012.

FHC Jr Dupont. Hmisc: Harrell Miscellaneous. 2015.

Wei T, Simko V. corrplot: visualization of a correlation matrix. MMWR Morb Mortal Wkly Rep. 2013;52(12):145–51.

Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin P, O’Hara RB, Simpson G, Solymos P, Stevens MHH, Szöcs E, Wagner H. Vegan community ecology package version 2.5-7 November 2020. 2020. https://www.researchgate.net/publication/346579465_vegan_community_ecology_package_version_25-7_November_2020.

Rohart F, Gautier B, Singh A, Lê Cao K-A. mixOmics: An R package for ’omics feature selection and multiple data integration. PLoS Comput Biol. 2017;13(11):e1005752.

Sing T, Sander O, Beerenwinkel N, Lengauer T. ROCR: visualizing classifier performance in R. Bioinformatics. 2005;21(20):3940–1.

J Brownlee. Probability for machine learning. Discover how to harness uncertainty with python. 2020.

Chen L, Wang D, Garmaeva S, Kurilshikov A, Vich Vila A, Gacesa R, Sinha T, Segal E, Weersma RK, Wijmenga C, Zhernakova A, Fu J. The long-term genetic stability and individual specificity of the human gut microbiome. Cell. 2021;184(9):2302.

Yamada S, Takiyama H, Isozaki Y, Shinoto M, Ebner DK, Koto M, Tsuji H, Miyauchi H, Sekimoto M, Ueno H, Itabashi M, Ikeda M, Matsubara H. Carbon ion radiotherapy for locally recurrent rectal cancer of patients with prior pelvic irradiation. Ann Surg Oncol. 2022;29(1):99.

Ferreira MR, Sands CJ, Li JV, Andreyev JN, Chekmeneva E, Gulliford S, Marchesi J, Lewis MR, Dearnaley DP. Impact of pelvic radiation therapy for prostate cancer on global metabolic profiles and microbiota-driven gastrointestinal late side effects: a longitudinal observational study. Int J Radiat Oncol Biol Phys. 2021;111(5):1204–13.

Gong S, Feng Y, Zeng Y, Zhang H, Pan M, He F, Wu R, Chen J, Lu J, Zhang S, Yuan S, Chen X. Gut microbiota accelerates cisplatin-induced acute liver injury associated with robust inflammation and oxidative stress in mice. J Transl Med. 2021;19(1):147.

Viaud S, Daillère R, Boneca IG, Lepage P, Langella P, Chamaillard M, Pittet MJ, Ghiringhelli F, Trinchieri G, Goldszmid R, Zitvogel L. Gut microbiome and anticancer immune response: really hot Sh*t! Cell Death Differ. 2015;22(2):199–214.

Joyce K, Saxena S, Williams A, Damurjian C, Auricchio N, Aluotto S, Tynan H, Demain AL. Antimicrobial spectrum of the antitumor agent, cisplatin. J Antibiot. 2010;63(8):530–2.

Tong J, Zhang X, Fan Y, Chen L, Ma X, Yu H, Li J, Guan X, Zhao P, Yang J. Changes of intestinal microbiota in ovarian cancer patients treated with surgery and chemotherapy. Cancer Manag Res. 2020;12:8125–35.

Kong C, Gao R, Yan X, Huang L, He J, Li H, You J, Qin H. Alterations in intestinal microbiota of colorectal cancer patients receiving radical surgery combined with adjuvant CapeOx therapy. Sci China Life Sci. 2019;62(9):1178–93.

Liu J, Liu C, Yue J. Radiotherapy and the gut microbiome: facts and fiction. Radiat Oncol. 2021;16(1):9.

Routy B, Le Chatelier E, Derosa L, Duong CPM, Alou MT, Daillère R, Fluckiger A, Messaoudene M, Rauber C, Roberti MP, Fidelle M, Flament C, Poirier-Colame V, Opolon P, Klein C, Iribarren K, Mondragón L, Jacquelot N, Qu B, Ferrere G, Clémenson C, Mezquita L, Masip JR, Naltet C, Brosseau S, Kaderbhai C, Richard C, Rizvi H, Levenez F, Galleron N, Quinquis B, Pons N, Ryffel B, Minard-Colin V, Gonin P, Soria J-C, Deutsch E, Loriot Y, Ghiringhelli F, Zalcman G, Goldwasser F, Escudier B, Hellmann MD, Eggermont A, Raoult D, Albiges L, Kroemer G, Zitvogel L. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science. 2018;359(6371):91–7.

Guo H, Chou W-C, Lai Y, Liang K, Tam JW, Brickey WJ, Chen L, Montgomery ND, Li X, Bohannon LM, Sung AD, Chao NJ, Peled JU, Gomes ALC, van den Brink MRM, French MJ, Macintyre AN, Sempowski GD, Tan X, Sartor RB, Lu K, Ting JPY. Multi-omics analyses of radiation survivors identify radioprotective microbes and metabolites. Science. 2020;370(6516):eaay9097.

Shiao SL, Kershaw KM, Limon JJ, You S, Yoon J, Ko EY, Guarnerio J, Potdar AA, McGovern DPB, Bose S, Dar TB, Noe P, Lee J, Kubota Y, Maymi VI, Davis MJ, Henson RM, Choi RY, Yang W, Tang J, Gargus M, Prince AD, Zumsteg ZS, Underhill DM. Commensal bacteria and fungi differentially regulate tumor responses to radiation therapy. Cancer Cell. 2021;39(9):1202.

Thông tin
Thông tin xuất bản

Journal of Translational Medicine

Thông tin tác giả