Dynamics of the intratumoural microbiome across malignant transformation and treatment in breast cancer

Abstract

Breast cancer (BC) is the most common malignancy in women, yet the dynamics of the intratumoural microbiome during tumour initiation, progression, and treatment remain poorly understood. Prior studies are predominantly cross-sectional and limited by indirect microbial inference from RNA-seq data. This study presents a comprehensive analysis of intratumoural microbiota across breast tissue samples by high-depth 16S rRNA sequencing (11 W tags), featuring two longitudinally paired cohorts for dynamic microbial profiling during tumour progression and treatment. Samples included 165 benign nodules (82 non-transforming, 83 that later progressed to cancer with matched malignant tissues); 180 primary BC tissues and 165 benign controls; and 101 neoadjuvant therapy (NAT) specimens (15 pCR, 86 non-pCR, with paired pre/post-treatment samples). We identified a cluster of taxa (Aeromicrobium, Halomonas, Dietzia, Nesterenkonia, Delftia, Nitriliruptor) depleted in nodules undergoing malignant transformation, declining with disease progression and partially restored after NAT, with transient enrichment early in transformation. Opposing trends were observed for Paenibacillus and Methyloversatilis. These changes corresponded to shifts in amino acid, lipid, and glycan metabolism. FISH and TEM analyses identified Paenibacillus pasadenensis and Halomonas hamiltonii within tumour cells, with opposing effects on tumour proliferation and activation. In addition, we developed two predictive models with high clinical relevance: one stratifying malignancy risk in nodules, and another predicting NAT response, both of which achieved strong performance in external validation. This longitudinal characterisations of intratumoural microbiota during breast tumourigenesis and treatment offer novel insights for precision oncology and microbiome-based interventions in breast cancer.

Keywords

16s RNA / breast cancer / cancer development / intratumoural microbiome / malignant transformation / neoadjuvant therapy / predictive model

References

[1] Barzaman K, Karami J, Zarei Z, et al. Breast cancer: biology, biomarkers, and treatments. Int Immunopharmacol. 2020; 84: 106535.

[2] Zhang Y, Ji Y, Liu S, et al. Global burden of female breast cancer: new estimates in 2022, temporal trend and future projections up to 2050 based on the latest release from GLOBOCAN. JNCC. 2025.

[3] Xu H, Xu B. Breast cancer: epidemiology, risk factors and screening. Chin J Cancer Res. 2023; 35(6): 565-583.

[4] Bi Z, Wang Y. Advances in regional nodal management of early-stage breast cancer. Chin J Cancer Res. 2024; 36(2): 215-225.

[5] Wang Y, Xu B. Recent advances in systematic therapy of breast cancer: Chinese contribution for international progress. Chin J Cancer Res. 2024; 36(6): 587-591.

[6] Yang L, Li A, Wang Y, Zhang Y. Intratumoral microbiota: roles in cancer initiation, development and therapeutic efficacy. Signal Transduct Target Ther. 2023; 8(1): 35.

[7] Tumor-resident intracellular microbiota promotes metastatic colonization in breast cancer. Cell. 2022; 185(8): 1356-1372.e26.

[8] Chang CM, Pekkle Lam HY. Intratumoral microbiota: unraveling their oncogenic impact on cancer progression with focus on breast cancer therapeutic outcomes. Anticancer Res. 2024; 44(6): 2271-2285.

[9] Untch M, von Minckwitz G. Neoadjuvant chemotherapy: early response as a guide for further treatment: clinical, radiological, and biological. J Natl Cancer Inst Monogr. 2011; 2011(43): 138-141.

[10] Tan W, Yang M, Yang H, Zhou F, Shen W. Predicting the response to neoadjuvant therapy for early-stage breast cancer: tumor-, blood-, and imaging-related biomarkers. Cancer Manag Res. 2018; 10: 4333-4347.

[11] Qin W, Li J, Gao N, et al. Multiomics-based molecular subtyping based on the commensal microbiome predicts molecular characteristics and the therapeutic response in breast cancer. Molecular Cancer. 2024; 23(1).

[12] Magoč T, Salzberg SL. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics. 2011; 27(21): 2957-2963.

[13] Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014; 30(15): 2114-2120.

[14] Bolyen E, Rideout JR, Dillon MR, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019; 37(8): 852-857.

[15] Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016; 13(7): 581-583.

[16] Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007; 73(16): 5261-5267.

[17] Quast C, Pruesse E, Yilmaz P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013; 41(Database issue): D590-6.

[18] GOWER JC. Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika. 1966; 53(3-4): 325-338.

[19] Segata N, Izard J, Waldron L, et al. Metagenomic biomarker discovery and explanation. Genome Biology. 2011; 12(6): R60.

[20] Deng Y, Jiang YH, Yang Y, He Z, Luo F, Zhou J. Molecular ecological network analyses. BMC Bioinformatics. 2012; 13: 113.

[21] Han J, Guzman JA, Chu ML. Prediction of gully erosion susceptibility through the lens of the SHapley Additive exPlanations (SHAP) method using a stacking ensemble model. J Environ Manage. 2025; 383: 125478.

[22] Li Y, Xing S, Chen F, et al. Intracellular Fusobacterium nucleatum infection attenuates antitumor immunity in esophageal squamous cell carcinoma. Nat Commun. 2023; 14(1): 5788.

[23] Fu K, Cheung AHK, Wong CC, et al. Streptococcus anginosus promotes gastric inflammation, atrophy, and tumorigenesis in mice. Cell. 2024; 187(4): 882-896.e17.

[24] Zolfaghar M, Amoozegar MA, Khajeh K, Babavalian H, Tebyanian H. Isolation and screening of extracellular anticancer enzymes from halophilic and halotolerant bacteria from different saline environments in Iran. Mol Biol Rep. 2019; 46(3): 3275-3286.

[25] Kokoulin MS, Sigida EN, Kuzmich AS, Ibrahim IM, Fedonenko YP, Konnova SA. Structure and antiproliferative activity of the polysaccharide from Halomonas aquamarina related to Cobetia pacifica. Carbohydr Polym. 2022; 298: 120125.

[26] Patkar S, Shinde Y, Chindarkar P, Chakraborty P. Evaluation of antioxidant potential of pigments extracted from Bacillus spp. and Halomonas spp. isolated from mangrove rhizosphere. BioTechnologia (Pozn). 2021; 102(2): 157-169.

[27] Mostafa YS, Alamri SA, Alfaifi MY, et al. L-glutaminase synthesis by marine Halomonas meridiana isolated from the red sea and its efficiency against colorectal cancer cell lines. Molecules. 2021; 26(7).

[28] Desalegn Z, Smith A, Yohannes M, et al. Human breast tissue microbiota reveals unique microbial signatures that correlate with prognostic features in adult Ethiopian women with breast cancer. Cancers (Basel). 2023; 15(19).

[29] Tavano F, Napoli A, Gioffreda D, et al. Could the microbial profiling of normal pancreatic tissue from healthy organ donors contribute to understanding the intratumoral microbiota signature in pancreatic ductal adenocarcinoma? Microorganisms. 2025; 13(2).

[30] Ozer MS, Incir C, Yildiz HA, et al. Comparison of tissue and urine microbiota in male, intervention naive patients with and without non-invasive bladder cancer. Urol Int. 2025; 109(1): 81-88.

[31] D'Afonseca V, Muñoz EV, Leal AL, Soto P, Parra-Cid C. Implications of the microbiome and metabolic intermediaries produced by bacteria in breast cancer. Genet Mol Biol. 2024; 47(Suppl 1): e20230316.

[32] Dou Y, Ma C, Wang K, et al. Dysbiotic tumor microbiota associates with head and neck squamous cell carcinoma outcomes. Oral Oncol. 2022; 124: 105657.

[33] Sun Y, Gan Z, Wang X, et al. Integrative metagenomic, transcriptomic, and proteomic analysis reveal the microbiota-host interplay in early-stage lung adenocarcinoma among non-smokers. J Transl Med. 2024; 22(1): 652.

[34] Proffitt C, Bidkhori G, Moyes D, Disease ShoaieS. Drugs and dysbiosis: understanding microbial signatures in metabolic disease and medical interventions. Microorganisms. 2020; 8(9).

[35] Bi X, Wang J, Liu C. Intratumoral microbiota: metabolic influences and biomarker potential in gastrointestinal cancer. Biomolecules. 2024; 14(8).

[36] Zhu Z, Cai J, Hou W, et al. Microbiome and spatially resolved metabolomics analysis reveal the anticancer role of gut Akkermansia muciniphila by crosstalk with intratumoral microbiota and reprogramming tumoral metabolism in mice. Gut Microbes. 2023; 15(1): 2166700.

[37] Pacheco JHL, Elizondo G. Interplay between estrogen, kynurenine, and AHR pathways: an immunosuppressive axis with therapeutic potential for breast cancer treatment. Biochem Pharmacol. 2023; 217: 115804.

[38] Ye LY, Zhang Q, Bai XL, Pankaj P, Hu QD, Liang TB. Hypoxia-inducible factor 1α expression and its clinical significance in pancreatic cancer: a meta-analysis. Pancreatology. 2014; 14(5): 391-397.

[39] Tan Z, Xu J, Zhang B, Shi S, Yu X, Liang C. Hypoxia: a barricade to conquer the pancreatic cancer. Cell Mol Life Sci. 2020; 77(16): 3077-3083.

[40] Huang J, Mao Y, Wang L. The crosstalk of intratumor bacteria and the tumor. Front Cell Infect Microbiol. 2023; 13: 1273254.

[41] Zheng X, Liu R, Zhou C, et al. ANGPTL4-mediated promotion of glycolysis facilitates the colonization of Fusobacterium nucleatum in colorectal cancer. Cancer Res. 2021; 81(24): 6157-6170.

[42] Hong J, Guo F, Lu SY, et al. F. nucleatum targets lncRNA ENO1-IT1 to promote glycolysis and oncogenesis in colorectal cancer. Gut. 2021; 70(11): 2123-2137.

[43] Liu H, Du J, Chao S, et al. Fusobacterium nucleatum promotes colorectal cancer cell to acquire stem cell-like features by manipulating lipid droplet-mediated numb degradation. Adv Sci (Weinh). 2022; 9(12): e2105222.

[44] Wang J, Li D, Wu R, Feng D. Cutting-edge advancements in the antibiotics-gut microbiota-urinary tumour axis. Cell Prolif. 2025; 58(5): e70023.

[45] Li D, Wu R, Yu Q, et al. Microbiota and urinary tumor immunity: mechanisms, therapeutic implications, and future perspectives. Chin J Cancer Res. 2024; 36(6): 596-615.

[46] Urbaniak C, Cummins J, Brackstone M, et al. Microbiota of human breast tissue. Appl Environ Microbiol. 2014; 80(10): 3007-3014.

[47] Bhatt AP, Redinbo MR, Bultman SJ. The role of the microbiome in cancer development and therapy. CA Cancer J Clin. 2017; 67(4): 326-344.

[48] Dzutsev A, Goldszmid RS, Viaud S, Zitvogel L, Trinchieri G. The role of the microbiota in inflammation, carcinogenesis, and cancer therapy. Eur J Immunol. 2015; 45(1): 17-31.

RIGHTS & PERMISSIONS

2025 The Author(s). Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics.