3D models for cancer-microbe interaction studies via pattern screening: a focus on lung and breast tumoroids

The complexity and cell heterogeneity of the tumour microenvironment have been extensively delineated in recent years. In particular, the roles of the intratumor microbiome and mycobiome in cancer pathogenesis are increasingly recognised [1,2]. For instance, β-glucan, the cell wall component of the fungus Aspergillus sydowii, promotes lung adenocarcinoma (ADC) progression by modulating interleukin (IL)-1β signalling and the accumulation of immunosuppressive regulatory T cells [3]. In renal carcinoma, the intratumoral mycobiota signature and the intratumoral mycobiota-related gene expression signature strongly predicted prognosis and immunotherapy outcomes. In this study, Aspergillus tanneri was identified as a potential key fungal species affecting cancer prognosis by inducing T cell exhaustion [4]. Interestingly, breast cancer has been described as extremely rich in microbiome niches compared to other tumours [5].All these studies highlight the importance of investigating the expression and function of pattern recognition receptors (PRRs) on cancer cells, which serve as a critical bridge among cancer cells, the microbial environment, and the modulation of innate and adaptive immunity. and absent in melanoma-2 (AIM2)-like receptors (ALRs) [6]. To recognise both extracellular and intracellular threats, PRRs are located on the cell surface and within endosomal compartments. PRRs ligands are broadly classified as microbial-associated molecular patterns (MAMPs), pathogenassociated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs). MAMPs and PAMPs include highly conserved structures that are essential for the growth and proliferation of microorganisms (lipopolysaccharides, proteins, nucleic acids), whereas DAMPs are products of host cell metabolism and death (uric acid, bile acid salts) [7]. PRR-ligand interactions induce cytokine, chemokine, and growth factor secretion, promoting inflammation and adaptive immune responses.Tumour-derived debris and other products are recognised as DAMPs by PRR-expressing innate immune cells, triggering key responses such as antigen presentation to CD8 + T cells and subsequent cytotoxic activity against cancer cells [8]. However, PRRs are also expressed by different tumour cells, including colon, lung, breast and gastric cancer cells [7]. In this context, PRR activation mainly promotes tumour progression [9].Tumoroids represent an optimal three-dimensional system for modelling human tumours in vitro [10].To date, lung ADC tumoroids have been used to perform tumour gene profiling and evaluate tumour drug susceptibility, morphology, and biomarker expression patterns at the air-liquid interface [11].Genomic, epigenomic, proteomic, and histological analyses reliably demonstrated preservation of tumour-specific alterations and lineage features. Together, these studies establish lung tumoroids as realistic patient avatars, providing a robust foundation for functional precision drug testing. Similarly, breast cancer (BC) tumoroids have been used for diagnosis and high-throughput drug testing [12].Here, we assemble further insights into the dynamic links between PRRs, microbiota, and cancer, suggesting how 3D models may become a personalised strategy to investigate ex vivo how individual cancer cells may interact with microbial patterns and how this interaction may affect cancer development, progression, or therapy response. In addition, tumoroids can be used to monitor the levels of DAMPs or PAMPs along with PRR expression. The level of innate activation determines the ability to recognise bacterial, viral, or fungal components, eventually contributing to tumour progression by modifying the microenvironment.Lung cancer is the leading cause of cancer-related deaths worldwide [13]. Non-small-cell lung cancer (NSCLC) accounts for ~80% of cases [13], with ADC being the most common histotype. ADC is characterised by glandular differentiation and typically displays a mixture of histological patterns that serve as prognostic markers. The 5 th World Health Organisation (WHO) classification of thoracic tumours [14] recognises five ADC patterns: lepidic, acinar, papillary, micropapillary, and solid (listed in order of declining prognosis). Immune checkpoint inhibitors, particularly those targeting the programmed death ligand 1, have expanded treatment options, and advanced-stage, non-oncogene addicted tumours with a tumour proportion score ≥50% are eligible for first-line immunotherapy [15]. However, many ADCs are characterised by inadequate anti-tumour T lymphocyte activation, highlighting the need for tumour microenvironment (TME) remodelling to improve the antitumor response.The intratumor microbiome has recently emerged as a key determinant of lung ADC onset, progression, and prognosis [3,16]. By activating innate receptors, the intratumor microbiome strongly affects the TME [17]. Moreover, specific microbes may trigger chronic inflammation, which also affects tumor metastasis. A recent study characterised the intratumor microbiome in lung ADC [18]. Liu et al. demonstrated that the intratumor microbiome of ADC is enriched in the fungus Aspergillus sydowii. stage lung ADC exhibited a more complex intratumor microbiome than those with early-stage disease, providing evidence of microbiome differentiation [20]. Patients with advanced lung adenocarcinoma had significantly higher levels of the bacterial genera Pseudoalteromonas, Luteibacter, Caldicellulosiruptor, and Serratia than patients with less advanced disease [20]. More recently, multiomics analysis revealed that early-stage lung ADC was associated with intratumor lung dysbiosis.expression at both the transcript and protein levels. Finally, machine learning revealed that six bacterial markers successfully distinguished patients with early-stage lung ADC from healthy control subjects [21]. A recent study used a mouse model of lung ADC to show that, mechanistically, intratumor dysbiosis was linked