The comprehensive study presented here introduces a 3D in vitro patient derived BCa tumor model as a novel platform for drug screening. BCa poses a significant global health burden, and despite advancements in treatment, predicting the efficacy of neoadjuvant chemotherapy remains a challenge. The study ingeniously addresses this challenge by leveraging the benefits of 3D culture systems, specifically utilizing decellularized pig bladders as scaffolds to create a more clinically relevant microenvironment.
Since Eiraku et al. successfully generated cerebral cortex tissue from ESCs utilizing the 3D aggregation culture method in 2009, marking a pioneering endeavor in organoid research, there has been a growing recognition of the significance of developing in vitro cell models that more accurately mimic the complex cellular configurations found in native tissue [22]. Numerous researchers have successfully developed cancer organoid models derived from tumor patients. These three-dimensional (3D) models effectively recapitulate the histopathology, as well as genetic and transcriptional profiles of tumors. However, a notable limitation is the absence of the immune system and stromal components. This limitation becomes particularly pronounced when studying interactions between tumor cells and stromal or immune cells [16, 23–24].
Studies conducted with BCa organoids demonstrate a high degree of reliability in detecting mutated genes, such as PIK3CA, FGFR3, EGFR, HRAS, PTEN, MDM2, RB1, and TP53. However, the response to treatment appears heterogeneous, with reduced chemotherapeutic sensitivity compared to 2D culture systems [25–27]. This suggests the influence of both direct and indirect cell-cell interactions within the complex organotypic 3D culture. Conversely, it may also indicate limitations in terms of drug accessibility to the cells.
On the other hand, patient-derived xenograft models (PDXs) effectively recapitulate the original cancer's tissue structure and preserve its genetic and histological characteristics [28]. Despite these advantages, PDXs present several drawbacks. Establishing a PDX model is associated with a relatively low success rate, averaging between 30–40%, and demands a considerable amount of time, ranging from 2 to 10 months. Moreover, the creation and maintenance of PDX models entail significant financial costs and resource investments, which may limit statistical power and hinder the feasibility of high-throughput studies [29–31].
Given the limitations discussed above regarding current cancer models, we decided to integrate a natural extracellular matrix (ECM) into our approach. This ECM is capable of mimicking the microarchitecture of native tissue using innovative tissue engineering methodologies [32–33]. The success in decellularizing pig bladders and recreating the tumor microenvironment (TME) within these scaffolds demonstrates a sophisticated approach to capturing the complexities of BCa. The resulting 3D model better represents in vivo conditions compared to traditional 2D cultures. As we can see how in our model with UM-UC3 cells they preserver the histology [34] displaying dense cellular proliferation characterized by enlarged nuclei and prominent nucleoli, without necessity of in vivo experiments. After establishing a protocol with a BCa cell line, we treated our models with the first line of treatment for BCa [35], and the result demonstrate a correlation between cell viability with drug dose.
The comparative analysis with traditional 2D cultures and spheroids highlights the superiority of the 3D model. The enhanced reliability (83.3%) of the 3D model compared to spheroids (50%) and 2D culture (33.3%) underscores the importance of incorporating three-dimensional structures for more accurate drug screening.
Cancer cell lines are preferred over animal models for cancer research because they are faster, affordable, and easy to access. They are extensively employed in multiples cancer types as a preclinic platform for drug testing. Genetic data and drug sensitivity profiles are available in databases like COSMIC and CCLE proving all the valuable information we can get for 2D culture a cell line [35–36]. However, there are disadvantages associated with cell line use. Prolonged in vitro culture can lead to genetic and epigenetic changes diverging from the original tumors. Synthetic culture conditions and the absence of a supportive 3D environment can influence cell behavior. Moreover, cell lines often fail to accurately predict drug efficacy in clinical settings [37–38]. Organoids have been used as a preclinical platform for predicting response to chemotherapy in different cancer types with an heterogenous accuracy in comparation with clinical response.
The TUMOROID study, which represents the most extensive prospective clinical investigation to date regarding the predictive potential of human organoids for treatment response, enrolled 61 patients diagnosed with colorectal cancer. Organoids derived from metastatic colorectal cancer (CRC) were employed in this study to correlate their response with corresponding clinical outcomes. The organoids predicted the response to irinotecan-based therapies in over 80% of patients, without misclassifying individuals who exhibited positive responses to the treatment. However, this predictive capability was not observed for combined 5-fluorouracil (5-FU) and oxaliplatin treatment. The study reported a 63% success rate in establishing in vitro cultures from patient tissue, with only 29 out of 61 patients (~ 47%) yielding evaluable responses and achieving an 80% prediction rate [39]. Yao et al. treated 80 organoids derived from human colon cancer cells with neoadjuvant chemoradiation (5-FU and irinotecan) demonstrating a sensitivity of 78.01% and specificity of 91.97%. The responses to chemoradiation observed in patients closely corresponded to those in the organoids, with an accuracy of 84.43%. These findings suggest that patient-derived organoids (PDOs) can effectively predict responses of locally advanced rectal cancer (LARC) patients in the clinical setting and may serve as a valuable companion diagnostic tool in rectal cancer treatment [40]. However, the reliability of organoid treatment response compared to clinical outcomes varies among different cancer types. For instance, in the study by Kim et al., patient-derived cancer cells (PDCs) were generated from 77 individuals with advanced lung adenocarcinoma, achieving a success rate of only 24% [41].
Predicting response for drugs in BCa have been tested with organoid with not very solid results. Bladder cancer organoids underwent sensitivity testing to various chemotherapeutic agents (epirubicin, mitomycin C, gemcitabine, vincristine, doxorubicin, or cisplatin), yet no significant correlations with patient response were identified [15]. Another investigation adopted a drug screening strategy employing bladder cancer organoids, revealing robust but variable responses. For instance, in some organoids derived from patients harboring FGFR3 activating mutations, MEK/ERK inhibition proved effective, although not universally. Correlations emerged between more aggressive clinical phenotypes (such as metastasis and recurrence) and resistance to a broad spectrum of drugs in organoids [42]. Additionally, apart from organoids, various other in vitro 3D cultures have been explored and documented. For instance, Amaral et al. [43] described a hanging drop method and a floating method utilizing ultra-low attachment (ULA) plates. The drug sensitivity of bladder cancer cells using these techniques is comparable to that observed with patient-derived xenografts. After reviewing the aforementioned studies in BCa aimed at developing preclinical models for drug testing, it is evident that this area of research remains ongoing, and continued efforts are necessary to advance towards personalized medicine. To establish the most reliable model, it is required not only to retain the specific genetic characteristics of the patient but also to recreate a microenvironment that closely mimics the original tumor environment. This includes incorporating components such as the ECM, cancer cells, cancer-associated fibroblasts (CAFs), immune cells, and vasculature to achieve a more physiologically relevant model for drug screening and therapeutic development.
The inclusion of patient-derived tumor samples further enhances the model's clinical relevance. The successful recreation of tumors from 17 patient samples, with varying responses to drug treatments, strengthens the potential utility of the 3D model in personalized medicine.
The positive correlation observed between treatment responses in the 3D model and clinical outcomes in 83.3% of the cases is a significant finding. This suggests that the developed 3D BCa model has a high predictive reliability, which is crucial for preclinical drug testing.
Challenges and Future Directions:
While the 3D model successfully captures the cellular and extracellular matrix components, it is acknowledged that immune and stromal components, critical for immunotherapy studies, are not fully represented. Future modifications to incorporate these elements could further enhance the model's applicability. The study's reliability assessment is based on a limited number of patient samples. Extending the validation to a larger cohort of patients will strengthen the model's predictive capabilities and generalizability. The current study focuses on Cis and Gem, commonly used in BCa treatment. Expanding the investigation to include other clinically relevant drugs and combination therapies would provide a more comprehensive understanding of the model's utility. Delving deeper into the molecular and cellular mechanisms underlying the observed treatment responses could provide valuable insights. Understanding the specific pathways influenced by different drug combinations will contribute to refining treatment strategies.
In conclusion, this study pioneers the development of a 3D BCa model with promising applications in drug screening and personalized medicine. The model's ability to recreate patient-specific responses and predict treatment outcomes marks a significant step forward in the field of BCa research. Continued advancements in this direction hold the potential to revolutionize preclinical testing, ultimately improving therapeutic outcomes for BCa patients.