Defining the challenges and opportunities for using patient-derived models in prostate cancer research.

BACKGROUND: There are relatively few widely used models of prostate cancer compared to other common malignancies. This impedes translational prostate cancer research because the range of models does not reflect the diversity of disease seen in clinical practice. In response to this challenge, research laboratories around the world have been developing new patient-derived models of prostate cancer, including xenografts, organoids, and tumor explants. METHODS: In May 2023, we held a workshop at the Monash University Prato Campus for researchers with expertise in establishing and using a variety of patient-derived models of prostate cancer. This review summarizes our collective ideas on how patient-derived models are currently being used, the common challenges, and future opportunities for maximizing their usefulness in prostate cancer research. RESULTS: An increasing number of patient-derived models for prostate cancer are being developed. Despite their individual limitations and varying success rates, these models are valuable resources for exploring new concepts in prostate cancer biology and for preclinical testing of potential treatments. Here we focus on the need for larger collections of models that represent the changing treatment landscape of prostate cancer, robust readouts for preclinical testing, improved in vitro culture conditions, and integration of the tumor microenvironment. Additional priorities include ensuring model reproducibility, standardization, and replication, and streamlining the exchange of models and data sets among research groups. CONCLUSIONS: There are several opportunities to maximize the impact of patient-derived models on prostate cancer research. We must develop large, diverse and accessible cohorts of models and more sophisticated methods for emulating the intricacy of patient tumors. In this way, we can use the samples that are generously donated by patients to advance the outcomes of patients in the future.

Lineage plasticity in prostate cancer: Looking beyond intrinsic alterations.

Emergence of small cell prostate cancer is linked to the plasticity of tumour cells and avoidance of environmental pressures. This process is thought to be reversable, however to-date evidence of this has been demonstrated in small-cell prostate cancer. To study the plasticity of prostate tumours, we look to clinical cohorts of patients covering the spectra of malignancy subtypes and utilise in vitro and in vivo models of disease progression. Current models have assisted in the understanding of the extremities of this plasticity, elucidating internal mechanisms and adaptations to stressors through transition to altered cell states. By interrogating the tumour microenvironment and earlier time points, we are beginning to form a deeper understanding of the full spectra of tumour plasticity. It could be proffered that this deeper understanding will lead to better patient outcome, with earlier interventions more likely to reverse plasticity and prevent trans-differentiation to the aggressive, small cell phenotype.

Gene based prediction of clinically localized prostate cancer progression after radical prostatectomy.

PURPOSE: Accurate estimates of recurrence risk are needed for optimal treatment of patients with clinically localized prostate cancer. We combined an established nomogram and what to our knowledge are novel molecular predictors into a new prognostic model of prostate specific antigen recurrence. MATERIALS AND METHODS: We analyzed gene expression profiles from formalin fixed, paraffin embedded, localized prostate cancer tissues to identify genes associated with prostate specific antigen recurrence. Profiles of the identified markers were reproduced by reverse transcriptase-polymerase chain reaction. We used the profiles of 3 of these genes along with output from the Kattan postoperative nomogram to produce a predictive model of prostate specific antigen recurrence. RESULTS: After variable selection we built a model of prostate specific antigen recurrence combining expression values of 3 genes and the postoperative nomogram. The 3-gene plus nomogram model predicted 5-year prostate specific antigen recurrence with a concordance index of 0.77 in a validation set compared to a concordance index of 0.67 for the nomogram. This model identified a subgroup of patients at high risk for recurrence that was not identified by the nomogram. CONCLUSIONS: This new gene based classifier has superior predictive power compared to that of the 5-year nomogram to assess the risk of prostate specific antigen recurrence in patients with organ confined prostate cancer. Our classifier should provide more accurate stratification of patients into high and low risk groups for treatment decisions and adjuvant clinical trials.