Reproducible preclinical models of androgen receptor driven human prostate cancer bone metastasis

BACKGROUND

Preclinical models recapitulating the metastatic phenotypes are essential for developing the next-generation therapies for metastatic prostate cancer (mPC). We aimed to establish a cohort of clinically relevant mPC models, particularly androgen receptor positive (AR+) bone metastasis models, from LuCaP patient-derived xenografts (PDX) that reflect the heterogeneity and complexity of mPC.

METHODS

PDX tumors were dissociated into single cells, modified to express luciferase, and were inoculated into NSG mice via intracardiac injection. The progression of metastases was monitored by bioluminescent imaging. Histological phenotypes of metastases were characterized by immunohistochemistry and immunofluorescence staining. Castration responses were further investigated in two AR-positive models.

RESULTS

Our PDX-derived metastasis (PDM) model collection comprises three AR+ adenocarcinomas (ARPC) and one AR- neuroendocrine carcinoma (NEPC). All ARPC models developed bone metastases with either an osteoblastic, osteolytic, or mixed phenotype, while the NEPC model mainly developed brain metastasis. Different mechanisms of castration resistance were observed in two AR+ PDM models with distinct genotypes, such as combined loss of TP53 and RB1 in one model and expression of AR splice variant 7 (AR-V7) expression in another model. Intriguingly, the castration-resistant tumors displayed inter- and intra-tumor as well as organ-specific heterogeneity in lineage specification.

CONCLUSION

Genetically diverse PDM models provide a clinically relevant system for biomarker identification and personalized medicine in metastatic castration-resistant prostate cancer.

The Low Rate of Adherence to Checklist for Artificial Intelligence in Medical Imaging Criteria Among Published Prostate MRI Artificial Intelligence Algorithms

OBJECTIVE

To determine the rigor, generalizability, and reproducibility of published classification and detection artificial intelligence (AI) models for prostate cancer (PCa) on MRI using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines, a 42-item checklist that is considered a measure of best practice for presenting and reviewing medical imaging AI research.

MATERIALS AND METHODS

This review searched English literature for studies proposing PCa AI detection and classification models on MRI. Each study was evaluated with the CLAIM checklist. The additional outcomes for which data were sought included measures of AI model performance (eg, area under the curve [AUC], sensitivity, specificity, free-response operating characteristic curves), training and validation and testing group sample size, AI approach, detection versus classification AI, public data set utilization, MRI sequences used, and definition of gold standard for ground truth. The percentage of CLAIM checklist fulfillment was used to stratify studies into quartiles. Wilcoxon's rank-sum test was used for pair-wise comparisons.

RESULTS

In all, 75 studies were identified, and 53 studies qualified for analysis. The original CLAIM items that most studies did not fulfill includes item 12 (77% no): de-identification methods; item 13 (68% no): handling missing data; item 15 (47% no): rationale for choosing ground truth reference standard; item 18 (55% no): measurements of inter- and intrareader variability; item 31 (60% no): inclusion of validated interpretability maps; item 37 (92% no): inclusion of failure analysis to elucidate AI model weaknesses. An AUC score versus percentage CLAIM fulfillment quartile revealed a significant difference of the mean AUC scores between quartile 1 versus quartile 2 (0.78 versus 0.86, P = .034) and quartile 1 versus quartile 4 (0.78 versus 0.89, P = .003) scores. Based on additional information and outcome metrics gathered in this study, additional measures of best practice are defined. These new items include disclosure of public dataset usage, ground truth definition in comparison to other referenced works in the defined task, and sample size power calculation.

CONCLUSION

A large proportion of AI studies do not fulfill key items in CLAIM guidelines within their methods and results sections. The percentage of CLAIM checklist fulfillment is weakly associated with improved AI model performance. Additions or supplementations to CLAIM are recommended to improve publishing standards and aid reviewers in determining study rigor.

Abstract B050: Reproducible and effective preclinical models of prostate cancer metastasis established with PDXs

Metastatic castration resistant prostate cancer (mCRPC) is a lethal disease. Understanding the intricacy of this disease and developing targeted treatment is hampered by the lacking of clinically relevant animal models. LuCaP patient-derived xenografts (PDXs), established from advanced metastatic prostate cancer, are a large cohort that encompass the molecular and cellular heterogeneity of mCRPC. We have established four prostate cancer metastasis models from three androgen receptor (AR) positive lines (LuCaP136, LuCaP167 and LuCaP23.1) and one AR negative line LuCap145.2. The three AR positive LuCap lines metastasized to vertebrae and long bones with differing bone-associated histology while the AR negative LuCap145.2 metastasize to brain and spinal cord. The bone metastasis phenotypes of the models encompass: osteolytic (LuCaP136), mixed bone destruction and new bone formation (LuCaP167), and a highly osteoblastic histology (LuCaP23.1). LuCap136 and LuCap167 also metasize to soft tissues including adrenal, kidney and liver. Castration responses were studied on LuCap136 and LuCap167 models. Although both models are castration resistant, LuCap136 model initially are sensitive to castration with decreased tumor progression, and AR down-regulated in the metastatic sites. In contrast, tumor progression was not affected after castration in LuCap167 model. AR and ARV7 were up-regulated in metastatic cells. In summary, the LuCap PDX lines have the capacity for systemic growth in clinically accurate metastatic sites. In addition, the two AR positive models show different castration response which may be driven by different pathways. Understanding the complexity of these pathways would guide optimal selection of drug combinations to achieve the best outcome for patients with mCRPC.

Citation Format: JuanJuan I. Yin, Asha Daryanani, Aian Alilin, Ross Lake, Kathy Kelly. Reproducible and effective preclinical models of prostate cancer metastasis established with PDXs [abstract]. In: Proceedings of the AACR Special Conference: Cancer Metastasis; 2022 Nov 14-17; Portland, OR. Philadelphia (PA): AACR; Cancer Res 2022;83(2 Suppl_2):Abstract nr B050.

Development of a 3D CNN-based AI Model for Automated Segmentation of the Prostatic Urethra

RATIONALE AND OBJECTIVE

The combined use of prostate cancer radiotherapy and MRI planning is increasingly being used in the treatment of clinically significant prostate cancers. The radiotherapy dosage quantity is limited by toxicity in organs with de-novo genitourinary toxicity occurrence remaining unperturbed. Estimation of the urethral radiation dose via anatomical contouring may improve our understanding of genitourinary toxicity and its related symptoms. Yet, urethral delineation remains an expert-dependent and time-consuming procedure. In this study, we aim to develop a fully automated segmentation tool for the prostatic urethra.

MATERIALS AND METHODS

This study incorporated 939 patients' T2-weighted MRI scans (train/validation/test/excluded: 657/141/140/1 patients), including in-house and public PROSTATE-x datasets, and their corresponding ground truth urethral contours from an expert genitourinary radiologist. The AI model was developed using MONAI framework and was based on a 3D-UNet. AI model performance was determined by Dice score (volume-based) and the Centerline Distance (CLD) between the prediction and ground truth centers (slice-based). All predictions were compared to ground truth in a systematic failure analysis to elucidate the model's strengths and weaknesses. The Wilcoxon-rank sum test was used for pair-wise comparison of group differences.

RESULTS

The overall organ-adjusted Dice score for this model was 0.61 and overall CLD was 2.56 mm. When comparing prostates with symmetrical (n = 117) and asymmetrical (n = 23) benign prostate hyperplasia (BPH), the AI model performed better on symmetrical prostates compared to asymmetrical in both Dice score (0.64 vs. 0.51 respectively, p < 0.05) and mean CLD (2.3 mm vs. 3.8 mm respectively, p < 0.05). When calculating location-specific performance, the performance was highest at the apex and lowest at the base location of the prostate for Dice and CLD. Dice location dependence: symmetrical (Apex, Mid, Base: 0.69 vs. 0.67 vs. 0.54 respectively, p < 0.05) and asymmetrical (Apex, Mid, Base: 0.68 vs. 0.52 vs. 0.39 respectively, p < 0.05). CLD location dependence: symmetrical (Apex, Mid, Base: 1.43 mm vs. 2.15 mm vs. 3.28 mm, p < 0.05) and asymmetrical (Apex, Mid, Base: 1.83 mm vs. 3.1 mm vs. 6.24 mm, p < 0.05).

CONCLUSION

We developed a fully automated prostatic urethra segmentation AI tool yielding its best performance in prostate glands with symmetric BPH features. This system can potentially be used to assist treatment planning in patients who can undergo whole gland radiation therapy or ablative focal therapy.

Recent Advancements in CT and MR Imaging of Prostate Cancer

CT and MRI are both commonly used in prostate cancer (PCa) management, which includes a large spectrum from screening positive pre-diagnosis phase to metastatic disease. CT and MRI have continually evolved to meet the changing demands for PCa management. For CT, novel techniques such as dual energy CT and photon counting CT show promising results for tissue characterization and quantification. For MRI, the detection, staging, and management of prostate cancer has been significantly improved by the development of multiparametric, biparametric, and whole-body MRI techniques. Additionally, research on ultrasmall superparamagnetic particles of iron oxide contrast-enhanced MRI has revealed promising results for nodal staging of PCa. In this manuscript we aim to outline the current status and recent advancements of CT and MRI in PCa imaging.