Abstract Introduction: A priority in prostate cancer (PCa) research is development of precise risk stratification tools to enable early identification of men with aggressive tumours while minimizing overtreatment of others with indolent disease. With imaging playing a central role in the diagnosis and management of PCa, radiogenomics has been explored as a personalised medicine approach to improve risk stratification. This study aims to review and describe radiogenomic applications reported in the literature. Methods: Medline, Embase and Cochrane libraries were systematically searched using variations of search terms for articles reporting radiogenomic applications in PCa after 2010. Articles were included if the following were reported (1) genomic platform used; (2) method of determining region of interest (ROI) for radiomic feature extraction; (3) correlation analyses between radiomics and genomics. Results: A total of 267 articles were screened and 13 met the inclusion criteria following independent review by two authors. Majority (n=10/13) reported MRI-based applications involving 715 patients. Remaining modalities included ultrasound (n=1) and PET scan (n=2). Most (7/10) studies evaluating an MRI imaging modality, correlated bulk RNA-sequencing with radiomic features. Textural radiomics (n=6) features were most commonly reported to correlate with gene expression followed by histogram (n=2) and volumetric features (n=1). MRI radiomics significantly correlated with hypoxia related genes in 4 studies. The textural feature (Gray Level Co-occurrence Matrix) was seen to correlate with ANGPTL4 expression in 3 studies. Median AUC for a radiogenomic model to predict presence of clinically significant PCa was 0.746. Only 4 studies (MRI-based n=3, ultrasound-based n=1) externally validated the developed model. Most (9/13) studies used a manual qualitative approach to register imaging loci with site of tissue acquisition for genomic analysis. Conclusion: There is significant heterogeneity in the reporting and design of prostate cancer radiogenomic studies. A signal suggesting and association of MRI textural radiomic features have been consistently observed in several studies but lack validation. Citation Format: Thineskrishna Anbarasan, Matilda Dichmont, Sandy Figiel, Bartlomiej Papiez, Alastair Lamb, Richard Bryant, Ian Mills. A systematic review of radiogenomic applications in prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2621.

New spatial molecular technologies are poised to transform our understanding and treatment of urological cancers. By mapping the spatial molecular architecture of tumours, these platforms uncover the complex heterogeneity within and around individual malignancies, offering novel insights into disease development, progression, diagnosis, and treatment. They enable tracking of clonal phylogenetics in situ and immune-cell interactions in the tumour microenvironment. A whole transcriptome/genome/proteome-level spatial analysis is hypothesis generating, particularly in the areas of risk stratification and precision medicine. Current challenges include reagent costs, harmonisation of protocols, and computational demands. Nonetheless, the evolving landscape of the technology and evolving machine learning applications have the potential to overcome these barriers, pushing towards a future of personalised cancer therapy, leveraging detailed spatial cellular and molecular data. Patient summary Tumours are complex and contain many different components. Although we have been able to observe some of these differences visually under the microscope, until recently, we have not been able to observe the genetic changes that underpin cancer development. Scientists are now able to explore molecular/genetic differences using approaches such as “spatial transcriptomics” and “spatial proteomics”, which allow them to see genetic and cellular variation across a region of normal and cancerous tissue without destroying the tissue architecture. Currently, these technologies are limited by high associated costs, and a need for powerful and complex computational analysis workflows. Future advancements and results through these new technologies may assist patients and their doctors as they make decisions about treating their cancer.

Risk stratification remains a key challenge in prostate cancer (PCa) management involves risk stratification, and identification of the subgroup of patients at highest risk of progressing from localised to metastatic disease is critical. Multiparametric MRI (mpMRI) is key in the PCa diagnostic pathway. By integrating clinical parameters, mpMRI radiomics and spatial transcriptomics (ST), this novel “Radio-Spatial Genomics” platform offers an exciting opportunity to identify mpMRI radiomic features associated with important biological aspects of PCa linked to an aggressive disease phenotype. Multi-regional spatial transcriptomics (Visium 10x Genomics) was performed on archived formalin-fixed paraffin-embedded prostatectomy sections from patients recruited to a local trial (ISRCTN10046036). Axial sections were sequenced using ST (8 per patient) from 2 patients with Gleason 4+4 PCa and preoperative mpMRI available was used for this study. Anatomical landmarks on mpMRI were segmented by a radiologist. Using a proportional size algorithm and a convolutional neural network (ProsRegNet), T2-axial MRI slices were aligned and registered to histopathology sections. An application, SpatialStitcher, was developed on Python 3.7.0 to digitally stitch separate ST sections for image registration. Using the prostatic capsule and urethra as landmarks, histopathology sections from 2 patients were co-registered to corresponding T2-axial MRI slices. In total, a median of 114670 whole transcriptome sequenced barcoded ST spots were co-registered to 30424 pixels on MRI per patient. A median DICE correlation score of 0.942, 0.738 and 0.756 was achieved for capsule, tumour and BPH nodules respectively. AMACR (marker for PCa) expression inversely correlated with T2 MRI intensity-based radiomic features (r = -0.763), consistent with the tumour being hypointense. Differential gene expression analysis between hyperintense peri-tumoural and hypointense tumour regions revealed enrichment for genes involved in mucosal immune response. In this study, we report preliminary results of mapping MRI with ST using machine learning to identify genotypic changes based on radiomics. This novel “Radio-Spatial Genomics” model may allow the detection of clinically relevant genotypic features from diagnostic prostate mpMRI imaging. Thineskrishna Anbarasan, Sandy Figiel, Sophia M. Abusamra, Wencheng Yin, Nithesh Ranasinha, James T. Grist, Dan J. Woodcock, Richard J. Bryant, Ruth McPherson, Freddie C. Hamdy, Bartlomiej Papiez, Ian G. Mills, Alastair D. Lamb. Integrating multiparametric MRI with spatial transcriptomics to identify “Radio-Spatial Genomic” features of prostate cancer using artificial intelligence [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3684.

Patient-derived organoids (PDOs) offer new opportunities to model various cancers. However, their application in prostate cancer (PCa) has been hampered by poor success rates and overgrowth of cell types which are not representative of the patient samples. By exploiting a cohort of 164 PCa patient samples and tuning several culture parameters, we show that an extracellular matrix-free (ECM)-free culture system increases the take-rate of PDOs with luminal-like and PCa features. Single-cell RNA sequencing (scRNA-seq) reveals that ECM-free PDOs comprise cell populations associated with known PCa signatures and exhibit transcriptomic resemblance with their respective parental tumors. In addition, we define organoid-associated cell type signatures and identify markers discriminating tumors versus benign cells ex vivo and in situ. Furthermore, we generate the first prostate PDO single-cell atlas integrating previously-published scRNA-seq datasets and our newly-generated data. We show that Matrigel-based organoid cultures derived from primary PCa are essentially composed of benign-like epithelial cells, irrespective of the dataset or the malignant nature of the tissue of origin. In contrast, ECM-free conditions maintain heterogenous patient-specific luminal tumor cell populations and enrich in intermediate cell types. Ultimately, our work will significantly enhance the potential of PDOs in basic and translational PCa research. ### Competing Interest Statement The authors have declared no competing interest.

The application of patient-derived organoids (PDOs) in prostate cancer (PCa) research has been hampered by poor take rates and benign overgrowth. We highlight the limitations of existing culture conditions and identify extracellular matrix composition as a determinant of organoid outcome. Single-cell RNA sequencing reveals that Matrigel-free PDOs exhibit cellular heterogeneity, preserve patient-specific PCa cells with active androgen receptor signaling, and enrich in intermediate cells. In contrast, Matrigel fails to maintain primary PCa cells and produces in vitro basal-like features divergent from patient samples. Furthermore, we redefine cell-type signatures, identify biomarkers discriminating tumor versus other cell types, and show that expression of laminin-binding integrins is a hallmark of Matrigel-derived organoids. Finally, integrating previously published datasets with our data, we generate a prostate PDO single-cell atlas (PPScA), which captures a spectrum of cellular identities while revealing pathways altered in vitro. Our study provides methodological improvements for short-term culture and cellular biology insights.

Prostate cancer (PCa) is an androgen receptor (AR) driven, high-incidence disease significantly contributing to cancer mortality. PCa is in need of better risk stratification at diagnosis and treatment outcomes in patients at high risk of metastasis. The unfolded protein response (UPR) is an AR-dependent process. However, the impact of the UPR transducer IRE1 on AR-dependent biology and treatment resistance has not been defined. We use diverse pre-clinical models of stress response to describe IRE1 activity impact on multiple disease stages and demonstrate its involvement with poor prognosis (RB1 loss), and cell lineage determination (club phenotypes). Integrating clinical transcriptomic datasets, we chart IRE1 activity throughout PCa evolution by developing a PCa-specific, IRE1 activity gene set (IRE1\_18) reflecting both tumoral and micro-environmental niches. IRE1\_18 can determine tumoral identity, inform androgen deprivation treatment suitability, prognosticate localised and metastatic disease independently from AR activity, and guide IRE1 modulation as a novel combination therapeutic. ### Competing Interest Statement The authors have declared no competing interest.

Epithelial cancers are typically heterogeneous with primary prostate cancer being a typical example of histological and genomic variation. Prior studies of primary prostate cancer tumour genetics revealed extensive inter and intra-patient genomic tumour heterogeneity. Recent advances in machine learning have enabled the inference of ground-truth genomic single-nucleotide and copy number variant status from transcript data. While these inferred SNV and CNV states can be used to resolve clonal phylogenies, however, it is still unknown how faithfully transcript-based tumour phylogenies reconstruct ground truth DNA-based tumour phylogenies. We sought to study the accuracy of inferred-transcript to recapitulate DNA-based tumour phylogenies. We first performed in-silico comparisons of inferred and directly resolved SNV and CNV status, from single cancer cells, from three different cell lines. We found that inferred SNV phylogenies accurately recapitulate DNA phylogenies (entanglement = 0.097). We observed similar results in iCNV and CNV based phylogenies (entanglement = 0.11). Analysis of published prostate cancer DNA phylogenies and inferred CNV, SNV and transcript based phylogenies demonstrated phylogenetic concordance. Finally, a comparison of pseudo-bulked spatial transcriptomic data to adjacent sections with WGS data also demonstrated recapitulation of ground truth (entanglement = 0.35). These results suggest that transcript-based inferred phylogenies recapitulate conventional genomic phylogenies. Further work will need to be done to increase accuracy, genomic, and spatial resolution.

Prostate cancer (PCa) remains the leading cause of cancer deaths in men. The prostate-specific antigen (PSA) test is widely used for PCa screening, but it lacks specificity and can lead to over-diagnosis and over-treatment. New, effective and affordable markers are therefore needed. Using enzymatic methyl sequencing (EM-Seq), methylation-specific PCR (MS-PCR), and transcriptomics including a spatial approach, we analyzed tumor and non-tumor samples from radical prostatectomy specimens. Comprehensive methylome was performed in 15 paired samples of prostate cancer and their adjacent non-tumor tissue by EM-Seq. From over 4-million differentially methylated CpG sites, we identified 66 CpGs sites representing eight genes: CLDN5, GSTP1, NBEAL2, PRICKLE2, SALL3, TAMALIN/GRASP, TJP2, and TMEM106A which were hypermethylated in PCa tissues (p-value < 0.0001), and were confirmed by MS-PCR. A very good correlation between EM-Seq and MS-PCR results was observed (Pearson’s correlation of 0.93). Differential expression of these candidate genes was analyzed first, using an Affymetrix RNA array dataset from a cohort of 68 non-tumor samples and 101 tumors with different aggressiveness patterns and, second, by in situ expression using Visium 10X spatial genomics transcriptomics on eight prostate tissue sections with different tumor grades and non-tumor glands. Lower expression level was found, using RNA arrays, in tumor compared to non-tumor tissues for six of the eight genes (p ≤ 0.0001) and in tumor glands with high aggressiveness compared to non-tumor glands (p < 0.0001) for the eight genes using in situ transcriptomics. Our study identifies promising DNA methylation markers for the diagnosis of prostate cancer.

Extension of prostate cancer beyond the primary site by local invasion or nodal metastasis is associated with poor prognosis. Despite significant research on tumour evolution in prostate cancer metastasis, the emergence and evolution of cancer clones at this early stage of expansion and spread are poorly understood. We aimed to delineate the routes of evolution and cancer spread within the prostate and to seminal vesicles and lymph nodes, linking these to histological features that are used in diagnostic risk stratification. We performed whole-genome sequencing on 42 prostate cancer samples from the prostate, seminal vesicles and lymph nodes of five treatment-naive patients with locally advanced disease. We spatially mapped the clonal composition of cancer across the prostate and the routes of spread of cancer cells within the prostate and to seminal vesicles and lymph nodes in each individual by analysing a total of > 19,000 copy number corrected single nucleotide variants. In each patient, we identified sample locations corresponding to the earliest part of the malignancy. In patient 10, we mapped the spread of cancer from the apex of the prostate to the seminal vesicles and identified specific genomic changes associated with the transformation of adenocarcinoma to amphicrine morphology during this spread. Furthermore, we show that the lymph node metastases in this patient arose from specific cancer clones found at the base of the prostate and the seminal vesicles. In patient 15, we observed increased mutational burden, altered mutational signatures and histological changes associated with whole genome duplication. In all patients in whom histological heterogeneity was observed (4/5), we found that the distinct morphologies were located on separate branches of their respective evolutionary trees. Our results link histological transformation with specific genomic alterations and phylogenetic branching. These findings have implications for diagnosis and risk stratification, in addition to providing a rationale for further studies to characterise the genetic changes causally linked to morphological transformation. Our study demonstrates the value of integrating multi-region sequencing with histopathological data to understand tumour evolution and identify mechanisms of prostate cancer spread.

Abstract Introduction Prostate cancer (PCa) is a multifocal disease driven by heterogenous spatial and temporal branching evolution of tumour clones. Spatial transcriptomic analysis has revealed, clonally distributed copy number alterations even in histologically benign regions. We aim to evaluate if the genetic signature comprised within these genetically aberrant histologically benign regions, or “ancestral clones” may predict presence of high-risk disease. Methods Fresh-frozen, radical prostatectomy specimen was profiled using the 10x Genomics Visium Spatial Gene Expression platform. Via hierarchical clustering, spatial tumour clonal relations were inferred. Differentially expressed genes (DEGs) between high-grade tumour and histologically benign ancestral clones were identified. DEGs (|log2FC|>0.75) were input into a LASSO regression algorithm with normalised mRNA expression of normal adjacent tissue from the TCGA PCa database to train a genetic risk model to predict presence of high risk PCa (Gleason>8). Derived risk score was validated on an external cohort (GSE21032). Results A total of 683 DEGs were identified. These were enriched for organic acid metabolic pathways. Regression analysis on 57 normal adjacent tissue samples from TCGA PCa dataset was performed to develop a predictive model comprising of 3 genes (SLC22A3, CRIP2, LSAMP). External validation of the genetic risk score on matched histologically benign tissue from 131 patients, had good performance for prediction of high-risk PCa (AUC 0.807; 95% CI 0.693-0.921). Conclusion Our model has good performance for predicting high-risk PCa from matched normal tissue. Developed genetic risk score has translational potential to improve detection of unsampled clinically significant prostate cancer from histologically benign biopsy cores.

You have accessJournal of UrologyProstate Oncology/Penile & Testis Oncology/Misc. Oncology II (V12)1 May 2024V12-12 NOVEL METHODOLOGY FOR OPTIMISING FORMALIN FIXED PARAFFIN EMBEDDED PROSTATE CANCER SECTIONS FOR SPATIAL TRANSCRIPTOMICS ANALYSIS Thineskrishna Anbarasan, Sandy Figiel, Renuka Teague, Wencheng Yin, Richard Colling, Clare Verrill, Ian Mills, and Alastair Lamb Thineskrishna AnbarasanThineskrishna Anbarasan , Sandy FigielSandy Figiel , Renuka TeagueRenuka Teague , Wencheng YinWencheng Yin , Richard CollingRichard Colling , Clare VerrillClare Verrill , Ian MillsIan Mills , and Alastair LambAlastair Lamb View All Author Informationhttps://doi.org/10.1097/01.JU.0001009480.90141.21.12AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Spatial transcriptomics has revolutionised our approach to in situ evaluation of prostate cancer (PCa) heterogeneity. Previous spatial transcriptomic evaluations of prostate cancer sections have relied on fresh-frozen tissue. Transition of spatial transcriptomic chemistry to effective evaluation of fixed tissue provides the opportunity to leverage the breadth of archived tissue. We have developed and refined an approach to use formalin fixed paraffin embedded (FFPE) specimens for spatial transcriptomic evaluation. METHODS: In this video demonstration, we outline three stages in preparing a FFPE histological specimen for spatial transcriptomics analysis using the 10x Genomics Visium Spatial Gene Expression platform. In stage 1, the haematoxylin and eosin-stained specimen has to be digitalised and annotated by a pathologist. In stage 2 a novel digital planning schema to optimise sequencing coverage has been developed. Stage 3 involves sectioning of the FFPE specimen. RESULTS: Application of our methodology has enabled consistent high quality sequencing coverage of regions of interest for spatial transcriptomics. We provide technical tips to address potential challenges which may impact sample processing for sequencing including: handling a whole axial prostatectomy specimen, preventing creases & lifting, avoiding expansion artefact. We have used this methodology to successfully process 54 FFPE sections from 4 patients with PCa after robotic prostatectomy and lymph node dissection, allowing us to evaluate tumour clonality and phylogenetics both in the primary tumour alongside nodal metastases. CONCLUSIONS: This video presents an optimised workflow for efficient sample preparation of archival FFPE prostate sections for spatial transcriptomics analysis. Source of Funding: NIHR UK - Thineskrishna Anbarasan - Academic Clinical Fellowship © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e998 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Thineskrishna Anbarasan More articles by this author Sandy Figiel More articles by this author Renuka Teague More articles by this author Wencheng Yin More articles by this author Richard Colling More articles by this author Clare Verrill More articles by this author Ian Mills More articles by this author Alastair Lamb More articles by this author Expand All Advertisement PDF downloadLoading ...

Prostate-specific membrane antigen (PSMA) is increasingly used to image prostate cancer in clinical practice. We sought to develop and test a humanised PSMA minibody IAB2M conjugated to the fluorophore IRDye 800CW-NHS ester in men undergoing robot-assisted laparoscopic radical prostatectomy (RARP) to image prostate cancer cells during surgery. The minibody was evaluated pre-clinically using PSMA positive/negative xenograft models, following which 23 men undergoing RARP between 2018 and 2020 received between 2.5 mg and 20 mg of IR800-IAB2M intravenously, at intervals between 24 h and 17 days prior to surgery. At every step of the procedure, the prostate, pelvic lymph node chains and extra-prostatic surrounding tissue were imaged with a dual Near-infrared (NIR) and white light optical platform for fluorescence in vivo and ex vivo. Histopathological evaluation of intraoperative and postoperative microscopic fluorescence imaging was undertaken for verification. Twenty-three patients were evaluated to optimise both the dose of the reagent and the interval between injection and surgery and secure the best possible specificity of fluorescence images. Six cases are presented in detail as exemplars. Overall sensitivity and specificity in detecting non-lymph-node extra-prostatic cancer tissue were 100 https://www.isrctn.com/ISRCTN10046036 .

You have accessJournal of UrologyProstate Cancer: Basic Research & Pathophysiology II (PD09)1 May 2024PD09-06 EXPLORING STROMAL DYNAMICS IN PROSTATE CANCER: INSIGHTS FROM SPATIAL TRANSCRIPTOMIC ANALYSES Sandy Figiel, Wencheng Yin, Mengxiao He, Renuka Teague, Thineskrishna Anbarasan, Nithesh Ranasinha, Sophia Abusamra, Reema Singh, Dimitrios Doultsinos, Ninu Poulose, Andrew Erickson, Clare Verrill, Richard Colling, Pelvender Gill, Richard J. Bryant, Olivier Cussenot, Massimo Loda, Freddie C. Hamdy, Dan J. Woodcock, Ian G. Mills, Joakim Lundeberg, Solna Sweden, and Alastair D. Lamb Sandy FigielSandy Figiel , Wencheng YinWencheng Yin , Mengxiao HeMengxiao He , Renuka TeagueRenuka Teague , Thineskrishna AnbarasanThineskrishna Anbarasan , Nithesh RanasinhaNithesh Ranasinha , Sophia AbusamraSophia Abusamra , Reema SinghReema Singh , Dimitrios DoultsinosDimitrios Doultsinos , Ninu PouloseNinu Poulose , Andrew EricksonAndrew Erickson , Clare VerrillClare Verrill , Richard CollingRichard Colling , Pelvender GillPelvender Gill , Richard J. BryantRichard J. Bryant , Olivier CussenotOlivier Cussenot , Massimo LodaMassimo Loda , Freddie C. HamdyFreddie C. Hamdy , Dan J. WoodcockDan J. Woodcock , Ian G. MillsIan G. Mills , Joakim LundebergJoakim Lundeberg , Solna SwedenSolna Sweden , and Alastair D. LambAlastair D. Lamb View All Author Informationhttps://doi.org/10.1097/01.JU.0001008572.33286.63.06AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Prostate cancer's prognosis varies widely, from indolent to aggressive forms causing metastasis and death. The lack of reliable diagnostic tools and effective treatments poses substantial challenges in its management and research. Understanding the mechanisms driving prostate cancer's diversity is crucial for improved treatment. Spatial genomics has allowed us to define clonal heterogeneity, revealing insights into the cancer's origin and emphasising the stroma's role in tumour development, fostering growth and immune evasion. This study explores the stroma in primary and metastatic prostate cancer tissue, aiming to identify transformation-promoting factors. METHODS: We conducted spatial transcriptomic analyses (Visium v2) on FFPE tissues from primary and nodal metastatic tissues. We analysed expression data using spatial inferred copy number variations and constructed a phylogenetic tree to describe clonal events in tumour regions (https://github.com/aerickso/SpatialInferCNV). We performed differential gene expression analyses of stromal cells around each tumour clone, employing combined cellular deconvolution. RESULTS: We found significant variations within stromal cells around distinct tumour clones. Interestingly, we found upregulation of genes associated with antigen presentation and inflammatory response pathways in the stroma surrounding ancestor tumour clones that had not yet acquired metastatic potential. For example, gene CD74 was highly expressed in the stroma around ancestor clone (clone B) compared to descendant clone (clone C) in the apex of the prostate. We also observed this discrepancy in the base, where we identified a lethal clone that had spread to the lymph nodes. Indeed, the stroma around the ancestor clone (clone C1) is enriched in IGHA1, compared to the stroma around the lethal clone (clone X1). CONCLUSIONS: Our study reveals altered stromal gene expression surrounding different tumour clones, highlighting the dynamic interplay between cancer cells and their microenvironment. This distinctive stromal profile offers mechanistic insights underpinning phenotypic variability. It raises the possibility of using the stroma as a window to differentiate tumour lethality from indolent disease, as well as offering targets for personalised treatment strategies. Download PPT Source of Funding: The present work was supported by Cancer Research UK (CRUK), Hanson Trust Research, the John Black Charitable Foundation, Prostate Cancer Foundation, NIHR Oxford Biomedical Research Centre, European Research Council, Swedish Society for Cancer Research © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e181 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Sandy Figiel More articles by this author Wencheng Yin More articles by this author Mengxiao He More articles by this author Renuka Teague More articles by this author Thineskrishna Anbarasan More articles by this author Nithesh Ranasinha More articles by this author Sophia Abusamra More articles by this author Reema Singh More articles by this author Dimitrios Doultsinos More articles by this author Ninu Poulose More articles by this author Andrew Erickson More articles by this author Clare Verrill More articles by this author Richard Colling More articles by this author Pelvender Gill More articles by this author Richard J. Bryant More articles by this author Olivier Cussenot More articles by this author Massimo Loda More articles by this author Freddie C. Hamdy More articles by this author Dan J. Woodcock More articles by this author Ian G. Mills More articles by this author Joakim Lundeberg More articles by this author Solna Sweden More articles by this author Alastair D. Lamb More articles by this author Expand All Advertisement PDF downloadLoading ...

Atypical small acinar proliferation (ASAP), found in 5% of prostate biopsies, represents a focus of atypical cells that fall short of a cancer diagnosis.1 ASAP may be associated with a diagnosis of prostate cancer (PCa) upon repeat biopsy in 25%–50% of patients within 5 years.1 The proportion of these cases that may be classified as being intermediate- or high-grade PCa varies in the literature, ranging from 6.0% to 22.5%.2, 3 Until recently, diagnosis of ASAP was an indication for early repeat biopsy in international guidelines. However, recent studies referenced by the European Association of Urology (EAU) guidelines suggest low rates of subsequent Gleason grade group (GG) ≥ 2 PCa, similar to following a previous negative biopsy, leading to a softening of the recommendation for ASAP as an indication for performing early repeat biopsy.4 We therefore aimed to test the hypothesis that prostate cancer diagnosed on early re-biopsy after detection ASAP is always low grade by interrogating a large prospective pathology database. We also aimed to determine the time interval between detection of ASAP and diagnosis of csPCa, if present. We scrutinised pathology records according to a prospectively derived protocol (ID: CU96T) for all consecutive patients with ASAP on needle biopsy, transurethral resection of the prostate (TURP) chippings, or holmium laser enucleation of the prostate (HoLEP) specimens between January 2010 and November 2021 at a single tertiary institution. We classified pathological upgrading to csPCa as any Gleason pattern 4 disease identified within 2 years of the initial biopsy/TURP/HoLEP specimen detecting ASAP. Where available, we reviewed pre-biopsy multiparametric MRI (mpMRI) reports for PI-RADS scores at the time of ASAP diagnosis and obtained the prostate volume in order to derive the PSA density (PSAD). A multi-variable logistic regression model (including age, PSAD and PI-RADS) was constructed to determine factors associated with the development of csPCa. Approximately 13 240 prostate samplings were performed (11 240 needle biopsy and 2000 HoLEP/TURP specimens) over the 10-year period. ASAP was identified in 617 (4.7%) biopsy samplings, involving 523 patients. Of these, 51 (9.7%) patients had a pre-existing history of PCa and were excluded from further analysis, leaving a sample size of 472 individuals with de novo ASAP (Table 1). The baseline characteristics of the cohort are summarised in Table 1. Two hundred and thirty-seven (50.2%) patients had a repeat biopsy (Table S1) within a median of 92 days (IQR: 56–283). The median PSA within 3–6 months of ASAP detection was higher amongst patients who underwent repeat biopsy (6.7 vs. 5.08 ng/ml, p = 0.001) consistent with clinical judgement advocating repeat biopsy. In the 248 of 472 (52.5%) patients with pre-biopsy MRI, logistic regression revealed age <65 years (OR: 3.11; 95% CI: 1.74–5.69), PSAD > 0.15 ng/ml2 (OR: 2.06; 95% CI: 1.13–3.80), and PI-RADS ≥ 3 (OR: 1.88; 95% CI: 1.05–3.42) were independently associated with patients undergoing repeat biopsy following detection of ASAP. In the 237 patients who underwent repeat biopsies, intermediate- or high-grade PCa (GG ≥ 2) was found in 57 (24.1%) patients (18 high-grade [GG 4/5] versus 39 intermediate-grade [GG 2/3]) within 2 years at a median interval of 128 days (IQR: 61–260). Low-grade PCa (GG1) was detected in 77 (32.5%) patients. GG ≥ 2 PCa was detected on the ipsilateral side of ASAP diagnosis in 46 of 57 (80.8%) patients, which we therefore hypothesise to be related to the original diagnosis of ASAP. Amongst patients who underwent repeat biopsy following detection of ASAP, mpMRI was performed with PI-RADS data available for 74 patients (31.2%; from December 2014). Of these, 48 (64.8%) patients had PI-RADS ≥ 3. A PSAD > 0.15 ng/ml2 at time of ASAP diagnosis was independently associated (OR: 3.21; 95% CI: 1.12–9.74) with the detection of GG ≥ 2 PCa (Table 1). A subgroup of 16 patients with pre-biopsy PI-RADS 4–5 lesions were not found to have csPCa on repeat biopsy within 2 years. Repeat mpMRI (median interval of 526 days from detection of ASAP) was performed in 14 of 16 of these patients, of which five lesions were downgraded to PI-RADS 3 and a further five to PI-RADS 1/2. Expressed as a proportion of all men with ASAP, PI-RADS score 4/5 was associated with the development of GG ≥ 2 PCa (OR: 5.86; 95% CI: 2.01–19.6), as expected for MRI visible lesions. There were 103 patients without pre-biopsy PI-RADS score (predating regular pre-biopsy MRI), hence excluded from the above regression analysis, ROC curve analysis showed that the AUC for PSAD > 0.15 ng/ml2 was 0.734 (95% CI: 0.642–0.827) for the detection of GG ≥ 2 PCa within 2 years (Figure S1). The positive predictive value (PPV) and negative predictive value (NPV) for PSAD threshold of 0.15 ng/ml2 were 68.8% and 83.3%. At a higher PSAD threshold of 0.20 ng/ml2, the PPV and NPV were 74.0% and 80.0%, respectively (Table S2) . The rate of detection of ASAP in our cohort is 4.7%, which is consistent with the rate of approximately 5% previously reported in the literature. Younger patients, those with a raised PSAD, and those with positive mpMRI findings (PI-RADS ≥ 3) were more likely to undergo re-biopsy. It is also likely that the decision to re-biopsy was influenced by guideline recommendations, which have varied during this time period. The rate of clinically important PCa, adopting a definition of any Gleason pattern 4 disease within 2 years, was 24.1%. This is at odds with studies suggesting GG≥ 2 PCa is not diagnosed after ASAP4 and is consistent with Kim et al. who reported GG ≥ 2 PCa in 19.6% of patients with ASAP, this being similar to reports in other contemporary studies.2, 5, 6 In a meta-analysis including 16 studies and 1796 patients, those who underwent repeat biopsy within 6 months of ASAP diagnosis, had lower clinically important PCa detection (9%) compared to those who had repeat biopsy after (22.1%).7 On analysis of repeat MRIs, 71.4% of patients with PI-RADS 4–5 lesions who did not have GG ≥ 2 PCa on repeat biopsy had the PI-RADS score downgraded to ≤3 upon repeat mpMRI. Although we only focused on ASAP, this is consistent with the report by Meng et al. who observed a PI-RADS score downgrade of 73% from PI-RADS 4–5 to ≤3 amongst patients with any benign biopsy sub-type.8 This may suggest that microenvironmental changes associated with ASAP could predispose to false positive mpMRI changes. PSAD > 0.15 ng/ml2 at time of ASAP diagnosis was independently associated with the subsequent detection of GG ≥ 2 PCa at repeat biopsy. A reduced rate of false positives was observed with increasing PSAD thresholds. In a report by Warlick et al., PSAD was an independent predictor of detection of GG ≥ 2 PCa at repeat biopsy within 1 year of a diagnosis of ASAP.9 We observed an association between the laterality of ASAP diagnosis and subsequent GG ≥ 2 PCa detection, highlighting importance for adequate ipsilateral sampling given tissue heterogeneity.10, 11 This study has several limitations. Firstly, the number of men who underwent repeat biopsy will have been influenced by clinical decision-making. Secondly, we only adjusted for a limited number of baseline clinicopathological variables as per our protocol. Next, the lack of a control group with completely negative biopsies precludes comparison to determine whether ASAP is an independent predictor for development of GG ≥ 2 PCa. Finally, this is single-centre data reflecting one pathology department's criteria for reporting ASAP. Overall, we must reject our initial hypothesis that prostate cancer diagnosed after ASAP is always low grade. Contrary to this supposition, we have observed an important rate of detection of GG ≥ 2 PCa following a previous diagnosis of ASAP at needle biopsy or following histological analysis of TURP/HoLEP specimens. These findings support that patients diagnosed with ASAP should be followed up for consideration of repeat mpMRI and/or sampling. Further studies may guide development of risk-stratification tools based on clinicopathologic factors such as PSAD and pre-biopsy mpMRI PI-RADS score at the time of initial ASAP diagnosis. This would enable clinicians to better counsel patients and identify those requiring more stringent follow-up, potentially with re-biopsy or repeat mpMRI follow-up. Conceptualisation: Mutie Raslan, Claudia Mercader, Francisco Lopez and Alastair D. Lamb. Data curation: Kanchan Ghosh, Philip Macklin, Richard Colling, Lisa Browning, Ian Roberts. Formal analysis: Thineskrishna Anbarasan, Mutie Raslan and Alastair D. Lamb. Methodology: Thineskrishna Anbarasan, Mutie Raslan, Richard J. Bryant, Richard Colling, Clare Verrill and Alastaiir D. Lamb. Supervision: Richard J. Bryant, Clare Verrill, Freddie C. Hamdy and Alastair D. Lamb. Writing (original draft): Thineskrishna Anbarasan, Mutie Raslan and Alastair D. Lamb. Writing (review and editing): Thineskrishna Anbarasan, Mutie Raslan, Kanchan Ghosh, Philip Macklin, Claudia Mercader, Tom Leslie, Freddie C. Hamdy, Richard Colling, Lisa Browning, Ian Roberts, Clare Verrill, Richard J. Bryant, Francisco Lopez and Alastair D. Lamb. We acknowledge the contribution of patients included and the Oxford Centre for Histopathology Research. Lisa Browning receives funding for a study (ArticulatePro) evaluating Paige Prostate, which is funded by the NHSX Artificial Intelligence in Health and Care Award: Driving system-wide improvements with real-world economics evidence and subspecialist-led adoption guidelines for full workflow implementation of AI with Paige Prostate cancer detection (Paige AI 2020, New York), grading and quantification tool in histopathology departments (AI_AWARD02269). Richard Colling is part funded by UKRI (WCT WVI MAP project and PathLAKE project in-kind partnership with Philips), mdxhealth (research funding), NHSX (ARTICULATE PRO: evaluating Paige AI), and the Clarendon Fund (University of Oxford). Richard Bryant receives grant funding for TRANSLATE Trial (NIHR-HTA: NIHR131233), PART Trial Funding (NIHR-HTA: 17/150/01), Cancer Research Clinician Scientist Fellowship (A22748). Participates in STAMINA Trial Programme Steering Committee. Ian Roberts receives consulting fees from Novartis and Travere Therapeutics and support for attending meetings from American Society of Nephrology, International Academy of Pathology and Asia Pacific Society of Nephrology. Alastair Lamb receives grant funding for TRANSLATE Trial (NIHR-HTA: NIHR131233). ADL was supported by a Cancer Research UK Clinician Scientist Fellowship award (C57899/A25812). Figure S1. Schematic summary of the sub-group of patients with complete clinicopathologic data available for regression analysis. Table S1. Demographics and clinical characteristics of patients with ASAP with respect to whether repeat biopsy was performed. Table S2. Positive (PPV) and negative (NPV) predictive values at various PSA density (PSAD) thresholds for development of GG ≥ 2 PCa within 2 years of ASAP diagnosis in the sub-group of patients without pre-biopsy mpMRI data (n = 103). Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Background: Detection of metastatic disease is important to inform prostate cancer management. Objectives: Evaluate local and distant staging by initial 18F-PSMA-1007 PET in primary and secondary prostate cancer. Design, Setting, and Participants: We retrospectively identified a consecutive series of 18F-PSMA-1007 PET scans from the date of introduction of 18F-PSMA-1007 PET in September 2019 until April 2022 at a single UK tertiary referral center. Our protocol was registered in advance (OSF registration ID: KTE3R). Results: We identified 1335 PSMA-PET scans, from 1220 men. Across 623 initial scans for primary staging, we observed PSMA-PET avidity in 97.6% cases positive for local disease, 29.5% for nodal disease, and 26.5% for metastatic disease. PSMA-PET identified a 13.2% absolute increase in nodal lesions compared with MRI and a 24.0% absolute increase in metastatic lesions compared with MRI marrow. The sensitivity for detection of local disease among 79 patients who had radical prostatectomy was 96.2% for PSMA-PET vs 89.4% for multiparametric MRI. Across 612 scans for secondary staging, we observed PSMA-PET positive avidity in 51.2% of cases for local recurrence, 46.6% for nodal disease, and 43.0% for metastatic disease. When evaluated by the PSA range for patients receiving secondary staging, using the PSA values of 0.2 to 0.49, 0.5 to 0.99, 1 to 1.99, and ≥ 2 ng/mL, PSMA-PET scans were positive in 57.8%, 75.0%, 83.8%, and 95.5% of cases, respectively. PSMA-PET identified a 26.2% absolute increase in metastatic lesions compared with MRI marrow or other skeletal MRI (n = 61) and a 14.7% absolute increase in metastatic lesions compared with the bone scan (n = 42). Conclusion: 18F-PSMA-1007 PET identifies a higher number of nodal and metastatic lesions compared with conventional cross-sectional imaging. However, the high number of indeterminate lesions and stage migration necessitates discussion of 18F-PSMA-1007 PET imaging within a multidisciplinary team and places a higher burden on these teams.

Abstract Background The TRANSLATE (TRANSrectal biopsy versus Local Anaesthetic Transperineal biopsy Evaluation) trial assesses the clinical and cost-effectiveness of two biopsy procedures in terms of detection of clinically significant prostate cancer (PCa). This article describes the statistical analysis plan (SAP) for the TRANSLATE randomised controlled trial (RCT). Methods/design TRANSLATE is a parallel, superiority, multicentre RCT. Biopsy-naïve men aged ≥ 18 years requiring a prostate biopsy for suspicion of possible PCa are randomised (computer-generated 1:1 allocation ratio) to one of two biopsy procedures: transrectal (TRUS) or local anaesthetic transperineal (LATP) biopsy. The primary outcome is the difference in detection rates of clinically significant PCa (defined as Gleason Grade Group ≥ 2, i.e. any Gleason pattern ≥ 4 disease) between the two biopsy procedures. Secondary outcome measures are th eProBE questionnaire (Perception Part and General Symptoms) and International Index of Erectile Function (IIEF, Domain A) scores, International Prostate Symptom Score (IPSS) values, EQ-5D-5L scores, resource use, infection rates, complications, and serious adverse events. We describe in detail the sample size calculation, statistical models used for the analysis, handling of missing data, and planned sensitivity and subgroup analyses. This SAP was pre-specified, written and submitted without prior knowledge of the trial results. Discussion Publication of the TRANSLATE trial SAP aims to increase the transparency of the data analysis and reduce the risk of outcome reporting bias. Any deviations from the current SAP will be described and justified in the final study report and results publication. Trial registration International Standard Randomised Controlled Trial Number ISRCTN98159689, registered on 28 January 2021 and registered on the ClinicalTrials.gov (NCT05179694) trials registry.

You have accessJournal of UrologyProstate Cancer: Basic Research & Pathophysiology II (PD09)1 May 2024PD09-08 SPATIAL TRANSCRIPTOMIC CLONAL DECONVOLUTION IDENTIFIES THE 'LETHAL CLONE' IN PROSTATE CANCER AS DEFINED BY ABILITY TO METASTASIZE TO LYMPH NODES Wencheng Yin, Sandy Figiel, Mengxiao He, Renuka Teague, Thineskrishna Anbarasan, Nithesh Ranasinha, Reema Singh, Ninu Poulose, Dimitrios Doultsinos, Sophia Abusamra, Andrew Erickson, Massimo Loda, Clare Verrill, Richard Colling, Pelvender Gill, Richard Bryant, Olivier Cussenot, Freddie Hamdy, Dan Woodcock, Ian Mills, Joakim Lundeberg, and Alastair Lamb Wencheng YinWencheng Yin , Sandy FigielSandy Figiel , Mengxiao HeMengxiao He , Renuka TeagueRenuka Teague , Thineskrishna AnbarasanThineskrishna Anbarasan , Nithesh RanasinhaNithesh Ranasinha , Reema SinghReema Singh , Ninu PouloseNinu Poulose , Dimitrios DoultsinosDimitrios Doultsinos , Sophia AbusamraSophia Abusamra , Andrew EricksonAndrew Erickson , Massimo LodaMassimo Loda , Clare VerrillClare Verrill , Richard CollingRichard Colling , Pelvender GillPelvender Gill , Richard BryantRichard Bryant , Olivier CussenotOlivier Cussenot , Freddie HamdyFreddie Hamdy , Dan WoodcockDan Woodcock , Ian MillsIan Mills , Joakim LundebergJoakim Lundeberg , and Alastair LambAlastair Lamb View All Author Informationhttps://doi.org/10.1097/01.JU.0001008572.33286.63.08AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Prostate cancer (PCa) epitomises intratumoural heterogeneity, a phenomenon that has hindered accurate risk stratification & treatment. A distinctive feature of this heterogeneity is the frequent occurrence of copy number alterations acquired by dividing cells, which can be used to define clonal heritage. The precise contribution of local cancer clones to lethal metastatic PCa is unclear. This study leveraged spatial transcriptomics technology to infer clonal genomic identity in PCa to identify regions of primary tumour that can metastasize to lymph nodes. METHODS: We collated FFPE samples from primary & nodal metastatic tissue of 4 patients recruited in trial (ISRCTN10046036). We performed spatial transcriptomics (Visium v2), processed fastq files using SpaceRanger & analysed with our published protocol (github.com/aerickso/SpatialInferCNV). Each section contains around 5,000 (6.5 mm2) or 14,000 (11 mm2) spots of 55 µm diameter with meticulous histological annotation by pathologist. We show the clonal relationships, location & distribution throughout prostate & nodal environment. RESULTS: We identified 11 clonal subtypes on epithelial tissue. We show the distinct features of specific clones which exist in both primary & lymph nodes metastases ("X clones") alongside their clonal ancestry as a phylogenetic tree. Interestingly, we observed subclonal events within the lymph nodes & evidence for polyclonal colonisation of lymph nodes at distinct time points in the evolution of primary disease. Specifically, we observed Chr10q loss, the location of PTEN, was an early event in the clonal ancestry of lethal disease. PTEN is a well-known tumour suppressor gene, the loss of which causes chromosomal instability. Chr8 gain/loss was an important later event in the transition to migratory behaviour possibly due to phenotypic versatility resulting from amplification of genes such as c-Myc. CONCLUSIONS: For the first time, we detail the clonal heterogeneity in PCa, with direct linkage to nodal metastases. Spatial transcriptomics with careful case selection & highly granular pathology, is a powerful tool to decipher clonal hierarchies & trace the evolution of lethal disease. This has important implications for risk stratification & treatment selection in PCa. Download PPT Source of Funding: The present work was supported by Cancer Research UK (CRUK), Hanson Trust Research, the John Black Charitable Foundation, Prostate Cancer Foundation, NIHR Oxford Biomedical Research Centre, European Research Council, Swedish Society for Cancer Research © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e182 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Wencheng Yin More articles by this author Sandy Figiel More articles by this author Mengxiao He More articles by this author Renuka Teague More articles by this author Thineskrishna Anbarasan More articles by this author Nithesh Ranasinha More articles by this author Reema Singh More articles by this author Ninu Poulose More articles by this author Dimitrios Doultsinos More articles by this author Sophia Abusamra More articles by this author Andrew Erickson More articles by this author Massimo Loda More articles by this author Clare Verrill More articles by this author Richard Colling More articles by this author Pelvender Gill More articles by this author Richard Bryant More articles by this author Olivier Cussenot More articles by this author Freddie Hamdy More articles by this author Dan Woodcock More articles by this author Ian Mills More articles by this author Joakim Lundeberg More articles by this author Alastair Lamb More articles by this author Expand All Advertisement PDF downloadLoading ...