Daniel Maurice Berney

Testicular tumors are relatively rare, yet represent one of the most common solid neoplasms in young adult males. They are a heterogeneous group of neoplasms with distinct biological behaviors and can be diagnostically challenging due to their relative unfamiliarity. The vast majority are of germ cell origin [testicular germ cell tumors (TGCTs)], predominantly arising from the precursor lesion, germ cell neoplasia in situ (GCNIS), and are broadly classified as seminomas or nonseminomas. TGCTs are highly curable, with efforts focused on reducing treatment intensity. The second most common group of testicular neoplasms is sex cord-stromal tumors (SCSTs). These usually follow a benign clinical course, but 5% to 10% are malignant and metastasize. In contrast to TGCTs, SCSTs are largely unresponsive to chemotherapy and radiotherapy; therefore, surgical resection, such as early retroperitoneal lymph-node dissection, may be the only effective treatment. Accurate tumor classification, staging, and risk stratification of TGCTs and SCSTs have a profound effect on patient care and rely on a thorough histopathologic examination, from macroscopic evaluation to microscopic assessment. In this review, we summarize the clinicopathological features most relevant and robust for the prognostication of patients with testicular tumors, with a focus on practical guidance on reporting and the avoidance of diagnostic pitfalls. Recent molecular insights that refine classification and predict clinical outcomes are also discussed.

Aims: Testicular sex cord‐stromal tumours (TSCSTs) are rare neoplasms that arise from (or show differentiation to) elements derived from the sex cords or intertubular stroma. The current classification (World Health Organization, 2022) is based almost entirely on morphology, with many tumour types such as granulosa cell tumours being defined based on their resemblance to ovarian counterparts. Recent studies have uncovered new clinicopathological, molecular and biological nuances within this group of tumours, underscoring differences with ovarian homologues. Methods and results: Considering the new data, a group of genitourinary pathology experts (Testicular Sex Cord‐Stromal Tumour [TESST] group) has proposed a revised ‘TESST classification’ that incorporates the best available evidence. This classification is structured and hierarchical, composed of the following tiers: (1) group, (2) category, (3) entity and (4) histological pattern. ‘Group’ refers to the histological compartment a specific tumour belongs to (sex cords, stroma or both), whereas ‘category’ is defined by the phenotype of the tumour, taking non‐neoplastic elements of the ovary and testis as a reference. The third tier, ‘entity’, refers to neoplasms with shared clinicopathological and/or molecular features that justify classifying them as distinct types. Lastly, ‘histological patterns’ are recurrent morphological variations within entities that are not linked to distinct clinicopathological or molecular features. Conclusions: We propose a classification that will provide a framework to improve current clinical management and guide future research in the field.

Aims: Germ cell neoplasia in situ (GCNIS) is the precursor of GCNIS‐derived testicular germ cell tumours, including seminomas and non‐seminomas. Most non‐seminomas show mixed histology, often including components of seminoma. A small subset presents as pure non‐seminomatous germ cell tumors. In these neoplasms, the absence of invasive seminoma. raises the possibility that reprogramming to non‐seminoma may occur at an early in‐situ stage (i.e., within the spermatogonial niche). This study examined GCNIS associated with pure embryonal carcinoma (EC) and postpubertal‐type yolk sac tumour (YST) using immunohistochemistry for transcription factors characteristic of GCNIS/seminoma, EC, and YST phenotypes to determine if reprogramming to non‐seminoma occurs within the spermatogonial niche. Methods: GCNIS associated with pure testicular YST and EC was assessed using OCT4, FOXA2 (only YST), CD30 (only EC), SOX2 (only EC), SOX17, and NANOG immunohistochemistry. Adjacent foci of intratubular and invasive tumour were also evaluated, if present. Results: Sixty‐seven pure non‐seminomas (63 EC, 4 YST) were evaluated. In all cases, GCNIS expressed OCT4, SOX17, and NANOG. GCNIS associated with EC was consistently negative for CD30 and SOX2, and GCNIS associated with YST was consistently negative for FOXA2. Adjacent invasive EC (62/67 cases) and intratubular EC (19/67) showed the classic EC immunophenotype: OCT4+/NANOG+/SOX17–/SOX2+/CD30+. Similarly, invasive YST adjacent to GCNIS was OCT4–/NANOG–/SOX17+/FOXA2+. Conclusions: Assessment of transcription factors involved in the induction and maintenance of EC and YST phenotypes suggests that, in pure non‐seminomas, reprogramming occurs outside the spermatogonial niche.

Prostate cancers with high proliferation rates have shorter survival times but when spread has occurred (metastatic), there is increased sensitivity to hormone therapy with abiraterone.

Background: Approximately one‐fourth of patients with clinical stage I testicular cancer relapse. For decades, risk stratification has been based on different tumor characteristics for seminomas and non‐seminomas. Previous studies primarily used Cox proportional‐hazards models and included only a limited number of variables. Machine learning techniques can integrate large datasets and may uncover novel combinations of risk factors. Objectives: To develop and validate a unified machine learning‐based relapse prediction model for clinical stage I testicular cancer, regardless of histologic subtype, using nationwide histopathological and clinical data. Materials and Methods: A population‐based cohort study of 1377 patients diagnosed with clinical stage I testicular cancer in Denmark from 2013 to 2018. Histopathological and clinical data were obtained through centralized pathology assessment and systematic medical record review. Two tree‐based binary classifiers (CatBoost and LightGBM) were trained to predict relapse, and a random survival forest model was used to estimate time‐to‐relapse. Data were split into training (80%, 5‐fold cross‐validation) and a test set (20%), balanced by seminoma/non‐seminoma subtypes and outcome. Subgroup analyses were performed for seminoma and non‐seminoma. Binary models were evaluated using receiver operating characteristic area under the curve, precision‐recall area under the curve, and Matthew's correlation coefficient; random survival forest performance was assessed using concordance index and Integrated Brier Score. Results: CatBoost outperformed LightGBM (receiver operating characteristic area under the curve = 0.74) and demonstrated a high negative predictive value (0.86). The random survival forest achieved a concordance index of 0.71. Predictive performance was stronger in the non‐seminoma than in the seminoma subgroups. Top‐ranked predictive features included lymphovascular invasion, embryonal carcinoma, tumor necrosis, rete testis invasion, tumor size, and elevated lactate dehydrogenase and β‐human chorionic gonadotropin. Tumor necrosis and the anatomical location of lymphovascular invasion emerged as novel predictors. Discussion and Conclusion: A unified machine learning‐based model for relapse prediction in clinical stage I testicular cancer is feasible and demonstrates moderate predictive accuracy. It is particularly useful for ruling out relapse and shows greater robustness in non‐seminoma. These findings provide a framework for validation in independent cohorts and highlight key predictive features for future research.

Background/Objectives: Although 90% of prostate cancer (PCa) metastasis occurs in the bone, there are limited studies and rarely available genome-wide profiles at individual sample level for genomic copy number changes in the literature. Methods: We performed Affymetrix SNP 6.0 high-density microarray analysis to generate the genome-wide copy number change profiles for six cases of PCa bone metastases. A common genomic loss was confirmed by fluorescence in situ hybridization (FISH) in paraffin-embedded PCa bone metastasis samples together with primary PCa and benign prostate hyperplasia samples. We overexpressed the candidate miRNA in PCa cell lines and knocked down its target genes by siRNA transfection and investigated the effect on protein expression and cell viability, migration, and invasion abilities, respectively. Protein expression in PCa tissues was analyzed by immunohistochemical staining. Results: We provided high-resolution PCa bone metastasis profiles of six cases and identified potential bone metastasis-specific common genomic alterations, including a 1.6 mb region on 17q11.2, as well as those shared by non-bone metastatic PCa. The common 17q11.2 loss was confirmed by FISH in further 14/21 PCa bone metastasis samples but was only found in 9/151 primary PCa samples. The well-established tumor-suppressing miRNA located within this small genomic region, miR-193a-3p, was downregulated in both bone metastasis and primary PCa cases, leading to overexpression of cyclin D1 and uPA to promote cancer cell migration and invasion. Cyclin D1 was highly expressed in both localized PCa and bone metastasis samples, and the expression was significantly higher in the latter group (p = 0.013). Conclusions: We generated high-resolution copy number change profiles for bone metastasis samples. This led to the identification of a common, small genomic loss and downregulation of miR-193a-3p, which suppresses PCa bone metastasis through inhibition of its target proteins, providing new insight into bone metastasis development.

Conflicting practice recommendations regarding the grading of intraductal carcinoma of the prostate (IDCP) from two leading uropathology societies, the Genitourinary Pathology Society (GUPS) and the International Society of Urological Pathology (ISUP), are confusing for both pathologists and treating clinicians. The objectives of this consultation were to clarify unresolved issues regarding IDCP and atypical intraductal proliferation (AIP) terminology, diagnostic criteria, grading, and management implications, as well as to develop uniform reporting guidelines for IDCP and AIP, endorsed by both societies. A 32‐member expert panel, composed of five core members, 25 expert urological pathologists, and two expert urologists, employed a modified Delphi process consisting of multiple rounds of consultation and voting. These were supplemented by discussions at the 2025 United States and Canadian Academy of Pathologists Annual Meeting to achieve expert consensus (defined as at least 67% agreement). Consensus was reached on several key issues. IDCP was regarded most commonly as reflecting the retrograde spread of invasive prostate cancer (PCa). IDCP diagnosis should be based on the Guo and Epstein criteria, supported by basal cell immunohistochemistry in cases that are difficult to distinguish from invasive PCa. The term AIP should be used only in equivocal proliferations where IDCP is favoured but the criteria are not fully met, and these should be reported as ‘ AIP, suspicious for IDCP ’. In the presence of invasive PCa, IDCP should generally be incorporated into Gleason grading irrespective of Grade Group (GG). However, a significant minority (30%) favoured excluding IDCP from the Gleason score if the invasive component was solely Gleason pattern (GP) 3. Pure IDCP (not associated with invasive PCa) and AIP, suspicious for IDCP, should not be graded. IDCP should not be incorporated in the grading of invasive PCa when it is spatially distinct from invasive PCa. A second opinion from a senior or dedicated GU pathologist and discussion within a multidisciplinary management setting should be considered, in the rare settings of pure IDCP or GP3 + IDCP (formerly GG1 + IDCP scenario). This joint GUPS–ISUP consultation provides unified recommendations for the diagnosis, terminology, grading, and reporting of IDCP and AIP, and will pave the way for the development of future IDCP/AIP WHO guidelines. Their adoption should reduce interobserver variation, facilitate consistent communication with clinicians, and improve patient management.

To summarise the content and significance of the recently published second edition International Collaboration on Cancer Reporting (ICCR) histopathology dataset for testicular germ cell tumours, covering the Orchiectomy specimen dataset. We highlight key updates from the first editions, including alignment with the 5th edition World Health Organization (WHO) Classification, revised staging criteria, clarified core data elements versus non‐core elements and the evidentiary basis underpinning these changes. A review of the ICCR 2nd edition dataset for Orchiectomy specimens of primary testicular tumours was performed, focusing on their development by an international expert committee using a consensus‐based approach. Core (required) and non‐core (recommended) data elements were identified along with the level of evidence supporting each, following National Health and Medical Research Council (NHMRC) criteria. Changes from the first edition were extracted by comparing dataset content and notes, informed by up‐to‐date literature through July 2024. The 2nd edition Orchiectomy dataset provides an integrated, harmonised framework for reporting testicular germ cell tumours. The dataset incorporates the WHO 5th Edition Classification of Urinary and Male Genital Tumours. Pathological staging criteria have been updated to align with the 8th edition Union for International Cancer Control (UICC)/American Joint Committee on Cancer (AJCC) tumour‐node‐metastasis (TNM) definitions. The second edition of this dataset includes changes to align the dataset with the WHO Classification of Tumours, Urinary and Male Genital Tumours, 5th edition, 2022. The ICCR dataset includes the 5th edition Corrigenda, July 2024. It was agreed that this dataset is not suitable for non‐germ cell tumours, with the hope that a new dataset, especially for sex‐cord stromal tumours, would be developed. The 2nd edition Orchiectomy dataset represents an authoritative, up‐to‐date standard for pathology reporting of primary testicular germ cell tumours. By incorporating the WHO 5th edition classifications, current TNM staging and the latest evidence on prognostic factors, this dataset facilitates uniform reporting and prognostication. The ICCR dataset underscores core data required for patient management decisions (e.g., adjuvant therapy in Stage I disease, post‐chemotherapy management) while providing flexibility through non‐core elements for additional useful information. Adoption of this internationally vetted dataset will enhance consistency, assist multidisciplinary treatment planning and align pathology reports with modern consensus guidelines and classifications. The dataset can be used in both high‐resource and limited‐resource settings without compromising the essential reporting standards.