Analysis of 3D pathology samples using weakly supervised AI.

Human tissue, which is inherently three-dimensional (3D), is traditionally examined through standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can insufficiently represent the tissue due to sampling bias. To holistically characterize histomorphology, 3D imaging modalities have been developed, but clinical translation is hampered by complex manual evaluation and lack of computational platforms to distill clinical insights from large, high-resolution datasets. We present TriPath, a deep-learning platform for processing tissue volumes and efficiently predicting clinical outcomes based on 3D morphological features. Recurrence risk-stratification models were trained on prostate cancer specimens imaged with open-top light-sheet microscopy or microcomputed tomography. By comprehensively capturing 3D morphologies, 3D volume-based prognostication achieves superior performance to traditional 2D slice-based approaches, including clinical/histopathological baselines from six certified genitourinary pathologists. Incorporating greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, further emphasizing the value of capturing larger extents of heterogeneous morphology.

Outcome Analysis of a Series of Mixed-Grade, Non-muscle Invasive, Papillary Carcinomas of the Bladder.

Introduction. Papillary urothelial carcinomas are currently graded as either low- or high-grade tumors based on World Health Organization (WHO) 2022 guidelines for genitourinary tumors. However, a minority of tumors are mixed-grade tumors, composed predominantly of low-grade cancer with a minor high-grade component. In the 2022 WHO these cancers are recognized as having outcomes comparable to low-grade cancers, although data to date has been limited. Methods. The pathology records of a large academic institution were searched for mixed-grade, non-muscle invasive papillary carcinomas of the bladder and ureter in order to characterize prognosis of these cancers. Results. Of 136 cancers, the majority (n = 104, 76.5%) were solitary, mixed-grade tumors, while 21 (15.4%) had a concurrent low-grade cancer and 11 (8.1%) had multiple mixed-grade tumors at the time of diagnosis. At follow-up (median 48.3 months, range = 1.3 months-18.1 years), 71 cancers recurred (52.2%): 52 (38.2%) as low- or mixed-grade cancers and 18 (13.2%) as high-grade cancers. There were no instances of stage-progression to >pT2. Conclusions. The clinical outcome of mixed-grade carcinomas was similar to what has been reported for low-grade carcinomas. Based on our results, and prior congruent studies of mixed-grade lesions, these lesions may be regarded as a distinct sub-category with a better prognosis than high-grade tumors.

Single Cell Analysis of Treatment-Resistant Prostate Cancer: Implications of Cell State Changes for Cell Surface Antigen Targeted Therapies.

Targeting cell surface molecules using radioligand and antibody-based therapies has yielded considerable success across cancers. However, it remains unclear how the expression of putative lineage markers, particularly cell surface molecules, varies in the process of lineage plasticity, wherein tumor cells alter their identity and acquire new oncogenic properties. A notable example of lineage plasticity is the transformation of prostate adenocarcinoma (PRAD) to neuroendocrine prostate cancer (NEPC)--a growing resistance mechanism that results in the loss of responsiveness to androgen blockade and portends dismal patient survival. To understand how lineage markers vary across the evolution of lineage plasticity in prostate cancer, we applied single cell analyses to 21 human prostate tumor biopsies and two genetically engineered mouse models, together with tissue microarray analysis (TMA) on 131 tumor samples. Not only did we observe a higher degree of phenotypic heterogeneity in castrate-resistant PRAD and NEPC than previously anticipated, but also found that the expression of molecules targeted therapeutically, namely PSMA, STEAP1, STEAP2, TROP2, CEACAM5, and DLL3, varied within a subset of gene-regulatory networks (GRNs). We also noted that NEPC and small cell lung cancer (SCLC) subtypes shared a set of GRNs, indicative of conserved biologic pathways that may be exploited therapeutically across tumor types. While this extreme level of transcriptional heterogeneity, particularly in cell surface marker expression, may mitigate the durability of clinical responses to novel antigen-directed therapies, its delineation may yield signatures for patient selection in clinical trials, potentially across distinct cancer types.

An end-to-end workflow for nondestructive 3D pathology.

Recent advances in 3D pathology offer the ability to image orders of magnitude more tissue than conventional pathology methods while also providing a volumetric context that is not achievable with 2D tissue sections, and all without requiring destructive tissue sectioning. Generating high-quality 3D pathology datasets on a consistent basis, however, is not trivial and requires careful attention to a series of details during tissue preparation, imaging and initial data processing, as well as iterative optimization of the entire process. Here, we provide an end-to-end procedure covering all aspects of a 3D pathology workflow (using light-sheet microscopy as an illustrative imaging platform) with sufficient detail to perform well-controlled preclinical and clinical studies. Although 3D pathology is compatible with diverse staining protocols and computationally generated color palettes for visual analysis, this protocol focuses on the use of a fluorescent analog of hematoxylin and eosin, which remains the most common stain used for gold-standard pathological reports. We present our guidelines for a broad range of end users (e.g., biologists, clinical researchers and engineers) in a simple format. The end-to-end workflow requires 3-6 d to complete, bearing in mind that data analysis may take longer.

High expression of Trop2 is associated with aggressive localized prostate cancer and is a candidate urinary biomarker.

Distinguishing indolent from clinically significant localized prostate cancer is a major clinical challenge and influences clinical decision-making between treatment and active surveillance. The development of novel predictive biomarkers will help with risk stratification, and clinical decision-making, leading to a decrease in over or under-treatment of patients with prostate cancer. Here, we report that Trop2 is a prognostic tissue biomarker for clinically significant prostate cancer by utilizing the Canary Prostate Cancer Tissue Microarray (CPCTA) cohort composed of over 1100 patients from a multi-institutional study. We demonstrate that elevated Trop2 expression is correlated with worse clinical features including Gleason score, age, and pre-operative PSA levels. More importantly, we demonstrate that elevated Trop2 expression at radical prostatectomy predicts worse overall survival in men undergoing radical prostatectomy. Additionally, we detect shed Trop2 in urine from men with clinically significant prostate cancer. Our study identifies Trop2 as a novel tissue prognostic biomarker and a candidate non-invasive marker for prostate cancer.

Engineering the future of 3D pathology.

In recent years, technological advances in tissue preparation, high-throughput volumetric microscopy, and computational infrastructure have enabled rapid developments in nondestructive 3D pathology, in which high-resolution histologic datasets are obtained from thick tissue specimens, such as whole biopsies, without the need for physical sectioning onto glass slides. While 3D pathology generates massive datasets that are attractive for automated computational analysis, there is also a desire to use 3D pathology to improve the visual assessment of tissue histology. In this perspective, we discuss and provide examples of potential advantages of 3D pathology for the visual assessment of clinical specimens and the challenges of dealing with large 3D datasets (of individual or multiple specimens) that pathologists have not been trained to interpret. We discuss the need for artificial intelligence triaging algorithms and explainable analysis methods to assist pathologists or other domain experts in the interpretation of these novel, often complex, large datasets.

Characterization of HOXB13 expression patterns in localized and metastatic castration-resistant prostate cancer.

HOXB13 is a key lineage homeobox transcription factor that plays a critical role in the differentiation of the prostate gland. Several studies have suggested that HOXB13 alterations may be involved in prostate cancer development and progression. Despite its potential biological relevance, little is known about the expression of HOXB13 across the disease spectrum of prostate cancer. To this end, we validated a HOXB13 antibody using genetic controls and investigated HOXB13 protein expression in murine and human developing prostates, localized prostate cancers, and metastatic castration-resistant prostate cancers. We observed that HOXB13 expression increases during later stages of murine prostate development. All localized prostate cancers showed HOXB13 protein expression. Interestingly, lower HOXB13 expression levels were observed in higher-grade tumors, although no significant association between HOXB13 expression and recurrence or disease-specific survival was found. In advanced metastatic prostate cancers, HOXB13 expression was retained in the majority of tumors. While we observed lower levels of HOXB13 protein and mRNA levels in tumors with evidence of lineage plasticity, 84% of androgen receptor-negative castration-resistant prostate cancers and neuroendocrine prostate cancers (NEPCs) retained detectable levels of HOXB13. Notably, the reduced expression observed in NEPCs was associated with a gain of HOXB13 gene body CpG methylation. In comparison to the commonly used prostate lineage marker NKX3.1, HOXB13 showed greater sensitivity in detecting advanced metastatic prostate cancers. Additionally, in a cohort of 837 patients, 383 with prostatic and 454 with non-prostatic tumors, we found that HOXB13 immunohistochemistry had a 97% sensitivity and 99% specificity for prostatic origin. Taken together, our studies provide valuable insight into the expression pattern of HOXB13 during prostate development and cancer progression. Furthermore, our findings support the utility of HOXB13 as a diagnostic biomarker for prostate cancer, particularly to confirm the prostatic origin of advanced metastatic castration-resistant tumors. © 2023 The Pathological Society of Great Britain and Ireland.

Visual Assessment of 2-Dimensional Levels Within 3-Dimensional Pathology Data Sets of Prostate Needle Biopsies Reveals Substantial Spatial Heterogeneity.

Prostate cancer prognostication largely relies on visual assessment of a few thinly sectioned biopsy specimens under a microscope to assign a Gleason grade group (GG). Unfortunately, the assigned GG is not always associated with a patient's outcome in part because of the limited sampling of spatially heterogeneous tumors achieved by 2-dimensional histopathology. In this study, open-top light-sheet microscopy was used to obtain 3-dimensional pathology data sets that were assessed by 4 human readers. Intrabiopsy variability was assessed by asking readers to perform Gleason grading of 5 different levels per biopsy for a total of 20 core needle biopsies (ie, 100 total images). Intrabiopsy variability (Cohen κ) was calculated as the worst pairwise agreement in GG between individual levels within each biopsy and found to be 0.34, 0.34, 0.38, and 0.43 for the 4 pathologists. These preliminary results reveal that even within a 1-mm-diameter needle core, GG based on 2-dimensional images can vary dramatically depending on the location within a biopsy being analyzed. We believe that morphologic assessment of whole biopsies in 3 dimension has the potential to enable more reliable and consistent tumor grading.

Concurrent Targeting of HDAC and PI3K to Overcome Phenotypic Heterogeneity of Castration-resistant and Neuroendocrine Prostate Cancers.

Castration-resistant prostate cancer (CRPC) consists of multiple phenotypic subtypes including androgen receptor (AR)-active prostate cancer (ARPC) and neuroendocrine prostate cancer (NEPC). Tumor cells with these phenotypes can coexist between metastases within a patient and within an individual tumor. Treatments that are effective across CRPC subtypes are currently lacking. Histone deacetylation is crucial for the regulation of chromatin structure and maintenance of cancer cell state and activation of the PI3K/AKT/mTOR signaling cascade is a tumor growth-promoting pathway. We therefore investigated combined targeting of histone deacetylase (HDAC) and PI3K using a rationally designed dual inhibitor, fimepinostat, in CRPC subtypes in vitro and in vivo. Dual HDAC1/2 and PI3K/AKT pathway inhibition by fimepinostat led to robust tumor growth inhibition in both ARPC and NEPC models including cell line- and patient-derived xenografts. HDAC1/2 inhibition combined with PI3K/AKT inhibition was more effective than targeting each pathway alone, producing growth inhibitory effects through cell-cycle inhibition and apoptosis. Molecular profiling revealed on-target effects of combined HDAC1/2 and PI3K/AKT inhibition independent of tumor phenotype. Fimepinostat therapy was also associated with the suppression of lineage transcription factors including AR in ARPC and Achaete-scute homolog 1 (ASCL1) in NEPC. Together, these results indicate that fimepinostat represents a novel therapeutic that may be effective against both ARPC and NEPC through CRPC subtype-dependent and -independent mechanisms. SIGNIFICANCE: CRPC is a heterogeneous disease constituting multiple phenotypic subtypes that often co-occur within tumors or across metastases in patients. Existing targeted therapies for CRPC do not take this into account. Here we show that fimepinostat, a dual HDAC1/2 and PI3K/AKT inhibitor investigated clinically in other cancer types but not prostate cancer, may overcome this heterogeneity by effectively inhibiting both ARPC and NEPC subtypes of CRPC.

Tumor-derived biomarkers predict efficacy of B7H3 antibody-drug conjugate treatment in metastatic prostate cancer models.

Antibody-drug conjugates (ADCs) are a promising targeted cancer therapy; however, patient selection based solely on target antigen expression without consideration for cytotoxic payload vulnerabilities has plateaued clinical benefits. Biomarkers to capture patients who might benefit from specific ADCs have not been systematically determined for any cancer. We present a comprehensive therapeutic and biomarker analysis of a B7H3-ADC with pyrrolobenzodiazepine(PBD) payload in 26 treatment-resistant, metastatic prostate cancer (mPC) models. B7H3 is a tumor-specific surface protein widely expressed in mPC, and PBD is a DNA cross-linking agent. B7H3 expression was necessary but not sufficient for B7H3-PBD-ADC responsiveness. RB1 deficiency and/or replication stress, characteristics of poor prognosis, and conferred sensitivity were associated with complete tumor regression in both neuroendocrine (NEPC) and androgen receptor positive (ARPC) prostate cancer models, even with low B7H3 levels. Non-ARPC models, which are currently lacking efficacious treatment, demonstrated the highest replication stress and were most sensitive to treatment. In RB1 WT ARPC tumors, SLFN11 expression or select DNA repair mutations in SLFN11 nonexpressors governed response. Importantly, WT TP53 predicted nonresponsiveness (7 of 8 models). Overall, biomarker-focused selection of models led to high efficacy of in vivo treatment. These data enable a paradigm shift to biomarker-driven trial designs for maximizing clinical benefit of ADC therapies.

An end-to-end workflow for non-destructive 3D pathology.

Recent advances in 3D pathology offer the ability to image orders-of-magnitude more tissue than conventional pathology while providing a volumetric context that is lacking with 2D tissue sections, all without requiring destructive tissue sectioning. Generating high-quality 3D pathology datasets on a consistent basis is non-trivial, requiring careful attention to many details regarding tissue preparation, imaging, and data/image processing in an iterative process. Here we provide an end-to-end protocol covering all aspects of a 3D pathology workflow (using light-sheet microscopy as an illustrative imaging platform) with sufficient detail to perform well-controlled preclinical and clinical studies. While 3D pathology is compatible with diverse staining protocols and computationally generated color palettes for visual analysis, this protocol will focus on a fluorescent analog of hematoxylin and eosin (H&E), which remains the most common stain for gold-standard diagnostic determinations. We present our guidelines for a broad range of end-users (e.g., biologists, clinical researchers, and engineers) in a simple tutorial format.

Multi-level functional genomics reveals molecular and cellular oncogenicity of patient-based 3' untranslated region mutations.

3' untranslated region (3' UTR) somatic mutations represent a largely unexplored avenue of alternative oncogenic gene dysregulation. To determine the significance of 3' UTR mutations in disease, we identify 3' UTR somatic variants across 185 advanced prostate tumors, discovering 14,497 single-nucleotide mutations enriched in oncogenic pathways and 3' UTR regulatory elements. By developing two complementary massively parallel reporter assays, we measure how thousands of patient-based mutations affect mRNA translation and stability and identify hundreds of functional variants that allow us to define determinants of mutation significance. We demonstrate the clinical relevance of these mutations, observing that CRISPR-Cas9 endogenous editing of distinct variants increases cellular stress resistance and that patients harboring oncogenic 3' UTR mutations have a particularly poor prognosis. This work represents an expansive view of the extent to which disease-relevant 3' UTR mutations affect mRNA stability, translation, and cancer progression, uncovering principles of regulatory functionality and potential therapeutic targets in previously unexplored regulatory regions.

Sarcomatoid Urothelial Carcinoma Is Associated With Limited Response to Neoadjuvant Chemotherapy and Poor Oncologic Outcomes After Radical Cystectomy.

INTRODUCTION: To examine oncologic outcomes and response to neoadjuvant chemotherapy (NAC) in patients with sarcomatoid urothelial carcinoma (SUC) treated with radical cystectomy (RC). MATERIALS AND METHODS: We retrospectively queried our institutional database (2003-18) and Surveillance, Epidemiology, and End Results (SEER)-Medicare (2004-2015) for patients with cT2-4, N0-2, M0 SUC and conventional UC (CUC) treated with RC. Clinicopathologic characteristics were described using descriptive statistics (t test, χ2-test and log-rank-test for group comparison). Overall (OS) and recurrence-free-survival (RFS) after RC were estimated with the Kaplan Meier method and associations with OS were evaluated with Cox proportional hazards models. RESULTS: We identified 38 patients with SUC and 287 patients with CUC in our database, and 190 patients with SUC in SEER-Medicare. In the institutional cohort, patients with SUC versus CUC had higher rates of pT3/4 stage (66% vs. 35%, P < 0.001), lower rates of ypT0N0 (6% vs. 35%, P = .02), and worse median OS (17.5 vs. 120 months, P < .001). Further, patients with SUC in the institutional versus SEER-Medicare cohort had similar median OS (17.5 vs. 21 months). In both cohorts, OS was comparable between patients with SUC undergoing NAC+RC vs. RC alone (17.5 vs. 18.4 months, P = .98, institutional cohort; 24 vs. 20 months, P = .56, SEER cohort). In Cox proportional hazards models for the institutional RC cohort, SUC was independently associated with worse OS (HR 2.3, CI 1.4-3.8, P = .001). CONCLUSION: SUC demonstrates poor pathologic response to NAC and worse OS compared with CUC, with no OS benefit associated with NAC. A unique pattern of rapid abdominopelvic cystic recurrence was identified.

Nondestructive 3D pathology with analysis of nuclear features for prostate cancer risk assessment.

Prostate cancer treatment decisions rely heavily on subjective visual interpretation [assigning Gleason patterns or International Society of Urological Pathology (ISUP) grade groups] of limited numbers of two-dimensional (2D) histology sections. Under this paradigm, interobserver variance is high, with ISUP grades not correlating well with outcome for individual patients, and this contributes to the over- and undertreatment of patients. Recent studies have demonstrated improved prognostication of prostate cancer outcomes based on computational analyses of glands and nuclei within 2D whole slide images. Our group has also shown that the computational analysis of three-dimensional (3D) glandular features, extracted from 3D pathology datasets of whole intact biopsies, can allow for improved recurrence prediction compared to corresponding 2D features. Here we seek to expand on these prior studies by exploring the prognostic value of 3D shape-based nuclear features in prostate cancer (e.g. nuclear size, sphericity). 3D pathology datasets were generated using open-top light-sheet (OTLS) microscopy of 102 cancer-containing biopsies extracted ex vivo from the prostatectomy specimens of 46 patients. A deep learning-based workflow was developed for 3D nuclear segmentation within the glandular epithelium versus stromal regions of the biopsies. 3D shape-based nuclear features were extracted, and a nested cross-validation scheme was used to train a supervised machine classifier based on 5-year biochemical recurrence (BCR) outcomes. Nuclear features of the glandular epithelium were found to be more prognostic than stromal cell nuclear features (area under the ROC curve [AUC] = 0.72 versus 0.63). 3D shape-based nuclear features of the glandular epithelium were also more strongly associated with the risk of BCR than analogous 2D features (AUC = 0.72 versus 0.62). The results of this preliminary investigation suggest that 3D shape-based nuclear features are associated with prostate cancer aggressiveness and could be of value for the development of decision-support tools. © 2023 The Pathological Society of Great Britain and Ireland.

Comprehensive Surface Histology of Fresh Resection Margins With Rapid Open-Top Light-Sheet (OTLS) Microscopy.

OBJECTIVE: For tumor resections, margin status typically correlates with patient survival but positive margin rates are generally high (up to 45% for head and neck cancer). Frozen section analysis (FSA) is often used to intraoperatively assess the margins of excised tissue, but suffers from severe under-sampling of the actual margin surface, inferior image quality, slow turnaround, and tissue destructiveness. METHODS: Here, we have developed an imaging workflow to generate en face histologic images of freshly excised surgical margin surfaces based on open-top light-sheet (OTLS) microscopy. Key innovations include (1) the ability to generate false-colored H&E-mimicking images of tissue surfaces stained for < 1 min with a single fluorophore, (2) rapid OTLS surface imaging at a rate of 15 min/cm2 followed by real-time post-processing of datasets within RAM at a rate of 5 min/cm2, and (3) rapid digital surface extraction to account for topological irregularities at the tissue surface. RESULTS: In addition to the performance metrics listed above, we show that the image quality generated by our rapid surface-histology method approaches that of gold-standard archival histology. CONCLUSION: OTLS microscopy has the feasibility to provide intraoperative guidance of surgical oncology procedures. SIGNIFICANCE: The reported methods can potentially improve tumor-resection procedures, thereby improving patient outcomes and quality of life.

Targeting advanced prostate cancer with STEAP1 chimeric antigen receptor T cell and tumor-localized IL-12 immunotherapy.

Six transmembrane epithelial antigen of the prostate 1 (STEAP1) is a cell surface antigen for therapeutic targeting in prostate cancer. Here, we report broad expression of STEAP1 relative to prostate-specific membrane antigen (PSMA) in lethal metastatic prostate cancers and the development of a STEAP1-directed chimeric antigen receptor (CAR) T cell therapy. STEAP1 CAR T cells demonstrate reactivity in low antigen density, antitumor activity across metastatic prostate cancer models, and safety in a human STEAP1 knock-in mouse model. STEAP1 antigen escape is a recurrent mechanism of treatment resistance and is associated with diminished tumor antigen processing and presentation. The application of tumor-localized interleukin-12 (IL-12) therapy in the form of a collagen binding domain (CBD)-IL-12 fusion protein combined with STEAP1 CAR T cell therapy enhances antitumor efficacy by remodeling the immunologically cold tumor microenvironment of prostate cancer and combating STEAP1 antigen escape through the engagement of host immunity and epitope spreading.

Reversible epigenetic alterations mediate PSMA expression heterogeneity in advanced metastatic prostate cancer.

Prostate-specific membrane antigen (PSMA) is an important cell surface target in prostate cancer. There are limited data on the heterogeneity of PSMA tissue expression in metastatic castration-resistant prostate cancer (mCRPC). Furthermore, the mechanisms regulating PSMA expression (encoded by the FOLH1 gene) are not well understood. Here, we demonstrate that PSMA expression is heterogeneous across different metastatic sites and molecular subtypes of mCRPC. In a rapid autopsy cohort in which multiple metastatic sites per patient were sampled, we found that 13 of 52 (25%) cases had no detectable PSMA and 23 of 52 (44%) cases showed heterogeneous PSMA expression across individual metastases, with 33 (63%) cases harboring at least 1 PSMA-negative site. PSMA-negative tumors displayed distinct transcriptional profiles with expression of druggable targets such as MUC1. Loss of PSMA was associated with epigenetic changes of the FOLH1 locus, including gain of CpG methylation and loss of histone 3 lysine 27 (H3K27) acetylation. Treatment with histone deacetylase (HDAC) inhibitors reversed this epigenetic repression and restored PSMA expression in vitro and in vivo. Collectively, these data provide insights into the expression patterns and regulation of PSMA in mCRPC and suggest that epigenetic therapies - in particular, HDAC inhibitors - can be used to augment PSMA levels.

An update on computational pathology tools for genitourinary pathology practice: A review paper from the Genitourinary Pathology Society (GUPS).

Machine learning has been leveraged for image analysis applications throughout a multitude of subspecialties. This position paper provides a perspective on the evolutionary trajectory of practical deep learning tools for genitourinary pathology through evaluating the most recent iterations of such algorithmic devices. Deep learning tools for genitourinary pathology demonstrate potential to enhance prognostic and predictive capacity for tumor assessment including grading, staging, and subtype identification, yet limitations in data availability, regulation, and standardization have stymied their implementation.

ASSESSMENT OF CELL SURFACE TARGETS IN METASTATIC PROSTATE CANCER: EXPRESSION LANDSCAPE AND MOLECULAR CORRELATES.

Therapeutic approaches targeting proteins on the surface of cancer cells have emerged as an important strategy for precision oncology. To fully capitalize on the potential impact of drugs targeting surface proteins, detailed knowledge about the expression patterns of the target proteins in tumor tissues is required. In castration-resistant prostate cancer (CRPC), agents targeting prostate-specific membrane antigen (PSMA) have demonstrated clinical activity. However, PSMA expression is lost in a significant number of CRPC tumors, and the identification of additional cell surface targets is necessary in order to develop new therapeutic approaches. Here, we performed a comprehensive analysis of the expression and co-expression patterns of trophoblast cell-surface antigen 2 (TROP2), delta-like ligand 3 (DLL3), and carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5) in CRPC samples from a rapid autopsy cohort. We show that DLL3 and CEACAM5 exhibit the highest expression in neuroendocrine prostate cancer (NEPC), while TROP2 is expressed across different CRPC molecular subtypes, except for NEPC. We observed variable intra-tumoral and inter-tumoral heterogeneity and no dominant metastatic site predilections for TROP2, DLL3, and CEACAM5. We further show that AR amplifications were associated with higher expression of PSMA and TROP2 but lower DLL3 and CEACAM5 levels. Conversely, PSMA and TROP2 expression was lower in RB1-altered tumors. In addition to genomic alterations, we demonstrate a tight correlation between epigenetic states, particularly histone H3 lysine 27 methylation (H3K27me3) at the transcriptional start site and gene body of TACSTD2 (encoding TROP2), DLL3, and CEACAM5, and their respective protein expression in CRPC patient-derived xenografts. Collectively, these findings provide novel insights into the patterns and determinants of expression of TROP2, DLL3, and CEACAM5 with important implications for the clinical development of cell surface targeting agents in CRPC.

Comprehensive assessment of anaplastic lymphoma kinase in localized and metastatic prostate cancer reveals targetable alterations.

Anaplastic lymphoma kinase (ALK) is a tyrosine kinase with genomic and expression changes in many solid tumors. ALK inhibition is first line therapy for lung cancers with ALK alterations, and an effective therapy in other tumor types, but has not been well-studied in prostate cancer. Here, we aim to delineate the role of ALK genomic and expression changes in primary and metastatic prostate cancer. We determined ALK expression by immunohistochemistry and RNA-Seq, and genomic alterations by NGS. We assessed functional consequences of ALK overexpression and pharmacological ALK inhibition by cell proliferation and cell viability assays. Among 372 primary prostate cancer cases we identified one case with uniformly high ALK protein expression. Genomic analysis revealed a SLC45A3-ALK fusion which promoted oncogenesis in in vitro assays. We observed ALK protein expression in 5/52 (9%) of metastatic prostate cancer cases, of which 4 of 5 had neuroendocrine features. ALK-expressing neuroendocrine prostate cancer had a distinct transcriptional program, and earlier disease progression. An ALK-expressing neuroendocrine prostate cancer model was sensitive to pharmacological ALK inhibition. In summary, we found that ALK overexpression is rare in primary prostate cancer, but more frequent in metastatic prostate cancers with neuroendocrine differentiation. Further, ALK fusions similar to lung cancer are an occasional driver in prostate cancer. Our data suggest that ALK-directed therapies could be an option in selected patients with advanced prostate cancer.