Prostate cancer is the second most common malignancy among men, with androgen deprivation therapy (ADT) serving as the standard treatment due to the hormone sensitivity of prostate tumors. However, therapeutic resistance frequently develops, leading to castration-resistant prostate cancer (CRPC), an aggressive and lethal disease. A recently defined subtype, stem cell-like CRPC (CRPC-SCL), accounts for approximately 25% of CRPC cases and demonstrates poor responsiveness to ADT. CRPC-SCL is characterized by the expression of CD44, a glycoprotein that promotes hyaluronic acid binding and uptake. Single-cell RNA sequencing (scRNA-seq) analysis of CRPC patient samples revealed that CD44hi cells are predominantly enriched within a specific cluster and exhibit distinct stem cell-like characteristics. Functionally, in a CRPC-SCL patient-derived xenograft (PDX) model, the CD44hi subpopulation demonstrated enhanced tumorigenic and proliferative capacities, indicating that these cells represent a more aggressive and malignant fraction within the CRPC-SCL tumor. Mechanistically, iron metabolism emerged as a critical regulator of CD44hi cell function. These cells maintain elevated intracellular iron levels, which can translocate into the nucleus to activate the H3K9me2 demethylase KDM3A. Activation of KDM3A sustains CD44 expression and reinforces stem cell-like properties through modulation of H3K9me2 histone modification. Exploiting this metabolic vulnerability, inhibition of the iron-regulatory factor NRF2 was found to decrease ferritin expression and increase intracellular free iron. The resultant iron overload selectively induces ferroptosis in CD44hi cells. Collectively, these findings reveal that targeting iron metabolism to trigger ferroptotic cell death represents a promising therapeutic strategy for the treatment of CRPC-SCL. Wanli Cheng, Andrea Brunello, Panagiotis Chouvardas, Sofia Karkampouna, Marianna Kruithof-de. Julio. Iron Metabolism as a Therapeutic Vulnerability in Stem Cell-Like Castration-Resistant Prostate Cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Innovations in Prostate Cancer Research and Treatment; 2026 Jan 20-22; Philadelphia PA. Philadelphia (PA): AACR; Cancer Res 2026;86(2_Suppl):Abstract nr B011.

Abstract Prostate cancer displays substantial clinical and histopathological heterogeneity which is not fully captured by conventional Gleason grading. To resolve the spatial and phenotypic complexity of the prostate tumor microenvironment, we performed imaging mass cytometry using a prostate-tailored 34-plex antibody panel on a clinically annotated tissue microarray cohort of 195 patients of primary stage disease after radical prostatectomy (523 regions of interest; 2.19 million cells). We identified 34 distinct cell types spanning epithelial, endothelial, stromal and immune compartments, and further organized into 18 epithelial-dominated, cancer associated fibroblast-dominated, and immune-rich spatial niches. Within the epithelial compartment, we detected an ERG⁺p53⁺ luminal population whose abundance is independently associated with poor overall and progression-free survival. In the stroma, we defined extracellular matrix remodeling-related cancer associated fibroblast and smooth muscle cell lineages, including a periglandular CD105 high niche with strong stromal-immune connectivity that is selectively associated with worse clinical outcome. Finally, cumulative immune niche burden correlated with histological inflammation and stratifies for worse patient survival. Together, these data provide a spatially resolved single-cell atlas of primary PCa and reveal stromal-immune-epithelial niches with prognostic relevance beyond Gleason grade.

Emerging evidence demonstrates the pivotal role played by the tumor microenvironment, particularly cancer-associated fibroblasts, during the development of castration-resistant prostate cancer. In this study, the molecular composition of the tumor microenvironment of androgen-sensitive and castration-resistant metastatic prostate cancer is investigated by utilizing patient-derived xenograft models. Transcriptomic and histological analysis identify the presence of a pro-fibrotic stroma in the castration-resistant (LAPC9) versus the castration-sensitive (PNPCa, BM18) models, characterized by high levels of collagen and tenascin C deposition and upregulation of inflammatory markers. Intra-tumoral collagen- and tenascin-positive stromal areas specifically correlate with higher tumor invasiveness. Master regulator analysis identifies the transcription factor PU.1 as a mediator of the LAPC9 pro-fibrotic phenotype, whose transcriptional activity can be specifically inhibited by the small molecule DB1976. To test the effect of pharmacologic PU.1 inhibition a novel organoid-fibroblast 3D co-culture system, able to mimic features of the in vivo tumor microenvironment, is established. Inhibition of stromal PU.1 activity reverts the pro-fibrotic phenotype and, in turn, reduces tumor organoid growth. Besides identifying a novel molecular player of the pro-fibrotic stromal phenotype in prostate cancer, this study highlights the applicability of 3D tumor organoid-fibroblast co-cultures as in vitro tools to test the effect of stromal-targeting compounds.

Due to their pivotal roles in tumor progression and therapy resistance, cancer-associated fibroblasts (CAF) are considered key therapeutic targets with loss of stromal androgen receptor (AR) a poorly understood hallmark of aggressive prostate cancer (PCa). A paucity of pre-clinical models however has hampered functional studies of CAF heterogeneity. We demonstrate that our newly-generated CAF biobank contains three FAP+-fibroblast subtypes, each with unique molecular and functional traits. Cultures with an early-activated phenotype expressed the highest levels of AR and exhibited AR-dependent growth. Consistently, stromal cells expressing early-activation markers co-expressed nuclear AR in clinical specimens and were enriched in pre-neoplastic lesions/low-grade PCa. Conversely, myofibroblastic CAF (myCAF), which expressed low AR levels in vitro and in vivo and were proliferatively-insensitive to AR signaling modulation, constituted the predominant CAF subpopulation in stromogenic high-grade PCa and castration-resistant LACP9 patient-derived xenografts. Exacerbation of the myCAF state upon castration of LAPC9-bearing hosts underscored these findings. Mechanistically, AR loss in myCAF was driven by an NFκB-TGFβ1-YAP1 axis, whose combined targeting synergistically repressed myofibroblastic hallmarks and impaired autophagic flux, effects that were potentiated by enzalutamide resulting in myCAF cell death. Collectively, these findings provide a mechanistic rationale for adjuvant targeting of the YAP1-TGFβ signaling axis to improve patient outcomes. ### Competing Interest Statement The authors have declared no competing interest.

RNA therapeutics offer a promising approach to cancer treatment by precisely regulating cancer-related genes. While lipid nanoparticles (LNPs) are currently the most advanced nonviral clinically approved vectors for RNA therapeutics, their antitumor efficacy is limited by their unspecific hepatic accumulation after systemic administration. Thus, there is an urgent need to enhance the delivery efficiency of LNPs to target tumor-residing tissues. Here, we conjugated the cluster of differentiation 44 (CD44)-specific targeting peptide A6 (KPSSPPEE) to the cholesterol of LNPs via PEG, named AKPC-LNP, enabling specific tumor delivery. This modification significantly improved delivery to breast cancer cells both in vitro and in vivo, as shown by flow cytometry and confocal microscopy. We further used AKPC-siYT to codeliver siRNAs targeting the transcriptional coactivators YAP and TAZ, achieving potent gene silencing and increased cell death in both 2D cultures and 3D tumor spheroids, outperforming unmodified LNPs. In a breast tumor cell xenografted zebrafish model, systemically administered AKPC-siYT induced robust silencing of YAP/TAZ and downstream genes and significantly enhanced tumor suppression compared to unmodified LNPs. Additionally, AKPC-siYT effectively reduced proliferation in prostate cancer organoids and tumor growth in a patient-derived xenograft (PDX) model. Overall, we developed highly efficient AKPC-LNPs carrying RNA therapeutics for targeted cancer therapy.

The high intra-patient heterogeneity in multifocal primary prostate cancer (PCa) has curtailed the efficacy of current treatment options. By employing twin biopsies from multiple lesions with matched patient-derived organoids (PDO) models, the PCa molecular heterogeneity was investigated. We utilized genomics, transcriptomics and machine learning (ML) approaches to elucidate and predict the underlying mechanisms of pharmacological heterogeneity. Our data indicate a vulnerability of primary PCa organoids for small molecule inhibitors targeting receptor tyrosine kinases (MET, ALK, SRC). By exploring gene expression data from matched parental tissue in an unsupervised manner, we identified two distinct clusters of samples. Interestingly, the PDO drug responses were significantly different between the two clusters for 4/11 compounds tested. We developed a transcriptomics-based, cluster prediction model, which can accurately stratify samples into the two clusters. Notably, our prediction model is based on tissue profiles, therefore, it can be utilized to rapidly evaluate new cases and suggest promising drug candidates, even when PDO derivation is not feasible. Taken together, we propose a novel flexible stratified oncology approach that can swiftly and accurately highlight promising drug vulnerabilities of PCa patients.

Due to their pivotal roles in tumor progression and therapy resistance, cancer-associated fibroblasts (CAF) are considered key therapeutic targets with loss of stromal androgen receptor (AR) a poorly understood hallmark of aggressive prostate cancer (PCa). A paucity of pre-clinical models however has hampered functional studies of CAF heterogeneity. We demonstrate that our newly generated CAF biobank contains three FAP+-fibroblast subtypes, each with unique molecular and functional traits. Cultures with an early-activated phenotype expressed the highest levels of AR and exhibited AR-dependent growth, whereby AR inhibition suppressed their migration. Consistently, stromal cells expressing early-activation markers co-expressed nuclear AR in clinical specimens and were enriched in pre-neoplastic lesions/low-grade PCa. Conversely, myofibroblastic CAF (myCAF) expressed low AR levels in vitro and in vivo, were insensitive at the proliferative and migratory levels to AR signaling modulation and significantly promoted PCa cell invasion in 3D composite collagen networks. Accordingly, myCAF constituted the predominant CAF subpopulation in stromogenic high-grade PCa and were enriched in aggressive disease states in PCa single cell atlases and castration-resistant LACP9 patient-derived xenografts. Exacerbation of the myCAF state upon castration of LAPC9-bearing hosts underscored these findings. Mechanistically, AR loss in myCAF was driven by an NFκB-TGFβ1-YAP1 axis, whose combined pharmacological or genetic targeting synergistically repressed myofibroblastic hallmarks and impaired autophagic flux, effects that were potentiated by enzalutamide resulting in enhanced myCAF cell death. Collectively, data herein provide a mechanistic rationale for stromal AR loss in aggressive PCa and suggest that adjuvant targeting of the YAP1-TGFβ signaling axis may improve patient outcome.

Abstract Background: Androgen deprivation therapy is the standard treatment for prostate cancer (PCa). Nevertheless, despite initial effectiveness, pre-existing cancer stem cell (CSC) populations invariably lead to incurable castration-resistant prostate cancer. CSCs are a subset of cancer cells possessing self-renewal properties, driving tumor progression and regrowth. CD44+ PCa cells exhibit more stemness features and are enriched in tumorigenic and metastatic progenitor cells. Here, we aim to explore different subpopulations in tumors based on levels of CD44 expression and investigate whether they have distinct molecular properties and functional characteristics. Material and Method: The number of CD44+ cells was evaluated in a tissue microarray from primary prostate cancer patient samples (EMPACT cohort) with clinical follow-up. Flow cytometry sorted the CRPC model LAPC9 tumor into CD44 high (CD44-H) and CD44 low (CD44-L) cells. RNA sequencing was performed on these subpopulations to explore their transcriptomic profiles. CD44 expression was validated at the cDNA and protein levels using qPCR and Western blot (WB) analysis. Sorted cells were maintained as organoids in vitro. To trace the dynamics of CD44-H and CD44-L subpopulations in vivo, LAPC9 tumor was labeled by fluorescent and luciferent markers. The CD44 status was determined by flow cytometry when tumor growth. Results: Patients with elevated expression of CD44 (higher number of CD44+ cells) at the time of surgery exhibited a greater propensity for clinic progression (5-year progression-free survival: 75% vs 95%, P=0.03). Furthermore, CD44-H cell ratio increased in the LAPC9 after castration, indicating that CD44-H cells can survive after treatment. Subsequent WB analysis confirmed that CD44 was highly expressed in sorted CD44-H cells, validating the flow cytometry results. Analysis of the CD44 RNA-seq data showed CD44 transcript variants CD44v10 (CD44-201) and CD44v7-10 (CD44-209) were notably upregulated in CD44-H cells. In organoid culture, sorted CD44-H cells displayed enhanced formation, indicating they possessed higher proliferative capacity and clonogenicity than CD44-L cells. Furthermore, when GFP-labeled CD44-H cells and RFP-labeled CD44-L cells were recombined in vivo, CD44-H cells exhibited robust proliferation, while CD44-L cells transitioned to a CD44hi state to facilitate tumor growth. Moreover, those GFP and RFP subpopulations displayed the same CD44hi cell ratio at the endpoint. Conclusion: Elevated CD44 expression is related to an increased risk in primary PCa. In addition, CD44-H cells exhibit high tumorigenicity in vitro and in vivo and can convert to each other. Moreover, the ratio of CD44-H cells in tumor at endpoint is consistent despite the starting CD44 expression status. Furthermore, the upregulation of CD44v10 and v7-10 in the CD44-H subpopulation may promote tumor formation and metastasis. Citation Format: Wanli Cheng, Marta De Menna, Francesco Bonollo, Panagiotis Chouvardas, George N. Thalmann, Sofia Karkampouna, Marianna Kruithof Julio. Investigating key molecular players in putative stem cell subpopulations of prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 253.

Introduction: Prostate Specific Membrane Antigen Positron Emission Tomography (PSMA-PET) is routinely used for the staging of patients with prostate cancer, but data on response assessment are sparse and primarily stem from metastatic castration-resistant prostate cancer (mCRPC) patients treated with PSMA radioligand therapy. Still, follow-up PSMA-PET is employed in earlier disease stages in case of clinical suspicion of disease persistence, recurrence or progression to decide if localized or systemic treatment is indicated. Therefore, the prognostic value of PSMA-PET derived tumor volumes in earlier disease stages (i.e., hormone-sensitive prostate cancer (HSPC) and non-[Lu-177]Lu-PSMA-617 (LuPSMA) therapy castration resistant prostate cancer (CRPC) are evaluated in this manuscript. Methods: A total number of 73 patients (6 primary staging, 42 HSPC, 25 CRPC) underwent two (i.e., baseline and follow-up, median interval: 379 days) whole-body [Ga-68]Ga-PSMA-11 PET/CT scans between Nov 2014 and Dec 2018. Analysis was restricted to non-LuPSMA therapy patients. PSMA-PETs were retrospectively analyzed and primary tumor, lymph node-, visceral-, and bone metastases were segmented. Body weight-adjusted organ-specific and total tumor volumes (PSMAvol: sum of PET volumes of all lesions) were measured for baseline and follow-up. PSMAvol response was calculated as the absolute difference of whole-body tumor volumes. High metastatic burden (>5 metastases), RECIP 1.0 and PSMA-PET Progression Criteria (PPP) were determined. Survival data were sourced from the cancer registry. Results: The average number of tumor lesions per patient on the initial PET examination was 10.3 (SD 28.4). At baseline, PSMAvol was strongly associated with OS (HR 3.92, p <0.001; n = 73). Likewise, response in PSMAvol was significantly associated with OS (HR 10.48, p < 0.005; n = 73). PPP achieved significance as well (HR 2.19, p <0.05, n = 73). Patients with hormone sensitive disease and poor PSMAvol response (upper quartile of PSMAvol change) in follow-up had shorter outcome (p < 0.05; n = 42). PSMAvol in bones was the most relevant parameter for OS prognostication at baseline and for response assessment (HR 31.11 p < 0.001; HR 32.27, p < 0.001; n = 73). Conclusion: PPP and response in PSMAvol were significantly associated with OS in the present heterogeneous cohort. Bone tumor volume was the relevant miTNM region for OS prognostication. Future prospective evaluation of the performance of organ specific PSMAvol in more homogeneous cohorts seems warranted.

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

Despite the potential of dosimetry in optimizing personalized radiopharmaceutical therapy (RPT), its limited clinical implementation impedes the development of simplified protocols for routine adoption. However, simplifications may introduce errors in dosimetry, prompting questions about their impact on clinical practice. In this retrospective study, we analyzed data from 21 patients diagnosed with metastatic castration-resistant prostate cancer (mCRPC) who underwent multiple cycles of 177Lu-PSMA-617 RPT treatment. Cumulative dosimetry of all the treatment cycles was calculated using both the standard multi-time point dosimetry (MTPD) method and the single time-point dosimetry (STPD, Hänscheid approximation) method for the same cohort. Their correlations with treatment outcome (PSA decline rate and overall survival, OS) and complication risk (anaemia grade) were investigated. The Fisher's Z-Transformed test was performed to statistically evaluate the difference between the correlations. STPD showed a non-significant difference in correlation with PSA decline rate, despite a mean percentage error (MPE) of up to 36.44

On September 23-24 (2024) the 6th Workshop IRE on Translational Oncology, titled "Cancer Organoids as Reliable Disease Models to Drive Clinical Development of Novel Therapies," took place at the IRCCS Regina Elena Cancer Institute in Rome. This prominent international conference focused on tumor organoids, bringing together leading experts from around the world.A central challenge in precision oncology is modeling the dynamic tumor ecosystem, which encompasses numerous elements that evolve spatially and temporally. Patient-derived 3D culture models, including organoids, explants, and engineered or bioprinted systems, have recently emerged as sophisticated tools capable of capturing the complexity and diversity of cancer cells interacting within their microenvironments. These models address critical unmet needs in precision medicine, particularly in aiding clinical decision-making. The rapid development of these human tissue avatars has enabled advanced modeling of cellular alterations in disease states and the screening of compounds to uncover novel therapeutic pathways.Throughout the event, distinguished speakers shared their expertise and research findings, illustrating how organoids are transforming our understanding of treatment resistance, metastatic dynamics, and the interaction between tumors and the surrounding microenvironment.This conference served as a pivotal opportunity to strengthen international collaborations and spark innovative translational approaches. Its goal was to accelerate the shift from preclinical research to clinical application, paving the way for increasingly personalized and effective cancer therapies.

Understanding the spatial heterogeneity of tumours and its links to disease initiation and progression is a cornerstone of cancer biology. Presently, histopathology workflows heavily rely on hematoxylin and eosin and serial immunohistochemistry staining, a cumbersome, tissue-exhaustive process that results in non-aligned tissue images. We propose the VirtualMultiplexer, a generative artificial intelligence toolkit that effectively synthesizes multiplexed immunohistochemistry images for several antibody markers (namely AR, NKX3.1, CD44, CD146, p53 and ERG) from only an input hematoxylin and eosin image. The VirtualMultiplexer captures biologically relevant staining patterns across tissue scales without requiring consecutive tissue sections, image registration or extensive expert annotations. Thorough qualitative and quantitative assessment indicates that the VirtualMultiplexer achieves rapid, robust and precise generation of virtually multiplexed imaging datasets of high staining quality that are indistinguishable from the real ones. The VirtualMultiplexer is successfully transferred across tissue scales and patient cohorts with no need for model fine-tuning. Crucially, the virtually multiplexed images enabled training a graph transformer that simultaneously learns from the joint spatial distribution of several proteins to predict clinically relevant endpoints. We observe that this multiplexed learning scheme was able to greatly improve clinical prediction, as corroborated across several downstream tasks, independent patient cohorts and cancer types. Our results showcase the clinical relevance of artificial intelligence-assisted multiplexed tumour imaging, accelerating histopathology workflows and cancer biology. VirtualMultiplexer is a generative AI tool that produces realistic multiplexed immunohistochemistry images from tissue biopsies. The generated images could be used to improve clinical predictions, enhancing histopathology workflows and accelerating cancer research.

Introduction and Objective Several studies have shown that cancer-associated fibroblasts (CAFs), the most abundant component in prostate cancer (PCa) microenvironment, promote resistance to androgen deprivation therapies, and metastatic development. The aim of this study is to elucidate the mechanisms of CAF-induced therapy resistance through the establishment of in vitro 3D co-cultures. Methods In this study, the androgen-dependent PNPCa patient-derived xenograft (PDX) model (derived from soft tissue metastasis), and the androgen-independent LAPC9 PDX model (bone metastasis) are used. Epithelial and stromal cells (human and mouse origin, respectively) are separated through magnetic-associated cell sorting to generate PDX-derived organoids and fibroblasts. Fibroblast RNA and protein expression is assessed through RT-qPCR, Western Blot, and Immunofluorescence. Direct 3D co-cultures are obtained by combining fluorescently labelled organoids and fibroblasts in ultra-low attachment plates in defined media. In indirect co-cultures, organoids are cultured in Spherical Plates (Kugelmeiers) with fibroblasts on transwell inserts. Organoid viability is measured after 9 days by CellTiter-Glo 3D Assay. Results PDX-derived fibroblasts express typical CAF genes (e.g. α-smooth muscle actin and Tenascin C), and androgen receptor (AR), both at RNA and protein level. AR protein expression (as well as RNA expression of the AR target gene Fkbp5) is increased in CAFs upon treatment with dyhydrotestosterone (DHT) 10nM, while co-treatment with the AR inhibitor Enzalutamide 10mM abrogates this effect, suggestive of functional AR signaling. These CAFs can organize into tumor/CAF organoid 3D structures upon combination with PDX-derived tumor epithelial cells, providing an in vitro functional model to study tumor-stromal interactions. In indirect (transwell) co-cultures, CAFs increase the viability of the androgen dependent PNPCa organoids, but not that of androgen independent LAPC9. This growth-promoting effect is observed even upon culturing of PNPCa organoids in non-optimal growth conditions, at low DHT concentrations (DHT 0.5nM and 0.25nM versus the standard DHT 1nM). The factors mediating this process will be identified by gene expression profiling of CAFs and organoids in transwell co-cultures and/or monoculture. Conclusions Our data indicate that PCa metastatic cells modify the properties of neighbor stromal cells to support tumorigenesis, since PDX-derived fibroblasts, originally part of the mouse dermal stroma, display CAF features and active AR signaling. 3D co-cultures represent a useful tool to study the tumor-stromal interactions. Our transwell co-culture experiments suggest that CAFs play an important role in supporting the viability and growth of early metastatic androgen dependent PCa cells (PNPCa) by secreting pro-tumorigenic factors. The possibility that CAFs exert different effects depending on the tumor stage setting (early vs advanced) will be subsequently investigated. Citation Format: Francesco Bonollo, Natalie Sampson, George Thalmann, Sofia Karkampouna, Marianna Kruithof-de Julio. Tumor-stromal 3D co-cultures to study the role of cancer-associated fibroblasts in the acquisition of androgen-deprivation therapy resistance in prostate cancer [abstract]. In: Proceedings of the AACR Special Conference: Advances in Prostate Cancer Research; 2023 Mar 15-18; Denver, Colorado. Philadelphia (PA): AACR; Cancer Res 2023;83(11 Suppl):Abstract nr B008.

Bladder Cancer (BLCa) inter-patient heterogeneity is the primary cause of treatment failure, suggesting that patients could benefit from a more personalized treatment approach. Patient-derived organoids (PDOs) have been successfully used as a functional model for predicting drug response in different cancers. In our study, we establish PDO cultures from different BLCa stages and grades. PDOs preserve the histological and molecular heterogeneity of the parental tumors, including their multiclonal genetic landscapes, and consistently share key genetic alterations, mirroring tumor evolution in longitudinal sampling. Our drug screening pipeline is implemented using PDOs, testing standard-of-care and FDA-approved compounds for other tumors. Integrative analysis of drug response profiles with matched PDO genomic analysis is used to determine enrichment thresholds for candidate markers of therapy response and resistance. Finally, by assessing the clinical history of longitudinally sampled cases, we can determine whether the disease clonal evolution matched with drug response.