Citations

Abstract Background: Chromosomal instability (CIN) is frequent in patients with metastatic castration-resistant prostate cancer (mCRPC) and determines taxane resistance. A circulating tumor cell (CTC) assay which morphologically identifies CTCs with CIN (CTC-CIN) from a single tube of whole blood could predict clinical outcomes in this population. We tested this hypothesis as a pre-planned, secondary outcome of the CARD trial, where patients with mCRPC progressing within a year of treatment with an androgen receptor pathway inhibitor (ARPI; enzalutamide or abiraterone acetate + prednisolone/prednisone) were randomized to treatment with cabazitaxel or the alternative ARPI. Methods: CTCs were isolated from blood samples obtained at baseline; cycle two, and end of treatment. The associations of baseline CTC and counts with imaging-based progression free survival (ibPFS), overall survival (OS), time to prostate-specific antigen (PSA) progression, RECIST 1.1 objective response rate (ORR), and PSA50 response rate (PRR) were assessed. Results: Of 255 patients randomized, CTC counts were acquired from 237 patients at screening, 213 patients at C2D1, and 166 patients at EOT. High baseline CTC counts were significantly associated with worse OS (median OS, 14.9 vs 9.8 months; univariate HR, 1.81; 95% CI, 1.30 - 2.51; p < 0.001). High baseline CTC-CIN counts were significantly associated with worse OS after adjustment for confounding variables (median OS, 15.3 vs 8.9 months; univariate HR, 2.16; 95% CI, 1.52 - 3.06; p < 0.001; multivariate HR, 1.56; 95% CI, 1.01 - 2.43; p = 0.047). Low CTC-CIN at baseline predicted ibPFS, ORR, and PRR benefit with cabazitaxel; conversely, high at baseline predicted no different ibPFS or OS outcomes with cabazitaxel or a second ARPI. Conclusions: This preplanned biomarker analysis of CARD confirmed that CTC-CIN counts are prognostic for OS and may be predictive of cabazitaxel benefit. (Funded by Sanofi and Epic Sciences; CARD ClinicalTrials.gov number, NCT02485691.) Citation Format: Ossian Longoria, Jan Rekowski, Santosh Gupta, Nick Beije, Klaus Pantel, Eleni Efstathiou, Cora Sternberg, Daniel Castellano, Karim Fizazi, Bertrand Tombal, Adam Sharp, Oliver Sartor, Sandrine Mace, Christine Geffriaud-Ricouard, Richard Wenstrup, Ronald de Wit, Johann de Bono. Morphological evaluation of chromosomal instability in circulating tumor cells and prediction of taxane resistance in metastatic prostate cancer: a prespecified sub-analysis of the CARD trial [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 1145

Abstract Introduction: Most people who die from prostate cancer are older than 75 years. However, prostate cancer diagnosed at younger ages is often more aggressive. While epidemiologic studies have examined the association between age and survival in patients with prostate cancer overall, the clinical drivers of this relationship in patients with advanced disease remain unclear. We leveraged the International Registry of Men with Advanced Prostate Cancer (IRONMAN, NCT03151629) to (1) quantify the association between age and overall survival and (2) explore whether this association is explained by treatments. Methods: We included 3, 734 patients enrolled from 15 countries with new diagnoses of metastatic hormone sensitive (mHSPC, N=2, 642) or castration resistant prostate cancer (CRPC, N=1, 092). Age at enrollment was categorized as <55, 55-59, 60-64 (reference), 65-69, 70-74, 75-79, 80-84, 85+ years. We computed age-specific death rates (95% confidence intervals [CIs]) by disease state (mHSPC, CRPC) at enrollment. To explore whether this relationship was explained by treatment, we fitted disease state-stratified Cox models and sequentially adjusted for (1) de novo metastatic status and country, and (2) baseline treatment (androgen deprivation therapy [ADT] alone, ADT + androgen receptor pathway inhibitors [ARPI], ADT + chemotherapy, ADT + ARPI + chemotherapy, other). Results: Over 5.5 years of follow-up (median: 3.2 years, IQR: 1.7, 4.8), 995 patients died from any cause. Younger patients were more likely to have de novo metastatic disease. Among those with mHSPC at enrollment, the age-specific death rates per 1, 000 person-years followed a J-shaped pattern: 80 (95% CI: 54, 117) in patients under 55 years, 58 (46, 74) in patients aged 60-64, 102 (78, 133) in patients aged 80-84, and 172 (125, 236) in patients aged 85 and older. The death rates among those with CRPC followed a similar pattern, albeit rates were higher in magnitude: 212 (95% CI: 140, 323) in patients under 55 years, 153 (115, 202) in patients aged 60-64, 200 (155, 258) in patients aged 80-84, and 204 (142, 291) in patients aged 85 and older. After adjusting for disease state, country, and the timing of diagnosis of metastases, death rates were substantially higher for ages 75-79 (HR 1.4; 95% CI 1.1, 1.7), 80-84 years (HR 1.6; 95% CI 1.2, 2.1), and 85 years and older (HR 2.0; 95% CI: 1.5, 2.7) compared to ages 60-64 years. Patients aged under 55 years had 1.4-fold higher death rates (95% CI: 1.0, 1.9) compared to those 60-64 years. After additionally adjusting for baseline treatment, overall survival remained worse for the older patients and the youngest age group with hazard ratios remaining meaningfully unchanged. Conclusion: Age at enrollment is associated with overall survival in people with advanced prostate cancer enrolled in IRONMAN in a J-shaped pattern. This relationship is not explained by differences in baseline treatment. Citation Format: Hannah E. Guard, Michelle O. Sodipo, Colleen B. McGrath, Anna Siefkas, Lauren E. Howard, Konrad H. Stopsack, Christopher M. Sauer, Robert Dreicer, Emilio Esteban, Hassan M. Dogo, Ademola Popoola, Charles Waihenya, Anders Bjartell, Kim N. Chi, Sebastien Hotte, Richard Cathomas, Deborah Enting, Scott Tagawa, Michael Ong, Michael Kolinsky, Vincent Khoo, Joaquin Mateo, Chidiebere N. Ogo, Rana R. McKay, Stefanie Fischer, Heather H. Cheng, Russell Szmulewitz, Young E. Whang, Anand Sharma, Frédéric Pouliot, Simon Crabb, Monica S. Chatwal, Miguel A. Climent, Raymond McDermott, Ian D. Davis, Camille Ragin, Folakemi T. Odedina, Simon Anderson, Simone Badal, Natalie Greaves, Karen A. Autio, Laurel Cannon, Alyssa Chan-Cuzydlo, Marie Grant, Travis Gerke, Hannah D. McManus, Philip W. Kantoff, Daniel J. George, Lorelei A. Mucci, IRONMAN investigator team. Disentangling the association between age and overall survival in an international cohort of patients with advanced prostate cancer [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 4935.

Abstract Introduction: The standard of care for metastatic hormone-sensitive prostate cancer (mHSPC) has evolved with the expanded approval of androgen receptor pathway inhibitors (ARPIs) alongside androgen deprivation therapy (ADT). ADT alone has been shown to affect cognitive function. However, the cognitive impact of distinct treatment combinations remains unclear. We investigated the association between first-line systemic treatments and the trajectory of subjective cognitive function among mHSPC survivors in the International Registry for Men with Advanced Prostate Cancer (IRONMAN). Methods: This analysis included 2435 newly diagnosed mHSPC patients enrolled across 15 countries between July 2017 and August 2024. Treatments were reported by site personnel and physicians. For this analysis, we focused on first-line systemic treatment and classified treatment groups at enrollment as ADT monotherapy (referent), ADT + ARPIs, or ADT + chemotherapy. Subjective cognitive function was assessed via the EORTC QLQ-C30 Cognitive Function subscale, with patient-reported scores (range 0-100) collected at enrollment and every 3 months for up to 5 years; higher scores reflect better cognitive function. Associations were estimated using linear mixed effects models with clustering by study site and country to examine differences at enrollment and across trajectories of subjective cognitive function by first-line treatment, adjusting for demographic and clinical factors. Results: Median age at enrollment was 70 years, with a median follow-up of 1.7 years. 724 (30%) participants were treated with ADT monotherapy, 1317 (54%) with ADT + ARPIs, and 394 (16%) with ADT + chemotherapy. Mean cognitive function scores at enrollment were 85.4 (Standard Deviation [SD] 19.5) for participants treated with ADT monotherapy, 86.7 (SD 17.3) for ADT + ARPIs, and 86.7 (SD 18.0) for ADT + chemotherapy. Cognitive function scores decreased by -0.60 points (95% Confidence Interval [CI] -0.72, -0.48) per year among those treated with ADT monotherapy, with similar rates observed for those treated with ADT + chemotherapy. Participants treated with ADT + ARPIs had a slower decline in cognitive function, with scores decreasing by -0.36 points (95% CI -0.60, -0.08) per year. Conclusion: In this international cohort of mHSPC survivors, subjective cognitive function scores were generally high at enrollment but declined over time. There was no evidence of worse cognitive function in those treated with ADT + ARPIs nor ADT + chemotherapy over time compared to ADT monotherapy. Cognitive functioning is a key component of survivorship, and defining cognitive trajectories associated with first-line treatment can inform supportive interventions, patient treatment decision-making, and clinical guidelines. Citation Format: Michelle O. Sodipo, Alicia K. Morgans, Erica T. Warner, Emily M. Rencsok, Lauren E. Howard, Colleen B. McGrath, Anna Siefkas, Konrad H. Stopsack, Christopher Sauer, Robert Drecier, Emilio Esteban, Hassan M. Dogo, Ademola Popoola, Charles Waihenya, Anders Bjartell, Kim Chi, Sebastien Hotte, Richard Cathomas, Deborah Enting, Scott Tagawa, Michael Ong, David Lorente, Elisabeth Heath, Rana McKay, Stefanie Fischer, Heather H. Cheng, Young E. Whang, Frederic Pouliot, Simon Crabb, Monica Chatwal, Miguel A. Climent, Raymond McDermott, Ian D. Davis, Camille Ragin, Folakemi Odedina, Natalie Greaves, Simone Badal, Simon Anderson, Sharon Harrison, Karen Autio, Laurel Cannon, Marie Grant, Alyssa Chan-Cuzydlo, Travis Gerke, Hannah D. McManus, Philip W. Kantoff, Daniel George, Sebastien Haneuse, Lorelei A. Mucci, on behalf of the IRONMAN Registry. First-line therapies and subjective cognitive function trajectories among international metastatic hormone-sensitive prostate cancer survivors [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 3630.

Background: Prostate cancer is the most common cancer in men in the United States with an estimated 288,300 new cases diagnosed in 2023. The treatment strategies include androgen deprivation therapies (ADTs) for metastatic prostate cancer (mPC) but 10-20% of patients eventually progress to castration-resistant prostate cancer (CRPC). However, nearly all patients develop primary and acquired resistance overtime, and developing therapies and identifying novel molecular targets are essential in enhancing the clinical outcomes of patients with prostate cancer. In this study, we report a small molecule that belongs to a class of tricyclic quinolones, QL47 that was previously screened among various other compounds in prostate PDX organoid models and showed a potent anti-proliferative activity. Methods: QL47 was tested on prostate cancer cell lines (22Rv1, 22RV1 abiraterone-resistant and DU145) for its anti-proliferative potential. Further, we used the biotinylated QL47 (QL47B) to pulldown its potential targets and we identified the targets through immunoprecipitation-mass spectrometry (IP-MS) technique followed by Western blot analysis. Cell cycle and cell death analyses were performed after staining the cells with propidium iodide and Annexin V followed by flow cytometry analysis. Oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) was investigated using the Seahorse assay. Results: The initial screening of QL47 in prostate cancer cell lines showed that it inhibits the proliferation at an IC50 of 50-200 nM. Through IP-MS and Western blot analysis, we identified a member of the pyruvate dehydrogenase complex dihydrolipoamide S-acetyltransferase (DLAT) as a direct target of QL47. Mechanistically, we found that QL47 does not increase cell death or affect cell cycle, but inhibits metabolic function, with a decrease in both oxygen consumption rate (OCR) and extracellular acidification rate (ECAR). Conclusion: We have identified a novel mechanism of action of QL47 on the pyruvate dehydrogenase complex, and we plan to further validate the safety and efficacy for the treatment of prostate cancer. Citation Format: Grace Nirmala Jesuthankaraj, August John, Huanyao Gao, Arun Kanakkanthara, Richard Weinshilboum, Liewei Wang. Targeting metabolic dysfunction in prostate cancer: the anti-proliferative effects of ql47 through inhibition of dlat [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 4297.

Abstract Background: Second generation androgen receptor signaling inhibitors (ARSIs), such as abiraterone, have demonstrated improved survival in metastatic castration-resistant prostate cancer (mCRPC) patients, however, acquired resistance to these agents often arises. Novel and alternative therapies are needed to combat the ARSI resistant tumor. In the last two decades patient-derived xenograft (PDX) and PDX organoids have become pivotal models in anticancer drug development and biomarker discovery due to their ability to faithfully recapitulate the complex and heterogeneous tumor biology. Methods: We analyzed RNAseq data from mCRPC patients and corresponding PDX models (PROMOTE study, NCT #01953640) and employed L1000 Chem Perturbation down/up signatures database to find candidate drugs to overcome abiraterone resistance by reverse signature search. We then performed proliferation assay using 49 compounds in three PROMOTE-PDX organoid models (2 abiraterone-resistant and 1 abiraterone-sensitive) using our semi high-throughput drug screening platform. Results: Overall, we identified a total of 32 compounds with IC50<10µM in at least one of the models, among which, 5 agents are active in all three models and 5 in at least two of the models. (Table 1). Within active compounds we find selective kinase inhibitors (BRAF, JAK2, AKT, FLT3) and histone deacetylase inhibitors (belinostat and pracinostat). Particularly interesting are two drugs that are only active in the abiraterone resistant setting but not the sensitive PDX. Conclusions: In summary, we leveraged RNAseq data from mCRPC patients and PDX models and performed a high-throughput drug screen in PDX-derived organoid models to identify potential drugs that could overcome abiraterone-resistance in lethal advanced prostate cancer. These data highlight potential drug combinations or subsequent therapeutic strategies for these patients. Citation Format: Irene Marín-Goñi, Huanyao Gao, Joachim L. Petit, Shreya Indulkar, Adam M. Kase, Cassandra N. Moore, Krishna R. Kalari, Michael T. Barrett, Richard Weinshilboum, Winston Tan, John A. Copland, Liewei Wang. Overcoming resistance in mCRPC: drug discovery using PDX models and high-throughput drug screening [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 5658.

Abstract Deep learning has advanced digital pathology and multi-omics data analysis. However, there is still a critical need to integrate these modalities to better understand complex diseases. Deep learning models, such as Prov-Gigapath, a state-of-the-art pathology foundation model, have been developed for analyzing whole-slide images (WSI). Our previously published deep-learning framework, OmicsFootPrint (OFP), transforms high-dimensional multi-omics data into intuitive genomically-arranged circular images and utilizes transfer learning to overcome the challenges of high-dimensional, low-sample-size data. In this study, we aimed to combine WSI and multi-omics data to develop a framework capable of distinguishing between invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC), which is clinically significant as it may help explain differential responses to therapies, such as tamoxifen versus letrozole. We enhanced the performance of our OFP model by dividing each multi-omics circular image (1024x1024x3 pixels) into 16 equal-sized patches (256x256x3 pixels). Each patch was trained using a separate EfficientNetV2 architecture with pre-trained ImageNet weights. The probability outcomes from these 16 models were subsequently combined through a meta-learner model using the AutoGluon framework. Concurrently, WSI images corresponding to the same patients were independently trained, maintaining consistent train/validation/test splits. The Prov-Gigapath model, which was trained using a dataset from the Providence Health System, was employed to extract patch-level embeddings from the WSI data. These embeddings were fine-tuned using a LongNet-based masked autoencoder to predict outcomes. Finally, predictions from the OFP model and the WSI-based Prov-Gigapath model were integrated through a meta-learner, preserving the original train/validation/test split. Model performances (AUC) were evaluated using the held-out test set. The gold standard used for the analysis was histological identification of IDC versus ILC, against which the performance of omics data alone, WSI data alone, and the multi-modal integration approach were compared. We evaluated our integrated model as a proof-of-concept using The Cancer Genome Atlas (TCGA) breast cancer cohort, comprising 92 ILC and 391 IDC samples. Multi-omics data, including gene expression, microRNA, copy number variation, and RPPA, were analyzed using the OFP framework, while WSI data were processed using the Prov-Gigapath method. The results demonstrated that the multi-modal integration approach significantly outperformed individual modalities, achieving an AUC of 0.98 compared to 0.87 for OFP (original), 0.95 for OFP (16-patches), and 0.92 for WSI. Integrating WSI and multi-omics data improves predictive accuracy, significantly improving the classification of breast cancer subtypes over histology alone. Citation Format: Naresh Prodduturi, Xiaojia Tang, Hamid R. Tizhoosh, Kevin J. Thompson, Richard Weinshilboum, Karthik V. Giridhar, Judy C. Boughey, Eric W. Klee, Liewei Wang, Matthew P. Goetz, Vera Suman, Krishna R. Kalari. A multi-modal learning framework to integrate digital pathology image and multi-omics data in breast cancer [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 2429.

PURPOSE Survival for patients with melanoma has recently improved. The propensity of melanoma to metastasize to the brain remains a common and serious feature of this disease. The purposes of this study were to evaluate prognostic factors for patients with newly diagnosed melanoma brain metastases (MBMs) in a large cohort treated with modern multimodal therapies, compare those results with those in prior eras, and update the Melanoma Graded Prognostic Assessment (GPA). METHODS Univariable and multivariable (MVA) analyses of prognostic factors and treatments associated with survival were performed on 1,796 patients with newly diagnosed MBM treated between January 01, 2015, and December 31, 2021, using a multi-institutional retrospective database. Multiple imputation was used to address missingness of potential predictors. Significant variables in combined MVA were used to update the Melanoma GPA. Comparisons were made with legacy cohorts. RESULTS Median survivals for cohorts A (1985-2007, n = 481), B (2006-2015, n = 823), and C (2015-2021, n = 1,796) were 6.7, 9.8, and 16.6 months and median follow-up times were 40.1, 43.6, and 48.8 months, respectively. In combined MVA, significant prognostic factors for survival were higher Karnofsky Performance Status, fewer MBMs, absence of extracranial metastases, lower serum lactate dehydrogenase, and no immunotherapy before MBM. These factors were incorporated into the updated Melanoma GPA. The combined median and 3-year survivals for patients with GPA 0-1, 1.5-2, and 2.5-4.0 were 5.4, 13.2, and 43.2 months and 12.4%, 28.8%, and 51.6%, respectively. CONCLUSION Prognostic factors have changed and survival has improved for patients with MBM but varies widely by GPA. The updated Melanoma GPA calculator (BrainMetGPA), available free online, can be used to estimate survival, individualize treatment, stratify clinical trials, guide surveillance, and augment clinical trial eligibility. Multidisciplinary treatment is essential. Trials are needed to elucidate the optimal sequencing of various therapeutic modalities.

Abstract Background Recently, a model to predict 5-year recurrence-free survival (RFS) and melanoma-specific survival (MSS) after sentinel lymph node biopsy (SLNB) was published. The aim of this study was to validate that model in a large independent international cohort. Methods The database of the Sentinel Lymph Node Working Group (SLNWG) was analysed for patients with malignant melanoma who underwent SLNB. Patients with clinical stage III melanoma, a history of other malignancies, or receiving concomitant systemic therapies during follow-up were excluded. The model’s predictive performance was evaluated using discrimination and calibration metrics in the eligible cohort. Decision curve analysis was performed to assess the clinical value of the model. Results The external validation cohort consisted of 6174 patients of the SLNWG from the USA, Europe, and Israel. A positive sentinel node was found in 788 patients (12.8%). The area under the time-dependent receiver operating characteristic (ROC) curve of the external validation was 0.76 (95% c.i. 0.74 to 0.77) for RFS and 0.79 (95% c.i. 0.76 to 0.81) for MSS. The model was well calibrated, as the observed 5-year survival rates aligned closely with the predicted survival rates (calibration slope of 0.98 for RFS and calibration slope of 0.99 for MSS). The model provided a net benefit versus the ‘treat all’ and ‘treat none’ strategies at the predetermined probability threshold for recurrence of 45%. Conclusion The model demonstrated good performance in a large heterogeneous independent cohort, emphasizing its robustness. Decision curve analysis revealed a clear net benefit of the model over a treat all strategy, highlighting its potential for clinical use.

Abstract Richter transformation (RT) is defined as an aggressive lymphoma emerging in patients with chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL). Despite novel therapeutics developed in CLL, RT is associated with poor outcomes. In light of recent progress regarding the diagnostic procedures and therapeutic concepts of RT, an international group of experts, under the coordination of the European Research Initiative on CLL, has developed consensus recommendations for clinical procedures and future research on this disease. Patients with RT typically present with a rapid clinical decline, worsening B-symptoms, elevated lactate dehydrogenase, and/or rapidly enlarging lymphadenopathy. Workup should include a positron emission tomography–computed tomography scan for patients with suspected RT. An excisional biopsy should be taken from an accessible lesion, preferably with the highest fluorodeoxyglucose avidity, and analyzed for the presence of aggressive lymphoma. The molecular relationship to the original CLL clone(s) should be defined. Because no effective standard treatment for RT exists, patients should be treated in a clinical trial. Response of both RT and CLL should be assessed at an early time point, and survival end points should be prioritized in trial design. We hope that these recommendations can help to harmonize clinical and translational research and improve outcomes for patients with RT.