Citations

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.

Abstract Background: Pharmacogenomic studies on antidepressant treatment outcomes could be conducted using previously collected data from electronic health record (EHR)-linked biobanks. However, absence of EHR based outcome measures is an unmet need in designing such studies We aimed to define EHR-derived antidepressant outcome measures and explore their utility in showing associations between treatment outcomes and Cytochrome P450 (CYP) metabolizer phenotypes in a proof-of-concept study. Methods: Using data from the EHR-linked cohort, Right Drug, Right Dose, Right Time: Using Genomic Data to Individualize Treatment (RIGHT 10K) Study, we collected prescription data and patient health questionnaire 9 (PHQ-9) scores to compute 3 proxy measures for antidepressant response, efficacy, and acceptability: change in PHQ-9 scores, longest treatment interval with a single antidepressant, and antidepressant non-refill. Subsequently, we tested the association of both prescription-based outcomes with DNA-predicted CYP metabolizer phenotypes in European-ancestry participants. Results: We identified 3920 RIGHT 10K participants with at least 1 antidepressant prescription and European-ancestry. Participants had a mean age of 61 years and 72% were women. Implementation of the PHQ-9 outcome was not feasible because of missingness. Of both prescription-based outcomes, antidepressant non-refill reproduced several known antidepressant-CYP interactions. However, the pilot was limited by small subgroups of participants with non-normal metabolizer phenotypes. Conclusions: Derived from structured data, antidepressant non-refill is a promising outcome measure for EHR-linked biobanks that partially reproduced antidepressant-CYP interactions. However, testing on larger datasets is necessary to understand whether it would be a useful for pharmacogenomic research.

Alcohol use disorder (AUD) is the most prevalent substance use disorder. Excessive alcohol consumption leads to a range of health issues. We set out to identify inflammatory markers linked to alcohol consumption, which might ultimately offer novel insight into genetic underpinnings and have implications for alcohol-associated disease. Alcohol consumption and blood-based multi-omics data were collected by The Mayo Clinic Center for Individualized Treatment of Alcohol Dependence study. Plasma samples from patients with AUD were used for proteomics analysis using the OLINK “Explore Inflammation” panel (n=410). Liver enzymes were also measured. A genome-wide association study (GWAS) was performed to explore the relationship between genetic variants and plasma TREM2 levels. Our findings show thatplasma triggering receptor expressed on myeloid cells 2 (TREM2), a key gene associated with neurodegenerative disease, was the most significant signal correlated with alcohol consumption, and has also been associated with liver enzyme levels in patients with AUD. We identified the rs7232 single nucleotide polymorphism (SNP) in MS4A6A as a key genetic variant associated with plasma TREM2 levels, with the minor allele (A) linked to higher TREM2 levels and increased alcohol consumption, particularly in men. Furthermore, MA4A6A is an ethanol-responsive gene in a SNP-dependent manner, and the variant genotype of the rs7232 SNP was associated with lower expression for MA4A6A due to proteasome-mediated protein degradation. In summary, this study provides insight into the relationship between plasma TREM2 levels, alcohol consumption, and liver function in AUD patients, shedding light on genetic factors underlying alcohol-related diseases.

Abstract Opioid use disorder (OUD) affects over 40 million people worldwide, creating significant social and economic burdens. Medication for opioid use disorder (MOUD) is often considered the primary treatment approach for OUD. MOUD, including methadone, buprenorphine, and naltrexone, is effective for some, but its benefits may be limited by poor adherence to treatment recommendations. Immunopharmacotherapy offers an innovative approach by using vaccines to generate antibodies that neutralize opioids, blocking them from crossing the blood-brain barrier and reducing their psychoactive effects. To date, only 3 clinical trials for opioid vaccines have been published. While these studies demonstrated the potential of opioid vaccines for relapse prevention, there is currently no standardized protocol for evaluating their effectiveness. We have reviewed recent preclinical studies that demonstrated the efficacy of vaccines targeting opioids, including heroin, morphine, oxycodone, hydrocodone, and fentanyl. These studies showed that vaccines against opioids reduced drug reinforcement, decreased opioid-induced antinociception, and increased survival rates against lethal opioid doses. These studies also demonstrated the importance of vaccine formulation and the use of adjuvants in enhancing antibody production and specificity. Finally, we highlighted the strengths and concerns associated with the opioid vaccine treatment, including ethical considerations.

Background Clonal hematopoiesis (CH) is defined by somatic mutations in hematopoietic stem cells, often conferring a selection advantage based on clonal selection pressures. CH in DNA Damage Response and Repair (DDR, e.g. TP53, PPM1D) genes are frequent in individuals exposed to chemotherapy and radiation (chemo/XRT). In metastatic prostate cancer (mPC) patients, the prevalence of CH is estimated at 57% and is increased among those with prior exposure to chemo or XRT. In these patients, the use of radiotheranostics is becoming more prevalent and are now being tested in earlier lines of therapy. We have identified an increase in DDR-somatic variants post lutetium-177-PSMA, raising concern for long term hematologic safety. Here, we evaluate the spectrum of CH and CCUS (clonal cytopenias of undetermined significance) in mPC patients, prior to receiving radioisotope therapies. Methods We performed error corrected next-generation sequencing (NGS) on peripheral blood samples from 67 mPC patients enrolled in the Prostate Cancer Medically Optimized Genome Enhanced Therapy Study (PROMOTE) study (NCT #01953640). All patients enrolled also had whole exome sequencing (WES) performed on initial metastatic biopsies and on whole blood for germline genotyping. Somatic variants with a variant allele fraction (VAF) ≥0.5% were considered as CH, using standard bioinformatic variant calling algorithms. Results The median age at NGS was 74.4 years (range, 58.4-92.3). While all patients received androgen deprivation therapy (ADT) +/- androgen receptor pathway inhibitor (ARPI), 67.2% (n=45, pelvic XRT- 35, palliative XRT- 10) received prior XRT and 3% (n=2) received prior docetaxel. No patients in this cohort received prior platinum or PARP inhibitor. Of those treated with XRT or chemo, the median time from first exposure to CH sample collection was 4.1yrs (range, 0.12-29.6) and 2.3yrs (range, 0.6-4.0), respectively. Of the 67 patients, we identified a total of 111 pathogenic variants in 59 (88.1%) patients. We compared our NGS data to the germline data and tumor for each patient (All samples obtained at the same time point; Wang et al 2018). Our analysis excluded 2 ATM variants likely to be germline, and 1 TP53 variant likely to be tumor-derived. Each patient had at least one somatic variant, with a median of 2 variants (range, 1-6The median CH VAF was 1.8% (range, 0.5-50.2) with 91.0% of alterations detected at a VAF of <10%. Specific somatic variants, presence/absence of CH, number of variants, nor VAF was associated with age, prior chemo, or XRT exposures. At time of NGS, 54% of patients were anemic (hgb <13g/dL), 7% were thrombocytopenic (platelets <135 x109), 6% were leukopenic (WBC <3 x 109), and no patients were neutropenic (ANC <1 x109). Persistent cytopenias, lasting >3months, were present in 25% (n=17) of patients; however, bone marrow biopsies (BMB) were performed only in 6% (n=4). Three (75%) of these 4 patients had mPC infiltration in the BM, and 1 (25%) had mild dysplasia not meeting criteria for MDS but meeting criteria for CCUS (CHEK2 somatic variant). At last follow-up, there were no patients formally diagnosed to have myeloid neoplasm (MN). Median overall survival (OS) from prostate cancer diagnosis was 8.6yrs (range, 1.4-28.4). With a median follow up of 2.2yrs (range 0.1-10.8) after CH testing, 3.0% (n=2) were alive, with most deaths occurring due to mPC. The presence of CH did not impact overall survival (OS), however, presence of cytopenias at the time of NGS testing was associated with worse outcomes (median OS 2.5yrs vs 1.2 yrs., p=0.025). Conclusions We define the landscape of CH mutations in mPC patients predominantly treated with ADT/ARPI and XRT, demonstrating that DNMT3A, TET2, PPM1D and ASXL1 are frequently mutated (88%) and largely subclonal (91% with VAF ≤10%). While 25% of these patients had cytopenias, with cytopenias negatively impacting survival, due to inadequate BM biopsy work up, the true prevalence of CCUS and MN could not be estimated. Given the advancement of genotoxic therapies and the high prevalence of subclonal CH in mPC, future studies investigating CH as a predictive biomarker of hematological dysfunction are much needed.

Background: Dilated Cardiomyopathy (DCM) is a major cause of heart failure (HF) and carries a high mortality rate. Drug treatment response for DCM-related HF patients is highly variable, with some patients having no response. A pharmacogenomic approach, genome-wide association study (GWAS) followed by biologically functional validation, can potentially identify genetic determinants of prognosis in DCM and might provide insight into novel biology of DCM pharmacotherapy. Methods: A GWAS was performed using DNA from 686 patients with recent onset DCM who were on standard HF therapy using change in left ventricular ejection fraction (LVEF) at a median of 6 months after initial diagnosis. Cultured human cardiac fibroblasts (HCFs) were used to functionally validate the GWAS findings in vitro . RNA-seq followed by pathway analysis after NAV3 knock-down (KD) using siRNA in HCFs were performed to explore potential biological mechanisms. Results: A genetic variant, rs11105445(G>A), identified from the GWAS, mapping to the neuron navigator 3 (NAV3) gene (rs11105445, p=2.37E-07; beta 2.74 ± 0.53) was associated with improvement in LVEF. Functional genomic experiments demonstrated that, both in LV tissue and in cultured fibroblasts, the minor allele A was associated with decreased transcription of NAV3 (pvalue: 0.0335 and 0.0432) suggesting that ↓NAV3 expression might be associated with improvement in LVEF. TGF-β1 can induce HCF transdifferentiation into myofibroblasts and we demonstrate that it also increases NAV3 expression. NAV3 KD which mimics the GWAS variant significantly suppressed TGF-β1 induced HCF transdifferentiation demonstrated by decreased expression of α-smooth muscle actin (ACTA2) and collagen I (COL1A1). RNA-seq after NAV3 KD followed by pathway analysis suggested that NAV3 exerted its effect by regulating ECM and cell cycle processes, especially G1/S transition. We demonstrate that NAV3 KD suppressed TGF-β1 induced G1/S arrest by regulating CDK4, CDK6 and INK4B (cyclin-dependent kinase inhibitor 2B/P15, CDK4 inhibitor). Conclusion: Decreased expression of NAV3 is associated with myocardial recovery in DCM, most likely due to its role in suppressing HCF differentiation by regulating TGF-β1 induced G1/S arrest. The role of NAV3 as a novel therapeutic target in DCM needs to be explored.

Background: CDCP1 has been associated with reverse remodeling in human dilated cardiomyopathy (DCM) mediating its effect by reducing cardiac fibrosis which has been demonstrated in-vitro using human cardiac fibroblasts (CF) and in-vivo histologically in mice. However, the role of and the molecular mechanisms by which CDCP1 attenuates cardiac fibrosis in-vivo is unknown. Methods: To characterize the transcriptomic profiles of CDCP1 in cardiac fibrosis, Cdcp1 KO FVB/NJ mice were generated, and implanted with osmotic minipumps containing angiotensin II and phenylephrine (Ang II/PE) or saline at age of 10 weeks. There were 4 experimental groups (6 mice each), Saline_WT, Saline_KO, AngII/PE_WT, and AngII/PE_KO. After 4 weeks of AngII/PE induction, mice were euthanized, RNA was extracted from their heart tissue followed by RNA-seq to explore transcriptomic profiles. Fibrosis was histologically determined using picosirius (PSR) staining and was quantified by percentage of fibrosis area. Results: Histological analysis demonstrated that Cdcp1 KO attenuated severe cardiac fibrosis by 32.7% determined by quantification of percentage of fibrosis area. When comparing the transcriptome-wide gene expression in WT mice hearts from Saline to AngII/PE induction, a total of 316 differentially expressed genes (DEGs) were identified ( Fig. 1A ). Expression of Nppb (encodes ProBNP), a marker for heart failure, was significantly upregulated. The most significantly upregulated DEG after AngII/PE induction was Crlf1 ( Fig. 1A ), a gene predominantly expressed in CF, consistent with previous findings. We next compared transcriptomic profiles of AngII/PE induction WT and Cdcp1 KO mice hearts, to explore the role of CDCP1 in cardiac fibrosis. A total of 163 DEGs were identified ( Fig. 1B ). Notably, Cdcp1 -mediated DEGs enriched GO Pathways associated with extracellular matrix organization (Biological Process) and collagen-containing extracellular matrix (Cellular Component) ( Figs. 1C, 1D ), pivotal processes in cardiac fibrosis. In addition, Cdcp1 KO resulted in several “top” DEGs (including Rnase1, Ccl7, Ccl12 Il21r , and Il6 ) which have known function in inflammation, suggesting that CDCP1 might attenuate cardiac fibrosis through immune regulation . Conclusion: Our findings underscore the pivotal role of CDCP1 in modulating cardiac fibrosis in-vivo, potentially through immune regulation. Targeting CDCP1 may offer promising therapeutic avenues for mitigating myocardial fibrosis.

Abstract The OmicsFootPrint framework addresses the need for advanced multi-omics data analysis methodologies by transforming data into intuitive two-dimensional circular images and facilitating the interpretation of complex diseases. Utilizing deep neural networks and incorporating the SHapley Additive exPlanations algorithm, the framework enhances model interpretability. Tested with The Cancer Genome Atlas data, OmicsFootPrint effectively classified lung and breast cancer subtypes, achieving high area under the curve (AUC) scores—0.98 ± 0.02 for lung cancer subtype differentiation and 0.83 ± 0.07 for breast cancer PAM50 subtypes, and successfully distinguished between invasive lobular and ductal carcinomas in breast cancer, showcasing its robustness. It also demonstrated notable performance in predicting drug responses in cancer cell lines, with a median AUC of 0.74, surpassing nine existing methods. Furthermore, its effectiveness persists even with reduced training sample sizes. OmicsFootPrint marks an enhancement in multi-omics research, offering a novel, efficient and interpretable approach that contributes to a deeper understanding of disease mechanisms.