Wang, Liewei

Abstract Purpose: We previously reported that postmenopausal women with estrogen receptor-α–positive breast cancer receiving adjuvant anastrozole 1 mg/day (ANA1) with estrone (E1) ≥1.3 pg/mL and estradiol (E2) ≥0.5 pg/mL [inadequate estrogen suppression (IES)] had a threefold increased risk of a breast cancer event. The objective of this study was to determine if increasing anastrozole to 10 mg/day (ANA10) could result in adequate estrogen suppression (AES: E1 <1.3 pg/mL and/or E2 <0.5 pg/mL) among those with IES on ANA1. Patients and Methods: Postmenopausal women with estrogen receptor-α–positive breast cancer planning to receive adjuvant ANA1 were eligible. E1 and E2 were assessed pre- and post-8 to 10 weeks of ANA1. Those with IES were switched to 8- to 10-week cycles of ANA10 followed by letrozole 2.5 mg/day. E1 and E2 were assessed after each cycle. Anastrozole concentrations were measured post-ANA1 and post-ANA10. Primary analyses included patients who documented taking at least 80% of the planned treatment (adherent cohort). Results: In total, 132 (84.6%) of 156 eligible patients were ANA1 adherent. IES occurred in 40 (30.3%) adherent patients. Twenty-five (78.1%) of 32 patients who began ANA10 were adherent, and AES was achieved in 19 (76.0%; 90% confidence interval, 58.1%–89.0%) patients. Anastrozole concentrations post-ANA1 and post-ANA10 did not differ by estrogen suppression status among adherent patients. AES was maintained/attained in 21 (91.3%) of 23 letrozole-adherent patients. Conclusions: Approximately 30% of ANA1-adherent patients had IES. Among those who switched to ANA10 and were adherent, 76% had AES. Further studies are required to validate emerging data that ANA1 results in IES for some patients and to determine the clinical benefit of switching to ANA10 or an alternative aromatase inhibitor.

Abstract BACKGROUND: Cancer is typically treated with a combination of drugs rather than monotherapy stratagems, but identification of synergistic drug combinations for the treatment of cancers is challenging. Here we present the combination of PARP and Aurora Kinase inhibitors synergize through induction of the ferroptosis pathway of cell death. METHODS: We identified these drugs through an unbiased screen of enriched drug class response correlations. We then validated a synergistic cell killing effect of multiple drugs of each class by in vitro drug screening. Next, we performed reversal experiments using a ferroptosis inhibitor and lipid peroxide staining to confirm ferroptosis is a relevant cell death pathway. We performed Western immunoblot analysis in cells overexpressing putative Aurora Kinase phospho-substrates in the relevant pathway of interest, and performed site directed mutagenesis experiments to show the relevant amino acid altered by kinase activity. We also performed a series of qPCR and co-IP experiments to show PARPi effect on the antioxidant signaling pathway. RESULTS: We validated and identified synergy in a prostate cancer specific context. Next, we found Talazoparib and Alisertib induced ferroptosis after synergy was reduced with a ferroptosis inhibitor and lipid peroxides increased accumulation. We found a novel mechanism action for Alisertib, wherein Aurora Kinase inhibitors can alter the phospho-status and activity of the iron reduction enzyme Steap3.We also found that PARP inhibitors can affect the Nrf2-driven antioxidant signaling pathway on a transcriptional level. CONCLUSION: Aurora Kinase inhibitors and PARP inhibitors for the treatment of prostate cancer was identified and was validated as synergistic, which revealed new functional roles of both drugs in the context of cell killing. These findings represent a new clinical combination of drugs in prostate cancer and reveal a novel role for this drug class in ferroptosis. Citation Format: August John, Kuntian Luo, Zhenkun Lou, Liewei Wang. Ferroptotic mechanism of drug induced toxicity of PARPi and AurKi in 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 4563.

Abstract Grouping gene expression into gene set activity scores (GSAS) provides better biological insights than studying individual genes. However, existing gene set projection methods cannot return representative, robust, and interpretable GSAS. We developed NetActivity, a machine learning framework that generates GSAS based on a sparsely-connected autoencoder, where each neuron in the inner layer represents a gene set. We proposed a three-tier training that yielded representative, robust, and interpretable GSAS. NetActivity model was trained with 1518 GO biological processes terms and KEGG pathways and all GTEx samples. NetActivity generates GSAS robust to the initialization parameters and representative of the original transcriptome, and assigned higher importance to more biologically relevant genes. Moreover, NetActivity returns GSAS with a more consistent definition and higher interpretability than GSVA and hipathia, state-of-the-art gene set projection methods. Finally, NetActivity enables combining bulk RNA-seq and microarray datasets in a meta-analysis of prostate cancer progression, highlighting gene sets related to cell division, key for disease progression. When applied to metastatic prostate cancer, gene sets associated with cancer progression were also altered due to drug resistance, while a classical enrichment analysis identified gene sets irrelevant to the phenotype. NetActivity is publicly available in Bioconductor and GitHub.

Abstract Background: Dysregulated Notch signalling contributes to breast cancer development and progression, but validated tools to measure the level of Notch signalling in breast cancer subtypes and in response to systemic therapy are largely lacking. A transcriptomic signature of Notch signalling would thus be warranted, and in this report, we have established such a classifier. Methods: To generate the signature, we first identified Notch-regulated genes from six basal-like breast cancer cell lines subjected to elevated and reduced Notch signalling by culturing on immobilized Notch ligand Jagged1 or blockade of Notch by g-secretase inhibitors, respectively. From this cadre of Notch-regulated genes, we developed candidate transcriptomic signatures that were trained on a breast cancer patient dataset (the TCGA-BRCA cohort) and a broader breast cancer cell line cohort and sought to validate in independent data sets. Results: An optimal 20-gene transcriptomic signature was selected. We validated the signature on two independent patient datasets (METABRIC and Oslo2) and it showed an improved coherence score and tumour specificity compared with previously published signatures. Furthermore, the signature score was particularly high for basal-like breast cancer, indicating an enhanced level of Notch signalling in this subtype. The signature score was increased after neoadjuvant treatment in the PROMIX and BEAUTY patient cohorts, and a lower signature score generally correlated with better clinical outcome. Conclusions: The 20-gene transcriptional signature has the potential to better stratify patients and to evaluate the response of future Notch-based therapies for breast cancer. Citation Format: Eike-Benjamin Braune, Felix Geist, Xiaojia Tang, Krishna Kalari, Judy Boughey, Liewei Wang, Roberto A. Leon-Ferre, Antonino B. D’Assoro, James N. Ingle, Matthew P. Goetz, Kang Wang, Theodoros Foukakis, Anita Seshire, Dirk Wienke, Urban Lendahl. Identification of a Notch transcriptomic signature for breast cancer [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr B013.

Objective CYP2C9 and CYP2C19 are highly polymorphic pharmacogenes that encodes enzymes that metabolize a large number of clinically relevant drugs. While the majority of variability in drug metabolism can be explained by a few common alleles, identification of gene sequence variation that alters enzyme activity and incorporation of that information into pharmacogenomic rules for clinical decision support might lead to improved therapy. Using sequence data derived from the Right Drug, Right Dose, Right Time: Using Genomic Data to Individualize Treatment (RIGHT Protocol), we sought to identify variants of unknown significance (VUSs) for CYP2C9 and CYP2C19, and to characterize the function of the encoded enzymes. Methods Targeted next‐generation sequencing for 84 pharmacogenes using the PGRN‐Seq v1.0 capture reagent was performed with DNA samples from 1013 RIGHT Protocol participants. Plasmids containing VUSs were created using site directed mutagenesis and were transiently expressed in COS‐1 cells. Initial determination of enzyme activity was performed using fluorogenic substrates. Enzyme kinetic parameters (Km and Vmax) for each of the variant allozymes were determined using liquid chromatography mass spectrometry with tolbutamide (CYP2C9) and mephenytoin (CYP2C19) as prototypic substrates. We also compared our results with in silico functional predictions by SIFT and Polyphen. Results Seven VUSs were identified for each gene, none of which had been functionally characterized. There were significant positive correlations between protein content and fluorometric enzyme activity for all variants for both CYP2C9 (P<0.05) and CYP2C19 (P<0.005). Notably, CYP2C9 V237L and CYP2C19 A173V activity was much lower than predicted based on protein content. The substrate intrinsic clearance of CYP2C9 variants P73L , S115R , V237L [6.23E‐05±1.08E‐05; 2.55E‐05±4.1E‐06; 7.28E‐05±2.4E‐05 (uL/mg protein/min)], and CYP2C19 variants V113I , A173V , R186C and R186H [2.49E‐04±2.78E‐05; 1.80E‐05±2.19E‐06; 2.33E‐05±9.2E‐06;1.45E‐04±1.2E‐05 (uL/mg protein/min)] were less than 25% of their respective WT enzyme . Conclusion Sequencing of CYP2C9 and CYP2C19 from 1013 subjects identified low frequency variants that had not been functionally characterized. In silico predictions which showed variable results, can be helpful but are not always accurate, hence functional validation is necessary for VUSs requiring protein expression and determination of kinetic parameters. Efforts to identify variation in diverse populations is resulting in a very large number of VUSs, so there is a need for the application of high throughput mutagenesis approaches and functional characterization methods to help pharmacogenomics contribute to precision medicine. Support or Funding Information NIH RO1 GM28157 and the Mayo Center for Individualized Medicine Pharmacogenomics Program.

Background: Antidepressants are among the most prescribed medications in the United States. The aim of this study was to explore the prevalence of antidepressant prescriptions and investigate sex differences and age-sex interactions in adults enrolled in the Right Drug, Right Dose, Right Time: Using Genomic Data to Individualize Treatment (RIGHT) study. Materials and Methods: We conducted a retrospective analysis of the RIGHT study. Using electronic prescriptions, we assessed 12-month prevalence of antidepressant treatment. Sex differences and age-sex interactions were evaluated using multivariable logistic regression and flexible recursive smoothing splines. Results: The sample consisted of 11,087 participants (60% women). Antidepressant prescription prevalence was 22.24% (27.96% women, 13.58% men). After adjusting for age and enrollment year, women had significantly greater odds of antidepressant prescription (odds ratio = 2.29; 95% confidence interval = 2.07, 2.54). Furthermore, selective serotonin reuptake inhibitors (SSRIs) had a significant age-sex interaction. While SSRI prescriptions in men showed a sustained decrease with age, there was no such decline for women until after reaching ∼50 years of age. There are important limitations to consider in this study. Electronic prescription data were cross-sectional; information on treatment duration or adherence was not collected; this cohort is not nationally representative; and enrollment occurred over a broad period, introducing confounding by changes in temporal prescribing practices. Conclusions: Underscored by the significant interaction between age and sex on odds of SSRI prescription, our results warrant age to be incorporated as a mediator when investigating sex differences in mental illness, especially mood disorders and their treatment.

Abstract Enhanced DNA repair is an important mechanism of inherent and acquired resistance to DNA targeted therapies, including poly ADP ribose polymerase inhibition. Spleen associated tyrosine kinase (Syk) is a non-receptor tyrosine kinase known to regulate immune cell function, cell adhesion, and vascular development. Here, we report that Syk can be expressed in high grade serous ovarian cancer and triple negative breast cancers and promotes DNA double strand break resection, homologous recombination (HR) and therapeutic resistance. We found that Syk is activated by ATM following DNA damage and is recruited to DNA double strand breaks by NBS1. Once at the break site, Syk phosphorylates CtIP, a key mediator of resection and HR, at Thr-847 to promote repair activity, specifically in Syk expressing cancer cells. Syk inhibition or genetic deletion abolished CtIP Thr-847 phosphorylation and overcame the resistant phenotype. Collectively, our findings suggest that Syk drives therapeutic resistance by promoting DNA resection and HR through a novel ATM-Syk-CtIP pathway, and that Syk is a new tumor-specific target to sensitize Syk-expressing tumors to PARPi and other DNA targeted therapy.

Abstract Background: Cancer is usually treated with a combination of drugs rather than a single agent. Treating cancer with a drug combination decreases the likelihood of the cancer acquiring resistance to therapy. It also allows for lower dosages of individual drugs, which reduces the occurrence and severity of side effects. Using public datasets, we investigated potential anti-cancer drug combinations using a novel framework, ReCorDE (Recurrent Correlation of Drugs with Enrichment), which identifies correlated drug response patterns for drugs with different primary mechanisms of action. Methods: 250 drugs from CTRPv2, GDSCv2, and PRISM datasets were examined using normalized logIC50 or AUC measurements. ReCorDE consists of a correlation and an enrichment step. For each dataset, we constructed pairwise drug-drug relationships using Spearman’s correlation. Combinations significant (p &lt; 0.05) in less than two datasets after Benjamini-Hochberg (BH) correction were pruned. To turn each drug combination into a drug class combination, all drugs were mapped to their corresponding fourth-level Anatomical Therapeutic Chemical (ATC) codes. Then, using a hypergeometric test, for each class combination in the subset of significant drug combinations (the “inside” set), we tested for enrichment of that class combination in the inside set compared to the class combination universe, the distribution of class combinations for all unique pairwise drug-drug associations (250 unique drugs; 31,125 distinct drug combinations mapped to 1596 distinct class combinations). Multiple testing during enrichment was corrected using the BH method with a significance cutoff of p&lt; 0.05. Results: We found 2764 pairs to be significant at a = 0.05 using the AUC measure. With a correlation coefficient of 0.77, vincristine and YK-4-279, an EWS-FLI1/RNA Helicase A inhibitor, had the lowest computed p-value of of 1.39 × 10−120 Adjusted p-values for &gt;15% of these combinations had p-values &lt; 5 × 10−6. Class combination enrichment on the same set showed 132 class pairs were enriched in the inside set compared to the class combination universe after BH correction. Taxanes/Plk1 inhibitors (OR = 69.5, adjusted p = 5.6 × 10−11); Aurora Kinase inhibitors/histone modifying agents (OR=9.2, adjusted p = 6.13 × 10−11); and pyrimidine analogues/CDK inhibitors (OR=3.43, adjusted p = 0.02) are a few notable enriched class combinations. The enrichment of these class combinations in the inside set suggests that combinations of drugs from these classes may be particularly effective in treating cancer compared to other drug combinations. Conclusions: Our framework, ReCorDE, demonstrates that finding potential drug combinations and characterizing novel, frequently perturbed pathways outside of a drug's primary mechanisms of action can be accomplished by identifying correlated drug-drug pairs from large, publicly accessible databases. Citation Format: Emily T. Ghose, August John, Huanyao Gao, Krishna R. Kalari, Liewei Wang. ReCorDE: A novel computational framework to discover potential combinations of anti-cancer drugs. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4290.

Abstract Background: Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer-related death among women worldwide. Triple negative breast cancer (TNBC) represents 15-20% of all breast cancers and is an aggressive subtype. Chemotherapy-based treatments remain the standard of care for TNBC. Unfortunately, chemotherapy resistance is common, and for these patients, outcomes are poor and alternative treatment strategies remain an unmet need. circRNAs are a newly identified class of noncoding RNA molecules with covalently closed circular structures. An increasing number of recent studies including ours have indicated that circRNAs play crucial roles in regulating tumor development and chemoresistance. However, the role of circRNAs in the process of chemotherapy resistance and TNBC progression is not clear. Materials and Methods: As a first step towards identifying circRNAs that participate in the development of chemoresistance in TNBC cells, and to determine if targeting such circRNAs is a novel and efficacious therapeutic strategy, doxorubicin-resistant (Doxo-R), paclitaxel-resistant (PTX-R), and double-resistant (DP-R) cell lines were generated from MDA-MB-231. Human circRNA microarrays were utilized to profile the expression of approximately 14,000 known circRNAs in normal breast tissue, matched patient-derived xenografts (PDX) generated prior to and following neoadjuvant chemotherapy (NAC), and TNBC chemosensitive and chemoresistant cell lines. Top hits were validated using RT-PCR. Results: circRNA microarray profiling identified 429 and 310 transcripts differentially expressed in doxorubicin and paclitaxel resistant cells, respectively, compared to parental chemosensitive cell lines (|FC| ≥ 1.5; p value &lt; 0.05). In comparison to pre-NAC derived xenografts, 1,396 circRNAs were dysregulated among post-NAC PDX models. Further, three circRNAs (hsa_circ_001388, hsa_circ_104652, and hsa_circ_061260) were upregulated in Doxo-R, PTX-R, and post-NAC PDX samples compared to their respective controls. Among these three circRNAs, hsa_circ_001388 (also known as circNSD2) was the only transcript also predicted to be translated into a novel and uncharacterized protein given the presence of a high confidence translation initiation site and IRES sequence. Ongoing studies are aimed at determining the role of circNSD2 protein in breast cancer carcinogenesis, progression, and response to standard of care chemotherapeutics and the mechanistic process by which this protein functions. Additionally, the efficacy of specifically targeting this circRNA as a novel therapeutic approach is being explored. Conclusions: Increasing knowledge of the important functions of circRNAs underlying drug resistance will provide new opportunities for developing efficacious therapeutic strategies and prognostic/predictive biomarkers for TNBC. Citation Format: Xiyin Wang, Michael J. Emch, Xiaojia Tang, Jia Yu, Krishna R. Kalari, Liewei Wang, Matthew P. Goetz, John R. Hawse. The role of circular RNAs in triple negative breast cancer and chemotherapy resistance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1689.

Abstract Purpose: Men with metastatic castration-resistant prostate cancer (mCRPC) frequently develop resistance to androgen receptor signaling inhibitor (ARSI) treatment; therefore, new therapies are needed. Trophoblastic cell-surface antigen (TROP-2) is a transmembrane protein identified in prostate cancer and overexpressed in multiple malignancies. TROP-2 is a therapeutic target for antibody–drug conjugates (ADC). Experimental Design: TROP-2 gene (TACSTD2) expression and markers of treatment resistance from prostate biopsies were analyzed using data from four previously curated cohorts of mCRPC (n = 634) and the PROMOTE study (dbGaP accession phs001141.v1.p1, n = 88). EPCAM or TROP-2–positive circulating tumor cells (CTC) were captured from peripheral blood for comparison of protein (n = 15) and gene expression signatures of treatment resistance (n = 40). We assessed the efficacy of TROP-2–targeting agents in a mouse xenograft model generated from prostate cancer cell lines. Results: We demonstrated that TACSTD2 is expressed in mCRPC from luminal and basal tumors but at lower levels in patients with neuroendocrine prostate cancer. Patients previously treated with ARSI showed no significant difference in TACSTD2 expression, whereas patients with detectable AR-V7 expression showed increased expression. We observed that TROP-2 can serve as a cell surface target for isolating CTCs, which may serve as a predictive biomarker for ADCs. We also demonstrated that prostate cancer cell line xenografts can be targeted specifically by labeled anti–TROP-2 agents in vivo. Conclusions: These results support further studies on TROP-2 as a therapeutic and diagnostic target for mCRPC.