Wang, Liewei

AbstractObjectiveElevated serum chromogranin A (CGA) is associated with intrinsic or treatment‐related neuroendocrine differentiation (NED) in men with metastatic castration‐resistant prostate cancer (mCRPC). Fluctuations in serum CGA during treatment of mCRPC have had conflicting results. We analyzed the impact of (i) rising serum CGA and (ii) baseline CGA/PSA ratio during treatment to identify associations with abiraterone acetate (AA) therapy.MethodsBetween June 2013 and August 2015, 92 men with mCRPC were enrolled in a prospective trial with uniform serum CGA processing performed before initiating abiraterone acetate/prednisone (AA/P) and serially after 12 weeks of AA/P treatments. Serum CGA was measured using a homogenous automated immunofluorescent assay. Patients receiving proton pump inhibitors or with abnormal renal function were excluded due to possible false elevations of serum CGA (n = 21 excluded), therefore 71 patients were analyzed. All patients underwent a composite response assessment at 12‐weeks. Kaplan–Meier estimates and Cox Regression models were used to calculate the association with time‐to‐treatment failure analyses and overall survival.ResultsAn increase in chromogranin was associated with a lower risk of treatment failure (hazard ratio [HR]: 0.52, p = 0.0181). The median CGA/PSA ratio was 7.8 (2.6–16.0) and an elevated pretreatment CGA/PSA ratio above the median was associated with a lower risk of treatment failure (HR: 0.54 p value = 0.0185). An increase in CGA was not found to be associated with OS (HR: 0.71, 95% CI: 0.42–1.21, p = 0.207). An elevated baseline CGA/PSA ratio was not associated with OS (HR: 0.62, 95% CI: 0.37–1.03, p = 0.062). An increase in PSA after 12 weeks of treatment was associated with an increased risk of treatment failure (HR: 4.14, CI: 2.21–7.73, p = < 0.0001) and worse OS (HR: 2.93, CI: 1.57–4.45, p = < 0.0001).ConclusionsWe show that an increasing chromogranin on AA/P and an elevated baseline CGA/PSA in patients with mCRPC were associated with a favorable response to AA/P with no changes in survival. There may be limited clinical utility in serum CGA testing to evaluate for lethal NED as AA/P did not induce lethal NED in this cohort. This highlights that not all patients with an increasing CGA have a worse OS.

Abstract Introduction: We performed matched case-control studies utilizing cohorts of postmenopausal women with ER+ breast cancer receiving adjuvant aromatase inhibitors (AI) on MA.27 [anastrozole, exemestane] or PreFace [letrozole] to assess the association between estrogen suppression after 6 months of treatment and an early breast cancer (EBC) event within 5 years of AI initiation (Clin Cancer Res 2020;26:2986-98). We found a significant 3.0-fold increase in risk of an EBC event for those taking anastrozole with levels of estrone (E1) ≥1.3 pg/mL and estradiol (E2) ≥0.5 pg/mL, but not for exemestane or letrozole. Given these findings we designed a prospective pharmacodynamic (PD) study to evaluate the impact of anastrozole (1 mg/day: ANA1) on E1 and E2 levels, and among those with inadequate estrogen suppression (IES: E1 ≥1.3 pg/mL and E2 ≥0.5 pg/mL), to evaluate the safety and PD efficacy of high-dose anastrozole (10 mg/day: ANA10), which has been found to be safe in prior clinical trials (Cancer 1998;83:1142-52). Methods: Post-menopausal women with stage I-III, ER ≥1% positive/HER2-negative breast cancer who were candidates for anastrozole were eligible after completion of locoregional therapy and chemotherapy, as clinically indicated. Women who were pre-menopausal at diagnosis were not eligible. All patients received 8-10 weeks of ANA1, after which those with adequate estrogen suppression (AES: E1&lt; 1.3 pg/mL or E2&lt; 0.5 pg/mL) came off study. Those with IES went on to receive ANA10 for 8-10 weeks, followed by letrozole (2.5 mg/day: LET) for 8-10 weeks. All patients were managed at their treating oncologist’s discretion following study discontinuation. E1 and E2 blood levels were measured pre-treatment and after completion of each treatment cycle by a CLIA-approved liquid chromatography with tandem mass spectrometry in the Immunochemical Core Laboratory at Mayo Clinic. With a sample size of 29 patients with IES after ANA1, a one-sided binomial test of proportions with a significance level of 0.05 will have an 87% chance of rejecting the proportion with AES after ANA10 is at most 25% (Ho) when the true proportion is at least 50%. Specifically, the null hypothesis is rejected if the number of women with AES after ANA10 is 12 or more. Data lock was July 6, 2022. Results: Of the 161 women enrolled from April 2020 through May 2022, 3 withdrew consent prior to start of ANA1 and 2 were ineligible; thus, 156 women comprised the study cohort. Median patient age was 64 years (range 44-86), 10% of patients were of Hispanic ethnicity and/or non-white race, and 15% received chemotherapy. Six patients remain on ANA1, and 10 discontinued ANA1 due to refusal (7), adverse event (AE) (2), or COVID-19 (1). Forty-one of the remaining 140 patients (29.3%; 95%CI: 21.9-37.6%) had IES with ANA1. Nine of these 41 patients did not go on to ANA10 due to refusal (6) or AE (3). Of the 32 patients who started ANA10, 8 remain on treatment, 5 discontinued due to refusal (3) or AE (1-grade 2 urinary tract infection; 1-grade 1 palpitations), and 19 had a blood draw 45 days or more after starting ANA10. No grade 3-5 AEs or grade 2 hot flashes or arthralgias were reported. Of these 19 patients, 14 achieved AES with ANA10 (73.7%; 95%CI: 48.8-90.9%). All 19 patients switched to LET of which 3 remain on treatment, 1 is missing E1/E2 data, and 15 had a blood draw 45 days or more after starting LET. Of these 15 patients, 10 maintained AES, 2 acquired AES with LET, and 3 no longer had AES. Anastrozole and letrozole drug levels will be reported at the meeting. Conclusions: Approximately 29% of postmenopausal women with ER+/HER2- BC receiving adjuvant anastrozole 1 mg/daily had IES. A majority of these patients achieved AES with dose escalation to ANA10 without tolerability issues. E1 and E2 levels are logical biomarkers given the mechanism of action of anastrozole, and further study utilizing them to determine the optimal dose of anastrozole for a given patient should be performed. Citation Format: Tufia C. Haddad, Vera Suman, Karthik V. Giridhar, Alvaro Moreno-Aspitia, Donald Northfelt, Brenda Ernst, Kostandinos Sideras, Ciara C. O’Sullivan, Ravinder Singh, Zeruesenay Desta, Jodi Taraba, Barbara Goodnature, Matthew P. Goetz, Liewei Wang, James N. Ingle. Anastrozole dose escalation for optimal estrogen suppression in postmenopausal early stage breast cancer: A prospective trial [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr OT1-04-02.

Abstract Survival rates of patients with metastatic castration-resistant prostate cancer (mCRPC) are low due to lack of response or acquired resistance to available therapies, such as abiraterone (Abi). A better understanding of the underlying molecular mechanisms is needed to identify effective targets to overcome resistance. Given the complexity of the transcriptional dynamics in cells, differential gene expression analysis of bulk transcriptomics data cannot provide sufficient detailed insights into resistance mechanisms. Incorporating network structures could overcome this limitation to provide a global and functional perspective of Abi resistance in mCRPC. Here, we developed TraRe, a computational method using sparse Bayesian models to examine phenotypically driven transcriptional mechanistic differences at three distinct levels: transcriptional networks, specific regulons, and individual transcription factors (TF). TraRe was applied to transcriptomic data from 46 patients with mCRPC with Abi-response clinical data and uncovered abrogated immune response transcriptional modules that showed strong differential regulation in Abi-responsive compared with Abi-resistant patients. These modules were replicated in an independent mCRPC study. Furthermore, key rewiring predictions and their associated TFs were experimentally validated in two prostate cancer cell lines with different Abi-resistance features. Among them, ELK3, MXD1, and MYB played a differential role in cell survival in Abi-sensitive and Abi-resistant cells. Moreover, ELK3 regulated cell migration capacity, which could have a direct impact on mCRPC. Collectively, these findings shed light on the underlying transcriptional mechanisms driving Abi response, demonstrating that TraRe is a promising tool for generating novel hypotheses based on identified transcriptional network disruptions. Significance: The computational method TraRe built on Bayesian machine learning models for investigating transcriptional network structures shows that disruption of ELK3, MXD1, and MYB signaling cascades impacts abiraterone resistance in prostate cancer.

Abstract Prediction of the response of cancer patients to different treatments and identification of biomarkers of drug response are two major goals of individualized medicine. Here, we developed a deep learning framework called TINDL, completely trained on preclinical cancer cell lines (CCLs), to predict the response of cancer patients to different treatments. TINDL utilizes a tissue-informed normalization to account for the tissue type and cancer type of the tumors and to reduce the statistical discrepancies between CCLs and patient tumors. Moreover, by making the deep learning black box interpretable, this model identifies a small set of genes whose expression levels are predictive of drug response in the trained model, enabling identification of biomarkers of drug response. Using data from two large databases of CCLs and cancer tumors, we showed that this model can distinguish between sensitive and resistant tumors for 10 (out of 14) drugs, outperforming various other machine learning models. In addition, our small interfering RNA (siRNA) knockdown experiments on 10 genes identified by this model for one of the drugs (tamoxifen) confirmed that tamoxifen sensitivity is substantially influenced by all of these genes in MCF7 cells, and seven of these genes in T47D cells. Furthermore, genes implicated for multiple drugs pointed to shared mechanism of action among drugs and suggested several important signaling pathways. In summary, this study provides a powerful deep learning framework for prediction of drug response and identification of biomarkers of drug response in cancer. The code can be accessed at https://github.com/ddhostallero/tindl.

Abstract Androgen receptor (AR) is expressed in 80% to 90% of estrogen receptor α–positive (ER+) breast cancers. Accumulated evidence has shown that AR is a tumor suppressor and that its expression is associated with improved prognosis in ER+ breast cancer. However, both a selective AR agonist (RAD140) and an AR inhibitor (enzalutamide, ENZ) have shown a therapeutic effect on ER+ breast cancer, so the potential for clinical application of AR-targeting therapy for ER+ breast cancer is still in dispute. In this study, we evaluated the efficacy of ENZ and RAD140 in vivo and in vitro in AR+/ER+ breast cancer models, characterizing the relationship of AR and ER levels to response to AR-targeting drugs and investigating the alterations of global gene expression and chromatin binding of AR and ERα after ENZ treatment. In the AR-low setting, ENZ directly functioned as an ERα antagonist. Cell growth inhibition by ENZ in breast cancer with low AR expression was independent of AR and instead dependent on ER. In AR-high breast cancer models, AR repressed ERα signaling and ENZ promoted ERα signaling by antagonizing AR. In contrast, RAD140 activated AR signaling and suppressed AR-high tumor growth by deregulating ERα expression and blocking ERα function. Overall, analysis of the dynamic efficacies and outcomes of AR agonist, and antagonist in the presence of different AR and ERα levels reveals regulators of response and supports the clinical investigation of ENZ in selected ER+ tumors with a low AR/ER ratio and AR agonists in tumors with a high AR/ER ratio. Significance: The ratio of androgen receptor to estrogen receptor in breast cancer dictates the response to AR-targeted therapies, providing guidelines for developing AR-directed treatment strategies for patients with breast cancer.

Abstract Understanding the function of non-coding genomic sequence variants represents a challenge for biomedicine. Many diseases are products of gene-by-environment interactions with complex mechanisms. This study addresses these themes by mechanistic characterization of non-coding variants that influence gene expression only after drug or hormone exposure. Using glucocorticoid signaling as a model system, we integrated genomic, transcriptomic, and epigenomic approaches to unravel mechanisms by which variant function could be revealed by hormones or drugs. Specifically, we identified cis-regulatory elements and 3D interactions underlying ligand-dependent associations between variants and gene expression. One-quarter of the glucocorticoid-modulated variants that we identified had already been associated with clinical phenotypes. However, their affected genes were ‘unmasked’ only after glucocorticoid exposure and often with function relevant to the disease phenotypes. These diseases involved glucocorticoids as risk factors or therapeutic agents and included autoimmunity, metabolic and mood disorders, osteoporosis and cancer. For example, we identified a novel breast cancer risk gene, MAST4, with expression that was repressed by glucocorticoids in cells carrying the risk genotype, repression that correlated with MAST4 expression in breast cancer and treatment outcomes. These observations provide a mechanistic framework for understanding non-coding genetic variant-chemical environment interactions and their role in disease risk and drug response.

Prostate cancer (PCa) accounted for more than 34,000 deaths in US males [...]

Abstract We identified resistance mechanisms to abiraterone acetate/prednisone (AA/P) in patients with metastatic castration-resistant prostate cancer (mCRPC) in the Prostate Cancer Medically Optimized Genome-Enhanced Therapy (PROMOTE) study. We analyzed whole-exome sequencing (WES) and RNA-sequencing data from 83 patients with metastatic biopsies before (V1) and after 12 weeks of AA/P treatment (V2). Resistance was determined by time to treatment change (TTTC). At V2, 18 and 11 of 58 patients had either short-term (median 3.6 months; range 1.4–4.5) or long-term (median 29 months; range 23.5–41.7) responses, respectively. Nonresponders had low expression of TGFBR3 and increased activation of the Wnt pathway, cell cycle, upregulation of AR variants, both pre- and posttreatment, with further deletion of AR inhibitor CDK11B posttreatment. Deletion of androgen processing genes, HSD17B11, CYP19A1 were observed in nonresponders posttreatment. Genes involved in cell cycle, DNA repair, Wnt-signaling, and Aurora kinase pathways were differentially expressed between the responder and non-responder at V2. Activation of Wnt signaling in nonresponder and deactivation of MYC or its target genes in responders was detected via SCN loss, somatic mutations, and transcriptomics. Upregulation of genes in the AURKA pathway are consistent with the activation of MYC regulated genes in nonresponders. Several genes in the AKT1 axis had increased mutation rate in nonresponders. We also found evidence of resistance via PDCD1 overexpression in responders. Implications: Finally, we identified candidates drugs to reverse AA/P resistance: topoisomerase inhibitors and drugs targeting the cell cycle via the MYC/AURKA/AURKB/TOP2A and/or PI3K_AKT_MTOR pathways.

AbstractAlternative polyadenylation (APA) is a common genetic regulatory mechanism that generates distinct 3′ ends for RNA transcripts. Changes in APA have been associated with multiple biological processes and disease phenotypes. However, the role of hormones and their drug analogs in APA remains largely unknown. In this study, we investigated transcriptome‐wide the impact of glucocorticoids on APA in 30 human B‐lymphoblastoid cell lines. We found that glucocorticoids could regulate APA for a subset of genes, possibly by changing the expression of 142 RNA‐binding proteins, some with known APA‐regulating properties. Interestingly, genes with glucocorticoid‐mediated APA were enriched in viral translation‐related pathways, while genes with glucocorticoid‐mediated expression were enriched in interferon and interleukin pathways, suggesting that glucocorticoid‐mediated APA might result in functional consequences distinct from gene expression. For example, glucocorticoids, a pharmacotherapy for severe COVID‐19, were found to change the APA but not the expression of LY6E, an important antiviral inhibitor in coronavirus diseases. Glucocorticoid‐mediated APA was also cell‐type‐specific, suggesting an action of glucocorticoids that may be unique to immune regulation. We also observed evidence for genotype‐dependent glucocorticoid‐mediated APA (referred to as pharmacogenomic‐alterative polyadenylation quantitative trait loci), providing potential functional mechanisms for a series of common genetic variants that had previously been associated with immune disorders, but without a clear mechanism. In summary, this study reports a series of observations regarding the impact of glucocorticoids on APA, raising the possibility that this mechanism might have implications for both disease pathophysiology and drug therapy.

Neoadjuvant chemotherapy (NAC) remains the cornerstone of the treatment for triple negative breast cancer (TNBC), with the goal of complete eradication of disease. However, for patients with residual disease after NAC, recurrence and mortality rates are high and the identification of novel therapeutic targets is urgently needed. We quantified tyrosine phosphorylation (pTyr)-mediated signaling networks in chemotherapy sensitive (CS) and resistant (CR) TNBC patient-derived xenografts (PDX), to gain novel therapeutic insights. The antitumor activity of SFK inhibition was examined in vivo. Treated tumors were further subjected to phosphoproteomic and RNAseq analysis, to identify the mechanism of actions of the drug. We identified Src Family Kinases (SFKs) as potential therapeutic targets in CR TNBC PDXs. Treatment with dasatinib, an FDA approved SFK inhibitor, led to inhibition of tumor growth in vivo. Further analysis of post-treatment PDXs revealed multiple mechanisms of actions of the drug, confirming the multi-target inhibition of dasatinib. Analysis of pTyr in tumor specimens suggested a low prevalence of SFK-driven tumors, which may provide insight into prior clinical trial results demonstrating a lack of dasatinib antitumor activity in unselected breast cancer patients. Taken together, these results underscore the importance of pTyr characterization of tumors, in identifying new targets, as well as stratifying patients based on their activated signaling networks for therapeutic options. Our data provide a strong rationale for studying SFK inhibitors in biomarker-selected SFK-driven TNBC.