Weinshilboum, Richard M.

Objective To test the ability of machine learning (ML) approaches with clinical and genomic biomarkers to predict methotrexate treatment response in patients with early rheumatoid arthritis (RA). Methods Demographic, clinical, and genomic data from 643 patients of European ancestry with early RA (mean age 54 years; 70% female) subdivided into a training (n = 336) and validation cohort (n = 307) were used. The genomic data comprised 160 single‐nucleotide polymorphisms (SNPs) previously associated with RA or methotrexate metabolism. Response to methotrexate monotherapy was defined as good or moderate by the European Alliance of Associations for Rheumatology (EULAR) response criteria at the 3‐month follow‐up. Supervised ML methods were trained with 5 repeats and 10‐fold cross‐validation using the training cohort. Prediction performance was validated in the independent validation cohort. Results Supervised ML methods combining age, sex, smoking, rheumatoid factor, baseline Disease Activity Score in 28 joints (DAS28) scores and 160 SNPs predicted EULAR response at 3 months with the area under the receiver operating curve of 0.84 ( P = 0.05) in the training cohort and achieved a prediction accuracy of 76% ( P = 0.05) in the validation cohort (sensitivity 72%, specificity 77%). Intergenic SNPs rs12446816, rs13385025, rs113798271, and ATIC (rs2372536) had variable importance above 60.0 and along with baseline DAS28 scores were among the top predictors of methotrexate response. Conclusion Pharmacogenomic biomarkers combined with baseline DAS28 scores can be useful in predicting response to methotrexate in patients with early RA. Applying ML to predict treatment response holds promise for guiding effective RA treatment choices, including timely escalation of RA therapies.

Abstract Our previous matched case–control study of postmenopausal women with resected early-stage breast cancer revealed that only anastrozole, but not exemestane or letrozole, showed a significant association between the 6-month estrogen concentrations and risk of breast cancer. Anastrozole, but not exemestane or letrozole, is a ligand for estrogen receptor α. The mechanisms of endocrine resistance are heterogenous and with the new mechanism of anastrozole, we have found that treatment of anastrozole maintains fatty acid synthase (FASN) protein level by limiting the ubiquitin-mediated FASN degradation, leading to increased breast cancer cell growth. Mechanistically, anastrozole decreases the guided entry of tail-anchored proteins factor 4 (GET4) expression, resulting in decreased BCL2-associated athanogene cochaperone 6 (BAG6) complex activity, which in turn, prevents RNF126-mediated degradation of FASN. Increased FASN protein level can induce a negative feedback loop mediated by the MAPK pathway. High levels of FASN are associated with poor outcome only in patients with anastrozole-treated breast cancer, but not in patients treated with exemestane or letrozole. Repressing FASN causes regression of breast cancer cell growth. The anastrozole-FASN signaling pathway is eminently targetable in endocrine-resistant breast cancer.

Epinephrine is a sympathomimetic which is derived from phenylalanine and tyrosine. The final step of epinephrine synthesis, the conversion from norepinephrine, relies on phenylethanolamine N‐methyltransferase (PNMT). The beta‐2 adrenergic receptor (ADRB2) binds to epinephrine and has several common functional polymorphisms including a Glycine (Gly) for Arginine (Arg) substitution at amino acid 16. We assessed the effect of the gene‐to‐gene interaction of these proteins on the ventricular response to exercise in healthy humans (n=69, age=29±6yrs, ht=174±10cm, wt=71±13kg). We found that within the 2*1 haplotype of the 5′FR, which has previously demonstrated greater epinephrine synthesis compared to 2*7 haplotype, the Arg16 genotype demonstrates lower stroke volume (SV) than the Gly16 genotype despite similar epinephrine levels and synthesis rates (Arg16= 75±5, Gly16=89±6ml/beat). Similarly, within the 2*7 haplotype of the 5′FR, which has demonstrated attenuated epinephrine synthesis compared to 2*1, the Arg16 genotype demonstrates a smaller SV than the Gly16 genotype (Arg16= 59±3, Gly16=78±6ml/beat). Additionally, the Arg16 and Gly16 subjects within the 2*1 haplotype of PNMT have greater stroke volume than their ADRB2 counterparts of the 2*7 haplotype of PNMT. These data demonstrate an interaction between the genes that encode PNMT and ADRB2 on the ventricular response to exercise.

There is large variation in response to menopausal hormone treatments among women and it remains unclear as to what genetic factors might increase risk of breast cancer in women using such treatments to relieve symptoms of menopause. SULT1A1 is a ubiquitous enzyme involved in the metabolism of steroid hormones including estrogen. A number of polymorphisms in the gene for this enzyme are associated with various cancer risks and the activity level of the enzyme is correlated closely with variation in copy number of the gene. This pilot study evaluated SNP variation and copy number of SULT1A1 in a subset of women (n=156) being screened for the Kronos Early Estrogen Prevention Study (KEEPS). All participants gave written informed consent. DNA prepared from blood was genotyped for SULT1A1 copy number, rs3760091 and rs750155 from the promoter region of SULT1A1, and rs9282861 from the coding region (SULT1A1*2 variant). In this cohort, 5.2% had 1 copy of SULT1A1, 65.2% had 2 copies, 23.9% had 3 copies, and 5.8% had 4 copies. Previously reports of the allele frequency for the three SNPs did not take into account gene copy number. When copy number was taken into account, allele frequency was significantly different, a fact that will have to be taken into account in future genotype‐phenotype correlations. (Supported by grants from the Kronos Longevity Research Institute, NIH HL90639, RR24150 and GM61388.)

BACKGROUND Most functionally‐significant single nucleotide polymorphisms (SNPs) are outside of open reading frames. Mechanisms by which these SNPs contribute to gene regulation are poorly understood. Aryl hydrocarbon receptor (AHR) is a nuclear receptor that regulates drug metabolism, xenobiotic response and immune response. Our previous data indicate that SNPs outside response elements can impact transcription factor binding and gene regulation. We hypothesized that SNPs near but outside AHR response elements (AhREs) might impact AHR binding and gene regulation. METHODS We have collected dense SNP‐genotypes across 300 lymphoblastoid cell lines (LCLs) – a powerful tool for generating and testing pharmacogenomic hypotheses. Eight LCLs were used to generate hypotheses about AHR SNP‐dependent transcription utilizing RNA‐ and ChIP‐seq, which were tested in additional LCLs using qRT‐PCR and ChIP after AHR agonist or antagonist treatment. RESULTS 69 genes were highly significantly differentially expressed after agonist or antagonist treatment and 80% of those genes had an AhRE within 50kb. Five SNP‐gene pairs have been functionally validated in additional LCLs (Table 1). CONCLUSION SNPs that are outside of AhREs can alter AHR binding and gene transcription. This is a general phenomenon that may have large‐scale influence on gene transcriptional regulation and pharmacogenomics. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

Objectives Betaine‐homocysteine methyltransferase (BHMT) catalyzes the remethylation of homocysteine. BHMT in the human liver is responsible for up to 25% of homocysteine metabolism. Knowledge of the relationship between genetic polymorphisms and BHMT phenotypes could enhance our understanding of the role of this reaction in drug metabolism. Methods Levels of BHMT enzyme activity and immunoreactive protein were measured in 268 human liver biopsies. Twelve tag BHMT SNPs from our own gene resequencing and HapMap data were used to genotype DNA from these samples. Genotype‐phenotype association analysis was then performed. BHMT enzyme activity averaged 0.077 ± 0.028 units/μg, with a 12 fold variation. Results BHMT protein levels averaged 6.9 ± 3.3 units/μg, with a 50‐fold variation. Three SNPs (rs41272270, rs16876512, and rs6875201), located 28kb upstream, in the 5′‐UTR and in intron 1 of BHMT , respectively, were significantly correlated with both phenotypes (p<10 −8 for enzyme activity and p<10 −5 for protein level). rs41272270 was associated with altered protein binding during electrophoresis mobility shift assay. Conclusions Three tag SNPs were significantly associated with human hepatic BHMT enzyme activity and immunoreactive protein. These observations may help us understand the regulation of BHMT in the human liver and its role in drug metabolism. Supported by NIH U01 GM61388, R01 GM28157, R01 CA132780.

Aromatase inhibitors (AIs) are the treatment of choice for hormone receptor–positive early breast cancer in postmenopausal women. None of the third‐generation AIs are superior to the others in terms of efficacy. We attempted to identify genetic factors that could differentiate between the effectiveness of adjuvant anastrozole and exemestane by examining single‐nucleotide polymorphism (SNP)‐treatment interaction in 4,465 patients. A group of SNPs were found to be differentially associated between anastrozole and exemestane regarding outcomes. However, they showed no association with outcome in the combined analysis. We followed up common SNPs near LY75 and GPR160 that could differentiate anastrozole from exemestane efficacy. LY75 and GPR160 participate in epithelial‐to‐mesenchymal transition and growth pathways, in both cases with SNP‐dependent variation in regulation. Collectively, these studies identified SNPs that differentiate the efficacy of anastrozole and exemestane and they suggest additional genetic biomarkers for possible use in selecting an AI for a given patient.

Objectives We previously discovered that the single nucleotide polymorphisms (SNP) rs9940645 in the ZNF423 gene regulate ZNF423 expression and serve as a potential biomarker for response to selective estrogen receptor modulators (SERMs). Here we explored pathways involved in ZNF423-mediated SERMs response and drugs that potentially sensitize SERMs. Methods RNA sequencing and label-free quantitative proteomics were performed to identify genes and pathways that are regulated by ZNF423 and the ZNF423 SNP. Both cultured cells and mouse xenograft models with different ZNF423 SNP genotypes were used to study the cellular responses to metformin. Results We identified ribosome and AMP-activated protein kinase (AMPK) signaling as potential pathways regulated by ZNF423 or ZNF423 rs9940645 SNP. Moreover, using clustered regularly interspaced short palindromic repeats/Cas9-engineered ZR75-1 breast cancer cells with different ZNF423 SNP genotypes, striking differences in cellular responses to metformin, either alone or in the combination of tamoxifen, were observed in both cell culture and the mouse xenograft model. Conclusions We found that AMPK signaling is modulated by the ZNF423 rs9940645 SNP in estrogen and SERM-dependent fashion. The ZNF423 rs9940645 SNP affects metformin response in breast cancer and could be a potential biomarker for tailoring the metformin treatment.

Abstract Background: Androgen receptor (AR) is expressed in 60%-90% of breast cancers. The role of AR in breast cancer largely depends on estrogen receptor (ER) status and remains controversial. In ER+ cancers, AR expression is associated with improved prognosis. Enzalutamide (ENZ), an AR antagonist, impairs AR signaling, inhibits ER+/AR+ breast cancer cell proliferation and has been shown to mitigate resistance to anti-estrogen therapies. A recent study identified that RAD140, an AR agonist, suppressed the growth of ER+/AR+ breast cancer via stimulating AR signaling, resulting in down regulation of ERα. Both AR agonists and antagonists are effective in treating ER+ breast cancers. Identifying the subgroup of patients who most likely will benefit from an individual drug is important in clinical practice. Here, we sought to characterize the relative AR to ER levels and their relationships to drug response in ER+ breast cancers.Methods: We evaluated AR and ER expression levels among 68 samples with ER+ breast cancers. On each tumor, IHC staining for AR and ER were performed and results were scored by multiplying the percentage of positive cells by the intensity. TCGA-RPPA (reverse phase protein array) dataset was used to verify AR protein distribution in ER+ cancers. We created MCF7 and T47D cells with doxycycline-inducible AR and ER expression system to manipulate the relative AR to ER ratios and determined the antitumor activity of ENZ and RAD140 by cell proliferation assays. The levels of AR and ER in cells were measured by western blot. Linear regression was used to test the association between dose-response area under curve (AUC) and AR, ER levels. Drug preference was modeled against AR/ER expression levels (AR/ER ratios) using logistic regression.Results: Among 68 ER+ breast cancers, 69.12% were AR-positive. More than 2/3 cases (48 of 68) had more ER than AR expressed. The AR to ER ratios varied from 0 to 6. In the TCGA cohort, 84.15% of 347 ER+ patients had AR/ER ratios less than 1.The range of AR to ER ratios in TCGA dataset were comparable with our data (0.004 to 4.210). In our cell line models, the AR/ER ratios were controlled between 0.19 to 4.05. We found that the AUC of RAD140 was negatively associated with AR protein levels (P=0.008) and AR/ER ratios (P=0.013), and not significantly associated with ER expression levels. On the other hand, the AUC of ENZ was significantly negatively associated with ER protein (P=0.0016) but postiviely associated with AR/ER ratio (p=0.037). Preferred treatment comparing efficacy of the two drug can be best determined at extremes of AR/ER ratios. Using clinically relevant dosages, our model predicted that ENZ (10µM) would be a preferred treatment choice and have a better treatment efficacy compared with RAD140 when the AR/ER ratio was ≤ 0.42, whereas RAD140 (1µM) would be the preferred choice with a better treatment efficacy compared with ENZ when AR/ER ratios were ≥ 3.1 The efficacy preference of the two drugs are equivocal for AR/ER ratios between 0.42 and 3.10. Conclusion: We developed preclinical models using AR and ER expression levels to predict AR-targeting drug response. The results support the use of RAD140 in AR high patients and those with an AR/ER ratio &gt;3.10, and enzalutamide in AR low patients and those with an AR/ER ratio &lt;0.42. Equipoise on choice of drug was found for AR/ER ratios of 0.42-3.10. RAD140 and enzalutamide are compelling candidates for monotherapy or combination with anti-estrogen therapies in ER+/AR+ breast cancer. Future clinical validation of the models and therapeutic effect is warranted. Citation Format: Lixuan Wei, Huanyao Gao, Jia Yu, Duan Liu, Huan Zhang, Thanh Nguyen, Marie R Passow, Jodi M Carter, Richard M Weinshilboum, James N Ingle, Liewei Wang. Computational modeling of androgen receptor (AR) and estrogen receptor as predictive biomarkers of response to AR agonists and antagonists [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS5-36.