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Age at depressive onset (AAO) corresponds to unique symptomatology and clinical outcomes. Integration of genome-wide association study (GWAS) results with additional “omic” measures to evaluate AAO has not been reported and may reveal novel markers of susceptibility and/or resistance to major depressive disorder (MDD). To address this gap, we integrated genomics with metabolomics using data-driven network analysis to characterize and differentiate MDD based on AAO. This study first performed two GWAS for AAO as a continuous trait in (a) 486 adults from the Pharmacogenomic Research Network-Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS), and (b) 295 adults from the Combining Medications to Enhance Depression Outcomes (CO-MED) study. Variants from top signals were integrated with 153 p180-assayed metabolites to establish multi-omics network characterizations of early (<age 18) and adult-onset depression. The most significant variant (p = 8.77 × 10−8) localized to an intron of SAMD3. In silico functional annotation of top signals (p < 1 × 10−5) demonstrated gene expression enrichment in the brain and during embryonic development. Network analysis identified differential associations between four variants (in/near INTU, FAT1, CNTN6, and TM9SF2) and plasma metabolites (phosphatidylcholines, carnitines, biogenic amines, and amino acids) in early- compared with adult-onset MDD. Multi-omics integration identified differential biosignatures of early- and adult-onset MDD. These biosignatures call for future studies to follow participants from childhood through adulthood and collect repeated -omics and neuroimaging measures to validate and deeply characterize the biomarkers of susceptibility and/or resistance to MDD development.

Abstract Purpose: Cardiac toxicity is a serious potential complication of HER2-directed therapies and anthracyclines. HER2 codon 655 and SLC28A3 gene polymorphisms have been reported to be associated with cardiac toxicity from anti-HER2 and anthracycline therapy, respectively. Association of the polymorphism at HER2 codon 655 with prognosis has also been reported. Experimental Design: Whole blood samples from patients treated on a randomized adjuvant breast cancer trial (BCIRG-006) that compared chemotherapy with or without trastuzumab plus either anthracycline or nonanthracycline chemotherapy were tested for genetic polymorphisms in HER2 codon 655 and SLC28A3. Genotypes were correlated with cardiac function and disease-free survival (DFS) outcomes. Results: Of 3,222 patients enrolled in BCIRG-006, 662 patient samples were successfully genotyped for the rs1136201 allele in HER2 (codon 655): 424 (64%) were AA, 30 (4.5%) were GG, and 208 (31%) were AG genotype. In addition, 665 patient samples were successfully genotyped for the rs7853758 allele in the SLC28A3 gene: 19 (3%) were AA, 475 (71%) were GG, and 171 (26%) were AG genotype. Follow-up time was 10 years. No correlation between DFS, cardiac event rate, or mean left ventricular ejection fraction (LVEF) and rs1136201 genotype was seen in the trastuzumab-treated or non–trastuzumab-treated patients. Moreover, mean LVEF and cardiac event rates were similar in all rs7853758 genotype groups treated with anthracycline-based therapy. Conclusions: In the largest study to date to evaluate whether two polymorphisms are associated with DFS and/or cardiac toxicity in HER2-positive breast cancer treated with trastuzumab and/or anthracyclines, we observed no correlation.

Background and Purpose Acamprosate is an anti‐craving drug used for the pharmacotherapy of alcohol use disorder (AUD). However, only some patients achieve optimal therapeutic outcomes. This study was designed to explore differences in metabolomic profiles between patients who maintained sobriety and those who relapsed, to determine whether those differences provide insight into variation in acamprosate treatment response phenotypes. Experimental Approach We previously conducted an acamprosate trial involving 442 AUD patients, and 267 of these subjects presented themselves for a 3‐month follow‐up. The primary outcome was abstinence. Clinical information, genomic data and metabolomics data were collected. Baseline plasma samples were assayed using targeted metabolomics. Key Results Baseline plasma arginine, threonine, α‐aminoadipic acid and ethanolamine concentrations were associated with acamprosate treatment outcomes and baseline craving intensity, a measure that has been associated with acamprosate treatment response. We next applied a pharmacometabolomics‐informed genome‐wide association study (GWAS) strategy to identify genetic variants that might contribute to variations in plasma metabolomic profiles that were associated with craving and/or acamprosate treatment outcome. Gene expression data for induced pluripotent stem cell‐derived forebrain astrocytes showed that a series of genes identified during the metabolomics‐informed GWAS were ethanol responsive. Furthermore, a large number of those genes could be regulated by acamprosate. Finally, we identified a series of single nucleotide polymorphisms that were associated with acamprosate treatment outcomes. Conclusion and Implications These results serve as an important step towards advancing our understanding of disease pathophysiology and drug action responsible for variation in acamprosate response and alcohol craving in AUD patients.

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.

Major depressive disorder (MDD), the number one psychiatric disease and a major cause of medical disability worldwide, is a heterogeneous disease. Efforts to identify biomarkers for antidepressant therapy response by the use of genome‐wide association studies (GWAS) have generally yielded disappointing results, e.g. only a few markers have been replicated across studies. By applying a “pharmacometabolomics‐informed pharmacogenomic” research strategy, we previously identified a genome‐wide significant single‐nucleotide polymorphism (SNP) signal in the glutamate‐rich 3 ( ERICH3 ) gene that was associated with plasma serotonin concentrations which were themselves associated with selective serotonin reuptake inhibitor (SSRI) response in MDD patients enrolled in the Mayo Clinic PGRN‐AMPS SSRI trial. The same ERICH3 SNPs were associated with SSRI response in other GWAS, including the STAR*D and ISPC studies. However, the function of ERICH3 and the molecular mechanism(s) by which it might be associated with plasma serotonin concentrations and with SSRI response remain unknown. Functional characterization of ERICH3 and its encoded protein might make it possible to identify novel mechanisms of antidepressant treatment response. In the present study, ERICH3 mRNA and protein were studied in cell lines from human peripheral blood and brain. ERICH3 cDNA constructs and specific antibodies were generated to make it possible to study its function. Two ERICH3 mRNA splice variants and protein isoforms, one “full‐length” and the other encoding a C‐terminal protein, were identified in a variety of human cell lines and in human brain tissue. ERICH3 mRNA expression and immunofluorescent staining suggested both cytosolic and vesicular localizations for ERICH3 as well as co‐localization with serotonin and dopamine in both mouse and human neurons. Co‐immunoprecipitation studies identified several ERICH3 interacting proteins, including PIK3C2A, CLTC and AP2A2, proteins known to play a role in vesicular trafficking, as well as ALK and CUX1, both of which display genetic polymorphisms that have been associated with SSRI response in GWAS studies of antidepressant treatment response in MDD patients. This series of experiments suggests that ERICH3 may play a role in vesicular trafficking and, as a result, may influence neurotransmitter actions in the brain that are associated with antidepressant treatment response. Support or Funding Information This work was supported by the NIH grants U19 GM61388, R01 GM28157 and T32 GM008685. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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.)

Single nucleotide variants in the open reading frames (ORFs) of pharmacogenes are important causes of interindividual variability in drug response. Because of the very large number of missense variants in ORFs, it is difficult to link the genotypes of these variants to their phenotypes using “one at a time” expression systems. Therefore, the functional characterization of variants of unknown significance in clinically important pharmacogenes remains a major challenge in pharmacogenomics. Deep mutational scanning (DMS) is a high‐throughput technique that make it possible to analyze hundreds of different missense variants in a parallel and scalable fashion. Engineered “landing pad” HEK 293T cells were used as a platform to integrate pooled variant libraries, resulting in one variant per cell for further assessment. We adapted this DMS system to analyze the function of missense variants in the ORFs of CYP2C9 and CYP2C19 . The ORFs of CYP2C9 and CYP2C19 were fused to green fluorescent protein, and individual variants with different fluorescence intensities were used to indicate different levels of protein expression. Pooled variant libraries were generated by nicking mutagenesis, and we then used recombinase to integrate the libraries into landing pad cells, one per cell. Multiplexed functional selection performed with Fluorescence Activated Cell Sorting separated cells into different bins by fluorescence readout at the single–cell level. Amplicon sequencing of DNA collected in each bin, followed by computational analysis of the frequency of variants appearing in each bin, was used to determine levels of protein expression. To identify potentially severely damaging variants for these two important pharmacogenes, we studied 243 missense variants in the CYP2C9 and CYP2C19 ORFs that had been identified by the Exome Aggregation Consortium Project and by the Mayo Clinic RIGHT 1K Study. We found that 13 of 115 CYP2C9 and 25 of 128 CYP2C19 missense variants displayed less than approximately 25% of the wildtype protein expression, a level that may have clinical relevance for drug metabolism. Variant calling by the DMS method for the severely damaging variants was compared with the results of individual variant assay by western blot and with the predictions of computational algorithms such as Polyphen and SIFT. Our results suggest that DMS is an efficient method for the high‐throughput identification of damaging ORF variants that might have potential clinical application in pharmacogenomics. Support or Funding Information Funded by NIH grant U19 GM61388 (The Pharmacogenomics Research Network), R01 GM28157, R01 GM125633, T32 GM08685 and by the Mayo Clinic Center for Individualized Medicine This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .