Weinshilboum, Richard M.

Purpose The majority of patients with cancer receive treatments that are minimally informed by omics data. We propose a precision medicine computational framework, PANOPLY (Precision Cancer Genomic Report: Single Sample Inventory), to identify and prioritize drug targets and cancer therapy regimens. Materials and Methods The PANOPLY approach integrates clinical data with germline and somatic features obtained from multiomics platforms and applies machine learning and network analysis approaches in the context of the individual patient and matched controls. The PANOPLY workflow uses the following four steps: selection of matched controls to the patient of interest; identification of patient-specific genomic events; identification of suitable drugs using the driver-gene network and random forest analyses; and provision of an integrated multiomics case report of the patient with prioritization of anticancer drugs. Results The PANOPLY workflow can be executed on a stand-alone virtual machine and is also available for download as an R package. We applied the method to an institutional breast cancer neoadjuvant chemotherapy study that collected clinical and genomic data as well as patient-derived xenografts to investigate the prioritization offered by PANOPLY. In a chemotherapy-resistant patient-derived xenograft model, we found that that the prioritized drug, olaparib, was more effective than placebo in treating the tumor ( P < .05). We also applied PANOPLY to in-house and publicly accessible multiomics tumor data sets with therapeutic response or survival data available. Conclusion PANOPLY shows promise as a means to prioritize drugs on the basis of clinical and multiomics data for an individual patient with cancer. Additional studies are needed to confirm this approach.

Abstract Objective: Response to menopausal hormone therapy (MHT) shows individual variation. SLCO1B1 encodes the OATP1B1 transporter expressed in the liver that transports many endogenous substances, including estrone sulfate, from the blood into hepatocytes. This study evaluated the relationship between genetic variation in SLCO1B1 and response to MHT in women enrolled in the Kronos Early Estrogen Prevention Study (KEEPS) at Mayo Clinic, Rochester, MN. Methods: KEEPS participants were randomized to oral conjugated equine estrogen (n = 33, oCEE), transdermal 17β-estradiol (n = 33, tE2), or placebo (n = 34) for 48 months. Menopausal symptoms (hot flashes, night sweats, insomnia, palpitations) were self-reported before treatment and at 48 months. Estrone (E1), E2, and sulfated conjugates (E1S, E2S) were measured using high-performance liquid chromatography-tandem mass spectrometry. SLCO1B1 rs4149056 (c.521T>C, p.Val174Ala) was genotyped using a TaqMan assay. Results: After adjusting for treatment, there was a significant association between the SLCO1B1 rs4149056 TT genotype (encoding normal function transporter) and lower E1S, E1S/E1, and E2S (P = 0.032, 0.010, and 0.008, respectively) compared with women who were heterozygous (TC) or homozygous (CC) for the reduced function allele. The interactions between genotype, treatment, and E2S concentration were stronger in women assigned to tE2 (P = 0.013) than the women taking oCEE (P = 0.056). Among women assigned to active treatment, women with the CT genotype showed a significantly greater decrease in night sweats (P = 0.041) than those with the TT genotype. Conclusions: Individual variation in sulfated estrogens is explained, in part, by genetic variation in SLCO1B1. Bioavailability of sulfated estrogens may contribute to relief of night sweats.

Response to a drug often differs widely among individual patients. This variability is frequently observed not only with respect to effective responses but also with adverse drug reactions. Matching patients to the drugs that are most likely to be effective and least likely to cause harm is the goal of effective therapeutics. Pharmacogenomics (PGx) holds the promise of precision medicine through elucidating the genetic determinants responsible for pharmacological outcomes and using them to guide drug selection and dosing. Here we survey the US landscape of research programs in PGx implementation, review current advances and clinical applications of PGx, summarize the obstacles that have hindered PGx implementation, and identify the critical knowledge gaps and possible studies needed to help to address them.

Antidepressants have reduced the symptom burden for many Major Depressive Disorder (MDD) patients, but drug‐related side effects and treatment resistance continue to present major challenges. Pharmacogenomics represents one approach to enhance antidepressant efficacy and avoid adverse reactions, but concerns remain with regard to the overall “value equation,” and several barriers must be overcome to achieve the full potential of MDD pharmacogenomics.

Sulfotransferase (ST) enzymes cata‐lyze the sulfate conjugation of many hormones, neu‐rotransmitters, drugs, and xenobiotic compounds. These reactions result in enhanced renal excretion of the sulfate‐conjugated reaction products, but they can also lead to the formation of “bioactivated” metabolites. ST enzymes are members of an emerging gene superfamily that presently includes phenol ST (PST), hydroxysteroid ST (HSST), and, in plants, flavonol ST (FST) “families,” members of which share at least 45% amino acid sequence iden‐tity. These families can be further subdivided into “subfamilies” that are at least 60% identical in amino acid sequence. For example, the PST family includes both PST and estrogen ST (EST) subfamilies. Amino acid sequence motifs exist within ST enzymes that are conserved throughout phylogeny. These signature sequences may be involved in the binding of 3 ‘‐phosphoadenosine‐5 ‘‐phosphosulfate, the cosubstrate for the sulfonation reaction. There are presently five known human cytosolic ST en‐zymes: an EST, an HSST, and three PSTs. cDNAs and genes for all of these enzymes have been cloned, and chromosomal localizations have been reported for all five genes. Genes for these human enzymes, as well as those of other mammalian cytosolic ST enzymes that have been cloned, show a high degree of structural homology, with conservation of the lo‐cations of most intron/exon splice junctions. Human ST enzyme expression varies among individuals. Functionally significant genetic polymorphisms for ST enzymes in humans have been reported, and other molecular genetic mechanisms that might be involved in the regulation of the expression of these enzymes are being explored. Knowledge of the mo‐lecular biology of cytosolic ST enzymes, when placed within a context provided by decades of biochemical research, promises to significantly enhance our understanding of the regulation of the sulfate conjugation of hormones, neurotransmitters, and drugs.—Weinshilboum, R. M., Otterness, D. M., Aksoy, I. A., Wood, T. C., Her, C., Rafto‐ gianis, R. B. Sulfotransferase molecular biology: cDNAs and genes. FASEB J. 11, 3‐14 (1997)

Biomedical research is undergoing rapid change, with the development of a series of analytical omics techniques that are capable of generating Biomedical Big Data. These developments provide an unprecedented opportunity to gain novel insight into disease pathophysiology and mechanisms of drug action and response—but they also present significant challenges. Pharmacogenomics is a discipline within Clinical Pharmacology that has been at the forefront in defining, taking advantage of, and dealing with the opportunities and challenges of this aspect of the Post‐Genome Project world. This overview will describe the evolution of germline pharmacogenomic research strategies as we have moved from an era of candidate genes to agnostic genome‐wide association studies (GWAS) coupled with the functional and mechanistic pursuit of GWAS signals. Germline pharmacogenomic studies of breast cancer endocrine therapy will be used to illustrate research strategies that are being applied broadly to omics studies of drug response phenotypes.

The testis‐specific Y‐encoded‐like protein (TSPYL) gene family includes TSPYL1 to TSPYL6. We previously reported that TSPYL5 regulates cytochrome P450 (CYP) 19A1 expression. Here we show that TSPYLs, especially TSPYL 1, 2, and 4, can regulate the expression of many CYP genes, including CYP17A1, a key enzyme in androgen biosynthesis, and CYP3A4, an enzyme that catalyzes the metabolism of abiraterone, a CYP17 inhibitor. Furthermore, a common TSPYL1 single nucleotide polymorphism (SNP), rs3828743 (G/A) (Pro62Ser), abolishes TSPYL1's ability to suppress CYP3A4 expression, resulting in reduced abiraterone concentrations and increased cell proliferation. Data from a prospective clinical trial of 87 metastatic castration‐resistant prostate cancer patients treated with abiraterone acetate/prednisone showed that the variant SNP genotype (A) was significantly associated with worse response and progression‐free survival. In summary, TSPYL genes are novel CYP gene transcription regulators, and genetic alteration within these genes significantly influences response to drug therapy through transcriptional regulation of CYP450 genes.