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Pre-Diagnostic Serum Metabolomic Profiling of Prostate Cancer Survival Jiaqi Huang, MS, Phd,1 Stephanie J

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Pre-Diagnostic Serum Metabolomic Profiling of Prostate Cancer Survival Jiaqi Huang, MS, Phd,1 Stephanie J Journals of Gerontology: Medical Sciences cite as: J Gerontol A Biol Sci Med Sci, 2019, Vol. 74, No. 6, 853–859 doi:10.1093/gerona/gly128 Advance Access publication June 7, 2018 Translational Section Bringing Men’s Health into the Limelight Original Article Downloaded from https://academic.oup.com/biomedgerontology/article/74/6/853/5033968 by guest on 23 September 2021 Pre-diagnostic Serum Metabolomic Profiling of Prostate Cancer Survival Jiaqi Huang, MS, PhD,1 Stephanie J. Weinstein, MS, PhD,1 Steven C. Moore, MPH, PhD,1 Andriy Derkach, MS, PhD,1 Xing Hua, PhD,1 Alison M. Mondul, MSPH, PhD,2 Joshua N. Sampson, MS, PhD,1 and Demetrius Albanes, MD1 1Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland. 2Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor. Address correspondence to: Demetrius Albanes, MD, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive-6e342, Bethesda, MD 20892. E-mail: [email protected] Received: February 23, 2018; Editorial Decision Date: May 24, 2018 Decision Editor: David Le Couteur, MBBS, FRACP, PhD Abstract Impaired metabolism may play a role in the development and lethality of prostate cancer, yet a comprehensive analysis of the interrelationships appears lacking. We measured 625 metabolites using ultrahigh performance liquid chromatography/mass spectrometry (LC-MS) and gas chromatography/mass spectrometry (GC-MS) of prediagnostic serum from 197 prostate cancer cases in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study (ages at diagnosis, 55–86 years). Cox proportional hazards models estimated associations between circulating metabolites and prostate cancer mortality for 1 SD differences (log-metabolite scale), adjusted for age, year of diagnosis, and disease stage. Associations between metabolite chemical classes and survival were examined through pathway analysis, and Cox models assessed the relationship with a sterol/steroid metabolite principal component analysis factor score. Elevated serum N-oleoyl taurine was significantly associated with prostate cancer-specific mortality (hazard ratios [HR] = 1.72 per 1 SD, p < .00008, Bonferroni-corrected threshold = 0.05/625; HR = 3.6 for highest vs lowest tertile, p < .001). Pathway analyses revealed a statistically significant association between lipids and prostate cancer death (p < .006, Bonferroni-corrected threshold = 0.05/8), and sterol/steroid metabolites showed the strongest chemical sub-class association (p = .0014, Bonferroni- corrected threshold = 0.05/45). In the principal component analysis, a 1-SD increment in the sterol/steroid metabolite score increased the risk of prostate cancer death by 46%. Prediagnostic serum N-oleoyl taurine and sterol/steroid metabolites were associated with prostate cancer survival. Keywords: Metabolomic profile, Prostate cancer mortality, N-oleoyl taurine, Sex sterol/steroid Prostate cancer is the most common non-cutaneous malignancy Agnostic or targeted metabolomic analyses that simultaneously among men in the United States, accounting for nearly 1 in 5 new measure a broad array of low molecular weight biochemical com- diagnoses and ranking third in cancer mortality, with an estimated pounds in blood or other tissue represent a relatively new approach 27,000 deaths in 2017 (1). At the same time, its etiology remains to investigating cancer risk and mortality that has the potential to largely unknown, with the exception of risk being higher in African identify underlying biological mechanisms (4). Studies have dem- Americans and in men with a positive family history or specific low- onstrated that dysregulated metabolism plays an important role in penetrance genetic variants, none of which are modifiable (2,3). prostate carcinogenesis, with malignant cells exhibiting a unique Elucidation of preventable factors related to lethal prostate cancer metabolic phenotype that differs from normal cells (5). As in other and survival outcome are needed. malignancies, proliferating prostate cancer cells require sufficient Published by Oxford University Press on behalf of The Gerontological Society of America 2018. 853 This work is written by (a) US Government employee(s) and is in the public domain in the US. 854 Journals of Gerontology: MEDICAL SCIENCES, 2019, Vol. 74, No. 6 anabolic substrates including amino acids, nucleic acids, peptides, control, and identification of 637 known metabolites, of which 12 and lipids to support neoplastic growth (6,7). This underlying tumor (2%) were excluded because more than 95% of subjects had values biology has been reflected in previous experimental studies and pop- below the limit of detection. The resulting 625 metabolites were cat- ulation-based metabolomic analyses that have identified alterations egorized into one of eight mutually exclusive chemical classes: amino in amino acids, energy and lipid metabolites, bile acids, polyamines, acids and amino acid derivatives (collectively referred to as “amino long chain fatty acids, phospholipids, and choline in prostate cancer acids”), carbohydrates, cofactors and vitamins, energy metabo- cell lines, tissues and case patients (6,8–11). Other experimental lites, lipids, nucleotides, peptides or xenobiotics, which are adapted data support sex steroid regulation of prostate cancer progression according to the Kyoto Encyclopedia of Genes and Genomics (12). At the same time, higher prostate cancer risk appears to be (KEGG) database. These metabolites are described in Supplementary related to metabolic factors including obesity, hyperinsulinemia, Table 1. Based on blinded duplicate quality control samples included elevated markers of inflammation, and the insulin-like growth factor in each batch (8%), the median coefficients of variation across all Downloaded from https://academic.oup.com/biomedgerontology/article/74/6/853/5033968 by guest on 23 September 2021 pathway, and these factors have been associated with increased pros- metabolites were 9% (intra-batch; interquartile range 4%–20%) tate cancer risk or mortality (13–17). For example, a recent meta- and 17% (inter-batch; interquartile range 10%–28%), respectively. analysis of 19 studies with 10,554 prostate cancer cases and 13,618 controls found elevated IGF-I concentration was associated with an Statistical Analysis increased prostate cancer risk. The association between IGF-I and Each metabolite was batch-normalized by dividing by the batch me- aggressive disease was not statistically significant, however, and it dian value. Missing values for each metabolite were assigned the min- did not differ significantly by tumor stage and grade, suggesting imum detectable value, and each metabolite was log-transformed and IGF-I may influence multiple stages of prostate carcinogenesis (16). normalized to have a mean of 0 and a variance of 1. Cox propor- Because of the high incidence and prevalence of this malignancy, tional hazards regression models were used to estimate the hazard and, to our knowledge, a comprehensive analysis of pre-diagnostic ratios (HR) and 95% confidence intervals (CIs), for a 1-SD increase in serum metabolomic profiling of prostate cancer survival has not log-metabolite level, on the risk of prostate cancer mortality, adjust- been reported, we conducted the present investigation. ing for age at diagnosis (years, continuous), tumor stage at diagnosis (cancer stage I–IV), and year of diagnosis (1986–1994, 1995–2000, Methods 2001–2007). Deaths from causes other than prostate cancer were cen- sored on the date of the event. The conservative Bonferroni corrected Study Population threshold (p < .00008 or less, 0.05/625) was used to define statistical Prostate cancer cases from the Alpha-Tocopherol, Beta-Carotene significance. For statistically significant metabolites, as well as top Cancer Prevention (ATBC) Study cohort were included. The Alpha- metabolites in associated pathways, we also present HR for tertiles. Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study was In secondary analyses, we adjusted for time from blood collec- a controlled trial that recruited 29,133 50- to 69-year-old Finnish tion to cancer diagnosis (0–≤5, 6–≤10, 11–≤15, 16–≤20 years), body male smokers from 1985 to 1988 and tested alpha-tocopherol and mass index (BMI, weight (kg)/height (m)2; continuous), number of beta-carotene supplementation for the prevention of lung and other cigarettes per day (continuous), total smoking years (continuous), cancers (18). At baseline, blood samples were collected after an over- total serum cholesterol (mmol/L; continuous), serum high-density night fast, processed to serum and stored at −70°C until assay. Height lipoprotein cholesterol (mmol/L; continuous), history of hyperten- and weight were measured, and risk factor questionnaires and writ- sion (yes/no), and history of diabetes mellitus (yes/no). We also per- ten informed consent were obtained. The research was approved formed sensitivity analyses stratified by median time from blood by the institutional review boards at both the U.S. National Cancer collection to cancer diagnosis, BMI, and cancer stage at diagnosis. Institute and the Finnish National Institute for Health and Welfare. In the pathway analyses, we examined the associations be- Cases were identified through December 31, 2007 by the Finnish tween chemical classes (and sub-classes) of metabolites and pros- Cancer Registry (International Classification of Disease 9, ICD-9, tate cancer-specific
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