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Archive for the ‘Reproductive Andrology, Embryology, Genomic Endocrinology, Preimplantation Genetic Diagnosis and Reproductive Genomics’ Category

Genetics and male endocrinology

Posted in Biological Networks, Gene Regulation and Evolution, Genome Biology, Genomic Testing: Methodology for Diagnosis, Molecular Genetics & Pharmaceutical, Personalized and Precision Medicine & Genomic Research, Population Health Management, Genetics & Pharmaceutical, Reproductive Andrology, Embryology, Genomic Endocrinology, Preimplantation Genetic Diagnosis and Reproductive Genomics, tagged , , , , , , on November 12, 2012| 2 Comments »

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

Genetics and Male Endocrinology

Image Source: Created by Noam Steiner Tomer 8/10/2020

Male sexual differentiation and development proceed under direct control of androgens.  Androgen action is mediated by the intracellular androgen receptor, which belongs to the superfamily of ligand-dependent transcription factors. Mutations in the androgen receptor gene cause phenotypic abnormalities of male sexual development that range from a:

  • female phenotype (complete testicular feminization), to that of
  • under-virilized or infertile men.

Using the tools of molecular biology, it was analyzed androgen receptor gene mutations in 31 unrelated subjects with androgen resistance syndromes. Most of the defects are due to nucleotide changes that cause premature termination codons or single amino acid substitutions within the open reading frame encoding the androgen receptor, and the majority of these substitutions are localized in three regions of the androgen receptor:

Less frequently, partial or complete gene deletions have been identified. Functional studies and immunoblot assays of the androgen receptors in patients with androgen resistance indicate that in most cases the phenotypic abnormalities are the result of impairment of receptor function or decreases in receptor abundance or both.

In the X-linked androgen insensitivity syndrome, defects in the androgen receptor gene have prevented the normal development of both internal and external male structures in 46, XY individuals.

The complete form of androgen insensitivity syndrome is characterized by

  • 46, XY karyotype,
  • external female phenotype,
  • intra-abdominal testes,
  • absence of uterus and ovaries,
  • blindly ending vagina, and
  • gynecomastia.

There is also a group of disorders of androgen action that result from partial impairment of androgen receptor function. Clinical indications can be abnormal sexual development of individuals with a

  • predominant male phenotype with
  • severe hypospadias and micropenis or of individuals with a
  • predominantly female phenotype with cliteromegaly,
  • ambiguous genitalia, and
  • gynecomastia.

Complete or gross deletions of the androgen receptor gene have not been frequently found in persons with the complete androgen insensitivity syndrome, whereas point mutations at several different sites in exons 2-8 encoding the DNA- and androgen-binding domain have been reported in both partial and complete forms of androgen insensitivity, with a relatively high number of mutations in two clusters in exons 5 and 7.

The number of mutations in exon 1 is extremely low, and no mutations have been reported in the hinge region, located between the DNA-binding domain and the ligand-binding domain.

The X-linked condition of spinal and bulbar muscle atrophy (Kennedy’s disease) is characterized by a progressive motor neuron degeneration associated with signs of androgen insensitivity and infertility. The molecular cause of spinal and bulbar muscle atrophy is an expanded length (> 40 residues) of one of the polyglutamine stretches in the N-terminal domain of the androgen receptor.

Source References:

http://www.ncbi.nlm.nih.gov/pubmed/8421085

http://www.ncbi.nlm.nih.gov/pubmed/8732995

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Every sperm is sacred: Sequencing DNA from individual cells vs “humans as a whole.”

Posted in Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, Chemical Genetics, Genome Biology, Genomic Testing: Methodology for Diagnosis, Medical and Population Genetics, Molecular Genetics & Pharmaceutical, Personalized and Precision Medicine & Genomic Research, Pharmaceutical R&D Investment, Population Health Management, Genetics & Pharmaceutical, Proteomics, Reproductive Andrology, Embryology, Genomic Endocrinology, Preimplantation Genetic Diagnosis and Reproductive Genomics, Reproductive Biology & Bio Instrumentation, Statistical Methods for Research Evaluation, Technology Transfer: Biotech and Pharmaceutical, tagged , on November 5, 2012| 5 Comments »

Reporter: Aviva Lev-Ari, PhD, RN

Genomics: The single life

Sequencing DNA from individual cells is changing the way that researchers think of humans as a whole.

31 October 2012

The tendency of sperm to swim alone makes the cells ideal for single-cell genomics. Adam Auton, a statistical geneticist at Albert Einstein College of Medicine in New York is using sperm to study recombination, the process that shuffles genes during the formation of germ cells and therefore influences which genes are inherited. “Recombination is one of the fundamental forces that shapes genetic diversity,” he says. “In recent years we’ve learned that there is considerable variation in the recombination rate between different populations, between the sexes and even between individuals.” But pinning down the rate in people once seemed impossible because it would have required finding individuals with hundreds of children and sequencing their genomes.

The ability to sequence single cells meant that researchers could take another approach. Working with a team at the Chinese sequencing powerhouse BGI, Auton sequenced nearly 200 sperm cells and was able to estimate the recombination rate for the man who had donated them. The work is not yet published, but Auton says that the group found an average of 24.5 recombination events per sperm cell, which is in line with estimates from indirect experiments2. Stephen Quake, a bioengineer at Stanford University in California, has performed similar experiments in 100 sperm cells and identified several places in the genome in which recombination is more likely to occur. The location of these recombination ‘hotspots’ could help population biologists to map the position of genetic variants associated with disease.

Quake also sequenced half a dozen of those 100 sperm in greater depth, and was able to determine the rate at which new mutations arise: about 30 mutations per billion bases per generation3, which is slightly higher than what others have found. “It’s basically the population biology of a sperm sample,” Quake says, and it will allow researchers to study meiosis and recombination in greater detail.

SOURCE:  

VIEW ARTICLE IN NATURE

http://www.nature.com/news/genomics-the-single-life-1.11710#/genome

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Leptin and Puberty

Posted in Population Health Management, Genetics & Pharmaceutical, Reproductive Andrology, Embryology, Genomic Endocrinology, Preimplantation Genetic Diagnosis and Reproductive Genomics, Reproductive Biology & Bio Instrumentation, tagged , , , , , , , on November 5, 2012| 5 Comments »

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

It is well established that food restriction delays pubertal onset, whereas refeeding abolishes this delay. In addition, murine and human genetic models of leptin deficiency fail to enter puberty, and treatment with leptin can establish a pulsatile secretory pattern of gonadotropins that is characteristic of early puberty. The female transgenic skinny mouse, which is an in vivo model of chronic hyperleptinemia in the absence of adipose tissue, enters puberty precociously. Data regarding the effects of leptin administration on pubertal onset are controversial. It has been shown that intracerebroventricular leptin administration prevents the delay in vaginal opening induced by chronic food restriction in the rat. By contrast, it has been found that artificially raised leptin levels are not sufficient to abolish the delay of pubertal onset caused by food deprivation. Thus, the question arises whether leptin might be a ‘permissive factor’ (tonic mediator), whose concentration above a certain threshold is required for pubertal onset, or a ‘trigger’ (phasic mediator) that determines the pubertal spurt through a rise in serum concentration at an appropriate time of development.

The temporal correlation between increases in leptin concentration and the initiation of LH pulsatility over the peripubertal period has been studied in several species. In men it has been shown that leptin levels rise by 50% before the onset of puberty, and decrease to baseline after the initiation of puberty. Other cross-sectional studies showed that age has a significant effect on serum leptin concentrations through prepuberty into early puberty. It has been reported repeatedly that there are no significant changes in leptin levels over the peripubertal period in male rhesus macaques; however, more recent studies performed in castrated male monkeys showed that nocturnal levels of leptin increase just before the nocturnal prepubertal increase in pulsatile LH release.

A possible explanation for such contrasting reports in monkeys could be the sampling of nocturnal rather than diurnal blood. Indeed, in primates, prepubertal changes in nocturnal LH release occur approximately five months before diurnal variations. Another reason might be the use of different models: agonadal monkeys were treated with intermittent exogenous GnRH to sensitize the pituitary to endogenous GnRH, thus magnifying the LH release independently from gonadal influences. In the same study, the leptin rise was accompanied by a sustained increase in nocturnal GH and IGF-I concentrations before the onset of puberty, which is defined as the increase in nocturnal pulsatile LH secretion. It is not clear whether one of the two metabolic signals has a predominant role or whether both act in concert. Indeed, it has been reported that the maximum increase in GH and leptin occurs simultaneously, about 10–30 days before the onset of puberty. However, these conclusions were based on results from a study that used castrated animals, which in the strictest sense do not undergo puberty. Thus, it remains to be clarified whether the same mechanisms that result in the onset of the pubertal rise in LH secretion in castrated animals are also responsible for the reactivation of the HPG axis in intact animals.

The sexual dimorphism in leptin concentrations becomes evident after puberty. In males, leptin levels rise throughout childhood, reach a peak in the early stages of puberty and then decline, whereas they increase steadily during pubertal development in females. Consequently, leptin levels are three to four times higher in females than in males. The reason for this postpubertal sexual dimorphism in leptin levels is not clear. After puberty, serum testosterone and testicular volume are inversely related to leptin levels in males, whereas in females, when adjusted for adiposity indexes, estradiol is directly correlated with leptin levels. These observations indicate that androgens and estradiol might account, at least in part, for the gender differences in circulating leptin levels. This is also supported by in vitro studies which show that androgens and estrogens inhibit and stimulate leptin expression and release from human adipocytes in culture, respectively.

Thus, puberty represents a turning point in the sexual dimorphic relationships between the HPG axis and leptin by determining the steroid milieu that leads to a different regulation of leptin secretion in the sexes.

Source References:

http://www.sciencedirect.com/science/article/pii/S1043276000003520#

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Pregnancy with a Leptin-Receptor Mutation

Posted in Cell Biology, Signaling & Cell Circuits, Medical and Population Genetics, Population Health Management, Genetics & Pharmaceutical, Population Health Management, Nutrition and Phytochemistry, Reproductive Andrology, Embryology, Genomic Endocrinology, Preimplantation Genetic Diagnosis and Reproductive Genomics, tagged , , on October 31, 2012| 5 Comments »

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

Leptin is considered to have an important role in reproductive functions, including menstrual-cycle regulation, pregnancy, and lactation. The absence of leptin action caused by functional mutations in the leptin gene (LEP) or the leptin receptor gene (LEPR) has been linked to infertility in rodents and humans. A pregnancy was reported in a woman despite absent leptin signaling.

In 1998, it was reported the case of a morbidly obese patient with a rare homozygous LEPR mutation, which was shared by several affected siblings. The mutation was found in the patient’s blood and adipose tissue, indicating no evidence of chimerism. She had been followed for morbid obesity since early childhood, with abnormal compulsive-feeding behaviors and reduced levels of growth hormone and thyrotropin. She entered puberty late, with irregular cycles after the age of 17 years. Repeated evaluations of sex-hormone levels were considered to be normal after the age of 18 years. The patient underwent abdominoplasty at the age of 16 years and gastric-bypass surgery at the age of 24 years. Six months after gastric bypass, her weight had decreased from 220 kg (485 lb) to 170 kg (375 lb), with a concurrent decrease in the body-mass index (the weight in kilograms divided by the square of the height in meters) from 81 to 62. She was counseled regarding contraception and was prescribed oral contraceptives. Two years after gastric bypass, just before an unplanned pregnancy, she had no diabetes, hypertension, respiratory disorders, or other recognized complications of obesity.

Ultrasonographic examinations during pregnancy were considered normal except for suspected macrosomia in the third trimester. The patient’s total weight gain during pregnancy was 50 kg (110 lb) from a prepregnancy weight of 180 kg (397 lb). Routine screening for gestational diabetes was normal. Although occasional elevated blood sugar levels were documented during the pregnancy, the glycated hemoglobin level in the third trimester was 5.6%. At 37 weeks 5 days of gestation (on the basis of first-trimester ultrasonography), the patient delivered a son by elective cesarean section, which was performed because of breech presentation and suspected macrosomia under epidural anesthesia after the administration of glucocorticoids for fetal lung maturation. The birth weight was 3720 g (8.2 lb), and the length was 50 cm (19.7 in.); the head circumference was 36.5 cm (14.4 in.), which was above the 90th percentile. The patient’s postpartum course was complicated by a wound infection. The infant’s neonatal course was complicated by hypoglycemia, hypocalcemia, and jaundice requiring phototherapy. The patient briefly breast-fed her child. The child’s growth and development have been normal; his weight at 1 year was 14 kg (31 lb).

This case of a natural pregnancy in a woman with a homozygous LEPR mutation calls into question the belief that leptin function is critical to reproductive function.

 

Source References:

 

http://www.nejm.org/doi/full/10.1056/NEJMc1200116

 

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The contribution of comparative genomic hybridization in reproductive medicine

Posted in Genome Biology, Genomic Testing: Methodology for Diagnosis, Molecular Genetics & Pharmaceutical, Personalized and Precision Medicine & Genomic Research, Reproductive Andrology, Embryology, Genomic Endocrinology, Preimplantation Genetic Diagnosis and Reproductive Genomics, tagged , , , , on October 26, 2012| 4 Comments »

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

Cytogenetic research has had a major impact on the field of reproductive medicine, providing an insight into the frequency of chromosomal abnormalities that occur during gametogenesis, embryonic development and pregnancy. In humans, aneuploidy has been found to be relatively common during fetal life, necessitating prenatal screening of high-risk pregnancies. Aneuploidy rates are higher still during the preimplantation stage of development. An increasing number of IVF laboratories have attempted to improve pregnancy rates by using preimplantation genetic diagnosis (PGD) to ensure that the embryos transferred to the mother are chromosomally normal. This paper reviews some of the techniques that are key to the detection of aneuploidy in reproductive samples including comparative genomic hybridization (CGH). CGH has provided an unparalleled insight into the nature of chromosome imbalance in human embryos and polar bodies. Methods for chromosomal analysis have become increasingly powerful, benefiting enormously from the fusion of traditional cytogenetic techniques and molecular genetics. Fluorescence in situ hybridization and comparative genomic hybridization have been amongst the most significant methodological advances. CGH has overcome many of the technical limitations that beset earlier cytogenetic methods, allowing detailed chromosomal data to be obtained from a variety of tissues that were previously considered problematic. In the field of reproductive medicine, as in other fields, CGH has been employed for the ascertainment of chromosomal duplications, amplifications and deletions that contribute to neoplastic transformation. This has revealed the chromosomal location of tumor suppressor genes and oncogenes that play a role in neoplasia affecting tissues of the reproductive system. The application of CGH to prenatal and pediatric samples has also proven extremely beneficial, allowing the delineation of complex or cryptic chromosomal rearrangements that could not be defined using classical cytogenetic techniques. CGH has also been applied to the analysis of mitotically inactive cells derived from products of conception, shedding light on the spectrum of chromosomal abnormalities causing miscarriage. Finally, the use of CGH to analyze human preimplantation embryos has provided unique scientific data concerning the variety and rate of aneuploidy at this early developmental stage. Most recently, this has led to methods for screening IVF embryos, assisting in the identification of those with the greatest potential for further development. In the future, CGH or related techniques such as M-CGH, will allow IVF clinics to screen embryos for any form of aneuploidy. It is hoped that this will enable the preferential transfer of the embryos most likely to form a viable pregnancy and thus lead to improvements in the outcome of assisted reproductive procedures.

Source References:

http://www.ncbi.nlm.nih.gov/pubmed?term=Cytogenetics%20in%20reproductive%20medicine%3A%20the%20contribution%20of%20comparative%20genomic%20hybridization%20(CGH)

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Pancreatic cancer genomes: Axon guidance pathway genes – aberrations revealed

Posted in Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, CANCER BIOLOGY & Innovations in Cancer Therapy, Cell Biology, Signaling & Cell Circuits, Chemical Biology and its relations to Metabolic Disease, Chemical Genetics, Computational Biology/Systems and Bioinformatics, Genome Biology, Genomic Testing: Methodology for Diagnosis, Liver & Digestive Diseases Research, Medical and Population Genetics, Metabolomics, Molecular Genetics & Pharmaceutical, Personalized and Precision Medicine & Genomic Research, Population Health Management, Nutrition and Phytochemistry, Proteomics, Reproductive Andrology, Embryology, Genomic Endocrinology, Preimplantation Genetic Diagnosis and Reproductive Genomics, Statistical Methods for Research Evaluation, Technology Transfer: Biotech and Pharmaceutical, tagged , , , , , on October 24, 2012| 6 Comments »

Reporter: Aviva Lev-Ari, PhD, RN

Screen Shot 2021-07-19 at 7.33.50 PM

Word Cloud By Danielle Smolyar

Pancreatic cancer genomes reveal aberrations in axon guidance pathway genes

Nature (2012) 

doi:10.1038/nature11547 Received 09 January 2012  Accepted 04 September 2012 

Published online 24 October 2012

Pancreatic cancer is a highly lethal malignancy with few effective therapies. We performed exome sequencing and copy number analysis to define genomic aberrations in a prospectively accrued clinical cohort (n = 142) of early (stage I and II) sporadic pancreatic ductal adenocarcinoma. Detailed analysis of 99 informative tumours identified substantial heterogeneity with 2,016 non-silent mutations and 1,628 copy-number variations. We define 16 significantly mutated genes, reaffirming known mutations (KRAS, TP53, CDKN2A, SMAD4, MLL3, TGFBR2, ARID1A andSF3B1), and uncover novel mutated genes including additional genes involved in chromatin modification (EPC1 and ARID2), DNA damage repair (ATM) and other mechanisms (ZIM2,MAP2K4, NALCN, SLC16A4 and MAGEA6). Integrative analysis with in vitro functional data and animal models provided supportive evidence for potential roles for these genetic aberrations in carcinogenesis. Pathway-based analysis of recurrently mutated genes recapitulated clustering in core signalling pathways in pancreatic ductal adenocarcinoma, and identified new mutated genes in each pathway. We also identified frequent and diverse somatic aberrations in genes described traditionally as embryonic regulators of axon guidance, particularly SLIT/ROBO signalling, which was also evident in murine Sleeping Beauty transposon-mediated somatic mutagenesis models of pancreatic cancer, providing further supportive evidence for the potential involvement of axon guidance genes in pancreatic carcinogenesis.

Figures at a glance

Contributions

The research network comprising the Australian Pancreatic Cancer Genome Initiative, the Baylor College of Medicine Cancer Genome Project and the Ontario Institute for Cancer Research Pancreatic Cancer Genome Study (ABO collaboration) contributed collectively to this study as part of the International Cancer Genome Consortium. Biospecimens were collected at affiliated hospitals and processed at each biospecimen core resource centre. Data generation and analyses were performed by the genome sequencing centres, cancer genome characterization centres and genome data analysis centres. Investigator contributions are as follows: S.M.G., A.V.B., J.V.P., R.L.S., R.A.G., D.A.W., M.-C.G., J.D.M., L.D.S and T.J.H. (project leaders); A.V.B., S.M.G. and R.L.S. (writing team); A.L.J., J.V.P., P.J.W., J.L.F., C.L., M.A., O.H., J.G.R., D.T., C.X., S.Wo., F.N., S.So., G.K. and W.K. (bioinformatics/databases); D.K.M., I.H., S.I., C.N., S.M., A.Chr., T.Br., S.Wa., E.N., B.B.G., D.M.M., Y.Q.W., Y.H., L.R.L., H.D., R. E. D., R.S.M. and M.W. (sequencing); N.W., K.S.K., J.V.P., A.-M.P., K.N., N.C., M.G., P.J.W., M.J.C., M.P., J.W., N.K., F.Z., J.D., K.C., C.J.B., L.B.M., D.P., R.E.D., R.D.B., T.Be. and C.K.Y. (mutation, copy number and gene expression analysis); A.L.J., D.K.C., M.D.J., M.P., C.J.S., E.K.C., C.T., A.M.N., E.S.H., V.T.C., L.A.C., E.N., J.S.S., J.L.H., C.T., N.B. and M.Sc. (sample processing and quality control); A.J.G., J.G.K., R.H.H., C.A.I.-D., A.Cho., A.Mai., J.R.E., P.C. and A.S. (pathology assessment); J.W., M.J.C., M.P., C.K.Y. and mutation analysis team (network/pathway analysis and functional data integration); K.M.M., N.A.J., N.G.C., P.A.P.-M., D.J.A., D.A.L., L.F.A.W., A.G.R., D.A.T., R.J.D., I.R., A.V.P., E.A.M., R.L.S., R.H.H. and A.Maw. (functional screens); E.N., A.L.J., J.S.S., A.J.G., J.G.K., N.D.M., A.B., K.E., N.Q.N., N.Z., W.E.F., F.C.B., S.E.H., G.E.A., L.M., L.T., M.Sam., K.B., A.B., D.P., A.P., N.B., R.D.B., R.E.D., C.Y., S.Se., N.O., D.M., M-S.T., P.A.S., G.M.P., S.G., L.D.S., C.A.I.-D., R.D.S., C.L.W., R.A.M., R.T.L., S.B., V.C., M.Sca., C.B., M.A.T., G.T., A.S. and J.R.E. (sample collection and clinical annotation); D.K.C., M.P., C.J.S., E.S.H., J.A.L., R.J.D., A.V.P. and I.R. (preclinical models).

Competing financial interests

The authors declare no competing financial interests.

International Team Reports on Large-Scale Pancreatic Cancer Analysis

October 24, 2012

NEW YORK (GenomeWeb News) – A whole-exome sequencing and copy number variation study of pancreatic cancer published online today in Nature suggests that the disease sometimes involves alterations to genes and pathways best known for their role in axon guidance during embryonic development.

The work was conducted as part of the International Cancer Genome Consortium effort by researchers with the BCM Cancer Genome Project, the Australian Pancreatic Cancer Genome Initiative, and the Ontario Institute for Cancer Research Pancreatic Cancer Genome Study.

As they reported today, the investigators identified thousands of somatic mutations and copy number alterations in pancreatic ductal adenocarcinoma cancer, the most common form of pancreatic cancer. Some of the mutations affected known cancer genes and/or pathways implicated in pancreatic cancer in the past. Other genetic glitches pointed to processes not previously linked to the disease including mutations to axon guidance genes such as SLIT2, ROBO1, and ROBO2.

“This is a category of genes not previously linked to pancreatic cancer,” Baylor College of Medicine researcher William Fisher, a co-author on the new paper, said in a statement. “We are poised to jump on this gene list and do some exciting things.”

Pancreatic cancer is among the deadliest types of cancer, he and his colleagues explained, with a grim five-year survival rate of less than 5 percent. But despite its clinical importance, direct genomic studies of primary tumors had been stymied in the past due to difficulties obtaining large enough samples for such analyses.

“Genomic characterization of pancreatic ductal adenocarcinoma, which accounts for over 90 [percent] of pancreatic cancer, has so far focused on targeted polymerase chain reaction-based exome sequencing of primary and metastatic lesions propagated as xenografts or cell lines,” the study authors noted.

“A deeper understanding of the underlying molecular pathophysiology of the clinical disease is needed to advance the development of effective therapeutic and early detection strategies,” they added.

For the current study, researchers started with a set of tumor-normal samples from 142 individuals with stage I or stage II sporadic pancreatic ductal adenocarcinoma. Following a series of experiments to assess tumor cellularity and other features that can impact tumor analyses, they selected 99 patients whose samples were assessed in detail.

For whole-exome sequencing experiments, the investigators nabbed coding sequences from matched tumor and normal samples using either Agilent SureSelectII or Nimblegen capture kits before sequencing the exomes on SOLiD 4 or Illumina sequencing platforms. They also used Ion Torrent and Roche 454 platforms to validate apparent somatic mutations in the samples.

For its copy number analyses, meanwhile, the team tested the pancreatic cancer and normal tissue samples using Illumina HumanOmni1 Quad genotyping arrays.

When they sifted through data for the 99 most completely characterized pancreatic tumors, researchers uncovered 1,628 CNVs and roughly 2,000 non-silent, somatic coding mutations. More than 1,500 of the non-silent mutations were subsequently verified through additional sequencing experiments.

On average, each of the tumors contained 26 coding mutations. And despite the variability in mutations present from one tumor to the next, researchers identified 16 genes that were mutated in multiple tumor samples.

Some were well-known cancer players such as KRAS, which was mutated in more than 90 percent of the 142 pancreatic tumors considered initially. Several other genes belonged to cell cycle checkpoint, apoptosis, blood vessel formation, and cell signaling pathways, researchers reported, or to pathways involved in chromatin remodeling or DNA damage repair.

For example, some 8 percent of tumors contained mutations to ATM, a gene participating in a DNA damage repair pathway that includes the ovarian/breast cancer risk gene BRCA1.

Genes falling within axon guidance pathways turned up as well. That pattern was supported by the researchers analyses of data from published pancreatic cancer studies — including two studies based on mutagenesis screens in mouse models of the disease — and by their own gene expression experiments in mice.

The team also tracked down a few more pancreatic ductal adenocarcinoma cases involving mutations to axon guidance genes such as ROBO1, ROBO2, and SLIT2 through targeted testing on 30 more pancreatic cancer patients.

The findings are consistent with those found in some other cancer types, according to the study’s authors, who noted that there is evidence indicating that some axon guidance components feed into signaling pathways related to cancer development, such as the WNT signaling pathway. If so, they explained, it’s possible that mutations to axon guidance genes might influence the effectiveness of therapies targeting such downstream pathways or serve as potential treatment targets themselves.

Still, those involved in the study cautioned that more research is needed not only to explore such possibilities but also to distinguish between driver and passenger mutations in pancreatic cancer.

“The potential therapeutic strategies identified will … require testing in appropriate clinical trials that are specifically designed to target subsets of patients stratified according to well-defined molecular markers,” the study’s authors concluded.

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Genomic Medicine in MD Education: Med Students practice clinical interactions with digital avatars that can mimic patients with various Genetic conditions.

Posted in Bio Instrumentation in Experimental Life Sciences Research, Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, Cardiovascular Pharmacogenomics, Chemical Genetics, Computational Biology/Systems and Bioinformatics, Genome Biology, Genomic Testing: Methodology for Diagnosis, Health Economics and Outcomes Research, Medical and Population Genetics, Metabolomics, Molecular Genetics & Pharmaceutical, Personalized and Precision Medicine & Genomic Research, Population Health Management, Genetics & Pharmaceutical, Reproductive Andrology, Embryology, Genomic Endocrinology, Preimplantation Genetic Diagnosis and Reproductive Genomics, Scientist: Career considerations, tagged , , , , , on October 24, 2012| Leave a Comment »

Reporter: Aviva Lev-Ari, PhD, RN

Educating Physicians on Genomic Medicine

October 2012

By Turna Ray

Medical schools across the US are busy this fall, preparing students for the impending transformation in healthcare that advances in genomic knowledge promise to bring.

After only eight weeks of medical coursework, students at Ohio State University will be thrown into a real-world learning environment where they will use patients’ genomic and behavioral risk factors to encourage healthier lifestyles. Medical and PhD students at Stanford University, meantime, have the opportunity to get their own DNA tested and learn how genes influence disease risk and drug response in the context of their own health. And at the University of Florida, medical and pharmacy students will soon be able to practice clinical interactions with digital avatars that can mimic patients with various genetic conditions.

Medical schools are developing such innovative curricula as it becomes increasingly clear that physicians are ill-equipped to practice genomically guided personalized medicine — a discipline that requires doctors to consider a patient’s genomic data in the context of other medical and family history and craft a unique treatment plan. A survey of 800 physicians from last year revealed that, although the majority of respondents believes personalized medicine will influence how they care for patients in coming years, only 10 percent of primary care doctors and cardiologists and 30 percent of oncologists feel they are up to speed with the latest advances in the field.

The same survey, conducted by healthcare communications firm CAHG, found that only 20 percent of practicing physicians had received any training on how to administer genomically guided medicine. The outlook improves somewhat for more recently minted doctors, with around 50 percent of those who graduated from medical school in the past five years reporting that they have had some form of training in personalized medicine.

The challenge of keeping doctors up to date on the latest medical advances looms particularly large considering that, by 2021, spending on genetic testing is projected to jump to $25 billion from $5 billion currently. However, physicians’ limited genomics know-how isn’t the only barrier to the adoption of personalized medicine into mainstream care. While many healthcare providers are enthusiastic about using genomic tools to improve their patients’ health, there are a number of systemic challenges — slow turnaround times for test results, insurers’ reluctance to pay for new technologies, and the lack of genomic data in electronic medical records — that keep them from effectively using these tests.

“Personalized medicine is an ecosystem or a value chain,” says Larry Lesko, who left the US Food and Drug Administration last year to head Florida’s new Center for Pharmacometrics and Systems Pharmacology. “In this ecosystem … there is a lot more than physician education.”

Even if medical students leave academia with knowledge of genomic medicine, in the short term very few will get to apply those principles at a community practice or a hospital. “Unless what we’re teaching them is what they see in the clinical environment, wherever they go from here [they will face] substantial barriers,” says Daniel Clinchot, associate dean for medical education at Ohio State’s medical school. “[Unless] we can ensure that, across the US, we are holding physicians accountable for using the most up-to-date information and the way that information is applied, that sort of undoes the … medical education they received.”

Simulated reality

Physicians today have plenty of reasons not to practice genomic medicine. Take the anticoagulant warfarin for example. Although there is evidence that with genetic testing doctors can dose the drug more accurately than with standard methods and avoid hospitalizations due to adverse reactions, most doctors don’t use it because turnaround times for test results are too long to be useful for patients with acute conditions. For the majority of genetic tests, however, doctors find limited evidence backing their validity and utility in improving patients’ health. Even for genetic tests that are well validated, physicians are wary of coverage denials from insurance companies because there is little proof that the test is cost-effective compared to standard interventions. Meanwhile, healthcare providers who are eager to implement genetic testing more broadly in their practices find it difficult to do so with the dearth of genetic counselors and within the average eight-minute physician-patient interaction.

When developing genomic medicine courses, universities are keeping these realities in mind. With Lesko’s leadership, Florida is testing out the theory that physicians will be more likely to use genomic data in patient care if the information is readily available in electronic medical records.

Patients treated at Florida’s catheterization lab will receive a multi-gene test that doctors will use to discern whether the patients are likely to be poor responders to the antiplatelet drug Plavix and are at heightened risk for cardiac events. If, at a later time, a physician prescribes Plavix to a patient deemed to be a poor responder by genetic testing, the doctor will receive a “best practices advisory alert” in the patient’s EMR, recommending a different treatment strategy.

For the time being, only the test results related to Plavix response are included in the EMR. With patient consent, data on 249 other gene variations the test gauges will be stored in a secure database for research use.

Through this effort, doctors will learn how to consider genomic data in the context of a patient’s overall medical history, but they won’t have to worry about some of the procedural headaches, such as lengthy turnaround times for results, that deter the adoption of many tests by primary care physicians. “You have to focus on education of physicians at the right time,” Lesko says. “If you do it too early, when the infrastructure in somebody’s practice isn’t set up, I don’t think physicians will care, and they won’t retain the knowledge. But if you have the test results already available in the EMR, like we’re doing, then that’s the right time to do the training.”

Similarly, Florida plans to teach its medical students how to discuss genomic information with patients, with the help of digital simulations. Lesko envisions that medical and pharmacy students will be “able to practice clinical care” by interacting with avatars that can “realistically imitate patients with different genetic [data].”

For a few hundred dollars, consumers increasingly have access to genetic testing for numerous health conditions from companies such as 23andMe and Decode Genetics. A doctor with limited genomic knowledge could be at a loss for what to do with a patient who brings in a report with a slew of genetic test results. Under the Florida program, students would learn how to discuss genetic test results with an avatar that behaves like a patient with such a report.

“The idea is to get medical and pharmacy students involved in an active learning process,” Lesko says. “Retention of information [through such simulation programs] is usually fairly high.”

At Ohio State, meanwhile, the focus is on teaching medical students not just how to treat patients, but how to inspire them to stay healthy. “The students learn to be health coaches, which is extremely important in the transformation of medicine,” says Ohio State’s Clinchot. Genomics, particularly in the context of oncology, as well as the principles of P4 medicine — short for predictive, preventive, personalized, and participatory medicine — will be a big part of the students’ four-year training.

“We really try to focus on healthy behaviors by teaching students that they not only need to care for patients with disease, but also care for patients who are healthy currently, but have risk factors for certain things — whether they are genetic or behavioral — so they can [learn] how to prevent the development of things like type 2 diabetes,” Clinchot says.

In creating this program, Ohio State ran a pilot effort where students helped type 2 diabetes patients make lifestyle changes. The project showed that the students’ efforts resulted in patients adhering better to their medication regimens and feeling more in control of their diabetes. This pilot didn’t gauge the impact of DNA information on patient behavior, but Clinchot says that when genetic risk data is conveyed in the context of a more in-depth patient-physician interaction, the effect will be similarly positive.

Previous studies, such as one from the Multiplex Initiative by the National Human Genome Research Institute and a behavioral project conducted by the Scripps Translational Research Institute, have reported that genetic data has a limited impact on people’s behavior and that a minority of people share their test reports with genetic counselors or doctors. However, these surveys also found those who shared their test results with their doctors were the most motivated to make lifestyle changes.

“It’s not enough that you tell a patient [their genetic test results], sort of go over their risk factors and let them go and that’s it,” Clinchot says. “It’s [with] long-term follow up and the coaching aspect of it … that you’ll see a big difference.”

Real world data

Back in the real world, insurers get a little nervous every time a university starts implementing forward-thinking genomic testing programs, such as UF’s multiplex testing effort. They fear that if more people find out about these academic programs, it will raise consumer expectations that these tests — most of which insurers currently consider investigational and not ready for broad implementation — will soon be available at community practices and hospitals.

At the 2010 ECRI Institute’s annual conference, which brought together insurers and academics involved in personalized medicine, Barry Straube, then chief medical officer of the Centers for Medicare & Medicaid Services, expressed concern over efforts at Brigham and Women’s Hospital in Boston to conduct genetic testing to personalize cancer treatment and include this data alongside patients’ medical information in an electronic database for research.

“The reality, although all this is very important and absolutely essential to clinical research, is that when the rubber hits the road, and patients … start coming into medical offices and requesting access to various genetic tests and treatments … the enormity of the cost to society is frightening,” Straube said at the time.

It is no surprise, then, that outside of academia, insurance hurdles seem to be the biggest headache for community physicians administering genetic testing. “Over the last few years genetic testing has become more available, but some of the insurance companies haven’t really acquiesced [with coverage], which has been a real problem with providing testing to families with genetic disorders,” says Michael Mirro, a cardiologist and the medical director of the research center at Parkview Health, a non-profit health services provider in northeast Indiana.

“Medical students may be getting more genomics education, but they’re going to be really frustrated when they start practicing,” Mirro adds.

As an example, Mirro had to work for years, appealing a string of coverage denials, to convince insurer Anthem Blue Cross Blue Shield to pay for a $500 genetic test to see if a patient’s seven children had inherited the heart condition hypertrophic cardiomyopathy — the most common cause of sudden cardiac death in athletes and individuals 35 years old and younger. Since the patient, 38-year-old Matt Christman, carries a gene mutation for hereditary HCM, there is a 50 percent chance that his children are also carriers of this mutation. Mirro thought that testing Christman’s children for the mutations would be a better option than the alternatives — a $1,000 annual heart ultrasound or even pricier imaging tests — and would allow the family to more closely monitor the at-risk children carrying the HCM-associated gene mutation.

After patient groups started lobbying on behalf of Christman’s children and their story was recounted in the media, WellPoint’s Anthem Blue Cross Blue Shield unit agreed to pay for genetic testing for three of the oldest children. However, this was an exception, and the insurer’s latest coverage policy for genetic testing for HCM still deems the intervention “investigational and not medically necessary.” While the American Heart Association and the American College of Cardiology recommend genetic testing of HCM patients’ close relatives, Anthem has said it will require evidence from larger, more rigorously conducted studies that show genetic testing is useful in determining whether someone is at risk for the disease.

“Only with extreme lobbying and pressure are most genetic tests covered,” Mirro says. “Right now, it’s one battle at a time. … Even if physicians know the value of a genetic test most won’t order it because coverage of genetic tests requires an incredible sequence of bureaucratic events that chews up not only their time, but their staff’s time, which costs money.”

Mirro’s difficulties getting coverage for HCM genetic testing for the Christman children didn’t deter him, though, from providing genetic testing services at Parkview Research Center. If anything, it was a learning experience that inspired him to make changes at the research facility. He recently hired a genetic counselor to educate patients about diseases and discuss what test results might mean for their health and families.

Additionally, the research unit is in the process of setting up genetic testing to gauge whether patients who have recently undergone a stent procedure harbor mutations that make them more likely to be poor responders to Plavix. Mirro and his colleagues will follow patients who received this testing and collect data on whether the intervention helped avoid costs due to adverse events and if treating patients with other anti-platelet drugs improved their health.

Having learned that the only way to broadly affect payor policies on genetic tests is with evidence of their usefulness and cost effectiveness, Mirro says he has gotten “very involved with trying to look at the clinical outcomes of patients who have undergone testing and their families to see if there is value in providing these tests.”

With insurers’ increasing data demands for genetic tests, universities are also taking on this kind of research. On the one hand, by setting up a genetic testing program for Plavix and inputting the results into EMRs, the University of Florida is enabling academic physicians to practice personalized medicine. On the other hand, the project is also testing the hypothesis that analyzing many gene variations at once — and before certain conditions manifest in patients — is a cheaper and more efficient way to implement genomic testing in mainstream care.

As the cost of developing genomic tools decreases, the diagnostics industry is moving toward multiplex tests that analyze tens or hundreds of genes at once. However, unwilling to pay for the analysis of gene markers that have the potential to affect future healthcare decisions — but have no immediate impact on treatment — insurance firms currently pay for very few genetic tests that gauge multiple genes linked to a variety of conditions.

If the data collected as part of the Florida project show that multiplex testing is cost-effective, that may convince some payors to cover it. The program is “really a test of the information and the theory that having genetic testing information preemptively is good, having the data in the EMR is a good place to put it, and having it ready at the bedside is a way to facilitate adoption,” Lesko says.

Learning moments

For emerging technologies competing for adoption with established standards of care, industry is often in the best position to not only educate end users, but also lower many of the hurdles hindering uptake. As one of the first companies to commercialize gene expression profiling for breast cancer recurrence, molecular diagnostics company Genomic Health has found physician education to be a critical component of its success.

In 2004, when Genomic Health began marketing Oncotype DX — a test that assesses whether a patient’s disease will return and if she would benefit from chemotherapy — oncologists were used to tracking disease progression by examining the features of a tumor under a microscope, and genomic medicine wasn’t on medical schools’ radar screens. So it was up to the company to address the barriers keeping doctors from using its test, including convincing doctors of its value, making it easier for doctors to provide testing, and getting insurers to cover the diagnostic, which costs several thousand dollars.

Over the years, the company has focused not just on increasing the number of doctors who use Oncotype DX, but on teaching them how to use the test in the proper clinical scenario. For example, clinical validation studies for Oncotype DX have shown that the test determines recurrence risk and chemotherapy benefit only in patients whose tumors are driven by estrogen — a fact the company prominently highlights in brochures, in patient reports, through its sales teams, and in scientific publications. However, in the early days when Oncotype DX was a new test for oncologists, for every tumor sample submitted for testing, Genomic Health’s lab technicians looked at the estrogen receptor level in the tumor sample, and, if it seemed more typical for an ER-negative tumor, the company called the doctor to double-check the ER status of the tumor and reemphasize that Oncotype DX is only for ER-positive disease.

“We knew that one of our obligations was to inform physicians who were ordering the test that they should only test tumors that are ER positive,” says Genomic Health Chief Medical Officer Steve Shak. “We did catch some ER-negative samples that way and cancelled the tests. It was a tremendous educational moment for us and for the physicians.”

Moreover, Genomic Health has published studies involving more than 4,000 patient samples showing that by using the Oncotype DX risk score, in addition to traditional risk factors, physicians can better assess which women are at high or low risk of breast cancer recurrence. Those women Oncotype DX deems to be at low risk of recurrence can be treated with hormonal treatment, avoiding the adverse reactions and costs of chemotherapy.

The strength of the available evidence on Oncotype DX has had the most influence on physician adoption of the test and on insurance companies’ coverage policies, the company says. Genomic Health recently reported data from a Canadian study showing that after receiving Oncotype DX results, physicians changed their decision to give patients chemotherapy for 30 percent of women with early stage, localized breast cancer. In the US, 98 percent of women with breast cancer that hasn’t spread to the lymph nodes have coverage from private payors for Oncotype DX. Medicare also pays for the test.

Meanwhile, Genomic Health’s team of 120 so-called regional oncologic liaisons help physicians figure out the logistical issues that might keep them from using the test, such as how to order the diagnostic, what types of samples they need to submit, and how long it will take to get the results back. Genomic Health also operates a customer service call center that fields an average of 10,000 calls per month.

“This is the type of investment in physician education it takes to be a successful molecular diagnostics company,” Shak says. Genomic Health, which reported more than $200 million in revenues last year, wouldn’t disclose how much it spends on physician education efforts for Oncotype DX. The company, though did report spending about $84 million on sales and marketing efforts in 2011. To date Genomic Health’s strategy has swayed 10,000 physicians to order the test for more than 300,000 patients.

While, industry marketing might drive physician adoption, too aggressive marketing that doesn’t conform to treatment guidelines may raise red flags among insurers. Myriad Genetics’ BRACAnalysis dominates the BRCA1/2 mutation testing market for hereditary breast and ovarian cancer, but insurers have said that 20 percent or more of those tests are being performed for women who don’t meet testing guidelines.

Further, industry-driven education efforts are usually centered around specific products and target a particular physician specialty. These piecemeal programs don’t address the overwhelming need to educate doctors across disciplines and in an independent forum about genomic medicine. Cardiologist Eric Topol has said that he wants to develop a free online certification course on genomic medicine for all physicians, but the effort has been hindered by limited funding and the fragmented nature of medical practice today.

According to Topol, chief academic officer of Scripps Health, there isn’t one group or venue where such a broadly targeted genomics course can be housed. WebMD reaches only half of the 700,000 doctors in the US, while the American Medical Association has around 200,000 members.

“If we just set up a website and say, ‘Come to us,’ that’s not going to work,” he says. Introducing the course by specialty would take too long and cost even more, Topol adds. Although organizers of the program, called the College of Genomic Medicine, have already laid out a curriculum, the main roadblock remains: “How do we get to the physicians?”

Turna Ray is the editor of GenomeWeb’s Pharmacogenomics Reporter. She covers pharmacogenomics, personalized medicine, and companion diagnostics. E-mail her here or follow her GenomeWeb Twitter account at @PGxReporter.

 

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Biomarker tool development for Early Diagnosis of Pancreatic Cancer: Van Andel Institute and Emory University

Posted in Bio Instrumentation in Experimental Life Sciences Research, Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, CANCER BIOLOGY & Innovations in Cancer Therapy, Cell Biology, Signaling & Cell Circuits, Chemical Biology and its relations to Metabolic Disease, Chemical Genetics, Computational Biology/Systems and Bioinformatics, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Genome Biology, Genomic Testing: Methodology for Diagnosis, Liver & Digestive Diseases Research, Medical and Population Genetics, Molecular Genetics & Pharmaceutical, Personalized and Precision Medicine & Genomic Research, Population Health Management, Genetics & Pharmaceutical, Reproductive Andrology, Embryology, Genomic Endocrinology, Preimplantation Genetic Diagnosis and Reproductive Genomics, Systemic Inflammatory Response Related Disorders, Technology Transfer: Biotech and Pharmaceutical, tagged , , , , , , on October 24, 2012| 7 Comments »

Biomarker tool development for Early Diagnosis of Pancreatic Cancer: Van Andel Institute and Emory University

Article 10.4. Biomarker tool development for Early Diagnosis of Pancreatic Cancer: Van Andel Institute and Emory University

Reporter: Aviva Lev-Ari, PhD, RN

Van Andel, Emory to Develop Early Pancreatic Cancer Dx

October 19, 2012
 

NEW YORK (GenomeWeb News) – Van Andel Institute and Emory University researchers will use a $2.3 million grant from the National Cancer Institute to fund an effort to develop new biomarker tools that can aid in the early diagnosis of pancreatic cancer.

The Van Andel and Emory team plan to use gene expression studies and a shotgun glycomics approach to try to develop useful diagnostic tests for a certain carbohydrate structure that is prevalent in most, but not all, pancreatic cancer tumors.

In a shotgun glycomics approach, all of the glycans from a sample are tagged with a fluorescent tag and separated from each other to create a tagged glycolipid library. This library will be developed through gene expression studies on the tumor tissue.

“One of the most common features of pancreatic cancers is the increased abundance of a carbohydrate structure called the CA 19-9 antigen,” Brian Haab, head of Van Andel’s Laboratory of Cancer Immunodiagnostics, said in a statement.

Because CA 19-9 is attached to many different proteins that the tumor secretes into the blood it is used to confirm diagnosis of and to manage disease progression of pancreatic cancer. Tests for this structure have not yet been useful for early detection or diagnosis, however, because around 20 to 30 percent off incipient tumors produce low levels of CA 19-9.

“The low levels are usually due to inherited genetic mutations in the genes responsible for the synthesis of CA 19-9,” Haab explained. “However, patients who produce low CA 19-9 produce alternate carbohydrate structures that are abnormally elevated in cancer.”

This study aims to characterize and identify these glycans to improve the ability to detect cancer in patients with low CA 19-9 levels.

The research will integrate the use affinity reagents, a type of proteins called lectins, as well as shotgun glycomics, to detect these glycan structures and develop a diagnostic test for pancreatic cancer.

Because pancreatic cancer tends to spread before it is diagnosed and because of its resistance to chemotherapy, it has one of the lowest survival rates of any major cancer. It will affect more than 43,000 Americans in 2012 and will kill more than 37,000, according to NCI.

“We anticipate these new approaches advancing pancreatic cancer diagnostics as well as benefiting other glycobiology research in cancer,” Haab said.

Researchers from the Fred Hutchinson Cancer Research Center, Palo Alto Research Center, the University of Georgia, and the University of Pittsburgh Medical Center also are participating in the project.

 

 

 

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Dopamine Gene Variant Predictive of Placebo Response in IBS Patients

Posted in Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, Cell Biology, Signaling & Cell Circuits, Chemical Biology and its relations to Metabolic Disease, Chemical Genetics, Computational Biology/Systems and Bioinformatics, Disease Biology, Small Molecules in Development of Therapeutic Drugs, FDA Regulatory Affairs, Genome Biology, Genomic Testing: Methodology for Diagnosis, Human Immune System in Health and in Disease, Liver & Digestive Diseases Research, Medical and Population Genetics, Metabolomics, Molecular Genetics & Pharmaceutical, Nutrigenomics, Personalized and Precision Medicine & Genomic Research, Pharmaceutical R&D Investment, Proteomics, Regulated Clinical Trials: Design, Methods, Components and IRB related issues, Reproductive Andrology, Embryology, Genomic Endocrinology, Preimplantation Genetic Diagnosis and Reproductive Genomics, Technology Transfer: Biotech and Pharmaceutical, tagged , , , , , , on October 24, 2012| Leave a Comment »

Reporter: Aviva Lev-Ari, PhD, RN

 

Study Finds Dopamine Gene Variant Predictive of Placebo Response in IBS Patients

October 24, 2012
 

Researchers led by a group at Beth Israel Deaconess Medical Center have identified a genetic marker associated with the placebo effect in patients with irritable bowel syndrome.

According to the group, the finding is the first to show “genetic modulation of true placebo effects,” and supports the possibility of using genomic information to better design placebo-controlled clinical trials.

The researchers described their results in PLOS One this week. The project used genotyping to measure whether a polymorphism in the dopamine pathway‘s COMT gene was associated with differences in placebo response among 104 IBS patients enrolled in a three-arm trial of different placebo treatments.

After studying the distribution of the val158met polymorphism among the trial’s three arms — no treatment (a waitlist), treatment with placebo alone, and placebo treatment with an “augmented” physician-patient interaction involving more support — the group found that the strongest placebo response occurred in met/met homozygotes who received the augmented placebo treatment.

The researchers identified a weaker link between met/met and response in the placebo-only arm. And patients in the waitlist control arm showed no difference in response based on their genotype.

The study’s first author, Kathryn Hall, told PGx Reporter this week that having a genetic predictor of placebo response could allow researchers to stratify future placebo-controlled drug trials by potential responders and non-responders.

IBS is known to have a high placebo response rate. Hall said it’s likely that the use of genetic predictors for placebo response will be most relevant to trials of drugs for conditions that are similarly associated with high placebo response levels, such as depression, headache, allergies, and pain.

“In conditions where there tends to be a high placebo response, oftentimes a drug fails because it can’t prove efficacy above the placebo response. In those cases, the pharmaceutical companies are basically losing quite a bit of money and time and resources,” Hall said.

“So the question is – is this a possibility? Obviously, it hasn’t been done before and probably will need a lot more validation before anyone actually wants to do it,” she said. “But if it does hold true at least for some conditions and treatments, it would allow you to focus in on just the people who are [not going to respond to the placebo] – so it would build your power [and] reduce your cost, since you don’t have this set of people that are inflating the placebo response.”

Hall cited diseases like Parkinsons and schizophrenia, which involve dopamine metabolism, as examples where new treatments might see their efficacy estimation confounded by the placebo effect.

At a minimum, Hall suggested that drug developers might improve the success rates of their products by balancing the number of patients who are predisposed to respond and not respond to the placebo effect in both the treatment and placebo arms of a trial.

In the study, Hall and her colleagues evaluated a subset of patients from an earlier randomized controlled IBS trial.

In the previous trial, the group measured differences in response, based on patient-reported symptoms, after either placebo treatment alone, “augmented” placebo treatment in which patients were given extra physician interaction and support, or no treatment, and placement on a waiting list.

In the genetic follow-up, the researchers genotyped 104 patient samples to look for associations between val158met genotype and placebo-response, based on reported symptoms and quality of life.

The group coded each patient according to the presence of the COMT met allele and found that patients with the met/met genotype had the greatest level of improvement — based on their scores in a measure called the IBS-Symptom Severity Scale — while those with the val/val type had the least. Val/met patients fell in the middle.

While patients homozygous for the COMT val158met allele were the most responsive to placebo overall, the strongest signal was in the augmented treatment arm, with a smaller effect in the placebo-alone arm, and virtually no effect, or even a reverse effect, in the waitlist control arm.

Overall, the group concluded that the results “strongly suggest that COMT val158met, specifically the met/met genotype, is a potential marker for placebo response in IBS.”

The fact that the genotype is associated with a positive outcome only in groups given a placebo, and not in the control group, indicates that it is a true predictor of placebo effect, not just improvement in general, the group wrote.

While previous studies have looked for a genetic link to placebo response, they have not included this control arm, according to the Beth Israel team. Additional studies that hypothesize a COMT involvement and include a no-treatment arm “will be critical to confirm our findings,” the group added.

According to Hall, the field is likely still far away from using genomic information to influence the design of placebo-controlled trials. However, her group’s results suggest a path forward, she said.

The results may also have implications for more personalized treatment strategies, she said.

“On one hand, you could hypothesize that there are situations where people are placebo responders and taking a drug with a lot of side effects … Obviously giving people placebo pills is a long way off, but [perhaps you could] minimize someone’s drug intake if they are having more of a placebo response so they don’t have to have all the side effects,” she said.

At the same time, she said, the trial highlighted the influence of the “warm, caring doctor relationship.”

“Having a mechanistic understanding of what’s going on there, I think, will reinforce the need and the importance of this part of medicine,” she said, at least for some. The fact that val/val subjects, for example, showed the same lack of response in both the placebo-alone and augmented arms of the study may shed some light on why, “despite their best efforts, many a warm and caring physician has had patients that seemed to derive minimum benefit from their empathic attentions,” the study authors wrote.

      Molika Ashford is a GenomeWeb contributing editor and covers personalized medicine and molecular diagnostics. E-mail her here.

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Metabolite Identification Combining Genetic and Metabolic Information: Genetic association links unknown metabolites to functionally related genes

Posted in Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, Cardiovascular Pharmacogenomics, Cell Biology, Signaling & Cell Circuits, Chemical Biology and its relations to Metabolic Disease, Chemical Genetics, Computational Biology/Systems and Bioinformatics, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Genome Biology, Genomic Testing: Methodology for Diagnosis, Liver & Digestive Diseases Research, Medical and Population Genetics, Metabolomics, Molecular Genetics & Pharmaceutical, Origins of Cardiovascular Disease, Personalized and Precision Medicine & Genomic Research, Pharmaceutical R&D Investment, Population Health Management, Genetics & Pharmaceutical, Proteomics, Reproductive Andrology, Embryology, Genomic Endocrinology, Preimplantation Genetic Diagnosis and Reproductive Genomics, Statistical Methods for Research Evaluation, Technology Transfer: Biotech and Pharmaceutical, tagged , , on October 22, 2012| 4 Comments »

Reporter: Aviva Lev-Ari, PhD, RN 

Mining the Unknown: A Systems Approach to Metabolite Identification Combining Genetic and Metabolic Information

Jan Krumsiek1, Karsten Suhre1,2, Anne M. Evans3, Matthew W. Mitchell3, Robert P. Mohney3, Michael V. Milburn3, Brigitte Wägele1,4, Werner Römisch-Margl1, Thomas Illig5,6, Jerzy Adamski7,8, Christian Gieger9, Fabian J. Theis1,10, Gabi Kastenmüller1*

 

1 Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany, 2 Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Education City, Qatar Foundation, Doha, Qatar, 3 Metabolon, Research Triangle Park, North Carolina, United States of America, 4 Department of Genome-Oriented Bioinformatics, Life and Food Science Center Weihenstephan, Technische Universität München, Freising, Germany, 5 Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany, 6 Biobank of the Hanover Medical School, Hanover Medical School, Hanover, Germany, 7 Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, Neuherberg, Germany, 8 Lehrstuhl für Experimentelle Genetik, Technische Universität München, Freising-Weihenstephan, Germany, 9 Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany, 10 Department of Mathematics, Technische Universität München, Garching, Germany

Abstract 

Recent genome-wide association studies (GWAS) with metabolomics data linked genetic variation in the human genome to differences in individual metabolite levels. A strong relevance of this metabolic individuality for biomedical and pharmaceutical research has been reported. However, a considerable amount of the molecules currently quantified by modern metabolomics techniques are chemically unidentified. The identification of these “unknown metabolites” is still a demanding and intricate task, limiting their usability as functional markers of metabolic processes. As a consequence, previous GWAS largely ignored unknown metabolites as metabolic traits for the analysis. Here we present a systems-level approach that combines genome-wide association analysis and Gaussian graphical modeling with metabolomics to predict the identity of the unknown metabolites. We apply our method to original data of 517 metabolic traits, of which 225 are unknowns, and genotyping information on 655,658 genetic variants, measured in 1,768 human blood samples. We report previously undescribed genotype–metabotype associations for six distinct gene loci (SLC22A2, COMT, CYP3A5, CYP2C18, GBA3, UGT3A1) and one locus not related to any known gene (rs12413935). Overlaying the inferred genetic associations, metabolic networks, and knowledge-based pathway information, we derive testable hypotheses on the biochemical identities of 106 unknown metabolites. As a proof of principle, we experimentally confirm nine concrete predictions. We demonstrate the benefit of our method for the functional interpretation of previous metabolomics biomarker studies on liver detoxification, hypertension, and insulin resistance. Our approach is generic in nature and can be directly transferred to metabolomics data from different experimental platforms.

Introduction 

Recently, genome-wide association studies (GWAS) on metabolic quantitative traits have proven valuable tools to uncover the genetically determined metabolic individuality in the general population [1]–[5]. Interestingly, a great portion of the genetic loci that were found to significantly associate with levels of specific metabolites are within or in close proximity to metabolic enzymes or transporters with known disease or pharmaceutical relevance. Moreover, compared to GWAS with clinical endpoints the effect sizes of the genotypes are exceptionally high.

The number and type of the metabolic features that went into these GWAS was mainly defined by the metabolomics techniques used: Gieger et al. [1] and Illig et al. [2] used a targeted mass spectrometry (MS)-based approach giving access to the concentrations of 363 and 163 metabolites, respectively. Suhre et al. [3] and Nicholson et al. [4] applied untargeted nuclear magnetic resonance (NMR) based metabolomics techniques, yielding 59 metabolites that had been identified in the spectra prior to the GWAS and 579 manually selected peaks from the spectra, respectively. In Suhre et al. [5], 276 metabolites from an untargeted MS-based approach were analyzed.

While these previous GWAS focused on metabolic features with known identity, untargeted metabolomics approaches additionally provide quantifications of so-called “unknown metabolites”. An unknown metabolite is a small molecule that can reproducibly be detected and quantified in a metabolomics experiment, but whose chemical identity has not been elucidated yet. In an experiment using liquid chromatography (LC) coupled to MS, such an unknown would be defined by a specific retention time, one or multiple masses (e.g. from adducts), and a characteristic fragmentation pattern of the primary ion(s). An unknown observed by NMR spectroscopy would correspond to a pattern in the chemical shifts. Unknowns may constitute previously undocumented small molecules, such as rare xenobiotics or secondary products of metabolism, or they may represent molecules from established pathways which could not be assigned using current libraries of MS fragmentation patterns [6], [7] or NMR reference spectra [8].

The impact of unknown metabolites for biomedical research has been shown in recent metabolomics-based discovery studies of novel biomarkers for diseases and various disease-causing conditions. This includes studies investigating altered metabolite levels in blood for insulin resistance [9], type 2 diabetes [10], and heart disorders [11]. A considerable number of high-ranking hits reported in these biomarker studies represent unknown metabolites. As long as their chemical identities are not clarified the usability of unknown metabolites as functional biomarkers for further investigations and clinical applications is rather limited.

In mass-spectrometry-based metabolomics approaches, the assignment of chemical identity usually involves the interpretation and comparison of experiment-specific parameters, such as accurate masses, isotope distributions, fragmentation patterns, and chromatography retention times [12]–[14]. Various computer-based methods have been developed to automate this process. For example, Rasche and colleagues [15] elucidated structural information of unknown metabolites in a mass-spectrometry setup using a graph-theoretical approach. Their approach attempts to reconstruct the underlying fragmentation tree based on mass-spectra at varying collision energies. Other authors excluded false candidates for a given unknown by comparing observed and predicted chromatography retention times [16], [17], or by the automatic determination of sum formulas from isotope distributions [18]. Furthermore, Gipson et al. [19] and Weber et al. [20] integrated public metabolic pathway information with correlating peak pairs in order to facilitate metabolite identification. However, these methods might not be applicable for high-throughput metabolomics datasets that have been produced in a fee-for-service manner, since the mass spectra as such might not be readily available.

Approaching the problem from a conceptually different perspective, we here present a novel functional metabolomics method to predict the identities of unknown metabolites using a systems biological framework. By combining high-throughput genotyping data, metabolomics data, and literature-derived metabolic pathway information, we generate testable hypotheses on the metabolite identities based solely on the obtained metabolite quantifications (Figure 1). No further experiment-specific data such as retention times, isotope patterns and fragmentation patterns are required for this analysis.

 

Figure 1. Data integration workflow for the systematic classification of unknown metabolites.

We combine high-throughput metabolomics and genotyping data in Gaussian graphical models (GGMs) [21] and in genome-wide association studies (GWAS) [5] in order to produce testable predictions of the unknown metabolites’ identities. These hypotheses are then subject to experimental verification by mass-spectrometry. Six such cases have been fully worked through and are presented in Table 3. doi:10.1371/journal.pgen.1003005.g001

 http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003005?imageURI=info:doi/10.1371/journal.pgen.1003005.g001#pgen-1003005-g001

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Discussion 

We developed and validated a novel integrative approach for the biochemical characterization of “unknown metabolites” from high-throughput metabolomics and genotyping datasets. Our method allows for the functional annotation of previously unidentified metabolites and, as a consequence, enhances the interpretability of metabolomics data in genome-wide association studies and biomarker discovery. For the first time, we systematically evaluated genetic associations of unknown metabolites, thereby discovering seven new loci of metabolic individuality. By classifying a series of unknown metabolites, we gained new insights into the functional interplay between genetic variation and the metabolome both for previously reported and new loci. Furthermore, several of the unknown compounds that we identified as well as their newly associated loci were independently reported in disease-related studies. In the following, we discuss three genetic loci and their associated phenotypes.

COMT and hepatic detoxification

The first example is a recent biomarker study, where Milburn et al. [34] reported an association of X-11593 with hepatic detoxification. In our GWAS, we find a strong association of X-11593 with the COMT locus, which encodes the catechol-O-methyltransferase enzyme. COMT is responsible for the inactivation of catecholamines such as L-dopa and various neuroactive drugs by O-methylation [35]. Following our identification approach, we experimentally confirmed the identity of X-11593 as O-methylascorbate. Notably, O-methylascorbate is a known product of ascorbate (vitamin C) O-methylation by COMT [36], [37]. Thus, our observations establish a link between O-methylascorbate blood levels, common genetic variation in the COMT locus and COMT-mediated liver detoxification processes.

ACE and hypertension

The second example relates to the ACE gene locus, which is a known risk locus for cardiovascular disease, hypertension and kidney failure. The protein encoded by the ACE locus, angiotensin-converting enzyme, is an exopeptidase which cleaves dipeptides from vasoactive oligopeptides, and plays a central role in the blood pressure-controlling renin-angiotensin system [38]. Moreover, the ACE protein is a target for various pharmaceuticals (ACE inhibitors), especially in the treatment of hypertension [39]. In our study, we identified three unknowns as dipeptides (X-14205, X-14208 and X-14478), two of which also associated with the ACE locus. These dipeptides could thus represent novel, interesting biomarkers for the activity of ACE. Moreover, Steffens et al. [11] reported a connection between heart failure and X-11805, which is in close proximity to angiontensin-related peptides in the GGM. This connection might be revisited after a successful identification of X-11805 in a future study.

UGT1A/ACADM and insulin resistance

The third example is an explorative study to detect biomarkers for insulin sensitivity. Gall et al. [9] reported several known metabolites (most prominently α-hydroxybutyrate) as biomarkers for insulin resistance. They also reported a series of unknown metabolites among their top hits. In the present study, we investigated three of these unknowns: X-11793 associates with UGT1A (UDP glucuronosyltransferase 1) and represents a bilirubin-related substance. Moreover, we experimentally validated X-11421 and X-13431, which display a strong association with ACADM (acyl-Coenzyme A dehydrogenase, C-4 to C-12 straight chain), as acylcarnitines containing 10 and 9 carbon atoms, respectively. The identification of these latter two unknown metabolites as medium-chain length acylcarnitines is coherent with reports by Adams et al. [40]. The authors found elevated blood plasma acylcarnitine levels in women with type 2 diabetes. Functionally, they attributed this finding to incomplete β-oxidation. Thus, our identification of X-11421 and X-13431 now suggests incomplete β-oxidation as an explanation for the associations found by Gall et al. and implies that acylcarnitines containing 10 and 9 carbon atoms are potential biomarkers for insulin resistance.

Conclusion

In summary, we integrated high-throughput metabolomics and genotyping data from a large population cohort for elucidating the biochemical identities of unknown metabolites. To this end, we applied metabolomics genome-wide association studies and Gaussian graphical modeling in order to link these unknown metabolites with known metabolic classes and biological processes. For six specific scenarios, we went from systematic hypothesis generation over detailed investigation and identity prediction to direct experimental confirmation. Similar validations may now be undertaken for the remaining predictions that we report in Table S1. Finally, we demonstrated the benefit of our method by discussing several of these newly identified metabolites in the context of existing biomarker discovery studies on liver detoxification, hypertension and insulin resistance.

It is to be noted that our method does not specifically require genotyping data. Even metabolomics measurements alone, analyzed through the GGMs, may provide sufficient information for the classification and even precise identity prediction. The unknowns with GGM evidence but without GWAS hits in Figure 4 as well as the HETE scenario represent examples for this approach.

One limitation of our approach is the requirement for associations with functionally described loci or known metabolites. Certain metabolite groups might thus systematically not be identifiable. For instance, if the identity of a whole class of biochemically related molecules is unknown (which might be due to experimental reasons), then the GGM associations between those compounds will not aid in identity elucidation. The 118 unknown compounds for which we could not derive any classification might represent such cases. Thus, our functionally oriented method should be regarded as a complementary extension to the existing identity determination methods.

Accordingly, our approach can be extended in several directions. It can be combined with method-specific, automated techniques that further exclude sets of metabolites. Previously mentioned methods relying on mass-spectra [15] or chromatographic properties [17] are suitable candidates here. Moreover, the method can be directly transferred to other types of metabolomics datasets not specifically originating from MS experiments, such as NMR-based metabolomics.

Beyond the application to metabolite identification, our study demonstrates the general potential of functional metabolomics in the context of genome-wide association studies. The comprehensive metabolic picture provided by GGMs in combination with GWAS allows for the detailed analysis of metabolic functions, chemical classes, enzyme-metabolite relationships and metabolic pathways.

Author Contributions 

Conceived and designed the experiments: JK KS FJT GK. Performed the experiments: AME MWM RPM MVM. Analyzed the data: JK GK. Contributed reagents/materials/analysis tools: BW WR-M TI JA CG. Wrote the paper: JK KS FJT GK.

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