Posts Tagged ‘Methylation’

Epigenetic mechanisms

Epigenetic mechanisms

Image Source: |Author=National Institute of Health |Date=2005

The Underappreciated EpiGenome

Author:  Demet Sag, PhD

Early 1990’s Kavai group developed a method called Restriction Landmark Genomic Scanning using Methylation-sensitive endonucleases (RLGS-M) to identify differential methylation during development based on CpG islands. In their study they showed that the appearance and disappearance of the spots were specific to tissue and affecting gene regulation.

English: Revised definition of gene and flow o...

Revised definition of gene and flow of genetic information (adapted from Mattick JS (2003). Challenging the dogma: the hidden layer of non-protein-coding RNAs in complex organisms. BioEssays 25:930. doi:10.1002/bies.10332).

Epigenetics is getting a big attention recently to understand genomics and provide better results. However, this field is studied for many years under functional genomics and developmental biology for cellular and molecular biology. Stem cells have a free drive that we have not figured out yet. So genomics must be studied essentially with people training in developmental biology and comparative molecular genetics knowledge to make heads and tail for translational medicine.

There are three main routes of epigenetic modifications one

In 1993, Kavai group showed brain development assays of mice showed that only 0.7% genome has tissue and cellular specificity, and 1.7% of genome was able to turn on and off. This conclusion is relevant to genome sequencing data. Also, previous studies in genome and RNA biology presented that RNA directed DNA modifications lead into splicing and transcriptional silencing for gene regulation in Arapsidosis, mice, and Drosophila. (Borge, F. and. Martiensse, R.A. 2013; Di Croce L, Raker VA, Corsaro M, et al. 2002; Piferrer, F, 2013; Jun Kawai1 et al. 1993)

Comparative developmental biology studies and genetics in organisms give away clues that can be applied or open the door for a new discovery in human disease models.  Plants do utilize methylation and transposomes, which are viral particles that can affect the genome structure and present in all organisms including human, for their gene regulation and development extensively (The EMBO Journal, (22 March 2013) | doi:10.1038/emboj.2013.49). Thus, Arapsidosis is a good model.

The environment and gene expression define inheritable materials at transcriptional levels. He group (Cui-Jun Zhang et al, 2013, doi:10.1038/emboj.2013.49) suggested splicing machinery affecting RdDM and transcriptional silencing to control gene regulation during development since an RNA-directed DNA methylation (RdDM) pathway directs de novo methylation. Furthermore, these differences are highly regulated at RNA level with silencing, splicing, transposon activation/inactivation and modulation at epigenetic level.

In fruit fly with more than five hundreds years of accumulated data, the sex determination pathways, soma or germline, share common genes but they act differently in each pathway. Sxl (sex-lethal), which is key gene of somatic sex determination and is an RNA binding protein, regulates the genes through splicing in one its mechanisms (DOI: 10.1002/dvdy.23924). Yet, in germline ovo, which is a DNA binding protein, regulates the expression and development with different sets of rules yet these two paths always communicate to make the final outcome. The effects of RNA on epigenetics and gene regulation during development will be another topic to discuss. However, the three major epigenetic factors do not run in order necessarily, but always have three targeted outcome: initiate, differentiate and maintain. Thus, from environment to phenotype there are places/parts scientists can modulate or reprogram but there parts must be kept intact.

Yang et al, 2012 presented a study on PHD Finger Protein 7 (PHF7), which is an important factor for male germline sexual identity in Drosophila, and called this gene as a “epigenetic reader’ since the expression of this gene in XX soma started a female germline development. This type of epigenetic readers may also alter the outcome to balance cell metabolism towards desired phenotype in stem cells.

Adenine methylation

Adenine methylation (Photo credit: Allen Gathman)

The environment creates the epigenerators including temperature, differentiation signals and metabolites that trigger the cell membrane proteins for development of signal transduction within the cell to activate gene(s) and to create cellular response.  These changes can be modulated but they are not necessary for modulation. The second step involves epigenetic initiators that require precise coordination to recognize specific sequences on a chromatin in response to epigenerator signals. These molecules are

After they are involved they are on for life and controlled by autoregulatory mechanisms, like Sxl (sex lethal) RNA binding protein in somatic sex determination and ovo DNA binding protein in germline sex determination of fruit fly. Both have autoregulation mechanisms, cross talks, differential signals and cross reacting genes since after the final update made the soma has to maintain the decision to stay healthy and develop correctly.  Then, this brings the third level mechanism called epigenetic maintainers that are DNA methylating enzymes, histone modifying enzymes and histone variants.  The good news is they can be reversed. As a result the phonotype establishes either a

  • short term phenotype, transient for transcription,
  • DNA replication and repair or
  • long term phenotype outcomes that are chromatin conformation and heritable markers.

Early in development things are short term and stop after the development seized but be able to maintain the short term phonotype during wound healing, coagulation, trauma, disease and immune responses. Some cells will loose their ability to differentiate to very low levels. Yet, in life everything is possible even with less than 1% chance because nothing is accidental.

X-chromosome has fascinating characteristics simply because they present unusual mechanisms among female and male differentiation in fruit flies and mammals but their distinct characteristics in evolution marry with surprising parallel mechanisms in regulation. These features are the importance of noncoding RNAs, and epigenetic spreading of chromatin-modifying activities, and at the end of the actions most part of the Y chromosome is lost and one of the X-chromosome is downregulated in the big picture.

Figure 2: DNA methylation analysis methods not...

DNA methylation analysis methods not based on methylation-specific PCR. Following bisulfite conversion, the genomic DNA is amplified with PCR that does not discriminate between methylated and non-methylated sequences. The numerous methods available are then used to make the discrimination based on the changes within the amplicon as a result of bisulfite conversion. (Photo credit: Wikipedia)

Revisiting RNA directed DNA methylation study once again shows that unread sequence has the word on gene expression; it can still create the diversity that may help rebirth of stem cells with a correct program and develop tools for unmet human diseases. This will be the next topic to discuss.

Personal Impression

While I was listening Dr. Ecker, I remembered these studies. The question becomes what we know then what we know now. “The Underappreciated Epigenome: Methylation of Brain” by Joseph Ecker, Ph.D. of Salk Institute gave a talk on differential expression in adult vs. fetal brain development at Future of Genomics VI Medicine on March 7, 2013.

I like to give snapshot of his talk, and relating to the third wheel of the epigenetics: non coding RNAs for epigenetics, stem cell biology and development. He also reconnecting the dots and demonstrated that there is a linear relation between gene regulation region and methylation type.  As a result the plasticity of development takes place with the extensive mutation reconfigurations during early post natal stages up to two years at synaptogenesis. 

Ecker’s Study

The study focused onto inheritability of methylation in different organisms and comparative expression pattern. The data from chip sequencing for

  • histone modifications,
  • whole genome bisulfide sequencing for DNA modifications and
  • methylome profiling

projected a differential expression pattern between

  • adult and
  • fetal brain

for 5 hydroxymethyl cytosine hmC and 5 methyl cytosine mC.

Completion of base resolution of human methylation and aberrant epigenomic reprogramming in induced stem cells showed that the density of genic mCH is positively correlated with gene expression.

  • There was an increased mCH and an elevated gene expression pattern, unlike mCG that the gene is silenced at stem cell differentiation.
  • mCH expression was not only tissue specific but also cell specific based on comparative expression study.
  • There was no mCH expression in fetal frontal cortex unlike adult frontal cortex with accumulation of mCH during synaptogenesis. Also
  • deserts of methylation can be counted as heterochromatic regions and protective transfactors between mCG and mCH methylation.
  • In DNMT pattern showed neurons enriched with mCH but glia was depleted.  Furthermore, these
  • sites are not randomly but occurring with correlation.

Ecker group also published (Lister et al, 2009) the first genome-wide study in a mammalian genome, from both

  • human embryonic stem cells and
  • fetal fibroblasts, along with
  • comparative analysis of messenger RNA and
  • small RNA components of the transcriptome, several
  • histone modifications, and
  • sites of DNA–protein interaction for several key regulatory factors.

Like the related review paper by Spivakov and Fisher pointed out the search for molecular signatures of ‘stemness’ and pluripotency is becoming important for cell therapy. Thus, there is a huge effort on transcription machinery of key genes during early development and understanding of stem cells, but working on the epigenetic profiles and their interaction with transcription machinery is equally important. This poised but activable factors under the stem cell genome may open new doors for diagnostics and therapies.  As a result, “restricted” human diversity will open doors for a personalized medicine and delivery mechanisms.

Until human genome was sequenced the expected number of genes was high but only 1% of genome is read producing about 25000 genes. That brings up three modules to be concerned:

1. Use the RNA wisely as the ancestor of transferable genetic material that viruses used, even human has embedded over 90% natural viral in their genome;

2. Apply epigenetics with all three types from a scratch;

3. Inheritance.

RNA is regulating the methylation on genome through transposons and silencing the genes at transcriptional level to create intergenerational or transgenerational reprograming. This makes sense since after the decision is made there are two intentions passing onto next generation and maintaining the decision consistently.  If

  • in soma by mitosis to daughter cells, or
  • in germline by meiosis

the characters are transferred to the next generation. However, we also need to mind after the fact because no one is choosing what they get let alone not being able to choose their parents. Choosing the healthy tolerance levels in genome for future medicine is the key.


Borge, F. and Martiense, R.A. “Establishing epigenetic variation during genome reprogramming” RNA Biology, Volume 10, Issue 4, April 2013,  doi: org/10.4161/rna.24085

Dalakouras,A. and Wassenegger, M. “Revisiting RNA-directed DNA methylation” RNA Biology, Volume 10, Issue 3 March 2013 Pages 453 – 455

Piferrer, F. “Epigenetics of sex determination and gonadogenesis” Developmental Dynamics 8 FEB 2013 DOI: 10.1002/dvdy.23924

Yang SY, Baxter EM, Van Doren M. “Phf7 controls male sex determination in the Drosophila germline” Dev Cell. 2012 May 15;22(5):1041-51. doi: 10.1016/j.devcel.2012.04.013.

Yongkyu Park,  Mitzi I. KurodaEpigenetic Aspects of X-Chromosome Dosage Compensation” Science 10 August 2001: Vol. 293 no. 5532 pp. 1083-1085  DOI: 10.1126/science.1063073

Okano M, Xie S, Li E (July 1998). “Cloning and characterization of a family of novel mammalian DNA (cytosine-5) methyltransferases” Nat Genet 19 (3): 219–20. doi:10.1038/890    

Di Croce L, Raker VA, Corsaro M, et al. (2002). “Methyltransferase recruitment and DNA hypermethylation of target promoters by an oncogenic transcription factor” Science 295 (5557): 1079–82. doi:10.1126/science.1065173

Jun Kawai1,+, Shinji Hirotsune2,3,Kenji Hirose1,3,+,Shinji Fushiki4, Sachihiko Watanabe1,+ and Yoshihide Hayashizaki2,3, “Methylation profiles of genomic DNA of mouse developmental brain detected by restriction landmark genomic scanning (RLGS) method” Nucl. Acids Res. (1993) 21 (24): 5604-5608. doi: 10.1093/nar/21.24.5604

Cui-Jun Zhang, Jin-Xing Zhou, Jun Liu, Ze-Yang Ma, Su-Wei Zhang, Kun Dou, Huan-Wei Huang, Tao Cai, Renyi Liu, Jian-Kang Zhu and Xin-Jian He. “The splicing machinery promotes RNA-directed DNA methylation and transcriptional silencing in Arabidopsis” The EMBO Journal , (22 March 2013) | doi:10.1038/emboj.2013.49

Ryan Lister1,9, Mattia Pelizzola1,9, Robert H. Dowen1, R. David Hawkins2, Gary Hon2, Julian Tonti-Filippini4, Joseph R. Nery1, Leonard Lee2, Zhen Ye2, Que-Minh Ngo2, Lee Edsall2, Jessica Antosiewicz-Bourget5,6, Ron Stewart5,6, Victor Ruotti5,6, A. Harvey Millar4, James A. Thomson5,6,7,8, Bing Ren2,3 & Joseph R. Ecker1 “Differential methylation between stem cells and adult stem cellsNature 462, 315-322 (19 November 2009) | doi:10.1038/nature08514

Mikhail Spivakov & Amanda G. Fisher “Epigenetic signatures of stem-cell identity” Nature Reviews Genetics 8, 263-271 (April 2007) | doi:10.1038/nrg2046

Louise C Laurent1,2,3,15, Caroline M Nievergelt4,15, Candace Lynch2,3, Eyitayo Fakunle2,3, Julie V Harness5, Uli Schmidt6, Vasiliy Galat7,8, Andrew L Laslett9,10,11, Timo Otonkoski12,13, Hans S Keirstead5, Andrew Schork4, Hyun-Sook Park14 & Jeanne F Loring2  “Restricted human ethnic diversity in human stem cell lines.” Nature Methods 7, 6 – 7 (2010) doi:10.1038/nmeth0110-06

Other related articles on this topic were published on this Open Access Online Scientific Journal, including the following:

Prostate Cancer Cells: Histone Deacetylase Inhibitors Induce Epithelial-to-Mesenchymal Transition

SJ Williams, PhD

How mobile elements in “Junk” DNA promote cancer. Part 1: Transposon-mediated tumorigenesis.

SJ Williams, PhD

Diagnosing Diseases & Gene Therapy: Precision Genome Editing and Cost-effective microRNA Profiling 

Aviva Lev-Ari, PhD, RN, March 28, 2013

Genomics-based cure for diabetes on-the-way 

Ritu Saxena, PhD, March 4, 2013

How Genes Function

Larry H Bernstein, MD, FACP, March 4, 2013

Long noncoding RNA: UCSF Researchers have Uncovered its role in Brain Development and in Neurological Diseases

Aviva Lev-Ari, PhD, RN, April 17, 2013

Bibliographies on Genomics by Subject Matter

Genomics and Genetics Articles on this Open Access Online Scientific Journal 2/2012 — 1/2013

Aviva Lev-Ari, PhD, RN, 2/25/2013

The Initiation and Growth of Molecular Biology and Genomics – Part I

Larry H Bernstein, MD, FACP, 2/8/2013

CRACKING THE CODE OF HUMAN LIFE: Recent Advances in Genomic Analysis and Disease – Part IIC

Larry H Bernstein, MD, FACP, February 14, 2013

Genomic Endocrinology and its Future

Sudipta Saha, December 27, 2012

Exome sequencing of serous endometrial tumors shows recurrent somatic mutations in chromatin-remodeling and ubiquitin ligase complex genes
Sudipta Saha, PhD, December 18, 2012

Pancreatic Cancer: Genetics, Genomics and Immunotherapy
Tilda Barlyia, PhD, April 11, 2013

Exome sequencing of serous endometrial tumors shows recurrent somatic mutations in chromatin-remodeling and ubiquitin ligase complex genes
Sudipta Saha, December 18, 2012

Genomics & Genetics of Cardiovascular Disease Diagnoses: A Literature Survey of AHA’s Circulation Cardiovascular Genetics
3/2010 – 3/2013 

Aviva Lev-Ari, PhD, RN and Larry H Bernstein, MD, FACP, March 7, 2013

What is the Future for Genomics in Clinical Medicine?

Larry H Bernstein, MD, FACP, February 17, 2013

CRACKING THE CODE OF HUMAN LIFE: Recent Advances in Genomic Analysis and Disease – Part IIC

Larry H Bernstein, MD, FACP, February 14, 2013



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A perspective on where we are on carcinogenesis, cancer variability and predictors of time to recurrence and future behavior

Author: Larry H Bernstein, MD, FCAP

I.     Background

In “Tumor Imaging and Targeting: Predicting Tumor Response to Treatment: Where we stand? “ ( Dec 13, 2012) Dr. Ritu Saxena  attempts to integrate three posts and to embed all comments made to all three papers, allowing the reader a critically thought compilation of evidence-based medicine and scientific discourse.

Dr. Dror Nir authored a post on October 16th titled “Knowing the tumor’s size and location, could we target treatment to THE ROI by applying imaging-guided intervention?” The article attracted over 20 comments from readers including researchers and oncologists debating the following issues:

imaging technologies in cancer

  • tumor size, and
  • tumor response to treatment.

The debate lead to several new posts authored by:

Dr. Bernstein’s (What can we expect of tumor therapeutic response),

Dr. Saxena, the Author of this post’s, (Judging ‘tumor response’-there is more food for thought) and

Dr. Lev-Ari’s post on Personalized Medicine: Cancer Cell Biology and Minimally Invasive Surgery (MIS)

The post was a compilation of the views of authors representing different specialties including research and medicine. In medicine: Pathology, Oncology Surgery and Medical Imaging, are represented.

Dror Nir added a fresh discussion in “New clinical results supports Imaging-guidance for targeted prostate biopsy” based on a study of “Artemis”, a system that is adjunct to ultrasound and performs 3D Imaging and Navigation for Prostate Biopsy by Eigen (a complementary post to “Imaging-guided biopsies: Is there a preferred strategy to choose?”).

Image fusion is the process of combining multiple images from various sources into a single representative image. Ultrasound is the imaging modality used to guide Artemis in performing the biopsies. In this study MRI is used to overcome the “blindness” regarding tumor location.  This supports the detection reliability issue made in his “ Imaging-guided biopsies: Is there a preferred strategy to choose?” and  “Fundamental challenge in Prostate cancer screening.”

This makes the case that In the future, MRI-ultrasound fusion for lesion targeting is likely to result in fewer and more accurate prostate biopsies than the present use of systematic biopsies with ultrasound guidance alone.   Nevertheless, we haven’t completed the case for prediction of recurrence, even if we may eliminate the unnecessary consequences of radical prostatectomy.

Let’s look a little further. A discussion opens up more questions for discussion. I just read an interesting related article. The door has opened  wider.

II.               Novel technology to detect cancer in early stages

A. nanoparticles

Researchers have developed novel technology to detect the tumors in the body in early stages with the help of nanoparticles .( Nature Biotechnology).

Cancer cells produce many of the proteins that could be used as biomarkers to detect the cancer in the body but the amount of these proteins is not up to the mark or they may get diluted in the body of the patients making it nearly impossible to detect them in early stages.

This new technology has been developed by the researchers from MIT . Nanoparticles (brown) coated with peptides (blue) cleaved by enzymes (green) at the disease site. Peptides than come into the urine to be detected by mass spectrometry. (Credit: Justin H. Lo/MIT)

In this technology, nanoparticles will interact with the tumor proteins helping to make thousands of biomarkers secreted by the cancer cells. We had this ‘aha’ moment: What if you could deliver something that could amplify the signal?”

  • Scientists administered ‘synthetic biomarkers’ having peptides bonded to the nanoparticles and
  • the particles interact with the protease enzymes often found in large quantities in cancer cells

as they help them to cut the proteins normally holding the cells in place and to spread in other parts of the body.

Researchers found that the proteases break down hundreds of peptides from the nanoparticles and release them in the bloodstream. These peptides are then excreted in the urine, where the process of mass spectrometry could help to detect such peptides.

These “Synthetic biomarkers” perform three functions in vivo:

  1. they target sites of disease,
  2. sample dysregulated protease activities and
  3. emit mass-encoded reporters into host urine (for multiplexed detection by MS).

According to Bhatia, this biomarker amplification technology could also be used to manage the advancement of the disease and to check the response of the tumors to the drugs.


Kwong, G., von Maltzahn, G., Murugappan, G., Abudayyeh, O., Mo, S., Papayannopoulos, I., Sverdlov, D., Liu, S., Warren, A., Popov, Y., Schuppan, D., & Bhatia, S. (2012). Mass-encoded synthetic biomarkers for multiplexed urinary monitoring of disease Nature Biotechnology

IIB    Synthetic Nucleosides

J Gong and SJ Sturla published “A Synthetic Nucleoside Probe that Discerns a DNA Adduct from Unmodified DNA” in JACS Communications on web 4/03/2007).  They state that biologically reactive chemicals alkylate DNA and induce structural modifications in the form of covalent adducts that can persist, escape repair, and serve as templates for polymerase-mediated DNA synthesis. Therefore, correlating chemical structures and quantitative levels of adducts with toxicity is essential for targeting specific agents to carcinogenesis.

  • DNA adducts are formed at exceedingly low levels.
  • Minor lesions may have greater biological impact than more abundant products.
  • New molecular approaches for addressing specific low-abundance adducts are needed

They describe the first example of a synthetic nucleoside that may serve as the chemical basis for a probe of a bulky carcinogen-DNA adduct

IIC.  MicroRNAs caused by DNA methylation

Another molecular approach “ A microRNA DNA methylation signature for human cancer metastasis” was published in PNAS [2008;105(36):13556-13561)] by A Lujambio , Calin GA, Villanueva A et al.

Different sets of miRNAs are usually deregulated in different cancers, and some miRNAs are aberrantly methylated and silenced, causing tumorigenesis. The authors

  • identified aberrantly methylated and silenced miRNAs that are cancer-specific
  • using miRNA microarray techniques.

Functional analyses for the selected genes proved that these miRNAs act on C-MYC, E2F3, CDK6 and TGIF2, resulting in metastasis through aberrant methylation of the miRNAs. The authors suggest that these may be applicable to advance research in the clinical setting.

III.              New methods require advanced mathematical prediction methods

A.  First Case …ProsVue PSA

One of the most elegant papers I have seen in several years  has been published in Clinical Biochemistry (CLB–12-00159), by Mark J. Sarnoa1 and Charles S. Davis2. [1Vision Biotechnology Consulting, 19833 Fortuna Del Este Road, Escondido, CA 92029, USA (, 2CSD Biostatistics, Inc., San Diego, CA, 4860 Barlows Landing Cove, San Diego, CA 92130, USA (]

Robustness of ProsVue™ linear slope for prediction of prostate cancer recurrence: Simulation studies on effects of analytical imprecision and sampling time variation.
Keywords: ProsVue, slope, prostate cancer, random variates.
Financial support for the investigation was provided by Iris Molecular Diagnostics

Abstract: Objective: The ProsVue assay measures

  • serum total prostate-specific antigen (PSA) over three time points post-radical prostatectomy and
  • calculates rate of change expressed as linear slope. Slopes ≤2.0 pg/ml/month are associated with reduced risk for prostate cancer recurrence.

However, an indicator based on measurement at multiple time points, calculation of slope, and relation of slope to a binary cutpoint may be subject to effects of analytical imprecision and sampling time variation.

They performed simulation studies to determine the presence and magnitude of such effects.

Design and Methods: Using data from a two-site precision study and a multicenter retrospective clinical trial of 304 men, they carried out simulation studies to assess whether analytical imprecision and sampling time variation can drive misclassification of patients with stable disease or classification switching for patients with clinical recurrence.


  • Analytical imprecision related to expected PSA values in a stable disease population results in ≤1.2% misclassifications.
  • For recurrent populations, an analysis taking into account correlation between sampling time points demonstrated that classification switching across the 2.0 pg/ml/month cutpoint occurs at a rate ≤11%.
  • Lastly, sampling time variation across a wide range of scenarios results in 99.7% retention of proper classification for stable disease patients with linear slopes up to the 75th percentile of the distribution.


  • These results demonstrate the robustness of the ProsVue assay and the linear slope indicator.
  • Further, these simulation studies provide a potential framework for evaluation of future assays that may rely on the rate of change principle

The ProsVue Assay has been cleared for commercial use by the US Food and Drug Administration (FDA) as “a prognostic marker in conjunction with clinical evaluation as an aid in

  • identifying those patients at reduced risk for recurrence of prostate cancer for the eight year period following prostatectomy.”

The assay measures

  1. serum total prostate specific antigen (PSA) in post-RP samples and
  2. calculates rate of change of PSA over the sampling period,

expressing the outcome as linear slope. The assay is novel in at least a few respects.

  • the assay is optimized to identify patients at reduced risk for recurrence.

In order to demonstrate efficacy for this indication, the assay employs the immuno-polymerase chain reaction (immuno-PCR) to achieve sensitivity

  • an order of magnitude lower than existing “ultrasensitive” PSA assays.

The improved sensitivity allows quantification of PSA at levels exhibited in stable disease (<5 pg/ml), which have been historically below the

measurement range of ultrasensitive assays.

Secondly, the assay is the first to receive clearance based on

  • linear slope of tumor marker concentration versus time post-surgery.
  • Specifically, PSA is measured in three samples taken between 1.5 and 20 months post-RP and
  • the slope calculated using simple least squares regression.
  • The calculated slope is compared to a threshold of 2.0 pg/ml/month with values at or below the threshold associated with reduced risk for PCa recurrence.

Does analytical imprecision present a potential risk for misclassification by driving errors in the calculated slope that result in classification switching?  Since excursions of precision can occur as point sources in single sampling points or in cumulative effect from the three sampling points, the question is worthy of consideration. They carried out studies

  • to address these questions specific to ProsVue and also
  • provide a potential framework for evaluation of future assays.
  • Similarly, does variation in the time at which samples are taken drive errors resulting in classification switching?

Both questions require evaluating the robustness of the ProsVue Assay and are properly presented for clinical chemists and physicians evaluating use of the assay in clinical practice. Furthermore, since future diagnostic assays may employ the rate of change principle, it is important to develop statistical methods to evaluate effects of variation.

The point is that more sophisticated methods are needed to measure scarce analytes associated with risk for eventual clinical events.

  • Accurate measurement at post-RP levels to identify patients with reduced risk of recurrence represents a new development.
  • Furthermore, measurement of PSA at multiple time points and calculation of rate of change using linear regression extends application of the analyte markedly beyond traditional use.

Such use presents certain questions of variation effects.

Their results indicate that analytical imprecision in the range of concentrations exhibited in patients at reduced risk for recurrence (the focus of the assay) presents no significant risk of misclassification.

  • Classification switching in this population occurs at a frequency of ≤1.2%.
  • Slopes for recurrent patients and clinical classification are substantively insensitive to analytical variation even in a subpopulation of recurrent patients with slowly rising PSA values.
  • Sampling time variation negligibly affects clinical classification for stable disease patients with slopes at and below the 75th percentile.
Table 1. Side-effects and effects on recovery ...

Table 1. Side-effects and effects on recovery of treatments for newly diagnosed prostate cancer. The Prostate Brachytherapy Advisory Group: (Photo credit: Wikipedia)


IIIB. Other interesting developments are going to need further development and validation.

For instance, research has been published online in the journal Cancer Cell, reports a cellular component that is involved in mobility of cancer to other body parts and inhibition of which could increase the tumor formation. These investigators worked on various animal models including chicken, zebrafish and mouse, and patient samples and have found a cellular component; Prrx1 that stops the cancer cells from staying in organs.  Epithelial-mesenchymal transition (EMT) is the process that is required by the cancer cells to spread to other organs. This process helps the cells to become mobile and move with the bloodstream. These cells must lose their mobility before attaching to other body parts.

In the final analysis the cells have to lose the component Prrx1 to lose mobility and to become stationary. Researchers wrote, “Prrx1 loss reverts EMT & induces stemness, both required for metastatic colonization.”  Consequently,  Prrx1 has to be turned off for these cells to group together to form other tumours.” It has been found that the tumors with elevated levels of Prrx1 cannot form new tumors.

IIIC.  PXR and AhR Nuclear Receptor Activation

  • The primary mechanism of cytochrome P450 induction is via increased gene transcription which typically occurs through nuclear receptor activation.
  • The most common nuclear receptors involved in the induction of drug metabolizing enzymes include the pregnane X receptor (PXR), the aryl hydrocarbon receptor (AhR), and the constitutive androstane receptor (CAR) which are known to regulate CYP3A4, CYP1A2 and CYP2B6, respectively.
  • An industry survey of current practices and recommendations (Chu et al., (2009) Drug Metab Dispos 37: 1339-1354) indicates 64% of survey respondents routinely use nuclear receptor transactivation assays to assess the potential of test compounds to cause enzyme induction
  • ‘Because reporter assays are relatively high throughput and cost effective, they can be a valuable tool in drug discovery.’(Chu V, Einolf HJ, Evers R, Kumar G, et.  (2009) Drug Metab Dispos 37; 1339-1354)
  • Luciferase reporter gene assay

No cytotoxicity was observed for any of six compounds at the concentration range tested with the exception of troglitazone for which cytotoxicity was observed at the highest concentration of 50μM.  This data point was excluded in this instance and not used for calculating the Emax or EC50.

In brief, CAR and PXR regulate distinct but overlapping sets of target genes, which include certain phase 1 P450 enzymes (e.g., CYP2B, CYP3A, and CYP2C), phase II conjugation enzymes such as UDP glucuronosyltransferase UGT1A1 and sulfotransferase SULT2A, and phase III transporters such as P-glycoprotein (MDR-1). The AhR receptor has been shown to regulate the expression of CYP1A.

Will this be combined with the other methods for drug selection and prediction of drug free survival?

I have mentioned an improved molecular assay of PSA at the pcg/ml level that is approved for use with an acceptable linear prediction of survival for 8 years post radical prostatectomy.  Then there is a report of a method of measuring nanoparticles in urine, to amplify the signal detected by mass spectrometry. This new technology has been developed by the researchers from MIT and led by Sangeeta Bhatia at MIT. (Novel technology to detect cancer in early stages, Nature Biotechnology, Dec 16, 2012).  There is still another recent report about using gene expression profiles to predict breast cancer, and a number of articles have shown variability in breast cancer types.   I view with reservations until I can see long term predictions of prognosis.

IIID.  Prediction of Breast Cancer Metastasis by Gene Expression Profiles

The report in Cancer Informatics (; open access)  by M Burton, M Thomassen, Q Tan, and TA Kruse is “Prediction of Breast Cancer Metastasis by Gene Expression Profiles: A Comparison of Metagenes and Single Genes.”   The authors state “The diversity of microarray platforms has made the full validation of gene expression  profiles across studies difficult and, the classification accuracies are rarely validated in multiple independent datasets. The individual genes between such lists may not match, but genes with comparable function are included across gene lists. However,  genes can be grouped together as metagenes (MGs) based on common characteristics such as pathways, regulation, or genomic location. Such MGs might be used as features in building a predictive model applicable for classifying independent data.”

Microarray gene expression analysis has in several previous studies been applied to elucidate the relation between clinical outcome and gene expression patterns in breast cancer and has demonstrated improvement of recurrence prediction. In some studies, genes in such profiles might be fully or partially missing in the test data used for validation due to the choice of microarray platform or the presence of missing values associated with a given probe.

To overcome the obstacles, these authors propose that individual genes could be considered part of a larger network such that their expression being controlled by the expression level of other genes or that a group of genes belong to a specific pathway performing a well-defined task. These genes may be controlled by the same transcription factor or located in the same chromosomal region. In fact these groupings have been collected in public databases (the Kyoto Encyclopedia of Genes and Genomes (KEGG), the Molecular Signature Database (MsigDB), the Gene Ontology database (GO)). This could be upregulation or deregulation of pathways associated with metastasis. Metastasis progressionas well as tumor grading (in breast cancer) are associated with accumulated mutations in several genes, leading to amplification or inactivation of genes.

Several studies have defined metagene/gene modules derived from microarray data using various methods such as penalized matrix decomposition which clusters similar genes but without similar expression profiles – hierarchical clustering, correlation, or combining a priori protein-protein interactions with microarray gene expression data defining interaction networks as features. Few studies have attempted to use such predefined gene sets for prediction models.

Their study compared the performance of either metagene- or single gene-based feature sets and classifiers using random forest and two support vector machines for classifier building. The performance within the same dataset, feature set validation performance, and validation performance of entire classifiers in strictly independent datasets were assessed by 10 times repeated 10-fold cross validation, leave-one-out cross validation, and one-fold validation, respectively. To test the significance of the performance difference between MG- and SG-features/classifiers, we used a repeated down-sampled binomial test approach.

They found MG- and SG-feature sets are transferable and perform well for training and testing prediction of metastasis outcome in strictly independent data sets, both between different and within similar microarray platforms.  Further, The study showed that MG- and SG-feature sets perform equally well in classifying independent data. Furthermore, SG-classifiers significantly outperformed MG-classifier when validation is conducted between datasets using similar platforms, while no significant performance difference was found when validation was performed between different platforms.

  • The MG- and SG-classifiers had similar performance when conducting classifier validation in independent data based on a different microarray platform.
  • The latter was also true when only validating sets of MG- and SG-features in independent datasets, both between and within similar and different platforms.

This study appears to be unique in the same way that the PCa prediction study is unique in that genome-based expression patterns are used to classify and predict metastatic potential.

These studies have the potential to materialize into practice changing behavior.

IIIE. Colon Cancer and Treatment Recurrence

Cancer scientists led by Dr. John Dick at the Princess Margaret Cancer Centre have found a way to follow single tumour cells and observe their growth over time. By using special immune-deficient mice to propagate human colorectal cancer, they found that genetic mutations, regarded by many as the chief suspect driving cancer growth, are only one piece of the puzzle. The team discovered that biological factors and cell behaviour — not only genes — drive tumour growth, contributing to therapy failure and relapse. The findings are published December 13 online ahead of print in Science, are “a major conceptual advance in understanding tumour growth and treatment response” according to Dr. Dick.

[1] only some cancer cells are responsible for keeping the cancer growing.

[2] these kept the cancer growing for long time periods (up to 500 days of repeated tumour transplantation)

[3] a class of propagating cancer cells that could lie dormant before being activated.

[4] the mutated cancer genes were identical for all of these different cell behaviours.

[5] given chemotherapy the long-term propagating cells were generally sensitive to treatment, but dormant cells were not killed by drug treatment.

[6] these became activated andpropagated new tumour.

IV. Related References

Diagnostic efficiency of carcinoembryonic antigen and CA125 in the cytological evaluation of effusions.
M M Pinto, L H Bernstein, R A Rudolph, D A Brogan, M Rosman
Arch Pathol Lab Med 1992; 116(6):626-631 ; ICID: 825503

Medically significant concentrations of prostate-specific antigen in serum assessed.
L H Bernstein, R A Rudolph, M M Pinto, N Viner, H Zuckerman
Clin Chem 1990; 36(3):515-518 ; ICID: 825497

Entropy and Information Content of Laboratory Test Results
R T Vollmer
Am J Clin Pathol.  2007;127(1):60-65.


This article introduces the use of information theoretic concepts such as entropy, S, for the evaluation of laboratory test results, and it offers a new measure of information, 1 – S,
which tells us just how far toward certainty a laboratory test result can predict a binary outcome. The derived method is applied to the serum markers troponin I and
prostate-specific antigen and to histologic grading of HER-2/neu staining, to cytologic diagnosis of cervical specimens, and to the measurement of tumor thickness in malignant
melanoma. Not only do the graphic results provide insight for these tests, they also validate prior conclusions. Thus, this information theoretic approach shows promise for
evaluating and understanding laboratory test results.

A map of protein-protein interactions involving calmodulin. Protein-protein interactions are both numerous and incredibly complex, and they can be mapped using the Database of
Interacting Proteins (DIP). This image depicts a DIP map for the protein calmodulin. The interactions with the most confidence are drawn with wider connecting lines. This diagram
highlights one level of complexity involved in understanding the downstream effects of gene regulation and expression.

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