Disease Status - D

Environmental exposure - X

Gene Status - G (Mutation, SNP, Haplotype)

Logistic regression is robust - no assumptions

• Joint distribution of gene and environment in the population (can't get from a case control sample)
• Interactions are tough to assess in a logistic regression
• Sample size requirements are much higher.

Gene-Environment interaction studies - assumptions

• Gene and Environmental variables are independently distributed.
• But this does not always hold.
• Assumptions about distribution of the covariates in the population increases efficiency - particularly in a case-control scenario.
• Or Put a restraint on the restrospective likelihood to achieve more power.

Carlo Emilio Bonferroni and his beady guilt-trippy eyes.

The Problem with the Bonferroni Adjustment

is that he was not a clinician and so his adjustment:

• Assumes that its okay to lower Type I errors AT THE COST OF Type II errors - a big blunder in the clinic/population studies.
• Factors in the independent tests shown in the published paper - into the Bonferroni adjustment, but disregards the numerous unpublished descriptive tests which went into the preparation of that paper.
• Bonferroni adjustments are concerned with the wrong hypothesis - the study-wide error rate that the Bonferroni adjustment generates applies to the universal null hypothesis that:

1. All groups are identical on all the variables - thus we cannot say which or even how many variables differ. Thus there is no clinical relevance to this "global hypothesis". Detail is sorely missing. Bonferroni adjustments provide the correct answer to a completely irrelevant question.

• Comparisons are interpreted differently according to how many other tests were performed.
• Using Bonferroni Adjustments introduces cynicism in research (for those who are not idealists). These researchers would publish one p value at a time to avoid censure by statistical reviewers. Scientific relevance is thrown out the window.

The Bonferroni adjustment MUST die

From the producer of Senseless Statistics. Guess who? Yep. Neyman-Pearson in a misguided attempt to improve decision making through statistical inference. See previous post.

• Bonferroni adjustments might be okay for repeated decision making - but this is not the setting of most research study settings. They are not helpful in determining what the data say in one particular study.
• The Bonferroni adjustment gives the reader an illusion that the final test result has somehow been corrected for the uncountable number of independent tests that have been carried out across the study. But this is not the case, because an equal number (or perhaps more) unpublished independent tests are missed.
• So if the null hypothesis is rejected under the Bonferroni adjustment, we can only conclude that the universal null hypothesis (across all the independent tests that the Bonferroni adjustment corrects for) has been rejected. We would have no idea of which of the individual tests might be significant. This is NOT a clinically useful result at all.
• They *might* be useful if the same test is conducted in many sub-samples without any a-priori hypothesis - reminiscent of repeated sampling from the same lot.
• Might be useful when searching for significant associations without pre-established hypotheses.
• They should NOT BE USED when assessing evidence about specific hypotheses.

===================

• Perneger TV. What's wrong with Bonferroni adjustments. BMJ. 1998 April;316(7139):1236-1238. Available from: http://www.bmj.com/cgi/content/abstract/316/7139/1236.
• Morgan JF. p Value fetishism and use of the Bonferroni adjustment. Evid Based Ment Health. 2007 May;10(2):34-35. Available from: http://dx.doi.org/10.1136/ebmh.10.2.34.

Differences in the Fisher and Neyman-Pearson School of Statistical Thought

Fisher (and his cloud of p-value inference)

Knowledge is created via inductive inference

• p value is P of an event X | Null hypothesis of no effect or relationship is true.
• Smaller the p value, greater the evidence against the null hypothesis
• p value is a measure of inductive evidence against Ho
• Data dependent random variable (Exploratory)

Neyman-Pearson (and their vices)

Statistical Testing as a mechanism for making decisions and guiding behaviour

• Two Hypotheses H0 and Ha
• Decision between two distinct courses of action
• Type I error is the false rejection of Ho
• Type II error is the false acceptance of Ho
• Statistical testing is aimed at ERROR CONTROL
• Not concerned with gathering evidence
• Must be fixed before gathering data to control Type I errors
• P value plays no role in NP theory

See Royall RM 1997 Statistical evidence - A likelihood paradigm. New York, Chapman and Hall Chapter 5 for further discussion on this point

The Confusion

• P value is compared to Type I error rate for rejecting Ho over Ha if P < Type I error
• When used this way, the specific value of p is irrelevant and should not be reported
• Can only say whether or not the result fell in the rejection region and not where it fell (as might be induced through a precise p value).
• The exact value of p cannot be reported in an NP test because alpha is the probability of a set of outcomes that may fall anywhere in the tail area of the distribution under a null hypothesis. We cannot know ahead of time which of these particular outcomes will occur
• The tail area for the p value is known only after the outcome is observed (Its not a probability of a set of outcomes, its the specific result of one outcome?)
• If the alpha is fixed, the p values cannot be re-interpreted at different values (p<0.05, p<0.01 etc)
• "Level of significance" cannot be interpreted by the p value.
• The p value is NOT a data dependent adjustable Type I error rate
• If the researcher is concerned with error probabilities, the specific p value is irrelevant
• If the researcher is interested in the "measure of evidence" from a p value, there is no point in also reporting the error probabilities

Advice from this article

• If the focus of the study is on controlling errors (e.g., in quality control experiments), use the N-P approach. Make a serious

attempt to calculate the costs of committing Type I and II errors.

• If the focus of the study is evidential in nature (which will be most of the time), use p values. Indeed, use exact p values, such as p = .04, whenever possible.
• Do not report p = .04 as p < .05
• Furthermore, do not present p values at fixed levels such as p < .05, p < .01, p < .001, and so on. This makes them look like Type I error rates.
• Recall that the p value is a measure of evidence against the null hypothesis.
• Be aware that p values can greatly exaggerate this evidence against H0.
• Remember, also, that the p value is not a measure of support for the alternative hypothesis, HA.
• Do not mistake p values for Type I error rates.
• P values are data dependent measures, not fixed levels. Alphas are pre-selected levels, not data-dependent values.
• It is completely inadmissible to use true N-P α values in a roving manner.
• Do not use the p < α criterion of statistical significance
• Present other information, such as confidence intervals, alongside or instead of significance levels.

Alternatives to obsessive p-value testing

• Report effect sizes, sample statistics and their confidence intervals
• CI stress the importance of estimation over testing
• Scientific progress depends on arriving at credible estimates of the magnitudes of effect sizes.
• CI yields a range of estimates deemed likely for the population
• Width of the CI provide a measure of reliability or precision of the estimate
• CIs make it easier to determine if a finding has any substantive as opposed to statistical significance.
• CIs are in the same metric as the risk estimate and are easier to interpret within the context of the problem
• CIs hold the true error rate to the chosen level.
• A 95% CI that does not include the null value is equivalent to rejecting the hypothesis at the 0.05 level.
• The use of CIs allows for the possibility of unifying a seemingly fragmented literature

Standing on the shoulders of...

• Overlapping CIs indicate consistent results if Risk estimates are in the same direction

even if

1. a) the CIs cross the null
2. b) p values are insignificant

• Consider publication bias when looking at average risk estimates.

1. Since "insignificant" results are seldom published, the average effect size is bloated

---------------------------

Hubbard, R. and Armstrong, J. S. (2006). Why we don't really know what statistical significance means: Implications for educators. Journal of Marketing Education, 28(2):114-120.

HIV Example:

• Null hypothesis: Person is not infected.
• P(Positive|Null hypothesis true) = .0001,

which is the exact level of significance.

• Therefore, the null hypothesis of no infection is rejected with high confidence, and the alternative hypothesis that the person is infected is accepted.

In a Bayesian calculation:

• P(Infected) = 0.0001
• P(Positive|Infected) = 0.999
• P(Positive| Not infected) = 0.0001 <--- Null hypothesis.

Bayes:

• P (Infected|Positive) = P (Infected)* P(Positive|Infected) / P (Infected)* P(Positive|Infected) + P (Non-Infected)* P(Positive|Not Infected)

= 0.5

• Wilson VS, Bobseine K, Gray EL. Development and Characterization of a Cell Line That Stably Expresses an Estrogen-Responsive Luciferase Reporter for the Detection of Estrogen Receptor Agonist and Antagonists. Toxicol Sci. 2004 September;81(1):69-77. Available from: http://dx.doi.org/10.1093/toxsci/kfh180.

Used this plasmid

MATERIALS AND METHODS

• Chemicals. (Sigma Chemical Company)

1. 17b-Estradiol (E2, 99%),
2. 17a-ethynylestradiol (EE, >98%),
3. diethylstibestrol (DES, min >99%),
4. 5a-dihydrotestosterone (DHT, min >99%),
5. dexamethasone (DEX, 100%),
6. genestein (Gen, >98%),
7. tamoxifen (Tam, >99%) and
8. methoxychlor (Meth, >98%)

1. 4-Nonylphenol (4-NP, a mixture of branched side chains containing 85% p-isomers) was purchased from Fluka Chemical Corp.(Ronkonkoma, NY).

1. The synthesis of 2,2-bis(p-hydroxyphenyl)-1,1,1-trichloroethane(HPTE), a methoxychlor (Sigma, purity >99%) metabolite, was previously published (Waller et al., 1996).

1. The antiestrogen, ICI 182,780, was supplied by ICI Pharmaceuticals (Macclesfield, England, Lot #C42710).

1. Cadmium chloride (CdCl, purity 99.5%) was purchased from Fisher Chemical

Co. (Fig. 1)

Construction of reporter plasmid pGL2.TATA.Inr.luc.neo.

• Remove Neomycin Gene from puc9neo

1. Neomycin gene was removed from puc9.neo (provided by Phillip Hartig, U.S. EPA, Research Triangle Park, NC) using a BamHI digest.

• Incorporate Neomycin gene into pGL2.TATA.Inr.Luc.ERE(3) plasmid

1. The 1.8 kb fragment was ligated to pGL2.TATA.Inr.luc containing three estrogen-responsive elements(ERE) (provided by Donald McDonnell, Duke University, Durham,NC) that had been linearized with SmaI.

• The resulting plasmid was pGL2.TATA.Inr.luc.neo

Preparing Mammalian cells: T47D Breast Adenocarcinoma

• T47D cells:

1. The T-47 line was isolated by I. Keydar from a pleural effusion obtained from a 54 year old female patient with an infiltrating ductal carcinoma of the breast.
2. This differentiated epithelial substrain (T-47D) was found to contain cytoplasmic junctions and receptors to 17 beta estradiol, other steroids and calcitonin.
3. It will form colonies in soft agar.
4. Purified DNA from this line is available as ATCC 45528 (25 micrograms) and ATCC 45529 (100 micrograms).

• The human breast cancer cell line T-47D (ATCC No. HTB 133), ERa/b 1 /GR 1, was used for transfection.
• Cells were screened for sensitivity to the selection antibiotic, geneticin (Gibco/BRL).
• Concentrations of the antibiotic were selected to produce 100% lethality over a two week culture period (data not shown).
• Growth media was RPMI (Gibco) supplemented with 2.5 g/l glucose, 10 mM HEPES, 1 mM sodium pyruvate, 1.5 g/l NaHCO2, 0.2 U/ml insulin, 10% FBS (fetal bovine serum, Hyclone, characterized), 100 mg/ml penicillin, 100 U/ml streptomycin, and 0.25 mg/ml amphotericin B (Gibco BRL, purchased as a 1003 mixture of penicillin, streptomycin, and amphotericin B).

Transfection procedure.

• T47D Cells were seeded 23105 cells per 60mmculture dish.
• T47D Cells were transfected using Fugene 6 (Roche) per manufacturer protocol with 5 mg pGL2.TATA.Inr.luc.neo per dish.
• T47D cells were placed in selection media (growth media plus 500 mg/ml gentamycin) 24 h after transfection.
• Cells were grown in selection media until colony formation was observed.
• Colonies were transferred by trypsinization to 24-well plates and then to T-25 cm2 flasks for continued culture.

Initial screening of clones.

• For initial screening of colonies, cells were plated at 104 per well in 96-well luminometer plates and allowed to attach 5–6 h.
• After attachment, growth media were replaced with fresh media, except 5% dextran-charcoal treatedFBSwas substituted for 10%regular FBS.
• After 40 to 48h cells were dosed with 100 ml dosing media/well (5% dextran-charcoal treated

FBS media plus test chemical) and incubated for 24 h.

• Stock chemicals were prepared in 95% ethanol.
• Dosing solutions were prepared by diluting the chemical stock with fresh dosing media to the desired concentration. In no case did the ethanol concentration exceed 0.2%.
• Negative control wells were dosed with media plus 0.1% ethanol.
• Positive control wells were dosed with 0.1 nM or 1.0nM 17b-estradiol (E2).
• Both controls (vehicle and E2) were also competed with 1mM ICI, an ERantagonist, to assess ER-specific responsiveness and background.
• Cells were washed with phosphate buffered saline at room temperature and then 25 ml lysis buffer (Ligand Pharmaceuticals) was added per well and incubated until cells were lysed (15–30 min).
• Relative light units per well were determined using a 96-wellMLXLuminometer (Dynex, Chantilly, VA).
• The final clone was chosen based on

1. appropriate ligand responsiveness and
2. genetic stability over time and
3. renamed T47D-KBluc.

Chemical screening.

• Stock cells from the chosen clone, T47D-KBluc, were maintained in standard growth media as detailed above.
• Cells were placed in growth media modified by replacement of 10% FBS with 10% dextran-charcoal

treated FBS (Hyclone) without antibiotic supplement one week prior to assay.

• Dosing media was further modified by reduction of dextran-charcoal treated FBS to 5%.
• Cells were seeded at 104 cells per well in 96-well luminometer plates and allowed to attach overnight.
• Media was then replaced with 100 ml/well of dosing media and the test chemical and incubated 24 h. Ethanol vehicle did not exceed 0.2%.
• Cells were washed with phosphate buffered saline at room temperature, then harvested in 25ml lysis buffer (Ligand Pharmaceuticals) per well.
• Luciferase activity was determined using an MLX microtiter plate luminometer (Dynex, Chantilly, VA) and quantified as relative light units (RLU).
• Each well received 25ml reactionbuffer (25mMglycylglycine, 15mMMgCl2, 5mMATP, 0.1 mg/ml

BSA, pH 7.8), followed by 25 ml 1 mMD-luciferin 5 s later.

• Each chemical was assayed independently at least three times (three replicate assay plates) with a

minimum of four wells per each replicate assay unless otherwise noted in the text.

• Cells were screened with a battery of chemicals using agonist positive (E2), negative (vehicle only), antagonist (E2 plus ICI), and background (vehicle plus ICI) controls on every plate.
• Each chemical was tested both alone and in the presence of an appropriate competitor such as 0.1 nM E2 (suspected antagonist) or ICI (suspected agonists).
• E2 positive controls were monitored over time as an assessment of the stability of the cells line. * In instances where cytotoxicity of a chemical was suspected, duplicate plates were dosed in parallel.
• Luciferase activity was assayed in one plate as described above and the second plate was

tested for cell toxicity.

• Cytotoxicity was evaluated by determining the mitochondrial function of the cells using the tetrazolium dye 3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide (MTT) following treatment with the compound.
• MTT is a yellow vital dye that is actively converted by mitochondrial oxidation-reduction reactions into blue formazan crystals. The formation of the blue MTTcrystals within the cell decreases in direct proportion to the viability of cells (Li et al., 1999).

Statistical analysis.

• These data were collected from several independent experiments, with three or more replicates (plates) per experiment.
• A replicate was a 96-well plate which included 4–8 independent observations (wells) each of

the vehicle control, positive control (either 0.1 or 1.0 nM E2), antiestrogen control (E2 plus ICI), background control (vehicle plus ICI), and all other treatment groups.

• Data were analyzed by two-way ANOVA (main effects being replicate [a nuisance or blocking factor] and treatment) using Proc GLM available from SAS version 6.09 (SAS Institute, Cary, NC.) on the U.S. EPA IBM mainframe computer.
• Relative light units were converted to fold induction above the vehicle control value or as percent of E2 positive control response for each replicate for statistical analysis.
• Data were analyzed in a GLM model that included the concentrations and replicates.
• Statistically significant effects ( p < 0.05) were examined using the least squares means (LSMEANS) procedure available on SAS.
• Means and standard errors were calculated using PROC means.
• For agonists, which stimulate luciferase expression, treatments were compared to the vehicle (media plus ETOH) control group or to the relative response of their respective E2 control (either 0.1 or 1.0 nM E2).
• Estrogen antagonist, ICI, which blocks E2-induced luciferase expression, was compared to the E2 positive control group.
• Graphs were prepared using Origin scientific graphing software (OriginLab, Northampton, MA). Best fit curves were generated using logistic fit of the data.

• Legler J, van&nbsp;den Brink CE, Brouwer A, Murk AJ, van&nbsp;der Saag PT, Vethaak AD, et al. Development of a stably transfected estrogen receptor-mediated luciferase reporter gene assay in the human T47D breast cancer cell line. Toxicol Sci. 1999 March;48(1):55-66. Available from: http://dx.doi.org/10.1093/toxsci/48.1.55.

------------------------

Drawbacks of Other Assay Systems

• Competitive ligand binding assays for the estrogen receptor cannot distinguish between estrogenic and anti-estrogenic substances and do not provide insight into a substance’s ability to initiate the molecular cascade of events leading to altered gene expression.
• Cell proliferation assays are limited by a lack of specificity as mitogens other than estrogens are able to influence the proliferation of human breast cancer cells (van der Burg et al., 1988; Dickson and Lippman, 1995).
• Yeast-based assays are simple and sensitive, however drawbacks include lack of responsiveness to anti-estrogens (Lyttle et al., 1992; Kohno et al., 1994) as well as possible differences in permeability of compounds through the yeast cell wall, relative to mammalian cell membranes.

Reporter Gene Expression Systems that exist

Description: Estrogen Responsive Cell line with

1. a) Recombinant receptor
2. b) Reporter genes

Examples

• MCF-7 Cells with pVit-tk-Luc construct

1. Has a Xenopus Laevis vitellogenin A2 promoter region
2. Contains one ERE upstream of the Herpes Simplex virus thymidine kinase promoter - controls expression of the firefly luciferase gene.

• HeLa cell line with Gal4-regulated chimeric estrogen receptor and luciferase gene constructs

Drawbacks

• Modest sensitivity and responsiveness to estradiol reported

Legler Gene Reporter Assay

• Stably transfected CALUX (chemical-activated luciferase gene expression) cell-line assay
• T47D breast adenocarcinoma cells
• + pEREtata-Luc estrogen responsive luciferase gene

Construct of the pEREtat-Luc Estrogen Responsive Luciferase gene

• Three ERE upstream from a TATA box regulating expression of an enhanced luciferase reporter gene construct.
• The TATA box (also called Goldberg-Hogness box) is a DNA sequence (cis-regulatory element) found in the promoter region of most genes in eukaryotes and Archaea. Considered to be the core promoter sequence, it is the binding site of either transcription factors or histones (the binding of a transcription factor blocks the binding of a histone and vice versa) and is involved in the process of transcription by RNA polymerase. (Wikipedia)

Materials and Methods

Chemicals

Sigma Chemical Co.

• 17b-Estradiol (E2, 99%)
• Methoxychlor (95%)
• Tamoxifen (99%)
• Genistein (99%)
• Al-trans retinoic acid
• Ethanol (100%, pa)

Fluka

• 4-Nonylphenol (92.7%)

Aldrich

• Bisphenol A (99%)

Riedel-de Haan, The Netherlands.

• Chlordane (97.7%)
• Endosulfan (99%),
• Dieldrin (98.5%)

Dr. A. Wakeling, Zeneca Pharmaceuticals, U.K.

• ICI 182,780 was a kind gift

Dutch State Institute for Quality Control of Agricultural Products (RIKILT-DLO).

• o,p’DDT
• Tetrachlorodibenzo-p-dioxin (TCDD)

Organon B. V., Oss, The Netherlands.

• Synthetic progestin Org 2058

NEN (Boston, MA) and Dr. A. Brinkman, Erasmus University, Rotterdam.

• Synthetic androgen R1881 (methyltrienolone) was purchased from

Acros

• Ethanol or dimethyl sulfoxide (DMSO, 99.9%, spectrophotometric grade).

Duchefa, The Netherlands.

• Antibiotics used for selection of stable clones (puromycin, hygromycin, and G418)

Cell Cultures

Dr. R. L. Sutherland (Garvan Institute of Medical Research, Sydney, Australia)

• The T47D human breast adenocarcinoma cell line

DF, Gibco

• 1:1 mixture of Dulbecco’s modified Eagle’s medium and Ham’s F12 medium (DF, Gibco) supplemented with
• sodium bicarbonate,
• non-essential amino acids,
• sodium pyruvate, and

FCS, Integro, Austria)

• 7.5% fetal calf serum

Culture Conditions

• T47D cells were cultured at 37°C, 7.5% CO2.

ER-CALUX assays

• T47D, MCF-7 and Hepa cells were maintained in assay medium without phenol red, supplemented with 5% dextran-coated charcoal treated FCS (DCC-FCS).
• DCC-FCS was prepared by heat inactivation (30 min at 56°C) of FCS, followed by two 45-min DCC treatments at 45°C as described by Horwitz and McGuire, 1978.

Basics of a Gene Reporter Assay

A reporter gene assay is used to study the regulation of a gene of interest.

• Clone upstream regulatory elements for this gene of interest and integrate them upstream of the firefly luciferase gene - luc.

• Regulatory elements include

1. potential promoters
2. known promoters
3. portions of a promoter or known response and enhancer elements (ERE)
4. Three tandem repeats of the consensus ERE oligo (GAGCTTAGGTCACTGTGACCT) upstream of the minimal human E1B TATA promoter sequence (GGGTATATAAT) were inserted in the Sma1-BglII site of the multiple cloning site of pGL3-basic.

• A variety of plasmids including the pGL4 vectors are utilized for this purpose.

1. pGL3-basic: The pGL3-Basic Vector(a,b,c) lacks eukaryotic promoter and enhancer sequences, allowing maximum flexibility in cloning putative regulatory sequences. Expression of luciferase activity in cells transfected with this plasmid depends on insertion and proper orientation of a functional promoter upstream from luc+. Potential enhancer elements also can be inserted upstream of the promoter or in the BamHI or SalI sites downstream of the luc+ gene.

• The cloned upstream regulatory elements for the gene of interest and the integrated downstream firefly luciferase gene - luc are incorporated into the plasmid vector.

• This plasmid with the regulatory element and the luc gene is transfected into a cell line of choice.

• In dual-luciferase assays, a renilla luciferase-containing control plasmid is co-transfected. This plasmid uses a constitutively active promoter and provides a reference to compare results against the plasmid with the test promoter to the gene of interest. It reduces error due to experimental variations such as transfection and cell-handling.

• Once the plasmids are transfected, mRNA is transcribed by active promoters. The stronger a promoter is, the more mRNA it makes. With regulatory elements the proper signal transduction pathways need to be activated to induce expression.

• Each reporter mRNA is translated into protein. The amount of firefly luciferase is dependent on the activity of the promoter or the regulatory element being studied. The production of the Renilla

luciferase is independent of the experimental promoter or regulatory element activity.

• Results are read on a luminometer. Add luciferase assay reagent to generate luminescent signal and add stop and glo reagent to quench firefly luciferase reaction and start Renilla luciferase reaction.

Receptor and reporter gene constructs.

• The reporter gene pEREtata-Luc was constructed using the enhanced luciferase reporter gene pGL3-basic (Promega, U.S.A.).
• Three tandem repeats of the consensus ERE oligo (GAGCTTAGGTCACTGTGACCT) upstream of the minimal human E1B TATA promoter sequence (GGGTATATAAT) were inserted in the Sma1-Bgl II site of the multiple cloning site of pGL3-basic (Fig. 2).

• The Gal4-HEGO chimeric receptor and Gal4-regulated luciferase reporter gene 17m5-G-Luc were kindly provided by Prof. P. Chambon, INSERM U184, Strasbourg, France.
• Gal4-HEGO consists of the ligand binding domain of the ER linked to the DNA binding domain (1–147) of the yeast transcription factor Gal4 (Green et al., 1988).
• The plasmid 17m5-G-Luc consists of luciferase cDNA regulated by the rabbit b-globin basal promoter and five tandem consensus Gal4 17-mer response elements (Jausons-Loffreda et al., 1994).
• For stable transfection, a second 17m5 luciferase reporter gene (p17m5-G-neo-Luc) was constructed by cloning the Pvu1-EcoRV fragment of pMAMneo-Luc (ClonTech, U.S..) containing the sequence for neomycin (G418) resistance in the Pvu1-EcoRV sites of p17m5-G-Luc.
• For selection of stable transfected clones, the following selection plasmids were used: for puromycin selection, pPur (ClonTech, U.S.); for hygromycin selection, pGK-Hyg (Te Riele et al., 1990); and for G418 selection, pSV2-Neo (Hoglund et al., 1992).

Stable transfection: pEREtata-Luc in T47D cell line.

• Two days prior to transfection, T47D cells were plated at a density of 250,000 cells per 5-cm petri dish on 5 ml of culture medium.
• Per dish, 18 mg pEREtata-Luc and 2 mg pGK-Hyg were transfected using the calcium phosphate precipitation method described above. (Sambrook 1989)
• Cells were incubated with transfection mixture for 8 h, after which the medium was renewed. The following day, the cells were trypsinized and plated over four 5-cm dishes in culture medium supplemented with 100 mg/ml hygromycin (selection medium).
• Clones were allowed to grow for 10 days, during which time the selection medium was renewed every two to three days.
• Dishes containing 10–15 large colonies were trypsinized for 1–3 min at room temperature.
• Individual clones were resuspended in 2 ml medium and added to 24-well plates containing 2 ml selection medium.
• Smaller clones were left an extra 2 weeks to grow and were then isolated in the same manner.
• To test for luciferase induction, confluent 24 wells were trypsinized and one well per clone was seeded in culture medium and incubated for 24 h.
• Non-transfected T47D cells were used as control. Medium was removed and cells were lysed in 55 ml triton-lysis buffer containing 1% triton X-100, 25 mM glycylglycin, 15 mM MgSO4, 4 mM EGTA (pH 7.8), and 1 mM DTT. A 50-ml portion of cell lysate was transferred to a black 96-well plate (Packard) to which 50 ml luciferine substrate (LucLite reporter gene assay kit, Packard) was added.
• Luciferase activity was measured in a scintillation counter (Packard Top Count) for 0.1 min per well.

ER-CALUX assay procedure.

• T47D cells, stably transfected with pEREtata-Luc from the most responsive clone, were plated in 24-well plates at a density of 50,000 cells in 0.8 ml DF without phenol red 1 5% DCC-FCS (assay medium) per well, or in black 96-well viewplates (Packard) at a density of 5000 cells per well in 0.1 ml of assay medium.
• Following a 24-h incubation, cells were approximately 50% confluent. Assay medium was renewed, and the cells were incubated another 24 h. The medium was again renewed, and the cells were dosed in triplicate in 24-well plates by direct addition of the chemical to be tested, dissolved in ethanol or DMSO to the medium above cells.
• For 96-well plates, the assay medium containing chemicals was first prepared in 0.8 ml assay medium in 48-well plates, mixed well, and transferred to the microtiter plate at a volume of 0.1 ml per well.
• In addition to one E2 standard curve in triplicate per experiment, control wells, solvent control wells, and E2 calibration points (6 pM and 30 pM) were included in triplicate on each plate. The maximum solvent concentration used was 0.2%.
• Cells were dosed for 24 h prior to luciferase measurement. For 24-well plates, medium was

removed, and the cells were lysed in 100 ml triton-lysis buffer. Lysis was carried out by gentle shaking at 4°C for a minimum of one h. A 50-ml sample of the cell lysate was then transferred to a black 96-well plate, 50 ml luciferin substrate was added and the luciferase activity was assayed in a scintillation counter for 0.1 min per well. For 96-well viewplates, 50 ml LucLite was added directly to the medium above cells and the plates were shaken gently for 10 min at room temperature to stimulate cell lysis. The transparent bottom of the viewplates was covered by a black sticker prior to luciferase measurement in a scintillation counter (Packard Top Count).

Potential cytotoxicity of pseudo-estrogens was controlled by microscopic visualization of the cells. In addition, a “CytoLite” (Packard) luminescent, non-separation assay kit for the determination of viable cell numbers was used according to manufacturer’s specifications, in black 96-well viewplates. T47D.Luc cells were seeded and exposed to pseudo-estrogens in the same manner as outlined in the ER-CALUX assay procedure.

Data and statistical analysis.

• To determine the EC50 and detection limit of E2 in the ER-CALUX assay, a complete standard curve was included in each assay. The standard curve was fitted (sigmoidal fit, function: y 5 a0

1 a1/ (1 1 exp(-(x–a2) / a3)) using SlideWrite 3.0 for Windows, which determines the fitting coefficients by an iterative process minimizing the c2 merit function (least squares criterion). The EC50 for E2 and pseudo-estrogens was calculated by determining the concentration at which 50% of the maximum luciferase activity was reached using the sigmoidal fit equation. The detection limit was calculated as the luciferase activity elicited by the solvent control plus three times the standard deviation.

• To determine estradiol equivalents (EEQs), the luciferase response by (combinations of) pseudo-estrogens was interpolated in the linear range of the corresponding E2 standard curve for the same assay. Data shown are representative of a minimum of 2 independent assays. Statistical analysis was

performed on luciferase activity by combinations of pseudo-estrogens, as compared to the arithmetic sum of luciferase activity by individual compounds using ANOVA (a 5 0.01) in SPSS Version 6.0 for Windows.

• Standard deviation around the arithmetic sum was calculated by taking the square root of the pooled variance of the luciferase activity by the two individual compounds.

Remove: ERα has also been detected at low levels in the rat thymus (Kuiper 1997).

The lack of Progesterone Receptor expression might be informative. It could indicate aberrant growth factor signaling and molecular crosstalk between Estrogen Receptor and growth factor signaling pathways (Cui 2003, Fuqua 2005). Tumors that are Estrogen Receptor positive and Progesterone Receptor negative are more likely to develop resistance to treatment with hormonal analogs such as Tamoxifen (Arpino 2005, Cui 2005). The Progesterone Receptor negative tumors are also more likely to possess activated transcriptional profiles for oncogenic signaling pathways (Creighton 2008).

• Are these Oncogenic signaling pathways the same second messenger pathways that are initiated through non-traditional non-ligand activation of the estrogen receptor? Could it be that in estrogen receptor defective stage, these non-ligand activation pathways still operate and this is what is demonstrated by the absence of progesterone receptors - that need non-defective estrogen receptor to be expressed. How about other xenoestrogens? Can they activate the receptor even if the receptor is "defective"?

• In bacteria and yeast, fluctuations are assumed to exist mostly in the ergodic regime. This is because, in these simple cells, the fluctuations in the expression of an exogenous fluorescent reporter gene that is not embedded in an endogenous network of gene regulation are fast14–16. moreover, short cell division times.

-----------------

• 14. Elowitz, M. B., Levine, A. J., Siggia, E. D. & Swain, P. S. Stochastic gene expression in a single cell. Science 297, 1183–1186 (2002).
• 15. Rosenfeld, N., Young, J. W., Alon, U., Swain, P. S. & Elowitz, M. B. Gene regulation at the single-cell level. Science 307, 1962–1965 (2005).
• 16. Yu, J., Xiao, J., Ren, X., Lao, K. & Xie, X. S. Probing gene expression in live cells, one protein molecule at a time. Science 311, 1600–1603 (2006).

-----------------

Brock A, Chang H, Huang S. Non-genetic heterogeneity — a mutation-independent driving force for the somatic evolution of tumours. Nature Reviews Genetics. 2009 May;10(5):336-342. Available from: http://dx.doi.org/10.1038/nrg2556.

What are the keyboard Shortcuts on CiteULike?

1. On the posting page

• Ctrl-Enter = Post and Review
• Enter = Post
• Tab = Autocomplete tag suggestion

2. On the "New Blog Article" or "Edit Blog Article" page

• Ctrl-S = Create New Blog Article = Update Blog Article

• Target pathways not genes
• NSAIDs decrease risk
• Chronic inflammation increases risk
• COX2 is present in tumors --> How does that affect these dynamics?
• Chemotherapy promotes resistant clones

Polyak K. Breast cancer: origins and evolution. The Journal of clinical investigation. 2007 November;117(11):3155-3163. Available from: http://dx.doi.org/10.1172/JCI33295.

Breast cancer has pathologically defined stages such as ADH, DCIS and IDC. These pathological stages of breast cancer are heterogenous with respect to mitotic activity and cellular differentiation. This heterogeneity is captured by tumour grading. These pathological stages are similar to each other at the level of the transcriptome.

Distinct stages of progression are evolutionary products of the same clonal origin. Genes conferring invasive growth are active in the preinvasive stage.

Tumour grades are:

• associated with distinct transcriptional signatures
• linked to the DCIS-IDC stage transition.

Samples

• Phenotypically normal epithelial cells from the TDLU
• Phenotypically abnormal epithelial cells constituting different pathological stages of cancer.
• Phenotypically normal breast epithelial cells from mammoplasty reduction specimens - noncancerous.

Aim

1. Discover the consistently up and down regulated genes at each stage of disease progression.

• Patient-matched normal samples are highly similar to those from the mammoplasty reduction specimens. Thus patient-matched normal breast epithelium can serve as an appropriate baseline control for evaliating tumour progression.

• Different synchronous stages of progression within an individual patient cluster more closely to one another than to their respective stage from different patients.
• A pattern of gene-expression correlated with high tumour grade.
• A reciprocal gradient exists in the intensities of grade I and grade III signatures.
• Grade II lesions exihibit a hybrid of grade I and grade III signatures.
• All ADH samples demonstrate a grade I gene-expression signature and cluster with low-grade DCIS and IDC samples.
• Greatest alterations in gene-expression are seen among different histological grades of breast cancer.
• No consistent gene-expression alterations are unique to each of the three different pathological stages of breast cancer.
• Reference 17: Warnberg Nordgren 2001: Several prominent tumour markers correlate with tumour grade but not with distinction between DCIS and grade.

1. ### Cross-Ref: The gene-lists correlate with race but not with tumour markers. (Ambs et. al.) That indicates that these genes are independent of the biological factors that are represented by the tumour markers and indicate independent additional pathways that contribute to tumour biology in addition to the ones already known.
2. ### Link up these genes with survival data
3. ### Put patients on a CRON diet and see how the gene-expression is affected.
4. ### Put patient on CRON diet and see how survival is affected.
5. ### Does survival correlate with gene-expression?
6. ### Does modified survival correlate with gene-expression?

1. RT-PCR verifies microarray-derived results at the level of cellular resolution.

• Is the DCIS-IDC transition associated with subtle quantitative differences in gene-expression?
• A cluster of 29 genes is consistently over-expressed in IDC relative to its matched DCIS especially grade III samples.
• Some of these genes are found within the 100-gene grade III signature
• Genes that are upregulated in grade III DCIS relative to grade I DCIS are further elevated in IDC revealing a link between grade and stage progression.
• Poorly differentiated DCIS are more likely to be associated with occult invasive disease than lower grade disease counterparts
• The transcriptional program that drives tumour cells to an advanced tumour grade may also confer invasiveness.
• Clonal relationship between between distinct pathological stages.
• Gene-expression of early stage disease may reflect progressive potential of the pathological lesion.
• The hybrid nature of grade 2 reflect

1. (a) Mixture of grade 1 and grade 2 cells
2. (b) Transition state from grade 1 to grade 2

The LCM and DNA microarray techniques perform cellular-based, rather than tissue-based gene-expression profile analyses. Since premalignant and preinvasive stages are microscopic in nature, these methods are better than extracting RNA from traditional tissue.

• Hallmarks Weinberg paper
• 4-6: Poor grade is associated with significantly poorer clinical outcome.

Ma, X. J., Salunga, R., Tuggle, J. T., Gaudet, J., Enright, E., McQuary, P.,Payette, T., Pistone, M., Stecker, K., Zhang, B. M., Zhou, Y. X., Varnholt, H., Smith, B., Gadd, M., Chatfield, E., Kessler, J., Baer, T. M., Erlander,M. G., & Sgroi, D. C. (2003). Gene expression profiles of human breast cancer progression. Proceedings of the National Academy of Sciences of the United States of America , 100 (10), 5974-5979.URL http://dx.doi.org/10.1073/pnas.0931261100

Sotiriou C, Pusztai L. Gene-Expression Signatures in Breast Cancer. N Engl J Med. 2009 February;360(8):790-800. Available from: http://dx.doi.org/10.1056/NEJMra0801289.

Gene-Expression signatures can be used only after the validation prospective studies regarding their prognostic significance are concluded.

The meaning of a p-value from a permutation procedure differs from the meaning of a model-based p-value.

• The model-based p-value is the probability of the test statistic, assuming that the gene levels in both the groups follow the model (eg. a Normal distribution).

• A permutation-based p-value tells how rare that test statistic is, among all the random partitions of the actual samples into pseudo-group A and pseudo-group B.

The steps in a permutation-based computation of the significance level of a test statistic are as follows:

* Choose a test statistic, eg. a t-score for a comparison of two groups, * Compute the test statistic for the gene of interest, * Permute the labels on samples at random, and re-compute the test statistic for the rearranged labels; repeat for a large number (perhaps 1,000) permutations, and finally, * Compute the fraction of cases in which the test statistics from permutation exceed the real test statistic from ii).

The p-value for the gene is the fraction of cases in which the randomly permuted samples give a test statistic for that gene, at least as extreme as the one that occurs in the properly labelled samples. Tells us how likely it is that the given test statistics occurs by chance alone

The idea is that if the gene is distributed similarly in both treatment and control groups, then the difference statistic (a t-statistic or any other) will appear about as big in the permuted arrangement, as in the true arrangement. If the gene levels in the treatment group are higher than any levels in the control group, then no value of the permuted statistic will be as great as the true value.

A permutation test needs at least two groups of six samples, in order to have enough different permutations. For two groups of six, there are C(12,6) = 924 permutations that give different groups; although half of these permutations are mirror images of the other half, so the true number of distinct pseudo-scores is 462.

Some statisticians use balanced permutations: where each pseudo-group has roughly equal representation from both the true treatment and the true control group. The true test statistics typically stand out better from this group of permutations, giving more extreme p-values, but at the cost of requiring larger numbers of samples; for example for two groups of six there are only C(6,3)2 /2 = 200 distinct balanced pseudo-groupings.

Sequential progression Vs. Heterogeneity

Roylance R, Gorman P, Papior T, Wan YL, Ives M, Watson JE, et al. A comprehensive study of chromosome 16q in invasive ductal and lobular breast carcinoma using array CGH. Oncogene. 2006 May;25(49):6544-6553. Available from: http://dx.doi.org/10.1038/sj.onc.1209659.

• Conventional CGH suggested that 16q deletions were much less common in Grade II and Grade III Invasive Ductal Cancer than in Grade I Invasive Ductal Cancers and Invasive Lobular Cancers.
• Further support is through morphological data (Millis et al., 1998)
• Led to the hypothesis that for the majority of breast tumours there were parallel pathways of tumour development, with Grade I tumours having a separate pathway from the higher grade tumours (Buerger et al., 1999; Roylance et al., 1999).
• For a minority of high-grade tumours with 16q loss, a progression from low-grade tumours was thought likely and mathematical modelling seemed to support this hypothesis (Korsching et al., 2004).
• It was further hypothesized, from the pattern of genetic changes in Grade I IDCs and ILCs, that ILCs developed along a similar pathway to GI IDCs with loss of E-cadherin leading to the ILC phenotype (Roylance et al., 1999, 2003; Cleton-Jansen, 2002).

Results

• Using the 16q-specific array, although we still found a differential loss of 16q, there was a higher frequency of changes on 16q in the higher grade lesions than expected.
• Furthermore, there were more complex changes seen in all cancers, but especially in the higher grade ductal lesions.
• Interestingly, the peaks and troughs of the frequencies of gains and losses tended to occur at the same sites in each of the four tumour types.
• Cluster analysis showed that most cancers with predominant loss on 16q clustered into a single group, but that, in the other two groups, ILCs were underrepresented compared with IDCs.
• We suspect, therefore, that the relationship between different morphological types of breast cancer is complex and that the previous hypothetical model needs refining.
• Our data remain consistent with a model in which ILCs and Grade I IDCs have a common progenitor and in which the lineages diverge at or soon after 16q loss owing to E-cadherin mutation or silencing causing the lobular phenotype to develop.
• However, unlike in the previous model, our aCGH data are more consistent with a significant number of IDCs showing progression through grades, with subsequent accumulation of segmental gains (and, perhaps, further losses) in the higher grade lesions.
• The fact that aCGH continues to find a lower frequency of any 16q loss in higher grade IDCs compared to GI tumours continues to support the hypothesis that some Grade II/Grade III IDCs develop along a pathogenic pathway which does not involve a precursor stage as a Grade I cancer.

Some high grade tumours have 16q losses - are these products of tumour progression from low grade lesions? Or the ADDITIONAL mutational changes have made them high grade tumours. Maybe the 16q loss becomes irrelevant when the tumours have other mutations tacked on.

Melchor L, Honrado E, Huang J, Alvarez S, Naylor TL, Garcia MJ, et al. Estrogen Receptor Status Could Modulate the Genomic Pattern in Familial and Sporadic Breast Cancer. Clinical Cancer Research. 2007 December;13(24):7305-7313. Available from: http://dx.doi.org/10.1158/1078-0432.CCR-07-0711.

The article talks about the importance of the estrogen receptor in defining some of the characteristics of the heterogeneous prognosis of breast cancer IN ADDITION to the known ER-independent genomic instabilities. Important to defend use of ER in the outcome for breast cancer aggressiveness.

Heterogeneity already exists in pre-invasive lesions such as DCIS.

Vincent-Salomon A, Lucchesi C, Gruel N, Raynal V, Pierron G, Goudefroye R, et al. Integrated genomic and transcriptomic analysis of ductal carcinoma in situ of the breast. Clinical cancer research : an official journal of the American Association for Cancer Research. 2008 April;14(7):1956-1965. Available from: http://dx.doi.org/10.1158/1078-0432.CCR-07-1465.

Loss of heterozygosity (LOH)

• Loss of normal function of one allele of a gene in which -
• The other allele was already inactivated.

This term is mostly used in the context of oncogenesis;

• after an inactivating mutation in one allele of a tumor suppressor gene occurs in the parent's germline cell, it is passed on to the zygote resulting in an offspring that is heterozygous for that allele.

• In oncology, loss of heterozygosity occurs when the remaining functional allele in a somatic cell of the offspring becomes inactivated by mutation.

• This results in no normal tumor suppressor being produced and almost certainly results in tumorigenesis.

If most grade I tumors do not progress to grade III tumors this suggests that groups of breast tumors defined by different grades arise by distinct genetic processes and should be regarded as having distinct biological behaviors. This is a view supported by recent morphological studies in which primary tumors and subsequent recurrences and metastases remain, in most cases, within grade (10).

• Millis, R. R., Barnes, D. M., Lampejo, O. T., Egan, M. K., and Smith, P. Tumor grade does not change between primary and recurrent mammary carcinoma. Eur. J. Cancer, 34: 548–553, 1998.

Epidemiology of Grade of Breast Cancer (UK)

• 6. Elston, C. W., Gresham, G. A., Rao, G. S., Zebro, T., Haybittle, J. L., Houghton, J., and Kearney, G. The Cancer Research Campaign (King’s/Cambridge) trial for early breast cancer: clinico-pathological aspects. Br. J. Cancer, 45: 655–669, 1982.

--> Check References (US)

8Q, 2q correlations with shorter survival and overall aggressive phenotype

• 17. Isola, J. J., Kallioniemi, O. P., Chu, L. W., Fuqua, S. A. W., Hilsenbeck, S. G.,

Osborne, C. K., and Waldman, F. M. Genetic aberrations detected by comparative genomic hybridization predict outcome in node-negative breast cancer. Am. J. Pathol., 147: 905–911, 1995.

Measurement

• Spaced Radiographic measurements
• Counting cycling cells
• Proliferation index
• Ki67 Index
• Determined during exponential growth phase because very small tumours are detected by mammography

Factors

• Proliferation
• Neo-angiogenesis
• Immune System
• Apoptosis
• Necrosis

Variation

• Between patients
• Not between matched primaries and metastases
• Cancer growth rates are an inherent property of cells with similar origin
• More rapid in younger patients compared to older patients

Data: Goes against the linear progression hypothesis

• Median time of metastases detection from surgery is 35 and 20 months (3.x years) when it should be 6-12 years after the seeding of the mets just prior to study.
• Diagnosis of metastasis in early-stage breast cancer
• Cancers of unknown primary (or no detectable) primary tumours

--> Both cases would require that metastatic growth rates outstrip primary tumour growth rates - No evidence to support this scenario.

Data: Parallel Progression hypothesis

• Primary tumour size and early onset and frequency of metastatic disease are correlated

1. --> Lead time bias: The extra time the large tumour takes to grow compared to the small one.
2. --> The metastasis has had a longer time to grow and hence detected earlier.
3. --> Signals from the primary tumour help the secondary tumour to grow
4. --> Stimulation is a function of the size of the tumour

Disseminated cancer cells

• Chromosomal rearrangement differences are found between primary tumours and DTC and between DTCs themselves.
• Doesn't this mean their TVDT would differ as well? How does that remain similar to the primary then? If you base you parallel progression hypothesis on tumour volume doubling time then the DTCs would need to be genetically similar to the primary tumour.

Grade

• Limited genetic changes across cancers
• Therefore, the presence of restricted genetic changes does not provide conclusive insight into the clonality or descent of primary tumours and metastasis, whereas differences indicate the selection of different founder cells.
• Many factors that affect tumour propagation, genetic and epigenetic background (cellular origin), and microenvironment are shared by primary and secondary sites, so the genetic similarities between primary tumours and metastases may reflect convergent evolution.

Do some set of activated genes make cancers more successful than others? Should success be measured in mortality?

Organ Failure

Clinical Syndromes with Cytokine Overproduction

• Cachexia
• Thrombotic Syndromes

* *

Source: What are Cytokeratins?

• Intermediate filament keratins
• Intermediate filaments: Cytoskeletal elements - range in diameter between thin filaments (actin) and microtubules. Work together to enhance both structural integrity, cell shape, and cell and organelle motility. Intermediate filaments are stable, durable. They range in diameter from 8-10 nm (intermediate in size compared with thin filaments and microtubules). They are prominent in cells that withstand mechanical stress and are the most insoluble part of the cell. The intermediate filaments can be dissociated by urea.
• Keratins: Keratins are a family of fibrous structural proteins
• Found in the intracytoplasmic cytoskeleton of epithelial tissue.

Types of intermediate filament

• Type I: Acidic CK9 - CK20
• Type II: Basic CK1 - CK8
• Type III: Vimentin, Desmin, Glial Fibrillary acidic protein, peripherin
• Type IV: Neurofilaments, Nestin, Internexin
• Type V: Laminins

Mammary Cytokeratins

• Made up of pairs of Type I and II

Types

• Low molecular weight acidic type I cytokeratins
• High molecular weight basic or neutral type II cytokeratins.

Proof Check

1. HMW CK1, CK2, CK3, CK4, CK5, CK6, CK7, CK8 and CK9.
2. LMW CK10, CK12, CK 13, CK14, CK16, CK17, CK18, CK19 and CK20.

Location

• Moll table of cytokeratins
• Eg. CK7 is typically expressed in the ductal epithelium of the genitourinary (GU) tract
• Eg. CK20 most commonly in the gastrointestinal (GI) tract
• Cytokeratin profile tends to remain constant when an epithelium undergoes malignant transformation.
• CK18 and CK19 are ubiquitously expressed.

Staining Sidenote

• Basic dye hematoxylin, which colors basophilic structures with blue-purple hue.

1. Nucleic acids, such as the ribosomes
2. Chromatin-rich cell nucleus
3. Cytoplasmatic regions rich in RNA.

• Alcohol-based acidic eosin Y, which colors eosinophilic structures bright pink.

1. Intracellular or extracellular protein.
2. Lewy bodies and Mallory bodies
3. Most of the cytoplasm
4. Red blood cells are stained intensely red.

Mechanism of release and action

• CK18 is cleaved at 2 distinct sites by different caspases during apoptosis. So is CK19.
• CK14 is cleaved at the sequence VEMD/A, a sequence which is found in CK12–CK17
• All type I keratins with the exception of CK9 and CK10 are potential caspase substrates.
• Reorganization of the cytoskeleton is required for apoptosis of epithelial cells.
• Possibly released from apoptotic or necrotic tumour cells.
• Necrosis releases CKs not cleaved by caspases.
• Apoptosis releases CKs cleaved by caspases.

Linder S. Cytokeratin markers come of age. Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine. 2007;28(4):189-195. Available from: http://dx.doi.org/10.1159/000107582.

GENOMIC INSTABILITY The failure to transmit an accurate copy of the entire genome from one cell to its two daughter cells. Note that this term does not describe a state but, rather, a process.

• Increased rate of damage
• Defective repair systems
• Inappropriate segregation: Gains or losses of entire chromosomes
• Inappropriate recombination

Damage Mechanisms

• Exogenous damage
• Endogenous damage

Repair

• Deficiencies in repair enzymes
• Checkpoint defects
• BRCA1 and BRCA2 are both involved in homologous recombination repair of DNA double strand breaks. Sister chromosome used as a framework to ensure proper repair.

Chromosomal Level Disorders

• Trisomies - one extra chromosome - chromosome-wide gene amplification. Extra gene copies spanning an entire chromosome.
• Aneuploidy; The occurrence of one or more extra or missing chromosomes leading to an unbalanced chromosome complement, or, any chromosome number that is not an exact multiple of the haploid number
• Chromosomal Translocations
• Microsatellite Repeat Sequences

Techniques

• Comparative Genomic Hybridization: Comparison of fluorescent tagged PCR copies of tumour and normal cell DNA - reveals allelic imbalances in the form of amplifications and deletions. Translocations, inversions and insertions are not revealed

Gobert M, Treilleux I, Bendriss-Vermare N, Bachelot T, Goddard-Leon S, Arfi V, et al. Regulatory T Cells Recruited through CCL22/CCR4 Are Selectively Activated in Lymphoid Infiltrates Surrounding Primary Breast Tumors and Lead to an Adverse Clinical Outcome. Cancer Res. 2009 March;69(5):2000-2009. Available from: http://dx.doi.org/10.1158/0008-5472.CAN-08-2360.

Lubin JH, Gail MH. Biased selection of controls for case-control analyses of cohort studies. Biometrics. 1984 March;40(1):63-75. Available from: http://view.ncbi.nlm.nih.gov/pubmed/6375751.

• Cancer cells metabolize glucose by aerobic glycolysis instead of oxidative phosphorylation.

References:

Hsu PP, Sabatini DM. Cancer Cell Metabolism: Warburg and Beyond. Cell. 2008 September;134(5):703-707. Available from: http://dx.doi.org/10.1016/j.cell.2008.08.021.

Sakaguchi S, Yamaguchi T, Nomura T, Ono M. Regulatory T cells and immune tolerance. Cell. 2008 May;133(5):775-787. Available from: http://dx.doi.org/10.1016/j.cell.2008.05.009. Link

Follow-Up Blog Questions

Five types of Cell-intrinsic T-cell regulation against self-attack

• Receptor editing: Self-reactive receptors are edited out.
• Anergy of the self-reactive T-cells on encountering self-antigens
• Apoptosis of the self-reactive T-cells
• Activation thresholds are raised: armed with inhibitory receptors or negative signalling molecules
• Short life because of activation induced cell-death

Statistics for generating a continuous variable from a categorical variable with a latent construct.

Wei GCG, Tanner MA. A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms. Journal of the American Statistical Association. 1990;85(411):699-704. Available from: http://dx.doi.org/10.2307/2290005. Link

BALB/c

• Particularly well known for the production of plasmacytomas on injection with mineral oil: an important process for the production of monoclonal antibodies.
• Low mammary tumour incidence
• Develop other types of cancers in later life, most commonly reticular neoplasms, lung tumours, and renal tumours.
• Most substrains have a "long reproductive life-span"
• Noted for displaying high levels of anxiety
• Relatively resistant to diet-induced atherosclerosis, making them a useful model for cardiovascular research.
• Differences between different BALB/c substrains, due to mutation rather than genetic contamination. # Male BALB/c mice are aggressive and will fight other males if housed together.

1. BALB/Lac substrain is much more docile
2. BALB/cWt is also unusual in that 3% of progeny display true hermaphroditism.

The number of autoantibodies increase with age

References:

1. CiteULike Wilder RL. Neuroendocrine-immune system interactions and autoimmunity. Annual review of immunology. 1995;13:307-338. Available from: http://dx.doi.org/10.1146/annurev.iy.13.040195.001515.

1. Fairweather D, Rose NR: Immunopathogenesis of autoimmune disease. Immunotoxicology and Immunopharmacology, ed 3. Edited by Luebke R, House R, Kimber I. Boca Raton, CRC Press, 2007, pp 423–436

The number of Tregs increase with age

References:

1. CiteULike Simone R, Zicca A, Saverino D. The frequency of regulatory CD3+CD8+CD28-CD25+ T lymphocytes in human peripheral blood increases with age. Journal of leukocyte biology. 2008 December;84(6):1454-1461. Available from: http://dx.doi.org/10.1189/jlb.0907627.
2. CiteULike Sharma S, Dominguez AL, Lustgarten J. High Accumulation of T Regulatory Cells Prevents the Activation of Immune Responses in Aged Animals. J Immunol. 2006 December;177(12):8348-8355. Available from: http://www.jimmunol.org/cgi/content/abstract/177/12/8348.

The Adaptive Immune System

• Conservation of scarce resources by specifying a narrow immune response.
• Treg cells make it even more specific - and hence are even scarcer in old age because we can't afford a ton of energy spent in non-specific inflammation.

Impairment of antigen presentation

• Downregulation of specific antigen eg. Melan-A/MART-1 silenced via promoter (epigenetic silencing?)
• Mutation of antigen producing genes -> Antigenic drift.
• Defect in antigen presenting machinery: Antigen transporter (TAP) Immunoproteosome defects
• Loss of MHC molecules
• Decreased expression of HLA-A/HLA-B alleles
• Complete loss of HLA Class-I (Different mutations involving the Beta-2 microglobulin gene)
• Hemizygous loss of HLA - ABC alleles -> large deletions in Chromosome 6
• Loss of single HLA alleles
• Expression of non-classical HLA-G and HLA-E belong to MHC class Ib

Activation of negative costimulatory/Regulatory Pathways

• CTLA-4
• Rrogrammed death-1 (PD-1) and programmed death receptor ligand-1 (PD-L1)

represents a clear example of how negative costimulatory signals may contribute to create an immunosuppressive microenvironment at the tumor site [144]. Zha Y, Blank C, Gajewski TF (2004) Negative regulation of Tcell function by PD-1. Crit Rev Immunol 24:229–237

• PD-L1, engages the inducible inhibitory receptor on activated T cells called

PD-1 and induces phosphorylation of an immunoreceptor tyrosine-based inhibitory motif (ITIM) [144].

• Dong et al. demonstrated the presence of PD-L1 (also called B7-H1)

on the cell surface of a wide range of tumors [28]. The expression of PD-L1 on tumor cells of diverse histological origins suggested that this molecule might contribute to tumor-immune escape. In fact, it has been demonstrated that cancer cell-associated PD-L1 promotes apoptosis of antigen-specific human T cell clones in vitro and in vivo [8]. In addition, blockade of PD-L1 enhances DC-mediated T-cell activation and limited tumor growth, suggesting another potential mechanism by which PD-L1 restrains T cell–mediated anti-tumor immunity [15].

Active Immunosuppressive Pathways

Resistance to Apoptosis/Effector Mechanisms

• Overexpression of antiapoptotic molecules: FLIP L,S, Bcl2, Bcl xl, Mcl-1, Survivin
• Inhibition of perforin/granzyme cytotoxic pathway: Upregulation of serine protease inhibitor PI-9/SPI-6
• Soluble receptors that act as decoys for death ligands

Elaboration of immunosuppressive factors

• Indoleamine 2,3 dioxygenase IDO: Creates potent immunosuppression by thus far unknown mechanism. Under control of Bin1

Expansion and Recruitment of regulatory cell populations

• Tregs

1. Attract Tregs to tumour sites by expressing chemokines such as CCL22

• Myeloid Suppressor Cells
• Mature and immature regulatory dendritic cells

=== CD3-Zeta and Cancer Stage===

• Tumor induced degradation of the CD3-Zeta chain.
• Tumor cells can induce the activation of intracellular peptidases in T lymphocytes

that is responsible for decreased or absent expression of signal transduction molecules, including the CD3-Zeta chain in activated T cells [140].

• Generation of free oxygen radicals and increased arginase activity within the tumor microenvironment, have been proposed to account for decreased CD3-Zeta expression in cancer [57, 109].
• Human T cells stimulated and cultured in the absence of Larginine lose the expression of the CD3-Zeta chain and demonstrate impaired proliferation and decreased cytokine production [109].
• Regulation of L-arginine concentration in the microenvironment could represent an

important mechanism via which the expression of CD3-Zeta chain is modulated, with critical consequences in TCR mediated signaling and T cell function.

Tumour induced alteration of Signal Transduction Molecules

• Aberrant activation of STAT3 (Signal transducer and activator of transcription 3)
• STAT3 signaling suppresses all immune responses.
• Inhibition of STAT3 upregulates a subset of immunoregulatory genes - chemokines that promote recruitment of effector T-cells

Death signals

• Expression of FasL on tumours sent death signals to effector T-cells with Fas receptors

• Luminal turs generally express the ER with or without coexpression of the progesterone receptor(PR).

• Basal-like tumors are defined by expression of cytokeratins (CKs) 5, 14 and 17 and a lack of ER, PR, and HER2 expression.

• It must be emphasized that the distinction between the subtypes in these transcriptomic studies is based upon a large panel of genes rather than single markers. Nonetheless, ER seems to be one of a number of genes the expression of which is almost exclusively limited to one of these major subtypes, making it one of the strongest discriminatory genes.

• Tools that more accurately calculate this

risk, combined with the publication of treatment guidelines, have helped to guide clinicians and patients alike, but these advances have inevitably led to gross overtreatment for the majority of women concerned.65–67 65 Goldhirsch A et al. (2005) Meeting highlights: international expert consensus on the primary therapy of early breast cancer 2005. Ann Oncol 16: 1569–1583 66 Haybittle JL et al. (1982) A prognostic index in primary breast cancer. Br J Cancer 45: 361–366 67 Ravdin PM et al. (2001) Computer program to assist in making decisions about adjuvant therapy for women with early breast cancer. J Clin Oncol 19: 980–991

My most favourite support-person ever.

Epidemiological Evidence

Ref: http://www.citeulike.org/user/Zephyrus/article/4052332

• Higher estrogen-receptor positive cancers in the western world
• Higher breast cancer in urban versus rural areas.
• All known risk factors explain only 30-50% of breast cancer incidence.
• Marked changes in risk following migration
• Rates of breast cancer are increasing @ 0.5% annually. Estimated 1.35 million new cases in 2010
• Conservative increase of 3% (in China) -> 1.45 million new cases by 2010. 82% increase from 1990
• Caucasians have a higher prevalence of HR+ breast cancer
• Increase in the proportion of ER+ve breast cancer, ER-ve breast cancers have remained the same.
• Increase in incidence of breast cancer in the US has been due to ER+ cancer
• HR+ tumours are mostly associated with reproductive risk factors
• Reproductive risk factors have changed:

1. Earlier menarche
2. Later first childbirth
3. Lesser number of childbirths
4. Lesser duration of lactation
5. Later menopause
6. HRT

--> Explain only 50% of breast cancer

The other 50%

• Xenoestrogens?
• Equivocal results because

1. XE might be associated with specific breast cancer subtypes
2. Interaction of several xenoestrogens with each other.
3. Absence of correct measures to assess xenoestrogen exposure

TEXB assessment

• Linked postmenopausal breast cancer with highest levels of TEXB-Alpha
• Gene-environment interactions between xenoestrogens and the cytochrome enzyme system

Types of Xenoestrogens

• Long acting

1. Lipid Soluble
2. Remain in body for decades in adipose tissue eg. POP
3. Leach into circulation at a constant rate

• Short acting

1. Water Soluble
2. Sources: Plastics, bicarbonate bottles, cosmetics, food preservatives etc.
3. Bisphenol-A and parabens are excreted out the body - however, patients have a constant exposure to them in the form of what they ordinarily use.

Mechanism of Action

• Same pathway as natural estrogens eg BPA
• Stimulate ER in-vitro eg. nonylphenol
• Tamoxifen increases the agonistic action of xenoestrogens on mutant ERs - drug refractoriness and drug resistance

Three Types of Stem Cells: Differences in age at which these are expressed.

• HR Negative
• HR Heterogenous
• HR Positive

Public Health Significance

• Pesticide use has been increasing due to growing agricultural needs
• Developing countries may still use some banned pesticides
• Exponential use in short acting xenoestrogens due to increasing use of plastics
• North America and Europe use 80% of the plastic bags in the world
• Quarter of these bags made in Asia
• Apart from bags, plastic is also used in food and drink containers, electronics, medical products

etc.

• Short acting xenoestrogens are also seen in other categories of products such as food preservatives, cosmetics, detergents

etc.

Phase 1 trials

• 'The safe dose range (with escalating doses)'
• 'The side effects'
• How the body copes with the drug - pharmacodynamics and pharmacokinetics
• If the treatment shrinks the cancer - often have advanced disease
• Enrolment: Small, ditch attempt in advanced disease

Phase 2 trials

• Larger Sample than phase 1
• If the new treatment works well enough to test in a larger phase 3 trial
• Which types of cancer the treatment works for
• More about side effects and how to manage them
• More about the best dose to use

Phase 3 trials

• Larger Sample than phase 2
• Compare a completely new treatment with the standard treatment
• Different doses or ways of giving a standard treatment
• A new radiotherapy schedule with the standard one

Randomisation

• Drugs/Therapy/exposure is randomized.

Overview studies

• Meta-analysis of Phase III trials

Phase 4 trials

• More about the side effects and safety of the drug
• What the long term risks and benefits are
• How well the drug works when it’s used more widely than in clinical trials

Reference: Introduction to Apoptosis

• If the cells don't have estrogen receptors - they cannot transduce estrogenic signals.
• When we say that a breast cancer is ER-ve, do we mean that its stroma is ER-ve as well?

• G-Protein Coupled hormone receptors in other membranes such as Endoplasmic Reticulum and the plasma membrane bring about quick non-nuclear effects through G-protein initiated signaling cascades.

Hypothalamus → GnRH → Pituitary → FSH → Follicle → Estrogen Hypothalamus → GnRH → Pituitary → LH → Corpus luteum → Progesterone

Suppression:

• IFN-Gamma and IL-4 mutually suppress each other
• TGF-Beta suppresses Th1 and Th2
• IFN-Gamma and IL-4 inhibit Th17 differentiation

Mechanism -> Interaction of Master Genes -> Interaction between lineage-specific transcription factors

• T-bet suppresses GATA-3 function by direct binding of the factors
• ROR-Gamma is suppressed by direct protein-protein binding with Foxp3
• Low TGF-Beta induces ROR-Gammat and High TGF-Beta induces Foxp3

Mechanism: Competition for DNA binding

• Stat5 competes with Stat3 for binding to promoter of IL-17 (Consequence: IL-17 Suppressed)

Mechanism: Transcriptional Control of critical factors

• GATA3 (Th2) downregulates Stat4 (Th1)
• Stat5 (Treg) activation inhibits T-bet (Th1) expression
• T-bet (Th1) can suppress GATA3 (Th2) expression

Mechanism: At the level of cytokine transcription

• Foxp3 suppresses IL-2 by binding to NFAT as well as RUNX1
• RUNX3 blocks IL-4 production by binding to HSIV region of the IL-4 locus
• GATA-3 deficiency leads to spontaneous IFN-Gamma production
• Gfi1 (Th2) suppresses IFN-Gamma and IL-17
• Interchromosomal interaction between Ifng and IL4

Epigenetic Changes:

• The Locus Control Region of IL-4 and IL13 is under some epigenetic control.
• A site within this LCR called RHS7 is becomes DNAase I hypersensitive. (DNAase 1 is able to approach it easily and fragment the DNA)
• It is demethylated within 48 hours of initiation of Th2 differentiation.
• This demethylation seems to be partially dependent on IL-2 and Stat5 (Th2) signalling
• GATA-3 and Stat5 collaborate in regulating the LCR
• GATA-3 and Stat5 bind to sequences in the IL-4 locus and lead to accessibility - measured by patterns of histone modification or restriction enzyme accessibility.

Monoallelic Expression

• Some cytokine genes are expressed monoallelically.
• The probability of expression of the IL-4 or IL-3 allele is determined by its pattern of gene-accessibility.
• Cells only express only one of the two alleles during any one stimulation period.
• Some Th2 cells may not make IL-4 in one stimulation cycle and this will regulate the IL-4 control of immunoglobulin class switching to IgE

Chromatin is remodeled (and thus, nucleosome structure is altered) primarily through posttranslational modifications of histone proteins, which actually change the conformation of nearby DNA. For instance, histone acetylation reduces the positive charge of the histone proteins, thereby reducing their affinity for DNA and causing the chromatin to open (Figure 2). This in turn permits some transcription factors to bind to the DNA. Indeed, the "open" or "closed" state of the chromatin near a particular gene can be revealed by examining DNA sensitivity to the enzyme DNAse I by way of a procedure known as a DNAse sensitivity assay. This technique is based on the fact that DNAse I degrades open DNA more quickly than closed DNA. Not only can this type of assay provide information about whether gene expression changes are accompanied by chromatin remodeling, but it can also show where remodeling is taking place.

A major product of the differentiated cells is a principal stimulant, providing a potent positive feedback that can enforce the development of a high degree of polarization. The Jak/Stat pathways and a specific Stat in association with one of 4 master regulators, T-bet, GATA-3, ROR
t, and Foxp3, are essential for the differentiation process. In

Subsets:

• Discovered on the basis of their different cytokine products.

Other not so well-known T-cell subsets:

• Th3: Transforming growth factor-Beta producing cells induced by oral tolerance. Inducible regulatory T-cells that express Foxp3
• TR1: IL10 producing cells.

TH1

• Against intracellular pathogens
• Reistance to mycobacterial infections
• Cytokine Products: IFN-Gamma. Lymphotoxin-Alpha. IL-2
• IFN-Gamma -> Increase macrophage microbicidal activity
• IL-2: CD4 T-cell memory
• Lt-Alpha: Marker for disease progression in MS
• IL-2: Stimulates CD8 cells -> critical for CD8 memory formation
• IL-2: Also important for CD4 Tcell memory

TH2

• Allergy. Asthma. Defence againt parasites
• IL4, Il5, Il-9, IL-10, IL-13, IL-25. Amphiregulin.
• Il-4: Positive feedback for Th2 cell differentiation
• Il-4: IgE class switching in B cells.
• Il-5: Critical role in recruiting esosinophils
• IL-9: Mast cells. Lymphocytes. Mucin production in epithelial cells during allergic reactions.
• IL-10: Suppresses TH1 proliferation. Suppresses dendritic cell function
• IL-13: Effector cytokine in the expulsion of helminths. Induction of airway hypersensitivity.
• Amphiregulin: Member of the epithelial growth factor family. Induces epithelial cell proliferation. Induction of airway hypersensitivity. Expulsion of certain helminths are delayed in its absence.
• IL-25: Induce CCL5 and CCL11 tha recruit eosinophils. Enhances IL-4 IL-5 and IL13 by a unique non lymphocyte population --> C-kit Fc-Epsilon-TI-ve

TH17

• Against extracellular bacteria and fungi
• Do not produce Th1 and Th2 cytokine products
• Express low levels of Tbet (Th1) and GATA3 (TH2) <- The transcription factors
• IL-4 and IFN-Gamma suppress Th17 cell differentiation.
• Can arise from naïve cells by stimulation through T-cell receptor in the presence of IL6 and TGF-Beta
• ROR-Gamma is the master regulator gene.
• HIgh levels of TGF-Beta inhibit T-cell differentiation and favour iTreg differentiation.
• Il-6, IL-21 and IL-23 use STAT3 for signal transduction.
• IL-17: Induces IL-6 and IL-8. Recruit and activate neutrophils.
• IL-21: Positive feedback Amplifier.

iTreg

• Activated naïve CD4 T-cells stimulated by TGF-Beta in the absence of proinflammatory cytokines develop into iTreg cells.

''' Suppressive Function '''

• Production of IL-10. IL-35 and TGF-Beta
• IL-10 is a negative regulatory mechanism for limiting immune responses.

More Cytokines and Chemokines

• Th1

1. IL-12 Receptor Complex Hyperresponsiveness
2. IL-18 Receptor activation: Synergize with Il-12 to induce IFN-Gamma
3. CXCR3: preferential expression in Th1 cells
4. CCR5: preferential expression in Th1 cells

• Th2

1. IL-4 Receptor is upregulated by IL-4
2. CD25 expression is higher in Th2 cells than in Th1 cells (Action of c-Maf )
3. CD25 confers hyperresponsiveness to IL-2 <-Does this mean that Tregs are also hyperresponsive to IL2??
4. Cell Surface Marker: T1/ST2 (IL-33R-alpha) or (IL1 Receptor like 1)
5. CCR3, CCR4, CCR8 CRTh2

• Th2 Like

1. IL-9
2. IL-10

• Th17

1. IL-23 Receptor
2. IL1 Receptor1: Critical for Il-17 production
3. IL-18 Receptor-alpha
4. CCR6 and CCR4

• Treg

1. CD25 (IS the IL-2 receptor)

(These three can be induced in any other naïve conventional CD4 T-cell but are expressed in Tregs without need for activation. NO IL7 - Receptor Alpha)

1. CTLA-4
2. GITR
3. Folr4

---

1. CD103 (Alpha E Integrin) <-- Check recent integrin research

Transcription Factors

Common Factors in all lineages --> They are critically involved in cytokine production upon TCR and/or cytokine stimulation. Not directly determining T-helper lineage fates.

1. Nuclear Factor of Activated T Cell (NFAT)
2. NF-Kappab
3. Activator Protein 1 (AP1)

• Th1

1. T-bet -> master regulator of Th1
2. Stat1 --> Major transducer of IFN-Gamma signal. Induces IFN-Gamma mediated induction of T-bet.

The following are involved in IFN-Gamma Production

1. Eomesodermin (Eomes): A member of T-box family upregulated during Th1 differentiation. Also involved in IFN-gamma production
2. Stat4 --> An IL12 signal transducer. Amplifies Th1 responses. Directly induces IFN-Gamma in activated CD4 cells
3. IL-12 and Stat4 can cause IFN-Gamma production independent of TCR stimulation.
4. RunX3: Transcriptional Repressor important for silencing CD4 during CD8 development. Overexpression induces IFN-Gamma production.
5. H1X: Interacts with T-bet and enhances T-bet mediated IFN-Gamma production.

• Th2

1. Stat6: Signal Transducer in IL-4 mediated Th2 differentiation (Not essential for Th2 responses in vivo)
2. GATA-3: Th2 Master Regulator Gene: Overexpression induces Il-4 production. Deleting this leads to Th2 development failure
3. Deleting GATA3 in fully differentiated Th2 cells: Blocks production of Il-5 and IL-13, only modest effect on IL-4
4. Stst5a: Responsible for cytokine driven cell proliferation and cell survival
5. C-MAF: Enhances IL-4 production.
6. IR-4: Upregulates GATA-3
7. Gfi-1: IL-4 inducible gene. Selects GATA-3 high cells for growth.

• Th17

1. ROR-Gamma
2. ROR-Alpha: Little IL-17 production
3. STAT3: Signal transduction for Il-6, IL-21 and IL-23. Induces Il-23 Receptor. Deletion leads to loss of IL-17 producing cells.
4. IRF-4 (Interferon Regulatory Factor-4): Plays a role in ROR-Gamma Expression.

• Treg

1. FOXP3: Diminished production of FOXP3 expression converts Treg cells to Th2 like cells. Close relations: Tregs and Th2 lineages.
2. STAT-5: Activation by IL-2 is important for Th2 as well as Treg development

T-Helper Cell 1 Differentiation

• APC -> Activate -> Produce IL-12
• IL-12 activate NK cells to produce IFN-Gamma
• IFN-Gamma activates Stat1
• Stat1 upregulates T-bet expression
• T-bet induces IFN-Gamma production and Upregulates IL-12-Beta-2
• IL-12 also activates Stat4 -> induction of IFN-Gamma -> Sustains expression of IL-12 Receptor Beta2

Collaboration between IFN-Gamma and IL-12 induces full Th1 differentiation

• IL-12 and IL-18 induce IFN-Gamma production independent of TCR stimulation.

T-Helper Cell 2 Differentiation

• Exogenous IL-4 mediated STAT6 activation induces GATA-3 expression.
• IL-2 mediated Stat5 activation also produces IL-4 (Low strength signal scenario)
• IL-4/Stat6 pathway also induces Gfi-1
• GATA-3 binds to IL-4/IL-3 loci
• The IL-2/STAT5 activation maintains accessibility at second intron HSII and HSIII DNAase I hypersensitive sites of the IL-4 locus

IN VITRO: Stat5 and GATA collaboration account for full Th2 differentiation IL4 is not necessary for invivo differentiation.

• In vivo: IL-4 independent GATA-3 activation maybe due to hyperactivation of Stat5 by cytokines: Il2, Il7 or TSLP

T-Helper Cell 17 Differentiation

• TGF-Beta
• TGF-Beta induces TH-17 differentiation, IL-21 production, ROR-Gamma and Il-23R in the presence of IL-6
• IL21 can induce ROR-Gamma and IL-17 production. <-- Amplification stage
• IL23 <-- Stabilization stage: Critical for Th17 cell survival and maintaining its function
• IL-6, IL-21 and IL-23 activate Stat3.

T-Regulatory Cell Differentiation

• TGF-BEta activates Smad3
• TCR stimulation activates NFAT activation
• Smad3 and NFAT collaborate in remodeling Foxp3 enhancer region and promote Foxp3 expression
• Il-2 mediated Stat5 activation induces Foxp3
• Both IL-2 and TGF-Beta required for survival and function of tregs

• Control of multiple pathways and comorbidities.
• Energy imbalance roles may actually regulate recovery as well.

T-cell Biology

• Do concentrations of cytokines matter just as concentrations of hormones matter?

In hindsight, this is perhaps not unexpected because it is unlikely that just a few terminally differentiated effector T cell subsets (such as TH1 cells, TH2 cells and IL-17-producing T helper cells (TH-17 cells)) with fixed phenotypes could provide immunity against the multitude of parasites, bacteria and viruses that invade and infect hosts.

• But that's not the point of the cytokines released by the final phenotypes. They just attract the various foot soldiers to the scene to kill the invaders. The TCRs on the final phenotypes differ anyway. Right? Look up TCR variety on Th1 and Th2 cells. Also they don't bind soluble epitopes - they only bind peptide-MHC complexes. Each T-cell regardless of the family phenotype expresses a unique TCR.

T cells receive their ‘primary education’ in the thymus; they develop with very few distinct features and have limited functional characteristics. As these naive, inexperienced T cells enter the systemic circulation and move into lymph nodes, they receive their ‘secondary education’ as they encounter their cognate antigen. This interaction triggers ‘downstream’ signaling events and chromatin rearrangement to ‘imprint’ specific cytokine ‘signatures’ on these activated cells.

• Chromatin Rearrangement is triggered by signaling pathways?
• Does that mean epigenetic events are actually controlled by antigenic interaction?
• Thus these epigenetic events are almost pre-programmed because they are linked to signaling pathways.

Effector T cells may undergo further instruction when they finally enter the ‘real world’ of their target organs. In tissues such as the lung, intestine and skin, T cells are exposed to the local cytokine environment, which drives them to a final effector phenotype important for successful host defense.

• Thus the microenvironment of the tumour might determine the final fates of the T-cell actions? Does more positive and negative selections occur?

Thesis

• Need a Foxp3-GFP reporter system.

New Experimental evidence for differential transformation of the Th2 phenotype

• IL-6 and IL-27 inhibit TGF-Beta mediated induction of FoxP3
• IL-4 blocks TGF-Beta dependent Foxp3 expression, alternatively activated cells produce IL-9 instead of Il-4, IL-5 and IL-13.
• Activation of STAT6 is needed for activation of IL9 producing cells. IL-4 inhibition of Foxp3 expression is also STAT6 dependent.

====? Check STAT6 and breast cancer ====

• T-bet/STAT4(Th1), Foxp3/STAT5(Treg), ROR-Gamma/STAT3(Th17) and GATA3/STAT5(Th2) are absent from this IL-9 producing cells.
• But GATA3 is initially required for generation of IL9/IL10 cells. This opposed TGF-Beta induced FoxP3 activation
• Downregulation of Th2 signature cytokines when T-cells are stimulated with a combination of IL4 and TGF-B

• No evidence of a unique transcription factor that promotes the IL-9 Il-10 secreting phenotype. This phenotype also does not have regulatory functions. They are highly specialized offshoot fo Th2 cells. Similar offshoots may arise from the Th1 lineage as well. We already know of Treg suppressive variants.

• Target inflammatory cytokine levels not the cells themselves? Because T-cell therapies might fail according to the host microenvironment. A bigger upstream control might be hormones?

A higher control are genes but since manipulation is tough - maybe that's not doable.

• If cytokine mileau shapes phenotypic function then probably should be quantifying cytokine levels with the panel as well!?

Key regulatory cytokines could generate T-cells with relatively stable phenotype. MORE BLOOD! Misery. :(

• Be more flexible in viewing T-cell differentiation and function.

• A way to reverse positive and negative selection of T-cells in the thymus?

Positive and Negative Selection

Interaction with Transcription Factors

Pro: Proline rich region. ZnF: Zinc finger domain. LZ: Leucine Zipper Region. FHD: Forkhead box. FoxP3: Forkhead box P3

• NFAT: Nuclear Factor of Activated T-cells

Help Fox-P3 to:

1. Repress IL-2
2. Activate CTLA-4
3. Activate CD25
4. Suppress normal T-cells when expressed in them

• AML/Runx-1: Organizes and facilitates assembly of transcriptional activation complexes.

1. Knockdown causes autoimmune disease similar to Treg depletion.

• HATs: TIP60. HDACs

1. FOXP3 acetylated by TIP60 - enhances binding of FoxP3 to the IL2 promoter.

FoxP3 Target Genes

• IL-2
• CD25
• CTLA-4
• GITR

Genomewide FoxP3 targets

Expressed in natural Tregs and Foxp3-transduced T-cells

• Signalling: G protein coupled receptor 83
• Microenvironment: Extracellular matrix 1
• Gene encoding Granzyme B: Specific to Tregs but expressed independently of FoxP3
• Transcription factor Helios: Specific to Tregs but expressed independently of FoxP3

• FoxP3 binds to 10% of the hundreds of genes it controls.

?? How else does it control them?

1. Signal transduction molecules Zap70 and Ptpn22
2. Transcription factors (such as Crem)
3. Cytokines (e.g.,Il2)
4. Cell-surface molecules (such as Il2ra, Ctla4, and FasL)
5. Enzymes for cell metabolism (such as Pde3b)-> What metabolism does it control?
6. Intergenic microRNAs (such as miR-155).

Basic Mechanism of action of FoxP3

• Hijack of transcription of effector T-cells so that they morph into Tregs instead.\
• Helps in suppressive quality of Tregs as well.
• Interacts with ROR-Gamma - and inhibits differentiation naive T-cells to Th17 cells.
• Lack of Tregs leads to more spontaneous differentiation into other effector T-cells.

In a scenario of immune dysfunction

1. When there are low levels of effector T-cells is Foxp3 expression higher?
2. Is this foxp3 expression predominantly due to carcinomatous cells?
3. Maybe control of tregs is just a "side-effect" of one of the myriad effects of FoxP3.
4. Is a measure of Tregs a good proxy measure for how much the tumour has attempted to subvert the microenvironment and how much control of regular processes the tumour actually has?
5. What is estrogenic effect on FoxP3 - if its a hormonally responsive element then manipulating this effect might block the hormones key effects on signalling pathways as well as genetic pathways - via FoxP3.

• How are ERs imported into the mitochondria?
• Are both or either ER-alpha and ER-beta directly involved in E2 induced MRC protein synthesis?
• Do ERs mediate the via their interactions with transcription factors within mitochondria?

1. E2-induced MRC protein synthesis and activity

• What are the physiological and pathological implications of the over-abundance of E2/ER-mediated mitochondrial effects in cancer cells?

Effects of Estrogens

• Regulates MRC energy metabolism.
• Simulation of cell proliferation
• Inhibition of apoptosis
• Oxidative damage to the mitochondrial DNA.

Gail

• Personal Information

1. Age

• Hormonal Factors

1. Menarche
2. First Live birth

• Breast disease

1. Breast Biopsy
2. Atypical Ductal Hyperplasia

• Family history

1. 1st degree relatives

Tyler-Cuzick

• Personal Information

1. Age
2. BMI

• Hormonal Factors

1. Menarche
2. First Live Birth
3. Menopause
4. HRT

• Breast disease

1. Breast Biopsy
2. Atypical Ductal Hyperplasia
3. Lobular carcinoma in situ

• Family history

1. 1st degree relatives
2. 2nd degree relatives
3. Age of onset of breast cancer
4. Bilateral Breast cancer
5. Ovarian cancer

Claus

• Personal Information

1. Age

• Family history

1. 1st degree relatives
2. 2nd degree relatives
3. Age of onset of breast cancer

Ford

• Personal Information

1. Age

• Family history

1. 1st degree relatives
2. 2nd degree relatives
3. Age of onset of breast cancer
4. Bilateral Breast cancer
5. Ovarian cancer
6. Male breast cancer

Extracellular information to intracellular events that mediate the intended physiological response

Characteristic and reproducible cellular response

• Extracellular stimulus

1. Hormones, Neurotransmitters, Growth Factors, Odorants, Metal ions, fluid shear, pressure, photons, NO (soluble gases)

• Transducer: Receptors

Receptors

• Proteins that recognize the stimulus, change their conformation and activate intracellular proteins.

• Penetrate the plasma membranes with intrinsic enzymatic activity.

1. Tyrosine Kinases: PGDF, insulin EGF, FGF

Autophosphorylation and Phosphorylation of other substrates

1. Tyrosine Phosphatases: CD45 protein of T-cells and macrophages
2. Guanylate Cyclases: Natriuretic peptide receptors
3. Serine/Threonine Kinases: Activin, TGF-Beta receptors

• Coupled with intracellular G-proteins

1. have 7-transmembrane spanning domains: Serpentine receptors
2. Adrenergic receptors, Odorant receptors, Hormone Receptors (Glucagon, Angiotensin, Vasopressin, Bradykinin)

• Intracellular receptors

1. Upon ligand binding they migrate to the nucleus where they affect gene transcription
2. Steroid and thyroid hormone receptors
3. have a ligand binding, a DNA-binding and transcriptional activator domain.

• Intracellular signal

1. Enzymes
2. Accessory/Structural Proteins
3. Lipid Substrates
4. Ion Channels

• Second Messengers

1. Lipids (Arachidonic Acid, Diacylglycerol)
2. Ions (Ca2+ K+)
3. Nucleotides (cGMP and cAMP)
4. Gases (NO)

Types of receptors

• Receptor tyrosine kinases.

1. Four domains: Extracellular ligand binding, Intracellular tyrosine binding, intracellular regulatory domain, transmembrane domain.
2. Some have a non-kinase domain inserted into the kinase domain -> kinase insert
3. 14 families with different kinase inserts and extracellular structural features.

14 families of Receptor-Tyrosine=Kinases

• Cysteine-Rich: EGF receptor, HER2-neu, HER3
• Cysteine-Rich with disulfide-linked heterotetramers: Insulin receptors, IGF-1
• 5 Ig-like domains + kinase insert: PGDF receptors, c-kit
• 3 Ig-like domains + kinase insert acidic domain: FGF receptors.
• 7 Ig-like domains + kinase insert: Vascular endothelial ground factor receptor (VEGF)
• Heterodimeric (one protein subunit completely extracellular): Hepatocyte Growth Factor and Scatter Factor receptor.
• Leucine rich domain: NGF receptor
• No/few cysteine rich domains: Neurotrophin receptor family (TRKA TRKB TRKC)

Mechanism of action: Receptor bound protein tyrosine kinases

• Receptors are activated by phosphorylation
• Activated receptors interact with proteins of the signaling cascade
• These interacting proteins have SH2 and SH3 domains
• Leads to the tyrosine phosphorylation of the interacting proteins
• If the interacting proteins are enzymatic proteins, there is +/- in their activity
• Examples: Phospholipase Cy. ras(associated)G(TPase)A(ctivating)P(rotein). PI3K. Protein-Phosphatase-1C (PTP-1C), members of the PTKs

Mechanism of action: NON-Receptor protein tyrosine kinases

• Class of receptors that have no enzymatic activity: All cytokine receptors, CD4, CD8 cell surface glycoproteins. T-cell receptors
• Receptor signaling through protein interaction: Insulin receptor

1. Principal IR substrate is a protein called IRS-1
2. IRS-1 contains motifs that bind catalytically active subunit of PI3K
3. Thus IRS-1 is a docking protein that couples IR with SH2 containing signaling proteins.
4. Nicotinic Ach receptor: 4 subunits are phosphorylated in reponse to acetylcholine binding that leads to an increase in the rate of desensitization to ACh

Mechanism of action: Receptor Serine/Threonine kinases

• Examples include: TGF-Beta, activin and BMP receptors
• Signaling pathways are different from the Tyrosine Kinase deal.
• Ligands first bind to type 1 receptors and then interact with Type 2
• Type 2 receptor phosphorylates the type 1 receptor leading to the initiation of the signaling cascade.

MAP Kinase

• Mitogen Activated kinase
• Activation in response to growth factor stimulation of cells in culture.
• They transmit information from increased intracellular tyrosine phosphorylatin to serine-threonine phosphorylation
• Ultimate targets of MAPK are proto-oncogenes FOS, MYC and JUN

Phospholipases and Phospholipids

• Protein Kinase C -> Maximum activity in the presence of Calcium and Diacyl Glycerol (Ca2+ and DAG)
• DAG is generated when phospholipases are activated.
• PLC-Gamma has SH2 domains that enables interaction with tyrosine kinases
• PLC-Gamma cleaves Phosphotidylinositol bisphosphate (PIP2) to DAG and inositol trisphosphate (IP3)
• IP3 + intracellular membrane receptors -> increased Ca2+ from stores
• DAG + Ca2+ -> increase Protein Kinase C activity
• Phosphotidylinositol 3 kinase (PI3K) is activated via SH2 domain binding by PDGF, EGF, insulin, IGF-1, HGF and NGF receptors.

G-Protein Coupled Receptors

• Modulate adenylate cyclase activity.
• Activate PLC-gamma
• May be photoreceptors

G-Protein Regulators

• These are GTPase activating proteins
• Regulation of GTPases associated with some proto-oncogenes such as RAS
• Other examples: NF1 tumour suppressor gene, BCR locus gene

Hormone Receptors

• Bind hormone as well as directly activate gene transcription
• Reside in the cytoplasm and penetrate the plasma membrane
• H-R translocates to the nucleus and binds to the HRE altering trancription rates

Phosphatases

• Removing phosphates turn off signaling
• Thus phosphatases could be anti-oncogenes, or growth-suppressor genes
• But sometime dephosphorylation is required for cell growth
• CD45 is associated with a protein tyrosine phosphatase.
• PTP-1B dephosphorylates the autophosphorylation that follows insulin binding.
• Protein serine phosphatases also exist.

Mechanism of action: Non-Receptor Serine/Threonine kinases

• Examples: cAMP-dependent protein kinases, protein kinase C, Mitogen activated protein kinases
• PKC: phosphorylates the EGF receptor that down-regulates the tyrosine kinase activity of the receptor.

Mechanisms

• Phosphorylation: Kinase
• Dephosphorylation: Phosphatase

Interaction between different signalling pathways

• Crosstalk is by feedback inhibition
• An example of cytosolic interaction is the SH2 binding to pTyr.

Src Homology 2 (SH2) Domains

• Src Homology 2 (SH2) domains are protein modules of about 100 amino acids in size which are found in a large number of proteins involved in signal transduction.
• They specifically recognize the phosphorylated state of tyrosine residues, thereby allowing SH2 domain-containing proteins to localize to tyrosine-phosphorylated sites.
• When a receptor is activated by the binding of an extracellular ligand it induces activation of kinase activities on the other side of the membrane, resulting in specific phosphorylation on tyrosine residues located in the intracellular domains of the receptor. This process constitutes the fundamental event of signal transduction through a membrane, in which a signal in the extracellular compartment is "sensed" by a receptor and is converted in the intracellular compartment to a different chemical form, i.e. that of a phosphorylated tyrosine.
• Tyrosine phosphorylation leads to activation of a cascade of protein-protein interactions whereby SH2 domain-containing proteins are recruited to tyrosine-phosphorylated sites. This process initiates a series of events which eventually result in altered patterns of gene expression or other cellular responses.
• Recognition of tyrosine-phosphorylated sites by SH2 domains must be strictly specific. Misreading of phosphorylated sites by SH2 domains would lead to recruitment of inappropriate SH2 domain-containing proteins to the receptor and hence to undesirable activation of pathways.
• Specificity is conferred by the sequence context of the phosphotyrosine within the tyrosine-phosphorylated site, and, more specifically, by the three residues immediately C-terminal to the phosphotyrosine.

• The peptide-binding specificity of a large number of SH2 domains was then investigated using libraries of peptides phosphorylated on a tyrosine residue and randomized at the +1, +2 and +3 positions C-terminal to the phosphotyrosine.
• Because SH2 domains play fundamental roles in a variety of signal transduction pathways, SH2 domains have been the targets of extensive drug design efforts.

Example of specificity

• Activation of the epidermal growth factor receptors: > 6 pTyr residues.
• SH2 binding domain containing proteins: Phospholipase. PI3K. Tyrosine Kinase. Adaptor proteins. Tyrosine Phosphatase.
• Another protein similar to the SH2 domain with similar pTyr binding function is:

PTB -> Phosphotyrosine Binding Domain. Specificity in the PTB is in the context of residues located N-terminal to the target pTyr However, signalling complexes organized by PTB are unknown.

• The SH3 binding domain has more stable and long-term interactions than the the SH2 and PTB binding domains. Determinants for specificity are not known.
• Pleckstrin-homology (PH) binding domain.
• PDZ -> Postsynaptic density protein - disclarge, zo-1

Hormonal Etiology of Breast Cancer

Structure

Early reproductive years

• 20% Epithelium
• 20% Fat
• 60% Connective Tissue.

1. More Stroma
2. More "Microenvironment" to get through
3. More physical barriers to metastasis
4. More intense inflammation because of more stromal elements?
5. Correlated with density
6. Density declines with age
7. Fat increases with age

• Density is associated with risk. However, higher age is also associated with risk.
• Perhaps the relative composition of breast tissue is important

Prediction

Models predict number of cancers likely to be seen in a population

Cannot identify a particular woman who might develop breast cancer

1. Based on the relative risk within the whole population, differences in risk between high risk and low risk needs to be greater than a 100 fold

• Family History Alone (BRCA1 BRCA2 TP53)

1. Claus Model
2. Ford Model

• Hormonal and Reproductive Models + Family History

1. Gail Model
2. Tyrer-Cuzick Model

• Additional Useful Factors:

1. Mammographic Density
2. Weight Gain
3. Serum Steroid hormone

Focussing on other tissue

• Lymphocytes: Genetic instability might reflect the same process in breast epithelial cells (but the estrogen environment is completely different!)
• Defective DNA repair and increased chromosomal radiosensitivity in both these cells.
• Do changes in lymphocytes reveal possible risk?

Prevention Efforts

• SERM (38% - 59%)
• Aromatase (50% -> ?70%)
• Oophorectomy (50%)
• ? Temporary ovarian ablation
• Gonadorelin
• Weight Reduction (20% - 40%)
• Energy restricting mimetic agents that inhibit glycolysis
• Exercise (20% - 30%)

Problems

• COX-2 inhibitor such as rofecoxib increase risk of colorectal cancer
• 100mg every two days is ineffective for breast cancer

Breast Development

• Does not develop when

1. Parathyroid hormone-related peptide in the epithelium
2. Its receptors on fibroblast are knocked out
3. Absence of white adipose tissue or leptin

• Essential for breast development

1. Hedghog (Hh) and Notch signaling pathways.

Mediates stromal-epithelial interactions during ductal development

1. Through two Hh signal transduction network genes - Patched-1 (Ptc-1) and Gli2
2. Hh signaling can be blocked through cyclopamine
3. Notch pathway can be blocked with gamma secretase inhibitors.
4. Estrogen signaling
5. Notch signaling plays a role in cell-fate determination, cell-survival and proliferation.
6. Vertebrate Notch4 -> normal mammary development.

Hyperplastic Enlarged Lobular Unit

• Proportion of ER positive cells increase from 20% in normal breast to over 80% in hyperplasias and HELUs
• Breast cancers arise from ER-negative stem cells and ER-positive progenitor cells

Genetics

• Silencing of tumour suppressor gene p16(INK4) is associated with an increased expression of stress activated protein kinase p38 and COX-2 in 30% of women with histologically normal breasts.
• Reduced apoptosis
• Increased ER expression
• Reduced integrin expression
• Loss of heterozygosity
• Senescent fibroblasts secrete hepatocyte growth factor and other factors that increase epithelial branching
• If these fibroblast are "transduced" with genes for the hepatocyte growth factor and transforming growth factor-beta (TGF-beta), they help in the uptake of human epithelial cells in the fat-pad of immune deprived mice.
• '''???''' How important are fibroblasts and their secretions in the risk associated with Mammographic density.
• '''???''' Does caloric restriction act via the adipocytes in the breast?
• '''???''' Do NSAIDs act directly on the epithelial cells or via resident breast macrophages.

Leptin

• Adipocytes secrete leptin and Collagen VI
• Stimulates aromatase synthesis and ER transactivation

Cytokines

• IL-6 and TNF-alpha can stimulate aromatase activity

Gene Expression

• Parous Breast: Differentiation markers were expressed in parous breasts.
• Nulliparous Breast: Growth and extracellular matrix genes were expressed in nulliparous breast.
• Hyperplastic Enlarged Lobular unit: G-protein over-expression and increased retinoic acid pathway signalling was seen in HELUs.
• Myoepithelial cells and Myofibroblasts: CXCL12 and CXCL14 Chemokines overexpressed in myoepithelial cells and myofibroblasts and bind to receptors on epithelial cells, enhance proliferation, migration and invasion. They act as paracrine factors.
• Senescent Fibroblasts: Wound repair genes
• Preadipocytes: Extracellular matrix components
• Subcutaneous fat: Increased proinflammatory genes and decreased anti-inflammatory genes. This reverted to normal after a period of diet induced weight reduction.

Prevention

• Obesity and inflammation: Changes in intracellular signalling pathways {{Pregnancy change in fat mass and is an inflammatory process. Quelled by estrogen}}
• Environmental influences change intracellular signalling pathways.

NF-kappa-B

• Mediator of development of malignancy in epithelia
• Tumour promotion by macrophage infiltration.
• Inhibition of NF-Kappa-B: Decreased tumour formation, increased apoptosis.
• Activity is reduced by:

1. Dietary energy restriction
2. Reduction of AMP-related kinase
3. Akt/protein-kinase B,
4. Increased SIRT1 (silent mating type information regulation 2 homolog) activity
5. Resveratrol increases SIRT1

• NF-kappa-B inhibits the activity of the Estrogen receptor.
• Parthenolide: NF-kappa-B inhibitor - reverse tamoxifen and fulvestrant resistance.
• Upregulates COX-2 via retinoids.
• Increased prostaglandin synthesis, aromatase activity, and cell proliferation

Prevention pathway focus

• Adipocytes: Energy restriction/ mimetic agents
• Macrophages: NSAIDS
• ER-positive epithelial cells: SERMS, AIs
• ER-negative epithelial cells: Growth-factor pathway inhibitors
• Block NF-Kappa-B
• *** Identify a common pathway and block that instead?

Macrophages

• M1: Proinflammatory
• M2: Anti-inflammatory
• Adipose Promote conversion of M2 macrophages to M1 macrophages.

IL-6

• Pronounced systemic increase might and small local increase play opposite roles in glucose metabolism.
• ??Source: Adipocytes or Macrophages?
• Blocking IL-6 receptor binding -> Tocilizumab (Rx for RA? How about Diab Type-II)

Adipose Tissue

• M1 cells are attracted to adipocytes that release cytokines and fatty acids. ??
• Activated M1 cells produce IL-6 and Tumour-Necrosis Factor-Alpha -> that interfere with responses to insulin by cells including hepatocytes.
• Serum concentrations of proinflammatory cytokines correlate with adiposity or with insulin resistance.

????

• ??Fat -> Macrophages -> IL-6 -> Insulin Resistance
• ?Fat (High Fat Diet) -> Stress -> Proinflammatory Cytokines -> Destruction of beta cells
• Stresses including inflammation activate the c-jun Nh2-terminal kinase 1 (JNK1)
• JNK1 activates the expression of proinflammatory cytokine genes.
• JNK1 phosphorylates insulin receptor substrate - blocks the insulin signal.
• JNK1 in adipocytes NOT in myeloid cells are required for obesity induced insulin resistance.

JNK1 Knockout in adipose tissue

• Restored the effect of insulin on adipose tissue
• In response to high-fat diet, Akt-activation was no longer blocked. Akt is a protein kinase downstream of insulin receptor substrate.
• Suppressive effect of insulin on gluconeogenesis in the liver was retained.
• IL-6 production in response to a high-fat diet were completely eliminated.
• SOCS3 is a signalling molecule in hepatocytes that is activated by IL-6. Inhibits insulin signal transduction.
• SOCS3 expression in liver decreased when JNK1 was ablated in adipocytes

Adipose JNK1 Role

• Regulates circulating concentrations of IL-6
• Stress signalling during "fat inflammation".

Arguments for Late Dissemination

• Stochastic accumulation of mutations
• Correlation between primary tumour size and metastasis

Arguments against stochastic model

• Association with tumour grade with metastatic proclivity (????)
• Occurence of bone micrometastasis early in the evolution of cancer
• DNA microarrays detect metastatic signatures in early-stage primary tumours.

Studies:

• A poor prognosis gene signature is present in a majority of early-stage primary tumours.
• The size of the tumour would be irrelevant and even small tumours may be expected to contain cells with metastatic potential.
• A 17-gene signature distinguished primary carcinomas from metastatic adenocarcinomas. Numerous primary solid tumours also had this signature.
• Single viable disseminated breast cancer cells had an abundance of chromosomal copy number changes in their genome with significant intercellular heterogeneity.
• Disseminated cells evolve independently of the primary tumour.
• Gene signature -> Short interval to disease recurrence, distant metastasis and death following therapy.
• Display features that are reminiscent of those of normal stem cells.
• Some of these unique poor prognosis genes had stromal cell-transcripts - part of the predictive stromal gene set. Thus, stroma may participate actively in the ability of the cancer to metastasize.

Stem Cells

• Express multi-drug resistance genes
• Slow proliferation rate

Summary

• Capacity of a tumour to disseminate is acquired at early steps during the multistep process of tumorigenesis
• A combined effect of oncogene signalling and tumour suppressor gene loss in the appropriate cellular environment is likely to determine whether a cancer cell has the potential to colonize distant organs.

Three candidate mechanisms:

     1. Chemokine-receptor mediated chemotaxis
     2. The establishment of a metastatic niche
     3. Tumour cell genetic program that facilitates adaptation to a particular microenvironment.

Chemokine Receptor Mediated Chemotaxis

• Tumour cells express chemokine receptors including CXCR4 serves as a receptor for CXCL12/SDF
• SDF is secreted by host tissue stromal fibroblasts
• Determines localization of metastasis of certain tumour types.

????

1. Which other cells express these receptors?
2. Are these receptors abnormal?

Preparing the soil: The metastatic environment

• Tumours and Stromal-Cell secrete cytokines.
• They can recruit endothelial (EPC) and hematopoietic progenitor cells (HPC) to the relevant organ prior to tumour arrival.
• Tumour-cells deposit fibronectin
• Anti-VEGFR-1 neutralizing antibodies inhibit these cells.

???

• Estrogen helps in the recruitment of progenitors as well.
• Is estrogen controlling this cytokine secretion?
• If so, how?

UNCLEAR

• Mechanisms that govern HPC recruitment to potential metastatic sites
• How do HPCs render any site permissive for metastatic tumour growth??
• Do "pre-prepared" metastatic niches exist where local HPCs alter the microenvironment after the arrival of the initial tumour cells.

???

What are the functions of these niches in a non-metastatic condition???

Gene Expression patterns that determine organ-specific homing

• Eg. B16 Melanoma cells that express an upregulated RhoC gene preferentially homed to the lung.
• Eg. MDA-MB231 cell line (BrCa) is derived from the pleural effusion of a patient with wide-spread metastasis. Sublines of these cells predictably formed tumours in given organs (following intravenous injections). Gene expression profiling was done in these sublines and gene signatures were identified.

1. Genes correlating with general metastasis proclivity
2. Additional gene signatures correlated to the organ of mets.
3. 54 genes that predicted lung tropism

• In combination, these genes could induce the poorly metastatic parental MDA-MB231 cells to colonize the organ from which the metastatic cell variants were retrieved.
• MDA-MB231 also used to identify genes that help mets home to bone sites.

Functionality of these over-expressed genes

• Encoded cell membrane or secreted molecules relevant to the bone microenvironment.
• ??? How about the lung mets ones? What were their functions??
• ??? Immune related ones?

1. Immune microenvironment would be utilized for mets?
2. Mets predominantly "immune-oriented" in nature?
3. Immune dependent mets?
4. Can these cancers be targeted with immune therapy?

• A combination of these genes augmented the metastatic activity of the parental cells to levels comparable to those displayed by the most aggressive cell lines expressing the entire bone metastasis gene set.
• ??? So were these genes tested in other cancer lines to see if they would upregulate the bone mets?
• The cells containing these gene signature were identified in the parental tumour cell population.
• Do these cells have different surface antigenic profiles???

New Colonies

• The ability of the tumour cells to subvert the local microenvironment will most likely determine their fate.
• Some micrometastasis may be dormant because of:

1. Inadequate blood supply and failure to induce angiogenesis.
2. Isolated cells that are unable to divide but retain tumorigenicity.
3. Shift in the equilibrium between natural stimulators and inhibitors of angiogenesis.

????

• Estrogen effects in the microenvironment?
• Weight gain, increased adipose cells in the microenvironment
• Estrogen and Cytokines
• Dormancy broken

Three Events - Selectin-mediated low-affinity interactions -> leukocyte rolling on the endothelium - Endothelial cell derived chemokine-mediated leukocyte activation that changes leukocyte Beta-2 integrin conformation from low to high affinity. - High affinity interaction between leukocyte Beta-2 integrins and endothelial ICAM-1 -> required for the leukocyte arrest that precedes and is necessary for diapedesis and extravasation.

Carcinomatous cells can use some of these processes to interact with the vascular endothelium but its relevance in human cancer metastasis has not yet been demonstrated.

Tumour-cell-platelet/leukocyte interactions favour metastasis in experimental animal models. Are such mechanisms important in human metastasis??

• Effect of Tregs.
• Effect of Th1/Th2

• Shear Stress -> resist shear stress aided by platelets and leukocytes.
• Interaction with leukocytes leading to destruction: use mechanisms used by leukocytes to adhere to the endothelium.

• Transformed cells express and altered Glycosyltransferase repertoire with respect to normal counterparts.
• Glycosyl and Sialyl transferases expressed in many carcinomas decorate cell surface receptors with oligosaccharide structures that correspond to ligands of selectins.
• Selectins are a c-type lectin class of cell-surface adhesion molecule that regulate leukocyte-endothelial interactions and leukocyte trafficking.
• P-selectin/CD62P expressed on the surface of activated platelets and endothelial cells
• E-selectin/CD62E is predominantly induced in activated endothelium.
• Circulating tumour cells coated with these selectin ligands are coated with platelets and leukocytes and create microemboli that may obstruct capillaries of various organs.
• May also adhere to activated to endothelial cells.

• Platelets and leukocytes can also interact with tumor cells via αvβ3-dependent

adhesion.

• Integrins and Immunoglobulin superfamily are also implicated in tumour cell adhesion to the endothelium.
• The α4β1 integrin, associated primarily with lymphocytes, is expressed on a variety of tumor cell types, and its ligand VCAM-1 was shown to support melanoma cell adhesion to endothelial cells

Distant Organ Metastasis Debate:

• Are they from the primary tumour sites?
• Are they from cells colonizing the lymph nodes?
• Both mechanisms might be operational and that the relative ease of lymph vessel invasion and penetration might explain that lymph nodes are usually the first metastatic site in carcinomas. It is also possible, however, that colonization of both lymphoid and nonlymphoid organs occurs within a comparable time frame, but owing to local conditions tumor growth proceeds more rapidly in lymphoid tissues.

-------- Arguments favouring degradation of BM and irruption into circulation

• Depends on MMP-9 Activity
• Rate-limiting step in metastasis

Angiogenic Factors: Vascular Endothelial Growth Factors (VEGF)

• VEGF-A expression is induced by:

1. MAPK signalling pathway
2. Hypoxia that accompanies rapid primary tumour growth.
3. Hypoxia inducible factor 1-alpha drives VEGF-A transcription.

• Fibroblast growth factor and TGF-Beta increased as a result of tissue remodeling.

Lymphangiogenesis:

• Metastasis to regional draining lymph nodes of a tumour.
• VEGF-C and VEGF-D members of the VEG-F family which bind to VEGFR-3 on the surface of lymphatic endothelial cells.
• VEGF-C regulated by IL-1Beta and TNF-Alpha signalling
• VEGF-D: product of Fos regulated gene, regulated by oncogenic signalling pathways

Following neural tube closure, cells in the blastoderm or ectoderm migrate in sheets. Morphogenesis of mammary glands and ducts.

• Carcinoma metastasize in such a communal fashion
• There is some functional specialization in multicellular migration of cancer cells.

Inflammation:

• Myeloid Cells: Neutrophils, monocytes and macrophages
• Tumour associated macrophages: present antigen and secrete cytokines
• This may promote tumour progression
• Express receptors for several growth factors - eg. VEGFR1 binds tumour derived VEGF-A and placental growth factor PIGF
• Recruited via tumour signals to the tumour microenvironment where they differentiate into TAM and promote tumour growth and dissemination.
• Secrete MMP-9 that plays a key role in angiogenesis.

E-cadherin

• Found in epithelial cells
• E-cadherin-containing cell-to-cell junctions often adjacent to actin-containing filaments of the cytoskeleton
• 5 cadherin repeats (EC1 ~ EC5) in the extracellular domain
• 1 transmembrane domain
• 1 intracellular domain that binds p120-catenin and beta-catenin.

1. Has a highly-phosphorylated region vital to beta-catenin binding and therefore to E-cadherin function.
2. Beta-catenin can also bind to alpha-catenin.
3. Alpha-catenin participates in regulation of actin-containing cytoskeletal filaments.

Development

• First expressed in the 2-cell stage of mammalian development
• Becomes phosphorylated by the 8-cell stage, where it causes compaction.
• In adult tissues, E-cadherin is expressed in epithelial tissues.
• Constantly regenerated with a 5-hour half-life on the cell surface.

Cancer

• E-cadherin is a tumour suppressor.
• Loss of function/expression implicated in cancer progression and metastasis.
• Downregulation decreases strength of cellular adhesion within a tissue
• Increase in cellular motility.
• May allow cancer cells to cross the basement membrane and invade surrounding tissues

Loss of function in cancer

• Transcription repression (Snail, Slug, SIP1, Twist, dEF1 and E12/E47)
• Promoter methylation (epigenetic)
• Disruption of cytoskeletal connections (Beta-catenin mutations abrogate its binding to Alpha-catenin and result in a non-adhesive phenotype)
• Increased intracellular degradation
• Proteolytic cleavage of extracellular domain by matrix metalloproteinases (MMPs)
• Tyrosine Phosphorylation of E-cadherin-Beta-catenin complexes by activated RTKs and Srcs leads to their recognition by ubiquitin ligases and then downregulated by endocytosis. Dependent on integrin signalling and focal adhesion kinase phosphorylation. Integrin Signalling operates through Snail and Slug and suppresses E-cadherin expression and disrupts adherens junctions. In some cells, this is mediated by integrin linked kinase.
• Proteolytic cleavage of the extracellular domain of E-cadherin by MMP3 and MMP7 disrupts its ability to promote cellular interactions.
• Disruption of E-cadherin mediated cell-cell adhesion results in detachment of tumour cells from the epithelial cell layer.
• This affects signalling pathways

1. Rho GTPase mediated modulation of the actin cytoskeleton
2. Canonical Wnt signalling.

Cadherin Switch

• E-cadherin switches to N-cadherin.
• N-cadherin mediates cell-cell interactions

1. Enables tumour cells to direct host responses
2. Regulates fibroblast growth factor receptors and triggers downstream signals

## Phospholipase C-γ. ## Phosphatidylinositol 3 kinase ## Mitogen activated protein kinase

N-cadherin

• Extracellular domain of N-cadherin susceptible to proteolytic cleavage by MMPs
• Block cell-cell adhesion
• Stimulate fibroblast-growth-factor receptor signalling in paracrine fashion

Integrins

• Large family of adhesion receptors
• Alpha and beta transmembrane chain
• Various combos of 18 alpha and beta chains
• 24 integrins in toto
• Recognize distinct ECM ligands
• Help form focal adhesions or ECM contacts
• Organize and remodel cytoskeleton of the cell
• Adhesive and migratory interactions with the ECM
• Impart polarity
• Control proliferation and survival
• Cooperate with receptor tyrosine kinases and control survival and mitogenic pathways.
• 1. Mediate cell adhesion
• 2. Impose positional control on RTK activity
• 1 + 2 -> whether cells migrate in response to cytokines and growth factors.
• alpha-2-beta-1 and alpha-3-beta-1 Maintain epithelial cell adhesions to the basal lamina and maintain them in a quiescent state.
• alpha-v-beta-3 and alpha-6-beta-4 are upregulated: promote migration, invasion and proliferation.

Matrix-Metalloproteinases

• Secreted MMPs tethered to the surface of tumour/stromal cells
• Activate relevant growth factors
• Promote angiogenesis
• Disrupt ECM barriers to invading cells

Fibroblasts and Soluble Regulators

• Carcinoma-associated fibroblasts
• Platelet-derived Growth Factors and TGF-Beta alter fibroblast phenotype to one reminiscent of myofibroblasts.
• Source of proteolytic enzymes including MMP and cathepsins
• Monocyte Chemotactic Protein - 1
• Regulate inflammatory response to tumour invasion
• Secrete Stromal Cell derived factor-1 that recruits bone marrow-derived endothelial cell precursor recruitment

TGF-Beta

• Tumour-host cross talk
• Inhibits proliferation of normal epithelial cells and early carcinoma cells
• Stimulates fibroblast growth
• ECM secretion
• Promotes late-stage carcinoma invasiveness
• Induce EMT
• Activation of normal fibroblasts.

Malignant Transformation:

• Normal epithelial cell communication with its microenvironment is regulated by

E-cadherin-mediated cell-cell interaction and β1-integrin-mediated adhesion to the basement membrane (BM).

• Transformation results in E-cadherin loss and replacement by N-cadherin

1. Regulates fibroblast growth factor receptor (FGFR) function.

• Carcinomas frequently express αvβ3 and α6β4 integrins.

1. Interact with receptor tyrosine kinases, including ErbB2 and Met.

• Changes in glycosylation of cell surface proteoglycans such as CD44

1. Coordinate MMP-7-mediated heparin-binding
2. Epidermal growth factor (HB-EGF) activation
3. ErbB4 signaling.

• Changes in the glycosyltransferase repertoire can also result in the

decoration of cell surface receptors by oligosaccharides that constitute selectin ligands.

• Several transmembrane MMPs are expressed by tumor cells and mediate BM degradation.

• Targets that would work for everyone regardless of the heterogeneity of cancer
• Must be uniformly operative in all tumour cells.
• There must be no escape-resistance.
• Target downstream?

• Leptin: produced by adipocytes.
• Obesity: High levels of leptin + IGF-1 (produced by liver)
• Both stimulate breast cancer cells to grow faster
• Activate EGFR molecule.
• Erlotinib and Cetuximab are EGFR inhibitors.
• Checked the metastatic behaviour of breast cancer but not proliferation.
• Maybe helpful for estrogen-receptor negative breast cancer.

Acquiring the hallmarks of cancer.

Testosterone and Estrogen are opposite. They counteract each other's effects? Estrogen synergizes with androgens. The estrogen:androgen ratio determines cancer. May be I should be looking at testosterone immunomodulation in the context of poorer survival and more metastasis?

• Organ Specificity of Cancer.

1. Context of cell-type
2. Epigenetics
3. DNA is organized differently in every cell type.

• Species Specificity of Cancer.

1. Evolution didn't keep up with drastic environment changes?
2. Lifestyle and diet?

• Geographical Specificity in Acquired Cancers

1. Migrant rates increase
2. Role of the environment

• Tissue Specificity of Inherited Defects in Oncogenes and Suppressor Gene Function in Carcinogenesis.

1. Some cell cycle control genes only act in certain cancer
2. Some DNA repair genes only act to produce cancer in some tissues and not in others.
3. What is the "context of the cell" (am I violating this by doing gene reporter assays?)
4. What is the structural hardwiring of cells?
5. How does this influence epigenetics?

• Common denominators in carcinogenic change

1. The role of the microenvironment matrix

• Differential curability of cancers

1. Faster it gets caught the lesser the heterogeneity and the lesser the genomic instability

{Does estrogen also fuel this??}

• Cancer cells become resistant to therapy, normal cells do not.

1. Evolution and heterogeneity of cancer cells

{Estrogen role in evolution?}

1. UNstable genomes
2. Solid tumours that are the most resistant to therapy have imbalanced reciprocal translocations and chromosome rearrangements
3. Darwinian therapy?

-----------------------Some common tenets---------------------

• Every hypothesis is true.
• All observations are true.
• Generate at least five explanations.
• You don't have to assume anything you can prove.
• Figure out ways to test theories to determine if they really are true.
• Think about the results as you perform the experiment.
• The most successful experiment of them all -> Evolution. Study it

- Part of evolution if its linked to

• Insulin signalling
• Dietary Restriction
• Innate immunity
• Inflammation

• Cyclical Changes in hormone levels
• Hormonal determinants

• Estrogen Alone?
• Prolactin?
• Other Hormones?

''' Neuroendocrine Hormones '''

• Are these other hormones confounders?

Inclusion/Exclusion Criteria

• Other Hormone distribution should be fairly non-variant in study subjects chosen

Factors and overall survival

• How sound is this type of investigation?
• Won't confounders muddy the result?
• Is it right to look at overall survival at all?
• List lengthening or logic??

One Carbon Metabolism and Epigenetics in fetal plasticity.

Research estrogen assays.

Obesity

Epidemiology

• Disproportionately affects African-American and Afro-Caribbeans in the United States
• Incidence of obesity in urban West Africa is double of what it was earlier

Candidate Genes involved in Obesity

African-Americans, Mende tribe, Sierra Leone

• Human Uncoupling Gene UCP3 (Lower resting energy expenditure)

African-American Children

• Human Uncoupling Gene UCP2

Nigerians, African-Americans

• Polymorphism in promoter of Angiotensin Converting Enzyme (ACE)

People of African Origin

• Polymorphism in the promoter of Agouti-related protein AGRP

West Africans

• Strong linkage to Chromosome 1, 2, 5, 7, 8, 11

Africans: Hunter Gatherer Vs Agriculturists

• NAT2 Genes metabolizing anti-TB drug Isoniazid - Fast Acetylation Vs Slow Acetylation Phenotypes

Diabetes Mellitus

• Prevalence in lower in Africa than among people of African descent.

Candidate Genes involved in DM2

Mexican Americans, Nigerians

• Calpain 10 gene on Chromosome 2

Africans, African-Americans

• AGRP - Agouti-related protein gene
• The transcription factor 7-like 2 gene (TCF7L2)
• The proprotein convertase subtilisin/kexin type 2 (PCSK2) gene

West Africa

• Linkage to Chromosome 12, 19, 20
• Linkage to Chromosomes 4, 6, 8, 10, 15, 16, 17 and 18
• Thrifty gene hypothesis -> Changes in selective pressure: Genes that once promoted the efficient absorption, storage and utilization of nutrients in their ancestral environment are now maladaptive in modern environments increasing risk for disease.

Hypertension

• 1.6 times more in African-Americans than in European Americans
• Growing numbers in Nigeria, Tanzania and Cameroon

African Ancestry (Heritibility low in African-Americans) Nigerian (Heritability High)

• G protein Beta-3 subunit
• Angiotensin (AGT)
• Angiotensin II receptor (AGTR1)

Epistatic Interaction in Nigerian Families

• Polymorphisms in Angiotensin Converting Enzyme (ACE, ACE4 and ACE8)

West-African Families

• ACE and G-Protein Coupled Receptor Kinase 4. (GRK4)

African Americans

• Chromosomes 6q24, 21q21

African Americans, Mexican Americans

• Chromosome 6: Polymorphisms in Vanin 1 (VNN1) gene

Climate -> Hot/Humid -> Low Salt Diets / Low-Salt Environments

• Adaptation/Selection: Salt retaining Phenotype

Drug Metabolism / Pharamacogenetics

• Cytochrome P450
• N-acetyl Transferases
• Multidrug Transporters

Africans

• CYP2B6 -> Artemisinin (Malaria) and Efavirenz (HIV)

Future Directions in Evolutionary Research in Africa

GWAS

''' East Africans'''

• Testing origin and dispersal OUT OF AFRICA

''' West Africans '''

• Information about African-Americans

The following are indicative of positive selection for a particular trait:

• Low Macrosatellite Variability
• High Linkage Disequilibrium

Malarial Resistance

Impact on the human population in the last 10,000 years (corresponding with development of agriculture and pastoralism in Africa).

Plasmodium Falciparum

Decreased Susceptibility

Beta-Globin Gene

====== ''' East and West Africa''' ======

HbS

• Sickle cell anaemia in homozygous individuals
• Malaria Resistance and higher reproductive fitness in heterozygous individuals

HbC

HbE

Alpha-Globin Gene

====== ''' East Africa''' ======

• Alpha+ thalassemia

G6PD A-

• 25% in Malaria Endemic Regions
• High LD near this variant (>400kb)

Plasmodium Vivax

Duffy Gene on Chromosome I

• Codes for a receptor on the erythrocyte surface
• FY*A, FY*B and FY*O alleles
• Only FY*O allele found in sub-Saharan Africans
• Variation at the FY locus is three-fold less than in other populations
• Reduced sequence variation around the FY*O locus
• Unusally large Fst values for the FY*A and FY*O variants -> across African, European and Asian populations
• A large Fst indicates large difference in DNA sequence at this locus
• Thus the variant possessed is a positive selection in different geographic regions -> A local adpatation

Human Leukocyte Antigen: HLA Locus

====== ''' West Africa''' ======

• HLA-B53
• HLA-DR.B1_1302

Interleukin 4

====== ''' Fulani of Mali and Burkina Faso''' ======

• IL-4 - 590C

Increased Susceptibility

Alpha-and-Beta Globin Gene

====== ''' East Africa''' ======

• HBS and Alpha+ Thalassemia inherited together

Chromosome 10

====== ''' West Africa''' ======

• 10p 15.3-10p14

Dietary Adaptations

Lactase Persistence

• Lactase Persistence (LP) gives an individual the ability to digest dairy.
• Present in pastoral populations (Northern Europe, African and Arabian Nomadic tribes) and low in non-pastoral populations (East Asians and West Africans).
• Associated with the T variant of a C/T allele SNP located 13910kb upsteam from LCT - the lactase gene
• T-13910 + LAC promoter -> Enhances gene transcription
• However in African studies a different set of SNPs near the lactase gene were found to be associate with Lactase Persistence. Thus, though these SNPs were positively selected due to shared cultural traits (Cattle domestication and milk consumption) they are at different relative positions compared to the lactase gene in two different populations. (And also within various different African populations).

Taste Bitter Compounds

• Low sensitivity to bitter taste variant occurs in moderately high frequency in malaria endemic regions in Central Africa
• Intake of bitter substances - such as organic cyanogens - protective against malarial infection

Recent Infections

Acquired Immune Deficiency Syndrome

Resistance

• CCR5 Chemokine Receptor (Delta-32 Mutation) in North Europeans -> HIV Resistance

Delayed Progression

• CCR2-641 allele -> Delayed disease progression (African/African-American Population)
• KIR3DS1 (Killer Immunoglobin-Like Receptors) + HLA-B Bw4-80Ile: Delayed disease progression (West-Africans/African-American Population)

Decreased Infection

• HLA A2/6802 Human Leukocyte Antigen Locus supertype and HLA DRB1*01 -> Decreased HIV infection in female sex workers in East Africa
• IRF-1 (Interferon Regulatory Factor) (Polymorphism 610, Microsatellite Region, Gene 6516): Decreased Susceptibility (East-Africa)
• TRIM5-alpha: 136Q and 43Y alleles protect against HIV infection in African Americans

Increased Progression

• APOBEC3G: 186R allele -> Increased disease progression in African-Americans
• CUL5 Haplotype 10 -> Increased disease progression of HIV-1 in African Americans

Tuberculosis

Increased Risk/Susceptibility

Polymorphisms of the NRAMP1 gene

(Human homolog of natural-resistance-associated macrophage protein 1) gene

====== East and West Africa ======

• 5' Microsatellite Repeats
• SNP in intron 4
• Deletion in 3' UTR region

Several regulatory sequences are found in the 3' U.nT.ranslated R.egion

• A polyadenylation signal, usually AAUAAA, or a slight variant. This marks the site of cleavage of the transcript approximately 30 base pairs past the signal, followed by the addition of several hundred adenine residues (poly-A tail).
• Binding sites for proteins, that may affect the mRNA's stability or location in the cell, like SECIS elements (which direct the ribosome to translate the codon UGA as selenocysteines rather than as a stop codon).
• AU-rich elements (AREs), stretches consisting of mainly adenine and uridine nucleotides (which can either stabilize or destabilize the mRNA depending on the protein bound to it).
• Binding sites for miRNAs, a type of RNAi.

UBE3A

====== West Africa ======

• 7-bp deletion at 5' end

Chr 15, Chr X

====== West and South Africa ======

• Unknown Loci

{People with long eyelashes -> phenotypic SNP in linkage with a TB specific locus}

HLA Locus

====== Venda Population from South Africa ======

• DB*1302 allele
• DQB1∗0301–0304 alleles

Vitamin D Receptor Locus

====== West Africa ======

• Polymorphism Fok1 and ApaI

CD209

====== West Africa ======

• Intron 6 variant

Decreased Risk/Susceptibility

Vitamin D Receptor Locus

====== South Africa ======

• F-b-A-T Haplotype

====== Gambia ======

• Polymorphism at codon 352 of VDR gene

CD209

====== Malawi and West Africa ======

• Promoter -336G allele

====== South Africa ======

• Promoter -336A and -871G

Pentraxin 3 (PTX3)

====== West Africa ======

• G-A-G haplotype

Selective Forces Outside Africa - Two Courses

Positive selections or Adaptive Evolution

• Novel and diverse environments (infectious agents, diets and climates)

Variation due to Demographic History

• Founder Effect
• Population Bottlenecks
• Genetic Drift
• Non-Random Mating
• Reduction in Effective Population Sizes

The Hardy-Weinberg Equilibrium states that the allelic as well as genotype frequencies and distribution in the offspring population would be similar (or in equilibrium) to the ones in the parent population just by chance unless specific disturbances are introduced.

Deviations from HWE are caused by some demographic scenarios such as:

• Natural Selection
• Population Bottlenecks
• Reduction of population sizes
• Founder Effect
• Non-random mating
• Mutations
• Genetic Drift
• Gene Flow

These demographic scenarios can be identified by:

• Simulating expected pattern of variation under different scenarios (Computer Modelling)
• The Outlier approach

Outliers show an unusual pattern of variation or population differentiation compared with empirical data collected from other loci across the genome.

Two deadly abilities.

• Are macro-evolutionary principles applicable to cancer cells?

• Metastatic dissemination occurs continually throughout the course of primary tumour development, yielding a diverse spectrum of disseminated cells, including ones that are at the moment of dissemination almost indistinguishable from normal cells. <-- Do these exhibit any characteristics of cell stress? Can these targeted by the T-cells.

Phenomenon where the allelic distribution in the offspring population differs significantly from the allelic distribution in the parent population because of a purely random selection of allelic variants at the parent population level. This selection of which individuals (and thus which allelic variant) from the parent population go on to reproduce could also be chosen through selection pressures. When the offspring population is different in its allelic distribution due to chance alone, its called genetic drift. In a small population, genetic drifts (due to random fertile individual selection) are much more likely to happen as compared to large populations. Genetic drift often introduces more allelic homogeneity among the offspring generation population (eg. Non-African Populations).

Thus, reasons for offspring allelic frequencies being different from parent gene pool allelic distributions:

• Founder effect
• Genetic Drift
• Selective pressure (due to climate, diet, infections etc.)

Related Terms:

• Hardy-Weinberg Equilibrium: The principle that parent and offspring allelic distribution will remain fairly similar from generation to generation.

Exceptions to the Hardy-Weinberg Equilibrium happen due to selective evolution, founder effect or genetic drift.

• Markov Property: The predicted distribution of alleles in the offspring population is not dependent on any grand-parent allelic distributions. They are thus memory-less and only dependent on parent allelic distributions.

In small populations: (Non-Africa)

• Genetic Drifts play a larger role in determining final offspring population allelic frequencies

In large populations: (Africa)

• Even weak selection forces are predicted to influence final offspring population allelic frequencies because random chance always predicts more uniformity among parent and offspring allelic distributions.

Population Bottleneck: A catastrophic event that reduces the size of a population thus exposing this resulting smaller population to all the effect of a genetic drift in the future. This might lead to a founder event.

Coalescent Theory: Tracing back ancestors to see when two distinct populations with different allelic distributions shared a common coalescent parent. The probability that two lineages coalesce in the immediately preceding generation is the probability that they share a parent.

• Rapidly expanding populations: Rare polymorphisms also expand.
• Population Bottleneck: Loss of low-frequency rare variants.

During founding events, the particular pattern of pairwise allelic association might have differed from the parental African population, depending on the genetic constitution of the founders relative to that of the parental gene pool. Only a few individuals from the parent gene pool that are responsible for establishing a wholly different population group. This population group has far less diversity as compared to the parent gene pool simply because it started out with a fewer number of individuals. Thus:

• LD blocks are longer
• More linkage disequilibrium
• Less markers needed
• Start association studies with these populations
• Use preliminary results to then study the effects of finer diversity in African Populations.

There are three types of Evolution

• Selective Evolution

Elimination of diversity and retention of just the phenotypes (and genotypes) that can withstand the evolutionary pressure.

• Adaptive Evolution

Introduction of diversity as a means to combat evolutionary pressures. Important for how environment shapes our genomes.

• Stochastic Evolution

Allelic selection by chance alone eg. genetic drift. (Not strictly evolution).

The Code to the Book of Life

• Locus -> Position of any of the following elements in the book, specified by a number-alphabet sequence.
• Locus Example -> (Chromosome, Arm, Region, Band, Sub-band) -> (Chapter, Section, Page, Paragraph, Line)
• Bases -> Alphabets
• Genes -> Sentences that add to the story.
• Sections of DNA -> Paragraphs
• Chromosomes -> Chapters
• Arms of the Chromosomes -> Sections within the Chapters. p (short arm); q (long arm)
• Centromere -> Planned Break in the Chapter between Sections
• Region of the Chromosome -> Specified range of pages within sections of a chapter
• Band of the Chromosome -> Paragraph/s of interest within a given region
• Sub-band of the Chromosome -> Lines of interest in a given region
• Tangible structures made of proteins -> Story
• Complete human body -> Novel
• Population -> Library

Variations

• Structural Variants -> Differences of a group of words collectively made up of larger than a thousand (1kb) alphabets or bases.

• Copy Number Variation -> Group of words (made up of larger than a 1000 alphabets - 1kb) that repeat a varied number of times in different books. These may include insertions, deletions and duplications. Large Scale CNVs are more than 50,000 alphabets long. They can be detected by clone-based array comparative genome hybridization (array-CGH)

• Copy Number Polymorphism -> A CNV (an allele with a specific number of > 1kb repeats) that occurs in more than 1% of the population.

• Microsatellite: Two, Three or Four Alphabet Repeat. In a polymorphism, these di/tri/tetra alphabets are repeated a varied number of times at the same locus. Alleles are the loci with different numbers of repeats. Microsatellites with few variant alleles come from populations with a low frequency of mutation whereas alleles with many variant microsatellites come from populations with a high frequency of mutations. Individuals that are most closely related will have the most similar alleles at each, of the many examined, loci. Thus, similarity of fingerprints will provide the necessary information.

• Single nucleotide polymorphism -> Single Alphabet Typo

In some individuals, whole paragraphs of the text were duplicated, whereas in others, large passages were missing, or even printed backwards. These major revisions turned up in all kinds of people, including many who seemed healthy and normal. Suddenly, it seemed possible that there was actually no standard version of the Book of Life, and researchers wondered whether we are all much more different from each other than they had thought. In the Book of Life analogy, polymorphisms in copy number variations are sections of text where certain paragraphs are repeated different numbers of times in different individuals. The single nucleotide polymorphisms or SNPs are merely the single-letter ‘typos’ in the Book of Life.

Practically, some of the genes, if duplicated, can have beneficial or deleterious effects on a person's susceptibility to a diseases and prognosis of the disease. Extra copies of CCL3L1, helps protect people against HIV. Extra copies of CCL3L1 delay the progression of HIV positivity to AIDS.

Structural Variation would be important in the context of polygenic and complex diseases - when just one gene or one SNP does not explain variations in disease distributions. If a huge chunk of DNA varies among populations, explaining polygenic influences on a disease would be more plausible.

Gene-Environment interactions may also be explained better with the structural variation model. If regions of structural variation in human beings are still evolving - then these might be the locations that are gradually targeted for selection among people who are suited to a post-paleolithic environment and people who aren't. Thus should we be looking at structural variations that have been selected already instead of SNPs? Should we be looking at the paragraph scale of evolution and not at an alphabet or sentence scale - when we look at evolutionary natural selections? Structural Variation might well be responsible for our divergence from the apes in the evolutionary tree.

Where to look for these trouble-causing CNVs, and indels.

• Segmental Duplications - 5% of the genome. A segment of DNA greater than a 1000 bases (alphabets) in size that occurs in two or more copies per haploid genome. Each copy shares a greater than 90% sequence identity with each other (is more than 90% similar).

• Regions of inversions (which in turn occur in regions of segmental duplications: Reversed in orientation with respect to the rest of the chromosome - A palindrome? Pericentric involves the centromere. Paracentric don't.

Copy number variation is the number of duplications, insertions and deletions of the same segment of the DNA in different individuals. Thus a "control" group that is normal may not necessarily be "normal" or even comparable to the cases who have the CNVs. If the same copy numbers variation occur in more than one individual, at the same location, they are "polymorphisms" - particular spots that regularly differ between individuals. More than one person has an alternative copy number variant of that particular stretch of DNA.

However, the range of what is normal might be quite wide when it comes to structural variation. Normal people might have a wide variety of structural variation.

Immune cells activated against estrogen receptors?