Muhammad Jalal

Dr. Hoopes

Genetic Regulation Seminar

4/17/2015

Epigenetic Features and Treatment of Acute Myeloid Leukemia

I. Introduction

The world of eukaryotic genetic regulation is exceedingly complex and multifaceted,

with an incredible diversity of pathways, proteins, genes, and signals to regulate the fundamental

on-off switch of transcription. In this paper, epigenetic regulations of acute myeloid leukemia

will be considered through an in-depth analysis of five primary literature articles. Despite such a

short scope of one of the many diseases that result from improper genetic regulation pathways,

many themes which are commonly seen throughout genetic regulation will be studied

extensively here.

The proliferation of cancer over recent years has posed a call to extensive research on

characterizing and targeting cancer related molecules and pathways. Understanding epigenetics,

or mechanisms for transcriptional control which lie outside of that coded within the actual

genome, has become increasingly important in molecular biology and cancer research. Besides

genetic mutations, many types of cancers have been linked to epigenetic regulation. In this paper,

three epigenetic modifications will be considered: DNA methylation of CpG sequences in the

genome, changes to histone modification, and microRNA inhibition of tumor suppressors.

Within these broad groups are further classifications; histone modification includes acetylation,

methylation, deacetylation, demethylation, and others. The focus will be for the most part on

acute myeloid leukemia (AML), the most common adult form of leukemia. The striking patterns

of epigenetic regulation in such a small subset of all types of cancer will illuminate the

complexity of epigenetics and add consideration for therapeutic targeting of these modifications.

AML is a subset of leukemia, a group of cancers which affect the development of bone

marrow derived cells such as white blood cells and platelets. The term myeloid refers to a

particular lineage of hematopoiesis, the production of a variety of blood cells. A multipotent

hematopoietic stem cell (MHSC) can mature into either a myeloid progenitor cell or a lymphoid

progenitor cell; these cells can mature into more specialized cells, such as T-cells, macrophages,

and platelets. In AML, either the myeloid progenitor cell, known as a myeloblast, or a myeloid

stem cell, is unable to differentiate and as such accumulates in the bone marrow. Because these

undifferentiated cells have self-renewing potential, tumors can result; more dangerously, levels

of myeloid derived blood cells can decrease dramatically. Understanding the mechanism through

which AML tumor cells can be made to differentiate is of importance as a therapeutic option.

Some treatments, such as inhibitors of demethylases and deacetylases, have proven to be

extremely valuable in targeting these epigenetic differences and can provide a way to target

cancer cells without affecting normal cells. Two treatments, vorinostat and tranylcypromine, will

be considered in the primary literature.

The concepts that are illustrated in these papers and have also been shown in the class

include CpG Islands, LSD1, acetylation, methylation, microRNA, hematopoietic stem cells,

progenitor cells, and histone modification. Techniques seen in class such as western blotting,

bisulfite genomic analysis, immunofluorescence, a variety of assays, and heat maps of

microarray data will be seen in these papers. Furthermore, while outside of the scope of this

class, double stranded breaks and DNA repair mechanisms are commonly studied in molecular

biology and will also be studied in the histone deacetylase paper.

II. Concurrent DNA hypermethylation of multiple genes in acute myeloid leukemia (Melki

et al. 1999)

Melki et al. (1999) sought to understand the methylation patterns of CpG islands in

normal patients compared to AML patients. CpG islands are regions of the gene with unusually

large numbers of CG nucleotides. Cancer cells often contain hypermethylation of tumor

suppressor genes, which enables their proliferation; the suggested reason is due to elevated levels

of methyltransferase in leukemia (Melki et al. 3730). Certain subsets of leukemia contain

hypomethylated sites, and others contain hypermethylated cites. However, a large scale CpG

island methylation study across several genes in one subset of leukemia has not been studied;

furthermore, the mechanism for unusual methylation is not understood. Melki was particularly

interested in assessing whether this phenomena was a general process or limited to particular

genomes.

To study aberrant methylation, the team extracted bone marrow samples from twenty

patients with AML and 9 patients with no evidence of leukemia. The group could have been

better controlled, with age ranges varying widely; however, the authors’ data suggests that

methylation patterns of normal vs AML patients was conserved regardless of the age of the

patient. Bisulfite genomic sequencing, which involved the PCR amplification of many

implicated genes and was used to construct methylation maps, enabled comparisons in

methylation patterns for each patient across several genes.

The genes the authors chose were calcitonin, ER, E-Cadherin, p15, p16, HIC1, Rb, and

GST-Pi. Their reasoning for picking these genes was well justified. The first three have been

shown to be hypermethylated in many forms of acute leukemia through Southern blot analysis.

p16, a similar analogue of the cyclin dependent kinase inhibitor p15, did not have the same

property. E-cadherin, Rb, and GST-Pi has been shown to be hypermethylated in other cancer cell

lines. HIC1 contains an unusually high number of CpG islands even in intron regions, thus

enabling them to see how this property differs between introns and exons. The set of genes the

authors select contain a good number of positive controls and interesting variations that add

worth to their paper; it would however have been beneficial to isolate a negative control, such as

a hypomethylated gene. Furthermore, the functional roles of these proteins are seldom explained,

save for p15 and p16. Why might a cancer cell hypermethylate and thus silence these particular

genes? In particular, the calcitonin gene and cadherin gene are shown to be calcium dependent,

and as will be shown by their data, both genes are hypermethylated in AML patients. These

relationships may have been an important stepping stone forward to characterize AML further,

but are not discussed by the paper.

The gene maps (Figures 1-6) for each gene are similarly organized. The label A

highlights the CpG sites on the gene by lines and shows which range of CPG sites were studied

in the genome. B marks the sequence of the CpG island. C shows the methylation map of each

CpG site in N and R patients; N patients are normal, and R patients have AML. The shading of

the boxes in each map correspond to the percent methylation; dots in the boxes indicate that the

site was not determined. A representative version of these figures is shown in Picture A, which is

the gene map for p15.

In the case of calcitonin, ER, E-cadherin, p15, and HIC1 (Figures 1-4, 6), much more

methylation seems to be present in a select number of AML patients compared to the normal

patients. Nonetheless, each figure illustrates the heterogeneity of methylation patterns from one

patient to the other. In Picture A, the red arrow points to the middle patients with AML, who

have almost no methylation in their CpG sites. Right above and below the arrow are AML

patients who have extensive methylation in nearly every CpG site. The authors do a good job

presenting caution for their results, pointing out this feature instead of overemphasizing the

higher general methylation in AML patients. Nonetheless, the methodology has some problems.

First of all, the authors overemphasize that CpG sites near the promoter are more likely to

be methylated. This is seen clearly in the calcitonin region (Figure 1), but as seen in Picture A,

there is no seeming pattern between proximity to the promoter (marked by the arrow) and the

methylation of the CpG site. Not every site has a clearly defined transcription site, making this a

difficult point to assess across various genes. Another issue is that the region chosen for the CpG

site analysis seems rather arbitrary. In Picture A, the authors select a large portion of the gene

containing the intron and exon portions of the gene. However, in the calcitonin gene and the p16

gene, a much smaller portion is selected. It is not made clear why these discrepancies exist; as

CpG sites are ubiquitous in the genome. It would have been more prudent to study every CpG

site in every CpG island, as the heterogeneity of the data is a defining feature of the result.

The authors sum up the methylation results for each gene in Figure 7 (Picture B). For

each patient, they shade in the circle corresponding to a gene if at least 25% of the CpG sites are

methylated or if there is at least one site with greater than 25% methylation. This is a rather

arbitrary distinction to draw the line from, though it does ensure a cross-understanding of patient

variances for each gene. As is clear, most AML patients have at least one gene that is

hypermethylated, and the proportion of patients who meet this criteria are significantly more than

the normal group for most genes. The results also add context to other properties of genes; some

genes which have been shown to be hypermethylated in other cancers, such as Rb and GST-Pi,

are not hypermethylated in leukemia, and there is a difference in the hypermethylation of the

introns of HIC1 compared to the exons in the normal and AML group. Ultimately, these results

do confirm that hypermethylation of the CpG islands in several genes is a defining feature of

AML in contrast to normal patients. There is substantial heterogeneity between various patients,

suggesting that CpG methylation is non-specific.

Aesthetically, the paper is a bit cluttered and difficult to read. For instance, Picture A

presents the data for every one of the 47 CpG islands. It would have been worthwhile to

incorporate the data in a different way, such as percentage of sites showing a certain ratio of

methylation. Because figures like A are repeated six times, the paper feels longer than it needs to

be and is difficult to contextualize with such a massive number of data points. Picture B does a

good job contextualizing each gene for each patient, though not every single dot is needed to

make the point.

PICTURE A. Figure 4 from Melki et al. 1999. Figure 1 legend is included since it contains the

legend for understanding the shading patterns of the methylation map (C). Red arrow highlights

patients with AML who have similar methylation patterns as normal patients.

PICTURE B. Figure 7 from Melki et al. 1999.

III. Loss of acetylation at Lys16 and trimethylation at Lys20 of histone H4 is a common

hallmark of human cancer (Fraga et al. 2005)

Fraga et al. (2005) contextualize epigenetic studies in a variety of cancers and focus

particularly on histone modification, a less characterized (at that time) epigenetic feature of a

variety of cancers. Referencing the heavy focus on aberrant DNA methylation of CpG islands in

past research, the authors provide an interesting transition from the previous paper. In particular,

the authors consider methylation and acetylation at histone 4 (H4) in a variety of cells. Because a

substantial number of their experiments study leukemia and lymphoma, and because their

ultimate conclusion is that certain histone modifications are cancer “signatures” (ubiquitous

along most cancer cells), these findings could likely be extended to AML, with some

reservations to be discussed later.

The authors use a variety of methods to study the presence of histone modifications.

High-performance capillary electrophoresis (HPCE) produces spectra corresponding to a variety

of histone modifications; the area under the curve represents the frequency of these sites. Picture

C corresponds to Figure 1 of their paper and shows the differences between the normal cell line

of lymphocytes (NL) and a leukemia cell line (HL60). As is shown in the boxed area, the ratio of

tri-methylated H4 and single acetylated H4 falls in HL60 compared to NL. The authors confirm

these findings in a variety of cancers, and show that more advanced stages of cancer have more

advanced loss of these modifications (Figure 2/Picture D). Figure 2 as a whole is excellent,

accounting for a variety of factors, such as freshly extracted tumor cells, cell cycle controls, and

a variety of cell lines. The only issue is that in Figure 2A, it is difficult to to assess the statistical

reliability of the acetylated data. With these findings, the authors transition into the next logical

step- what amino acid is affected within the histone for each modification?

The problems of the paper become apparent in the next figures (3 and 4). The authors use

western blot analysis to pull down specific acetylated sites of H4, such as K5, K8, K12, and K16,

as well as the trimethylated K20. Excerpts from each figure is shown in Picture E. The authors

justifiably show that the acetylation of K16 decreases in the lymphomas, but do not explain the

apparent hyperacetylation with the other lysine residues. In figure 4, the authors show that there

is a decrease in TrimeK20H4 for two of the four lymphoma cell lines, but do not mention why

the Jurkat cells or Column 1 are apparently similar in their amounts of trimethylation. The

bottom part of 4A shows that the HPCE quantification results in a decreased amount of 3Me, but

the staining of the Jurkat lymphoma is inconsistent, with the right cell expressing far less

3MeK20H4, but the authors do not explain this finding. It is likely that the left cell is a control

and the right cell is a distorted cancer cell, but this is not indicated at all in the paper. By not

addressing these significant findings, the authors lose some legitimacy in trying to overstate the

universality of these phenomena across a variety of cancer cells.

After isolating K16 and K20 as the important sites, the authors transition into identifying

links between genes and these modifications. They begin first with CDKN1A, a gene which is

epigenetically silenced by hypoacetylation at the promoter. Comparing the histone modifications

of NL and HL60 cells (through a CHIP assay) shows no apparent change in trimethylation or

acetylation at lysine 16, though other histone modifications differ between the two cells. The

reasoning for choosing to study CDKN1A (or any histone-modified gene) is not explained well;

in the figure legend, the authors suggest that they are expecting a shift in the acetylation pattern

to K16, but it seems that this shift has already been identified previously as the other acetylated

histones as the difference between cancer and normal cells. It would have been better to study a

gene which is epigenetically regulated in the same way through histone modification in both the

normal and cancer cells.

The authors continue linkage studies by studying genes which have hypermethylated

CpG sites in HL60 cells but not in normal lymphocytes: MGMT and PRDM2. They use a

negative control GSTP1 and a positive control IVD to ensure that their methylation studies are

consistent, which is good experimental design, and find that both HL60 and NL cells have the

same K16 acetylation patterns and trimethyl K20 (Picture F). However, as previously indicated,

they also show acetylation patterns across the two cell lines, and is seen, they are clearly

underexpressed in the HL60 cells (Picture F). These results are not explained at all nor are they

made significant, even though they are anything but insignificant. If the authors propose that

there is no association between the studied histone modification and the gene, it would have

made a stronger argument if they did not have another histone modification which does appear to

be important. Similar to design as the above two experiments, the authors do show an association

of hypomethylated repetitive sequences with their histone modifications of choice, but they

continue to incorporate other histone modifications which are also significant, yet not studied in

the paper. If the goal of the paper is to isolate signature histone modifications of cancer, it would

have been important to study the ubiquity of these trends across a variety of cell lines.

The above issue touches with a significant consideration of the paper. The authors intend

to highlight a common phenomena across a variety of cancers, and they do so successfully,

clearly showing decreased levels of K16 acetylation and K20 trimethylation of H4. However,

they focus almost exclusively on leukemia and lymphoma, which are derived from a linked

immune-lymphatic system. It would have been more fair to suggest that these features are a

hallmark of lymph derived cancers, rather than as a “cancer signature” or the general “human

cancer”. Furthermore, the inclusion of other significant histone modifications that clearly show

up in a variety of genes might be more characteristic of a cancer signature across a variety of

genes.

Aesthetically, the paper is in general well organized and logically structured. The figures

are generally strong and make a strong case for the author’s main points, though comparisons

could benefit from a t-test. The major issue with the paper, however, is that there are a variety of

confounding factors in the data that are not expanded upon in either the results or the conclusion.

A minor issue is that not all of the experiments seem as important to make the point that the

authors intend. For instance, not outlined above is a variety of demethylating drug studies done

at the end of the paper to show that there is an increase in hypomethylation (Figure 8) in addition

to the already existing hypomethylation. This data does not seem to be very important in

establishing the importance of the histone modifications.

PICTURE C. Figure 1 from Fraga et al.

PICTURE D. Figure 2 from Fraga et al.

PICTURE E. Figure 3 (left) and part of Figure 4 (right) from Fraga et al. In 3A, the authors

correctly show that the acetylation of K16 is decreased in lymphomas compared to the normal

lymph cells, but they make no mention of the hyperacetylation of K12 or K5 (red circles). 3B is

not explained in the paper at all beyond the figure legend. In 4A, the trimethylation decreases in

lymphoma cell lines 2 and 3, but not in 1 or the Jurkat cell lines (red circle). The authors do not

mention this discrepancy. Furthermore, in their immunofluorescence of the K20 site, two Jurkat

cells vary widely in their relative amounts of H4-Lys20.

PICTURE F. Excerpt of Figure 6 from Fraga et al. The red boxes indicate acetylation levels of

K5, K8, and K12 in NL and HL60 cells.

IV. microRNA-29a induces aberrant self-renewal capacity in hematopoietic progenitors,

biased myeloid development, and acute myeloid leukemia (Han et al. 2010)

The next paper, by Han et al. (2010), is the most recent of the three papers studied here

which focus on epigenetic trends and patterns of acute myeloid leukemia: particularly of

microRNA (miRNA). As the authors mention, miRNAs have been linked to carcinogenesis by

affecting a variety of important normal processes such as apoptosis and proliferation. The

author’s unique methodology from other studies of miRNA is that they are studying the role of 1

particular miRNA and its role in directing differentiation of stem cells. Unlike Melki’s paper,

which looks particularly at AML, the authors study an extensive number of cell types, including

stem cells, cancer stem cells, immature progenitors, and mature progenitor cells. While this is

arguably the most difficult to read paper of the five due to its extensive use of a variety of cells

and complex flow-cytometry data, it presents a very strong case that microRNA-29a is

overexpressed hematopoietic stem cells and leads to a biased differentiation pattern which

ultimately results in acute myeloid leukemia. This paper is important because it highlights a

potential mechanism through which AML can develop. Unlike treatment options which look for

ways to reduce AML potency, treating for the microRNA and inhibiting it before it manifests

may be a viable option to prevent the emergence of AML.

To break down the paper in easier to understand terms, I will use the terms HSC to refer

to hematopoietic stem cells, LSC to refer to leukemia stem cells, MPP to refer to multipotent

progenitors (which are more mature than stem cells but not as mature as the next group), and

CLP to refer to lineage committed progenitor cells. The authors describe how they isolated

miRNA29a from a QT-PCR study of 315 RNAs. They very clearly show that miR-29a is more

expressed in HSCs, MPPs, and LSCs than in CLPs through heat map expression (Picture G).

However, in relative expressions in human and mice cells of miR-29a, the bottom half of Picture

G, their data is quite confounding. While both cells show an overexpression of 29a in HSCs and

an under expression of 29a in CLPs, their MPP data is different; one is significantly lower, while

the other is not. Furthermore, LSC and non-LSC cells are not studied in the mouse genome. It is

not clear why the authors chose to do their methodology that way.

Next, the authors transduce normal BM mouse cells with either 29a or a control EMP.

Through flow cytometry, the authors show that 29a transduced cells and spleen cells show

myeloid lineage bias by overexpressing monocytes and granulocytes (Picture H). The data is

confusingly organized. It seems that the left four boxes are the EMP control, and the right four

boxes are the 29a. But the third column corresponding to the 29a is the weakest expressing of

them all. The authors also use staining to show that there is a myeloid lineage bias in the wild

type cells compared to the chimeric miR-29a cells, though an arrow pointing to different cells

would have made for a better figure. However, contrary to what is expected, there is an increase

of CLPs as well. It makes sense that the levels of HSCs or MPPs will rise up in the cell after 29a

transduction, but the data before doesn’t seem to support that there would be an increase in

CLPs. Thankfully, this confusion is cleared up in the next figure, but it weakens their argument

and should have been addressed when it was seen.

In their next set of experiments (Picture I), the authors show that there is a rise in the

numbers of MPP in 29a cells, while the numbers of common myelin progenitors and granulocyte

myelin progenitors remains the same. The proliferative ability of MPPs is thus enhanced by 29a

transduction and can be the reason for a biased lineage. Logically, their next experiment is to see

what the levels of differentiated cells look like, and they see a rise in macrophages and

granulocytes, as well as a corresponding decrease in megakaryocytes. AML is a myeloid

disorder, and myeloblasts are important in producing granulocytes. Furthermore, leukocytes

include macrophages and granulocytes; erythrocytes and thrombocytes are not part of the

leukocytic pathway. While this is not clearly mentioned in the paper, the lineage bias points

towards elevated levels of myeloid progenitors, which is characteristic of myeloproliferative

disorder (MPD). There is also a decreased expression of T-cells and B cells (Picture I, part D),

which are leukocytes as well, but are not seen in leukemia, suggesting that a particular branch of

leukocytes are upregulated. As the authors logically address in their next figure, the question

becomes why these particular progenitors are favored, and of what implications they have for

leukemic stem cells.

After a few months of transplantation with the overexpressed granulocyte and

macrophage progenitors, the cells show splenomegaly and hepatomegaly; they also are more

densely packed with immature myeloid blasts, a hallmark feature of AML (Picture J, part A).

Furthermore, AML cells have higher levels of mi-R29a. This suggests that the myeloproliferative

disease has evolved into AML, and that the progenitors have become capable of self-renewal,

which is a concerning implication of how AML might initiate. Mature progenitors are not

normally capable to self-renew, so it may be that AML adds this element to them and enables

them to revert back to immature blasts which can continue to create these self-renewing

progenitors. This is the most substantial and striking data point that they have, and it adds a new

lens to consider in AML research of the importance of the progenitors themselves. As

highlighted in the introduction, it has been demonstrated that AML is the result of immature

myeloblasts which interfere with normal cells. It may be useful to study whether the self-

renewing progenitors create functional mature cells.

To better characterize the role of mi-r29a, the authors include experiments of cell cycle

progression. After 10 minutes, the mi-r29a cells are much quicker in reaching the S and G-2

phase than the control cells. This enhances the understanding of the proliferative potential of mi-

r29a cells. The authors also show preliminary data of the target genes of mi-r29a and note in

particular HBP1- a tumor suppressor. These experiments show a logical transition into the next

part of further characterizing mi-r29a and add legitimacy to the author’s proposal of the

importance of this epigenetic feature.

Aesthetically, the paper is well organized and exhaustive; while there are a few misplaced

points here and there, each data point is important in explaining how mi-r29a is expressed in a

variety of different cells, of how mi-r29a creates a biased lineage, and of how this biased lineage

evolves into AML. In general, the conclusions derived match the data presented by the

beautifully made figures.

PICTURE G. Figure 1 from Han et al. The boxes represent some of the confounding variances

that make the data difficult to generalize. The heat map clearly shows that committed progenitor

cells express less miR-29a than AML cells (LSC/non-LSC) and HSC/MPP. In normalized

studies in human cells (B), the MPP levels of miR-29a are not significantly different from the

HSC cells, while the LSC levels are. In mouse cells (C), the MPP levels are significantly

different. No LSC cells are studied.

PICTURE H. Excerpt of figure 2 from Han et al.

PICTURE I. Figure 3 from Han et al. 2010

PICTURE J. Figure 4 from Han et al. 2010

V: Inhibition of the LSD1 (KDM1A) demethylase reactivates the all-trans-retinoic acid

differentiation pathway in acute myeloid leukemia (Schenk et al. 2012)

The previous three papers studied epigenetic features which may be a hallmark for acute

myeloid leukemia: CpG island methylation, loss of trimethylation and monoacetylation at a

particular histone, and overexpression of the microRNA 29A. Targeting these and other

epigenetic modifications may serve as a useful therapeutic option to combat AML. In the first of

two papers that will be considered about this goal, Schenk et al. study how inhibiting lysine

specific demethylase 1 (LSD1) is a precursor for enabling the all-trans-retinoic acid (ATRA)

differentiation pathway. While some subtypes of AML, known as acute promyelocytic leukemia

(APL), can be successfully targeted by ATRA alone, other subtypes of AML do not follow the

same pathway. This may be because ATRA target genes are blocked from access. In choosing to

target LSD1, the authors had previously considered the RARA2 promoter, which is an ATRA

target gene and is normally methylated in the H3K4 site (this modification corresponds to

activation). A decrease of the methylation in H3K4 of RARA2 has been identified in AML,

implicating a demethylase such as LSD1 and preventing ATRA from receiving full efficacy.

The authors do a variety of studies to consider how inhibiting LSD1 (either

through a drug LSD1 inhibitor (LSD1i) or shRNA inhibition) affects the ability of ATRA to

counter cancer cell properties, such as engraftment, the rise of mature cell markers, and the rise

in H3K4 histone methylation clusters. Two cell lines are studied: the ATRA responsive HL60

(which was also studied in a previous paper), and the ATRA non-responsive TEX cells. The

authors begin with drug combinations of ATRA with 2d and TCP- two different LSD1is. Using

flow cytometry FACS analysis, they show that there is a substantial rise in the number of cells

expressing Cd11B+ (a myeloid differentiation marker) in both HL-60 cells and TEX cells after

treatment with ATRA and TCP (Picture K, A and D). As expected, HL-60 cells are more

responsive to ATRA treatment in general, with 4x as many cells expressing Cd11B+ with just

ATRA compared to the TEX cells. While there is a substantial rise in the presence of Cd11B+

cells in the ATRA + TCP, the absolute percentage is the same as ATRA alone in the HL-60 cells.

The authors also do a superoxide anion luminescence study to consider the mechanism through

which ATRA and TCP might function. They see a rise in superoxide formation (Picture K, B);

the last paper will consider the implications of reactive oxygen species and may present a link to

why this may be. The authors also consider shRNA knockdown to ensure that it is actually LSD1

inhibition that is causing this attenuation by ATRA; they observe that the percentage of Cd11B+

cells rises with a combination of ATRA and shLSD1 (Picture K, E). These data are very strong,

well-controlled (there is an additional shScram control), and point to the potential of LSD1i to

enhance ATRA mediated differentiation. One problem with this set of experiments is that the

cell morphology data among different conditions (Picture K, C and D) is difficult to understand.

Better labeling would have ensured a better understanding of what morphological features to

look for, as there is no clear indication of what the differences are.

In the next experiments, the authors focus on HL-60 and TCP and study the colonogenic

properties and engraftment properties after treatment (Picture L). One significant problem with

this set of studies is that TEX is not studied at all. If a goal of the authors is to compare ATRA

responsive to ATRA non responsive cells, it would be valuable to include TEX cells for all of the

studies. As will be discussed soon, a problem results from studying HL-60. The colony assay

(Picture L, A) shows a substantial decline in colonies of the ATRA + TCP cells compared to the

untreated cells. Doing shLSD1 as an alternative to TCP also results in a significant decline

compared to just ATRA with shScram or just shScram (Picture L, A). However, in the untreated

ATRA case, shLSD1 still results in a substantial decline in the colony numbers by itself. This is

not explained in the paper and is a significant confounding factor. Next, the authors look at the

engraftment of the cells in a variety of sites in two mice strain: NOD-SCID (Picture L, B) and

NSG (Picture L, C and D). The NOD-SCID data is for the most part very strong. There is a

substantial decrease in engraftment with treatment of both ATRA and TCP compared to just

ATRA itself; the control, which is cord blood, does not exhibit this phenomenon. This is a great

control and enhances their case because it shows that normal HSCs are not affected by TCP. The

NSG data is not as strong (Picture L, C), but shows a similar phenomenon. One particularly

interesting experiment is the strain 0840, which is derived from ATRA/TCP treated cells. As is

evident, even in the untreated case is there a substantial decrease in engraftment, suggesting that

this morphology follows and maintains despite a lack of continual ATRA/TCP treatment.

However, as the authors point out, a similar phenomenon is seen in the strain 8058 derived from

ATRA treatment alone. Because of their use of HL60 instead of TEX, it is difficult to assess the

importance of TCP for the maintaining of the ATRA differentiated pathway in all subsets of

AML.

Gene mapping and expression makes up the next experiments that the authors study. The

results are quite striking (Picture M). The heat maps for the genes in HL60 after ATRA + TCP

treatment are very different from the other conditions (Picture M, A). The difference is less

striking in the TEX cells; while the untreated and the ATRA + TCP heat maps are quite different,

the ATRA and the ATRA + TCP are not. There appears to be a link in the genes that are changed

in both the TEX and ATRA cells (Picture M, B). The combined treatment seems to add a

synergistic effect to the amounts of upregulated and down regulated genes (Picture M, D),

though it would have been valuable to separate the TEX and HL60 data. In their last figures, the

authors show that H3K4 clusters rise across the genome as a result of ATRA and TCP treatment,

suggesting that LSD1 functionality is being blocked (Picture N, A) Another heat map is shown

comparing both the relative expressions of the histone cluster and the expressions of the gene.

Unusually, in this case the TEX genes are differently expressed as a result of ATRA and TCP

treatment, which adds confusion of the previously described data. The histone patterns are

strikingly different in HL60 cells as a result of the combined treatment, but subtly different in

TEX cells. It would be useful to understand where this difference may stem from, since it would

be hypothesized that TEX is the more responsive cell to the double treatment.

Aesthetically, the paper is fantastic. It is the shortest of the papers discussed, but it has a

substantial amount of fascinating and strong data that enhances understanding of the ATRA

pathway. It would have been better if it were clearly organized in sections or if a conclusion was

mentioned which describes the next steps, but it had to be formatted to the style of Nature letters.

While the authors do find an importance in the role of ATRA and TCP in ATRA-insensitive cells

(though further investigations remain), they also find that it is useful in ATRA sensitive cells like

HL60.

PICTURE K. Figure 1 from Schenk et al. 2012

Picture L. Figure 2 from Schenk et al. 2012.

Picture M. Excerpt of Figure 3 from Schenk et al. 2012. Part C has been removed since it is not

discussed in this paper.

Picture N. Excerpt of Figure 3 from Schenk et al. 2012. Part B has been removed since it is not

discussed in this paper.

VI: Vorinostat Induces Reactive Oxygen Species and DNA Damage in Acute Myeloid

Leukemia Cells (Petruccelli et al. 2011)

In the last paper, Petruccelli et al. 2011 study vorinostat, a histone deacetylase inhibitor

(HDACi), for its therapeutic potential in treating AML. While it has been made clear in the past

that HDACi arrest growth and induce apoptosis, the authors were particularly interested in

characterizing the pathways that vorinostat might affect in AML cells. Unlike the previous paper,

which focuses more on intrinsic cell features, this paper considers cell cycle and apoptotic

markers as well as features of DNA damage such as reactive oxidative species (ROS) and double

strand (DS) breaks.

The authors first study the presence of double strand breaks (Picture O). It would have

been valuable to include more text about why DS breaks are dangerous for cells and what their

presence indicates. A COMET assay is used to look at the tail moment and length of control and

vorinostat treated NB4 and U937 cells in alkaline situations; an increase in these numbers

indicates increased presence of DS breaks. There is a substantial increase in tail length for both

cells after vorinostat exposure, though increased exposure doesn’t seem to significantly affect the

length of the tails or their moments (Picture O, A). The authors also include this data in neutral

conditions (Picture O, B), and find better attenuation and separation of exposure time and the tail

properties. It is not clear why the data was studied in alkaline conditions to begin with. The

authors anticipate the question of how tail length data might directly suggest the presence of DS

breaks by also including data of the binding of phosphorylated H2AX, which binds to DSBs after

they form. As part C in Picture O shows, western blot analysis confirms an increased amount of

phosphorylated yH2AX data over time, though it would have been valuable to include data for

no vorinostat exposure over time. The next data points, also a part of figure 1, feel a bit

misplaced since they study ROS; there’s a latter figure that studies these more extensively. These

points will be looked at last and should have been combined together to enable a consistent flow

of logic.

In the next data points, the authors study cell cycle properties of the above strains after

exposure to vorinostat (Picture P). Using BrdU, which is a marker for replicating cells, the

authors see an increase in the number of cells in the G2 phase, which suggests that it might be

implicated in arrest. They see decreases in G1 phase cells and a rise in S phase cells after

vorinostat exposure (Picture P, A), suggesting that vorinostat may be causing arrest at the G2

phase. Unusually enough, they also see a rise in replicating cells through increased uptake of

cyclin-E (Picture P, B). This point is not explained in the paper. The authors use taxol to increase

the number of Ser10-H2 cells (a marker of mitosis), and show in their next data point that much

more cells are in the G1, S, and G2 phase after exposure to vorinostat (Picture P, C and D).

While this does confirm the previous data point, it also contradicts it since the G1 phase is

elevated. The legitimacy of the data is difficult to assess as a result and calls into question the

author’s conclusions: “These data suggest that vorinostat causes cells to exit the G1 phase of the

cell cycle and that cycling cells from G1 eventually arrest in the G2-M phase, likely due to an

accumulation of DNA damage” (Petrucelli et al. 4).

In their next assay, the authors consider apoptosis of both strains (Picture Q). This is their

strongest data. A substantial number of cells have DNA content below the Sub-Go peak, which

is a marker for apoptosis, after exposure with vorinostat after 24 or 48 hours (Picture Q, A). It

takes 18 hours for the initiation of apoptosis to begin, suggesting that an important pathway is

being turned on over time. One pathway could be the caspase pathway. Caspase-3/7, an

important protein for apoptosis, is upregulated substantially after vorinostat exposure in both

cells (Picture Q, B). Reversing apoptosis by adding Z-VAD-FMK to vorinostat shows that

vorinostat is in fact affecting apoptosis levels. It would have been useful to include data of

normal lymphocytes to see if this process is limited to just leukemia.

The authors next consider reactive oxidative species and how they are expressed as a

result of vorinostat (Picture R). In figure 1 (Picture R, D2), they show an increased amount of

oxidative DNA lesion 8-oxoguanine, suggesting that there is DNA damage caused by oxidative

species. The first part of their experiment shows a rise in the percent of cells that show peroxide,

a marker for ROS, over time with vorinostat exposure (Picture R, A). Another marker for ROS,

the intake of HO-1, is increased over time in both AML cell lines after exposure to vorinostat

(Picture R, B). These effects can be countered by the antioxidant NAC (Picture R, C and D). In

their last experimental figure, the authors study ex vivo AML derived blasts and confirm these

phenomena in them as a result of vorinostat exposure. In particular, cell viability of AML blasts

decreases (Picture S). This methodology calls into question the type of cells the authors are

exploring, as blasts and progenitors are quite different. As will be discussed in the conclusion,

accounting for the type of cell studied would be important for all AML studies since immature

blasts and progenitors may have different patterns.

Aesthetically, this paper is clear and easy to understand, with well-made figures, a

generally solid train of logic, and substantial evidence linking AML cell treatment by vorinostat

to other processes. However, several parts of the data are difficult to explain in the same way that

the authors choose to do. One frustrating aspect of the paper is that figure legends can be on the

next page. It would have been easy to shrink the figures and put in a figure legend on the same

page, but the authors choose not to do this.

Picture O. Excerpt of Figure 1 from Petruccelli et al. 2011

Picture P. Figure 2 from Petruccelli et al. 2011

Picture Q. Figure 2 from Petruccelli et al. 2011

Picture R. Figure 4 (left) and excerpt from Figure 1 (right) from Petruccelli et al. 2011

Picture S. Excerpt of Figure 5 from Petruccelli et al. 2011

VII: Conclusion

The links between these papers are quite remarkable. The identification of aberrant CpG

island methylation (studied by Melki et al.) is referenced as a commonly understood cancer

feature in Fraga’s paper, which comes six years later. The loss of acetylation and methylation

histone modifications which is identified by Fraga is seen in both of the treatment papers;

vorinostat is a histone deacetylase inhibitor, and tranylcypromine is a histone demethylase

inhibitor, most noticeably of lysine specific demethylase I. In the microRNA paper, it is

referenced that cells exhibiting mi-R29A are regulated at the G1 to S phase transition, which

enables them to proliferate rapidly. Vorinostat, however, is shown to arrest the cells at the G2

phase. Bisulfite genoming sequencing is used both in Melki’s paper and Fraga’s paper to study

methylation, albeit at different places- either the naked DNA, or the histone. Gene heat maps are

used in both the microRNA paper and the LSD1 paper to show how genes are differentially

expressed relative to control cells. Double stranded breaks and reactive oxidative species, while

not included in the scope of any other paper studied, are commonly used in the Negritto lab to

understand DNA stability and protein binding; the interlink between other fields of research

beyond just cancer is exciting to see.

As a whole, these papers reveal a great deal of the complexity of epigenetics. In Melki’s

paper, despite showing on average that patients with AML had hypermethylated sites, the results

across each patient were extremely heterogeneous, with some AML patients not showing any

hypermethylation at all. However, in Fraga’s paper, the histone modifications of focus were seen

in a variety of cell types, including leukemia, lymphoma, and melanoma, suggesting that some

epigenetic markers may be homogenous across a variety of cell lines. In the ATRA-LSD1 paper,

it was revealed that some cell types are more responsive to ATRA treatment than others, even if

these cell types are from the same general umbrella disorder such as AML. This adds an element

of consideration to all epigenetic research of the scope of view of the modification and treatment.

As such, it would be to the interest of Fraga’s team to confirm that their modifications are

consistently seen in a variety of AML morphologies. It would be valuable for Han’s team to see

if the overexpression of mi-r29A is a feature of other leukemias. These results point to the

cautioning of the generalizability of cancer and highlight the importance of individualized

medicine in seeking the most representative treatments that align with the patient’s own

manifestation of the disease.

A particularly interesting implication is of therapeutically targeting AML with a variety

of epigenetic treatments. The reason I chose this topic was because I studied drug combinations

of vorinostat and tranylcypromine in acute lymphoblastic leukemia (the other side of acute

leukemia) and observed synergistic cell death as a result. Would drug combinations of these

drugs, along with drugs which counter the effects of microRNA, result in a similar synergistic

phenomena in AML? One thing to note is of the response of these treatments to normal cells.

Results such as studying normal HSC under these treatments, such as the cord-blood derived

HSCs from the LSD1 paper, are important to elucidate so that the side effects of the treatment

are minimal.

Lastly, the microRNA paper has highlighted an extremely important feature of AML

which should now be incorporated in AML treatment studies. AML is not only characterized by

the presence of immature blast cells, but also of more mature progenitor cells which have self-

renewing properties and can revert back to immature blast cells. The problem may not be just

that the immature blast cells can not differentiate, but that progenitor cells which are derived may

be non-functional. A further study of these cells is required since it has been made clear by the

ATRA/TCP paper that differentiation patterns can vary among cells of the same disorder. If it is

shown that AML consists of these dangerous progenitor cells, new treatment options would be

sought beyond just those which lead to the differentiation of immature blast cells into functional

progenitors.

VIII: Works Cited

1. Fraga MF1, Ballestar E, Villar-Garea A, Boix-Chornet M, Espada J, Schotta G,

Bonaldi T, Haydon C, Ropero S, Petrie K, Iyer NG, Pérez-Rosado A, Calvo E,

Lopez JA, Cano A, Calasanz MJ, Colomer D, Piris MA, Ahn N, Imhof A, Caldas C,

Jenuwein T, Esteller M. 2005. Loss of acetylation at Lys16 and trimethylation at Lys20

of histone H4 is a common hallmark of human cancer. Nature Genetics 37:391-400

2. Han YC1, Park CY, Bhagat G, Zhang J, Wang Y, Fan JB, Liu M, Zou Y, Weissman

IL, Gu H. 2010. microRNA-29a induces aberrant self-renewal capacity in hematopoietic

progenitors, biased myeloid development, and acute myeloid leukemia. Journal of

Experimental Medicine 207:475-89.

3. Melki JR, Vincent PC, Clark SJ. 1999. Concurrent DNA hypermethylation of multiple

genes in acute myeloid leukemia. Cancer Research 59:3730-40.

4. Petruccelli LA, Dupéré-Richer D, Pettersson F, Retrouvey H, Skoulikas S, Miller

WH Jr. 2011. Vorinostat induces reactive oxygen species and DNA damage in acute

myeloid leukemia cells. PLoS One 6:e20987. doi: 10.1371/journal.pone.0020987

5. Schenk T, Chen WC, Göllner S, Howell L, Jin L, Hebestreit K, Klein HU, Popescu

AC, Burnett A, Mills K, Casero RA Jr, Marton L, Woster P, Minden MD, Dugas M,

Wang JC, Dick JE, Müller-Tidow C, Petrie K, Zelent A. 2012. Inhibition of the LSD1

(KDM1A) demethylase reactivates the all-trans-retinoic acid differentiation pathway in

acute myeloid leukemia. Nature Medicine 18:605-11