AJP_12-0313_Araten_et_al_Word_Version

Leukemic Blasts with the PNH Phenotype in Children with Acute Lymphoblastic Leukemia

David J. Araten1, Katie J. Sanders1, Dan Anscher1, Leah Zamechek1, Stephen P. Hunger2, Jonathan Karten3, Sherif Ibrahim3

1Division of Hematology, NYU School of Medicine, NYU Langone Clinical Cancer Center, and the New York VA Medical Center

2Children's Hospital Colorado and the University of Colorado School of Medicine, Aurora, CO

3Department of Pathology, NYU School of Medicine

Institution where work performed: Division of Hematology, Department of Medicine, NYU School of Medicine, NYU Langone Cancer Center, and the New York VA Medical Center

Number of text pages: 15

Number of figures: 3

Number of tables: 2

Running Head: ALL blasts with the PNH phenotype

Corresponding author: David J. Araten, MD; NYU Langone Clinical Cancer Center, Hematology Division, 160 East 34th Street, 7th floor, New York, NY 10016; Ph 212-731-5186; Fax 212-731-5540 e-mail: [email protected]

Reprint requests: David J. Araten, MD; 160 East 34th Street, 7th floor, New York, NY 10016

Grant Support: NIH RO1-CA109258, VA Merit Review 1IO1BX-000670-01, grants to the Children’s Oncology Group including the COG Chair’s grant (CA98543), U10 CA98413 (COG Statistical Center), and U24 CA114766 (COG Specimen Banking), and the Michael Saperstein Medical Scholars Award.

Disclosures: the authors have no relevant disclosures

1

Abstract

It has been proposed that genomic instability is essential to account for the multiplicity

of mutations often seen in malignancies. Using the X-linked PIG-A gene as a sentinel

for spontaneous inactivating somatic mutations, we previously showed that normal

individuals harbor granulocytes with the PIG-A mutant (PNH) phenotype at a median

frequency (f) of 12 x 10-6. Here we have used a similar approach to determine f

among blast cells derived from 19 individuals with acute lymphoblastic leukemia (ALL),

in comparison with immortalized EBV transformed B cell cultures (BLCLs) from healthy

donors. The BLCLs exhibited a unimodal distribution, with a median value of 11 x 10 -6.

In contrast, analysis of the f values for the ALL samples revealed at least two distinct

populations: one population, representing about half of the samples, had a median f

value of 13 x 10-6. The remaining half of the samples had a median f value of 566 x 10-6.

We conclude that in ALL, there are two distinct phenotypes with respect to

hypermutability, which we hypothesize will correlate with the number of pathogenic

mutations required to produce the leukemia.

2

Introduction

For a few “sentinel” genes, such as HPRT1, 2, GPA3-5, XK6, HLA7, and PIG-A8, it is

possible to use a phenotypic screen to quantitate the frequency (f) of spontaneously

arising mutants among blood cells from normal individuals. In these models, f generally

ranges from 1 x 10-6 to over 60 x 10-6, depending upon the sentinel gene and the age of

the individual. Such estimates are critical for quantitative models of carcinogenesis. For

example, considering that mutations in n different oncogenes or tumor suppressor

genes are required for the development of malignancy, if each one were to occur

independently, then the probability of n mutations coinciding in the same cell should

approximate f

n, where f represents the geometric mean of the frequencies for the

different oncogenic mutations. Since the adult body has <1014 cells, it has been argued

that given these measured values for f, it would be impossible for malignancy ever to

occur if n >2, unless spontaneous mutation rates were to somehow increase during the

process of malignant transformation9, 10.

Hypermutability could result from environmental mutagenesis, or genetic or epigenetic

inactivation of repair genes. Abnormalities in the expression or fidelity of DNA

polymerases and/or DNA repair genes10, 11 could also result in hypermutability. In

support of this model, results from cancer genome sequencing projects have generally

demonstrated a surprisingly high number of mutations12-14. However, mutations in repair

genes or polymerases have not been commonly found. An alternative model to account

for the multiplicity of mutations in cancer in the absence of hypermutability would involve

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successive rounds of clonal selection. Here, each oncogenic mutation would result in a

partial growth advantage in a dividing pre-malignant cell population. According to this

model, we might not expect to see a high frequency of phenotypic variants using a

sentinel gene that is not itself an oncogene or tumor suppressor gene.

To evaluate these models, we considered it important to investigate whether there is

evidence of hypermutability among ex vivo leukemic blasts. However, in applying a

phenotypic screen for rare mutants within a leukemic blast population, we are limited by

three considerations: (i) for some of the sentinel genes mentioned above (e.g., XK and

GPA), mutants can be detected only among red cells; (ii) for HPRT, the cells must grow

well in vitro-- which ex vivo blast cells do not readily do; (iii) for autosomal genes, the

effect of a loss of function mutation on one chromosome may be complemented by the

unmutated allele on the homologous chromosome. For a few autosomal genes that

have well characterized polymorphic alleles (e.g. HLA and GPA), it is possible to identify

spontaneous loss of one allele—but only in cells from only certain individuals who have

a specific compound heterozygote genotype.

PIG-A15 does not have these limitations and has several advantages as a sentinel gene

for spontaneous somatic mutations. Because PIG-A is X-linked (as are HPRT and XK),

a single inactivating mutation can produce the mutant phenotype, due to Lyonization in

females and hemizygosity in males. PIG-A has been well-characterized due to its

association with Paroxysmal Nocturnal Hemoglobinuria (PNH), and it is known that a

very broad spectrum of mutations can inactivate the gene 16, 17, providing a model for the

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inactivation of tumor suppressor genes as well as many of the point mutations that

would activate oncogenes. We and others have demonstrated occult populations of

cells with the PIG-A mutant (PNH) phenotype and genotype among diverse cell types

including granulocytes8, lymphocytes18, 19, human B-lymphoblastoid cell lines (BLCLs)20,

21, and marrow progenitors from normal donors22, as well as cell lines derived from

neoplasms23. Animals also harbor rare populations of spontaneously arising blood cells

with the PIG-A mutant phenotype, and the frequency can be shown to increase as a

result of mutagen exposure, as recently reviewed24.

A further advantage of using PIG-A as a sentinel gene is that its inactivation confers

loss from the cell surface of all proteins that require glycosylphosphatidylinositol (GPI),

resulting in a phenotype that can be detected by flow cytometry, without a requirement

for in vitro cell growth. PIG-A is widely expressed, and GPI is present in diverse cell

types, including primitive hematopoietic cells such as leukemic blasts. In addition,

antibodies specific for more than one GPI-linked protein can be used simultaneously,

along with the FLAER reagent25, which binds to the GPI-structure directly, in order to

maximize the specificity of any assay. Our previous work using PIG-A has

demonstrated hypermutability in many but not all cell lines derived from hematologic

malignancies26. Here we have applied this approach to determine whether

hypermutability can be demonstrated in populations of blasts from patients with ALL.

5

Methods

Frozen aliquots of de-identified ficol-sedimented marrow samples were obtained from

the Children’s Oncology Group repository and from the NYU Department of Pathology

in accordance with institutional protocols. All of the samples analyzed were known to

have been derived from the initial diagnosis of leukemia, before the administration of

chemotherapy, with the exception of the sample from patient 2, which was de-identified

in a way such that this information is not available. As a control, samples of whole blood

were donated by patients with PNH, who signed informed consent. EBV transformed B

cell lines (BLCL’s) were generated using EBV stock obtained from ATCC to infect

lymphocytes obtained from cord blood samples from discarded placentas as well as

whole blood from healthy adult donors providing consent as per an IRB approved

protocol. Six established BLCLs were obtained directly from the Coriell Cell Repository.

To generate BLCLs, for the first several weeks, until the cells BLCLs started to grow

and exhaust the media, cyclosporine was added at a concentration of 2 µg/ml to prevent

T cell activation. The cells were then grown in RPMI with 15% fetal bovine serum, L-

glutamine, and Pen/Strep, and non-essential amino acids.

Samples from patients with ALL were first thawed and diluted into DMEM media with at

least 20% fetal bovine serum and then incubated with the Alexa-488 conjugated FLAER

reagent (obtained from Pinewood Scientific Services, Victoria, BC, Canada) for 30

minutes at 37°C, at a concentration of 5 x 10-7M. The cells were then placed on ice for

the remainder of the experiment and then incubated with mouse anti-CD55 and anti-

CD59 antibodies (Serotec, 1:20 dilution). The cells were then washed twice and

incubated with FITC-conjugated rabbit-anti-mouse immunoglobulin (DAKO, 1:5 dilution).

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The cells were washed twice again and incubated with PE-conjugated murine anti-

CD45 (Serotec, 1:5 dilution), and washed once again. In order to ensure that the entire

sample population came in contact with the reagents, antibodies were added to pelleted

cells, which were resuspended, briefly centrifuged, and then resuspended again at the

start of each incubation. Propidium iodide was added at a concentration of 0.1 ug/ml

prior to analysis on a Becton-Dickinson FacScan instrument. As a control, using this

protocol, we stained lymphocytes from a patient with PNH, BLCL’s from normal donors,

the T cell leukemia line Jurkat, and a GPI (-) subclone of Jurkat that had been selected

with proaerolysin. By this approach, GPI (-) cells appear in the upper left quadrant, and

GPI (+) cells appear in the upper right quadrant. Of note, the emission spectrum of

Alexa 488 and FITC are extremely close, allowing for detection of both fluorochromes

together in a single channel (FL1). When analyzing ALL blasts and control BLCLs from

healthy donors, we gated on cells based on forward and side scatter, and we excluded

dead cells, which take up propidium iodide, which registers in FL3.

Voltage settings were applied to the PMTs such that unstained blast cells would exhibit

mean FL1 and FL2 values of approximately 2.5, so that over 80% of the unstained cells

would exhibit FL1 values of less than 5 (figure 1D). In our studies of spontaneously

arising GPI (-) cell populations in other cell types, we have found that after appropriate

fluorochrome compensation, GPI (-) cells can be reproducibly identified as having <4%

of the fluorescence of the wild type population. We therefore defined GPI (-) cells as

having less than 4% of the FL1 fluorescence of the wild type population; in cases

where this value would be less than 5, we used a value of 5 fluorescence units to

define the GPI (-) cells, based on the characteristics of unstained blast cells. In order to

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exclude cells with a global defect in membrane proteins, we gated on CD45 (+) events,

excluding any cells with an FL2 fluorescence <10% of the mean of the overall

population-- which allowed inclusion of at least 99.7% of the analyzed cells. In order to

maximize the chances of identifying rare events, we aimed to include at least 1 million

gated events in each analysis. The frequency of phenotypic variants was calculated as

the number of live CD45 (+) GPI (-) events divided by the total number of live CD45 (+)

cells analyzed.

Results

As expected, analysis of peripheral blood lymphocytes (PBLs) from a patient with PNH

who was known to have a substantial PNH clone within the lymphocyte, granulocyte,

and red cell lineages revealed two distinct populations with respect to the expression of

the GPI-linked proteins CD55 and CD59 and uptake of the FLAER reagent (figure 1A).

Similarly, a GPI (-) subclone of Jurkat registered in the upper left quadrant (figure 1B),

whereas the parental Jurkat culture registered in the upper right quadrant (data not

shown).

We then analyzed EBV immortalized BLCLs from healthy donors. A representative

example is shown in figure 1C, where the vast majority of the cells are seen to express

GPI-linked proteins, take up the FLAER reagent, and express the transmembrane

protein CD45. Almost the entire population, therefore, registers in the upper right

quadrant. However, there are rare events in the upper left quadrant that appear

phenotypically identical to the control GPI (-) cells in figure 1A and B. Twenty-five such

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events were counted out of a total of 1,041,825 cells analyzed, and the frequency of

these spontaneously arising GPI (-) phenotypic variants in this example is therefore 24 x

10-6.

In a panel of 19 BLCLs from normal donors, a median of 1.2 million gated events were

analyzed (range 0.4 to 1.9 million). In all but one BLCL cell line, at least one

spontaneously appearing GPI (-) event was identified that registered in the upper left

quadrant. The mean frequency of these phenotypic variants was 26 x 10 -6, with a

median value of 11 x 10-6, and a range of 0 to 149 x 10-6 (table 1). Using a value of

0.25 in a Box-Cox transformation, this distribution of values was unimodal and

symmetric, possibly with one “high” outlier, and the transformed data plotted on a q-q

plot demonstrated a nearly straight line, suggesting a near normal distribution.

We also applied this analysis to ALL blasts (figure 2), and we had available 25 frozen

samples. In 6 cases either there was either a lack of viability, extensive cell clumping

after thawing, insufficient cells for analysis, or a “tail” of the distribution curve with

respect to FL1 fluorescence that precluded discrimination of GPI (+) from GPI (-) cells.

In the remaining 19 cases, representing 4 cases of T cell ALL and 15 cases of B lineage

ALL, it was possible to identify spontaneously arising phenotypic variants. Looking at

the f values, the distribution clearly differed from the values derived from the analysis of

BLCLs from normal donors. Here the f values spanned 4 orders of magnitude, ranging

from 2.5 x 10-6 to 16,374 x 10-6. The mean value was 1046 x 10-6 and the median value

was 65 x 10-6. The f values for the ALL samples, overall, were significantly higher than

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the f values obtained from the BLCLs (p = 0.03 , 1 sided Mann Whitney U test). In

contrast to the distribution obtained for the BLCLs (figure 3A), using different possible

values ranging from -1 to +1 in the Box-Cox formula, there was no transformation that

could produce a straight line on the q-q plot or a histogram with a unimodal distribution

for the ALL samples (figure 3B). The ten ALL samples with the lowest f values had a

median f value of 13 x 10-6. Representative samples with a low frequency of GPI (-)

variants are shown in figure 2A and 2B. The remaining 9 samples had a median f value

of 566 x 10-6. Representative samples with a high frequency of phenotypic variants are

shown in figure 2D, 2E, and 2F. Using a log transformation of the f values, it is seen that

there are at least two distinct populations (figure 3B). In fact, the distribution may be

trimodal, and figure 2C shows a representative sample with an intermediate frequency

of phenotypic variants, in this case 88 x 10-6.

Discussion

We have taken advantage of the unique properties of the PIG-A gene to develop a

novel sensitive assay for the presence of phenotypic variants among leukemic blasts

from patients with ALL. Because PIG-A mutations disrupt the synthesis of the GPI

structure and the expression of GPI-linked membrane proteins, the PIG-A mutant

phenotype can be detected by flow cytometry, using monoclonal antibodies against

GPI-linked proteins, together with FLAER, a fluorescent reagent that binds to GPI-

directly. This approach allows for screening of a large number of cells to identify rare

spontaneously arising phenotypic variants, which is otherwise not possible to do. Here

we have found two distinct patterns: about half the samples we analyzed exhibited a

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frequency of phenotypic variants that is similar to results obtained from non-malignant

blood cells from normal donors. On the other hand, half of the samples we analyzed

demonstrated a high frequency of spontaneously arising GPI (-) cells—which is highly

suggestive of genomic instability.

The simplest interpretation of this data is that there are two different pathways to

developing leukemia. In the first case, a small number of mutations—perhaps only one

mutation in addition to a translocation13—are sufficient to initiate the process of

leukemogenesis. In this case, hypermutability might not be necessary, and non-

oncogenic mutations in genes such as PIG-A will be rare, with a frequency comparable

to that of non-malignant cells. In the second pathway, a large number of oncogenic

mutations are required, which could most easily occur as a result of genomic instability,

which will be reflected by an increased number of mutations in oncogenes as well as an

increase in non-oncogenic mutations27. In this pathway, we would therefore expect an

increased frequency of GPI (-) phenotypic variants. Individuals with germline variations

in repair genes resulting in constitutional hypermutability20 as well as those with

acquired repair defects occurring specifically in the cells of origin of the malignancy

could achieve the requisite number of oncogenic mutations through this second

pathway.

It is possible that an initial oncogenic translocation will determine whether the leukemia

demonstrates a high or a low mutator phenotype: for example, leukemias harboring the

t(12;21) translocation resulting in the ETV6/RUNX1 (TEL-AML1) fusion have been

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shown to have a higher number of deletion mutations than those with an MLL

translocation13. Indeed, here we have found that 4 out of 5 of our samples harboring the

ETV6/RUNX1 translocation (patients 4, 6, 14, 15, 19, table 2) demonstrated a markedly

elevated f value, as was the case for the sample from patient 1, which harbored a BCR-

ABL translocation. Interestingly, the BCR-ABL translocation has recently been

associated with intra-tumoral genetic diversity28, and a mechanism has been proposed

whereby the BCR-ABL fusion protein directly results in oxidative stress and secondary

mutations29. Two of the samples we analyzed were considered to be hyperdiploid

based on trisomies of chromosomes 4 and 10 (patients 13 and 18), and both of these

had a low f value. We believe that with a large number of samples each harboring the

same cytogenetic abnormality, we may be able to investigate the biologic factors that

are associated with hypermutability using this method.

We believe that an elevation in f --as detected in our assay-- is due to an increase in the

mutation rate rather than increased cell turnover. In our previous work using cell lines,

we were able to control for cell divisions, measure the mutation rate directly, and

demonstrate that it is frequently --but not universally-- elevated in hematologic

malignancies26. We have recently analyzed the mutation rate in a panel of cell lines

derived from Burkitt’s neoplasms and found that the distribution of mutation rates in this

type of malignancy is bimodal as well (manuscript in preparation). In studying ALL, we

can not control for cell divisions, because ex vivo leukemic cells do not often adapt to

tissue culture. However, our control cells, the BLCLs, had been growing well in culture

for a median of 5 months before they were analyzed. These BLCL lines did not

demonstrate any increase in f compared with f values from our previous work in

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granulocytes8 or estimates of f by others using other model systems1-7, 20, 30, 31. Indeed,

even though they were growing rapidly in vitro, their f values overall were significantly

lower than those of the ALL samples. This suggests that hypermutability in a subgroup

of ALL samples is likely to be a feature of the malignant phenotype rather than

proliferation per se.

Our assay is set up to detect mutations in the PIG-A gene, which can be inactivated by

a very broad spectrum of mutations16, 17, including nonsense, missense, and splice site

mutations, frameshifts, small in-frame deletions, as well as very large deletions.

Although in PNH, the GPI (-) phenotype, as a rule, results from mutations in PIG-A,

strictly speaking, the GPI (-) phenotype could be produced by loss (or epigenetic

silencing32) of any of the ~20 genes involved in GPI anchor synthesis 33-35- -or the genes

necessary for GPI trafficking36. However, with the exception of PIG-A, these genes are

autosomal34, and would probably require biallelic inactivation to produce the GPI (-)

phenotype, which would probably occur less frequently than a single PIG-A mutation.

We can not completely rule out the possibility that the GPI (-) phenotype could be

positively or negatively selected at various stages in the development of leukemia,

which could, respectively, increase or decrease the f values we observe. However, it is

widely believed that PIG-A mutations are growth neutral in vivo and in vitro20, 37-39 in

situations apart from the special case of aplastic anemia40. Of note, PIG-A is not

emerging as a “driver” gene in genome-wide analyses12, 41-46, arguing that selection in

favor of PIG-A mutants is an unlikely explanation for our findings. While it is highly likely

that an increase in the frequency of phenotypic variants as measured here is due to

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genomic or epigenetic instability, we can not say that a low f value rules out all forms of

hypermutability. Specifically, a propensity toward translocations and gene amplifications

would probably escape detection here. In addition, theoretically, it is possible that

successive rounds of clonal selection could periodically reduce the observed frequency

of phenotypic variants, as has been reported in yeast growing in culture over a

prolonged period 47.

Another caveat is that we can not be certain that PIG-A is reflective of the mutation rate

in other genes. This is an issue any time that a sentinel gene is chosen, particularly

because a phenotypic screen is possible for only a very few genes for comparison. Of

note, our studies on the mutation rate in non-malignant human cells using PIG-A have

generally corresponded to mathematical models of the mutation rate in HPRT20.

Although it is possible to perform deep sequencing for a large number of genes in order

to demonstrate intra-tumoral diversity, in a recent report using this technology48, this

approach had a sensitivity of detecting a heterozygous point mutation of 1/166 cells,

below which mutations could not be distinguished from sequencing errors. Random

Mutation Capture (RMC) is a highly sensitive assay developed by Bielas et al27 to detect

rare point mutations at recognition sites for a highly efficient restriction enzyme and may

in the future complement the assay described here. However, RMC is unlikely to be as

easily implemented as an assay based on flow cytometry.

In spite of these caveats, we believe that we have developed the first clinically

applicable test that is reflective of hypermutability and tumoral genetic diversity in

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leukemic blasts, and we believe that the parameter we measure here is likely to be

clinically relevant. For example, a high f value might correlate with the probability of

mutations in genes associated with relapse and chemotherapy resistance45, 49, and

indeed, mutations in PIG-A itself could confer resistance to alemtuzumab, which targets

CD52, a GPI-linked protein18. In fact, there is recent data from an animal model of

human ALL that this may occur50. Conversely, leukemias that demonstrate

hypermutability may be more susceptible to the effects of DNA damaging drugs such as

alkylating agents and anthracyclines, which might increase the mutation rate above the

threshold at which viability would be compromised. Our findings suggest that it will be

possible to apply this analysis at the time of routine phenotyping of leukemia and to

investigate these questions further by following patient outcomes prospectively.

Acknowledgments: Grant Support RO1-CA109258, VA Merit Review 1IO1BX-000670-01, grants to the Children’s Oncology Group including the COG Chair’s grant (CA98543), U10 CA98413 (COG Statistical Center), and U24 CA114766 (COG Specimen Banking), and the Michael Saperstein Medical Scholars Award. SPH is the Ergen Family Chair in Pediatric Cancer. We are indebted to Dr. Meenakshi Devidas, Dr. I-Ming Chen, and Dr. Mignon Loh from the Children’s Oncology Group for their assistance coordinating the sharing of samples, and Bridget Lane, RN for her assistance obtaining blood samples from healthy donors.

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Table 1: BLCL controls from healthy donors

Cell line sex age of donor# gated GPI (-)

cells total gated cells

frequency of GPI (-) cells per

million (f x 106)

BLCL 1 F 77 36 1,329,700 27

BLCL 2 M 73 11 1,319,757 8.3

BLCL 3 M 29 13 1,240,805 11

BLCL 4 M 71 174 1,165,695 149

BLCL 5 N/A N/A 1 1,091,817 0.9

BLCL 6 N/A cord blood 3 750,602 4.0

BLCL 7 N/A cord blood 0 396,746 0.0

BLCL 8 N/A cord blood 5 663,771 7.5

BLCL 9 N/A cord blood 55 884,288 62

BLCL 10 M N/A 8 504,941 16

BLCL 11 F 83 24 789,240 30

BLCL 12 F 60 25 1,041,825 24

BLCL 13 M 31 8 738,969 11

BLCL 14 (GM03299) F 8 6 1,882,614 3.2

BLCL 15 (GM03715) F 12 17 1,411,330 12

BLCL 16 (GM00130) M 25 176 1,671,951 105

BLCL 17 (GM14583) M 31 3 1,342,490 2.2

BLCL 18 (GM00131) F 23 11 1,642,549 6.7

BLCL 19 (GM14537) M 20 22 1,450,815 15

N/A : not available

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Table 2: Samples from patients with leukemia

Patient Age(yrs)

m/f

WBCper µl x 103

lineage Metaphase Cytogenetics BCR-ABL

(FISH)

MLL

(FISH)

Trisomy 4 &10(FISH)

ETV6-Runx1

(FISH)

Hypo-diploid(FISH)

# gated GPI (-) cells

total gatedcells

frequency of GPI(-) cells per million (f x 106)

Pt 1 41 M N/A B N/A Pos N/A N/A N/A N/A 510 1,844,838 276

Pt 2 13 F N/A T N/A N/A N/A N/A N/A N/A 16 331,368 48

Pt 3 18 M N/A T t(13q;18q) Neg N/A N/A N/A N/A 49 1,618,408 30

Pt 4 4 M N/A B N/A N/A N/A N/A Pos N/A 148 244,609 605

Pt 5 15 M 4.5 B N/A Neg Neg Neg Neg No 579 1,022,860 566

Pt 6 4.5 F 8.6 B N/A Neg Neg N/A Pos No 13 749,933 17

Pt 7 7 F 2.8 B N/A Neg Neg Neg Neg No 6 1,057,166 5.7

Pt 8 3.5 M 38 B 47,XY,+5[16]/46,XY[4] Neg Neg Neg Neg No 133 2,059,699 65

Pt 9 6 F 588 T 46, XX [40] Neg Neg Neg Neg No 83 983,522 84

Pt 10 3 M 18 B 52,XX,+X,+4,+14,+17,+21,+21[8]/46,XY[4] Neg Neg Neg Neg No 2 787,833 2.5

Pt 11 9 M 1.9 B N/A Neg Neg Neg Neg No 62 708,410 88

Pt 12 4 F 5.8 B 46, XX[20] Neg Neg Neg Neg No 200 1,196,416 167

Pt 13 11 M 4.8 T 85~87,XXYY,+Z,-4,-11,-15,-21[CP18]/46,XY[2] Neg Neg Pos Neg No 24 1,242,719 19

Pt 14 2 F 63 B N/A Neg Neg Neg Pos No 635 811,457 783

Pt 15 5 F 23 B 46, XX[14] Neg Neg Neg Pos No 1053 1,438,327 732

Pt 16 4 M 13 B N/A Neg Neg Neg Neg No 8 1,917,780 4.2

Pt 17 3 M 53 B 52,XY,+X,DUP(1)(q21q42),+10,+14,+17,+21,+21[4]/53,IDEM,+3[4] Neg Neg Neg Neg No 12 2,200,433 5.5

Pt 18 19 F 9.3 B 58,XX,+X,+4,+6,+8,+9,+10,+11,+14,+14,DER(16) t(11;16)(q21;q22),ADD(17)(p12),+18,+21,+21[17]/46,XX[2]

Neg Neg Pos Neg No 2 220,314 9.1

Pt 19 17 M 3.9 B 46,XY,t(4;11)(q27;q24),DEL(6)(q21),t(13;14)(q32;q13),ADD(15)(q26)[4]/46,XY[6] Neg Neg Neg Pos No 1791 109,384 16374

N/A : not available

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18

Figure 1

19

Figure 2

20

Figure 3

Figure Legends

Figure 1: Flow cytometry dot plot analyses of controls. FITC and Alexa-488 register on FL1 (horizontal axis) and reflect density of the GPI-linked proteins (CD55 and CD59) and the GPI-anchor itself respectively on the surface of the cell. PE registers on FL2 (vertical axis), reflecting density of CD45, a non-GPI-linked membrane protein. GPI (-) cells register in the upper left quadrant, and GPI (+) cells register in the upper right quadrant. (A) peripheral blood lymphocytes (PBLs) isolated from a patient with PNH. There are two distinct populations, representing GPI (+) and GPI (-) cells; (B) A spontaneously arising GPI (-) clone of the Jurkat cell line, registering in the upper left quadrant; (C) A representative BLCL derived from a healthy donor (BLCL 12): the vast majority of the cells are GPI (+) with a small but distinct subpopulation of GPI (-) cells registering in the upper left quadrant. The frequency of these spontaneously arising phenotypic variants is 24 x 10-6 in this example. (D) Unstained thawed blasts from a patient with ALL.

Figure 2: Flow cytometry dot plot analyses of samples derived from ALL blast populations. (A-B): representative examples of samples with a low frequency of spontaneously arising GPI (-) phenotypic variants-- patient 7 and patient 17 respectively; (C) patient 11, an example of a sample with an intermediate-sized population of GPI (-) phenotypic variants; (D-F) representative examples of samples exhibiting a very high frequency of GPI (-) phenotypic variants-- patient 5, patient 14, and patient 19 respectively.

Figure 3: Histogram of f values for BLCLs and ALL samples. (A) Using a value of 0.25 in a Box-Cox transformation, the f values for the BLCLs are unimodal and nearly symmetric, and they fall on a nearly straight line in a q-q plot, suggesting that these values are near normally distributed. (B) There is no transformation that could produce a unimodal symmetric distribution for the f values measured in ALL samples. Using a log transformation of the f values, it is seen that the distribution is bimodal or trimodal.

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17. O'Keefe C, Sugimori C, Afable M, Clemente M, Shain K, Araten D, List A, Epling-Burnette P, Maciejewski J: Deletions of Xp22.2 including PIG-A locus lead to paroxysmal nocturnal hemoglobinuria, Leukemia 2011, 25:379-382

18. Rawstron AC, Rollinson SJ, Richards S, Short MA, English A, Morgan GJ, Hale G, Hillmen P: The PNH phenotype cells that emerge in most patients after CAMPATH-1H therapy are present prior to treatment, Br J Haematol 1999, 107:148-153

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29. Nowicki M, Falinski R, Koptyra M, ASlupianek, Stoklosa T, Gloc E, M Nieborowska-Skorska, Blasiak J, Skorski T: BCR/ABL oncogenic kinase promotes unfaithful repair of the reactive oxygen species–dependent DNAdouble-strand breaks, BLOOD 2004, 104:3746-3753

30. Albertini R, Nicklas J, O’Neill J, Robison S: In vivo somatic mutations in humans: measurement and analysis, Annu Rev Genet 1990, 24:305-326

31. Morley A, Trainor K, Seshadri R, Ryall R: Measurement of in vivo mutations in human lymphocytes, Nature 1983, 302:155-156

32. Hu R, Mukhina G, Lee S, Jones R, Englund P, Brown P, Sharkis S, Buckley J, Brodsky R: Silencing of genes required for glycosylphosphatidylinositol anchor biosynthesis in Burkitt lymphoma, Experimental Hematology 2009, 37:423-434

33. Almeida A, Murakami Y, Layton D, Hillmen P, Sellick G, Maeda Y, Richards S, Patterson S, Kotsianidis I, Mollica L, Crawford D, Baker A, Ferguson M, Roberts I, Houlston R, Kinoshita T, Karadimitris A: Hypomorphic promoter mutation in PIGM causes inherited glycosylphosphatidylinositol deficiency, Nature Medicine 2006, 12:846-851

34. Kinoshita T: Overview of PNH. Edited by Omine M, Kinoshita T. Tokyo, Springer-Verlag, 2003, p. 6

35. Kinoshita T, Inoue N: Dissecting and manipulating the pathway for glycosylphosphatidylinositol-anchor biosynthesis, Current Opinion in Chemical Biology 2000, 4:632–638, 4:632-638

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38. Keller P, Payne JL, Tremml G, Greer PA, Gaboli M, Pandolfi PP, Bessler M: FES-Cre Targets Phosphatidylinositol Glycan Class A (PIGA) Inactivation to Hematopoietic Stem Cells in the Bone Marrow, J Exp Med 2001, 194:581-590

39. Rosti V, Tremml G, Soares V, Pandolfi P, Luzzatto L: Murine Embryonic Stem Cells Without pig-a Gene Activity Are Competent for Hematopoiesis with the PNH Phenotype but Not for Clonal Expansion, Journal of Clinical Investigation 1997, 100:1028-1036

40. Rotoli B, Luzzatto L: Paroxysmal nocturnal haemoglobinuria, Baillieres Clinical Haematology 1989, 2:113-138

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42. Dunwell T, Hesson L, Rauch T, Wang L, Clark R, Dallol A, Gentle D, Catchpoole D, Maher E, Pfeifer G, Latif F: A Genome-wide screen identifies frequently methylated genes in haematological and epithelial cancers, Mol Cancer 2010, 9:44

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44. Mullighan C, Miller C, Radtke I, Phillips L, Dalton J, Ma J, White D, Hughes TP, Beau ML, Pui C, MVRelling, Shurtleff S, Downing J: BCR–ABL1 lymphoblastic leukaemia is characterized by the deletion of Ikaros, Nature 2008, 453:110-114

45. Mullighan C, Phillips L, Su X, Ma J, Miller C, Shurtleff S, Downing J: Genomic Analysis of the Clonal Origins of Relapsed Acute Lymphoblastic Leukemia Science 2008, 322 1377-1380

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48. Walter M, Shen D, Ding L, Shao J, Koboldt D, Chen K, Larson D, McLellan M, Dooling D, Abbott R, Fulton R, Magrini V, Schmidt H, Kalicki-Veizer J, O'Laughlin M, Fan X, Grillot M, Witowski S, Health S, Frater J, Eades W, Tomasson M, Westervelt P, DiPersio J, Link D, Mardis E, Ley T, Wilson R, Graubert T: Clonal Architecture of Secondary Acute Myeloid Leukemia, N Eng J Med 2012, 366:1090-1098

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Jonathan Karten The Basics Flow Cytometry and Multiple Myeloma ExperimentFlow CytometryIntro

Cytometry is the measurement of physical and chemical characteristics obtained from individual

cells. Flow Cytometry is a technique used to acquire this information by means of Fluidics, Optics, and

Electronics. The Flow Cytometer is an apparatus that suspends “flow” single-file cells through a laser

beam where they scatter light and emit fluorescence, which is collected, filtered and converted to digital

values that are then stored in a computer, gated, and plotted. Later this data is analyzed, compared, and

diagnosed. The data acquired by the Flow Cytometer, whether it is through the simple (FACS) Scans,

Quadruple Laser Cytometry, or Sorter Cytometer, is of vital importance to biology, chemistry, and even

physics. As a whole, such data procured from the Flow Cytometer aids to branches including, but not

limited to, clinical study, genealogy, diagnoses, and almost all research and medical practice. Although

the Flow Cytometer can be used in cell cycle, ploidy analysis, immunophenotyping, and determining the

aggressiveness of tumors, the popularity of the Flow Cytometer comes from the fact that thousands of

cells can be statistically analyzed in a short time. Computer programs such as FlowJo allow for flexibility

of the acquired data, and the ability to use and “re-gate” old acquisitions for comparison and/or long

standing experiments.

Optics

Light scatter provides semi-quantitative information linked to the cell size or internal

complexity of the cell. Everything that passes through the laser beam scatters light. The computer then

determines the size and granularity of the object. Side Scatter (SSC) is used to indicate the granularity of

the object, and is obtained when the object is deflected at roughly a right angle from the laser beam, see

figure 2 for FSC vs. SSC graph. Forward Side Scatter(FSC) is when the object or lack of object is

translated to the forward optic lens or fluorescence detector. The forward side-scatter optic scanner lies

adjacent to the laser and past the Flow cell. The bigger the cells the more electrical current is blocked 27

from being picked up by the FSC optic lens or fluorescence detector. The amount of data picked up by the

FSC optic lens is then used to establish the size of the cell (see figure 1 below for an example of the

chamber schematic in relationship to FSC SSC). Both FSC and SSC are vital in helping to recognize

whether the cell is dead or alive. No staining is required for light scatter testing.

*Figure 1, chamber schematic in *Figure 2, FSC vs. SSC graph:

relationship to FSC SSC:

In order to determine the variety of characteristics in a cell-line, a specific extrinsic or intrinsic

fluorescent antibody must be bound to each individual cell. Fluorescent excitation is when energy is

absorbed and the molecule is excited. As the molecule returns to its normal state a specific, wavelength is

emitted corresponding to a particular section of the electromagnetic spectrum. This is known as

fluorescence emission. Only a very small part of the electromagnetic spectrum is visible to the human eye

or in this case fluorescence detector. Any wavelength between 380nm to 750 nm is expressed in the

“optical window” by the colors red, orange, yellow, blue, indigo, and violet, (see figure 3 for optical

window in electromagnetic spectrum). These colors are also deflected at a 90˙ angle and later re-directed

by a variety of dichotic mirrors and filters, to be read (see figure 4 for optical schematic). The Flow

Cytometer documents these colors emitted and turns them into data for the computer, however without a

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fluorescent molecule no data is collected. A molecule must be bound to the cell first before entering the

Flow Cytometer, a process known as staining.

*Figure 3, “optical window” in

electromagnetic spectrum in relationship

to wavelenth (nm): *Figure 4, optical schematic:

Antibodies/ Fluorescence

Intracellular Flow Cytometry staining is where the antibody is located or placed inside the cell

rather than on the surface of the cell. Although intracellular Flow Cytometry is a commonly used practice,

there are many different methods to bind fluorescence to a cell. The most common of these being

attaching an antibody to an antigen located on the cell via an anchor or transmembrane protein (see figure

5 for GPI linked protein on active site and transmembrane protein).

A polyclonal antibody is an antibody that is made in the ascites (the accumulation of fluids found

in the serous membrane lining of the abdomen) of an animal. Polyclonal antibodies get their names

because they bind to many of the epitotes that are located on the antigen, which increases the probability

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of cross-reactivity, later to be discussed. An epitiote is the active site on an antigen where it binds to the

antibody. An antigen is much more easily bound to an antibody if the antibody is of high affinity or if it

requires only one epitope to bind. Monoclonal antibodies are made in hybridomas (the resulting hybrid

cell from the fusion of an lymphocyte and a tumor cell). Monoclonal anti-bodies bind to a single epitope

on the antigen. See figure 6 for a diorama of monoclonal vs. polyclonal anti-bodies.

*Figure 5, GPI linked protein on active site *Figure 6, monoclonal vs. polyclonal and transmembrane protein: anti-bodies:

There are different ways antibodies bind to cells. A specific binding is usually a directly

conjugated antibody to eptiope, but sometimes the bond may occur between an Fc to an Fc receptor.

However, this is not a successful bond; neither is any bond of low affinity. Bonds of low affinity create a

sticky environment for the cells and cause cells to clump.

Different clones of the same CD-specific antibody have a higher affinity than others, however all

clones produce antibodies to one epitope, so the staining procedure becomes a much simpler process. In

1975, Kohler and Milstien were awarded the Noble Prize for making this discovery.

After successfully binding a fluorescently labeled antibody to the cell-line the sample is ready to

be excited by the laser. The sample then goes through the aspiration rod (the tubing inside the

instrument), combines with sheath fluid, becomes a flow cell suspended in a stream (where it intersects

with the laser), and then travels to the waste or in Flow Cytometry Sorters is put in collection tubes for

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further analysis. See figure 7 for the Flow Cytometer schematic in association with flow chamber and

electronic console.

*Figure 7, Flow Cytometer schematic in association

with flow chamber and electronic console:

Examples of extrinsic fluorescence can be probes or dyes such as FITC, PE, or PI. Nevertheless,

these probes aren’t the only way for a cell to be stained. Some cell-lines don’t need to be stained at all,

but rather come intrinsically stained. This is known as “autofluorescence” because either the cell-line

naturally has tryptophan, tyrosine, natural pigments, or hemoglobin, or the cell-line is bought

fluorescence added. There are also fluorescent proteins as well attenuated transfected stains that don’t

need to be bound to the anchor attached antigen but rather directly attach to the cell. Transfected stains,

such as FLAER (a commonly used transmembrane binding stain), come from viruses or bacteria, that

have been immunized so that they don’t cause their negative effects, but rather just bind to the cell.

Titrating reagents

Before staining cells, the appropriate staining concentration must determined so that all antigens

are saturated, this is called reagent titration and it improves the accuracy of staining, avoids non-specific

binding, and saves money. One must take into account a number of factors before titrating, including the

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final volume of the reaction, the concentration of the reaction, the number of cells needed to be stained,

the temperature during titration, and the time at which the titration takes place.

The concentration of the sample is always dependent on the volume. It is important to know the

final volume and the dilution factor to determine the concentration and the necessary amount of

fluorescent molecules needed to bind. There may be more total antigen molecules than antibody

molecules in the tube, for example 20 l of stain may be appropriate for 1 million cells, but not for 20

million cells. If 20 l was used to stain 20 million cells the dilution would be off and the molecule

excitation and emission would be drastically lowered.

Compensation

Because most fluorescent molecules express a different wavelength when excited than when

emitted, sometimes molecules may overlap in fluorescence. One molecule’s excitation wavelength may

be the same as another's emission wavelength and vise versa (see figure 8 for FITC and PE excitation to

emission graph and overlap). In-order to prevent this from disturbing the data one needs to counterweigh

the affect. In Flow Cytometry this action is called compensation. The goal of compensation is to correctly

quantify each dye with which a particular cell is labeled. Through setting controls with naturally known

outcomes and calibrating the Flow Cytometers voltage of the laser to each cell accordingly. Then by

changing the Threshold of samples’ voltages, a portion of one detector's signal may be subtracted from

another leaving only the desired signal. See figure. See figure 9 for stained and unstained compensation

graph

*Figure 8, FITC and PE excitation to *Figure 9, stained and unstained

emission graph and overlap: compensation graph:

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Fluidics

Flow Cytometry reads the characteristics of each individual cell. To do so, the cells must be

suspended in single file order for the FALS sensor (the “eyeball” of the Flow Cytometry), to pick up and

transmit the information. To suspend cells into Flow Cells, all Flow Cytometers add their sample through

a small (50-400m) orifice and simultaneously, a pressurized torrent of sheath fluid passes at the same

velocity, causing the cells to break up and form their individual lines. The act of suspending flow cells is

called Hydrodynamic focusing. There are two main pressure systems to control flow rates and achieve

Laminar Flow. Laminar Flow is the state where flow cells are suspended individually in-between sheath

fluid. These two systems are differential pressure and Volumetric Injection pressure.

Differential Pressure uses air or other gases to separately pressurize and regulate the sample

pressure and sheath fluid pressure, before they come in contact and interact with one another. This is

known as the sheath flow rate. The difference in pressure between the sample and sheath fluid is called

sample flow rate. Although this is a good technique, the control is not absolute and changes in friction

may alter the sample flow rate. See figure 10 for differential pressure schematic sketch

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*Figure 10, Differential Pressure System:

The Volumetric Injection System also uses air or other gases, but only to set the sheath fluid flow

rate, not the sample rate. The Volumetric injection system uses the syringe pump that is attached to the

piston, which is inserted in the sample to regulate the sample rate. In this system the control is absolute.

See figure 11 for Volumetric Injection System.

*Figure 11, Volumetric Injection System:

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Flow chambers are the chambers that hold the tips, nozzles and flow cells. There are two types of

flow chambers, “Jet-In-Air” and “Flow-Through Cuvette”. Jet-In-Air is best used in sorting optical

properties, and Flow-through Cuvette is best used for sorting, but usually appears in analyzers as well.

See figure 12 for Flow Through Cuvette chamber vs. Jet-In-Air chamber.

*Figure 12, Flow Through Cuvette chamber vs. Jet-In-Air chamber:

-Flow Through Cuvette -Jet-In-Air

The Laser(s) focus through the quartz on the Flow Through Cuvette chambers and at the stream

in the Jet-In-Air chambers. For the laser to give a good intersection all the components of the chamber

must be properly aligned with the stream or quartz, and the fluidics must be stable. See figure for Flow

Through Cuvette chamber vs. Jet-In-Air chamber.

The Laser usually is either composed of a single wavelength or tunable wavelength, which is

altered after passing through a prism. Different Flow Cytometers use different lasers such as Ion lasers,

Dyed lasers, or Diode High Efficiency lasers.

In multi-laser Flow Cytometers, florescence and side scatter get determined with the use of Long

and Short Pass mirrors and filters, as well as a series of differently calibrated optic lenses. These optic

lenses only pick up certain types of electrical current and emitted light. Flow Cytometers can have up to

four lasers and many more optic lenses. Each laser has its own purpose and each lens has its own specific

read on the sample. See figure 13 for the optic schematic.35

*Figure 13, optic schematic:

Filter and Mirror Types

The specificity of detection is controlled by the wavelength’s selectitivity of optical lenses and

filters. There are many filter types such as absorbance filters, interference filters and neutral density

filters. Absorbance filters work by the absorption of wanted wavelength. Interference filters, are basically

just mirrors comprised of very thin-sandwiched metal layers, which work to deflect the wavelength to the

proper detector. Neutral filters cut down on the amount light getting through, but unlike the absorbance

filters, do not absorb all the wavelength currents.

Long pass filters transmit wavelengths above a certain wavelength, like LP55 which amplifies

any wavelength above 550nm, emitting red color. Short pass filters transmit wavelengths below a certain

wavelength; for example SP550 would transmit any wavelength to less than 550nm.

Analyzers and Sorters

The main difference between Analyzers and Sorters is that particles passing through Analyzers

are just detected, analyzed and split between PMTs via a series of filters, while particles passing through

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sorters can be sorted into separate tubes or wells. Although the Sorter is far more flexible in being able to

change the configuration of PMTs and/or Filters, the Sorter is far larger than the Analyzer and must be

calibrated weekly. See figure 14 for an example of an Analyzer Flow Cytometer vs. the Sorter.

*Figure 14 for an example of an Analyzer Flow Cytometer vs. a Sorter Flow Cytometer:

-Sorter Flow Cytometer -Analyzer Flow Cytometry (FACS) scan

Like the analyzer the Sorter achieves Laminar Flow, however Sorters also contain the ability to

purify materials. The Sorter purifies materials by suspending them into droplets through high-speed

oscillations. According to whether it is programmed to keep or dispose of the sample the Sorter

selectively charges and deflects droplets in 2-4 directions or into multiple plates or slides, through

electrically charged plates. The Sorters capability to transform a sample flow-cell stream into droplets

comes from the Piezo Electric Crystal that, through vibrations of up to 200,000 waves per second break

up the stream into droplets. See figure 15 for an example of an Analyzer Flow Cytometer schematic vs. a

Sorter Flow Cytometry schematic.

*Figure 15 for an example of an Analyzer Flow Cytometer

schematic vs. a Sorter Flow Cytometry schematic:

-Sorter Flow Cytometry schematic -Analyzer Flow Cytometer schematic

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Electronics and Data Analysis

The processes of converting emitted fluoresce into FCS or List Mode Files, is as follows: first the

flow Cytometer collects photons emitted by the sample. The varying number of photons reaching the

detector is then converted to a corresponding number of electrons by the PMTs. If need be, the number of

electrons exciting a detector can be magnified by increasing the voltage of the PMTs. The current

generated is put into the log of a linear amplifier and is changed to a voltage pulse. The voltage pulse is

an analog signal and becomes digitalized by the ADC and placed into a List Mode File to be analyzed.

Cytometry is made up of Fluidics, Optics, and Electronics (see figure 16); however, without

statistics the Flow Cytometer is rendered useless. Through the use of statistics doctors and researchers are

able to discover the percentages of dead to live, mutated to un-mutated cell populations, as well as the

size and the brightness of the cell and its antigen. This information helps figure out the mutation rate of a

certain cell-line, and in some cases frequency. This helps treat the cell-lines and/or patients accordingly.

Also doctors and researchers may check and see if the treatment will work on all or some of the cell lines

and then compare them to clinical history, to diagnose a patient or recommend new treatment options.

*Figure 16 Fluidics, Optics, and Electronics:

38

Multiple Myeloma

Background

Multiple Myeloma is a hematological cancer of the plasma cells, this is when collections of

abnormal plasma cells accumulate in the bone marrow and interfere with the production of normal blood

cells. A Plasma cell is a type of white blood cell that, when normal, produces antibodies, however when

mutated causes a different affect. Plasma mutation may result in Multiple Myeloma, also regarded Plasma

Cell Myeloma and Kahler’s Disease. Most cases of Myeloma feature the production of a paraprotein;

other side affects of this are kidney malfunctions, bone lesions, and hypercalcaemia (high calcium levels).

Myeloma can be diagnosed with blood tests such as serum free kappa/lambda light chain assay and serum

protein electrophoresis. Urine protein electrophoresis and bone x-rays can also diagnose myeloma.

39

Although Myeloma is somewhat treatable with the use of steroids, chemotherapy, proteasome inhibitors,

and other immunomodulatory drugs, it is still incurable. Myeloma affects 1-4 per 100,000 adults and is

more common in men, and twice more common in African-American than white American for reasons

unknown. After diagnosed with Multiple Myeloma the survival median is 3-4 years, which may be

extended to 5-7 years with advanced treatment. Multiple Myeloma constitutes 1% of all cancers and is the

second most common hematological cancer.

Pig-A/ GPI linked Proteins

As a person ages, there cells go through natural divisions, unfortunately mutations are an inherent

risk of cell duplication. Although inheritable mutations are the catalyst of biological evolution, the

accumulations of mutations in some somatic cells play the key mechanisms for the development of

cancer. The frequency of mutants (f) and the rate of mutation (µ) are biological features of any cell

population. Frequency and rate measurements may provide important information regarding the risk of

oncogenesis and the exposure to carcinogenic agents.

We have found that Pig-A meets the requirements for a good snetiniel gene and therefore is a

good model for calculating f and µ. This is because the Pig-A gene encodes one subunit of the enzyme

essential in the biosynthesis of glycosylphosphatidylinositol (GPI). When the PIG-A gene is mutated the

resulting phenotype is known as PNH (Paroxysmal nocturnal hemoglobinuria). In our previous studies we

have found that there is no selection for mutants and all mutations are growth neutral. PIG-A is an X-

linked gene so there is only a single copy in males. In this way, a single mutation leads to phenotypic

change in males. In addition multiple types of mutations such as, frame shifts, point mutation, deletions,

etc, all can affect the PIG-A gene, making it susceptible to entire chromosome mutations not just RNA

mutations. See figure 17 for diagram of the PIG-A gene mutation.

*Figure 17 diagram of the PIG-A gene mutation:

40

When the PIG A gene is mutated, the resulting disease, PNH leades to cells that do not display

the proteins that required for GPI attachment. A GPI- negative surface phenotype can be easily detected

by flow Cytometry. Through Flow Cytometry we set our control. We have found that the normal BLCLs

demonstrated a frequency of PNH cells of 6.3 x 10 -6 and 18.4 x 10-6, in normal adults. Which means that

normal adults have a less than 1% population of a PNH-like phenotype.

By counting the GPI-negative phenotypes using the Flow Cytometry we have been able to

calculate the measurement of PIG-A mutants, this information proved to be effective in measuring mutant

frequency in peripheral blood cells of humans and other animals. Although, it has proven difficult to

measure the m of PIG-A mutations in human cells, by using the PIG-A gene in lymphoblastoid cell lines

we now have a test that makes it practical to measure m in human cells.

Mutation rate

The mutation rate (m) is a key biological feature of somatic cells. M determines the risk for

malignant transformation and has been exceedingly difficult to measure in human cells. A potential

sentinel is the X-linked PIG-A gene. The Pig-A gene inactivation causes lack of GPI-linked membrane

41

proteins. We previously found that the frequency (f) of PIG-A mutant cells can be measured accurately by

flow cytometry, even when f is very low.

We now measure both f and m by culturing B-lymphoblastoid cell lines and first eliminating

preexisting PIG-A mutants by flow sorting. After expansion in culture, the frequency of new mutants is

determined by flow Cytometry using antibodies specific for GPI-linked proteins (e.g., CD48, CD55, and

CD59).

The mutation rate is then calculated by the formula m = f/d, where d is the number of cell

divisions occurring in culture and f is the negative cells over the total population. By measuring the mean

of the normal population versus the negitive mutated population and setting the controls, the mutation rate

can now be measured routinely in the B-lymphoblastoid cell lines. This system can be useful in

evaluating cancer risk and in design of preventive strategies.

Here we have used a similar approach to determine f among blast cells derived from 19

individuals with acute lymphoblastic leukemia (ALL), in comparison with immortalized EBV

transformed B cell cultures (BLCLs) from healthy donors. When we looked at BLAST cells analysis in

the ALL sample, we only used frequency and not µ because ALL BLAST cells do not grow in culture.

Using these methods and calculating for f in ALL (acute lymphoblastic leukemia) cell samples,

aided and led to the discovery of a bimodal grouping pattern. We concluded that in the ALL samples we

analyzed, there are two distinct phenotypes, High mutaters and Low mutaters. One population,

representing about half of the samples, had a median f value of 13 x 10-6. The remaining half of the

samples had a median f value of 566 x 10-6. We hypothesis that these phenotype populations will

correlate with the amount of mutations required to produce leukemia. Based on these results, we later

tested Multiple Myeloma cell-lines to see if the same patterns emerged.

Procedure

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In order to quantitate the frequency of myeloma cells with the PNH phenotype, we analyzed

thawed ficolled samples from patients with a heavy burden of myeloma cells in the marrow. Cells were

stained sequentially with Flaer-Alexa 488 at a 1:20 dilution. Flaer is unique in the sense that it stains at 37

degrees Celsius in comparison to -4 degrees Celsius; this is because Flaer is not actually an antibody but

rather a genetically modified pathogen. Flaer is derived from Proaerolysin. Proaerolysin enters through

the GPI protein and kills the cell, while Flaer being a genetically modified pathonegen, just enters and

attaches to the cell. We then use RAM-FITC as our florescent protein. Attaching RAM-FITC is a two-

step process; first we attach a mouse anti-human monoclonal anti-body so that we can conjugate the cell

anchor to RAM-FITC; RAM-FITC being a polyclonal rabbit anti-mouse antibody. The monoclonal

antibodies we use are CD59, CD55, and CD48. To stain our transmembrane we use 1 of 3 different

transmembrane binding proteins CD138-PE, CD38-PE, which are both Plasma transmembrane binding

proteins. Another binding protein we use is CD 45-PE, which is a BLCL transmembrane binding protein

used as a control. Live myeloma cells were identified by forward/side scatter and propidium iodide

exclusion and expression of CD38 or CD138. For a negative control, we analyzed 2 non-malignant B-

lymphoblastoid cell lines (BLCLs) from normal donors, and for a positive control, we analyzed the

mantle cell lymphoma cell line HBL2A (in this case using CD45-PE to identify transmembrane

proteins).

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Results:

The normal BLCLs demonstrated a frequency of PNH cells of 6.3 x 10 -6 and 18.4 x 10-6, which is

in the range that we have previously reported for BLCLs and granulocytes from normal individuals. Also

In contrast, as we have previously reported, the mantle cell line demonstrated a markedly higher

frequency of cells with the PNH phenotype- 1034 x 10-6.

We found that there were at least two 2 distinct groups. One group, which constituted 14 of the

20 samples or (70%), showed a mutant frequency comparable to non-malignant cell populations, with its

median value of 9.5 x 10-6 (range 2.4 to 37 x 10-6). See figure 20 for the compiled Flow Cytometry

Statistic graphs of hypothesized non-malignant cell population samples.

44

*Figure 20 Compiled Flow Cytometry Statistic graphs of hypothesized non-malignant cell population samples:

100 101 102 103 104100

101

102

103

104

Tube 5 MM-BM #12ÉPI Neg

FL1-H

FL

2-H

1.15e-3 99.7

0.250.021

The remaining 6 samples (30%) demonstrated a markedly increased frequency of PNH cells, with

a median value of 90 x 10-6 (range 73 to 11,763 x 10-6). See figure 21 for compiled Flow Cytometry

Statistic graphs of hypothesized malignant cell population samples.

*Figure 21, Compiled Flow Cytometry Statistic graphs of hypothesized malignant cell population samples:

100 101 102 103 104

100

101

102

103

104

Tube 7 MM-BM #20ÉPI Neg

FL1-H

FL

2-H

0.15 99

0.460.35

45

Compiled Flow Cytometry Statistic graphs of hypothesized non-malignant cell population

samples Most of the samples we analyzed came from patients who had received prior therapy, but one of

the samples, sample-20, demonstrating a very high frequency of PNH cells (1314 x 10 -6). This sample

was derived from a patient who had not had prior therapy, but was known to have had an abnormality of

p53. p53 is a tumor suppressing gene that when mutated, obviously, no-longer suppresses tumors; we

hypothesize this to be the reason Sample 20 had such a high mutant frequency. See figure 22 for the Flow

Cytometry statistic graph of Sample-20

*Figure 22, Flow Cytometry statistic graph of Sample-20:

100 101 102 103 104

100

101

102

103

104

Tube 7 MM-BM #20ÉPI Neg

FL1-H

FL

2-H

0.15 99

0.460.35

Conclusion

The data acquired demonstrates that an increase in inactivating mutations is not essential for the

development of myeloma, although it does seem to be a common feature of this condition. This flow-

based assay could be applied at the time of diagnosis and this may facilitate investigations as to whether

hypermutability correlates with outcome in patients with myeloma. While we still hypothesis that

Multiple Myeloma acquires to distinct phenotypes like ALL, not enough information has been discovered

46

yet to confirm this. In any case, the experiment is still ongoing, and our future prospects entail gathering

and analyzing more sample as well as clinical history and hopefully publishing our findings.

47