This document describes a study that analyzed the frequency of cells with a PIG-A mutant phenotype (PNH phenotype) in samples from 19 patients with acute lymphoblastic leukemia (ALL) and immortalized B cell lines (BLCLs) from healthy donors. The BLCLs showed a median frequency of 11 x 10-6, while the ALL samples showed two distinct populations - about half of samples had a median frequency of 13 x 10-6, similar to controls, while the other half had a significantly higher median frequency of 566 x 10-6. This suggests that in ALL, there are two phenotypes with respect to hypermutability, which may correlate with the number of mutations required to cause the leukemia.

1. 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
1
Division of Hematology, NYU School of Medicine, NYU Langone Clinical Cancer
Center, and the New York VA Medical Center
2
Children's Hospital Colorado and the University of Colorado School of Medicine,
Aurora, CO
3
Department 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: david.araten@nyumc.org
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

2. 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

3. 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
successive rounds of clonal selection. Here, each oncogenic mutation would result in a
3

4. 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
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
4

5. 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.
Methods
5

6. 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 10-7
M. 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).
The cells were washed twice again and incubated with PE-conjugated murine anti-
6

7. 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
exclude cells with a global defect in membrane proteins, we gated on CD45 (+) events,
7

8. 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
events were counted out of a total of 1,041,825 cells analyzed, and the frequency of
8

9. 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 λ 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
the f values obtained from the BLCLs (p = 0.03 , 1 sided Mann Whitney U test). In
9

10. contrast to the distribution obtained for the BLCLs (figure 3A), using different possible λ
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
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
10

11. 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
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
11

12. 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
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
12

13. 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
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
13

14. 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
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
14

15. 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.
15

16. 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
Table 2: Samples from patients with leukemia
16

17. Patient Age
(yrs)
m
/f
WBC
per
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 gated
cells
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
17

18. 18
Figure 1

19. 19
Figure 2

20. 20
Figure 3

21. 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 λ 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.
21

22. References
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5940
Jonathan Karten
26

27. The Basics Flow Cytometry and Multiple Myeloma Experiment
Flow Cytometry
Intro
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
from being picked up by the FSC optic lens or fluorescence detector. The amount of data picked up by the
27

28. 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
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.
28

29. *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
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
29

30. 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
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
30

31. 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
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.
31

32. 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 µl of stain may be appropriate for 1 million cells, but not for 20
million cells. If 20 µl 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:
32

33. 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 (50-400µm) 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
33

34. *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:
34

35. 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

36. *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
sorters can be sorted into separate tubes or wells. Although the Sorter is far more flexible in being able to
36

37. 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
37

38. 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

39. 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

40. 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

41. 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
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.
41

42. 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
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
42

43. 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).
Results:
43

44. 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.
*Figure 20 Compiled Flow Cytometry Statistic graphs of hypothesized non-malignant cell population
samples:
44

45. 100
101
102
103
104
10
0
101
102
103
104
Tube 5 MM-BM #12ÉPI Neg
FL1-H
FL2-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:
10
0
10
1
10
2
10
3
10
4
10
0
101
10
2
10
3
104
Tube 7 MM-BM #20ÉPI Neg
FL1-H
FL2-H
0.15 99
0.460.35
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
45

46. 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:
10
0
10
1
10
2
10
3
10
4
10
0
10
1
10
2
10
3
10
4
Tube 7 MM-BM #20ÉPI Neg
FL1-H
FL2-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
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.
46