Characterization of breast cancer DNA content profiles as a prognostic tool
Worldwide, breast cancer in women remains to be the most common malignancy that in a considerable proportion shows the resistance to genotoxic treatments and poor outcome. Chromosomal instability manifested as aneuploidy represents an integral characteristics of the malignant genotype not only beca...
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| Цитувати: | Characterization of breast cancer DNA content profiles as a prognostic tool / B.I. Gerashchenko, A. Huna, J. Erenpreisa // Experimental Oncology. — 2014. — Т. 36, № 4. — С. 219-225. — Бібліогр.: 74 назв. — англ. |
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Gerashchenko, B.I. Huna, A. Erenpreisa, J. 2019-01-21T08:27:56Z 2019-01-21T08:27:56Z 2014 Characterization of breast cancer DNA content profiles as a prognostic tool / B.I. Gerashchenko, A. Huna, J. Erenpreisa // Experimental Oncology. — 2014. — Т. 36, № 4. — С. 219-225. — Бібліогр.: 74 назв. — англ. 1812-9269 https://nasplib.isofts.kiev.ua/handle/123456789/145371 Worldwide, breast cancer in women remains to be the most common malignancy that in a considerable proportion shows the resistance to genotoxic treatments and poor outcome. Chromosomal instability manifested as aneuploidy represents an integral characteristics of the malignant genotype not only because of the selection of mutated aneuploid sub-clones that stipulate the tumor progression, but also because of the reversible endopolyploidy of tumor cells that serves for the endless maintenance of therapy-resistant tumor stem cells. Therefore, cytometric determination of DNA content in tissue samples for detecting malignancy, monitoring responses to therapy, and prognosing disease outcome needs to be revived. Both flow and image cytometry are most frequently used for generation of DNA content profiles (histograms), interpretation of which, however, may have some caveats. This review presents the major characterization criteria and analysis tools for breast cancer DNA histograms. Key Words: breast cancer, aneuploidy, DNA content analysis, DNA histogram, flow cytometry, image cytometry. This work is supported by the European Social Fund (grant number: 1DP/1.1.1.2/APIA/VIAA/037). en Інститут експериментальної патології, онкології і радіобіології ім. Р.Є. Кавецького НАН України Experimental Oncology Reviews Characterization of breast cancer DNA content profiles as a prognostic tool Article published earlier |
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Characterization of breast cancer DNA content profiles as a prognostic tool Gerashchenko, B.I. Huna, A. Erenpreisa, J. Reviews |
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characterization of breast cancer dna content profiles as a prognostic tool |
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Experimental Oncology |
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Інститут експериментальної патології, онкології і радіобіології ім. Р.Є. Кавецького НАН України |
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Worldwide, breast cancer in women remains to be the most common malignancy that in a considerable proportion shows the resistance to genotoxic treatments and poor outcome. Chromosomal instability manifested as aneuploidy represents an integral characteristics of the malignant genotype not only because of the selection of mutated aneuploid sub-clones that stipulate the tumor progression, but also because of the reversible endopolyploidy of tumor cells that serves for the endless maintenance of therapy-resistant tumor stem cells. Therefore, cytometric determination of DNA content in tissue samples for detecting malignancy, monitoring responses to therapy, and prognosing disease outcome needs to be revived. Both flow and image cytometry are most frequently used for generation of DNA content profiles (histograms), interpretation of which, however, may have some caveats. This review presents the major characterization criteria and analysis tools for breast cancer DNA histograms. Key Words: breast cancer, aneuploidy, DNA content analysis, DNA histogram, flow cytometry, image cytometry.
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1812-9269 |
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Characterization of breast cancer DNA content profiles as a prognostic tool / B.I. Gerashchenko, A. Huna, J. Erenpreisa // Experimental Oncology. — 2014. — Т. 36, № 4. — С. 219-225. — Бібліогр.: 74 назв. — англ. |
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Experimental Oncology 36, 219–225, 2014 (December) 219
CHARACTERIZATION OF BREAST CANCER DNA CONTENT
PROFILES AS A PROGNOSTIC TOOL
B.I. Gerashchenko1, 2, A. Huna1, J. Erenpreisa1,*
1Latvian Biomedical Research and Study Centre, Riga LV-1067, Latvia
2R.E. Kavetsky Institute of Experimental Pathology, Oncology and Radiobiology,
National Academy of Sciences of Ukraine, Kyiv 03022, Ukraine
Worldwide, breast cancer in women remains to be the most common malignancy that in a considerable proportion shows the resis-
tance to genotoxic treatments and poor outcome. Chromosomal instability manifested as aneuploidy represents an integral cha-
racteristics of the malignant genotype not only because of the selection of mutated aneuploid sub-clones that stipulate the tumor
progression, but also because of the reversible endopolyploidy of tumor cells that serves for the endless maintenance of therapy-
resistant tumor stem cells. Therefore, cytometric determination of DNA content in tissue samples for detecting malignancy,
monitoring responses to therapy, and prognosing disease outcome needs to be revived. Both flow and image cytometry are most
frequently used for generation of DNA content profiles (histograms), interpretation of which, however, may have some caveats.
This review presents the major characterization criteria and analysis tools for breast cancer DNA histograms.
Key Words: breast cancer, aneuploidy, DNA content analysis, DNA histogram, flow cytometry, image cytometry.
INTRODUCTION
Breast cancer is among the most common malignan-
cies in women worldwide, and its manifestation consid-
erably varies based on cell proliferation state, karyotype
and nuclear DNA content testifying aneuploidy. There
is also the well-known cancer hallmark such as genome
instability resulting in aneuploidy-linked elevated mu-
tagenicity [1, 2]. The latest research conducted on cell
cultures and patient tumors as well showed that stem-
ness of tumor cells is tightly associated with polyploidi-
zation, the process that results in the numerical gain
of the whole set of chromosomes [3–5]. This property
stipulates the resistance to anti-cancer treatments and
poor outcome [6]. It is supposed that the new treat-
ment strategies aimed to eradicate the highly evolvable
polyploid cells would help to maintain tumors in a drug
sensitive state [7]. In light of this newly emerged aspect
in tumor cell biology, cytometric determination of DNA
content in tissue samples “gets a second breath”, thus
becoming a very important and demanding method not
only for detecting malignancies, but also for assessing
responses to therapy and disease outcome. Both flow
and image cytometry are most frequently used for
generation of DNA content profiles (histograms). Their
interpretation, however, may have some caveats, and
therefore needs to be univocal and accurate. The aim
of this review is to present the major characterization
criteria and analysis tools for breast cancer DNA histo-
grams that together can be used for gaining prognostic
and treatment response information.
ANEUPLOIDY AND BREAST CANCER
Chromosomal instability (CIN) is the main reason
of aneuploidy in cancer cells [8, 9]. Aneuploidy implies
a condition in which the chromosome number is not
exact multiple of the haploid karyotype. As a result
of improper segregation of chromosomes during
division of mother cell, two daughter cells become
aneuploid. For example, one daughter cell has chro-
mosomal gain (2n+x), whereas another daughter cell
has chromosomal loss (2n−x). However, in some other
cases chromosomal gains and losses may not neces-
sarily be concordant. The fact that cells in the majo-
rity of aneuploid tumors have 40−60 chromosomes
(while diploid cells have 46 chromosomes) indicates
that the accumulation of chromosome imbalances
generated by the sequential loss and gain of single
chromosomes through CIN may be the most common
pathway to aneuploidy [9]. Missegregation of chro-
mosomes can be due to defects in the kinetochore-
microtubule attachments and dynamics, centrosome
number, spindle-assembly checkpoint, and chromo-
some cohesion [2]. The disruption of multiple genes
and pathways is believed to play in the aforementioned
defects [10].
Aneuploidy that was first proposed as a cause
of cancer by D. Hansemann and Th. Boveri at the turn
of 20th century was based on observations of multipo-
lar cell divisions and bipolar asymmetric segregation
of chromosomes in large polyploid cells [11, 12]. With
the beginning of the molecular biology era the most
attention was switched to elementary genetic and
molecular changes in cancer, while during the last two
decades aneuploidy was again recognized as a main
driver of cancer progression, and from some views,
Submitted: July 08, 2014.
*Correspondence: Fax: +371–67442407
E-mail: katrina@biomed.lu.lv
Abbreviations used: 7-AAD — 7-aminoactinomycin D; BRCA1 and 2 —
breast cancer associated, types 1 and 2; CCD — charge-coupled
device; CIN — chromosomal instability; DAPI — 4´,6-diamidino-2-
phenylin dole; DI — DNA index; HER2 — human epidermal growth
factor receptor, type 2; IF — integrated fluorescence; IOD — inte-
grated optical density; MAD2 — mitotic arrest deficient, type 2;
NANOG — homeobox transcription factor; OCT4 — octamer-binding
transcription factor 4; PCNA — proliferating cell nuclear antigen;
SOX2 — SRY (sex determining region Y)-box 2; SPF — S-phase frac-
tion; TOP2A — DNA topoisomerase, type 2-alpha.
Exp Oncol 2014
36, 4, 219–225
REVIEWS
220 Experimental Oncology 36, 219–225, 2014 (December)
aneuploidy together with CIN is considered as an origin
of cancer [13–16].
Both structural and numerical chromosomal abnor-
malities, that can currently be detected by conventional
karyotyping and other more advanced cytogenetic
techniques such as fl uorescence in situ hybridization,
spectral karyotyping and array-based comparative
genomic hybridization, are characteristic of cancer
genomes. Contrary to structural chromosomal abnor-
malities, the role of numeric chromosomal abnormali-
ties (termed also as “whole-chromosome aneuploidy”
or simply “aneuploidy”) in tumor development is much
less well-understood largely because of difficulties
to identify tumorigenesis related genes on aneuploid
chromosomes [2, 17]. Although at least at low fre-
quency aneuploidy is likely to promote tumorigenesis,
it is currently observed in most tumors including breast
cancer (≈ 90% of solid tumors and 85% of hemato-
poietic malignancies) [16]. Understanding the role
of aneuploidy specifically in these tumors is of great
importance to uncover pathogenesis of disease and
develop new strategies for treatment [2, 18, 19] and
prognostication [20].
In breast cancer, the spindle-assembly checkpoint
appears to be affected causing CIN [9]. Expression
of MAD2 gene is essential to control this check-
point [21]. Mutations in BRCA1 and BRCA2 genes
can also contribute to CIN. While the former gene
is required for the proper spindle checkpoint func-
tion [22], the latter gene is required for proper cytoki-
nesis [23]. Of 200 breast cancer cases, there are ≈ 15%
of tumors having cells with 46 chromosomes (these
cells are not free of translocations, inversions, dele-
tions and/or additions), 70% of tumors having cells
with > 46 but ≤ 68 chromosomes, and ≈ 15% of tumors
having cells with ≥ 69 chromosomes [16]. Among
those chromosomes that can be affected [24–27],
chromosome 17 is affected most frequently (both
numerical and structural abnormalities in this chromo-
some are common in breast cancer [27]). The majo-
rity of breast tumors (54%) have whole-chromosome
17 aneuploidy, of which 14% are monosomic and 86%
are polysomic [28]. Numerical aberrations of chromo-
some 17 (either gains or losses) are linked to breast
cancer initiation and progression, and possibly to treat-
ment response [27]. Notably, this chromosome con-
tains such genes as HER2, BRCA1, P53, and TOP2A,
whose alterations are of importance in breast cancer
pathogenesis [27].
Polyploidization that occurs due to unscheduled
whole-genome duplications has been proposed
to constitute an important step in the development
of cancer aneuploidy since it holds the probability
to amortize consequences of chromosome damage
or even loss [29, 30]. Transient and reversible polyploi-
dy works as a pro-survival mechanism after genotoxic
treatment by activating pluripotency and self-renewal
cassette (OCT4/SOX2/NANOG) characteristic for
most aggressive tumors [6] and giving rise to resistant
survivals after de-polyploidization [3,4].
In breast cancer cell lines as well as in breast can-
cer primary specimens, ionizing radiation can induce
dose-dependent polyploidization together with mani-
festation of breast cancer stem cell phenotype in poly-
ploid cells [5]. Self-renewal activation in polyploid cells
displays the property to overcome therapy-induced
cell senescence [31]. Moreover, the development
of rare polyploid cells in normal senescent fibroblasts
correlates with self-renewal signaling, the fact that
suggests that these polyploid cells can be potential
cancer candidates [32]. Because of the definite role
of polyploid cells in therapy resistance and tumor
repopulation after therapy, they are currently consi-
dered as critical drug targets for tackling cancers [7].
Interestingly, tetraploidy is more frequent in BRCA2-
mutated than in sporadic breast carcinomas, the fact
that prompts to propose that BRCA2 mutations can
facilitate polyploidization through cytokinesis failure
as well as formation of chromosome bridges [33].
BASIC DEFINITIONS IN DNA CYTOMETRY
DNA histogram is the distribution of the fre-
quency of integrated optical density (IOD) or inte-
grated fluorescence (IF) values obtained by cytometric
measurements of cells stoichiometrically stained for
DNA. In DNA histogram diploidy is shown by the po-
sition of the modal value of the peak corresponding
to G0-/G1-phase cells having diploid chromosomal set
2n (this position is usually expressed as 2c). In case
if the modal value of the peak differs from that of nor-
mal diploid cells (< 2c or > 2c, excluding 4c), one
could conclude that sample contains cells with aneu-
ploidy. The term “aneuploidy” also implies a biological
phenomenon (concisely considered in the previous
section). Appearance of additional peak(s) (4c, 8c,
16c, etc.) corresponding to cells with geometrically
doubled set of chromosomes is characteristic to poly-
ploidy. In case if DNA distribution in the examined
sample cannot be differentiated from that of normal
(resting, proliferating, or polyploidizing) cell popula-
tion, there could be euploidy. Diploid (2c) and tetra-
ploid (4c) tumors are often considered as euploid. DNA
stemline is the G0-/G1-phase cell fraction of prolife-
rating cell population with a unique chromosomal
outfit. In DNA histogram stemline shows a distinct
peak (Xc) with a second doubling one (2Xc) [20].
TYPING OF DNA HISTOGRAMS
FOR BREAST CANCER PROGNOSIS
(Auer’s classification)
Based on the analysis of Feulgen method-stained
tumor biopsy material, Auer et al. [34] first proposed
classification of breast cancer DNA histograms. These
histograms are empirically divided into 4 groups.
The type I histogram is characterized by a single
distinct modal DNA value in the diploid (or near-
diploid) region (2c) of normal cells with only a minor
fraction of cells exhibiting higher DNA values (> 2c).
The type II histogram has either a distinct peak
in tetraploid (or near-tetraploid) region (4c) of nor-
mal cells or a couple of well-defined peaks in 2c and
Experimental Oncology 36, 219–225, 2014 (December) 221
4c regions, although the presence of the minor peak
corre sponding to octoploid cells (8c) is also pos-
sible (< 5%). There are no at all or there are few cells
that have DNA amounts corresponding to the DNA
synthesis phase of normal cells (< 5%). Although
type III histogram like type II histogram often shows
two peaks in 2c and 4c regions, it is similar to that
of proliferating normal cell population with DNA values
scattered between the normal 2c and 4c region. There
is a sizable number (> 5%) of cells with DNA amounts
similar to those of normal S-phase cells. Later, it has
been recognized that this type of histogram is most
difficult to delineate [35, 36]. The type IV histogram
is characterized by the large fraction of aneuploid
tumor cells with highly increased and scattered DNA
values significantly exceeding the normal 4c region.
There is a distinct correlation between the type of DNA
distribution pattern of breast cancer and the survival
time of the individual. Histograms of types III and IV are
indicative of worst prognosis. Auer et al. [37] also
demonstrated that metastases and primary tumors
shared the same DNA histogram pattern. Thus, while
tumors exhibiting DNA values within the limits of normal
tissue correlate with favorable prognosis, tumors with
increased and scattered DNA values are indicative
of poor prognosis.
ADDITIONAL PARAMETERS FOR
SUCCESSFUL EVALUATION OF BREAST
CANCER DNA HISTOGRAMS
Opfermann et al. [36] noted that Auer’s classifica-
tion is limited in that “it did not take into account gen-
eral important biologic parameters which could alter
the biologic behavior of cells” (assuming the necessity
to estimate individual cells based on the phase of mitotic
cycle, and cell population growth fraction). In this re-
gard, the authors proposed to introduce two additional
parameters such as ploidy balance and proliferation
index that can be derived by distributing breast can-
cer biopsy cells in 10 different ploidy intervals ranging
from 2c to > 8c. This way of histograms classification
is free of uncertainties related to the statements such
as “near-diploid” or “near-tetraploid” used in the defi-
nition of Auer histogram patterns. The ploidy balance
is simply calculated by subtraction of the percentage
of aneuploid cells (2.5c, 3c, 3.5c, 5c, 6c, 7c, and > 8c)
from the percentage of euploid cells (2c, 4c, and 8c).
Accordingly, the difference can fluctuate from +100%
(all cells are euploid) to −100% (all cells are aneuploid).
Proliferation index is the percentage of cells between
major and related peaks (regardless of the ploidy level
of the major peak). Although these parameters de-
monstrate that aneuploidy and cell proliferation does
correlate with tumor aggressiveness as derived from
the “tumor aggressiveness triangle”, at least 15%
of the patients failed to be correctly classified in terms
of long vs. short survival time [36]. Therefore, additional
parameters should be taken into consideration.
There is more precise approach based on calcu-
lation of DNA index (DI) which is defined as the ratio
of the modal DNA value of the tumor cells to the modal
DNA value of the internal control cells (2c). This pa-
rameter that was first introduced by Barlogie et al.
[38, 39] is being actively used since the early 80th
of the last century when the flow cytometry era
started to bloom. Fernö et al. [40] proposed to cate-
gorize the ploidy of breast cancer cell populations
based on DI distribution as follows: hypodiploid
(DI < 0.95), diploid (DI = 0.95−1.04), near-hyperdiploid
(DI = 1.05−1.14), hyperdiploid (DI = 1.15−1.91), tet-
raploid (DI = 1.92−2.04), hypertetraploid (DI ≥ 2.05),
and multiploid (in case if DNA histogram has ≥ 2 peaks
corresponding to aneuploid/polyploid cell population).
This type of DI categorization compared with those
proposed by other authors also includes the class
of near-hyperdiploid tumors whose cells can yet
be distinguished from diploid (2c) cells.
Determination of DI is often supplemented with de-
tection of cell proliferation level by measurements
of S-phase fraction (SPF), which is expressed
as a percentage of tumor cells in DNA synthesis phase
of the cell-cycle. This parameter correlates with proli-
ferative activity of tumor cells. At present, quantifica-
tion of SPF is usually performed by means of special
mathematical models in computer programs for cell-
cycle analysis. Notably, the earliest method that was
actively used for SPF quantification is a planimetric
method of Baisch et al. [41] assuming that the S-phase
compartment constitutes a rectangle distribution be-
tween the modal values of G0/G1 and G2 peaks. Flow cy-
tometrically measured SPF correlates with the labeling
index measured by autoradiography of tumor biopsies
pulse-labeled with tritiated thymidine [42, 43].
SPF parameter appears to be prognostically very
informative for some human cancers, including breast
cancer [44, 45]. Starting from the middle of 80th
of the last century, SPF became an object of exten-
sive exploration for delineating of its prognostic value
in breast cancer. In one of the earliest works on this is-
sue, Hedley et al. [46] demonstrated that disease-free
survival with SPF ≤ 10% was significantly longer than
that with SPF > 10%, and the latter SPF value strongly
correlated with high tumor grade and abnormal DI but
weakly correlated with nodal, hormone (estrogen)
receptor, and menopausal status. The fact that SPF
values are significantly higher in aneuploid tumors than
in diploid tumors is confirmed in other reports [47−51].
According to Clark et al. [48], SPF is highly predictive
of di sease-free survival in patients with diploid tumors
but does not provide additional prognostic informa-
tion for aneuploid cases. Sigurdsson et al. [49, 50]
proposed to gain prognostic information by dividing
the SPF into 3 prognostic categories: low (< 7.0%),
intermediate (7.0−11.9%) and high (≥ 12%). These
cate gories allow grouping the patients according
to their level of risk. The risk of death or recurrence for
diploid and non-diploid cases is up to 50% higher for
the high S-phase category than for the intermediate
category, and approximately 50% higher for the inter-
mediate category than for the low category [49]. In this
222 Experimental Oncology 36, 219–225, 2014 (December)
situation, ploidy does not provide additional prognostic
information with reference to any of S-phase catego-
ries. In spite of different techniques (e.g., whether
the tissue is fresh, frozen, or paraffin-embedded, etc.)
and cut-points (e.g., whether the SPF is di- or tri-
chotomized, etc.), correlations between SPF and other
prognostic markers are relatively consistent across
studies: higher SPF is generally associated with worse
tumor grade, negative receptor status, larger tumors,
and positive axillary nodes [52]. Moreover, higher
SPF is generally associated with worse disease-free
and overall survival in both univariate and multivariate
analyses [52].
Since polyploidy can correlate with aggressiveness
of cancer, the detection and analysis of polyploid cells
in DNA histograms is of importance. Diploid (in cluding
near-hyperdiploid) and hyperdiploid tumors are most
common comprising about 45 and 36% of all breast
cancer ploidy types, respectively [40]. Favo rable
prognosis is more characteristic for diploid and near-
hyperdiploid cases (low-risk group) [40, 53, 54],
though Fernö et al. [40] claim that prognosis for near-
hyperdiploid tumors that comprise only about 4.0%
of all cases is even somewhat better than for diploid
tumors. As for hypodiploid, hypertetraploid, and mul-
tiploid cases, all of them fall into the category of worse
prognosis (high-risk group). Interestingly, in this group
hypodiploid cases can be most aggressive [40, 55]. Hy-
perdiploid cases are probably to fall between low- and
high-risk groups [40, 54–57]. However, there is some
controversy regarding prognosis of tetraploid cases
(≈ 5.0% of all cases). Some authors reported better
prognosis for tetraploid cases [53, 56−58] than other
authors did [40, 54]. Notably, Stål et al. [56] reported
even a slightly longer survival in tetraploid cases than
in diploid cases. Ewers et al. [59] found that the recur-
rence rate in early-stage disease was twice as low
among patients with euploid (diploid and tetraploid)
tumors as among patients with aneuploid tumors.
Although some investigators did report the lack
of prognostic significance of DNA ploidy and/or SPF
in breast cancer, those reports, however, are not
numerous [52, 60]. In fact, the assignment of DI and
SPF can be subjected to a number of various techni-
cal pitfalls that may take place starting with specimen
preparation/processing and ending with instrument
alignment and cell-cycle analysis [52, 61]. Also, tumors
may not be usually consistent in their ploidy (there
is variation in DNA content between different areas
of the tumor) [62]. In this case, examination of tissue
samples from several different areas of the tumor may
be needed.
FLOW CYTOMETRY VERSUS IMAGE
CYTOMETRY
Numerical and structural chromosomal abnor-
malities together can lead to DNA aneuploidy which
is quantified cytometrically in a cell population. There
is a variety of DNA staining methods using light-
absorbing or fluorescent stains that stoichiometrically
bind to DNA, so DNA content can be measured based
on IOD or IF values. As for staining of DNA with light-
absorbing dyes, the Feulgen reaction by using of Schiff
reagent is still most popular, although this staining
technique was developed almost a century ago [63].
As for fluorescence staining of DNA, there is a variety
of nucleic acid fluorescent dyes, and some of them
(e.g., Hoechst dyes, DAPI, and 7-AAD) selectively
or preferably bind to DNA [64]. Both flow and image
cytometry are frequently used for generation of DNA
histograms of DNA-stained cell samples, although none
of these techniques is free of disadvantages (Table).
Table. Major differences between flow and image cytometry that can af-
fect the quality of assessment of DNA content histograms obtained from
cancer samples
Flow cytometry Image cytometry*
1. Few thousand cells or cell nu-
clei can be analyzed for a short time
(≈ 1 min)
At least few dozen cell nuclei
can be analyzed for a short time
(≈ 1 min)
2. While performing analysis, mor-
phological assessment of cells
is impossible
While performing analysis, morpho-
logical assessment of cells is pos-
sible
3. Quantification of S-phase cells
is relatively precise
If DNA histogram peaks are
too broad, additional staining
of S-phase cells may be needed
4. More sensitive in detection
of near-diploid aneuploidy
Less sensitive in detection of near-
diploid aneuploidy unless a larger
cohort of nuclei is analyzed
5. Analysis requires cell suspension Samples can be prepared from cell
suspension, tissue imprints or histo-
logical sections
Note: *image cytometry implies software-based analysis of IOD from digi-
talized images obtained by microscope equipped with CCD camera.
Nevertheless, flow cytometry is superior in terms
of speedy acquiring a large cohort of cells or cell nuclei
in sufficient numbers for precise statistical certainty.
Because of this advantage, DNA histogram peaks look
sharper (since coefficients of variation of the peaks
become lower), and thus S-phase cells can be quanti-
fied with a special DNA content/cell-cycle analysis
software. Several models have been proposed to es-
timate S-phase in overlapping populations that may
take place in some tumors [43, 65]. Taking into account
the aforementioned advantages, flow cytometry is gen-
erally well-described and widely used technique [66].
Contrary to flow cytometry, a few hundred cells are usu-
ally analyzed by image cytometry resulting in broader
DNA histogram peaks (coefficients of variation of the
peaks are relatively high). In this regard, the assessment
of S-phase seems somewhat problematic, although
there could be a good option for solving this situation
by specific staining of those cells that proliferate (e.g.,
using antibodies to proliferation-associated antigens
such as Ki-67 and PCNA [67, 68]). While performing
analysis, flow cytometry compared with image cytom-
etry, however, is incapable of discriminating non-ma-
lignant cells of other types (lymphocytes, granulocytes,
macrophages, and some stromal cells such as fibro-
blasts) unless they are specifically labeled. Using micro-
scope, an experienced operator can easi ly discriminate
Experimental Oncology 36, 219–225, 2014 (December) 223
these cells mainly based on size and shape of their nu-
clei and some nuclear texture features. Thereby, image
cytometry-based generation of DNA histograms is less
likely to be interfered by “contamination” with unwanted
cells. As for detection of aneuploid cell populations,
a combination of both techniques is superior to either
of them alone [69]. However, none of these techniques
can necessarily detect gains or losses of single intact
chromosomes or deletions in individual chromosomes
especially if a tissue sample contains a very small fraction
of aneuploid cells. In general, there is a good correlation
in DNA content analysis data between flow and image
cytometry [69], although a thorough under standing and
appreciation of the methodological problems associated
with both techniques may be needed [70].
In 2001 there was the last consensus report on di-
agnostic DNA image cytometry proposed by the group
of prominent European analytical cytologists [20].
With regard to identification of neoplasia and grading
of tumor malignancy, this report contains several main
clinically-oriented recommendations such as short
description of DNA histogram (histogram type),
interpretation of DNA histogram concerning the oc-
currence of DNA aneuploidy and/or the histogram
type, prognostic interpretation of DNA histogram,
and summarized morphologic/cytometric diagnosis.
In that time when this report was written the usual
precision of DNA image cytometric measurements
should at least allow DNA stemlines to be identified
as abnormal (or aneuploid), if they deviate more than
10% from the diploid (2c) or tetraploid (4c) region, i.e.,
if they are outside 2c ± 0.2c or 4c ± 0.4c.
CONCLUDING REMARKS
Like two decades ago, DNA content profile re-
mains to be a subject of thorough examination to gain
the information on prognosis of disease and response
to treatment. For this purpose, combination of various
characterization criteria and analysis tools for DNA
histograms may be needed. Determination of consen-
sus values in interlaboratory comparisons is required.
Finally, it is worth to note the another principally dif-
ferent analytical approach of analysis of DNA-stained
cancer specimens that currently shows promising data
from diagnostic and prognostic points of view. This
is nuclear texture measured by digital image analysis
that allows detailing the phenotype by objective clas-
sifying of visual and sub-visual changes in nuclear
chromatin [71]. Qualitative and quantitative changes
in chromatin structure are of importance for under-
standing the neoplastic process as well as for identifying
structural changes that may indicate functional altera-
tions [72]. Nuclear textural features have been proven
to be successful in clinics for prognosis in several can-
cers including breast cancer [73]. However, the nuclear
texture analysis should be performed on undistorted
nuclei. For this purpose, monolayers of nuclei carefully
isolated from the sections of formalin-fixed and paraffin-
embedded tissues are usually used [74].
ACKNOWLEDGEMENTS
This work is supported by the European Social Fund
(grant number: 1DP/1.1.1.2/APIA/VIAA/037).
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