Bin-Free Fitting of Probability Distributions Emphasizing DNA Histograms

It is often challenging to find the right bin size when constructing a histogram to represent a noisy experimental data set. This problem is frequently faced when assessing whether a cell synchronization experiment was successful or not. In this case the goal is to determine whether the DNA content is best represented by a unimodal, indicating successful synchronization, or bimodal, indicating unsuccessful synchronization, distribution. This choice of bin size can greatly affect the interpretation of the results; however, it can be avoided by fitting the data to a cumulative distribution function (CDF). Fitting data to a CDF removes the need for bin size selection. The sorted data can also be used to reconstruct an approximate probability density function (PDF) without selecting a bin size. A simple CDF-based approach is presented and the benefits and drawbacks relative to usual methods are discussed.

DNA IMAGE CYTOMETRY IN PROGNOSTICATION OF COLORECTAL CANCER: PRACTICAL CONSIDERATIONS OF THE TECHNIQUE AND INTERPRETATION OF THE HISTOGRAMS

The role of DNA content as a prognostic factor in colorectal cancer (CRC) is highly controversial. Some of these controversies are due to purely technical reasons, e.g. variable practices in interpreting the DNA histograms, which is problematic particularly in advanced cases. In this report, we give a detailed account on various options how these histograms could be optimally interpreted, with the idea of establishing the potential value of DNA image cytometry in prognosis and in selection of proper treatment. Material consists of nuclei isolated from 50 ƒĘm paraffin sections from 160 patients with stage II, III or IV CRC diagnosed, treated and followed-up in our clinic. The nuclei were stained with the Feulgen stain. Nuclear DNA was measured using computer-assisted image cytometry. We applied 4 different approaches to analyse the DNA histograms: 1) appearance of the histogram (ABCDE approach), 2) range of DNA values, 3) peak evaluation, and 4) events present at high DNA values. Intra-observer reproducibility of these four histogram interpretation was 89%, 95%, 96%, and 100%, respectively. We depicted selected histograms to illustrate the four analytical approaches in cases with different stages of CRC, with variable disease outcome. In our analysis, the range of DNA values was the best prognosticator, i.e., the tumours with the widest histograms had the most ominous prognosis. These data implicate that DNA cytometry based on isolated nuclei is valuable in predicting the prognosis of CRC. Different interpretation techniques differed in their reproducibility, but the method showing the best prognostic value also had high reproducibility in our analysis.

DAPI Fluorescence in Nuclei Isolated from Tumors

In DNA histograms of some human solid tumors stained with nuclear isolation medium-4,6-diamidino-2-phenylindole dihydrochloride (NIM-DAPI), the coefficient of variation (CV) of the G0/G1 peak was broad, and in nuclear volume vs DNA scattergrams, a prominent slope was seen. To determine the cause for this, nuclei from frozen breast tumors were stained with NIM-DAPI and analyzed after dilution or resuspension in PBS. In two-color (blue vs red) analysis, most of the slope and broad CV was due to red fluorescence of nuclei stained with NIM-DAPI, which was reduced on dilution or resuspension in PBS, resulting in elimination of the slope and tightening of the CV.

Confocal 3D DNA Cytometry: Assessment of Required Coefficient of Variation by Computer Simulation

Background: Confocal Laser Scanning Microscopy (CLSM) provides the opportunity to perform 3D DNA content measurements on intact cells in thick histological sections. So far, sample size has been limited by the time consuming nature of the technology. Since the power of DNA histograms to resolve different stemlines depends on both the sample size and the coefficient of variation (CV) of histogram peaks, interpretation of 3D CLSM DNA histograms might be hampered by both a small sample size and a large CV. The aim of this study was to analyze the required CV for 3D CLSM DNA histograms given a realistic sample size. Methods: By computer simulation, virtual histograms were composed for sample sizes of 20000, 10000, 5000, 1000, and 273 cells and CVs of 30, 25, 20, 15, 10 and 5%. By visual inspection, the histogram quality with respect to resolution of G0/1 and G2/M peaks of a diploid stemline was assessed. Results: As expected, the interpretability of DNA histograms deteriorated with decreasing sample sizes and higher CVs. For CVs of 15% and lower, a clearly bimodal peak pattern with well distinguishable G0/1 and G2/M peaks were still seen at a sample size of 273 cells, which is our current average sample size with 3D CLSM DNA cytometry. Conclusions: For unambiguous interpretation of DNA histograms obtained using 3D CLSM, a CV of at most 15% is tolerable at currently achievable sample sizes. To resolve smaller near diploid stemlines, a CV of 10% or better should be aimed at. With currently available 3D imaging technology, this CV is achievable.

Dependence of DNA-Histograms on The Sampling Techniques in Fine Needle Aspirates of the Breast

48 fine needle aspiration biopsy (FNAB) samples from 25 breast cancer cases, originally used for cytodiagnosis were subjected to DNA cytometry. There were air dried smears stained with the MGG method, and samples stained with HE or PAP stain after 50% ethanol fixation and cytocentrifugation. Different sampling strategies were applied. Four methods were tested: method 1: cell groups measured, method 2: all cells measured, method 3: free cells measured, and method 4: atypical free cells measured. Method 4 showed most often DNA aneuploid histogram patterns, sampling method 1 had the highest number of DNA diploid histogram patterns. Diagnostic approaches may benefit from a sampling method detecting the hiding aneuploid cell population. Grading of neoplasm could potentially benefit from other approaches.

Heterogeneity of DNA Distribution in Diploid Cells: A New Predicitive Discriminant Factor for Solid Tumour Behaviour

Spatial nuclear DNA heterogeneity distribution of Feulgen‐stained DNA diploid cells was studied by image cytometry in voided urine of 119 patients without bladder tumour (n=20) and with initial (n=23) or previous (n=76) diagnosed bladder tumour. For each patient, repetitive DNA measurements were performed during 1–4 years of follow up. Only cells of diploid DNA histograms and diploid subpopulations of aneuploid DNA histograms were used for analysis. DNA heterogeneity distribution of these diploid cells was quantified by statistical parameters of each nuclear optical density distribution. Discriminant analysis was performed on three groups of DNA histograms. Group A (n=44): aneuploid DNA histograms of patients with bladder tumour. Group D (n=55): 38 diploid DNA histograms of the 20 patients without bladder tumour (subgroup D1) and 17 diploid DNA histograms of patients with a non‐recurrent bladder tumour (subgroup D2). Group R (n=27): diploid DNA histograms of patients with bladder tumour recurrence. No statistically significant discriminant function was found to separate D1 and D2. However, the first canonical discriminant function C1 differentiated diploid cells of diploid DNA histograms (group D and group R) from diploid cell subpopulations of aneuploid DNA histograms (group A). Mean C1 values were 1.06, 0.84 and –1.45 for groups R, D and A, respectively. The second canonical discriminant function C2 differentiated diploid DNA histograms of patients with bladder tumour recurrence (group R) from diploid DNA histograms of patients without bladder tumour or without bladder tumour recurrence (group D). Mean C2 values were 1.78 and –0.76 for groups R and D, respectively. In 95% confidence limit, the rate of rediscrimination using the two first canonical discriminant functions C1 and C2 were 86.4, 74.5 and 74.1% for groups A, D and R, respectively. Percent of “grouped” cases correctly classified was 78.6%. Thus spatial DNA heterogeneity distribution of diploid cells seems to quantitate probable genetic instability as a function of clinical evolution such as tumour recurrence, and suggests the possible presence of aneuploid stemlines in a heterogeneous tumour, even if a diploid DNA histogram is observed in a single sample. From standardized C1 and C2 canonical discriminant function coefficients, a DNA heterogeneity index (2c‐HI) is proposed to characterize diploid cells providing a descriptive and predictive discriminant factor for solid tumour behaviour.