DNA Ploidy and Cell Cycle Analysis in Cancer Diagnosis and Prognosis
DNA Ploidy and Cell Cycle Analysis in Cancer Diagnosis and Prognosis
The article by Dr. Ross provides an overview of the current status of the medical literature regarding the role of DNA ploidy and cell cycle analysis in cancer diagnosis and patient prognosis. The scope of the article is quite broad, covering virtually every organ system and, as such, provides only a brief summary of the data in each diagnostic category. From these data, there is general agreement about the value of detection of aneuploidy in tumor specimens but a lack of consensus about the importance of cell proliferation analyses, such as S-phase fraction (SPF) measurements. This conclusion reflects the inherent variability in the two determinations. Detection of aneuploidy by analytic cytometry is reliable; it is accurate and depends upon the specimen (frozen vs formaldehyde- fixed, presence of necrosis, cellularity) as well as the quality of specimen preparation. Thus, DNA ploidy analysis can easily be standardized, minimizing intralaboratory variation. Cell cycle analysis, however, is more complex and as yet is not standardized.
Although other methods are reviewed by Dr. Ross, the most frequently reported method to quantitate tumor cell proliferation in clinical studies is SPF analysis using DNA histograms. The complexity of the DNA histograms of clinical specimens leads to greater variability in proliferation determinations, substantially increasing the difficulty in cross-institutional comparisons of SPF data. Measurements obtained from breast carcinomas provide an excellent example of the impact that this variability can have on the attempt to confer a prognostic value for the SPF. Technically excellent studies involving large cohorts of patients have indicated that breast cancer can be subdivided into good, borderline, and poor prognostic categories based upon S-phase cut-off values [1-4]. Yet when other laboratories attempt to duplicate these data, they are unable to arrive at the same conclusion . Does this indicate a lack of prognostic importance for S-phase values, or perhaps variations in the method of SPF determination? We believe the latter.
Complex Computer Modeling
Besides an a priori requirement for technical excellence in specimen preparation, S-phase analysis relies upon complex computer modeling to obtain S-phase measurements in DNA histograms. These models must subtract cellular debris and doublets that contaminate the cell cycle compartments for accurate SPF determinations. In a recent, prospective series of 185 breast carcinoma specimens, we used 4 different computer programs to study the effect of debris subtraction on S-phase analysis. Depending upon the program used, the placement of debris boundaries, the coefficient of variation of the G0/G1 peak of the DNA histogram, and the relative proportion of debris, a significantly bias in S-phase calculations was observed .
Variations in the computer debris model and debris boundaries can affect S-phase values; for example, with one set of modeling conditions, a specimen may fall into a borderline S-phase category, yet when modeled differently, the same specimen may fall into a high S-phase category, conferring a poor prognosis. Thus, it is likely that individuals using different models may be unable to reproduce the findings of other published studies on the prognostic importance of S-phase determinations in breast cancer. In fact, this has been demonstrated in interlaboratory studies. In a study by Collan et al, S-phase analysis sufficiently varied among laboratories in 32% of the specimens to merit placement in different prognostic categories . In a second interinstitutional study, only 5 of 29 specimens would be placed in the same S-phase prognostic category by all five participating laboratories .
From a practical point of view, oncologists must have a basic understanding of the limitations of DNA cell cycle analysis and be able to assess the quality of the test results received on any specimen. Both aneuploidy and S-phase analysis can be measured on any DNA histogram and are not separate tests, although the complexity of the analysis does differ between the two. Detection of aneuploidy is fairly straightforward and of clinical importance in numerous diseases. As with SPF measurements, specimen quality and other technical factors can affect the detection of aneu-ploidy in tumor specimens. False-negative results are frequently obtained when the quality of the DNA histogram is poor. The quality of a DNA histogram is routinely assessed by the coefficient of variation (CV) of the G0/G1, peak. A high value denotes that this peak is broad, and aneuploid populations may be undetected or poorly resolved. Large CVs also confound the complex SPF analyses. In the past, clinical laboratories have often reported aneuploidy or SPF based on DNA histograms of poor quality, when, in fact, a diagnosis of "unsatisfactory" should have been made. Clinicians can avoid making decisions based upon this type of unsatisfactory data by demanding the report of the CV, which is often not reported, or by ascertaining the value that a particular laboratory used for exclusion from analysis.
S phase analysis is highly complex, making the routine analysis of the SPF in smaller diagnostic laboratories difficult to interpret in terms of published studies. Due to the lack of standardization and inherent variability associated with SPF analysis, when cutoff values obtained from the literature for prognostic group assignment in a particular disease process are used, it is good practice to perform S-phase measurement in the laboratory that reported the values. A reference laboratory cannot be relied upon to generate the same values attained in a major clinical study; however, a good laboratory can be relied upon to reproduce its own values. Therefore, the choice of laboratory for DNA content analysis should not be based solely on cost, but also on its experience and ability to compare a value attained within a diagnostic setting to intralaboratory-generated prognostic categories.
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