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Developing a reliable and quantitative assessment of the potential virulence of a malignancy has been a long-standing goal in clinical cytometry. DNA histogram analysis provides valuable information on the cycling activity of a tumor population through S-phase estimates; it also identifies nondiploid populations, a possible indicator of genetic instability and subsequent predisposition to metastasis. Because of conflicting studies in the literature, the clinical relevance of both of these potential prognostic markers has been questioned for the management of breast cancer patients. The purposes of this study are to present a set of 10 adjustments derived from a single large study that optimizes the prognostic strength of both DNA ploidy and S-phase and to test the validity of this approach on two other large multicenter studies. Ten adjustments to both DNA ploidy and S-phase were developed from a single node-negative breast cancer database from Baylor College (n = 961 cases). Seven of the adjustments were used to reclassify histograms into low-risk and high-risk ploidy patterns based on aneuploid fraction and DNA index optimum thresholds resulting in prognostic P values changing from little (P < 0.02) or no significance to P < 0.000005. Other databases from Sweden (n = 210 cases) and France (n = 220 cases) demonstrated similar improvement of DNA ploidy prognostic significance, P < 0.02 to P < 0.0009 and P < 0.12 to P < 0.002, respectively. Three other adjustments were applied to diploid and aneuploid S-phases. These adjustments eliminated a spurious correlation between DNA ploidy and S-phase and enabled them to combine independently into a powerful prognostic model capable of stratifying patients into low, intermediate, and high-risk groups (P < 0.000005). When the Baylor prognostic model was applied to the Sweden and French databases, similar significant patient stratifications were observed (P < 0.0003 and P < 0.00001, respectively). The

Both DNA ploidy and S-phase ploidy are promising prognostic factors for node-negative breast cancer patients. Based largely on the analysis of one large study, much of the reported problems with these factors have been caused by some unappreciated complexities in categorizing DNA ploidy into low- and high-risk groups and the lack of some necessary adjustments to eliminate unwanted correlations between DNA S-phase and ploidy. When both DNA ploidy and S-phase are compensated properly, they become independent prognostic markers, forming a powerful prognostic model.

Recently a set of ten adjustments that optimizes the prognostic strength of both DNA ploidy (P) and S-phase (S) was published (Cytometry, 46(3), 2001). Also presented was an optimal method of combining P and S (P+S) that stratifies node-negative patients into highly significant risk groups. The adjustments compensate for many unappreciated complexities in categorizing P into low and high risk groups and eliminate unwanted correlation between P and S. The purpose of this study is to examine P+S in the context of other well-known prognostic factors such as primary size (pT), estrogen and progesterone receptor (ER,PR) and menopausal status (MS). Methods: DNA histograms derived from frozen primary tumors and clinical databases were provided by Baylor College, n=935; Sweden, n=210 (Lund, Linkoping, Stockholm) and France, n=220 (Angers, Marseille, Saint Cloud, Tours). Time to metastasis was the tested clinical outcome. Results: Cox proportional hazards analysis of theBaylor data revealed P+S, p<0.000002, and pT, p<0.003, as independent significant prognostic factors. The Sweden study also showed P+S the mostsignificant prognostic factor, p<0.002, as well as MS, p<0.004 and ER, p<0.007. The French study results were MS, p<0.0005, P+S, p<0.002 and pT, p<0.007.A P+S, MS and pT prognostic model stratified patients in all studies into highly significant categories, Baylor, p<0.000005, Sweden, p<0.00001, and French, p<0.000005, with low and high risk 10-year relapse-free survival fractions of 0.92-0.69, 0.95-0.58 and 0.96-0.60 respectively. Conclusion: A combined P+S, MS and pT prognostic model is a powerful and reliable method of stratifying node-negative breast cancer patients into highly significant prognostic groups.