Examination of the proliferative activity of tumor cells in human lymphoid neoplasms using a morphometric approach

Background: The genesis of lymphoid neoplasia is accompanied by alterations in cell proliferation control mechanisms. Thus, proliferative indices (PIs) provide valuable prognostic information in this setting. Nonetheless, one shortcoming of PI measurements is that they depend not only on the proliferative activity of the neoplastic cells in a given lesion, but also on the activity of any admixed reactive cells. The current report describes an approach to identifying neoplastic and reactive subpopulations in cytologic preparations and to comparatively characterizing the morphologic features of neoplastic cells and assessing their proliferative activity.

Methods: Reactive and malignant lymph node samples were obtained from 37 patients during surgical biopsy. Giemsa stained touch imprints were used for morphometric and high-resolution videomicroscopic analyses. Immunofluorescence-based detection of pKi-67-positive cells was used in conjunction with morphometric analysis to assess the proliferative activity of tumor cells.

Results: Morphometric analysis allowed the selective identification of neoplastic cells in large cell lymphomas (LCLs). The morphologic characteristics of neoplastic cells in primary and secondary LCLs were found to be similar. The PI was highly dependent on the proportion of tumor cells present in the tissue sample analyzed, and in some samples with large proportions of reactive cells, the overall percentage of pKi-67-positive cells was low, but the majority of neoplastic cells nonetheless were positive for pKi-67. Most primary LCLs had very high tumor cell PIs (80-100%), whereas all secondary LCLs had moderate tumor cell PIs.

Conclusions: Neoplastic cell subpopulations in LCLs can be identified on morphometric analysis, which can be performed using touch imprints; this technique allows evaluation of the proliferative activity of tumor cells. The authors propose that the use of tumor cell PIs rather than PIs for overall cell populations will result in more accurate assessment of disease prognosis.