PATHOLOGIC PROGNOSTIC FACTORS IN DIFFUSE AGGRESSIVE NON-HODGKIN'S LYMPHOMA - 09/09/11
Resumen |
Diffuse aggressive non-Hodgkin's lymphomas (NHL) encompass a diverse group of histologic subtypes that have a predominance of large neoplastic cells and lack any follicular architecture. This group of neoplasms demonstrates a spectrum of histologic, cytologic, immunophenotypic, and cytogenetic subtypes that are reflected by the marked biologic heterogeneity of diffuse large-cell lymphomas (DLCLs).19, 66 Although these are considered clinically aggressive tumors, the DLCLs are highly responsive to chemotherapy and are curable in approximately 40% to 55% of advanced-stage cases.61 Because the outcome for any individual patient is uncertain, prognostic models based on analysis of pretreatment variables are needed to facilitate treatment decisions. Those patients considered at high risk for treatment failure are potential candidates for alternative therapies, whereas those patients deemed to have a good prognosis may benefit from less aggressive approaches. This review focuses on a select group of pathologic, or so-called biologic, variables that may add additional prognostic information beyond that derived from an analysis of clinical factors.
Important clinical prognostic models have been developed that accurately predict the outcome of individual patients and have proven useful for studying risk-adjusted therapies.24, 61 These factors are easily measured and are quantifiable. In general, they can be divided into four basic categories: (1) the tumor's growth and invasive potential (serum lactate dehydrogenase [LDH], stage, size of mass, number of nodal or extranodal sites of disease, bone marrow involvement); (2) the patient's response to the tumor (performance status, B symptoms); (3) the patient's ability to tolerate treatment (age, performance status, bone marrow involvement); and (4) a variety of treatment-related factors including time to achieve complete remission (CR) and dose delivery and intensity schedules.60, 61 An analysis of some of these variables led to the recent development of the International Prognostic Factor Index (IPI), which has proven to be an extremely useful model for predicting treatment failure risk in individual patients.61 Briefly, this large collaborative study of more than 3000 patients found that age, stage, LDH, performance status, and the number of extranodal sites of disease involvement were important clinical factors for predicting the outcome of patients with DLCLs. Each patient was assigned a score, and four relative-risk categories were determined including low, low-intermediate, high-intermediate, and high risk. The predicted 5-year survivals for these groups were 73%, 51%, 43%, and 26%, respectively. A separate age-adjusted model was also developed for those patients under age 60 years, considered potential candidates for intensive experimental regimens.61
Importantly, these clinical variables are surrogates for the underlying biologic heterogeneity of the DLCLs. This has led both pathologists and basic researchers to search for the biologic correlates that underlie the clinical behavior of NHLs, with the hope that acquisition of new knowledge will not only improve our understanding of these entities but also lead to the recognition of independent factors that strengthen our ability to accurately predict prognosis in this group of patients.
Many pathologic prognostic factors have been studied in patients with DLCL, including cell lineage, growth fraction, chromosomal abnormalities, oncogene rearrangements, major histocompatibility antigen expression, cell adhesion molecule expression, and tumor-infiltrating lymphocyte responses, to name a few.14, 17, 45 This review will focus on four of these factors: cell lineage (B versus T cell); bcl-2 gene rearrangements; bcl-2 protein expression; and the role of p53 expression and mutations.
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| Address reprint requests to Randy D. Gascoyne, MD, FRCPC, Department of Pathology, B. C. Cancer Agency, 600 West 10th Avenue, Vancouver, BC, Canada, V5Z 4E6 This work was supported in part by Grant 104 (91-2) from the British Columbia Health Research Foundation. |
Vol 11 - N° 5
P. 847-862 - octobre 1997 Regresar al número
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