Profiling: "Editorial on ‘Survival after progression in patients with follicular lymphoma: analysis of prognostic factors’ ", by S. Montoto et al. (Ann Oncol 2002; 13: 523–530)

P. McLaughlin

Department of Lymphoma/Myeloma, University of Texas MD Anderson Cancer Center, Houston, TX, USA

Profiling is not just a telemarketing or law enforcement tool. As physicians, we profile our patients so that we can make intelligent management recommendations. The paper by Montoto et al. [1] can help us do our job better for patients with recurrent follicular lymphoma.

Most prognostic models pertain to previously untreated patients. Such analyses focus on disease and host features, but do not take into account the impact of therapy. A model such as the International Prognostic Index (IPI) [2] for aggressive lymphoma is highly useful to predict outcome, to provide a basis for comparing different patient populations, and to recognize situations in which standard therapy is likely to be successful. It can provide a compelling rationale for selection of non-standard therapy in high-risk situations. The same logic applies in the setting of indolent lymphoma. A variety of schemes have been used to stratify patients and to guide management choices [3, 4]. Regrettably though, virtually all patients with follicular lymphoma will ultimately relapse, regardless of the initial prognostic factors or therapy.

A salvage model has to account for the influence of prior therapy. Since no standard therapy for patients with advanced stage indolent lymphoma is curative, many initial management approaches have been employed. The heterogeneous treatment and the variable treatment-free interval which ensues add substantial complexity to the analysis. Montoto et al. and others [5, 6] have shown that the prior therapy response is a significant prognostic factor, which emphasizes the need for a model designed specifically for relapsing patients.

For patients with recurrent indolent lymphoma, retreatment with alkylating agents can be temporarily effective [7]. However, patients eventually become refractory to the retreatment approach. Moreover, there are now numerous other appealing options for patients, including relatively non-toxic approaches such as monoclonal antibody therapy, conventional dose chemotherapy alternatives such as the nucleoside analogs, or intensive therapy such as autologous or allogeneic transplant [8]. Our patients need to be informed about the threat of their disease so that the risks of therapy can be put in perspective. For innovative milder therapies [4], selection of appropriate low-risk patients (and the patience to await long-term follow-up information!) is probably warranted. The converse is not necessarily true. In the absence of any truly satisfactory (i.e. curative) therapy, there is no mandate to reserve intensive therapy only for high-risk or last ditch situations [9]. But to interpret treatment results, a profile is needed of the patients’ risk status.

A model such as the one proposed by Montoto et al. [1] does not always leave us with simple decisions. Not surprisingly, age is an important prognostic factor [6]. Consequently, a Catch-22 situation exists for many patients who are sufficiently high-risk to warrant a transplantation strategy, but who are too old to tolerate this treatment approach. The advent of the ‘mini-allo’ transplant strategy may provide an attractive option for these patients [10].

Whether aggressive or mild therapy is selected, the conscientious collection of relevant prognostic information provides a much-needed basis for comparisons between treatments. The issue of selection bias, which is often subtle and unintentional, can be substantially addressed through a critical analysis of the prognostic features of patients on a clinical trial. For instance, in aggressive lymphoma, a careful analysis of prognostic factors provided insight into the failure of several single institutions’ encouraging experiences with ‘second-generation’ and ‘third-generation’ regimens to be confirmed, in a large multi-center trial that compared some of those regimens with standard therapy [11]. The model by Montoto et al. [1] provides a framework, for use on patients with relapsed indolent lymphoma, to take a critical look at whether promising innovative therapies are of genuine merit.

The patients in the Montoto analysis were treated between 1977 and 1997, which provided the investigators the necessary follow-up time to make their important prognostic observations. Complete data were not available on all patients. Some of their important observations were made on just a subset of the group. The highly significant prognostic value of serum ß2-microglobulin was particularly notable. This is a simple, quantitative and under-appreciated test that merits consideration for lymphoma patients. Routine assessment of performance status is also simple and advisable. Measurement of tumor bulk is important as well. Additional biological data is now recognized to be of interest, such as bcl-2 data [12]. With hindsight, it is easy to speculate that a prognostic model might be enhanced by the inclusion of such molecular genetic or gene expression data [13, 14]. Such wishful thinking should be kept in mind as we assess patients; we should try to anticipate how we might capitalize in the future on technologies and insights that are evolving today.

The most satisfying use of a prognostic model is to make a confident prediction that standard therapy is likely to be curative. From this perspective, all prognostic models for indolent lymphoma are humbling, since no subset of patients is consistently cured. This is testimony that we need better therapy. A prognostic model that facilitates comparisons between patient groups should help us reach rational and more rapid assessments of our expanding list of treatment options.

P. McLaughlin

Department of Lymphoma/Myeloma, University of Texas, MD Anderson Cancer Center, Houston, TX, USA

References

1. Montoto S, López-Guillermo A, Ferrer A et al. Survival after progression in patients with follicular lymphoma: analysis of prognostic factors. Ann Oncol 2002; 13: 523–.530.[Abstract/Free Full Text]

2. A predictive model for aggressive non-Hodgkin’s lymphoma. The International Non-Hodgkin’s Lymphoma Prognostic Factors Project. N Engl J Med 1993; 329: 987–994.[Abstract/Free Full Text]

3. López-Guillermo A, Montserrat E, Bosch F et al. Applicability of the International Index for aggressive lymphomas to patients with low-grade lymphoma. J Clin Oncol 1994; 12: 1343–1348.[Abstract]

4. Colombat P, Salles G, Brousse N et al. Rituximab (anti-CD20 monoclonal antibody) as single first-line therapy for patients with follicular lymphoma with a low tumor burden: clinical and molecular evaluation. Blood 2001; 97: 101–106.[Abstract/Free Full Text]

5. Spinolo JA, Cabanillas F, Dixon DO et al. Therapy of relapsed or refractory low-grade follicular lymphomas: factors associated with complete remission, survival and time to treatment failure. Ann Oncol 1992; 3: 227–232.[Abstract]

6. Weisdorf DJ, Andersen JW, Glick JH, Oken MM. Survival after relapse of low-grade non-Hodgkin’s lymphoma: implications for marrow transplantation. J Clin Oncol 1992; 10: 942–947.[Abstract]

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8. van Besien K, Sobocinski KA, Rowlings PA et al. Allogeneic bone marrow transplantation for low-grade lymphoma. Blood 1998; 92: 1832–1836.[Abstract/Free Full Text]

9. Horning SJ, Negrin RS, Hoppe RT et al. High-dose therapy and autologous bone marrow transplant for follicular lymphoma in first complete or partial remission: results of a phase II clinical trial. Blood 2001; 97: 404–409.[Abstract/Free Full Text]

10. Khouri IF, Keating M, Körbling M et al. Transplant-lite: Induction of graft-versus-malignancy using fludarabine-based nonablative chemotherapy and allogeneic blood progenitor-cell transplantation as treatment for lymphoid malignancies. J Clin Oncol 1998; 16: 2817–2824.[Abstract]

11. Fisher RI. Diffuse large-cell lymphoma. Ann Oncol 2000; 11 (Suppl 1): 29S–33S.

12. López-Guillermo A, Cabanillas F, McLaughlin P et al. The clinical significance of molecular response in indolent follicular lymphomas. Blood 1998; 91: 2955–2960.[Abstract/Free Full Text]

13. Alizadeh AA, Eisen MB, Davis RE et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 2000; 403: 503–511.[ISI][Medline]

14. Husson H, Carideo EG, Neuberg D et al. Gene expression profiling of follicular lymphoma and normal germinal center B cells using cDNA arrays. Blood 2002; 99: 282–289.[Abstract/Free Full Text]





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