Development of a comprehensive prognostic index for patients with chronic lymphocytic leukemia

Abstract

In addition to clinical staging, a number of biomarkers predicting overall survival (OS) have been identified in chronic lymphocytic leukemia (CLL). The multiplicity of markers, limited information on their independent prognostic value, and a lack of understanding of how to interpret discordant markers are major barriers to use in routine clinical practice. We therefore performed an analysis of 23 prognostic markers based on prospectively collected data from 1948 CLL patients participating in phase 3 trials of the German CLL Study Group to develop a comprehensive prognostic index. A multivariable Cox regression model identified 8 independent predictors of OS: sex, age, ECOG status, del(17p), del(11q), IGHV mutation status, serum β2-microglobulin, and serum thymidine kinase. Using a weighted grading system, a prognostic index was derived that separated 4 risk categories with 5-year OS ranging from 18.7% to 95.2% and having a C-statistic of 0.75. The index stratified OS within all analyzed subgroups, including all Rai/Binet stages. The validity of the index was externally confirmed in a series of 676 newly diagnosed CLL patients from Mayo Clinic. Using this multistep process including external validation, we developed a comprehensive prognostic index with high discriminatory power and prognostic significance on the individual patient level. The studies were registered as follows: CLL1 trial (NCT00262782, https://ichgcp.net/clinical-trials-registry/NCT00281918" title="See in ClinicalTrials.gov">NCT00281918, http://clinicaltrials.gov).

© 2014 by The American Society of Hematology.

Figures

Figure 1

The survival curves according to clinical staging in the training data set. (A) OS of Binet stages within the training data set (N = 1863); (B) OS of Rai stages within the training data set (N = 1863).

Figure 2

Time-to-event curves according to the prognostic index in the training data set. (A) OS of risk groups of the training data set according to the prognostic index (score population: N = 1223); (B) PFS of patients starting treatment according to the prognostic index (training data set N = 807); (C) TFS of untreated patients according to the prognostic index (training data set N = 416).

Figure 3

Survival curves of the Binet and Rai stages by prognostic index risk categories in the training data set. (A) Patients in Binet stage A (N = 531); (B) Patients in Binet stage B (N = 443); (C) Patients in Binet stage C (N = 241); (D) Patients in Rai stage 0 (N = 232); (E) Patients in Rai stage I/II (N = 702); (F) Patients in Rai stage III/IV (N = 281).

Figure 4

Survival curves of genetic subgroups by prognostic index risk categories in the training data set. (A) OS of patients with unmutated IGHV mutation status within risk groups of the prognostic index (training data set N = 645); (B) OS of patients with deletion 17p13 within risk groups of the prognostic index (training data set N = 74).

Figure 5

Time-to-event curves according to the prognostic index in the validation data set. (A) OS of risk groups of the Mayo validation cohort according to the prognostic index (N = 673); (B) TFS of the Mayo validation cohort according to the prognostic index (N = 673).

Figure 6

Survival curves of Rai stages by prognostic index risk categories in the validation data set. (A) Patients in Rai stage 0 (N = 384); (B) Patients in rai stage I/II (N = 269); (C) Patients in Rai stage III/IV (N = 20).

Figure 7

Survival curves of genetic subgroups by prognostic index risk categories in the validation data set. (A) OS of patients with unmutated IGHV mutation status within risk groups of the prognostic index (validation data set N = 292); (B) OS of patients with deletion 17p13 withihn risk groups of the prognostic index (validation data set N = 29).