New Prognostic Factors for Chronic lymphocytic leukemia

1. Dr. Ajay Yadav
Medical Oncologist
AIIMS, New Delhi
Lancet Oncol 2016; 17: 779–90
1

2. CLL - Epidemiology
• Most common lymphoproliferative disorder in the West (30)
• Incidence in India 2–4%
• Median age: 59 years
• Chlorambucil based : ORR 69%, CR 3%
• Fludarabine-based : ORR 89%, CR 44%
• Median overall survival:5.1yr
• Event-free survival 4.6 years
Gogia A. et al.Leuk Lymphoma. 2012 Oct;53(10):1961-5. doi: 10.3109/10428194.2012.672734. Epub 2012 May 21
50%
required
treatment at
presentation
0
10%
1
16%
2
33%
3
20%
4
21%
Rai Stage
2

3. 3
Rai Stage Characteristics
Median survival
(months)
0 Lymphocytosis 150
I Lymphadenopathy 101
II Organomegaly 71
III Anemia 19
IV Thrombocytopenia 19
Prognosis of CLL

4. 4
Binet Stagging

5. 5
Newer prognostic markers

6. 6
Newer Prognostic Markers

7. 7

8. Aim of study:
• To create an international prognostic index for CLL patients that
integrates the major prognostic parameters
8

9. Study Design
• Meta-analysis individual patient data from eight phase III trials
• Study Place : France, Germany, Poland, the United Kingdom, and the United States
• N=3472
• Treatment naïve patients with CLL, both early and advanced stage
9

10. End Point
• Primary End Point:
Overall survival
10

11. Methods
• Prospective, Clinical phase 3 trials of CLL, published between Jan 1, 1950 and Dec 31,
2010
• Chemo naïve patients with all stages
• Phase II or III trials
• At least one of the following new prognostic factors: del(17p), del(13q), del(6q), del(11q),
trisomy 12, TP53 and IGHV mutational status, and ZAP-70 and CD38 expression
• Eligible clinical trials : 13 (phase III trials)
• Eight investigators - agreed to provide individual patient data
11

12. Overview of study datasets
12

13. Patient Segregation
• Total 3,472 patients after univariate analysis of randomized into
– Training dataset N=2308 (66%)
– Internal-validation data set N=1164 (34%)
• Two additional dataset (838 and 416) used as external validation sets
(population-based case-control study)
13

14. Statistical analysis
• Kaplan-Meier method, including the log-rank test used for estimations and
comparisons of overall survival
• Hazard ratios (HR) was calculated using Cox proportional hazard regression
analyses
14

15. • Univariate analyses :
• Univariate analysis was done with 27 baseline variables
• Categorized laboratory variables by published thresholds and quartiles
• Categorized variables used for further analyses if significantly associated with overall survival
• Out of 27, 17 had significant association with survival
• Random allocation of participant data to Training dataset (66%) and Internal-validation data set (34%)
• Multivariate analysis:
• All factors that were significantly associated with overall survival in univariate analyses were included for the
multivariate analysis
15

16. Results
16

17. Baseline Characteristics
• Median age: 61 years (range 27–86)
• 1542 (44%) died from any cause
• Median observation time of 79·9 months (IQR 79·9–101·4)
• Median overall survival: 95·3 months [95% CI 89·7–98·5]
17

18. Univariate Analysis for OS
18

19. 19

20. 20

21. Multivariate Analysis
21

22. CLL- IPI Prognostic Model
Risk Group CLL-IPI risk
score
Low-risk 0-1
Intermediate-risk 2-3
High-risk 4-6
Very High-risk 7-10
1) TP53 status (no abnormalities v/s del[17p]
or TP53 mutation or both)
2) IGHV mutational status (mutated v/s
unmutated)
3) Serum β2-microglobulin concentration
(≤3.5 mg/L v/s >3.5 mg/L)
4) Clinical stage (Binet A or Rai 0 v/s Binet B–C
or Rai I–IV)
5) Age (≤65 years v/s >65 years)
22

23. Analysis at Training dataset (N: 1214 )
23

24. OS: Training dataset of 1214 patients
24

25. Internal-validation cohort (N:585 )
25

26. Internal-validation cohort (N:585 )
26

27. External Validation :Mayo Clinic (N: 838)
27

28. External Validation:Mayo Clinic (N:838)
28

29. External Validation: SCAN cohort (N=416)
29

30. OS: External validation in SCAN cohort of 416 patients
30

31. Risk Group CLL-IPI risk
score
Management
Low-risk 0-1 Do not touch : watch-and-wait approach
CLL –IPI : How to implement
Intermediate-risk 2-3 Do not treat (except when the patient is symptomatic)
Very High-risk 7-10 Treat in experimental protocol or with non-cytotoxic drugs if
possible (no chemotherapy or chemo -immunotherapy)
High-risk 4-6 Treat (except when the patient is asymptomatic)
31

32. Discussion
Rai and Binet clinical staging systems developed :No biological or genetic variable available
CLL-IPI working group collected data from 8 international, phase III clinical trials from 5 countries
Evaluated the data of Individual patients
CLL-IPI combines genetic, biochemical, and clinical parameters into a prognostic model
32

33. Discussion
Easily reproducible prognostic model
Identified four distinct groups of patients, on the basis of five parameters
Both Internally and externally validated
More realistically classify CLL patients
More targeted management of patients with CLL in clinical practice and in trials testing novel
drugs
33

34. Limitations of the Study
At the time of analysis, phase 3 trials of novel oral inhibitors (idelalisib, ibrutinib, or
venetoclax) did not have sufficiently long follow-up to be included
Median age was lower than the general median age of patients at diagnosis (61 years vs
72 years)
34

35. Further Study
CLL-IPI recently validated externally in two independent, prospective cohorts of
newly diagnosed patients from the USA and Europe
Caspar et al The International prognostic Index for patients with chronic lymphocytic leukemia (CLL-IPI) applied in a population-based cohort Blood 2016 :blood-2016-07-724740
Molica et al Is the International Prognostic INDEX for CLL (CLL-IPI) Useful to Predict Time to First Treatment of Patients with early Disease? Results of a Prospective
Multicenter Analysis:ASH 57th ASH Annual Meeting & Exposition 2015
35

36. Take Home
• CLL-IPI combines the most important genetic risk factors with clinical stage, age, and β2-microglobulin
• An easily applicable prognostic score for CLL patients
• Cost effective, detection of number of markers can be avoided
• Provides an important framework for treatment recommendations
• Identifies very poor risk groups, who may be benefited with novel therapies
36

37. Thank You

38. 38
Dr. Kanti Roop Rai