8/14/2019 Ash Poster 02122009_2230 Rr Mak

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IntroductionIntroduction

Recent studies have proposed the RNA-based

markers LPL, TCL1, ZAP70, CLLU1 and MCL1,

to be novel predictors of clinical outcome in CLL.

However, a comprehensive evaluation of these

RNA markers is lacking.

Material and MethodsMaterial and Methods

mRNA expressions levels of LPL, ZAP70, CLLU1,

TCL1 and MCL1 were measured in unsorted samples

from 252 newly diagnosed CLL patients from a

Scandinavian population-based cohort by RQ-PCR

RNA markers were evaluated as single markers and

in combination with established markers i.e. Binet

staging, IGHV mutation status, recurrent genomic

aberrations and CD38 expression.

ROC curve analysis was applied to define the

threshold values for survival analyses of the RNA-

based markers.

AimAim

TTo investigate the potential of the RNA-based

markers LPL, ZAP70, CLLU1, TCL1, andMCL1 in CLL prognostication, either as single

markers or in combination with established

markers.

ConclusionsConclusions

Among the RNA-based prognostic

markers, LPL was most successful in

predicting clinical outcome in CLL and

remained as the strongest independent

marker in multivariate analysis.

LPL expression could also further stratify

good-prognosis patient subgroups based on

established markers.

Combinations ofLPL and CD38 expression

could further subdivide Binet stage A CLL

patients.

Our results suggest that LPL analysis

could be applied in the clinical laboratory,

particularly in combination with established

markers.

ResultsResults

3.3. All of the RNA-based markers added further prognostic

information to established markers in subgroups of patients, withLPL expression status giving the most significant results (Table 3).

1.1. All investigated RNA-based markers except MCL1

could significantly predict overall survival (Figure 1)

and time to treatment (Figure 2). LPL gave the most

significant results in all the analyses.

2.2. In multivariate analysis including the RNA markers,

LPL expression was the only independent prognostic

factor for OS and TTT (Table 1). LPL lost its

significance when including established markers, likely

due to its close association to IGHV mutation status.

Once the latter marker was excluded, LPL regained its

independent prognostic strength (Table 2).

LPL is the strongest prognostic factor in a comparative analysis of RNA-based

markers in chronic lymphocytic leukemia

Mohd Arifin Kaderi1, Meena Kanduri1, Mahmoud Mansouri1, Anne Mette Buhl2, , Marie Sevov1, Nicola Cahill1, Rebeqa Gunnarsson3, Mattias

Jansson1, Karin Ekstrm Smedby4, Henrik Hjalgrim5, Gunnar Juliusson3, Jesper Jurlander2, Richard Rosenquist1

1Department of Genetics and Pathology, Uppsala University, Uppsala, Sweden, 2Department of Hematology, Leukemia Laboratory, Rigshospitalet, Copenhagen, Denmark, 3Department of Laboratory Medicine, Stem Cell Center,

Hematology and Transplantation, Lund University, Lund, Sweden, 4Department of Medicine, Clinical Epidemiology Unit, Karolinska Institutet, Stockholm, Sweden, 5Department of Epidemiology Research, Statens Serum Institut,

Copenhagen, Denmark.

A. LPL

0 2 4 4 8 7 2 9 6 1 2 00

2 5

5 0

7 5

10 0 Lo w C L L U 1 , n = 1 3 4

H i g h C L L U 1 , n = 1 1 8

p = 0 . 0 0 0 8 7

%

Untreated

Time (months)

D. CLLU1 C. TCL1

0 2 4 4 8 7 2 9 6 1 2 00

2 5

5 0

7 5

1 0 0 L o w T C L 1 , n = 1 1 6

H i g h T C L 1 , n = 1 0 3

p < 0 . 0 1

%

Untreated

Time (months)

E. MCL1

0 2 4 4 8 7 2 9 6 1 2 00

25

50

75

10 0 Lo w M C L 1 , n = 1 2 6

H i g h M C L 1 , n = 1 2 2

%

Untreated

Time (months)0 2 4 4 8 7 2 9 6 1 2 0

0

25

50

75

10 0 L o w Z A P 7 0 , n = 1 2 6

H i g h Z A P 7 0 , n = 1 2 6p = 0 . 0 1

%

Untreated

Time (months)

B. ZAP70

A. LPL B. ZAP70

0 2 4 4 8 7 2 9 6 1 2 00

25

50

75

10 0

Lo w Z A P 7 0 , n = 1 2 6

H i g h Z A P 7 0 , n = 1 2 6

p < 0 . 0 1%

Surv

iving

Time (months)0 2 4 4 8 7 2 9 6 1 2 0

0

25

50

75

10 0

L ow T C L 1 , n = 1 2 9

H i g h T C L 1 , n = 1 2 3

p < 0 . 0 1%

Surviving

Time (months)

D. TCL1

0 2 4 4 8 7 2 9 6 1 2 00

25

50

75

1 00

Lo w M C L 1 , n = 1 2 6

H i g h M C L 1 , n = 1 2 2

%

Surviving

Time (months)

E. MCL1

Timefrom diagnosis (months)

%

Surviving

0 2 4 4 8 7 2 9 6 1 2 00

25

50

75

10 0

L ow C L L U 1 , n = 1 3 4

H i g h C L L U 1 , n = 1 1 8

p = 0 . 0 3

C. CLLU1

%

Surviving

Time (months)

4.4. We observed that LPL expression in combination with CD38

expression could further subdivide Binet stage A CLL patients(Figure 4).

Figure 1. Expression status of RNA-based markers and overall survival.

Figure 2. Expression status of the RNA-based markers and time to treatment.

Variable LPL

OS TTT

Table 3. The prognostic information of RNA-based markers in subgroups of established markers.

a

only 3 cases with low LPL expression in this subgroup* high CLLU1 had longer OS accoding to Kaplan-Meier curveNS not significant

NA not available; reliable log-rank test could not be performed due to very low number of cases

Table 2. Multivariate Cox-regression analysis of LPL and established markers (excluding IGHV mutation status).

The threshold values used in the analysis were as follows; age at diagnosis: median (63.9); Binet stage: A vs B/C;

IGHV mutation status: mutated vs unmutated; genomic aberrations: del(13q)/no aberration vs trisomy12/del(11q)

vs del(17p); CD38: 7%; LPL: threshold value based on ROC curve analysis.

HR: Hazard ratio, CI: Confidence interval.

Table 1. Multivariate Cox-regression analysis of RNA-based markers.

The threshold values used in the analysis were determined based on ROC

curve analysis.

HR: Hazard ratio, CI: Confidence interval.

Variable Overall survival (N=248

HR CI p

TCL1 1.38 0.86 2.19 0.18LPL 4.63 2.65 8.09