Paper: Pre-Transplant Serum Biomarkers Predict Early Relapse in Classical Hodgkin Lymphoma Patients Undergoing Autologous Stem Cell Transplantation

Poster Abstracts

Grand Hall CD (Manchester Grand Hyatt)

Bryan Trottier, MD , Blood and Marrow Transplantation, University of Minnesota, Minneapolis, MN

Holly Miller, MD , Blood and Marrow Transplantation Program, University of Michigan, Ann Arbor, MI

Qing Cao, MS , Biostatistics and Bioinformatics, University of Minnesota, Minneapolis, MN

Jeffrey S. Miller, MD , Blood and Marrow Transplantation Program, University of Minnesota, Minneapolis, MN

Michael R. Verneris, MD , Pediatric Hematology and Oncology, University of Minnesota Medical Center, Fairview, Minneapolis, MN

Daniel J. Weisdorf, MD , University of Minnesota Medical Center, Minneapolis, MN

John Levine, MD, MS , Pediatric Blood and Marrow Transplant Program, University of Michigan, Ann Arbor, MI

Linda J. Burns, MD , National Marrow Donor Program, Minneapolis, MN

Presentation recording not available for download or distribution as requested by the presenting author.

Background: Serum biomarkers in classical Hodgkin Lymphoma (cHL) reflect both tumor biology and burden in the non-transplant setting. We sought to determine the prognostic value of cHL serum biomarkers in predicting early relapse following autologous stem cell transplantation (ASCT).

Methods: Nine serum biomarkers (IL-6, IL-10, IL-13, sCD30, sIL-2R, CCL17, Galectin-1, CD68 and CD163), selected for their prognostic capacities in the upfront setting, were collected pre-ASCT and measured in 61 patients undergoing transplantation at the University of Minnesota or University of Michigan. Using recursive partitioning methods, we calculated cutpoints for each biomarker to optimally separate patients with early relapse (<2 years from ASCT) from those with late relapse / complete remission (CR).

Results: The majority of patients (89%) were in CR (47%) or partial remission (PR) (41%) at the time of transplant. Twenty-six patients experienced early relapse following ASCT, including 9 (31%) in CR and 12 (48%) in PR pre-ASCT. Seven biomarkers identified patients with early relapse from late relapse / CR (Table). By pre-ASCT disease status, no biomarker distinguished early relapse among patients in CR prior to transplantation. Six biomarkers identified early relapse among patients in PR pre-ASCT.

Conclusions: Elevated serum biomarkers in cHL may identify chemosensitive patients in pre-transplant PR at increased risk for relapse. Identification of these high-risk patients may offer opportunities for intervention, such as alternative salvage therapy pre-ASCT or post-ASCT maintenance therapy.

Table: Estimates and Relative Risks (RR) for 2-Year Relapse by Biomarker Strata Among All Patients

All Patients

PR Patients

Biomarker

Group

N

2-Year Relapse
Estimate
(95% CI)

P-value

2-Year RR
for Relapse
(95% CI)

P-value

N

2-YearRelapse Estimate
(95% CI)

P-value

IL-6 (pg/mL)

< 2.15

18

11% (3-38%)

5

0%

³ 2.15

42

57% (42-72%)

<0.01

7.11 (1.67-30.27)

<0.01

19

59% (38-80%)

0.04

IL-10 (pg/mL)

< 1.65

44

30% (19-47%)

17

30% (14-58%)

³ 1.65

16

75% (53-92%)

<0.01

4.17 (1.88-9.29)

<0.01

7

86% (54-99%)

<0.01

sCD30 (U/mL)

< 8.65

39

32% (19-49%)

18

33% (17-60%)

³ 8.65

21

63% (43-82%)

<0.01

2.88 (1.31-6.32)

<0.01

6

100%

0.01

sIL-2R (U/mL)

< 1276

52

37% (26-52%)

19

37% (20-63%)

³ 1276

8

75% (44-96%)

<0.01

3.38 (1.34-8.54)

0.01

5

80% (42-99%)

0.03

CCL17 (pg/mL)

< 343.6

26

16% (6-36%)

8

13% (2-61%)

³ 343.6

34

63% (47-79%)

<0.01

6.10 (2.08-17.93)

<0.01

16

63% (47-79%)

0.04

Galectin-1 (ng/mL)

< 9.8

13

8% (1-43%)

3

0%

³ 9.8

47

52% (38-67%)

<0.01

8.60 (1.16-63.54)

0.04

21

53% (34-75%)

0.14

CD68 (pg/mL)

< 3.895

13

8% (1-43%)

5

0%

³ 3.895

47

51% (38-67%)

<0.01

8.53 (1.15-63.08)

0.04

19

59% (38-80%)

0.04

		All Patients	PR Patients
IL-6 (pg/mL)	< 2.15	18	11% (3-38%)				5	0%
	³ 2.15	42	57% (42-72%)	<0.01	7.11 (1.67-30.27)	<0.01	19	59% (38-80%)	0.04
IL-10 (pg/mL)	< 1.65	44	30% (19-47%)				17	30% (14-58%)
	³ 1.65	16	75% (53-92%)	<0.01	4.17 (1.88-9.29)	<0.01	7	86% (54-99%)	<0.01
sCD30 (U/mL)	< 8.65	39	32% (19-49%)				18	33% (17-60%)
	³ 8.65	21	63% (43-82%)	<0.01	2.88 (1.31-6.32)	<0.01	6	100%	0.01
sIL-2R (U/mL)	< 1276	52	37% (26-52%)				19	37% (20-63%)
	³ 1276	8	75% (44-96%)	<0.01	3.38 (1.34-8.54)	0.01	5	80% (42-99%)	0.03
CCL17 (pg/mL)	< 343.6	26	16% (6-36%)				8	13% (2-61%)
	³ 343.6	34	63% (47-79%)	<0.01	6.10 (2.08-17.93)	<0.01	16	63% (47-79%)	0.04
Galectin-1 (ng/mL)	< 9.8	13	8% (1-43%)				3	0%
	³ 9.8	47	52% (38-67%)	<0.01	8.60 (1.16-63.54)	0.04	21	53% (34-75%)	0.14
CD68 (pg/mL)	< 3.895	13	8% (1-43%)				5	0%
	³ 3.895	47	51% (38-67%)	<0.01	8.53 (1.15-63.08)	0.04	19	59% (38-80%)	0.04

Disclosures:

D. J. Weisdorf, Alexion, Consultant, data sharing: Consultancy and Research Funding
Amgen, Consultant: Consultancy
Pharmacyclics, Consultant, study planning: Consultancy
Enlivez, Study planning: Consultancy
Therakos, Speaking/Teaching: Educational lecture
Millenium, Consultation: Consultancy

J. Levine, University of Michigan, Professor: Financial Benefit and/or patents

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