Role of HLA in Unrelated

Donor Transplantation

Effie Petersdorf

13th ESH-EBMT Training Course

Blood and Marrow Transplantation

25 May 2009 Latimer, UK

Theme

Human genetic variation is a reflection of the

ethnic/racial background of the population.

• MHC and non-MHC

The biologic implications of genetic diversity

are best examined in ethnically and

racially diverse populations.

• Autoimmunity, infectious diseases, cancer,

transplantation.

The Human Major

Histocompatibility Complex

• The most gene-dense region in the entire

human genome: over 300 loci of which 30%

have immune-related function.

• Functional genes in infection, inflammation,

autoimmunity, cancer and transplantation:

recent genome-wide association studies confirm

MHC region genes involved in T1D, RA, CD,

HIV/AIDs progression.

• High degree of sequence variation and strong

positive long-range linkage disequilibrium.

Challenges in Unrelated

Hematopoietic Cell

Transplantation

1. Diversity of the HLA System

Many patients do not have allele-matched donors.

Can the bar be lowered without compromising safety?

2. Linkage Disequilibrium

Even with precise donor matching, GVHD remains a significant complication.

How can LD be used advantageously to identify new transplantation determinants?

Hallmark of the MHC:

Polymorphism

No.

Alleles*

A B C DRB1 DQB1

697 1109 381 1603 95

9.0 x 1024 Unique Genotypes!

*2008 WHO Nomenclature

Hallmark of the MHC:

Linkage DisequilibriumNon-random association of alleles at nearby loci

Ethnicity/Race A1-B8-

DR0301

A33-

B58-

DR0301

A30-B42-

DR0302

A29-B44-

DR0701

Caucasian 0.062 0.0002 0.00009 0.016

Asian 0.003 0.019 0.00005 0.0007

African 0.012 0.0004 0.0143 0.004

Hispanic 0.017 0.0002 0.002 0.018

www.nmdpresearch.org/HLA/em_haplotype_freq.html

State-of-the-Art: May 2009

Clinical outcome is shaped by:

I. Recipient-donor race/ethnicity

II. Donor HLA match status

III. HLA haplotypes and MHC resident

variation

IV. Non-HLA variation

International Histocompatibility Working

Group in Hematopoietic Cell

TransplantationEst 1996

Theme

The safety and efficacy of HCT may be

increased through a more complete

understanding of the nature of genetic

and non-genetic factors that affect

transplant outcome.

International Histocompatibility Working

Group in Hematopoietic Cell Transplantation

Established 1996

• International collaboration: 37 laboratories,

240 transplant centers and 21 transplant

and donor registries.

• Database: 17,341 (16,198 accepted)

unrelated transplants as of September,

2008.

IHWG Study Population

N=17,341 September 2008

Donor Registry Transplant Registry

HLA Match Status

Characteristics of the 15th WS

IHWG HCT Study PopulationSeptember 200810/10 HLA matched 9 of 10 HLA matched 8 of 10 or less HLA matchedN=7579 N=4136 N=4483

Median recipient age, y (range) 35.8 (0.38-74.5) 33.9 (0.35-75) 30.0 (0.47-72.0)

Year of transplantation - no. (%)1983-1991 202 (2.7) 113 (2.7) 224 (5.0)

1992-1997 1580 (20.8) 920 (22.2) 1285 (28.7)

1998-2002 3115 (41.1) 1722 (41.6) 1817 (40.5)

2003-2007 2626 (34.6) 1329 (32.1) 1116 (24.9)

Unknown 56 (0.7) 52 (1.3) 41 (0.9)

Recipient/donor gender, no. (%)Male/male 3089 (40.8) 1516 (36.7) 1651 (36.8)Male/female 1315 (17.4) 812 (19.6) 1011 (22.6)

Female/male 1645 (21.7) 925 (22.4) 906 (20.2)Female/female 1386 (18.3) 812 (19.6) 865 (19.3)

Unknown 144 (1.9) 71 (1.7) 50 (1.1)

Diagnosis/disease severity*, no. (%)/ no.Acute leukemia/ high-risk, intermediate-risk,missing 3917 (51.7)/ 1088,2773,56 2198 (53.1)/641,1514,43 2342 (52.2)/732,1569,41

Myeloproliferative disorders/ high-risk, intermediate-risk,

low-risk, missing 1843 (24.3)/ 87,454,1246,56 994 (24.0)/ 42,253,654,45 1153 (25.7)/ 98,351,666,38

MDS/ high-risk, intermediate-risk 1009 (13.3)/ 292,435,282 507 (12.2)/151,216,140 476 (10.6)/ 97,142,237Other /high-risk, intermediate-risk, missing 810 (10.7)/ 208,406,196 437 (10.6)/ 108,237,92 512 (11.4)/ 99,303,110

Recipient/donor CMV serology, no. (%)Positive/positive 901 (11.9) 550 (13.3) 475 (10.6)Positive/negative 1323 (17.5 754 (18.2) 617 (13.8)

Negative/positive 657 (8.7) 409 (9.9) 666 (14.9)

Negative/negative 1672 (22.1) 925 (22.4) 366 (8.2)

Unknown 3026 (39.9) 1498 (36.2) 2359 (52.6)

Characteristics of the 15th WS

IHWG HCT Study PopulationSeptember 2008

10/10 HLA matched 9 of 10 HLA matched 8 of 10 or less HLA matchedN=7579 N=4136 N=4483

Transplant type/ Conditioning regimen, no. (%)Ablative/ No TBI 1044 (13.8) 514 (12.4) 336 (7.5)Ablative/ TBI 3134 (41.4) 1763 (42.6) 2094 (46.7)Nonablative 901 (11.9) 535 (12.9) 546 (12.2)Unknown 2500 (33.0) 1324 (32.0) 1507 (33.6)

Source of cells - no. (%)Bone marrow 5857 (77.3) 3208 (77.6) 4058 (90.5)Peripheral blood stem cells 1646 (21.7) 865 (20.9) 363 (8.1)Unknown 76 (1.0) 63 (1.5) 62 (1.4)

GVHD ProphylaxisAny single agent by itself 221 (2.9) 128 (3.1) 104 (2.3)T-cell depletion ± other 829 (10.9) 709 (17.1) 690 (15.4)Two or more agents mixed together 5849 (77.2) 2905 (702) 3484 (77.7)Other or unknown 680 (9.0) 394 (9.5) 205 (4.6)

State-of-the-Art: May 2009

Clinical outcome is shaped by:

I. Recipient-donor race/ethnicity

II. Donor HLA match status

III. HLA haplotypes and MHC resident

variation

IV. Non-HLA variation

Impact of Donor-Recipient Ethnicity on Risk

of Acute GVHD and Survival

in HLA-A, B, C, DRB1, DQB1 Matched

Unrelated Transplantation

Yasuo Morishima, Takakazu Kawase, Satoko

Morishima

on behalf of the IHWG

Aichi Cancer Center, Nagoya, Japan

Japan Marrow Donor Program

Background• The association of ethnicity with GVHD and other

outcomes after HLA identical BMT is well documented.

Comparison of GVHD and survival after HLA-identical sibling BMT in

ethnic population. (Oh H, et al. Blood 105:1408-1416, 2005 )

2-4 A-GVHD

Purpose

● Comparison of the incidence of acute GVHD between ethnic groups based on the same background

1. Large scale IHWG HCT database: 5555 pairs2. HLA allele matched transplant. 10/10 match3. GVHD prophylaxis: T cell replete stem cell

source 4. Leukemia and MDS patients

● Results obtained from this analysis will become basic data for further international analysis of HLA mismatched unrelated HSCT and for donor exchange of unrelated donor.

Ethnicity

Patient – Donor No. of pairs

Asian/Pacific – Asian/Pacific (AA) 2062

(Japanese – Japanese) (2039)

Caucasian – Caucasian (CC) 2419

Black – Black (BB) 39

Hispanic – Hispanic (HH) 21

Native American – Native American (NN) 2

Mismatch race pair (in non-JMDP) (MM) 268

Unknown donor ethnicity 744

Acute GVHD and EthnicityMultivariate models adjusted for clinical factors

Overall SurvivalMultivariate models adjusted for clinical factors

SurvivalLow and intermediate-risk leukemia

HLA - A – DPB1 (12/12) matched

A A (n=505)

C C (n=332)

H H (n=11)

Mismatch ethinicity (n=30)

A:Asian/Pacific C:Caucasian H:Hispanic

Days after transplantation

0 1000 2000 3000 3650

Surv

ival(%

)

0

20

40

60

80

100

State-of-the-Art: May 2009

Clinical outcome is shaped by:

I. Recipient-donor race/ethnicity

II. Donor HLA match status

III. HLA haplotypes and MHC resident

variation

IV. Non-HLA variation

HLA Genetic Variation

• Complex: not SNPs

• Substitutions distributed across 3 class I

exons, one class II exon

• Nucleotide substitutions are concentrated

at positions that are functionally important

for T cell and NK cell allorecognition.

• “Patchwork” overlap in amino acid motifs.

“Patchwork” Nature of HLA

Variation: HLA-DPB1A B C D E F

Courtesy of Bronwen Shaw, MD

Background

Risks after unrelated donor HCT are

influenced by:

• the number of HLA mismatches

• the locus that is mismatched

• the allele and antigen mismatch defined

by their sequence (residues)

Effect of Number of HLA

Mismatches on Mortality

*tally=4, 335/488=69%; tally=5, 125/182=69%; tally=6, 34/51=67%; tally=7, 13/13=100%; tally=8, 2/4=50%; tally=9, 1/2=50%

Tally HR 95% CI p-value

0 (3894/7549=52%)

1 (2403/4116=58%)

2 (1567/2502=63%)

3 (821/1232=67%)

≥ 4* (510/740=69%)

1

1.21

1.45

1.68

1.84

---

1.15-1.27

1.37-1.54

1.56-1.82

1.68-2.02

---

<.0001

<.0001

<.0001

<.0001

Risk of mortality increases as the number of HLA mismatches increases:

Assumption: all mismatches contribute equally to outcome.

Impact of Single Mismatches Relative

to Single HLA-DQB1 Mismatch

Single Mismatch HR 95% CI P-Value

HLA-DQB1 1 --- ---

HLA-A 1.2 1.05 - 1.37 .007

HLA-B 1.29 1.10 - 1.53 .002

HLA-C 1.18 1.05 - 1.33 .004

HLA-DRB1 1.08 0.89 - 1.32 .45

Compared to single DQB1 mismatches, single A, B, C, and DRB1

mismatches are associated with increased risk. Model adjusts for disease risk.

The Impact of HLA-DPB1 Epitope

Matching on the Outcome of URD

HCT

Bronwen Shaw, Katharina Fleischhauer, Elisabetta

Zino, Mari Malkki, Ted Gooley, Effie Petersdorf

HypothesisMismatching for T cell epitopes (TCE) in the HLA-DP molecule may be as

(more) important as allele-matching for predicting outcome of URD HSCT

Zino et al., 2004

DPB1*1001

DPB1*1401

DPB1*1701

DPB1*0301

DPB1*4501

DPB1*0401

and others

DPB1*0901

group 1

group 2

group 3

+ -

-

T cell clones

+ +

TCE

Group-specific rather than allele-specific DPB1 matching:

• TCE-Disparate = “Non-Permissive”

• TCE-Matched = All Others

Methods and Results• 5838 pairs matched for HLA-A,-B,-C,-DRB1,-DQB1 at high

resolution (4 digit) “10/10 matched”

– DPB1 allele-mismatched (n=4490) or 12/12 (n=1348)

– TCE disparate (TCED) in GvH or HvG, or TCE matched (TCEM)

– Results adjusted for classical variables

– Reference: TCEM, DPB1 allele mismatched

HR 95% CI P

TRM 10/10 TCED

12/12

1.26

0.837

1.134-1.399

0.735-0.954

0.00002

0.007

aGvHD 3-4 10/10 TCED

12/12

1.289

0.77

1.104-1.505

0.636-0.933

0.0013

0.007

Overall

mortality

10/10 TCED

12/12

1.156

0.966

1.065-1.255

0.879-1.062

0.00054

0.481

Relapse 10/10 TCED

12/12

0.92

1.316

0.802-1.055

1.151-1.505

0.23613

0.00006

Conclusion and Future Plans

• Ongoing work

– Define impact of the TCE in the 9/10 context

– Analysis of particular diseases (B cell malignanices)

– Role of hypervariable region (HvR) disparity in defining non-

permissive mismatches

10/10 (80%)12/12 (20%)

aGvHD

TRM

Relapse

OS =

14th IHWS

TCED (35%)

OS

TRM

aGvHD

Relapse =

TCEM (55%)

OS

TRM

aGvHD

Relapse =

15th IHWS

Summary

Clinical outcome after unrelated HCT is influenced by:

• The number of HLA mismatches: as the number of mismatches increases, mortality increases. Choose donors with the fewest number of HLA mismatches.

• The locus that is mismatched: compared to single HLA-DQB1 mismatches, other single locus mismatches are associated with increased risk of mortality. There is no “good” mismatch, but relative degrees of “bad” mismatches. When matched donors are not available, avoid HLA-A,B,C and probably DRB1 mismatches.

• The disease risk at the time of HCT: influence of a single HLA mismatch is most evident among low-risk patients. Potential “gain” in a prolonged URD search in the hopes of finding a better matched donor at the expense of disease progression should be weighed very carefully.

• HLA-DPB1 mismatching: an epitope-based matching algorithm may provide choices in optimizing donor selection for up to 55% of patients. Beneficial GVLE associated with mismatching.

State-of-the-Art: May 2009

Clinical outcome is shaped by:

I. Recipient-donor race/ethnicity

II. Donor HLA match status

III. HLA haplotypes and MHC resident

variation

IV. Non-HLA variation

Traherne in International Journal of Immunogenetics 35:179, 2008

Haplotypes

• A combination of markers (alleles) at two or more sites physically linked on the same chromosomal strand.

• The markers are inherited together.

• Markers that are always observed together on the same haplotype can serve as “tags” for one another.

• Tags can be used to identify unknown variation that are linked on the same haplotype.

• How does knowledge of haplotype-linked tag markers facilitate disease mapping?

Tag SNPs

Manolio et al J Clinical Invest 118:1590, 2008

HLA-DR Haplotypes

DR1, 10, 103

DR Haplotype

DRB1 ψ ψ DRA

DRAψ ψDR15, 16

DRB1

DR8

DRAψDRB1

DRB5

DR3, 11, 12, 13, 14

DRAψ ψDRB1 DRB3

DR4, 7, 9

DRAψ ψψDRB1 DRB4

DR Genes

Global Survey of

Haplotype Variation

Conrad et al Nature

Genetics 38:1251, 2006

How similar are ethnically

diverse populations at the

haplotype level?

Surveyed 927 individuals

from 52 populations for

3,024 SNPs in 36 genomic

regions .

Conclusions:

Nearby populations (same

continent) share haplotype

structure.

Number of distinct

haplotypes per region

declines from Africa.

Hypotheses

• MHC haplotypes are a tool for mapping novel transplantation determinants.

• HLA alleles on haplotypes are markers for functional linked MHC resident variation.

• We can query the MHC with the use of Msat and SNP markers.

• We can measure risks associated with donor genotype, recipient genotype, and donor-recipient mismatching.

Father Mother

A1

B8

DR3

DQ2

A2

B44

DR7

DQ2

A3

B7

DR2

DQ1

A11

B60

DR4

DQ4

DonorPatient

A1

B8

DR3

DQ2

A3

B7

DR2

DQ1

A1

B8

DR3

DQ2

A3

B7

DR2

DQ1

a b c d

aa cc a d

Family Segregation of HLA

← Identical by Descent →

PotentialPotential

Unrelated Unrelated

DonorsDonors

PatientPatient

A1

B8

DR3

DQ2

A3

B7

DR2

DQ1

Unrelated Donor Search

? ?

Identical by State

HLA-A,B,DR Matched

Unrelated Pairs

The HLA Complex

6p21.3

HLA-B-Specific Capture and

Haplotype Separation Guo et al PNAS 103:6964,2006

MHC Haplotypes in TransplantationPetersdorf et al PLoS Medicine 4:e8, 2007

• 248 HLA-A,B,C,DRB1,DQB1 allele-identical unrelated donor transplants

• Myeloablative conditioning: TBI-Cy or Bu-Cy

• GVHD prophylaxis: Csp and Mtx

• T-replete Marrow (88%) or PBSC (12%)

• Heterozygous at HLA-B plus at least one other locus (HLA-A and/or DRB1)

Haplotype Matching Defines

Transplant Outcome

A: Grades 3-4 acuteGVHD; B: Relapse; C: TRM; D: Overall survival

Petersdorf et al PLoS Med, 4(1):e8, 2007

IHWG: Degree of Mismatching for Class I, III, II Microsatellites in

HLA-A, C, B, DRB1, DQB1 Allele-Matched URD Transplants

(n= 819)

Association of Msat mismatching with GVHD or overall mortality.

Malkki et al BBMT 2006

State-of-the-Art: May 2009

Clinical outcome is shaped by:

I. Recipient-donor race/ethnicity

II. Donor HLA match status

III. HLA haplotypes and MHC resident

variation

IV. Non-HLA variation

Immune Response Gene (IRG) and

Cytokine Gene (CG) Polymorphisms in

Unrelated HCT

Hypotheses

CGs/IRGs lower or enhance risks of GVHD and mortality after unrelated HCT.

May account for GVHD after HLA matched HCT.

Pathophysiology of GVHD

Ferrara JML, Best Pract Res Clin Haematol. 20: 91-97, 2007

Study Study Population and

Methods• 1215 10/10 matched BM (95%) and PBSC (5%)

URD transplants (1041 NMDP, 23 FRABET, 151

FHCRC)

• Diagnoses: AML 24%, ALL 16%, CML 60%

• Transplant regimens: TBI 84%; non-TBI 16%

• aGVHD prophylaxis: T-replete 86%; TCD 14%

• 43 SNPs representing 27 IRGs/CGs selected from

literature, dbSNP, Seattle SNPs, SNP 500 CA db

• MAF > 5%

• TaqMan chemistry

Comparison with Current Literature

Bottom line: Comparison of results challenging; larger transplant populations

with diverse ethnicity required through collaborative research

Gene No. aGVHD Relapse Mortality

� � - � � - � � -

CD31 14 7 7 1 2 2 3

IL10 15 4 1 10 1 1 2 9

IL1B 7 1 6 1 1 1 5

MTHFR 7 2 2 2 1 1 1 3

TGFB1 4 2 2 1 3

TLR4 3 3 2 1 1

IL6 9 2 7 1 7

IL4 2 2 1 2

HLA-E 3 1 1 1 2 1 2

NOD2 6 2 1 3 1 1 2 2 2

Theme

• Human genetic variation (MHC and non-

MHC) is a reflection of the ethnic/racial

background of the population.

• The biologic implications of genetic

diversity are best examined in ethnically

and racially diverse populations.

Novembre et al Nature 456:98, 2008

3000 Europeans genotyped for > 500,000

SNPs. Close correlation between genetic

and geographic distances: 2D picture of

genetic variation and geographic map.

Disease associations must account for

genetic structure of population to avoid

spurious associations.

Haplotype Studies

FHCRC/ Petersdorf Lab

Zhen Guo, PhD

Mari Malkki, PhD

Paul Martin, MD

Ted Gooley, PhD

Mark Gatterman

Michelle Baillie

Charlie Du

Sharie Cheng

Institute for Systems

Biology

Leroy Hood, MD,

PhD

HCT Working GroupHCT Working Group

Australia Jeremy Chapman, Jim McCluskey, Frank Christiansen, Peter Bardy,

Campbell Witt, Heather Dunckley, Zlatibor Velickovic, John-Paul Hogbin,

Greg Bennett, Ian Humphries, Patrick Hogan, Rhonda Holdsworth, Ian

Nicholson, Simon Knowles, Mike Varney, Carmel Kanaan, Mary Diviney,

Brian Tait, Linda Smith

Austria Wolfgang Mayr, Gottfried Fischer

Belgium Marie-Paule Emonds, Marc A. Boogaerts, Tribaut Gervais, Augustin

Ferrant, Dominique Latinne

Brazil Noemi Pereira, Ricardo Pasquini

Canada Marlis Schroeder, Luvinia Kwan, Shaunna Kawaleski

Czech Republic Jindra Pavel, Vladimir Koza, Petra Venigova

Denmark Mette Voldby Larsen, Soren Buus, Ilka Hoof, Ole Lund

France Colette Raffoux, Jean Dausset, Jean-Denis Bignon, Eliane Gluckman ,

Katia Gagne, Valerie Dubois, Lucette Gebuhner, Agnes Moine, Lena

Absi, Dominique Charron, Pascale Loiseau, Monique Bois, Marie-Lorraine

Appert, Marc Busson, Anne Dormoy, Dominique Masson, Muriel

DeMatteis, Christophe Picard, Agnes Basire, Coralie Frassati, Anne

Cambon-Thomsen, Pierre Antoine Gourrard

Germany Gerhard Ehninger, Carlheinz Muller, Shraga Goldman, Ekkehard Albert

Dietger Niederwieser, Ute Hegenbart, Martin Bornhauser, Monika

Fuessel

Italy Domenico Adorno, Sonia Nesci, Miryam Martinetti, Angelica Canossi,

Paolo Di Bartolomeo, Annamaria Pasi, Emilio Paolo Alessandrino,

Giuseppe Visani, Katarina Fleischhauer, Elisabeta Zino, Andrea Velardi

Japan Takehiko Sasazuki, Takakazu Kawase,Takeo Juji, Yoshika Kodera, Yasuo

Morishima

Netherlands Jan van Rood, Machteld Oudshoorn, Jan Cornelissen, Marcel Tilanus, John

Lee, Martin Heemskerk, Anja van Biezen

Norway Torstein Egeland, Erik Thorsby, Lorentz Brinch

South Africa Ernette DuToit, Peter Jacobs, Sister Lucille Wood

Sweden Mats Bengsson, Olle Olerup, Olle Ringden, Marie Schaffer

Switzerland Jean-Marie Tiercy, Alois Gratwohl, U. Schanz, R. Seger, Jacob Passweg

Taiwan Tsung Dao Lee

United Kingdom John Goldman, Alejandro Madrigal, Steven Marsh, Bronwen Shaw,

Ann-Margaret Little, Anne Dickinson, Kim Pearce, Arlene Frater,

Brendan Clarke, Gordon Cook, Cristina Navarrete, Carmen Ruiz de Elvira,

Marco Miretti

USA Mary Horowitz, Steve Spellman, Michelle Setterholm, Michael Haagenson,

Martin Maiers, Loren Gragert, John Klein, Waleska Perez, Seth Ketelsen,

Doug Rizzo, Mary Carrington, Katharine Hsu, Bo Dupont, Michelle Chiu,

David Senitzer, John Hansen, Eric Mickelson, Gary Olsem, Gary Schoch,

Lue Ping Zhao, Sue Li

Registries ABMDR, ANRC, CIBMTR/NMDP, EBMT, Europdonor, JMDP, SFGM, Typhon

NIH NCBI, NLM; Francesca Macchiarini, Mike FeoloSeattle Lab Mari Malkki, Mark Gatterman, Dawn MoranBiostatistics Ted Gooley, John Klein, Tao Wang