How to Find an Unrelated Donor Theory & Technology

How to Find an Unrelated Donor Theory & Technology Carlheinz R. Müller Zentrales Knochenmarkspender-Register für die Bundesrepublik Deutschland (ZKRD) Ulm, Germany

How to Find an Unrelated Donor HLA-Basics polymorphism, haplotypes, frequencies Selection Criteria and Supporting Evidence Donor Registries geography, quantity and quality Global Networks EMDIS, BMDW and the future Current Matching Technology The principles behind OptiMatch, HapLogic e.a.

The Most Important MHC Loci for HSCT HLA-A: 21 / 1243 variants (+863) HLA-B: 44 / 1773 variants (+1093) HLA-C: 9 / 884 variants (+681) HLA-DR(B1): 14 / 736 variants (+331) HLA-DQ(B1): 7 / 109 variants (+46) HLA-DP(B1): 6 / 129 variants (+18) serological antigens / ARS protein variants as of July 2011 (increase since March 2005) class II class III class I DP.. DQ.. DR.. B C A

Virtually Unlimited Possibilities alleles genotypes@locus haplotypes phenotypes A 1,243 773,146 1.2 10 3 7.7 10 5 B 1,773 1,572,651 1.1 10 6 6.1 10 11 C 884 391,170 4.9 10 8 1.2 10 17 DRB1 736 271,216 1.8 10 11 1.6 10 22 DQB1 109 5,995 9.8 10 12 4.8 10 25 ARS protein variants as of July 2011

Haplotype Frequency frequ uency 1e-08 1e-06 1e-04 1e-02 1e+01 1e+02 1e+03 1e+04 1e+05 number of haplotypes

Cumulative Haplotype Frequency cumulative frequency 0.0 0.2 0.4 0.6 0.8 1.0 1e+01 1e+02 1e+03 1e+04 1e+05 number of haplotypes

Quiz What would have happened if HLA-A,-B, -C,-DRB1 and -DQB1 were located on three separate chromosomes? What is the probability that two siblings have the same two haplotypes for five chromosomes?

General / HLA-C: DPB1 Basis of Evidence for Our Current Practice (Selection) Flomenberg: Impact of high-resolution matching... (Blood, 2004) Lee: High-resolution... HLA matching (Blood, 2007) Woolfrey: HLA-C antigen mismatch... (BBMT, 2011) Fürst: High-resolution HLA matching... (Blood, 2013) Shaw: HLA-DPB1 matching status... (Blood, 2006) Fleischhauer:... HLA-DPB1 T-cell epitope matching... (BMT, 2014) Pidala: Non-permissive HLA-DPB1 mismatch... (Blood, 2014) Low expression HLA loci: Fernández-Viña: Multiple mm at low expr. loci... (Blood, 2013)

Survival for Early Disease High Resolution HLA-matching in Hematopoietic Stem Cell Transplantation, Fürst e.a., Blood, 2013 100 90 80 10/10 n=674 9/10 n=319 8/10 n=89 p=0.019 p=0.002 70 Surviva al % 60 50 40 30 20 10 0 0 12 24 36 48 60 Months after Transplantation

Survival for Intermediate Disease High Resolution HLA-matching in Hematopoietic Stem Cell Transplantation, Fürst e.a., Blood, 2013 100 90 80 10/10 n=622 9/10 n=272 8/10 n=70 p=0.002 p=0.008 70 Surviva al % 60 50 40 30 20 10 0 0 12 24 36 48 60 Months after Transplantation

Survival for Advanced Disease High Resolution HLA-matching in Hematopoietic Stem Cell Transplantation, Fürst e.a., Blood, 2013 100 90 80 10/10 n=431 9/10 n=196 8/10 n=44 p=0.008 p=0.019 70 Surviva al % 60 50 40 30 20 10 0 0 12 24 36 48 60 Months after Transplantation

Hazard Ratio for Overall Survival High Resolution HLA-matching in Hematopoietic Stem Cell Transplantation, Fürst e.a., Blood, 2013 HLA-match n RR 95% CI p 10/10 1511 1.00 9/10 803 1.26 1.11-1.43 <0.001 8/10 247 1.86 1.51-2.29 <0.001 8/10 versus 9/10 247 1.47 1.19-1.82 <0.001

Hazard Ratio for Overall Survival High Resolution HLA-matching in Hematopoietic Stem Cell Transplantation, Fürst e.a., Blood, 2013 HLA-match n RR 95% CI p 10/10 1511 1.00 9/10 803 1.26 1.11-1.43 <0.001 2 MM HLA-class I+II 67 2.20 1.60-3.03 <0.001 2 MM only HLA-class I 162 1.68 1.27-2.22 <0.001 2 MM only HLA-class II 18 1.83 1.04-3.22 0.036

Impact of Single Locus Mismatch High-resolution donor-recipient HLA-matching contributes to the success of unrelated donor marrow transplantation, Lee e.a. Blood 2007

What is Quite Well Established Matching HLA-A,-B,-C and DRB1 is important! Mismatching is bad. More mismatching is worse. Younger donors are better. Don t wait too long for a better match. Most other criteria are new, not unequivocally validated and/or explicitly controversial.

Common Practices and New Trends Ignore mismatches outside the ARS (antigen recognition site = exon 2+3 for class I and exon 2 for class II loci) Matching or permissible mismatching for HLA-DPB1 Accept DQB1-MM on 8/8 donors. Prefer male donors (in the malignant setting) or gender-identical donors (non-malignant disease). Accept C-allele variants first if necessary. Prefer donors with identical CMV-status.

Age Distribution of German Donors At Collection Age Distribution of Donors at Collection 300 250 2014 Male 2014 Female 2013 Male 2013 Female 200 Num mber 150 100 50 0 20 25 30 35 40 45 50 55 60 Age

Limits of knowledge KIR? If so, which model to use and when? (haplotype, ligand, missing ligand,...) Weighing criteria is difficult. Which locus should I mismatch? Is some allele mm better than an antigen mismatch What is more important: age, gender, CMV status?

ZKRD Donor Numbers ZKRD Donor Numbers 6.000.000 5.000.000 Total HLA A,B,DRB1 typed HLA DRB1 high res. 4.000.000 Num mber 3.000.000 2.000.000 1.000.000 0 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 Year

Donor Numbers Worldwide Donor Numbers Worldwide (according to BMDW) 25.000.000 20.000.000 Num mber 15.000.000 10.000.000 5.000.000 0 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 Year Total HLA A,B,DRB1 typed

data provided by Joris&Foeken, WMDA Office, Leiden NL

data provided by Joris&Foeken, WMDA Office, Leiden NL

ZKRD Donor Numbers ZKRD Donor Numbers 3.500.000 3.000.000 HLA A,B,DRB1 high res. HLA A,B,C,DRB1 high res. HLA A,B,C,DRB1,DQB1 high res. 2.500.000 Num mber 2.000.000 1.500.000 1.000.000 500.000 0 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Year

BMDW FIRST EDITION (Bone Marrow Donors Worldwide) Initiated by Professor Jon van Rood in Leiden in 1988 Operated by the Dutch Registry Europdonor HLA phenotype data from 75 registries with 25m donors in 54 countries 50 cord blood banks and registries in 34 countries with 634k units continuously updated online at http://www.bmdw.org

The Global EMDIS B2B-Network (European Marrow Donor Information System) DC DC DC DC ZKRD DC DC DC DC DC Simple Principles DC DC DC DC DC DC IBMDR DC DC DC DC DC DC FGM DC DC DC in a Complex Reality

Mission of WMDA The World Marrow Donor Association (WMDA) is working towards the goal that high-quality and secure haematopoietic stem cell products are available for all patients worldwide while maintaining the health and welfare of the stem cell donors. https://wmda.info mail@wmda.info

World Marrow Donor Association (WMDA) Network of more than 70 registries and cord blood banks from over 40 countries active in the international field of HSCT Five Working Groups (Registries, Cord Blood, Quality and Regulation, Medical, IT) Six Board Committees (Standard, Accreditation, S(P)EAR, Finance, Nomination, Conference Programme) International Donor Registry Conference (last London 2014; next Singapore 2016)

Current Challenges in Donor Search Donor searches with few potential matches: Accepting mismatches early may be crucial. Prospects not be as bad as they look. Donor searches with many potential matches: It is difficult to pick the most promising donors out of a huge set. Chances may be still be pretty bad due to incomplete or low resolution typing, rare alleles or rare associations.

Do not create unnecessary concern

Current Challenges in Donor Search Donor searches with few potential matches: Accepting mismatches early may be crucial. Prospects not be as bad as they look. Donor searches with many potential matches: It is difficult to pick the most promising donors out of a huge set. Chances may be still be pretty bad due to incomplete or low resolution typing, rare alleles or rare associations.

Do not create false hope

4 2x10 5 2x10 11 5x10 17

You need good tools to become stronger!

Virtually Unlimited Possibilities alleles haplotypes A 1,243 1.2 10 3 B 1,773 1.1 10 6 C 884 4.9 10 8 DRB1 736 1.8 10 11 DQB1 109 9.8 10 12 but only 10 5 relevant haplotypes in the German population So just 1 in 10 8 is really present!

Donor #1 alleles/genotypes ARS-group probabilities 4-digits A*02:CVYA,24:ENHC 5*46=230 5*34=170 97% B*35:EKNR,14:BH 14*2=28 10*2=20 99% C*04:EKPB,08:EKPD 19*4=76 16*4=64 99% DRB1*01:02,14:01 1*1=1 1*1=1 100% DQB1*05:01,05:03 1*1=1 1*1=1 100% genotypen 489440 217600 97% diplotypes 1.1024e-06 02:01-35:01-04:01-14:01-05:03 24:02-14:02-08:02-01:02-05:01 5.7291e-07 02:01-14:02-08:02-01:02-05:01 24:02-35:01-04:01-14:01-05:03 4.4895e-08 02:17-35:01-04:01-14:01-05:03 24:02-14:02-08:02-01:02-05:01 ARS-genotypes 1.6753e-06 4.4895e-08 02:01P,24:02P-14:02P,35:01P-04:01P,08:02P-01:02P,14:01P-05:01P,05:03P 02:17P,24:02P-14:02P,35:01P-04:01P,08:02P-01:02P,14:01P-05:01P,05:03P

Donor #2 alleles/genotypes ARS-group probabilities 4-digits A2s,24s 194*99=19206 169*82=13858 87% B65s,35s 16*99=1584 16*91=1456 81% Cw4,8 48*29=1392 42*26=1092 99% DRB1*01:02,14:BCAD 2*1=2 1*1=1 100% DQB1*05:RV 4*(4+1)/2=10 4*(4+1)/2=10 99% genotypes 8*10^11 2*10^11 73% diplotypes 1.1024e-06 02:01-35:01-04:01-14:01-05:03 24:02-14:02-08:02-01:02-05:01 5.7291e-07 02:01-14:02-08:02-01:02-05:01 24:02-35:01-04:01-14:01-05:03 4.233e-07 02:01-14:02-08:02-01:02-05:01 24:02-35:01-04:01-14:54-05:03 2.2055e-07 02:01-35:01-04:01-14:54-05:03 24:02-14:02-08:02-01:02-05:01 2.0273e-07 02:01-14:02-08:02-01:02-05:01 24:02-35:03-04:01-14:01-05:03 1.1743e-07 02:01-14:02-08:02-01:02-05:01 24:02-35:08-04:01-14:01-05:03 6.8989e-08 02:05-14:02-08:02-01:02-05:01 24:02-35:01-04:01-14:01-05:03... total 106 ARS-genotypes 2.3192e-06 02:01P,24:02P-14:02P,35:01P-04:01P,08:02P-01:02P,14:01P-05:01P,05:03P... total 76

Donor #3 alleles/genotypes ARS-group probabilities 4-digits A2s,24s 194*99=19206 169*82=13858 90% B65s,35s 16*99=1584 16*91=1456 68% Cw 808*(808+1)/2=326836 757*(757+1)/2=286903 77% DRB1*01:02,14:JFA 1*6=6 1*6=6 99% DQB1 581*(581+1)/2=169071 218*(218+1)/2=23871 99% genotypes 9*10^18 5*10^17 56% diplotypes 1.1024e-06 02:01-35:01-04:01-14:01-05:03 24:02-14:02-08:02-01:02-05:01 5.7291e-07 02:01-14:02-08:02-01:02-05:01 24:02-35:01-04:01-14:01-05:03 2.3207e-07 02:01-14:02-08:02-01:02-05:01 24:02-35:03-12:03-14:01-05:03 2.0273e-07 02:01-14:02-08:02-01:02-05:01 24:02-35:03-04:01-14:01-05:03 1.5522e-07 02:01-35:03-12:03-14:01-05:03 24:02-14:02-08:02-01:02-05:01 1.1743e-07 02:01-14:02-08:02-01:02-05:01 24:02-35:08-04:01-14:01-05:03 6.8989e-08 02:05-14:02-08:02-01:02-05:01 24:02-35:01-04:01-14:01-05:03... total 332 ARS-genotypes 1.6753e-06 02:01P,24:02P-14:02P,35:01P-04:01P,08:02P-01:02P,14:01P-05:01P,05:03P... total 234

4 2x10 5 2x10 11 5x10 17

reduces the number of possibilities using HLAhaplotype frequencies 1 2 76 234

indicates the probability of allele identity

the shift from the haystack to the silver tray.

What You Need to Search for Unrelated Donors expertise in HLA and population diversity a lot of background knowledge adequate infrastructure link to / accreditation by a registry

Matching resolution and donor identification rates in Germany f patients with donors percentage of 50 60 70 80 90 100 0 10 20 30 40 HLA A, B, C, DRB1, DQB1 high res. matching 0 mismatch 1 mismatch 2 mismatches 1.000 10.000 100.000 1.000.000 10.000.000 100.000.000 number of donors in the registr y

With all enthusiasm for novel cell- and gene-based medicines: Take into account the donors rights and needs! They are not like that:

Thanks to all volunteer donors worldwide for their dedication all colleagues in our partner transplant centres, search units and donor centres for their excellent cooperation IMGT-HLA database: Prof. Steven G. E. Marsh and James Robinson http://hla.alleles.org/wmda/index.html WMDA: Lydia Foeken and Monique Jöris BMDW: Jon van Rood, Machteld Oudshoorn and others EMDIS too many names... and the -Team: Werner Bochtler, Hans-Peter Eberhard, Ulrike and Markus Beth, Heiko Hummler, Andrea Timm and Hans-Georg Rist

We hope to see you some time in Ulm

In theory there is no difference between theory and practice. In practice, however, there is. Yogi Berra