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BE THE MATCH
The Role of HLA in Finding a Match for Bone Marrow or Peripheral Blood
Stem Cell Transplantation
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Leukemia• Leukemia is a type of cancer of the
blood or bone marrow. It is characterized by an increase in white blood cells. There are four types:– Acute lymphocytic leukemia (ALL)– Acute myelogenous leukemia (AML)– Chronic lymphocytic leukemia (CLL)– Chronic myelogenous leukemia (CML)
• Leukemia is treated with chemotherapy, radiation therapy, or hematopoietic cell transplant.
• Incidence rates per 100,000– World: 5.0– More developed: 7.3– Less developed: 4.0– http://www.wcrf.org/cancer_statistics/d
eveloped_countries_cancer_statistics.php
Source of figure: http://en.wikipedia.org/wiki/Leukemia
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PART I
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The Story of Xilan Part I: Related Donor
• At age 13, Xilan was diagnosed with acute myeloid leukemia (AML), which was treated with chemotherapy. Xilan has been in remission for five years. She is now 18 years old, and of lately, she is feeling quite tired and running a fever. Her parent takes her to her doctor where a blood smear discovers a recurrence of the leukemia. Although she tolerated the first treatment well, her doctor recommends a hematopoietic cell transplant. She has 4 older siblings who immediately volunteer to be tested for being a donor.
Bone marrow aspirate showing acute myeloid leukemia. Several blasts have Auer rods.Source: http://en.wikipedia.org/wiki/Acute_myeloid_leukemia
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TASK 1
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HLA and Matching
• Go to the following website to learn about related donors– http://www.stanford.edu/
dept/HPS/transplant/html/hla.html
• HLAs (human leukocyte antigens) are proteins located on white blood cells and other tissues that are inherited as “sets” from parents
There is a 1 in 4 chance of being an identical match with a sibling.
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Gene Map of the HLA Region
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HLA Nomenclature
Source: http://hla.alleles.org/nomenclature/nomenclature_2009.html
Serological antigen
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Types of Matching• Sibling
– Full match– haploidentical (haplo) match– No match
• Parent– haploidentical (haplo) match
• Unrelated donor– HLA full (8/8 HLA‐ A,‐B,‐C,‐DR) match– Partial match
• Umbilical cord blood (UCB) – HLA 6/6 (‐A,‐ B, ‐DR matches) – Partial match
Source: http://www.stanford.edu/dept/HPS/transplant/html/hla.html
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TASK 2
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Related Donor
• Below is Xilan’s family tree. Draw genotypes of the four siblings of Xilan so that one is a full match, one is a haplo match but not a full match, and two are no matches.
Figure modified from: http://www.stanford.edu/dept/HPS/transplant/html/hla.html
Xilan
1,8,10
3,14,17
2,7,11
10,16,8
10,16,8
3,14,17
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TASK 3
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Related Donor
• Investigate through simulations how likely it is that at least one of Xilan’s siblings is a full or haplomatch.
Figure modified from: http://www.stanford.edu/dept/HPS/transplant/html/hla.html
Xilan
1,8,10
3,14,17
2,7,11
10,16,8
10,16,8
3,14,17
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Excel Simulation
1,8,10
3,14,17
2,7,11
10,16,8
1,8,10
10,16,8 10,16,8
3,14,17 3,14,17
2,7,11 2,7,11
1,8,103,14,17
10,16,8
Type 0 1Parent 1 1 8 10 3 14 17Parent 2 2 7 11 10 16 8
Sibling 1 0 1 1 8 10 10 16 8Sibling 2 1 1 3 14 17 10 16 8Sibling 3 1 0 3 14 17 2 7 11Sibling 4 0 0 1 8 10 2 7 11
Full Match
Haplo Match
Sibling 1 0 1Sibling 2 1 1Sibling 3 0 1Sibling 4 0 0Total 1 3
Xilan is of type 1‐1
Xilan
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A B C D E F G12 Type 0 13 Parent 1 1 8 10 3 14 174 Parent 2 2 7 11 10 16 856 Sibling 1 0 1 1 8 10 10 16 87 Sibling 2 1 1 3 14 17 10 16 88 Sibling 3 1 0 3 14 17 2 7 119 Sibling 4 0 0 1 8 10 2 7 11
Xilan is of type 1‐1
Given
=IF(RAND()<0.5,0,1) =IF(C9=0,$C$3,$D$3)
=IF(D9=0,$C$4,$D$4)
Code I
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Full and Haplo Match
Full 0 1
0 No No
1 No Yes
Haplo 0 1
0 No Yes
1 Yes Yes
Full match (both are “1”) if both haplotypes match: Logical AND
Haplo match (at least one “1”) if at least one haplotypes matches: Logical OR
Xilan is of type 1‐1
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Code IIaA B C D E F G
12 Type 0 13 Parent 1 1 8 10 3 14 174 Parent 2 2 7 11 10 16 856 Sibling 1 0 1 1 8 10 10 16 87 Sibling 2 1 1 3 14 17 10 16 88 Sibling 3 1 0 3 14 17 2 7 119 Sibling 4 0 0 1 8 10 2 7 1110
11Full Match
Haplo Match
Full Match
Haplo Match
12 Sibling 1 FALSE TRUE 0 113 Sibling 2 TRUE TRUE 1 114 Sibling 3 FALSE TRUE 0 115 Sibling 4 FALSE FALSE 0 016 Total 1 3
Xilan is of type 1‐1
=AND(C9,D9) =OR(C9,D9)
=IF(D12,1,0)
=SUM(G12:G15)
Excel cannot count the number of “TRUE” or “FALSE.” A “TRUE” or “FALSE” needs to be converted first.
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Code IIbA B C D E F G
12 Type 0 13 Parent 1 1 8 10 3 14 174 Parent 2 2 7 11 10 16 856 Sibling 1 0 1 1 8 10 10 16 87 Sibling 2 1 1 3 14 17 10 16 88 Sibling 3 1 0 3 14 17 2 7 119 Sibling 4 0 0 1 8 10 2 7 1110
11Full Match
Haplo Match
12 Sibling 1 0 113 Sibling 2 1 114 Sibling 3 0 115 Sibling 4 0 016 Total 1 3
Xilan is of type 1‐1
=C6*D6
=1‐(1‐C6)*(1‐D6)
=SUM(C12:C15)
If at least one entry is a “1”, the product is 0, and hence 1‐product is equal to 1.
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FURTHER EXPLORATION
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Expand to larger families…
Type 0 1Parent 1 1 8 10 3 14 17Parent 2 2 7 11 10 16 8
Match 1‐1Sibling 1 1 0 3 14 17 2 7 11 0 0Sibling 2 0 1 1 8 10 10 16 8 0 0Sibling 3 1 1 3 14 17 10 16 8 1 1Sibling 4 1 1 3 14 17 10 16 8 1 2Sibling 5 0 1 1 8 10 10 16 8 0 2Sibling 6 1 0 3 14 17 2 7 11 0 2Sibling 7 1 1 3 14 17 10 16 8 1 3Sibling 8 1 1 3 14 17 10 16 8 1 4Sibling 9 1 1 3 14 17 10 16 8 1 5Sibling 10 0 1 1 8 10 10 16 8 0 5Sibling 11 1 0 3 14 17 2 7 11 0 5Sibling 12 1 1 3 14 17 10 16 8 1 6Sibling 13 1 1 3 14 17 10 16 8 1 7Sibling 14 0 1 1 8 10 10 16 8 0 7Sibling 15 1 1 3 14 17 10 16 8 1 8Sibling 16 1 0 3 14 17 2 7 11 0 8Sibling 17 1 0 3 14 17 2 7 11 0 8Sibling 18 1 0 3 14 17 2 7 11 0 8Sibling 19 1 1 3 14 17 10 16 8 1 9
New baby is of type 1‐1
Cumulative number of matching siblings
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Binomial and Geometric Distribution
• Write a macro to simulate 500 families• Explore through simulations
– Number of siblings needed for first match– Number of matches for fixed number of siblings
• Theory– See worksheet
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PART II
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The Story of Xilan Part II: Unrelated Donor
• It turns out that none of Xilan’s four siblings is a suitable donor. Her friends from college learn about Xilan’sdisease and volunteer to help.
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• NMDP—History – http://www.youtube.com/watch?v=uXwUzEkrWf0
• Transplant sources– Umbilical cord blood– Bone marrow– Peripheral blood stem cells– For more detail
• http://www.cancer.gov/cancertopics/factsheet/Therapy/bone‐marrow‐transplant
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Ballen, K. K., King, R. J., Chitphakdithai, P., Bolan Jr, C. D., Agura, E., Hartzman, R. J., & Kernan, N. A. (2008). The national marrow donor program 20 years of unrelated donor hematopoietic cell transplantation. Biology of blood and marrow transplantation: journal of the American Society for Blood and Marrow Transplantation, 14(9 Suppl), 2.
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Pediatric and adult transplants facilitated by NMDP
Ballen, K. K., King, R. J., Chitphakdithai, P., Bolan Jr, C. D., Agura, E., Hartzman, R. J., & Kernan, N. A. (2008). The national marrow donor program 20 years of unrelated donor hematopoietic cell transplantation. Biology of blood and marrow transplantation: journal of the American Society for Blood and Marrow Transplantation, 14(9 Suppl), 2.
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Survival of patients with early and intermediate disease depending on degree of HLA matching (8/8, 7/8, and 6/8) for HLA-A, -B, -C, and -DRB1.
Lee S J et al. Blood 2007;110:4576-4583©2007 by American Society of Hematology
Eligible diagnoses included acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic myeloid leukemia (CML), and myelodysplasticsyndrome (MDS).
Modified slide
HLA Matching of Unrelated Donors:Survival of Recipients
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Student Engagement• NMDP—Community engagement
– http://www.youtube.com/watch?v=3L8p_rhiPuw• Doctors choose registry members between 18 & 44 more than 90% of the time.– College drives to sign up potential donors
Kollman, C., Howe, C. W., Anasetti, C., Antin, J. H., Davies, S. M., Filipovich, A. H., ... & Confer, D. L. (2001). Donor characteristics as risk factors in recipients after transplantation of bone marrow from unrelated donors: the effect of donor age. Blood, 98(7), 2043‐2051.
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TASK 1
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Finding a donor in your county I
• Census Data– Go to http://www.census.gov/2010census/
– Select a state– Click on “Areas within”
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Finding a donor in your county II
• Click on “Search”
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Finding a donor in your county III
• Select a county• Click “Display”
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Finding a donor in your county IV
• Display data from county
• Population by Race
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Finding a donor in your county V• Population by Ethnicity
– Hispanic or Latino– Non Hispanic or Latino
• Population by Race– White– African American– Asian– American Indian and Alaska
Native– Native Hawaiian and Pacific
Islander– Other– Identified by two or more
• NMDP Classification• EUR
– White but not Hispanic/Latino• AFA
– African American• API
– Asian, Native Hawaiian and Pacific Islander
• HIS – Hispanic
• Find the population size according to NMDP in your county of choice
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Example: Ramsey County, MN
• NMDP Classification• EUR
– White but not Hispanic/Latino• AFA
– African American• API
– Asian, Native Hawaiian and Pacific Islander• HIS
– Hispanic
White 356,547 African American 56,170 Asian 59,301 American Indian and Alaska Native 4,043 Native Hawaiian and Pacific Islander 247 Other 14,776 Identified by two or more 17,556
Hispanic or Latino 36,483 Non Hispanic or Latino 472,157
Ramsey County, Minnesota EUR 320,064
AFA 56,170
API 59,548
HIS 36,483
Other 36,375
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TASK 2
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Tab: Unrelated Donor
HLA‐A serotype frequencies
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TASK 3
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Example: Ramsey County, MN
• Type A1 (%) from NMDP data• Calculate the number of people in each ethnic/racial group in
Ramsey county who are of type A1• Add up the number of people with this type• The likelihood of finding a match within these four groups is
the relative frequency
EUR AFA API HIS Total320,064 56,170 59,548 36,483 472,265
Type A1 (%) 17.21% 5.41% 5.08% 7.00%Type A1 55,070 3,038 3,026 2,555 63,689
63,689Likelihood of match 0.1349 13.5%472,265
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TASK 4
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How many?
• The worksheet explains how to find the expected number of people needed for a match and the number of people required to find at least one match with a specified probability.
• Go to the worksheet and read the instructions under Task 4.
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PART III
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The Story of Xilan Part II: Genetic Distance
• Xilan’s family comes from the Tujia people, an ethnic minority in China. Which ethnic/racial group in the NMDP data base is closest based on the HLA‐A locus?
Tujia is location #3
Source: Zhang, L., Cheng, D., Tao, N., Zhao, M., Zhang, F., Yuan, Y., & Qiu, X. (2012). Distribution of HLA‐A,‐B and‐DRB1 Genes and Haplotypes in the TujiaPopulation Living in the Wufeng Region of Hubei Province, China. PloS one,7(6), e38774.
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Genetic Distance between Populations
• Population 1 gene frequencies– [p1, p2, …, pn]
• Population 2 gene frequencies– [q1, q2, …, qn]
• Distance between the two populations
Cavalli‐Sforza, L. L., & Edwards, A. W. (1967). Phylogenetic analysis. Models and estimation procedures. American journal of human genetics, 19(3 Pt 1), 233.
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Where does this come from?• Population 1 gene frequencies: [p1, p2]• Population 2 gene frequencies: [q1, q2]• Square root transformation
1 2
1 2 1 2
1 1 2 2
1 1 2 2
cos cos ︵ ︶
cos cos sin sin
p q p qp q p q
1
2
1 2,p p
1 2,q q
1 2
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TASK 1
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Genetic Distances I
• Calculate the genetic distances between the Tujia population and the ethnic/racial groups based on HLA‐A
A EUR_freq AFA_freq API_freq HIS_freq Tujia01 17.21% 5.41% 5.08% 7.00% 2.02%02 30.81% 18.78% 23.66% 26.96% 35.48%03 14.64% 8.22% 3.13% 8.08% 1.21%11 5.69% 1.58% 19.48% 4.67% 28.23%23 1.68% 10.80% 0.23% 3.72% 0.00%24 8.81% 2.37% 21.48% 13.10% 15.73%25 1.93% 0.50% 0.06% 0.88% 0.00%26 2.99% 1.52% 4.77% 3.04% 2.82%29 3.50% 3.74% 1.58% 4.52% 0.00%30 2.33% 13.33% 2.46% 5.32% 2.42%31 2.36% 1.06% 3.28% 4.97% 4.84%32 3.13% 1.41% 1.30% 2.74% 0.00%33 1.15% 6.57% 9.54% 3.26% 6.86%34 0.04% 3.41% 1.75% 0.35% 0.00%36 0.01% 2.43% 0.00% 0.28% 0.00%43 0.00% 0.02% 0.00% 0.00% 0.00%66 0.26% 2.50% 0.03% 0.65% 0.00%68 3.36% 10.23% 1.92% 8.91% 0.40%69 0.08% 0.04% 0.14% 0.55% 0.00%74 0.03% 5.37% 0.11% 0.75% 0.00%80 0.00% 0.71% 0.00% 0.25% 0.00%TOTAL 99.99% 100.00% 100.00% 100.00% 100.01%
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Cosine
• The genetic distance αis calculated as cos α
• To find α, we use the Excel function ACOS
• Examples• “=COS(π/4)” results in
0.707107• “=ACOS(0.707107)”
results in 0.785398, which is approximately π/4 or 45⁰
=COS(PI()/4)
=ACOS(0.707107)
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Genetic Distances II
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FURTHER EXPLORATION
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Pairwise Distances
• Use the HLA‐A frequencies to calculate all pairwise genetic distances
• Use the distances to construct a tree that reflects the genetic distances
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Further Exploration• Table has frequencies for
all HLA‐A serotypes by ethnic/racial group
• Use this table and the demographic data for your county to calculate the likelihood of a match for each type
• See http://en.wikipedia.org/wiki/HLA‐A for more information on HLA‐A
A EUR_freq AFA_freq API_freq HIS_freq1 0.17206 0.05408 0.05082 0.070032 0.30806 0.18782 0.23659 0.269583 0.14639 0.08215 0.03133 0.0808311 0.05686 0.01581 0.1948 0.0466823 0.01684 0.10795 0.00226 0.0371524 0.08812 0.02372 0.21484 0.1310225 0.01931 0.00499 0.00056 0.0087926 0.02992 0.01519 0.04771 0.0303729 0.03495 0.03744 0.01581 0.0451830 0.02332 0.13332 0.02456 0.0532131 0.02357 0.01061 0.03275 0.049732 0.03133 0.01414 0.01299 0.0273633 0.01149 0.06572 0.09543 0.0326334 0.00044 0.03412 0.0175 0.0035136 0.00006 0.02434 0 0.0027643 0 0.00021 0 066 0.00261 0.02496 0.00028 0.0065268 0.0336 0.10233 0.01919 0.0891169 0.00076 0.00042 0.00141 0.0055274 0.00025 0.05365 0.00113 0.0075380 0 0.00707 0 0.00251
TOTAL 0.99994 1.00004 0.99996 0.99999