Volume 13, Issue 2, Pages 127-144 (February 2007)

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ABSTRACT

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New Directions in the Genomics of Allogeneic Hematopoietic Stem Cell Transplantation

Received 12 September 2006; accepted 10 October 2006.

Abstract

Despite optimal supportive care and high-resolution HLA matching, complications such as GVHD and infection remain major barriers to the success of allogeneic HCT (allo-HCT). This has led to growing interest in the non-HLA genetic determinants of complications after allo-HCT. Most studies have examined genetic predictors of GVHD, relapse, and mortality and have focused on 3 main areas: minor histocompatibility antigen (miHAs), inflammatory mediators of GVHD, and more recently NK cell-mediated allorecognition. The genetic basis of other outcomes such as infection and drug toxicity are less well studied but are being actively investigated. High-throughput methodologies such as single nucleotide polymorphism arrays are enabling the study of hundreds of thousands of genetic markers throughout the genome and the interrogation of novel genetic variants such as copy number variations. These data offer the opportunity to better predict those at risk of complications and to identify novel targets for therapeutic intervention. This review examines the current data regarding the non-HLA genomics of allo-HCT and appraises the promises and pitfalls for integration of this new genetic information into clinical transplantation practice.

Key words: Genomics, Polymorphism, Hematopoietic, Transplantation, Cytokine, Pharmacogenomics, Tumor necrosis factor, Interleukin, Mannose-binding lectin, Natural killer cell

Article Outline

• Abstract

• Spectrum of complications of allogeneic HCT

• Minor histocompatibility antigens and allo-HCT outcome

• The Multistep pathogenesis of GVHD

• Non-HLA immunogenetic associations with GVHD

• TNF genomics and allo-HCT outcome

• IL10 genomics and allo-HCT outcome

• Other non-HLA immunogenetic associations with GVHD

• Genetic determinants of other complications after allo-HCT

• Pharmacogenomic determinants of allo-HCT outcome

• NK cells and allo-HCT

• “Missing Self” and NK Alloreactivity

• “Missing Ligand” and NK Alloreactivity

• Incorporating non-HLA genomic data into clinical transplantation practice: Promise and pitfalls

• Confirmation of associations in large cohorts

• Correlation of genotype with phenotype

• Risk prediction

• Future directions

• Acknowledgment

• References

• Copyright

Spectrum of complications of allogeneic HCT

Allogeneic HCT (allo-HCT) is widely used to treat a diverse range of malignant and nonmalignant diseases. The growing indications for allo-HCT and the development of reduced intensity conditioning regimens has seen the number of allogeneic transplantations continue to increase, with >15 000 performed worldwide in 2002 [1]. However, transplant-related complications remain a major obstacle [2]. The 100-d mortality rate for standard-risk patients receiving a transplant from an HLA-identical sibling is 15%-20%. Mortality is higher for patients who are older, those with significant comorbidity, those undergoing transplantation for other indications, and those receiving unrelated donor transplants. Disease relapse is the leading cause of death (30%-34% of deaths after HLA-identical sibling transplantations) [1, 2], but transplant-related complications are also important. These include GVHD (15%-25%), major infection (10%-17%), and other treatment-related toxicities (35% of deaths) such as interstitial pneumonitis and hepatic veno-occlusive disease. Long-term causes of morbidity include cGVHD and infection. These complications remain significant in transplantations using reduced intensity conditioning regimens [3].

Prevention of these complications has traditionally relied on meticulous supportive care, prophylactic antimicrobials and immunosuppression, and accurate matching of donors and recipients for HLA alleles. HLA matching to minimize graft rejection and the leading cause of transplant-related morbidity, GVHD, remains a cornerstone of modern transplantation management [4]. GVHD arises from the recognition of recipient tissues as foreign by donor T cells. Three key requirements for the development of GVHD were described by Billingham in 1966 [5]. These are the presence of immunologically competent cells in the graft, the inability of the recipient to reject the transplanted cells, and the presence of antigens, HLA antigens, in the recipient that are lacking in the graft. However, despite optimal HLA matching using high-resolution molecular techniques, aGVHD remains a major problem [6]. More than 30% of patients with chronic myeloid leukemia receiving an HLA-matched unrelated donor transplant develop severe (grades III-IV) acute GVHD [7].

If HLA disparity is central to the development of GVHD, why doesn’t HLA matching prevent it? It is now appreciated that the initiation and propagation of GVHD is a multifactorial, multistep process that includes, but is not restricted to, classic HLA allorecognition [8]. GVHD is critically dependent on additional pathways including non-classic HLA allorecognition (such as by NK cells), allorecognition of miHAs, and an inflammatory milieu. The relative importance of each of these pathways is context dependent and is influenced by recipient characteristics, donor type, stem cell source, intensity of conditioning, and degree of HLA matching. Importantly, the mediators of these pathways are encoded by highly polymorphic immunoregulatory genes, and there are considerable data implicating such “non-HLA” immunogenetic polymorphisms in the risk and severity of GVHD. Further, the pathophysiology and genetic determinants influencing risk of other complications may be distinct from GVHD and less dependent on HLA.

Consequently, there has been intense interest in defining the best non-HLA genetic markers of allo-HCT outcomes and incorporating these into routine tissue typing strategies. However, the profusion of genetic markers poses formidable challenges to the development of robust predictive models. This review examines the evidence for non-HLA genetic risk factors in allo-HCT, highlights problems raised by existing studies, and discusses potential strategies to overcome these issues. In addition, newer high-throughput approaches to identify genetic risk factors are discussed.

The continuing centrality of HLA matching in allo-HCT cannot be overemphasized, and optimal HLA typing approaches continue to evolve. In particular, high-resolution HLA typing approaches may reduce the number of fully HLA-matched unrelated donors. Recent elegant studies have begun to address issues of permissible HLA mismatches and tradeoffs with other factors such as time to transplantation [9, 10]. A full discussion of these issues is beyond the scope of this review, and the reader is referred elsewhere [6, 11].

Minor histocompatibility antigens and allo-HCT outcome

The miHAs are of interest in allo-HSCT as risk factors for allo-HCT outcome and as potential targets for immunotherapy [12, 13, 14, 15]. The miHAs are short peptides, frequently derived from intracellular processing of proteins, that are presented on host HLA molecules and can stimulate alloreactive T cell immune responses between HLA-matched individuals. The tissue distribution of miHAs is variable, with some (eg, HA-1) expressed only by cells of hematopoietic origin, and others being ubiquitously expressed. Some miHAs are expressed by hematopoietic tumors, such as proteinase 3 in CML, and are thus potential targets of graft-versus-leukemia (GVL) responses. The genes encoding miHAs are frequently polymorphic, resulting in variation in peptide sequence that can influence intracellular processing or presentation of miHA peptide, and the stimulation of an alloreactive response if donors and recipients are disparate (mismatched) for miHA genotype.

miHA gene polymorphism may result in missense changes in the miHA peptide presented by HLA and in potential disparity in miHA genotype between donor and recipient. miHA peptides are usually only presented by a single HLA allele; eg, HA-1 is presented by HLA-A*0201. Hence, HA-1 mismatching is only relevant to the subset of transplants whose donors and recipients are HLA-A*0201 positive.

The most extensively studied miHA is HA-1, a nonapeptide encoded by the HMHA1 (HA-1 or KIAA0223) gene with 2 alleles defined by histidine (HA-1H) or arginine (HA-1R) at the third amino acid of the peptide. Because the HA-1H peptide binds with much greater affinity to HLA-*0201 than the HA-1R allele, transplants from HA-1R/R donors to HA-1H positive recipients can potentially cause GVHD, but not the reverse [16]. Numerous groups have examined HA-1 disparity as a risk factor for GVHD (Table 1). Goulmy et al [17] detected a significant association between HA-1 disparity and aGVHD in sibling myeloablative transplants, suggesting that HA-1 disparity could be used clinically as a predictor of GVHD. Subsequent studies have been conflicting, and the largest studies have concluded that the effect of HA-1 disparity is, at best, weak [18, 19, 20, 21, 22, 23, 24, 25]. Conflicting results have also been reported for polymorphisms in the PECAM1 gene (CD31) and allo-HCT outcome [20, 23, 24, 25, 26, 27, 28, 29, 30] (Table 1). Many of the studies are small and clinically heterogenous, and it is difficult to be conclusive about the role of miHA genetic polymorphism as a predictor of transplantation outcome. As discussed below, further studies will need to examine the role of miHA variants simultaneously with other immunogenetic and clinical variables.

Table 1.

Associations between miHAs and Allo-HSCT Outcome


Study	Donor, n	miHA Studied	Associated Genetic Variant	Associations
Goulmy et al [17]	Sibling, 148	HA-1, −2, −4, −5, HY	HA-1 mismatch ≥1 mismatch in HA-1,-2, −4, or −5	↑ aGVHD II-IV ↑ aGVHD II-IV
Behar et al [26]	Sibling, 46	CD31 codon 125	CD31 mismatch	↑ aGVHD
Nichols et al [27]	Sibling, 301	CD31 codon 125		No association with aGVHD
Da Costa et al [174]	Sibling, 96 URD, 4	CD31 codon 125		No association with aGVHD
Maruya et al [28]	Sibling, 118	CD2, CD31, CD42, CD49b, CD54, CD62L	CD31 codons CD62L+CD49b CD31	↑ aGVHD ↑ aGVHD
Balduini et al [20]	Paediatric REL 37, URD 70	HPA-1, −2, −3, −5, CD31 (codon 125)	codon 125 HPA-3	↑ aGVHD II-IV ↑ aGVHD
Juji et al [175]	URD, 715	HPA 2-6	HPA-5 mismatching	↓ Survival
Tseng et al [18]	Sibling, 237	HA-1	HA-1 disparity	Trend to ↑ aGVHD
Lin et al [19]	Sibling, 613	HA-1		No associations with aGVHD, cGVHD, survival
Grumet et al [179]	Sibling, 118	CD31 codons 125, 563, 670	Codon 563/670⁎ mismatch Codon 125 mismatch	↓ Survival ↑ aGVHD III-IV ↓ Survival ↑ aGVHD III-IV
Balduini et al [20]	Sibling, 92 URD 58	HA-1, H-Y CD31 codons 125 and 563	CD31 codon 563 D/R mismatch	↑ aGVHD II-IV
Gallardo et al [180]	Sibling, 215	HA-1	HA-1 mismatch	↑ aGVHD II-IV,
Tait et al [22]	Sibling, 129	HA-1		No association with aGVHD
Socie et al [74]	Sibling, 100	HA-1	HA-1 mismatch	↑ aGVHD, RR 2.8
Rocha et al [23]	Sibling, 107	CD31		No association with aGVHD
Kogler et al [24]	UCB, 115	HY, HA-1, CD31 codon 125		No associations with aGVHD II-IV
Akatsuka et al [176]	Sibling, 577	HA-8 (KIAA0020)	HA-8 D/R disparity	↑ aGVHD II-IV
Nesci et al [177]	Sibling, thalassemic, 94	HA-1		No association with aGVHD
Rozman et al [110]	Sibling, 223	HPA-1, −2, −3, −4, −5		No associations with GHVD or survival
Heinemann et al [25]	Sibling, 94 URD, 69	HA-1, CD31, CD49b		No independent associations
Nishida et al [178]	URD, 320	ACC-1	ACC-1 disparity	No associations with aGHVD, cGVHD, relapse, DFS
Cavanagh et al [29]	Sibling, 74	CD31	V/N/G haplotype (D) Codon 563 heterozygosity (D)	↑ aGVHD ↓OS
Perez-Garcia et al [21]	Sibling, 146	HA-8	HA-8 D/R disparity	↑ aGVHD III-IV, ↓DFS, ↓OS
Katagiri et al [30]	Sibling 60, URD46	HA-1, CD31 codons 125, 563, CD49b, CD62L	CD62L and CD31 incompatibility	↓ Relapse

HPA indicates human platelet alloantigen; OS, overall survival; REL, related (donor transplant); UCB, unrelated cord blood; URD, unrelated donor; D, donor; R, recipient; ↑, increased; ↓, decreased.

⁎

Identical associations seen for codons 563 and 670.

Interest in miHAs as potential therapeutic targets for graft-versus-tumor responses has been driven by the observations that miHA expression is often limited to hematopoietic tumor cells [31, 32, 33], and that expansion of miHA-specific CTLs accompanies attainment of full donor lymphoid chimerism and disease remission [13, 34, 35]. miHA-specific donor T cells have been shown to mediate GVH reactions ex vivo using a skin explant model [35]; conversely, murine experimental systems demonstrating the capability of miHA-specific CTLs to mediate GVL effects without GVHD have been described [36]. Strategies to expand miHA-specific T cells ex vivo to enhance antitumor responses without increasing GVHD are consequently being actively explored. These complex approaches are presently restricted to a small number of centers but are likely to become more widely used.

The Multistep pathogenesis of GVHD

It is now well established that the pathogenesis of aGVHD is critically dependent on a number of inflammatory pathways in addition to classic T cell/HLA allorecognition. Activation of these pathways occurs before infusion of donor T cells and may result from underlying disease and prior treatment, even before administration of conditioning regimens. Many inflammatory mediators of these pathways, including cytokines, chemokines, and adhesion molecules, are encoded by polymorphic genes, and there is now compelling evidence that these polymorphisms influence allo-HCT outcome. Before examining these data, it is important to review this model of GVHD pathogenesis.

The development of GVHD may be divided into 3 interrelated stages (Figure 1) [8]. In the first stage, the conditioning regimen, prior disease, and comorbidity result in liberation of proinflammatory cytokines such as TNF and IL-1 [37]. This proinflammatory milieu upregulates expression of HLA and costimulatory molecules on APCs, which facilitates an alloimmune response. Microbial mediators cross damaged gut mucosa, further amplifying this systemic inflammatory response [37, 38, 39]. The importance of this “cytokine storm” [40] is supported by extensive evidence from mouse models of GVHD [37, 41, 42, 43, 44] and clinical allo-HCT data showing that increases in circulating levels of cytokines such as TNF, IL-2, IL-6, IL-8, IL-12, and IFN-γ, and polymorphisms in cytokine genes predict the occurrence and severity of aGVHD [45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]. The role of pro- and anti-inflammatory cytokines in the development of GVHD is complex and is likely dependent on transplantation variables such as intensity of conditioning, stem cell source and dose, GVHD prophylaxis, and site of GVHD [64, 65]. In a recent study examining levels of 10 pro- and anti-inflammatory cytokines in 113 nonmyeloablative transplants, an increase in the level of IL-12, but no other cytokines, accompanied the development of aGVHD [66]. IL-12 drives the expansion of TH1 T lymphocytes, which was also observed in patients demonstrating GVHD. The striking association of GVHD with levels of IL-12, but none of the other inflammatory cytokines commonly associated with GVHD in myeloablative allo-HCT, suggests the pathogenesis and role of cytokine dysregulation differs between the 2 transplant settings. This has important implications for the design of non-HLA genomic studies, as discussed below.

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Figure 1. The 3-stage pathogenesis of aGVHD. In stage 1, conditioning causes damage and activation of host tissues. Bacterial LPS translocates from the intestinal lumen to the circulation, resulting in stimulation of inflammatory cytokine secretion and upregulated expression of MHC antigens and adhesion molecules on host tissues, which in turn enhance the recognition of MHC and miHAs by mature donor T cells. In stage 2, TH1 T cells proliferate in the presence of IL-12 and secrete IL-2 and IFN-γ. IL-2 and IFN-γ induce further T cell expansion and CTL and NK cell responses, and activate mononuclear phagocytes. CTL and NK effectors damage tissue by perforin/granzyme, FasL, and TNF. In phase 3, effector functions of activated mononuclear phagocytes are triggered by LPS and other stimulatory molecules that leak through the intestinal mucosa damaged during phases 1 and 2. This amplifies local tissue injury and further drives an inflammatory response. Gut damage amplifies LPS release and leads to the “cytokine storm” of aGVHD. Reproduced with permission from Hill and Ferrara [39].

The second stage of GVHD involves presentation of recipient antigen on MHC molecules by APCs to donor T cells, followed by activation, proliferation, and secretion of cytokines such as IL-2 and IFN-γ by these cells. These cytokines activate cytotoxic T cells and NK cells, prime macrophages to release TNF, resulting in further inflammation, and induce skin and gut GVHD pathology. In the final, effector phase of GVHD, soluble and cellular effectors mediate recipient tissue damage. These include cytokines such as TNF and IL-1 and cell-mediated cytotoxicity mediated by Fas and perforin. The relative importance of each of these pathways may be organ dependent. Murine models suggest Fas/Fas ligand-mediated apoptosis and cytotoxicity mediates GVHD in the liver [41, 67].

Non-HLA immunogenetic associations with GVHD

Numerous studies have examined the role of polymorphisms in immunoregulatory genes and transplantation outcome [23, 24, 41, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109] (Table 2). Most have examined related, myeloablative, HLA-matched transplants. Pro- and anti-inflammatory cytokine genes have been most commonly studied, including TNF, IL1, ILRN (encoding the IL-1 receptor antagonist), IL2, IL6, IL10, and IFNG. Acute GVHD and mortality measurements such as TRM, DFS, and overall survival are the most widely studied outcomes. There are much less data available regarding cGVHD and non-GVHD transplant-related morbidities such as infection, noninfectious pulmonary dysfunction, veno-occlusive disease, and mucositis. Several studies have examined unrelated donor transplants [24, 70, 76, 87, 89], and 1 study has examined CBT [24]. Similarly, there are few data specifically examining the non-HLA genomics of nonmyeloablative SCT.

Table 2.

Studies of Non-HLA Immunogenetic Variants and Outcome of Allo-HSCT‡


Study	Type of Transplant, n	Genetic Variants Examined	Genetic Variants Associated with Outcome	Association
Middleton et al [68]	Sibling, 49	TNF d VNTR,−308 IL10−1064 VNTR	TNF d3 homozygosity (R) IL10 −1064 ↑ allele length (R)	↑ aGVHD III-IV ↑ aGVHD III-IV
Cavet et al [69]	Sibling, 144	TNF d VNTR IL10−1064 VNTR,−1082	TNF d 3/3 homozygous (R) IL10−1064 ↑ allele length (R)	↑ Mortality ↑ aGVHD III-IV
Takahashi et al [70]	Sibling, 49; URD, 13	TNF−308 IL-10.G (CA)n VNTR	TNF−308A (D) IL-10.G ↑ allele length (D)	↑ aGVHD III-IV OR 29.4 ↑ cGVHD OR 4.5
Cavet et al [71]	Sibling, 80	IFNG intron 1 (CA)n VNTR IL6−174, IL6 3′ (AT) minisatellite TNF d VNTR, IL10#x2212;1064 VNTR,−1082, LTA NcoI-AspIH, CTLA4 VNTR, TGFB1−509	IFNG 3/3 (R) IL6 −174 G/G (R) TNF d3/3 (R and D) IL10 −1064 (R)	↑ aGVHD III-IV, OR 3.9 ↑ cGVHD, OR 4.25 ↑ aGVHD III-IV, OR 3.3 ↑ aGVHD, OR 4.6
Cullup et al [72]	Sibling, 99	IL1B −511, +3953, IL1RN VNTR	IL1RN2* (D)	↓ aGVHD III-IV
Lorenz et al [73]	Sibling, 237	TLR4 codons 299, 399	None
Socie et al [74]	Sibling, 100	TNF −308,−238, d VNTR, LTA +252, IL6−174, IL10−1082,−819,−592, IFNG +874	IL10 GCC homozygosity (R and D) IL6 (R)	↑ aGVHD RR = 3.5 (D), 7.9 (R) ↑ cGVHD RR 4.2
Tambur et al [75]	Pediatric sibling, 24	TNF −308, TGFB1 codons 10, 25, IL6 −174, IL10 −1082,−819,−592, IFNG +874	TGFB1	↑ aGVHD
Hattori et al [41]	Pediatric sibling, 67	IL4, IL4R, IL10, TGFB1, TGFB1RII, IFNG, IFNGR2, and IRF1	TGFB1 codon 10 Pro TGFB1RII 1167T	↑ aGVHD OR 3.0 ↑ aGVHD OR 4.1
Ishikawa et al [76]	URD, 462	TNF −1031,−863,−857 TNFRSF1B codon 196	TNF TCC haplotype TNFRSF1B 196R (D)	↑ aGVHD III-IV ↓ Relapse ↑ aGVHD III-IV
Kogler et al [24]	Unrelated cord blood, 115	TNF d VNTR, IL10−1064 VNTR	No associations
Middleton et al [77]	Sibling, 88	VDR intron 8 BsmI and ApaI RFLPs	VDR ApaI A (R) VDR ApaI A (D)	↑ aGVHD ↓ Survival
Mullighan et al [78]	Related, 96	MBL2−550(H/L),−221(X/Y), codons 52, 54, 57	MBL2 coding mutation (D) MBL2 HYA haplotype (R)	↑ Major infection ↓ Major infection
Nordlander et al [79]	URD, 111; sibling, 85	TNF−308, TNFd VNTR, IL-10−1064 VNTR, IL10−1082	TNFd⁎4 (sibling donors) IL10−1082 b1 homozygosity (R) (URD) IL10−1064⁎13 homozygosity (R)	↑ aGVHD II-IV ↑ aGVHD ↑ aGVHD II-IV
Rocha et al [23]	Sibling, 107	TNF−308, TNFB +252, IL1RA VNTR, IL6−174, IL10−1084, FCGR2A, FCGR3A, FCGR3B (HNA-1); MBL2 codons 52, 54, 57 MPO−463; ICAM1	FCGR2A 131R (R) MPO −463A (D) FCGR3B genotype (D) IL1RN⁎2 absence (D) IL10 GG (R), IL1RN (R)	Shorter time to first infection Severe bacterial infection, ↑ Day 180 mortality Faster neutrophil recovery, ↑ Day 180 mortality ↑ aGVHD II-IV ↑ cGVHD
Wang et al [80]	Not stated, 49	TNF−308,−238	TNF−308 (D)	↑ aGVHD III-IV ↑ Extensive cGVHD
Bogunia-Kubik et al [81]	Sibling, 70	TNF−308, LTA +249 (NcoI)	TNF−308 and LTA NcoI genotype	Non-GVHD III-IV organ toxicity
Cullup et al [82]	Sibling, 115	IL1A −889, IL1A VNTR, IL1B−511, +3953, IL1RN VNTR	IL1A−889⁎2 (D), IL1A VNTR⁎2 (D)	↑ cGVHD
Lin et al [83]	Sibling, 993	IL1B −511 and +3954, IL1RN −9261, IL6−174, IL10 −1082 and−592, TNF−308	IL10−592A/A (R)	↓ aGVHD III-IV, death in remission
MacMillan et al [108]	URD, 95	IL2−330	IL2−330G (R)	↑ aGVHD
MacMillan et al [84]	URD, 90	IL1A −889, IL1B −511, IL1RN VNTR	IL1A −889T (R and D) IL1B −511T (R and D)	↑ Survival, ↑TRM ↑ Survival
Middleton et al [85]	Sibling, 108	ESR1 intron 1PvuII and XbaI RFLP, promoter VNTR ESR2 intron 5 VNTR, +1082, +1730; TNF d, IL10, IL6, IFNG, IL1RN, VDR	ESR1 intron 1 PX haplotype (R)	↑ aGVHD ↓ survival
Stark 2003 [86]	Sibling, 104	TNFRSF1B codon 196; IL6−174, IL10−1082,−819,−592; IL1RN VNTR	TNFRSF1B 196R (R) TNFRSF1B 196R (D)	↑ aGVHD ↑ cGVHD
Cardoso et al [87]	URD, 157	IL18−137,−607,−656	IL18 GCG haplotype	↓TRM and ↑ survival
Holler et al [88]	Sibling, 78; related, 4; URD, 87	CARD15 SNPs 8, 12, 13	CARD15 mutations (D, or R and D together)	↑ aGVHD III-IV, gut aGVHD, TRM
Keen et al [89]	URD, 182	TNF−1031,−308,−238; TNFa, TNFd VNTRs; IL10 VNTR,−1082,−819,−592.	TNF a5, d4, TNF−1031C IL10 R2-GCC haplotype (D) IL10 R3-GCC haplotype (D)	↓ Survival ↓ Survival ↑ Survival No associations with aGVHD.
Mullighan et al [90]	Related, 160	TNF−308,−238, +488; IL1A−889; IL1B−511, +3953, IL1RN +11100, IL1R1 +970, IFNG +5297, TNFRSF6−1377 and −670; MBL2	TNF 488A (D/R) IL6−174 (D) IL10 ATA TNFRSF6−670G (R)	↑ aGVHD, early death, ↑ cGVHD ↑ aGVHD ↑ cGVHD ↑ aGVHD, ↑ major infection
Mlynarczewska et al [91]	Sibling, 71 adult, 31 pediatric	IFNG intron 1 VNTR, 874 SNP	IFNG intron 1 non 2/2 genotypes	↑ aGVHD II-IV
Pihusch et al [92]	Related, 46, URD, 43	F5 1691, F2 20210A, MTHFR 677, ITGB3 a1/a2, FGB 455, SERPINE1−675, ACE In/del	SERPINE1−675 4G/4G	↑ Catheter thrombosis, VOD
Shaw et al [93]	URD, 189	TNF −308,−238	TNF −308A	Delayed neutrophil engraftment
Srivastava et al [94]	Pediatric thalassemic, 116	GSTM1, GSTT1	GSTM1 null genotype	VOD
Bogunia-Kubik et al [95]	160 (49 pediatric, 85 NMSCT)	IFNG intron 1 VNTR	IFNG 3/3 (R)	↑ aGVHD and ↑ cGVHD
Bogunia-Kubik and Lange [96]	133 (52 pediatric, 70 haploidentical/URD)	HSPA1L 2763	HSPA1L 2763 AA (R)	↑ aGVHD
Bogunia-Kubik et al [97]	83 (15 pediatric, 34 haploidentical/URD)	IFNG intron 1 VNTR	IFNG 3/3 (R)	↑ aGVHD and EBV reactivation
Karabon et al [98]	93†	IL6−174; IL10 −1082,−819,−592	IL10 GCC haplotype (R) and IL6−174G (D)	↑ aGVHD
Kesh et al [130]	127†	TLR1, 2, 4, and 6 (multiple SNPs)	TLR1 R80T TLR1 N248S + TLR6 S249P	Invasive aspergillosis
Kim et al [99]	Sibling, 60	IL10 −1082,−819,−592	IL10 ATA haplotype	↑ cGVHD
Lin et al [100]	Sibling, 953	IL10RB 238, IL10,−592	IL10 −592A (R) and IL10RB c238G (D)	↓ aGVHD III-IV
Onizuka et al [101]	Sibling, 118	ACE intron 16 In/Del	ACE D/D	↑ NIPD
Seo et al [102]	Related, 90; URD, 15	IL10 −1082,−819,−592	IL10 non-ACC haplotype (R)	Invasive pulmonary aspergillosis
Laguila Visentainer et al [103]	Sibling, 118	TNF−308, IL6−174, IL10−1082,−819,−592, IFNG−874, TGFB1 +869, +915	IL6 −174 (R or D)	↑ cGVHD
Chien et al [104]	572†	BPI, TLR2-4,6, CD14, IL1B, IL1RN, IL6, IL6, LBP, TIRAP, IRAK4, TRAF6 and TNF (69 SNPs)	4 BPI haplotypes (D and R)	Airflow decline
Bettens et al [111]	URD, 131	IL10−1082, IL1B−511, IL4R−3223 and−1902 SNPs; TNFa,d, IL1RN, IL10 −1064 VNTRs	TNF d3/d3, d4, d5 (R) IL10 −1064 (12,14,15) alleles (R)	↓ Survival ↓ Survival
Granell et al [109]	T-depleted†	CARD15 SNPs 8, 12, 13	CARD15 variants (R)	↓ DFS
Holler et al [105]	Sibling, 225	CARD15 SNPs 8, 12, 13	CARD15 variants (D or R)	↑TRM, ↑aGVHD III-IV
Shamim et al [106]	Sibling, 120; URD, 75	IL7R 510, 1237, 2087, 3110	IL7R 1237G (D)	↓ Survival, ↑ TRM
Terakura et al [107]	URD, 373	GSTM1 and GSTT1 deletions	GSTM1 deletion	↓ Survival, ↑ TRM

↑ indicates increased; ↓, decreased; ACE, angiotensin I converting enzyme, BPI, bactericidal/permeability-increasing protein; D, donor; ESR1, estrogen receptor 1 (α); ESR2, oestrogen receptor 2 (β); FCGR2A, FCGR3A, FCGR3B, low affinity receptor for Fc fragment of IgG, types IIa, IIIa and IIIb; F2. factor 2 (prothrombin); F5, factor V; FGB, fibrinogen β; GSTM1, glutathione S-transferase M1; GSTT1, glutathione S-transferase θ1; HSPA1L, heat shock 70-kDa protein 1-like (HSP70-HOM); IL, interleukin, eg, IL1RN, IL-1 receptor antagonist; IRAK, IL-1 receptor-associated kinase 4; IRF, IFN regulatory factor; LBP, LPS binding protein; LTA, gene encoding lymphotoxin α (also known as TNFα); MBL2, mannose-binding lectin; MPO, encoding myeloperoxidase; NIPD, noninfectious pulmonary dysfunction; NMSCT, nonmyeloablative SCT; R, recipient; RFLP, restriction fragment length polymorphism; SERPINE1, plasminogen activator inhibitor 1; TGF, transforming growth factor; TIRAP, toll-interleukin 1 receptor (TIR) domain containing adaptor protein; TLR, Toll-like receptor; TNF, gene encoding tumour necrosis factor; TNFRSF1B, gene encoding TNF receptor 2; TNFRSF6, gene encoding Fas (Apo-1); TRAF6, TNF receptor-associated factor 6; TRM, VDR, gene encoding vitamin D receptor; VNTR, variable number tandem repeats (microsatellite); VOD, hepatic veno-occlusive disease

⁎

HLA 5-6/6 antigen match.

†

Donor type not stated.

‡

All studies listed examine HLA-matched myeloablative transplants unless otherwise stated. All polymorphisms are SNPs unless otherwise stated. Gene symbols are italicized and have been adapted where appropriate from the original publications according to HUGO Gene Nomenclature Committee guidelines (http://www.gene.ucl.ac.uk/nomenclature/).

TNF genomics and allo-HCT outcome

Polymorphisms in the genes encoding the cytokines TNF, IL-1, IL-6, IL-10, and IFN-γ have been most consistently associated with outcomes such as aGVHD and mortality. TNF alleles were the first non-HLA variants reported to be associated with GVHD in sibling myeloablative allo-HCT [68]. Studies of TNF genomics in allo-HCT illustrate several important issues in non-HLA genomic studies that warrant detailed discussion. The cytokine genes TNF and LTA (encoding lymphotoxin α) lie in the class III region of the MHC and are highly polymorphic (Figure 2). TNF gene contains multiple single nucleotide polymorphisms (SNPs) that have been studied in many disease settings, including SNPs in the promoter (eg, at nucleotides −1031, −863, −857, −376, −308, and −238 relative to the transcription start site), first intron (+488), and several microsatellite polymorphisms named TNFa, TNFb, TNFc, TNFd, and TNFe. An association between the TNFd3 allele and aGVHD was the first report of an association between cytokine genotype and allo-HCT outcome [68] and was confirmed in subsequent cohorts and in multivariable analyses [69, 110, 111]. This suggests that the TNFd3 association is biologically important and potentially clinically useful; however, it should be noted that the TNFd microsatellite lies approximately 10 kb distal to the TNF gene in intron 3 of the leukocyte-specific transcript gene, LST1. From these studies, it is thus unclear if the TNFd polymorphism directly influences TNF secretion, and whether it is the TNF variant most strongly linked with GVHD. Further, this association has not been detected in subsequent studies [74, 79, 89].

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Figure 2. Genomic polymorphism of TNF. TNF and the adjacent LTA gene lie on the short arm of chromosome 6 separated by 1 kb. HLA-DRB1 and TNF are separated by approximately 850 kb, and LTA and HLA-B by 250 kb. Locations of commonly studied TNF and flanking SNPs and microsatellites (variable number tandem repeats) are shown. TNFd and e microsatellites lie in the LST1 gene. Exons are represented by shaded boxes, and translated regions of the gene by black boxes. Arrows represent direction of transcription.

Other studies have examined TNF SNPs that lie in closer proximity to the TNF gene. The most widely studied TNF polymorphism is an SNP at position −308 in the TNF promoter, which has been associated with a diverse array of inflammatory, infectious, and malignant diseases [112]. Associations between TNF −308 genotype and GVHD and organ toxicity have been identified in small cohorts [80, 81]; however, most studies, including several examining large or multiple cohorts and incorporating multivariable analysis, have found no association with this polymorphism [23, 68, 74, 79, 83, 89, 90]. Several groups including our own have investigated other SNPs in the TNF gene. In a study of 2 cohorts of related myeloablative transplants (total n = 160), we detected a strong association between the intronic TNF 488A allele and aGVHD, higher grade GVHD, early mortality, and cGVHD [90]. Other investigators have reported associations between other TNF polymorphisms and allo-HCT outcome, including the −1031T/−863C/−857C haplotype and high-grade aGVHD [76], and the TNF a5, d4, and −1031C alleles with GVHD and inferior survival [89, 111].

The balance of evidence suggests that TNF variants are important risk factors for GVHD, but the question remains as to which variant is most strongly associated with GVHD, and would serve as the best clinical marker. To address this question, we performed extended genotyping of the TNF promoter and intronic SNPs (−1031, −863, −857, −308, −238, and +488) in the cohort described previously [90] and a prospective cohort of sibling, HLA-matched myeloablative and nonmyeloablative transplants. This identified 9 TNF SNP haplotypes and showed that the TNF 488A allele was in strong, but not absolute, linkage disequilibrium with the TNF promoter −857T allele. TNF −857T, which lies in closer proximity to the regulatory regions of TNF than does 488A, was more strongly associated with GVHD than was 488A, and these associations were confirmed in the second cohort (Mullighan and Bardy, unpublished data). Importantly, these associations were observed in myeloablative allo-HCT but not in nonmyeloablative SCT. These observations illustrate the importance of detailed genotyping of multiple polymorphisms in candidate genes and careful characterization of transplant variables such as conditioning intensity. Our study has also collected peritransplant sera to examine associations between cytokine genotype and blood levels and further clarify the functional effects of the associated variants.

One additional important issue is the location of TNF within the HLA region and the strong linkage disequilibrium across the region [93, 113]. Comprehensive studies examining TNF SNPs, microsatellites, and HLA genotype will be important and may demonstrate interaction among multiple TNF SNP and microsatellite alleles and allo-HCT outcome, as has been demonstrated in other settings [114].

IL10 genomics and allo-HCT outcome

IL10 polymorphisms have also been extensively studied and merit discussion because they have been examined in the largest studies of non-HLA allo-HCT genomics reported to date. The IL10 gene contains ≥2 promoter microsatellite polymorphisms and multiple SNPs. In one of the earliest studies, Middleton et al [68, 69] reported associations between increased allele length at the IL10 -1064 microsatellite and risk/severity of GVHD, a finding confirmed by other groups [79]. We and others have detected associations between SNPs or haplotypes of SNP alleles in the IL10 promoter and risk of aGVHD and/or cGVHD [23, 74, 83, 90, 98, 99]. IL-10 is a pleiotropic cytokine that is frequently anti-inflammatory and antagonizes the release or action of proinflammatory TNF. One might expect that IL10 genotypes associated with reduced in vitro IL-10 production, such as the −1082A/−819T/−592A haplotype [115], would be associated with increased risk of GVHD. This was found in several studies cited previously [90, 99]. However, in a study of 993 sibling HLA-matched transplants, Lin et al [83] studied 5 cytokine genes and detected an association between IL10 −592A homozygosity and protection from high-grade aGVHD. This appears counterintuitive, because this genotype has previously been associated with reduced IL-10 levels, and thus the reverse association with GVHD might be expected. The investigators suggested that this genotype is associated with increased IL-10 production in this context, but most evidence regarding IL10 genotype and IL-10 levels suggests the opposite is true [115, 116, 117, 118]. Despite the size and power of this study, these contradictory findings emphasize the need to replicate associations in independent cohorts from different centers and to carefully examine associations between genotype and functional readouts, such as blood levels or in vitro production, before incorporation into clinical tissue typing protocols. Lin et al [100] also recently reported an association between donor IL-10 receptor β (IL10RB) genotype and severe GVHD, although the functional effects of this polymorphism have not been investigated.

Other non-HLA immunogenetic associations with GVHD

Several other gene variants have been reproducibly associated with GVHD, including polymorphisms in the IL1 gene cluster [72, 82, 84], IL6 [71, 74, 90, 103], IFNG [71, 91, 95, 97], and the TNFR genes [76, 86] (Table 2). The reader is referred to other reviews for additional discussion of these associations [119, 120, 121]. Recent studies have also examined non-cytokine immunoregulatory gene polymorphisms including the estrogen and vitamin D receptor genes and the gene encoding the apoptosis molecule Fas [77, 85, 90]. Notable are reports of associations between polymorphisms in CARD15, which encodes NOD2, a molecule involved in the nuclear factor κB pathway, and aGVHD and mortality in two mixed cohorts of related and unrelated donor transplants [88, 105]. CARD15 is a mediator of innate immunity in the gut and is implicated in the pathogenesis of inflammatory bowel disease [122, 123]. Moreover, CARD15 variants were associated with gut GVHD, and the strength of association was influenced by the type of gut decontamination used. A recent report also identified associations between CARD15 genotype and DFS in HLA-matched, T cell-depleted allo-HCT [109]. This highlights the potential role of genetic variability in mediators of innate immunity in allo-HCT, as discussed below.

Genetic determinants of other complications after allo-HCT

Most studies of allo-HCT genomics have examined the risk of aGVHD and mortality. Fewer studies have investigated the genetic basis of other complications such as cGVHD and infection, two other leading causes of morbidity and mortality. This is partly due to the greater difficulty in accurately documenting, characterizing, and confirming these more complex and longer-term outcomes. However, existing data support a role for cytokine polymorphisms and cGVHD risk [23, 70, 71, 74, 82, 86, 90]. It is important to note that one of the strongest risk factors for cGVHD is prior aGVHD, and this potentially confounding factor is frequently not addressed. This will be important if genetic determinants unique to cGVHD pathogenesis are to be identified.

Our group has actively pursued the question of genetic predisposition to infection risk after allo-HCT. There is intense interest in the role of innate immune host defenses in clinical settings where adaptive immune responses (such as specific humoral responses and antigen-specific T cells) are compromised, such as after allo-HCT [124]. Mannose-binding lectin (MBL) is an innate host-defense molecule that recognizes a diverse array of pathogens independently of antibody and triggers activation of the complement cascade [125]. The gene encoding MBL, MBL2, contains several common polymorphisms that profoundly influence circulating levels of complement-fixing MBL. Up to 40% of individuals have low circulating MBL levels [126], and reduced MBL levels are associated with a range of infective diseases and complications when normal adaptive immunity is compromised [125]. There is intense interest in MBL replacement therapy to prevent or treat infection in MBL-compromised individuals.

In a retrospective study of 96 myeloablative transplantations [78], we detected associations between MBL2 genotype and risk of major infection after transplantation. MBL2 coding polymorphisms associated with low MBL levels were associated with a high risk of major infection, and that MBL2 promoter variants that are associated with the highest blood levels of MBL are protective [78]. Subsequent studies have detected associations between MBL2 genotype status and infection in autologous HCT [127, 128], but data in allo-HCT are conflicting [23, 129]. Further, MBL is an acute-phase reactant primarily synthesized by the liver, and the effect of hepatotoxic conditioning regimens on peritransplantation MBL levels is unclear.

To clarify the importance of MBL status in allo-HCT, we conducted a prospective study examining MBL2 genotype, blood MBL levels, and risk of major infection in myeloablative and nonmyeloablative allo-HCTs (Mullighan and Bardy, unpublished observations). This showed that pretransplantation MBL levels are higher in recipients than in their sibling donors, reflecting an acute-phase response induced by disease and prior treatment. Moreover, there was a significant increase in recipient MBL levels from baseline to day 14 after transplantation. This was most evident in recipients lacking MBL2 coding mutations and receiving myeloablative TBI. An association between major infection and MBL status, as assessed by MBL2 genotype or MBL levels, was detected but was critically dependent on transplant type. A significant interaction was observed between recipient MBL2 coding mutations and myeloablative TBI. For example, 70.6% of recipients with a coding MBL2 mutation receiving TBI developed major sepsis compared with 20% of those without mutations not receiving TBI. No association between MBL status and infection was detected in nonmyeloablative transplantations. These results suggest that MBL replacement therapy may be beneficial in MBL-deficient individuals receiving TBI conditioned transplants and further emphasize the importance of confirmatory studies in well-characterized cohorts.

Several other studies highlight the relevance of the genomics of innate immunity in allo-HCT. MPO (myeloperoxidase) and Ig receptor gene variants have been associated with infection [23], TLR (toll-like receptor) variants with invasive aspergillosis [130], and IFNG variants with EBV reactivation [97]. Recently, a screen of a number of innate immunity genes identified haplotypes in the bactericidal/permeability-increasing protein BPI as risk factors for pulmonary dysfunction and airflow decline after transplantation [104].

Pharmacogenomic determinants of allo-HCT outcome

Toxicities of cytoreductive and immunosuppressive agents such as methotrexate and busulfan are important causes of morbidities such as mucositis, delayed engraftment, and hepatic dysfunction, which in turn may influence risks of GVHD and infection. Pharmacogenomic approaches are increasingly used to predict risk of complications and guide individualized dosing [131, 132]. Several groups have used targeted busulfan dosing based on individualized pharmacokinetic measurements to reduce toxicity and optimize engraftment [133, 134, 135].

Pharmacogenomic approaches examine the relation among polymorphisms in drug-metabolizing enzymes, drug pharmacokinetics, tumor response, and treatment toxicity. For example, polymorphisms in the TPMT gene encoding thiopurine methyltransferase are associated with myelosuppression and response to treatment in patients receiving 6-mercaptopurine and azathioprine [136]. Polymorphisms in other drug-metabolizing enzymes such as cytochrome P450 enzymes, glutathione-S-transferases, and methylenetetrahydrofolate reductase (MTHFR) have been associated with drug response and toxicity and even tumor susceptibility in patients with cancer [131, 136].

Pharmacogenomic factors also influence allo-HCT outcome. Methotrexate is widely used as an immunosuppressive drug for GVHD prophylaxis in allo-HCT. MTHFR is a critical regulatory enzyme in folate metabolism, and methotrexate metabolites influence MTHFR activity. Two missense MTHFR SNPs, C677T and A1298C, influence MTHFR activity [137, 138]. Ulrich et al [139] examined a MTHFR 677 SNP in allo-HCT patients receiving methotrexate and found that the MTHFR 677TT genotype, associated with lower enzyme activity, was associated with increased mucositis and delayed platelet engraftment [139]. This group has confirmed these findings and also detected an association between combined MTHFR 677 and 1298 genotypes and risk relapse of CML after transplantation [140, 141, 142]. A smaller study found no associations between MTHFR genotype and mucositis or engraftment [143]. Two recent studies have detected associations between MTHFR 677T and thymidylate synthase (TS) genotypes and reduced risk of GVHD, possibly due to the greater sensitivity to methotrexate associated with these alleles [144, 145].

Veno-occlusive disease is a less frequent complication but commonly treatment refractory and fatal. Drugs used in pretransplantation conditioning such as busulfan contribute to the risk of veno-occlusive disease. Glutathione-S-transferases are involved in busulfan metabolism, and a recent study of pediatric patients undergoing allo-HCT for thalassemia observed associations among GSTM1 null genotype (which results in reduced GSTM1 activity), elevated levels of busulfan metabolites, and veno-occlusive disease. These results of MTHFR and GSTM1 genotype suggest that pharmacogenetic factors are important determinants of drug toxicity and clinical outcome in allo-HCT.

NK cells and allo-HCT

The potential for NK cell alloreactivity to mediate GVL effects without severe GVHD in HLA-mismatched transplants has stimulated intense interest in NK biology in allo-HCT [146, 147]. NK cells are large granular lymphocytes capable of causing lysis of infected or malignant cells in vitro. NK cells recognize self MHC class I molecules through activating and inhibitory KIRs. NK autoreactivity is normally prevented by inhibitory interactions between MHC class I molecules and inhibitory KIR. The KIR gene locus contains 15 genes on chromosome 19, and KIR diversity arises from the number of genes on each KIR haplotype, the combination of maternal and paternal haplotypes, and allelic polymorphism.

“Missing Self” and NK Alloreactivity

The interaction between KIR and HLA class I is a key determinant of NK alloreactivity and can, in part, be predicted by the HLA class I type of donor and recipient. The inhibitory KIR2DL1 receptor recognizes group 2 HLA-Cw alleles with asparagine at codon 77 and lysine at codon 80 of HLA-Cw, and KIR2DL2/3 recognizes group 1 alleles (serine 77, asparagine 80).

The importance of this “missing self” model of NK alloreactivity in allo-HCT was first appreciated in haploidentical transplants (haplo-HCT), in which donor and recipient are mismatched for 1 HLA haplotype. Such transplantations increase the number of available donors, but generally require intensive immunosuppression or T cell depletion to avoid graft rejection and severe GVHD. In a study of T-depleted unrelated haplo-HCT, NK alloreactivity in the GVH direction, as predicted by HLA-Cw typing, was associated with low rates of graft rejection, and disease relapse and mortality were observed in transplants for AML, but not for ALL [148]. This was accompanied by low rates of GVHD, attributed to NK-mediated depletion of recipient APCs [148]. The researchers confirmed these striking results in a larger cohort of patients with AML [149], suggesting that NK ligand matching could be used to select haploidentical or HLA-mismatched unrelated donors. Although some centers have adopted this practice, other studies have yielded conflicting data, with one study finding a survival advantage for KIR ligand incompatibility [149], but others showing no effect or trends toward increased GVHD and lower survival in those with GVH NK alloreactivity [150, 151, 152, 153, 154]. Notably, a recent multicenter retrospective study of 1571 HLA-matched and mismatched, predominantly T-replete unrelated donor transplants found no beneficial effect of KIR ligand incompatibility upon relapse or mortality. These discrepant results may be explained by the high cell dose, T depletion, and lack of post-transplantation immunosuppression used by Ruggeri et al [148], particularly as residual T cells in the graft may adversely affect T cell reconstitution [155].

“Missing Ligand” and NK Alloreactivity

The “missing self” model described above is insufficient to predict NK alloreactivity in all situations, such as in HLA-matched transplants. Because the KIR genes at 19q13 segregate independently from HLA at 6p21, individuals may express inhibitory KIR but no corresponding HLA ligand and, conversely, HLA ligands but no corresponding KIR. It is thought that most individuals possess a complete complement of inhibitory KIR [156, 157], but the frequency of class I ligand expression is highly variable. In a small study, this missing ligand model was a better predictor of outcome than was KIR ligand incompatibility in haplo-HCT [158], and a study of 178 HLA-matched sibling allo-HCT found that missing KIR ligand was common (63%) and associated with reduced relapse and improved survival [159]. A multicenter retrospective analysis of 1770 myeloablative T-replete HLA-matched and -mismatched allo-HCTs found that HLA homozygosity predicting missing KIR ligand was associated with reduced relapse in AML, ALL, and CML [160].

Further, these models do not consider activating KIR gene complement and polymorphism [147], which in combination with KIR ligand genotype has been reported to influence survival in sibling HLA-matched allo-HCT [161]. Two recent reports also suggested that donor-activating KIR genotype influences the risk of CMV reactivation after allo-HCT [162, 163].

How might prediction of NK alloreactivity be used clinically? Recent large retrospective studies have suggested that selection of HLA-mismatched donors based on predicting “missing self” alone is unrealistic, at least outside the high cell dose, T-deplete haplo-HCT setting described by Ruggeri et al [148]. These larger studies suggest that consideration of missing KIR ligand is important in HLA-matched and -mismatched allo-HCT [160, 164]. Modeling of NK alloreactivity may also require consideration of activating KIR genotype, although larger prospective studies genotyping KIR in addition to HLA are required.

Incorporating non-HLA genomic data into clinical transplantation practice: Promise and pitfalls

The studies discussed above describe numerous non-HLA immunogenetic variants that appear to influence allo-HCT outcome and may prove useful clinical markers to assist patient counseling, donor selection, tailoring of immunosuppressive therapy, and identification of novel therapeutic targets. Examples of these strategies are listed in Table 3, but important questions remain. Which associations may be deemed conclusive, and for genes consistently associated with outcome such as TNF or IL10, which markers are most appropriate? We suggest that few, if any, non-HLA markers are ready for routine clinical use without further studies that address the limitations of existing data. The chief of these is sample size and the need for confirmation. Many studies are small and clinically heterogeneous, examine only a small number of genes/polymorphisms, and are underpowered to perform the multivariable analyses necessary to determine primacy of association. There is also an urgent need to examine unrelated donor and nonmyeloablative transplants and transplants using alternative sources of stem cells such as umbilical cord blood. We suggest the following priorities for future studies.

Table 3.

Potential Clinical Integration of Non-HLA Genomic Data in Allo-HSCT


	Potential Application	Example
	Refine donor selection	Cytokine genotypes that predict severe GHVD (eg, TNF, IL6, IL10, IFNG, CARD15) KIR/KIR ligand genotyping
	Modify conditioning	Pharmacogenomic data, eg, busulfan dose according to GSTM1 genotype Avoid TBI in MBL-deficient recipients
	Modify immunosuppression	Modify methotrexate dose according to MTHFR (and folate pathway) genotype Intensify immunosuppression in those at highest risk of GVHD
	Novel therapies to prevent GVHD	Cytokine blockade in those with high-risk genotypes
	Novel therapies to prevent infection	MBL replacement therapy
	Adoptive immunotherapy to promote GVL	Engineered NK cells Ex vivo expanded anti-miHA CTLs

Confirmation of associations in large cohorts

It is important to comprehensively genotype genes repeatedly associated with outcome such as TNF, IL10, IL6, IFNG, and MBL in large cohorts. It is important that genotyping of all putatively functional polymorphisms in each gene and genotyping multiple genes are performed. Even the largest studies to date have genotyped relatively few genes and polymorphisms [83]. Such comprehensive genotyping is important to determine the strongest association within a gene, to examine interaction between variants, and to examine associations with functional readouts. Very few centers will have sufficiently large cohorts, and carefully curated repositories of DNA with comprehensive clinical will be invaluable [165]. It is likely that these studies can realistically only be performed in a collaborative fashion, and we and others are involved in such projects.

Correlation of genotype with phenotype

Determining the functional effect of associated polymorphisms remains an important challenge, as shown by the associations between IL10 genotype and GVHD. Although many studies have examined cytokine genotype or levels and allo-HCT outcome, few have assessed both. Even for widely studied genes such as TNF, data examining the functional effects of polymorphisms are limited and conflicting [43, 112, 166], and there are very few data regarding the variants reproducibly associated with GVHD, TNF 488A and d3 [167, 168]. These data are derived from nontransplantation settings, and it is possible that the biologic effects of each cytokine and polymorphism are context dependent. Our studies of MBL status and infection show that there remains a role for smaller studies that carefully correlate genetic and functional measurements with allo-HCT outcome. Prospective studies that collect comprehensive genotypic data and examine changes in encoded mediators in the peritransplantation period (such as blood cytokine levels or in vitro cytokine production) and clinical outcome will be important.

Risk prediction

The ultimate goal of these studies is to construct a clinically useful model of risk that incorporates clinical and genetic information to improve risk prediction and aid clinical decision making. It is conceptually attractive that a single genetic variant might be used to assist clinical decision making, but this is likely overly simplistic in practice, especially in the HLA-matched setting, in which additional genetic variables may further shrink the donor pool. A more feasible approach will be the construction of a risk “index” of clinical and genetic variables that not only distinguishes multiple potential donors but also highlights recipients at highest risk of complications for whom alternative therapies should be considered. This will entail large studies of multiple test and validation cohorts, and these collaborative efforts, such as those of the International HLA and Immunogenetics Workshop [169], are in progress.

Future directions

In addition to confirming and clarifying existing immunogenetic associations, systematic discovery of new associations will be important in the field of allo-HCT genomics. Completion of the Human Genome Project, the cataloging of >1 million SNPs by the HapMap project [170], and the advent of high-throughput, genome-wide, and targeted genotyping technologies are transforming the practice of genetic disease-association studies. Allo-HCT genomics is no exception. Most existing studies have used relatively laborious strategies to genotype small numbers of candidate genes and polymorphisms. Several technologies are currently available to genotype hundreds of thousands of SNPs [171] in a nonselective, genome-wide fashion or targeting known genes and pathways at high resolution. Studies examining associations of >500 000 SNPs with GVHD in a large Japanese allo-HCT cohort are in progress [172]. Further, these genome-wide approaches have identified a novel type of polymorphism, genomic copy number variation, at exceptionally high resolution, the significance of which is unclear in most clinical contexts, including allo-HCT [173].

Together these studies promise to provide detailed and comprehensive information regarding the genomic basis of allo-HCT outcome. Unifying this information and integrating with functional and clinical markers will pose a major challenge, but one that will be met with enthusiasm by the allo-HCT community.

Acknowledgments

We thank our colleagues in the Australian Red Cross Blood Service and Royal Adelaide, Royal Melbourne, Alfred and Westmead Hospitals for their contributions to the studies described in this review. The work was supported by the Anti-Cancer Foundation for South Australia, the Cooperative Research Centre for Vaccine Technology at the Australian Red Cross Blood Service, the Royal Adelaide Hospital and Institute of Medical and Veterinary Science, Adelaide, Australia, and the Royal College of Pathologists of Australasia Kanematsu Foundation. CGM is supported by the American Lebanese Syrian Associated Charities (ALSAC) of St Jude Children’s Research Hospital, and a National Health and Medical Research Council (Australia) CJ Martin Fellowship.

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1 Pathology, St Jude Children’s Research Hospital, Memphis, Tennessee

2 Division of Haematology, Institute of Medical and Veterinary Science, Adelaide, Australia

3 Department of Haematology and Oncology, The Queen Elizabeth Hospital, Adelaide, Australia

Correspondence and reprint requests: Charles Mullighan, MD, Pathology, MS 342, St Jude Children’s Research Hospital, 332 N Lauderdale, Memphis, TN 38105

PII: S1083-8791(06)00717-8

doi:10.1016/j.bbmt.2006.10.018

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