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Systematic identification of minor histocompatibility antigens predicts outcomes of allogeneic hematopoietic cell transplantation

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Systematic identification of minor histocompatibility antigens predicts outcomes of allogeneic hematopoietic cell transplantation

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Data availability

WES and RNA-seq data from the training HCT, DFCI-MRD and HP-MRD cohorts is available through the dbGaP portal (accession phs003394.v1.p1). The original mass spectra, PSMs and protein sequence databases used for searches have been deposited in the public proteomics repository MassIVE (https://massive.ucsd.edu) and are accessible at ftp://massive.ucsd.edu/v08/MSV000095025/. Original mass spectrometry data for the previously published B721.221 monoallelic immunopeptidomes are accessible at ftp://massive.ucsd.edu/MSV000080527. The GI GvHD scRNA-seq dataset is available from the corresponding author upon reasonable request. All external datasets used in this study (identified by reference number) are summarized in Supplementary Fig. 18, and their accession numbers are as follows: GSE164403 (ref. 29); GSE124395 (ref. 30); GSE115469 (ref. 31); EGAS00001002649(ref. 32); GSE123904 (ref. 33); GSE116222 (ref. 34); GSE125970 (ref. 35); GSE164241 (ref. 36); GSE164403 (ref. 37); GSE116256 (ref. 39); dbGAP study ID 30641 (ref. 40), accession ID phs001657.v1.p1; E-MTAB-8581 (ref. 62) accessed online through https://developmentcellatlas.ncl.ac.uk; www.proteinatlas.org/about/download (ref. 43); GSE109093 (ref. 44); GSE113046 (ref. 45). GTEx data were accessed from https://gtexportal.org/home/, IEDB from https://www.iedb.org/database_export_v3.php and 1000 Genomes project from https://www.internationalgenome.org/data. Additional databases/datasets used include Interferome (www.interferome.org), MutSigDB (https://www.gsea-msigdb.org/gsea/msigdb/). Source data are provided with this paper.

Code availability

The source code and documentation for the mHAg pipeline are available under https://github.com/nidhih2/mhags (https://doi.org/10.5281/zenodo.11658572 (ref. 97) for autosomal mHAg prediction and https://doi.org/10.5281/zenodo.11658599 (ref.98) for Y mHAg prediction).

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Acknowledgements

We are grateful for expert assistance from S. Pollock, H. Lyon and F. Dao for their help in sample collection and management at the Broad Institute; K. Rizza and the OTTR team (DFCI Department of Cellular Therapy) for assistance with clinical databases; A. Gusev for fruitful discussion on correlative analysis design; D. Hearsey and the members of the DFCI Ted and Eileen Pasquarello Tissue Bank in Hematologic Malignancies for provision of samples; the patients who generously consented for the research use of these samples and all members of the Wu Laboratory for productive discussions. This research was supported by grants from the National Institutes of Health (NIH/NCI-P01 CA229092 and NIH/NHLBI P01 HL158505 to C.J.W. and NIH R01 HL157174 to D.B.K.) and from the Leukemia & Lymphoma Society (SCOR-22937-22 to C.J.W. and R.J.S.). Statistical analysis was supported by the DF/HCC Cancer Center Support Grant 5P30 CA006516. Mass spectrometry-based immunopeptidomics data acquisition and analysis was supported in part by NIH P01CA206978 (to S.A.C.), NCI Clinical Proteomic Tumor Analysis Consortium program U24CA270823 and U01CA271402 (to S.A.C.), as well as a grant from the Dr. Miriam and Sheldon G. Adelson Medical Research Foundation (to S.A.C.). N.C. was supported by the 2020 American Association for Cancer Research-Incyte Immuno-oncology Research Fellowship (20-40-46-CIER) and the Helen Gurley Brown Foundation. H.J. was supported by the NCI CaNCURE (grant 5R25CA174650). L.P. is a scholar of the American Society of Hematology (ASH), is a participant in the BIH Charité Digital Clinician Scientist Program funded by the DFG, the Charité—Universitätsmedizin Berlin and the Berlin Institute of Health at Charité (BIH) and is supported by the Max-Eder program of the German Cancer Aid (Deutsche Krebshifle), by the Else Kröner-Fresenius-Stiftung (2023_EKEA.102) and the DKMS John Hansen Research Grant. D.A.B. acknowledges support from the Department of Defense Early Career Investigator grant (KCRP AKCI-ECI and W81XWH-20-1-0882), the Louis Goodman and Alfred Gilman Yale Scholar Fund and the Yale Cancer Center (supported by NIH/NCI research grant P30CA016359). G.O. was supported by the Claudia Adams Barr Program for Innovative Cancer Research and by DF/HCC Kidney Cancer SPORE P50 CA101942. S.L. is supported by the NCI Research Specialist Award (R50CA251956). L.S.K. is supported by the NIH under grants NIH/NIAID U19 Al1051731, NIH/NHLBI R01 HL095791, NIH/NHLBI P01 HL158504, NIH/NHLBI P01 HL158505 and NIH/NIAID U19 AI174967. Visual elements in Fig. 1 were created with BioRender.com.

Author information

Author notes

  1. Susan Klaeger

    Present address: Department of Proteomic and Genomic Technologies, Genentech Inc, South San Francisco, CA, USA

  2. David A. Brown

    Present address: Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA

Authors and Affiliations

  1. Department of Medical Oncology, Dana–Farber Cancer Institute, Boston, MA, USA

    Nicoletta Cieri, Nidhi Hookeri, Kari Stromhaug, Raphael Kfuri-Rubens, Yiren Shao, Kaila Powell, Helen Ji, Cleo Forman, David A. Braun, Livius Penter, Giacomo Oliveira, Shuqiang Li, Kenneth J. Livak, Derin B. Keskin, Vincent T. Ho, Jerome Ritz, Robert J. Soiffer & Catherine J. Wu

  2. Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA

    Nicoletta Cieri, Nidhi Hookeri, Kari Stromhaug, Liang Li, Raphael Kfuri-Rubens, Yiren Shao, Gabrielle M. Hernandez, Jennifer Abelin, Susan Klaeger, Karl R. Clauser, Siranush Sarkizova, David A. Braun, Livius Penter, Giacomo Oliveira, Steven A. Carr, Derin B. Keskin, Chip Stewart, Gad Getz & Catherine J. Wu

  3. Harvard Medical School, Boston, MA, USA

    Nicoletta Cieri, Kameron A. Kooshesh, David A. Braun, Livius Penter, William J. Lane, Giacomo Oliveira, Leslie S. Kean, Derin B. Keskin, Vincent T. Ho, Jerome Ritz, Robert J. Soiffer, Gad Getz & Catherine J. Wu

  4. Department of Data Science, Dana–Farber Cancer Institute, Boston, MA, USA

    Nidhi Hookeri, Julia Keating, Yiren Shao, Cleo Forman, Haesook T. Kim & Donna Neuberg

  5. Department of Immunology, Princesa Health Research Institute (IIS-IP), La Princesa University Hospital, Madrid, Spain

    Paula Díaz-Fernández & Cecilia Muñoz-Calleja

  6. Department of Hematology, Princesa Health Research Institute (IIS-IP), La Princesa University Hospital, Madrid, Spain

    Gomez-Garcia Valley of Soria

  7. Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA

    Jonathan Stevens & William J. Lane

  8. Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA

    David A. Braun, Vincent T. Ho, Jerome Ritz, Robert J. Soiffer & Catherine J. Wu

  9. Department of Hematology, Oncology, and Tumorimmunology, Campus Virchow Klinikum, Berlin, Charité—Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany

    Livius Penter

  10. Division Hematology/Oncology, Dana–Farber/Boston Children’s Cancer and Blood Disorders Center, Boston, MA, USA

    Leslie S. Kean

  11. Translational Immunogenomics Lab, Dana–Farber Cancer Institute, Boston, MA, USA

    Shuqiang Li, Kenneth J. Livak & Derin B. Keskin

  12. Department of Computer Science, Metropolitan College, Boston University, Boston, MA, USA

    Deep B. Sharp

  13. Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, Lyngby, Denmark

    Deep B. Sharp

  14. Department of Medicine, School of Medicine, Autonomous University of Madrid, Madrid, Spain

    Cecilia Munoz-Calleja

  15. Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA

    Gad Getz

  16. Department of Pathology, Massachusetts General Hospital, Boston, MA, USA

    Gad Getz

Contributions

N.C. and C.J.W. conceived the project and directed the overall study. N.C. designed and performed the experimental and data analysis together with H.J., K.P. and L.P. K.S. developed the computational pipeline under the supervision of N.C., C.J.W., C.S. and G.G. N.H. analyzed public single-cell datasets, docked the pipeline on Terra and applied it to the DFCI-MRD patient cohort. L.S.K. provided the allo-HCT GI single-cell libraries. Y.S. analyzed the GvHD single-cell dataset. R.K.-R. and Y.S. ran the pipeline on the HP-MRD cohort. S.L. and K.J.L. assisted with NGS preparation and analysis. N.C. curated the clinical annotation of the patient cohort with the help of K.A.K., H.T.K. and V.T.H J.S. and W.J.L. provided the DFCI-MRD cohort DNA samples. P.D.-F., V.G.-G.S. and C.M.-C. provided the samples and clinical annotation for the HP-MRD cohort. L.L. performed the 1000 Genomes simulation under the guidance of N.C., C.S. and G.G. J.K. and N.C. designed and performed the statistical analyses under the supervision of D.N. C.F. and S.S. curated AML cell line genomic and transcriptomic analysis. G.M.H., S.K., J.A., S.S., G.O., D.A.B., D.B.K., K.R.C. and S.A.C. generated and analyzed mass spectrometry results. G.O., R.J.S., J.R. and V.T.H. contributed to data discussion and interpretation. N.C. and C.J.W. wrote the manuscript. All authors discussed the results and read and approved the manuscript.

Corresponding author

Correspondence to Catherine J. Wu.

Ethics declarations

Competing interests

C.J.W. holds equity in BioNTech and receives research support from Pharmacyclics. D.B.K is a scientific advisor for Immunitrack and Breakbio and owns equity in Affimed N.V., Agenus, Armata Pharmaceuticals, Breakbio, BioMarin Pharmaceutical, Celldex Therapeutics, Editas Medicine, Gilead Sciences, Immunitybio, IMV, Lexicon Pharmaceuticals and Neoleukin Therapeutics. BeiGene supported unrelated SARS-COV-2 research at Translational Immunogenomics Lab. R.J.S. consults or is on the advisory board of Kiadis, Juno Therapeutics, Gilead, Jasper, Jazz Pharmaceuticals, Precision Biosciences, Rheo Therapeutics, Takeda and NMDP—Be the Match. J.R. receives research funding from Kite/Gilead, Novartis and Oncternal and consults or is on advisory boards for Clade Therapeutics, Garuda Therapeutics, LifeVault Bio, Smart Immune and TriArm Bio. V.T.H. receives funding from Jazz Pharmaceuticals and consults or is on advisory boards for Jazz Pharmaceuticals, Janssen, Alexion Pharmaceuticals and Omeros. W.J.L. consults or is on the advisory board of CareDx, One Lambda and Thermo Fisher Scientific and receives royalty payments from Thermo Fisher Scientific. K.J.L. holds equity in Standard BioTools and is on the scientific advisory board for MBQ Pharma. S.A.C. is a member of the scientific advisory boards of PTM BioLabs, Kymera, Seer and PrognomIQ and holds equity in the latter three. D.A.B. reports honoraria from LM Education/Exchange Services; advisory board fees from Exelixis and AVEO; personal fees from Schlesinger Associates, Cancer Expert Now, Adnovate Strategies, MDedge, CancerNetwork, Catenion, OncLive, Cello Health BioConsulting, PWW Consulting, Haymarket Medical Network, Aptitude Health, ASCO Post/Harborside, Targeted Oncology, AbbVie, DLA Piper and Elephas; equity in CurIOS Therapeutics, Elephas and Fortress Biotech (subsidiary); research support from Exelixis (US) and AstraZeneca (UK), outside of the submitted work. G.O. is a consultant for Bicycle Therapeutics. L.S.K. is on the scientific advisory board for Mammoth Biosciences and HiFiBio; received research funding from Magenta Therapeutics, Tessera Therapeutics, Novartis, EMD Serono, Gilead Pharmaceuticals and Regeneron Pharmaceuticals; consulting fees from Vertex; grants/personal fees from Bristol Myers Squibb and royalties/partial funding for the current study from Bristol Myers Squibb. L.S.K.’s conflict of interest with Bristol Myers Squibb is managed under an agreement with Harvard Medical School. D.N. holds equity in Madrigal Pharmaceutics. G.G. receives research funds from Pharmacyclics, Ultima Genomics and IBM. G.G. receives research funds from Pharmacyclics, Bayer, Genentech, Ultima Genomics and IBM; is an inventor of patent applications related to MSMuTect, MSMutSig, MSIDetect, POLYSOLVER, SignatureAnalyzer-GPU and MinimuMM-seq; is a founder and consultant and holds privately held equity in Scorpion Therapeutics and is a founder and holds privately held equity in PreDICTA Biosciences. The other authors declare no competing interests.

Peer review

Peer review information

Nature Biotechnology thanks Marcel van den Brink and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Pipeline details.

aDetailed workflow for the prediction of autosomal (left) and Y-encoded (right) mHAgs. bPipeline outputs for the training AML cohort composed of 11 D–R pairs (see Supplementary Table 3). Shown are the median number (and interquartile range) of hits for each step of the pipeline for matched-related donor (MRD, blue; n = 2) and unrelated donor (URD, orange; n = 9) transplants. The number of discordant variants between donors and recipients was, as expected, higher in URD than in MRD transplants. Pie charts (on the right)—distribution of the types of discordant variants in MRD (top) and URD (bottom) D–R pairs, that is, SNPs, single-nucleotide polymorphisms; DEL, deletions; INS, insertions. GvHD, graft-versus-host disease; GvL, graft-versus-leukemia.

Extended Data Fig. 2 Single-cell data analysis to define the GvHD filter gene set.

aSummary of the single-cell datasets used, related to the following organs target of GvHD: oral mucosa, lacrimal gland (eye), skin, liver, colon (GI) and lung. For each organ analyzed, the number of datasets and their accession numbers are shown, together with the total number of cells after standard single-cell data QC analysis, as well as after removal of immune cells. The ID of the second lung dataset has been abbreviated for ease of visualization, but the full identifier is reported in c legend. bUMAP plots showing clustering of the resident cell types for each organ. Note that for ‘eye’, the dataset includes both primary cells and organoids (derived from ductal cells), which were both analyzed. cViolin plots of lineage-defining markers for each cell type across the different organ datasets. dUMAP plots showing the relative contribution of individual datasets, for those organs with 2 available. Vasc., vascular; Lymph., lymphatic; KC, keratinocyte; LSEC, liver sinusoidal endothelial cells; CT, crypt top.

Extended Data Fig. 3 Threshold definition for single-cell-based expression atlas.

aTo minimize the drop-out effect common in single-cell RNA-seq data, gene expression was analyzed in a pseudo-bulk fashion for each cluster. To define the threshold for positive expression having the best signal-to-noise ratio, the expression levels of lineage-specific markers such as MALANA (melanin, expressed only in melanocytes), SFTB (surfactant B, expressed only in the lung) and ALB (albumin, expressed only in the liver) were analyzed, using as control a pan-expressed gene, B2M. The dot plot shows the expression levels for all single-cell clusters (with the tiles next to their names indicating the organ of origin: green for ‘liver’, light blue for ‘lung’, yellow for ‘skin’, orange for ‘GI’, red for ‘oral mucosa’ and navy blue for ‘eye’). bComparison of the expression profile of fibroblasts from 2 independent single-cell datasets (oral mucosa and skin). Results of the linear regression analysis (R squared and p value) are reported and show a substantial transcriptional identity between fibroblasts across different anatomical sites and datasets. cComparison of the expression profile of fibroblasts derived from single-cell sequencing data versus bulk sequencing available through the GTEx repository. Fibroblasts were chosen as they were the only purified cell type available in both GTEx and single-cell datasets. Results of the linear regression analysis are reported, showing a significant transcriptional similarity across single-cell versus bulk RNA sequencing. Vasc., vascular; Lymph., lymphatic; KC, keratinocyte; LSEC, liver sinusoidal endothelial cells; CT, crypt top; CPM, counts per million; TPM, transcript per million.

Source data

Extended Data Fig. 4 GI single-cell RNA-sequencing of allo-HCT patients.

aSchema depicting the patients included in the allo-HCT dataset and the analytic pipeline for the single-cell RNA-sequencing analysis. Briefly, single-cell RNA-sequencing data wre generated from the biopsies of 3 allo-HCT patients undergoing diagnostic colonoscopy for suspected GI GvHD at a median time from transplant of 90 days (range: 22–103). Upon standard processing and QC, viable cells were clustered and manually annotated (see Supplementary Fig. 2). Immune cell clusters were excluded, and remaining resident non-immune cells were merged and harmonized with the healthy subject GI dataset used for the generation of the GvHD filter. CPM, counts per million. bUMAP showing cluster annotations from the merged Seurat object containing both allo-HCT and healthy subject-derived GI cells (top) and violin plots depicting the lineage-defining markers used for cluster annotation (bottom). cUMAP depicting the clusters colored based on the dataset of origin. dVenn diagram showing the number of genes that were present in the allo-HCT GI dataset vs. the GI healthy subject dataset. eVenn diagram showing the overlap of the genes expressed in the allo-HCT GI dataset vs. the overall GvHD filter. fEnrichment analysis of interferon-related signatures (ref. 88; MSigDB IFNa and IFNg89) in the allo-HCT vs. GI healthy subject datasets (p < 0.0001, 2-tailed Mann–Whitney test).

Source data

Extended Data Fig. 5 GvL filter details.

aSchematic depicting the generation of the 2 components of the GvL filter, that is, AML and Hematopoietic filters. For the ‘AML filter’, a single-cell-based classifier39 was applied to bulk RNA-seq data from the Beat AML cohort40to fully capture the AML transcriptional heterogeneity. For the ‘heme filter’, bulk RNA-seq from 18 purified mature hemopoietic cell types43 as well as from hematopoietic stem and progenitor cells (HSPCs)44,45 were the starting source. From the list of expressed genes (TPM > 2), all those with expression in adult non-hematopoietic tissues per the GTEx RNA and protein repositories were excluded to define a set of 650 genes with preferential expression in AML and/or hematopoietic cells. bGender-specificity of the GvL filters: the GTEx filtering step was performed in a gender-specific fashion, as genes expressed in the male reproductive organs are not filtered out if the patient is female, and vice versa genes expressed in the female reproductive organs are maintained if the patient is male. cHistograms depicting the chromosomal location of the 650 genes comprising the ‘AML’ and ‘heme’ filters; below each bar, relative chromosome size is depicted. For the X chromosome, only the pseudo-autosomal regions have been included in the analysis. dSubcellular localization of the genes in the ‘AML’ and ‘heme’ filters. eBiological functions of the genes included in the filters. Biological functions (from GO and superpaths) have been manually clustered in macro-groups as specified in Supplementary Table 2.

Extended Data Fig. 6 Y-encoded mHAg filter.

aSchematic depicting the structure of the Y chromosome with a special focus on the genes in the male-specific region (MSY). Heatmap showing the expression pattern of the genes in the MSY across different healthy adult tissues: only the first 9 genes (RPS4Y1, DDX3Y, KDM5D, EIF1AY, ZFY, USP9Y, TMSB4Y, UTY and NLGN4Y) have evidence of expression (≥1 TPM) in ≥1 adult tissue site of GvHD. PAR, pseudo-autosomal region. bStacked histograms showing the number of predicted Y epitopes across individual HLA-A, HLA-B and HLA-C alleles and divided based on the MSY gene of origin. cBubble plot showing the median number of predicted epitopes for each MSY gene grouped based on the HLA peptide-binding motif from ref.28.

Extended Data Fig. 7 Antigenicity and immunogenicity of Y mHAgs.

aCorrelation matrix showing the peptide-binding motifs of the HLA-A, HLA-B and HLA-C alleles from ref. 28; lateral panels display the individual HLA alleles belonging to each peptide-binding motif, whose corresponding monoallelic B721.221 immunopeptidomes have been analyzed in Fig. 2b. bHydrophobicity scores of the 410 Y mHAg peptides tested for immunogenicity, grouped by individual HLA restrictions: HLA-A0201 had the highest number of predicted binders with a score>0 (boxplots show min to max and median values; Kruskal–Wallis test with Dunn’s multiple comparisons test). cHydrophobicity scores of the 410 Y mHAgs grouped by HLA groups. Whiskers indicate min and max values, with all individual values shown (Kruskall–Wallis test with Dunn’s multiple comparisons test). dHydrophobicity scores of the predicted binders for each HLA allele, grouped based on the experimental evidence of T cell immunogenicity (per Fig. 2f): only for HLA-A0101 and HLA-C0501 was hydrophobicity significantly associated with immunogenicity (assessed with 2-tailed unpaired t test). Whiskers indicate min and max values, with all individual values shown. eUMAP showing cluster annotations of single-cell thymic epithelial cells (TECs) from ref.62 (left), with feature plots of cluster-defining markers (middle); normalized expression of HLA-A, HLA-B and HLA-C genes per cluster (right).

Source data

Extended Data Fig. 8 Tracking of Y mHAg-specific T cells ex vivo.

aFlow cytometry plots showing the percentage of circulating CD8+ T cells specific for the indicated Y mHAgs at the listed time points, including donor before allo-HCT in a patient transplanted from his HLA-identical sister and experiencing severe chronic GvHD. An irrelevant epitope from the EBV EBNA3A protein (HLA-B0702-restricted) was used as control, as both patient and donor were EBV seropositive. bTimeline depicting the patient clinical course, highlighting the onset and course of the severe chronic GvHD, involving primarily skin and liver as shown by the liver function tests (ALT in red and total bilirubin in green). Triangles, peripheral blood samples used for Y mHAg-specific T cell tracking; diamonds, EBV reactivation. MMF, mycophenolate mofetil; tx: transplant. cQuantification of ZFY-C0501-specific T cells in the leukapheresis (LK) products of additional 7 female donors to male patients (F to M), compared with T cells stained with a control C0501-dextramer. Boxplots show min to max and median values; significance was assessed with 2-tailed Wilcoxon paired t test.

Source data

Extended Data Fig. 9 Autosomal mHAgs and GvHD.

aNormal distribution of the autosomal mHAg load in the DFCI-MRD cohort. bCumulative incidence of NIH moderate/severe chronic GvHD stratifying patients based on the overall autosomal mHAg load below (orange) or above (yellow) the median: no differences in 5-year cumulative incidences are observed: CIs are 42% (95% confidence interval: 33–52%) and 39% (95% confidence interval: 30–49%) for median, respectively, 2-sided p = 0.8 (Gray’s test). cDistribution of patients experiencing grade II–IV skin (left) and GI (right) acute GvHD across deciles of skin and GI mHAgs, respectively. dDistribution of patients experiencing NIH moderate/severe organ-specific chronic GvHD across deciles of mHAgs expressed in the indicated GvHD target organs: from left to right—skin, GI, liver, eye and oral. eHeatmap depicting the co-occurrence of the 7 SNPs associated with liver acute GvHD in: from left to right, patients with liver acute GvHD, patients experiencing acute GvHD without liver involvement and patients with chronic liver GvHD. fNumber of co-occurring driver liver mHAgs in the 3 patient groups outlined in e and defined with the same color code. Boxplots show min to max and median values (Kruskall–Wallis test with Dunn’s multiple comparison test). gPromoter analysis of the genes harboring the SNPs associated with liver acute GvHD: 4 of 7 genes have interferon-responsive elements in their promoter region. Transcription factor binding site locations within 1500 base pairs (bp) upstream of the transcription start site (position 0) and the 5′ UTR are indicated.

Source data

Extended Data Fig. 10 Population coverage simulating a T cell-based immunotherapy approach targeting GRFS mHAgs.

aHeatmap showing the donor–recipient pairs (DRPs) that are informative for the pool of 54 GRFS epitopes indicated in the columns. DRPs (rows) are grouped by population of origin of the simulated recipient, as shown in the inner right bar. The outer right bar shows the number of predicted epitopes per DRP. Gray histograms on the bottom indicate the number of informative DRPs for each epitope. bPopulation coverage analysis for: from top to bottom, overall simulation cohort, EUR, EAS, SAS, AFR and AMR. The histogram bars denote the percentage of DRPs that are informative for the indicated number of epitope hits, while the open circles indicate the cumulative percentage of population coverage for each number of epitope hits. The percentage indicated in the top-right corner of each graph shows the % of population for which ≥1 GRFS epitope could be potentially targeted. The red line denotes the 90% threshold of population coverage, which is considered optimal.

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Cieri, N., Hookeri, N., Stromhaug, K. et al. Systematic identification of minor histocompatibility antigens predicts outcomes of allogeneic hematopoietic cell transplantation. Nat Biotechnol (2024). https://doi.org/10.1038/s41587-024-02348-3

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  • DOI: https://doi.org/10.1038/s41587-024-02348-3

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