Abstract
The scarcity of malignant Hodgkin and Reed–Sternberg cells hampers tissue-based comprehensive genomic profiling of classic Hodgkin lymphoma (cHL). By contrast, liquid biopsies show promise for molecular profiling of cHL due to relatively high circulating tumour DNA (ctDNA) levels1,2,3,4. Here we show that the plasma representation of mutations exceeds the bulk tumour representation in most cases, making cHL particularly amenable to noninvasive profiling. Leveraging single-cell transcriptional profiles of cHL tumours, we demonstrate Hodgkin and Reed–Sternberg ctDNA shedding to be shaped by DNASE1L3, whose increased tumour microenvironment-derived expression drives high ctDNA concentrations. Using this insight, we comprehensively profile 366 patients, revealing two distinct cHL genomic subtypes with characteristic clinical and prognostic correlates, as well as distinct transcriptional and immunological profiles. Furthermore, we identify a novel class of truncating IL4R mutations that are dependent on IL-13 signalling and therapeutically targetable with IL-4Rα-blocking antibodies. Finally, using PhasED-seq5, we demonstrate the clinical value of pretreatment and on-treatment ctDNA levels for longitudinally refining cHL risk prediction and for detection of radiographically occult minimal residual disease. Collectively, these results support the utility of noninvasive strategies for genotyping and dynamic monitoring of cHL, as well as capturing molecularly distinct subtypes with diagnostic, prognostic and therapeutic potential.
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Data availability
The sequencing data are available in the database of Genotypes and Phenotypes dbGaP (study accession phs003435.v1.p1) in compliance with European General Data Protection Regulation (GDPR) and US Health Insurance Portability and Accountability Act (HIPAA) as applicable. In addition, an online tool to assign new samples to the genetic subtypes H1 and H2 is available at https://hodgkin.stanford.edu. Mutation calls can be downloaded from the same website.
Code availability
The code for LDA clustering and RePhyNER is also available at https://hodgkin.stanford.edu.
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Acknowledgements
This work was supported by the National Cancer Institute (R01CA257655 and R01CA233975 to A.A.A. and M.D., the Department of Defense (CA210872, award number W81XWH-22-1-0336) the Virginia and D.K. Ludwig Fund for Cancer Research (to A.A.A. and M.D.), Hanna and Michael Murphy family gift (to A.A.A.), the Stanford Cancer Institute (to A.A.A.), the Damon Runyon Cancer Research Foundation (DR-CI#71-14 to A.A.A.), the American Society of Hematology Scholar Award (to A.A.A.), V Foundation for Cancer Research Abeloff Scholar Award (to A.A.A.), the Emerson Collective Cancer Research Fund (to A.A.A.), the Stinehart/Reed Award (to A.A.A.), the CRK Faculty Scholar Fund (to M.D.), the Lung Cancer Research Foundation (to M.S.E.), K08CA241076 (to D.M.K.) and the SDW/DT, the Shanahan Family Foundations (to A.A.A. and D.M.K.), the Terry Fox Research Institute (grants 1061 and 1108 to C.S.) and the Canadian Institutes of Health Research (180613 to C.S.). A.A.A. is the Moghadam Family Professor of Medicine at Stanford University. S.K.A. is a scholar of the German Cancer Aid (Deutsche Krebshilfe, Dr. Mildred Scheel scholarship 57406718) and A.A.A. is a scholar of the Leukemia & Lymphoma Society. M.Y.L. is a PhD candidate at the University of British Columbia, Canada and is supported by an Elizabeth C. Watters Research Fellowship. T.F. and J.E.F. were supported by the American Syrian Lebanese Associated Charities and the US National Institutes of Health Cancer Support Core grant (R03CA21765). The Children Oncology Group (COG) was supported by the NCTN Operations Center grant U10CA180886, the NCTN Statistics & Data Center grant U10CA180899 and the NCTN HM-ITSC grant UG1CA233249. The content is solely the responsibility of the authors and does not necessarily represent the official views of the US National Institutes of Health.
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S.K.A., M.S.E., A.G. and M.Y.L. contributed equally. S.K.A., M.S.E., A.G., A.A.A. and M.D. developed the concept, designed the experiments, analysed the data and wrote the initial draft of the manuscript. M.Y.L., A.T., S.R. and C.S. designed and performed the in vitro experiments. T.F., J.E.F., D.L., K.F.P.-P. and S. Surman planned, performed and analysed the single-cell experiments. S.K.A., A.G., C.R., R. Adams, M.S.B., M.C.J., M.O, S. Su, X.K. and M.J.S. performed and analysed the sequencing studies and assembled data. S.K.A. and M.S.E. developed all the machine learning modules in this study with input from A.A.A. and M.D. S.K.A., M.S.E., J.G.S.-M. and J.A.M. conducted the experiments and analysed the sequencing data related to EPIC-seq. S.K.A., M.S.B., S. Su, M.O., X.K. and Y.N. performed the laser microdissection experiments, and tumour and RNA sequencing. S.K.A. and N.S. analysed the plasma TCR rearrangements. S.K.A., M.S.E., A.G., T.F., B.J.S., J.G.S.-M., D.A.K., A.S., J.B., K.R.K., C.L.L., F.T. and D.M.K. performed the bioinformatics analyses. C.R., J.E.F., V.S., T.T., L.B., R.H., Y.N., L.-M.F., S.M.C., R. Advani, D.R., R.L., H.G., O.C., L.J.M., M.P.L., M.A. and P.V. provided patient samples and assembled clinical data. All authors reviewed the manuscript.
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Competing interests
S.K.A. reports speaker honoraria from Takeda. M.S.E. reports consultancy for Foresight Diagnostics. J.E.F. reports research funding from Seattle Genetics. B.J.S. reports consultancy for Foresight Diagnostics. A.S. is currently employed by Foresight Diagnostics. Y.N. reports consultancy for Roche & Leica Biosystems and research funding from Kite Pharma. D.R. reports consultancy, honoraria, research funding and travel support from AstraZeneca, Janssen, AbbVie, Gilead, MSD, BMS and BeiGene. R.L. reports research funding from TG Therapeutics, Incyte, Bayer, Cyteir, Genentech, SeaGen and Rapt, as well as consultancy for Cancer Study Group, SeaGen, Foresight Diagnostics and AbbVie. D.M.K. reports consultancy for Roche, Adaptive Biotechnologies and Genentech, equity ownership interest in Foresight Diagnostics, and patent filings, including patent issued, licensed and with royalties paid from Foresight. M.A. reports consultancy for Takeda, BMS, Karyopharm, Gilead and Incyte, research funding from Roche, Johnson & Johnson and Takeda, as well as travel support from Roche, BMS, Celgene, Gilead, AbbVie, AstraZeneca and Takeda. C.S. has performed consultancy for Bayer, and has received research funding from Epizyme and Trillium Therapeutics. M.D. reports research funding from AstraZeneca, Genentech, Varian Medical Systems and Illumina, ownership interest in CiberMed and Foresight Diagnostics, consultancy from AstraZeneca, Boehringer Ingelheim, BMS, Genentech, Gritstone Oncology, Illumina, Regeneron and Roche, and multiple issued and pending patents including patents licensed to Foresight Diagnostics and Roche. A.A.A. reports consultancy for Celgene, Chugai, Genentech, Gilead, Janssen, Pharmacyclics and Roche, scientific advisory board membership in the Lymphoma Research Foundation, and Professional Affiliations with the American Society of Hematology, American Society of Clinical Oncology, American Society of Clinical Investigation, Leukemia & Lymphoma Society, Research Funding from the National Cancer Institute, the National Heart, Lung, and Blood Institute, the US National Institutes of Health, Celgene, BMS and Pfizer, patent filings including patent issued, licensed and with royalties paid from FortySeven, a patent pending and Licensed to Foresight, a patent pending relating to MARIA, a patent issued and licensed to CiberMed, a patent issued and a patent pending to CiberMed, patents issued to Idiotype Vaccines, and a patent issued, licensed and with royalties paid from Roche, and equity ownership interests in CiberMed Inc., ForeSight Diagnostics, FortySeven Inc. and CARGO Therapeutics. S.K.A., M.S.E., M.Y.L., B.J.S., D.M.K., C.S., M.D. and A.A.A. also report patent filings related to cancer biomarkers. The remaining authors declare no competing interests.
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Extended data figures and tables
Extended Data Fig. 1 Noninvasive profiling of cHL.
(A) Study overview. MRD: Minimal residual disease; PET2: Positron emission tomography after 2 cycles of chemotherapy; preTx: pretreatment; AF: allelic fraction; SNV: single nucleotide variants; LDA: Latent Dirichlet Allocation; SCNA: somatic copy number aberration; EPIC-Seq: epigenetic expression inference from cell-free DNA-sequencing. (B) Line plot summarizing sensitivity of the Gradient Boosting Machine (GBM) model to call exome-wide SNVs as a function of plasma VAF in 2 non-Hodgkin lymphoma validation samples as compared with a conventional workflow. Sensitivity was calculated considering mutations with VAFs ranging from x-1 to x + 1%. Tumour mutation calls were considered ground truth for sensitivity estimation. (C) Line plot summarizing positive predictive values (PPV) of the GBM model as a function of plasma VAF in 2 non-Hodgkin lymphoma validation samples as compared with a conventional workflow. PPV was calculated considering mutations with VAFs ranging from x-1 to x + 1%. The union of tumour and deep plasma exome mutation calls were considered ground truth for PPV estimation. (D) Bar plot summarizing MutSig2CV −log10 Q values of 41 genes found to be significantly mutated in targeted or whole exome sequencing (Q value < 0.05). The heat of the bars reflects mutation recurrence frequency. Genes that have not been recurrently described in the cHL literature (i.e. ≤ 1 study comprising at least 50 patients) are highlighted in bold. (E) Cophenetic coefficients (sample, feature and final) by number of clusters (k) when applied to the Latent Dirichlet Allocation (LDA) framework to identify genetic subtypes in 293 cHL cases.
Extended Data Fig. 2 Oncoprint visualizing targeted SNV and indel variant calls.
Genes with mutation frequencies ≥5% are visualized. If more than 1 variant per gene was identified in a patient, colour coding was derived using the following hierarchy: nonsense > start codon mutation > frameshift indel > nonstop > splice-site > missense > inframe indel. N = 293 pretreatment plasma samples were included in the analysis.
Extended Data Fig. 3 Oncoprint visualizing whole exome SNV and indel variant calls.
Genes with mutation frequencies ≥7.5% are visualized. If more than 1 variant per gene was identified in a patient, colour coding was derived using the following hierarchy: nonsense > start codon mutation > frameshift indel > nonstop > start gain > splice-site > missense > inframe indel. N = 119 cases profiled by plasma exome sequencing were included in the analysis.
Extended Data Fig. 4 GISTIC2 peaks identified in 61 plasma samples profiled by whole exome sequencing with AFs ≥ 5%.
Selected gene symbols of genes of interest falling within a peak are annotated with the cytobands. Top x-axis: G-score, bottom x-axis: Q value.
Extended Data Fig. 5 Mutual exclusivity/ co-association analysis using the DISCOVER R-package.
Heatmaps summarizing (A) unadjusted −log10 P values or (B) −log10 Q values for mutual exclusivity/ co-association, respectively. Alterations that tend to co-occur are visualized in blue, while those with a tendency for mutual exclusivity are depicted in red colours. Non-silent mutations and SCNA as summarized in the LDA clustering matrix observed in >20 patients among 293 cases were included in the analysis. Statistics were performed using the DISCOVER R-package.
Extended Data Fig. 6 Genetic subtypes are independent of EBV status and validate in external data.
(A) Overview figure summarizing genetic cHL subtype discovery and validation. LDA: Latent Dirichlet allocation; cfDNA: cell-free DNA; TCR: T-cell receptor. The figure in panel A was created using BioRender (biorender.com). (B-C) Boxplots and Wilcoxon P values (two-sided) summarizing the targeted SNV burden (B), and fraction of the genome affected by SCNAs (C) in cluster H1 (n = 200), H2 EBV− (n = 56) and H2 EBV+ (n = 37) in the plasma sequencing cohort. (D) Heatmap summarizing non-silent mutations and SCNAs (rows) of 61 patients with cHL (columns) as published in Maura et al.20 Clusters were assigned using the probabilistic model generated by LDA from the plasma discovery cohort as shown in Fig. 2. (E) Bar plot visualizing recurrence frequencies of features associated with subtype H1 (top) and H2 (bottom) as presented in panel D. Dark colours denote frequencies from plasma genotyping (H1: n = 200; H2: n = 93, as visualized in Fig. 2) while light colours reflect frequencies as described in Maura et al. (H1: n = 33; H2: n = 28). Spearman rhos and P values (algorithm AS 89) provided in the graphs describe the correlation of recurrence frequencies from all 30 features visualized in D between this study and Maura et al. within H1 and H2, respectively. (F) Boxplots summarizing the whole genome mutational burden in cluster H1 (n = 16), H2 EBV− (n = 5) and H2 EBV+ (n = 3) in patients with available whole genome sequencing and known EBV status from Maura et al. Wilcoxon P values (two-sided) are provided. (G) Loess regression describing the association of age and the probability of assignment to the H2 subtype in n = 292 patients from the plasma genotyping cohort (black line: mean; ribbon: standard deviation*1.96). Each dot represents a group of 10 patients from Maura et al. (n = 60 total) with x and y illustrating average age, and the fraction of H2 cases within the group, respectively. Patients were sorted by age prior to grouping. (H) Pie chart summarizing EBV status of patients from Maura et al. assigned to the H1 (n = 33) and H2 (n = 26) clusters. Two-sided Fisher’s exact test P value is provided. Panels B,C,F: each box represents the interquartile range (the range between the 25th and 75th percentile) with the median of the data, whiskers indicate the upper and lower value within 1.5 times the IQR.
Extended Data Fig. 7 Mutual exclusivity analysis in the validation cohort20.
Mutual exclusivity/co-association analysis using the DISCOVER R-package. The heatmap summarizes −log10 P values for mutual exclusivity/co-association, respectively. The mutual exclusivity of H1- and H2- defining features (black boxes) was compared to a null distribution constructed by random shuffling of the matrix while maintaining the number of cluster-defining features. To provide a single measure of mutual exclusivity, P values (one-sided, mutual exclusivity) were combined using Fisher’s method. The empirical P value provided on the top of the heatmap was defined by comparing the observed combined P value with the null distribution. Features from Extended Data Fig. 6d with greater than 4 occurrences were visualized. In addition, only one cytoband per chromosome was visualized selected by their rank in the feature list of H1/H2.
Extended Data Fig. 8 Truncating IL4R mutations enhance STAT6 signalling.
(A) Immunoblot showing protein levels of IL-4Rα and GAPDH in transduced DEV and KM-H2 cells. (B) Gene set enrichment plots from RNA-Sequencing showing enrichment of canonical KEGG pathways in mutant (E684Kfs2, n = 6) vs WT (n = 6) expressing DEV cells. Normalized enrichment scores (NES) and adjusted P values (fgsea R-package) are provided. (C) Scatter plot visualizing base mean expression and log2 fold change comparing gene expression in mutant (E684Kfs2, n = 6) vs WT (n = 6) expressing DEV cells. Absolute log2 fold changes >1 are highlighted in orange or black, respectively. (D) Phospho-STAT6 levels (flow) in transduced DEV (n = 5 each) and KM-H2 (n = 4 each) cells under unstimulated, IL-13-stimulated, IL-13-stimulated + IL-4R-Ab treated as well as IL-13-stimulated + STAT6-I. treated conditions. Unadjusted Wilcoxon P values (two-sided) compared to IL-13 stimulation alone are provided. (E) Representative phospho-STAT6 flow raw data for KM-H2 Empty, WT, Q666* and Q698* constructs under IL-13-stimulated conditions. (F) Phospho-STAT6 levels in WT, E684Kfs2 and I242N (PMBL hotspot) DEV cells under unstimulated, IL-4-stimulated, IL-4-stimulated + IL-4R-Ab treated as well as IL-4-stimulated + STAT6-I. treated conditions (n = 5 each). Unadjusted Wilcoxon P values (two-sided) compared to IL-4 stimulation alone are provided. (G) Representative immunoblot showing protein levels of pSTAT6, STAT6 and GAPDH in KM-H2 cells as a function of IL4R construct expression under unstimulated, IL-13-stimulated, IL-13-stimulated + IL-4R-Ab treated as well as IL-13-stimulated + STAT6-I. treated conditions. All conditions were run on the same gel. Ladders run between IL4R constructs were cropped and are not shown. (H) Immunoblot showing protein levels of pSTAT6, STAT6 and GAPDH in IL4R I242N expressing KM-H2 cells under unstimulated, IL-13-stimulated, IL-13-stimulated + IL-4R-Ab treated as well as IL-13-stimulated + STAT6-I. treated conditions. (I-J) CCL17 (TARC) concentrations in supernatant of transduced (I) DEV (n = 5 for unstimulated and IL-13; n = 4 for IL-4R-Ab and STAT6-I.) and (J) KM-H2 (n = 3 each) cells under unstimulated, IL-13-stimulated, IL-13-stimulated + IL-4R-Ab treated as well as IL-13-stimulated + STAT6-I. treated conditions. Unadjusted Wilcoxon P values (two-sided) are provided. (K) Boxplots and Wilcoxon P values (two-sided) comparing IL13 and IL4 expression in RNA-Sequencing of primary bulk tumour specimens visualized as normalized counts (n = 86 cHL, n = 66 LBCL32). (L) Log2 copy number ratio (L2CNR, boxplot and Wilcoxon P value, two-sided) of the 5q31.1 cytoband harboring IL13 stratified by IL4R mutation status in n = 119 patients with plasma exome sequencing (IL4R mutant: n = 12; IL4R WT: n = 107). Panels A,G-H: At least 2 independent experiments were performed for each condition. Panels D,F,I,J: mean +/− standard error (se). Panels K-L: each box represents the interquartile range (the range between the 25th and 75th percentile) with the median of the data, whiskers indicate the upper and lower value within 1.5 times the IQR.
Extended Data Fig. 9 Pretreatment ctDNA correlates with clinical risk factors and MRD is an independent prognostic factor in cHL.
(A-E) Previously untreated, adult cHL patients were considered for associations between pretreatment ctDNA levels and clinical variables (n = 309). Boxplots summarizing pretreatment ctDNA levels by (A) stage (n = 309, Kruskal-Wallis P value), (B) bulky disease (n = 184, Wilcoxon P value), (C) B-symptoms (n = 308, Wilcoxon P value), (D) EBV status (n = 309, Wilcoxon P value) and (E) histological subtype (n = 247, Wilcoxon P value). Patients with lymphocyte rich and lymphocyte depleted subtypes are not visualized due to small numbers (n = 9). All P values were derived from two-sided tests. (F-G) Waterfall plots showing log10 ctDNA changes from baseline at (F) C1D15 and (G) the >C(ycle)4/EoT (End of Treatment) timepoint. Bars are coloured by PFS event status. Top annotation visualizes PET2 readings according to the 5-point scale (5PS Deauville). (H) Kaplan-Meier curves and logrank P value showing PFS stratified by ctDNA detection at >C4/EoT. (I) Kaplan-Meier curves and logrank P values showing progression-free survival (PFS) stratified by ctDNA detection at C3D1 in PET2 negative and PET2 positive patients. Panels A-E: each box represents the interquartile range (the range between the 25th and 75th percentile) with the median of the data, whiskers indicate the upper and lower value within 1.5 times the IQR.
Extended Data Fig. 10 Model depicting the potential pathogenesis of H1 and H2 cHL subtypes.
GC: Germinal centre; SHM: Somatic hypermutation; SNV: Single nucleotide variant; SCNA: Somatic copy number aberrations. The figure was created using BioRender (biorender.com).
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Alig, S.K., Shahrokh Esfahani, M., Garofalo, A. et al. Distinct Hodgkin lymphoma subtypes defined by noninvasive genomic profiling. Nature 625, 778–787 (2024). https://doi.org/10.1038/s41586-023-06903-x
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DOI: https://doi.org/10.1038/s41586-023-06903-x
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