Distinct evolutionary paths in chronic lymphocytic leukemia during resistance to the graft-versus-leukemia effect

Abstract

Leukemic relapse remains a major barrier to successful allogeneic hematopoietic stem cell transplantation (allo-HSCT) for aggressive hematologic malignancies. The basis for relapse of advanced lymphoid malignancies remains incompletely understood and may involve escape from the graft-versus-leukemia (GvL) effect. We hypothesized that for patients with chronic lymphocytic leukemia (CLL) treated with allo-HSCT, leukemic cell-intrinsic features influence transplant outcomes by directing the evolutionary trajectories of CLL cells. Integrated genetic, transcriptomic, and epigenetic analyses of CLL cells from 10 patients revealed that the clinical kinetics of post-HSCT relapse are shaped by distinct molecular dynamics. Early relapses after allo-HSCT exhibited notable genetic stability; single CLL cell transcriptional analysis demonstrated a cellular heterogeneity that was static over time. In contrast, CLL cells relapsing late after allo-HSCT displayed notable genetic evolution and evidence of neoantigen depletion, consistent with marked single-cell transcriptional shifts that were unique to each patient. We observed a greater rate of epigenetic change for late relapses not seen in early relapses or relapses after chemotherapy alone, suggesting that the selection pressures of the GvL bottleneck are unlike those imposed by chemotherapy. No selective advantage for human leukocyte antigen (HLA) loss was observed, even when present in pretransplant subpopulations. Gain of stem cell modules was a common signature associated with leukemia relapse regardless of posttransplant relapse kinetics. These data elucidate the biological pathways that underlie GvL resistance and posttransplant relapse.

Fig. 1.. Timing of relapse after allo-HSCT is defined by distinct evolutionary trajectories and stem cell expression programs.

(A) Time to clinical progression after allo-HSCT for 10 patients with CLL indicated along with relevant clinical histories and times of sample procurement. (B) Evolutionary patterns of mutation clusters shown by their cancer cell fractions (CCFs), inferred using PhylogicNDT. Time to progression is shown by the purple color bar. Putative CLL drivers detected for each cluster are shown. (C) Box plot of time to relapse after allo-HSCT in patients with (n=6) and without (n=3) clonal evolution. The P value was calculated by a two-sided Wilcoxon ranked sum test. “Evolution” was defined as having any cluster with absolute difference ≥0.2 between pre-HSCT and relapse timepoints. (D) Unadjusted P values of enriched stem cell gene sets in pretreatment allo-HSCT samples per GSEA, comparing samples collected from early versus late relapses. (E) Unadjusted P values of enriched signaling pathways per GSEA, comparing post- versus pre-HSCT samples of late relapses.

Fig. 2.. Phenotypic changes in relation to the kinetics of genetic evolution.

(A) Sample schematic and experimental workflow for capturing single CLL cell transcriptomes through the inDrops system from paired, pre- and post-HSCT PBMCs from two early and two late relapses. (B) Joint graph visualized using largeVis embedding showing clustering and annotation of cell subsets from PBMCs from all four patients. (C) Joint graphs using UMAP embedding of computationally identified CD19⁺CD5⁺ CLL cells, colored by cluster (left), patient (center) and relapse kinetics (right). (D) Individual joint graphs for both early and late relapse patients colored by timing. (E) kNN-based quantification of timepoint intermixing across clusters for each patient. The P value was calculated from a two-sided Welch t-test comparing means of individual cell values per patient, grouped into early (n=2) versus late (n=2) relapses. (F) Extent of gene expression change between pre- and post-transplant CLL cells for each patient. The P value was calculated from a one-sided Welch t-test.

Fig. 3.. Transcriptional programs define inter- and intra-leukemic heterogeneity during late relapse.

(A) Cells (columns) from patients 5328 and 5341 are organized by cluster assignment. For each cell, relapse kinetics, timing and principal component (PC)/aspect score are displayed. For each aspect, overdispersion score is shown by white/black color bar. Row labels summarize key functional annotations of gene sets for each aspect. For each aspect, gene expression patterns of top-loading genes are shown. (B) Joint graphs of CLL cells are visualized to demonstrate differential downregulation of tumor suppressor genes (TP53 and BACH2) and differential upregulation of oncogenic signalling (PIM2, MCL1) after allo-HSCT among late relapse clusters 4, 5 and 6.

Fig. 4.. scRNA-seq analysis of late relapse clusters highlights distinctive features of post-transplant CLL cells.

(A) CLL joint graph colored by individual cell score for the aspect defined as antigen presentation. (B) CLL joint graph displaying only cells from the indicated late relapse patient and their associated expression of HLA class I or II genes. (C) Left, probability densities of the range of HLA class I or II gene expression values. P <10⁻¹⁴ for all four comparisons, by two-sample Kolmogorov-Smirnov test. Right, stacked barplots indicating the lack of expansion of HLA class I or II ‘low’ expressing cells after allo-HSCT. * = P <0.01 for contraction determined from Fisher’s exact test. (D) Bulk gene set expression values for HLA class I (left) and II (right) genes from purified normal B cells, untreated CLL, and paired pre- and post-HSCT CLL cells. P value determined from Student’s t test (paired t test for pre- vs post-HSCT). (E, F) Joint graph of CLL cells from patient 5341 colored by RPS15 mutation status (E) or gene expression (F). Dotted lines indicated approximate cluster boundaries. (G) Box plots showing RPS15 gene expression by cluster for cells from patient 5341. Cluster 4 vs 5 (dotted box), P value calculated from two-sided Wilcoxon rank sum test. (H) Joint graph of all CLL cells colored by the aspect defined as ribosomal biogenesis.

Fig. 5.. Methylome instability characterizes late relapse after allo-HSCT.

(A) Boxplots of the change in locally disordered methylation as measured by the percentage of discordant methylated reads (PDR). Shown are values for early relapse (n=4), late relapse (n=4) and matched post-chemotherapy treated relapse patients (n=7). * = p<0.05 determined from Kruskal Wallis test. (B) Graph depicts change in PDR as function of change in time to progression. Dotted line represents Bayesian linear model fitted to each group. Points represent weighted averages of all genomic regions per patient. Bayes factor=1.6. (C) Stem cell gene sets are enriched from sites of methylation differences (absolute change >10%) between pre-HSCT and relapse samples in late relapse patients. P values were calculated from Fisher’s exact test. (D-F) Ordered and disordered methylated reads for patients 5334 (early relapse) and 5328 (late relapse), respectively, for promoters corresponding to (D) CACNA1C, (E) DLC1, and (F) ID2 stem cell genes.