Genetic and evolutionary patterns of treatment resistance in relapsed B-cell lymphoma

Abstract

Diffuse large B-cell lymphoma (DLBCL) patients are typically treated with immunochemotherapy containing rituximab (rituximab, cyclophosphamide, hydroxydaunorubicin-vincristine (Oncovin), and prednisone [R-CHOP]); however, prognosis is extremely poor if R-CHOP fails. To identify genetic mechanisms contributing to primary or acquired R-CHOP resistance, we performed target-panel sequencing of 135 relapsed/refractory DLBCLs (rrDLBCLs), primarily comprising circulating tumor DNA from patients on clinical trials. Comparison with a metacohort of 1670 diagnostic DLBCLs identified 6 genes significantly enriched for mutations upon relapse. TP53 and KMT2D were mutated in the majority of rrDLBCLs, and these mutations remained clonally persistent throughout treatment in paired diagnostic-relapse samples, suggesting a role in primary treatment resistance. Nonsense and missense mutations affecting MS4A1, which encodes CD20, are exceedingly rare in diagnostic samples but show recurrent patterns of clonal expansion following rituximab-based therapy. MS4A1 missense mutations within the transmembrane domains lead to loss of CD20 in vitro, and patient tumors harboring these mutations lacked CD20 protein expression. In a time series from a patient treated with multiple rounds of therapy, tumor heterogeneity and minor MS4A1-harboring subclones contributed to rapid disease recurrence, with MS4A1 mutations as founding events for these subclones. TP53 and KMT2D mutation status, in combination with other prognostic factors, may be used to identify high-risk patients prior to R-CHOP for posttreatment monitoring. Using liquid biopsies, we show the potential to identify tumors with loss of CD20 surface expression stemming from MS4A1 mutations. Implementation of noninvasive assays to detect such features of acquired treatment resistance may allow timely transition to more effective treatment regimens.

Graphical abstract

Figure 1.

Mutation landscape of lymphoma-related genes in 135 rrDLBCL cases. Exonic mutations affecting the top 50 most recurrently mutated genes in our cohort of 135 rrDLBCL samples representing 5 different cohorts (“Methods”). The inferred effect of each mutation is indicated by color. Noncoding mutations are suppressed with the exception of NFKBIZ, which includes 3′ UTR mutations that have been previously described as driver mutations. The 2 covariate tracks on the bottom show COO information (where available) and the source cohort for each sample. Bar plots above and to the right of the plot indicate number of mutations per patient and number of patients with a mutation in that gene, respectively. Although the mutation landscape closely resembles untreated DLBCL, there are some notable differences. For example, approximately half of all rrDLBCLs harbored mutations in either TP53 (51%) or the histone methyltransferase KMT2D (50%) with 31% of cases harboring mutations in both genes.

Figure 2.

Differentially mutated genes between rrDLBCL and untreated DLBCL. (A) Mutation type and frequency of each differentially mutated gene in the untreated and rrDLBCL cohorts, using a significance threshold of 0.1 following false discovery rate correction. Untreated cases with insufficient coverage (not callable) in the gene of interest were not counted in the denominator for that gene (supplemental Methods). (B) Forest plot showing the odds ratio for all differentially mutated genes, as determined by the Fisher’s exact test, for all differentially mutated genes (supplemental Table 6). CI, confidence interval.

Figure 3.

Mutation patterns in genes enriched for mutations within the population of rrDLBCLs. Lollipop plots displaying the mutations discovered in the 6 genes (KMT2D [A], TP53 [B], CREBBP [C], FOXO1 [D], NFKBIE [E], MS4A1 [F]) found to be significantly enriched for mutations at relapse compared with untreated DLBCL. Mutations in rrDLBCL are displayed above each gene, and mutations in the untreated cohort are displayed below each gene. The number of mutated cases and percentage of cases with mutations in that gene are shown beside each gene (red: relapse; blue: untreated). The size of a lollipop and vertical displacement represent the number of patients with nonsilent mutations observed at that position. Note that lollipops were scaled down in the untreated cohort, and thus, the size of a lollipop cannot be directly compared between the untreated and relapse cohorts. Relevant protein domains are displayed for genes with differing mutation patterns within these domains. There is a general enrichment for recurrent mutations in the untreated cohort, most pronounced in KMT2D. These are attributed to rare germline variants that we were unable to filter due to their absence in any database of common variants.

Figure 4.

Clonal evolution patterns of regression and selection in rrDLBCL. (A) Number of cases with a mutation that regressed (blue), expanded (pink), or remained stable (gray) in a given gene following therapy. Time points are from a tumor biopsy before treatment (T1) and a plasma sample after treatment (P2). Genes in clusters that predominately undergo clonal expansion treatment are near the bottom, and genes in clusters depleted following treatment are near the top. (B-I) Clonal evolution plots for several patients following therapy, using a pretreatment tumor tissue biopsy and a posttreatment plasma sample. Each line represents a single coding mutation, and the relative CCF of each mutation before and after therapy is used to flag mutations that undergo clonal expansion (pink), depletion (blue), or remain stable (gray). The 8 genes differentially mutated are labeled and highlighted, and the mutation type is indicated by the adjacent symbol.

Figure 5.

Distribution and functional impact of MS4A1 mutations in rrDLBCL. (A) Topology of MS4A1 transmembrane domains and extracellular loops, as annotated by Uniprot and elsewhere. MS4A1 mutations observed in the rrDLBCL cohort have been labeled, along with the predicted binding epitope of 4 different CD20 mAbs. (B) Comparison of antibody binding between CHO-S cells transfected with plasmids expressing either WT CD20 or 1 of 3 mutants (Tyr86His, Tyr86Cys, and Leu66Arg) for 4 different CD20 antibodies: rituximab (RTX), tositumomab (B1), obinutuzumab (BHH-2), or ofatumumab (OFA). The percent of positively stained cells was compared between mutants within each antibody (adjusted P values from 2-way analysis of variance of 3 replicates: *P > .1, **P > .01, ***P > .001, ****P > .0001). See also supplemental Figure 5. (C) Representative western blot (of 2 independent experiments performed) showing CD20 expression of CHO-S cells transfected with WT or mutant CD20 (Y86H, Y86C, and L66R) and a nontransfected (NT) control. (D) Immunohistochemistry of CD20 in a cell line and tumor tissue biopsy harboring WT CD20 as well as 2 patient-derived cell lines harboring G98R (PT255), and Y86H along with a frameshift mutation, respectively. CD20 is stained red using the L26 CD20 antibody and B-cell nuclei were stained purple using a Pax5 antibody, visualized at ×20 original magnification.

Figure 6.

Plasma and single-cell sequencing of multiple time points in a DLBCL patient (PT255). (A) Timeline of events for PT255. Clinical time point shows the timing of diagnosis (D) and relapses (R2, relapse 2; and R3, relapse 3) relative to blood sample collection (P1 to P6). Bulk tumor DNA was separately obtained from a biopsy at diagnosis, circulating tumor cells extracted at R2 and R3, and cfDNA extracted from plasma samples P1 to P6 after R2. Varying types of sequencing was performed on DNA from each time point, as summarized below. (B) Results from running PyClone on exome sequencing of DNA obtained from diagnosis, R2(P1,) and R3(P5). Clusters 0 and 1 contain trunk mutations seen at both P1 and P5; cluster 2 contains R2-specific mutations, and cluster 3 contains mutations that were subclonal at R2 and clonal at R3. (C) Amplicon sequencing of a subset of mutations found in the clusters in panel B from all 6 plasma time points reveal a more complete but similar evolution of the tumor as inferred from bulk sequence analysis in panel B. Below shows the suspected proportion of the tumor made up of each clone at individual time points. (D) Single-cell amplicon sequencing of circulating tumor cells taken at R2 and R3 revealed 2 distinct populations of cells containing mutations specific to each of R2 and R3. Genes are ordered by group and by frequency of mutation detected (top to bottom), suggesting a relative order of mutation acquisition.