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Letters to the Editor
Molecular attributes underlying central nervous system and systemic relapse in diffuse large B-cell lymphoma
Refractory or relapsed diffuse large B-cell lymphoma (DLBCL) is challenging to treat in general and progres- sion in the central nervous system (CNS) is associated with particularly dismal outcomes. Indeed, the median survival of patients diagnosed with CNS relapse is typi- cally 2-4 months.1 The incidence of CNS relapse is 2-5% in unselected patient cohorts treated with modern immunochemotherapy,2,3 presenting a challenge for risk prediction. Individual risk factors for CNS relapse are well established, and include clinical parameters such as age, stage, elevated lactate dehydrogenase and perform- ance status.1,4 Moreover, the involvement of certain extranodal sites such as uterus/testis or kidney/adrenal gland confers higher risk of CNS relapse.5 These individ- ual clinical risk factors have been integrated into a risk score (CNS International Prognostic Index [CNS-IPI]).3 Molecular attributes such as double-hit translocation sta- tus, BCL2/MYC dual protein expression as well as the activated B-cell-like (ABC) subtype (or non-germinal center B [GCB]), have also been associated with a higher risk of CNS relapse.6,7 However, the major shortcoming of available risk prediction models is their limited dis- criminative power. While the CNS-IPI classifies 12% of patients into the high-risk group, only 10% of patients within this high-risk group experience CNS relapse.3
Herein, to comprehensively assess molecular determi- nants of CNS relapse, we chose three distinct approach- es. Firstly, we performed exome sequencing in paired tis- sue biopsies (peripheral and CNS parenchymal tumors) and reconstructed clonal phylogenies that underlie CNS invasion. Secondly, we assembled a dataset to assess the association of specific gene mutations with CNS relapse. Lastly, we performed gene expression and pathway analysis on an enriched cohort of diagnostic tissue sam- ples from 222 patients with diverging clinical outcomes. An overview of all samples used in this study is shown in the Online Supplementary Figure S1.
In order to describe changes in clonal structure associ- ated with CNS relapse, we used the PyClone computa- tional tool to infer clonal structures in five paired CNS samples (Online Supplementary Table S1). Recurrent gene mutations included PIM1 (n=4), CD44 (n=3), ETV6 (n= 3), MALAT1 (n=3), ARHGAP5 (n=2), CD79B (n=2), CXCR4 (n=2) and RUNX1T1 (n=2). We observed striking clonal divergence in all five cases, with biopsies from the time of CNS invasion characterized by discrete sets of mutations (Figure 1). In general, the expanding sub- clones were not sampled at the time point of diagnosis, with the main exception being case LY_CNSrel_006. The number of mutations found in CNS-relapsing clones tended to be higher than the number of shared muta- tions, and higher than the number of mutations exclu- sively found at diagnosis (Online Supplementary Figure S2). The most frequently mutated gene was PIM1, with 22 exonic, intronic and untranslated region mutations identified in samples from four patients, in keeping with PIM1 being recognized as a known target of aberrant somatic hypermutation in DLBCL.8 These mutations were mostly subclonal (95%), identified in either expanding (14 of 22 mutations, 64%) or extinguished clones (seven of 22 mutations, 32%). The ETS family member transcription factor ETV6 was found to be mutated in expanding clones in two cases, and in an ancestral clone in another case, with all mutations map-
ping to the exon 1 donor splice site. Our findings docu- ment the existence of intratumoral heterogeneity that is associated with the emergence of subclones that are observed in the CNS. They also suggest that the disease leading to relapse has features that are distinct from the disease found at primary diagnosis.
Next, we asked whether a larger cohort of diagnostic samples could delineate gene mutations associated with either CNS or systemic relapse. We compiled mutation data from 223 diagnostic DLBCL samples, derived from several datasets of both population-based and clinical trial series. Information on 45 genes was available for 72 cases with CNS relapse, 62 cases with systemic relapse and 89 cases without relapse (Online Supplementary Table S2). The strongest signal for enrichment within cases with subsequent CNS relapse was found for MYD88, although this gene was almost as frequently mutated in cases with subsequent systemic relapse (Figure 2). On the other hand, several gene mutations appeared most robustly associated with systemic relapse, and were comparatively uncommon in cases with subsequent CNS relapse. This pattern was most striking for TP53, with mutations found in 45% of patients experiencing systemic relapse, in 20% of patients with no relapse, and in only 11% of patients with subsequent CNS relapse. Furthermore, we found that certain gene mutations were relatively depleted in CNS relapse cases, compared to cases with either systemic or no relapse. The most strik- ing example was KMT2D, mutated in 22% of patients with CNS relapse, compared to 39% and 37% of patients with no relapse and systemic relapse, respec- tively.
Lastly, we asked whether specific pathways were enriched in diagnostic samples from patients who subse- quently experienced either CNS or systemic relapse. We included a total of 222 patients with samples available for gene expression profiling: 50 patients with CNS relapse (n=39) or CNS involvement documented at diag- nosis (n=11), 64 patients with systemic relapse and 108 patients without relapse (Online Supplementary Table S3). We performed gene set enrichment analysis to identify biological processes deregulated in the three risk groups (Online Supplementary Table S4). Interestingly, we observed a significant downregulation of pathways in ABC-DLBCL when comparing systemic versus no relapse, and an upregulation of pathways in GCB- DLBCL (Figure 3A). Downregulation of pathways was also observed in CNS versus no relapse in ABC-DLBCL but not GCB-DLBCL. We observed that systemic relapse in GCB-DLBCL appeared to be driven by MYC, metabol- ic and protein translation signatures, to a higher degree than CNS relapse (Figure 3B). We assessed whether the double-hit signature9 allowed the delineation of varying risks of CNS and/or systemic relapse in our cohort. Overall, 39 of 90 classifiable GCB cases (43%) were pos- itive for a double-hit signature. The percentage of dou- ble-hit signature-postive-positive cases was similar between systemic (64%) and CNS relapse (58%), and lower in cases without relapse (29%, χ2 test P=0.008, Online Supplementary Figure S3). These results link expression of the DHIT signature with CNS relapse, mir- roring the increased risk of CNS relapse conferred by dual expression of MYC and BCL2.6 ABC-DLBCL cases with subsequent relapse were characterized by depletion of stromal signatures, an effect that was most pro- nounced in those ABC-DLBCL cases with systemic relapse. In summary, while cases with subsequent CNS relapse displayed similar pathway enrichment patterns to cases with subsequent systemic relapse, there are like-
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haematologica | 2021; 106(5)