Classic Hodgkin lymphoma: the journey over the destination Free

In this issue of Blood, Pirosa et al provide novel, complex insights into classic Hodgkin lymphoma (cHL) genetics, proposing that cHL genetic subgroups are driven by mechanisms of genetic instability and not by the functional associations of identified DNA alterations. They also report frequent mutations of noncoding regulatory regions.¹ 

Genetic studies of cHL are limited due to the low proportion of malignant cells within cHL tumors. The Hodgkin/Reed-Sternberg (HRS) cHL tumor cells could form as little as 0.1% of the tumor, which represents a substantial technical challenge for their analysis. Therefore, liquid biopsy-based approaches for cHL genetic studies, specifically analysis of circulating tumor DNA (ctDNA), are particularly appealing. Despite the scarcity of HRS cells, it has been documented that relatively high levels of ctDNA are detectable in plasma of patients with cHL, allowing for its reliable analysis.^2,3 Thus, large-scale cHL genetic studies quickly moved to next-generation sequencing-based analysis of ctDNA, opening specific questions related to cHL biology and genetics: are there any genetic subgroups of cHL? What are the mechanisms of mutagenesis? Are there any genetic factors that would allow risk stratification? Can we use ctDNA dynamics for treatment personalization? To address these outstanding questions, Pirosa at al performed a ctDNA-based analysis of a large cohort of cHL patients using patient profiling by deep sequencing (CAPP-Seq),⁴ focusing on the coding regions of 155 genes recurrently mutated in mature B-cell malignancies and, importantly, also on 33 regulatory noncoding regions frequently affected by activation-induced cytidine deaminase (AID)-mediated somatic hypermutation. These findings were correlated with disease biology and outcome, including positron emission tomography/computed tomography (PET/CT) restaging.

Pirosa et al identified 2 cHL genetic subtypes. Surprisingly, these subtypes were not driven by biological implications of identified DNA alterations, but by underlying mechanisms of genetic instability (see Figure 1 in the article by Pirosa et al, which begins on page 1207). The first subtype, called C1 (64% of cases), was characterized by higher mutation load, and its DNA alterations were linked to the AID and microsatellite instability signatures. The dominant features of the second subtype, labeled C2 (36% of cases), were chromosomal instability and a high number of copy number alterations. Very interestingly, a high proportion of cHL tumors (83%) had DNA alterations also in at least one of the analyzed noncoding regulatory regions (frequently with AID signature characteristics). Pirosa at al discovered a new cHL mutational hot spot within one of the noncoding regulatory regions. Specifically, 30.7% of cHL cases had mutation in the superenhancer of BCL6 (B-cell lymphoma 6) gene. BCL6 is a key transcriptional repressor responsible for maintenance of the germinal center phenotype with many lymphomas depending on its continuous expression.⁵ Identified mutations in the BCL6 superenhancer corresponded with a known somatic expression quantitative trait locus of BCL6, and Pirosa et al documented that these mutations contribute to sustained expression of BCL6 and consequent aberrant gene expression program in a subset of cHL cases.

Pirosa et al further used gene expression profiling (and immunohistochemical analysis) of diagnostic biopsies and identified 2 cHL microenvironmental classes: macrophage enriched and T-cell enriched. Neither of these 2 classes was linked to a particular genetic feature or group. Instead, cHL microenvironmental composition seems to be at least partially driven by neoantigens; a higher load of predicted neoantigens was detected in the T-cell-enriched microenvironmental group. Furthermore, Pirosa et al (1) confirmed previous reports that neoantigen clonal structure predicts response to checkpoint inhibitors (rather than the total number of neoantigens)⁶ as patients with neoantigens derived from tumor subclones had shorter progression-free survival following checkpoint blockade and (2) provided additional evidence that ctDNA-based molecular response determination is an excellent tool for reliable treatment outcome prediction, especially in a clinical situation with ambiguous PET/CT results.

The capability of ctDNA analysis to noninvasively profile cHL for its genetic or molecular classification was shown recently by 2 additional studies. Although showing similarities, there is only a partial overlap between identified cHL genetic subgroups. Alig at al described 2 cHL genetic clusters.³ Cluster H1 was associated with a high somatic mutational burden and alterations of genes involved in NFκB, JAK/STAT, and PI3K signaling pathways, and cluster H2 was associated with frequent copy number alterations and mutations of known lymphoma driver genes KMT2D and TP53. Additionally, Heger et al identified 3 distinct cHL subgroups.⁷ By proposing that cHL genetic subgroups are driven by the mechanism of genetic instability, rather than by biological consequences of genetic alterations, Pirosa et al provide a possible explanation of this partial inconsistency in cHL genetic studies. This contrasts with other lymphoma types where the genetic clusters are consistent and dominantly driven by functional categories.⁸ 

The comprehensive analysis of cHL genetics shows for the first time the AID-mediated mutagenesis at noncoding regulatory regions with substantial consequences on gene expression regulation in cHL. This discovery reveals an important overlap of cHL and diffuse large B-cell lymphoma biology and suggests that whole genome studies or wide analysis of noncoding regulatory regions might identify additional critical events in cHL tumorigenesis. The current study clearly documents such important novel noncoding regulatory mutational hot spot in the BCL6 gene, but there might be other such hot spots contributing to the deregulated gene expression program of cHL.

With high cHL cure rates, treatment personalization is one of the directions for further therapy improvement. Better risk stratification or early reliable treatment response evaluation would allow therapy deescalation or intensification to reduce acute and long-term complications as appropriate. Pirosa et al show how various genetic events such as novel mutations of the BCL6 locus shape the cHL biology and treatment outcome but also support the exploration of the clinical use of ctDNA for monitoring response to treatment. Robust validation in prospective clinical trials is necessary before ctDNA analysis could be incorporated into routine clinical use as a valuable diagnostics and disease-monitoring tool. For example, the PRECISE-HL trial (NCT06745076) evaluates ctDNA-based chemotherapy adjustment and the RAFTING trial (NCT04866654), the ctDNA-based treatment monitoring. All effort should be put also into strict technological standardization of ctDNA assessment at its all levels, from preanalytical phase to bioinformatics data processing and results evaluation.

Conflict-of-interest disclosure: The authors declare no competing financial interests.