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Editorial

What Is Still Unclear or Unresolved in Classic Hodgkin Lymphoma Pathobiology, Diagnosis, and Treatment

by
Antonino Carbone
1,*,
Mohamed Nazem Alibrahim
2 and
Annunziata Gloghini
3
1
Centro di Riferimento Oncologico, Istituto di Ricovero e Cura a Carattere Scientifico, 33081 Aviano, Italy
2
Department of Internal Medicine, Faculty of Medicine, Zagazig University, Governorate 7120001, Egypt
3
Department of Advanced Pathology, Fondazione IRCCS, Istituto Nazionale Tumori Milano, 20133 Milano, Italy
*
Author to whom correspondence should be addressed.
Hemato 2025, 6(3), 20; https://doi.org/10.3390/hemato6030020
Submission received: 17 June 2025 / Accepted: 24 June 2025 / Published: 4 July 2025
Browse Figures
Versions Notes

1. Introduction

In recent decades, significant progress in medicine has improved the outcomes for Hodgkin lymphoma (HL), a malignancy affecting approximately 8570 new patients annually in the United States and causing around 910 deaths per year [1]. The etiology of HL remains poorly understood, although associations have been identified, including familial predisposition, viral infections (especially Epstein–Barr virus), and immunosuppression [2,3].
Ninety-five percent of HL cases are classified as classic Hodgkin lymphoma (cHL), which is defined by the presence of characteristic CD30-expressing Hodgkin and Reed–Sternberg cells (HRSCs), which are of follicular center B-cell origin. Classic HL should be distinguished from the nodular lymphocyte-predominant subtype, now renamed nodular lymphocyte-predominant B-cell lymphoma (NLPBL) due to its distinct biology [4].
Classic HL is further categorized into four histologic subtypes: nodular sclerosis, mixed cellularity, lymphocyte-rich, and lymphocyte-depleted, each with distinct clinical and pathological features [5]. Among patients with cHL, those aged 75–84 years have the highest mortality rate, accounting for 23.5% of all cHL-related deaths. The overall 5-year relative survival rate for HL is 87.4% [5,6,7,8]. Notably, survival outcomes have improved significantly over the past three decades, with the 5-year survival rate rising from 71.6% in 1982 to 90.38% in 2012 [6].
Despite these therapeutic advances, approximately 10–20% of patients initially achieve remission but subsequently relapse, while an additional 5–10% demonstrate primary refractory disease, failing to respond adequately to standard first-line therapy [9]. Identifying these high-risk patient subsets early remains a critical yet unresolved clinical and research challenge. Current prognostic models lack precise biomarkers or robust clinical predictors capable of accurately stratifying patients at diagnosis.
In this Editorial, we aim to comprehensively address what remains unclear or unresolved in the pathobiology, diagnosis, and treatment of cHL, highlighting major knowledge gaps and proposing directions for future research. We systematically discuss diagnostic ambiguities, explore mechanisms underlying resistance to established and novel therapeutic agents, evaluate complexities within the tumor microenvironment, and outline translational research opportunities. By doing so, we seek to guide the identification of precise biomarkers and targeted interventions, ultimately enhancing patient risk stratification and clinical outcomes.

2. Diagnostic Challenges and Uncertainties

Table 1 lists the main diagnostic challenges and uncertainties addressed in this Editorial.

2.1. Lack of a Specific Marker for Diagnosis

No single biomarker definitively diagnoses cHL. HRS cells uniformly express CD30 and are frequently positive for CD15, with CD20 present in a subset of cases (~22%) (Figure 1). Their immunophenotype reflects an activated state, characterized by the expression of markers such as CD25, CD71, and HLA-DR. While lineage tracing often points to a B-cell or occasionally a T-cell origin, surface antigen expression is typically aberrant or attenuated [10,11,12].
However, these features are not entirely specific to cHL. In fact, reactive immunoblasts in benign lymphadenitis, EBV-driven proliferations, or other lymphomas, such as cutaneous CD30-positive lymphoproliferative disorders, diffuse large B-cell lymphoma (DLBCL), primary mediastinal B-cell lymphoma (PMBL), peripheral T-cell lymphoma (PTCL), mycosis fungoides, and adult T-cell leukemia/lymphoma, which can express CD30 to varying extents [13,14,15,16,17,18]. CD30 positivity was observed, especially in cases associated with EBV. Moreover, since some cHL cases lack CD15 or even CD30 [19], the correct diagnosis relies on pattern recognition (morphology + multiplex IHC) rather than a unique stain.

2.2. Morphologic Variability of Diagnostic Tumor Cells

The morphologic variability of HRS cells in cHL is substantial and closely influenced by the histological subtype, making contextual interpretation essential for accurate diagnosis. HRS cells, which constitute only a small fraction (typically 0.1–10%) of the cellular infiltrate, appear in diverse forms, including mononuclear Hodgkin cells and classic binucleated or multinucleated HRS cells, which exhibit prominent eosinophilic nucleoli and an “owl-eye” appearance (Figure 2). However, this prototypical form may be a minority amidst more subtle variants, which require diligent identification, especially in small biopsies.
In nodular sclerosis cHL (NSCHL), lacunar cells are a hallmark, resulting from cytoplasmic retraction during fixation and creating an artifactual “lacuna.” These often reside in fibrotic nodules surrounded by sclerotic bands and are associated with capsular fibrosis and a mixed inflammatory background. NSCHL may also show syncytial sheets of neoplastic cells and necrosis in areas of high tumor burden.
Mixed-cellularity cHL (MCCHL) presents with more frequent and readily identifiable classic HRS cells, diffusely scattered in a rich inflammatory infiltrate composed of small lymphocytes, eosinophils, plasma cells, and histiocytes. Epithelioid granulomas and a reticular fibrosis pattern are common, especially in EBV-positive cases, potentially mimicking reactive or immunodeficiency-associated processes.
Lymphocyte-rich cHL (LRCHL) features fewer HRS cells within nodules dominated by small reactive B cells, mimicking nodular lymphocyte-predominant Hodgkin lymphoma (NLPHL). These HRS cells may resemble mononuclear lacunar forms or lymphocyte-predominant variants, necessitating immunophenotypic confirmation to distinguish them from NLPHL or small lymphocytic lymphoma.
Lymphocyte-depleted cHL (LDCHL), the rarest subtype, is marked by abundant pleomorphic, “pseudosarcomatous” HRS cells in sheets, minimal background lymphocytes, and often extensive fibrosis or histiocytic infiltration. Its morphology frequently overlaps with EBV-positive diffuse large B-cell lymphoma or grey-zone lymphomas, especially in immunocompromised hosts.
The significant morphologic variability of HRS cells across subtypes—and their frequent overlap with features seen in other lymphoid neoplasms or reactive conditions—introduces notable inter-observer variability. This variability is further compounded by sampling limitations, fixation artifacts, and the often-sparse presence of neoplastic cells. As a result, even expert pathologists may differ in their interpretations, particularly in small biopsies or atypical presentations. Rather than reflecting sharply demarcated subtypes, the histologic landscape of cHL often behaves more like a continuum, challenging the adequacy of rigid diagnostic categories.

2.3. Tumor Microenvironment Variability

One of the most unresolved and complex aspects of cHL lies in the heterogeneity of its TME. Despite comprising the vast majority of the tumor mass, the cellular composition, spatial architecture, and functional states of the TME vary significantly between patients and even within individual tumors [20,21]. Although the TME frequently consists of a mixed, reactive infiltrate with immune cells, the composition of the TME can also be rich in T cells or B cells, or tumor-associated macrophages (TAMs) or fibroblasts associated or not with a band of sclerosis (Figure 3).
A key and often underappreciated dimension of TME variability is its age dependence. Pediatric patients (age < 10 years) demonstrate a TME enriched in M1-polarized macrophages and cytotoxic/Th1-skewed CD8+ T cells, often in association with Epstein–Barr virus (EBV) positivity [22]. This inflammatory milieu likely contributes to the generally favorable outcomes seen in this age group. In adolescents and young adults (age 15–39), a shift occurs toward a more immunosuppressive environment, marked by M2-polarized macrophages and LAG3+ type 1 regulatory T cells (Tr1) [22]. Elderly patients display an even more suppressive TME, enriched in FOXP3+ regulatory T cells and PD-L1+ macrophages, which frequently cluster near PD-1+ CD4+ T cells [22]. These configurations suggest active immune exhaustion and tolerance, contributing to poorer outcomes in older adults.
EBV status further influences the TME. EBV+ cHL in children typically exhibits enhanced immune surveillance, with increased cytotoxic T cells expressing TIA-1, T-bet, and abundant M1-like TAMs [22,23,24]. In contrast, adult EBV+ cHL shows higher FOXP3+ Treg infiltration and the overproduction of immunosuppressive cytokines such as IL-10, IL-13, and TGF-β [23,24,25]. These observations suggest an “aged” or senescent immune milieu in adults, which may partly explain the differential outcomes observed by age and EBV status.
Advanced techniques such as single-cell RNA sequencing (scRNA-seq), time-of-flight cytometry (CyTOF), and multiplexed imaging (MPI) have elucidated the spatial and phenotypic organization of immune subsets in the TME [21,26,27]. These methods have identified novel T-cell populations, including PD-1+ and LAG3+ Tr1 cells, and demonstrated spatial interactions critical to immune evasion. For instance, PD-L1+ macrophages outnumber and co-localize with PD-1+ T cells more than PD-L1+ HRS cells, suggesting a key mechanism of checkpoint inhibitor efficacy [28,29]. Spatial profiling has also revealed a CXCR5+ HRS–CXCL13+ TAM axis that correlates with treatment failure in relapsed disease, leading to the development of a prognostic spatial model (RHL4S), which predicts outcomes in relapsed cHL based on spatial scores of four cell types: CXCR5+ HRS cells, PD-1+ CD4+ T cells, CD68+ macrophages, and CXCR5+ non-malignant B cells. Each variable independently correlates with failure-free survival after autologous stem cell transplant, and RHL4S stratifies relapsed patients into high- vs. low-risk groups. Such work suggests that spatial context (cell–cell neighborhood patterns) may become a valuable prognostic tool, but these approaches are still in their infancy and need validation in larger cohorts. [30,31].
A broad spectrum of immune and stromal cell populations—including T cells (CD4+, CD8+, Tregs, Th1/Th2/Th17), B cells, natural killer (NK) cells, macrophages, dendritic cells, myeloid-derived suppressor cells, eosinophils, and mast cells—interact with neoplastic HRS cells in ways that are often context-dependent and highly plastic [20,32,33,34]. Many of these cells exhibit overlapping phenotypes or transition between functional states (e.g., M1- and M2-like macrophages, exhausted vs. cytotoxic T cells), thereby blurring traditional immunologic classifications [28,34,35,36,37,38,39]. Furthermore, the spatial distribution of immune cells within the TME plays a pivotal role, with immunosuppressive elements clustering near HRS cells and cytotoxic components often sequestered at the tumor periphery [22,34].
These dynamic and sometimes contradictory features complicate prognostication and limit the predictive value of single-cell biomarkers. As such, the field is shifting toward identifying complex, integrative “functional immune signatures” that better reflect the interplay between tumor and microenvironment, with the goal of improving therapeutic stratification and understanding mechanisms of treatment resistance. Gene expression profiling (GEP)-based risk models such as HL-27, derived from adult cohorts, fail to predict outcomes in pediatric populations, indicating possible biological variations in the TME across different age groups, prompting the development of age-specific models like PHL-9C, which is currently under investigation for its potential utility in guiding brentuximab vedotin-containing treatment regimens in children with cHL [40,41,42]. While spatial and single-cell data have unveiled promising immune signatures, they are not yet integrated into clinical practice.
Unresolved questions include how to best stratify patients based on TME characteristics, how age-related immune remodeling affects therapeutic response, and how to target TME components to reverse immune suppression in older or EBV+ patients.

2.4. Extent of Disease at Presentation Variability

CHL, accounting for approximately 85% of all HL cases, demonstrates substantial variability in the extent of disease at presentation, influenced by its histologic subtype, age distribution, and geographic context. NSCHL predominantly affects adolescents and young adults (15–34 years), particularly in higher socioeconomic regions, and it typically presents with bulky mediastinal involvement in up to 80% of cases, often at an early stage but with central thoracic disease [43]. In contrast, MCCHL and LDCHL are more frequently seen in children and older adults, with a bimodal age distribution and strong associations with Epstein–Barr virus (EBV) infection, which is present in 70–80% of these cases [44,45]. These subtypes are more prevalent in developing countries and are more likely to present with advanced-stage disease, including frequent extranodal involvement, particularly in LDCHL, which often shows dissemination into bone marrow and visceral organs at diagnosis [46]. LRCHL, which is less common (~5% of CHL cases), occurs mostly in older patients and tends to present with limited-stage disease, minimal B symptoms, and clinical behavior resembling NLPHL [47,48]. While extranodal presentation is rare in CHL overall, when it occurs, it usually arises from hematogenous spread rather than primary extranodal origin [49].
Symptoms and sites vary (e.g., mediastinal vs. peripheral nodes, with/without “B” symptoms), which can blur with other diseases. This phenotypic variability means that a case of HL in a child vs. a septuagenarian may differ biologically and diagnostically, yet clear age-related criteria are lacking.

2.5. Overlap with Other Lymphoma Entities

cHL sits at several diagnostically hazardous “grey-zone” interfaces where morphology, immunophenotype, and genetics merge with other lymphomas [50]. The prototypic example of this is the mediastinal continuum between NSHL and PMBL; so-called mediastinal grey-zone lymphomas display hybrid histology, retain at least one B-cell marker alongside strong CD30 (±CD15), and share 9p24/REL gains and JAK-STAT/NF-κB activation, which underpin a true biological bridge, but they fare worse clinically than either parent entity, underscoring the need for tailored therapy [10,51,52,53,54,55,56,57].
Outside of the mediastinum, “non-mediastinal” grey-zone cases are molecularly heterogeneous; one subset clusters with germinal-center DLBCL (TP53/BCL2/KMT2D mutations) and another with SOCS1/STAT6 lesions, highlighting the absence of consensus criteria for classification and management [53].
NLPHL can disguise itself as LRHL when LP cells aberrantly express CD30, CD15, or EBV, blurring boundaries that currently rely on qualitative immunoprofiles instead of robust molecular signatures [58]. EBV-driven entities such as EBV-positive DLBCL and mucocutaneous ulcer, as well as post-transplant or senescence-related lymphoproliferations, frequently harbor HRS-like blasts that mimic cHL yet differ in latency pattern and clinical course, demanding vigilant clinico-pathologic correlation [59,60,61]. Conversely, rare cHL cases that aberrantly express T-cell or cytotoxic markers challenge distinction from ALK-negative anaplastic or other peripheral T-cell lymphomas and may require clonality studies for resolution [61,62,63,64,65,66].
Finally, a spectrum of “mimics” (e.g., CLL with HRS-like cells, infectious mononucleosis, peripheral T-cell lymphoma with HRS-like cells) illustrates that not all overlaps denote shared biology; many are morphological illusions that can only be disentangled with comprehensive immunophenotyping and, increasingly, genomic profiling. Collectively, these persisting grey zones epitomize what remains unresolved in cHL pathobiology and diagnosis [50]. The field lacks universally accepted molecular benchmarks to demarcate true biological hybrids from phenotypic look-alikes, thereby leaving treatment algorithms and prognostication empiric.

3. Treatment Challenges and Unmet Needs

Table 2 lists the main treatment challenges and unmet needs discussed in this Editorial.

3.1. Refractoriness and Relapse Rates

First-line chemo-radiotherapy cures the majority of HL patients, but roughly 15–20% of cHL cases fail to achieve durable remission. Among those with relapsed/refractory (R/R) disease, high-dose chemotherapy with autologous stem cell transplant cures only about half [67,68,69]. This leaves a significant fraction of patients (especially R/R) with dismal outcomes. Identifying which patients will not be cured by standard therapy (and why) is an unresolved issue.

3.2. Brentuximab and Checkpoint Inhibitor Resistance

A large multicenter study provides critical insights into the outcomes of cHL patients who are refractory or intolerant (DR/INT) to both brentuximab vedotin (BV) and anti-PD-1 therapy, a subset for whom therapeutic options remain unclear. Among 173 patients, median survival from diagnosis (OS-1) was 14.8 years, while median survival from the onset of DR/INT (OS-2) was 7.4 years, with improved outcomes observed in those who had previously undergone autologous stem cell transplant. Rechallenge with either BV or anti-PD-1 therapies yielded moderate short-term responses but limited durability. In contrast, patients who proceeded with allogeneic transplant or CD30-directed CAR-T therapy showed significantly better survival [70].
Resistance to BV in cHL is driven by a convergence of biological and pharmacological mechanisms that compromise drug efficacy despite persistent CD30 expression on HRS cells. CD30 downregulation is not typically observed in resistant cases; instead, studies show that CD30 remains expressed, suggesting that target loss is not the main issue [71,72,73]. A dominant resistance mechanism involves MDR1 (P-glycoprotein) overexpression, which actively exports the cytotoxic payload MMAE from the cytosol, significantly reducing its intracellular accumulation and antimitotic action [71]. This efflux is confirmed by decreased rhodamine-123 retention and elevated MDR1 mRNA and protein levels in resistant cell lines [71]. Additional resistance arises from defective linker–payload cleavage, particularly the broad specificity of the valine–citrulline linker to multiple cathepsins beyond tumor-specific cathepsin B, which can cause premature payload release and off-target toxicity [74]. The TME also plays a critical role, promoting drug resistance through metabolic reprogramming, ECM remodeling, angiogenesis, immune suppression, and exosome-mediated drug sequestration [75,76,77]. Other contributors include the ectodomain shedding of CD30 by ADAM10/17 proteases, leading to increased soluble CD30 that traps BV in circulation [78]. Strategies to counteract resistance include using cathepsin B-specific linkers, charged payloads to limit the bystander effect, MDR1 inhibitors (e.g., cyclosporine and vorinostat), and gene editing approaches like CRISPR-Cas9 to silence ABC transporters. Combination therapies involving BV and immune checkpoint inhibitors, such as nivolumab or pembrolizumab, aim to overcome immune escape. Nanoparticle-mediated MMAE delivery and epigenetic therapies targeting DNA methylation and histone modification pathways are emerging as promising adjuncts [74]. Still, these approaches remain largely experimental, and BV resistance in cHL continues to lack clinically validated predictive biomarkers or universally effective interventions.
Resistance to PD-1 blockade in cHL arises from a complex interplay of tumor-intrinsic factors and a profoundly immunosuppressive TME. Primary resistance mechanisms include inadequate T-cell activation due to impaired antigen presentation, particularly the loss or dysfunction of HLA class II molecules critical for CD4+ T-cell recognition. This impairment is often driven by genetic alterations in CIITA or HLA-DM, especially in EBV-negative tumors [79,80,81,82]. HRS cells also remodel the TME to favor immunosuppression by secreting chemokines (e.g., CCL17/TARC and CCL22) that recruit Th2 and regulatory T cells (Tregs), further limiting effective anti-tumor immunity [83,84]. Moreover, high levels of indoleamine 2,3-dioxygenase and adenosine accumulate in the TME, impairing T-cell and NK cell function [85,86,87]. TAMs and PD-L1+ monocytes enhance this suppressive niche, and their expression of PD-L1 contributes to T-cell exhaustion [27,88]. Acquired resistance can develop through the upregulation of alternative immune checkpoints like LAG-3 and TIM-3, often co-expressed with PD-1 on tumor-infiltrating lymphocytes [89]. VEGF expression by HRS cells may further exacerbate T-cell exhaustion and PD-L1 upregulation [89]. A related challenge is quantifying the ultimate benefit of immunotherapy in HL. PD-1 inhibitors are moving into frontline trials, but it is not yet clear whether they will improve cure rates or simply reduce toxicity. Biomarkers that predict which patients truly need immunotherapy (versus conventional chemo) are lacking. In summary, overcoming refractory HL, whether via better drugs or better use of immunotherapy, remains an active research priority.

4. Future Directions and Open Questions

Table 3 summarizes future directions and open questions in cHL.

4.1. Diagnostic Biomarkers

Efforts to identify cHL-specific biomarkers should continue. Possibilities include novel immunohistochemical markers of HRS cells, molecular (gene expression or mutation) signatures, or even circulating tumor DNA (ctDNA). In fact, recent work shows that HRS-derived ctDNA can be detected at diagnosis and might track disease burden [90,91,92]. Developing assays (IHC panels and genomic tests) that improve the sensitivity/specificity of HL diagnosis would address a major gap in this area.

4.2. Microenvironment Targeting

Given the TME’s importance, new therapies may aim at TME components. For instance, trials of LAG-3 or CSF1R inhibitors in HL are logical given recent findings. Research should identify which TME features predict therapy response (e.g., are high LAG-3^+ T-cell clusters biomarkers of PD-1 resistance?). Spatial and single-cell studies could guide such biomarker development.

4.3. Immunotherapy Optimization

Defining the long-term benefit of checkpoint blockade (as frontline vs. salvage) remains critical. Biomarker-driven trials (e.g., using PD-L1 expression, T-cell clonality, or spatial signatures) could personalize immunotherapy. Novel combinations (checkpoint + other immune modulators or targeted agents) should be explored, especially for early relapsers.

4.4. Innovative Technologies

New methods like spatial transcriptomics, high-dimensional proteomics, and “liquid biopsy” are poised to impact HL research. For example, utilizing the RHL4S spatial assay in routine practice or deploying ctDNA sequencing to monitor minimal residual disease could transform prognostication. As these technologies mature, integrating them into clinical studies will be important.

5. Conclusions

While the treatment landscape for cHL has evolved considerably in recent decades, a persistent subset of patients continues to experience treatment failure, either through relapse or primary refractoriness, highlighting critical unmet needs in both clinical practice and research. The present Editorial underscores that the biological underpinnings of cHL remain incompletely defined. The absence of disease-specific diagnostic markers forces continued reliance on morphologic pattern recognition and multiplex immunohistochemistry, which introduces significant inter-observer variability, particularly in small or atypical biopsies.
Equally complex is the TME, which, despite constituting the majority of the tumor mass, remains poorly characterized in routine settings. Its dynamic composition, shaped by age, EBV status, and immune exhaustion, has been shown to influence disease behavior and treatment response but is not yet utilized in patient stratification. Similarly, the mechanisms driving resistance to key agents such as brentuximab vedotin and PD-1 inhibitors are multifactorial, involving drug efflux systems, impaired antigen presentation, and TME-mediated immune suppression. These insights underscore the urgent need for predictive biomarkers that can identify patients unlikely to respond to standard therapies.
Beyond biologic factors, cHL is increasingly recognized as a spectrum of overlapping disease entities rather than a single uniform diagnosis. Grey-zone lymphomas and immunophenotypic mimics challenge current classification systems and expose the limitations of rigid diagnostic categories. A more nuanced, molecularly guided taxonomy may be necessary to align diagnosis with biology and clinical behavior.
Looking forward, progress will depend on integrating high-resolution technologies, such as spatial transcriptomics, single-cell profiling, and ctDNA monitoring, into clinical and translational pipelines. These tools not only hold promise for refining diagnosis and risk assessment but also for identifying actionable targets within the TME. Addressing the unresolved complexities of cHL will require a shift toward more biologically informed, patient-specific models of diagnosis, prognosis, and therapy.

6. Summary

This Editorial synthesizes current evidence and spotlights four priority gaps; addresses unresolved issues across biology, diagnostics, and treatment; and highlights open questions.
  • Diagnostic ambiguity: cHL lacks a pathognomonic marker; CD30/CD15 expression and HRS morphology overlap with grey-zone lymphomas and EBV-driven proliferations, leading to inter-observer variability.
  • Microenvironmental complexity: Spatial and single-cell studies reveal prognostic immune niches (e.g., CXCR5+ HRS–CXCL13+ macrophage axis), yet these insights have not been translated into routine risk stratification or therapy.
  • Resistance mechanisms: Predictors of failure to standard ABVD, brentuximab vedotin, or PD-1 blockade remain undefined. Data implicate MDR1 upregulation, HLA-II loss, and alternative checkpoints, but no biomarkers guide clinical decisions.
  • Translational opportunities: Circulating tumor DNA, refined imaging, and next-generation patient-derived models promise better monitoring and drug testing but need validation.

Author Contributions

A.C. was responsible for designing the Editorial; he wrote the paper and supervised the paper and the figures. M.N.A. wrote the paper. A.G. prepared the figures and their legends. All authors have read and agreed to the published version of the manuscript.

Conflicts of Interest

The authors declare no competing interests.

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Figure 1. Marker expression in Hodgkin Reed–Sternberg (HRS) cells. CD30 is a hallmark marker of classic Hodgkin lymphoma (cHL), since it is expressed in nearly all cases of cHL. CD15 is expressed in most cHL cases and the majority of HRS cells. Additionally, PAX5 is usually expressed in HRS cells, although its expression is characteristically weaker than in reactive B cells. Epstein–Barr virus (EBV) positivity in HRS cells can be demonstrated by in situ hybridization for EBERs, showing a nuclear positivity. EBV-infected HRS cells express, as demonstrated by immunohistochemistry, latent membrane protein 1 (LMP1) with a cytoplasmic staining. This figure also lists other markers that are usually positive (CD40), usually negative (CD20), and negative (CD45).
Figure 1. Marker expression in Hodgkin Reed–Sternberg (HRS) cells. CD30 is a hallmark marker of classic Hodgkin lymphoma (cHL), since it is expressed in nearly all cases of cHL. CD15 is expressed in most cHL cases and the majority of HRS cells. Additionally, PAX5 is usually expressed in HRS cells, although its expression is characteristically weaker than in reactive B cells. Epstein–Barr virus (EBV) positivity in HRS cells can be demonstrated by in situ hybridization for EBERs, showing a nuclear positivity. EBV-infected HRS cells express, as demonstrated by immunohistochemistry, latent membrane protein 1 (LMP1) with a cytoplasmic staining. This figure also lists other markers that are usually positive (CD40), usually negative (CD20), and negative (CD45).
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Figure 2. Hodgkin Reed–Sternberg cells. Classic and variants. HRS cells are large, multinucleated cells or binucleated “owl’s eye” cells with huge eosinophilic nucleoli. In addition to these classic HRS cells, there are several morphologic variants, including the mononucleated and macronucleolated “Hodgkin cells”, the “lacunar cells” (which appear to be surrounded by a clear space or lacuna), the “pseudosarcomatous” cells, and the “mummified” cells, which display condensed cytoplasm and pyknotic eosinophilic or basophilic nuclei.
Figure 2. Hodgkin Reed–Sternberg cells. Classic and variants. HRS cells are large, multinucleated cells or binucleated “owl’s eye” cells with huge eosinophilic nucleoli. In addition to these classic HRS cells, there are several morphologic variants, including the mononucleated and macronucleolated “Hodgkin cells”, the “lacunar cells” (which appear to be surrounded by a clear space or lacuna), the “pseudosarcomatous” cells, and the “mummified” cells, which display condensed cytoplasm and pyknotic eosinophilic or basophilic nuclei.
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Figure 3. Tumor microenvironment (TME) in classic Hodgkin lymphoma (cHL). The TME in cHL displays a variable cellularity. The TME frequently consists of a mixed, reactive infiltrate with B cells and T cells, neutrophils, histiocytes, plasma cells, and mast cells. However, the composition of the TME can also be rich in T cells or B cells, tumor-associated macrophages (TAMs), or fibroblasts associated or not with a band of sclerosis. Drawings (top row) show cellular characteristics of different TMEs in cHL.
Figure 3. Tumor microenvironment (TME) in classic Hodgkin lymphoma (cHL). The TME in cHL displays a variable cellularity. The TME frequently consists of a mixed, reactive infiltrate with B cells and T cells, neutrophils, histiocytes, plasma cells, and mast cells. However, the composition of the TME can also be rich in T cells or B cells, tumor-associated macrophages (TAMs), or fibroblasts associated or not with a band of sclerosis. Drawings (top row) show cellular characteristics of different TMEs in cHL.
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Table 1. Diagnostic challenges and uncertainties in cHL.
Table 1. Diagnostic challenges and uncertainties in cHL.
Diagnostic ChallengeDetails
Lack of disease-specific biomarkersCD30 expression alone or CD30 and CD15 co-expression are non-specific, since they overlap with atypical lymphoproliferations and other lymphomas.
Morphologic variability of HRS cellsDiverse appearances (mononuclear, multinucleated, or lacunar); sometimes subtype-specific; variations cause diagnostic ambiguity.
Grey-zone lymphomasOverlap with PMBL, NLPHL, DLBCL, PTCL, and EBV-driven proliferations, complicating definitive classification.
TME heterogeneitySignificant variation influenced by age, EBV status, and spatial organization complicates a standard diagnostic approach.
Inter-observer variabilityDue to sparse malignant cells, variable morphology, fixation artifacts, or small biopsies.
Table 2. Treatment challenges and unmet needs in cHL.
Table 2. Treatment challenges and unmet needs in cHL.
Treatment ChallengeDetails
Refractoriness and relapse rates~15–20% of cHL patients fail initial therapy; ~50% cure rate with salvage therapy (high-dose chemo + transplant)
Resistance to brentuximab vedotinDriven by MDR1-mediated payload efflux, linker–payload cleavage issues, TME-mediated immune evasion, ectodomain shedding (ADAM10/17)
Resistance to PD-1 inhibitorsInvolves impaired antigen presentation (CIITA/HLA-DM alterations), chemokine-driven T-cell suppression (Tregs), IDO and adenosine accumulation, alternative checkpoints (LAG-3, TIM-3)
Lack of predictive biomarkersNo validated biomarkers to predict therapeutic resistance or guide immunotherapy vs. chemotherapy decisions
Limited therapeutic options for double-refractory patientsPatients resistant to both BV and PD-1 inhibitors have poor outcomes; few effective alternatives (e.g., CAR-T or allo-transplantation)
Table 3. Open questions and future research directions in cHL.
Table 3. Open questions and future research directions in cHL.
Open QuestionPotential Approaches and Opportunities
Diagnostic biomarkersDevelopment of molecular diagnostics, novel IHC markers, genomic signatures, and ctDNA assays
Targeting the tumor microenvironmentExploiting TME biology, trials with LAG-3, CSF1R inhibitors, and biomarker-driven stratification
Optimization of immunotherapyClarifying the benefit of frontline vs. salvage checkpoint inhibitors; personalized therapy using biomarkers
Integrative technologies (spatial transcriptomics, single-cell profiling, liquid biopsy)Integration into clinical trials; prognostic models like RHL4S, routine monitoring of minimal residual disease
Resolving grey-zone casesDefining molecular criteria to distinguish true biological hybrids from phenotypic mimics
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Carbone, A.; Alibrahim, M.N.; Gloghini, A. What Is Still Unclear or Unresolved in Classic Hodgkin Lymphoma Pathobiology, Diagnosis, and Treatment. Hemato 2025, 6, 20. https://doi.org/10.3390/hemato6030020

AMA Style

Carbone A, Alibrahim MN, Gloghini A. What Is Still Unclear or Unresolved in Classic Hodgkin Lymphoma Pathobiology, Diagnosis, and Treatment. Hemato. 2025; 6(3):20. https://doi.org/10.3390/hemato6030020

Chicago/Turabian Style

Carbone, Antonino, Mohamed Nazem Alibrahim, and Annunziata Gloghini. 2025. "What Is Still Unclear or Unresolved in Classic Hodgkin Lymphoma Pathobiology, Diagnosis, and Treatment" Hemato 6, no. 3: 20. https://doi.org/10.3390/hemato6030020

APA Style

Carbone, A., Alibrahim, M. N., & Gloghini, A. (2025). What Is Still Unclear or Unresolved in Classic Hodgkin Lymphoma Pathobiology, Diagnosis, and Treatment. Hemato, 6(3), 20. https://doi.org/10.3390/hemato6030020

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