---
title: "No prognostic value of c-MET, HLA class II, or CD68 by IHC in early-stage classic Hodgkin lymphoma"
id: "plos-one-14-immunohistochemistry-for-c-met-hla-class-ii-and-cd68-does-not-show-prognostic"
canonical_url: "https://medichelpline.com/clinical-feed/plos-one-14-immunohistochemistry-for-c-met-hla-class-ii-and-cd68-does-not-show-prognostic"
content_type: "clinical_feed_article"
specialty: "Oncology"
source_name: "PLOS ONE (Medicine)"
source_url: "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360080"
published_at: "2026-10-08T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# No prognostic value of c-MET, HLA class II, or CD68 by IHC in early-stage classic Hodgkin lymphoma
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/plos-one-14-immunohistochemistry-for-c-met-hla-class-ii-and-cd68-does-not-show-prognostic
- **Specialty:** [Oncology](https://medichelpline.com/clinical-feed/oncology.md)
- **Primary Source:** PLOS ONE (Medicine)
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360080)
- **Published At:** 2026-10-08T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- The study tested three tissue biomarkers—**c-MET**, **HLA class II**, and **CD68**—by immunohistochemistry (IHC) in diagnostic tissue from early-stage classic Hodgkin lymphoma (cHL) patients enrolled in the randomized EORTC-GELA H9 trial. - A case-cohort design was used: 580 patients had available tissue; a 33% random sub-cohort (n = 193) stratified by risk and treatment plus all additional cases produced a biomarker analysis cohort of 257 patients, including 105 PFS events. - IHC methods and scoring replicated original reports: c-MET positive if ≥30% HRS cells stained; HLA class II positive if membranous staining exceeded surrounding lymphocytes and at least 50 HRS cells were present; CD68 scored by automated digital morphometry with high defined above the median (~9.4% positive pixels). - c-MET was observed in tumor cells in 67% (108/162) of evaluable cases; membranous **HLA class II** expression was present in 51% (70/137); CD68 high was defined above the median 9.4% of tissue surface area stained. - In univariate weighted Cox regression for the primary endpoint **progression-free survival (PFS)**, none of the biomarkers showed significant association: HR 0.752 (c-MET), HR 0.854 (HLA class II), HR 0.864 (CD68); all 95% CIs included 1. - Because none were significant in univariate analysis, no combined biomarker classifier was constructed. - Authors conclude these three candidate tissue biomarkers provide insufficient prognostic value for implementation in clinical practice in this early-stage trial population, noting trial-specific factors and scoring challenges may have influenced results. - Study adhered to pre-specified statistical methods (Barlow weighting for case-cohort Cox models) and used the EORTC-GELA H9 trial population with registered protocol and ethics approval; individual participant data are not publicly available due to GDPR constraints.
## Clinical Analysis & Structured Key Points
Immunohistochemistry for c-MET, HLA class II, and CD68 does not show prognostic value in early-stage classic Hodgkin lymphoma patients in the EORTC-GELA H9 intergroup randomised trial | PLOS One Browse Subject Areas ? Click through the PLOS taxonomy to find articles in your field. For more information about PLOS Subject Areas, click here . Article Authors Metrics Comments Media Coverage Reader Comments Figures Figures Abstract Background In classic Hodgkin lymphoma (cHL), identifying high-risk patients is crucial for tailored treatment. Clinical risk factors are useful, but there is room for improvement. Although many diagnostic tissue-based biomarkers have been described to correlate with prognosis, none have achieved the accuracy to be included into daily clinical practice. Methods The most promising tissue biomarkers, c-MET, HLA class II, and CD68, were tested in early-stage cHL for prognostic validation, individually and in combination with each other and established clinical risk factors. All biomarkers were assessed by immunohistochemistry in diagnostic tissue of patients included in the EORTC-GELA H9 trial. A case-cohort design was used, with progression-free survival (PFS) as the primary endpoint (n = 257; 105 PFS events). Hazard ratios (HRs) were estimated using Cox regression models with Barlow weighting to account for the case-cohort structure. Results c-MET expression was detected in the tumor cells in 67% (108/162) of evaluable cases, and cell-surface expression of HLA class II was observed in 51% (70/137), consistent with earlier studies. CD68 expression was scored as the percentage of tissue surface area stained, with scores above the median (9.4%) designated high. In univariate Cox regression analyses, the HRs for PFS were: 0.752 for c-MET (95% confidence interval (CI): 0.456–1.241); 0.854 for HLA class II (95% CI: 0.503–1.448); and 0.864 for CD68 (95% CI: 0.518–1.440). Since none of the biomarkers were significantly associated with PFS in univariate analysis, no further analyses combining the three biomarkers were performed. Conclusions Using a rigorous statistical approach, we found that none of the three candidate biomarkers correlated with patient outcomes. While this lack of validation may be explained by trial-specific factors, such as risk-adapted treatment, the exclusion of advanced-stage patients, or challenges in scoring, these biomarkers provide insufficient prognostic value for implementation in clinical practice. Registration NCT00005584 ( https://clinicaltrials.gov/study/NCT00005584 ). Citation: Martinez Alcala R, Visser L, Fortpied C, Fermé C, Henry-Amar M, Aleman B, et al. (2026) Immunohistochemistry for c-MET, HLA class II, and CD68 does not show prognostic value in early-stage classic Hodgkin lymphoma patients in the EORTC-GELA H9 intergroup randomised trial. PLoS One 21(10): e0360080. https://doi.org/10.1371/journal.pone.0360080 Editor: Semir Vranic, Qatar University College of Medicine, QATAR Received: April 17, 2026; Accepted: September 22, 2026; Published: October 8, 2026 Copyright: © 2026 Martinez Alcala et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: Individual participant-level data cannot be made publicly available due to legal restrictions under the General Data Protection Regulation (GDPR). The dataset has not been demonstrated and documented as anonymised and still contains coded or pseudonymised participant-level data. Such data remain personal data under the GDPR. Public disclosure would make the data available to an undefined and unlimited number of recipients, for purposes that EORTC cannot review or control, without access controls or a data sharing agreement. This would not comply with GDPR requirements applicable to EORTC as data controller, including purpose limitation, data minimisation, integrity and confidentiality, and appropriate safeguards. Data access requests may be submitted to EORTC via Data And Sample Sharing - EORTC: https://www.eortc.org/data-sharing/ . Requests will be assessed through EORTC’s controlled data sharing process, subject to review of the scientific purpose, applicable legal and ethical conditions, and execution of an appropriate data sharing agreement where required. Funding: The author(s) received no specific funding for this work. Competing interests: The authors declare no conflict of interest. Introduction Classic Hodgkin lymphoma (cHL) stands out as a distinctive type of B-cell malignancy, characterized by unique clinical and biological traits. In cHL, the tumor microenvironment (TME) is dominated by a rich and immunosuppressive non-malignant immune infiltrate, while the malignant Hodgkin Reed-Sternberg (HRS) tumor cells make up less than 1% of the tumor mass. The immune infiltrate includes CD4 and CD8 T cells, and more variably, B cells, macrophages, dendritic cells, eosinophils, neutrophils, mast cells, and natural killer cells. This heterogenous cellular composition and the lymphoma architecture form the basis of the histological classification of cHL into four subtypes: nodular sclerosis (NS), lymphocyte-rich (LR), mixed cellularity (MC), and lymphocyte-depleted (LD) [ 1 ]. Infection with Epstein-Barr Virus (EBV), present in up to 25–30% of the cases, is an additional factor that shapes the TME composition by enhancing the immunomodulatory and pro-survival features of the HRS cells [ 2 ]. Latent EBV infection has been linked to a slightly adverse prognosis in patients over the age of 50 years [ 3 ]. Despite significant advances with standard first-line treatments, comprising chemotherapy, with or without radiotherapy, about 15% of cHL patients either fail to enter complete remission or relapse shortly after finishing treatment [ 4 ]. Current treatment strategies seek to reach higher complete response rates while reducing severe immediate and long-term treatment-related toxicities such as cardiac disease, pulmonary side-effects, infertility, and an increased risk of secondary cancers, among others. Reduction of these toxic effects has high priority since the majority of cHL patients are young adults. Clinical risk factors, encompassing disease stage, patient age, and others, have proven helpful in guiding treatment decisions [ 4 ]. In addition, early assessment of metabolic tumor response through [ 18 F]FDG_PET scanning has been established as a helpful tool, allowing for tailored treatment adjustments [ 4 , 5 ]. However, even with these tools in use, the same 15% of patients either do not respond to initial treatment or encounter relapse [ 6 – 8 ], underscoring the need for better predictive biomarkers and treatment strategies. Over the past decades, researchers have investigated a wide range of tissue-based biomarkers for cHL, identifying more than a hundred candidates with potential prognostic value. However, most of these studies aimed to evaluate the biological significance of a novel marker, but were not (entirely) corrected for clinical risk factors and therefore were not designed to ensure prognostic impact in a clinical setting. CD68 has gained the most attention among all these biomarkers since it was first described in 2010, and mainly identifies tumor-associated macrophages within the cHL microenvironment [ 9 ]. High numbers of CD68-positive cells are associated with a significant adverse prognosis, and this finding has been supported by several other studies that included correction for clinical risk factors [ 10 – 19 ]. However, several other publications have not found a significant correlation [ 20 – 27 ]. This contradiction may be related to technical reproducibility issues and challenges in standardization. In addition to high CD68, a lack of tumor cell markers c-MET or HLA class II was found to have significant adverse prognostic value independent of clinical risk factors [ 28 , 29 ]. These findings have not yet been replicated by other research groups or in other patient cohorts. To validate CD68, c-MET, and HLA class II as prognostic biomarkers in a rigorously controlled setting, we evaluated diagnostic tissue samples from patients included in the multi-center randomized clinical trial EORTC-GELA H9 (protocol number 20982) [ 30 , 31 ]. In short, patients were randomly assigned to receive either six or four cycles of ABVD followed by Involved-Field Radiotherapy (IFRT) (6-ABVD-IFRT and 4-ABVD-IFRT), or four cycles of BEACOPP baseline followed by IFRT (4-BEACOPP baseline -IFRT). We designed our biomarker study to assess the prognostic significance of each biomarker individually and in combination, aiming to develop a multiparameter risk classifier. For all three markers, antibodies, staining protocols, and scoring were similar to the initial scientific papers identifying prognostic impact [ 9 , 10 , 28 , 29 ]. Materials and methods Patients and lymphoma tissue Tissue samples embedded in paraffin were retrospectively collected from the EORTC-GELA H9 trial (protocol number 20982). The purpose of the randomized phase III trial was to compare the effectiveness of different regimens of combination chemotherapy with or without radiation therapy in treating HL patients ( Fig 1 ). In this prospective controlled trial in clinical stage I and II (supradiaphragmatic) cHL patients, a distinction was made between patients with favorable clinical characteristics (H9-F) and patients with unfavorable clinical characteristics (H9-U). The flow diagrams for the H9-F and H9-U trials have been reported previously and are shown in S1 Fig and S2 Fig [ 30 , 31 ]. In H9-F, patients with complete response (CR or CR unconfirmed (CRu)) after 6x EBVP chemotherapy, standard radiotherapy treatment (36 Gy IFRT) was compared to 20 Gy IFRT and to no additional radiotherapy with the intent to reduce late toxicity. The primary endpoint was relapse-free survival, defined as the time from the date of assessment of complete response (CR or CRu) until relapse or death from any cause. In H9-U patients, 6x ABVD was compared to 4x ABVD and 4x BEACOPP baseline, followed by 30 Gy IFRT in all arms. The primary endpoint was event-free survival, defined as the time from registration until progression/relapse or death from any cause. Download: PNG larger image TIFF original image Fig 1. Flow diagram of patient samples used for this study. Cases are defined as patients who experienced an event for progression-free survival. https://doi.org/10.1371/journal.pone.0360080.g001 The H9 study included 1591 cHL patients enrolled between 01/10/1998 and 01/09/2002, and for 580 patients (36%), appropriate unstained whole tissue sections were available for analysis. These tissue sections were obtained from 50 centers in ten European countries: Belgium, Croatia, France, Italy, the Netherlands, Poland, Portugal, the Slovak Republic, Slovenia, and Switzerland. The trial was approved by the EORTC Protocol Review Committee and by ethics committees of each participating center or countries according to local laws. All patients gave written informed consent before study entry. The trial was registered at ClinicalTrials.gov under NCT00005584. Permission to conduct research on patient samples was obtained from the institutional review board of the University Medical Center Groningen (UMCG) (METc 2012.343). Data from patient samples were accessed for research purposes on 09/04/2020. Authors had no access to information that could identify individual participants during or after data collection. This study adheres to CONSORT principles by reporting key eligibility criteria, randomization procedures, and analysis population relevant to the evaluation of the collected patient samples 30,31 . Immunohistochemistry and in situ hybridization Immunohistochemistry (IHC) was performed on consecutive slides of paraffin-embedded tissue sections. Depending on the origin of the samples, tissue sections were fixed with formalin or Bouin’s solution. Antibodies against the biomarkers c-MET, HLA class II, and CD68 were used. A rabbit polyclonal antibody to human antigen c-MET C-28 (RRID: AB_631941; dilution 1:200) was obtained from Santa Cruz Biotechnology (Cat. No sc-161). A mouse monoclonal antibody to human antigen HLA class II (clone CR3/43; RRID: AB_2313661; dilution 1:200) was obtained from Dako Denmark (Cat. No M0775). A mouse monoclonal antibody to human antigen CD68 (clone KP1; RRID: AB_2661840; dilution 1:2000) was obtained from Dako Denmark (Cat. No GA60961−2). IHC for c-MET, HLA class II, and CD68 was performed as previously described [ 9 , 10 , 28 , 29 ]. Appropriate positive and negative controls were included for each staining. Fig 2 shows representative IHC staining images for the three biomarkers. To enable determination of tumor cell EBV status, Epstein-Barr virus-encoded small RNAs (EBER) in situ hybridization (ISH) was performed manually using the EBER RNA CISH kit (Dako Omnis). Download: PNG larger image TIFF original image Fig 2. Representative immunohistochemistry of cHL tissue. For c-MET, panel (A) shows a case with expression in the cytoplasm of HRS cells, while panel (B) shows a patient with c-MET-negative HRS cells. For HLA class II, panel (C) shows a case with expression on the membrane of HRS cells and panel (D) displays a case with no expression. For CD68, panel (E) shows a case with >9.8% of surface area stained while panel (F) shows a case with ≤9.8% of positive surface area. Red arrows indicate HRS cells. https://doi.org/10.1371/journal.pone.0360080.g002 Immunohistochemistry scoring Stained slides for c-MET and HLA class II were analyzed by an experienced pathologist who also scored these stains in the original papers (A.D.), and CD68 stains were evaluated by automated digital scoring. The evaluation method for CD68 was selected to replicate the approach used in the original study, in which automated scoring was specifically performed in tumor-rich areas. For c-MET, scoring of HRS cell staining was performed as previously reported [ 28 ]. Stained slides were considered suitable for evaluation only if at least ten HRS cells were present; otherwise, slides were classified as tissue with missing data. Cytoplasmic staining in at least 30% of the HRS cells was scored positive. This cut-off was based on a bimodal distribution of c-MET expression observed in the original study, where 30% of c-MET-positive tumor cells reflected two prognostically different groups. HLA class II was scored using the same approach as originally described [ 29 ]. Stained slides were scored only when the positive control showed consistent staining of lymphocytes or sometimes vascular structures, and when at least 50 HRS cells were present for evaluation. Tissue sections that did not meet these criteria were considered unsuitable for scoring and classified as missing data. Membranous HLA class II expression was scored positive if it was more pronounced than in surrounding lymphocytes or present between adjacent HRS cells. Lack of membranous staining between adjacent HRS cells was denoted as negative. In heterogeneous staining between tumor cells, the predominant pattern was scored. Heterogeneous staining without a predominant pattern was categorized as indecisive. Images of the CD68 staining were acquired by digital scanning (Aperio) and analyzed using Aperio Image Scope (version 11). Four tumor-rich areas (selected by L.V.) were selected for each case for morphometric analysis using a hue value of 0.1, a hue width of 0.5, and a color saturation threshold of 0.04. Cases were excluded and categorized as missing if there was an insufficient number of tumor cells to identify the staining reliably. For the remaining evaluable cases, the number of CD68-positive pixels was divided by the total tissue pixels and multiplied by 100 to calculate the percentage of positive pixels. The cut-off used to classify the expression of CD68 as high or low was based on the median value of positive pixels, consistent with the approach used in the original study with automated scoring [ 10 ]. Statistical analysis The statistical analysis methods were pre-defined before the immunohistochemistry and ISH were performed. The primary endpoint of this study was progression-free survival (PFS), defined as the time from registration until disease progression/relapse or death from any cause, as a compromise between the primary endpoints of H9-F and H9-U. Overall survival (OS) was the secondary endpoint. A case-cohort design was used due to its flexibility, as the sampling is done irrespective of patient outcome [ 32 ]. Such a design combines the advantages of a cohort study with the efficiency of a case-control study. By definition, a case-cohort is composed of a random subcohort of patients from the overall population, irrespective of patient outcome, and supplemented with all cases. In our study, the case-cohort was constructed among the set of patients with diagnostic tissue available for biomarker measurement (n = 580), which included 105 cases, i.e., 105 patients who experienced an event for PFS. The random sub-cohort consisted of a random sample of 33% of the patients with biological material, stratified for risk group (favorable vs. unfavorable) and treatment arm. The fraction of 33% was chosen to ensure adequate power to confirm the magnitude of biomarker effects observed in previous studies [ 33 ]. The resulting random sub-cohort was composed of 193 patients, including 41 cases. The 64 cases not included in the random subcohort were added, resulting in a case-cohort of 257 patients, including 105 cases. Chi-square test for binary variables or the Kruskal-Wallis test for continuous variables were used to check whether the subset of patients with tissue available (580 patients) and the sub-cohort (193 patients) were representative of the overall study population (1591 patients). The distribution of biomarker values, the association between biomarkers, and clinical and demographic characteristics were described in the sub-cohort. The prognostic value of the biomarkers was analyzed in the case-cohort population. The over-representation of cases in the case-cohort was adjusted in the statistical analysis using weighting methods. A separate Cox regression model was used to assess the prognostic value of each of the biomarkers on PFS, with case and control observations weighted by their inverse sampling probabilities using the Barlow weighting scheme [ 32 , 34 ]. The estimated hazard ratios (HR) and their 95% confidence interval were provided, using robust variance estimation. The analysis was conducted separately for each risk group (favorable and unfavorable) and the whole group. An additional analysis was conducted, stratifying the primary model by treatment arm. Other weighting schemes for case and control observations were used as sensitivity analyses to evaluate the robustness of the results obtained with the primary model: Prentice (1986) and Self & Prentice (1988) [ 35 , 36 ]. With this design and the available number of patients and events, the statistical power to detect a HR of 2.0, 2.5, or 3.0 was 79%, 96% and 99% respectively, when analyzing favorable and unfavorable risk patients together. The calculations were based on the method of Cai et al.[ 37 ], assuming a significance level of 0.05 (two-sided) and a maximum imbalance between biomarker positive: negative of 71%: 29%. Results Patient and tumor characteristics In the random sub-cohort (193 patients), 51% of patients were female, the median age was 31 years (range 15–69), 70% were stage II, 26% had B-symptoms, and 19% had bulky mediastinal disease. The basic characteristics of the sub-cohort are presented in Table 1 . There were no significant differences between the patients included and excluded from the sub-cohort, indicating that it was
## Related Clinical Research

- [Naive-like T cell proportion predicts durable response to axi-cel in lymphoma](https://medichelpline.com/clinical-feed/nature-1-determinants-of-response-to-car-t-cell-therapy-in-a-multicenter-multi-trial.md)
- [Ifupinostat phase 2 trial in relapsed/refractory DLBCL: study report and open questions](https://medichelpline.com/clinical-feed/pubmed-42893991.md) (DOI: 10.1038/s41392-026-02926-8)
- [mRNA LNP co-delivery of autoactivated cGAS and membrane-tethered IL-12 induces systemic tumor cont](https://medichelpline.com/clinical-feed/biorxiv-1-combining-an-autoactivated-cgas-mutant-with-membrane-tethered-il-12-via-mrna.md)
- [ComBat outperforms alternatives for cross-batch prediction in B‑cell lymphoma harmonization](https://medichelpline.com/clinical-feed/biorxiv-14-2-234-attempts-to-harmonize-a-multi-platform-b-cell-lymphoma-bulk.md)
- [Sintilimab plus R-CHOP vs DA-EPOCH-R in PMBCL — article content unavailable on source page](https://medichelpline.com/clinical-feed/frontiers-in-immunology-15-sintilimab-plus-r-chop-versus-da-epoch-r-as-first-line-therapy-for-primary.md)

## Navigation
- [← Back to Oncology Feed](https://medichelpline.com/clinical-feed/oncology.md)
- [← All Clinical Specialties](https://medichelpline.com/clinical-feed.md)
## Medical & Regulatory Disclaimer

> [!CAUTION]
> MedicHelpline content is structured for research, educational, and professional discovery purposes. It does not constitute individual medical advice, clinical diagnosis, or treatment recommendations.
> Always verify dosing, contraindications, and regulatory alerts against official product labeling and primary regulatory sources before clinical decision-making.