Real-world lymphoma cohort in the HIV-endemic setting: Impact of implementing novel reclassification standards

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Andera, Dharshnee R. Chetty, Zainab Mohamed, Diana Oelofse, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6948575/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Apr, 2026 Read the published version in BMC Cancer → Version 1 posted 14 You are reading this latest preprint version Abstract Background: Lymphoma real-world observational data and accurate diagnostic systems are lacking in low-resource settings. We established a diagnostic registry for lymphoma classification to generate internationally comparable, clinically validated data. Methods: The descriptive retrospective cohort included patients ≥13 years of age newly diagnosed with lymphoma from 2005 to 2020. Patients were enrolled at a single site in a registry with hierarchical groupings to capture, interrogate, and subtype lymphoma diagnoses. These were standardised on sequential versions of the World Health Organisation Classification of Haematolymphoid Tumours (WHO-HAEM) and correlated with the International Consensus Classification of mature lymphoid neoplasms (ICC). Differences due to nomenclature and diagnostic category were annotated. Results: The cohort consisted of 2354 incident lymphoma cases; 1891 (80.3%) non-Hodgkin lymphoma and 463 (19.7%) Hodgkin lymphoma (HL). Twenty-one lymphoma NOS cases were excluded due to inadequate specimen for standardised sub-classification. Overall reclassification according to WHO-HAEM5 was 25.8% (n=608). Major differences between WHO-HAEM5 and ICC included 44 (1.9%) transformations of indolent B-cell lymphomas; also 957 (40.7%) lymphoid proliferations and lymphomas associated with immune deficiency/dysregulation due to HIV (33.1%) and EBV (31.8%). EBV-association was highest among HL cases, 77 (50.3%) were HIV reactive. Conclusion: We report here the impact of adopting international lymphoma classification standards in an HIV-endemic setting. Our findings highlight the persistent prevalence of large B-cell lymphoma, confirm inadequate viral suppression as a key disease driver, and provide further evidence for EBV-associated HL as a distinct entity. Consolidating HIV-associated and other immune deficiency/dysregulation lymphomas into a unified framework could have significant implications and warrants further consideration for inclusion in future WHO-HAEM classifications. Figures Figure 1 Figure 2 Figure 3 Figure 4 Background The accurate classification of lymphoma is crucial for effective treatment and prognostication yet the diagnostic pathway in the sub-Saharan African (SSA) context is complicated by sub-optimal pathology services, and the more pressing clinical demands of the human immunodeficiency virus (HIV) and tuberculosis (TB) endemic environment [1-6]. Most of the historical lymphoma diagnostic pathology and epidemiology studies were conducted by working groups from high income countries (HIC) with comparatively few independently generated from within the SSA region [7-18]. Real-world observational data have been identified as important adjuncts to bridge the knowledge gap and are under-utilised in low-resource settings [19-21]. To yield translational data, such cohorts need to be meticulously designed and should adhere to recognised quality standards. The process of generating internationally comparable, clinically validated data in these settings is challenging. Population-based cancer registries in low- and middle-income countries (LMIC), where they exist, contribute data classified within broad topographical categories to the collaborative platforms of the World Health Organisation (WHO): International Agency for Research on Cancer (IARC) and International Association of Cancer Registries (IACR) [1, 2, 10, 22]. A few specialised lymphoma cohorts have, nonetheless, been established in recent decades; intermittently reporting tentatively disaggregated lymphoma data to the international scientific community. These cohorts were often representative of single institutions and focused on the impact of HIV as the primary driver of lymphoma in the early anti-retroviral treatment (ART) era [12, 13, 15, 16]. These studies were, furthermore, mostly pathology based, with data classified along the directives of earlier versions of the WHO Classification of Haematolymphoid Tumours, hereafter referred to as WHO-HAEM, and in some instances utilised obsolete morphology based classification [8, 9, 23]. The continually updated WHO-HAEM multi-modal framework provides the standard for accurate contemporary diagnosis and subtyping of lymphoma [24-27]. Implementation of this refined system for subclassification of lymphoma in the LMIC setting, is limited by a lack of access to pathology resources [28]. In addition, incomplete implementation of updated versions of the WHO-HAEM impacts subsequent data collection by cancer registries [29]. In 2022, evolving insights concerning pathogenesis, clinical features, molecular genetics, and changes in taxonomy brought the successor to WHO-HAEM4R in WHO-HAEM5 [26, 27]. This advanced understanding concurrently catalysed a second classification standard, the International Consensus Classification (ICC) of mature lymphoid neoplasms [26, 27, 30]. Both classification systems, WHO-HAEM5 and ICC, share fundamental concepts of disease classification that integrate clinical, pathological and molecular data [27, 30]. With these new insights, the University of Cape Town Lymphoma Working Group (UCT LWG), a multidisciplinary team of pathologists, haematologists, database developers and data capturers affiliated with Groote Schuur Hospital (GSH) in the Western Cape province of South Africa, set out to develop a specialised cancer registry empowered to deliver high quality real-world clinically validated lymphoma data. We report here on the operational impact of implementing novel classification systems in a resource restricted setting, and report findings pertinent to an HIV-endemic setting. The UCT LWG lymphoma cohort encompasses all lymphoma cases diagnosed from 2005 and continues to operate prospectively from the Division of Clinical Haematology. Methods 1. Study design The GSH haematology patient registry, a large parent cohort of haematological malignancies, was established in 2018 by the UCT Division of Clinical Haematology. This observational cohort is prospectively maintained and systematically enriched with retrospective data. The registry utilises a secure web-based Research Electronic Data Capture platform (REDCap software 2024, version 13.7.9 Vanderbilt University, Nashville, TN, USA) developed and hosted at UCT to collect and store patient information [31, 32]. For this study, the UCT LWG recruited and reviewed all lymphoma patients ≥13 years of age with a new lymphoma diagnosis managed at GSH between 1 st January 2005 to 31 st December 2020. The inclusion of the adolescent group, 13 years and older, is reflective of local hospital admission practice [33]. The study patients were identified from GSH clinical records in the Division of Clinical Haematology, the Department of Radiation Oncology, and from the accredited Divisions of Anatomical Pathology and Haematology of the National Health Laboratory Services (NHLS). All relapsed/refractory cases, transformations from previously diagnosed indolent B-cell lymphomas, and dendritic cell and histiocytic neoplasms were excluded. Other exclusions leading to the cohort of interest are highlighted in Figure 1. 2. Study population The catchment area comprises both urban and rural settings in the Western Cape province of South Africa and surrounding regions. GSH is a 975-bed regional academic treatment centre that receives around half of all tertiary, adult public sector referrals in the province (South Africans and resident foreign nationals) [34, 35]. Patients with medical insurance are usually treated in the private sector and seldom diagnosed at GSH. According to the latest South African general household survey conducted in 2021, 47.9% of the Western Cape population is dependent on public health facilities [36]. 3. Data collection Demographic and baseline clinical data were obtained from paper and electronic records. Patient characteristics such as sex and age were obtained from the patient clinical management and booking system of the South Africa National Department of Health, Clinicom (Citrix 2024 Cloud Software Group, Inc. South Africa). The lymphoma diagnosis, date of diagnosis, Epstein-Barr virus (EBV) and HIV status were obtained from the NHLS laboratory information systems, DISA (DISA*LAB 2017 Johannesburg, South Africa) (archived) and TrakCare (TrakCare Lab 2012, version L6.10, InterSystems Corporation, Morningside, South Africa) (active), theunified healthcare information systems, and electronic medical records tool for healthcare professionals in South Africa’s public healthcare facilities. 4. Disease categorisation 4.1 Hierarchical classification The REDCap data dictionary categorisation pathway of the lymphoma cohort was modelled on a hierarchical taxonomy of lymphoid neoplasms [37]. This universally accepted methodology, embedded in the WHO-HAEM4R and International Classification of Diseases for Oncology 3 rd edition (ICD-O-3), systematically categorise diseases from general to increasingly specific entities and subtypes aligned with their respective International Classification of Diseases 10 th revision (ICD-10) codes and International Classification of Diseases for Oncology, 3 rd edition (ICD-O-3) codes [26, 38, 39]. To reach the most accurate downstream diagnosis a combination of pathology morphology reports and all available ancillary modalities were interrogated and validated with clinical case notes. Cases were next delineated into two subgroups: non-Hodgkin lymphoma (NHL) and Hodgkin lymphoma (HL). NHL first degree delineation is based on the binary lineage directed WHO methodology of B-cell or T/NK-cell, followed by clinical presentation before treatment – aggressive or indolent [40]. Whilst some lymphoma cases were concluded upstream in more general categories, most were fully subclassified downstream to specific subtypes. 4.2 Creating a baseline standardised cohort Patients in our settingwere historically diagnosed and classified according to their contemporaneous WHO iteration set out in either the WHO-HAEM3, WHO-HAEM4, or WHO-HAEM4R [24-26]. To that end, the primary researcher first allocated, or where required, reclassified all cases to a baseline WHO-HAEM4R category [26]. Classifications were based on a primary source morphology diagnosis obtained from soft tissue, bone marrow, peripheral blood or body cavity fluid and maximally integrated with immunohistochemistry (IHC) stains, immunophenotype on flow cytometry and karyotype fluorescence in-situ hybridisation (FISH) andgene rearrangement studies, and also clinical features. Pathology reports that contained incomplete or differential diagnoses were subjected to review and escalated to the UCT LWG histopathologists and haematopathologists (DRC, DO, GP, SC, JJO, EV) to reconsider and complete subclassification. 4.3 Cohort enrichment with retrospective investigations in high-grade lymphoma Prior to WHO-HAEM4 our pathology department followed an "essential for diagnosis" approach to refined subtyping of high-grade B-cell lymphomas (HGBL), with limited immunohistochemistry and an absence of molecular diagnostic methods. Following the implementation of WHO-HAEM4 in 2008, the introduction of cell-of-origin (COO) classification, Hans algorithm and the concept of double-hit HGBL, supplementary investigations were introduced incrementally [25, 41-43]. To achieve diagnostic refinement across the study cohort, some cases required additional IHC stains ( CD10/ BCL6/ MUM1/ BCL2/ C-MYC ) and FISH investigations ( MYC/ BCL2/ BCL6 ) retrospectively[39]. Sub-cohort studies provided additional laboratory data. These studies included (i) Diffuse large B-cell lymphoma, not otherwise specified (DLBCL, NOS ) which examined COO subtypes, EBV co-infection and CD4 counts in the setting of high HIV prevalence; (ii) EBV viral load in newly diagnosed Hodgkin lymphoma; and (iii) molecular testing and CD4 counts in patients with newly diagnosed HIV-Burkitt lymphoma [44-46]. 4.4 Cohort reclassification - nested within the novel classification standards of WHO-HAEM and ICC To assess the operational impact of implementing novel classification standards we re-classified our WHO-HAEM4R standardised cohort according to the newly published guidelines of WHO-HAEM5 and ICC. To enable comparative analysis, the number of specialised investigations required to facilitate reclassification from WHO-HAEM4R to respectively WHO-HAEM5 and ICC were computed. WHO-HAEM5 was utilised as the denominator informing temporal changes. In addition, some new entries not previously found in WHO-HAEM4R included IgG4-related disease, transformations of indolent B-cell lymphomas and lymphoid proliferations and lymphomas associated with immune deficiency/dysregulation (IDD). 4.5 Temporal analysis To assess the translation of temporal changes in our own laboratory approach to the diagnosis of HGBL, we audited our diagnostic algorithm used in the diagnosis of HGBL and enumerated the number of FISH investigations required to achieve refined downstream subtyping. We disaggregated our data across the three previous WHO-HAEM versions. 4.6 Integrated reporting with dynamic quality improvement The cooperative platform of the specialised lymphoma registry facilitated the introduction of a process of integrated reporting, with dynamic quality improvement. All incident lymphoma diagnoses prospectively enrolled into the registry since 2019 subsequently benefit from a quality check for missing ancillary tests and gaps addressed in real-time. Once WHO-HAEM5 was published, incident cases were also automatically conformed to the new standard. 5. Data analysis and Statistical Methods StataCorp. 2023 Stata Statistical Software: Release 18 (StataCorp LLC, College Station, TX, USA) was used for analysis. Categorical variables were described by frequencies and percentages. Age at diagnosis was described by medians and interquartile ranges as data were non-parametric. Age and sex were compared in the HIV-negative and HIV-positive groups by the Mann–Whitney U test and chi-square test, respectively. Focus areas included numbers and percentages of lymphoma cases by subtype, EBV status, HIV status, and cytogenetics performed over 16 consecutive years. The infographics were created using the online design tools Canva https://www.canva.com/) and SankeyMATIC (https://sankeymatic.com/build/). Time trends were displayed using bar graphs created in Microsoft Excel. Results 1. Establishing the lymphoma cohort Cohort design and workflow are illustrated in Figure 1. Overall, 2399 lymphoma cases consecutively diagnosed between 1 January 2005 and 31 December 2020 were analysed. Isolated cases that lacked HIV status were excluded. Among other exclusions were 21 lymphoma - NOS cases and instances where patients with insufficient data for definitive sub-classification were lost to follow-up. Table 1 shows the final cohort reclassified to WHO-HAEM5. Additional disaggregation for HIV status represents the WHO-HAEM5 conceptual non-hierarchical entity IDD in the setting of HIV. The final cohort comprised 2354 newly diagnosed patients - 1891 (80.3%) NHL and 463 (19.7%) HL (Table 1). The evolution in disease categorisation, as informed by WHO-HAEM4R to WHO-HAEM5 and ICC, is captured in Figure 2. For 19 patients known to have had more than one type of high-grade NHL or HL diagnosis during the study period, only the first diagnosis was analysed. The distribution of cases according to WHO-HAEM4R is provided in supplementary Table 1. 2. Reclassifications Table 2 shows that reclassification from WHO-HAEM4R to conform to WHO-HAEM5 was required for 25.8% (n=608) of all cases. We reclassified 44 (1.9%) cases as transformations of indolent B-cell lymphomas and reassigned 957 (40.7%) cases to the conceptual non-hierarchical entity - IDD. Notable entity changes demonstrated in Figure 2 and supplementary Table 2 illuminate disparities between WHO-HAEM4R, WHO-HAEM5 and ICC. Figure 2 further illustrates the challenge of applying WHO-HAEM5 in an HIV-endemic setting; as highlighted by the emergence of a large IDD group. To conform to the WHO-HAEM5, more than 50% of HGBL required reclassification. To conform to the ICC, the number of HGBL that required reclassification was comparable to WHO-HAEM4R. The diagnostic algorithm used for this category is shown in Figure 3. 3. Implementation of molecular tests Figure 4 illustrates FISH investigations carried out to refine HGBL diagnoses gradually increased over time. FISH studies were performed for 53 (7.5%) DLBCLspecimens, 26 (50.0%) HGBL, NOS and 107 (54.6%) BL. HGBL diagnosis was guided by a high proliferation index (Ki67>90%) in 124 cases (66.7%). Although 186 (15.3%) of all HGBL had at least one FISH investigation, only 16 cases with a confirmed positive IHC MYC result, had all three FISH probes MYC , BCL2 and BCL6, performed. An analysis of the temporal trends showed that the FISH testing kick-started by the prior sub-cohort studies, gradually enabled more comprehensive testing [44, 45]. The WHO-HAEM4R entity DLBCL NOS with MYC and BCL6 gene rearrangements was found in a single case, but after WHO-HAEM5 reclassification, no ‘double hit lymphomas’ with MYC and BCL2 gene rearrangements were identified. 4. EBV cohort EBV stain results from in-situ hybridisation with EBV encoded RNA (EBER-ish) and latent membrane protein 1 (LMP1) are summarised in Table 3 [44, 46, 47]. In total 770 (32.7%) cases of the cohort were tested. EBV positivity was found in 31.8% (n=245), with a significant proportion concurrently HIV reactive (n=167, 68.2%; P <0.001). The majority of EBV-positive DLBCL (67.3%) were PLWH. Among plasmablastic lymphoma (PBL) cases (92.6% in PLWH), 82.6% (n=46) were EBV positive. Although only a small sample 13.2% (n=10) of KSHV/HHV8-associated multicentric Castleman disease (MCD) cases (92.1% in PLWH) were analysed, 60% were EBV-associated and therefore triple positive for EBV, HHV8 and HIV. EBV results were likewise only available for a small sample of BL 25.3% (n=49), with comparatively few EBV positive (22.4%); the majority in PLWH (88.1%). Newly diagnosed HL in PLWH (n=153) was also very commonly EBV-associated (n=77, 50.3%). We additionally, identified a rare example of EBV-associated nodular lymphocyte predominant HL (NLPHL). 5. HIV cohort HIV prevalence was 33.1% and ART status at lymphoma diagnosis included: ART naïve [n=334, (42.9%)], on ART and virally suppressed [n=285, (36.6%)], and on ART, but virally unsuppressed [n=160, (20.5%)]. PLWH presented at a significantly younger age - median 38.3 years (IQR 32.5-45.3) compared to the HIV-negative group - median 56.6 years (IQR 41.0-67.3); P <0.001. The most frequent HIV-associated lymphoma entities were DLBCL, NOS [n=173 (22.2%)], BL [n=171 (22.0%)], and classic HL [n=152 (19.5%)]. Primary large B-cell lymphomas of immune privileged sites were rare. Most low-grade NHL B-cell entities were HIV non-reactive. Splenic B-cell lymphomas/ leukaemia and lymphoplasmacytic lymphoma were notably absent, whereas rare cases of chronic lymphocytic leukaemia/ small lymphocytic lymphoma, marginal zone lymphoma, follicular lymphoma and mantle cell lymphoma were HIV reactive. Subtype analysis of HL showed that nodular sclerosis HL cases were the most common among PLWH. NLPHL was a rare entity (1 of n=27). Finally, T-cell and NK-cell leukaemia and lymphomas (mature, cutaneous and other), represented a small proportion of lymphoma cases [n=181, (7.7%)] with [n=25, (13.8%)] presenting in PLWH. Discussion In this study from an HIV-endemic developing country, we demonstrate how rigorous lymphoma classification could be established in a real-world diagnostic registry, by gradual implementation of WHO-HAEM and ICC directives. The impact of reclassifying to ICC was lower than WHO-HAEM5, with less disparities compared to the conversion from WHO-HAEM4R to the WHO-HAEM5. Data disaggregation revealed that the most frequent groups reclassified were the high-grade varieties of lymphoma, notably HGBL (> 50% of cases). This included reassignment of cases to the WHO-HAEM5 conceptual non-hierarchical entity IDD that endorse the “Three Part Nomenclature Framework” for IDD as suggested by international working groups concerned with immunodeficiency-associated lymphoproliferative disorders [ 27 , 48 ]. This entity, that amalgamates all post-transplant lymphoproliferative disorders (PTLD), HIV-positive and EBV tumour positive cases contrasts with the ICC approach to retain the traditional hierarchical type and subtype categories. In the absence of the IDD entity, we found reasonable comparability between WHO-HAEM4R, WHO-HAEM5 and ICC. This agrees with data published by a large prospective HIC cohort reporting only 0.8% major diagnostic differences [ 49 ]. We, furthermore, identified only a single case of HGBL with MYC and BCL6 gene rearrangements that, in a departure from previous WHO-HAEM4R methodology, would be categorised as DLBCL, NOS according to WHO-HAEM5, and as a defined stand-alone subtype by ICC. Similar to another South African cohort, HGBL with MYC and BCL2 gene rearrangements was not identified in our cohort [ 50 ]. Another major difference between WHO-HAEM5 and ICC is the consolidation of transformations of indolent B-cell lymphomas [ 27 , 30 ]. In our cohort, we were comfortably able to identify cases of indolent B-cell lymphomas that transformed to high-grade. However, in some instances, de novo high-grade lymphoma, that morphologically resembled transformation from indolent B-cell lymphoma, imparted a degree of uncertainty. Of note also, is the annotation of residual discordant indolent B-cell lymphomas (with bone marrow involvement) following treatment of high-grade lymphomas. In addition, indolent B-cell lymphomas are diverse entities, implying significant pathological, clinical and therapeutic divergence. The consolidation of these cases as a single conceptual entity once transformation occurs, may require further evidence-based validation and refining. Earlier regional studies that mostly focussed on lymphoma prevalence, highlighted the prominence of high-grade lymphoma and formerly rare varieties, with HIV as the primary driver [ 8 , 12 , 13 , 15 , 16 ]. The most prevalent lymphoma entities among PLWH in our cohort agrees with data reported from HIC and confirms the ongoing prominence of large B-cell lymphoma [ 51 ]. On interrogation of the clinical features of HL, we found that it frequently behaved in a “clinically aggressive” fashion not dissimilar to other established histology driven clinically aggressive high-grade entities [ 52 ]. HIV prevalence for this study cohort was slightly lower than the range (37–66%) reported by earlier cohorts in South Africa [ 12 , 13 , 16 ]. The significantly younger age at presentation for PLWH with lymphoma [median 38.3 years (IQR 32.5–45.3)] is in stark contrast to the HIV-negative patients in our cohort [median 56.6 years (IQR 41.0-67.3)] and the difference is even more striking when comparing it with the median age of presentation of lymphoma cohorts in HIC; for example, the United Kingdom [median 69.9 years (IQR 59.1–78.3)], USA [median 62 years (range, 18–99)], Australia and New Zealand [median 64.3 years (IQR 52.1–73.5)] [ 49 , 53 , 54 ]. More than half of the subpopulation of PLWH were ART naïve, or on ART but unsuppressed at lymphoma diagnosis highlighting viral non-suppression as an important driver of lymphomagenesis. The analysis of EBV testing data in our lymphoma cohort indicate a high overall EBV positivity and illuminates significant concurrent HIV reactivity. The WHO-HAEM5 entity EBV-positive DLBCL, typically a disease of older patients in HIC, occurs in comparatively younger patients in our setting, and frequently occurs in PLWH. The tentative EBV characterisation in our cohort supports theorisation around the increasing prominence of virally driven cancers in LMICs [ 27 , 55 , 56 ]. Although EBV testing was not performed in all our lymphoma cases, which may have introduced selection bias, on balance our results argue for the potential utility of routinely incorporating EBV testing in lymphoma diagnostic algorithms, in select settings [ 27 , 30 , 56 , 57 ]. Similarly, the karyotypes for some BL cases were found to be frequently and unusually complex in the case of HIV-associated BL [ 45 ]. The availability of karyotypes in these instances obsoleted the need for MYC testing by FISH. This stands in contrasts to the WHO-HAEM5 diagnostic algorithm for HGBL that advocates for further testing despite a MYC negative result. Nonetheless, the distinction between the HGBL entities DLBCL and BL, both frequently HIV-associated and clinically aggressive, but with different treatment modalities, remain of clinical importance in our setting. Our results underscore the importance of FISH analysis as a crucial adjunct to diagnostic clarity in high-grade varieties of B-cell lymphoma and reveal rare instances of double and triple viral oncogene or viral vector association. In LMIC, bespoke evidence-based diagnostic algorithms for HGBL are ultimately needed to direct the selective use of costly complimentary techniques, including FISH studies, and reserved for cases where results are imperative to guide and optimise therapy [ 58 – 62 ]. The solid hierarchical framework of the WHO-HAEM5 and ICC enabled incorporation of high-quality real-world data maximising available information and minimising impact of incomplete data [ 63 ]. Challenges in our setting include scarce technical expertise, limited access to and the financial cost of advanced instrumentation and bioinformatics support. The relatively high reclassification rate illustrates how in-house operational challenges in a resource restricted setting, was ameliorated by incremental introduction of novel diagnostics in support of progressive sophistication in lymphoma diagnosis and classification. In HIC, representing around a fifth of the world’s population and benefiting from wider implementation of universal healthcare access, the categorisation of some lymphoma entities along the more comprehensive ICC diagnostic pathways, may be more achievable [ 30 ]. Both WHO-HAEM5 and ICC, nonetheless, provide improved classification methodology and can be implemented to a reasonable level of downstream refinement, provided relatively basic laboratory modalities are available. Limitations of this study, include instances of sample insufficiency for FISH testing among some HGBL cases and insufficient IHC stains performed to achieve subclassification. Contingent on clinically extreme presentations, which are not rare in the SSA setting, bias may also be introduced by the omission of diagnoses based on isolated pre-terminal samples. Additionally, missing clinical data and early deaths that preclude further diagnostic investigations led to exclusion of some cases. The high reclassification rate in this study may at least be partly attributed to additions and developments within the sequential versions of the WHO-HAEM [ 24 – 27 ]. NHL cases frequently required additional IHC stains or complementary tests to reach refined diagnoses or to upgrade to disease entities previously defined as provisional entities [ 19 , 25 ]. Despite these limitations, this is the first study that comprehensively describes the population of lymphoma patients in our institution with disaggregated subtyping. Strengths of this study include the large sample size, single site integrated diagnosis and categorisation standardised to the WHO-HAEM5 providing real-world data that allows comparability to other centres, local and international [ 27 ]. Conclusions This study takes the first steps to highlight the complexities and challenges encountered to implement refined diagnosis and classification of lymphoma in an HIV and TB endemic LMIC setting. It also argues for improvement in cost-effective diagnostic and classification algorithms appropriate for LMIC settings [ 6 , 64 ]. Additionally, it supports the development of regional specialised cancer registries and provides a platform that fosters much needed translational research. Finally, from a public health standpoint, it is hoped that it may yield actionable guidance for equitable healthcare strategy development in our local context. Abbreviations ABC activated B-cell ART antiretroviral therapy BCLu B-cell lymphoma unclassifiable BL Burkitt lymphoma CHL classic Hodgkin lymphoma CLL chronic lymphocytic leukaemia CNS central nervous system COO cell-of-origin DLBCL diffuse large B-cell lymphoma EBER-ish Epstein-Barr virus-encoded small RNAs in-situ hybridisation EBV Epstein-Barr virus FISH fluorescence in-situ hybridisation FL follicular lymphoma GCB germinal centre B-cell GSH Groote Schuur Hospital HGBL high-grade B-cell lymphoma HIC high income countries HIV human immune deficiency virus HHV8 human herpesvirus 8 HL Hodgkin lymphoma HREC human research ethics committee HTLV-1 human T-lymphotropic virus IACR International Agency for Research on Cancer IARC International Association of Cancer Registries ICC Internation Consensus Classification of mature lymphoid tissues ICD-03 International Classification of Diseases for Oncology, 3 rd edition ICD-10 International Classification of Diseases for Oncology, 10 th revision IDD lymphoid proliferations and lymphomas associated with immune deficiency / dysregulation IHC immunohistochemistry IP-LBCL primary large B-cell lymphoma of immune-privileged sites IQR Inter Quartile Range KSHV Kaposi sarcoma herpes virus LMIC low- and middle-income countries LMP-1 latent membrane protein 1 LPD lymphoproliferative disorder LPL lymphoplasmacytic lymphoma LWG Lymphoma Working Group MALT mucosa-associated lymphoid tissue MCD multicentric Castleman disease MCL mantle cell lymphoma MGZL mediastinal grey zone lymphoma MZL marginal zone lymphoma NLPBL nodular lymphocyte predominant B-cell lymphoma NLPHL nodular lymphocyte predominant Hodgkin lymphoma NHL non-Hodgkin lymphoma NHLS National Health Laboratory Services NOS not otherwise specified PBL plasmablastic lymphoma PCNSL primary central nervous system lymphoma PLWH people living with HIV PMLBCL primary mediastinal large B-cell lymphoma PTCL peripheral T-cell lymphoma PTLD post-transplant lymphoproliferative disorders REDCap Research Electronic Data Capture SSA sub-Sarahan Africa T-DLBCL testicular diffuse large B-cell lymphoma THRLBCL T-cell/histiocyte-rich large B-cell lymphoma tIL transformations of indolent B-cell lymphomas UCT University of Cape Town WHO-HAEM World Health Organisation Classification of Haematolymphoid Tumours Declarations Acknowledgements The authors thank the GSH NHLS staff: Haematology, Molecular, Cytogenetics and Anatomical Pathology and all other colleagues who supported the study through its many phases; and also, Harvey Lusaka Binamu for his expert assistance with the infographics. Funding Research reported in this publication was supported by the Cancer Association of South Africa (CANSA) as well as the Fogarty International Centre and National Heart, Lung, and Blood Institute of the National Institutes of Health under award number D43 TW010345. The content is solely the responsibility of the authors and does not necessarily represent the official views of CANSA and the National Institutes of Health. Data availability statement The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request. Authors’ contributions EV was responsible for funding acquisition, designed and implemented the GSH haematology patient registry, oversaw study design as well as reviewed disputed clinical cases treated in the Division of Clinical Haematology. LFA conceptualised study design, acquired the data, and wrote the manuscript as part of her PhD. DC reviewed disputed tissue histology cases and verified the data from the Division of Anatomical Pathology. DO reviewed disputed bone marrow or flow cytometry cases and verified the data. ZM reviewed disputed clinical cases treated in the Department of Radiation Oncology and verified the data. JB and KB designed, maintained and managed the registry as well as extracted the data and performed statistical analyses. GP reviewed and approved disputed cases. KA, KS, GK, SC, JJO performed sub-studies within this lymphoma cohort and provided baseline data for this study. AR reviewed molecular studies. VJL critically revised the final manuscript. All co-authors reviewed and edited the manuscript draft. Ethics approval and consent to participate Ethical approval for the GSH haematology patient registry (HREC R024/2018), and for this study (HREC 479/2020), were obtained from the human research ethics committee (HREC) at UCT and the GSH ethics review board. Patient consent was waived due to the retrospective nature of data collection. Disclosure of interests The authors declare no conflict of interest. References GLOBOCAN: South Africa [https://gco.iarc.fr/today/data/factsheets/populations/913-southern-africa-fact-sheets.pdf] Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F: Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries . CA Cancer J Clin 2021, 71 (3):209-249. Global HIV Statistics [https://unaids.org/en/resources/fact-sheet] South Africa Department of Statistics: Statistical Release P0302. Mid-year population estimates 2022 . In . Pretoria; 2023. Antel K, Louw VJ, Maartens G, Oosthuizen J, Verburgh E: Diagnosing lymphoma in the shadow of an epidemic: lessons learned from the diagnostic challenges posed by the dual tuberculosis and HIV epidemics . Leuk Lymphoma 2020, 61 (14):3417-3421. Antel K, Oosthuizen J, Brown K, Malherbe F, Loebenberg P, Seaton C, Baloyi S, Simba K, Chetty D, Louw VJ et al : Focused investigations to expedite cancer diagnosis among patients with lymphadenopathy in a tuberculosis and HIV-endemic region . AIDS 2023, 37 (4):587-594. Gopal S, Patel MR, Yanik EL, Cole SR, Achenbach CJ, Napravnik S, Burkholder GA, Reid EG, Rodriguez B, Deeks SG et al : Temporal trends in presentation and survival for HIV-associated lymphoma in the antiretroviral therapy era . J Natl Cancer Inst 2013, 105 (16):1221-1229. Perry AM, Perner Y, Diebold J, Nathwani BN, MacLennan KA, Müller-Hermelink HK, Bast M, Boilesen E, Armitage JO, Weisenburger DD: Non-Hodgkin lymphoma in Southern Africa: review of 487 cases from The International Non-Hodgkin Lymphoma Classification Project . British Journal of Haematology 2016, 172 :716-723. Perry AM, Diebold J, Nathwani BN, MacLennan KA, Müller-Hermelink HK, Bast M, Boilesen E, Armitage JO, Weisenburger DD: Non-Hodgkin lymphoma in the developing world: review of 4539 cases from the International Non-Hodgkin Lymphoma Classification Project . Haematologica 2016, 101 (10):1244-1250. Miranda-Filho A, Pineros M, Znaor A, Marcos-Gragera R, Steliarova-Foucher E, Bray F: Global patterns and trends in the incidence of non-Hodgkin lymphoma . Cancer Causes Control 2019, 30 (5):489-499. Anderson JR, O. AJ, Weisenburger DDftN-HLCP: Epidemiology of non-Hodgkin lymphomas: Distributions of the major subtypes differ by geographic locations . Annals of Oncology 1998, 9 :717-720. Mantina H, Wiggill TM, Carmona S, Perner Y, Stevens WS: Characterization of Lymphomas in a High Prevalence HIV Setting . J Acquir Immune Defic Syndr 2010, 53 :656–660. Wiggill TM, Mantina H, Willem P, Perner Y, Stevens WS: Changing Pattern of Lymphoma Subgroups at a Tertiary Academic Complex in a High-Prevalence HIV Setting: A South African Perspective . Jaids-Journal of Acquired Immune Deficiency Syndromes 2011, 56 (5):460-466. Oelofse D, Truter I: Incidence of haematological malignancies, Eastern Cape Province; South Africa, 2004-2013 . Cancer Epidemiol 2018, 53 :166-171. Abayomi EA, Somers A, Grewal R, Sissolak G, Bassa F, Maartens D, Jacobs P, Stefan C, Ayers LW: Impact of the HIV epidemic and Anti-Retroviral Treatment policy on lymphoma incidence and subtypes seen in the Western Cape of South Africa, 2002-2009: preliminary findings of the Tygerberg Lymphoma Study Group . Transfus Apher Sci 2011, 44 (2):161-166. Patel MR, Philip V, Omar T, Turton D, Candy G, Lakha A, Pather S: The Impact of Human Immunodeficiency Virus Infection (HIV) on Lymphoma in South Africa . Journal of Cancer Therapy 2015, 06 (06):527-535. Oluwasola AO, Olaniyi JA, Otegbayo JA, Ogun GO, Akingbola TS, Ukah CO, Akang EEU, Aken’Ova YA: A Fifteen-year Review of Lymphomas in a Nigerian Tertiary Healthcare Centre . J Health Popul Nutr 2011, 29 (4):310-316. Vaughan J, Perner Y, McAlpine E, Wiggill T: HIV-Associated Hodgkin Lymphoma Involving the Bone Marrow Identifies a Very High-Risk Subpopulation in the Era of Widescale Antiretroviral Therapy Use in Johannesburg, South Africa . AIDS 2020, 83 (4):345-349. Hershman DL, Wright JD: Comparative effectiveness research in oncology methodology: observational data . J Clin Oncol 2012, 30 (34):4215-4222. El-Galaly TC, Cheah CY, Villa D: Real world data as a key element in precision medicine for lymphoid malignancies: potentials and pitfalls . Br J Haematol 2019, 186 (3):409-419. Chihara D, Hobbs BP, Maurer MJ, Flowers CR: Utilization of Real-World Data to Facilitate Clinical Trials for Patients with Lymphoma . Pharmacoepidemiology 2024, 3 (3):252-264. Mafra A, Laversanne M, Gospodarowicz M, Klinger P, De Paula Silva N, Pineros M, Steliarova-Foucher E, Bray F, Znaor A: Global patterns of non-Hodgkin lymphoma in 2020 . Int J Cancer 2022, 151 (9):1474-1481. Naresh KN, Raphael M, Ayers L, Hurwitz N, Calbi V, Rogena E, Sayed S, Sherman O, Ibrahim HA, Lazzi S et al : Lymphomas in sub-Saharan Africa--what can we learn and how can we help in improving diagnosis, managing patients and fostering translational research? Br J Haematol 2011, 154 (6):696-703. Jaffe ES, Harris NL, Stein H, Vardiman JW: World Health Organization Classification of Tumours, Pathology and Genetics of Tumours of Haematopoietic and Lymphoid Tissues : IARC Press, Lyon, France; 2001. Swerdlow SH, Campo E, Harris NL, Jaffe ES, Pileri SA, Stein H, Thiele J, Vardiman JW: WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues . Lyon, France: IARC Press; 2008. Swerdlow SH, Campo E, Harris NL, Jaffe ES, Pileri SA, Stein H, Thiele J: WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues, Revised 4th ed . Lyon, France: IARC Press; 2017. Alaggio R, Amador C, Anagnostopoulos I, Attygalle AD, Araujo IBO, Berti E, Bhagat G, Borges AM, Boyer D, Calaminici M et al : The 5th edition of the World Health Organization Classification of Haematolymphoid Tumours: Lymphoid Neoplasms . Leukemia 2022, 36 (7):1720-1748. Tomoka T, Montgomery ND, Powers E, Dhungel BM, Morgan EA, Mulenga M, Gopal S, Fedoriw Y: Lymphoma and Pathology in Sub-Saharan Africa: Current Approaches and Future Directions . Clin Lab Med 2018, 38 (1):91-100. Smith A, Roman E, Howell D, Jones R, Patmore R, Jack A, Haematological Malignancy Research N: The Haematological Malignancy Research Network (HMRN): a new information strategy for population based epidemiology and health service research . Br J Haematol 2010, 148 (5):739-753. Campo E, Jaffe ES, Cook JR, Quintanilla-Martinez L, Swerdlow SH, Anderson KC, Brousset P, Cerroni L, de Leval L, Dirnhofer S et al : The International Consensus Classification of Mature Lymphoid Neoplasms: A Report from the Clinical Advisory Committee . Blood 2022. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG: Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support . J Biomed Inform 2009, 42 (2):377-381. Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O'Neal L, McLeod L, Delacqua G, Delacqua F, Kirby J et al : The REDCap consortium: Building an international community of software platform partners . J Biomed Inform 2019, 95 . Stefan C, van der Merwe P-L: Treating adolescents in South Africa . SAMJ 2008, 8 (3). South Africa Department of Health: Referral and support zones public sector health institutions in the Western Cape province . In . Pretoria: National Department of Health; 1997. Richards DB, Jacquet GA: Analysis of referral appropriateness in the Western Cape, South Africa, and implications for resource allocation . African Journal of Emergency Medicine 2012, 2 (2):53-58. South Africa Department of Statistics: Statistical Release P0318. General Household Survey 2021 . In . Pretoria; 2022. Turner JJ, Morton LM, Linet MS, Clarke CA, Kadin ME, Vajdic CM, Monnereau A, Maynadié M, Chiu BC, Marcos-Gragera R et al : InterLymph hierarchical classification of lymphoid neoplasms for epidemiologic research based on the WHO classification (2008): update and future directions . Blood 2010, 116 (20):e90-98. Fritz A, Percy C, Jack A, Shanmugaratnam K, Sobin L, Parkin DM, Whelan S: International Classification of Diseases for Oncology , 3rd edn. Malta: World Health Organisation; 2013. WHO: International statistical classification of diseases and related health problems , vol. 2, 5th edn. France: World Health Organisation; 2016. Connors JM: Non-Hodgkin lymphoma: the clinician's perspective--a view from the receiving end . Mod Pathol 2013, 26 Suppl 1 :S111-S1118. Campo E, Swerdlow SH, Harris NL, Pileri S, Stein H, Jaffe ES: The 2008 WHO classification of lymphoid neoplasms and beyond: evolving concepts and practical applications . Blood 2011, 117 (19):5019-5032. Hans CP, Weisenburger DD, Greiner TC, Gascoyne RD, Delabie J, Ott G, Muller-Hermelink HK, Campo E, Braziel RM, Jaffe ES et al : Confirmation of the molecular classification of diffuse large B-cell lymphoma by immunohistochemistry using a tissue microarray . Blood 2004, 103 (1):275-282. Choi WW, Weisenburger DD, Greiner TC, Piris MA, Banham AH, Delabie J, Braziel RM, Geng H, Iqbal J, Lenz G et al : A new immunostain algorithm classifies diffuse large B-cell lymphoma into molecular subtypes with high accuracy . Clin Cancer Res 2009, 15 (17):5494-5502. Cassim S, Antel K, Chetty DR, Oosthuizen J, Opie J, Mohamed Z, Verburgh E: Diffuse large B-cell lymphoma in a South African cohort with a high HIV prevalence: an analysis by cell-of-origin, Epstein-Barr virus infection and survival . Pathology 2020:453-459. Opie J, Antel K, Koller A, Novitzky N: In the South African setting, HIV-associated Burkitt lymphoma is associated with frequent leukaemic presentation, complex cytogenetic karyotypes, and adverse clinical outcomes . Ann Hematol 2020, 99 (3):571-578. Opie J, Mohamed Z, Chetty D, Bailey J, Brown K, Verburgh E, Hardie D: Hodgkin lymphoma: the role of EBV plasma viral load testing in an HIV-endemic setting . Clin Exp Med 2024, 25 (1):10. Antel K, Chetty D, Oosthuizen J, Mohamed Z, Van der Vyver L, Verburgh E: CD68-positive tumour associated macrophages, PD-L1 expression, and EBV latent infection in a high HIV-prevalent South African cohort of Hodgkin lymphoma patients . Pathology 2021, 53 (5):628-634. Natkunam Y, Gratzinger D, Chadburn A, Goodlad JR, Chan JKC, Said J, Jaffe ES, D. dJ: Immunodeficiency-associated lymphoproliferative disorders: time for reappraisal? Blood 2018, 132 (18):1871-1878. Cerhan JR, Maurer MJ, Link BK, Feldman AL, Habermann TM, Jaye DL, Burack WR, McDonnell TJ, Vega F, Chapman JR et al : The Lymphoma Epidemiology of Outcomes cohort study: Design, baseline characteristics, and early outcomes . Am J Hematol 2024, 99 (3):408-421. Vaughan J, Perner Y, Wiggill T: Diffuse Large B-Cell Lymphoma in the Public-Sector of Johannesburg, South Africa, in the Era of Widescale Antiretroviral Therapy Use . J Acquir Immune Defic Syndr 2022, 91 (4). Silverberg MJ, Lau B, Achenbach CJ, Jing Y, Althoff KN, D'Souza G, Engels EA, Hessol NA, Brooks JT, Burchell AN et al : Cumulative Incidence of Cancer Among Persons With HIV in North America: A Cohort Study . Ann Intern Med 2015, 163 (7):507-518. Simba K, Mohamed Z, Opie JJ, Andera LF, Brown K, Oosthuizen J, Antel K, Dawood T, Van der Vyfer L, Du Toit C et al : The International Prognostic Score and HIV status predict red cell concentrate transfusion needs in Hodgkin lymphoma . Leuk Lymphoma 2022:1-8. Lamb M, Painter D, Howell D, Barrans S, Cargo C, de Tute R, Tooze R, Burton C, Patmore R, Roman E et al : Lymphoid blood cancers, incidence and survival 2005-2023: A report from the UK's Haematological Malignancy Research Network . Cancer Epidemiol 2024, 88 . Investigators LaRDR: Improving outcomes for patients with lymphoma: design and development of the Australian and New Zealand Lymphoma and Related Diseases Registry . BMC Med Res Methodol 2022, 22 (1). Tumwine LK, Orem J, Kerchan P, Byarugaba W, Pileri SA: EBV, HHV8 and HIV in B cell non Hodgkin lymphoma in Kampala, Uganda . Infect Agent Cancer 2010, 5 (12). Briercheck EL, Ravishankar S, Ahmed EH, Carias Alvarado CC, Barrios Menendez JC, Silva O, Solorzano-Ortiz E, Siliezar Tala MM, Stevenson P, Xu Y et al : Geographic EBV variants confound disease-specific variant interpretation and predict variable immune therapy responses . Blood Adv 2024, 8 (14):3731-3744. Kanakry JA, Hegde AM, Durand CM, Massie AB, Greer AE, Ambinder RF, Valsamakis A: The clinical significance of EBV DNA in the plasma and peripheral blood mononuclear cells of patients with or without EBV diseases . Blood 2016, 127 (16):2007-2017. Arber DA, Hasserjian RP, Orazi A, Mathews V, Roberts AW, Schiffer CA, Roug AS, Cazzola M, Dohner H, Tefferi A: Classification of myeloid neoplasms/acute leukemia: Global perspectives and the international consensus classification approach . Am J Hematol 2022, 97 (5):514-518. Jaffe ES, Barr PM, Smith SM: Understanding the New WHO Classification of Lymphoid Malignancies: Why It's Important and How It Will Affect Practice . Am Soc Clin Oncol Educ Book 2017, 37 :535-546. Ok CY, Medeiros LJ: High-grade B-cell lymphoma: a term re-purposed in the revised WHO classification . Pathology 2020, 52 (1):68-77. Petrich AM, Gandhi M, Jovanovic B, Castillo JJ, Rajguru S, Yang DT, Shah KA, Whyman JD, Lansigan F, Hernandez-Ilizaliturri FJ et al : Impact of induction regimen and stem cell transplantation on outcomes in double-hit lymphoma: a multicenter retrospective analysis . Blood 2014, 124 (15):2354-2361. Sesques P, Johnson NA: Approach to the diagnosis and treatment of high-grade B-cell lymphomas with MYC and BCL2 and/or BCL6 rearrangements . Blood 2017, 129 (3):280-288. Ghesquieres H, Cherblanc F, Belot A, Micon S, Bouabdallah KK, Esnault C, Fornecker LM, Thokagevistk K, Bonjour M, Bijou F et al : Challenges for quality and utilization of real-world data for diffuse large B-cell lymphoma in REALYSA, a LYSA cohort . Blood Adv 2024, 8 (2):296-308. Valvert F, Silva O, Solorzano-Ortiz E, Puligandla M, Siliezar Tala MM, Guyon T, Dixon SL, Lopez N, Lopez F, Carias Alvarado CC et al : Low-cost transcriptional diagnostic to accurately categorize lymphomas in low- and middle-income countries . Blood Adv 2021, 5 (10):2447-2455. Tables Tables 1 to 3 are available in the Supplementary Files section Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.docx Tables.docx Cite Share Download PDF Status: Published Journal Publication published 02 Apr, 2026 Read the published version in BMC Cancer → Version 1 posted Editorial decision: Revision requested 28 Oct, 2025 Reviews received at journal 24 Oct, 2025 Reviews received at journal 16 Oct, 2025 Reviewers agreed at journal 11 Oct, 2025 Reviewers agreed at journal 10 Oct, 2025 Reviews received at journal 18 Aug, 2025 Reviewers agreed at journal 11 Aug, 2025 Reviews received at journal 02 Aug, 2025 Reviewers agreed at journal 28 Jul, 2025 Reviewers invited by journal 21 Jul, 2025 Editor invited by journal 25 Jun, 2025 Editor assigned by journal 24 Jun, 2025 Submission checks completed at journal 24 Jun, 2025 First submitted to journal 22 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Louw","email":"","orcid":"","institution":"University of Cape Town and Groote Schuur Hospital","correspondingAuthor":false,"prefix":"","firstName":"Vernon","middleName":"J.","lastName":"Louw","suffix":""},{"id":488562708,"identity":"b04206c3-b9ae-4623-9df1-cc29f5d00783","order_by":14,"name":"Estelle Verburgh","email":"data:image/png;base64,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","orcid":"","institution":"University of Cape Town and Groote Schuur Hospital","correspondingAuthor":true,"prefix":"","firstName":"Estelle","middleName":"","lastName":"Verburgh","suffix":""}],"badges":[],"createdAt":"2025-06-22 08:53:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6948575/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6948575/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12885-026-15807-8","type":"published","date":"2026-04-02T15:59:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87663780,"identity":"741eae5e-281d-45a8-9aab-bf5ced664b7d","added_by":"auto","created_at":"2025-07-27 10:54:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":67831,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe exclusive cohort of newly diagnosed lymphoma according to the WHO-HAEM5.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea,,b\u003c/sup\u003e cases reclassified from lymphoid to myeloid.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026nbsp;c\u003c/sup\u003e excluded indolent lymphoma diagnosis in cases with subsequent diagnosis of transformation of indolent B-cell lymphoma\u003c/p\u003e\n\u003cp\u003eHL, Hodgkin lymphoma; NHL, non-Hodgkin lymphoma.\u0026nbsp;\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6948575/v1/3d637935326cb067c2490642.png"},{"id":87665086,"identity":"304977a5-c828-4c2f-8a96-c8f41b63a1bf","added_by":"auto","created_at":"2025-07-27 11:02:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":154394,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe evolving classification of lymphoma demonstrating correlation of the WHO-HAEM5 and the ICC using a Groote Schuur Hospital patient cohort from 2005-2020*\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e*Cases diagnosed using previous WHO-HAEM versions before 2017 were standardised using the WHO-HAEM4R.\u003c/p\u003e\n\u003cp\u003eA: WHO-HAEM5 diagnoses showing the conceptual IDD group. B: WHO-HAEM5 diagnoses without IDD group.\u003c/p\u003e\n\u003cp\u003eBL Burkitt lymphoma; BCLu B-cell lymphoma unclassifiable, intermediate between diffuse large B-cell lymphoma and classic Hodgkin lymphoma; CHL classic Hodgkin lymphoma; DLBCL, \u003cem\u003eNOS\u003c/em\u003e diffuse large B-cell lymphoma, \u003cem\u003enot otherwise specified\u003c/em\u003e; EBV+ DLBCL, \u003cem\u003eNOS\u003c/em\u003e Epstein-Barr virus positive diffuse large B-cell lymphoma not otherwise specified; HGBL-MYC\u0026amp;BCL6 high-grade B-cell lymphoma with MYC and BL6 gene rearrangements; HGBL, \u003cem\u003eNOS\u003c/em\u003e; high grade B-cell lymphoma, not otherwise specified; IDD immune deficiency/dysregulation-associated lymphomas; IP-LBCL Primary large B-cell lymphoma of immune-privileged sites; MCL Mantle cell lymphoma; MGZL Mediastinal grey zone lymphoma; NLPHL nodular lymphocyte predominant Hodgkin lymphoma; NLPBL nodular lymphocyte predominant B-cell lymphoma; PBL plasmablastic lymphoma; PCNSL primary central nervous system lymphoma; PMLBCL primary mediastinal large B-cell lymphoma; tIL transformations of indolent B-cell lymphomas; T-DLBCL testicular diffuse large B-cell lymphoma; THRLBCL T-cell/histiocyte-rich large B-cell lymphoma.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6948575/v1/2ccefb7368aabc728ad176fe.png"},{"id":87666074,"identity":"7df1f2d4-939d-4dc3-9472-b7da61717917","added_by":"auto","created_at":"2025-07-27 11:10:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":96808,"visible":true,"origin":"","legend":"\u003cp\u003eStandardised approach to differential diagnosis of high-grade B-cell lymphomas on tissue or lymph nodes at Groote Schuur Hospital.\u003c/p\u003e\n\u003cp\u003e* The diagnostic modalities for BL or DLBCL with leukaemic involvement include immunophenotype characterisation by flow cytometry.\u003c/p\u003e\n\u003cp\u003eBL Burkitt lymphoma, DLBCL diffuse large B-cell lymphoma, HGBL high-grade B-cell lymphoma, \u003cem\u003eNOS \u003c/em\u003enot otherwise specified.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6948575/v1/9da6c76321532bd8e85ee3e3.png"},{"id":87663790,"identity":"d0d142e8-494f-4dc9-bc2a-8e8b444ecae6","added_by":"auto","created_at":"2025-07-27 10:54:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":46927,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImplementation of molecular tests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA: DLBCL (n=710), B: HGBL, \u003cem\u003eNOS\u003c/em\u003e (n=52) and C: BL (n=196).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6948575/v1/8f85efb9524bbb5e9494087b.png"},{"id":106344494,"identity":"73591771-bea3-41b8-b440-5169216321d1","added_by":"auto","created_at":"2026-04-07 16:15:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4101894,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6948575/v1/d05c9b79-c015-4b6f-872f-9a530a86881f.pdf"},{"id":87663781,"identity":"3a5874db-40f3-457a-9ecd-de638723471d","added_by":"auto","created_at":"2025-07-27 10:54:29","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":27709,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6948575/v1/c45c08f779320ed2f14dc7ec.docx"},{"id":87663783,"identity":"1a02e347-3caf-4977-b1e0-c3b8020741f0","added_by":"auto","created_at":"2025-07-27 10:54:29","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":89671,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6948575/v1/700d4b4e990a116f99c0bfa7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Real-world lymphoma cohort in the HIV-endemic setting: Impact of implementing novel reclassification standards","fulltext":[{"header":"Background","content":"\u003cp\u003eThe accurate classification of lymphoma is crucial for effective treatment and prognostication yet the diagnostic pathway in the sub-Saharan African (SSA) context is complicated by sub-optimal pathology services, and the more pressing clinical demands of the human immunodeficiency virus (HIV) and tuberculosis (TB) endemic environment [1-6]. Most of the historical lymphoma diagnostic pathology and epidemiology studies were conducted by working groups from high income countries (HIC) with comparatively few independently generated from within the SSA region [7-18]. Real-world observational data have been identified as important adjuncts to bridge the knowledge gap and are under-utilised in low-resource settings [19-21]. To yield translational data, such cohorts need to be meticulously designed and should adhere to recognised quality standards.\u0026nbsp;The process of generating internationally comparable, clinically validated data in these settings is challenging.\u003c/p\u003e\n\u003cp\u003ePopulation-based cancer registries in low- and middle-income countries (LMIC), where they exist, contribute data classified within broad topographical categories to the collaborative platforms of the World Health Organisation (WHO): International Agency for Research on Cancer (IARC) and International Association of Cancer Registries (IACR) [1, 2, 10, 22]. A few specialised lymphoma cohorts have, nonetheless, been established in recent decades; intermittently reporting tentatively disaggregated lymphoma data to the international scientific community. These cohorts were often representative of single institutions and focused on the impact of HIV as the primary driver of lymphoma in the early anti-retroviral treatment (ART) era [12, 13, 15, 16]. These studies were, furthermore, mostly pathology based, with data classified along the directives of earlier versions of the WHO\u0026nbsp;Classification of Haematolymphoid Tumours, hereafter referred to as WHO-HAEM,\u0026nbsp;and in some instances utilised obsolete morphology based classification\u0026nbsp;[8, 9, 23].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe continually updated WHO-HAEM\u0026nbsp;multi-modal framework provides the standard for accurate contemporary diagnosis and subtyping of lymphoma [24-27].\u0026nbsp;Implementation of this refined system for subclassification of lymphoma in the LMIC setting, is limited by a lack of access to pathology resources [28].\u0026nbsp;In addition, incomplete implementation of updated versions of the WHO-HAEM impacts subsequent data collection by cancer registries\u0026nbsp;[29]. In 2022, evolving insights concerning pathogenesis, clinical features, molecular genetics, and changes in taxonomy brought the successor to WHO-HAEM4R in WHO-HAEM5\u0026nbsp;[26, 27]. This advanced understanding concurrently catalysed\u0026nbsp;a second classification standard, the International\u0026nbsp;Consensus Classification (ICC) of mature lymphoid neoplasms\u0026nbsp;[26, 27, 30].\u0026nbsp;Both classification systems, WHO-HAEM5 and ICC, share fundamental concepts of disease classification that integrate clinical, pathological and molecular data [27, 30].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWith these new insights, the University of Cape Town Lymphoma Working Group (UCT LWG), a multidisciplinary team of pathologists, haematologists, database developers and data capturers affiliated with Groote Schuur Hospital (GSH) in the Western Cape province of South Africa, set out to develop a specialised cancer registry empowered to deliver high quality real-world clinically validated lymphoma data. We report here on the operational impact of implementing novel classification systems in a resource restricted setting, and report findings pertinent to an HIV-endemic setting. The UCT LWG lymphoma cohort encompasses all lymphoma cases diagnosed from 2005 and continues to operate prospectively from the Division of Clinical Haematology.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003e1. Study design\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GSH haematology patient registry, a large parent cohort of haematological malignancies, was established in 2018 by the UCT Division of Clinical Haematology. This observational cohort is prospectively maintained and systematically enriched with retrospective data. The registry utilises a secure web-based Research Electronic Data Capture platform (REDCap software 2024, version 13.7.9 Vanderbilt University, Nashville, TN, USA) developed and hosted at UCT to collect and store patient information [31, 32]. For this study, the UCT LWG recruited and reviewed all lymphoma patients \u0026ge;13 years of age with a new lymphoma diagnosis managed at GSH between 1\u003csup\u003est\u003c/sup\u003e January 2005 to 31\u003csup\u003est\u003c/sup\u003e December 2020. The inclusion of the adolescent group, 13 years and older, is reflective of local hospital admission practice [33]. The study patients were identified from GSH clinical records in the Division of Clinical Haematology, the Department of Radiation Oncology, and from the accredited Divisions of Anatomical Pathology and Haematology of the National Health Laboratory Services (NHLS). All relapsed/refractory cases, transformations from previously diagnosed indolent B-cell lymphomas, and dendritic cell and histiocytic neoplasms were excluded. Other exclusions leading to the cohort of interest are highlighted in Figure 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2. Study population\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe catchment area comprises both urban and rural settings in the Western Cape province of South Africa and surrounding regions. GSH is a 975-bed regional academic treatment centre that receives around half of all tertiary, adult public sector referrals in the province (South Africans and resident foreign nationals) [34, 35]. Patients with medical insurance are usually treated in the private sector and seldom diagnosed at GSH. According to the latest South African general household survey conducted in 2021, 47.9% of the Western Cape population is dependent on public health facilities [36].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3. Data collection\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDemographic and baseline clinical data were obtained from paper and electronic records. Patient characteristics such as sex and age were obtained from the patient clinical management and booking system of the South Africa National Department of Health, Clinicom (Citrix 2024 Cloud Software Group, Inc. South Africa). The lymphoma diagnosis, date of diagnosis, Epstein-Barr virus (EBV) and HIV status were obtained from the NHLS laboratory information systems, DISA (DISA*LAB 2017 Johannesburg, South Africa) (archived) and TrakCare (TrakCare Lab 2012, version L6.10, InterSystems Corporation, Morningside, South Africa) (active), theunified healthcare information systems, and electronic medical records tool for healthcare professionals in South Africa\u0026rsquo;s public healthcare facilities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e4. Disease categorisation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e4.1 Hierarchical classification\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe REDCap data dictionary categorisation pathway of the lymphoma cohort was modelled on a hierarchical taxonomy of lymphoid neoplasms [37]. This universally accepted methodology, embedded in the WHO-HAEM4R and International Classification of Diseases for Oncology 3\u003csup\u003erd\u003c/sup\u003e edition (ICD-O-3), systematically categorise diseases from general to increasingly specific entities and subtypes aligned with their respective International Classification of Diseases 10\u003csup\u003eth\u003c/sup\u003e revision (ICD-10) codes and International Classification of Diseases for Oncology, 3\u003csup\u003erd\u0026nbsp;\u003c/sup\u003eedition (ICD-O-3) codes [26, 38, 39]. To reach the most accurate downstream diagnosis a combination of pathology morphology reports and all available ancillary modalities were interrogated and validated with clinical case notes. Cases were next delineated into two subgroups: non-Hodgkin lymphoma (NHL) and Hodgkin lymphoma (HL). NHL first degree delineation is based on the binary lineage directed WHO methodology of B-cell or T/NK-cell, followed by clinical presentation before treatment \u0026ndash; aggressive or indolent [40]. Whilst some lymphoma cases were concluded upstream in more general categories, most were fully subclassified downstream to specific subtypes. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e4.2 Creating a baseline standardised cohort\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients in our settingwere historically diagnosed and classified according to their contemporaneous WHO iteration set out in either the WHO-HAEM3, WHO-HAEM4, or WHO-HAEM4R [24-26]. To that end, the primary researcher first allocated, or where required, reclassified all cases to a baseline WHO-HAEM4R category [26].\u0026nbsp;Classifications were based on a primary source morphology diagnosis obtained from soft tissue, bone marrow, peripheral blood or body cavity fluid and maximally integrated with immunohistochemistry (IHC) stains,\u0026nbsp;immunophenotype on flow cytometry and karyotype fluorescence in-situ hybridisation (FISH) andgene rearrangement studies, and also clinical features. Pathology reports that contained incomplete or differential diagnoses were subjected to review and escalated to the UCT LWG histopathologists and haematopathologists (DRC, DO, GP, SC, JJO, EV) to reconsider and complete subclassification.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e4.3 Cohort enrichment with retrospective investigations in high-grade lymphoma\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrior to WHO-HAEM4 our pathology department followed an \u0026quot;essential for diagnosis\u0026quot; approach to refined subtyping of high-grade B-cell lymphomas (HGBL), with limited immunohistochemistry and an absence of molecular diagnostic methods. Following the implementation of WHO-HAEM4 in 2008, the introduction of cell-of-origin (COO) classification, Hans algorithm and the concept of double-hit HGBL, supplementary investigations were introduced incrementally [25, 41-43]. To achieve diagnostic refinement across the study cohort, some cases required additional IHC stains (\u003cem\u003eCD10/ BCL6/ MUM1/ BCL2/ C-MYC\u003c/em\u003e) and FISH investigations (\u003cem\u003eMYC/ BCL2/ BCL6\u003c/em\u003e) retrospectively[39]. Sub-cohort studies provided additional laboratory data. These studies included (i) Diffuse large B-cell lymphoma, not otherwise specified (DLBCL, \u003cem\u003eNOS\u003c/em\u003e) which examined COO subtypes, EBV co-infection and CD4 counts in the setting of high HIV prevalence; (ii) EBV viral load in newly diagnosed\u0026nbsp;Hodgkin lymphoma; and\u0026nbsp;(iii) molecular testing and CD4 counts in patients with newly diagnosed HIV-Burkitt lymphoma\u0026nbsp;[44-46].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e4.4 Cohort reclassification - nested within the novel classification standards of WHO-HAEM and ICC\u003c/em\u003e\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo assess the operational impact of implementing novel classification standards we re-classified our WHO-HAEM4R standardised cohort according to the newly published guidelines of WHO-HAEM5 and ICC. To enable comparative analysis, the number of specialised investigations required to facilitate reclassification from WHO-HAEM4R to respectively WHO-HAEM5 and ICC were computed. WHO-HAEM5 was utilised as the denominator informing temporal changes. In addition, some new entries not previously found in WHO-HAEM4R included IgG4-related disease, transformations of indolent B-cell lymphomas and lymphoid proliferations and lymphomas associated with immune deficiency/dysregulation (IDD).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e4.5 Temporal analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the translation of temporal changes in our own laboratory approach to the diagnosis of HGBL, we audited our diagnostic algorithm used in the diagnosis of HGBL and enumerated the number of FISH investigations required to achieve refined downstream subtyping. We disaggregated our data across the three previous WHO-HAEM versions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e4.6 Integrated reporting with dynamic quality improvement\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cooperative platform of the specialised lymphoma registry facilitated the introduction of a process of integrated reporting, with dynamic quality improvement. All incident lymphoma diagnoses prospectively enrolled into the registry since 2019 subsequently benefit from a quality check for missing ancillary tests and gaps addressed in real-time. Once WHO-HAEM5 was published, incident cases were also automatically conformed to the new standard.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e5. Data analysis and Statistical Methods\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStataCorp. 2023 \u003cem\u003eStata Statistical Software: Release 18\u003c/em\u003e (StataCorp LLC, College Station, TX, USA) was used for analysis. \u0026nbsp;Categorical variables were described by frequencies and percentages. Age at diagnosis was described by medians and interquartile ranges as data were non-parametric. Age and sex were compared in the HIV-negative and HIV-positive groups by the Mann\u0026ndash;Whitney U test and chi-square test, respectively. Focus areas included numbers and percentages of lymphoma cases by subtype, EBV status, HIV status, and cytogenetics performed over 16 consecutive years. The infographics were created using the online design tools Canva https://www.canva.com/) and SankeyMATIC (https://sankeymatic.com/build/). Time trends were displayed using bar graphs created in Microsoft Excel. \u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003e1. Establishing the lymphoma cohort\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCohort design and workflow are illustrated in Figure 1. Overall, 2399 lymphoma cases consecutively diagnosed between 1 January 2005 and 31 December 2020 were analysed. Isolated cases that lacked HIV status were excluded. Among other exclusions were 21 lymphoma - \u003cem\u003eNOS\u003c/em\u003e cases and instances where patients with insufficient data for definitive sub-classification were lost to follow-up. Table 1 shows the final cohort reclassified to WHO-HAEM5. Additional disaggregation for HIV status represents the WHO-HAEM5 conceptual non-hierarchical entity IDD in the setting of HIV. The final cohort comprised 2354 newly diagnosed patients - 1891 (80.3%) NHL and 463 (19.7%) HL (Table 1). The evolution in disease categorisation, as informed by WHO-HAEM4R to WHO-HAEM5 and ICC, is captured in Figure 2. For 19 patients known to have had more than one type of high-grade NHL or HL diagnosis during the study period, only the first diagnosis was analysed. The distribution of cases according to WHO-HAEM4R is provided in supplementary Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2. Reclassifications\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 shows that reclassification from WHO-HAEM4R to conform to WHO-HAEM5 was required for 25.8% (n=608) of all cases. We reclassified 44 (1.9%) cases as transformations of indolent B-cell lymphomas and reassigned 957 (40.7%) cases to the conceptual non-hierarchical entity - IDD. Notable entity changes demonstrated in Figure 2 and supplementary Table 2 illuminate disparities between WHO-HAEM4R, WHO-HAEM5 and ICC. Figure 2 further illustrates the challenge of applying WHO-HAEM5 in an HIV-endemic setting; as highlighted by the emergence of a large IDD group. To conform to the WHO-HAEM5, more than 50% of HGBL required reclassification. To conform to the ICC, the number of HGBL that required reclassification was comparable to WHO-HAEM4R. The diagnostic algorithm used for this category is shown in Figure 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3. Implementation of molecular tests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 4 illustrates FISH investigations carried out to refine HGBL diagnoses gradually increased over time. FISH studies were performed for 53 (7.5%) DLBCLspecimens, 26 (50.0%) HGBL, \u003cem\u003eNOS\u003c/em\u003e and 107 (54.6%) BL. HGBL diagnosis was guided by a high proliferation index (Ki67\u0026gt;90%) in 124 cases (66.7%). Although 186 (15.3%) of all HGBL had at least one FISH investigation, only 16 cases with a confirmed positive IHC \u003cem\u003eMYC\u003c/em\u003e result, had all three FISH probes \u003cem\u003eMYC\u003c/em\u003e, \u003cem\u003eBCL2\u003c/em\u003e and \u003cem\u003eBCL6,\u0026nbsp;\u003c/em\u003eperformed. An analysis of the temporal trends showed that the FISH testing kick-started by the prior sub-cohort studies, gradually enabled more comprehensive testing [44, 45]. The WHO-HAEM4R entity DLBCL \u003cem\u003eNOS\u003c/em\u003e with \u003cem\u003eMYC\u003c/em\u003e and \u003cem\u003eBCL6\u0026nbsp;\u003c/em\u003egene rearrangements was found in a single case, but after WHO-HAEM5 reclassification, no ‘double hit lymphomas’ with \u003cem\u003eMYC\u003c/em\u003e and \u003cem\u003eBCL2\u003c/em\u003e gene rearrangements were identified.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e4. EBV cohort\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEBV stain results from in-situ hybridisation with EBV encoded RNA (EBER-ish) and latent membrane protein 1 (LMP1) are summarised in Table 3 [44, 46, 47]. In total 770 (32.7%) cases of the cohort were tested. EBV positivity was found in 31.8% (n=245), with a significant proportion concurrently HIV reactive (n=167, 68.2%; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001).\u0026nbsp;The majority of EBV-positive DLBCL (67.3%) were PLWH. Among plasmablastic lymphoma (PBL) cases (92.6% in PLWH), 82.6% (n=46) were EBV positive. Although only a small sample 13.2% (n=10) of KSHV/HHV8-associated multicentric Castleman disease (MCD) cases (92.1% in PLWH) were analysed, 60% were EBV-associated and therefore triple positive for EBV, HHV8 and HIV. EBV results were likewise only available for a small sample of BL 25.3% (n=49), with comparatively few EBV positive (22.4%); the majority in PLWH (88.1%). Newly diagnosed HL in PLWH (n=153) was also very commonly EBV-associated (n=77, 50.3%). We additionally, identified a rare example of EBV-associated nodular lymphocyte predominant HL (NLPHL).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e5. HIV cohort\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHIV prevalence was 33.1% and ART status at lymphoma diagnosis included: ART naïve [n=334, (42.9%)], on ART and virally suppressed [n=285, (36.6%)], and on ART, but virally unsuppressed [n=160, (20.5%)].\u0026nbsp;PLWH presented at a significantly younger age - median 38.3 years (IQR 32.5-45.3) compared to the HIV-negative group - median 56.6 years (IQR 41.0-67.3); \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001. The most frequent HIV-associated lymphoma entities were DLBCL, \u003cem\u003eNOS\u003c/em\u003e [n=173 (22.2%)], BL [n=171 (22.0%)], and classic HL [n=152 (19.5%)]. Primary large B-cell lymphomas of immune privileged sites were rare. Most low-grade NHL B-cell entities were HIV non-reactive. Splenic B-cell lymphomas/ leukaemia and lymphoplasmacytic lymphoma were notably absent, whereas rare cases of chronic lymphocytic leukaemia/ small lymphocytic lymphoma, marginal zone lymphoma, follicular lymphoma and mantle cell lymphoma were HIV reactive. Subtype analysis of HL showed that nodular sclerosis HL cases were the most common among PLWH. NLPHL was a rare entity (1 of n=27). Finally, T-cell and NK-cell leukaemia and lymphomas (mature, cutaneous and other), represented a small proportion of lymphoma cases [n=181, (7.7%)] with [n=25, (13.8%)] presenting in PLWH.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study from an HIV-endemic developing country, we demonstrate how rigorous lymphoma classification could be established in a real-world diagnostic registry, by gradual implementation of WHO-HAEM and ICC directives. The impact of reclassifying to ICC was lower than WHO-HAEM5, with less disparities compared to the conversion from WHO-HAEM4R to the WHO-HAEM5. Data disaggregation revealed that the most frequent groups reclassified were the high-grade varieties of lymphoma, notably HGBL (\u0026gt;\u0026thinsp;50% of cases). This included reassignment of cases to the WHO-HAEM5 conceptual non-hierarchical entity IDD that endorse the \u0026ldquo;Three Part Nomenclature Framework\u0026rdquo; for IDD as suggested by international working groups concerned with immunodeficiency-associated lymphoproliferative disorders [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. This entity, that amalgamates all post-transplant lymphoproliferative disorders (PTLD), HIV-positive and EBV tumour positive cases contrasts with the ICC approach to retain the traditional hierarchical type and subtype categories. In the absence of the IDD entity, we found reasonable comparability between WHO-HAEM4R, WHO-HAEM5 and ICC. This agrees with data published by a large prospective HIC cohort reporting only 0.8% major diagnostic differences [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. We, furthermore, identified only a single case of HGBL with \u003cem\u003eMYC\u003c/em\u003e and \u003cem\u003eBCL6\u003c/em\u003e gene rearrangements that, in a departure from previous WHO-HAEM4R methodology, would be categorised as DLBCL, \u003cem\u003eNOS\u003c/em\u003e according to WHO-HAEM5, and as a defined stand-alone subtype by ICC. Similar to another South African cohort, HGBL with \u003cem\u003eMYC\u003c/em\u003e and \u003cem\u003eBCL2\u003c/em\u003e gene rearrangements was not identified in our cohort [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAnother major difference between WHO-HAEM5 and ICC is the consolidation of transformations of indolent B-cell lymphomas [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In our cohort, we were comfortably able to identify cases of indolent B-cell lymphomas that transformed to high-grade. However, in some instances, \u003cem\u003ede novo\u003c/em\u003e high-grade lymphoma, that \u003cem\u003emorphologically\u003c/em\u003e resembled transformation from indolent B-cell lymphoma, imparted a degree of uncertainty. Of note also, is the annotation of residual discordant indolent B-cell lymphomas (with bone marrow involvement) following treatment of high-grade lymphomas. In addition, indolent B-cell lymphomas are diverse entities, implying significant pathological, clinical and therapeutic divergence. The consolidation of these cases as a single conceptual entity once transformation occurs, may require further evidence-based validation and refining.\u003c/p\u003e\u003cp\u003eEarlier regional studies that mostly focussed on lymphoma prevalence, highlighted the prominence of high-grade lymphoma and formerly rare varieties, with HIV as the primary driver [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The most prevalent lymphoma entities among PLWH in our cohort agrees with data reported from HIC and confirms the ongoing prominence of large B-cell lymphoma [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. On interrogation of the clinical features of HL, we found that it frequently behaved in a \u0026ldquo;clinically aggressive\u0026rdquo; fashion not dissimilar to other established histology driven clinically aggressive high-grade entities [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. HIV prevalence for this study cohort was slightly lower than the range (37\u0026ndash;66%) reported by earlier cohorts in South Africa [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The significantly younger age at presentation for PLWH with lymphoma [median 38.3 years (IQR 32.5\u0026ndash;45.3)] is in stark contrast to the HIV-negative patients in our cohort [median 56.6 years (IQR 41.0-67.3)] and the difference is even more striking when comparing it with the median age of presentation of lymphoma cohorts in HIC; for example, the United Kingdom [median 69.9 years (IQR 59.1\u0026ndash;78.3)], USA [median 62 years (range, 18\u0026ndash;99)], Australia and New Zealand [median 64.3 years (IQR 52.1\u0026ndash;73.5)] [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. More than half of the subpopulation of PLWH were ART na\u0026iuml;ve, or on ART but unsuppressed at lymphoma diagnosis highlighting viral non-suppression as an important driver of lymphomagenesis.\u003c/p\u003e\u003cp\u003eThe analysis of EBV testing data in our lymphoma cohort indicate a high overall EBV positivity and illuminates significant concurrent HIV reactivity. The WHO-HAEM5 entity EBV-positive DLBCL, typically a disease of older patients in HIC, occurs in comparatively younger patients in our setting, and frequently occurs in PLWH. The tentative EBV characterisation in our cohort supports theorisation around the increasing prominence of virally driven cancers in LMICs [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Although EBV testing was not performed in all our lymphoma cases, which may have introduced selection bias, on balance our results argue for the potential utility of routinely incorporating EBV testing in lymphoma diagnostic algorithms, in select settings [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSimilarly, the karyotypes for some BL cases were found to be frequently and unusually complex in the case of HIV-associated BL [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The availability of karyotypes in these instances obsoleted the need for \u003cem\u003eMYC\u003c/em\u003e testing by FISH. This stands in contrasts to the WHO-HAEM5 diagnostic algorithm for HGBL that advocates for further testing despite a \u003cem\u003eMYC\u003c/em\u003e negative result. Nonetheless, the distinction between the HGBL entities DLBCL and BL, both frequently HIV-associated and clinically aggressive, but with different treatment modalities, remain of clinical importance in our setting. Our results underscore the importance of FISH analysis as a crucial adjunct to diagnostic clarity in high-grade varieties of B-cell lymphoma and reveal rare instances of double and triple viral oncogene or viral vector association. In LMIC, bespoke evidence-based diagnostic algorithms for HGBL are ultimately needed to direct the selective use of costly complimentary techniques, including FISH studies, and reserved for cases where results are imperative to guide and optimise therapy [\u003cspan additionalcitationids=\"CR59 CR60 CR61\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe solid hierarchical framework of the WHO-HAEM5 and ICC enabled incorporation of high-quality real-world data maximising available information and minimising impact of incomplete data [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Challenges in our setting include scarce technical expertise, limited access to and the financial cost of advanced instrumentation and bioinformatics support. The relatively high reclassification rate illustrates how in-house operational challenges in a resource restricted setting, was ameliorated by incremental introduction of novel diagnostics in support of progressive sophistication in lymphoma diagnosis and classification. In HIC, representing around a fifth of the world\u0026rsquo;s population and benefiting from wider implementation of universal healthcare access, the categorisation of some lymphoma entities along the more comprehensive ICC diagnostic pathways, may be more achievable [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Both WHO-HAEM5 and ICC, nonetheless, provide improved classification methodology and can be implemented to a reasonable level of downstream refinement, provided relatively basic laboratory modalities are available.\u003c/p\u003e\u003cp\u003eLimitations of this study, include instances of sample insufficiency for FISH testing among some HGBL cases and insufficient IHC stains performed to achieve subclassification. Contingent on clinically extreme presentations, which are not rare in the SSA setting, bias may also be introduced by the omission of diagnoses based on isolated pre-terminal samples. Additionally, missing clinical data and early deaths that preclude further diagnostic investigations led to exclusion of some cases. The high reclassification rate in this study may at least be partly attributed to additions and developments within the sequential versions of the WHO-HAEM [\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. NHL cases frequently required additional IHC stains or complementary tests to reach refined diagnoses or to upgrade to disease entities previously defined as provisional entities [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Despite these limitations, this is the first study that comprehensively describes the population of lymphoma patients in our institution with disaggregated subtyping. Strengths of this study include the large sample size, single site integrated diagnosis and categorisation standardised to the WHO-HAEM5 providing real-world data that allows comparability to other centres, local and international [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study takes the first steps to highlight the complexities and challenges encountered to implement refined diagnosis and classification of lymphoma in an HIV and TB endemic LMIC setting. It also argues for improvement in cost-effective diagnostic and classification algorithms appropriate for LMIC settings [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Additionally, it supports the development of regional specialised cancer registries and provides a platform that fosters much needed translational research. Finally, from a public health standpoint, it is hoped that it may yield actionable guidance for equitable healthcare strategy development in our local context.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eABC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;activated B-cell\u003c/p\u003e\n\u003cp\u003eART\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;antiretroviral therapy\u003c/p\u003e\n\u003cp\u003eBCLu\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;B-cell lymphoma unclassifiable\u003c/p\u003e\n\u003cp\u003eBL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Burkitt lymphoma\u003c/p\u003e\n\u003cp\u003eCHL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;classic Hodgkin lymphoma\u003c/p\u003e\n\u003cp\u003eCLL chronic lymphocytic leukaemia\u003c/p\u003e\n\u003cp\u003eCNS central nervous system\u003c/p\u003e\n\u003cp\u003eCOO\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;cell-of-origin\u003c/p\u003e\n\u003cp\u003eDLBCL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;diffuse large B-cell lymphoma\u003c/p\u003e\n\u003cp\u003eEBER-ish\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Epstein-Barr virus-encoded small RNAs in-situ hybridisation\u003c/p\u003e\n\u003cp\u003eEBV Epstein-Barr virus\u003c/p\u003e\n\u003cp\u003eFISH\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;fluorescence in-situ hybridisation\u003c/p\u003e\n\u003cp\u003eFL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;follicular lymphoma\u003c/p\u003e\n\u003cp\u003eGCB\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;germinal centre B-cell\u003c/p\u003e\n\u003cp\u003eGSH\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Groote Schuur Hospital\u003c/p\u003e\n\u003cp\u003eHGBL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;high-grade B-cell lymphoma\u003c/p\u003e\n\u003cp\u003eHIC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;high income countries\u003c/p\u003e\n\u003cp\u003eHIV human immune deficiency virus\u003c/p\u003e\n\u003cp\u003eHHV8 human herpesvirus 8\u003c/p\u003e\n\u003cp\u003eHL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Hodgkin lymphoma\u003c/p\u003e\n\u003cp\u003eHREC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;human research ethics committee\u003c/p\u003e\n\u003cp\u003eHTLV-1 human T-lymphotropic virus\u003c/p\u003e\n\u003cp\u003eIACR International Agency for Research on Cancer\u003c/p\u003e\n\u003cp\u003eIARC International Association of Cancer Registries\u003c/p\u003e\n\u003cp\u003eICC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Internation Consensus Classification of mature lymphoid tissues\u003c/p\u003e\n\u003cp\u003eICD-03 International Classification of Diseases for Oncology, 3\u003csup\u003erd\u0026nbsp;\u003c/sup\u003eedition\u003c/p\u003e\n\u003cp\u003eICD-10 International Classification of Diseases for Oncology, 10\u003csup\u003eth\u003c/sup\u003e revision\u003c/p\u003e\n\u003cp\u003eIDD lymphoid proliferations and lymphomas associated with immune deficiency / dysregulation\u003c/p\u003e\n\u003cp\u003eIHC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;immunohistochemistry\u003c/p\u003e\n\u003cp\u003eIP-LBCL primary large B-cell lymphoma of immune-privileged sites\u003c/p\u003e\n\u003cp\u003eIQR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Inter Quartile Range\u003c/p\u003e\n\u003cp\u003eKSHV Kaposi sarcoma herpes virus\u003c/p\u003e\n\u003cp\u003eLMIC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;low- and middle-income countries\u003c/p\u003e\n\u003cp\u003eLMP-1\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;latent membrane protein 1\u003c/p\u003e\n\u003cp\u003eLPD lymphoproliferative disorder\u003c/p\u003e\n\u003cp\u003eLPL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;lymphoplasmacytic lymphoma\u003c/p\u003e\n\u003cp\u003eLWG\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Lymphoma Working Group\u003c/p\u003e\n\u003cp\u003eMALT mucosa-associated lymphoid tissue\u003c/p\u003e\n\u003cp\u003eMCD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;multicentric Castleman disease\u003c/p\u003e\n\u003cp\u003eMCL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;mantle cell lymphoma\u003c/p\u003e\n\u003cp\u003eMGZL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;mediastinal grey zone lymphoma\u003c/p\u003e\n\u003cp\u003eMZL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;marginal zone lymphoma\u003c/p\u003e\n\u003cp\u003eNLPBL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;nodular lymphocyte predominant B-cell lymphoma\u003c/p\u003e\n\u003cp\u003eNLPHL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;nodular lymphocyte predominant Hodgkin lymphoma\u003c/p\u003e\n\u003cp\u003eNHL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;non-Hodgkin lymphoma\u003c/p\u003e\n\u003cp\u003eNHLS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;National Health Laboratory Services\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNOS\u003c/em\u003e \u003cem\u003enot otherwise specified\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePBL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;plasmablastic lymphoma\u003c/p\u003e\n\u003cp\u003ePCNSL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;primary central nervous system lymphoma\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePLWH\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;people living with HIV\u003c/p\u003e\n\u003cp\u003ePMLBCL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;primary mediastinal large B-cell lymphoma\u003c/p\u003e\n\u003cp\u003ePTCL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;peripheral T-cell lymphoma\u003c/p\u003e\n\u003cp\u003ePTLD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;post-transplant lymphoproliferative disorders\u003c/p\u003e\n\u003cp\u003eREDCap\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Research Electronic Data Capture\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSSA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;sub-Sarahan Africa\u003c/p\u003e\n\u003cp\u003eT-DLBCL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;testicular diffuse large B-cell lymphoma\u003c/p\u003e\n\u003cp\u003eTHRLBCL T-cell/histiocyte-rich large B-cell lymphoma\u003c/p\u003e\n\u003cp\u003etIL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;transformations of indolent B-cell lymphomas\u003c/p\u003e\n\u003cp\u003eUCT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;University of Cape Town\u003c/p\u003e\n\u003cp\u003eWHO-HAEM \u0026nbsp; World Health Organisation Classification of Haematolymphoid Tumours\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the GSH NHLS staff: Haematology, Molecular, Cytogenetics and Anatomical Pathology and all other colleagues who supported the study through its many phases; and also, Harvey Lusaka Binamu for his expert assistance with the infographics.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch reported in this publication was supported by the Cancer Association of South Africa (CANSA) as well as the Fogarty International Centre and National Heart, Lung, and Blood Institute of the National Institutes of Health under award number D43 TW010345. The content is solely the responsibility of the authors and does not necessarily represent the official views of CANSA and the National Institutes of Health.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEV was responsible for funding acquisition, designed and implemented the GSH haematology patient registry, oversaw study design as well as reviewed disputed clinical cases treated in the Division of Clinical Haematology. LFA conceptualised study design, acquired the data, and wrote the manuscript as part of her PhD. DC reviewed disputed tissue histology cases and verified the data from the Division of Anatomical Pathology. DO reviewed disputed bone marrow or flow cytometry cases and verified the data. ZM reviewed disputed clinical cases treated in the Department of Radiation Oncology and verified the data. JB and KB designed, maintained and managed the registry as well as extracted the data and performed statistical analyses. GP reviewed and approved disputed cases. KA, KS, GK, SC, JJO performed sub-studies within this lymphoma cohort and provided baseline data for this study. AR reviewed molecular studies. VJL critically revised the final manuscript. All co-authors reviewed and edited the manuscript draft.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for the GSH haematology patient registry (HREC R024/2018), and for this study (HREC 479/2020), were obtained from the human research ethics committee (HREC) at UCT and the GSH ethics review board.\u0026nbsp;Patient consent was waived due to the retrospective nature of data collection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure of interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eGLOBOCAN: South Africa\u0026nbsp;\u003c/strong\u003e[https://gco.iarc.fr/today/data/factsheets/populations/913-southern-africa-fact-sheets.pdf]\u003c/li\u003e\n \u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F: \u003cstrong\u003eGlobal Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries\u003c/strong\u003e. \u003cem\u003eCA Cancer J Clin\u0026nbsp;\u003c/em\u003e2021, \u003cstrong\u003e71\u003c/strong\u003e(3):209-249.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eGlobal HIV Statistics\u0026nbsp;\u003c/strong\u003e[https://unaids.org/en/resources/fact-sheet]\u003c/li\u003e\n \u003cli\u003eSouth Africa Department of Statistics: \u003cstrong\u003eStatistical Release P0302. Mid-year population estimates 2022\u003c/strong\u003e. In\u003cem\u003e.\u003c/em\u003e Pretoria; 2023.\u003c/li\u003e\n \u003cli\u003eAntel K, Louw VJ, Maartens G, Oosthuizen J, Verburgh E: \u003cstrong\u003eDiagnosing lymphoma in the shadow of an epidemic: lessons learned from the diagnostic challenges posed by the dual tuberculosis and HIV epidemics\u003c/strong\u003e. \u003cem\u003eLeuk Lymphoma\u0026nbsp;\u003c/em\u003e2020, \u003cstrong\u003e61\u003c/strong\u003e(14):3417-3421.\u003c/li\u003e\n \u003cli\u003eAntel K, Oosthuizen J, Brown K, Malherbe F, Loebenberg P, Seaton C, Baloyi S, Simba K, Chetty D, Louw VJ\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e: \u003cstrong\u003eFocused investigations to expedite cancer diagnosis among patients with lymphadenopathy in a tuberculosis and HIV-endemic region\u003c/strong\u003e. \u003cem\u003eAIDS\u0026nbsp;\u003c/em\u003e2023, \u003cstrong\u003e37\u003c/strong\u003e(4):587-594.\u003c/li\u003e\n \u003cli\u003eGopal S, Patel MR, Yanik EL, Cole SR, Achenbach CJ, Napravnik S, Burkholder GA, Reid EG, Rodriguez B, Deeks SG\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e: \u003cstrong\u003eTemporal trends in presentation and survival for HIV-associated lymphoma in the antiretroviral therapy era\u003c/strong\u003e. \u003cem\u003eJ Natl Cancer Inst\u0026nbsp;\u003c/em\u003e2013, \u003cstrong\u003e105\u003c/strong\u003e(16):1221-1229.\u003c/li\u003e\n \u003cli\u003ePerry AM, Perner Y, Diebold J, Nathwani BN, MacLennan KA, Müller-Hermelink HK, Bast M, Boilesen E, Armitage JO, Weisenburger DD: \u003cstrong\u003eNon-Hodgkin lymphoma in Southern Africa: review of 487 cases from The International Non-Hodgkin Lymphoma Classification Project\u003c/strong\u003e. \u003cem\u003eBritish Journal of Haematology\u0026nbsp;\u003c/em\u003e2016, \u003cstrong\u003e172\u003c/strong\u003e:716-723.\u003c/li\u003e\n \u003cli\u003ePerry AM, Diebold J, Nathwani BN, MacLennan KA, Müller-Hermelink HK, Bast M, Boilesen E, Armitage JO, Weisenburger DD: \u003cstrong\u003eNon-Hodgkin lymphoma in the developing world: review of 4539 cases from the International Non-Hodgkin Lymphoma Classification Project\u003c/strong\u003e. \u003cem\u003eHaematologica\u0026nbsp;\u003c/em\u003e2016, \u003cstrong\u003e101\u003c/strong\u003e(10):1244-1250.\u003c/li\u003e\n \u003cli\u003eMiranda-Filho A, Pineros M, Znaor A, Marcos-Gragera R, Steliarova-Foucher E, Bray F: \u003cstrong\u003eGlobal patterns and trends in the incidence of non-Hodgkin lymphoma\u003c/strong\u003e. \u003cem\u003eCancer Causes Control\u0026nbsp;\u003c/em\u003e2019, \u003cstrong\u003e30\u003c/strong\u003e(5):489-499.\u003c/li\u003e\n \u003cli\u003eAnderson JR, O. AJ, Weisenburger DDftN-HLCP: \u003cstrong\u003eEpidemiology of non-Hodgkin lymphomas: Distributions of the major subtypes differ by geographic locations\u003c/strong\u003e. \u003cem\u003eAnnals of Oncology\u0026nbsp;\u003c/em\u003e1998, \u003cstrong\u003e9\u003c/strong\u003e:717-720.\u003c/li\u003e\n \u003cli\u003eMantina H, Wiggill TM, Carmona S, Perner Y, Stevens WS: \u003cstrong\u003eCharacterization of Lymphomas in a High Prevalence HIV Setting\u003c/strong\u003e. \u003cem\u003eJ Acquir Immune Defic Syndr\u0026nbsp;\u003c/em\u003e2010, \u003cstrong\u003e53\u003c/strong\u003e:656\u0026ndash;660.\u003c/li\u003e\n \u003cli\u003eWiggill TM, Mantina H, Willem P, Perner Y, Stevens WS: \u003cstrong\u003eChanging Pattern of Lymphoma Subgroups at a Tertiary Academic Complex in a High-Prevalence HIV Setting: A South African Perspective\u003c/strong\u003e. \u003cem\u003eJaids-Journal of Acquired Immune Deficiency Syndromes\u0026nbsp;\u003c/em\u003e2011, \u003cstrong\u003e56\u003c/strong\u003e(5):460-466.\u003c/li\u003e\n \u003cli\u003eOelofse D, Truter I: \u003cstrong\u003eIncidence of haematological malignancies, Eastern Cape Province; South Africa, 2004-2013\u003c/strong\u003e. \u003cem\u003eCancer Epidemiol\u0026nbsp;\u003c/em\u003e2018, \u003cstrong\u003e53\u003c/strong\u003e:166-171.\u003c/li\u003e\n \u003cli\u003eAbayomi EA, Somers A, Grewal R, Sissolak G, Bassa F, Maartens D, Jacobs P, Stefan C, Ayers LW: \u003cstrong\u003eImpact of the HIV epidemic and Anti-Retroviral Treatment policy on lymphoma incidence and subtypes seen in the Western Cape of South Africa, 2002-2009: preliminary findings of the Tygerberg Lymphoma Study Group\u003c/strong\u003e. \u003cem\u003eTransfus Apher Sci\u0026nbsp;\u003c/em\u003e2011, \u003cstrong\u003e44\u003c/strong\u003e(2):161-166.\u003c/li\u003e\n \u003cli\u003ePatel MR, Philip V, Omar T, Turton D, Candy G, Lakha A, Pather S: \u003cstrong\u003eThe Impact of Human Immunodeficiency Virus Infection (HIV) on Lymphoma in South Africa\u003c/strong\u003e. \u003cem\u003eJournal of Cancer Therapy\u0026nbsp;\u003c/em\u003e2015, \u003cstrong\u003e06\u003c/strong\u003e(06):527-535.\u003c/li\u003e\n \u003cli\u003eOluwasola AO, Olaniyi JA, Otegbayo JA, Ogun GO, Akingbola TS, Ukah CO, Akang EEU, Aken\u0026rsquo;Ova YA: \u003cstrong\u003eA Fifteen-year Review of Lymphomas in a Nigerian Tertiary Healthcare Centre\u003c/strong\u003e. \u003cem\u003eJ Health Popul Nutr\u0026nbsp;\u003c/em\u003e2011, \u003cstrong\u003e29\u003c/strong\u003e(4):310-316.\u003c/li\u003e\n \u003cli\u003eVaughan J, Perner Y, McAlpine E, Wiggill T: \u003cstrong\u003eHIV-Associated Hodgkin Lymphoma Involving the Bone Marrow Identifies a Very High-Risk Subpopulation in the Era of Widescale Antiretroviral Therapy Use in Johannesburg, South Africa\u003c/strong\u003e. \u003cem\u003eAIDS\u0026nbsp;\u003c/em\u003e2020, \u003cstrong\u003e83\u003c/strong\u003e(4):345-349.\u003c/li\u003e\n \u003cli\u003eHershman DL, Wright JD: \u003cstrong\u003eComparative effectiveness research in oncology methodology: observational data\u003c/strong\u003e. \u003cem\u003eJ Clin Oncol\u0026nbsp;\u003c/em\u003e2012, \u003cstrong\u003e30\u003c/strong\u003e(34):4215-4222.\u003c/li\u003e\n \u003cli\u003eEl-Galaly TC, Cheah CY, Villa D: \u003cstrong\u003eReal world data as a key element in precision medicine for lymphoid malignancies: potentials and pitfalls\u003c/strong\u003e. \u003cem\u003eBr J Haematol\u0026nbsp;\u003c/em\u003e2019, \u003cstrong\u003e186\u003c/strong\u003e(3):409-419.\u003c/li\u003e\n \u003cli\u003eChihara D, Hobbs BP, Maurer MJ, Flowers CR: \u003cstrong\u003eUtilization of Real-World Data to Facilitate Clinical Trials for Patients with Lymphoma\u003c/strong\u003e. \u003cem\u003ePharmacoepidemiology\u0026nbsp;\u003c/em\u003e2024, \u003cstrong\u003e3\u003c/strong\u003e(3):252-264.\u003c/li\u003e\n \u003cli\u003eMafra A, Laversanne M, Gospodarowicz M, Klinger P, De Paula Silva N, Pineros M, Steliarova-Foucher E, Bray F, Znaor A: \u003cstrong\u003eGlobal patterns of non-Hodgkin lymphoma in 2020\u003c/strong\u003e. \u003cem\u003eInt J Cancer\u0026nbsp;\u003c/em\u003e2022, \u003cstrong\u003e151\u003c/strong\u003e(9):1474-1481.\u003c/li\u003e\n \u003cli\u003eNaresh KN, Raphael M, Ayers L, Hurwitz N, Calbi V, Rogena E, Sayed S, Sherman O, Ibrahim HA, Lazzi S\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e: \u003cstrong\u003eLymphomas in sub-Saharan Africa--what can we learn and how can we help in improving diagnosis, managing patients and fostering translational research?\u003c/strong\u003e \u003cem\u003eBr J Haematol\u0026nbsp;\u003c/em\u003e2011, \u003cstrong\u003e154\u003c/strong\u003e(6):696-703.\u003c/li\u003e\n \u003cli\u003eJaffe ES, Harris NL, Stein H, Vardiman JW: \u003cstrong\u003eWorld Health Organization Classification of Tumours, Pathology and Genetics of Tumours of Haematopoietic and Lymphoid Tissues\u003c/strong\u003e: IARC Press, Lyon, France; 2001.\u003c/li\u003e\n \u003cli\u003eSwerdlow SH, Campo E, Harris NL, Jaffe ES, Pileri SA, Stein H, Thiele J, Vardiman JW: \u003cstrong\u003eWHO Classification of Tumours of Haematopoietic and Lymphoid Tissues\u003c/strong\u003e. Lyon, France: IARC Press; 2008.\u003c/li\u003e\n \u003cli\u003eSwerdlow SH, Campo E, Harris NL, Jaffe ES, Pileri SA, Stein H, Thiele J: \u003cstrong\u003eWHO Classification of Tumours of Haematopoietic and Lymphoid Tissues, Revised 4th ed\u003c/strong\u003e. Lyon, France: IARC Press; 2017.\u003c/li\u003e\n \u003cli\u003eAlaggio R, Amador C, Anagnostopoulos I, Attygalle AD, Araujo IBO, Berti E, Bhagat G, Borges AM, Boyer D, Calaminici M\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e: \u003cstrong\u003eThe 5th edition of the World Health Organization Classification of Haematolymphoid Tumours: Lymphoid Neoplasms\u003c/strong\u003e. \u003cem\u003eLeukemia\u0026nbsp;\u003c/em\u003e2022, \u003cstrong\u003e36\u003c/strong\u003e(7):1720-1748.\u003c/li\u003e\n \u003cli\u003eTomoka T, Montgomery ND, Powers E, Dhungel BM, Morgan EA, Mulenga M, Gopal S, Fedoriw Y: \u003cstrong\u003eLymphoma and Pathology in Sub-Saharan Africa: Current Approaches and Future Directions\u003c/strong\u003e. \u003cem\u003eClin Lab Med\u0026nbsp;\u003c/em\u003e2018, \u003cstrong\u003e38\u003c/strong\u003e(1):91-100.\u003c/li\u003e\n \u003cli\u003eSmith A, Roman E, Howell D, Jones R, Patmore R, Jack A, Haematological Malignancy Research N: \u003cstrong\u003eThe Haematological Malignancy Research Network (HMRN): a new information strategy for population based epidemiology and health service research\u003c/strong\u003e. \u003cem\u003eBr J Haematol\u0026nbsp;\u003c/em\u003e2010, \u003cstrong\u003e148\u003c/strong\u003e(5):739-753.\u003c/li\u003e\n \u003cli\u003eCampo E, Jaffe ES, Cook JR, Quintanilla-Martinez L, Swerdlow SH, Anderson KC, Brousset P, Cerroni L, de Leval L, Dirnhofer S\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e: \u003cstrong\u003eThe International Consensus Classification of Mature Lymphoid Neoplasms: A Report from the Clinical Advisory Committee\u003c/strong\u003e. \u003cem\u003eBlood\u0026nbsp;\u003c/em\u003e2022.\u003c/li\u003e\n \u003cli\u003eHarris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG: \u003cstrong\u003eResearch electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support\u003c/strong\u003e. \u003cem\u003eJ Biomed Inform\u0026nbsp;\u003c/em\u003e2009, \u003cstrong\u003e42\u003c/strong\u003e(2):377-381.\u003c/li\u003e\n \u003cli\u003eHarris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O\u0026apos;Neal L, McLeod L, Delacqua G, Delacqua F, Kirby J\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e: \u003cstrong\u003eThe REDCap consortium: Building an international community of software platform partners\u003c/strong\u003e. \u003cem\u003eJ Biomed Inform\u0026nbsp;\u003c/em\u003e2019, \u003cstrong\u003e95\u003c/strong\u003e.\u003c/li\u003e\n \u003cli\u003eStefan C, van der Merwe P-L: \u003cstrong\u003eTreating adolescents in South Africa\u003c/strong\u003e. \u003cem\u003eSAMJ\u0026nbsp;\u003c/em\u003e2008, \u003cstrong\u003e8\u003c/strong\u003e(3).\u003c/li\u003e\n \u003cli\u003eSouth Africa Department of Health: \u003cstrong\u003eReferral and support zones public sector health institutions in the Western Cape province\u003c/strong\u003e. In\u003cem\u003e.\u003c/em\u003e Pretoria: National Department of Health; 1997.\u003c/li\u003e\n \u003cli\u003eRichards DB, Jacquet GA: \u003cstrong\u003eAnalysis of referral appropriateness in the Western Cape, South Africa, and implications for resource allocation\u003c/strong\u003e. \u003cem\u003eAfrican Journal of Emergency Medicine\u0026nbsp;\u003c/em\u003e2012, \u003cstrong\u003e2\u003c/strong\u003e(2):53-58.\u003c/li\u003e\n \u003cli\u003eSouth Africa Department of Statistics: \u003cstrong\u003eStatistical Release P0318. General Household Survey 2021\u003c/strong\u003e. In\u003cem\u003e.\u003c/em\u003e Pretoria; 2022.\u003c/li\u003e\n \u003cli\u003eTurner JJ, Morton LM, Linet MS, Clarke CA, Kadin ME, Vajdic CM, Monnereau A, Maynadi\u0026eacute; M, Chiu BC, Marcos-Gragera R\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e: \u003cstrong\u003eInterLymph hierarchical classification of lymphoid neoplasms for epidemiologic research based on the WHO classification (2008): update and future directions\u003c/strong\u003e. \u003cem\u003eBlood\u0026nbsp;\u003c/em\u003e2010, \u003cstrong\u003e116\u003c/strong\u003e(20):e90-98.\u003c/li\u003e\n \u003cli\u003eFritz A, Percy C, Jack A, Shanmugaratnam K, Sobin L, Parkin DM, Whelan S: \u003cstrong\u003eInternational Classification of Diseases for Oncology\u003c/strong\u003e, 3rd edn. Malta: World Health Organisation; 2013.\u003c/li\u003e\n \u003cli\u003eWHO: \u003cstrong\u003eInternational statistical classification of diseases and related health problems\u003c/strong\u003e, vol. 2, 5th edn. France: World Health Organisation; 2016.\u003c/li\u003e\n \u003cli\u003eConnors JM: \u003cstrong\u003eNon-Hodgkin lymphoma: the clinician\u0026apos;s perspective--a view from the receiving end\u003c/strong\u003e. \u003cem\u003eMod Pathol\u0026nbsp;\u003c/em\u003e2013, \u003cstrong\u003e26 Suppl 1\u003c/strong\u003e:S111-S1118.\u003c/li\u003e\n \u003cli\u003eCampo E, Swerdlow SH, Harris NL, Pileri S, Stein H, Jaffe ES: \u003cstrong\u003eThe 2008 WHO classification of lymphoid neoplasms and beyond: evolving concepts and practical applications\u003c/strong\u003e. \u003cem\u003eBlood\u0026nbsp;\u003c/em\u003e2011, \u003cstrong\u003e117\u003c/strong\u003e(19):5019-5032.\u003c/li\u003e\n \u003cli\u003eHans CP, Weisenburger DD, Greiner TC, Gascoyne RD, Delabie J, Ott G, Muller-Hermelink HK, Campo E, Braziel RM, Jaffe ES\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e: \u003cstrong\u003eConfirmation of the molecular classification of diffuse large B-cell lymphoma by immunohistochemistry using a tissue microarray\u003c/strong\u003e. \u003cem\u003eBlood\u0026nbsp;\u003c/em\u003e2004, \u003cstrong\u003e103\u003c/strong\u003e(1):275-282.\u003c/li\u003e\n \u003cli\u003eChoi WW, Weisenburger DD, Greiner TC, Piris MA, Banham AH, Delabie J, Braziel RM, Geng H, Iqbal J, Lenz G\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e: \u003cstrong\u003eA new immunostain algorithm classifies diffuse large B-cell lymphoma into molecular subtypes with high accuracy\u003c/strong\u003e. \u003cem\u003eClin Cancer Res\u0026nbsp;\u003c/em\u003e2009, \u003cstrong\u003e15\u003c/strong\u003e(17):5494-5502.\u003c/li\u003e\n \u003cli\u003eCassim S, Antel K, Chetty DR, Oosthuizen J, Opie J, Mohamed Z, Verburgh E: \u003cstrong\u003eDiffuse large B-cell lymphoma in a South African cohort with a high HIV prevalence: an analysis by cell-of-origin, Epstein-Barr virus infection and survival\u003c/strong\u003e. \u003cem\u003ePathology\u0026nbsp;\u003c/em\u003e2020:453-459.\u003c/li\u003e\n \u003cli\u003eOpie J, Antel K, Koller A, Novitzky N: \u003cstrong\u003eIn the South African setting, HIV-associated Burkitt lymphoma is associated with frequent leukaemic presentation, complex cytogenetic karyotypes, and adverse clinical outcomes\u003c/strong\u003e. \u003cem\u003eAnn Hematol\u0026nbsp;\u003c/em\u003e2020, \u003cstrong\u003e99\u003c/strong\u003e(3):571-578.\u003c/li\u003e\n \u003cli\u003eOpie J, Mohamed Z, Chetty D, Bailey J, Brown K, Verburgh E, Hardie D: \u003cstrong\u003eHodgkin lymphoma: the role of EBV plasma viral load testing in an HIV-endemic setting\u003c/strong\u003e. \u003cem\u003eClin Exp Med\u0026nbsp;\u003c/em\u003e2024, \u003cstrong\u003e25\u003c/strong\u003e(1):10.\u003c/li\u003e\n \u003cli\u003eAntel K, Chetty D, Oosthuizen J, Mohamed Z, Van der Vyver L, Verburgh E: \u003cstrong\u003eCD68-positive tumour associated macrophages, PD-L1 expression, and EBV latent infection in a high HIV-prevalent South African cohort of Hodgkin lymphoma patients\u003c/strong\u003e. \u003cem\u003ePathology\u0026nbsp;\u003c/em\u003e2021, \u003cstrong\u003e53\u003c/strong\u003e(5):628-634.\u003c/li\u003e\n \u003cli\u003eNatkunam Y, Gratzinger D, Chadburn A, Goodlad JR, Chan JKC, Said J, Jaffe ES, D. dJ: \u003cstrong\u003eImmunodeficiency-associated lymphoproliferative disorders: time for reappraisal?\u003c/strong\u003e \u003cem\u003eBlood\u0026nbsp;\u003c/em\u003e2018, \u003cstrong\u003e132\u003c/strong\u003e(18):1871-1878.\u003c/li\u003e\n \u003cli\u003eCerhan JR, Maurer MJ, Link BK, Feldman AL, Habermann TM, Jaye DL, Burack WR, McDonnell TJ, Vega F, Chapman JR\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e: \u003cstrong\u003eThe Lymphoma Epidemiology of Outcomes cohort study: Design, baseline characteristics, and early outcomes\u003c/strong\u003e. \u003cem\u003eAm J Hematol\u0026nbsp;\u003c/em\u003e2024, \u003cstrong\u003e99\u003c/strong\u003e(3):408-421.\u003c/li\u003e\n \u003cli\u003eVaughan J, Perner Y, Wiggill T: \u003cstrong\u003eDiffuse Large B-Cell Lymphoma in the Public-Sector of Johannesburg, South Africa, in the Era of Widescale Antiretroviral Therapy Use\u003c/strong\u003e. \u003cem\u003eJ Acquir Immune Defic Syndr\u0026nbsp;\u003c/em\u003e2022, \u003cstrong\u003e91\u003c/strong\u003e(4).\u003c/li\u003e\n \u003cli\u003eSilverberg MJ, Lau B, Achenbach CJ, Jing Y, Althoff KN, D\u0026apos;Souza G, Engels EA, Hessol NA, Brooks JT, Burchell AN\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e: \u003cstrong\u003eCumulative Incidence of Cancer Among Persons With HIV in North America: A Cohort Study\u003c/strong\u003e. \u003cem\u003eAnn Intern Med\u0026nbsp;\u003c/em\u003e2015, \u003cstrong\u003e163\u003c/strong\u003e(7):507-518.\u003c/li\u003e\n \u003cli\u003eSimba K, Mohamed Z, Opie JJ, Andera LF, Brown K, Oosthuizen J, Antel K, Dawood T, Van der Vyfer L, Du Toit C\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e: \u003cstrong\u003eThe International Prognostic Score and HIV status predict red cell concentrate transfusion needs in Hodgkin lymphoma\u003c/strong\u003e. \u003cem\u003eLeuk Lymphoma\u0026nbsp;\u003c/em\u003e2022:1-8.\u003c/li\u003e\n \u003cli\u003eLamb M, Painter D, Howell D, Barrans S, Cargo C, de Tute R, Tooze R, Burton C, Patmore R, Roman E\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e: \u003cstrong\u003eLymphoid blood cancers, incidence and survival 2005-2023: A report from the UK\u0026apos;s Haematological Malignancy Research Network\u003c/strong\u003e. \u003cem\u003eCancer Epidemiol\u0026nbsp;\u003c/em\u003e2024, \u003cstrong\u003e88\u003c/strong\u003e.\u003c/li\u003e\n \u003cli\u003eInvestigators LaRDR: \u003cstrong\u003eImproving outcomes for patients with lymphoma: design and development of the Australian and New Zealand Lymphoma and Related Diseases Registry\u003c/strong\u003e. \u003cem\u003eBMC Med Res Methodol\u0026nbsp;\u003c/em\u003e2022, \u003cstrong\u003e22\u003c/strong\u003e(1).\u003c/li\u003e\n \u003cli\u003eTumwine LK, Orem J, Kerchan P, Byarugaba W, Pileri SA: \u003cstrong\u003eEBV, HHV8 and HIV in B cell non Hodgkin lymphoma in Kampala, Uganda\u003c/strong\u003e. \u003cem\u003eInfect Agent Cancer\u0026nbsp;\u003c/em\u003e2010, \u003cstrong\u003e5\u003c/strong\u003e(12).\u003c/li\u003e\n \u003cli\u003eBriercheck EL, Ravishankar S, Ahmed EH, Carias Alvarado CC, Barrios Menendez JC, Silva O, Solorzano-Ortiz E, Siliezar Tala MM, Stevenson P, Xu Y\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e: \u003cstrong\u003eGeographic EBV variants confound disease-specific variant interpretation and predict variable immune therapy responses\u003c/strong\u003e. \u003cem\u003eBlood Adv\u0026nbsp;\u003c/em\u003e2024, \u003cstrong\u003e8\u003c/strong\u003e(14):3731-3744.\u003c/li\u003e\n \u003cli\u003eKanakry JA, Hegde AM, Durand CM, Massie AB, Greer AE, Ambinder RF, Valsamakis A: \u003cstrong\u003eThe clinical significance of EBV DNA in the plasma and peripheral blood mononuclear cells of patients with or without EBV diseases\u003c/strong\u003e. \u003cem\u003eBlood\u0026nbsp;\u003c/em\u003e2016, \u003cstrong\u003e127\u003c/strong\u003e(16):2007-2017.\u003c/li\u003e\n \u003cli\u003eArber DA, Hasserjian RP, Orazi A, Mathews V, Roberts AW, Schiffer CA, Roug AS, Cazzola M, Dohner H, Tefferi A: \u003cstrong\u003eClassification of myeloid neoplasms/acute leukemia: Global perspectives and the international consensus classification approach\u003c/strong\u003e. \u003cem\u003eAm J Hematol\u0026nbsp;\u003c/em\u003e2022, \u003cstrong\u003e97\u003c/strong\u003e(5):514-518.\u003c/li\u003e\n \u003cli\u003eJaffe ES, Barr PM, Smith SM: \u003cstrong\u003eUnderstanding the New WHO Classification of Lymphoid Malignancies: Why It\u0026apos;s Important and How It Will Affect Practice\u003c/strong\u003e. \u003cem\u003eAm Soc Clin Oncol Educ Book\u0026nbsp;\u003c/em\u003e2017, \u003cstrong\u003e37\u003c/strong\u003e:535-546.\u003c/li\u003e\n \u003cli\u003eOk CY, Medeiros LJ: \u003cstrong\u003eHigh-grade B-cell lymphoma: a term re-purposed in the revised WHO classification\u003c/strong\u003e. \u003cem\u003ePathology\u0026nbsp;\u003c/em\u003e2020, \u003cstrong\u003e52\u003c/strong\u003e(1):68-77.\u003c/li\u003e\n \u003cli\u003ePetrich AM, Gandhi M, Jovanovic B, Castillo JJ, Rajguru S, Yang DT, Shah KA, Whyman JD, Lansigan F, Hernandez-Ilizaliturri FJ\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e: \u003cstrong\u003eImpact of induction regimen and stem cell transplantation on outcomes in double-hit lymphoma: a multicenter retrospective analysis\u003c/strong\u003e. \u003cem\u003eBlood\u0026nbsp;\u003c/em\u003e2014, \u003cstrong\u003e124\u003c/strong\u003e(15):2354-2361.\u003c/li\u003e\n \u003cli\u003eSesques P, Johnson NA: \u003cstrong\u003eApproach to the diagnosis and treatment of high-grade B-cell lymphomas with MYC and BCL2 and/or BCL6 rearrangements\u003c/strong\u003e. \u003cem\u003eBlood\u0026nbsp;\u003c/em\u003e2017, \u003cstrong\u003e129\u003c/strong\u003e(3):280-288.\u003c/li\u003e\n \u003cli\u003eGhesquieres H, Cherblanc F, Belot A, Micon S, Bouabdallah KK, Esnault C, Fornecker LM, Thokagevistk K, Bonjour M, Bijou F\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e: \u003cstrong\u003eChallenges for quality and utilization of real-world data for diffuse large B-cell lymphoma in REALYSA, a LYSA cohort\u003c/strong\u003e. \u003cem\u003eBlood Adv\u0026nbsp;\u003c/em\u003e2024, \u003cstrong\u003e8\u003c/strong\u003e(2):296-308.\u003c/li\u003e\n \u003cli\u003eValvert F, Silva O, Solorzano-Ortiz E, Puligandla M, Siliezar Tala MM, Guyon T, Dixon SL, Lopez N, Lopez F, Carias Alvarado CC\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e: \u003cstrong\u003eLow-cost transcriptional diagnostic to accurately categorize lymphomas in low- and middle-income countries\u003c/strong\u003e. \u003cem\u003eBlood Adv\u0026nbsp;\u003c/em\u003e2021, \u003cstrong\u003e5\u003c/strong\u003e(10):2447-2455.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 3 are available in the Supplementary Files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6948575/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6948575/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eLymphoma real-world observational data and accurate diagnostic systems are lacking in low-resource settings. We established a diagnostic registry for lymphoma classification to generate internationally comparable, clinically validated data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e The descriptive retrospective cohort included patients ≥13 years of age newly diagnosed with lymphoma from 2005 to 2020. Patients were enrolled at a single site in a registry with hierarchical groupings to capture, interrogate, and subtype lymphoma diagnoses. These were standardised on sequential versions of the World Health Organisation Classification of Haematolymphoid Tumours (WHO-HAEM) and correlated with the International Consensus Classification of mature lymphoid neoplasms (ICC). Differences due to nomenclature and diagnostic category were annotated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The cohort consisted of 2354 incident lymphoma cases; 1891 (80.3%) non-Hodgkin lymphoma and 463 (19.7%) Hodgkin lymphoma (HL). Twenty-one lymphoma \u003cem\u003eNOS \u003c/em\u003ecases were excluded due to inadequate specimen for standardised sub-classification. Overall reclassification according to WHO-HAEM5 was 25.8% (n=608). Major differences between WHO-HAEM5 and ICC included 44 (1.9%) transformations of indolent B-cell lymphomas; also 957 (40.7%) lymphoid proliferations and lymphomas associated with immune deficiency/dysregulation due to HIV (33.1%) and EBV (31.8%). EBV-association was highest among HL cases, 77 (50.3%) were HIV reactive.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e We report here the impact of adopting international lymphoma classification standards in an HIV-endemic setting. Our findings highlight the persistent prevalence of large B-cell lymphoma, confirm inadequate viral suppression as a key disease driver, and provide further evidence for EBV-associated HL as a distinct entity. Consolidating HIV-associated and other immune deficiency/dysregulation lymphomas into a unified framework could have significant implications and warrants further consideration for inclusion in future WHO-HAEM classifications.\u003c/p\u003e","manuscriptTitle":"Real-world lymphoma cohort in the HIV-endemic setting: Impact of implementing novel reclassification standards","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-27 10:54:24","doi":"10.21203/rs.3.rs-6948575/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-28T10:25:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-24T09:15:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-16T13:09:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191519952192382544548427308110890102812","date":"2025-10-11T10:13:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1581679467150246626467252850147223921","date":"2025-10-10T11:14:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-18T11:00:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"113979997887186317464928447460115610898","date":"2025-08-11T06:24:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-02T08:07:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"268792235102912353718723362043185436041","date":"2025-07-28T07:26:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-21T04:21:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-25T08:58:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-24T05:18:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-24T05:17:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-06-22T08:47:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f94638de-13ad-4098-87d6-308e76a15c9a","owner":[],"postedDate":"July 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T16:11:08+00:00","versionOfRecord":{"articleIdentity":"rs-6948575","link":"https://doi.org/10.1186/s12885-026-15807-8","journal":{"identity":"bmc-cancer","isVorOnly":false,"title":"BMC Cancer"},"publishedOn":"2026-04-02 15:59:52","publishedOnDateReadable":"April 2nd, 2026"},"versionCreatedAt":"2025-07-27 10:54:24","video":"","vorDoi":"10.1186/s12885-026-15807-8","vorDoiUrl":"https://doi.org/10.1186/s12885-026-15807-8","workflowStages":[]},"version":"v1","identity":"rs-6948575","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6948575","identity":"rs-6948575","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}