Hans Algorithm and Its Role in Lymphoma Classification

Accurate classification of diffuse large B-cell lymphoma (DLBCL) is essential for guiding treatment decisions and predicting patient outcomes. The Hans algorithm, a widely used immunohistochemistry-based method, distinguishes between key subtypes of DLBCL in settings where molecular testing may not be readily available.

By utilizing specific biomarkers, this algorithm provides a practical approach to categorizing cases with significant clinical implications.

Immunohistochemical Markers

The Hans algorithm differentiates between the germinal center B-cell-like (GCB) and activated B-cell-like (ABC) subtypes of DLBCL based on immunohistochemical markers. These markers—CD10, BCL6, and MUM1 (also known as IRF4)—serve as surrogates for gene expression profiling.

CD10, a metalloproteinase expressed in germinal center B cells, strongly suggests a GCB phenotype. If CD10 is absent, BCL6 is assessed next. While its expression is associated with GCB origin, it is not exclusive to this subtype, requiring further evaluation. MUM1, a transcription factor linked to post-germinal center differentiation, helps classify cases where CD10 is negative. If MUM1 is expressed and BCL6 is either negative or weakly positive, the case is categorized as ABC.

Immunohistochemical staining is performed on formalin-fixed, paraffin-embedded tissue sections, with antigen retrieval techniques enhancing marker detection. Standardized scoring criteria, typically using a 30% positivity threshold, guide classification. However, variations in staining intensity and interpretation can introduce challenges, requiring experienced pathologists to ensure accuracy. Interobserver variability underscores the need for standardized protocols and quality control in immunohistochemical analysis.

Steps In Classification

The Hans algorithm follows a structured approach to classify DLBCL based on immunohistochemical marker expression. The process begins with selecting representative tumor tissue, usually obtained through biopsy and preserved in formalin-fixed, paraffin-embedded (FFPE) blocks. Proper tissue handling is crucial, as fixation time and antigen retrieval techniques can affect staining outcomes.

CD10 is evaluated first, as its expression is strongly linked to the GCB subtype. Tumors with at least 30% CD10 positivity are classified as GCB. If CD10 is absent, BCL6 is assessed. While BCL6 positivity can indicate a GCB phenotype, it is not definitive. MUM1 is examined in cases where CD10 is negative. If MUM1 is expressed in more than 30% of tumor cells and BCL6 is negative or weakly positive, the case is categorized as ABC. This sequential approach ensures accurate classification and minimizes misclassification risks.

Subtypes Identified By Algorithm

The Hans algorithm categorizes DLBCL into two primary subtypes: GCB and ABC. These classifications reflect distinct cellular origins and biological behaviors, influencing treatment responses.

GCB-DLBCL arises from germinal center B cells, where affinity maturation and somatic hypermutation occur. This subtype frequently harbors genetic alterations such as BCL2 translocations and EZH2 mutations, contributing to uncontrolled proliferation.

ABC-DLBCL originates from post-germinal center B cells undergoing plasmablastic differentiation. These tumors often exhibit chronic NF-κB pathway activation, driven by mutations in MYD88 and CD79B, which promote survival and resistance to apoptosis. Structural abnormalities in PRDM1 further reinforce the aggressive nature of this subtype.

Correlation With Molecular Profiles

Although based on immunohistochemistry, the Hans algorithm aligns closely with molecular classifications of DLBCL. Gene expression studies confirm that GCB-DLBCL is enriched for BCL6, LMO2, and EZH2, all associated with germinal center development. In contrast, ABC-DLBCL exhibits elevated expression of IRF4, MYD88, and NF-κB pathway components, reflecting its reliance on chronic signaling for survival.

Whole-exome sequencing has identified frequent mutations in CREBBP and KMT2D in GCB-DLBCL, both involved in chromatin remodeling. ABC-DLBCL commonly harbors structural variations in CARD11 and CD79B, reinforcing its dependence on B-cell receptor signaling. These genetic distinctions have therapeutic implications, as targeted inhibitors like ibrutinib, which disrupts BCR signaling, show greater efficacy in ABC cases.

Observed Clinical Variations

Patients with DLBCL display diverse clinical presentations, with Hans algorithm subtypes exhibiting distinct disease behaviors and treatment responses.

GCB-DLBCL is generally associated with a more favorable prognosis and responds well to standard immunochemotherapy regimens such as R-CHOP. It typically presents with nodal involvement in sites like cervical, axillary, and inguinal lymph nodes, with extranodal manifestations being less common.

ABC-DLBCL often presents with aggressive features, including extranodal disease at diagnosis, particularly in the gastrointestinal tract, central nervous system, and bone marrow. Higher lactate dehydrogenase (LDH) levels are common, indicating increased tumor proliferation. This subtype is more resistant to standard therapies, with higher relapse rates necessitating alternative treatments. BTK inhibitors like ibrutinib, which impair B-cell receptor signaling, have shown promise in ABC cases, though responses vary. Additionally, CAR T-cell therapy is being explored for refractory cases, emphasizing the importance of precise subtype identification for optimizing treatment.

Prognostic Indicators In Subgroups

GCB and ABC subtypes of DLBCL differ in prognostic indicators that influence survival outcomes and treatment planning. The International Prognostic Index (IPI), incorporating factors such as age, stage, extranodal involvement, performance status, and LDH levels, remains a key risk stratification tool. However, the Hans algorithm enhances prognosis by refining risk assessment based on tumor biology.

GCB-DLBCL generally has higher overall survival rates, with five-year progression-free survival exceeding 70% in some cohorts. This is attributed to greater chemotherapy sensitivity and fewer genetic alterations driving resistance.

ABC-DLBCL, in contrast, has poorer survival outcomes, with five-year overall survival rates below 50%, particularly in high-risk cases. MYD88 L265P mutations contribute to chemoresistance through NF-κB activation, increasing the likelihood of early progression. Emerging prognostic biomarkers such as circulating tumor DNA (ctDNA) and minimal residual disease (MRD) assessments are being explored to improve risk stratification and treatment decisions. By integrating molecular insights with immunohistochemical classification, clinicians can better predict disease progression and tailor therapies accordingly.