Tag Archives: Hans’ Algorithm

Organ Systems, WHO Classification

Hans’ Algorithm

Diffuse Large B-Cell Lymphoma represents a heterogeneous group of non-Hodgkin B-cell lymphoma cases that share a common architectural pattern and large cell size.  Subcategorization have been attempted with varying success based on morphology, immunophenotype, and molecular characteristics.  Gene expression profiling (GEP) has demonstrated two important groups for both prognosis and treatment.  Alizadeh, et al showed significant survival differences in cases of DLBCL with either a germinal center B-cell-like pattern or an activated B-cell-like pattern.
 
Unfortunately, GEP is not available in routine clinical practice, and multiple surrogate immunohistochemistry (IHC) based algorithms have been developed as a surrogate to GEP.  The Hans’ algorithm (classifier) has been one of the most popular methods because it uses only three IHC markers (CD10, Bcl-6, & MUM-1) that are commonly available in most pathology laboratories.  The following figure highlights the algorithm for the Hans’ classifier as described in the original paper.  The Hans’ algorithm appears to match GEP in 75-80% of cases.
 
Hans Classifier - CD10, Bcl-6, MUM-1
Hans classifier to subtype DLBCL as to germinal center B-cell-like (GCB) or non-germinal center B-cell-like (non-GCB) which has prognostic significance.
Alternative Algorithm(s)
The University of Nebraska group that originally developed the Hans algorithm has developed a new IHC stain algorithm that reportedly classifies cases of DLBCL more accurately compared to the corresponding molecular subtypes (~80% concordance).  This algorithm uses GCET1, CD10, BCL-6, MUM-1, and FOXP1 with differing cutoff values for positive/negative. (WW Choi, et al)  New algorithms with IHC markers not commonly used in many laboratories has probably limited popularity compared to the Hans’ algorithm.  The 2016 WHO hematopathology revision requires that cases of DLBCL be characterized at GCB vs. non-GCB by some acceptable methodology (molecular or IHC).
References
Hans CP, Weisenburger DD, Greiner TC, Gascoyne RD, Delabie J, Ott G, et al. Confirmation of the molecular classification of diffuse large B-cell lymphoma by immunohistochemistry using a tissue microarray. Blood. 2004;103: 275–282. doi:10.1182/blood-2003-05-1545
 
Haarer CF, Roberts RA, Frutiger YM, Grogan TM, Rimsza LM. Immunohistochemical classification of de novo, transformed, and relapsed diffuse large B-cell lymphoma into germinal center B-cell and nongerminal center B-cell subtypes correlates with gene expression profile and patient survival. Arch Pathol Lab Med. 2006;130: 1819–1824.
 
Chang C-C, McClintock S, Cleveland RP, Trzpuc T, Vesole DH, Logan B, et al. Immunohistochemical expression patterns of germinal center and activation B-cell markers correlate with prognosis in diffuse large B-cell lymphoma. Am J Surg Pathol. 2004;28: 464–470.
 
Choi WWL, Weisenburger DD, Greiner TC, Piris MA, Banham AH, Delabie J, et al. A new immunostain algorithm classifies diffuse large B-cell lymphoma into molecular subtypes with high accuracy. Clin Cancer Res. 2009;15: 5494–5502. doi:10.1158/1078-0432.CCR-09-0113
 
Alizadeh AA, Elsen MB, Davis RE, Ma C. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature. 2000.
 
Swerdlow SH, Campo E, Pileri SA, Harris NL, Stein H, Siebert R, et al. The 2016 revision of the World Health Organization classification of lymphoid neoplasms. Blood. 2016;127: 2375–2390. doi:10.1182/blood-2016-01-643569 
CD Markers, Antibody List

CD10

Hematopathology
CD10 (a.k.a. CALLA, common acute lymphoblastic leukemia antigen) is a useful marker for cells of germinal center cell origin and is expressed during the lymphoblastic phase of development.  Therefore, this marker is diagnostically helpful in several areas in hematopathology:  acute lymphoblastic leukemia (ALL), follicular lymphoma, diffuse large B-cell lymphoma (DLBCL), and Burkitt lymphoma.
 
ALL will often show expression of CD10.  In fact, CD10 co-expression with TdT is characteristic of ALL (additional expression of T- or B-cell markers will help further classify).
 
CD10 is  a marker of follicle center cell origin, which is characteristic of certain lymphomas including: follicular lymphoma, Burkitt lymphoma, and a subset of DLBCLs.
 
CD10 can be used as part of a prognostic panel (CD10, bcl-6, and MUM-1) in DLBCL to help separate cases into germinal center and non-germinal center subtypes.  The Hans’ (classifier) algorithm method is the most popular, probably due to the simplicity of the algorithm and utilization of IHC markers already present in most laboratories.
Non-Hematopathology
CD10 is a useful marker in non-lymphoid malignancies:  renal cell carcinoma and hepatocellular carcinoma.  CD10 will have a “bile canaliculi” pattern in HCC.  CD10 will also stain endometrial stromal sarcoma, and the “brush boarder” in GI tumors.
Pitfalls
CD10 can appear to have a lot of “non-specific” staining because of staining of dendritic stomal cells.  This can cause a pattern similar to reticular fibers, and many describe this as a “reticular pattern,” but the staining does not directly correlate with reticulin staining.  Caution should be exercised in using this stain in isolation given its lack of specificity (see below).
CD10 Expression in tumors often studied by CD10 IHC staining
Other tumors/tissues with CD10 expression (20-100% expression)
  • Hepatocellular Carcinoma
  • Breast myoepithelial cells and stromal fibroblasts
  • Cutaneous adnexal neoplasms
  • Mesothelioma
  • Epithelioid hemangioendotheliomas
  • Ovarian carcinoma
  • Urothelial carcinoma
  • Prostatic adenocarcinoma
  • Colon adenocarcinoma
  • Melanoma
  • Spindle cell carcinoma 
  • Lung carcinomas
  • Pancreatic solid pseudo papillary carcinoma
Photomicrographs
CD10 - Germinal Center
CD10 – Germinal Center
CD10 - Metastatic Renal Cell Carcinoma
CD10 – Metastatic Renal Cell Carcinoma
References
Hans CP, Weisenburger DD, Greiner TC, Gascoyne RD, Delabie J, Ott G, et al. Confirmation of the molecular classification of diffuse large B-cell lymphoma by immunohistochemistry using a tissue microarray. Blood. 2004;103: 275–282. doi:10.1182/blood-2003-05-1545
 
Tan, P.-H., Cheng, L., Rioux-Leclercq, N., Merino, M. J., Netto, G., Reuter, V. E., et al. (2013). Renal tumors: diagnostic and prognostic biomarkers. (Vol. 37, pp. 1518–1531). Presented at the The American journal of surgical pathology. doi:10.1097/PAS.0b013e318299f12e
 
Chang, C.-C., McClintock, S., Cleveland, R. P., Trzpuc, T., Vesole, D. H., Logan, B., et al. (2004). Immunohistochemical expression patterns of germinal center and activation B-cell markers correlate with prognosis in diffuse large B-cell lymphoma. The American Journal of Surgical Pathology, 28(4), 464–470.  
 
Tan P-H, Cheng L, Rioux-Leclercq N, Merino MJ, Netto G, Reuter VE, et al. Renal tumors: diagnostic and prognostic biomarkers. 2013. pp. 1518–1531. doi:10.1097/PAS.0b013e318299f12e
 
Truong LD, Shen SS. Immunohistochemical diagnosis of renal neoplasms. Arch Pathol Lab Med. 2010;135: 92–109. Available: http://www.archivesofpathology.org/doi/pdf/10.1043/2010-0478-RAR.1
 
Dewar R, Fadare O, Gilmore H, Gown AM. Best practices in diagnostic immunohistochemistry: myoepithelial markers in breast pathology. Arch Pathol Lab Med. 2011;135: 422–429. doi:10.1043/2010-0336-CP.1
 
Alizadeh AA, Elsen MB, Davis RE, Ma C. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature. 2000.
 
Swerdlow SH, Campo E, Pileri SA, Harris NL, Stein H, Siebert R, et al. The 2016 revision of the World Health Organization classification of lymphoid neoplasms. Blood. 2016;127: 2375–2390. doi:10.1182/blood-2016-01-643569
 
Bone Marrow IHC.  Torlakovic, EE, et. al. American Society for Clinical Pathology Pathology Press © 2009.  pp. 38.