A decade ago, gene expression profiling (GEP) was successfully introduced in haematological research. Considering the heterogeneity of haematological malignancies, the growing arsenal of compounds, allowing targeted therapy, e.g. in myelodysplastic syndromes (MDS) or chronic myeloid leukaemia (CML), and the more differentiated indication to allogeneic stem cell transplantation, routine diagnostic procedures would highly benefit from an introduction of this novel methodology: by now, the majority of genetically defined leukaemia subtypes has been accurately reproduced on the basis of distinct gene expression patterns by various independent research groups. Moreover, classification of histomorphologically overlapping lymphoma subentities (e.g. Burkitt lymphoma and diffuse large B-cell lymphoma, DLBCL), was considerably improved by GEP. Beyond that, differential gene expression has provided the basis for assays being able to predict prognosis of individual patients as well as the response to specific treatment approaches, e.g. to lenalidomide in MDS. In a high proportion of Philadelphia positive acute lymphoblastic leukaemia (ALL) patients, prognostically adverse deletions of the IKZF1 gene coding for a specific transcription factor were identified with GEP analysis, which revealed new insights in the clinical variability of this disorder. Given these advantages of GEP, the introduction of this methodology in current diagnostic algorithms of haematological malignancies should further be validated in clinical studies.
A decade ago, gene expression profiling (GEP) was successfully introduced in haematological research. Considering the heterogeneity of haematological malignancies, the growing arsenal of compounds, allowing targeted therapy, e.g. in myelodysplastic syndromes (MDS) or chronic myeloid leukaemia (CML), and the more differentiated indication to allogeneic stem cell transplantation, routine diagnostic procedures would highly benefit from an introduction of this novel methodology: by now, the majority of genetically defined leukaemia subtypes has been accurately reproduced on the basis of distinct gene expression patterns by various independent research groups. Moreover, classification of histomorphologically overlapping lymphoma subentities (e.g. Burkitt lymphoma and diffuse large B-cell lymphoma, DLBCL), was considerably improved by GEP. Beyond that, differential gene expression has provided the basis for assays being able to predict prognosis of individual patients as well as the response to specific treatment approaches, e.g. to lenalidomide in MDS. In a high proportion of Philadelphia positive acute lymphoblastic leukaemia (ALL) patients, prognostically adverse deletions of the IKZF1 gene coding for a specific transcription factor were identified with GEP analysis, which revealed new insights in the clinical variability of this disorder. Given these advantages of GEP, the introduction of this methodology in current diagnostic algorithms of haematological malignancies should further be validated in clinical studies.