Gene expression profiling (GEP) plays a pivotal role in characterizing various tissues and tumors. However, distinguishing between different tissue types, whether normal or abnormal, within the same organ based on gene expression remains a formidable challenge.

In a recent comprehensive meta-analysis, I introduced a novel approach: an expression intensity signature panel specific to brain tissues. By leveraging the characteristic expression patterns of thousands of genes for each brain tissue type and tumor, this reference panel demonstrates remarkable alignment with histological diagnoses. Notably, this alignment persists even when accounting for variations in transcriptomic techniques, platforms, and species.

More recently, a similar expression panel has been assessed in hematological tumors. In this regard, it should be noted that despite the infrequent misdiagnosis of lymphoproliferative diseases (LPDs), the introduction of novel agents directed against specific targets—such as CD30, PD1, or PDL1—has underscored the need for nearly 100% accurate diagnosis. The presence of mixed cellular populations around the tumor cells in various LPDs, such as Hodgkin's and T-cell lymphoma, complicates a definitive histological diagnosis. Additionally, the difficulty in confirming clonality further complicates the diagnosis of these LPDs. However, the current meta-analysis focused on Hodgkin's lymphoma suggests that the normal microenvironment surrounding the tumor cells can itself aid in accurate diagnosis due to its typical expression profile. Thus, the gene expression signature of the whole biopsy specimen, reflecting the expression of both the tumor cells and their normal microenvironment, may help discriminate among different LPDs. More importantly, the inclusion of thousands of probes in the analysis—in contrast to the common use of a limited number of probes (only those that show >2-fold changes)—can explain the very high concordance between the expression signature and the histological diagnosis of seen in the current analysis.