ERG expression in multiple myeloma-A potential diagnostic pitfall

Tissue-Specific Variations in Transcription Factors Elucidate Complex Immune System Regulation

Gene expression plays a key role in health and disease. Estimating the genetic components underlying gene expression can thus help understand disease etiology. Polygenic models termed “transcriptome imputation” are used to estimate the genetic component of gene expression, but these models typically consider only the cis regions of the gene. However, these cis-based models miss large variability in expression for multiple genes. Transcription factors (TFs) that regulate gene expression are natural candidates for looking for additional sources of the missing variability. We developed a hypothesis-driven approach to identify second-tier regulation by variability in TFs. Our approach tested two models representing possible mechanisms by which variations in TFs can affect gene expression: variability in the expression of the TF and genetic variants within the TF that may affect the binding affinity of the TF to the TF-binding site. We tested our TF models in whole blood and skeletal muscle tissues and identified TF variability that can partially explain missing gene expression for 1035 genes, 76% of which explains more than the cis-based models. While the discovered regulation patterns were tissue-specific, they were both enriched for immune system functionality, elucidating complex regulation patterns. Our hypothesis-driven approach is useful for identifying tissue-specific genetic regulation patterns involving variations in TF expression or binding.

ERG Immunoreactivity in Blastic Hematolymphoid Neoplasms: Diagnostic Pitfall in the Workup of Undifferentiated Malignant Neoplasms

Undifferentiated malignant neoplasms pose diagnostic challenges, and reliable immunohistochemical markers with well-characterized staining profiles are desirable when characterizing them. Our initial observation of erythroblast transformation specific regulated gene-1 (ERG) reactivity in myeloid sarcomas led us to broadly explore the utility of ERG as a marker of immature hematolymphoid neoplasms presenting in extramedullary sites. We stained 207 immature and mature hematolymphoid lesions as well as 39 benign hematolymphoid tissues and found weak-to-moderate ERG immunopositivity in 15 of 16 (94%) acute myeloid leukemias/myeloid sarcomas, including 4 of 5 (80%) CD34-negative/CD117-negative acute myeloid leukemias/myeloid sarcomas. ERG positivity was also seen in all 9 cases of B-lymphoblastic and T-lymphoblastic leukemia/lymphoma, all 3 cases of hematogone hyperplasia, and all 4 cases of systemic mastocytosis. ERG was negative in 148 mature B-cell and T-cell lymphomas, including 2 high-grade B-cell lymphomas and 2 blastoid variant mantle cell lymphomas; 23 histiocytic/dendritic cell neoplasms; 2 indolent T-lymphoblastic proliferations; and 2 blastic plasmacytoid dendritic cell neoplasms. We conclude that ERG immunoreactivity may pose a significant diagnostic pitfall in the workup of undifferentiated malignant neoplasms, particularly those presenting in extramedullary sites.

Construction of a prognosis‑associated long noncoding RNA‑mRNA network for multiple myeloma based on microarray and bioinformatics analysis

At present, the association between prognosis-associated long noncoding RNAs (lncRNAs) and mRNAs is yet to be reported in multiple myeloma (MM). The aim of the present study was to construct prognostic models with lncRNAs and mRNAs, and to map the interactions between these lncRNAs and mRNAs in MM. LncRNA and mRNA data from 559 patients with MM were acquired from the Genome Expression Omnibus (dataset GSE24080), and their prognostic values were calculated using the survival package in R. Multivariate Cox analysis was used on the top 20 most significant prognosis-associated mRNAs and lncRNAs to develop prognostic signatures. The performances of these prognostic signatures were tested using the survivalROC package in R, which allows for time-dependent receiver operator characteristic (ROC) curve estimation. Weighted correlation network analysis (WGCNA) was conducted to investigate the associations between lncRNAs and mRNAs, and a lncRNA-mRNA network was constructed using Cytoscape software. Univariate Cox regression analysis identified 39 lncRNAs and 1,445 mRNAs that were significantly associated with event-free survival of MM patients. The top 20 most significant survival-associated lncRNAs and mRNAs were selected as candidates for analyzing independent MM prognostic factors. Both signatures could be used to separate patients into two groups with distinct outcomes. The areas under the ROC curves were 0.739 for the lncRNA signature and 0.732 for the mRNA signature. In the lncRNA-mRNA network, a total of 143 mRNAs were positively or negatively associated with 23 prognosis-associated lncRNAs. NCRNA00201, LOC115110 and RP5-968J1.1 were the most dominant drivers. The present study constructed a model that predicted prognosis in MM and formed a network with the corresponding prognosis-associated mRNAs, providing a novel perspective for the clinical diagnosis and treatment of MM, and suggesting novel directions for interpreting the mechanisms underlying the development of MM.

Practical Application of Lineage-Specific Immunohistochemistry Markers: Transcription Factors (Sometimes) Behaving Badly

CONTEXT.—

Transcription factors (TFs) are proteins that regulate gene expression and control RNA transcription from DNA. Lineage-specific TFs have increasingly been used by pathologists to determine tumor lineage, especially in the setting of metastatic tumors of unknown primary, among other uses. With experience gathered from its daily application and increasing pitfalls reported from immunohistochemical studies, these often-touted highly specific TFs are not as reliable as once thought.

OBJECTIVES.—

To summarize the established roles of many of the commonly used TFs in clinical practice and to discuss known and potential sources for error (eg, false-positivity from cross-reactivity, aberrant, and overlap "lineage-specific" expression) in their application and interpretation.

DATA SOURCES.—

Literature review and the authors' personal practice experience were used. Several examples selected from the University Health Network (Toronto, Ontario, Canada) are illustrated.

CONCLUSIONS.—

The application of TF diagnostic immunohistochemistry has enabled pathologists to better assess the lineage/origin of primary and metastatic tumors. However, the awareness of potential pitfalls is essential to avoid misdiagnosis.