A novel method of amplification of FFPET-derived RNA enables accurate disease classification with microarrays

Assessment of 2-Year Storage Conditions on Protein, RNA, and DNA in Unstained Human Tissue Sections, Including a Novel Multiplex Digital Gene Expression Profiling Method with Implications for Biobanking

Background: Formalin-fixed, paraffin-embedded (FFPE) tissues are a valuable resource for clinical and basic science research. Paraffin blocks and the resulting unstained sections (USS) are often stored for years before being used. Previous studies have evaluated the effects of time, temperature, humidity, and inert gases on preservation of USS; however, no study has examined all four variables together. Methods: In the current work, we prospectively and blindly assessed time points from 0 to 24 months, room versus refrigerated temperature, and presence of a desiccant and/or nitrogen atmosphere on a variety of benign and malignant tissues from North America and Africa. End points included immunohistochemistry (IHC), in situ hybridization (ISH), extracted RNA and DNA quantity and quality, and messenger RNA performance in a novel, multiplexed digital gene expression profiling assay of both housekeeping and tumor-specific genes. Results: We found that using current methods of antigen retrieval, staining, and extraction, the end points of IHC, ISH, RNA, and DNA were well preserved under the various conditions tested, with implications that pre-embedding factors contribute to variability in subsequent tissue integrity. We also document that spectrophotometric estimations of nucleic acid concentrations were in general estimated to be higher than with fluorimetric methods, which may be pertinent to end assay development. We further describe a new multiplex assay, the PlexSet digital gene expression assay, suitable for evaluating RNA quality in FFPE tissues. Conclusion: Altogether, these results may provide helpful guidance with regard to approaches for long-term storage conditions for USS.

Integrative analysis of hub genes and key pathway in two subtypes of diffuse large B-cell lymphoma by bioinformatics and basic experiments

Background

The germinal center B‐cell (GCB) and activated B‐cell (ABC) subtypes of diffuse large B‐cell lymphoma (DLBCL) have a significant difference in prognosis. This study aimed to identify potential hub genes, and key pathways involved in them.

Methods

Databases including Gene Expression Omnibus (GEO), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and STRING were accessed to obtain potential crucial genes and key pathways associated with the GCB and ABC. Then qRT‐PCR and Western blot experiments were performed to verify the most clinically significant gene and pathway.

Results

Three cohort datasets from the GEO database were analyzed, including 195 GCB and 169 ABC samples. We identified 1113 differentially expressed genes (DEGs) between the GCB and ABC subtypes. The DEGs were mainly enriched in biological processes (BP). The KEGG analysis showed enrichment in cell cycle and Wnt signaling pathways. We selected the top 10 genes using the STRING database and Cytoscape software. We used 5 calculation methods of the cytoHubba plugin, and found 3 central genes (IL‐10, CD44, CCND2). CCND2 was significantly related to the prognosis of DLBCL patients. Besides, our experimental results demonstrated a significantly higher expression of CCND2 in the ABC‐type cell line than in the GCB‐type; it was proportional to the expression of key proteins in the Wnt signaling pathway.

Conclusion

CCND2 overexpression and Wnt pathway activation might be the main reasons for the poor prognosis of ABC‐DLBCL.

A refined cell-of-origin classifier with targeted NGS and artificial intelligence shows robust predictive value in DLBCL

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous entity of B-cell lymphoma. Cell-of-origin (COO) classification of DLBCL is required in routine practice by the World Health Organization classification for biological and therapeutic insights. Genetic subtypes uncovered recently are based on distinct genetic alterations in DLBCL, which are different from the COO subtypes defined by gene expression signatures of normal B cells retained in DLBCL. We hypothesize that classifiers incorporating both genome-wide gene-expression and pathogenetic variables can improve the therapeutic significance of DLBCL classification. To develop such refined classifiers, we performed targeted RNA sequencing (RNA-Seq) with a commercially available next-generation sequencing (NGS) platform in a large cohort of 418 DLBCLs. Genetic and transcriptional data obtained by RNA-Seq in a single run were explored by state-of-the-art artificial intelligence (AI) to develop a NGS-COO classifier for COO assignment and NGS survival models for clinical outcome prediction. The NGS-COO model built through applying AI in the training set was robust, showing high concordance with COO classification by either Affymetrix GeneChip microarray or the NanoString Lymph2Cx assay in 2 validation sets. Although the NGS-COO model was not trained for clinical outcome, the activated B-cell-like compared with the germinal-center B-cell-like subtype had significantly poorer survival. The NGS survival models stratified 30% high-risk patients in the validation set with poor survival as in the training set. These results demonstrate that targeted RNA-Seq coupled with AI deep learning techniques provides reproducible, efficient, and affordable assays for clinical application. The clinical grade assays and NGS models integrating both genetic and transcriptional factors developed in this study may eventually support precision medicine in DLBCL.

Molecular Subtyping of Diffuse Large B-Cell Lymphoma Using a Novel Quantitative RT-PCR Assay

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease. Cell-of-origin classification in DLBCL has identified activated B cell (ABC) and germinal center B cell (GCB) as two major subtypes. Patients with the ABC subtype show reduced overall survival with standard therapies. Development of a quantitative RT-PCR-based lymphoma cell-of-origin (LCOO) assay to determine ABC, GCB, and unclassifiable subtypes in formalin-fixed, paraffin-embedded tissue (FFPET) DLBCL samples is reported. The LCOO classifier was trained on two DLBCL cohorts with validation performed by using an analytical grade assay in an independent cohort of 60 FFPET DLBCL samples. In the validation cohort, LCOO classification was 88.1%, 84.7%, and 84.7% concordant with microarray, immunohistochemistry (Hans classification), and Lymphoma Subtyping Test, respectively. Importantly, LCOO and Lymphoma Subtyping Test assays commonly assigned subtypes in 17 (94.4%) of 18 ABC samples and 34 (89.5%) of 38 GCB DLBCL samples from this cohort. Progression-free survival and overall survival of ABC and GCB subtypes, as classified by all platforms, were not significantly different in the validation cohort. LCOO classification using publicly available microarray gene expression from two independent data sets (414 fresh frozen and 474 FFPET DLBCL biopsies) revealed a significantly worse outcome for the ABC subtype compared with that of the GCB subtype. Thus, a sensitive, reproducible, LCOO assay developed on an easy to standardize quantitative RT-PCR platform may be an important clinical tool for DLBCL cell-of-origin classification.

Regional Molecular Signature of the Symptomatic Atherosclerotic Carotid Plaque

BACKGROUND

Many studies have explored molecular markers of carotid plaque development and vulnerability to rupture, usually having examined whole carotid plaques. However, there are regional differences in plaque morphology and known shear-related mechanisms in areas surrounding the lipid core.

OBJECTIVE

To determine whether there are regional differences in protein expression along the long axis of the carotid plaque and how that might produce gaps in our understanding of the carotid plaque molecular signature.

METHODS

Levels of 7 inflammatory cytokines (IL-1β, IL-6, IL-8, IL-10, IL-12 p70, IFN-γ, and TNF-α) and caspase-3 were analyzed in prebifurcation, bifurcation, and postbifurcation segments of internal carotid plaques surgically removed from symptomatic and asymptomatic patients. Expression profiles of miRNAs and mRNAs were determined with microarrays for the rupture-prone postbifurcation segment for comparison with published whole plaque results.

RESULTS

Expression levels of all proteins examined, except IL-10, were lowest in the prebifurcation segment and significantly higher in the postbifurcation segment. Patient group differences in protein expression were observed for the prebifurcation segment; however, no significant differences were observed in the postbifurcation segment between symptomatic and asymptomatic patients. Expression profiles from postbifurcation carotid plaques identified 4 novel high priority miRNAs differentially expressed between patient groups (miR-214, miR-484, miR-942, and miR-1287) and 3 high-confidence miRNA:mRNA targets, including miR-214:APOD, miR-484:DACH1, and miR-942:GPR56.

CONCLUSION

The results demonstrate regional differences in protein expression for the first time and show that focus on the rupture-prone postbifurcation region leads to prioritization for further study of novel miRNA gene regulation mechanisms.

Remaining challenges in predicting patient outcomes for diffuse large B-cell lymphoma

Introduction: Diffuse large B-cell lymphoma (DLBCL) is the most common non-Hodgkin lymphoma and is an aggressive malignancy with heterogeneous outcomes. Diverse methods for DLBCL outcomes assessment ranging from clinical to genomic have been developed with variable predictive and prognostic success.Areas covered: The authors provide an overview of the various methods currently used to estimate prognosis in DLBCL patients. Models incorporating cell of origin, genomic features, sociodemographic factors, treatment effectiveness measures, and machine learning are described.Expert opinion: The clinical and genetic heterogeneity of DLBCL presents distinct challenges in predicting response to therapy and overall prognosis. Successful integration of predictive and prognostic tools in clinical trials and in a standard clinical workflow for DLBCL will likely require a combination of methods incorporating clinical, sociodemographic, and molecular factors with the aid of machine learning and high-dimensional data analysis.

Bioinformatic validation identifies candidate key genes in diffuse large-B cell lymphoma

Aim: In this study, four datasets concerning 167 diffuse large B-cell lymphoma (DLBCL) patients versus 56 controls and seven datasets involving 280 germinal center B-cell like (GCB) versus 224 activated B-cell like (ABC) DLBCL were included. Materials & methods: We identified 80 different expression genes (DEGs) for DLBCL versus nontumor and 77 DEGs for GCB versus ABC DLBCL. Results: These DEGs were found to be enriched in cell activity, signal transduction and extracellular region. Then ten central node genes for DLBCL versus nontumor and two hub genes for GCB versus ABC DLBCL were identified. Last, PAICS, IRF4 and PTPN1 were explored to be correlated with poor prognosis in DLBCL patients. Conclusion: Our study has identified critical genes from transcriptional profiles for DLBCL.

Integrating precision medicine through evaluation of cell of origin in treatment planning for diffuse large B-cell lymphoma

Precision medicine is modernizing strategies for clinical study design to help improve diagnoses guiding individualized treatment based on genetic or phenotypic characteristics that discriminate between patients with similar clinical presentations. Methodology to personalize treatment choices is being increasingly employed in clinical trials, yielding favorable correlations with improved response rates and survival. In patients with diffuse large B-cell lymphoma (DLBCL), disease characteristics and outcomes may vary widely, underscoring the importance of patient classification through identification of sensitive prognostic features. The discovery of distinct DLBCL molecular subtypes based on cell of origin (COO) is redefining the prognosis and treatment of this heterogeneous cancer. Owing to significant molecular and clinical differences between activated B-cell-like (ABC)- and germinal center B-cell-like (GCB)-DLBCL subtypes, COO identification offers opportunities to optimize treatment selection. Widespread adoption of COO classification would greatly improve treatment and prognosis; however, limitations in interlaboratory concordance between immunohistochemistry techniques, cost, and availability of gene expression profiling tools undermine universal integration in the clinical setting. With advanced methodology to determine COO in a real-world clinical setting, therapies targeted to specific subtypes are under development. The focus here is to review applications of precision medicine exemplified by COO determination in DLBCL patients.

MEF2B is a member of the BCL6 gene transcriptional complex and induces its expression in diffuse large B-cell lymphoma of the germinal center B-cell-like type

Myocyte enhancer-binding factor 2B (MEF2B) has been implicated as a transcriptional regulator for BCL6. However, details about the interaction between MEF2B and BCL6 during expression, as well as the relationship of MEF2B to the expression of other germinal center (GC) markers, have not yet been fully explained. Using germinal center B-cell-like diffuse large B-cell lymphoma (GC-DLBCL) and activated B-cell diffuse large B-cell lymphoma (ABC-DLBCL) cell lines, we analyzed the expression of MEF2B and its associations with BCL6, CD10, and ERK. Furthermore, small interfering RNA (siRNA) was used to study the possible effects of MEF2B knockdown on these proteins and cell growth. Analysis of the BCL6 transcriptional complex was performed using electrophoretic mobility shift assay. The correlation between MEF2B expression and the genetic type of DLBCL was assessed using immunohistochemistry on 111 patient samples, and via in silico analysis of publicly available microarray (Gene Expression Omnibus (GEO)) datasets. Our results indicate that the expression of MEF2B protein is important for the growth of GC-DLBCL cells, as evidenced by MEF2B knockdown inhibition of cell growth and the subsequent suppression of BCL6, CD10, and ERK phosphorylation. Analysis of BCL6 transcription factors in nuclear extracts of MEF2-expressing DLBCL cells showed involvement of MEF2B with AP-2α and BCL6 proteins in the formation of the BCL6 gene transcriptional complex. Indeed, differential expression of MEF2B in the GC-DLBCL is statistically significant compared to the ABC-DLBCL in the GEO datasets, as well as in tissue microarray, as indicated via immunohistochemistry (Visco-Young algorithm). Our findings indicate that MEF2B is an essential component of the BCL6 gene transcriptional complex for the regulation of DLBCL growth via the promotion of BCL6 expression. Beyond its regulatory role in DLBCL growth, MEF2B expression correlated positively with BCL6 and CD10 expression, and was preferentially expressed in the GBC-DLBCL group.

Abnormal Small Intestinal Epithelial Microvilli in Patients With Crohn's Disease

BACKGROUND & AIMS

Crohn disease (CD) presents as chronic and often progressive intestinal inflammation, but the contributing pathogenic mechanisms are unclear. We aimed to identify alterations in intestinal cells that could contribute to the chronic and progressive course of CD.

METHODS

We took an unbiased system-wide approach by performing sequence analysis of RNA extracted from formalin-fixed paraffin-embedded ileal tissue sections from patients with CD (n = 36) and without CD (controls; n = 32). We selected relatively uninflamed samples, based on histology, before gene expression profiling; validation studies were performed using adjacent serial tissue sections. A separate set of samples (3 control and 4 CD samples) was analyzed by transmission electron microscopy. We developed methods to visualize an overlapping modular network of genes dysregulated in the CD samples. We validated our findings using biopsy samples (110 CD samples for gene expression analysis and 54 for histologic analysis) from the UNITI-2 phase 3 trial of ustekinumab for patients with CD and healthy individuals (26 samples used in gene expression analysis).

RESULTS

We identified gene clusters that were altered in nearly all CD samples. One cluster encoded genes associated with the enterocyte brush border, leading us to investigate microvilli. In ileal tissues from patients with CD, the microvilli were of decreased length and had ultrastructural defects compared with tissues from controls. Microvilli length correlated with expression of genes that regulate microvilli structure and function. Network analysis linked the microvilli cluster to several other down-regulated clusters associated with altered intracellular trafficking and cellular metabolism. Enrichment of a core microvilli gene set also was lower in the UNITI-2 trial CD samples compared with controls; expression of microvilli genes was correlated with microvilli length and endoscopy score and was associated with response to treatment.

CONCLUSIONS

In a transcriptome analysis of formalin-fixed and paraffin-embedded ileal tissues from patients with CD and controls, we associated transcriptional alterations with histologic alterations, such as differences in microvilli length. Decreased microvilli length and decreased expression of the microvilli gene set might contribute to epithelial malfunction and the chronic and progressive disease course in patients with CD.

Feasibility of developing reliable gene expression modules from FFPE derived RNA profiled on Affymetrix arrays

The reliability of differential gene expression analysis on formalin-fixed, paraffin-embedded (FFPE) expression profiles generated using Affymetrix arrays is questionable, due to the high range of percent-present values reported in studies which profiled FFPE samples using this technology. Moreover, the validity of gene-modules derived from external datasets in FFPE microarray expression profiles is unknown. By generating matched gene expression profiles using RNAs derived from fresh-frozen (FF) and FFPE preserved breast tumors with Affymetrix arrays and FF/FFPE RNA specific amplification-and-labeling kits, the reliability of differential expression analysis and the validity of gene modules derived from external datasets were investigated. Specifically, the reliability of differential expression analysis was investigated by developing de-novo ER/HER2 pathway gene-modules from the matched datasets and validating them on external FF/FFPE gene expression datasets using ROC analysis. Spearman's rank correlation coefficient of module scores between matched FFPE/frozen datasets was used to measure the reliability of gene-modules derived from external datasets in FFPE expression profiles. Independent of the array/amplification-kit/sample preservation method used, de-novo ER/HER2 gene-modules derived from all matched datasets showed similar prediction performance in the independent validation (AUC range in FFPE dataset; ER: 0.93-0.95, HER2: 0.85-0.91), except for the de-novo ER/HER2 gene-module derived from the FFPE dataset using the 3'IVT kit (AUC range in FFPE dataset; ER: 0.79-0.81, HER2: 0.78). Among the external gene modules considered, roughly ~50% gene modules showed high concordance between expression profiles derived from matching FF and FFPE RNA. The remaining discordant gene modules between FF and FFPE expression profiles showed high concordance within matching FF datasets and within matching FFPE datasets independently, implying that microarrays still require improved amplification-and-sample-preparation protocols for deriving 100% concordant expression profiles from matching FF and FFPE RNA.

Robust transcriptional tumor signatures applicable to both formalin-fixed paraffin-embedded and fresh-frozen samples

Formalin-fixed paraffin-embedded (FFPE) samples represent a valuable resource for clinical researches. However, FFPE samples are usually considered an unreliable source for gene expression analysis due to the partial RNA degradation. In this study, through comparing gene expression profiles between FFPE samples and paired fresh-frozen (FF) samples for three cancer types, we firstly showed that expression measurements of thousands of genes had at least two-fold change in FFPE samples compared with paired FF samples. Therefore, for a transcriptional signature based on risk scores summarized from the expression levels of the signature genes, the risk score thresholds trained from FFPE (or FF) samples could not be applied to FF (or FFPE) samples. On the other hand, we found that more than 90% of the relative expression orderings (REOs) of gene pairs in the FF samples were maintained in their paired FFPE samples and largely unaffected by the storage time. The result suggested that the REOs of gene pairs were highly robust against partial RNA degradation in FFPE samples. Finally, as a case study, we developed a REOs-based signature to distinguish liver cirrhosis from hepatocellular carcinoma (HCC) using FFPE samples. The signature was validated in four datasets of FFPE samples and eight datasets of FF samples. In conclusion, the valuable FFPE samples can be fully exploited to identify REOs-based diagnostic and prognostic signatures which could be robustly applicable to both FF samples and FFPE samples with degraded RNA.

Molecular subtyping of breast cancer improves identification of both high and low risk patients

BACKGROUND

Transcriptome analysis enables classification of breast tumors into molecular subtypes that correlate with prognosis and effect of therapy. We evaluated the clinical benefits of molecular subtyping compared to our current diagnostic practice.

MATERIALS AND METHODS

Molecular subtyping was performed on a consecutive and unselected series of 524 tumors from women with primary breast cancer (n = 508). Tumors were classified by the 256 gene expression signature (CIT) and compared to conventional immunohistochemistry (IHC) procedures.

RESULTS

More than 99% of tumors were eligible for molecular classification and final reports were available prior to the multidisciplinary conference. Using a prognostic standard mortality rate index (PSMRi) developed by the Danish Breast Cancer Group (DBCG) 39 patients were assigned with an intermediate risk and among these 16 (41%) were furthermore diagnosed by the multi-gene signature assigned with a luminal A tumor and consequently spared adjuvant chemotherapy. There was overall agreement between mRNA derived and IHC hormone receptor status, whereas IHC Ki67 protein proliferative index proved inaccurate, compared to the mRNA derived index. Forty-one patients with basal-like (basL) subtypes were screened for predisposing mutations regardless of clinical predisposition. Of those 17% carried pathogenic mutations.

CONCLUSION

Transcriptome based subtyping of breast tumors evidently reduces the need for adjuvant chemotherapy and improves identification of women with predisposing mutations. The results imply that transcriptome profiling should become an integrated part of current breast cancer management.

KLHL6 Is Preferentially Expressed in Germinal Center-Derived B-Cell Lymphomas

OBJECTIVES

KLHL6 is a recently described BTB-Kelch protein with selective expression in lymphoid tissues and is most strongly expressed in germinal center B cells.

METHODS

Using gene expression profiling as well as immunohistochemistry with an anti-KLHL6 monoclonal antibody, we have characterized the expression of this molecule in normal and neoplastic tissues. Protein expression was evaluated in 1,058 hematopoietic neoplasms.

RESULTS

Consistent with its discovery as a germinal center marker, KLHL6 was positive mainly in B-cell neoplasms of germinal center derivation, including 95% of follicular lymphomas (106/112). B-cell lymphomas of non-germinal center derivation were generally negative (0/33 chronic lymphocytic leukemias/small lymphocytic lymphomas, 3/49 marginal zone lymphomas, and 2/66 mantle cell lymphomas).

CONCLUSIONS

In addition to other germinal center markers, including BCL6, CD10, HGAL, and LMO2, KLHL6 immunohistochemistry may prove a useful adjunct in the diagnosis and future classification of B-cell lymphomas.

ESMO consensus conference on malignant lymphoma: general perspectives and recommendations for prognostic tools in mature B-cell lymphomas and chronic lymphocytic leukaemia

The European Society for Medical Oncology (ESMO) consensus conference on mature B-cell lymphomas and chronic lymphocytic leukaemia (CLL) was held on 20 June 2015 in Lugano, Switzerland, and included a multidisciplinary panel of 25 leading experts. The aim of the conference was to develop recommendations on critical subjects difficult to consider in detail in the ESMO Clinical Practice Guidelines. The following areas were identified: (i) the elderly patient, (ii) prognostic factors suitable for clinical use and (iii) the 'ultra-high-risk' group. Before the conference, the expert panel was divided into three working groups; each group focused on one of these areas in order to address four clinically relevant questions relating to that topic. All relevant scientific literature, as identified by the experts, was reviewed in advance. During the consensus conference, each working group developed recommendations to address each of the four questions assigned to their group. These recommendations were then presented to the entire panel and a consensus was reached. This manuscript presents recommendations dedicated to the second area of interest, i.e. prognostic factors suitable for clinical use. The four topics [i.e. interim positron emission tomography (PET), TP53 mutations, cell of origin (COO) and minimal residual disease (MRD)] were primarily chosen because of the bulk of available data together with the lack of clear guidance regarding their use in clinical practice and within clinical trials. Results, including a summary of evidence supporting each recommendation, are detailed in this manuscript. The panel acknowledged that detection of TP53 inactivation by deletion or mutation in CLL should be implemented in clinical practice (level of evidence I, strength of recommendation A). Due to their potentially high prognostic value, at least in some lymphoma entities, implementation of interim PET, COO and MRD was highly recommended in the context of clinical trials. All expert panel members approved this final article.

Advances in the molecular diagnosis of diffuse large B-cell lymphoma in the era of precision medicine

INTRODUCTION

The adoption of high-throughput technologies has led to a transformation in our ability to classify diffuse large B-cell lymphoma (DLBCL) into unique molecular subtypes. In parallel, the expansion of agents targeting key genetic and gene expression signatures has led to an unprecedented opportunity to personalize cancer therapies, paving the way for precision medicine. Areas covered: This review summarizes the key molecular subtypes of DLBCL and outlines the novel technology platforms in development to discriminate clinically relevant subtypes. Expert commentary: The application of emerging diagnostic tests into routine clinical practise is gaining momentum following the demonstration of subtype specific activity by novel agents. Co-ordinated efforts are required to ensure that these state of the art technologies provide reliable and clinically meaningful results accessible to the wider haematology community.

Comparison of Nanostring nCounter® Data on FFPE Colon Cancer Samples and Affymetrix Microarray Data on Matched Frozen Tissues

The prognosis of colorectal cancer (CRC) stage II and III patients remains a challenge due to the difficulties of finding robust biomarkers suitable for testing clinical samples. The majority of published gene signatures of CRC have been generated on fresh frozen colorectal tissues. Because collection of frozen tissue is not practical for routine surgical pathology practice, a clinical test that improves prognostic capabilities beyond standard pathological staging of colon cancer will need to be designed for formalin-fixed paraffin-embedded (FFPE) tissues. The NanoString nCounter^® platform is a gene expression analysis tool developed for use with FFPE-derived samples. We designed a custom nCounter^® codeset based on elements from multiple published fresh frozen tissue microarray-based prognostic gene signatures for colon cancer, and we used this platform to systematically compare gene expression data from FFPE with matched microarray array data from frozen tissues. Our results show moderate correlation of gene expression between two platforms and discovery of a small subset of genes as candidate biomarkers for colon cancer prognosis that are detectable and quantifiable in FFPE tissue sections.

Cell-of-Origin in Diffuse Large B-Cell Lymphoma: Are the Assays Ready for the Clinic?

Diffuse large B-cell lymphoma (DLBCL) is the most common lymphoma worldwide and consists of a heterogeneous group of cancers classified together on the basis of shared morphology, immunophenotype, and aggressive clinical behavior. It is now recognized that this malignancy comprises at least two distinct molecular subtypes identified by gene expression profiling: the activated B-cell-like (ABC) and the germinal center B-cell-like (GCB) groups-the cell-of-origin (COO) classification. These two groups have different genetic mutation landscapes, pathobiology, and outcomes following treatment. Evidence is accumulating that novel agents have selective activity in one or the other COO group, making COO a predictive biomarker. Thus, there is now a pressing need for accurate and robust methods to assign COO, to support clinical trials, and ultimately guide treatment decisions for patients. The "gold standard" methods for COO are based on gene expression profiling (GEP) of RNA from fresh frozen tissue using microarray technology, which is an impractical solution when formalin-fixed paraffin-embedded tissue (FFPET) biopsies are the standard diagnostic material. This review outlines the history of the COO classification before examining the practical implementation of COO assays applicable to FFPET biopsies. The immunohistochemistry (IHC)-based algorithms and gene expression-based assays suitable for the highly degraded RNA from FFPET are discussed. Finally, the technical and practical challenges that still need to be addressed are outlined before robust gene expression-based assays are used in the routine management of patients with DLBCL.

Subtype-Specific Metagene-Based Prediction of Outcome after Neoadjuvant and Adjuvant Treatment in Breast Cancer

PURPOSE

In spite of improvements of average benefit from adjuvant/neoadjuvant treatments, there are still individual patients with early breast cancer at high risk of relapse. We explored the association with outcome of robust gene cluster-based metagenes linked to proliferation, ER-related genes, and immune response to identify those high-risk patients.

EXPERIMENTAL DESIGN

A total of 3,847 publicly available gene-expression profiles were analyzed (untreated, N = 826; tamoxifen-treated, N = 685; chemotherapy-treated, N = 1,150). Genes poorly performing in formalin-fixed samples were removed. Outcomes of interest were pathologic-complete response (pCR) and distant metastasis-free survival (DMFS). In ER(+)HER2(-), the proliferation and ER-related metagenes were combined to define three risk groups. In HER2(+) and ER(-)HER2(-) risk groups were defined by tertiles of an immune-related metagene.

RESULTS

The high-proliferation/low-ER group of ER(+)HER2(-) breast cancer had significantly higher pCR rate [OR, 5.01 (1.76-17.99), P = 0.005], but poorer outcome [HR = 3.73 (1.63-8.51), P = 0.0018] than the low-proliferation/high-ER. A similar association with outcome applied to patients with residual disease (RD) after neoadjuvant chemotherapy (P = 0.01). In ER(-)HER2(-) and HER2(+) breast cancer, immune metagene in the high tertile was linked to higher pCR [33.7% vs. 11.6% in high and low tertile, respectively; OR, 3.87 (1.79-8.95); P = 0.0009]. In ER(-)HER2(-), after adjuvant/neoadjuvant chemotherapy, 5-year DMFS was 85.4% for high-tertile immune metagene, and 43.9% for low tertile. The outcome association was similar in patients with RD (P = 0.0055). In HER2(+) breast cancer treated with chemotherapy the association with risk of relapse was not significant.

CONCLUSIONS

We developed metagene-based predictors able to define low and high risk of relapse after adjuvant/neoadjuvant therapy. High-risk patients so defined should be preferably considered for trials with investigational agents.

Gene expression meta-analysis reveals immune response convergence on the IFNγ-STAT1-IRF1 axis and adaptive immune resistance mechanisms in lymphoma

BACKGROUND

Cancers adapt to immune-surveillance through evasion. Immune responses against carcinoma and melanoma converge on cytotoxic effectors and IFNγ-STAT1-IRF1 signalling. Local IFN-driven immune checkpoint expression can mediate feedback inhibition and adaptive immune resistance. Whether such coupled immune polarization and adaptive resistance is generalisable to lymphoid malignancies is incompletely defined. The host response in diffuse large B-cell lymphoma (DLBCL), the commonest aggressive lymphoid malignancy, provides an empirical model.

METHODS

Using ten publicly available gene expression data sets encompassing 2030 cases we explore the nature of host response in DLBCL. Starting from the "cell of origin" paradigm for DLBCL classification, we use the consistency of differential expression to define polarized patterns of immune response genes in DLBCL, and derive a linear classifier of immune response gene expression. We validate and extend the results in an approach independent of "cell of origin" classification based on gene expression correlations across all data sets.

RESULTS

T-cell and cytotoxic gene expression with polarization along the IFNγ-STAT1-IRF1 axis provides a defining feature of the immune response in DLBCL. This response is associated with improved outcome, particularly in the germinal centre B-cell subsets of DLBCL. Analysis of gene correlations across all data sets, independent of "cell of origin" class, demonstrates a consistent association with a hierarchy of immune-regulatory gene expression that places IDO1, LAG3 and FGL2 ahead of PD1-ligands CD274 and PDCD1LG2.

CONCLUSION

Immune responses in DLBCL converge onto the IFNγ-STAT1-IRF1 axis and link to diverse potential mediators of adaptive immune resistance identifying future therapeutic targets.

Use of formalin-fixed paraffin-embedded samples for gene expression studies in breast cancer patients

To obtain gene expression profiles from samples collected in clinical trials, we conducted a pilot study to assess feasibility and estimate sample attrition rates when profiling formalin-fixed, paraffin-embedded specimens. Ten matched fresh-frozen and fixed breast cancer samples were profiled using the Illumina HT-12 and Ref-8 chips, respectively. The profiles obtained with Ref 8, were neither technically nor biologically reliable since they failed to yield the expected separation between estrogen receptor positive and negative samples. With the use of Affymetrix HG-U133 2.0 Plus chips on fixed samples and a quantitative polymerase chain reaction -based sample pre-assessment step, results were satisfactory in terms of biological reliability, despite the low number of present calls (M = 21%±5). Compared with the Illumina DASL WG platform, Affymetrix data showed a wider interquartile range (1.32 vs 0.57, P<2.2 E-16,) and larger fold changes. The Affymetrix chips were used to run a pilot study on 60 fixed breast cancers. By including in the workflow the sample pre-assessment steps, 96% of the samples predicted to give good results (44/46), were in fact rated as satisfactory from the point of view of technical and biological meaningfulness. Our gene expression profiles showed strong agreement with immunohistochemistry data, were able to reproduce breast cancer molecular subtypes, and allowed the validation of an estrogen receptor status classifier derived in frozen samples. The approach is therefore suitable to profile formalin-fixed paraffin-embedded samples collected in clinical trials, provided that quality controls are run both before (sample pre-assessment) and after hybridization on the array.

Diffuse Large B-cell Lymphoma Classification Tied Up Nicely with a "String"

Molecular classification of diffuse large B-cell lymphoma (DLBCL) is a critical biologic, prognostic, and predictive distinction. Since the discovery of two molecular types of DLBCL in otherwise morphologically indistinguishable cases, numerous methodologies for making this distinction have been published. A method and technical platform may now be reaching consensus.

SPIB and BATF provide alternate determinants of IRF4 occupancy in diffuse large B-cell lymphoma linked to disease heterogeneity

Interferon regulatory factor 4 (IRF4) is central to the transcriptional network of activated B-cell-like diffuse large B-cell lymphoma (ABC-DLBCL), an aggressive lymphoma subgroup defined by gene expression profiling. Since cofactor association modifies transcriptional regulatory input by IRF4, we assessed genome occupancy by IRF4 and endogenous cofactors in ABC-DLBCL cell lines. IRF4 partners with SPIB, PU.1 and BATF genome-wide, but SPIB provides the dominant IRF4 partner in this context. Upon SPIB knockdown IRF4 occupancy is depleted and neither PU.1 nor BATF acutely compensates. Integration with ENCODE data from lymphoblastoid cell line GM12878, demonstrates that IRF4 adopts either SPIB- or BATF-centric genome-wide distributions in related states of post-germinal centre B-cell transformation. In primary DLBCL high-SPIB and low-BATF or the reciprocal low-SPIB and high-BATF mRNA expression links to differential gene expression profiles across nine data sets, identifying distinct associations with SPIB occupancy, signatures of B-cell differentiation stage and potential pathogenetic mechanisms. In a population-based patient cohort, SPIBhigh/BATFlow-ABC-DLBCL is enriched for mutation of MYD88, and SPIBhigh/BATFlow-ABC-DLBCL with MYD88-L265P mutation identifies a small subgroup of patients among this otherwise aggressive disease subgroup with distinct favourable outcome. We conclude that differential expression of IRF4 cofactors SPIB and BATF identifies biologically and clinically significant heterogeneity among ABC-DLBCL.

Accurate data processing improves the reliability of Affymetrix gene expression profiles from FFPE samples

Formalin fixed paraffin-embedded (FFPE) tumor specimens are the conventionally archived material in clinical practice, representing an invaluable tissue source for biomarkers development, validation and routine implementation. For many prospective clinical trials, this material has been collected allowing for a prospective-retrospective study design which represents a successful strategy to define clinical utility for candidate markers. Gene expression data can be obtained even from FFPE specimens with the broadly used Affymetrix HG-U133 Plus 2.0 microarray platform. Nevertheless, important major discrepancies remain in expression data obtained from FFPE compared to fresh-frozen samples, prompting the need for appropriate data processing which could help to obtain more consistent results in downstream analyses. In a publicly available dataset of matched frozen and FFPE expression data, the performances of different normalization methods and specifically designed Chip Description Files (CDFs) were compared. The use of an alternative CDFs together with fRMA normalization significantly improved frozen-FFPE sample correlations, frozen-FFPE probeset correlations and agreement of differential analysis between different tumor subtypes. The relevance of our optimized data processing was assessed and validated using two independent datasets. In this study we demonstrated that an appropriate data processing can significantly improve the reliability of gene expression data derived from FFPE tissues using the standard Affymetrix platform. Tools for the implementation of our data processing algorithm are made publicly available at http://www.biocut.unito.it/cdf-ffpe/.

Point of Care Technologies for HIV

Effective prevention of HIV/AIDS requires early diagnosis, initiation of therapy, and regular plasma viral load monitoring of the infected individual. In addition, incidence estimation using accurate and sensitive assays is needed to facilitate HIV prevention efforts in the public health setting. Therefore, more affordable and accessible point-of-care (POC) technologies capable of providing early diagnosis, HIV viral load measurements, and CD4 counts in settings where HIV is most prevalent are needed to enable appropriate intervention strategies and ultimately stop transmission of the virus within these populations to achieve the future goal of an AIDS-free generation. This review discusses the available and emerging POC technologies for future application to these unmet public health needs.

Molecular classification, pathway addiction, and therapeutic targeting in diffuse large B cell lymphoma

The rapid emergence of molecularly based techniques to detect changes in the genetic landscape of diffuse large B cell lymphoma (DLBCL), including gene expression, DNA and RNA sequencing, and epigenetic profiling, has significantly influenced the understanding and therapeutic targeting of DLBCL. In this review, we briefly discuss the new methods used in the study of DLBCL. We describe the influence of the generated data on DLBCL classification and the identification of new entities and altered cell survival strategies, with a focus on the renewed interest in some classic oncogenic pathways that are currently targeted for new therapy. Finally, we examine the molecular genomic studies that revealed the importance of the tumor microenvironment in the pathogenesis of DLBCL.

An optimized workflow for improved gene expression profiling for formalin-fixed, paraffin-embedded tumor samples

BACKGROUND

Whole genome microarray gene expression profiling is the 'gold standard' for the discovery of prognostic and predictive genetic markers for human cancers. However, suitable research material is lacking as most diagnostic samples are preserved as formalin-fixed, paraffin-embedded tissue (FFPET). We tested a new workflow and data analysis method optimized for use with FFPET samples.

METHODS

Sixteen breast tumor samples were split into matched pairs and preserved as FFPET or fresh-frozen (FF). Total RNA was extracted and tested for yield and purity. RNA from FFPET samples was amplified using three different commercially available kits in parallel, and hybridized to Affymetrix GeneChip® Human Genome U133 Plus 2.0 Arrays. The array probe set was optimized in silico to exclude misdesigned and misannotated probes.

RESULTS

FFPET samples processed using the WT-Ovation™ FFPE System V2 (NuGEN) provided 80% specificity and 97% sensitivity compared with FF samples (assuming values of 100%). In addition, in silico probe set redesign improved sequence detection sensitivity and, thus, may rescue potentially significant small-magnitude gene expression changes that could otherwise be diluted by the overall probe set background.

CONCLUSION

In conclusion, our FFPET-optimized workflow enables the detection of more genes than previous, nonoptimized approaches, opening new possibilities for the discovery, validation, and clinical application of mRNA biomarkers in human diseases.

The molecular biology of diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) represents the most common type of malignant lymphoma. In the last few years, significant progress has been achieved in the understanding of the molecular pathogenesis of this entity. Gene expression profiling has identified three molecular DLBCL subtypes, termed germinal-center B-cell-like (GCB) DLBCL, activated B-cell-like (ABC) DLBCL, and primary mediastinal B-cell lymphoma (PMBL). In this review, we summarize our current understanding of the biology of these DLBCL subtypes with a special emphasis on novel diagnostic and therapeutic approaches.

A microarray platform-independent classification tool for cell of origin class allows comparative analysis of gene expression in diffuse large B-cell lymphoma

Cell of origin classification of diffuse large B-cell lymphoma (DLBCL) identifies subsets with biological and clinical significance. Despite the established nature of the classification existing studies display variability in classifier implementation, and a comparative analysis across multiple data sets is lacking. Here we describe the validation of a cell of origin classifier for DLBCL, based on balanced voting between 4 machine-learning tools: the DLBCL automatic classifier (DAC). This shows superior survival separation for assigned Activated B-cell (ABC) and Germinal Center B-cell (GCB) DLBCL classes relative to a range of other classifiers. DAC is effective on data derived from multiple microarray platforms and formalin fixed paraffin embedded samples and is parsimonious, using 20 classifier genes. We use DAC to perform a comparative analysis of gene expression in 10 data sets (2030 cases). We generate ranked meta-profiles of genes showing consistent class-association using ≥6 data sets as a cut-off: ABC (414 genes) and GCB (415 genes). The transcription factor ZBTB32 emerges as the most consistent and differentially expressed gene in ABC-DLBCL while other transcription factors such as ARID3A, BATF, and TCF4 are also amongst the 24 genes associated with this class in all datasets. Analysis of enrichment of 12323 gene signatures against meta-profiles and all data sets individually confirms consistent associations with signatures of molecular pathways, chromosomal cytobands, and transcription factor binding sites. We provide DAC as an open access Windows application, and the accompanying meta-analyses as a resource.

Whole genome profiling and other high throughput technologies in lymphoid neoplasms--current contributions and future hopes

The development of high throughput technologies based on the knowledge of the human genome has opened the possibility to search for global genomic alterations in tumors responsible for their development and progression that may have important clinical implications. One of the major applications of this genomic knowledge has been the design of different types of microarray platforms for the analysis of DNA alterations and gene expression profiling (GEP). The main contributions of the DNA studies in lymphoid neoplasms include the definition of relatively characteristic genomic profiles for specific disease entities, the demonstration of common chromosomal alterations across entities, the identification of genes and pathways targeted by the altered chromosomal regions, and the identification of chromosomal alterations with prognostic implications. RNA GEP studies in these tumors have enhanced the molecular characterization of known entities and facilitated the recognition of new subtypes and categories of lymphoid neoplasms, the identification of new biomarkers and prognostic models, and the detection of oncogenic pathways with potential implications for targeted therapies. The recent development of the next generation sequencing (NGS) technologies and its application in lymphoid neoplasms already have provided an initial view of the complex landscape of somatic mutations in these tumors and some findings with important functional and clinical implications. This review addresses the major contributions and limitations of the microarray technologies in the understanding of lymphoid neoplasms and discusses how this knowledge may be transferred into the clinics. The initial results of the NGS studies are also presented.

Expression profiling of archival tumors for long-term health studies

PURPOSE

More than 20 million archival tissue samples are stored annually in the United States as formalin-fixed, paraffin-embedded (FFPE) blocks, but RNA degradation during fixation and storage has prevented their use for transcriptional profiling. New and highly sensitive assays for whole-transcriptome microarray analysis of FFPE tissues are now available, but resulting data include noise and variability for which previous expression array methods are inadequate.

EXPERIMENTAL DESIGN

We present the two largest whole-genome expression studies from FFPE tissues to date, comprising 1,003 colorectal cancer (CRC) and 168 breast cancer samples, combined with a meta-analysis of 14 new and published FFPE microarray datasets. We develop and validate quality control (QC) methods through technical replication, independent samples, comparison to results from fresh-frozen tissue, and recovery of expected associations between gene expression and protein abundance.

RESULTS

Archival tissues from large, multicenter studies showed a much wider range of transcriptional data quality relative to smaller or frozen tissue studies and required stringent QC for subsequent analysis. We developed novel methods for such QC of archival tissue expression profiles based on sample dynamic range and per-study median profile. This enabled validated identification of gene signatures of microsatellite instability and additional features of CRC, and improved recovery of associations between gene expression and protein abundance of MLH1, FASN, CDX2, MGMT, and SIRT1 in CRC tumors.

CONCLUSIONS

These methods for large-scale QC of FFPE expression profiles enable study of the cancer transcriptome in relation to extensive clinicopathological information, tumor molecular biomarkers, and long-term lifestyle and outcome data.

Whole genome expression profiling based on paraffin embedded tissue can be used to classify diffuse large B-cell lymphoma and predict clinical outcome

This study tested the validity of whole-genome expression profiling (GEP) using RNA from formalin-fixed, paraffin-embedded (FFPE) tissue to sub-classify Diffuse Large B-cell Lymphoma (DLBCL), in a population based cohort of 172 patients. GEP was performed using Illumina Whole Genome cDNA-mediated Annealing, Selection, extension & Ligation, and tumours were classified into germinal centre (GCB), activated B-cell (ABC) and Type-III subtypes. The method was highly reproducible and reliably classified cell lines of known phenotype. GCB and ABC subtypes were each characterized by unique gene expression signatures consistent with previously published data. A significant relationship between subtype and survival was observed, with ABC having the worst clinical outcome and in a multivariate survival model only age and GEP class remained significant. This effect was not seen when tumours were classified by immunohistochemistry. There was a significant association between age and subtype (mean ages ABC - 72·8 years, GC - 68·4 years, Type-III - 64·5 years). Older patients with ABC subtype were also over-represented in patients who died soon after diagnosis. The relationship between prognosis and subtype improved when only patients assigned to the three categories with the highest level of confidence were analysed. This study demonstrates that GEP-based classification of DLBCL can be applied to RNA extracted from routine FFPE samples and has potential for use in stratified medicine trials and clinical practice.

Report on emerging technologies for translational bioinformatics: a symposium on gene expression profiling for archival tissues

Background

With over 20 million formalin-fixed, paraffin-embedded (FFPE) tissue samples archived each year in the United States alone, archival tissues remain a vast and under-utilized resource in the genomic study of cancer. Technologies have recently been introduced for whole-transcriptome amplification and microarray analysis of degraded mRNA fragments from FFPE samples, and studies of these platforms have only recently begun to enter the published literature.

Results

The Emerging Technologies for Translational Bioinformatics symposium on gene expression profiling for archival tissues featured presentations of two large-scale FFPE expression profiling studies (each involving over 1,000 samples), overviews of several smaller studies, and representatives from three leading companies in the field (Illumina, Affymetrix, and NuGEN). The meeting highlighted challenges in the analysis of expression data from archival tissues and strategies being developed to overcome them. In particular, speakers reported higher rates of clinical sample failure (from 10% to 70%) than are typical for fresh-frozen tissues, as well as more frequent probe failure for individual samples. The symposium program is available at http://www.hsph.harvard.edu/ffpe.

Conclusions

Multiple solutions now exist for whole-genome expression profiling of FFPE tissues, including both microarray- and sequencing-based platforms. Several studies have reported their successful application, but substantial challenges and risks still exist. Symposium speakers presented novel methodology for analysis of FFPE expression data and suggestions for improving data recovery and quality assessment in pre-analytical stages. Research presentations emphasized the need for careful study design, including the use of pilot studies, replication, and randomization of samples among batches, as well as careful attention to data quality control. Regardless of any limitations in quantitave transcriptomics for FFPE tissues, they are often the only biospecimens available for large patient populations with long-term history and clinical follow-up. Current challenges can be expected to remain as RNA sequencing matures, and they will thus motivate ongoing research efforts into noise reduction and identification of robust, translationally relevant biological signals in expression data from FFPE tissues.

Comprehensive gene expression profiling and immunohistochemical studies support application of immunophenotypic algorithm for molecular subtype classification in diffuse large B-cell lymphoma: a report from the International DLBCL Rituximab-CHOP Consortium Program Study

Gene expression profiling (GEP) has stratified diffuse large B-cell lymphoma (DLBCL) into molecular subgroups that correspond to different stages of lymphocyte development-namely germinal center B-cell like and activated B-cell like. This classification has prognostic significance, but GEP is expensive and not readily applicable into daily practice, which has lead to immunohistochemical algorithms proposed as a surrogate for GEP analysis. We assembled tissue microarrays from 475 de novo DLBCL patients who were treated with rituximab-CHOP chemotherapy. All cases were successfully profiled by GEP on formalin-fixed, paraffin-embedded tissue samples. Sections were stained with antibodies reactive with CD10, GCET1, FOXP1, MUM1 and BCL6 and cases were classified following a rationale of sequential steps of differentiation of B cells. Cutoffs for each marker were obtained using receiver-operating characteristic curves, obviating the need for any arbitrary method. An algorithm based on the expression of CD10, FOXP1 and BCL6 was developed that had a simpler structure than other recently proposed algorithms and 92.6% concordance with GEP. In multivariate analysis, both the International Prognostic Index and our proposed algorithm were significant independent predictors of progression-free and overall survival. In conclusion, this algorithm effectively predicts prognosis of DLBCL patients matching GEP subgroups in the era of rituximab therapy.

Microarray gene expression analysis of fixed archival tissue permits molecular classification and identification of potential therapeutic targets in diffuse large B-cell lymphoma

Refractory/relapsed diffuse large B-cell lymphoma (DLBCL) has a poor prognosis. Novel drugs targeting the constitutively activated NF-κB pathway characteristic of ABC-DLBCL are promising, but evaluation depends on accurate activated B cell-like (ABC)/germinal center B cell-like (GCB) molecular classification. This is traditionally performed on gene microarray expression profiles of fresh biopsies, which are not routinely collected, or by immunohistochemistry on formalin-fixed, paraffin-embedded (FFPE) tissue, which lacks reproducibility and classification accuracy. We explored the possibility of using routine archival FFPE tissue for gene microarray applications. We examined Affymetrix HG U133 Plus 2.0 gene expression profiles from paired archival FFPE and fresh-frozen tissues of 40 ABC/GCB-classified DLBCL cases to compare classification accuracy and test the potential for this approach to aid the discovery of therapeutic targets and disease classifiers in DLBCL. Unsupervised hierarchical clustering of unselected present probe sets distinguished ABC/GCB in FFPE with remarkable accuracy, and a Bayesian classifier correctly assigned 32 of 36 cases with >90% probability. Enrichment for NF-κB genes was appropriately seen in ABC-DLBCL FFPE tissues. The top discriminatory genes expressed in FFPE separated cases with high statistical significance and contained novel biology with potential therapeutic insights, warranting further investigation. These results support a growing understanding that archival FFPE tissues can be used in microarray experiments aimed at molecular classification, prognostic biomarker discovery, and molecular exploration of rare diseases.

Accurate classification of diffuse large B-cell lymphoma into germinal center and activated B-cell subtypes using a nuclease protection assay on formalin-fixed, paraffin-embedded tissues

Classification of diffuse large B-cell lymphoma (DLBCL) into cell-of-origin (COO) subtypes based on gene expression profiles has well-established prognostic value. These subtypes, termed germinal center B cell (GCB) and activated B cell (ABC) also have different genetic alterations and overexpression of different pathways that may serve as therapeutic targets. Thus, accurate classification is essential for analysis of clinical trial results and planning new trials by using targeted agents. The current standard for COO classification uses gene expression profiling (GEP) of snap frozen tissues, and a Bayesian predictor algorithm. However, this is generally not feasible. In this study, we investigated whether the qNPA technique could be used for accurate classification of COO by using formalin-fixed, paraffin-embedded (FFPE) tissues. We analyzed expression levels of 14 genes in 121 cases of R-CHOP-treated DLBCL that had previously undergone GEP by using the Affymetrix U133 Plus 2.0 microarray and had matching FFPE blocks. Results were evaluated by using the previously published algorithm with a leave-one-out cross-validation approach. These results were compared with COO classification based on frozen tissue GEP profiles. For each case, a probability statistic was generated indicating the likelihood that the classification by using qNPA was accurate. When data were dichotomized into GCB or non-GCB, overall accuracy was 92%. The qNPA technique accurately categorized DLBCL into GCB and ABC subtypes, as defined by GEP. This approach is quantifiable, applicable to FFPE tissues with no technical failures, and has potential for significant impact on DLBCL research and clinical trial development.

Exon-array profiling unlocks clinically and biologically relevant gene signatures from formalin-fixed paraffin-embedded tumour samples

Background:

Degradation and chemical modification of RNA in formalin-fixed paraffin-embedded (FFPE) samples hamper their use in expression profiling studies. This study aimed to show that useful information can be obtained by Exon-array profiling archival FFPE tumour samples.

Methods:

Nineteen cervical squamous cell carcinoma (SCC) and 9 adenocarcinoma (AC) FFPE samples (10–16-year-old) were profiled using Affymetrix Exon arrays. The gene signature derived was tested on a fresh-frozen non-small cell lung cancer (NSCLC) series. Exploration of biological networks involved gene set enrichment analysis (GSEA). Differential gene expression was confirmed using Quantigene, a multiplex bead-based alternative to qRT–PCR.

Results:

In all, 1062 genes were higher in SCC vs AC, and 155 genes higher in AC. The 1217-gene signature correctly separated 58 NSCLC into SCC and AC. A gene network centered on hepatic nuclear factor and GATA6 was identified in AC, suggesting a role in glandular cell differentiation of the cervix. Quantigene analysis of the top 26 differentially expressed genes correctly partitioned cervix samples as SCC or AC.

Conclusion:

FFPE samples can be profiled using Exon arrays to derive gene expression signatures that are sufficiently robust to be applied to independent data sets, identify novel biology and design assays for independent platform validation.

Aggressive B-cell lymphomas: a review of new and old entities in the WHO classification

Aggressive B-cell lymphomas are clinically and pathologically diverse and reflect multiple pathways of transformation. The 2008 World Health Organization (WHO) classification reflects this complexity with the addition of several new entities and variants. Whereas MYC translocations have long been associated with Burkitt lymphoma (BL), deregulation of MYC has been shown to occur in other aggressive B-cell lymphomas, most often as a secondary event. Lymphomas with translocations of both MYC and BCL2 are highly aggressive tumors, with a high failure rate with most treatment protocols. These "double-hit" lymphomas are now separately delineated in the WHO classification as B-cell lymphoma, unclassifiable, with features intermediate between diffuse large B-cell lymphoma (DLBCL) and BL. A MYC translocation is also found uncommonly in DLBCL, but the clinical consequences of this in the absence of a double hit are not yet fully delineated. Most recently, MYC translocations have been identified as a common secondary event in plasma cell neoplasms, seen in approximately 50% of plasmablastic lymphoma. Another area that has received recent attention is the spectrum of EBV-driven B-cell proliferations in patients without iatrogenic or congenital immunosuppression; most of these occur in patients of advanced age and include the EBV-positive large B-cell lymphomas of the elderly.