Adding a Gene Expression Profile Test to Aid Differential Diagnosis and Treatment in Aggressive Large B-Cell Lymphoma: An Early Exploratory Economic Evaluation.

BACKGROUND AND OBJECTIVE: Adding gene expression profiles (GEPs) to the current diagnostic work-up of aggressive large B-cell lymphomas may lead to the reclassification of patients, treatment changes and improved outcomes. A GEP test is in development using TempO-Seq® technology to distinguish Burkitt lymphoma (BL) and primary mediastinal large B-cell lymphoma (PMBCL) from diffuse large B-cell lymphoma (DLBCL), and to classify patients with DLBLC and to predict the benefit of (e.g.) adding bortezomib to R-CHOP therapy (RB-CHOP). This study aims to estimate the potential impact of a GEP test on costs and health outcomes to inform pricing and evidence generation strategies. METHODS: Three decision models were developed comparing diagnostic strategies with and without GEP signatures over a lifetime horizon using a UK health and social care perspective. Inputs were taken from a recent clinical trial, literature and expert opinion. We estimated the maximum price of the test using a threshold of Great Britain Pound (GBP) 30,000 per quality-adjusted life-year (QALY). Sensitivity analyses were conducted. RESULTS: The estimated maximum threshold price for a combined test to be cost effective is GBP 15,352. At base-case values, the BL signature delivers QALY gains of 0.054 at an additional cost of GBP 275. This results in a net monetary benefit at a threshold of GBP 30,000 per QALY of GBP 1345. For PMBCL, the QALY gain was 0.0011 at a cost saving of GBP 406 and the net monetary benefit was GBP 437. The hazard ratio for the impact of treating BL less intensively must be at least 1.2 for a positive net monetary benefit. For identifying patients with the DLBCL subtype responsive to bortezomib, QALY gain was 0.2465 at a cost saving of GBP 6175, resulting in a net monetary benefit of GBP 13,570. In a probabilistic sensitivity analysis using 1000 simulations, a testing strategy was superior to a treat all with R-CHOP strategy in 81% of the simulations and with a cost saving in 92% assuming a cost price of zero. CONCLUSIONS: Our estimates show that the combined test has a high probability of being cost effective. There is good quality evidence for the benefit of subtyping DLBCL but the evidence on the number of patients reclassified to or from BL and PMBCL and the impact of a more precise diagnosis and the cost of treatment is weak. The developers can use the price estimate to inform a return on investment calculations. Evidence will be required of how well the TempO-Seq® technology performs compared to the testing GEP technology used for subtyping in the recent clinical trial. For BL and PMBCL elements of the test, evidence would be required of the number of patients reclassified and improved costing information would be useful. The diagnostic and therapeutic environment in haematological malignancies is fast moving, which increases the risk for developers of diagnostic tests.

classifieR a flexible interactive cloud-application for functional annotation of cancer transcriptomes.

BACKGROUND: Transcriptionally informed predictions are increasingly important for sub-typing cancer patients, understanding underlying biology and to inform novel treatment strategies. For instance, colorectal cancers (CRCs) can be classified into four CRC consensus molecular subgroups (CMS) or five intrinsic (CRIS) sub-types that have prognostic and predictive value. Breast cancer (BRCA) has five PAM50 molecular subgroups with similar value, and the OncotypeDX test provides transcriptomic based clinically actionable treatment-risk stratification. However, assigning samples to these subtypes and other transcriptionally inferred predictions is time consuming and requires significant bioinformatics experience. There is no "universal" method of using data from diverse assay/sequencing platforms to provide subgroup classification using the established classifier sets of genes (CMS, CRIS, PAM50, OncotypeDX), nor one which in provides additional useful functional annotations such as cellular composition, single-sample Gene Set Enrichment Analysis, or prediction of transcription factor activity. RESULTS: To address this bottleneck, we developed classifieR, an easy-to-use R-Shiny based web application that supports flexible rapid single sample annotation of transcriptional profiles derived from cancer patient samples form diverse platforms. We demonstrate the utility of the " classifieR" framework to applications focused on the analysis of transcriptional profiles from colorectal (classifieRc) and breast (classifieRb). Samples are annotated with disease relevant transcriptional subgroups (CMS/CRIS sub-types in classifieRc and PAM50/inferred OncotypeDX in classifieRb), estimation of cellular composition using MCP-counter and xCell, single-sample Gene Set Enrichment Analysis (ssGSEA) and transcription factor activity predictions with Discriminant Regulon Expression Analysis (DoRothEA). CONCLUSIONS: classifieR provides a framework which enables labs without access to a dedicated bioinformation can get information on the molecular makeup of their samples, providing an insight into patient prognosis, druggability and also as a tool for analysis and discovery. Applications are hosted online at https://generatr.qub.ac.uk/app/classifieRc and https://generatr.qub.ac.uk/app/classifieRb after signing up for an account on https://generatr.qub.ac.uk .

Extraction-free whole transcriptome gene expression analysis of FFPE sections and histology-directed subareas of tissue.

We describe the use of a ligation-based targeted whole transcriptome expression profiling assay, TempO-Seq, to profile formalin-fixed paraffin-embedded (FFPE) tissue, including H&E stained FFPE tissue, by directly lysing tissue scraped from slides without extracting RNA or converting the RNA to cDNA. The correlation of measured gene expression changes in unfixed and fixed samples using blocks prepared from a pellet of a single cell type was R2 = 0.97, demonstrating that no significant artifacts were introduced by fixation. Fixed and fresh samples prepared in an equivalent manner produced comparable sequencing depth results (+/- 20%), with similar %CV (11.5 and 12.7%, respectively), indicating no significant loss of measurable RNA due to fixation. The sensitivity of the TempO-Seq assay was the same whether the tissue section was fixed or not. The assay performance was equivalent for human, mouse, or rat whole transcriptome. The results from 10 mm2 and 2 mm2 areas of tissue obtained from 5 µm thick sections were equivalent, thus demonstrating high sensitivity and ability to profile focal areas of histology within a section. Replicate reproducibility of separate areas of tissue ranged from R2 = 0.83 (lung) to 0.96 (liver) depending on the tissue type, with an average correlation of R2 = 0.90 across nine tissue types. The average %CVs were 16.8% for genes expressed at greater than 200 counts, and 20.3% for genes greater than 50 counts. Tissue specific differences in gene expression were identified and agreed with the literature. There was negligible impact on assay performance using FFPE tissues that had been archived for up to 30 years. Similarly, there was negligible impact of H&E staining, facilitating accurate visualization for scraping and assay of small focal areas of specific histology within a section.

Comparison of base-line and chemical-induced transcriptomic responses in HepaRG and RPTEC/TERT1 cells using TempO-Seq.

The utilisation of genome-wide transcriptomics has played a pivotal role in advancing the field of toxicology, allowing the mapping of transcriptional signatures to chemical exposures. These activities have uncovered several transcriptionally regulated pathways that can be utilised for assessing the perturbation impact of a chemical and also the identification of toxic mode of action. However, current transcriptomic platforms are not very amenable to high-throughput workflows due to, high cost, complexities in sample preparation and relatively complex bioinformatic analysis. Thus, transcriptomic investigations are usually limited in dose and time dimensions and are, therefore, not optimal for implementation in risk assessment workflows. In this study, we investigated a new cost-effective, transcriptomic assay, TempO-Seq, which alleviates the aforementioned limitations. This technique was evaluated in a 6-compound screen, utilising differentiated kidney (RPTEC/TERT1) and liver (HepaRG) cells and compared to non-transcriptomic label-free sensitive endpoints of chemical-induced disturbances, namely phase contrast morphology, xCELLigence and glycolysis. Non-proliferating cell monolayers were exposed to six sub-lethal concentrations of each compound for 24 h. The results show that utilising a 2839 gene panel, it is possible to discriminate basal tissue-specific signatures, generate dose-response relationships and to discriminate compound-specific and cell type-specific responses. This study also reiterates previous findings that chemical-induced transcriptomic alterations occur prior to cytotoxicity and that transcriptomics provides in depth mechanistic information of the effects of chemicals on cellular transcriptional responses. TempO-Seq is a robust transcriptomic platform that is well suited for in vitro toxicity experiments.

Characterization of carfilzomib-resistant non-small cell lung cancer cell lines.

PURPOSE: We previously showed that carfilzomib (CFZ) has potent anti-proliferative and cytotoxic activity in a broad range of lung cancer cell lines. Here we investigate possible mechanisms of CFZ acquired resistance in lung cancer cell lines. METHODS: CFZ-resistant non-small cell lung cancer (NSCLC) cell lines were developed by exposing A549 and H520 cells to stepwise increasing concentrations of CFZ. Resistance to CFZ and cross-resistance to bortezomib and other chemotherapy drugs was measured using the MTT assay. Cytotoxicity to CFZ was determined using a CytoTox assay. Western blot was used to measure apoptosis, autophagy, and drug efflux transporter-related proteins. Quantitative targeted whole transcriptome sequencing and quantitative RT-PCR was used to measure gene expression. Flow cytometry was used to analyze intracellular accumulation of doxorubicin. RESULTS: The CFZ IC50 value of the resistant cells increased versus parental lines (2.5-fold for A549, 122-fold for H520). Resistant lines showed reduced expression of apoptosis and autophagy markers and reduced death versus parental lines following CFZ treatment. Both resistant lines exhibited higher P-glycoprotein (Pgp) gene (TempO-Seq® analysis, increased 1.2-fold in A549, > 9000-fold in H520) and protein expression levels versus parental lines. TempO-Seq® analysis indicated other drug resistance pathways were upregulated. The resistant cell lines demonstrated less accumulation of intracellular doxorubicin, and were cross-resistant to other Pgp client drugs: bortezomib, doxorubicin, and paclitaxel, but not cisplatin. CONCLUSIONS: Upregulation of Pgp appears to be an important, but not the only, mechanism of CFZ resistance in NSCLC cell lines.

Whole-transcriptome sequencing identified gene expression signatures associated with aggressive clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the most prevalent subtype of kidney cancer, yet molecular biomarkers have not been used for the prognosis of ccRCC to aide clinical decision making. This study aimed to identify genes associated with ccRCC aggressiveness and overall survival (OS). Samples of ccRCC tumor tissue were obtained from 33 patients who underwent nephrectomy. Gene expression was determined using whole-transcriptome sequencing. The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) RNA-seq data was used to test association with OS. 290 genes were differentially expressed between tumors with high and low stage, size, grade, and necrosis (SSIGN) score (≥7 vs. ≤3) with P ADJ<0.05. Four genes, G6PD, APLP1, GCNT3, and PLPP2, were also over-expressed in advanced stage (III and IV) and high grade (3 and 4) ccRCC and tumor with necrosis (P ADJ<0.05). Investigation stratifying by stage found that APLP1 and PLPP2 overexpression were significantly associated with poorer OS in the early stage (Quartile 1 vs. Quartile 4, HR = 3.87, 95% CI:1.25-11.97, P = 0.02 and HR = 4.77, 95% CI:1.37-16.57, P = 0.04 respectively). These genes are potential biomarkers of ccRCC aggressiveness and prognosis that direct clinical and surgical management.

A trichostatin A expression signature identified by TempO-Seq targeted whole transcriptome profiling.

The use of gene expression signatures to classify compounds, identify efficacy or toxicity, and differentiate close analogs relies on the sensitivity of the method to identify modulated genes. We used a novel ligation-based targeted whole transcriptome expression profiling assay, TempO-Seq®, to determine whether previously unreported compound-responsive genes could be identified and incorporated into a broad but specific compound signature. TempO-Seq exhibits 99.6% specificity, single cell sensitivity, and excellent correlation with fold differences measured by RNA-Seq (R2 = 0.9) for 20,629 targets. Unlike many expression assays, TempO-Seq does not require RNA purification, cDNA synthesis, or capture of targeted RNA, and lacks a 3' end bias. To investigate the sensitivity of the TempO-Seq assay to identify significantly modulated compound-responsive genes, we derived whole transcriptome profiles from MCF-7 cells treated with the histone deacetylase inhibitor Trichostatin A (TSA) and identified more than 9,000 differentially expressed genes. The TSA profile for MCF-7 cells overlapped those for HL-60 and PC-3 cells in the Connectivity Map (cMAP) database, suggesting a common TSA-specific expression profile independent of baseline gene expression. A 43-gene cell-independent TSA signature was extracted from cMAP and confirmed in TempO-Seq MCF-7 data. Additional genes that were not previously reported to be TSA responsive in the cMAP database were also identified. TSA treatment of 5 cell types revealed 1,136 differentially expressed genes in common, including 785 genes not previously reported to be TSA responsive. We conclude that TSA induces a specific expression signature that is consistent across widely different cell types, that this signature contains genes not previously associated with TSA responses, and that TempO-Seq provides the sensitive differential expression detection needed to define such compound-specific, cell-independent, changes in expression.

A chemical-biological similarity-based grouping of complex substances as a prototype approach for evaluating chemical alternatives.

Comparative assessment of potential human health impacts is a critical step in evaluating both chemical alternatives and existing products on the market. Most alternatives assessments are conducted on a chemical-by-chemical basis and it is seldom acknowledged that humans are exposed to complex products, not individual substances. Indeed, substances of Unknown or Variable composition, Complex reaction products, and Biological materials (UVCBs) are ubiquitous in commerce yet they present a major challenge for registration and health assessments. Here, we present a comprehensive experimental and computational approach to categorize UVCBs according to global similarities in their bioactivity using a suite of in vitro models. We used petroleum substances, an important group of UVCBs which are grouped for regulatory approval and read-across primarily on physico-chemical properties and the manufacturing process, and only partially based on toxicity data, as a case study. We exposed induced pluripotent stem cell-derived cardiomyocytes and hepatocytes to DMSO-soluble extracts of 21 petroleum substances from five product groups. Concentration-response data from high-content imaging in cardiomyocytes and hepatocytes, as well as targeted high-throughput transcriptomic analysis of the hepatocytes, revealed distinct groups of petroleum substances. Data integration showed that bioactivity profiling affords clustering of petroleum substances in a manner similar to the manufacturing process-based categories. Moreover, we observed a high degree of correlation between bioactivity profiles and physico-chemical properties, as well as improved groupings when chemical and biological data were combined. Altogether, we demonstrate how novel in vitro screening approaches can be effectively utilized in combination with physico-chemical characteristics to group complex substances and enable read-across. This approach allows for rapid and scientifically-informed evaluation of health impacts of both existing substances and their chemical alternatives.

Effects of ß4 integrin expression on microRNA patterns in breast cancer.

The integrin α6ß4 is defined as an adhesion receptor for laminins. Referred to as 'ß4', this integrin plays a key role in the progression of various carcinomas through its ability to orchestrate key signal transduction events and promote cell motility. To identify novel downstream effectors of ß4 function in breast cancer, microRNAs (miRNAs) were examined because of their extensive links to tumorigenesis and their ability to regulate gene expression globally. Two breast carcinoma cell lines and a collection of invasive breast carcinomas with varying ß4 expression were used to assess the effect of this integrin on miRNA expression. A novel miRNA microarray analysis termed quantitative Nuclease Protection Assay (qNPA) revealed that ß4 expression can significantly alter miRNA expression and identified two miRNA families, miR-25/32/92abc/363/363-3p/367 and miR-99ab/100, that are consistently downregulated by expression of this integrin. Analysis of published Affymetrix GeneChip data identified 54 common targets of miR-92ab and miR-99ab/100 within the subset of ß4-regulated mRNAs, revealing several genes known to be key components of ß4-regulated signaling cascades and effectors of cell motility. Gene ontology classification identified an enrichment in genes associated with cell migration within this population. Finally, gene set enrichment analysis of all ß4-regulated mRNAs revealed an enrichment in targets belonging to distinct miRNA families, including miR-92ab and others identified by our initial array analyses. The results obtained in this study provide the first example of an integrin globally impacting miRNA expression and provide evidence that select miRNA families collectively target genes important in executing ß4-mediated cell motility.

Integrin ß4 regulates SPARC protein to promote invasion.

The α6ß4 integrin (referred to as "ß4" integrin) is a receptor for laminins that promotes carcinoma invasion through its ability to regulate key signaling pathways and cytoskeletal dynamics. An analysis of published Affymetrix GeneChip data to detect downstream effectors involved in ß4-mediated invasion of breast carcinoma cells identified SPARC, or secreted protein acidic and rich in cysteine. This glycoprotein has been shown to play an important role in matrix remodeling and invasion. Our analysis revealed that manipulation of ß4 integrin expression and signaling impacted SPARC expression and that SPARC facilitates ß4-mediated invasion. Expression of ß4 in ß4-deficient cells reduced the expression of a specific microRNA (miR-29a) that targets SPARC and impedes invasion. In cells that express endogenous ß4, miR-29a expression is low and ß4 ligation facilitates the translation of SPARC through a TOR-dependent mechanism. The results obtained in this study demonstrate that ß4 can regulate SPARC expression and that SPARC is an effector of ß4-mediated invasion. They also highlight a potential role for specific miRNAs in executing the functions of integrins.

Biodosimetry on small blood volume using gene expression assay.

This paper reports the development of a biodosimetry device suitable for rapidly measuring expression levels of a low-density gene set that can define radiation exposure, dose and injury in a public health emergency. The platform comprises a set of 14 genes selected on the basis of their abundance and differential expression level in response to radiation from an expression profiling series measuring 41,000 transcripts. Gene expression is analyzed through direct signal amplification using a quantitative Nuclease Protection Assay (qNPA). This assay can be configured as either a high-throughput microplate assay or as a handheld detection device for individual point-of-care assays. Recently, we were able to successfully develop the qNPA platform to measure gene expression levels directly from human whole blood samples. The assay can be performed with volumes as small as 30 microL of whole blood, which is compatible with collection from a fingerstick. We analyzed in vitro irradiated blood samples with qNPA. The results revealed statistically significant discrimination between irradiated and non-irradiated samples. These results indicate that the qNPA platform combined with a gene profile based on a small number of genes is a valid test to measure biological radiation exposure. The scalability characteristics of the assay make it appropriate for population triage. This biodosimetry platform could also be used for personalized monitoring of radiotherapy treatments received by patients.

Transcriptional analysis of intracytoplasmically stained, FACS-purified cells by high-throughput, quantitative nuclease protection.

Analyzing specialized cells in heterogeneous tissues is crucial for understanding organ function in health and disease. Thus far, however, there has been no convenient method for studying gene expression in cells purified by fluorescence-activated cell sorting (FACS) using intracellular markers. Here we show that the quantitative nuclease protection assay (qNPA) enables transcriptional analysis of intracytoplasmically stained cells sorted by FACS. Applying the method to mouse pancreatic islet-cell subsets, we detected both expected and unknown lineage-specific gene expression patterns. Some beta cells from pregnant animals were found to express Mafb, previously observed only in immature beta cells during embryonic development. The four 'housekeeping' genes tested were expressed in purified islet-cell subpopulations with a notable variability, dependent on both cell lineage and developmental stage. Application of qNPA to intracellularly stained, FACS-sorted cells should be broadly applicable to the analysis of gene expression in subpopulations of any heterogeneous tissue, including tumors.

Gene expression predicts overall survival in paraffin-embedded tissues of diffuse large B-cell lymphoma treated with R-CHOP.

Gene expression profiling (GEP) on frozen tissues has identified genes predicting outcome in patients with diffuse large B-cell lymphoma (DLBCL). Confirmation of results in current patients is limited by availability of frozen samples and addition of monoclonal antibodies to treatment regimens. We used a quantitative nuclease protection assay (qNPA) to analyze formalin-fixed, paraffin-embedded tissue blocks for 36 previously identified genes (N = 209, 93 chemotherapy; 116 rituximab + chemotherapy). By qNPA, 208 cases were successfully analyzed (99.5%). In addition, 15 of 36 and 11 of 36 genes, representing each functional group previously identified by GEP, were associated with survival (P < .05) in the 2 treatment groups, respectively. In addition, 30 of 36 hazard ratios of death trended in the same direction versus the original studies. Multivariate and variable cut-off point analysis identified low levels of HLA-DRB (< 20%) and high levels of MYC (> 80%) as independent indicators of survival, together distinguishing cases with the worst prognosis. Our results solve a clinical research problem by demonstrating that prognostic genes can be meaningfully quantified using qNPA technology on formalin-fixed, paraffin-embedded tissues; previous GEP findings in DLBCL are relevant with current treatments; and 2 genes, representing immune escape and proliferation, are the common features of the most aggressive DLBCL.

Quantitative nuclease protection assay in paraffin-embedded tissue replicates prognostic microarray gene expression in diffuse large-B-cell lymphoma.

Gene expression profiling (GEP) has identified genes whose expression levels predict patient survival in diffuse large-B-cell lymphoma (DLBCL). Such discovery techniques generally require frozen samples unavailable for most patients. We developed a quantitative nuclease protection assay to measure expression levels of prognostic DLBCL genes using formalin-fixed, paraffin-embedded (FFPE) tissue. FFPE tissue was sectioned, permeabilized, denatured in the presence of specific probes, and hybridized to mRNA in situ. Nuclease subsequently destroyed non-hybridized probe. Alkaline hydrolysis freed mRNA-bound probes from tissue, which were transferred to ArrayPlates for probe capture and chemiluminescent quantification. We validated assay performance using frozen, fresh, and FFPE DLBCL samples, then used 39 archived DLBCL, previously microarray analyzed, to correlate GEP and ArrayPlate results. We compared old (>18 years) with new (<2 months) paraffin blocks made from previously frozen tissue from the original biopsy. ArrayPlate gene expression results were confirmed with immunohistochemistry for BCL2, BCL6, and HLA-DR, showing agreement between mRNA species and the proteins they encode. Assay performance was linear to approximately 1 mg sample/well. RNase and DNase treatments demonstrated assay specificity for RNA detection, both fixed and soluble RNA detection. Comparisons were excellent for lysate vs snap-frozen vs FFPE (R(2)>0.98 for all comparisons). Coefficients of variation for quadruplicates on FFPE were generally <20%. Correlation between new and old paraffin blocks from the same biopsy was good (R(2)=0.71). Comparison of ArrayPlate to Affymetrix and cDNA microarrays showed reasonable correlations. Insufficient power from small sample size prevented successfully correlating results with patient survival, although hazard ratios trended the expected directions. We developed an assay to quantify expression levels of survival prediction genes in DLBCL using FFPE, fresh, or frozen tissue. While this technique cannot replace GEP for discovery, it indicates that expression differences identified by GEP can be replicated on a platform applicable to archived FFPE samples.

High-throughput, high-sensitivity analysis of gene expression in Arabidopsis.

High-throughput gene expression analysis of genes expressed during salt stress was performed using a novel multiplexed quantitative nuclease protection assay that involves customized DNA microarrays printed within the individual wells of 96-well plates. The levels of expression of the transcripts from 16 different genes were quantified within crude homogenates prepared from Arabidopsis (Arabidopsis thaliana) plants also grown in a 96-well plate format. Examples are provided of the high degree of reproducibility of quantitative dose-response data and of the sensitivity of detection of changes in gene expression within limiting amounts of tissue. The lack of requirement for RNA purification renders the assay particularly suited for high-throughput gene expression analysis and for the discovery of novel chemical compounds that specifically modulate the expression of endogenous target genes.

Multiplexed screening assay for mRNA combining nuclease protection with luminescent array detection.

The principles and performance are described for the ArrayPlate mRNA assay, a multiplexed mRNA assay for high-throughput and high-content screening and drug development. THP-1 monocytes grown and subjected to compound treatments in 96-well plates were subjected to a multiplexed nuclease protection assay in situ. The nuclease protection assay destroyed all cell-derived mRNA, but left intact stoichiometric amounts of 16 target-specific oligonucleotide probes. Upon transfer of processed cell lysates to a microplate that contained a 16-element oligonucleotide array at the bottom of each well, the various probe species were separated by immobilization at predefined elements of the array. Quantitative detection of array-bound probes was by enzyme-mediated chemiluminescence. A high-resolution charge-coupled device imager was used for the simultaneous readout of all 1536 array elements in a 96-well plate. For the measurement of 16 genes in samples of 25000 cells, the average standard deviation from well to well within a plate was 8.6% of signal intensity and was 10.8% from plate to plate. Assay response was linear and reproducibility was constant for all detected genes in samples ranging from 1000 to 50000 cells. When THP-1 monocytes were differentiated with phorbol ester and subsequently activated with bacterial lipopolysaccharide that contained different concentrations of dexamethasone, dose-dependent effects of dexamethasone on the mRNA levels of several genes were observed.