Vigdis Nygaard

Options available for profiling small samples: a review of sample amplification technology when combined with microarray profiling

The possibility of performing microarray analysis on limited material has been demonstrated in a number of publications. In this review we approach the technical aspects of mRNA amplification and several important implicit consequences, for both linear and exponential procedures. Amplification efficiencies clearly allow profiling of extremely small samples. The conservation of transcript abundance is the most important issue regarding the use of sample amplification in combination with microarray analysis, and this aspect has generally been found to be acceptable, although demonstrated to decrease in highly diluted samples. The fact that variability and discrepancies in microarray profiles increase with minute sample sizes has been clearly documented, but for many studies this does appear to have affected the biological conclusions. We suggest that this is due to the data analysis approach applied, and the consequence is the chance of presenting misleading results. We discuss the issue of amplification sensitivity limits in the light of reports on fidelity, published data from reviewed articles and data analysis approaches. These are important considerations to be reflected in the design of future studies and when evaluating biological conclusions from published microarray studies based on extremely low input RNA quantities.

Effects of mRNA amplification on gene expression ratios in cDNA experiments estimated by analysis of variance

BackgroundA limiting factor of cDNA microarray technology is the need for a substantial amount of RNA per labeling reaction. Thus, 20–200 micro-grams total RNA or 0.5–2 micro-grams poly (A) RNA is typically required for monitoring gene expression. In addition, gene expression profiles from large, heterogeneous cell populations provide complex patterns from which biological data for the target cells may be difficult to extract. In this study, we chose to investigate a widely used mRNA amplification protocol that allows gene expression studies to be performed on samples with limited starting material. We present a quantitative study of the variation and noise present in our data set obtained from experiments with either amplified or non-amplified material.ResultsUsing analysis of variance (ANOVA) and multiple hypothesis testing, we estimated the impact of amplification on the preservation of gene expression ratios. Both methods showed that the gene expression ratios were not completely preserved between amplified and non-amplified material. We also compared the expression ratios between the two cell lines for the amplified material with expression ratios between the two cell lines for the non-amplified material for each gene. With the aid of multiple t-testing with a false discovery rate of 5%, we found that 10% of the genes investigated showed significantly different expression ratios.ConclusionAlthough the ratios were not fully preserved, amplification may prove to be extremely useful with respect to characterizing low expressing genes.

Constitutive Expression of the AP-1 Transcription Factors c-jun, junD, junB, and c-fos and the Marginal Zone B-Cell Transcription Factor Notch2 in Splenic Marginal Zone Lymphoma

Splenic marginal zone lymphoma (SMZL) is a lymphoma type of putative marginal zone B-cell origin. No specific genetic alterations have yet been demonstrated in SMZL. Clinically, SMZL is a low-grade B-cell non-Hodgkin lymphoma. However, the presence of p53 mutation, 7q22-7q32 deletion or the absence of somatic hypermutations of immunoglobulin genes has been correlated with a worse prognosis. In this study, we analyzed genome-wide gene expression of 24 cases of SMZL using the microarray technique. The AP-1 transcription factors c-jun, junD, junB, and c-fos as well as Notch2 were found to be specifically up-regulated. These data were confirmed by real-time PCR and immunohistochemical staining of tissue sections. The absence of concordant high expression of the MAP kinases, the signaling cascade leading to AP-1 up-regulation, suggests autoregulation of the AP-1 transcription factors and an important role in SMZL oncogenesis. High expression of Notch2, a transcription factor that induces marginal zone B-cell differentiation, is highly suggestive for a marginal zone B-cell origin of SMZL. In addition, SMZL with the 7q deletion showed high expression of TGF-beta1 and low expression of the DNA helicase XPB, a crucial part of the nucleotide excision repair complex, possibly explaining the more aggressive clinical course of those cases.

Limitations of mRNA amplification from small-size cell samples

BackgroundGlobal mRNA amplification has become a widely used approach to obtain gene expression profiles from limited material. An important concern is the reliable reflection of the starting material in the results obtained. This is especially important with extremely low quantities of input RNA where stochastic effects due to template dilution may be present. This aspect remains under-documented in the literature, as quantitative measures of data reliability are most often lacking. To address this issue, we examined the sensitivity levels of each transcript in 3 different cell sample sizes. ANOVA analysis was used to estimate the overall effects of reduced input RNA in our experimental design. In order to estimate the validity of decreasing sample sizes, we examined the sensitivity levels of each transcript by applying a novel model-based method, TransCount.ResultsFrom expression data, TransCount provided estimates of absolute transcript concentrations in each examined sample. The results from TransCount were used to calculate the Pearson correlation coefficient between transcript concentrations for different sample sizes. The correlations were clearly transcript copy number dependent. A critical level was observed where stochastic fluctuations became significant. The analysis allowed us to pinpoint the gene specific number of transcript templates that defined the limit of reliability with respect to number of cells from that particular source. In the sample amplifying from 1000 cells, transcripts expressed with at least 121 transcripts/cell were statistically reliable and for 250 cells, the limit was 1806 transcripts/cell. Above these thresholds, correlation between our data sets was at acceptable values for reliable interpretation.ConclusionThese results imply that the reliability of any amplification experiment must be validated empirically to justify that any gene exists in sufficient quantity in the input material. This finding has important implications for any experiment where only extremely small samples such as single cell analyses or laser captured microdissected cells are available.

Metastasis-associated protein S100A4 induces a network of inflammatory cytokines that activate stromal cells to acquire pro-tumorigenic properties

Tumor cells have the ability to exploit stromal cells to facilitate metastasis. By using malignant melanoma as a model, we show that the stroma adjacent to metastatic lesions is enriched in the known metastasis-promoting protein S100A4. S100A4 stimulates cancer cells to secrete paracrine factors, such as inflammatory cytokines IL8, CCL2 and SAA, which activate stromal cells (endothelial cells and monocytes) so that they acquire tumor-supportive properties. Our data establishes S100A4 as an inducer of a cytokine network enabling tumor cells to engage angiogenic and inflammatory stromal cells, which might contribute to pro-metastatic activity of S100A4.

Metabolic reprogramming supports the invasive phenotype in malignant melanoma

Invasiveness is a hallmark of aggressive cancer like malignant melanoma, and factors involved in acquisition or maintenance of an invasive phenotype are attractive targets for therapy. We investigated melanoma phenotype modulation induced by the metastasis-promoting microenvironmental protein S100A4, focusing on the relationship between enhanced cellular motility, dedifferentiation and metabolic changes. In poorly motile, well-differentiated Melmet 5 cells, S100A4 stimulated migration, invasion and simultaneously down-regulated differentiation genes and modulated expression of metabolism genes. Metabolic studies confirmed suppressed mitochondrial respiration and activated glycolytic flux in the S100A4 stimulated cells, indicating a metabolic switch toward aerobic glycolysis, known as the Warburg effect. Reversal of the glycolytic switch by dichloracetate induced apoptosis and reduced cell growth, particularly in the S100A4 stimulated cells. This implies that cells with stimulated invasiveness get survival benefit from the glycolytic switch and, therefore, become more vulnerable to glycolysis inhibition. In conclusion, our data indicate that transition to the invasive phenotype in melanoma involves dedifferentiation and metabolic reprogramming from mitochondrial oxidation to glycolysis, which facilitates survival of the invasive cancer cells. Therapeutic strategies targeting the metabolic reprogramming may therefore be effective against the invasive phenotype.

Methods for quantitation of gene expression.

Gene expression of protein encoding genes can be quantitatively measured at the transcriptional level by a number of low- to high-throughput methods. The sensitivity of each method is dependent on both the intrinsic properties of the respective technology and the absolute number of each mRNA molecule to be measured. For these reasons, the correlation of measurements between technological platforms may be variable. Due to the complexity of the transcriptome, the purpose of a gene expression study dictates the choice of method as each is connected to a set of advantages and disadvantages. Strategies such as global mRNA amplification of small samples, have been implemented to overcome previous limitations. However, stochastic events will limit quantitative measurements of any tool when in-put levels are extremely low. Due to the versatile nature of microarray technology, this method will likely persist as a highly applied tool to query the levels of non-coding transcripts, a new expansion in the field of gene expression analysis although possible advances of the technology may occur.

Fibroblast-induced switching to the mesenchymal-like phenotype and PI3K/mTOR signaling protects melanoma cells from BRAF inhibitors.

The knowledge on how tumor-associated stroma influences efficacy of anti-cancer therapy just started to emerge. Here we show that lung fibroblasts reduce melanoma sensitivity to the BRAF inhibitor (BRAFi) vemurafenib only if the two cell types are in close proximity. In the presence of fibroblasts, the adjacent melanoma cells acquire de-differentiated mesenchymal-like phenotype. Upon treatment with BRAFi, such melanoma cells maintain high levels of phospho ribosomal protein S6 (pS6), i.e. active mTOR signaling, which is suppressed in the BRAFi sensitive cells without stromal contacts. Inhibitors of PI3K/mTOR in combination with BRAFi eradicate pS6high cell subpopulations and potentiate anti-cancer effects in melanoma protected by the fibroblasts. mTOR and BRAF co-inhibition also delayed the development of early-stage lung metastases in vivo. In conclusion, we demonstrate that upon influence from fibroblasts, melanoma cells undergo a phenotype switch to the mesenchymal state, which can support PI3K/mTOR signaling. The lost sensitivity to BRAFi in such cells can be overcome by co-targeting PI3K/mTOR. This knowledge could be explored for designing BRAFi combination therapies aiming to eliminate both stroma-protected and non-protected counterparts of metastases.

Validation of oligoarrays for quantitative exploration of the transcriptome

BackgroundOligoarrays have become an accessible technique for exploring the transcriptome, but it is presently unclear how absolute transcript data from this technique compare to the data achieved with tag-based quantitative techniques, such as massively parallel signature sequencing (MPSS) and serial analysis of gene expression (SAGE). By use of the TransCount method we calculated absolute transcript concentrations from spotted oligoarray intensities, enabling direct comparisons with tag counts obtained with MPSS and SAGE. The tag counts were converted to number of transcripts per cell by assuming that the sum of all transcripts in a single cell was 5·105. Our aim was to investigate whether the less resource demanding and more widespread oligoarray technique could provide data that were correlated to and had the same absolute scale as those obtained with MPSS and SAGE.ResultsA number of 1,777 unique transcripts were detected in common for the three technologies and served as the basis for our analyses. The correlations involving the oligoarray data were not weaker than, but, similar to the correlation between the MPSS and SAGE data, both when the entire concentration range was considered and at high concentrations. The data sets were more strongly correlated at high transcript concentrations than at low concentrations. On an absolute scale, the number of transcripts per cell and gene was generally higher based on oligoarrays than on MPSS and SAGE, and ranged from 1.6 to 9,705 for the 1,777 overlapping genes. The MPSS data were on same scale as the SAGE data, ranging from 0.5 to 3,180 (MPSS) and 9 to1,268 (SAGE) transcripts per cell and gene. The sum of all transcripts per cell for these genes was 3.8·105 (oligoarrays), 1.1·105 (MPSS) and 7.6·104 (SAGE), whereas the corresponding sum for all detected transcripts was 1.1·106 (oligoarrays), 2.8·105 (MPSS) and 3.8·105 (SAGE).ConclusionThe oligoarrays and TransCount provide quantitative transcript concentrations that are correlated to MPSS and SAGE data, but, the absolute scale of the measurements differs across the technologies. The discrepancy questions whether the sum of all transcripts within a single cell might be higher than the number of 5·105 suggested in the literature and used to convert tag counts to transcripts per cell. If so, this may explain the apparent higher transcript detection efficiency of the oligoarrays, and has to be clarified before absolute transcript concentrations can be interchanged across the technologies. The ability to obtain transcript concentrations from oligoarrays opens up the possibility of efficient generation of universal transcript databases with low resource demands.

Molecular signatures reflecting microenvironmental metabolism and chemotherapy-induced immunogenic cell death in colorectal liver metastases

BACKGROUND Metastatic colorectal cancer (CRC) is associated with highly variable clinical outcome and response to therapy. The recently identified consensus molecular subtypes (CMS1-4) have prognostic and therapeutic implications in primary CRC, but whether these subtypes are valid for metastatic disease is unclear. We performed multi-level analyses of resectable CRC liver metastases (CLM) to identify molecular characteristics of metastatic disease and evaluate the clinical relevance. METHODS In this ancillary study to the Oslo-CoMet trial, CLM and tumor-adjacent liver tissue from 46 patients were analyzed by profiling mutations (targeted sequencing), genome-wide copy number alteration (CNAs), and gene expression. RESULTS Somatic mutations and CNAs detected in CLM were similar to reported primary CRC profiles, while CNA profiles of eight metastatic pairs suggested intra-patient divergence. A CMS classifier tool applied to gene expression data, revealed the cohort to be highly enriched for CMS2. Hierarchical clustering of genes with highly variable expression identified two subgroups separated by high or low expression of 55 genes with immune-related and metabolic functions. Importantly, induction of genes and pathways associated with immunogenic cell death (ICD) was identified in metastases exposed to neoadjuvant chemotherapy (NACT). CONCLUSIONS The uniform classification of CLM by CMS subtyping may indicate that novel class discovery approaches need to be explored to uncover clinically useful stratification of CLM. Detected gene expression signatures support the role of metabolism and chemotherapy in shaping the immune microenvironment of CLM. Furthermore, the results point to rational exploration of immune modulating strategies in CLM, particularly by exploiting NACT-induced ICD.Background Metastatic colorectal cancer (CRC) is associated with highly variable clinical outcome and response to therapy. The recently identified consensus molecular subtypes (CMS1-4) have prognostic and therapeutic implications in primary CRC, but whether these subtypes are valid for metastatic disease is unclear. We performed multi-level analyses of resectable CRC liver metastases (CLM) to identify molecular characteristics of metastatic disease and evaluate the clinical relevance. Methods In this ancillary study to the Oslo-CoMet trial, CLM and tumor-adjacent liver tissue from 46 patients were analyzed by profiling mutations (targeted sequencing), genome-wide copy number alteration (CNAs), and gene expression. Results Somatic mutations and CNAs detected in CLM were similar to reported primary CRC profiles, while CNA profiles of eight metastatic pairs suggested intra-patient divergence. A CMS classifier tool applied to gene expression data, revealed the cohort to be highly enriched for CMS2. Hierarchical clustering of genes with highly variable expression identified two subgroups separated by high or low expression of 55 genes with immune-related and metabolic functions. Importantly, induction of genes and pathways associated with immunogenic cell death (ICD) was identified in metastases exposed to neoadjuvant chemotherapy (NACT). Conclusions The uniform classification of CLM by CMS subtyping may indicate that novel class discovery approaches need to be explored to uncover clinically useful stratification of CLM. Detected gene expression signatures support the role of metabolism and chemotherapy in shaping the immune microenvironment of CLM. Furthermore, the results point to rational exploration of immune modulating strategies in CLM, particularly by exploiting NACT-induced ICD.