Vorinostat and Mithramycin A in combination therapy as an interesting strategy for the treatment of Sézary T lymphoma: a transcriptomic approach

SAHA (vorinostat) is a histone deacetylase inhibitor approved by the USA Food and Drug Administration (FDA) for treating advanced refractory cutaneous T cell lymphomas. As SAHA alters the expression of many genes under control of the Sp1 transcription factor, we examined the effect of its association with the FDA-approved anticancer antibiotic Mithramycin A (MTR, plicamycin), a competitive inhibitor of Sp1 binding to DNA. Sézary syndrome (SS) cells, expanded ex vivo from peripheral blood mononuclear cells of 4 patients, were tested for their sensitivity to the drugs regarding cytotoxicity and differential responsive gene expression. Multivariate statistical methods were used to identify genes whose expression is altered by SAHA, MTR, and the synergist effect of the two drugs. MTR, like SAHA, induced the apoptosis of SS cells, while the two drugs in combination showed clear synergy or potentiation. Expression data stressed a likely important role of additive or synergistic epigenetic modifications in the combined effect of the two drugs, while direct inhibition of Sp1-dependent transcription seemed to have only limited impact. Ontological analysis of modified gene expression suggested that the two drugs, either independently or synergistically, counteracted many intertwined pro-survival pathways deregulated in SS cells, resistance of these tumors to intrinsic and extrinsic apoptosis, abnormal adhesion migration, and invasive properties, as well as immunosuppressive behavior. Our findings provide preliminary clues on the individual and combined effects of SAHA and MTR in SS cells and highlight a potential therapeutic interest of this novel pair of drugs for treatment of SS patients.

Responder and nonresponder patients exhibit different peripheral transcriptional signatures during major depressive episode

To date, it remains impossible to guarantee that short-term treatment given to a patient suffering from a major depressive episode (MDE) will improve long-term efficacy. Objective biological measurements and biomarkers that could help in predicting the clinical evolution of MDE are still warranted. To better understand the reason nearly half of MDE patients respond poorly to current antidepressive treatments, we examined the gene expression profile of peripheral blood samples collected from 16 severe MDE patients and 13 matched controls. Using a naturalistic and longitudinal design, we ascertained mRNA and microRNA (miRNA) expression at baseline, 2 and 8 weeks later. On a genome-wide scale, we detected transcripts with roles in various biological processes as significantly dysregulated between MDE patients and controls, notably those involved in nucleotide binding and chromatin assembly. We also established putative interactions between dysregulated mRNAs and miRNAs that may contribute to MDE physiopathology. We selected a set of mRNA candidates for quantitative reverse transcriptase PCR (RT-qPCR) to validate that the transcriptional signatures observed in responders is different from nonresponders. Furthermore, we identified a combination of four mRNAs (PPT1, TNF, IL1B and HIST1H1E) that could be predictive of treatment response. Altogether, these results highlight the importance of studies investigating the tight relationship between peripheral transcriptional changes and the dynamic clinical progression of MDE patients to provide biomarkers of MDE evolution and prognosis.

Gene expression profiling identifies molecular subgroups among nodal peripheral T-cell lymphomas

The classification of peripheral T-cell lymphomas (PTCL) is still a matter of debate. To establish a molecular classification of PTCL, we analysed 59 primary nodal T-cell lymphomas using cDNA microarrays, including 56 PTCL and three T-lymphoblastic lymphoma (T-LBL). The expression profiles could discriminate angioimmunoblastic lymphoma, anaplastic large-cell lymphoma and T-LBL. In contrast, cases belonging to the broad category of 'PTCL, unspecified' (PTCL-U) did not share a single molecular profile. Using a multiclass predictor, we could separate PTCL-U into three molecular subgroups called U1, U2 and U3. The U1 gene expression signature included genes known to be associated with poor outcome in other tumors, such as CCND2. The U2 subgroup was associated with overexpression of genes involved in T-cell activation and apoptosis, including NFKB1 and BCL-2. The U3 subgroup was mainly defined by overexpression of genes involved in the IFN/JAK/STAT pathway. It comprised a majority of histiocyte-rich PTCL samples. Gene Ontology annotations revealed different functional profile for each subgroup. These results suggest the existence of distinct subtypes of PTCL-U with specific molecular profiles, and thus provide a basis to improve their classification and to develop new therapeutic targets.

Feature extraction and signal processing for nylon DNA microarrays

Background

High-density DNA microarrays require automatic feature extraction methodologies and softwares. These can be a potential source of non-reproducibility of gene expression measurements. Variation in feature location or in signal integration methodology may be a significant contribution to the observed variance in gene expression levels.

Results

We explore sources of variability in feature extraction from DNA microarrays on Nylon membrane with radioactive detection. We introduce a mathematical model of the signal emission and derive methods for correcting biases such as overshining, saturation or variation in probe amount. We also provide a quality metric which can be used qualitatively to flag weak or untrusted signals or quantitatively to modulate the weight of each experiment or gene in higher level analyses (clustering or discriminant analysis).

Conclusions

Our novel feature extraction methodology, based on a mathematical model of the radioactive emission, reduces variability due to saturation, neighbourhood effects and variable probe amount. Furthermore, we provide a fully automatic feature extraction software, BZScan, which implements the algorithms described in this paper.

[DNA arrays: technological aspects and applications]

The Human Genome Project has allowed considerable progress in the construction of physical and genetic maps and the identification of genes involved in human sicknesses. The accelerated accumulation of biological information and knowledge is due in large part to the sequencing projects of other organisms, which in fact paved the way for the Human Genome Project. In parallel, recently developed techniques which take advantage of genomic sequences allow large scale molecular analyses resulting in the functional annotation of many of the proteins represented by these genes. This is the goal of functional genomics. These progresses are at the origin of the present revolution in biomedical research. DNA microarrays are playing a dominant role compared to the other developing technologies since they are relatively easy to make and use and are applicable to numerous scientific inquiries. They allow the simultaneous analysis of several thousands of genes in biological samples from sick or healthy tissues, at the genome or transcriptome level. The data obtained is expected to result in major advances in the health sciences. In addition to an improved understanding of the complex molecular interaction networks of healthy cells and tissues, a more precise genetic characterization of the molecular mechanisms involved in pathology should result in the identification of new therapeutic targets and the development of new medicines. The genetic profiles thus obtained should also permit the definition of new pathologic subclasses not recognizable by traditional clinical factors, as well as new markers for susceptibility to certain illnesses, and new prognostic markers or methods of predicting responses to treatment. In this article, we present the different approaches and potential applications of DNA microarray technology, in particular as applied to cancer research.

[Molecular typing of breast cancer: transcriptomics and DNA microarrays]

Breast cancer is the most frequent and deadly cancer of women. Its great heterogeneity makes prognosis and response to current treatments highly variable and difficult to predict. Mammary oncogenesis remains poorly understood. These issues should benefit from recent development of techniques capable of large-scale molecular analyses. The use of cDNA array techniques allows for the simultaneous analysis of the mRNA expression levels of thousands of genes in mammary tumor cell lines and breast tumors. Expression profiles will help classify tumors and provide new prognostic tools and potential therapeutic targets. They will also boost our knowledge of the molecular events responsible for the development and progression of this cancer.

Gene expression profiling of primary breast carcinomas using arrays of candidate genes

Breast cancer is characterized by an important histoclinical heterogeneity that currently hampers the selection of the most appropriate treatment for each case. This problem could be solved by the identification of new parameters that better predict the natural history of the disease and its sensitivity to treatment. A large-scale molecular characterization of breast cancer could help in this context. Using cDNA arrays, we studied the quantitative mRNA expression levels of 176 candidate genes in 34 primary breast carcinomas along three directions: comparison of tumor samples, correlations of molecular data with conventional histoclinical prognostic features and gene correlations. The study evidenced extensive heterogeneity of breast tumors at the transcriptional level. A hierarchical clustering algorithm identified two molecularly distinct subgroups of tumors characterized by a different clinical outcome after chemotherapy. This outcome could not have been predicted by the commonly used histoclinical parameters. No correlation was found with the age of patients, tumor size, histological type and grade. However, expression of genes was differential in tumors with lymph node metastasis and according to the estrogen receptor status; ERBB2 expression was strongly correlated with the lymph node status (P < 0.0001) and that of GATA3 with the presence of estrogen receptors (P < 0.001). Thus, our results identified new ways to group tumors according to outcome and new potential targets of carcinogenesis. They show that the systematic use of cDNA array testing holds great promise to improve the classification of breast cancer in terms of prognosis and chemosensitivity and to provide new potential therapeutic targets.

Sensitivity issues in DNA array-based expression measurements and performance of nylon microarrays for small samples

DNA or oligonucleotide arrays are widely used for large-scale expression measurements, using various implementations: macroarrays in which DNA is spotted onto nylon membranes of relatively large dimensions (with radioactive detection) on the one hand; microarrays on glass slides and oligonucleotide chips, both used with fluorescent probes, on the other hand. Nylon micro-arrays with colourimetric detection have also been described recently. The small physical dimensions of miniaturized systems allow small hybridization volumes (2-100 microl) and provide high probe concentrations, in contrast to macroarrays. We show, however, that actual sensitivity (defined as the amount of sample necessary for detection of a given mRNA species) is in fact similar for all these systems and that this is mostly due to the very different amounts of target material present on the respective arrays. We then demonstrate that the combination of nylon microarrays with(33)P-labelled radioactive probes provides 100-fold better sensitivity, making it possible to perform expression profiling experiments using submicrogram amounts of unamplified total RNA from small biological samples. This has important implications in basic and clinical research and makes this alternative approach particularly suitable for groups operating in an academic context.

Expression scanning of an array of growth control genes in human tumor cell lines

Analysis of gene expression on a medium- or large-scale is an increasingly recognized method for functional and clinical investigations based on the now extensive catalog of known or partially sequenced genes. The accessibility of this approach can be enhanced by using readily available technology (macroarrays on Nylon, radioactive detection) and the IMAGE resource to assemble sets of targets. We have set up such a medium-scale, flexible system and validated it by the study of quantitative expression levels for 120 genes in six cell lines, including three mammary carcinoma cell lines. A number of important parameters are identified as necessary for the assembly of a valid set and the obtention of good-quality quantitative data. The extensive data assembled in this survey identified potential targets of carcinogenesis, for example the CRABP2 and GATA3 transcription factor genes. We also demonstrate the feasibility of this procedure for relatively small tumor samples, without recourse to probe amplification methods.