Highlighting type A RRs as potential regulators of the dkHK1 multi-step phosphorelay pathway in Populus

In previous studies, we highlighted a multistep phosphorelay (MSP) system in poplars composed of two hybrid-type Histidine aspartate Kinases, dkHK1a and dkHK1b, which interact with three Histidine Phosphotransfer proteins, dkHPt2, 7, and 9, which in turn interact with six type B Response Regulators. These interactions correspond to the dkHK1a-b/dkHPts/dkRRBs MSP. This MSP is putatively involved in an osmosensing pathway, as dkHK1a-b are orthologous to the Arabidopsis osmosensor AHK1, and able to complement a mutant yeast deleted for its osmosensors. Since type A RRs have been characterized as negative regulators in cytokinin MSP signaling due to their interaction with HPt proteins, we decided in this study to characterize poplar type A RRs and their implication in the MSP. For a global view of this MSP, we isolated 10 poplar type A RR cDNAs, and determined their subcellular localization to check the in silico prediction experimentally. For most of them, the in planta subcellular localization was as predicted, except for three RRAs, for which this experimental approach gave a more precise localization. Interaction studies using yeast two-hybrid and in planta BiFC assays, together with transcript expression analysis in poplar organs led to eight dkRRAs being singled out as partners which could interfere the dkHK1a-b/dkHPts/dkRRBs MSP identified in previous studies. Consequently, the results obtained in this study now provide an exhaustive view of dkHK1a-b partners belonging to a poplar MSP

Exquisite Sensitivity of TP53 Mutant and Basal Breast Cancers to a Dose-Dense Epirubicin−Cyclophosphamide Regimen

BACKGROUND: In breast cancers, only a minority of patients fully benefit from the different chemotherapy regimens currently in use. Identification of markers that could predict the response to a particular regimen would thus be critically important for patient care. In cell lines or animal models, tumor protein p53 (TP53) plays a critical role in modulating the response to genotoxic drugs. TP53 is activated in response to DNA damage and triggers either apoptosis or cell-cycle arrest, which have opposite effects on cell fate. Yet, studies linking TP53 status and chemotherapy response have so far failed to unambiguously establish this paradigm in patients. Breast cancers with a TP53 mutation were repeatedly shown to have a poor outcome, but whether this reflects poor response to treatment or greater intrinsic aggressiveness of the tumor is unknown. METHODS AND FINDINGS: In this study we analyzed 80 noninflammatory breast cancers treated by frontline (neoadjuvant) chemotherapy. Tumor diagnoses were performed on pretreatment biopsies, and the patients then received six cycles of a dose-dense regimen of 75 mg/m(2) epirubicin and 1,200 mg/m(2) cyclophosphamide, given every 14 days. After completion of chemotherapy, all patients underwent mastectomies, thus allowing for a reliable assessment of chemotherapy response. The pretreatment biopsy samples were used to determine the TP53 status through a highly efficient yeast functional assay and to perform RNA profiling. All 15 complete responses occurred among the 28 TP53-mutant tumors. Furthermore, among the TP53-mutant tumors, nine out of ten of the highly aggressive basal subtypes (defined by basal cytokeratin [KRT] immunohistochemical staining) experienced complete pathological responses, and only TP53 status and basal subtype were independent predictors of a complete response. Expression analysis identified many mutant TP53-associated genes, including CDC20, TTK, CDKN2A, and the stem cell gene PROM1, but failed to identify a transcriptional profile associated with complete responses among TP53 mutant tumors. In patients with unresponsive tumors, mutant TP53 status predicted significantly shorter overall survival. The 15 patients with responsive TP53-mutant tumors, however, had a favorable outcome, suggesting that this chemotherapy regimen can overcome the poor prognosis generally associated with mutant TP53 status. CONCLUSIONS: This study demonstrates that, in noninflammatory breast cancers, TP53 status is a key predictive factor for response to this dose-dense epirubicin–cyclophosphamide regimen and further suggests that the basal subtype is exquisitely sensitive to this association. Given the well-established predictive value of complete responses for long-term survival and the poor prognosis of basal and TP53-mutant tumors treated with other regimens, this chemotherapy could be particularly suited for breast cancer patients with a mutant TP53, particularly those with basal features

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Наук. кер.: А.Ю. Жулавськи

Chronic T cell receptor stimulation unmasks NK receptor signaling in peripheral T cell lymphomas via epigenetic reprogramming.

Peripheral T cell lymphomas (PTCLs) represent a significant unmet medical need with dismal clinical outcomes. The T cell receptor (TCR) is emerging as a key driver of T lymphocyte transformation. However, the role of chronic TCR activation in lymphomagenesis and in lymphoma cell survival is still poorly understood. Using a mouse model, we report that chronic TCR stimulation drove T cell lymphomagenesis, whereas TCR signaling did not contribute to PTCL survival. The combination of kinome, transcriptome, and epigenome analyses of mouse PTCLs revealed a NK cell-like reprogramming of PTCL cells with expression of NK receptors (NKRs) and downstream signaling molecules such as Tyrobp and SYK. Activating NKRs were functional in PTCLs and dependent on SYK activity. In vivo blockade of NKR signaling prolonged mouse survival, demonstrating the addiction of PTCLs to NKRs and downstream SYK/mTOR activity for their survival. We studied a large collection of human primary samples and identified several PTCLs recapitulating the phenotype described in this model by their expression of SYK and the NKR, suggesting a similar mechanism of lymphomagenesis and establishing a rationale for clinical studies targeting such molecules

A refined molecular taxonomy of breast cancer

The current histoclinical breast cancer classification is simple but imprecise. Several molecular classifications of breast cancers based on expression profiling have been proposed as alternatives. However, their reliability and clinical utility have been repeatedly questioned, notably because most of them were derived from relatively small initial patient populations. We analyzed the transcriptomes of 537 breast tumors using three unsupervised classification methods. A core subset of 355 tumors was assigned to six clusters by all three methods. These six subgroups overlapped with previously defined molecular classes of breast cancer, but also showed important differences, notably the absence of an ERBB2 subgroup and the division of the large luminal ER+ group into four subgroups, two of them being highly proliferative. Of the six subgroups, four were ER+/PR+/AR+, one was ER−/PR−/AR+ and one was triple negative (AR−/ER−/PR−). ERBB2-amplified tumors were split between the ER−/PR−/AR+ subgroup and the highly proliferative ER+ LumC subgroup. Importantly, each of these six molecular subgroups showed specific copy-number alterations. Gene expression changes were correlated to specific signaling pathways. Each of these six subgroups showed very significant differences in tumor grade, metastatic sites, relapse-free survival or response to chemotherapy. All these findings were validated on large external datasets including more than 3000 tumors. Our data thus indicate that these six molecular subgroups represent well-defined clinico-biological entities of breast cancer. Their identification should facilitate the detection of novel prognostic factors or therapeutical targets in breast cancer

Should We Abandon the t-Test in the Analysis of Gene Expression Microarray Data: A Comparison of Variance Modeling Strategies

High-throughput post-genomic studies are now routinely and promisingly investigated in biological and biomedical research. The main statistical approach to select genes differentially expressed between two groups is to apply a t-test, which is subject of criticism in the literature. Numerous alternatives have been developed based on different and innovative variance modeling strategies. However, a critical issue is that selecting a different test usually leads to a different gene list. In this context and given the current tendency to apply the t-test, identifying the most efficient approach in practice remains crucial. To provide elements to answer, we conduct a comparison of eight tests representative of variance modeling strategies in gene expression data: Welch's t-test, ANOVA [1], Wilcoxon's test, SAM [2], RVM [3], limma [4], VarMixt [5] and SMVar [6]. Our comparison process relies on four steps (gene list analysis, simulations, spike-in data and re-sampling) to formulate comprehensive and robust conclusions about test performance, in terms of statistical power, false-positive rate, execution time and ease of use. Our results raise concerns about the ability of some methods to control the expected number of false positives at a desirable level. Besides, two tests (limma and VarMixt) show significant improvement compared to the t-test, in particular to deal with small sample sizes. In addition limma presents several practical advantages, so we advocate its application to analyze gene expression data