Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma (NHL) and is a clinical, pathological, and molecular heterogeneous disease with highly variable clinical outcomes. Currently, valid prognostic biomarkers in DLBCL are still lacking. To optimize targeted therapy and improve the prognosis of DLBCL, the performance of proposed biomarkers needs to be evaluated in multiple cohorts, and new biomarkers need to be investigated in large datasets. Here, we developed a consensus Online Survival analysis web server for Diffuse Large B-Cell Lymphoma, abbreviated OSdlbcl, to assess the prognostic value of individual gene. To build OSdlbcl, we collected 1100 samples with gene expression profiles and clinical follow-up information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. In addition, DNA mutation data were also collected from the TCGA database. Overall survival (OS), progression-free survival (PFS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI) are important endpoints to reflect the survival rate in OSdlbcl. Moreover, clinical features were integrated into OSdlbcl to allow data stratifications according to the user\'s special needs. By inputting an official gene symbol and selecting desired criteria, the survival analysis results can be graphically presented by the Kaplan-Meier (KM) plot with hazard ratio (HR) and log-rank p value. As a proof-of-concept demonstration, the prognostic value of 23 previously reported survival associated biomarkers, such as transcription factors FOXP1 and BCL2, was evaluated in OSdlbcl and found to be significantly associated with survival as reported (HR = 1.73, P < .01; HR = 1.47, P = .03, respectively). In conclusion, OSdlbcl is a new web server that integrates public gene expression, gene mutation data, and clinical follow-up information to provide prognosis evaluations for biomarker development for DLBCL. The OSdlbcl web server is available at https://bioinfo.henu.edu.cn/DLBCL/DLBCLList.jsp.

Regulatory T cell (Treg)-mediated immunosuppression is considered a major obstacle for successful cancer immunotherapy. The association between clinical outcome and Tregs is being studied extensively in clinical trials, but unfortunately, no consensus has been reached about (a) the markers and (b) the gating strategy required to define human Tregs in this context, making it difficult to draw final conclusions. Therefore, we have organized an international workshop on the detection and functional testing of Tregs with leading experts in the field, and 40 participants discussing different analyses and the importance of different markers and context in which Tregs were analyzed. This resulted in a rationally composed ranking list of \"Treg markers\". Subsequently, the proposed Treg markers were tested to get insight into the overlap/differences between the most frequently used Treg definitions and their utility for Treg detection in various human tissues. Here, we conclude that the CD3, CD4, CD25, CD127, and FoxP3 markers are the minimally required markers to define human Treg cells. Staining for Ki67 and CD45RA showed to provide additional information on the activation status of Tregs. The use of markers was validated in a series of PBMC from healthy donors and cancer patients, as well as in tumor-draining lymph nodes and freshly isolated tumors. In conclusion, we propose an essential marker set comprising antibodies to CD3, CD4, CD25, CD127, Foxp3, Ki67, and CD45RA and a corresponding robust gating strategy for the context-dependent analysis of Tregs by flow cytometry.

BACKGROUND: The Forkhead (FKH) transcription factor FOXM1 is a key regulator of the cell cycle and is overexpressed in most types of cancer. FOXM1, similar to other FKH factors, binds to a canonical FKH motif in vitro. However, genome-wide mapping studies in different cell lines have shown a lack of enrichment of the FKH motif, suggesting an alternative mode of chromatin recruitment. We have investigated the role of direct versus indirect DNA binding in FOXM1 recruitment by performing ChIP-seq with wild-type and DNA binding deficient FOXM1.
RESULTS: An in vitro fluorescence polarization assay identified point mutations in the DNA binding domain of FOXM1 that inhibit binding to a FKH consensus sequence. Cell lines expressing either wild-type or DNA binding deficient GFP-tagged FOXM1 were used for genome-wide mapping studies comparing the distribution of the DNA binding deficient protein to the wild-type. This shows that interaction of the FOXM1 DNA binding domain with target DNA is essential for recruitment. Moreover, analysis of the protein interactome of wild-type versus DNA binding deficient FOXM1 shows that the reduced recruitment is not due to inhibition of protein-protein interactions.
CONCLUSIONS: A functional DNA binding domain is essential for FOXM1 chromatin recruitment. Even in FOXM1 mutants with almost complete loss of binding, the protein-protein interactions and pattern of phosphorylation are largely unaffected. These results strongly support a model whereby FOXM1 is specifically recruited to chromatin through co-factor interactions by binding directly to non-canonical DNA sequences.

A single, recurrent somatic point mutation (402C→G) in FOXL2 has been described in almost all adult-type granulosa cell tumors but not other ovarian neoplasms. Histopathological features of adult-type granulosa cell tumors can be mimicked by a variety of other tumors, making diagnosis of adult-type granulosa cell tumor challenging. It has been suggested that molecular testing for FOXL2 mutation might be a useful tool in the diagnosis of adult-type granulosa cell tumors. The aim of this study was to demonstrate how testing for the FOXL2 mutation can be used in a gynecological pathology consultation service and to establish clear procedural guidelines for FOXL2 testing. Immunohistochemistry for FOXL2 was done using an anti-FOXL2 polyclonal antiserum. If immunohistochemistry was positive, FOXL2 mutation status was subsequently analyzed using a TaqMan assay. A dilution experiment was done to assess the sensitivity and minimum tumor cellularity requirements for our TaqMan assay. Twenty problematic cases were assessed, where the differential diagnosis after the initial investigations included adult-type granulosa cell tumors. Differential diagnoses included: thecoma, Sertoli-Leydig cell tumor, juvenile granulosa cell tumor, endometrial stromal sarcoma and others. In all cases, FOXL2 immunohistochemistry was positive and in six samples the FOXL2 mutation was detected, thus confirming a diagnosis of adult-type granulosa cell tumor. The TaqMan assay was able to reliably detect the FOXL2 mutation with input DNA in the range of 2.5-20 ng, and with a minimum of 25% tumor cell nuclei. The analysis of the FOXL2 mutational status in clinical samples is a useful diagnostic tool in situations where the differential diagnosis is between adult-type granulosa cell tumor and other ovarian tumors. The TaqMan assay requires a minimum of 2.5 ng DNA, with optimal assay performance for 5 to 10 ng DNA input. Laser capture or needle-macrodissection should be undertaken to enrich samples with tumor cell content below 25%.

Lupus-prone (NZB x NZW)F1 mice spontaneously develop elevated titers of anti-DNA Abs that contain T cell determinants in their V(H) regions. We have previously shown that tolerization with an artificial peptide based on these T cell determinants (pConsensus (pCons)) can block production of anti-DNA Abs and prolong survival of the mice. In this study, we show that this protection depends in part on the generation of peripheral TGFbeta- and Foxp3-expressing inhibitory CD8+ (Ti) cells. These CD8+ Ti cells suppress anti-DNA IgG production both in vitro and in vivo and require up-regulated expression of both Foxp3 and TGFbeta to exert their suppressive function, as indicated by microarray analyses, small interfering RNA inhibition studies, and blocking experiments. Additionally, CD8+ Ti cells from pCons-tolerized mice were longer-lived suppressors that up-regulated expression of Bcl-2 and were more resistant to apoptosis than similar cells from naive mice. These data indicate that clinical suppression of autoimmunity after administration of pCons depends in part on the generation of CD8+ Ti cells that suppress secretion of anti-DNA Ig using mechanisms that include Foxp3, TGFbeta, and resistance to apoptosis.

daf-16 is a forkhead-type transcription factor, functioning downstream of insulin-like signals, and is known to be critical to the regulation of life span in Caenorhabditis elegans. Mammalian DAF-16 homologues include AFX, FKHR and FKHRL1, which contain a conserved forkhead domain and three putative phosphorylation sites for the Ser/Thr kinase Akt/protein kinase B (PKB), as well as for DAF-16. To assess the function of the homologues, we examined tissue distribution patterns of mRNAs for DAF-16 homologues in mice. In the embryos, expressions of AFX, FKHR and FKHRL1 mRNAs were complementary to each other and were highest in muscle, adipose tissue and embryonic liver. The characteristic expression pattern remained in the adult, except that signals of FKHRL1 became evident in more tissues, including the brain. In order to clarify whether each DAF-16 homologue had different target genes, we determined the consensus sequences for the binding of DAF-16 and the mouse homologues. The binding sequences for all four proteins shared a core sequence, TTGTTTAC, daf-16 family protein-binding element (DBE) binding protein. However, electrophoretic mobility shift assay showed that the binding affinity of DAF-16 homologues to the core sequence was stronger than that to the insulin-responsive element in the insulin-like growth factor binding protein-1 promoter region, which has been identified as a binding sequence for them. We identified one copy of the DBE upstream of the first exon of sod-3 by searching the genomic database of C. elegans. Taken together, DAF-16 homologues can fundamentally regulate the common target genes in insulin-responsive tissues and the specificity to target genes of each protein is partially determined by the differences in their expression patterns.