SOCS2 is part of a highly prognostic 4-gene signature in AML and promotes disease aggressiveness

Acute myeloid leukemia (AML) is a heterogeneous disease with respect to its genetic and molecular basis and to patients´ outcome. Clinical, cytogenetic, and mutational data are used to classify patients into risk groups with different survival, however, within-group heterogeneity is still an issue. Here, we used a robust likelihood-based survival modeling approach and publicly available gene expression data to identify a minimal number of genes whose combined expression values were prognostic of overall survival. The resulting gene expression signature (4-GES) consisted of 4 genes (SOCS2, IL2RA, NPDC1, PHGDH), predicted patient survival as an independent prognostic parameter in several cohorts of AML patients (total, 1272 patients), and further refined prognostication based on the European Leukemia Net classification. An oncogenic role of the top scoring gene in this signature, SOCS2, was investigated using MLL-AF9 and Flt3-ITD/NPM1c driven mouse models of AML. SOCS2 promoted leukemogenesis as well as the abundance, quiescence, and activity of AML stem cells. Overall, the 4-GES represents a highly discriminating prognostic parameter in AML, whose clinical applicability is greatly enhanced by its small number of genes. The newly established role of SOCS2 in leukemia aggressiveness and stemness raises the possibility that the signature might even be exploitable therapeutically

SOCS2 is part of a highly prognostic 4-gene signature in AML and promotes disease aggressiveness.

Acute myeloid leukemia (AML) is a heterogeneous disease with respect to its genetic and molecular basis and to patients´ outcome. Clinical, cytogenetic, and mutational data are used to classify patients into risk groups with different survival, however, within-group heterogeneity is still an issue. Here, we used a robust likelihood-based survival modeling approach and publicly available gene expression data to identify a minimal number of genes whose combined expression values were prognostic of overall survival. The resulting gene expression signature (4-GES) consisted of 4 genes (SOCS2, IL2RA, NPDC1, PHGDH), predicted patient survival as an independent prognostic parameter in several cohorts of AML patients (total, 1272 patients), and further refined prognostication based on the European Leukemia Net classification. An oncogenic role of the top scoring gene in this signature, SOCS2, was investigated using MLL-AF9 and Flt3-ITD/NPM1c driven mouse models of AML. SOCS2 promoted leukemogenesis as well as the abundance, quiescence, and activity of AML stem cells. Overall, the 4-GES represents a highly discriminating prognostic parameter in AML, whose clinical applicability is greatly enhanced by its small number of genes. The newly established role of SOCS2 in leukemia aggressiveness and stemness raises the possibility that the signature might even be exploitable therapeutically

Third CECOG consensus on the systemic treatment of non-small-cell lung cancer

The current third consensus on the systemic treatment of non-small-cell lung cancer (NSCLC) builds upon and updates similar publications on the subject by the Central European Cooperative Oncology Group (CECOG), which has published such consensus statements in the years 2002 and 2005 (Zielinski CC, Beinert T, Crawford J et al. Consensus on medical treatment of non-small-cell lung cancer—update 2004. Lung Cancer 2005; 50: 129-137). The principle of all CECOG consensus is such that evidence-based recommendations for state-of-the-art treatment are given upon which all participants and authors of the manuscript have to agree (Beslija S, Bonneterre J, Burstein HJ et al. Third consensus on medical treatment of metastatic breast cancer. Ann Oncol 2009; 20 (11): 1771-1785). This is of particular importance in diseases in which treatment options depend on very particular clinical and biologic variables (Zielinski CC, Beinert T, Crawford J et al. Consensus on medical treatment of non-small-cell lung cancer—update 2004. Lung Cancer 2005; 50: 129-137; Beslija S, Bonneterre J, Burstein HJ et al. Third consensus on medical treatment of metastatic breast cancer. Ann Oncol 2009; 20 (11): 1771-1785). Since the publication of the last CECOG consensus on the medical treatment of NSCLC, a series of diagnostic tools for the characterization of biomarkers for personalized therapy for NSCLC as well as therapeutic options including adjuvant treatment, targeted therapy, and maintenance treatment have emerged and strongly influenced the field. Thus, the present third consensus was generated that not only readdresses previous disease-related issues but also expands toward recent developments in the management of NSCLC. It is the aim of the present consensus to summarize minimal quality-oriented requirements for individual patients with NSCLC in its various stages based upon levels of evidence in the light of a rapidly expanding array of individual therapeutic option

Combined effects of cigarette smoking, gene polymorphisms and methylations of tumor suppressor genes on non small cell lung cancer: a hospital-based case-control study in China

<p>Abstract</p> <p>Background</p> <p>Cigarette smoking is the most established risk factor, and genetic variants and/or gene promoter methylations are also considered to play an essential role in development of lung cancer, but the pathogenesis of lung cancer is still unclear.</p> <p>Methods</p> <p>We collected the data of 150 cases and 150 age-matched and sex-matched controls on a Hospital-Based Case-Control Study in China. Face to face interviews were conducted using a standardized questionnaire. Gene polymorphism and methylation status were measured by RFLP-PCR and MSP, respectively. Logistic regressive model was used to estimate the odds ratios (OR) for different levels of exposure.</p> <p>Results</p> <p>After adjusted age and other potential confounding factors, smoking was still main risk factor and significantly increased 3.70-fold greater risk of NSCLC as compared with nonsmokers, and the ORs across increasing levels of pack years were 1, 3.54, 3.65 and 7.76, which the general dose-response trend was confirmed. Our striking findings were that the risk increased 5.16, 8.28 and 4.10-fold, respectively, for NSCLC with promoter hypermethylation of the <it>p16</it>, <it>DAPK </it>or <it>RARβ </it>gene in smokers with <it>CYP1A1 </it>variants, and the higher risk significantly increased in smokers with null <it>GSTM1 </it>and the OR was 17.84 for NSCLC with <it>p16 </it>promoter hypermethylation, 17.41 for <it>DAPK</it>, and 8.18 for <it>RARβ </it>in smokers with null <it>GSTM1 </it>compared with controls (all p < 0.01).</p> <p>Conclusion</p> <p>Our study suggests the strong combined effects of cigarette smoke, <it>CYP1A1 </it>and <it>GSTM1 </it>Polymorphisms, hypermethylations of <it>p16</it>, <it>DAPK </it>and <it>RARβ </it>promoters in NSCLC, implying complex pathogenesis of NSCLC should be given top priority in future research.</p

Differential DNA methylation profiles in gynecological cancers and correlation with clinico-pathological data

BACKGROUND: Epigenetic gene silencing is one of the major causes of carcinogenesis. Its widespread occurrence in cancer genome could inactivate many cellular pathways including DNA repair, cell cycle control, apoptosis, cell adherence, and detoxification. The abnormal promoter methylation might be a potential molecular marker for cancer management. METHODS: For rapid identification of potential targets for aberrant methylation in gynecological cancers, methylation status of the CpG islands of 34 genes was determined using pooled DNA approach and methylation-specific PCR. Pooled DNA mixture from each cancer type (50 cervical cancers, 50 endometrial cancers and 50 ovarian cancers) was made to form three test samples. The corresponding normal DNA from the patients of each cancer type was also pooled to form the other three control samples. Methylated alleles detected in tumors, but not in normal controls, were indicative of aberrant methylation in tumors. Having identified potential markers, frequencies of methylation were further analyzed in individual samples. Markers identified are used to correlate with clinico-pathological data of tumors using χ(2 )or Fisher's exact test. RESULTS: APC and p16 were hypermethylated across the three cancers. MINT31 and PTEN were hypermethylated in cervical and ovarian cancers. Specific methylation was found in cervical cancer (including CDH1, DAPK, MGMT and MINT2), endometrial cancer (CASP8, CDH13, hMLH1 and p73), and ovarian cancer (BRCA1, p14, p15, RIZ1 and TMS1). The frequencies of occurrence of hypermethylation in 4 candidate genes in individual samples of each cancer type (DAPK, MGMT, p16 and PTEN in 127 cervical cancers; APC, CDH13, hMLH1 and p16 in 60 endometrial cancers; and BRCA1, p14, p16 and PTEN in 49 ovarian cancers) were examined for further confirmation. Incidence varied among different genes and in different cancer types ranging from the lowest 8.2% (PTEN in ovarian cancer) to the highest 56.7% (DAPK in cervical cancer). Aberrant methylation for some genes (BRCA1, DAPK, hMLH1, MGMT, p14, p16, and PTEN) was also associated with clinico-pathological data. CONCLUSION: Thus, differential methylation profiles occur in the three types of gynecologic cancer. Detection of methylation for critical loci is potentially useful as epigenetic markers in tumor classification. More studies using a much larger sample size are needed to define the potential role of DNA methylation as marker for cancer management