ALBERT

Description: The timing of initiation of human impacts on the global climate system is actively debated. Anthropogenic effects on the global climate system are evident since the Industrial Revolution, but humans may have altered biomass burning, and hence the climate system, for millennia. We use the specific biomarker levoglucosan to produce the first high-temporal resolution hemispheric reconstruction of Holocene fire emissions inferred from ice core analyses. Levoglucosan recorded in the Greenland NEEM ice core significantly increases since the last glacial, resulting in a maximum around ~2.5 ka and then decreasing until the present. Here, we demonstrate that global climate drivers fail to explain late-Holocene biomass burning variations, and that the levoglucosan maximum centred on ~2.5 ka may be due to anthropogenic land clearance.

Description: Acute lymphoblastic leukemia (ALL) is the most frequent malignant disease in childhood. Advances in therapy, in particular stratification of patients to appropriate treatment according to risk factors improved long term survival attaining cure rates of up to 80 %. Despite the efforts achieved by the stratification strategies the majority of relapse patients are recruited from the low risk groups emphasizing the need for additional independent risk factors. In a recent study we transplanted primary leukemia samples obtained from pediatric patients with newly diagnosed B cell precursor ALL (BCP-ALL) into NOD/SCID mice. Time to leukemia (TTL) was analyzed for each patient sample transplanted from date of transplant to date of leukemia manifestation in the recipients. We demonstrated that patients whose leukemia cells engrafted rapidly leading to manifestation of the disease within 10 weeks (TTLshort) showed a clearly inferior relapse free survival in contrast to patient samples with prolonged in vivo growth (TTLlong). Interestingly, the same distinct difference in relapse free survival was observed in the low risk groups only. Multivariate analysis showed an almost 45- fold increased risk for relapse in TTLshort patients. In order to further characterize the biological properties of the leukemia cells in the two groups, gene expression profiles of samples with short versus long TTL in the xenograft model were analyzed using a human whole genome array (Affymetrix U133 Plus 2.0). Here, we used quantitative traits analysis (QTA) correlating gene expression values (relative expression) to the time from transplant to manifestation of leukemia in the NOD/SCID mice (TTL, in weeks). 14 different xenograft samples (TTLshort n= 7, mean TTL 8.14 weeks; TTLlong n= 7, mean TTL 19.71 weeks; T-test P = .03) isolated from leukemia bearing mice were investigated. All 14 patients included were stratified in low (standard or intermediate) risk groups. All patients were negative for TEL/AML1- fusion, BCR/ABL- fusion or MLL rearrangement. By QTA we identified 5 genes that were significantly correlated (Spearman correlation, for all 5 genes P 〈 .0001) to time from transplant to leukemia manifestation in the recipient mice (TTL). Analysis of the 14 xenograft samples using the 5 genes identified showed clustering of all but one sample in one group. The 5 genes were than explored for their power to predict relapse in an independent cohort of pediatric ALL patients. For these patients expression profiles were analyzed in leukemia samples obtained at diagnosis. All patients in this cohort were also stratified in low risk groups and negative for the presence of TEL/AML1-, BCR/ABL- or MLL- fusion transcripts. 8 of the 38 patients encountered relapse, 5 at early and 3 at late time points (〈 or 〉 24 months after diagnosis). Clustering according to the 5 genes identified by QTA lead to separation of the patients into two groups. Interestingly, all relapsed patients except one clustered in the group associated with the signature characteristic for TTLshort. Most importantly, all 5 patients with early relapse gathered in this cluster. Taken together, we used a novel approach directly correlating gene expression values to the continuous variable time to leukemia (TTL) which might be reflected more accurately by this strategy than comparing two groups. We applied this gene signature characteristic for TTLshort, thereby identifying poor overall survival in a set of independent pediatric ALL patients and found clustering of all early relapse patients in one group. This new independent risk factor might identify patients with high risk for early relapse avoiding xenografting in the mouse model.

Description: Gene expression microarrays had been used to classify known tumor types and various hematological malignancies (Yeoh et al, Cancer Cell 2002; Kohlmann et al, Genes Chromosomes Cancer 2003), enforcing the objective that microarray analysis could be introduced soon in the routine classification of cancer (Haferlach et al, Blood 2005). However, there’re still doubts about gene expression experiments performance in clinical laboratory diagnosis. For instance, the quality of starting material is a major concern in microarray technology and there are no data on the variation in gene expression profiles ensuing from different RNA extraction procedures. Here, as part of the internal multicenter MILE Study program, we assess the impact of different RNA preparation methods on gene expression data, analyzing 27 patients representative of nine different subtypes of pediatric acute leukemias. We compared the three currently most used protocols to isolate RNA for routine diagnosis (PCR assays) and microarray experiments. They are named as method A: lysis of mononuclear leukemia cells, followed by lysate homogeniziation, followed by total RNA isolation; method B: TRIzol RNA isolation, and method C: TRIzol RNA isolation followed by total RNA purification step. The methods were analyzed in triplicates for each sample (24) and additional three samples were performed in technical replicates of three data sets for each preparation (HG-U133 Plus 2.0). Method A results in better total RNA quality as demonstrated by 3′/5′ GAPD ratios and by RNA degradation plots. High comparability of gene expression data is found between samples in the same leukemia subclasses and collected with different RNA preparation methods thus demonstrating that sample preparation procedures do not impair the overall signal distribution. Unsupervised analyses showed clustering of samples first by each patient’s replicate conditions, then by leukemia type, and finally by leukemia lineage. In fact, B-ALL samples are clustered together, separately from T-ALL and AML, demonstrating that clustering reflects biological differences between leukemias and that the RNA isolation method is a secondary effect. Also, supervised cluster analyses highlight that samples are grouped depending on intra-lineage features (i.e. chromosomal aberrations) thus confirming the clustering organizations as reported in recent gene expression profiling studies of acute leukemias. Our study shows that biological features of pediatric acute leukemia classes largely exceed the variations between different total RNA sample preparation protocols. However, technical replicates analyses reveal that gene expression data from method A have the lowest degree of variation, are more reproducible and more precise as compared to the other two methods. Furthermore, compared to methods B and C, method A produces more differentially expressed probe sets between distinct leukemia classes and is therefore considered the more robust RNA isolation procedure for gene expression experiments using high-density microarray technology. We therefore conclude that method A (initial homogenization of the leukemia cell lysate followed by total RNA isolation) combined with a standardized microarray analysis protocol is highly reproducible and contributes to robustness of gene expression data and that this procedure is most practical for a routine laboratory use.

Description: A number of clinical and biological prognostic factors are currently used for B-cell precursor Acute Lymphoblastic Leukemia (BCP-ALL) patient risk stratification into (standard risk) SR, (medium risk) MR and (high risk) HR treatment protocols. In spite of careful stratification patients stratified in the same risk class may respond differently to therapy suggestive of a need for additional prognostic markers in BCP-ALL. Here we analyzed gene expression profiles (GEP) of BCP-ALL patients at diagnosis in search for correlations with the diverse responses to therapy among these patients. We specifically searched for expression signatures that predict relapse or the absence of relapse in BCP-ALL. Among BCP-ALL 50% of patients are characterized by recurrent genomic aberrations that are strong indicators for risk stratification of which BCR/ABL and MLL rearrangements have a HR to relapse; TEL/AML1 and hyperdiploid have a standard risk to relapse. BCPALL patients without these aberrations form a heterogeneous risk group in which new prognostic markers will be very important to help stratify these patients. A GEP data set of 86 BCP-ALL patients (47 males; 39 females) without known aberrations was obtained using Affymetrix HG-U133Plus2.0 arrays that were analyzed using Partek and R software packages. Patients were treated according to AIEOP ALL 2000 protocols. Nine out of 86 patients relapsed of which 6 within 24 months; the minimum follow-up time was 24 months. For each probe set the variance among patients was computed and 10 % of the probe sets with the major variance were selected for further analysis. Unsupervised hierarchical cluster analysis separated patients in two groups: one group of 19 patients that were all in complete remission (CR) and a second group of 67 patients in which all patients that eventually relapsed (14%) and CR patients (86%) cluster together. The median follow-up time in the first group of patients was 40 months and the median patient age at diagnosis was 7 years. In the second group median follow-up was 29 months and median patient age at diagnosis was 5.5 years. The separation of patients into two groups was not related to risk group (SR, MR or HR), gender, age or response to induction therapy. A t-test (multiplicity corrections were adopted to control for false positives) was used to find probe sets that were differentially expressed between the two groups. Functional evaluation of the top 200 probe sets of this analysis established 3 major biological categories according to the GO database and revealed differential expression of genes involved in cell adhesion, signal transduction and immune response. We identified a subtype of patients with a gene expression signature that is correlated with excellent prognosis using GEP analysis on BCP-ALL patients that present no recurrent known genomic aberrancies. The signature of excellent prognosis involves about 20% of BCP-ALL patients without common genotypic aberrations and 8% of all BCP-ALL patients and is independent of currently applied prognostic risk factors.

Description: Background Microarray gene expression (MAGE) signatures allow insights into the transcriptional processes of leukemias and may evolve as a molecular diagnostic test. Introduction of MAGE into clinical practice of leukemia diagnosis will require comprehensive assessment of variation due to the methodologies. Here we systematically assessed the impact of three different total RNA isolation procedures on variation in expression data: method A: lysis of mononuclear cells, followed by lysate homogenization and RNA extraction; method B: organic solvent based RNA isolation, and method C: organic solvent based RNA isolation followed by purification. Results We analyzed 27 pediatric acute leukemias representing nine distinct subtypes and show that method A yields better RNA quality, was associated with more differentially expressed genes between leukemia subtypes, demonstrated the lowest degree of variation between experiments, was more reproducible, and was characterized with a higher precision in technical replicates. Unsupervised and supervised analyses grouped leukemias according to lineage and clinical features in all three methods, thus underlining the robustness of MAGE to identify leukemia specific signatures. Conclusion The signatures in the different subtypes of leukemias, regardless of the different extraction methods used, account for the biggest source of variation in the data. Lysis of mononuclear cells, followed by lysate homogenization and RNA extraction represents the optimum method for robust gene expression data and is thus recommended for obtaining robust classification results in microarray studies in acute leukemias.