Project description:Diffuse large B-cell lymphoma (DLBCL) has striking clinical and molecular variability. Although a more precise identification of the multiple determinants of this variability is still under investigation, there is a consensus that high-clinical-risk DLBCL cases require a risk-adapted therapy, since intensification of chemotherapy with autologous stem-cell transplantation (ASCT) has been shown to improve the prognosis for high-risk patients in randomised clinical trials. In spite of this, the protocols used for these patients have a high morbidity, associated with ASCT and the use of multiple drugs. This makes it important to identify patients that may take benefit from risk-adapted therapies, through the recognition of biological markers that provide information about both the tumoral cells and the microenvironment. Unfortunately, many of the studies so far performed have relied on heterogeneous series of patients, staged or treated with different protocols. For instance, some of the variability in DLBCL arises from the fact that this diagnosis is applied to de novo and secondary tumours, nodal and extranodal, irrespective of clinical stage, patient age and associated infections. Additionally, DLBCL includes some specific variants, such as mediastinal DLBCL and T/HRBCL, with specific prognostic parameters. This could prevent the identification of potential predictive biomarkers, because the results of many studies show that the search for predictive biomarkers should be promoted in the context of samples of clinically homogeneous patients enrolled in clinical trials. A further source of variability is the dependence of some predictive markers on specific therapeutic approaches, as is the case for the Bcl6 expression in DLBCL, since Bcl-6+ cases have been shown not to benefit from the addition of R to CHOP. Here we have analysed a series of high-clinical-risk DLBCLs by a two-stage approach, first identifying functional signatures by expression analysis, then analysing surrogate biomarkers using tissue microarrays (TMAs). This eclectic approach could reveal new aspects of the relationship between the neoplastic cells and the microenvironment, leading to the identification of previously unknown prognostic markers. At the same time, the use of functional signatures to analyse expression-profiling data avoids the poor reproducibility of the data obtained from gene-by-gene analysis, and benefits from the existence of a growing body of data concerning the major pathways deregulated in DLBCL. To avoid the bias of semiquantitative scoring, in this study we have quantified the markers included in the multivariate analysis. Keywords: new biological variables, risk-adapted therapies

Project description:Diffuse large B-cell lymphoma (DLBCL) has striking clinical and molecular variability. Although a more precise identification of the multiple determinants of this variability is still under investigation, there is a consensus that high-clinical-risk DLBCL cases require a risk-adapted therapy, since intensification of chemotherapy with autologous stem-cell transplantation (ASCT) has been shown to improve the prognosis for high-risk patients in randomised clinical trials. In spite of this, the protocols used for these patients have a high morbidity, associated with ASCT and the use of multiple drugs. This makes it important to identify patients that may take benefit from risk-adapted therapies, through the recognition of biological markers that provide information about both the tumoral cells and the microenvironment. Unfortunately, many of the studies so far performed have relied on heterogeneous series of patients, staged or treated with different protocols. For instance, some of the variability in DLBCL arises from the fact that this diagnosis is applied to de novo and secondary tumours, nodal and extranodal, irrespective of clinical stage, patient age and associated infections. Additionally, DLBCL includes some specific variants, such as mediastinal DLBCL and T/HRBCL, with specific prognostic parameters. This could prevent the identification of potential predictive biomarkers, because the results of many studies show that the search for predictive biomarkers should be promoted in the context of samples of clinically homogeneous patients enrolled in clinical trials. A further source of variability is the dependence of some predictive markers on specific therapeutic approaches, as is the case for the Bcl6 expression in DLBCL, since Bcl-6+ cases have been shown not to benefit from the addition of R to CHOP. Here we have analysed a series of high-clinical-risk DLBCLs by a two-stage approach, first identifying functional signatures by expression analysis, then analysing surrogate biomarkers using tissue microarrays (TMAs). This eclectic approach could reveal new aspects of the relationship between the neoplastic cells and the microenvironment, leading to the identification of previously unknown prognostic markers. At the same time, the use of functional signatures to analyse expression-profiling data avoids the poor reproducibility of the data obtained from gene-by-gene analysis, and benefits from the existence of a growing body of data concerning the major pathways deregulated in DLBCL. To avoid the bias of semiquantitative scoring, in this study we have quantified the markers included in the multivariate analysis. Experiment Overall Design: Patients Experiment Overall Design: The study was performed in a series of 74 high-clinical-risk DLBCL patients included in a prospective trial from the GEL/TAMO Spanish Group (Grupo EspaÃ±ol de Linfomas / Trasplante de MÃ©dula Ã?sea) between June 2001 and June 2005. High risk was defined as having either an age-adjusted IPI score >=2 or IPI <2 with a high Î²2 microglobulin level. Experiment Overall Design: All patients received therapy with MegaCHOP, after which, patients were again evaluated for response by CT and Ga 67S. Patients with CT response and negative Ga 67S received another MegaCHOP cycle followed by BEAM and ASCT. Those patients with positive Ga 67S or without CT response underwent salvage treatment with Ifosfamide and Etoposide (IFE) for two courses, followed by BEAM and ASCT whenever a response had been achieved. From June 2003 Rituximab was added to the schema, before administering ASCT. Those patients with less than a Partial Response (PR) or progressive disease after IFE were removed from the study. Experiment Overall Design: Diagnoses were revised by a panel of pathologists, following the World Health Organization (WHO)9 criteria. Only primary DLBCLs were included in this analysis. T-cell/histiocyte-rich large B-cell lymphomas (T/HRLBCLs) and mediastinal DLBCLs were excluded. Experiment Overall Design: Patients included in this biological analysis were consecutive, selected only on the basis of the availability of formalin-fixed paraffin-embedded or frozen tissue representative of the tumour at diagnosis. The final number of cases analysed was 50. Frozen diagnostic tissue was available from a subset of ten of these patients. Experiment Overall Design: Informed consent was obtained from the patients included in the trial under the supervision of the Local Ethical Committees. Experiment Overall Design: Identifying biological function surrogate markers for clinical prediction Experiment Overall Design: The expression profile was initially analysed in the ten samples from which frozen tissue was available. These included four patients who had died after treatment failure and six who were alive, with complete remission, and free of disease. Experiment Overall Design: All frozen tissue corresponded to a representative area of the tumour with more than 60% of viable tumoral cells present. Experiment Overall Design: We used a multistep gene-set-enrichment approach to identify functional pathways deregulated in DLBCLs resistant to the treatment strategy. Experiment Overall Design: The design of the analysis was as follows (details on the procedure are included as Supplementary information on material and Methods) : Experiment Overall Design: 1. Microarray analysis, using both cDNA and oligonucleotide microarrays in a set of 10 frozen diagnostic samples. Experiment Overall Design: 2. Identification of biological function representative of differentially expressed genes, using the Gene Ontology database (http://bioinformatics.weizmann.ac.il/cards) Experiment Overall Design: 3. Selection of surrogate immunohistochemical markers (representative of the biological function identified) Experiment Overall Design: 4. Immunohistochemical study on Tissue Microarrays containing diagnostic initial biopsy tissue in a set of 50 patients Experiment Overall Design: a. Semiquantitative scoring Experiment Overall Design: b. Quantification of the most relevant markers Experiment Overall Design: Identification of biological markers predicting treatment response Experiment Overall Design: The relations between the semiquantitative measurement of the 60 proteins evaluated and the clinical outcome were analysed. The clinical end-points were: a) CR after 3 MegaCHOP, b) CR after IFE and, c) absence of progression after consolidation with HDT/ ASCT. Experiment Overall Design: The chi-square statistic was used to assess associations between the protein-expression levels and the intermediate step of treatment. Significant relationships (p<0.05) and non-significant trends (p<0.1) were identified. Experiment Overall Design: Failure was defined as progression or death attributable to the tumour. Failure-free survival (FFS) curves were plotted using the Kaplan-Meier method. Statistical significance of associations between individual variables and FFS was determined using the log-rank test. Significant relationships (p<0.05) and non-significant trends (p<0.1) were identified. Experiment Overall Design: Genes were clustered on the basis of their biological similarities. Experiment Overall Design: The accumulations or alternations of marker alterations were analysed by the chi-square and Spearman correlation tests. Only significant relationships (p<0.05) were considered further. Experiment Overall Design: One surrogate marker for each altered main cellular function was selected. These markers were considered in order to develop a multivariate logistic regression model predicting failure of treatment. Proteins found to be potentially informative in this analysis were automatically scored. Experiment Overall Design: Various multivariate models were fitted using step-up (forward) and step-down (backwards) variable selection and other heuristic procedures. By comparing the fits of these models, a final predictive logistic regression model was obtained. The final model estimates the odds ratio (OR), 95% confidence interval (CI) and significance (P) of each variable. General applicability of the model was tested by leave-one-out cross-validation. Different predictor models were found when using leave-one-out cross validation but these showed only small variations in the weight of each marker. Accuracy was also tested by the Receiver Operating Characteristic (ROC) curve, which estimates the discriminating ability of the model. The fit of the final model was assessed by the Hosmer-Lemeshow test. Experiment Overall Design: To demonstrate the predictive capacity of the model, patients were ranked according to this score and then divided into quartiles. Experiment Overall Design: Statistical analyses were performed using SPSS and R tools.

Project description:Background: Validated markers to predict outcome in rectal cancer patients treated with multimodal therapy remain elusive. Identifying molecular profiles for disease prognosis would be required for the design of clinical trials aimed at optimizing risk-adapted therapies. We have therefore used whole genome expression profiling of tumors of a large cohort of patients enrolled in the multicenter trials of the German Rectal Cancer Study Group (GRCSG) to identify molecular profiles for individualized therapy. Methods: We prospectively collected pretherapeutic biopsies from patients (n=300) treated according to the GRCSG trial guidelines from seven different German surgical departments using rigid quality controls. These samples were profiled by global gene expression analysis and a classifier developed to predict postoperative lymph node status and Disease Free Survival (DFS). The performance of the classifier was validated with an independent, prospectively collected set of samples. Findings: The final training and test set included 198 patients. Analyzes for postoperative nodal status and DFS revealed 69 and 674 differentially regulated genes, respectively. Depending on the classifier the accuracy to predict lymph node status ranged from 64% to 69% with negative predictive values between 72% and 74%. Stratification according to DFS resulted in a good (n=99), bad (n=96) and a small very bad prognosis group (n=3). Based on linear discriminant analysis the classifier for positive lymph node status was validated in 47 independent patients and revealed an accuracy of 72% and a positive predictive value of 100%. Thereby, prediction based on molecular profiling is superior to prediction based on conventional clinical markers. Interpretation: Whole genome expression analysis of pretherapeutical biopsies resulted in a molecular classifier of disease prognosis. This classifier was successfully validated. The high positive predictive value for post therapeutic lymph node status allows identification of patients requiring alternative or intensified treatment protocols. These data are currently validated for their clinical applicability and should be taken as basis for future molecular driven clinical trials to develop risk-adapted treatments.

Project description:The mTOR (mammalian Target of Rapamycin) pathway is constitutively activated in Diffuse Large B-Cell Lymphoma (DLBCL). mTOR inhibition has been shown to have clinical activity in patients with DLBCL, although overall response rates remain low. We therefore evaluated differences in the transcriptome between DLBCL cell lines with differential sensitivity to the mTOR inhibitor Rapamycin, to (A) identify gene-expression patterns(GEP) capable of identifying sensitivity to Rapamycin, (B) understand the underlying mechanisms of resistance to Rapamycin in DLBCL and (C) identify bioactive molecules likely to synergize with mTOR inhibitors. Using Affymetrix HuGene ST 1.0 microarrays, we were able to identify a gene expression signature capable of accurately predicting sensitivity and resistance to Rapamycin in DLBCL cell lines. Pathway analysis identified the serine/threonine kinase Akt as central to the differentially-expressed gene network. Connectivity mapping of our datasets identified compounds targeting the AKT pathway with a high likelihood of reversing the GEP associated with resistance to Rapamycin. Specifically, we evaluated the HIV protease inhibitor (PI) Nelfinavir, which is known to have anti-cancer and Akt-inhibitory properties, as well as the small molecule Akt inhibitor MK-2206, for their potential to synergize with to Rapamycin in DLBCL. Nelfinavir and MK-2206 caused profound inhibition of cell viability in combination with Rapamycin in DLBCL cell lines. Low nanomolar concentrations of Rapamycin inhibited phosphorylation of Akt and also downstream targets of activated mTOR when used in combination with these Akt inhibitors. These findings have the potential to significantly improve patient selection for mTOR inhibitor therapy, and to improve rates and depths of response. More broadly, they support the use of global RNA expression and connectivity mapping to improve patient selection and identify synergistic drug combinations for cancer therapy. DLBCL cell lines were tested for Rapamycin sensitivity and classified as "sensitive" or "resistant." Genome-wide analysis of all cell lines were performed using the Affymetrix HuGene ST 1.0 Array Platform. Genes with differential expression between sensitive and resistant cell lines were analyzed using Statistical Analysis of Microarrays (SAM) software, and a signature of genes determnined. This signature was found to accurately predict sensitivity or resistance of other DLBCL cell lines, and to identify the protein kinase Akt as central to resistance.

Project description:Identification of novel molecular subgroups Background: International consensus recognises four medulloblastoma molecular subgroups - WNT, SHH, Group 3 and Group 4 - each defined by their characteristic genome-wide transcriptomic and DNA methylomic profiles. Subgroups harbor distinct clinico-pathological and molecular features, underpin current disease sub-classification and initial subgroup-directed therapies are underway in clinical trials (i.e. reduced risk-adapted treatments for favorable-risk WNT patients; SMO inhibitors for SHH patients). However, significant biological heterogeneity and differences in survival are apparent within each subgroup, which remain to be resolved. Methods: We undertook comprehensive molecular profiling and unsupervised class discovery (non-negative matrix factorization, t-SNE) of test and validation cohorts to identify consensus primary molecular subgroups within childhood medulloblastoma (<16.0 years), and characterize their clinical and biological significance. Survival modeling was performed in clinically-annotated centrally-reviewed patients (>3.0 years). Findings: Seven robust and reproducible primary molecular subgroups of childhood medulloblastoma were identified, characterized by distinct biological/clinical features. For instance, SHH comprised two age-dependent subgroups, while Grp3 and Grp4 each split into two subgroups with significantly different survival rates. Survival analysis identified secondary features predictive of outcome. Cross-validated subgroup-dependent models incorporating these novel subgroups along with secondary features and established disease risk-factors, outperformed current disease risk-stratification schemes. These schema stratified patients into four clinical risk-groups - favorable-risk (91% 5-year survival, 25% of patients), standard-risk (81%, 23%), high-risk (42%, 38%) and very high-risk (28%, 13%) - to be considered for treatment reduction, intensification or novel therapies respectively. Interpretation: The discovery of seven novel, clinically-significant, subgroups significantly improves disease risk-stratification and provides a new foundation for future research and clinical investigations.

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