G Wright's research while affiliated with National Cancer Institute (USA) and other places

The median survival of patients with mantle cell lymphoma (MCL) ranges from 3 to 5 years with current chemotherapeutic regimens. A common secondary genomic alteration detected in MCL is chromosome 13q31-q32 gain/amplification, which targets a microRNA (miRNA) cluster, miR-17∼92. On the basis of gene expression profiling, we found that high level expression of C13orf25, the primary transcript from which these miRNAs are processed, was associated with poorer survival in patients with MCL (P=0.021). We demonstrated that the protein phosphatase PHLPP2, an important negative regulator of the PI3K/AKT pathway, was a direct target of miR-17∼92 miRNAs, in addition to PTEN and BIM. These proteins were down-modulated in MCL cells with overexpression of the miR-17∼92 cluster. Overexpression of miR-17∼92 activated the PI3K/AKT pathway and inhibited chemotherapy-induced apoptosis in MCL cell lines. Conversely, inhibition of miR-17∼92 expression suppressed the PI3K/AKT pathway and inhibited tumor growth in a xenograft MCL mouse model. Targeting the miR-17∼92 cluster may therefore provide a novel therapeutic approach for patients with MCL.

Activating mutations in the KRAS gene are among the most prevalent genetic changes in human cancers. To identify synthetic lethal interactions in cancer cells harbouring mutant KRAS, we performed a large-scale screen in isogenic paired colon cancer cell lines that differ by a single allele of mutant KRAS using an inducible short hairpin RNA interference library. Snail2, a zinc finger transcriptional repressor encoded by the SNAI2 gene, was found to be selectively required for the long-term survival of cancer cells with mutant KRAS that have undergone epithelial-mesenchymal transition (EMT), a transdifferentiation event that is frequently seen in advanced tumours and is promoted by RAS activation. Snail2 expression is regulated by the RAS pathway and is required for EMT. Our findings support Snail2 as a possible target for the treatment of the broad spectrum of human cancers of epithelial origin with mutant RAS that have undergone EMT and are characterized by a high degree of chemoresistance and radioresistance.

The addition of rituximab to combination chemotherapy with cyclophosphamide, doxorubicin, vincristine, and prednisone (CHOP), or R-CHOP, has significantly improved the survival of patients with diffuse large-B-cell lymphoma. Whether gene-expression signatures correlate with survival after treatment of diffuse large-B-cell lymphoma is unclear. We profiled gene expression in pretreatment biopsy specimens from 181 patients with diffuse large-B-cell lymphoma who received CHOP and 233 patients with this disease who received R-CHOP. A multivariate gene-expression-based survival-predictor model derived from a training group was tested in a validation group. A multivariate model created from three gene-expression signatures--termed "germinal-center B-cell," "stromal-1," and "stromal-2"--predicted survival both in patients who received CHOP and patients who received R-CHOP. The prognostically favorable stromal-1 signature reflected extracellular-matrix deposition and histiocytic infiltration. By contrast, the prognostically unfavorable stromal-2 signature reflected tumor blood-vessel density. Survival after treatment of diffuse large-B-cell lymphoma is influenced by differences in immune cells, fibrosis, and angiogenesis in the tumor microenvironment.

Gene expression profiling of diffuse large B-cell lymphoma (DLBCL) has revealed biologically and prognostically distinct subgroups: germinal center B-cell-like (GCB), activated B-cell-like (ABC) and primary mediastinal (PM) DLBCL. The BCL6 gene is often translocated and/or mutated in DLBCL. Therefore, we examined the BCL6 molecular alterations in these DLBCL subgroups, and their impact on BCL6 expression and BCL6 target gene repression. BCL6 translocations at the major breakpoint region (MBR) were detected in 25 (18.8%) of 133 DLBCL cases, with a higher frequency in the PM (33%) and ABC (24%) subgroups than in the GCB (10%) subgroup. Translocations at the alternative breakpoint region (ABR) were detected in five (6.4%) of 78 DLBCL cases, with three cases in ABC and one case each in the GCB and the unclassifiable subgroups. The translocated cases involved IgH and non-IgH partners in about equal frequency and were not associated with different levels of BCL6 mRNA and protein expression. BCL6 mutations were detected in 61% of DLBCL cases, with a significantly higher frequency in the GCB and PM subgroups (>70%) than in the ABC subgroup (44%). Exon-1 mutations were mostly observed in the GCB subgroup. The repression of known BCL6 target genes correlated with the level of BCL6 mRNA and protein expression in GCB and ABC subgroups but not with BCL6 translocation and intronic mutations. No clear inverse correlation between BCL6 expression and p53 expression was observed. Patients with higher BCL6 mRNA or protein expression had a significantly better overall survival. The biological role of BCL6 in translocated cases where repression of known target genes is not demonstrated is intriguing and warrants further investigation.

Background: FL is a common NHL that has a broad spectrum of clinical outcomes. Over time some pts will transform to an aggressive histology (Tly) associated with inferior survival. In 2004, the LLMPP constructed a model that was predictive of overall survival (OS) based on the gene expression profiles (GEP) of 191 specimens taken from pts with untreated FL. The genes associated with survival were derived from the non-neoplastic immune response (IR) cells. However the risk of developing Tly was not addressed in this study. Thus we re-analyzed the GEP with updated clinical data. Our goal was to validate our previous model with extended follow-up and to create a model that would predict the risk of developing TLy. Methods: 170 of 191 previously untreated FL pts had updated clinical information but only 142 had transformation outcome. Transformation was defined as biopsy proven DLBCL or clinically based on the presence of at least one of the following: hypercalcemia, a sudden rise in LDH >twice baseline, unusual extranodal growth or rapid discordant nodal growth. Raw CEL files from Affymetrix U133A arrays were pre-processed and normalized using Bioconductor’s GCRMA package. Models were developed using SignS package (http://signs/bioinfo.cnio.es/), with 10 times cross-validation. All gene lists produced in these analyses were then re-tested for association with outcome using Bioconductor’s Globaltest package. Over Representation Analysis of signature components was performed using Dchip. Results: The median OS of these patients was 8 yrs. A new 7-component survival model (85 genes) was developed that was significantly associated with survival (p= 2.9×10−13). In Globaltest, these gene lists were associated with survival at a level of (p=2.6×10−5). The previous model using IR-1 and IR-2 signatures was associated with survival at a level of p=2.6×10−4. Although there is little overlap between the 2 models, the new model confirms the importance of IR genes and extracellular matrix genes as being prognostically important. Interestingly, one component containing 10 genes on chromosome 6q was associated with a superior survival (p<1×107). 27% developed Tly over a median follow-up time of 11.2 yrs (69% biopsy proven). Our transformation model included 53 genes divided into 3 components (p=0.001). The Globaltest analysis for association of these genes with transformation was significant (p=0.018). 54 genes overlapped between the survival genes and transformation genes that were present in >1 cross validation run. These were significantly enriched in genes important in immune response like T cell and macrophage activation. Conclusion: Our survival model is stable and confirms the importance of key genes involved in the immune response and lymph node remodeling. It also introduces new genes that are potentially important for survival. Our transformation model may shed light on the mechanisms involved in the progression of FL to DLBCL but it is less stable and less reliable than our survival model at predicting outcome.

Background Burkitt lymphoma(BL) is a potentially curable, aggressive lymphoma. The distinction between BL and diffuse large B-cell lymphoma (DLBCL) is important because they differ significantly in clinical management. The distinction can be difficult because DLBCL can resemble BL in morphology, immunophenotype and cytogenetics. We investigated whether gene expression profiling (GEP) could create a molecular definition of BL that can reliably distinguish it from DLBCL. Methods Biopsy samples were collected from 312 patients with a diagnosis of sporadic BL or Burkitt-like lymphoma, or DLBCL. All cases were reviewed by a panel of expert hematopathologists. GEP of all the samples was carried out using a specialized oligonucleotide microarray. We constructed a predictor using 197 genes to distinguish BL from each molecular subtype of DLBCL. Leave-one-out cross-validation was used to evaluate the predictor’s performance. Chemotherapy treatments were grouped into either CHOP-like(CHOP, CNOP) or intensive(BFM, CODOX-M IVAC, regimens requiring stem cell rescue). Results After pathology review, the samples were reclassified as:classic BL(25 cases), atypical BL(19), DLBCL(261), and unclassifiable lymphoma(7). All classic BL and 18/19 cases of atypical BL shared a profile that was strikingly different from that of all the molecular subtypes of DLBCL, including those DLBCL cases that have a c-myc translocation. C-myc and its target genes, and genes related to germinal center differentiation were expressed at high levels in BL. NF-kB and its target genes and MHC class-I genes were expressed at very low levels in BL. Interestingly, 10 cases that were DLBCL by pathology were classified as BL by the predictor. The diagnosis of BL was supported by FISH analysis indicating a c-myc translocation. Among adults identified as having BL by the predictor (with full clinical data in N=15), overall survival was markedly superior for those receiving intensive regimens compared to CHOP-like regimens(Fig 1). The groups were similar with regard to age, stage, performance status and sites of involvement. Conclusion This study demonstrates that the molecular characteristics of BL can be used to accurately distinguish it from DLBCL. Importantly, a subgroup of BL was identified by the predictor that could not be diagnosed as BL by conventional criteria. The ability of the predictor to identify patients who benefit from aggressive therapies suggests that it will be useful in the diagnosis and management of patients with Burkitt lymphoma. Figure Figure

Clinical management differs significantly for the various types of non-Hodgkin lymphoma (NHL), and the diagnosis of these lymphomas can be challenging in some cases. Further, existing NHL categories include subgroups that can differ substantially in gene expression, response to therapy and overall survival. We have created a custom oligonucleotide microarray, named LymphDx, which could prove clinically useful for molecular diagnosis and outcome prediction in NHL. Biopsy specimens were obtained from 559 patients with a variety of lymphomas and lymphoproliferative conditions. Gene expression profiles of these samples were obtained using Affymetrix U133 A and B microarrays. The 2653 genes on LymphDx were chosen to include:(1)Genes most differentially expressed among NHL types based on Affymetrix U133 or Lymphochip microarrays (2)Genes predicting length of survival in diffuse large B cell lymphoma(DLBCL), follicular lymphoma(FL) and mantle cell lymphoma(MCL) (3)Genes encoded in the EBV and HHV-8 viral genomes (4)Genes encoding all known surface markers, kinases, cytokines and their receptors, as well as oncogenes, tumor suppressors, and other genes relevant to lymphoma. The LymphDx microarray was used to profile gene expression in 434 biopsy samples. These data were used to create a diagnostic algorithm that can distinguish various NHL types and benign follicular hyperplasia(FH) based on gene expression. The algorithm classifies a sample into one of the following categories: Burkitt’s lymphoma(BL), DLBCL, FL, MCL, small lymphocytic lymphoma(SLL) or FH. The algorithm further distinguishes the 3 recognized DLBCL subgroups: germinal center B cell-like, activated B cell-like or primary mediastinal lymphoma. Using a leave one out, cross validation strategy, the algorithm was found to agree well with the pathology diagnosis (see Figure). Some samples were deemed unclassified when their gene expression did not adequately match with that of any of the NHL categories. For a few samples, the gene expression-based diagnosis and the pathology diagnosis were discordant. Pathology review showed that two NHL types coexisted (eg FL and DLBCL) in many of these cases, potentially explaining the results of the diagnostic algorithm. LymphDx could also reliably predict the overall survival of patients with DLBCL, FL and MCL. Prospective evaluation of the LymphDx microarray is warranted since it could be used to provide objective molecular diagnostic, and prognostic information for patients with NHL. Figure Figure