Bryant, Barbara M.

Abstract The biological heterogeneity of myeloma can be largely explained by genetic subtypes characterized by primary translocations (t(4;14), t(11;14), and t(14;16)) or trisomies of odd-numbered chromosomes (hyperdiploid myeloma). In newly diagnosed patients, it is well established that these genetic subtypes have different survival. However, the prevalence and survival of the main genetic subtypes in relapse patients is unknown. In this study, we examined the prevalence and survival according to TC classes (a gene expression-based classification system which identifies the main genetic subtypes of myeloma) in patients with relapsed myeloma entered into bortezomib trials who had gene expression profiling (GEP) performed as part of a pharmacogenomics study (Bortezomib cohort). In comparison, we analyzed a large publicly available GEP dataset of newly diagnosed patients treated with total therapy II from the University of Arkansas for Medical Science (UAMS cohort, GEP data available on Gene Expression Omnibus, Zhan et al Blood 2006 epub). GEP was performed using the U133A and B chip for the bortezomib cohort and U133plus chip for the UAMS cohorts (Affymetrix, Santa Clara, CA). For both dataset, raw gene expression were log-transformed, median centered and analyzed using Genespring 7.0 (Agilent Technologies, Palo Alto, CA). A proliferation index (PI) based on the expression of 7 genes (TYMS, TOP2A, CCNB1, KIAA0101, CKS1B, UBE2C, TRIP13) was also calculated. As shown previously (Mulligan et al ASH 2005 abstract) the prevalence of the different TC classes in relapse patients was very similar to newly diagnosed patients (Chi-square p=0.25). In the bortezomib cohort, patients with t(11;14) had the worst survival when compared to patients with t(4;14), maf translocation and hyperdiploidy who had similar survival (survival of t(11;14) versus others, log-rank p=0.036). In contrast, among newly diagnosed patients (UAMS cohort) those with t(4;14) and maf translocations had the worst prognosis, whereas t(11;14) and hyperdiploid patients had the best prognosis (log-rank p=0.02). This interesting contrast in the prognosis within these main genetic subtypes of myeloma at diagnosis and relapse suggest that at relapse, the survival of these patients may be modulated by secondary events or possibly the treatment received. Indeed, a greater proportion of relapsed t(11;14) have a high PI (Chi-square p = 0.04) than newly diagnosed t(11;14). In contrast, there is no difference between the PI of newly diagnosed and relapsed hyperdiploid MM (TC D1, p=0.19). Our observations suggest that although the composition of the various genetic subtypes at diagnosis and relapse appear similar, the prognosis of some subtypes (especially t(11;14)) can be markedly different at relapse when modulated by secondary events (e.g. increase proliferation) whereas others (such as hyperdiploid MM) appear to behave similarly at diagnosis and relapse. Our observation also has important therapeutic implications as it suggests relapsed t(11;14) should be treated aggressively and drug combinations or addition of new agents may be more appropriate. Conversely, bortezomib seemed to be an effective therapy in relapse t(4;14) patients, improving the prognosis of these patients to that of genetic subgroups with good prognosis. Our study highlights the fact that different treatment strategies may be needed for newly diagnosed and relapsed patients even within same genetic subtypes.

Abstract Purpose: The initiation, progression, and maintenance of pancreatic ductal adenocarcinoma (PDAC) results from the interplay of genetic and epigenetic events. While the genetic alterations of PDAC have been well characterized, epigenetic pathways regulating PDAC remain, for the most part, elusive. The goal of this study was to identify novel epigenetic regulators contributing to the biology of PDAC. Experimental Design: In vivo pooled shRNA screens targeting 118 epigenetic proteins were performed in two orthotopic PDAC xenograft models. Candidate genes were characterized in 19 human PDAC cell lines, heterotopic xenograft tumor models, and a genetically engineered mouse (GEM) model of PDAC. Gene expression, IHC, and immunoprecipitation experiments were performed to analyze the pathways by which candidate genes contribute to PDAC. Results: In vivo shRNA screens identified BRD2 and BRD3, members of the BET family of chromatin adaptors, as key regulators of PDAC tumor growth. Pharmacologic inhibition of BET bromodomains enhanced survival in a PDAC GEM model and inhibited growth of human-derived xenograft tumors. BET proteins contribute to PDAC cell growth through direct interaction with members of the GLI family of transcription factors and modulating their activity. Within cancer cells, BET bromodomain inhibition results in downregulation of SHH, a key mediator of the tumor microenvironment and canonical activator of GLI. Consistent with this, inhibition of BET bromodomains decreases cancer-associated fibroblast content of tumors in both GEM and xenograft tumor models. Conclusions: Therapeutic inhibition of BET proteins offers a novel mechanism to target both the neoplastic and stromal components of PDAC. Clin Cancer Res; 22(16); 4259–70. ©2016 AACR.

AbstractCentrosome amplification is common in myeloma and may be involved in disease pathogenesis. We have previously derived a gene expression–based centrosome index (CI) that correlated with centrosome amplification and was an independent prognostic factor in a small cohort of heterogeneously treated patients. In this study, we validated the prognostic significance of the CI in 2 large cohorts of patients entered into clinical trials and showed that a high CI is a powerful independent prognostic factor in both newly diagnosed and relapsed patients, whether treated by intensive therapy (total therapy II) or novel agents (bortezomib). Tumors with high CI overexpressed genes coding for proteins involved in cell cycle, proliferation, DNA damage, and G2-M checkpoints, and associated with the centrosome and kinetochore/ microtubules. In particular, aurora kinases are significantly overexpressed in patients with high CI, with concordant increase in protein expression. Human myeloma cell lines with higher CI are more responsive to treatment with a novel aurora kinase inhibitor. Aurora kinase may represent novel therapeutic targets in these patients with very poor prognosis.

Abstract Hyperdiploid multiple myeloma (H-MM) is the most common form of myeloma. In this gene expression profiling study, we show that H-MM is defined by a protein biosynthesis signature that is primarily driven by a gene dosage mechanism as a result of trisomic chromosomes. Within H-MM, four independently validated patient clusters overexpressing nonoverlapping sets of genes that form cognate pathways/networks that have potential biological importance in multiple myeloma were identified. One prominent cluster, cluster 1, is characterized by high expression of cancer testis antigen and proliferation-associated genes. Tumors from these patients were more proliferative than tumors in other clusters (median plasma cell labeling index, 3.8; P < 0.05). Another cluster, cluster 3, is characterized by genes involved in tumor necrosis factor/nuclear factor-κB signaling and antiapoptosis. These patients have better response to bortezomib as compared with patients within other clusters (70% versus 29%; P = 0.02). Furthermore, for a group of patients generally thought to have better prognosis, a cluster of patients with short survival (cluster 1; median survival, 27 months) could be identified. This analysis illustrates the heterogeneity within H-MM and the importance of defining specific cytogenetic prognostic factors. Furthermore, the signatures that defined these clusters may provide a basis for tailoring treatment to individual patients. [Cancer Res 2007;67(7):2982–9]

Abstract The aims of this study were to assess the feasibility of prospective pharmacogenomics research in multicenter international clinical trials of bortezomib in multiple myeloma and to develop predictive classifiers of response and survival with bortezomib. Patients with relapsed myeloma enrolled in phase 2 and phase 3 clinical trials of bortezomib and consented to genomic analyses of pretreatment tumor samples. Bone marrow aspirates were subject to a negative-selection procedure to enrich for tumor cells, and these samples were used for gene expression profiling using DNA microarrays. Data quality and correlations with trial outcomes were assessed by multiple groups. Gene expression in this dataset was consistent with data published from a single-center study of newly diagnosed multiple myeloma. Response and survival classifiers were developed and shown to be significantly associated with outcome via testing on independent data. The survival classifier improved on the risk stratification provided by the International Staging System. Predictive models and biologic correlates of response show some specificity for bortezomib rather than dexamethasone. Informative gene expression data and genomic classifiers that predict clinical outcome can be derived from prospective clinical trials of new anticancer agents.