The Center for Cancer Research: Finding Opportunities, Facing Challenges

n 2001, the NCI intramural Divisions of Basic Sciences and Clinical Sciences were merged to form the Center for Cancer Research (CCR). This reengineering was fueled by the rapid pace of biotechnology advancement and the growing need for multidisciplinary approaches to the complex scientific problems NCI researchers are increasingly tackling. CCR’s mission is to reduce the burden of cancer through exploration, discovery, and translation. This integrated structure is intended to promote rapid bench-to-bedside translation of promising cancer therapies. In turn, results from the clinic are informing the work of laboratory investigators to further refine therapies. In CCR, we value high-quality, investigator-initiated research, but we are also challenging the customary ways of thinking and organizing, fostering cross-disciplinary and multi-institutional research to solve complex problems in cancer research.

Within the last year, research initiated and developed at the Center culminated in a number of notable advances, including a vaccine against cervical cancer, a promising new immunotherapy against melanoma and renal carcinoma, a U.S. Food and Drug Administration (FDA)–approved drug to treat oral mucositis, a protective agent to prevent hair loss in cancer patients undergoing radiotherapy, and a cutting-edge cancer-patient molecular profiling technology. These advances are having an impact on the NCI Challenge Goal of eliminating the suffering and death due to cancer by 2015 and improving the quality of lives of cancer survivors. At present, a number of additional therapies are working their way through clinical trials to reach the patients.

Going forward, we are leveraging our strengths to respond to emerging needs and opportunities as well as quickly establishing programs in high-priority areas. We are pursuing an interdisciplinary and multidisciplinary “team-science” approach to address the complexity of cancer research, exemplified by the formation of several Centers of Excellence. One example is the Center of Excellence in Immunology (CEI), created to foster discovery, development, and delivery of novel immunologic approaches to prevent and treat cancer and cancer-associated viral diseases. CEI’s objectives include defining emerging opportunities, overseeing programs in specific areas in immunology and virology, and fine-tuning immunotherapeutic approaches in cancer treatment. The CEI sponsored a highly successful national conference in immunotherapy September 22–23, 2005, on the NIH campus.

We also are leveraging our significant strengths in the fields of immunology and carcinogenesis to address one of the major causes of cancer: chronic inflammation. In 2005, we launched the Inflammation and Cancer Initiative, which includes four key areas of investigative opportunity: cancer-prone chronic inflammatory diseases, innate and adaptive immunity, stem cells, and inflammation-related molecular targets.

Another guiding principle is the redeployment of existing resources into new and promising areas where CCR can make a distinct contribution. An excellent example of this is the realignment of the Laboratory of Experimental and Computational Biology to support NCI’s nanotechnology effort, creating an Intramural Cancer Nanotechnology Program (ICNP). CCR investigators seized the opportunity in NCI’s new National Advanced Technologies Initiative for Cancer, redirecting their scientific expertise to develop a research portfolio to complement the NCI Alliance for Nanotechnology in Cancer—especially the Nanotechnology Standards Laboratory, and molecular targets/molecular oncology efforts.

While our challenges are many, the staff of CCR will continue to seek innovative solutions to the complex problems of cancer by leveraging our internal strengths, identifying new opportunities, and forging fruitful collaborations.

Robert H. Wiltrout, PhD
Director

Keratinocyte Growth Factor Decreases Oral Mucositis Resulting from Intensive Therapy for Hematologic Malignancies

Spielberger R, Stiff P, Bensinger W, Gentile T, Weisdorf D, Kewalramani T, Shea T, Yanovich S, Hansen K, Noga S, McCarty J, LeMaistre CF, Sung EC, Blazar BR, Elhardt D, Chen M-G, and Emmanouilides C. Palifermin for oral mucositis after intensive therapy for hematologic cancers. N Engl J Med 351: 2590–8, 2004.

eratinocyte growth factor (KGF) was identified and cloned in the Laboratory of Cellular and Molecular Biology, Division of Cancer Etiology, NCI, in the late 1980s. It was purified from fibroblast culture fluid based on its ability to stimulate DNA synthesis in a keratinocyte cell line and was subsequently shown to be active on a variety of epithelial cells, but not other cell types. KGF is a member of the fibroblast growth factor (FGF) family (FGF-7) and acts through a subset of FGF receptor isoforms (FGFR2b) that are expressed almost exclusively by epithelial cells.

KGF functions as a mesenchymally derived, paracrine mediator of epithelial homeostasis, with remarkable cytoprotective effects. The upregulation of KGF after epithelial injury suggests that it has an important role in tissue regeneration. In addition to stimulating repair, other studies demonstrated that the timely administration of recombinant KGF could prevent or reduce damage from a variety of toxic agents, including chemotherapy and radiation. In 1992, KGF technology was licensed to Amgen, Inc., for the development of therapeutic products. Among several potential applications, the decision was made to initially focus on the reduction of damage to the oral cavity that results from high-dose chemo/radiotherapy.

Oral mucositis is a major debilitating side effect of intensive cancer treatments. Severe oral mucositis is associated with pain, difficulty eating and speaking, and gastrointestinal bleeding. It has a negative effect on patients’ quality of life and often results in a delay or reduction in cancer therapy. Until now, there has been no effective way to prevent or limit this condition. Encouraging results were obtained with KGF in a series of clinical trials, leading to a pivotal phase 3 trial reported in the New England Journal of Medicine (referenced above).

Patients in this study received autologous peripheral blood progenitor cell transplants for hematologic malignancies. Prior to the transplants, they were treated with a standard combination of fractionated total body irradiation for 3 or 4 days, followed by VP-16 and cyclophosphamide. Patients received either the vehicle control or KGF (60 micrograms/kg/day) in three daily intravenous injections both before the start of radiation and after chemotherapy. Clinical staff monitored the appearance of the patients’ mouths on a daily basis. Severe mucositis was characterized by widespread erythema and ulceration in the oral cavity, and the ability to eat either only a liquid diet or nothing at all. Additional information was gathered from hospital records and from patients’ diaries about their health.

KGF markedly reduced the duration of severe oral mucositis: the placebo group averaged 9.0 days, whereas the KGF cohort averaged only 3.0 days (P < 0.001). The incidence of severe oral mucositis also was significantly lower in the KGF group, 63% versus 98% for the placebo. This effect was due to a decline in the most debilitating form of mucositis, associated with an inability to eat, that corresponded to 62% of the placebo population but only 20% of the KGF group. Consistent with the decline in mucositis, there was a substantial reduction in the amount of analgesic medicine required by patients treated with KGF (P < 0.001), and a decrease in the use of total parenteral nutrition to supplement oral intake (P < 0.001). These favorable results were corroborated by the patients’ reports of mouth/throat soreness and functional status (e.g., ability to drink, eat, talk, and sleep). Furthermore, patients treated with KGF were less likely to experience episodes of febrile neutropenia, reinforcing the idea that a decrease in damage to the mucosa would reduce infection. Side effects of KGF were mild to moderate in severity, transient, and attributable to its pharmacologic action on skin and oral epithelium (e.g., rash, pruritis, erythema, and taste alteration).

Based on these results, the U.S. Food and Drug Administration (FDA) approved KGF1 to reduce severe oral mucositis in patients with hematologic malignancies who were receiving chemotherapy and radiation prior to autologous bone marrow/peripheral blood progenitor cell transplants. Approximately 10,000 adults in the United States undergo transplantation each year. Additional clinical trials have been initiated to test the safety and efficacy of KGF in the solid tumor setting, particularly head/neck, lung, and colorectal carcinomas. Positive results in these populations could lead to a substantial increase in the number of patients treated with KGF. By decreasing the toxicity of therapeutic agents, KGF might also foster the development of more potent and effective cancer treatments.

1 Palifermin is the generic name for KGF in the clinic, and KepivanceTM is the trade name of the product from Amgen that went on the market in January 2005.

Note: as a co-inventor on patents pertaining to KGF, the author acknowledges that he has a financial interest in its commercial development.

Jeffrey S. Rubin, MD, PhD
Senior Principal Investigator
Laboratory of Cellular and Molecular Biology
NCI-Bethesda, Bldg. 37/Rm. 2042
Tel: 301-496-4265
Fax: 301-496-8479
rubinj@mail.nih.gov

Metastasis Susceptibility

Park YG, Zhao X, Lesueur F, Lowy DR, Lancaster M, Pharoah P, Qian X, and Hunter KW. Sipa1 is a candidate for underlying the metastasis efficiency modifier locus Mtes1. Nat Genet 37: 1055–62, 2005.

etastasis, the final stage of cancer progression and the source of most cancer-related mortality, is usually thought to be the result of oncogenic mutation and somatic evolution of tumor cells, either within the primary tumor mass or at distant sites. This hypothesis, while consistent with much of the data, does not entirely explain all experimental and clinical observations. Additional variables that contribute to metastatic progression therefore need to be identified and investigated to develop a more comprehensive model of the terminal stages of cancer progression.

One variable that may contribute to the complexity of this process is genetic background. Germline polymorphism has long been associated with human cancer risk. Much of human molecular epidemiology is based on the premise that certain constitutional polymorphisms are associated with different susceptibilities. Numerous examples of this type of cancer-associated variation also exist in experimental organisms, and hundreds of modifier or quantitative trait loci have been mapped in the mouse and rat that influence a wide variety of pathological conditions common in human populations, including disorders such as cancer, diabetes, and hypertension. These data suggest that many, probably most, phenotypes have a significant genetic contribution, even traits as complex as tumor dissemination.

Evidence supporting the role of germline polymorphism in metastasis has come from the recent identification of a candidate for the metastasis efficiency modifier locus, Mtes1. Previous genetic studies demonstrated the presence of polymorphic genes in the mouse genome that suppressed the ability of a highly aggressive transgene-induced mammary tumor to metastasize to the lung. Genetic mapping studies revealed that one of these metastasis-suppressing genes, designated Mtes1, was located on proximal mouse chromosome 1, in a region orthologous to human chromosome 11q13. Genomic analysis of the region identified several interesting polymorphic genes, and subsequent in vitro and in vivo experiments identified a polymorphism in the negative regulator of Rap1 GTPase, Sipa1, which significantly influenced its protein-protein interactions and enzymatic function. In tumor cell lines, the rate of metastases was increased by Sipa1 overexpression and decreased by knocking down its expression in spontaneous metastasis assays in mice (Figure 1). The potential role of SIPA1 in human metastasis was investigated by examination of publicly available gene-expression profiles, which revealed higher expression of SIPA1 in metastatic prostate cancer compared with localized tumor, in agreement with the mouse data. In toto, the mouse results strongly support the concept of naturally occurring genetic variants playing an important role in the final, lethal stages of cancer, and the human data implicate SIPA1 in the metastatic process in human cancer.

Figure 1. Modulation of Sipa1 levels significantly influences the metastatic capacity of a highly metastatic mouse mammary tumor cell line. Wild-type cells (center), cells overexpressing (right; FVB Sipa1), or cells silenced (left; siRNA) for the Sipa1 gene were implanted into the flanks of mice and allowed to develop into tumors. The metastatic capacity was then determined by counting lung surface nodules. Representative images of the lungs from each group are shown above the scatterplots. The black bars represent the median value for each experimental group.

The existence of these polymorphic metastasis-susceptibility genes may have a significant impact on clinical prognosis. At present, evidence of lymph node metastasis is one of the most powerful prognostics for disease course in breast cancer. However, about 30% of women who are node negative at diagnosis develop metastatic disease, whereas 30% of women who are node positive are disease free a decade after local therapy. As a result, many women who do not benefit from aggressive systemic treatment may be receiving adjuvant therapy, with its associated side effects and morbidity. Conversely, there may be individuals who would benefit from systemic adjuvant therapy but are not treated due to the apparent localized nature of the tumor. Identification and screening of allelic variants of metastasis-susceptibility genes may therefore significantly improve patient stratification based on inherited risk assessment instead of lymph node status. This may ultimately enable more accurate tailoring of treatment, and thereby reduce the overall morbidity and mortality of cancer.

Kent W. Hunter, PhD
Principal Investigator
Laboratory of Population Genetics
NCI-Bethesda, Bldg. 41/Rm. D702
Tel: 301-435-8957
Fax: 301-435-8963
hunterk@mail.nih.gov

Differential Functions of the Ubiquitin-associated Domains of Cbl and Cbl-b Proteins: “Cblings,” But Not Twins

Davies GC, Ettenberg SA, Coats AO, Mussante M, Ravichandran S, Collins J, Nau MM, and Lipkowitz S. Cbl-b interacts with ubiquitinated proteins; differential functions of the UBA domains of c-Cbl and Cbl-b. Oncogene 23, 7104–15, 2004.

bl proteins are a highly conserved family of proteins found in metazoans. Upon activation of a variety of tyrosine kinases, the Cbl proteins are tyrosine phosphorylated, and they associate with other proteins via SH2- and SH3-mediated interactions. These diverse interactions regulate signaling through numerous pathways. Our laboratory has focused on the downregulation of epidermal growth factor receptor (EGFR) as a model in which to study Cbl protein function in epithelial cells. Mechanistic data from many labs, including our own, have demonstrated that Cbl proteins mediate ubiquitination of the activated EGFR and enhance its endocytosis and degradation. Thus, they are negative regulators of EGFR signaling. Parallel studies on other receptor and non-receptor tyrosine kinases have demonstrated that Cbl proteins similarly regulate a wide range of signaling pathways. Together, these data indicate that the Cbl proteins are important regulators of intracellular signaling and, consequently, of cell function and development.

There are three mammalian Cbl proteins: Cbl, Cbl-b, and Cbl-c (also known as Cbl-3). The Cbl proteins are characterized by several highly conserved domains. They contain an N-terminal tyrosine kinase binding (TKB) domain that mediates interactions with tyrosine phosphorylated proteins, a C3HC4 RING finger (which is the catalytic domain for ubiquitin ligase [E3] activity), and proline-rich regions (which mediate interactions with SH3-containing proteins). Cbl and Cbl-b share additional areas of homology in the C-terminal region, including more extensive proline-rich regions and a ubiquitin-associated (UBA) domain. This study characterized biochemical differences in ubiquitin binding of the Cbl and Cbl-b proteins in order to better understand the unique function of each.

While studying EGFR downregulation by Cbl-b, we observed that high molecular weight ubiquitinated proteins constitutively coimmunoprecipitated with transfected and endogenous Cbl-b, but not Cbl. The binding site for these ubiquitinated proteins was mapped to the UBA domain of Cbl-b (UBAb). A glutathione S-transferase (GST) fusion protein containing the UBAb interacted with ubiquitinated proteins from cell lysates and purified polyubiquitin chains in vitro. The UBAb had a much greater affinity for polyubiquitin chains than for monoubiquitin. The UBA domain of Cbl-b is necessary and sufficient for the interaction of Cbl-b with ubiquitin chains and ubiquitinated proteins. Interestingly, the homologous UBA domain of Cbl (UBAc) did not bind to ubiquitin or ubiquitin chains and did not mediate association with high molecular weight ubiquitinated proteins in cells.

UBA domains are short domains consisting of three alpha helices that are found in a number of proteins associated with ubiquitin-mediated processes. UBA domains from a number of proteins bind to ubiquitin. Proteins containing UBA domains or structurally related ubiquitin-binding domains (i.e., CUE and UIM domains) have been shown to bind to ubiquitinated membrane proteins via these domains and to mediate ubiquitin-driven endocytosis. Because the Cbl proteins mediate ubiquitination and endocytosis of activated receptor tyrosine kinases, the interaction of Cbl-b with ubiquitinated proteins via its UBA domain was very intriguing. It is also somewhat surprising that the homologous UBA region of Cbl did not bind ubiquitin. This work is the first description of such a dramatic difference in the ubiquitin binding ability of two closely related UBA domains.

Receptor endocytosis in both yeast and mammalian cells is mediated, in part, by ubiquitination. In yeast, proteins containing UBA or other ubiquitin-binding domains have been shown to mediate the internalization of monoubiquitinated proteins and their trafficking to the vacuole. Thus, we investigated whether UBAb plays a role in EGFR downregulation. We did not find a significant difference in the downregulation of EGFR by wild-type Cbl-b or Cbl-b with the UBA deleted. Thus, UBAb does not seem necessary for EGFR trafficking. In contrast to the deletion of the UBA domain of Cbl-b, overexpression of UBAb, but not of UBAc, inhibited degradation of ubiquitinated EGFR as well as other proteins (i.e., Mdm-2 and Siah-1). This is most likely attributable to non-specific ubiquitin binding of isolated UBAb. This in vivo result is consistent with the differences in ubiquitin binding observed in vitro between UBAb and UBAc.

The difference in ubiquitin-binding of the UBA domains of Cbl-b and Cbl likely reflects distinct regulatory functions of the proteins and warrants further investigation. One possible function of the ubiquitin binding of UBAb would be to target Cbl-b to a specific protein or subcellular localization. Another possible function would be the regulation of ubiquitin-mediated protein degradation. One mechanism by which the UBA domains can regulate ubiquitin-mediated processes is by inhibition of chain elongation of nascent ubiquitin chains. Interestingly, Cbl proteins have been shown to monoubiquitinate activated EGFR at multiple sites and target it for lysosomal degradation. Other published data suggest that Cbl proteins can polyubiquitinate some substrates and target them for proteasomal degradation. Thus, it is possible that the UBA domain of Cbl-b can regulate the length of the ubiquitin chains added to substrates. Another mechanism proposed for the inhibition of proteasomal degradation by UBA domains is masking of the ubiquitin chains, thus preventing the ubiquitinated protein from binding to the proteasome. In our work, we have found that overexpression of UBAb (but not of UBAc) inhibits ubiquitin-mediated protein degradation of a variety of proteins. This is likely due to the masking of the ubiquitin molecules attached to these proteins by the overexpressed UBAb, which in turn, prevents the proper recognition and degradation of the ubiquitinated proteins by the proteasome or the lysosome.

Although further work is necessary to understand the physiologic function of UBAb, the differences in ubiquitin binding between the UBA domains of Cbl and Cbl-b provide clear evidence that these highly homologous proteins have distinct roles in epithelial cells.

Gareth C. Davies, PhD
Visiting Fellow

Stan Lipkowitz, MD, PhD
Principal Investigator
Laboratory of Cellular and Molecular Biology
NCI-Bethesda, Bldg. 37/Rm. 2050
Tel: 301-402-4276
Fax: 301-496-8479
lipkowis@mail.nih.gov

Genome-scale Profiling of Gene Expression in Hepatocellular Carcinoma: Classification and Survival Prediction

Lee J-S, Chu I-S, Heo J, Calvisi DF, Sun Z, Roskams T, Durnez A, Demetris AJ, and Thorgeirsson SS. Classification and prediction of survival in hepatocellular carcinoma by gene expression profiling. Hepatology 40: 667–76, 2004.

uch is known about the sequential cellular changes that precede the formation of hepatocellular carcinoma (HCC) and the etiologic agents (i.e., hepatitis B virus [HBV] and hepatitis C virus [HCV] infection and alcohol) responsible for the majority of HCC cases. Nevertheless, the molecular pathogenesis of HCC is not well understood. Although much progress has been made by using clinical information and pathological classification to provide information at diagnosis on survival and treatment options, many issues still remain unresolved. For example, a staging system that reliably separates patients with early and intermediate-to-advanced HCC into homogeneous groups with respect to prognosis does not exist. This is important because the natural course of early HCC is unknown and the progression of intermediate and advanced HCC are quite heterogeneous. Thus, improving the classification of HCC patients would at minimum improve the application of currently available treatment modalities and at best provide new treatment strategies.

While gene expression profiling technology has previously been applied to some specific aspects of HCC, we investigated the possibility that variations in gene expression of HCC at diagnosis would permit the identification of distinct subclasses of HCC patients with different prognoses. We applied three independent but complementary approaches for data analysis to uncover subclasses of HCC and the underlying biological differences between them. First, unsupervised classification methods based solely on gene expression patterns were applied. Hierarchical clustering of the data as well as multidimensional scaling (MDS) plot revealed two subclasses of HCC strongly associated with the length of patients’ survival (Figure 1). Second, we applied five independent prediction algorithms to determine whether gene expression patterns could be used to predict survival. HCC patients were randomly divided into two equal groups: a training set (n = 45) that was used to develop the HCC classifiers, and a validation set (n = 44) that was used to evaluate the test. Briefly, we started to identify the most differentially expressed genes between two clusters in the training set. The number of genes in the classifiers was optimized to minimize misclassification errors during the leave-one-out cross-validation of the tumors in the training set. When applied to the validation set, all five models successfully separated patients with poor survival (cluster/subclass A) from patients with longer survival (cluster/subclass B). These results demonstrated not only a strong association between gene expression patterns and the survival of patients but also a robust reproducibility of these gene expression-based predictors. Third, a univariate Cox regression model was used to identify individual genes whose expression is highly correlated with the length of survival. Application of survival-associated genes for subclass prediction was highly accurate, as illustrated by the fact that averaged gene expression indices from the selected 406 “survival genes” were sufficient to segregate the two subclasses even without the use of sophisticated prediction models.

Figure 1. Classification of hepatocellular carcinoma (HCC) based on genome-wide survey of gene expression. A) Hierarchical clustering of 91 HCC tumors. Genes with an expression ratio that had at least a 2-fold difference relative to reference in at least 9 tissues were selected for hierarchical analysis (4,187 gene features). B) Multidimensional scaling (MDS) plot of HCC tissues using 4,187 genes. MDS plotting was based on a matrix of Pearson correlation coefficients from the complete pair-wise comparison of all experiments. The MDS plot displays the position of each HCC tissue in three-dimensional Euclidean space with the distance between HCC tissues reflecting their approximate degree of correlation. Red and green balls represent HCC tissues in cluster A and cluster B, respectively. C) Kaplan-Meier plot of overall survival of HCC patients grouped on the basis of gene expression profiling.

Information obtained from knowledge-based annotation of the 406 survival genes provided insight into the underlying biological differences between the two subclasses of HCC. Out of several biological groups of the survival genes, the cell proliferation group was the best predictor of an unfavorable outcome of the disease. Expression of typical cell proliferation markers like PNCA, and cell cycle regulators such as CDK4, CCNB1, CCNA2, and CKS2 was greater in subclass A than in subclass B. Not surprisingly, many genes that are expressed more in subclass A are anti-apoptotic. Interestingly, higher expression of genes involved in ubiquitination and sumoylation was observed in subclass A. The ubiquitin system is often deregulated in cancers. In HCC, the degree of ubiquitination is highly correlated with cell proliferation and survival of patients and has also been proposed as a possible predictive marker for recurrence of human HCC. Also, enhanced activation of ubiquitin-dependent protein degradation may account for deregulation of cell cycle control and faster cell proliferation in the poor survival group (subclass A). This result is highly concordant with our recent study with mouse models (Lee JS et al. Comparative functional genomics to identify the best-fit mouse cancer models for studying human HCC. Nat Genet 36: 1306–11, 2004). We found that the ubiquitination index is much higher in mouse models that mimic poor human prognosis (subclass A).

The severity of HCC and the lack of good diagnostic markers and treatment strategies have rendered the disease a major challenge. Systematic analysis of gene expression patterns provides an insight into the biology and pathogenesis of HCC. Our results indicate that HCC prognosis can be predicted from the gene expression profiles of the primary tumors. Since the microarray-based measurement of gene expression reflects the abundance of expressed mRNA and proteins in the HCC, a limited set of quantitative RT-PCR and/or immunohistochemical staining assays may be sufficient to predict the prognosis of patients at the time of diagnosis.

Ju-Seog Lee, PhD
Research Fellow
leeju@mail.nih.gov

Snorri S. Thorgeirsson, MD, PhD
Principal Investigator
Laboratory of Experimental Carcinogenesis
NCI-Bethesda, Bldg. 37/Rm 4146
Tel: 301-496-5688
Fax: 301-496-0734
snorri_s_thorgeirsson@nih.gov

Scientific Advisory Committee

If you have scientific news of interest to the CCR research community, please contact one of the scientific advisors (below) responsible for your areas of research.

CCR Frontiers in Science—Staff

Center for Cancer Research

Robert H. Wiltrout, PhD, Director
Lee J. Helman, MD, Acting Scientific Director for Clinical Research
Frank M. Balis, MD, Clinical Director
L. Michelle Bennett, PhD, Associate Director for Science

Deputy Directors

Douglas R. Lowy, MD
Jeffrey N. Strathern, PhD
Lawrence E. Samelson, MD

Editorial Staff

Tracy Thompson, Editor-in-Chief
Sue Fox, BA/BSW, Senior Editor
Lamont Williams, Managing Editor *
Ave Cline, Editor
Terry Taylor, Copy Editor *
Emily R. Krebbs, MA, Copy Editor *
Amy Schneider, Copy Editor *
Rob Wald, Publications Manager *
Michael Fleishman, Graphic Artist *
Yvonne Bersofsky, Web Developer *

* Palladian Partners, Inc.