K. Stephen Suh

Genomewide DNA methylation analysis reveals novel targets for drug development in mantle cell lymphoma

Mantle cell lymphoma (MCL) is a mostly incurable malignancy arising from naive B cells (NBCs) in the mantle zone of lymph nodes. We analyzed genomewide methylation in MCL patients with the HELP (HpaII tiny fragment Enrichment by Ligation-mediated PCR) assay and found significant aberrancy in promoter methylation patterns compared with normal NBCs. Using biologic and statistical criteria, we further identified 4 hypermethylated genes CDKN2B, MLF-1, PCDH8, and HOXD8 and 4 hypomethylated genes CD37, HDAC1, NOTCH1, and CDK5 when aberrant methylation was associated with inverse changes in mRNA levels. Immunohistochemical analysis of an independent cohort of MCL patient samples confirmed CD37 surface expression in 93% of patients, validating its selection as a target for MCL therapy. Treatment of MCL cell lines with a small modular immunopharmaceutical (CD37-SMIP) resulted in significant loss of viability in cell lines with intense surface CD37 expression. Treatment of MCL cell lines with the DNA methyltransferase inhibitor decitabine resulted in reversal of aberrant hypermethylation and synergized with the histone deacetylase inhibitor suberoylanilide hydroxamic acid in induction of the hypermethylated genes and anti-MCL cytotoxicity. Our data show prominent and aberrant promoter methylation in MCL and suggest that differentially methylated genes can be targeted for therapeutic benefit in MCL.

Ovarian cancer biomarkers for molecular biosensors and translational medicine

Multiple omics researches in the past two decades have identified over 200 potential biomarkers for ovarian cancer. Discoveries during the 1990s were more focused on clinicopathology-based biomarkers that were targeted to support diagnosis, but the emphasis has shifted to the identification of prognostic biomarkers in the past 10 years. The post-genomic era has opened the door for personalized cancer treatments and the trend of discovery is moving forward to identify more stratified biomarkers to accurately predict the progression of disease, as well as efficacy biomarkers to precisely determine drug response. To better meet future challenges, biomedical research needs the reformed and standardized infrastructure of tissue banks/biorepositories, with national and international initiatives. Of the hundreds of biomarker candidates for ovarian cancer, only a small number are actively being validated with clinical samples, owing to the lack of biomaterials that are linked with accurate clinical data. The purpose of this article is to present selected biomarkers from the past 20 years of ovarian cancer research, placing special emphasis on biomarkers that are strongly associated with positive or negative clinical outcomes. The article also presents a global view of all known potential biomarkers and mutations for ovarian cancer from NCI’s Cancer Gene Index developed by Sophic, and Sanger’s Catalogue of Somatic Mutations in Cancer database.

Clinical decision support systems for improving diagnostic accuracy and achieving precision medicine.

As research laboratories and clinics collaborate to achieve precision medicine, both communities are required to understand mandated electronic health/medical record (EHR/EMR) initiatives that will be fully implemented in all clinics in the United States by 2015. Stakeholders will need to evaluate current record keeping practices and optimize and standardize methodologies to capture nearly all information in digital format. Collaborative efforts from academic and industry sectors are crucial to achieving higher efficacy in patient care while minimizing costs. Currently existing digitized data and information are present in multiple formats and are largely unstructured. In the absence of a universally accepted management system, departments and institutions continue to generate silos of information. As a result, invaluable and newly discovered knowledge is difficult to access. To accelerate biomedical research and reduce healthcare costs, clinical and bioinformatics systems must employ common data elements to create structured annotation forms enabling laboratories and clinics to capture sharable data in real time. Conversion of these datasets to knowable information should be a routine institutionalized process. New scientific knowledge and clinical discoveries can be shared via integrated knowledge environments defined by flexible data models and extensive use of standards, ontologies, vocabularies, and thesauri. In the clinical setting, aggregated knowledge must be displayed in user-friendly formats so that physicians, non-technical laboratory personnel, nurses, data/research coordinators, and end-users can enter data, access information, and understand the output. The effort to connect astronomical numbers of data points, including ‘-omics’-based molecular data, individual genome sequences, experimental data, patient clinical phenotypes, and follow-up data is a monumental task. Roadblocks to this vision of integration and interoperability include ethical, legal, and logistical concerns. Ensuring data security and protection of patient rights while simultaneously facilitating standardization is paramount to maintaining public support. The capabilities of supercomputing need to be applied strategically. A standardized, methodological implementation must be applied to developed artificial intelligence systems with the ability to integrate data and information into clinically relevant knowledge. Ultimately, the integration of bioinformatics and clinical data in a clinical decision support system promises precision medicine and cost effective and personalized patient care.

Early Detection Biomarkers for Ovarian Cancer

Despite the widespread use of conventional and contemporary methods to detect ovarian cancer development, ovarian cancer remains a common and commonly fatal gynecological malignancy. The identification and validation of early detection biomarkers highly specific to ovarian cancer, which would permit development of minimally invasive screening methods for detecting early onset of the disease, are urgently needed. Current practices for early detection of ovarian cancer include transvaginal ultrasonography, biomarker analysis, or a combination of both. In this paper we review recent research on novel and robust biomarkers for early detection of ovarian cancer and provide specific details on their contributions to tumorigenesis. Promising biomarkers for early detection of ovarian cancer include KLK6/7, GSTT1, PRSS8, FOLR1, ALDH1, and miRNAs.

Molecular and clinical significance of fibroblast growth factor 2 (FGF2 /bFGF) in malignancies of solid and hematological cancers for personalized therapies

Fibroblast growth factor (FGF) signaling is essential for normal and cancer biology. Mammalian FGF family members participate in multiple signaling pathways by binding to heparan sulfate and FGF receptors (FGFR) with varying affinities. FGF2 is the prototype member of the FGF family and interacts with its receptor to mediate receptor dimerization, phosphorylation, and activation of signaling pathways, such as Ras-MAPK and PI3K pathways. Excessive mitogenic signaling through the FGF/FGFR axis may induce carcinogenic effects by promoting cancer progression and increasing the angiogenic potential, which can lead to metastatic tumor phenotypes. Dysregulated FGF/FGFR signaling is associated with aggressive cancer phenotypes, enhanced chemotherapy resistance and poor clinical outcomes. In vitro experimental settings have indicated that extracellular FGF2 affects proliferation, drug sensitivity, and apoptosis of cancer cells. Therapeutically targeting FGF2 and FGFR has been extensively assessed in multiple preclinical studies and numerous drugs and treatment options have been tested in clinical trials. Diagnostic assays are used to quantify FGF2, FGFRs, and downstream signaling molecules to better select a target patient population for higher efficacy of cancer therapies. This review focuses on the prognostic significance of FGF2 in cancer with emphasis on therapeutic intervention strategies for solid and hematological malignancies.

Tissue Banking, Bioinformatics, and Electronic Medical Records: The Front-End Requirements for Personalized Medicine

Personalized medicine promises patient-tailored treatments that enhance patient care and decrease overall treatment costs by focusing on genetics and “-omics” data obtained from patient biospecimens and records to guide therapy choices that generate good clinical outcomes. The approach relies on diagnostic and prognostic use of novel biomarkers discovered through combinations of tissue banking, bioinformatics, and electronic medical records (EMRs). The analytical power of bioinformatic platforms combined with patient clinical data from EMRs can reveal potential biomarkers and clinical phenotypes that allow researchers to develop experimental strategies using selected patient biospecimens stored in tissue banks. For cancer, high-quality biospecimens collected at diagnosis, first relapse, and various treatment stages provide crucial resources for study designs. To enlarge biospecimen collections, patient education regarding the value of specimen donation is vital. One approach for increasing consent is to offer publically available illustrations and game-like engagements demonstrating how wider sample availability facilitates development of novel therapies. The critical value of tissue bank samples, bioinformatics, and EMR in the early stages of the biomarker discovery process for personalized medicine is often overlooked. The data obtained also require cross-disciplinary collaborations to translate experimental results into clinical practice and diagnostic and prognostic use in personalized medicine.

CLIC4 is a tumor suppressor for cutaneous squamous cell cancer

Chloride intracellular channel (CLIC) 4 is a member of a redox-regulated, metamorphic multifunctional protein family, first characterized as intracellular chloride channels. Current knowledge indicates that CLICs participate in signaling, cytoskeleton integrity and differentiation functions of multiple tissues. In metabolically stressed skin keratinocytes, cytoplasmic CLIC4 is S-nitrosylated and translocates to the nucleus where it enhances transforming growth factor-β (TGF-β) signaling by protecting phospho-Smad 2 and 3 from dephosphorylation. CLIC4 expression is diminished in multiple human epithelial cancers, and the protein is excluded from the nucleus. We now show that CLIC4 expression is reduced in chemically induced mouse skin papillomas, mouse and human squamous carcinomas and squamous cancer cell lines, and the protein is excluded from the nucleus. The extent of reduction in CLIC4 coincides with progression of squamous tumors from benign to malignant. Inhibiting antioxidant defense in tumor cells increases S-nitrosylation and nuclear translocation of CLIC4. Adenoviral-mediated reconstitution of nuclear CLIC4 in squamous cancer cells enhances TGF-β-dependent transcriptional activity and inhibits growth. Adenoviral targeting of CLIC4 to the nucleus of tumor cells in orthografts inhibits tumor growth, whereas elevation of CLIC4 in transgenic epidermis reduces de novo chemically induced skin tumor formation. In parallel, overexpression of exogenous CLIC4 in squamous tumor orthografts suppresses tumor growth and enhances TGF-β signaling. These results indicate that CLIC4 suppresses the growth of squamous cancers, that reduced CLIC4 expression and nuclear residence detected in cancer cells is associated with the altered redox state of tumor cells and the absence of detectable nuclear CLIC4 in cancers contributes to TGF-β resistance and enhances tumor development.

Blocking the adhesion cascade at the premetastatic niche for prevention of breast cancer metastasis.

Shear-resistant adhesion and extravasation of disseminated cancer cells at the target organ is a crucial step in hematogenous metastasis. We found that the vascular adhesion molecule E-selectin preferentially promoted the shear-resistant adhesion and transendothelial migration of the estrogen receptor (ER)(-)/CD44(+) hormone-independent breast cancer cells, but not of the ER(+)/CD44(-/low) hormone-dependent breast cancer cells. Coincidentally, CD44(+) breast cancer cells were abundant in metastatic lung and brain lesions in ER(-) breast cancer, suggesting that E-selectin supports hematogenous metastasis of ER(-)/CD44(+) breast cancer. In an attempt to prevent hematogenous metastasis through the inhibition of a shear-resistant adhesion of CD44(+) cancer cells to E-selectin-expressing blood vessels on the premetastatic niche, an E-selectin targeted aptamer (ESTA) was developed. We demonstrated that a single intravenous injection of ESTA reduced metastases to a baseline level in both syngeneic and xenogeneic forced breast cancer metastasis models without relocating the site of metastasis. The effect of ESTA was absent in E-selectin knockout mice, suggesting that E-selectin is a molecular target of ESTA. Our data highlight the potential application of an E-selectin antagonist for the prevention of hematogenous metastasis of ER(-)/CD44(+) breast cancer.

Informatics-guided procurement of patient samples for biomarker discovery projects in cancer research

Modern cancer research for biomarker discovery program requires solving several tasks that are directly involved with patient sample procurement. One requirement is to construct a highly efficient workflow on the clinical side for the procurement to generate a consistent supply of high quality samples for research. This undertaking needs a network of interdepartmental collaborations and participations at various levels, including physical human interactions, information technology implementations and a bioinformatics tool that is highly effective and user-friendly to busy clinicians and researchers associated with the sample procurement. Collegial participation that is sequential but continual from one department to another demands dedicated bioinformatics software coordinating between the institutional clinic and the tissue repository facility. Participants in the process include admissions, consenting process, phlebotomy, surgery center and pathology. During this multiple step procedures, clinical data are collected for detailed analytical endpoints to supplement logistics of defining and validating the discovery of biomarkers.

Mantle cell lymphoma in the era of precision medicine-diagnosis, biomarkers and therapeutic agents

Despite advances in the development of clinical agents for treating Mantle Cell Lymphoma (MCL), treatment of MCL remains a challenge due to complexity and frequent relapse associated with MCL. The incorporation of conventional and novel diagnostic approaches such as genomic sequencing have helped improve understanding of the pathogenesis of MCL, and have led to development of specific agents targeting signaling pathways that have recently been shown to be involved in MCL. In this review, we first provide a general overview of MCL and then discuss about the role of biomarkers in the pathogenesis, diagnosis, prognosis, and treatment for MCL. We attempt to discuss major biomarkers for MCL and highlight published and ongoing clinical trials in an effort to evaluate the dominant signaling pathways as drugable targets for treating MCL so as to determine the potential combination of drugs for both untreated and relapse/refractory cases. Our analysis indicates that incorporation of biomarkers is crucial for patient stratification and improve diagnosis and predictability of disease outcome thus help us in designing future precision therapies. The evidence indicates that a combination of conventional chemotherapeutic agents and novel drugs designed to target specific dysregulated signaling pathways can provide the effective therapeutic options for both untreated and relapse/refractory MCL.