Terry Hermiston, Ph.D.
Vice President, US Biologics Research Site Head, US Innovation Center Bayer Healthcare, USA
ColoAd1 – An oncolytic adenovirus derived by directed evolution
Attempts at developing oncolytic viruses have been primarily based on rational design. However, this approach has been met with limited success. An alternative approach employs directed evolution as a means of producing highly selective and potent anticancer viruses. In this method, viruses are grown under conditions that enrich and maximize viral diversity and then passaged under conditions meant to mimic those encountered in the human cancer microenvironment. Using the “Directed Evolution” methodology, we have generated ColoAd1, a novel chimeric oncolytic adenovirus. In vitro, this virus demonstrated a >2 log increase in both potency and selectivity when compared to ONYX-015 on colon cancer cells. These results were further supported by in vivo and ex vivo studies. Importantly, these results have validated this methodology as a new general approach for deriving clinically-relevant, highly potent anti-cancer virotherapies. This virus is currently in clinical trials as a novel treatment for cancer.
Sanjeeva Srivastava, Ph.D.
Assistant Professor, Proteomics Lab, IIT-Bombay, India
Identification of Potential Early Diagnostic Biomarkers for Gliomas and Various Infectious Diseases using Proteomic Technologies
The spectacular advancements achieved in the field of proteomics research during the last decade have propelled the growth of proteomics for clinical research. Recently, comprehensive proteomic analyses of different biological samples such as serum or plasma, tissue, CSF, urine, saliva etc. have attracted considerable attention for the identification of protein biomarkers as early detection surrogates for diseases (Ray et al., 2011). Biomarkers are biomolecules that can be used for early disease detection, differentiation between closely related diseases with similar clinical manifestations as well as aid in scrutinizing disease progression. Our research group is performing in-depth analysis of alteration in human proteome in different types of brain tumors and various pathogenic infections to obtain mechanistic insight about the disease pathogenesis and host immune responses, and identification of surrogate protein markers for these fatal human diseases.
Applying 2D-DIGE in combination with MALDI-TOF/TOF MS we have analyzed the serum and tissue proteome profiles of glioblastoma multiforme; the most common and lethal adult malignant brain tumor (Gollapalli et al., 2012) (Figure 1). Results obtained were validated by employing different immunoassay-based approaches. In serum proteomic analysis we have identified some interesting proteins like haptoglobin, ceruloplasmin, vitamin-D binding protein etc. Moreover, proteomic analysis of different grades (grade-I to IV) of gliomas and normal brain tissue was performed and differential expressions of quite a few proteins such as SIRT2, GFAP, SOD, CDC42 have been identified, which have significant correlation with the tumor growth. While proteomic analysis of cerebrospinal fluid from low grade (grade I & II) vs. high grade (grade III & IV) gliomas revealed modulation of CSF levels of apolipoprotein E, dickkopf related protein 3, vitamin D binding protein and albumin in high grade gliomas. The prospective candidates identified in our studies provide a mechanistic insight of glioma pathogenesis and identification of potential biomarkers. We are also studying the role of JAK/STAT interactome and therapeutic potential of STAT3 inhibitors in gliomas using proteomics approach. Several candidates of the JAK/STAT interactome were identified with altered expression and a significant correlation was observed between STAT3 and PDK1 transcript expression level.
We have also investigated the changes in human serum proteome in different infectious diseases including falciparum and vivax malaria (Ray et al., 2012a; Ray et al., 2012b), dengue (Ray et al., 2012c) and leptospirosis (Srivastava et al., 2012). Although, quite a few serum proteins were found to be commonly altered in different infectious diseases and might be a consequence of inflammation mediated acute phase response signaling, uniquely modulated candidates were identified in each pathogenic infection indicating the some inimitable responses. Further, a panel of identified proteins consists of six candidates; serum amyloid A, hemopexin, apolipoprotein E, haptoglobin, retinol-binding protein and apolipoprotein A-I was used to build statistical sample class prediction models employing PLSDA and other classification methods to predict the clinical phenotypic classes and 91.37% overall prediction accuracy was achieved (Figure 2). ROC curve analysis was carried out to evaluate the individual performance of classifier proteins. The excellent discrimination among the different disease groups on the basis of differentially expressed proteins demonstrates the potential diagnostic implications of this analytical approach.
Keywords: Diagnostic biomarkers, Gliomas, Infectious Diseases, Proteomics, Serum proteome
Acknowledgments: This disease biomarker discovery research was supported by Department of Biotechnology, India grant (No. BT/PR14359/MED/30/916/2010), Board of Research in Nuclear Sciences (BRNS) DAE young scientist award (2009/20/37/4/BRNS) and a startup grant 09IRCC007 from the IIT Bombay. The active support from Advanced Center for Treatment Research and Education in Cancer (ACTREC), Tata Memorial Hospital (TMH), and Seth GS Medical College and KEM Hospital Mumbai, India in clinical sample collection process is gratefully acknowledged.
References :
- Ray S, Reddy PJ, Jain R, Gollapalli K. Moiyadi A, Srivastava S. Proteomic technologies for the identification of disease biomarkers in serum: advances and challenges ahead. Proteomics 11: 2139-61, 2011.
- Gollapalli K, Ray S, Srivastava R, Renu D, Singh P, Dhali S, Dikshit JB, Srikanth R, Moiyadi A, Srivastava S. Investigation of serum proteome alterations in human glioblastoma multiforme. Proteomics 12(14): 2378-90, 2012.
- Ray S, Renu D, Srivastava R, Gollapalli K, Taur S, Jhaveri T, Dhali S, Chennareddy S, Potla A, Dikshit JB, Srikanth R, Gogtay N, Thatte U, Patankar S, Srivastava S. Proteomic investigation of falciparum and vivax malaria for identification of surrogate protein markers. PLoS One 7(8): e41751, 2012a.
- Ray S, Kamath KS, Srivastava R, Raghu D, Gollapalli K, Jain R, Gupta SV, Ray S, Taur S, Dhali S, Gogtay N, Thatte U, Srikanth R, Patankar S, Srivastava S. Serum proteome analysis of vivax malaria: An insight into the disease pathogenesis and host immune response. J Proteomics 75(10): 3063-80, 2012b.
- Srivastava R, Ray S, Vaibhav V, Gollapalli K, Jhaveri T, Taur S, Dhali S, Gogtay N, Thatte U, Srikanth R, Srivastava S. Serum profiling of leptospirosis patients to investigate proteomic alterations. J Proteomics 76: 56-68, 2012.
- Ray S, Srivastava R, Tripathi K, Vaibhav V, Srivastava S. Serum proteome changes in dengue virus-infected patients from a dengue-endemic area of India: towards new molecular targets? OMICS 16(10): 527-36, 2012c.
* Correspondence: Dr. Sanjeeva Srivastava, Department of Biosciences and Bioengineering, IIT Bombay, Mumbai 400 076, India: E-mail: sanjeeva@iitb.ac.in; Phone: +91-22-2576-7779, Fax: +91-22-2572-3480
![Figure 1 (a) Differentially expressed proteins in GBM identified using 2D-DIGE. Representative 2D- DIGE image to compare serum proteome of HC and GBM patients. GBM and HC samples were labeled with Cy3 and Cy5 respectively, while the protein reference pool (internal standard) was labeled with Cy2. Graphical and 3D fluorescence intensity representations of few selected statistically significant (p < 0.05) differentially expressed proteins in GBM patients identified in biological variation analysis (BVA) using DeCyder 2D software. (b) Involvement of different essential physiological pathways with differentially expressed proteins in GBM. Members of multiple essential physiological processes including cell growth and proliferation, vitamin D metabolism, lipoprotein metabolism and transport, oxidative stress regulation, complement cascade, and platelet activation found to be modulated in the GBM patients (Gollapalli et al., Proteomics 2012).](http://www.amritabioquest.org/conference/2013/wp-content/uploads/sites/2/2015/02/sanjeeva.jpg)
![Figure 2 (a) Western blot analysis of haptoglobin (HP), serum amyloid A (SAA), and clusterin (CLU) from serum samples of healthy control (HC) [n = 12], falciparum malaria (FM) [n = 12], vivax malaria (VM) [n = 12], Leptospirosis (Lep) [n = 6], dengue fever [DF] [n = 6] and non infectious disease control (NIDC:GBM) [n = 12]. Representative blots of the target proteins are depicted along with their respective relative abundance volumes (volume X 104). All the data are represented as mean ± SE. (b) Discrimination of malaria from dengue, leptospirosis and GBM using PLS-DA analysis. PLS-DA scores Plot for FM (blue spheres, n = 8), VM (green spheres, n = 8), DF (red spheres, n = 6), Lep (grey spheres, n = 6) and GBM (brown spheres, n = 8) samples based on 6 differentially expressed proteins (serum amyloid A, hemopexin, apolipoprotein E, haptoglobin, retinol-binding protein and apolipoprotein A-I) identified using DIGE. The axes of the plot indicate PLSDA latent variables t0-t2.](http://www.amritabioquest.org/conference/2013/wp-content/uploads/sites/2/2015/02/sanjeeva-2.jpg)
Karmeshu, Ph.D.
Dean & Professor, School of Computer & Systems Sciences & School of Computational & Integrative Sciences, Jawaharlal Nehru University, India.
Interspike Interval Distribution of Neuronal Model with distributed delay: Emergence of unimodal, bimodal and Power law
The study of interspike interval distribution of spiking neurons is a key issue in the field of computational neuroscience. A wide range of spiking patterns display unimodal, bimodal ISI patterns including power law behavior. A challenging problem is to understand the biophysical mechanism which can generate the empirically observed patterns. A neuronal model with distributed delay (NMDD) is proposed and is formulated as an integro-stochastic differential equation which corresponds to a non-markovian process. The widely studied IF and LIF models become special cases of this model. The NMDD brings out some interesting features when excitatory rates are close to inhibitory rates rendering the drift close to zero. It is interesting that NMDD model with gamma type memory kernel can also account for bimodal ISI pattern. The mean delay of the memory kernels plays a significant role in bringing out the transition from unimodal to bimodal ISI distribution. It is interesting to note that when a collection of neurons group together and fire together, the ISI distribution exhibits power law.
Gokul Das, Ph.D.
Co-Director, Breast Disease Site Research Group, Roswell Park Cancer Institute, Buffalo, NY
Probing Estrogen Receptor−Tumor Suppressor p53 Interaction in Cancer: From Basic Research to Clinical Trial
Tumor suppressor p53 and estrogen receptor have opposite roles in the onset and progression of breast cancer. p53 responds to a variety of cellular of stresses by restricting the proliferation and survival of abnormal cells. Estrogen receptor plays an important role in normal mammary gland development and the preservation of adult mammary gland function; however, when deregulated it becomes abnormally pro-proliferative and greatly contributes to breast tumorigenesis. The biological actions of estrogens are mediated by two genetically distinct estrogen receptors (ERs): ER alpha and ER beta. In addition to its expression in several ER alpha-positive breast cancers and normal mammary cells, ER beta is usually present in ER alpha-negative cancers including triple-negative breast cancer. In spite of genetically being wild type, why p53 is functionally debilitated in breast cancer has remained unclear. Our recent finding that ER alpha binds directly to p53 and inhibits its function has provided a novel mechanism for inactivating genetically wild type p53 in human cancer. Using a combination of proliferation and apoptosis assays, RNAi technology, quantitative chromatin immunoprecipitation (qChIP), and quantitative real-time PCR (qRT-PCR), in situ proximity ligation assay (PLA), and protein expression analysis in patient tissue micro array (TMA), we have demonstrated binding of ER alpha to p53 and have delineated the domains on both the proteins necessary for the interaction. Importantly, ionizing radiation inhibits the ER-p53 interaction in vivo both in human cancer cells and human breast tumor xenografts in mice. In addition, antiestrogenstamoxifen and faslodex/fulvestrant (ICI 182780) disrupt the ER-p53 interaction and counteract the repressive effect of ER alpha on p53, whereas 17β-estradiol (E2) enhances the interaction. Intriguingly, E2 has diametrically opposite effects on corepressor recruitment to a p53-target gene promoter versus a prototypic ERE-containing promoter. Thus, we have uncovered a novel mechanism by which estrogen could be providing a strong proliferative advantage to cells by dual mechanisms: enhancing expression of ERE-containing pro-proliferative genes while at the same time inhibiting transcription of p53-dependent anti-proliferative genes. Consistently, ER alpha enhances cell cycle progression and inhibits apoptosis of breast cancer cells. Correlating with these observations, our retrospective clinical study shows that presence of wild type p53 in ER-positive breast tumors is associated with better response to tamoxifen therapy. These data suggest ER alpha-p53 interaction could be one of the mechanisms underlying resistance to tamoxifen therapy, a major clinical challenge encountered in breast cancer patients. We have launched a prospective clinical trial to analyze ER-p53 interaction in breast cancer patient tumors at Roswell Park Cancer Institute. Our more recent finding that ER beta has opposite functions depending on the mutational status of p53 in breast cancer cells is significant in understanding the hard-to-treat triple-negative breast cancer and in developing novel therapeutic strategies against it. Our integrated approach to analyze ER-p53 interaction at the basic, translational, and clinical research levels has major implications in the diagnosis, prognosis, and treatment of breast cancer.