Gillian Murphy, Ph.D.
Professor, Department of Oncology, University of Cambridge, UK
A novel strategy for targeting metalloproteinases in cancer
Epithelial tumours evolve in a multi-step manner, involving both inflammatory and mesenchymal cells. Although intrinsic factors drive malignant progression, the influence of the micro-environment of neoplastic cells is a major feature of tumorigenesis. Extracellular proteinases, notably the metalloproteinases, are key players in the regulation of this cellular environment, acting as major effectors of both cell-cell and cell-extracellular matrix (ECM) interactions. They are involved in modifying ECM integrity, growth factor availability and the function of cell surface signalling systems, with consequent effects on cellular differentiation, proliferation and apoptosis.This has made metalloproteinases important targets for therapeutic interventions in cancer and small molecule inhibitors focussed on chelation of the active site zinc and binding within the immediate active site pocket were developed. These were not successful in early clinical trials due to the relative lack of specificity and precise knowledge of the target proteinase(s) in specific cancers. We can now appreciate that it is essential that we understand the relative roles of the different enzymes (of which there are over 60) in terms of their pro and anti tumour activity and their precise sites of expression The next generations of metalloproteinase inhibitors need the added specificity that might be gained from an understanding of the structure of individual active sites and the role of extra catalytic domains in substrate binding and other aspects of their biology. We have prepared scFv antibodies to the extra catalytic domains of two membrane metalloproteinases, MMP-14 and ADAM17, that play key roles in the tumour microenvironment. Our rationale and experiences with these agents will be presented in more detail.
Srisairam Achuthan, Ph.D.
Senior Scientific Programmer, Research Informatics Division, Department of Information Sciences, City of Hope, CA, USA
Applying Machine learning for Automated Identification of Patient Cohorts
Srisairam Achuthan, Mike Chang, Ajay Shah, Joyce Niland
Patient cohorts for a clinical study are typically identified based on specific selection criteria. In most cases considerable time and effort are spent in finding the most relevant criteria that could potentially lead to a successful study. For complex diseases, this process can be more difficult and error prone since relevant features may not be easily identifiable. Additionally, the information captured in clinical notes is in non-coded text format. Our goal is to discover patterns within the coded and non-coded fields and thereby reveal complex relationships between clinical characteristics across different patients that would be difficult to accomplish manually. Towards this, we have applied machine learning techniques such as artificial neural networks and decision trees to determine patients sharing similar characteristics from available medical records. For this proof of concept study, we used coded and non-coded (i.e., clinical notes) patient data from a clinical database. Coded clinical information such as diagnoses, labs, medications and demographics recorded within the database were pooled together with non-coded information from clinical notes including, smoking status, life style (active / inactive) status derived from clinical notes. The non-coded textual information was identified and interpreted using a Natural Language Processing (NLP) tool I2E from Linguamatics.
Sunilkumar Sukumaran, Ayyappan Nair, Madhuri Subbiah, Gunja Gupta, Lakshmi Rajakrishna, Pradeep Savanoor Raghavendra, Subbulakshmi Karthikeyan, Salini Krishnan Unni and Ganesh Sambasivam
Genotoxicity is defined as DNA damage that leads to gene mutations which can become tumorigenic. Genotoxicity testing is important to ensure drug safety and is mandatory prior to Phase I/II clinical trials of new drugs. The results from genetic toxicology studies help to identify hazardous drugs and environmental genotoxins. Currently, among others there are four tests recommended by regulatory authorities (Ames test-bacterial, chromosome aberrations; in vitro gene mutation-eukaryotic cells and in vivo test). These assays are laborious, time consuming, require large quantities of test compounds and limited by throughput challenges. The site and mechanism of genotoxicity are not revealed by these assays and data obtained from bacterial tests might not translate the same in mammals. To address these we have developed a novel, versatile, human cell based, high throughput, reporter based genotoxicity screen (Anthem’s Genotox screen). This screen is performed on genetically engineered human cell lines that express 3 reporter genes under transcriptional control of ‘early DNA damage sensors’ (p53, p21 and GADD153). These genes are involved in DNA repair, cell cycle arrest and/or apoptosis. p21 and GADD are also known to be induced in a p53 independent manner. p53 blocks G1/S transition of cell cycle while the p53 independent DNA damage block G2/M transition. Identification of the mechanism of genotoxicity helps in rational drug designing. Additionally, the platform can be used to screen other potential genotoxins from cosmetics, food and environment. Initial validation studies of the Genotox screen was performed with over 60 compounds chosen from a variety of chemical classes. The genotoxic potential of metabolites was tested using rat liver S9 fractions. The results demonstrated a sensitivity of 86.7–92.3% and a specificity of 70–78.6% when compared with currently available in vitro genotoxicity assays. This Genotox screen would prove to be an invaluable human cell based tool to weed out potential genotoxins in various industries.