Cancer biotherapy resource

`Cancer Biotherapy\u27 - as opposed to cancer chemotherapy- is the use of macromolecular, biological agents instead of organic chemicals or drugs to treat cancer. Biotherapy is a treatment modality that blocks the growth of cancer cells by interfering with specific, targeted molecules needed for carcinogenesis and tumor growth instead of simply interfering with rapidly dividing cells as in chemotherapy1. In light to the much higher selectivity of biological agents than chemical agents for cancer cells over normal cells, there is a much less toxic side effect in biotherapy as compared to chemotherapy. As solid tumor cancer continues to be analyzed as a chronic condition, there is an absolute need for long-term treatment with minimal side effects. The International Society for Biological Therapy of Cancer, being the only available information database for cancer biotherapy, lacks some crucial information about various cancer biotherapy regimens and the information presented seemed unorganized and unsystematic making it difficult to search for results. With the increasing rate of cancer deaths across the world and biotherapy studies, it is acutely necessary to have a comprehensive curetted cancer biotherapy database. The database accessible to cancer patients and also should be a sounding board for scientific ideas by cancer researchers. The database/web server has information about main families of cancer biotherapy regimens to date, namely, 1.) Protein Kinase Inhibitors, 2.) Ras Pathway Inhibitors, 3.) Cell-Cycle Active Agents, 4.) MAbs (monoclonal antibodies), 5.) ADEPT (Antibody-Directed Enzyme Pro-Drug Therapy), 6.) Cytokines (interferons, interleukins, etc.), 7.) Anti-Angiogenesis Agents, 8.) Cancer Vaccines (peptides, proteins, DNA), 9.) Cell-based Immunotherapeutics, 10.) Gene Therapy, 11.) Hematopoietic Growth Factors, and 12.) Retinoids 13.) CAAT. For each biotherapy regimen, we will extract the following attributes in populating the database: (a.) Cancer type, (b.) Gene/s and gene product/s involved, (c.) Gene sequence (GenBank ID), (d.) Organs affected (e.) Chemo treatment, (f.) Reference papers, (g.) Clinical phase/stage, (h.) Survival rate (chemo. Vs. biother.), (i.) Clinical test center locations, (j.) Cost, (k.) Patient blog, (l.) Researcher blog, (m.) Future work. The database accessible to public through a website and had FAQs for making it understandable to the laymen and discussion page for researchers to express their views and ideas. In addition to information about the biotherapy regimens, the website is linked to other biologically significant databases like structural proteomics, metabolomics, glycomics, and lipidomics web servers. Also, the websites presented the news in the field of biotherapy and other links which are relevant from biotherapy point of view. The database attributes would be regularly updated for novel attributes as discoveries would be made

MEASURING SINGLE CELL RESPONSES TO LAPATINIB IN A HETEROGENEOUS POPULATION

Cancer is notonedisease butasaga of diseases and is the outcome of disturbed homeostasis in the normal cells due to the deregulation of its genetic makeup. With advent of technologies thatallowdetailed molecular characterizationoftumors, targeted therapies have emerged as a more promising and specific mode of treatment. However, a major challenge with targeted therapy is the acquired resistance in the cancer cells to these therapies, quite often very rapidly in the course of a few months. One of the major targets in cancer has been the EGFR/ErbB2 network in breast and other cancer types. Prior work from our lab and others have shown alterations in the cellular network whereby compensatory upregulation of alternative pathways such as glucose uptake and metabolism can lead to acquired resistance to anti- EGFR/ErbB2 therapy in breast cancer to Lapatinib [1]. However, one the of the very important unanswered questions at the cellular and molecular level is the mechanismsthatleadstoselection of cells that are resistant to Lapatinib whereby there exists two possibilities: 1. Cells are intrinsically resistant and are less likely to respond to the drug and get selected for2.Cellsswitch response phenotype over time leading to increased metabolism and resistance. In this proposal I will develop a predictive computational model that can be used to dynamically model the response of cellstolapatinibanddetermine what underlying response mechanisms can lead to adaptive resistance cell populations based on single cell dynamics. Models to predict the internal environment of the cell by the phenotype and vice versa will be a very novel approach to understand the adaptive resistance mechanism and to overcome it. Here, I propose to utilize an Agent-based cellular automata model to represent the cellularmicroenvironment, which can track the cellular response to drugs by tracking the metabolite or signaling levels which can then be experimentally constrained and tested using live cell FRET reporter constructs