Development of an Implantable Device to Determine Cancer Cell Response to Chemotherapy in Real Time

John Basile (UMB, School of Dentistry) and Elizabeth Smela (UMCP, School of Engineering)

Conventional cancer treatments are limited by lack of efficacy, toxicity toward normal cells and tissues, and drug resistance of targeted cells. Emerging technologies attempting to overcome these limitations represent a revolution in cancer research often called "personalized medicine," or the acquisition of a profile of a particular patient's malignancy in an attempt to optimize therapy. The broad objective of our project is to develop an implantable device to isolate and contain cancer cells for the purpose of measuring their viability and response to chemotherapeutic agents. Such information would hasten drug development and advance personalized medicine by monitoring the response of a patient's tumor cells to a specific treatment regimen.

A Non-Invasive & Cost-Effective Approach for Lung Cancer Screening

Feng Jiang (UMB, School of Medicine/Pathology) and Jiuzhou "John" Song (UMCP, College of Agriculture and Natural Resources)

Lung cancer is the No. 1 cancer killer in both men and women in the USA. The early detection of lung cancer by computerized tomography (CT) followed by appropriate treatments can greatly reduce the death rate. However, CT increases the number of uncertain lung nodules in asymptomatic individuals, whereas only a small fraction of lung nodules are lung tumors. The diagnostic dilemma of the uncertain nodules incidentally found on screening CT has created an urgent need for reliable biomarkers capable of identifying lung cancer. The objective of our project is to develop biomarkers that can be used in sputum for accurately and noninvasively diagnosing lung cancer among the nodules found by CT. Future use of the biomarkers will reduce the death rates from lung cancer.

Use of Visualization Assisted Clustering to Detect Diverse Patterns of Disease Progression in Parkinson's disease

Lisa Shulman (UMB, School of Medicine/Neurology) and Amitabh Varshney (UMCP, College of Computer, Mathematical and Natural Sciences)

Parkinson's disease (PD) is a neurologic disorder that impacts approximately one million people in the United States and up to 10 million worldwide. PD is characterized by motor symptoms such as rigidity, tremors, and gait impairment, along with a wide range of non-motor symptoms including cognitive decline, depression and fatigue. Despite these well-known defining features, there is great diversity between patients in the clinical presentation of PD; thus the identification of PD subtypes is crucial for research into underlying disease mechanisms, as patients with similar clinical presentations are likely to share pathologic and genetic features. While early attempts to define PD subtypes have used traditional data analysis approaches, relying on small datasets of recently-diagnosed patients, our study will use cutting-edge visual data mining and clustering to identify novel PD subtypes in a unique, longitudinal, 2000-patient dataset, based on patterns of progression of disease severity and selected symptom clusters.

Nanotopographic Diagnostic Panel for Breast Cancer Metastasis

Stuart Martin (UMB, School of Medicine/Oncology), and Wolfgang Losert and John Fourkas (UMCP, College of Computer, Mathematical & Natural Sciences)

This project brings together investigators with expertise in physics (Losert), chemistry (Fourkas) and tumor cell biology (Martin) to examine the differences between how normal cells and tumor cells respond to surface texture. Leveraging unique capabilities to manufacture a range of different surface textures and advanced imaging and analysis of cell movement, the project will define cell behaviors that are associated with a higher probability of cancer metastasis. By testing these principles on tumor cells isolated from breast cancer patients, the project will also develop methods for rapidly testing the risk that patient tumor cells will metastasize, which is the principal cause of cancer patient death.