In search of protein biomarkers in ovarian cancer and Gaucher disease

In this thesis we used proteomic technologies to identify biomarkers for two very different types of disease, i.e., ovarian cancer and Gaucher disease. In order to obtain reliable and reproducible classification of different sample groups in large datasets, we first experimented with optimizing study protocols and data acquisition. In chapter 2 we evaluated different pre-processing and classification methods in search of methods best suited for discovering (multivariable) biomarkers from large proteomic datasets. Using these results we showed that surface-enhanced laser desorption/ionization-time-of-flight-mass spectrometry (SELDI-TOF-MS) can produce reliable classification results in serum and tissue samples of ovarian cancer patients in chapter 3. Chapter 4 gives an overview of the existing literature describing the research on biomarkers in Gaucher disease such as chitotriosidase and the search for novel markers. It also highlights research using novel techniques such as SELDI-TOF-MS for the discovery of CCL18.Label-free liquid chromatography mass spectrometry (LC-MSe) in combination with laser capture microdissection of macrophages from spleen of Gaucher disease patients resulted in the discovery of glycoprotein nonmetastatic melanoma protein B (gpNMB) as a new marker in Gaucher disease. The results of which are presented in chapter 5.A label-free LC-MS approach was also used on serum and tissue of patients with an ovarian tumor identifying proteins which were significantly differentially expressed between benign and malignant samples in chapter 6. In chapter 7 we used LC-MSe on ovarian tumor tissue of patients with a difference in disease-free survival in an attempt to find biomarkers predicting patient survival and chemoresistance

Establishment of a General Medicine Residency Training Program in Rural West Africa

Ghana, a developing country in West Africa, has major medical burdens in taking care of a large population with limited resources. Its three medical schools produce more than 200 graduates per year, but most emigrate to developed lands after training. Ghana is working to educate and retain locally trained physicians, but it is difficult to get them to work in rural settings where the need is greatest. This article details the establishment of a General Medicine residency at a 150-bed hospital in rural Ghana. Early training comprises 6 months each in Medicine, Surgery, OB/GYN, and Pediatrics; the hospital in Techiman also has a Surgery residency. House officers choose the program for more hands-on experience than they can get in larger centers. They perform many tasks, including surgery, sooner and more independently than do residents in developed countries. The training program includes a morning report, clinical teaching rounds, and rotations on in-patient wards and in the Emergency Department and clinics. Teaching focuses on history, physical examination, good communication, and proper follow-up, with rigorous training in the OR and some clinical research projects pertinent to Ghana. Trainees work hard and learn from one another, from a dedicated faculty, and by evaluating and treating very sick patients. Ghana’s rural residencies offer rigorous and attractive training, but it is too soon to tell whether this will help stem the “brain drain” of young physicians out of West Africa

The Medical System in Ghana

Ghana is a developing country in West Africa with a population of about 25 million. Medical illnesses in Ghana overlap with those in developed countries, but infection, trauma, and women’s health problems are much more prominent. Medical practice in rural Africa faces extremely limited resources, a multiplicity of languages (hundreds in Ghana), and presentation of severe illnesses at later stages than seen elsewhere. Despite these limitations, Ghana has established a relatively successful national medical insurance system, and the quality of medical practice is high, at least where it is available. Ghana also has a well-established and sophisticated administrative structure for the supervision of medical education and accreditation, but it has proven very difficult to extend medical training to rural areas, where health care facilities are particularly short of personnel. Physicians are sorely needed in rural areas, but there are few because of the working conditions and financial limitations. Hospital wards and clinics are crowded; time per patient is limited. This article details some of the differences between medical practice in Ghana and that in wealthier countries and how it functions with very limited resources. It also introduces the medical education and training system in Ghana. The following article describes an attempt to establish and maintain a residency training program in General Medicine in a rural area of Ghana

A critical assessment of SELDI-TOF-MS for biomarker discovery in serum and tissue of patients with an ovarian mass

<p>Abstract</p> <p>Background</p> <p>Less than 25% of patients with a pelvic mass who are presented to a gynecologist will eventually be diagnosed with epithelial ovarian cancer. Since there is no reliable test to differentiate between different ovarian tumors, accurate classification could facilitate adequate referral to a gynecological oncologist, improving survival. The goal of our study was to assess the potential value of a SELDI-TOF-MS based classifier for discriminating between patients with a pelvic mass.</p> <p>Methods</p> <p>Our study design included a well-defined patient population, stringent protocols and an independent validation cohort. We compared serum samples of 53 ovarian cancer patients, 18 patients with tumors of low malignant potential, and 57 patients with a benign ovarian tumor on different ProteinChip arrays. In addition, from a subset of 84 patients, tumor tissues were collected and microdissection was used to isolate a pure and homogenous cell population.</p> <p>Results</p> <p>Diagonal Linear Discriminant Analysis (DLDA) and Support Vector Machine (SVM) classification on serum samples comparing cancer versus benign tumors, yielded models with a classification accuracy of 71-81% (cross-validation), and 73-81% on the independent validation set. Cancer and benign tissues could be classified with 95-99% accuracy using cross-validation. Tumors of low malignant potential showed protein expression patterns different from both benign and cancer tissues. Remarkably, none of the peaks differentially expressed in serum samples were found to be differentially expressed in the tissue lysates of those same groups.</p> <p>Conclusion</p> <p>Although SELDI-TOF-MS can produce reliable classification results in serum samples of ovarian cancer patients, it will not be applicable in routine patient care. On the other hand, protein profiling of microdissected tumor tissue may lead to a better understanding of oncogenesis and could still be a source of new serum biomarkers leading to novel methods for differentiating between different histological subtypes.</p