The Speicher laboratory is pursuing eight major projects as well as additional collaborative projects. Most projects utilize proteomics and mass spectrometry as major tools, whereas other projects take conventional structure-function approaches to understanding specific proteins and macromolecular complexes in disease-associated processes. These projects are briefly summarized below, and in greater detail in the following sections.
The first project uses proteomics to identify and characterize cancer biomarkers associated with ovarian cancer. This includes use of both xenograft mouse models and in vitro, short-term organ cultures of ovarian cancer tissue removed at time of surgery.
The second project takes a similar approach to identification of colorectal cancer biomarkers.
The third project focuses on identification of biomarkers that can distinguish aggressive and indolent prostate cancer. This study primarily involves proteome analysis of formalin-fixed, paraffin-embedded specimens with known clinical outcome.
The fourth project uses state-of-the-art proteomics approaches to identify biomarkers that distinguish ectopic pregnancy from normal intrauterine pregnancy and non-viable intrauterine pregnancy.
The fifth project involves a systems biology approach to study the role of autophagy and melanoma progression and resistance to conventional therapies.
A sixth project involves the use of proteomics to characterize innate immunity and its role in resistance to HIV infection.
The seventh project involves the structure-function analysis of spectrin and examines its role in hereditary hemolytic anemias. In addition, molecular modeling coupled with high resolution, high mass accuracy mass spectrometry of chemically crosslinked proteins are being used to develop medium resolution structures of spectrin and larger membrane-associated complexes.
The eighth project is a structure-function analysis of Peroxyredoxin-6 (PRDX6), a protein that plays a unique role in protecting lung tissue from damage due to excessive oxidative stress. This enzyme has both peroxidase and phospholipase activities.
The goal of this project is to discover and validate novel protein biomarkers of human ovarian cancer that can improve clinical management of this disease. Ovarian cancer is the leading cause of death from gynecological cancers in the United States. More than 24,000 women are diagnosed with ovarian cancer every year, and approximately 15,000 die of this disease. Ovarian cancers are biologically very heterogeneous with common epithelial tumors consisting of serous, endometrioid, mucinous, and clear cell sub-types. Serous sub-type tumors account for about half of the epithelial tumors and generally occur in women between 40 and 60 years of age. They are usually highly aggressive and account for the greatest number of ovarian cancer-related deaths.
The five-year survival for invasive epithelial ovarian cancer is about 90% when the disease is confined to the ovaries, but is only about 30% when the cancer has spread to other organs. Even high-grade serous tumors do well if diagnosis occurs when the tumor is confined to the ovary. However, about 75% of ovarian cancers are not diagnosed until after the cancer has spread, primarily because early-stage tumors are generally asymptomatic.
There is no effective molecular test for ovarian cancer at present. So far, the best plasma biomarker that detects ovarian cancer prior to symptoms is CA125, but CA125 suffers from a large number of false positives and false negatives, which prevents it from being used as a routine screen for ovarian cancer. The goal of this project is to identify new, better, molecular biomarkers for ovarian cancer that would form the basis for a minimally invasive blood test for either early diagnosis or clinical management of the disease after initial diagnosis.
Two complementary approaches are being used to identify novel ovarian cancer biomarkers. The first method uses a xenograft model, where human ovarian cancer cell lines are subcutaneously implanted in immune-compromised mice. After tumors grow, the blood is collected and blood biomarkers are identified using high-dimensional fractionation of the plasma or serum and high performance mass spectrometry to identify human proteins. An advantage of this system is that any protein identified as having a human rather than a mouse sequence was unambiguously shed by the human tumor into the mouse blood. One hypothesis is that such proteins specifically shed by the tumor into the blood will be more specific than more general host responses to the tumor, and the second hypothesis is that proteins that are low abundance or not detectable in normal human serum or plasma are expected to be the most specific biomarkers. A second method for discovering novel biomarkers is to place minced human ovarian tumor tissue obtained shortly after surgery and incubate it for a short time in organ culture, followed by in-depth proteome analysis of the shed proteins. These two discovery methods have already identified more than 100 high-priority candidate biomarkers.
We are now focusing on validating some of the high-priority ovarian cancer candidate biomarkers from the above discovery studies. The major strategy we are using for laboratory-scale validation in modest-sized patient cohorts is to use label-free multiple reaction monitoring (MRM), a targeted mass spectrometry approach that is quite different from the mass spectrometry approaches used for discovery. In this method, targeted mass spectrometry (MS) assays are set up to quantitate selected peptides for proteins of interest. Due to the low abundance of the proteins and the associated peptides in serum or plasma patient samples, it is necessary to deplete high-abundance proteins followed by further fractionation prior to the MRM-MS assays using a triple quadropole mass spectrometer. In initial proof-of-principle experiments, several ovarian cancer biomarkers have been shown to be significantly higher in ovarian cancer patients compared with normal controls or patients with benign ovarian conditions.
Colorectal cancer is a major international health problem that results in more than 100,000 deaths worldwide every year. In the United States, it is the second highest cause of cancer deaths, with approximately 140,000 new cases reported annually, and mortality approaches about 60,000 cases each year. About 90% of these colorectal cancers arise from adenomatous polyps, and surgical resection is an effective treatment for these precancerous lesions as well as localized disease.
An effective screening method for colorectal cancer, which can identify and remove pre-cancerous polyps as well as localized small tumors with good prognosis, is colonoscopy. Widespread use of this screening method could greatly reduce colon cancer mortality. However, although this procedure is effective, it is expensive, uncomfortable, has moderate risk of complications and is not readily available to all patients. As a result, only about 30% of the at-risk population has ever been screened using colonoscopy, and the percentage of patients who are screened on a regular basis consistent with existing clinical guidelines is even lower.
Our goal is to develop a first-tier screen using novel plasma protein biomarkers of colon cancer to pre-screen the general at-risk population to identify those patients who would benefit from colonoscopy. That is, patients who are very likely to have pre-cancerous or cancerous lesions. By focusing expensive colonoscopy resources on a subpopulation of patients with a high probability of precancerous or early stage cancer lesions, we could greatly reduce the financial burden associated with colonoscopy and increase compliance with this screening technique.
The approach we have taken to identify biomarkers in this case is to use a number of early-stage colon cancer cell lines that were established at The Wistar Institute by our colleague Dr. Dorothee Herlyn. These cell lines were implanted in immunocompromised mice, and after tumors had grown in situ, blood was collected and fractionated using a four-dimensional, in-depth plasma proteome fractionation method that we developed. These fractions were analyzed by LC-MS/MS and the large datasets were analyzed using a data analysis pipeline that we developed. This resulted in identification of hundreds of high-priority colon cancer biomarkers.
We are currently validating approximately 100 of these candidate biomarkers in small patient cohorts using label-free MRM MS, which is described in greater detail in Project 1.
Prostate cancer is the second-leading cause of cancer death among men in the United States. The disease is currently diagnosed using the PSA blood test, usually in conjunction with a digital rectal exam. But, these screening methods have resulted in over-diagnosis and over-treatment of the disease, which results in extra healthcare costs and serious side effects from the surgery and therapeutic treatments that do not benefit a substantial portion of the patients. A critical challenge for better clinical management of prostate cancer after initial diagnosis is that some tumors are very slow growing (indolent disease) and such tumors do not represent a major health risk. However, some prostate cancer tumors are aggressive and life-threatening, and these patients will benefit from surgery and other therapeutic interventions. Using existing diagnostic methods, some prostate cancer tumors can be competently assigned as being indolent or aggressive. But a large percentage of cases are ambiguous or in the gray zone and the proper approach for handling these patients is not currently well define. Hence, better minimally invasive biomarkers are needed to better classify indolent and aggressive prostate cancer.
We have discovered approximately 100 novel candidate prostate cancer biomarkers using two complementary methods; that is, a mouse xenograft model (see projects 1 and 2), and quantitative comparisons of formalin-fixed, paraffin-embedded (FFPE) surgical tissue from patients where 5-year clinical outcome was known. The surgical specimens allowed us to compare indolent disease with aggressive disease, where initial diagnosis was ambiguous. Label-free LC-MS/MS analysis and indolent and aggressive tumors identified approximately 50 high priority candidate biomarkers.
Our plan is to set up quantitative label-free MRM-MS assays for approximately 100 high-priority candidate biomarkers, which will include the 50 proteins from the FFPE specimen comparisons and the 50 best candidates from the xenograft mouse model. We will initially screen large numbers of patient surgical specimens where the aggressiveness of the tumor has been defined based upon patient outcome and other clinical criteria. We will then evaluate the best biomarkers form the tissue analysis in plasma of patients with aggressive and indolent prostate cancer. We expect these studies will identify a panel of prostate cancer biomarkers that can be used to analyze surgical tissue using immunochemistry and related techniques. A subset of these biomarkers will be useful in initial screening of patient serum or plasma samples prior to surgery or biopsy.