It is well known that between 1950 and the early part of this century, deaths due to cardiovascular insult dropped precipitously while cancer death rates were virtually unaltered. How can we change these dismal statistics?
According to Anna Lokshin, Ph.D., associate professor at the University of Pittsburgh Cancer Institute, “Cancer biomarkers serve many uses ranging from early detection to differential diagnosis to therapeutic monitoring. So because of its lethality in the later stages of the disease, we feel that it is critical to develop new and more effective screening tools.”
Given that the lifetime risk of ovarian cancer is 1.8%, an effective screening strategy must have a sensitivity of at least 80% and a specificity of 99.6% in the early stages of the disease. Dr. Lokshin and her colleagues have designed a serum multimarker panel using a group of well-known biomarkers, which for ovarian cancer yielded a sensitivity of 90% and a specificity of 98%. However, to be effective, higher sensitivity and specificity is required, so there is a need for biomarkers able to recognize preclinical disease at an earlier stage.
“This means we need to examine additional biomarkers, combine different classes of biomarkers, for example, proteins and nucleic acids, or look at biomarkers in other bodily fluids,” Dr. Lokshin concluded. Indeed, for a number of cancers, including ovarian, pancreatic, lung, and breast, marker levels in the urine of healthy individuals compared with that of cancer patients proved to be more accurate in terms of both sensitivity and specificity than in serum.
These studies, while tantalizing, leave open a number of questions for future investigations. This includes a need to verify the performance of expanded sets of serum biomarkers of these cancers while further optimizing the biomarker panels through combinations of serum and urine biomarkers.
Another strategy would be to develop recombinant antibodies that are optimized for urine biomarkers using selective screening of phage-display antibody libraries. Yet another approach would be to examine combinations of urine proteins with DNA isolated from urine or from microRNA.
An alternative task is to initiate prospective collection of matching urine/serum samples in symptomatic patients. Specifically included would be ovarian cancer patients with a pelvic mass, breast cancer patients who are positive on mammogram studies, lung cancers diagnosed through CT scan, and pancreatic cancer diagnosed by MRI. All these materials would be preserved for future validation.
But, perhaps the most challenging need is to convince the principle investigators of large-scale cancer screening trials to collect urine samples from their participants, given the fact that urine has been ignored by cancer researchers over the years as a source of diagnostic information.
Network Analysis of Cancer Mutations
Ali Torkamani, Ph.D., of the Scripps Institute of Genomic Medicine, discussed recent studies on somatic mutations in cancer cells. He stressed that the analysis of the frequency of specific mutations among different tumors has identified a number of mutated genes that contribute to tumor initiation and progression. “Network-based analyses, in which we consider tumor profiles as a system rather than as discrete events, may provide increased power in detecting driver mutations and predictive signatures.”
Dr. Torkamani described this approach in cancer biomarker discovery, both for somatic mutation analysis and the identification of predictive signatures. The group’s protocol applies co-expression, gene ontology, literature, and interaction searches to various cancers. From these studies they derived modules indicating the functional relationships of the mutated genes making up the networks.
“Through this analysis, we identified Wnt/TGF-beta cross-talk, Wnt/VEGF signaling, and MAPK/focal adhesion kinase pathways as targets of rare driver mutations in breast cancer, colorectal cancer, and glioblastoma, respectively,” Dr. Torkamani asserted. “It is our belief that these mutations contribute to a refined shaping or ‘tuning’ of these pathways in such a way as to result in the inhibition of their tumor-suppressive signaling arms, and thereby, conserve or enhance tumor-promoting processes.”
Previous efforts to detect rare driver mutations have focused on known pathways or known direct interactions between mutated genes, resulting in descriptions of tumorigenic processes only in general terms, lacking specificity with respect to the role of these individual mutations in the tumorigenic process.
However, Dr. Torkamani stated that the network reconstruction and gene co-expression module-based approach was based on identifying a larger number of mutated genes than expected by chance. This unbiased approach does not rely on prior knowledge of the biological relationships between genes, but rather attempts to reconstruct sets of coordinately acting genes in order to define, de novo, biological processes affected by cancer mutations.
While Scripps’ approach to identifying cancer biomarkers does not offer immediate clinical applications, it does demonstrate how network-based approaches can be incorporated into a genomic medicine strategy directed toward the understanding of tumor development. These findings can serve a basis for new therapeutic and diagnostic tools.