As the complex biology of cancer continues to be revealed in laboratories around the world, kinases have emerged as key players in the evolution and progression of malignancies.
Therapeutic strategies based on inhibition of kinase activity have begun to emerge. Some have already reached clinical practice, such as gefitinib (Iressa), the epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor approved for treatment of lung cancer and under investigation as therapy for other malignancies.
"It will become important to think of targeted agents as cytostatic rather than cytotoxic," said Dr. Ingo Mellinghoff, an assistant professor of molecular pharmacology at the University of California, Los Angeles.
Many emerging clinical strategies employ targeted therapies aimed at disrupting the biological chatter that conveys the essential instruction for tumor growth. In that context, the communication surrounding a malignancy often takes precedence over anatomic features.
"Eventually, a combination of targeted agents might be the way to go," Mellinghoff said.
TWO-PRONGED APPROACH
Mellinghoff and his UCLA colleagues have embarked on a two-pronged investigational approach to defining and establishing the clinical role of molecular imaging as it applies to kinases in cancer. One avenue of investigation focuses on developing a better understanding of the role of existing PET tracers such as FDG and FLT.
"We want to understand what these tracers really read, besides glucose and nucleoside metabolism," Mellinghoff said. "They might actually home in on kinases that regulate tumor cell metabolism and tracer retention."
The second area of research relates to development of new tracers for detecting and monitoring kinase activity. The goal is to develop novel probes that have the appropriate radar to identify kinase-driven cancers and monitor inhibition in the tumor itself, rather than relying on surrogate markers of drug activity. Whether or not treatment failures are due to incomplete kinase inhibition or kinase-independent tumor regrowth will be critical to choosing second-line agents, Mellinghoff said.
Ongoing clinical trials at UCLA include patients with glioblastoma or lung cancer who are being treated with regimens that include gefitinib and erlotinib (Tarceva), a small-molecule inhibitor of EGFR. Molecular imaging studies seek to relate the uptake of FDG and FLT to kinase activity.
"It has become clear over the past decade or so that FDG uptake is often reduced shortly after chemotherapy, and that might be prognostically useful to identify patients who are likely to respond to treatment," Mellinghoff said. "We read a metabolic response that might or might not correlate with the long-term response to chemotherapy. Understanding how kinase targets contribute to tracer metabolism will refine the use of PET imaging in oncology."
PROBE DEVELOPMENT
Development of imaging probes specific for kinase activity remains in an early stage of investigation. Mellinghoff's group is one of several working toward similar goals because of what they perceive as the immense potential of such probes. The current approach to probe development involves labeling kinase inhibitors and then following their distribution and metabolism in tumor-bearing mice. Preliminary experimental results suggest that at least certain kinase inhibitors accumulate in tumors. If imaged at the appropriate time, that type of information could prove invaluable for planning and monitoring treatment. Much of the work has been limited to in vitro studies; in vivo studies have yet to produce much useful information. Other approaches to imaging kinase activities in vivo have yet to bear scientific fruit, in large part because key aspects of the underlying science have not been worked out. Exploration of new imaging approaches will press forward because of the pot!entially large clinical payoff.
"We're in a very early stage of study, and the chemistry is nowhere near prime time," Mellinghoff said. "But we and others are pursuing this very actively because it appears that there is a subset of kinase-dependent tumors, and those are patients who respond to the targeted drugs. In lung cancer, for example, about 15% of patients respond to EGFR kinase inhibition."
In addition to lung cancer and glioblastoma, several other types of cancer clearly involve kinase activation in at least some patients. Breast cancer offers one of the best known examples, as about 30% of patients carry upregulation of the HER2 gene. HER2-positive tumors are identified by immunohistochemistry or fluorescence in situ hybridization assay, which quantifies HER2 and presumably reflects activation. However, the story is obviously more complicated, Mellinghoff said, because only a subset of HER2-positive patients respond to trastuzumab (Herceptin), an anti-HER2 antibody.
Other cancers with documented kinase activation include chronic myelogenous leukemia, gastrointestinal stromal tumors, and acute myelogenous leukemia. Additionally, about 60% of melanomas appear to have activating kinase mutations in the BRAF oncogene, an enormous frequency if the initial findings pan out. Whether the mutations are clinically meaningful and have therapeutic implications remains to be determined. Inhibitors that target BRAF have been developed and have demonstrated some promise in preclincal investigations, but clinical trials have yet to begin.
"The imaging community can play a huge role in the optimal deployment of kinase inhibitor therapy for cancer," Mellinghoff said. "That includes building a better understanding of the currently available tracers, such as FDG. There is in vitro evidence that several transporters and enzymes involved in glucose metabolism are modulated by oncogenic kinases. Whether such regulation explains the rapid FDG response of gastrointestinal stromal tumors treated with the kinase inhibitor imatinib (Gleevec) remains to be proven. Sorting though these types of issues will be very important to the advancement of clinical care."
Study Assesses Lung CT-Based AI Models for Predicting Interstitial Lung Abnormality
September 6th 2024A machine-learning-based model demonstrated an 87 percent area under the curve and a 90 percent specificity rate for predicting interstitial lung abnormality on CT scans, according to new research.