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Immunotheranostic MPI for Early Diagnosis of Tobacco-Related Lung Cancer

Institution: University of California, Berkeley
Investigator(s): Steven Conolly, Ph.D.
Award Cycle: 2017 (Cycle 26) Grant #: 26IP-0049S Award: $319,604
Subject Area: Early Diagnosis of Tobacco-Related Cancer
Award Type: High Impact Pilot Award
Abstracts

Initial Award Abstract

Immunotherapy has recently revolutionized cancer therapies, but the cancer immune response has not yet been exploited to improve early-stage cancer diagnosis, when survival rates are highest. Perhaps this is because today’s standard immune-cell imaging (the nuclear medicine Indium-111 WBC scan) subjects the patient to far too much radiation for a screening exam. A new imaging method, Magnetic Particle Imaging (MPI), has extraordinary potential to reveal the patient’s own immune cell homing to early-stage lung cancer, and it has zero radiation dose. My engineering group at UC Berkeley has designed and built all the MPI scanners in the USA. In “immunotheranostic MPI”, we will first pre-label immune cells with safe MPI magnetic tracers, then inject them into the body intravenously. We will then wait hours (or days) for the immune cells to home to areas of early lung cancer and then image the entire body in an MPI scanner. MPI can “see” pre-labeled cells with exquisite sensitivity. We recently demonstrated 200-cell sensitivity in the world’s first MPI cell tracking in the lungs, which is 100-fold greater sensitivity than any of our whole-animal cell tracking methods. We hypothesize that early tobacco-related lung tumors will be revealed by immunotheranostic MPI with high sensitivity and specificity because immune surveillance will be intact during the earliest stages of cancer. As one piece of evidence, Tumor Associated Neutrophils (TANs) constitute up to 25% of cells from isolated early stage lung tumors. The ACS notes that lung cancer is the leading cause of cancer death in the US, causing about 1 out of 4 cancer deaths, or about 158,000 deaths annually. The CDC notes “Cigarette smoking is the number one risk factor for lung cancer. In the United States, cigarette smoking is linked to about 80% to 90% of lung cancers.” Early-stage diagnosis is crucial to reduce lung cancer mortality, but, sadly, this remains very uncommon. The ALA states: “The five-year survival rate for lung cancer is 54 percent for cases detected when the disease is still localized. However, only 15 percent of lung cancer cases are diagnosed at an early stage. For distant tumors the five-year survival rate is only 4 percent.” Hence, improving imaging methods for early lung cancer diagnosis is our goal. Since today’s noninvasive imaging methods (MRI and ultrasound) are ineffective in lungs, Low-Dose CT (LDCT) is used to screen high-risk patients for lung cancer. The ACS notes that in the National Lung Screening Trial “people who got LDCT had a 16% lower chance of dying from lung cancer than those who got chest X-rays.” However, LDCT shows very poor specificity, with 98% of biopsies finding no malignancies. Clearly, we need a better and a less invasive imaging method for early-stage lung cancer screening. Here we aim to demonstrate, for the first time, that MPI targeted to the patient’s own immune cells will provide early diagnosis of tobacco-related lung cancer. Immune cells are thought to home to early cancers, when the immune surveillance system is still intact. MPI is a completely noninvasive biomedical imaging modality, relying on different physics and hardware from MRI, Ultrasound, X-ray and CT. MPI is a tracer imaging method, like nuclear medicine. My engineering group at UC Berkeley has designed and built all the MPI scanners in the USA. This year, we published the first demonstration of MPI cell tracking in lungs, showing 200-cell (200-nM tracer) sensitivity in vivo and also 87-day cell tracking. No other imaging modality can match this performance in the lungs. Moreover, MPI will be translatable to human studies since MPI uses zero radiation; safe magnetic fields; and safe iron oxide nanoparticle tracers.