Nearly 240,000 men are diagnosed with prostate cancer (PCa) every year in the United States but there is no clinical test that can effectively determine whether their tumors will progress to life-threatening disease or remain indolent. Consequently, a majority of men with low-risk PCa who should simply undergo active surveillance (AS) instead receive costly treatments with major long-term adverse effects. A multidisciplinary team of investigators comprising clinicians, biologists, and bioengineers, who individually have developed key technologies, now seek to combine resources to directly address this long-standing clinical problem. The proposed project focuses on validating a novel technology platform that combines label-free and quantum dot-labeled spectral imaging to predict PCa progression. Illustrating the need and utility of our technology is the specific choice of assays we are utilizing here. The Mayo team has shown that rearrangements and/or copy number variant levels of five genomic regions in tumor cells in formalin fixed and paraffin embedded (FFPE) biopsy specimens can be useful in determining risk of PCa aggressiveness. However, these markers cannot be developed into a robust, clinical assay due to the current limitations of technology: Needle biopsy specimens often contain only a small amount of cancer, and even when cut into thin sections, the employment of a 5-probe assay is often simply not possible because of the limited capacity to multiplex conventional FISH probes. Furthermore, it is not possible to simultaneously identify cell types in sections labeled for fluorescence, so it is not clear whether cells that stain positive or negative for the FISH probes comprise cancer or stromal cells. This is the general problem our technology will address ? lack of multiplexed molecular quantitation and identification of (non)responsive cells. The Illinois team has shown that using infrared (IR) spectral imaging, the tumor microenvironment can be profiled and new predictive information can be obtained. However, this approach needs to be validated in a larger trial. Our project addresses the technology and validation needs by combining (a) FISH-probes based on quantum dots to identify specific molecular alterations with (b) cell/tissue identification using label-free infrared spectroscopic imaging. While both technologies has been independently developed and demonstrated to be effective, they have not been integrated in a complementary manner nor together used to address PCa needs. Here, we propose a test and validation of this combination technology via a cohort of PCa specimens that have already been genomically profiled. The combined technology's validation will also test its effectiveness in providing a practical test for PCa prognosis with statistical models and measures that will be compared to the current gold standards. Success in this proposal will enable the production of a robust assay strategy to determine which men are best candidates for AS or for more aggressive treatment ? which would be transformative for prostate healthcare. Validation of this technology also paves the way for combined molecular and cellular analysis in all tissues and for all types of molecular targets, which can vastly expand our capacity to provide accurate and specific diagnoses that can guide therapy in a wide range of pathologies.
The goal of this project is to develop a combined label-free and labeled imaging technology for quantitative histologic and molecular analysis of tumors. Over 200,000 men are found with cancer in their prostate biopsies annually in the US and over 90% of these individuals undergo therapy whereas a majority do not need it (for every 100 prostate cancer patients treated, only an estimated 0-1 life is saved). Validation of the technology here will develop a clinically-translatable test that distinguishes aggressive from ?indolent? cancers by integrating molecular imaging, genomic, and computational technologies.
| Liu, Yang; Le, Phuong; Lim, Sung Jun et al. (2018) Enhanced mRNA FISH with compact quantum dots. Nat Commun 9:4461 |
| Ganguli, A; Ornob, A; Spegazzini, N et al. (2018) Pixelated spatial gene expression analysis from tissue. Nat Commun 9:202 |
