Cancer research at SSIM is currently focused on the use of Raman spectroscopy to identify and detect cancerous tissues down to the cell level, as described below. These technologies will enable realtime diagnosis and treatment of cancers in the operating room.
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We are performing research for the in vivo distinguishing or detection of normal, necrosis, and glioblastoma regions and their boundaries by a hand held Micro Raman Spectroscopy device and software algorithms. Raman spectroscopy is a non-destructive surface technique which provides a molecular signature of the region under examination. When light is incident on a sample, most of it is scattered back at the same energy and wavelength. However, in rare cases (1 in 107 photons), there is an energy exchange between the incident photon and the molecule under examination, causing the scattered photon to shift its wavelength, called the Raman effect.
We currently have a Raman unit in the Henry Ford Health Systems Neurosurgery OR investigating real time diagnosis under an approved IRB. We have performed 100 cases and over 500 spectra. The results are very encouraging as we can tell in real time tumor, gray matter, and white matter which is critical to tumor boundary identification and patient outcome.
Multimodal advanced imaging techniques (specifically Raman spectroscopy, fluorescence spectroscopy, and x-ray photoelectron spectroscopy {XPS}) can provide synergistic atomic and molecular information which can elucidate mechanisms of change in smoking and in lung cancer. This is joint with HFHS Pulmonary.
Raman Spectroscopy and X-Ray Photoelectron Analysis to Understand Atomic Changes Associated with Breast Cancer (with NCI)
This program maps out the molecular and chemical makeup of breast cancer. A joint program with NCI. The work provides molecular signatures of 80 breast cancer tumors from a small area in Poland that has high cancer rates. Molecular makeup and chemical constituents are correlated with outcomes to aid in determining what makes certain breast cancers resistant to treatment and others responsive. On a molecular level we seek to determine why some cancers are aggressive and others less invasive.
Patients undergoing FNA biopsy at the 5A UHC clinic were invited to participate in the study. After providing informed consent, patients underwent a routine FNA procedure using 4 passes per nodule (27 gauge needle). An on-site cytopathology team evaluated samples at the time of the procedure by staining using two needle rinse samples for cell block. If those samples were adequate for routine diagnosis, the remaining two samples were saved for Raman spectroscopy. Each pass was treated as a unique sample. In cases where additional material was needed for routine cytopathology, it took precedence over Raman specimens. In cases where no full passes were available for Raman spectroscopy, a portion of needle rinse was requested from cytology. At a later date, the patient’s medical records were reviewed for final cytopathologic and, if available, surgical diagnosis.
While the samples obtained and measured over the course of the study period did not display the range of clinical diagnoses we had hoped for, we were able to make significant progress toward developing a protocol for testing cells from thyroid fine needle aspiration, and in developing a Raman spectral database of cells from thyroid nodules. Spectra of three distinct cell types were measured, including platelets, presumed nodular cells, and an unknown cell type, and we began to work towards defining distinct Raman signatures of cells from colloid nodules versus follicular nodules.
Toward the goal of obtaining spectra from a larger variety of thyroid pathologies, we have developed a human subjects protocol to obtain cell scrapings from excised thyroid tumors. We feel that this will aid in rapidly developing a database of known cell types, as the diagnosis of the excised tissue can be obtained directly from the surgical pathology report, and abundant cells will be available for measurement.
In future work, we will also explore the use of immunohistochemistry and other dyes/stains which can be applied to provide accurate diagnosis of single cell measurements.
Smart Sensors and Integrated Microsystems (SSIM) Program
Wayne State University
College of Engineering Room 3172
5050 Anthony Wayne Drive
Detroit, MI 48202
Ph: 313-577-1306
Fax: 313-577-1101
