8 research outputs found
MOLECULAR ANALYSIS OF CANCER PROGRESSION WITH LABEL-FREE RAMAN SPECTROSCOPY
Due to its ability to probe water-containing samples using visible and near-infrared frequencies with high chemical specificity, Raman spectroscopy is an attractive tool for label-free investigation of biological samples. While Raman spectroscopy has been leveraged for exploratory studies in clinical cancer diagnostics, only limited studies have used it to understand the molecular mechanisms driving key characteristics of cancer progression. In this thesis, we present three progressively complex applications of Raman spectroscopy that take advantage of its specificity and synergistic combination with plasmonic nanoparticles and multivariate data analysis for molecular study of cancer.
First, we used Au@SiO2 shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS) to investigate the roles of microcalcification status and the composition of tumor microenvironment in breast tissue for identification of a range of breast pathologies. We developed a partial least squares-discriminant analysis-based classifier to correlate the spectra with their pathology to obtain high prediction accuracy. A parallel investigation of the genetic drivers of microcalcification formation in breast cancer cells revealed that stable silencing of the Osteopontin gene decreased the formation of hydroxyapatite in breast cancer cells and reduced their migration.
Next, we demonstrated the ability to detect premetastatic changes in the lungs of mice bearing breast tumors, in advance of tumor cell seeding, using Raman spectroscopy and multivariate data analysis. Our measurements showed reliable differences in the collagen and proteoglycan features of the premetastatic lungs which uniquely identify the metastatic potential of the primary tumor. Consistent with histological assessment, our results hint at a continuous premetastatic niche formation model dependent on the metastatic potential of primary tumor.
Finally, we exploited Raman mapping to elucidate radiation therapy-induced biomolecular changes in murine tumors and uncovered latent microenvironmental differences between treatment-resistant and -sensitive tumors. We used multivariate curve resolution-alternating least squares (MCR-ALS) and support vector machine (SVM) to quantify biomolecular differences in the tumor microenvironment and constructed classification models to predict therapy outcome and resistance. We found significant differences in lipid and collagen content between unirradiated and irradiated tumors.
Taken together, these studies pave the way for applications of Raman spectroscopy beyond clinical diagnostics such as metastatic risk assessment and treatment monitoring
Discerning the differential molecular pathology of proliferative middle ear lesions using Raman spectroscopy
Despite its widespread prevalence, middle ear pathology, especially the development of proliferative lesions, remains largely unexplored and poorly understood. Diagnostic evaluation is still predicated upon a high index of clinical suspicion on otoscopic examination of gross morphologic features. We report the first technique that has the potential to non-invasively identify two key lesions, namely cholesteatoma and myringosclerosis, by providing real-time information of differentially expressed molecules. In addition to revealing signatures consistent with the known pathobiology of these lesions, our observations provide the first evidence of the presence of carbonate- and silicate-substitutions in the calcium phosphate plaques found in myringosclerosis. Collectively, these results demonstrate the potential of Raman spectroscopy to not only provide new understanding of the etiology of these conditions by defining objective molecular markers but also aid in margin assessment to improve surgical outcome.National Institute for Biomedical Imaging and Bioengineering (U.S.) (9P41EB015871-26A1)Connecticut Institute for Clinical and Translational ScienceJHU Whiting School of Engineering (Startup Funds
Primary breast tumor induced extracellular matrix remodeling in premetastatic lungs
Abstract The premetastatic niche hypothesis proposes an active priming of the metastatic site by factors secreted from the primary tumor prior to the arrival of the first cancer cells. We investigated several extracellular matrix (ECM) structural proteins, ECM degrading enzymes, and ECM processing proteins involved in the ECM remodeling of the premetastatic niche. Our in vitro model consisted of lung fibroblasts, which were exposed to factors secreted by nonmalignant breast epithelial cells, nonmetastatic breast cancer cells, or metastatic breast cancer cells. We assessed ECM remodeling in vivo in premetastatic lungs of female mice growing orthotopic primary breast tumor xenografts, as compared to lungs of control mice without tumors. Premetastatic lungs contained significantly upregulated Collagen (Col) Col4A5, matrix metalloproteinases (MMPs) MMP9 and MMP14, and decreased levels of MMP13 and lysyl oxidase (LOX) as compared to control lungs. These in vivo findings were consistent with several of our in vitro cell culture findings, which showed elevated Col14A1, Col4A5, glypican-1 (GPC1) and decreased Col5A1 and Col15A1 for ECM structural proteins, increased MMP2, MMP3, and MMP14 for ECM degrading enzymes, and decreased LOX, LOXL2, and prolyl 4-hydroxylase alpha-1 (P4HA1) for ECM processing proteins in lung fibroblasts conditioned with metastatic breast cancer cell media as compared to control. Taken together, our data show that premetastatic priming of lungs by primary breast tumors resulted in significant ECM remodeling which could facilitate metastasis by increasing interstitial fibrillar collagens and ECM stiffness (Col14A1), disruptions of basement membranes (Col4A5), and formation of leaky blood vessels (MMP2, MMP3, MMP9, and MMP14) to promote metastasis
Rapid Identification of Biotherapeutics with Label-Free Raman Spectroscopy
Product identification is a critical
and required analysis for
biotheraputics. In addition to regulatory requirements for identity
testing on final drug products, in-process identity testing is implemented
to reduce business risks associated with fill operations and can also
be used as a tool against counterfeiting. Biotherapeutics, in particular
monoclonal antibodies, represent a challenging cohort for identity
determination because of their similarity in chemical structure. Traditional
methods used for product identification can be time and labor intensive,
creating a need for quick, inexpensive and reliable methods of drug
identification. Here, driven by its molecular-specific and nonperturbative
nature, we present Raman spectroscopy as an alternate analytical tool
for identity testing. By exploiting subtle differences in vibrational
modes of the biologics, we have developed partial least-squares-discriminant
analysis derived decision algorithms that offer excellent differentiation
capability using spontaneous Raman spectra as well as label-free plasmon-enhanced
Raman spectra. Coupled with the robustness to spurious correlations
due to its high information content, our results highlight the potential
of Raman spectroscopy as a powerful method for rapid, on-site biotherapeutic
product identification
Rapid Identification of Biotherapeutics with Label-Free Raman Spectroscopy
Product identification is a critical
and required analysis for
biotheraputics. In addition to regulatory requirements for identity
testing on final drug products, in-process identity testing is implemented
to reduce business risks associated with fill operations and can also
be used as a tool against counterfeiting. Biotherapeutics, in particular
monoclonal antibodies, represent a challenging cohort for identity
determination because of their similarity in chemical structure. Traditional
methods used for product identification can be time and labor intensive,
creating a need for quick, inexpensive and reliable methods of drug
identification. Here, driven by its molecular-specific and nonperturbative
nature, we present Raman spectroscopy as an alternate analytical tool
for identity testing. By exploiting subtle differences in vibrational
modes of the biologics, we have developed partial least-squares-discriminant
analysis derived decision algorithms that offer excellent differentiation
capability using spontaneous Raman spectra as well as label-free plasmon-enhanced
Raman spectra. Coupled with the robustness to spurious correlations
due to its high information content, our results highlight the potential
of Raman spectroscopy as a powerful method for rapid, on-site biotherapeutic
product identification