OraLiva awarded $1.9M National Institute of Dental and Craniofacial Research (NIDCR at NIH) Direct to Phase II SBIR grant for AI-linked cytomics-on-a-chip Read More
OraLiva Inc. and partners from the McDevitt laboratory have developed a platform to digitize biology using sensors that learn.
Our award-winning programmable platform has been adapted for oncology screening applications. Through these efforts, OraLiva Inc. is positioned to be the first company to provide a powerful single cell diagnostic platform for various oncological indications suitable for use at the point of care. These highly scalable diagnostic tools have potential to deliver new test and treat capabilities that can help to usher in arrival of precision oncology diagnostic care.
Cancer Cytopathology | February 7, 2020
McRae, M.P., Modak, S.S., Simmons, G.W., Trochesset, D.A., Kerr, A.R., Thornhill, M.H., Redding, S.W., Vigneswaran, N., Kang, S.K., Christodoulides, N.J., Murdoch, C., Dietl, S.J., Markham, R. and McDevitt, J.T
Abstract: Conversations with key stake holders involved in the treatment and management of oral cancer patients have resulted in the key insight that oral pathologists must be engaged properly for the development, optimization, and acceptance of new oral cancer adjunct testing methods. With this perspective in mind, the McDevitt lab has published a key paper that focusses on the development of a point of care oral cytology tool for the screening and assessment of potentially malignant oral lesions.
Journal of Dental Research | November 12, 2020
McRae M.P., Kerr A.R., Janal M, Thornhill M.H., Redding S.W., Vigneswaran N., Kang S.K., Niederman, R., Christodoulides N.J., Trochesset D.A., Murdoch C., Dapkins I., Bouquot, J., Modak S.S., Simmons G.W., McDevitt J.T.
Abstract: One of the significant outcomes derived from the above-described Grand Opportunity program was the development of a unique database involving 13M cells profiled at the single cell level. These efforts led to the bridging between big data analytics and oral lesion diagnostics for the first time. These efforts also provided fertile grounds for new oncology discoveries some of which are described in this publication. Here the clinical role of F-actin in malignant transformation in oral lesions was discovered. This paper is significant in that it includes the most accurate diagnostic models for a broad range of oral lesion disease stages.
Translational Oncology | February 24, 2018
Timothy J. Abram, Curtis R. Pickering, Alexander K. Lang, Nancy E. Bass, Rameez Raja, Cynthia Meena, Amin M. Alousi, Jeffrey N. Myers, John T. McDevitt, Ann M. Gillenwater and Nadarajah Vigneswaran
Abstract: Fanconi anemia (FA) is a hereditary genomic instability disorder with a predisposition to leukemia and oral squamous cell carcinomas (OSCCs). Hematopoietic stem cell transplantation (HSCT) facilitates cure of bone marrow failure and leukemia and thus extends life expectancy in FA patients; however, survival of hematologic malignancies increases the risk of OSCC in these patients. We developed a “cytology-on-a-chip” (COC)–based brush biopsy assay for monitoring patients with oral potentially malignant disorders (OPMDs). Using this COC assay, we measured and correlated the cellular morphometry and Minichromosome Maintenance Complex Component 2 (MCM2) expression levels in brush biopsy samples of FA patients’ OPMD with clinical risk indicators such as loss of autofluorescence (LOF), HSCT status, and mutational profiles identified by next-generation sequencing. Statistically significant differences were found in several cytology measurements based on high-risk indicators such as LOF-positive and HSCT-positive status, including greater variation in cell area and chromatin distribution, higher MCM2 expression levels, and greater numbers of white blood cells and cells with enlarged nuclei. Higher OPMD risk cores were associated with differences in the frequency of nuclear aberrations and differed based on LOF and HSCT statuses. We identified mutation of FAT1 gene in five and NOTCH-2 and TP53 genes in two cases of FA patients’ OPMD. The high-risk OPMD of a non-FA patient harbored FAT1, CASP8, and TP63 mutations. Use of COC assay in combination with visualization of LOF holds promise for the early diagnosis of high-risk OPMD. These minimally invasive diagnostic tools are valuable for long-term surveillance of OSCC in FA patients and avoidance of unwarranted scalpel biopsies.
Oral Surgery, Oral Medicine, Oral Pathology, and Oral Radiology | April 15, 2024
Michael P McRae, Kritika Srinivasan Rajsri, A Ross Kerr, Nadarajah Vigneswaran, Spencer W Redding, Malvin Janal, Stella K Kang, Leena Palomo, Nicolaos J Christodoulides, Meena Singh 1, Jeffery Johnston, John T McDevitt
Objective: A small fraction of oral lichenoid conditions (OLC) have potential for malignant transformation. Distinguishing OLCs from other oral potentially malignant disorders (OPMDs) can help prevent unnecessary concern or testing, but accurate identification by nonexpert clinicians is challenging due to overlapping clinical features. In this study, the authors developed a ‘cytomics-on-a-chip’ tool and integrated predictive model for aiding the identification of OLCs.
Study design: All study subjects underwent both scalpel biopsy for histopathology and brush cytology. A predictive model and OLC Index comprising clinical, demographic, and cytologic features was generated to discriminate between subjects with lichenoid (OLC+) (N = 94) and nonlichenoid (OLC-) (N = 237) histologic features in a population with OPMDs.
Results: The OLC Index discriminated OLC+ and OLC- subjects with area under the curve (AUC) of 0.76. Diagnostic accuracy of the OLC Index was not significantly different from expert clinician impressions, with AUC of 0.81 (P = .0704). Percent agreement was comparable across all raters, with 83.4% between expert clinicians and histopathology, 78.3% between OLC Index and expert clinician, and 77.3% between OLC Index and histopathology.
Conclusions: The cytomics-on-a-chip tool and integrated diagnostic model have the potential to facilitate both the triage and diagnosis of patients presenting with OPMDs and OLCs.
Accounts of Chemical Research | July 6, 2016
McRae, M. P.; Simmons, G.; Wong, J., and McDevitt, J. T.
Abstract: This publication describes the core science and engineering behind the OraLiva diagnostic engine. The work was developed in the McDevitt laboratory and includes two microchip sensor configurations that work within a common microfluidics-based imaging sensor ensemble. As such, this flexible sensor modality serves as a powerful platform to digitize biology. This paper demonstrates the platform’s flexibility through the completion of multiplex assays within the single-use disposable cartridges for three clinical applications: prostate cancer, ovarian cancer, and acute myocardial infarction. This work is published in Accounts of Chemical Research (impact factor of 23) that is in the top 1% of scientific journals.
Oral Oncology | March 13, 2019
Abram, T.J., Floriano, P.N., James, R., Kerr, A.R., Thornhill, M.H., Redding, S.W., Vigneswaran, N., Raja, R, McRae, M. P., and McDevitt, J. T
Abstract: This is OraLiva’s first product release in in the area of oral lesion adjunct testing. The diagnostic models that OraLiva now uses for the oral lesion clinical decision making were developed using data acquired from an international prospective clinical study that was funded by the National Institutes of Health. This unique clinical study involved recruitment of 999 patients and resulted in development of one of largest cytology data bases ever created for potentially malignant lesions. These efforts documented the clinical outcomes of the patients across 6 major diagnostic categories and included over 165 image-based features that were recorded for over 13M cells all characterized at the single cell level. Dr. McDevitt, the scientific founder for OraLiva, served as the Principal Investigator for this effort. The study was sponsored by a Grand Opportunity award (only top 1% of submitted proposals were selected for this distinction).
Speight, P.; Abram, T.; Floriano, P.; James, R.; Vick, J.; Thornhill, M.; Murdoch, C.; Freeman, C.; Hegarty, A.; D’Apice, K.; Kerr, A.; Phelan, J.; Corby, P.; Khouly, I.; Vigneswaran, N.; Bouquot, J.; Demian, N.; Weinstock, Y.; Redding, S.; Rowan, S.; Yeh, C.; McGuff, H.; Miller, F.; McDevitt, J.
Abstract: One of the major challenges that exist in the classification of oral lesions is that the gold standard based on histology and manual pathology examination is complicated by the large disagreement that exists in pathologist decisions. This is particularly a problem with dysplasia, as dysplasia is about 15 times more frequent than is oral cancer. From a clinical perspective, it is not practical on an individual patient basis to assemble an expert panel of distinguished pathologists from major academic centers to render a diagnosis. Thus, the development of accurate diagnostic methods for characterizing the risk of precancer circumstance is essential. This paper describes a 4-stage adjudication process that was developed to create an enhanced gold standard for the qualification of oral lesion samples including oral dysplasia. This ‘enhanced gold standard’ here developed served as the guiding principle to produce the new multivariate diagnostic models that underlie the OraLiva oncology models.
Lab on a Chip | June 21, 2020
McRae M.P., Simmons G.W., Christodoulides N.J., Lu Z., Kang S.K., Fenyo, D., Alcorn, T., Dapkins, I.P., Sharif, I., Vurmaz, D., Modak S.S., Srinivasan, K., Warhadpande, S., Shrivastav, R., McDevitt J.T
Abstract: This publication describes the efforts of the McDevitt laboratory to develop an integrated point of care assay system suitable for the profiling of disease severity in COVID-19 patients. This work created one of the first integrated disease severity tools and as such the work was considered high impact. The publication was featured on the flagship journal in Lab on a Chip. The work has been followed by a series of other efforts to use this and related tools in clinical practice. It should be noted that the work in this publication was completed over a three-week period. The rapid time frame is a testament to the programmable nature of this diagnostic platform.
Micromachines | April 16, 2019
Christodoulides N., McRae, M. P., Simmons, G. W., Modak, S. S., McDevitt, J.T
Abstract: The McDevitt group has sustained efforts to develop a programmable sensing platform that offers advanced, multiplexed/multiclass chem-/bio-detection capabilities covering a broad range of diagnostic indications. This scalable chip-based platform has been optimized to service real-world biological specimens and validated for analytical performance. Fashioned as a sensor that learns, the platform can host new content for the application at hand. These efforts serve to move to the point of big data acquisition alongside the linkage to machine learning and artificial intelligence. Here, exciting opportunities are afforded by multiparameter sensing that mimics the sense of taste, overcoming the limitations of salty, sweet, sour, bitter, and glutamate sensing and moving into fingerprints of health and wellness. This article describes a platform that digitizes biology and affords clinical decision support tools for a broad range of indications. A dynamic body of literature and key review articles that have contributed to the shaping of these activities are also highlighted. This fully integrated sensor promises more rapid transition of biomarker panels into wide-spread clinical practice yielding valuable new insights into health diagnostics, benefiting early disease detection.
