MultiOmyx multiplex assay reveals increases in tumor-infiltrating lymphocyte density and PD-L1 expression in tumors after oncolytic virotherapy.
Cancer claims more than half a million lives each year in the United States alone. Understanding the biology of cancer and developing treatment options is an ongoing challenge due to lack of a whole picture of cancerous cell growth and survival mechanisms. In recent years, however, immunotherapy has shown impressive results in terminal cancer patients where the patient is administered drugs that stimulate the immune system to fight cancer cells. More than half of the current cancer clinical trials include some immunotherapy methods. Understanding immunotherapy requires identifying different immune cells and their interaction with the tumor.
MultiOmyx is a novel multiplexed technology that enables visualization and characterization of multiple biomarkers on a single 4μm tissue section. The technology visualizes the immune cells in and around the tumor region and this equips our clients with the knowledge they need for developing personalized cancer treatments.
Furthermore, the final MultiOmyx report summarizes all the tumor-immune cell interaction data, quantitatively at the cell level. Even for a small tissue sample, clients get millions of data points. This is critical because clinical samples are precious and numbers of serial sections are limited.
MultiOmyx is a part of the Pharma Services division at NeoGenomics. MultiOmyx operations team consists of Ph.D. scientists with expertise in Oncology, Clinical Lab Scientists with 10+ years of experience, innovative Bioinformaticians, and dedicated System Engineers. After receiving FFPE samples or slides from our clients, an H&E is generated and a pathologist evaluates the quality of the tissue. The MultiOmyx Lab Scientists then proceed to stain the sample for two biomarkers at a time where after each round of staining is imaged. The dye is then inactivated by the use of a MultiOmyx proprietary dye inactivation chemistry, enabling repeated rounds of staining and deactivation. The number of biomarkers can be as high as 60 on the same sample. High quality images are the core of this assay. The Bioinformatics team has developed numerous computer algorithms that can identify each cell in the images as immune cells or any other type of cell. These algorithms use complex functions and routines that can zoom into a cell and identify features and measure biomarker expressions. The scientists work closely with the Bioinformatician team and make in depth investigation and interpretation of the results as it applies to the human body. This is an intense process that can take several weeks to analyze millions of cells.
Due to the significant increase in demand for MultiOmyx services and data, the Bioinformatics group has recently developed an in-house deep algorithm toolbox utilizing deep learning techniques. Deep learning is a subcategory of artificial intelligence and machine learning. In MultiOmyx, deep learning algorithms makes it possible to automatically and accurately detect proteins in an image at “machine speed”, this can dramatically decrease TAT and enhance our volume growth.
Over the last four years at NeoGenomics, MultiOmyx has proven its significant potential for facilitating cancer research. The technology can be integrated with RNAScope, DNA FISH, and NGS on the same sample. The final data gives a deeper and holistic view of proteins and genetic profiling within the complex tumor environment, with the potential to increase the pace of drug development and enable personalized cancer treatment. MultiOmyx has a huge growth potential in the coming years as its utility in immunotherapy trials becomes more pronounced, as well as outside the field of cancer in studies for infectious diseases such as HBV and in autoimmune disorders such as Celiac disease.
- Noone AM, Howlader N, Krapcho M, Miller D, Brest A, Yu M, Ruhl J, Tatalovich Z, Mariotto A, Lewis DR, Chen HS, Feuer EJ, Cronin KA (eds). SEER Cancer Statistics Review, 1975-2015, National Cancer Institute. Bethesda, MD, https://seer.cancer.gov/csr/1975_2015/, based on November 2017 SEER data submission, posted to the SEER web site, April 2018.