Earlier Signal Detection
Our immune system rapidly reacts to threats, triggering an internal amplification structure that causes changes to RNA expression levels within immune cells. By capitalizing on the immune surveillance apparatus, our ability for early detection is enhanced, identifying disease at the time of immune escape, as described in the theory of immunoediting.1,2 Particularly in oncology, it is at this point in the neoplastic process that small neoplasms may be most amenable to therapeutic intervention.
Phagocytic (CD14+) cells from the immune system therefore offer significant advantages for the identification of disease-specific signatures and subsequent detection of these signatures in healthy people and patients. Through lymphocyte normalization, using the patient’s own lymphocytes (CD2+) to filter out variabilities, we can isolate the disease-specific signal in phagocytes consequent to cleanup of apoptotic (tumor) cells. In addition, through the vast macrophage infiltration, the full tumor heterogeneity and multi-clonality can be captured. The subtraction-normalization procedure between these two cell types has the effect of stabilizing the otherwise noisy RNA expression signals such that meaningful conclusions about their relevance can be reached, providing a truly personalized assessment of the disease with higher accuracy.
Real-time surveillance of gene expression of monocytes and lymphocytes obtained from a patient enables the detection of immune-response signal changes that are caused by (i) intrinsic inter-individual variability, e.g. gender, genetic/ethnic background, (ii) epigenetic age-related and temporal variations, (iii) extrinsic intra-individual extracellular ‘milieu’ stimuli, e.g. food and drink intake both long-term and immediately prior to blood draw, smoking, recent vaccination, … (iv) specific diseases that the blood test aims to detect, e.g. prostate cancer, lung cancer, and pancreatic cancer, and (v) other diseases/conditions unrelated to the disease, e.g. arthritis, acute infection, … which are not part of the target disease panel. Since extracellular vesicles, apoptotic CTCs, and related cellular debris such as apoptotic bodies, exosomes, and nucleosomes are cleared from circulation by professional phagocytic cells, this process leads to the acquisition of disease-specific (epi)genomic signatures in these phagocytic cells and their absence in non-phagocytic cells.
Applying a combination of bioinformatics, machine learning and biostatistical approaches, we have developed sophisticated algorithms to address complex biological problems associated with high dimensionality and population, tumor, and outcome heterogeneity with complex or structured endpoints. We have created custom analytical pipelines for processing and assessing high-throughput molecular data based on state-of-the-art methods. We apply principled biostatistical approaches to account for statistical characteristics of data, in the context of the clinical use case on the intended population. Our first application addresses an unmet need for the identification of men harboring undiagnosed, aggressive prostate cancer. This signature will serve as our use case to demonstrate the applicability of our methodology in oncology as well as other disease processes.