Intelligentia Platform:

Early, Cellular, Individualized Disease Assessment

Intelligentia is a versatile platform offering the capability for individualized multi-omics profiling with the potential to address indications in oncology, metabolic, autoimmune and neurodegenerative diseases. Our immunogenomic approach enables improved screening, diagnosis, prognosis, therapeutic response prediction, and disease monitoring of patients.

Our promising foundational work demonstrates that our methodology is ideally suited to address a pan-disease indication, detecting multiple diseases from a single blood sample.

Early Signal Detection

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.

Our immunogenomic approach will allow for earlier signal detection than by looking for CTCs and cfDNA;cfNA which are often only sufficiently and readily detectable in more advanced disease stages. In oncology, solid tumors shed millions of tumor cells per gram tumor per day, potentially giving rise to CTCs.3,4 However, patients do not present with millions of clinical metastases since CTC survival in circulation is rare and most die due to a combination of factors including physical stress, oxidative stress, anoikis, and the lack of growth factors and cytokines.5-7 Although CTCs can be found in the blood of cancer patients, they are rare with one CTC per one to ten million leukocytes, or one per five billion red blood cells.8 Surviving CTCs often extravasate into the surrounding tissue or become lodged in a capillary bed.9

The vast majority of CTCs are apoptotic or broken down into apoptotic bodies which are rapidly cleared from circulation by circulating phagocytic WBCs.10,11 Apoptotic bodies contain various disease-originating intracellular molecules, including DNA, RNA, and proteins, which are clinically detectable within phagocytes that are in the process of cleaning up the apoptotic cells.12 Phagocytic WBCs migrate throughout the body, infiltrating tumors where they phagocytose apoptotic cells, and can constitute up to 25% of all solid tumor cells. As a result, macrophages can comprise up to 50% of solid tumor mass.13

cfNA includes DNA, RNA, and miRNA of various fragment sizes and characteristics (nucleosomes, exosomes, …), all of which are associated with inherent challenges for diagnostic strategies. cfNA is released by necrosis, apoptosis and active secretion of both normal and neoplastic cells by various pathologic processes (inflammation, trauma, sepsis, …) and routinely cleared from circulation by liver, kidney and the mononuclear phagocyte system with variable half-life ranging from minutes to several hours.14 The amount of tumor related cfNA is dependent on the tumor size, cellular turnover rate, and necrosis, so in the sea of normal cfNA, the quantity from small, early-stage tumors is relatively small compared to relatively large tumor burdens.15 In addition, most of the cfNA from tumor is normal and only an extremely small fraction harbors tumor specific mutations or epigenetic changes characteristic of tumor and useful as a diagnostic tool.

Personalized Assessment

Individualized Assessment

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

Real-Time Surveillance

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.

The comparison of gene expression levels of the CD14+ and CD2+ WBC subpopulations can (i) help eliminate (epi)genomic signals unrelated to the presence of the disease, (ii) detect disease-specific (epi)genomic signatures that are present in phagocytic cells, and (iii) lead to an approach that does not depend on population-derived, averaged profiles obtained from the blood of individuals whose genetic makeup differs from that of the patient being tested. The subtraction-normalized expression of phagocytes (SNEP) method can (i) eliminate the ‘inequality of baselines’, (ii) filter out (subtract) signals unrelated to the disease and facilitate the normalization of gene expression data, and (iii) lead to the identification of a (epi)genomic signature that can be useful in the management of many diseases, including prostate cancer. The SNEP methodology subtracts the gene expression signal of the non-phagocytic CD2+ cells from that of the phagocytic CD14+ cells by taking the log ratio of CD14/CD2 signals.
Compelling Data

Machine Learning

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.

References:

  1. Mittal D, Gubin M, Schreiber R, Smyth M; New Insights into cancer immunoediting and its three component phases – elimination, equilibrium and escape. Curr Opin Immunol. 2014
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  3. Katarzyna A. Rejniak; Integrated Circulating Tumor Cells: When a Solid Tumor Meets a Fluid Microenvironment; Adv Exp Med Biol. 2016; 936: 93–106.
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  5. Bailey P, Martin S; Insights on CTC Biology and Clinical Impact Emerging from Advances in Capture Technology. Cells 2019
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  8. Paterlini-Brechot P, Benali NL; Circulating tumor cells (CTC) detection: Clinical impact and future directions, Cancer Letters 2007
  9. Yamauchi K, Yang M, Jiang P, Yamamoto N, Xu M, Amoh Y, Tsuji K, Bouvet M, Tsuchiya H, Tomita K, Moossa AR, Hoffman RM. Real-time in vivo dual-color imaging of intracapillary cancer cell and nucleus and migration. Cancer Res. 2005;65(10):4246–4252. doi:10.1158/0008-5472.CAN-05-0069.
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  12. Halicka HD1, Bedner E, Darzynkiewicz Z, Segregation of RNA and separate packaging of DNA and RNA in apoptotic bodies during apoptosis. Exp Cell Res. 2000 Nov 1;260(2):248-56.
  13. Vitale I, Manic G, Coussens LM, Kroemer G, Galluzzi L; Macrophages and Metabolism in the Tumor Microenvironment. Cell Metab. 2019
  14. Khier S and Lohan L; Kinetics of circulating cell-free DNA for biomedical applications: critical appraisal of the literature, Future Sci OA. 2018 Apr; 4(4): FSO295.