The OncobiotaLUNGdetect is a multi-species, multi-omic liquid biopsy assay that will aid clinicians in risk stratification of pulmonary nodules. Micronoma is the first company to commercialize an assay that integrates metagenomic, proteomic, clinical and radiomic datasets to determine the malignancy status of a detected pulmonary lesion. Let us unravel what multi-species and multi-omics mean in the context of lung cancer detection.

Due to the widespread utilization of X-ray and computed tomography (LD CT scan) imaging of the chest, approximately 1.9 million pulmonary nodules – abnormal masses that appear on radiographic images and may indicate the presence of lung cancer – are detected each year in the United States. Patients with lung nodules have higher lung cancer risk than the general population, even when 90 – 95% of detected nodules prove benign. The clinical workflow needed to rule out that 90 – 95% of nodule cases is complex, involving surgeons, radiologists, pathologists and other specialists to perform further evaluations, some of which can be risky to patients and carry high complication rates. Currently, the only FDA approved test for discriminating malignant lung nodules from benign is PET-CT evaluation. PET-CT evaluation, however, is complicated by diabetic hyperglycemia, inconsistent performance in discriminating lung adenocarcinoma from benign nodules, and false positive test results in patients with certain pulmonary infections – such as histoplasmosis. The clinical and economic burdens of pulmonary nodule management and the documented shortcomings of existing diagnostic tools all point to an unmet need in patient care.

Micronoma developed The OncobiotaLUNGdetect  specifically to address this unmet need and deliver a minimally-invasive, blood-biomarker based method of risk stratifying lung nodules.

We think that our multi-omic approach to lung nodule categorization stands to benefit clinicians and their patients by catching lung cancer in its earliest stages (I and II), thereby expanding curative treatment options, but also benefits insurance companies and the healthcare system as whole by saving costs on unnecessary testing and

What do we mean by multi-species?
As we like to remind people, to properly understand human biology and its disease processes, we cannot ignore our symbionts – the 30 trillion microbes living on and within us – which contribute critically to our biology both in healthy and disease states.

Based on this view, Micronoma has developed a solution that interrogates both human and microbial molecules to improve cancer detection.

What do we mean by multi-omics?
Simply put, multi-omics is about harnessing biological data from diverse information streams obtained by “omics” analysis: genomics, proteomics, metagenomics (aka microbiomics), metabolomics, radiomics and more.

What we are doing is conceptually analogous to having blood work done at an annual physical exam where your doctor may order a lipid panel to help assess your cardiovascular health. That panel will measure different types of lipids in your blood – triglycerides, total cholesterol, and the three individual types of cholesterol – LDL, VLDL, and HDL. Your doctor does not use this information in isolation but rather combines it with other clinical data points – for example, your age, smoking history, BMI, family history of heart disease – to infer your risk for adverse cardiovascular events like stroke or heart attack. What we are doing is not that dissimilar, except instead of looking at a small number of variables, like in the lipid panel analogy, we leverage machine learning to examine thousands of laboratory-detected analytes and clinical data points to identify disease-associated patterns among those diverse features.

With this larger array of variables we can discover relationships that otherwise would have escaped detection than traditional low complexity biomarker assays where only a small number of proteins or tumor-derived mutations may be interrogated in parallel.

The OncobiotaLUNGdetect diagnostic assay consists of a multi-omic combination of metagenomics, proteomics, and clinical scores (Figure 1). From 500 microliters of a patient’s blood plasma, we analyze microbial DNA sequences and measure the level of two cancer-associated human plasma proteins (CEA and Osteopontin).

This sequencing data and proteomic analysis is then combined with clinical data that we receive from the patient’s medical chart.

 Figure 1. The OncobiotaLUNGdetect assay combines cell-free microbial DNA sequencing data, plasma protein analytes, and clinical data (including some radiomic data from the imaging report) to provide a lung cancer category of pulmonary nodules.

The combination of the CT scan-derived information with the protein data and metagenomic data provide a complete set of features that enable us to derive a cancer prediction category that is then delivered to the clinician and patient.

Multiomics gives us the power to achieve the difficult task of looking at samples from patients with lung nodules and determining whether they are cancerous or not.

The ability to find out what the worst-case-scenario is as early in the disease as possible is the best-case-scenario for the patient and the healthcare system as a whole.