Microbiome-Driven Liquid Biopsy for True Early Cancer Detection
Accurate, early cancer detection remains a challenge Micronoma seeks to overcome. Micronoma is focusing first on early lung cancer detection. There were approximately 228,000 new cases in the U.S. in 2020. Current lung cancer testing generally does not identify the disease until stages III and IV (75 percent of lung cancer cases fall into this category and, at that point, the five-year survival rate is less than 20 percent). Lung cancer patients currently rely on Low Dose CT scan for diagnosis. While this method is covered by Medicare for at-risk populations, less than 10 percent of the eligible population is tested. Besides not having access to testing centers, clinicians lack safe and reliable follow up tools when this method reveals a nodule to assess its malignant likelihood (which happens in only ~5% of the nodules). The only solution at this stage being tissue biopsies, which are both costly, and potentially risky. More modern technology for current liquid biopsy based upon circulating tumor DNA (ctDNA) and proteins, and/or methylation exist. Yet, these approaches often provide poor sensitivity for early-stage detection. Our Oncobiota™ solution can be a gateway solution when a nodule is discovered by LDCT to establish if it is benign or cancerous. This method would cost about half as much as tissue biopsy, has virtually no side effects (simple blood draw), and can significantly reduce the number of unnecessary tissue biopsies ordered by filtering out benign nodule patients.
Our Oncobiota™ platform technology is the only liquid biopsy that looks at the cancer microbiome. All other liquid biopsy companies are evaluating ctDNA and/or CTC, which largely depends on the size of the tumor. Because of the rich, diverse, and unique microbial markers identified, we can detect cancer in its earliest stages (stage I and II). Micronoma’s innovation also presents a huge cost savings for the healthcare system by avoiding unnecessary biopsy costs while flagging patients for surgery at early stages when treatment costs are lower and more effective. The mean cost of a diagnostic tissue biopsy for NSCLC runs above $10,000. Tissue biopsies can also be accompanied by potential serious complications, in about 20 percent of cases. Infection is a risk for all types of lung tissue biopsies. Other complications include severe bleeding, blood clots, or even pneumothorax. Rising costs come with these complications. Emergency teams must be brought into the picture, hospital admissions increase, and longer, more sophisticated care is needed. Given the many barriers and complications associated with early detection of lung cancer, the promise of identification through a simple and accessible liquid biopsy offers many advantages.
Our collaboration with UNSW, led by Associate Professor Amany Zekry and Professor Emad El-Omar from UNSW Medicine & Health, will enable the development of microbial-based biomarkers powered by artificial intelligence for early detection of liver cancer. Micronoma’s chief scientific officer, Eddie Adams, has joined these world-class, multi-disciplinary experts at UNSW in the fields of liver disease, liver cancer, microbiome, metabolomics, and artificial intelligence as a co-principal investigator on the grant. This research will use machine learning to examine thousands of microbiome plasma features to discover, validate, and translate microbial-derived biomarkers for the early detection of hepatocellular carcinoma (HCC), thus improving the chances of survival of HCC patients and making effective risk disease stratification possible. --We have collaborations in place with lung cancer key opinion leaders which are under confidentiality agreement until published --We have ongoing collaborations on other cancer types (pancreatic ductal adenocarcinoma, ovarian) as our vision is to tackle one cancer at a time --In addition, we are working toward a collaboration with a large pharmaceutical company to use our methodology to restratify their clinical trial with our method to define responders vs non-responders to their oncodrug