Have you noticed a pattern in the world of cancer diagnostics? Every new company bursts onto the scene claiming that their data type—whether it’s cell-free DNA, methylation patterns, exosomes, or something else—is the breakthrough we’ve all been waiting for. But despite all the hype, early cancer detection remains frustratingly elusive.

Why is that? Why do companies keep chasing the “next” data type, hoping it will unlock the secret? And how reliable are these bold claims, really?

After more than a decade of hands-on research, we’ve arrived at a clear answer: the problem isn’t the data type — it’s the analytical paradigm.

Cancer data is notoriously heterogeneous. Two patients with the same cancer type can show dramatically different molecular profiles, making it nearly impossible to identify reliable biomarkers using traditional statistical models or even machine learning. These approaches rely on consistency — something cancer biology simply doesn’t offer.

And it gets more complex. Cancer is a dynamic disease. Its biochemistry evolves over time — from the earliest initiation phase to full-blown metastasis. Trying to analyze this kind of variability with tools designed for static patterns is like trying to capture a moving train with a still camera.

So, is it hopeless? Absolutely not.

At Phyloncology, we’ve developed — and patented — a novel analytical method that’s designed to handle heterogeneity. Our approach has proven successful across multiple data types, including metabolomics, proteomics, and gene expression. It doesn’t just detect cancer — it identifies early, transitional states that other methods miss entirely.

And it works. We’ve tested it. We’ve published it. And now, we’re building a company around it.

We’re inviting you to be part of Phyloncology — a startup that’s not just joining the race for better cancer diagnostics, but redefining the rules.

Why are we so confident? Because we’ve solved the real problem.
It was never about the data. It was always about the analysis.