Tag Archives: #Caldogram

A rEvolutionary Approach to Cancer

The Problem

Cancer is one of the deadliest diseases of our time — yet we are still fighting it with outdated tools. Despite decades of research and billions in funding, there is still no accurate, non-invasive method for detecting cancer early.

Why? Because cancer isn’t one disease — it’s many. It evolves differently in every patient, making it incredibly difficult to pin down reliable biomarkers. As a result, early detection often feels like searching for a moving target in the dark.

The current standard — invasive biopsies — means waiting until there’s already a visible lump or symptoms, often when it’s too late. By then, cancer has already spread, reducing survival rates and narrowing treatment options.

We’re not just behind — we’re risking lives by staying there. It’s time for a breakthrough.

The Solution

We apply phylogenetics—a well-established and proven method in fields like Botany and Zoology—to analyze cancer data. This evolutionary approach effectively accounts for the heterogeneity of cancer, offering biologically meaningful classifications that group patients based on their genetic similarities. Our platform leverages these principles to uncover critical insights into cancer, such as susceptibility to development, accurate diagnosis and typing, staging of cancer, and post-treatment evaluation.

We’ve developed a patented software solution that transforms cancer data to identify key biological markers. This includes detecting under- or overexpressed genes, and in the case of serum data, identifying novel or disappeared mass spectrometry peaks, as well as peaks indicative of up- or down-regulated proteins. The transformation process preserves the integrity of the data, ensuring that all biologically relevant information remains intact without reducing data size.

In addition to our patented algorithm, we have dedicated years to creating two processes for the universal diagnosis of cancer, enabling the earliest possible detection of the disease. Our method is versatile, applicable to all cancer types, and provides early detection that can save both lives and resources. Unlike traditional methods that rely on specific biomarker panels, our approach is sensitive to all cancer types, focusing on the full range of biological markers found in the serum. This enables us to monitor disease progression and tailor treatments, whether for primary or metastatic cancer.

Our method is the fastest, most cost-effective, and accurate solution for cancer detection and diagnosis, offering unparalleled precision in early-stage identification and treatment planning.

Clinical Study

As part of our ongoing research and development efforts, we have applied our innovative method to multiple datasets, leading to the publication of over a dozen papers in prestigious, peer-reviewed journals.

In addition, we recently conducted a comprehensive clinical study focused on prostate cancer. In this study, we analyzed data from 78 patients, including 16 healthy women, 8 healthy men, and 54 prostate cancer patients—both before and after radiation therapy. The cladogram below illustrates the evolutionary relationships between these patients, visually depicting their clustering patterns. Notably, our method achieved a 100% separation between healthy and cancerous patients. Furthermore, using only serum samples, we successfully distinguished between women and men.

Operations
  1. Patients visit their doctors (typically as part of their annual check-up), hospitals, or labs, where they provide a blood sample. The sample is then processed using mass spectrometry proteomics or metabolomics to measure the molecules present. The lab analysis typically costs around $100.
  2. Once the analysis is complete, the patient, hospital staff, doctors, or lab personnel upload the measurement data (typically in an Excel file containing serum-related numbers) to our website from anywhere in the world.
  3. We then process these measurements using our proprietary algorithm and validated methods to provide accurate diagnoses and enable early detection of potential health issues.
Inside Look at Our Method

This software solution harnesses the extensive network of Metabolomics labs, both nationally and internationally. Launching this service requires only a minimal cost—just a unique identifier tied to each patient’s Metabolomics measurements or profile. We process these measurements in conjunction with our established baseline for each cancer type. Here’s how it works:

We will create a master cladogram for each cancer type, which will include data from 500 to 1,000 healthy individuals and a comparable number of cancer patients. This large dataset establishes a baseline that captures the variations present within the general population.

For example, for prostate cancer, we will process metabolomics data from 2,000 men of diverse demographics using our algorithm.

When a new, unknown specimen is submitted, it will be compared to this comprehensive database, and we will reprocess the data accordingly. Based on where the new specimen falls within the cladogram, there are three potential outcomes: healthy, transitional, or cancerous.

This approach allows us to diagnose and achieve early detection effectively.

Moreover, we can analyze the measurements from a single drop of a patient’s serum against the data from each population in the corresponding cladogram for every cancer type. The result is a non-invasive, comprehensive cancer screening with just one lab visit. This is the unique, compounded benefit we offer to our customers from the start.

Business Paths Forward

At this juncture, we see two paths forward:

1. FDA Approval

To advance towards FDA approval for cancer screening for a single cancer type, we need to raise $1M. This funding will cover the following:

$100K for legal and application fees.
$200K to $500K to conduct a new clinical study to meet FDA requirements.
$200K for creating the master cladogram.
$200K to develop a minimal viable, cloud-native website for diagnostic use in the first year.

FDA approval will not only allow us to offer the screening, but also enable insurance companies to cover the cost and doctors to prescribe it to patients.

Our pricing model will be $250 per screening, which includes a $100 lab analysis to produce mass spectrometry proteomics or metabolomics data.

After successfully launching and gaining adoption for the first cancer screening, we will replicate this process for additional cancer types, with the goal of eventually screening for the most common types of cancer.

2. Market Approach

Scenario A:

To create screenings for the five most common cancer types, we need to raise $2M. This budget will be allocated as follows:

$1M for the development of 5 master cladograms:
$200K per cladogram, which covers 2,000 patients for each cancer type.
$200K to create a minimal viable, cloud-native website for the first year.
$800K to fund sales and marketing efforts in the first year.

We will offer cancer screening for individual types at $250 per screening, which includes the $100 lab analysis for mass spectrometry proteomics or metabolomics data. Additionally, we will offer a bundled price of $350 for screenings across all 5 cancer types.

Scenario B:

To expand our offerings to the 10 most common cancer types, we need to raise $5M. The breakdown is as follows:

$2M for the development of 5 master cladograms:
$200K per cladogram, covering 2,000 patients for each cancer type.
$500K for the creation of a minimal viable, cloud-native website for the first year.
$2.5MK for office rental, along with hiring two cloud engineers and four sales and marketing representatives for the first year.

We will charge $250 per screening for each cancer type, which includes $100 lab analysis. For screenings across all 10 cancer types, the price will be $450 per screening, again including the $100 lab analysis.

Target Markets

Once we achieve the ability to diagnose and provide early cancer detection, we will focus on the following key markets:

  • Hospitals: Offering incentives for them to conduct cancer screenings via our platform
  • Laboratories: Partnering with labs specializing in blood analysis, encouraging them to provide cancer screening through our website
  • Insurance Companies: Providing incentives for insurers to offer these screenings at no cost to their customers, saving substantial long-term costs through early detection
  • Consumers: Targeting individuals directly who are proactive about monitoring their health, offering incentives to use our services

We will begin with the U.S. market, with plans to expand globally once our model is proven.

Thus Spoke the Cladogram

The following cladograms are examples of the Phyloncology analysis of cancer data:

The Phyloncology Application: Cancer diagnosis and early detection made possible!
A cladogram representing a modeling tool for early detection and diagnosis based on the phylogenetic analysis of serum metabolomic and/or proteomic data. This model is based on our published data (see examples below). When applied in a clinical setting, the analysis of an unknown specimen could turn out to be healthy (scenario A), suspect (scenario B, early detection), or cancerous (scenario C, diagnosis).
Separating the healthy from cancerous, and importantly separating primary from metastatic using gene-expression data!
A most parsimonious cladogram produced from dataset GDS1439 (http://www.ncbi.nlm.nih.gov/sites/entrez?db=gds). The dataset is comprised of six benign specimens, as well as seven primary and six metastatic prostate tumors. The cladogram shows a major bifurcation that delineates two clades; the first composed of all primary tumors and four benign specimens (node 6, supported by 24 synapomorphies), and the second composed of all metastatic tumors and two benign specimens (node 13, supported by 717 synapomorphies). A clade of primary tumors is delimited by 1,018 synapomorphies (node 8), whereas a clade of the metastatic tumors is delimited by 4,494 synapomorphies (node 15). Synapomorphies at nodes 6, 8, 13, and 15 are considered clonal (driver) expression aberrations. Pooled primary tumor specimens PX1 and PX2 grouped into a clade (node 12), whereas pooled metastatic specimens, WX1 and WX2 formed a clade (node 18).
The Tree of Cancer: Separating various cancer types using serum metabolomics
Tree of Cancer. A phylogenetic analysis of serum proteomic data showing the relationship of three types of cancers. The dataset is composed of 460 specimens representing ovarian, pancreatic, and prostate cancers as well as noncancerous specimens. Specimens of each cancer type are classified into two major clades, a terminal and middle, as well as transitional clades between the healthy and cancerous. Healthy specimens are classified into a major healthy clade and transitional clades.
Distinguishing healthy & benign tissues from cancer on the basis of gene-expression
A cladogram of a parsimony phylogenetic analysis of microarray gene-expression data representing normal myometrium (n = 4), leiomyoma (n = 7), leiomyosarcoma (n = 9), and extrauterine leiomyosarcoma (n = 4) specimens. The leiomyomas and leiomyosarcomas form a clade defined by 32 synapomorphies. The leiomyosarcoma specimens form a terminal clade that is circumscribed by 20 synapomorphies . Asterisk (*) denotes extrauterine leiomyosarcomas.
Serum metabolomics distinguish healthy men & women from prostate cancer patients (untreated and radiation treated)!
This cladogram is based on our own clinical trial of prostate cancer patients; it shows that serum metabolomics is useful tool for diagnosis and prognosis. The cladogram shows the phylogenetic analysis of serum metabolomic data representing healthy men (n=8), healthy women (n=16), prostate cancer before treatment (n=54), prostate cancer after radiation treatment (n=54). Notice that only two treated patients clustered with healthy men while the bulk of treated patients (n=50) formed their own cluster.