• Discover the Power of Live Primary Cell Phenomics

  • Learn About Cellanyx's Machine Vision and Machine Learning Technology

  • Advancing a Precision Risk Stratification Test

  • Meet the Team Developing a Personalized Risk Stratification Test

  • Cellanyx Has Unlocked Novel Phenotypic Biomarkers

  • Predictive Phenotypic Solutions



Cellanyx is developing proprietary live tumor cell phenotypic biomarker tests to improve cancer risk-stratification and inform clinical decision making. The Company’s initial tests, for prostate cancer and Stage 0 breast cancer, both of which have demonstrated clinical proof of concept, are being developed to augment traditional pathology tests.



Cellanyx’s technology analyzes multiple molecular, cellular and biophysical phenotypic markers of individual living cells and their behavior including protein dynamics and cell motility. The approach is enabled by a proprietary breakthrough technique for rapidly culturing primary tumor cells, coupled with microfluidics, microscopy, and advanced machine vision and machine learning artificial intelligence technologies.


Prostate Cancer Patient Risk Stratification Test

Cellanyx’s lead application is for enhanced risk stratification to predict adverse pathology at the time of radical prostatectomy in men with low and intermediate risk prostate cancer. The Company has demonstrated clinical validation in more than 300 prostate samples.


Prostate Cancer Test

Cellanyx’s lead test in development is designed to solve two major clinical challenges in prostate cancer diagnosis and treatment: risk stratification in men with low and intermediate grade prostate cancer (Gleason 6 or 7) and detection of cancer in men with a negative initial random prostate biopsy.

Prostate cancer is the most common cancer in men, but is very heterogeneous. Few patients die from prostate cancer and this presents a major challenge in distinguishing indolent from aggressive cancer and selecting the best clinical path: active surveillance or definitive treatment with surgery or radiation. The Gleason grading system is a pathological score based on formalin-fixed paraffin embedded (FFPE) prostate biopsy tissue samples that are microscopically evaluated by a board certified pathologist.

Prostate biopsies sample less than one percent of the prostate gland, and as a result, about one-half of approximately 1.3 million initial prostate cancer biopsies conducted in the US annually are negative, resulting in a high re-biopsy rate and use of expensive MRI imaging to ensure cancers are not missed.

Technology Platform

Cellanyx’s technology uniquely enables the use of live single cells cultured from tumors to analyze phenotypic tumor cell behavior. The technology measures dynamic molecular and cellular phenotypic and physical (morphologic) characteristics of the tumor cells using microfluidics, microscopy, and machine learning and vision algorithms to allow objective spatial-temporal resolution of phenotypic biomarker assessment with single cell resolution.

The technology generates quantitative and predictive measures of adverse pathology. The Company’s technology has resulted in a completely new generation of phenotypic biomarkers, such as cytoskeletal dynamics, adhesion dynamics, tumor micro-environment interactions, and assessment of protein sub-cellular localization, modification, and dynamics. In concert, these novel phenotypic markers predict adverse pathology at time of surgery. Cellanyx’s platform technology provides first-in-class phenotypic analysis of live tumor cells,

The Cellanyx phenotypic biomarker platform is objective and distinct, albeit complementary to, traditional light microscopic pathological evaluation of formalin-fixed paraffin-embedded (FFPE) tumor tissue. It is clearly distinct from cancer genomic tests which are based on FFPE tissues and generate predictive biomarkers based on a small number of pre-selected genes. The Cellanyx technology quantifies > 500 biomarkers via machine vision software. Biomarkers are then selected and objectively ranked by the machine learning algorithms to produce predictive and actionable biomarker scores for enhanced and precision patient and cancer risk stratification.

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