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Harnessing evolutionary search to find the right drug, patient, and resistance target network  

What is an evolutionary search?

Evolutionary Search: a naturally occurring biological search algorithm by which cancer makes multiple adaptations to find a resistance network that enables the cell to outsmart drug treatment.

Resistance Network: a set of resistance mechanisms to a given therapeutic selection pressure that enables a cancer cell to survive and thrive during treatment.

Each cell in the ResCu system is a Cancer Crawler that performs an evolutionary search to build a resistance network to a given treatment. Each search yields a molecular solution that enables the cancer crawlers to survive under treatment.

ResCu – an accurate model of pan cancer evolution

A resistance network is only as good as its physiological relevance.

ResCu bypasses cell passaging and creates a physiologically relevant tumor micro-environment with intra-cancer crawler population diversity to simulate tumor heterogeneity.  ResCu further mimics the tumor’s expected treatment exposure for translatable convergent evolutionary resistance network searches.

Until now, convergent evolutionary search has been impossible. Physiological accuracy determines how clinically relevant the resistance network being searched by each cancer crawler is in pre-clinical models. It also determines whether the result will be divergent resistance targets or convergent resistance target networks.  

There are currently two methodologies to predict treatment resistance to a novel compound preclinically:

Genetic screens, which may miss nuances and network effects, increase the noise to signal ratio on any discovery. 

Passaging experiments and traditional cell culture introduce artificial evolutionary pressures, leading to undruggable divergent evolutionary resistance targets.

Harnessing cancer cells as resistance network cancer crawlers

What is a cancer crawler?

A cancer crawler is an individual cancer cell in the ResCu system searching through the resistance adaptation space or resistance network to find a treatment resistance solution to stay alive. Each cancer crawler individually and cooperatively makes specific adaptations over months that allow them to survive and thrive. Adaptations that are unfavorable result in the Cancer Crawlers’ death.

After an evolutionary search, the surviving cancer crawlers have individually and collectively determined how to stay alive, and each Cancer Crawler holds a record of its path to becoming resistant. 

ResCu performs the same evolutionary search as in patients by mimicking native tumor biology and combining the Cancer Crawler population with drug selection in the ResCu System over months. Each evolutionary search results in a resistant Cancer Crawler population that has solved how to overcome the therapy, as it would in a patient. 

States of resistance: intrinsic, acquired, transient, and permanent

When is a tumor resistant, and is it a permanent change?

ResCu can identify resistance biomarkers that can determine:

The simple machines of treatment-resistance mechanisms

Resistance is universal but the devil is in the details

ResCu can simultaneously predict and model numerous resistance mechanisms without genetic manipulation within the same cancer crawler population.

While each resistance mechanism falls within a general category, like target alteration, metabolic bypass of the target, immune system evasion, and changes to the tumor microenvironment, each mechanism is specific to the cancer type and treatment.

The specificity and breadth of the ResCu system allows resistanceBio to predict potential clinical treatment resistance mechanisms of novel therapies before a clinical trial.

The evolutionary “Goldilocks” zone -tumor molecular diversity

The correct number and types of Cancer Crawlers are key.

We created a combination process/algorithm to accurately determine the correct number of crawlers and crawler cell lines needed to describe a compound’s resistance potential. This step is critical in ensuring that resistance mechanisms are present and identifiable through the noise.

The evolutionary “Goldilocks” zone – patient population diversity

All patients must be represented from the beginning.

We make therapies for all people. We compare evolutionary search results across multiple ethnically diverse backgrounds to find convergent resistance target networks that unite cancer patients and develop therapies to fight against treatment-resistant cancer.

ResistanceRank; reading evolutionary search results with single-cell analysis

Treatment resistance is caused by more than DNA.

Treatment resistance is multifactorial and often is predicated on non-genomic mechanisms. We deploy leading-edge multi-omic single-cell characterization and bioinformatics on the susceptible and resistant cancer crawlers to identify the resistance mechanisms to a given therapy.  This process creates a ResistanceRank, which measures the importance of a resistance target or resistance target network.    

So what? Is the resistance target or mechanism really in a patient?

The best drugs go after fundamental human disease biology.

We validate any predicted convergent resistance mechanism by its presence in patient samples and mechanistically confirm it in the lab.

Harnessing a therapy’s mechanism of action to drive tumor resistance convergence

Treatment modality affects resistance evolution.

By focusing on using therapies with lower mutation potential, we can prevent adding additional random mutations to the tumor and drive it to a convergent homogenous tumor state that is easier to treat with safer targeted approaches.

Traditional chemotherapy and radiation create divergent evolutionary outcomes compared to the convergent evolution of ImmunoTherapy, Targeted therapy, or Cell-based therapy. Chemo and radiation disrupt tumor DNA and create a plethora of mutations enabling the tumor to become more aggressive and resistant to treatment.

ResCu continuously improves

Like cancer, the ResCu System is constantly evolving.

We take our data outputs and compare them with patient outcomes. We characterize any differences between the results from ResCu and patient outcomes and methodically improve ResCu. The ResCu System becomes more human-like and predictive as it performs more evolutionary searches