Our Platform

Our foundational science

At Volastra we are focused on a key biological process that is not only a hallmark of cancer but its most targetable vulnerability: chromosomal instability, or CIN. CIN is present in 60-80% of all cancers and is associated with poor survival in many patients. While the genetic mutations that result from CIN have long been the focus of biotech research, targeting CIN itself has evaded discovery efforts — until now.

What is chromosomal instability?

When cells undergo mitosis, their chromosomes usually separate in an orderly fashion. When mitotic errors occur in normal cells, it is not tolerated leading to cell death through an array of inherent pathways. However, cancer cells develop unique adaptations to circumvent these intrinsic cellular defense mechanisms and continue to divide forming chromosomally unstable daughter cells. These daughter cells then go on to divide propagating this genomic chaos and genetic heterogeneity. This ongoing process is known as chromosomal instability (CIN).

Cancer cells also thrive under chromosomally unstable conditions. In cancers where CIN levels are high (CIN-high cancers), there are both genetic and non-genetic cellular consequences that lead to a host of biological glitches that increase the cancer cell’s ability to survive in a variety of conditions, driving not only disease progression but also treatment resistance. These consequences increase a patient’s risk of disease recurrence and death.


We are working to turn a deep understanding of CIN and its biological consequences into life-saving therapies leveraging our proprietary CINtech platform. Using our CIN focused tools and technologies we are equipped to identify relevant targets more quickly, create first-in-class drugs and select the best patients for our therapies. We believe that CIN is the key to unlocking new breakthroughs in cell biology that will revolutionize how cancer is treated.

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Paired CIN cell lines: Our biology team has built unique and proprietary paired cell lines to accurately model different types of CIN. This provides a way to test our targets and compounds in CIN-high versus CIN-low in-vitro systems.

CIN CRISPR screens: We run novel CIN-based screens to identify the most optimal genetic targets. Using synthetic lethal principles across broad CRISPR screens, we have discovered multiple genes essential in CIN-high cells, forming the basis of an extensive pipeline.

Organoid systems: We are proud to partner with Cornell to leverage innovative 3D cell cultures, known as organoids. Organoids can more effectively model tumor growth and response than 2D cell lines, resulting in significantly improved simulations of patient response.

2D and 3D design tools: Our chemistry team has made significant investments in property-based analytics that drive hypothesis-driven drug design. This ensures our future drugs have the best molecular and cellular characteristics, which we believe will translate into better patient response.

Biological and ADME assays: We have built unique, robust and translatable assays to minimize the burdensome design-make-test-analyze cycle for faster results. This deep understanding of the biological and metabolic underpinnings of our medicines will translate into better clinical results.

Structure-activity analysis: Our in-house biochemistry and medicinal chemistry expertise allows us to quantitatively map the relationship of structural parameters to PK/PD hypotheses and human PK and dose. This leads to the most optimal dosing regimens in our clinical trials.

Merged data analytics: We continue to build a wealth of CIN-specific data through our efforts in imaging and genomics. By merging this proprietary dataset with existing publicly available datasets (e.g. Broad Institute DepMap or TCGA) the team generates insights that integrate with our biological research to enhance our target ID and patient selection.

CIN computational screens: Our growing data science team uses state-of-the-art computational techniques to mine merged proprietary and public databases. This complements our biological screens to identify promising CIN-specific targets.

CIN imaging and genomic metrics: Through our collaboration with Microsoft, we have developed a unique way to measure CIN by leveraging machine learning imaging technology applied to routine H&Es. This, paired with novel genomic CIN-metrics, will contribute to more accurate patient selection.


  • Li et al., Metastasis and Immune Evasion from Extracellular cGAMP Hydrolysis. Cancer Discovery, 2021
  • Bakhoum SF, Cantley, LC. The multifaceted role of chromosomal instability and its microenvironment. Cell, 2018.
  • Bakhoum SF, Ngo B, Laughney AM, et al. Chromosomal instability drives metastasis through a cytosolic DNA response. Nature, 2018.