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Volastra Therapeutics Partners with Centaur Labs to Develop Data Annotation System for Faster Cancer Research

The Power of High-Quality Annotations and Machine Learning Models in Identifying Chromosome Instability at Scale

Leading biotechnology company, Volastra Therapeutics, has partnered with Centaur Labs to develop machine learning models that will help identify and quantify chromosome instability at scale. The aim is to use these models to power preclinical discovery work, to better understand the underlying biology and inform prioritization of targets for further development.

Volastra Therapeutics has developed a CINtech platform, which is based on its deep understanding of chromosomal instability (CIN), drug development, and data science. This platform is being used to identify high priority therapeutic targets, gain mechanistic biological insight, and develop novel drugs to treat chromosomally unstable cancers, including their lead KIF18A inhibitor program.

To develop these models, Volastra needed a training dataset of pathology slides annotated to identify the presence and type of mitotic events. However, the team knew that building the training datasets that power their models by hand would not scale. They needed a higher throughput data annotation system that could produce accurate annotations much more quickly than they could produce by themselves.

Volastra found the solution in Centaur Labs, the scalable data annotation platform for medical and life sciences. Volastra partnered with Centaur Labs because of the network of annotators with medical backgrounds, the scalability, the multi-reviewer scoring system, and the insights about data annotation quality the platform provides. Together, these capabilities gave the Volastra team the confidence to know they can get both quality annotations and increase throughput.

Centaur Labs segmented the DAPI stained images at a rate of approximately 3,220 images per week, and generated an average of 5 qualified opinions per image. For the H&E images, Centaur Labs segmented the cells in specific mitotic stages across the 119,000 tiled images at a rate of approximately 7,300 images per week, and generated an average of 8 qualified opinions per image.

In 61 – 89% of cases, depending on the task, the Centaur Labs annotations were the same as the Gold Standards. Working with Centaur Labs allowed Volastra to improve their timelines significantly and scale up the amount of annotation they could do.

The importance of speed becomes even clearer when considering the team’s longer-term model development plan, involving thousands of images across numerous datasets. Centaur Labs is also improving the workflow for Volastra’s pathologists wherever they still would like to annotate by hand. From a dataset of 53,000 tumor images, for example, Centaur Labs identified the 7,400 images the pathologists should focus on.

“Working with Centaur Labs has allowed us to use our experts more effectively and make faster progress,” said Sarah Bettigole, Head of Immunology and Data Science at Volastra.

Volastra is building a durable data annotation system for the long term. The flexibility of the Centaur Labs platform to support both multiple types of annotation tasks and large volumes of high-quality data enables Volastra to use a single platform for all data annotation throughout the model development lifecycle.

“We’re excited by the high throughput and quality of annotations we get with Centaur Labs – I would definitely recommend working with them,” added Bettigole.

Volastra Therapeutics is a Fierce15 award-winning biotechnology company, which is on a mission to stop cancer in its tracks. Instead of targeting genetic mutations, Volastra is targeting chromosomal instability or “CIN” underlying those genetic changes. CIN – present in 60 – 80% of human tumors – allows cancers to progress faster and more effectively resist treatment.

To build its CINtech platform, Volastra has combined its deep understanding of CIN, drug development, and data science. The company uses this platform to identify high-priority therapeutic targets, gain mechanistic biological insight, and develop novel

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