Blood Cancer Discovery Publication Further Validates Exscientia’s AI Precision Medicine Platform for Improving Patient Outcomes

Exscientia, ETH Zurich, the Medical University of Vienna, and the Center for Molecular Medicine (CeMM) announced a new publication in Blood Cancer Discovery, a journal of the American Association for Cancer Research, titled “Deep Morphology Learning Enhances Precision Medicine by Image-Based Ex Vivo Drug Testing” from the laboratory of Prof. Berend Snijder. This post-hoc analysis builds on the transformative work of the EXALT-1 trial, published in Cancer Discovery, by using deep learning algorithms to classify complex cell morphologies in patient cancer tissue samples into disease “morphotypes.”

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“Following results of the EXALT-1 study, these findings continue to validate that our AI-guided precision medicine platform has the ability to identify highly actionable clinical treatment recommendations for blood cancers, deepening our insights and enhancing the clinical predictive power of the platform to help patients”

EXALT-1 was the first prospective trial to demonstrate significantly improved outcomes for late-stage haematological cancer patients using an AI-supported precision medicine platform to guide personalised treatment recommendations as compared to physician’s choice of treatment. In EXALT-1, 40% of patients experienced exceptional responses lasting at least three times longer than expected for their respective disease. The post-hoc analysis published today in Blood Cancer Discovery shows that combining the technology as used in EXALT-1 with new deep learning advancements that take advantage of cell-specific features in high-content images revealed a potential to further increase these patient outcomes.

“Following results of the EXALT-1 study, these findings continue to validate that our AI-guided precision medicine platform has the ability to identify highly actionable clinical treatment recommendations for blood cancers, deepening our insights and enhancing the clinical predictive power of the platform to help patients,” said Gregory Vladimer, Ph.D., VP Translational Research at Exscientia and co-inventor of the platform technology. “Cell morphology, or assessing the characteristics of cells, is fundamental to the diagnosis of cancer. Within this research, we were able to utilise deep learning within the platform to improve our ability to identify personalised cancer treatments, leading to improved clinical outcomes for patients. At Exscientia, we are excited to expand the platform’s applications in order to bring personalised medicine to broader populations.”

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“We believe performing drug screens directly in tumour tissues of cancer patients is a great step forward in understanding tumour complexity compared to traditional cell model systems. The fact that we can now harness the power of deep learning to turn these terabytes of images into actionable insights is very exciting indeed,” added Prof. Berend Snijder, Principal Investigator at the Institute of Molecular Systems Biology of the ETH Zurich in Switzerland.

The impact of deep learning on the clinical predictive power of ex vivo drug screening was assessed in a post-hoc analysis of 66 patients over a period of three years in a combined data set of 1.3 billion patient cells across 136 ex vivo tested drugs across haematological diagnoses including acute myeloid leukaemia, T-cell lymphomas, diffuse large B-cell lymphomas, chronic lymphocytic leukaemia and multiple myeloma. Patients receiving treatments that were recommended by the platform’s immunofluorescence analysis or deep learning on cell morphologies showed an increased rate of achievement of exceptional clinical response, defined as a progression free survival period that lasted three times longer than expected for each patient’s respective disease. Post-hoc analyses confirmed that the clinical predictions became more accurate when also considering the drug toxicity on the healthy cells within the tested patient sample.

Exscientia’s precision medicine platform uses custom deep learning and computer vision techniques to extract meaningful single-cell data from high content images of individual patient tissue samples. This analysis generates clinically-relevant insights into which treatments will deliver the most benefit to an individual patient. Further evaluation of individual patient results through Exscientia’s genomics and transcriptomics capabilities may help Exscientia further understand which other patients may benefit from similar treatments. The underlying technology was developed by Dr. Gregory Vladimer and Prof. Berend Snijder while working in the laboratory of Giulio Superti-Furga at the CeMM Research Center for Molecular Medicine in Austria.

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