insitro and Genomics England Announce Partnership to Provide Multimodal Search Capabilities and Empower Data Exploration and Derivation of Novel Insights

March 9, 2022 |

5 min read

As part of the collaboration, insitro will deploy its machine-learning capabilities on Genomics England’s histopathology images and accompanying genetic data to learn a multimodal representation that captures the fine grained semantic structure in this high-dimensional clinical data.

SOUTH SAN FRANCISCO, Calif. & LONDON--(BUSINESS WIRE)-- insitro, a machine learning-driven drug discovery and development company, and Genomics England today announced a collaboration to bring insitro’s machine learning powered embedding search capabilities to Genomic England’s multimodal phenotypic and genetic database. Since it was established in 2013 to deliver the 100,000 Genomes Project, Genomics England has built an unparalleled resource of almost 150,000 whole genomes with corresponding phenotypic data from NHS patients with rare diseases and their families, as well as patients with cancer. Included in this resource, Genomics England has a growing corpus of multimodal and high-dimensional data, including genetic data and histopathology images from its cohort of cancer patients.

As part of the collaboration, insitro will deploy its machine-learning capabilities on Genomics England’s histopathology images and accompanying genetic data to learn a multimodal representation (an embedding) that captures the fine grained semantic structure in this high-dimensional clinical data. Powered by this representation, insitro’s embedding search engine enables users to search for related images, biopsies, or cases based upon biologically and clinically relevant semantic similarity rather than visual similarity. Under the terms of the agreement, insitro will make its embedding search engine available to Genomics England’s network of research partners within the secure Genomics England Research Environment. This capability will enable Genomics England’s research partners to more fully realize the promise of this multimodal data resource, accelerating cancer research by empowering scientists to derive multimodal insights that go beyond traditional diagnostic labels.

insitro will also become a Genomics England research partner more broadly, partnering with Genomics England to empower insitro’s mission of bringing medicines to patients in need through the combination of genetic data and machine learning.

“We are pleased to partner with Genomics England to support their mission of deriving novel insights from their world-class collection of complex, multimodal data, which can help in providing better care and new therapeutic interventions to patients with rare diseases and cancer,” said Daphne Koller, Ph.D., founder and chief executive officer of insitro. “We believe that cutting edge machine learning methods can go beyond automating predefined prediction tasks and enable clinicians and scientists to derive novel insights that might give rise to new discoveries and treatments. Following on our open sourcing of our redun workflow engine in November of 2021, the development of our embedding search engine for Genomics England demonstrates insitro’s commitment to building a truly enabling data driven discovery ecosystem for the benefit of patients. We believe this collaboration can be an exemplar of future collaborations that unlock the power of high-content data towards improved therapeutics and outcomes.”

“As we continue to advance our research strategy of making genomic data available and usable to global biopharma and academic researchers, we are now moving towards a future of enabling deeper genomic research through multimodal data, bringing genomic healthcare to all who need it,” said Parker Moss, Chief Commercial Officer at Genomics England. “insitro’s expertise in extracting insights from multimodal clinical data with machine learning is unparalleled, and this partnership is a key step forward to help our research partners maximize discovery in our research environment and enable our shared vision for the future.”

About insitro
insitro is a data-driven drug discovery and development company using machine learning and data at scale to transform the way that drugs are discovered and developed for patients. insitro is developing predictive machine learning models to discover underlying biologic states based on human cohort data and in-house generated cellular data at scale. These predictive models are being brought to bear on key bottlenecks in pharmaceutical R&D to advance novel targets and patient biomarkers, design therapeutics and inform clinical strategy. insitro is advancing a wholly owned and partnered pipeline of biologic insights and molecules in metabolism and neuroscience. Since formation in mid 2018, insitro has raised over $700 million from top tech, biotech and crossover investors, and from collaborations with pharmaceutical partners. For more information on insitro, please visit the company’s website at www.insitro.com.

Genomics England
Genomics England was set up in 2013 to deliver the 100,000 Genomes Project, which was established to sequence 100,000 whole genomes from around 85,000 NHS patients affected by a rare disease or cancer. The 100,000th genome was sequenced in December 2018. The Project has demonstrated how genomics insights can help doctors across the NHS and has built a foundation for the future by assembling a unique dataset. Genomics England is now working with the NHS to deliver genomic testing for cancer and rare disease patients as part of routine healthcare. As the world’s largest community in genomic healthcare and medical research, Genomics England brings together patients and participants from across the 50 million-member NHS, thousands of doctors and researchers, and dozens of biopharmaceutical and healthcare industry partners. The platform that has enabled the world’s first national Genomic Medicine Service within the NHS also provides the trusted research environment, consent and governance framework that enables scientists to securely study the deidentified data to develop the diagnostic and therapeutic solutions of tomorrow.