Insilico Medicine, a clinical-stage end-to-end artificial intelligence (AI)-driven drug discovery company, and the University of Copenhagen announced today the release of the first batch of results from the collaborative work directed to perform multi-omics-based analyses in the context of age-related diseases using an AI-driven target and drug discovery pipeline. The findings, supported by researchers from the University of Chicago, were published in the Nov. 26 edition of Nature Cell Death & Disease.
Existing therapeutic strategies for treating major cancer types may not be effective for all patients. The heterogeneous clinical outcomes seen among patients with the same cancer type as well as incomplete understanding of cancer-related molecular signatures contributes to failed clinical trials and limits the development of advanced tailored therapies. Discovery of the biomarkers associated with treatment response is urgently needed, researchers note, to optimize a patient’s selection criteria for clinical trials, reach efficacy endpoints, and improve already existing therapies.
In order to find these biomarkers, the team of scientists investigated gene expression datasets derived from DNA repair diseases with increased cancer risk to find commonly dysregulated genes potentially involved in cancer progression. With the most significantly perturbed genes serving as biomarkers, researchers performed survival analysis across 33 cancer types and selected those that showed high confidence stratification among cancer patients. The latter is particularly important for subsequent target discovery, since patients with unfavored clinical outcomes would benefit the most from more tailored therapies. Insilico Medicine’s AI-driven PandaOmics platform was used to perform a comprehensive differential gene expression analysis, survival stratification, and target discovery.
Researchers discovered 10 significantly perturbed genes with a similar expression pattern among the selected DNA repair deficient disorders. Importantly, the majority of the disclosed genes were further shown to stratify at least three cancer types based on the survival analysis. The researchers focused on the most downregulated gene, CEP135, which possesses a crucial function in centrosome biogenesis and cell division and correlates with the severity of survival in sarcoma patients. Applying PandaOmics, they discovered potential target candidates for the group of sarcoma patients with poor clinical outcome. The authors identified PLK1 as one of the top scoring hits that functions in the same molecular pathway as CEP135, and further validated the identified targets in vitro.
“It is amazing that we were able to generate and analyze data and, in general, to validate hypotheses in such a short period of time by using AI algorithms developed by Insilico Medicine,” said Garik Mkrtchyan, Assistant Professor at the Center for Healthy Aging, University of Copenhagen, one of the lead researchers of the study. “We were very happy to make contributions to the cancer field and highlight the importance of biomarker discoveries for patient stratification and further therapy improvements. I am also thankful for support from Dr. Evgeny Izumchenko, an expert cancer biologist at the University of Chicago, for his guidance and advice throughout the study.”
“This is the latest in a series of collaborations with the University of Copenhagen, and specifically Morten’s lab, one of the leading groups working in the field of healthy productive longevity,” said Alex Zhavoronkov, PhD, founder and CEO of Insilico Medicine. “This ongoing collaboration enabled the publication of the study we present today, highlighting the strong connection between DNA repair deficiencies and cancer. We are extremely proud that PandaOmics is capable of searching for and justifying relevant targets and biomarkers across multiple disease areas."
“I am very excited that we have been able to demonstrate the utility of the PandaOmics platform in a practical setting,” said Morten Scheibye-Knudsen, MD, PhD, Associate Professor at the Center for Healthy Aging at the University of Copenhagen, a corresponding author of the project. “The use of massive datasets will allow us to greatly increase the potential of personalized medicine in the future, an area where Insilico Medicine is taking a leading position.“
Researchers noted that the findings are not limited to the particular gene or cancer type in the study, but also feature the advantages of high confidence AI application for omics analysis broadly applicable to the cancer research community, as it provides a panel of genes and survival data for biomarker and target discovery across multiple cancer types.
PandaOmics is an artificial intelligence (AI)-driven target discovery platform developed by Insilico Medicine that applies deep learning models to identify therapeutic targets associated with a given disease through a combination of omics data analysis put in the context of prior information coming from publications, clinical trials and grant applications. The algorithm optimizes for the best potential therapeutic targets by scoring results on factors such as: novelty, confidence, commercial tractability, druggability, safety, and other key properties that drive target selection decisions. PandaOmics has been used to identify new targets for cancer, amyotrophic lateral sclerosis (ALS) and COVID-19 and related variants, among other diseases, and the novel target it discovered for idiopathic pulmonary fibrosis has been developed into a lead drug candidate (designed through Insilico Medicine’s Chemistry42 AI platform) that is currently in Phase 1 clinical trials, the first AI-discovered and AI-designed drug to reach this milestone.
About Insilico Medicine
Insilico Medicine, a clinical stage end-to-end artificial intelligence (AI)-driven drug discovery company, is connecting biology, chemistry, and clinical trials analysis using next-generation AI systems. The company has developed AI platforms that utilize deep generative models, reinforcement learning, transformers, and other modern machine learning techniques to discover novel targets and to design novel molecular structures with desired properties. Insilico Medicine is delivering breakthrough solutions to discover and develop innovative drugs for cancer, fibrosis, immunity, central nervous system diseases, and aging-related diseases. www.insilico.com
About the Scheibye-Knudsen lab
The Scheibye-Knudsen lab uses in silico, in vitro and in vivo models to understand the cellular and organismal consequences of DNA damage with the aim of developing interventions. Among others, the lab has discovered that DNA damage leads to changes in certain metabolites and that replenishment of these molecules may alter the rate of aging in humans and model organisms. These findings suggest that normal aging and age-associated diseases may be malleable to similar interventions. The hope is to develop interventions that will allow everyone to live healthier, happier and more productive lives.
Cell Death and Disease
Method of Research
Subject of Research
High-confidence cancer patient stratification through multiomics investigation of DNA repair disorders
Article Publication Date