Chemogenomic Atlas Using Next-Generation Tumor Models to Support Drug Discovery

Summary

• EIPM has compiled a proprietary library of 3D, patient-derived tumor organoids which retain cell-cell and cell-matrix interactions that closely resemble those of the original tumor
• While other oncology discovery platforms utilize commercial cell lines, the EIPM’s tumor organoids are patient matched to provide EMR data, histopathology, morphological changes, tumor content tracking, and whole-exome sequencing for deeper therapeutic insights
• By integrating the patient derived tumor organoids platform with chemo-sensitivity testing, researchers can compare the impact of:
• Novel chemical agents vs. SOC regimens
• Combination therapies in distinct patient populations
• Specific patient features which may drive response or non-response in clinical context
• Immunological activation landscape in response to therapy

Technical Overview

• Dr. Ben Hopkins is pioneering a novel oncology drug discovery platform which aims to leverage the extensive genomic insights of EIPM tumor organoid models to build a database of therapeutic responses to an automated, high-throughput chemical screen (SOC and NCE oncology compounds)
• EIPM has integrated this chemogenomic screening platform into their broader precision medicine effort, thus enabling research partners to find actionable therapeutic insights at multiple nodes and starting points in the workflow (Fig. 1), such as:
• Categorizing optimal patient population for given therapy
• Evaluating tumor or immune biomarkers correlating with tumor sensitivity
• Understanding mechanistic rationale for relapse / remission to reveal better combination therapies

Market Opportunity

• Dr. Ben Hopkins aims to leverage the extensive genomic insights of EIPM tumor organoid models to build a database of therapeutic responses through a scalable drug screening platform
• EIPM has integrated the chemogenomic screening platform in a way that allows research partners to find actionable therapeutic insights at any node of the workflow (Fig. 1), such as:
• Categorizing optimal patient population for given therapy
• Evaluating tumor or immune biomarkers correlating with tumor sensitivity
• Understanding mechanistic rationale for relapse / remission to reveal better combination therapies

Partnering Opportunity

Weill Cornell Medicine is seeking a partner with an interest in EIPM’s platform for de novo drug / organoid screening, hit-lead optimization, and mechanistic studies of tumor response across a range of cancers

Supporting Data / Figures

Figure 1: Overview of deep target mining and validation platform.