DURHAM, N.C.--(BUSINESS WIRE)--Xilis, Inc., a pioneering company developing its MicroOrganoSphereTM (MOS) technology to guide precision therapy for cancer patients and accelerate drug discovery and development, announced today the presentation of data on the company’s proprietary MOS technology. Data were presented at the 2021 European Society for Medical Oncology (ESMO) Congress in an oral presentation and two poster presentations.
“There is a significant unmet need for a patient-derived microtumor platform that captures the entire microenvironment and heterogeneity of the originating tumor tissue for accurate, rapid and high-throughput therapeutic profiling,” said Xiling Shen, PhD, Founder and Chief Executive Officer of Xilis. “In these three presentations, we provide evidence that our MOS technology retains the same characteristics as patient samples and has the capability to quickly evaluate cancer drug response. Our platform can support pharmaceutical companies in accelerating drug discovery and development and is being developed to help clinicians make more informed treatment decisions.”
“Patient-derived MicroOrganoSpheres Recapitulate Tumor Microenvironment and Heterogeneity for Precision Oncology”
Data from the oral presentation highlighted Xilis’ automated microfluidic platform to generate tens of thousands of MOS from resected or biopsied clinical tumor specimens. The studies showed that MOS could be derived from four primary cancer tissue types (colorectal cancer, lung tumor, breast tumor and kidney tumor), while retaining the genomic, transcriptomic and stromal features of the parent tissue. Additionally, we demonstrated that PD-1 immunotherapy activated tumor-infiltrating lymphocytes (TILs) within the MOS and induced cell death.
“MicroOrganoSpheres as a Novel Precision Oncology Platform in Colorectal Cancer”
In the MicroOrganoSphere Drug Screen to Lead Care (MODEL) proof-of-concept clinical trial, results from the poster presentation demonstrated that MOS technology could be used as a potential therapeutic diagnostic assay to help guide treatment strategies for metastatic colorectal cancer. In less than 14 days after individual patient tumor tissue were biopsied, MOS were generated and established followed by drug screening to predict each patient’s sensitivity or resistance to oxaliplatin. With results generated in less than two weeks, these studies are a significant step towards showing that MOS can be used to correlate drug response to clinical outcomes and supports future validation in clinical trials.
“MicroOrganoSphereTM: An Automated Platform for Rapid Drug Screening in Patient-Derived Breast Cancer Organoids”
Data from the poster presentation showed a positive correlation in breast cancer drug response for 10 FDA-approved therapies between MOS, conventional bulk organoids and patient-derived xenografts. Breast cancer MOS were established as early as 2-3 days and continued to grow over seven days while maintaining the genomic features of the parent tissue. These studies demonstrate that Xilis’ MOS technology can be utilized to evaluate drug sensitivities in high-throughput screening for drug discovery and once validated, offer guidance in patient treatment planning.
Presentations are available on the Xilis website.
Based in Durham, North Carolina, Xilis, Inc. is a biotechnology company developing a precision oncology platform that guides treatment decisions for oncologists to improve cancer care outcomes for patients and supports drug discovery and development for pharmaceutical companies. Xilis’ proprietary MicroOrganoSphere™ (MOS) technology consists of miniature patient tumors that capture the full microenvironment and heterogeneity and provides an automated and scalable solution. Using MOS and AI-driven algorithms, Xilis is developing a Xilis Response Score™ for the clinic, enabling oncologists to make informed and timely treatment decisions. Additionally, the MOS technology is speeding up development and clinical trials of cancer drugs by enabling analysis of authentic tumor microenvironments, high-throughput preclinical modeling, and clinical patient selection capabilities.