Rapid assessment of T cell health for enhanced cancer immunotherapy

Quick Facts:
Tags: Algorithm, Assay, Biomaterial, Cancer immunotherapy, Cell culture, Chronic lymphocytic leukemia, Immunotherapy, Machine learning, Mathematical optimization, Microscopy, Phenotype, T cell, Thin film, Tumors of the hematopoietic and lymphoid tissues
Inventors: Jia Guo, Lance Kam, Nicole Lamanna, Xin Wang
Manager: Dovina Qu
Departments: Biomedical Engineering, Medicine, Psychiatry
Divisions: Columbia University Medical Center (CUMC), Fu Foundation School of Engineering and Applied Science (SEAS)
Reference Number: CU24313
Release Date: 2024-12-18

This technology is a rapid assessment platform that integrates biomaterials, imaging, and machine learning models to evaluate the functionality of T cells for therapeutic applications.

Unmet Need: High-level readouts of functional capacity of T cells

Autologous anti-cancer T cell therapies rely on the health and functionality of patient-derived T cells. Current methods for assessing T cell functionality rely on biomarker-based evaluations, which are limited in their ability to provide comprehensive and actionable insights into T cell health. These approaches often fail to capture critical functional attributes, such as the proliferative capacity and effector phenotype, which are essential for successful therapeutic outcomes. There are currently no platforms that offer rapid, high-level readouts to directly evaluate the functional capacity of T cells before their clinical application.

The Technology: Integrated platform for rapid T cell functionality analysis

This technology is a platform that integrates biomaterials, imaging, and deep learning tools to measure T cell function. The platform assesses T cell functionality using phase contrast images of cells cultured on biomaterial-coated substrates. T cell spreading and morphological characteristics are measured over a short period, capturing key functional attributes such as proliferation potential and effector state. A machine learning algorithm processes the images for automated, unbiased classification of T cell health states. The assay can be extended to predict T cell proliferation capacity through a regression model, offering a rapid and actionable alternative to traditional long-term culture methods. By enabling precise selection and optimization of functional T cells, this technology could enhance the effectiveness and consistency of autologous immunotherapies.

This technology has been validated with samples from both healthy donors and patients with chronic lymphocytic leukemia.

Applications:

  • Platform for assessing the functionality of autologous T cells for immunotherapy
  • Method for improved in vitro T cell expansion for allogeneic transfers
  • Assessment of T cell proliferation potential
  • Diagnostic tool to evaluate T cell functionality in hematological malignancies
  • Research tool to optimize ex vivo cell culture

Advantages:

  • Automated and unbiased analysis
  • Compatible with standard microscopy
  • Rapid and cost-effective
  • Improved outcomes in immunotherapy

Lead Inventor:

Lance Kam, Ph.D

Patent Information:

Patent Pending

Related Publications:

Tech Ventures Reference: