A pipeline for malignancy and therapy agnostic assessment of cancer drug response using cell mass measurements

Area of Science:

• Biomedical Engineering
• Oncology
• Computational Biology

Background:

• Genomics-based therapies guide cancer treatment.
• Functional precision medicine tests drug efficacy directly on patient tumor cells.
• Current methods have limitations in detecting responses to slow-acting drugs and analyzing diverse sample types.

Purpose of the Study:

• To develop and validate a novel workflow for functional precision medicine in cancer.
• To enhance the clinical utility of functional drug testing using single-cell mass measurements and machine learning.
• To broaden the applicability of functional drug testing to various cancer types and sample formats.

Main Methods:

• Utilized single-cell mass measurements integrated with brightfield imaging.
• Employed machine learning for image-based classification of cell responses.
• Tested response signals for 60 drugs across 12 cell types and various primary tumor specimen formats (blood, bone marrow, FNA, malignant fluids).

Main Results:

• Demonstrated improved detection of responses to slow-acting drugs compared to standard viability assays.
• Successfully characterized mass response signals for a wide range of drugs and cell types.
• Generated reports within two days with results consistent with patient clinical responses across diverse sample types.

Conclusions:

• The developed workflow offers a high-resolution, rapid, and broadly applicable method for functional cancer drug response assessment.
• This approach complements genomics-based guidance by providing direct, functional insights into drug efficacy.
• The workflow is well-suited for clinically relevant functional assessment, potentially improving cancer therapy selection.