Cellular Mass Response to Therapy Correlates With Clinical Response for a Range of Malignancies.

PURPOSE: Cancer patients with advanced-stage disease have poor prognosis, typically having limited options for efficacious treatment, and genomics-based therapy guidance continues to benefit only a fraction of patients. Next-generation ex vivo approaches, such as cell mass-based response testing (MRT), offer an alternative precision medicine approach for a broader population of patients with cancer, but validation of clinical feasibility and potential impact remain necessary. MATERIALS AND METHODS: We evaluated the clinical feasibility and accuracy of using live-cell MRT to predict patient drug sensitivity. Using a unified measurement workflow with a 48-hour result turnaround time, samples were subjected to MRT after treatment with a panel of drugs in vitro. After completion of therapeutic course, clinical response data were correlated with MRT-based predictions of outcome. Specimens were collected from 104 patients with solid (n = 69) and hematologic (n = 35) malignancies, using tissue formats including needle biopsies, malignant fluids, bone marrow aspirates, and blood samples. Of the 81 (78%) specimens qualified for MRT, 41 (51%) patients receiving physician-selected therapies had treatments matched to MRT. RESULTS: MRT demonstrated high concordance with clinical responses with an odds ratio (OR) of 14.80 (P = .0003 [95% CI, 2.83 to 102.9]). This performance held for both solid and hematologic malignances with ORs of 20.67 (P = .0128 [95% CI, 1.45 to 1,375.57]) and 8.20 (P = .045 [95% CI, 0.77 to 133.56]), respectively. Overall, these results had a predictive accuracy of 80% (P = .0026 [95% CI, 65 to 91]). CONCLUSION: MRT showed highly significant correlation with clinical response to therapy. Routine clinical use is technically feasible and broadly applicable to a wide range of samples and malignancy types, supporting the need for future validation studies.

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

Functional precision medicine offers a promising complement to genomics-based cancer therapy guidance by testing drug efficacy directly on a patient's tumor cells. Here, we describe a workflow that utilizes single-cell mass measurements with inline brightfield imaging and machine-learning based image classification to broaden the clinical utility of such functional testing for cancer. Using these image-curated mass measurements, we characterize mass response signals for 60 different drugs with various mechanisms of action across twelve different cell types, demonstrating an improved ability to detect response for several slow acting drugs as compared with standard cell viability assays. Furthermore, we use this workflow to assess drug responses for various primary tumor specimen formats including blood, bone marrow, fine needle aspirates (FNA), and malignant fluids, all with reports generated within two days and with results consistent with patient clinical responses. The combination of high-resolution measurement, broad drug and malignancy applicability, and rapid return of results offered by this workflow suggests that it is well-suited to performing clinically relevant functional assessment of cancer drug response.