VisionGate Presents at the World Conference on Lung Cancer for 2017 in Tokyo: Morphometric Genotyping Identifies Lung Cancer Cells Harboring Target Mutations; Cell-CT® Platform Detects Gene Abnormalities

Morphometric Genotyping Identifies Lung Cancer Cells Harboring Target Mutations;
Cell-CT® Platform Detects Gene Abnormalities

Nelson, Meyer, Sussman, Katdare, Presley, Lakers, Hamilton, Wilbur, Mastrangelo, Doherty

Background:
The advent of genotype-directed therapy in personalized medicine requires the identification of
driver-mutations that are often under-diagnosed due to limitations in tissue biopsy and high false
negative rates associated with genomic tests.
Studies have demonstrated that the mutation status of cancer cells correlates with changes in
cellular morphology. The automated Cell-CT® platform produces isometric, high-resolution 3D
images of cells in liquid biopsies, such as sputum, where published studies have demonstrated
92% sensitivity to biopsy confirmed lung cancer with 95% specificity. This study reports the
development of cell classifiers for lung cancer cell lines that harbor known mutations, helping
pave the way to driver-mutation targeted therapy.

Methods:
Non-invasive sputum specimens from patients without lung cancer (“normal cells”) and the
following cell lines were analyzed using the Cell-CT® platform:
Small Cell Lung Cancer cell line
NCI-H69
Adenocarcinoma cell lines
A549 (EGFR wild-type, CDKN2A-c.1_471del471, KRAS- p.G12S)
NCI-H1650 (EGFR- p.E746_A750del, CDKN2A- c.1_471del471, TP53- c.673-2A>G)
NCI-H1975 (EGFR-T790M, CDKN2A- p.E69*, PIK3CA- p.G118D, TP53- p.R273H)
NCI-H2228 (EML4-ALK+, CDKN2A- c.1_471del471, RB1- p.E204fs*10, TP53- p.Q331*
high PD-L1).

Results:
15,000 normal cells from sputum and 500 malignant cells from each of the five cancer cell lines
were analyzed using Cell-CT® platform, measuring 704 structural biomarkers to sub-classify the
cancer cells by mutation status. Cell classifiers were operated to drive the highest specificity
(avoidance of false positives) while maintaining sensitivity above 50%. The area under ROC
(aROC), sensitivity and specificity for each classifier were:

Conclusions: This study demonstrates the feasibility of processing non-invasive sputum specimens by the Cell-CT® platform to accurately identify driver mutations in cancer cells to promote mutationdirected targeted therapy for the treatment of lung cancer.