Circulating Tumor Cell cluster phenotype and response to treatment and predicts survival in lung cancer

Background

Metastasis contributes to 90% of cancer deaths. Circulating tumor cells (CTCs) are cancer cells disseminated in blood and are considered to be pivotal in the metastatic cascade. cluster phenotype as a predictive indicator of treatment response and to determine the therapeutic efficacy of the drugs

Aim

To study CTC cluster formation and use the same to assess response to treatments

Methods

Blood samples were obtained from 52 lung cancer patients.The whole blood was separated by centrifugation and the remaining cell fraction was subjected to RBC lysis. Nucleated cells were re-suspended in Dulbecco’s modified Eagle medium (DMEM) (Sigma) supplemented with 10% fetal bovine serum (FBS) (Life Technologies) and 1% penicillin-streptomycin.The nucleated cells from patient samples were cultured in ellipsoidal microwells made on agar plates.Cultures were maintained for 3 weeks and imaged on day 7, 14, 21 with phase contrast microscope (Olympus IX71) and analyzed the images using ImageJ (NIH, Bethesda, MD).The threshold values to classify the clusters were calculated from the median of grey values from each cluster in a microwell, which reflects the fluorescent intensity of DAPI in clusters.

The median grey score obtained from theCTC clusters, were more than 100, which were categorized as very tight clusters, followed by grey score ranging between 10–100 as tight clusters,score was below 10 and was classified as loose clusters.

Results

The tight cluster formation correlated with shorter patient survival as against the loose clusters. We were able to correlate the same with prospective follow up on these patients with treatment and the imaging modalities. The dynamic change in cluster characteristics also correlated with response to ongoing treatment and development of resistance. The findings were consistent for the targeted therapies and chemotherapies.

Conclusion

CTC cluster characterization and serial follow up with the same can be conveniently used as a tool to predict responses and identification of resistance to ongoing therapies in lung cancer patients.