Hepatocellular carcinoma (HCC) is among the most common causes of cancer death in men. Whether or not a longitudinal follow-up of circulating tumor cells (CTCs) before and at different time points during systemic/targeted therapy is useful for monitoring the treatment response of patients with locally advanced or metastatic HCC has been evaluated in this study. Blood samples (n = 104) were obtained from patients with locally advanced or metastatic HCC (n = 30) for the enrichment of CTCs by a negative selection method. Analysis of the blood samples from patients with defined disease status (n = 81) revealed that those with progressive disease (PD, n = 37) had significantly higher CTC counts compared to those with a partial response (PR) or stable disease (SD; n = 44 for PR + SD, p = 0.0002). The median CTC count for patients with PD and for patients with PR and SD was 50 (interquartile range 21-139) and 15 (interquartile range 4-41) cells/mL of blood, respectively. A longitudinal analysis of patients (n = 17) after a series of blood collections demonstrated that a change in the CTC count correlated with the patient treatment response in most of the cases and was particularly useful for monitoring patients without elevated serum alpha-fetoprotein (AFP) levels. Sequential CTC enumeration during treatment can supplement standard medical tests and benefit the management of patients with locally advanced or metastatic HCC, in particular for the AFP-low cases.

Circulating Tumor Cells (CTCs) represent an easy, repeatable and representative access to information regarding solid tumors. However, their detection remains difficult because of their paucity, their short half-life, and the lack of reliable surface biomarkers. Flow cytometry (FC) is a fast, sensitive and affordable technique, ideal for rare cells detection. Adapted to CTCs detection (i.e. extremely rare cells), most FC-based techniques require a time-consuming pre-enrichment step, followed by a 2-hours staining procedure, impeding on the efficiency of CTCs detection. We overcame these caveats and reduced the procedure to less than one hour, with minimal manipulation. First, cells were simultaneously fixed, permeabilized, then stained. Second, using low-speed FC acquisition conditions and two discriminators (cell size and pan-cytokeratin expression), we suppressed the pre-enrichment step. Applied to blood from donors with or without known malignant diseases, this protocol ensures a high recovery of the cells of interest independently of their epithelial-mesenchymal plasticity and can predict which samples are derived from cancer donors. This proof-of-concept study lays the bases of a sensitive tool to detect CTCs from a small amount of blood upstream of in-depth analyses.

Various preclinical models have been developed to clarify the pathophysiology of prostate cancer (PCa). Traditional PCa cell lines from clinical metastatic lesions, as exemplified by DU-145, PC-3, and LNCaP cells, are useful tools to define mechanisms underlying tumorigenesis and drug resistance. Cell line-based experiments, however, have limitations for preclinical studies because those cells are basically adapted to 2-dimensional monolayer culture conditions, in which the majority of primary PCa cells cannot survive. Recent tissue engineering enables generation of PCa patient-derived xenografts (PDXs) from both primary and metastatic lesions. Compared with fresh PCa tissue transplantation in athymic mice, co-injection of PCa tissues with extracellular matrix in highly immunodeficient mice has remarkably improved the success rate of PDX generation. PDX models have advantages to appropriately recapitulate the molecular diversity, cellular heterogeneity, and histology of original patient tumors. In contrast to PDX models, patient-derived organoid and spheroid PCa models in 3-dimensional culture are more feasible tools for in vitro studies for retaining the characteristics of patient tumors. In this article, we review PCa preclinical model cell lines and their sublines, PDXs, and patient-derived organoid and spheroid models. These PCa models will be applied to the development of new strategies for cancer precision medicine.