Three-dimensional (3D) tumor cell cultures grown in laminin-rich-extracellular matrix (lrECM) are considered to reflect human tumors more realistic as compared to cells grown as monolayer on plastic. Here, we systematically investigated the impact of ECM on phenotype, gene expression, EGFR signaling pathway, and on EGFR inhibition in commonly used colorectal cancer (CRC) cell lines. LrECM on-top (3D) culture assays were performed with the CRC cell lines SW-480, HT-29, DLD-1, LOVO, CACO-2, COLO-205 and COLO-206F. Morphology of lrECM cultivated CRC cell lines was determined by phase contrast and confocal laser scanning fluorescence microscopy. Proliferation of cells was examined by MTT assay, invasive capacity of the cell lines was assayed using Matrigel-coated Boyden chambers, and migratory activity was determined employing the Fence assay. Differential gene expression was analyzed at the transcriptional level by the Agilent array platform. EGFR was inhibited by using the specific small molecule inhibitor AG1478. A specific spheroid growth pattern was observed for all investigated CRC cell lines. DLD-1, HT-29 and SW-480 and CACO-2 exhibited a clear solid tumor cell formation, while LOVO, COLO-205 and COLO-206F were characterized by forming grape-like structures. Although the occurrence of a spheroid morphology did not correlate with an altered migratory, invasive, or proliferative capacity of CRC cell lines, gene expression was clearly altered in cells grown on lrECM as compared to 2D cultures. Interestingly, in KRAS wild-type cell lines, inhibition of EGFR was less effective in lrECM (3D) cultures as compared to 2D cell cultures. Thus, comparing both 2D and 3D cell culture models, our data support the influence of the ECM on cancer growth. Compared to conventional 2D cell culture, the lrECM (3D) cell culture model offers the opportunity to investigate permanent CRC cell lines under more physiological conditions, i.e. in the context of molecular therapeutic targets and their pharmacological inhibition.

Significance: The infrequent detection of circulating tumor cells (CTCs) has hindered their clinical implication and their potential use in the sense of a “liquid biopsy” for cancer diagnosis and therapy. Hypothesizing that the limited blood volume commonly used for CTC analysis (1–10 mL) accounts for variable detection rates, we used leukapheresis to screen large blood volumes for CTCs. This enabled a more reliable detection of CTCs at high frequency even in nonmetastatic cancer patients. Thus, diagnostic leukapheresis may facilitate the routine clinical use of CTCs as biomarkers for personalized medicine. Combined with technologies for single-cell molecular genetics or cell biology, it may significantly improve prediction of therapy response and monitoring, especially in early systemic cancer.

After a CellSearch-processed circulating tumor cell (CTC) sample is imaged, a segmentation algorithm selects nucleic acid positive (DAPI+), cytokeratin-phycoerythrin expressing (CK-PE+) events for further review by an operator. Failures in this segmentation can result in missed CTCs. The CellSearch segmentation algorithm was not designed to handle samples with high cell density, such as diagnostic leukapheresis (DLA) samples. Here, we evaluate deep-learning-based segmentation method StarDist as an alternative to the CellSearch segmentation. CellSearch image archives from 533 whole blood samples and 601 DLA samples were segmented using CellSearch and StarDist and inspected visually. In 442 blood samples from cancer patients, StarDist segmented 99.95% of CTC segmented by CellSearch, produced good outlines for 98.3% of these CTC, and segmented 10% more CTC than CellSearch. Visual inspection of the segmentations of DLA images showed that StarDist continues to perform well when the cell density is very high, whereas CellSearch failed and generated extremely large segmentations (up to 52% of the sample surface). Moreover, in a detailed examination of seven DLA samples, StarDist segmented 20% more CTC than CellSearch. Segmentation is a critical first step for CTC enumeration in dense samples and StarDist segmentation convincingly outperformed CellSearch segmentation.

Purpose: Circulating tumor cells (CTCs) have clinical relevance, but their study has been limited by their low frequency. Experimental Design: We evaluated liquid biopsies by apheresis to increase CTC yield from patients suffering from metastatic prostate cancer, allow precise gene copy-number calls, and study disease heterogeneity. Results: Apheresis was well tolerated and allowed the separation of large numbers of CTCs; the average CTC yield from 7.5 mL of peripheral blood was 167 CTCs, whereas the average CTC yield per apheresis (mean volume: 59.5 mL) was 12,546 CTCs. Purified single CTCs could be isolated from apheresis product by FACS sorting; copy-number aberration (CNA) profiles of 185 single CTCs from 14 patients revealed the genomic landscape of lethal prostate cancer and identified complex intrapatient, intercell, genomic heterogeneity missed on bulk biopsy analyses. Conclusions: Apheresis facilitated the capture of large numbers of CTCs noninvasively with minimal morbidity and allowed the deconvolution of intrapatient heterogeneity and clonal evolution. Clin Cancer Res; 24(22); 5635–44. ©2018 AACR.

Circulating tumor cells (CTCs) are very attractive surrogate markers for systemic cancer. Currently, major efforts are being made to use these rare cells in the sense of a liquid biopsy to gain mol...