StarDist Image Segmentation Improves Circulating Tumor Cell Detection

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

StarDist Image Segmentation Improves Circulating Tumor Cell Detection

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

Serum microRNA profiles as prognostic or predictive markers in the multimodality treatment of patients with gastric cancer

Despite the implementation of multimodality treatment strategies, the persistently poor prognosis of gastric cancer patients is predominantly caused by the lack of predictive markers for response assessment in the neoadjuvant setting, preventing individualized therapy. Therefore, the identification of novel predictive and prognostic markers for application in the multimodality treatment of gastric cancer patients is required. The aim of the present study was to characterize the serum microRNA (miRNA/miR) profile of gastric cancer patients undergoing multimodality therapy to identify possible prognostic and predictive markers. The study consisted of 32 patients with gastric cancer who had undergone either primary surgical resection (n=14) or neoadjuvant therapy followed by surgical resection (n=18). Histopathological regression was defined as a major histopathological response when the resected specimens contained <10% vital residual tumor cells. Intratumoral miRNA was isolated from pre-operative or post-neoadjuvant blood serum samples. Initially, microarray analyses were performed in six of the patients that received neoadjuvant treatment (three responders versus three non-responders), to assess the amplification profile of dysregulated miRNAs. Based on these findings, possible predictive or prognostic markers were validated in all study patients by performing single reverse transcription-polymerase chain reaction (RT-PCR) analysis. Depending on the extent of the histopathological regression, a differential miRNA expression profile was identified in the microarray analyses. Based on the amplification profile, miR-21, miR-29a and miR-221 were selected for additional validation. However, the single RT-PCR measurements of the three selected miRNAs did not exhibit any prognostic or predictive value in the patients treated with primary resection or neoadjuvant therapy and resection. Thus, the current pilot study failed to identify a prognostic or predictive value in selected miRNAs using single RT-PCR measurements, however, the microarray results revealed a differential microRNA expression profile depending on the histopathological regression. The findings of the present study may have been affected by the small sample size

Diagnostic Leukapheresis Enables Reliable Transcriptomic Profiling of Single Circulating Tumor Cells to Characterize Inter-Cellular Heterogeneity in Terms of Endocrine Resistance

Circulating tumor cells (CTCs) hold great promise with regard to prognosis, treatment optimization, and monitoring of breast cancer patients. Single CTC transcriptome profiling might help reveal valuable information concerning intra-patient heterogeneity relevant to therapeutic interventions. In this study, we combined Diagnostic Leukapheresis (DLA), which is a microfluidic enrichment using the ParsortixTM system, micromanipulation with CellCelectorTM and subsequent single cell multi-marker transcriptome profiling. First, a PCR panel consisting of 30 different endocrine resistance and phenotypic marker genes was validated for single cell profiling by using different breast cancer cell lines. Second, this panel was applied to characterize uncultured and cultured CTCs, which were enriched from a cryopreserved DLA product obtained from a patient suffering from metastatic breast cancer resistant to endocrine therapy. Gene expression profiles of both CTC populations uncovered inter CTC heterogeneity for transcripts, which are associated with response or resistance to endocrine therapy (e.g., ESR1, HER2, FGFR1). Hierarchical clustering revealed CTC subpopulations with different expressions of transcripts regarding the CTCs&rsquo; differential phenotypes (EpCAM, CD44, CD24, MYC, MUC1) and of transcripts involved in endocrine signaling pathways (FOXO, PTEN). Moreover, ER-positive CTCs exhibited significant higher expression of Cyclin D1, which might be relevant for CDK4/6 inhibitor therapies. Overall, gene expression profiles of uncultured and cultured CTCs resulted in a partly combined grouping. Our findings demonstrate that multi-marker RNA profiling of enriched single uncultured CTCs and cultured CTCs form cryopreserved DLA samples may provide important insights into intra-patient heterogeneity relevant for targeted therapies and therapy resistance

Toward a real liquid biopsy in metastatic breast and prostate cancer: Diagnostic LeukApheresis increases CTC yields in a European prospective multicenter study (CTCTrap)

Frequently, the number of circulating tumor cells (CTC) isolated in 7.5 mL of blood is too small to reliably determine tumor heterogeneity and to be representative as a “liquid biopsy”. In the EU FP7 program CTCTrap, we aimed to validate and optimize the recently introduced Diagnostic LeukApheresis (DLA) to screen liters of blood. Here we present the results obtained from 34 metastatic cancer patients subjected to DLA in the participating institutions. About 7.5 mL blood processed with CellSearch® was used as “gold standard” reference. DLAs were obtained from 22 metastatic prostate and 12 metastatic breast cancer patients at four different institutions without any noticeable side effects. DLA samples were prepared and processed with different analysis techniques. Processing DLA using CellSearch resulted in a 0–32 fold increase in CTC yield compared to processing 7.5 mL blood. Filtration of DLA through 5 μm pores microsieves was accompanied by large CTC losses. Leukocyte depletion of 18 mL followed by CellSearch yielded an increase of the number of CTC but a relative decrease in yield (37%) versus CellSearch DLA. In four out of seven patients with 0 CTC detected in 7.5 mL of blood, CTC were detected in DLA (range 1–4 CTC). The CTC obtained through DLA enables molecular characterization of the tumor. CTC enrichment technologies however still need to be improved to isolate all the CTC present in the DLA