Imaging Of Circulating Tumor Cells Research Articles

Enhancing cancer detection through AI-driven high content analysis of circulating tumor cells.

3054 Background: The advancements in automated High Content Analysis (HCA) for Circulating Tumor Cells (CTCs) detection necessitate the integration of high-throughput screening (HTS) and Artificial Intelligence (AI) to improve cancer diagnosis efficiency and accuracy. Traditional approaches to cancer detection have faced limitations in speed, cost, and analysis depth, driving the need for innovative solutions in assay development and execution. Methods: We obtained peripheral blood from 150 asymptomatic individuals and 63 newly diagnosed cases of solid organ cancers including Lung, Breast, Head and Neck, Colorectal, Pancreas, Esophagus, Gynecologic cancers. We used differential apoptosis based negative enrichment technique and HCS fluorescent imaging for identification of CTCs and CTC like immune cells from the relevant blood samples. We devised AutoML algorithms which were trained on a cohort of 4,135 cropped region of interest (ROI) images of true CTCs and CTC-mimicking immune cells. These images were annotated with the coordinates of the top-left and bottom-right corners of rectangles to specify ROIs. This method employs a combination of automated sample processing, integration with the Laboratory Information Management System (LIMS) integration, digital filtration, and decision matrix algorithms for sample classification and smooth data flow. The image quality criteria were established prior to dataset analysis. The algorithms underwent training, testing, and validation based on expert recommendations, with dataset splitting of 80% for training, 10% for testing, and 10% for validation phases. Results: The automated deep learning model achieved remarkable performance metrics, presenting an accuracy of 96.66% (95% CI, 96.07%-97.19%), a specificity of 95.34% (95% CI, 94.37%-96.18%), and a sensitivity of 98.14% (95% CI, 97.44%-98.69%) in detecting true CTCs. Additional analysis included the area under the curve and confusion matrix evaluations, indicating the model's robustness in distinguishing between true positive and true negative specimens. This efficiency and precision in data handling, processing, and analysis demonstrate significant advancements in HCA screening, offering enhanced assay development, execution flexibility, and the facilitation of detailed cell-by-cell analysis for cancer detection from a large set of Peripheral Blood Mononuclear Cells (PBMCs) from samples obtained for screening or diagnosis. Conclusions: This study successfully evaluated a deep learning model for the detection of circulating tumor cells (CTCs) in peripheral blood mononuclear cells (PBMCs) using image data from HTS platform. The model achieved superior performance, demonstrating its potential for cancer detection from blood samples.

Intracellular MicroRNA Imaging and Specific Discrimination of Prostate Cancer Circulating Tumor Cells Using Multifunctional Gold Nanoprobe-Based Thermophoretic Assay.

Circulating tumor cells (CTCs) have emerged as powerful biomarkers for diagnosis of prostate cancer. However, the effective identification and concurrently accurate imaging of CTCs for early screening of prostate cancer have been rarely explored. Herein, we reported a multifunctional gold nanoprobe-based thermophoretic assay for simultaneous specific distinguishing of prostate cancer CTCs and sensitive imaging of intracellular microRNA (miR-21), achieving the rapid and precise detection of prostate cancer. The multifunctional gold nanoprobe (GNP-DNA/Ab) was modified by two types of prostate-specific antibodies, anti-PSMA and anti-EpCAM, which could effectively recognize the targeting CTCs, and meanwhile linked double-stranded DNA for further visually imaging intracellular miR-21. Upon the specific internalization of GNP-DNA/Ab by PC-3 cells, target aberrant miR-21 could displace the signal strand to recover the fluorescence signal for sensitive detection at the single-cell level, achieving single PC-3 cell imaging benefiting from the thermophoresis-mediated signal amplification procedure. Taking advantage of the sensitive miR-21 imaging performance, GNP-DNA/Ab could be employed to discriminate the PC-3 and Jurkat cells because of the different expression levels of miR-21. Notably, PC-3 cells were efficiently recognized from white blood cells, exhibiting promising potential for the early diagnosis of prostate cancer. Furthermore, GNP-DNA/Ab possessed good biocompatibility and stability. Therefore, this work provides a great tool for aberrant miRNA-related detection and specific discrimination of CTCs, achieving the early and accurate diagnosis of prostate cancer.

Glowing Octopus-Inspired Nanomachine: A Versatile Aptasensor for Efficient Capture, Imaging, Separation, and NIR-Triggered Release of Cancer Cells.

The separation and analysis of circulating tumor cells (CTCs) in liquid biopsy significantly facilitated clinical cancer diagnosis and personalized therapy. However, current methods face challenges in simultaneous efficient capturing, separation, and imaging of CTCs, and most of the devices cannot be reused/regenerated. We present here an innovative glowing octopus-inspired nanomachine (GOIN), capable of capturing, imaging, separating, and controlling the release of cancer cells from whole blood and normal cells. The GOIN comprises an aptamer-decorated magnetic fluorescent covalent organic framework (COF), which exhibits a strong affinity for nucleolin-overexpressed cancer cells through a multivalent binding effect. The captured cancer cells can be directly imaged using the intrinsic stable fluorescence of the COF layer in the GOIN. Employing magnet and NIR laser assistance enables easy separation and mild photothermal release of CTCs from the normal cells and the nanomachine without compromising cell viability. Moreover, the GOIN demonstrates a reusing capability, as the NIR-triggered CTC release is mild and nondestructive, allowing the GOIN to be reused at least three times.

Dual-source powered nanomotors coupled with dual-targeting ligands for efficient capture and detection of CTCs in whole blood and in vivo tumor imaging

Circulating tumor cells (CTCs) are important biomarkers in cancer diagnosis. However, the specific labeling of CTCs with high capture efficiency in whole blood remains a problem. Herein, a dual-source-driven nanomotor coupled with dual-targeting ligands (CD@NM) was designed for efficient capture, specific imaging and quantitative detection of CTCs. In both water and biological fluid, CD@NMs moved autonomously under the propulsion of a magnetic field and H2O2 solution, which improved the capture efficiency of CTCs to 97.50 ± 2.38%. More importantly, specific labeling of CTCs was achieved by fluorescence quenching and recovery of fluorescent carbon dots modified on the CD@NMs. As a result, the CD@NMs exhibited efficient CTC capture, specific CTC imaging and recognition in whole blood. CD@NMs were also successfully deployed in the specific imaging of tumor tissues in vivo. On this basis, CD@NMs are expected to provide a new platform for tumor diagnosis both in vitro and in vivo.

Detection and Characterization of Circulating Tumor Cells Using Imaging Flow Cytometry-A Perspective Study.

Simple SummaryLiquid biopsy is non-invasive approach used to prognose and monitor tumor progression based on the detection and examination of metastasis-related events found in the patients’ blood (such as circulating tumor cells (CTCs), extracellular vesicles, and circulating nucleic acids). Different ultrasensitive techniques are applied to study those events and the biology of tumor dissemination, which in the future might complement standard diagnostics. Here, we suggest that CTCs analysis could be improved by the usage of imaging flow cytometry, combining advantages of both standard flow cytometry (high-scale analysis) and microscopy (high resolution) to investigate detailed features of those cells. From this perspective, we discuss the potential of this technology in the CTC field and present representative images of CTCs from breast and prostate cancer patients analyzed with this method.Tumor dissemination is one of the most-investigated steps of tumor progression, which in recent decades led to the rapid development of liquid biopsy aiming to analyze circulating tumor cells (CTCs), extracellular vesicles (EVs), and circulating nucleic acids in order to precisely diagnose and monitor cancer patients. Flow cytometry was considered as a method to detect CTCs; however, due to the lack of verification of the investigated cells’ identity, this method failed to reach clinical utility. Meanwhile, imaging flow cytometry combining the sensitivity and high throughput of flow cytometry and image-based detailed analysis through a high-resolution microscope might open a new avenue in CTC technologies and provide an open-platform system alternative to CellSearch®, which is still the only gold standard in this field. Hereby, we shortly review the studies on the usage of flow cytometry in CTC identification and present our own representative images of CTCs envisioned by imaging flow cytometry providing rationale that this novel technology might be a good tool for studying tumor dissemination, and, if combined with a high CTC yield enrichment method, could upgrade CTC-based diagnostics.

Defining the dimensions of circulating tumor cells in a large series of breast, prostate, colon, and bladder cancer patients.

Circulating tumor cells (CTCs) in the blood of cancer patients are of high clinical relevance. Since detection and isolation of CTCs often rely on cell dimensions, knowledge of their size is key. We analyzed the median CTC size in a large cohort of breast (BC), prostate (PC), colorectal (CRC), and bladder (BLC) cancer patients. Images of patient-derived CTCs acquired on cartridges of the FDA-cleared CellSearch® method were retrospectively collected and automatically re-analyzed using the accept software package. The median CTC diameter (μm) was computed per tumor type. The size differences between the different tumor types and references (tumor cell lines and leukocytes) were nonparametrically tested. A total of 1962 CellSearch® cartridges containing 71612 CTCs were included. In BC, the median computed diameter (CD) of patient-derived CTCs was 12.4μm vs 18.4μm for cultured cell line cells. For PC, CDs were 10.3μm for CTCs vs 20.7μm for cultured cell line cells. CDs for CTCs of CRC and BLC were 7.5μm and 8.6μm, respectively. Finally, leukocytes were 9.4μm. CTC size differed statistically significantly between the four tumor types and between CTCs and the reference data. CTC size differences between tumor types are striking and CTCs are smaller than cell line tumor cells, whose size is often used as reference when developing CTC analysis methods. Based on our data, we suggest that the size of CTCs matters and should be kept in mind when designing and optimizing size-based isolation methods.

Time-gated luminescence imaging for background free in vivo tracking of single circulating tumor cells.

Fluorescence imaging is severely limited by the background and autofluorescence of tissues for in vivo detection of circulating tumor cells (CTCs). Time-gated luminescence (TGL) imaging, in combination with luminescent probes that possess hundreds of microsecond emission lifetimes, can be used to effectively suppress this background, which has predominantly nanosecond lifetimes. This Letter demonstrates the feasibility of TGL imaging using luminescent probes for the in vivo real time imaging and tracking of single CTCs circulating freely in the blood vessels with higher accuracy given by substantially higher signal-to-noise ratio. The luminescent probe used in this Letter was a commercial Eu3+ chelate (EuC) nanosphere with a super-long lifetime of near 800 µs, which enabled TGL imaging to achieve background-free detection with ∼5 times higher SNR versus steady state. Phantom and in vivo mouse studies indicated that EuC labeled tumor cells moving in medium or bloodstream at the speed of 1-2 mm/s could be captured in real time.

The Use of Three-Dimensional DNA Fluorescent In Situ Hybridization (3D DNA FISH) for the Detection of Anaplastic Lymphoma Kinase (ALK) in Non-Small Cell Lung Cancer (NSCLC) Circulating Tumor Cells.

Tumor tissue biopsy is often limited for non-small cell lung cancer (NSCLC) patients and alternative sources of tumoral information are desirable to determine molecular alterations such as anaplastic lymphoma kinase (ALK) rearrangements. Circulating tumor cells (CTCs) are an appealing component of liquid biopsies, which can be sampled serially over the course of treatment. In this study, we enrolled a cohort of ALK-positive (n = 8) and ALK-negative (n = 12) NSCLC patients, enriched for CTCs using spiral microfluidic technology and performed DNA fluorescent in situ hybridization (FISH) for ALK. CTCs were identified in 12/20 NSCLC patients ranging from 1 to 26 CTCs/7.5 mL blood. Our study revealed that 3D imaging of CTCs for ALK translocations captured a well-defined separation of 3′ and 5′ signals indicative of ALK translocations and overlapping 3′/5′ signal was easily resolved by imaging through the nuclear volume. This study provides proof-of-principle for the use of 3D DNA FISH in the determination of CTC ALK translocations in NSCLC.

388P - To identify circulating tumour cells by machine learning approach

BackgroundMore and more evidences suggest that circulating tumor cells (CTC) may be a good biomarker, not only as a prognostic indicator for tumors, but also for monitoring therapeutic effect and recurrence. However, the detection of CTC usually requires the final artificial judgment. This requires experienced pathologists and increases their workload. The application of machine learning in medical image recognition can effectively improve the level of automation and reduce the workload. So we hope to use machine learning to identify CTCs. MethodsFirst, the python's openCV software package is used to segment the images of CTCs by image denoising, image filtering, edge detection, image expansion and contraction techniques. Secondly, the segmented cell images as a training set are trained using the CNN deep learning network. The CNN deep learning network includes input layer, intermediate hidden layer, and output layer. The middle hidden layer contains three layers, namely layer1, layer2 and lager3. Each intermediate hidden layer further includes convolution layer, excitation layer, and pooling layer. After the input layer, the cell images first enter the first intermediate hidden layer. The convolution layer of the first intermediate hidden layer is composed of 32 5x5 convolution kernels, which are then output to the pooling layer for dimension reduction through the ReLU excitation layer. After dimension reduction, the data is output from the first hidden layer to complete an entire feature extraction process. Then, through the second and third intermediate hidden layers in sequence, all feature extraction is completed. Finally, it enters the output layer and output the result, ie, CTCs or non-CTCs. ResultsWe took 2920 cells from 732 patients for training and testing. Among them, 2000 cells were used as training set and 920 cells were used as testing set. The sensitivity and specificity of recognition reached 86.1% and 84.5%, respectively. ConclusionsTo identify CTC by machine learning can reach high sensitivity and specificity. We are further revising our methods of deep learning to achieve greater recognition effect. Legal entity responsible for the studyThe authors. FundingHas not received any funding. DisclosureAll authors have declared no conflicts of interest.

Circulating Tumor Cells: High-Throughput Imaging of CTCs and Bioinformatic Analysis.

Circulating tumor cells (CTCs) represent novel biomarkers, since they are obtainable through a simple and noninvasive blood draw or liquid biopsy. Here, we review the high-definition single-cell analysis (HD-SCA) workflow, which brings together modern methods of immunofluorescence with more sophisticated image processing to rapidly and accurately detect rare tumor cells among the milieu of platelets, erythrocytes, and leukocytes in the peripheral blood. In particular, we discuss progress in methods to measure CTC morphology and subcellular protein expression, and we highlight some initial applications that lead to fundamental new insights about the hematogenous phase of cancer, as well as its performance in early-stage diagnosis and treatment monitoring. We end with an outlook on how to further probe CTCs and the unique advantages of the HD-SCA workflow for improving the precision of cancer care.

Ultrasensitive Fluorescence Monitoring and in Vivo Live Imaging of Circulating Tumor Cell-Derived miRNAs Using Molecular Beacon System.

Circulating tumor cells (CTCs) have considerable clinical significance in cancer progression and prognosis. In this context, CTC-derived microRNAs (miRs) in blood and tissues have been proposed as the novel noninvasive biomarkers for monitoring tumor progression, especially at the early stages. To monitor circulating miRs, a tool should have high sensitivity, be a simple procedure, and allow detection in very small volumes. Thus, we designed a sensing tool for sensitive monitoring of blood or tissue miRs using a fluorophore-quencher probe-based molecular beacon (MB). This MB-based tool displayed an ultrasensitive limit of detection (LOD) level of 6.7 × 10-17 M and 8.7 × 10-17 for metastasis-derived miR-21a and miR-221, respectively. It also can discriminate miR-21a/221 from both guide strand miRs and its precursor forms (pre-miR). Furthermore, the tool discriminated between blood and tissue-related miR-21a/221-expression and detected metastasis and epithelial-mesenchymal transition and also describe a noninvasive miR fluorescence imaging of CTCs in a mouse model, showing the potential for clinical diagnosis and prognosis.

Enumeration of circulating tumor cells using a non–EpCAM-dependent device.

e23064 Background: One component of the metastatic process is the circulation of tumor cells. Identification of these circulating tumor cells (CTCs) provides important insights into cancer biology. There is enormous heterogeneity among CTCs, necessitating capture of both epithelial and non-epithelial subtypes. Detection and monitoring of CTCs will provide a non-invasive biomarker for the detection of early progression and assessing therapeutic response. Methods: The AxonDx nCyte™ System is designed to detect rare circulating cells from peripheral blood without enrichment or selection. An epi-fluorescence microscope scans pathology glass slides on which a sample has been applied, and high-resolution images of potential CTCs are captured at four emission wavelengths. The proprietary Cancer Cell Detection Cocktail is designed to detect CTCs from numerous solid tumors, including various stages (e.g., mesenchymal-epithelial transformation) and stem cells. The software distinguishes cancer-derived material (nucleus+/cytokeratin+/WBC marker-/ > 4 µm); from leukocytes (nucleus+/cytokeratin-/WBC marker+). Vimentin is used to distinguish epithelial from mesenchymal CTCs. We obtained informed consent from 32 genitourinary cancer patients to participate in this IRB-approved study. Red blood cells from 6 mL of whole blood were lysed and removed from 32 patient samples. Nucleated cells from the blood sample were fixed, permeabilized, and stained with AxonDx’s Cancer Cell Detection Cocktail. The prepared cells were dispensed onto two microscope slides, cover-slipped, and analyzed. Results: We detected CTCs in 32 of 32 samples (100%), in a range of 0.6–64.3 CTCs/mL. Number of CTCs correlated with metastatic tumor burden. Conclusions: The AxonDx nCYTE™ system can reliably detect CTCs from genitourinary patient samples. The AxonDx nCyte™ System has also successfully detected CTCs in other tumor types, e.g., breast, lung, CRC, and HCC. Because the system is not EpCAM-dependent, it captures the heterogeneous population of CTCs. Prospective longitudinal studies are ongoing to determine trends in CTC counts.

Circulating Tumor Cells: Fluid Surrogates of Solid Tumors.

Evaluation of circulating tumor cells (CTCs) has demonstrated clinical validity as a prognostic tool based on enumeration, but since the introduction of this tool to the clinic in 2004, further clinical utility and widespread adoption have been limited. However, immense efforts have been undertaken to further the understanding of the mechanisms behind the biology and kinetics of these rare cells, and progress continues toward better applicability in the clinic. This review describes recent advances within the field, with a particular focus on understanding the biological significance of CTCs, and summarizes emerging methods for identifying, isolating, and interrogating the cells that may provide technical advantages allowing for the discovery of more specific clinical applications. Included is an atlas of high-definition images of CTCs from various cancer types, including uncommon CTCs captured only by broadly inclusive nonenrichment techniques.

In Vivo Photoswitchable Flow Cytometry for Direct Tracking of Single Circulating Tumor Cells

Photoswitchable fluorescent proteins (PSFPs) that change their color in response to light have led to breakthroughs in studying static cells. However, using PSFPs to study cells in dynamic conditions is challenging. Here we introduce a method for invivo ultrafast photoswitching of PSFPs that provides labeling and tracking of single circulating cells. Using invivo multicolor flow cytometry, this method demonstrated the capability for studying recirculation, migration, and distribution of circulating tumor cells (CTCs) during metastasis progression. In tumor-bearing mice, it enabled monitoring of real-time dynamics of CTCs released from primary tumor, identifying dormant cells, and imaging of CTCs colonizing a primary tumor (self-seeding) or existing metastasis (reseeding). Integration of genetically encoded PSFPs, fast photoswitching, flow cytometry, and imaging makes invivo single cell analysis in the circulation feasible to provide insights into the behavior of CTCs and potentially immune-related and bacterial cells in circulation.

Imaging circulating tumor cells in freely moving awake small animals using a miniaturized intravital microscope.

Metastasis, the cause for 90% of cancer mortality, is a complex and poorly understood process involving the invasion of circulating tumor cells (CTCs) into blood vessels. These cells have potential prognostic value as biomarkers for early metastatic risk. But their rarity and the lack of specificity and sensitivity in measuring them render their interrogation by current techniques very challenging. How and when these cells are circulating in the blood, on their way to potentially give rise to metastasis, is a question that remains largely unanswered. In order to provide an insight into this "black box" using non-invasive imaging, we developed a novel miniature intravital microscopy (mIVM) strategy capable of real-time long-term monitoring of CTCs in awake small animals. We established an experimental 4T1-GL mouse model of metastatic breast cancer, in which tumor cells express both fluorescent and bioluminescent reporter genes to enable both single cell and whole body tumor imaging. Using mIVM, we monitored blood vessels of different diameters in awake mice in an experimental model of metastasis. Using an in-house software algorithm we developed, we demonstrated in vivo CTC enumeration and computation of CTC trajectory and speed. These data represent the first reported use we know of for a miniature mountable intravital microscopy setup for in vivo imaging of CTCs in awake animals.

Comparative morphometric analysis of breast-circulating tumor cells and their corresponding solid tumor cytology: A case study.

1151 Background: Evaluation of circulating tumor cells (CTCs) from solid tumors is emerging as a clinically relevant tool. Systematic cytometric measurements of CTCs and comparison to their solid tumor counterparts are lacking from the literature to date. Methods: We identified 51 CTCs in 0.5 mL of blood from a patient with metastatic breast cancer, using a slide-based fluorescence assay. We compared images of CTCs to images of tumor cells aspirated from a solid axillary lymph node metastasis. CTCs were imaged using a fluorescent microscope and measurements were made for the CTCs, and a control of 500 WBCs. Images of cells from a solid focus of metastatic tumor were obtained from archival air dried cytology aspirate smears, and similar measurements were made. Results: Measurements show CTC nuclei to be twice the size of circulating WBC nuclei in this patient. The N/C ratios of the tumor cells in the two sites are similar. Using WBCs as a control for nuclear size between specimens, we determined the fixati...

Abstract 3760: Development of automatic FISH probe counting in CTC

Abstract Introduction: Presence of Circulating Tumor Cells (CTC) in blood of patients with metastatic carcinomas has been associated with poor progression free and overall survival. Characterization of CTC can be performed by Fluorescence In Situ Hybridization (FISH), however counting of FISH dots by human reviewers can be tiring and subjective and thus likely produces variable outcomes. We investigated whether automated counting of FISH dots in CTC is comparable to the counts obtained by expert reviewers. Material and Methods: Samples processed on the CellSearchTM system for CTC counting were hybridized with fluorescent DNA probes targeting the HER2/neu gene region and the centromeric region of chromosome 17 or the centromeric regions of chromosome 1,7,8,17. (1) For optimization of the algorithm 492 Z-stacks from leukocytes carried over through the CellSearch procedure were recorded and a maximum intensity profile (MIP) was created. Five reviewers counted FISH dots in the MIP data set to create a ground truth. The automatic counting algorithm was validated in a set of stored images of CTC probed for chromosome 1,7,8,17 from castration resistant prostate cancer patients (CRPC).(1) Results: The data set with carried-over leukocytes was counted reliably by the algorithm: 97.8% of the HER2/neu FISH dots and 97.5% of the centromeric 17 dots were counted equally by the PC and the reviewers, regarding only the subset of images for which all the reviewers agreed. The mean intra-reviewer agreement was 96.5%. In the validation set copy number of chromosome 1 in carried-over leukocytes scored by an expert agreed in 50.8% with the automated count, for chromosome 7 34.4% for chromosome 8 22.8.% and for chromosome 17 55.0%. For CTC in the validation set copy number of chromosome 1 scored by an expert agreed in 71.6% with the automated count, for chromosome 7 56.2% for chromosome 8 64.8% and for chromosome 17 41.0%. For copy numbers larger than 6 both the expert and automated count were recorded as larger than 6. Agreement between the expert count and automated count did not significantly alter with the detected copy number. Over-count in the validation set was largely due to clustering of nuclei that were counted as one cell. Conclusions: Automatic FISH dot counting in CTC images is feasible for images where reviewers agree upon. The intra-reviewer variation of 3.5% shows that reviewers have ambiguous rules and are not reliable. This variation is closely related to the heterogeneity -size and shape- of the FISH dots. The PC has a reproducibility of 100% and is thus a good replacement for human reviewers. 1. Swennenhuis JF, Tibbe AGJ, Levink R, Sipkema RCJ, Terstappen LWMM. Characterization of Circulating Tumor Cells by Fluorescence In-Situ Hybridization. Cytometry Part A 75A: 520-527, 2009. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 3760.