Phenotyping of rare circulating cells in the blood of non-metastatic breast cancer patients using microfluidic Labyrinth technology

Antigen-independent single-cell circulating tumor cell detection using deep-learning-assisted biolasers

Circulating tumor cells (CTCs) in the bloodstream are important biomarkers for clinical prognosis of cancers. Current CTC identification methods are based on immuno-labeling, which depends on the differential expression of specific antigens between the cancer cells and white blood cells. Here we present an antigen-independent CTC detection method utilizing a deep-learning-assisted single-cell biolaser. Single-cell lasers were measured from nucleic-acid-stained cells inside optical cavities. A Deep Cell-Laser Classifier (DCLC) was developed to detect tumor cells from a patient CTC-derived pancreatic cell line using their unique single-cell lasing mode patterns. We further showed that the knowledge learned from one type of pancreatic cancer cell line can be transferred to detect other pancreatic cancer cell lines by the DCLC in zero-shot. A sensitivity of 94.3% and a specificity of 99.9% were achieved. Finally, enumeration was performed on CTCs obtained from pancreatic cancer patients. We further demonstrated the DCLC's ability in zero-shot generalization of enumeration on lung cancer patients' CTCs. The counting trends were consistent with those observed using conventional immunofluorescence imaging techniques. Employing our DCLC model, single-cell lasers open new avenues for both future biological studies and clinical applications, including classification of cell types and identification of rare cells.

The integrated on-chip isolation and detection of circulating tumour cells

Circulating tumour cells (CTCs) are cancer cells shed from a primary tumour which intravasate into the blood stream and have the potential to extravasate into distant tissues, seeding metastatic lesions. As such, they can offer important insight into cancer progression with their presence generally associated with a poor prognosis. The detection and enumeration of CTCs is, therefore, critical to guiding clinical decisions during treatment and providing information on disease state. CTC isolation has been investigated using a plethora of methodologies, of which immunomagnetic capture and microfluidic size-based filtration are the most impactful to date. However, the isolation and detection of CTCs from whole blood comes with many technical barriers, such as those presented by the phenotypic heterogeneity of cell surface markers, with morphological similarity to healthy blood cells, and their low relative abundance (∼1 CTC/1 billion blood cells). At present, the majority of reported methods dissociate CTC isolation from detection, a workflow which undoubtedly contributes to loss from an already sparse population. This review focuses on developments wherein isolation and detection have been integrated into a single-step, microfluidic configuration, reducing CTC loss, increasing throughput, and enabling an on-chip CTC analysis with minimal operator intervention. Particular attention is given to immune-affinity, microfluidic CTC isolation, coupled to optical, physical, and electrochemical CTC detection (quantitative or otherwise).

Single-Cell Proliferation Microfluidic Device for High Throughput Investigation of Replicative Potential and Drug Resistance of Cancer Cells

Introduction

Cell proliferation represents a major hallmark of cancer biology, and manifests itself in the assessment of tumor growth, drug resistance and metastasis. Tracking cell proliferation or cell fate at the single-cell level can reveal phenotypic heterogeneity. However, characterization of cell proliferation is typically done in bulk assays which does not inform on cells that can proliferate under given environmental perturbations. Thus, there is a need for single-cell approaches that allow longitudinal tracking of the fate of a large number of individual cells to reveal diverse phenotypes.

Methods

We fabricated a new microfluidic architecture for high efficiency capture of single tumor cells, with the capacity to monitor cell divisions across multiple daughter cells. This single-cell proliferation (SCP) device enabled the quantification of the fate of more than 1000 individual cancer cells longitudinally, allowing comprehensive profiling of the phenotypic heterogeneity that would be otherwise masked in standard cell proliferation assays. We characterized the efficiency of single cell capture and demonstrated the utility of the SCP device by exposing MCF-7 breast tumor cells to different doses of the chemotherapeutic agent doxorubicin.

Results

The single cell trapping efficiency of the SCP device was found to be ~ 85%. At the low doses of doxorubicin (0.01 µM, 0.001 µM, 0.0001 µM), we observed that 50-80% of the drug-treated cells had undergone proliferation, and less than 10% of the cells do not proliferate. Additionally, we demonstrated the potential of the SCP device in circulating tumor cell applications where minimizing target cell loss is critical. We showed selective capture of breast tumor cells from a binary mixture of cells (tumor cells and white blood cells) that was isolated from blood processing. We successfully characterized the proliferation statistics of these captured cells despite their extremely low counts in the original binary suspension.

Conclusions

The SCP device has significant potential for cancer research with the ability to quantify proliferation statistics of individual tumor cells, opening new avenues of investigation ranging from evaluating drug resistance of anti-cancer compounds to monitoring the replicative potential of patient-derived cells.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12195-023-00773-z.

Recent Progress in Enhanced Cancer Diagnosis, Prognosis, and Monitoring Using a Combined Analysis of the Number of Circulating Tumor Cells (CTCs) and Other Clinical Parameters

Analysis of circulating tumor cells (CTCs) holds promise to diagnose cancer or monitor its development. Among the methods, counting CTC numbers in blood samples could be the simplest way to implement it. Nevertheless, its clinical utility has not yet been fully accepted. The reasons could be due to the rarity and heterogeneity of CTCs in blood samples that could lead to misleading results from assays only based on single CTC counts. To address this issue, a feasible direction is to combine the CTC counts with other clinical data for analysis. Recent studies have demonstrated the use of this new strategy for early detection and prognosis evaluation of cancers, or even for the distinguishment of cancers with different stages. Overall, this approach could pave a new path to improve the technical problems in the clinical applications of CTC counting techniques. In this review, the information relevant to CTCs, including their characteristics, clinical use of CTC counting, and technologies for CTC enrichment, were first introduced. This was followed by discussing the challenges and new perspectives of CTC counting techniques for clinical applications. Finally, the advantages and the recent progress in combining CTC counts with other clinical parameters for clinical applications have been discussed.

Comprehensive Profiling of Cancer-Associated Cells in the Blood of Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy to Predict Pathological Complete Response

Neoadjuvant chemotherapy (NAC) can affect pathological complete response (pCR) in breast cancers; the resection that follows identifies patients with residual disease who are then offered second-line therapies. Circulating tumor cells (CTCs) and cancer-associated macrophage-like cells (CAMLs) in the blood can be used as potential biomarkers for predicting pCR before resection. CTCs are of epithelial origin that undergo epithelial-to-mesenchymal transition to become more motile and invasive, thereby leading to invasive mesenchymal cells that seed in distant organs, causing metastasis. Additionally, CAMLs in the blood of cancer patients are reported to either engulf or aid the transport of cancer cells to distant organs. To study these rare cancer-associated cells, we conducted a preliminary study where we collected blood from patients treated with NAC after obtaining their written and informed consent. Blood was collected before, during, and after NAC, and Labyrinth microfluidic technology was used to isolate CTCs and CAMLs. Demographic, tumor marker, and treatment response data were collected. Non-parametric tests were used to compare pCR and non-pCR groups. Univariate and multivariate models were used where CTCs and CAMLs were analyzed for predicting pCR. Sixty-three samples from 21 patients were analyzed. The median(IQR) pre-NAC total and mesenchymal CTC count/5 mL was lower in the pCR vs. non-pCR group [1(3.5) vs. 5(5.75); p = 0.096], [0 vs. 2.5(7.5); p = 0.084], respectively. The median(IQR) post-NAC CAML count/5 mL was higher in the pCR vs. non-pCR group [15(6) vs. 6(4.5); p = 0.004]. The pCR group was more likely to have >10 CAMLs post-NAC vs. non-pCR group [7(100%) vs. 3(21.4%); p = 0.001]. In a multivariate logistic regression model predicting pCR, CAML count was positively associated with the log-odds of pCR [OR = 1.49(1.01, 2.18); p = 0.041], while CTCs showed a negative trend [Odds Ratio (OR) = 0.44(0.18, 1.06); p = 0.068]. In conclusion, increased CAMLs in circulation after treatment combined with lowered CTCs was associated with pCR.

Phenotypic Plasticity in Circulating Tumor Cells Is Associated with Poor Response to Therapy in Metastatic Breast Cancer Patients

Circulating tumor cells (CTCs) are indicators of metastatic spread and progression. In a longitudinal, single-center trial of patients with metastatic breast cancer starting a new line of treatment, a microcavity array was used to enrich CTCs from 184 patients at up to 9 timepoints at 3-month intervals. CTCs were analyzed in parallel samples from the same blood draw by imaging and by gene expression profiling to capture CTC phenotypic plasticity. Enumeration of CTCs by image analysis relying primarily on epithelial markers from samples obtained before therapy or at 3-month follow-up identified the patients at the highest risk of progression. CTC counts decreased with therapy, and progressors had higher CTC counts than non-progressors. CTC count was prognostic primarily at the start of therapy in univariate and multivariate analyses but had less prognostic utility at 6 months to 1 year later. In contrast, gene expression, including both epithelial and mesenchymal markers, identified high-risk patients after 6-9 months of treatment, and progressors had a shift towards mesenchymal CTC gene expression on therapy. Cross-sectional analysis showed higher CTC-related gene expression in progressors 6-15 months after baseline. Furthermore, patients with higher CTC counts and CTC gene expression experienced more progression events. Longitudinal time-dependent multivariate analysis indicated that CTC count, triple-negative status, and CTC expression of FGFR1 significantly correlated with inferior progression-free survival while CTC count and triple-negative status correlated with inferior overall survival. This highlights the utility of protein-agnostic CTC enrichment and multimodality analysis to capture the heterogeneity of CTCs.