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.

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.

Detection of live breast cancer cells in bright-field microscopy images containing white blood cells by image analysis and deep learning.

SIGNIFICANCE: Circulating tumor cells (CTCs) are important biomarkers for cancer management. Isolated CTCs from blood are stained to detect and enumerate CTCs. However, the staining process is laborious and moreover makes CTCs unsuitable for drug testing and molecular characterization. AIM: The goal is to develop and test deep learning (DL) approaches to detect unstained breast cancer cells in bright-field microscopy images that contain white blood cells (WBCs). APPROACH: We tested two convolutional neural network (CNN) approaches. The first approach allows investigation of the prominent features extracted by CNN to discriminate in vitro cancer cells from WBCs. The second approach is based on faster region-based convolutional neural network (Faster R-CNN). RESULTS: Both approaches detected cancer cells with higher than 95% sensitivity and 99% specificity with the Faster R-CNN being more efficient and suitable for deployment presenting an improvement of 4% in sensitivity. The distinctive feature that CNN uses for discrimination is cell size, however, in the absence of size difference, the CNN was found to be capable of learning other features. The Faster R-CNN was found to be robust with respect to intensity and contrast image transformations. CONCLUSIONS: CNN-based DL approaches could be potentially applied to detect patient-derived CTCs from images of blood samples.

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

Label-free technologies for isolating rare circulating cells in breast cancer patients are widely available; however, they are mostly validated on metastatic patient blood samples. Given the need to use blood-based biomarkers to inform on disease progression and treatment decisions, it is important to validate these technologies in non-metastatic patient blood samples. In this study, we specifically focus on a recently established label-free microfluidic technology Labyrinth and assess its capabilities to phenotype a variety of rare circulating tumor cells indicative of epithelial-to-mesenchymal transition as well as cancer-associated macrophage-like (CAML) cells. We specifically chose a patient cohort that is non-metastatic and selected to undergo neoadjuvant chemotherapy to assess the performance of the Labyrinth technology. We enrolled 21 treatment naïve non-metastatic breast cancer patients of various disease stages. Our results indicate that (i) Labyrinth microfluidic technology is successfully able to isolate different phenotypes of CTCs despite the counts being low. (ii) Invasive phenotypes of CTCs such as transitioning CTCs and mesenchymal CTCs were found to be present in high numbers in stage III patients as compared to stage II patients. (iii) As the total load of CTCs increased, the mesenchymal CTCs were found to be increasing. (iv) Labyrinth was able to isolate CAMLs with the counts being higher in stage III patients as compared to stage II patients. Our study demonstrates the ability of the Labyrinth microfluidic technology to isolate rare cancer-associated cells from the blood of treatment naïve non-metastatic breast cancer patients, laying the foundation for tracking oncogenic spread and immune response in patients undergoing neoadjuvant chemotherapy.

Design of a Microfluidic Bleeding Chip to Evaluate Antithrombotic Agents for Use in COVID-19 Patients.

INTRODUCTION: Interventions that could prevent thrombosis, clinical decompensation, and respiratory compromise in patients with novel coronavirus disease (COVID-19) are key to decrease mortality rate. Studies show that profound cytokine release and excessive activation of blood coagulation appear to be key drivers of COVID-19 associated mortality. Since limited in vitro methods exist for assessing the effects of anticoagulants on hemostasis, the development of novel therapies to safely prevent thrombosis in COVID-19 patients relies on preclinical animal models and early phase human trials. Herein we present the design of a microfluidic "bleeding chip" to evaluate the effects of antithrombotic therapies on hemostatic plug formation in vitro. METHODS: The design of the microfluidic device consists of two orthogonal channels: an inlet that serves as a model blood vessel, and a bleeding channel to model hemostatic plug formation at sites of compromised endothelial barrier function. This is achieved by placing a series of 3 pillars spaced 10 µm apart at the intersection of the two channels. The pillars and bleeding channel are coated with the extracellular matrix protein collagen. RESULTS: Perfusion of human whole blood through the microfluidic bleeding chip led to initial platelet adhesion and aggregation at the pillars followed by hemostatic plug formation and occlusion of the bleeding channel. CONCLUSIONS: Safe and effective mitigating agents are needed for treatment and prevention of thrombotic complications in COVID-19 patients. This simple microfluidic device holds potential to be developed into a tool for assessing the effects of anticoagulant therapy on hemostasis.

Multi-sample deformability cytometry of cancer cells.

There is growing recognition that cell deformability can play an important role in cancer metastasis and diagnostics. Advancement of methods to characterize cell deformability in a high throughput manner and the capacity to process numerous samples can impact cancer-related applications ranging from analysis of patient samples to discovery of anti-cancer compounds to screening of oncogenes. In this study, we report a microfluidic technique called multi-sample deformability cytometry (MS-DC) that allows simultaneous measurement of flow-induced deformation of cells in multiple samples at single-cell resolution using a combination of on-chip reservoirs, distributed pressure control, and data analysis system. Cells are introduced at rates of O(100) cells per second with a data processing speed of 10 min per sample. To validate MS-DC, we tested more than 50 cell-samples that include cancer cell lines with different metastatic potential and cells treated with several cytoskeletal-intervention drugs. Results from MS-DC show that (i) the cell deformability correlates with metastatic potential for both breast and prostate cancer cells but not with their molecular histotype, (ii) the strongly metastatic breast cancer cells have higher deformability than the weakly metastatic ones; however, the strongly metastatic prostate cancer cells have lower deformability than the weakly metastatic counterparts, and (iii) drug-induced disruption of the actin network, microtubule network, and actomyosin contractility increased cancer cell deformability, but stabilization of the cytoskeletal proteins does not alter deformability significantly. Our study demonstrates the capacity of MS-DC to mechanically phenotype tumor cells simultaneously in many samples for cancer research.