Chunsheng Jiang

Microfluidics and Circulating Tumor Cells

Circulating tumor cells (CTCs) are shed from cancerous tumors, enter the circulatory system, and migrate to distant organs to form metastases that ultimately lead to the death of most patients with cancer. Identification and characterization of CTCs provides a means to study, monitor, and potentially interfere with the metastatic process. Isolation of CTCs from blood is challenging because CTCs are rare and possess characteristics that reflect the heterogeneity of cancers. Various methods have been developed to enrich CTCs from many millions of normal blood cells. Microfluidics offers an opportunity to create a next generation of superior CTC enrichment devices. This review focuses on various microfluidic approaches that have been applied to date to capture CTCs from the blood of patients with cancer.

Quest for the Ideal Cancer Biomarker: An Update on Progress in Capture and Characterization of Circulating Tumor Cells

Preclinical Research

Abstract 2389: A microfluidic system for the selection of circulating tumor cells that utilizes both affinity and size capture technologies

Cancer is a disease of uncontrolled cell growth and dissemination. As tumors increase in size and vascularity, populations of cells break off from the primary tumor, enter into the circulation, and are transported to distant parts of the host. These circulating tumor cells (CTCs) may constitute a seed population that is responsible for subsequent growth of additional tumors (metastasis) in different tissues. Therefore capture, and subsequent characterization of CTCs from patient9s blood, offers an opportunity to both study and monitor progression of the disease. One of the most common methodologies used for CTC capture is affinity based, where specific antibodies bind to cell surface antigens and allow cell capture. A second technique employs size filtration. CTCs are generally larger than normal leukocytes and this size differential can be exploited to enrich CTCs. Here we describe a CTC dual capture platform developed at On-Q-ity Inc. that combines both of these isolation techniques to offer more efficient CTC capture than either technology alone. Performance of On-Q-ity platform will be compared to Veridex9s CellSearch. Utility of On-Q-ity platform for sophisticated molecular and cellular characterization of captured cells will also be described. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 2389. doi:1538-7445.AM2012-2389

Abstract 3507: Multiparameter CTC characterization using dual capture microfluidic chips.

Proceedings: AACR 104th Annual Meeting 2013; Apr 6-10, 2013; Washington, DC Circulating tumor cells (CTCs) have become increasingly acceptable as a prognostic marker in stratifying metastatic cancer patients for treatment and as a predictive marker in monitoring therapeutic response. CTC enumeration is an established prognostic marker (gold standard) in metastatic prostate, breast and colorectal cancer. However, due to the heterogeneity with respect to CTC phenotypic expression, epithelial-mesenchymal transition, and morphologic variability of different cancer cells, it is impossible to simply use the counts of well defined cells to characterize a wide spectrum of cancer status and progression. In addition, manual counting of CTCs also introduces operator bias on the size, shape and expression levels. We have developed an automated CTC characterization system that extracts enumeration, cell morphology and expression level of all intact, irregular and fragmented CTCs in an automatic fashion. A multiparameter classification model was then developed to characterize patient clinical outcome. Whole blood from advanced stage cancer patients and non-diseased controls was processed through anti-EpCAM antibody coated OnQChipsTM. Chips were imaged at low magnification on a fully calibrated rapid automated platform. Captured CTC candidate events were processed by an automated CTC detection algorithm using a set of spatial and spectral features to initially remove non-cellular events and then to indentify CTC subclasses. All CTC subclasses as well as artifact classes were manually labeled and verified at high magnification by trained imaging technologists. Manual labels were used to assess performance of the automated algorithms. A multivariate model based on CART (Classification and Regression Trees) was used for the classifier development. A total of 27 prostate cancer patients and 33 normal controls with 7.5mL blood samples per patient were used to develop and validate the initial techniques. The preliminary results show that the automated CTC event detection algorithm achieved a sensitivity of 96% and specificity of 89%. The CTC subclass classification algorithm achieved classification accuracy from 82% to 95% across all subclasses. The algorithms were N-fold cross-validated with 80/20 random sampling. The preliminary clinical model achieved sensitivity and specificity values of 90% and 82% respectively for patient vs. normal classification. A method for automated patient CTC classification and clinical model has been developed. The performance data from all classification algorithms is very encouraging. The multivariate patient model discriminates cancer patients from normal donor samples with high sensitivity and specificity. Future work includes incorporation of image-based features as well as clinical patient data into the model to improve sensitivity and specificity and address specific clinical needs. Citation Format: Chunsheng Jiang, Oleg Gusyatin, David Tims, Aladin Milutinovic, Kam Sprott, Michael Stocum. Multiparameter CTC characterization using dual capture microfluidic chips. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3507. doi:10.1158/1538-7445.AM2013-3507

Abstract 5108: Capture and enumeration of mesenchymal CTCs on the OnQChip platform.

Circulating tumor cells (CTCs) are cancer cells that disseminate from the primary tumor and enter the blood. While cells in the primary tumor are generally epithelial in nature, as they become migratory and invasive, changes such as loss of epithelial markers (ie: EpCAM and cytokeratin (CK)) and a switch to a mesenchymal phenotype (EMT) often occur. CTC platforms must be flexible in capturing and identifying cells, and not limited to the use of only epithelial markers. Dual modal capture on the OnQChip provides this flexibility, allowing for CTC isolation based on size and independent of surface marker expression in addition to specific antibody mediated capture targeting markers such as EpCAM. For cell lines ranging in EpCAM expression, the OnQChip captures 90% or greater of mid to high EpCAM expressing cells, and 79% or greater of very low EpCAM expressing cells. Due to the OnQChip9s gradient design, distinct capture patterns related to EpCAM levels are observed, allowing for capture location to be informative about EpCAM expression. When Hs578T cells, a mesenchymal model with very little EpCAM and CK, are prestained around 79% of spiked cells are enumerated on the OnQChip, but when the same cells are not prestained and only CK staining is used, 0-2% of spiked cells are counted. When two new mesenchymal staining markers are incorporated alongside CK, 60-80% of spiked Hs578T cells are now enumerated, translating to a 75-100% staining efficiency of captured cells. Although very few cells are visualized with CK alone, pooling CK staining with mesenchymal staining shows increased staining intensity compared to mesenchymal staining alone, highlighting benefits of a multi-marker staining panel for visualizing cells with low expression of several different markers, likely the case for subpopulations of cells at different stages of the EMT. On a population of 44 prostate cancer samples, an increase of 28.5, 8.2, 12.7, and 7.6 in the mean number of total, intact, irregular, and fragmented CTCs, respectively, was observed with staining for CK plus the mesenchymal markers as compared to CK staining alone. Specifically, 70.5%, 18.2%, 50%, and 38.6% of patients showed an increase of 5 or more, and 40.9%, 9.1%, 31.8%, and 25% showed an increase of 10 or more, total, intact, irregular, and fragmented CTCs, respectively, when mesenchymal staining was included. Additionally, a few case studies from this population for which serial draws during treatment were obtained indicate changes in the ratio of CTCs enumerated with staining for CK plus mesenchymal markers compared to CK only, suggesting the ratio of mesenchymal to epithelial like CTCs may reflect a clinical outcome. Our data suggests that the OnQChip9s ability to capture CTCs based on size independent of surface marker expression and inclusion of two new stains, allow for capture and enumeration of mesenchymal CTCs that would be missed by platforms incorporating only epithelial markers such as EpCAM and CK. Citation Format: Keith D. Merdek, Kam Sprott, Chunsheng Jiang, Michael Stocum, Glenn Bubley, Walter Carney. Capture and enumeration of mesenchymal CTCs on the OnQChip platform. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5108. doi:10.1158/1538-7445.AM2013-5108 Note: This abstract was not presented at the AACR Annual Meeting 2013 because the presenter was unable to attend.

Detecting and classifying circulating tumor cell subclasses from On-Q-ity microfluidic chips using a customized imaging platform.

72 Background: Circulating tumor cells (CTCs) have clinical value in cancer diagnosis, prognosis, and treatment prediction. The On-Q-ity Microfluidic Chip (OnQchip) with a gradient design has the advantage of dual size and affinity capture resulting in increased efficiency. Accurately detecting and classifying these rare captured cells in patient blood however is difficult. We present an imaging platform for accurate and reproducible CTC detection, where identified cells and fragments are further classified into several subclasses that may have separate prognostic utilities. METHODS Whole blood from advanced stage cancer patients and non-diseased controls was processed within 36 hours of collection. Blood was run on OnQchips coated with anti-EpCAM antibody. Captured CTCs and cellular fragments were identified by on-chip immunofluorescence. Chips were imaged at 5X magnification on a rapid automated platform. Cytokeratin positive (CK+) events were identified and sorted via an advanced detection algorithm using a set of spatial and spectral features. Events were further validated and separated into subclasses by trained experts using 10X magnification. RESULTS Greater than 85% capture efficiency was repeatedly achieved in spiked blood with nearly 100% detection sensitivity and 3% false positive rate following automated sorting of CTC candidates. On a cohort of 27 prostate cancer patients and 33 normal subjects a median of 19 CK+events/7.5 ml blood were detected in cancer patients, compared to a median of only 6 in normals. ROC analysis yielded AUC values of 0.62 to 0.77 for separate subclasses of CTCs. An AUC of nearly 0.80 was achieved by combining two or more subclasses. CONCLUSIONS Preliminary evaluation of this imaging platform shows efficient and reproducible CTC detection in spiked and patient samples. Enumeration using subclasses achieved high sensitivities and specificities in separating cancer patients from normals and therefore, makes this technology feasible for both research and clinical studies. Future clinical studies with large sample sizes will be performed to validate clinical significance of CTC subclasses.

Computed aided classification of circulating tumor cell and CK+ subclasses.

54 Background: Fluorescence microscopy imaging system (OnQView, On-Q-ity, Waltham, MA) in combination with advanced cell capture techniques (OnQChip, On-Q-ity, Waltham, MA) provides necessary sensitivity to detect circulating tumor cells (CTCs) in a blood sample. The detection process involves automatic identification of CTC candidates from the collected imagery followed by CTC subclass identification. Subclass identification process is manual and usually leads to increased sample processing time. METHODS We have developed a fully automated CTC detection and classification system allowing for substantial increase in throughput while maintaining high sensitivity and specificity. Detection is accomplished by a robust segmentation technique. A set of 25 image-based features is automatically computed for each detected candidate. Features include texture measurements, morphology measurements, multichannel intensity and contextual characteristics. All CTC subclasses as well as artifact classes are manually labeled and verified by trained imaging technologists.A hierarchy of Multi-Layer Perceptron Neural Network (MLPNN) classifiers is then trained and used to identify and reject artifacts and to identify CTC subclasses. RESULTS A total of 27 prostate cancer patients and 33 normal controls with two 3.75ml blood samples per patient were used to validate techniques. Probability of successful artifact rejection was achieved to be 0.78 and probabilities of subsequent successful CTC subclass identification ranged between 0.79 and 0.98 (intact CTCs = 95%; irregular CTCs = 98%; fragmented CTCs = 82%). CONCLUSIONS A fully automated CTC detection and classification system was developed. Testing was conducted with 27 prostate cancer patients and 33 normal controls to yield an artifact rejection probability of 0.78 and CTC subclass identification probabilities of 0.79 to 0.98.