Inertial focusing for tumor antigen-dependent and -independent sorting of rare circulating tumor cells

Abstract

Circulating tumor cells (CTCs) are shed into the bloodstream from primary and metastatic tumor deposits. Their isolation and analysis hold great promise for the early detection of invasive cancer and the management of advanced disease, but technological hurdles have limited their broad clinical utility. We describe an inertial focusing-enhanced microfluidic CTC capture platform, termed "CTC-iChip," that is capable of sorting rare CTCs from whole blood at 10(7) cells/s. Most importantly, the iChip is capable of isolating CTCs using strategies that are either dependent or independent of tumor membrane epitopes, and thus applicable to virtually all cancers. We specifically demonstrate the use of the iChip in an expanded set of both epithelial and nonepithelial cancers including lung, prostate, pancreas, breast, and melanoma. The sorting of CTCs as unfixed cells in solution allows for the application of high-quality clinically standardized morphological and immunohistochemical analyses, as well as RNA-based single-cell molecular characterization. The combination of an unbiased, broadly applicable, high-throughput, and automatable rare cell sorting technology with generally accepted molecular assays and cytology standards will enable the integration of CTC-based diagnostics into the clinical management of cancer.

Conflict of interest statement

Competing interests: MGH filed for patent protection for the CTC-iChip technology. The authors declare that they have no competing interests.

Figures

Fig. 1

The CTC-iChip system. (A) Three microfluidic components of the CTC-iChip are shown schematically. Whole blood premixed with immunomagnetic beads and buffer comprises the inputs. The figure demonstrates the positive isolation method; however, the system can be operated in negative depletion mode. (B) Integrated microfluidic system. The debulking array sits in a custom polycarbonate manifold that enables fluidic connections to the inputs, waste line, and second-stage microfluidic channels. The inertial focusing and magnetophoresis chip is placed in an aluminum manifold that houses the quadrupole magnetic circuit. Magnetically deflected cells are collected in a vial. (C) Hydrodynamic size–based sorting. A mixture of 2-μm (red) and 10-μm (green) beads enters the channel (i). Whereas the 2-μm beads remain in laminar flow and follow the fluid streamlines, the 10-μm spheres interact with the post-array (ii and iii) as shown in the scanning electron microscope (SEM) image (right panel). Larger beads are fully deflected into the coincident running buffer stream by the end of the array (iv). Scale bars, 100 μm. (D) Cell focusing and magnetophoretic sorting. Magnetically labeled SKBR3 (red) and unlabeled PC3-9 (green) cell populations are mixed and enter the channel in random distribution (i). After passing through 60 asymmetric focusing units (pictured in the SEM, right panel), the cells align in a single central stream (ii). Magnetically tagged cells are then deflected (iii) using an external magnetic field, and separation is achieved by the end of the channel (iv). Scale bars, 100 μm.

Fig. 2

Modeling and magnetic sensitivity of the system. (A) A mathematical model describes the deflection of labeled cells (red) from a focused stream (white). Finite element method analysis of the quadrupole magnetic circuit and fluid flow in the channel provided estimates of the magnetic gradient (blue) and flow rate (green) across the deflection channel (left panel). This information, in conjunction with our experimental understanding of cell position in the focused stream (pink), was used to construct an overall model to predict the trajectories of focused cells with varying magnetic loads (right panel). (B) High sensitivity of inertial focusing enhanced magnetophoresis. Human PC3-9 cells were labeled with varying numbers of magnetic beads and collected in separate exit streams after traveling in the 4-cm-long magnetic deflection channel, fractionating the cells based on magnetic deflection distance. The beads on a representative population of cells were counted in each fraction. The deflection distance was measured from focused stream position to the channel wall. Fraction 6 included cells that deflected all the way and traveled at the wall; therefore, this data point did not match the simulation. The expected variations in cell sizes and the initial distribution of cells in the focused stream contribute to a variation in the deflection pattern that is reflected by shading the expected range around the model prediction. (C) The experimental “magnetic sensitivity” was determined by plotting histograms of bead loading density for deflected and undeflected cells for a given flow rate. The intersection of curve fits of these data represents the minimum number of beads required to deflect a cell. (D) The minimum required magnetic load increases with higher flow rates, as expected, and is accurately predicted by the model.

Fig. 3

Evaluation of overall system performance using cancer cell lines spiked into whole blood. (A) Quantitation of variable EpCAM expression in five cell lines using flow cytometry. (B) Capture yield of positive selection and negative depletion modes using various cell lines expressing different levels of EpCAM. (C) Background in posCTC-iChip product is measured, achieving >3.5-log depletion of WBCs. In contrast, negCTC-iChip has an order of magnitude lower purification. In both (B) and (C), each data point is an experimental result. Upper and lower bounds of the boxes signify the 75th and 25th quantiles, respectively. Perpendicular line in the box represents median value, and data points left above or below the error bars are outliers.

Fig. 4

CTC isolation by posCTC-iChip in cancer patients. (A) CTCs isolated from castrate-resistant prostate cancer (CRPC) patients were enumerated and compared with blood specimens processed from healthy donors. (B) EpCAM-based isolation using posCTC-iChip was compared with the Cell-Search system. Clinical samples were metastatic cancer patients of prostate (n = 19), breast (n = 12), pancreas (n = 6), colorectal (n = 2), and lung (n = 2). All counts were normalized to 7.5 ml. (C) For enumeration of CTCs from CRPC patients, CK8/18/19 staining was used (green). CD45 antigen (red) was used to identify contaminating leukocytes. Scale bars, 10 μm. (D) A CTC from a CRPC patient was stained for prostate-specific antigen (PSA) (red), prostate-specific membrane antigen (PSMA) (yellow), and DAPI (blue) to demonstrate dual immunofluorescence staining for PSAs. (E) Validation of EML4-ALK RT-PCR assay was completed with cell lines. posCTC-iChip products of whole blood from a healthy donor (HD) spiked with 0, 10, and 100 H3122 cells (expressing EML4-ALK variant 1) per 10 ml were subjected to RT-PCR for detection of the EML4-ALK fusion. Product isolated from healthy donor blood spiked with 500 VCaP cells/ml was processed as a negative control. (F) posCTC-iChip products from patient samples known to harbor the EML4-ALK translocation by FISH were similarly processed as in (E), and the bands were sequenced to confirm the presence of the fusion transcript. A representative sequence trace from patient 3 shows the translocation breakpoint between exon 13 of EML4 and exon 20 of ALK. CTC analysis of three patients whose cancer lacks the translocation was used to establish specificity: a prostate cancer patient (lane 1), an EGFR mutant lung cancer patient (lane 2), and a HER2-amplified lung cancer patient (lane 6).

Fig. 5

Classification of CTCs with cytopathology and ICC. (A) Specimens from H&E-stained primary and metastatic tumors (upper row) are compared with matched Pap-stained cytology samples from FNAs or pleural effusions (FNA/E) (middle row) and Pap-stained CTCs enriched from blood samples of the same patient using negCTC-iChip (lower row). Marked morphological similarity is seen between isolated CTCs and main tumors or cytology samples. (B) ICC profiles of primary and meta-static tumors (upper panel) matched to CTCs from the same patient (lower panel). All images: ×1000 original magnification. Scale bar is 30 μm and valid for all images.

Fig. 6

Variation of CTC sizes and morphologies. (A) CTCs from breast cancer and melanoma patients consecutively stained with Pap and either anti-CK (breast) or anti–Melan-A (melanoma) antibodies. (B) Quantitative analysis of the effective diameter (maximum feret diameter) for individual cells isolated in three cases. The top two panels are from different melanoma patients (M1 and M2). The bottom panel is from a breast cancer patient (B3). (C) Occasional very large cells with ample cytoplasm and multilobed nuclei were initially considered suspicious but were CK−. The same cells were subsequently restained for the platelet marker CD61, which supports their identification as circulating megakaryocytes. (D) CTCs were occasionally observed as clusters and confirmed by positive CK staining. All images: ×1000 original magnification. Scale bar, 30 μm.

Fig. 7

Heterogeneity of RNA expression between CTCs isolated from a prostate cancer patient. (A) Micromanipulation of single CTCs isolated from a blood specimen of a patient with prostate cancer using the negCTC-iChip and stained in solution with anti-EpCAM (green) and anti-CD45 (red) antibodies. Top panel shows a bright-field image merged. Wide arrow points to an EpCAM+/CD45− CTC. Thin arrow points to EpCAM−/CD45+ leukocytes. Arrowhead denotes an erythrocyte. Dashed line outlines the micromanipulator needle tip. Bottom two panels show distinct imaging channels. Scale bar, 20 μm. (B) EpCAM and bright-field images of 15 single prostate cancer CTCs from a single patient selected for transcriptional profiling. Scale bar, 10 μm. (C) Heat map of normalized gene expression (−ΔCt) of 43 genes in each of the single CTCs measured by microfluidic qRT-PCR. Columns list each individual prostate CTC, and rows show the panel of genes assayed, grouped thematically. The red asterisk highlights the gene expression patterns of PSA and PSMA, which provide a measure of AR signaling activity. NTC, no-template control. (D) Table listing the proportional distribution of various gene groups expressed in single CTCs isolated from the prostate cancer patient.