A Microfluidics Approach for Ovarian Cancer Immune Monitoring in an Outpatient Setting

Abstract

Among cancer diagnoses in women, ovarian cancer has the fifth-highest mortality rate. Current treatments are unsatisfactory, and new therapies are highly needed. Immunotherapies show great promise but have not reached their full potential in ovarian cancer patients. Implementation of an immune readout could offer better guidance and development of immunotherapies. However, immune profiling is often performed using a flow cytometer, which is bulky, complex, and expensive. This equipment is centralized and operated by highly trained personnel, making it cumbersome and time-consuming. We aim to develop a disposable microfluidic chip capable of performing an immune readout with the sensitivity needed to guide diagnostic decision making as close as possible to the patient. As a proof of concept of the fluidics module of this concept, acquisition of a limited immune panel based on CD45, CD8, programmed cell death protein 1 (PD1), and a live/dead marker was compared to a conventional flow cytometer (BD FACSymphony). Based on a dataset of peripheral blood mononuclear cells of 15 patients with ovarian cancer across different stages of treatment, we obtained a 99% correlation coefficient for the detection of CD8+PD1+ T cells relative to the total amount of CD45+ white blood cells. Upon further system development comprising further miniaturization of optics, this microfluidics chip could enable immune monitoring in an outpatient setting, facilitating rapid acquisition of data without the need for highly trained staff.

Keywords: flow cytometry; immune monitoring; immunotherapy; lab on chip; ovarian cancer.

Figure 2

Description of the chip cytometer setup. (a) Example photograph of the chip design that contains 5 fluidically independent microfluidic channels. (b) Illustration of one microfluidics channel. Buffer fluids were used that act as sheath fluid to hydrodynamically focus cells into the center of the laser beam (green vertical line). A sorting chamber was present but not used in current study (c). Diagram of the key features of different fluidic focusing principles. The relative score of the different concepts for footprint, scalability, ease of integration, and sample dependency were provided by the size of the color diamond in the axis of the property. The higher the concept scores on a property, the closer the diamond will extend to the maximum of the corresponding axis. Relative scores between the hydrodynamic, acoustic, inertial, and DEP concept were provided based on the results reported in the literature [22,23,24,25,26,28,29,30,31,32,33,34,35]. (d) Schematic of the optical system.

Figure 1

Overview of experimental study design in a six-step process. Peripheral blood samples were obtained from 15 patients with ovarian cancer. White blood cells were isolated by means of a density gradient centrifugation and frozen until further use. Batches of four to five samples were defrosted and labelled with fluorescent dyes. Each fluorescently stained patient sample was split into two equal parts to perform simultaneous but separate acquisition on a conventional flow cytometer (BD FACSymphony) and our own, silicon, microfluidics-based chip cytometer (Figure created in BioRender).

Figure 3

Pearson correlation and Bland–Altman agreement analysis for (a) total PD1+ cells, (b) CD8+PD1+ cells, and (c) CD8−PD1+ cells show high correlation and good agreement between chip cytometry and conventional flow cytometry in all three populations.

Figure 4

Clinical patterns of immune cells can be detected similarly on conventional and chip cytometry. (a) All CD45+ PD1+ cells as measured by flow and chip cytometry, respectively. No difference can be seen between the data obtained via both measurement methods. (b) Readout of CD8−PD1+ cells on flow and chip cytometer, respectively, shows the same impression of a non-significant higher mean of PD1 positivity in stage III (21.37% for conventional and 19.15% for chip flow cytometry) compared to stage IV (15.25% for conventional and 14.06% for chip flow cytometry). (c) Similar readout of CD8+PD1+ cells on flow and chip cytometer, respectively, after analysis. A two-tailed t-test was used to compare the datasets between both measurement methods (ns = p < 0.05).

Figure 5

Higher speed acquisition does not hamper measurement sensitivity. (a) Comparison of total detections and single cells acquired at speeds of 0.5 m/s and 1 m/s shows that the chip can handle doublet discrimination at higher sample concentrations and (b) still generates the same fluorescence sensitivity compared to lower speed acquisition, as demonstrated by the Pearson correlation resulting from linear fit statistical analysis for total PD1+ cells (R = 0.94, black dots), CD8+PD1+ (R = 0.99, blue, full-colored triangle), and CD8−PD1+ (R = 0.92, red upside-down triangle) populations.

Figure 6

Concept of a multiplexed cytometer chip combining both fluidics and photonics module. The fluidics module harbors besides 2D hydrodynamic focusing also the capacity to sort [22]. The photonics module, presented in the middle, comprises an SiN waveguide platform enabling coupling of laser light, routing of light throughout the chip, and emission of light into the fluidic channel [14,17,19]. The fluorescence emission by the cells can be collected through a quartz top plate, focused to an optical fiber with miniature lenses, such as grin lenses, and collected by an APD or PMT detector. Both modules can be monolithically integrated and multiplexed as represented in the figure on the right.