Multiplexed Prostate Cancer Companion Diagnostic Devices

Abstract

Prostate cancer (PCa) remains one of the most prominent forms of cancer for men. Since the early 1990s, Prostate-Specific Antigen (PSA) has been a commonly recognized PCa-associated protein biomarker. However, PSA testing has been shown to lack in specificity and sensitivity when needed to diagnose, monitor and/or treat PCa patients successfully. One enhancement could include the simultaneous detection of multiple PCa-associated protein biomarkers alongside PSA, also known as multiplexing. If conventional methods such as the enzyme-linked immunosorbent assay (ELISA) are used, multiplexed detection of such protein biomarkers can result in an increase in the required sample volume, in the complexity of the analytical procedures, and in adding to the cost. Using companion diagnostic devices such as biosensors, which can be portable and cost-effective with multiplexing capacities, may address these limitations. This review explores recent research for multiplexed PCa protein biomarker detection using optical and electrochemical biosensor platforms. Some of the novel and potential serum-based PCa protein biomarkers will be discussed in this review. In addition, this review discusses the importance of converting research protocols into multiplex point-of-care testing (xPOCT) devices to be used in near-patient settings, providing a more personalized approach to PCa patients' diagnostic, surveillance and treatment management.

Keywords: companion diagnostic devices; multiplex point-of-care testing (xPOCT); prostate cancer; protein biomarkers.

Figure 1

Desirable characteristics of ideal PCa-associated biomarkers. Reproduced with permission from ref. [43]. Copyright 2010 Ivyspring InternationalPublisher.

Figure 2

Schematic to multiplexing approaches in order to simultaneously detect multiple target analytes of interest.

Figure 3

Smartphone-based dual-color fluorescent LFIA reader; (A) Internal structure of the smartphone readout device. MQB625 and MQB525 conjugates captured on the test line were excited by a 365 nm UV LED light source. Red and green emission signals passed through a dual-band emission filter (524/628 nm) and an external plano-convex lens, before reaching the smartphone CMOS sensor, (B) Depiction of the smartphone readout device. Reproduced with permission from ref. [95]. Copyright 2019 Elsevier.

Figure 4

Illustration of microfluidic channel fabrication scheme for the quantification and glycoprofiling of fPSA. Reproduced with permission from ref. [100]. Copyright 2016 Elsevier.

Figure 5

A microfluidic immunoarray with a 30-well detection array attached to PCB-controlled micropumps and sample/reagent cassette. The Arduino microcontroller is the microprocessor used to function the micropumps in order to perform the assay. Reproduced with permission from ref. [107]. Copyright 2015 American Chemical Society.

Figure 6

Illustration of 3D-printed supercapacitor-powered immunoarray using ECL detection technique. Reproduced with permission from ref. [108]. Copyright 2015 Elsevier.

Figure 7

A 3D-printed immunoarray with touch screen user interface to control ECL measurements. A microfluidic array connected to a micropump is shown with dye-filled reagent chambers and graphite detection chip. Inset figures show multiple immunoassay steps along with messages to inform the user. Reproduced with permission from ref. [52]. Copyright 2018 American Chemical Society.

Figure 8

Detailed figure of the two types of multi-channel LFIA reaction columns; (a) Single-sample, multimarker reaction column (Type 1); (b) cross-sectional view of Type 1 LFIA column; (c) multisample, single-marker reaction column (Type 2); (d) Type 2 column’s cross-sectional view. Reproduced with permission from ref. [114]. Copyright 2020 Elsevier.

Figure 9

Illustration of portable SERS-based LFIA reader; (a) LFIA strip and (b) multi-channel LFIA reaction column, (c) Detailed schematic of the SERS-based LFIA reader and (d) the completed view of the portable reader. Reproduced with permission from ref. [114]. Copyright 2020 Elsevier.

Figure 10

Illustration of an automated microfluidic immunoarray platform, featuring; (a) Arduino Uno microcontroller, (b) syringe pump, (c) sample injector, (d) servo-actuated valves, (e) capture chambers and magnetic stirrers, (f) detection chambers, and (g) LCD displays. Reproduced with permission from ref. [55]. Copyright 2019 Wiley-VCH Verlag.

Figure 11

An electrochemical microfluidic immunoarray: (A) 256 individual working microelectrodes configuration; (B) 8 microfluidic immunoarrays are connected via miniaturized 8-port manifold; (C) molded PDMS microfluidic channel, and (D) deconstructed view of the integrated microfluidic immunoarray. Reproduced with permission from ref. [123]. Copyright 2016 American Chemical Society.

Figure 12

A flexible PDMS 8 × 8 electrode immunoarray; (A) Schematic of preparing the microchip along with the sensor surface modifications; (B) Illustration of the detection of PSA, PSMA and IL-6, with control measurements, in one microchannel. Reproduced with permission from ref. [69]. Copyright 2014 American Chemical Society.

Figure 13

Randles equivalent circuit to the model the charge flow during electrochemical impedance spectroscopy detection, R_CT represents the charge transfer resistance, R_SOL denotes the uncompensated solution resistance, C_DL represents the capacitance and W signifies the Warburg diffusion coefficient.

Figure 14

(a) Schematic representation of PSA antigens-related (cPSA and fPSA) and (b) device composed of two chambers for detecting the antigens: (c) one chamber is functionalized with anti-fPSA antibodies (Chamber 1) and the other one with anti-tPSA antibodies (Chamber 2). Reproduced with permission from ref. [129]. Copyright 2013 Royal Society of Chemistry.

Figure 15

Illustration of anti-PSA and PSAG-1 aptamers used in the dual aptamer-based impedimetric biosensor to detect PSA and PSA glycans. Using clinical serum samples, the EIS measurement were used to measure the glycan score (GS), which is the ratio between the concentration of the glycosylated PSA (detected with PSAG-1 aptamer) to tPSA (detected with anti-PSA aptamer), multiped by 100. According to the graphical data, the GS can be used to distinguish known PCa patients from benign and healthy patients. Reproduced with permission from ref. [131]. Copyright 2020 Elsevier.