Programmable Bio-Nano-Chip Systems for Serum CA125 Quantification: Toward Ovarian Cancer Diagnostics at the Point-of-Care

In 2010, approximately 21,880 women will be diagnosed with ovarian cancer in the United States and 13,850 women will succumb to this disease (1). Although 90% of ovarian cancers can be treated at stage I with the currently existing surgical and chemotherapeutic regimens, only 25% of ovarian cancers are detected at this treatable stage due to the nonspecific symptoms and the lack of effective screening procedures (2–4). Given the low prevalence of ovarian cancer (1 in 2,500 postmenopausal women), an extremely high specificity (99.6%) and sensitivity (>75%) are required to achieve a minimum positive predictive value (PPV) of 10% (i.e., 10 laparotomies/case of ovarian cancer detected; ref. 5). No screening test exists currently for recommended use in the general population, underscoring the need for novel early detection and easily completed screening methods (6).

For suspected pelvic masses, ovarian cancer diagnosis is realized by pelvic examination, transvaginal sonography (TVS), and serum CA125 leading to exploratory or diagnostic laparoscopy (4). TVS provides a precise image of the ovary and whereas PPVs in the most promising studies have been reported to be close to 10%, prohibitively high costs for implementation has precluded its use as a first-line screen (2, 7, 8).

The biomarker CA125 is a heavily glycosylated, high-molecular-weight protein, encoded by the MUC16 gene, with a potential role in contact and adhesion for metastasizing epithelial ovarian cancer cells (9, 10). The extracellular domain shed in serum from the surface of the ovarian cancer cells following cytoplasmic phosphorylation and proteolytic cleavage is elevated in 80% of advanced-stage carcinomas (11, 12). Hence, serum CA125 has been extensively used and has been U.S. Food and Drug Administration (FDA)-approved for recurrent disease detection and monitoring chemotherapy response (13). However, CA125 is elevated in only 50% to 60% of early-stage cancers with false-positives for a variety of nonmalignant gynecologic and physiologic conditions (14, 15).

The inaccessibility of the ovaries for screening has generated extensive interest in noninvasive serum biomarker–based methodologies for first-line screens and 2 approaches are being investigated. In multimodal screening, the patient's risk for ovarian cancer is stratified by monitoring longitudinal values of CA125, interpreted with a Risk of Ovarian Cancer Algorithm (16, 17). On the basis of assessed risk levels, patients are triaged for follow-up with CA125, TVS, or surgery (18). In an ongoing, large, randomized controlled trial of 202,638 women (UK Collaborative Trial of Ovarian Cancer Screening; UKCTOCS), the multimodal screening (MMS) showed promising PPVs of 43.3% (19). Nevertheless, 20% of ovarian cancers do not express CA125 and in multimarker panel (MMP) screening, multiple biomarkers taken together as a panel, have shown improved sensitivity and specificity compared with CA125 alone (2, 20, 21). Such biomarkers have been identified with proteomic techniques (22, 23), with the most promising panels comprising 3 to 4 biomarkers, presented as complementary to CA125 (24, 25).

The point-of-care (POC) implementation of aforementioned biomarker-based early detection and screening methodologies may potentially impact survival rates through early intervention with increased access to rapid, low-cost, and large-scale population screening. Aligned with promising research, such diagnostic platforms programmed for ovarian cancer should entail multiplexability, high-quality analytical performance (precision and sensitivity), and sample economy. Multiplexability and sample economy are crucial for validation of MMPs discovered by proteomic techniques with limited supply of early-stage preclinical samples (26, 27). Furthermore, longitudinal monitoring of CA125 and other biomarker levels necessitate high precision to discern biologic variations and low limit of detection (LOD) to facilitate early disease detection (28). Finally, rapid analysis times will permit immediate access to TVS, when required and minimize follow-up visits, with additional advantages of reduced patient discomfort and anxiety. A multifunctional diagnostic platform that can be used for assay development and validation and in turn can function as the endpoint diagnostic will significantly reduce residence time in the “diagnostic pipeline paradigm,” enabling speedier translation of biomarkers from discovery to diagnostics (23). Current gold-standard methodologies such as ELISA, dependent on centralized laboratory infrastructure, have typical turnaround times on the order of 24 to 48 hours (29). Furthermore, the single marker at a time approach and the need for trained personnel are inconducive for multiplexing and POC implementation (29, 30). While flow cytometric bead–based approaches tackle the issue of multiplexing and have served well for biomarker discovery, other technological constraints remain similar (31).

Microfluidic platforms have been proposed for POC applications in cancer diagnostics due to their inherent advantages of low cost, sample and reagent economy, low LODs, rapid analysis times, portability, automation, and ease of operation by nonexpert users (29). However, to realize widespread distribution, highly application-oriented systems tailored to address specific analytical and research needs of the pertinent clinical community (as discussed earlier for ovarian cancer) are needed (32). Unfortunately, technological maturation of microfluidic platforms has not complemented advances in cancer biomarker discovery attributable to the apparent disconnect between the relevant communities, crucially important for this highly interdisciplinary translational effort (33). In terms of technological constraints, reliance on centralized laboratory infrastructure akin to conventional immunoanalyzers needs to be overcome through miniaturized “analyzer” components to supplement the microfluidic device (34). Although an instrument-free setup would be preferred, high-sensitivity quantitative analysis for ovarian cancer biomarkers necessitates optical detection and consequently, miniaturized optical and mechanical components for POC implementation (35). Following sample input, all subsequent processes need to be automated with minimal user interaction in a closed format suitable for biohazardous material handling (29).

Rapid, reliable, and efficient measurement of multiple key biomarkers simultaneously at the POC has the potential to transform clinical laboratory science. Toward this goal, the McDevitt laboratory has sustained efforts to improve POC in vitro diagnostics through the development, validation, and implementation of programmable Bio-Nano-Chips (p-BNC; refs. 36, 37). The programmable feature of these systems refers to the capacity of the sensor ensemble to function as a standard platform that can be retasked (i.e., programmed) to serve a new application through insertion of molecular level code (i.e., the biomarker-specific reagents). The Bio terminology relates to the capacity to measure and extract the biosignatures associated with disease progression. The Nano element describes the capacity to miniaturize the system embodied in the use of nano-nets for efficient and rapid biomarker capture as well as quantum dots for increased signal generation. The Chip term emphasizes the capacity to mass-produce the sensor elements in ways analogous to those used by the microelectronics industry. In this work, we report our initial efforts to customize the p-BNC system, to address specific needs in ovarian cancer biomarker–based screening, using CA125 as the proof-of-concept biomarker. Here, we explore critical variables pertaining to reagent screening and assay development in a microfluidic environment that serve to influence the analytical performance of the minisensor ensemble under time constraints imposed for POC applications. This report further details CA125 quantitation in sera of patients with advanced-stage ovarian cancer and validation against gold-standard platforms and explores the potential of the p-BNC system in the context of MMS and MMP screening strategies.

All reagents used for conducting immunoassays in the p-BNCs were prepared in PBS (Thermo Fisher Scientific) containing 0.1% (w/v) bovine serum albumin (BSA; Sigma-Aldrich) needed for reagent stability and blocking nonspecific binding (PBSA). Anti-CA125 monoclonal antibodies (Clones M002201, M002203, M77161, M8072320, M8072321, and M8072322) were obtained from Fitzgerald Industries International. Low cross-reactivity, calibrator grade CA125 standards were acquired from Meridian Life Science. Human serum–based Liquicheck Tumor Marker controls were procured from Bio-Rad for precision studies.

The various generations of p-BNCs were fabricated using xurography-based rapid prototyping techniques. Technical details about construction are provided in the Supplementary Information section.

A typical assay was executed with analyte-specific beads and negative control beads [interleukin (IL)-6] localized in the individually addressable wells on the chip housed in the p-BNC microfluidic ensemble. Alternatively, beads of varying concentrations or antibody clones were multiplexed along adjoining columns and identified by location for optimization studies. Following a 1-minute high flow rate step (2,500 μL/min) for priming and microbubble elimination, the serially diluted analyte of predetermined concentration in PBSA or the serum sample was introduced to the bead array. Any remaining uncaptured analyte following this step was removed with a high-stringency, high flow rate PBS wash. The immunosandwich was completed in the agarose nano-nets with the introduction of Alexa Fluor 488–conjugated detecting antibody, followed by removal of excess antibody with a high-stringency wash and digital image capture of the fluorescent beads to quantify the captured analyte.

ELISAs were conducted at MD Anderson Cancer Center (MDACC; Houston, TX) and University of Texas, Heath Science Center at San Antonio (UTHSCSA; San Antonio, TX) with standard manufacturer protocols (Elecsys CA125 II electrochemiluminescence assay on the “cobas e” immunoanalyzer; Roche diagnostics).

Serum samples were collected from patients with advanced-stage (III and IV) ovarian cancer with informed consent following Institutional Review Board–approved procedures at the MDACC (n = 10) and UTHSCSA (n = 10) using routine protocols (See Supplementary Information). The aliquoted samples were analyzed by ELISA at the collection institutions and sent to the McDevitt laboratory over dry ice for analysis on the p-BNCs.

Images obtained were analyzed using open-source ImageJ (NIH, Bethesda, MD) software with custom image analysis macros to yield the mean fluorescence intensity (MFI) for the corresponding area of interest on the beads (38). Further details are provided in the Supplementary section.

Calibration curves were generated with SigmaPlot 10 (SPSS Inc.) and fitted to a 4-parameter logistic equation. Linear regression, determination of unknown concentrations from standard curves, and curve fitting were conducted using the same software. Precision analysis was conducted via the methods analysis module of Microsoft Excel. All optimization experiments were carried out in triplicate and error bars denote interassay precision unless otherwise noted.

The p-BNC system (Fig. 1) is a highly sensitive, multiplexable, POC-amenable microfluidic platform consistent with requirements set forth for the next generation of ovarian cancer diagnostic devices (see Introduction). This minisensor ensemble consists of 2 main parts: a disposable cartridge that contains the programmable chip module and portable instrumentation that serves as the user interface, thereby reading, analyzing, storing data, and reporting the results. This system is suitable for point-of-service quantitation of CA125 and expanded multiplexed panels that may be completed with finger-stick quantities of blood.

The completely integrated p-BNC (Fig. 2A, III) is composed of modular subassemblies, built around a micromachined stainless steel bead holder chip housing the programmable agarose microbead “immunoanalyzer” core, sandwiched in a plastic microfluidic card, that integrates various on-card fluid handling processes, a sample input port, and reagent containing blister packs. The programmable microbead immunoanalyzer core is identical between various generations of the p-BNCs (Fig. 2A, I, III) and is capable of high-efficiency analyte capture in a 3-dimensional (3D) agarose nano-net (Fig. 2B, III), sequestered from biologic matrices to aid rapid, ultrasensitive quantitative measurements of disease biomarkers in a fluorescence-based sandwich immunoassay format (Fig. 2C, I–IV). The integrated microfluidic assemblage contains a sample loop with an overflow chamber for precise on-card sample metering, in-line filters, and selectively permeable membranes for debris and bubble removal, reagent pads, and blister packs for on-card reagent and buffer storage, in-line micromixers for uniform reagent delivery to the immunoanalyzer core, and an on-card waste disposal chamber (Fig. 2A, III). Efforts from our translational partners have led to the development of a miniaturized analyzer (Fig. 1) with scaled-down light-emitting diode (LED)-based optics, a miniaturized microscope and camera, battery-powered source, and linear actuated stepper motors to crush the blister packs and to release buffer and reagents to drive the immunoassay to completion, followed by optical detection and output. These significant advances when fully developed and placed into widespread clinical practice have the potential to permit untethering from laboratory supplies such as pipettes, reagent and buffer cold-chain, microscopes, and pumps, limitations associated with the traditional laboratory infrastructure (Fig. 2A, I) to realize the POC potential of the p-BNCs.

Here, we focus on the adaptation of the immunoanalyzer module of the p-BNCs for rapid, sensitive, and precise quantification of serum CA125, as the proof-of-concept biomarker for ovarian cancer, with prominent implications in the multimodal and multimarker screening strategies, to derive the technical and operational specifications for the clinical-grade p-BNCs.

Like ELISA, the p-BNC system uses sandwich immunoassays (Fig. 2C); however, immunocomplexes are formed throughout the 3D bead matrix (Fig. 2B, III), rather than on a 2D flat surface in ELISA. Here, the p-BNC–enhanced dimensionality in a dynamic (i.e., nonequilibrium) microfluidic environment yields new challenges related to assay development and optimization. Efficient biomarker capture translating into high-performance immunoassays is influenced by a complex interplay of operating variables: epitope-guided matched pair screening, matched pair orientation, capturing and detecting antibody concentrations, sample and detecting antibody incubation times, and the flow rates corresponding to these incubation steps. Hence, these variables were individually assessed to derive optimal matched pairs and operating conditions (technical specifications) based on analytical performance criteria (slope, LOD, precision, and linearity).

The extracellular domain of CA125 shed in serum has multiple 156 amino acid repeat domains, each containing the 3 major nonoverlapping epitopes recognized by the OC125-like, M11-like, and OV197 antibodies (10, 39). On the basis of this established epitope mapping data, an array of antibodies were chosen to encompass the 3 distinct domains on the molecule; OC125-like antibodies (clones M002201 and M8072320), M11-like antibodies (clones M77161, M80272321, and M002203), and OV197-like antibody (clone M8072322). The matched pair identification for the CA125 sandwich immunoassay was enabled by screening each of the aforementioned clones in both capturing and detecting formats, thus potentially testing 30 such combinations.

“Nonrestrictive conditions” involving 30-minute incubation times for both the analyte and detecting antibody to permit sufficient reaction times at slow flow rates (250 μL/min), a flow-through format to avail excess analyte, relatively high analyte (100, 250, and 500 U/mL), and detecting antibody concentrations (1:250 dilution) to ensure adequate signal visualization were used to enable matched pair identification under unoptimized conditions. A small subset of the results from this extensive study is presented in Fig. 3A encompassing the matched pair selected eventually. Here, M8072320 (OC125) was chosen as the detecting antibody and the results (MFI against CA125 concentrations) represent the performance of the other 5 clones as capture antibodies immobilized on the agarose microbeads (107 ng/bead concentration) to form a matched pair for CA125.

The performance gradient of the various antibody pair combinations was concordant with expectations based on epitope specificities. Antibodies binding to the same domain and from the same clone performed the poorest (M8072320 capture and detection), represented by a relatively flat response in Fig. 3A and served as the negative control with the lowest slope and signal-to-noise ratio (SNR) at lowest tested concentration. The antibodies recognizing the same epitope, but from different clones (M002201 capture and M8072320 detection), exhibited poor, albeit slightly improved response above negative control, suggestive of strong competition between the clones for the same epitope. The other matched pairs formed between the detecting antibody [M8072320 (OC125)] with other capture antibodies recognizing distinctly different domains (M11 and OV197) showed good response over the tested range, with the performance gradient based on relative antibody affinities. M8072320 (OC125) clone for detection paired with M8072321 (M11) clone as the capture antibody exhibited the best performance in terms of aforementioned performance parameters with the highest slope, SNR, and linearity among the 30 tested combinations.

The assignment of the capturing and detecting antibody orientation for a given matched pair is critical to the p-BNC performance as illustrated by Fig. 3B. Here, a matched pair formed with the optimal “correct orientation” performed significantly better than the “reverse orientation,” where the capturing and detecting moieties were interchanged, in contrast to results reported in the literature for flow cytometric bead–based immunoassays and ELISAs for identical clones (40). Furthermore, microfluidic immunosensors, such as the p-BNCs, operate under dynamic (i.e., nonequilibrium) conditions, in contrast to the aforementioned systems, necessitating extensive screening to arrive at optimal matched pairs and orientation as elucidated by this study (40, 41).

To avoid prebiasing based on order of optimization, the capturing and detecting reagent concentrations were optimized concomitantly. “Nonrestrictive” incubation times and flow rates as previously described were used. Agarose microbeads with 320, 107, and 5 ng/bead immobilized capturing antibody concentrations were multiplexed via spatial recognition along each column (3 × 4 array), permitting simultaneous exploration of multiple reagent concentrations and calibration curves were generated with various detecting antibody dilutions (1:250, 1:500, and 1:1,000). Representative photomicrograph from this study at 50 U/mL CA125, 1:250 detecting antibody dilution, and various tested capturing antibody concentrations are presented in Fig. 4A.

A 3D surface plot was generated to simultaneously assess the effects of capturing and signaling antibody concentrations on the slope of the calibration curve (Fig. 4B). The slope was the highest with a capturing antibody concentration of 320 ng/bead at 1:250 detecting antibody dilution, but at the cost of linearity (R² = 0.7345). The effect of capturing antibody concentration on the slope at the optimal detecting antibody concentration (exhibiting the highest linearity) is depicted in Fig. 4C. The upper and lower bounds for exploring detecting antibody dilutions were set on the basis of deviation from linearity at the upper end and unacceptably low SNR at the lower end. Similarly, concentrations lower than 5 ng/bead of capturing antibody provided unacceptably low SNR and concentrations higher than 320 ng/bead did not offer any additional analytical advantages. This behavior is indicative of either the loading capacity of the beads or lack of diffusion into porous beads and access to the additional binding sites (generated with increased capture antibody concentration) due to steric hindrance resulting from the size of CA125. The optimal capturing and detecting antibody concentration combination with the highest linearity (R² = 0.999) along with the maximum SNR and slope were chosen as 320 ng/bead and 1:500 dilution, respectively, for further experimentation.

The flow rate/time dependence of reagent and sample delivery was explored to identify whether these crucial determinants can be tuned to practical time constraints without loss of performance. As such, upper limit for the total assay time for the optimization steps described previously was set at a maximum of 1 hour. Detecting antibody reagents have lower volume restrictions than serum samples and hence the corresponding incubation time was chosen first for optimization. With previously chosen optimal matched pairs and reagent concentrations, incubation times varied from 10 to 30 minutes under equimolar conditions (to preclude bias from lower reagent availability). Longer incubation times resulted in poor assay performance in terms of low slope and SNR along with high imprecision (Fig. 5A). Given that the detection step follows analyte capture by the immobilized capturing antibody (Fig. 2C, III), shorter incubation times are sufficient to complete the immunosandwich as indicated by these data. Longer incubation times and slower flow rates correspond to excess detecting antibody in the pores and necessitate higher wash times to discard the unbound reagent. However, wash times and wash flow rates were held constant in this study leading to the pronounced effect of longer incubation times on the slope and precision. Furthermore, given the molecular size of CA125, analyte capture is realized mainly on the periphery of the bead (Fig. 4A), enabling the detecting reagent easy access and fostering shorter incubation times. Consequently, a detecting antibody incubation time of 10 minutes was chosen as optimal as improvements in slope and precision resulting from lowering incubation times levels off at this time.

The sample incubation was carried out under the closed loop “recirculation mode” to increase analyte exploitation. A flow rate range of 250 to 1,500 μL/min was examined, translating into 7.5 to 45 passes (repeated introduction of the sample) to the array and the corresponding effect on slope is documented in Fig. 5B. The slope increases steeply with increase in flow rate reaching a maximum at 750 μL/min and then levels off. Initial increase in the slope can be attributed to overcoming transport limitations and consequently improved analyte exploitation, a frequently encountered situation in microfluidic systems (42). Beyond 750 μL/min, the reaction-limited regimen prevails and hence improvements in slope level off at this time (43). The optimal flow rate was selected as 750 μL/min. Similar trends were observed for lower immobilized concentrations, albeit at different optimal “cutoff” flow rates (Fig. 5B). The corresponding incubation time was held at 30 minutes as any further decrease resulted in unacceptably low SNR (data not shown). The CA125 p-BNC immunoassay conducted with the optimal parameters was completed in a total of 43 minutes inclusive of wash times making it ideally suited for implementation in a POC setting.

The LOD following each optimization step along with the corresponding linearity and percent of coefficient of variance (%CV) is tabulated (Table 1). These results indicate performance improvement in terms of LOD with each step, with the most increase noted for the sample incubation flow rate optimization step. This is expected as this step leads to increased analyte exploitation and is consistent with previous studies (44). Immunoassay variables interact in a complex manner with each other and the iterative approach presented here cannot account for these interactions and is subject to bias from the order of optimization steps. A statistical design of experiments method is currently being explored to address this issue.

Using the optimized CA125 immunoassay, standard curves were obtained over the range of 10 to 400 U/mL, pertinent to early disease detection. The calibration curve was linear (R² = 0.99) over this range and the corresponding dose–response curve fitted to a 4-parameter logistic equation is shown in Fig. 6A.

The zero analyte concentration was evaluated in triplicate and the concentration corresponding to 3 SDs above the mean signal at zero was determined as the LOD. The LOD of 1.0 U/mL was comparable with the values reported (0.05–1.45 U/mL range) for the currently available commercial systems (45).

To evaluate precision, serum-based tumor marker control standards corresponding to low (21.1 U/mL), medium (64.2 U/mL), and high (186 U/mL) concentrations were used. The intra-assay precision of the p-BNCs, denoting the variation between the analyte-sensitized beads placed within a chip (6 of 9 beads considered), was evaluated for these 3 standards. The interassay precision between the chips was estimated for the 3 standards over 3 consecutive days. For the low, medium, and high standards, the intra-assay precision (%CV) values were 1.4%, 3.0%, and 1.3%, respectively, and the interassay precision (%CV) values were 1.2%, 1.5%, and 0.82%, respectively. The precision values obtained on the minisensor ensemble were competitive with current commercial laboratory-based standards (45). The slightly higher intra-assay precision is reflective of bead sensor homogeneity and uniform fluid delivery. Furthermore, the automated p-BNC minimizes assay variations resulting in excellent interassay precision. Finally, potential interference from serum components were ruled out because of dilution linearity (R² = 0.98) tested for a preselected serum sample with a high CA125 value (data not shown).

To show clinical use of the p-BNC, CA125 levels were assessed in sera obtained from patients with advanced-stage ovarian cancer (n = 20). Sera (100 μL) were diluted 10-fold with 0.1% PBSA and CA125 values were determined from standard calibration curves on the p-BNCs. Accounting for differences in calibrators between the methods, the results from this study are presented in Fig. 6B. The concentrations obtained on the p-BNC correlated well with the current FDA-approved gold-standard ELISA (R² = 0.97). Samples with CA125 more than 4,000 U/mL were analyzed, but excluded from the results presented here because of the limited number of data points in the region.

The p-BNC synergizes components from microfluidics, biomarker discovery, clinical chemistry, and image analysis, and in this work, we have adapted the miniaturized integrated immunoanalyzer for specific applications in ovarian cancer diagnostics. The p-BNC harnesses inherent microfluidic advantages of reduced assay times, reagent and sample economy upon multiplexing and inexpensive construction, along with multiplexability and POC amenability compliant with requirements for novel early detection and screening platforms for ovarian cancer.

We chose CA125 as the proof-of-concept biomarker due to its prominent implications in the MMS and MMP strategies. For optimal matched pair identification, we investigated 30 antibody pair combinations and qualified them on the basis of rigorous analytical response criteria for the highest immunoassay slope, linearity, and precision along with the lowest LOD (Fig. 3A and B). We delineated and optimized the variables affecting p-BNC performance; reagent concentrations (Fig. 4B and C), sample and reagent incubation times (Fig. 5A), and the corresponding flow rates (Fig. 5B). We tapped the multiplexing potential of the p-BNC to simultaneously analyze multiple antibody clones and reagent concentrations on a single microchip (Fig. 4A). Each optimization step resulted in qualified improvement based on analytical response criteria (Table 1). The LOD (1.0 U/mL) and the inter- and intra-assay precision (1.2% and 1.9%, respectively) values obtained were at least comparable or better than most currently available commercial systems (45). The optimal conditions here defined through this study will now form the technical specifications for the “clinical-grade” p-BNCs.

The “clinical-grade” p-BNC retains identical microfluidic elements and most importantly, the programmable agarose microbead core, permitting rapid and rigorous optimization to be translated for widespread distribution and testing (Fig. 2A). This credit card–sized p-BNC can interface with and has coevolved along with a compact (13.5 lbs) toaster size analyzer (Fig. 1), developed by our commercial partners, which uses light-emitting diode–based optics along with a miniaturized charge-coupled device (CCD) and a mechanical actuator that circumvent the need for a microscope and external pumps, as necessitated for a POC setup (36). In the completely integrated p-BNCs (Fig. 2A, III), the loaded sample is accurately metered via a sample loop, following which the card is inserted into the battery-powered analyzer. Here, buffer containing blister packs are crushed and sample and reagents are released by actuation of a stepper motor and delivered to the nano-porous agarose beads housed in a stainless steel chip for assay sequence completion. Biohazardous wastes are contained in the waste chambers housed on the p-BNCs. Upon assay completion, optical signal capture followed by automated image analysis results in an output displayed on the built-in screen (37). Lack of such codeveloped scaled instrumentation has been a limiting factor for similar high-performance microfluidic systems to be distributed in the clinical settings and adapted to the POC (46), and this potential to untether from expensive traditional laboratory infrastructure is a particularly distinguishing feature of the p-BNCs.

The high-quality analytical performance here reported for the p-BNC is achieved within 43 minutes, well suited for POC adaptation, permitted by the high surface area and capture efficiency of the 3D agarose nanofibers compared with the traditional 2D platforms (38). Also, methods such as the p-BNCs circumvent the need for centralized laboratory infrastructure necessitated by ELISA, where turnaround times on the order of 24 to 48 hours are typical with 3 degrees of separation between the patient and the results (37). Traditional systems are further limited by one-marker-at-a-time approach and infeasibility for POC adaptation. Further reductions in assay times and sample volumes are anticipated with higher analyte capture efficiency, permitted by optimal bead holder chip geometry design powered by computational fluid dynamics.

Method validation of the p-BNCs using sera of patients with advanced-stage ovarian cancer showed good correlation (R² = 0.97) with the current gold-standard ELISA systems (Fig. 6B). To show proof of concept, serum (as used in current systems) was chosen. To minimize sample processing and to facilitate POC analysis, we are now investigating the feasibility of whole-blood samples obtained from a finger-stick to be applied directly to the p-BNCs via capillary loading. Future work will encompass diseased samples at early and preclinical stages with corresponding age-matched negative controls to encompass the low biomarker concentration range.

This work serves to define from a methods development perspective, the initial implementation of a chip-based ensemble for ovarian cancer biomarker testing at the POC. Further work is required to secure regulatory approval, a necessary and critical step before widespread clinical distribution is possible. Thus, given the preliminary nature of this work, it would be premature to claim full clinical use for early disease detection at this stage. However, the CA125 p-BNCs could envision use in a variety of scenarios. In this context, CA125 continues to be the single-best biomarker for ovarian cancer despite the search for novel markers (47). High analytical precision permitted by the CA125 p-BNCs will enable interpretation of biologic variations unconfounded by analytical variations, necessary for MMS. Poor assay precision translates into poor biomarker performance and these results are important in the light of high degree of variation in biomarker assay precision (2%–58%) noted recently (47). In addition, CA125 results obtained in 43 minutes can essentially permit TVS (if required) on the same day preempting follow-up visits. Harnessing the multiplexing ability of the p-BNCs, consistent with the MMP approach, we are currently placing newly discovered markers on to the p-BNCs alongside CA125 for increased PPV (31). Such a system could be used for longitudinal monitoring of multiple markers as a potential screening modality. Finally, the CA125 p-BNCs could potentially reduce the residence time in the “diagnostic pipeline paradigm by validating novel biomarkers on the system along with serving as the endpoint diagnostic (23).

Taken together, the CA125 p-BNC shows promising use for ovarian cancer diagnostics, with competitive analytical performance metrics, reduced assay time, potential for multiplexing and POC amenability, necessary for large-scale implementation of early detection and screening methodologies.

R.C. Bast, Jr, is a consultant/advisory board member of Vermillion, Fujirebio Diagnostics, and receives royalties for CA125. J.T. McDevitt has an ownership interest and commercial research grant from LabNow and is a Consultant/Advisory Board member for Force Diagnostics and LabNow. No potential conflicts of interests were disclosed by other authors. The content is solely the responsibility of the authors and does not necessarily represent or reflect views of CPRIT, the NIH, or the United States Government.

Conception and design: A. Raamanathan, G.W. Simmons, N. Christodoulides, P.N. Floriano, S.W. Redding, R.C. Bast Jr, J.T. McDevitt

Development of methodology: A. Raamanathan, G.W. Simmons, N. Christodoulides, J.T. McDevitt

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): A. Raamanathan, G.W. Simmons, W.B. Furmaga, S.W. Redding

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): A. Raamanathan, P.N. Floriano, J.T. McDevitt

Writing, review, and/or revision of the manuscript: A. Raamanathan, N. Christodoulides, P.N. Floriano, K.H. Lu, R.C. Bast Jr, J.T. McDevitt

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): W.B. Furmaga

Study supervision: N. Christodoulides, J.T. McDevitt

Funding for this work was provided the Cancer Prevention and Research Institute of Texas (CPRIT) (RP101382) as well as by the NIH through the National Institute of Dental and Craniofacial Research (U01 DE015017 and U01 DE017793). This work was also supported by funds from the National Cancer Institute (NCI) Ovarian SPORE (P5O CA 83639) and philanthropic support from Golfers against Cancer, the Tracey Jo Wilson Foundation, and the Mossy Foundation.

The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.