APtamer-Enhanced AP Assay: Dynamic Functional Profiling of Circulating Tumor Materials for Predicting TKI Resistance and Real-Time Treatment Monitoring

APtamer-Enhanced AP Assay: Dynamic Functional Profiling of Circulating Tumor Materials for Predicting TKI Resistance and Real-Time Treatment Monitoring | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article APtamer-Enhanced AP Assay: Dynamic Functional Profiling of Circulating Tumor Materials for Predicting TKI Resistance and Real-Time Treatment Monitoring Lin Chen, Yue Lu, Panpan Qi, Zhonglin Yang, Dongjiang Tang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8846023/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract The detection of circulating tumor cells (CTCs) is crucial for cancer management but remains challenging due to their rarity and heterogeneity. Alkaline phosphatase (AP) activity, particularly from cancer-associated isoforms ALPP and ALPG, offers a promising functional biomarker alternative. This study developed and validated a highly sensitive assay for detecting AP-expressing circulating tumor-related materials (CTRMs). Magnetic nanoparticles were conjugated with an optimized BG2-PEG-biotin aptamer for specific CTRM capture, with systematic optimization of probe design, blocking reagents, and background suppression. The assay demonstrated excellent linearity and reproducibility (%CV = 5.6%). Clinical validation across lung cancer (LC) and other cancers revealed significantly elevated AP activity in LC (p = 0.0372) and other cancer patients (p = 0.0576) versus healthy donors. Most notably, AP activity was substantially higher in tyrosine kinase inhibitor (TKI)-resistant patients (p = 0.0001). ROC analysis showed exceptional performance for distinguishing LC from healthy donors (AUC = 0.9340) and identifying TKI resistance (AUC = 0.9198). While no significant difference was found between CTC-positive and negative groups, AP activity strongly correlated with quantitative CTC burden in LC (r = 0.485, p = 0.000) and TKI-resistant subgroups (r = 0.369, p = 0.003). Longitudinal monitoring demonstrated that dynamic AP activity changes reflected treatment response. This study establishes AP activity as a sensitive, specific functional biomarker for CTRM detection that effectively predicts TKI resistance and dynamically monitors tumor burden, highlighting its significant clinical potential for non-invasive cancer management. Health sciences/Biomarkers Biological sciences/Cancer Health sciences/Oncology Alkaline Phosphatase Activity Circulating Tumor-Related Materials BG2 Aptamer TKI Resistance Tumor Burden Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Circulating tumor cells (CTCs) have emerged as crucial biomarkers for cancer diagnosis ( 1 ), prognosis ( 2 ), and monitoring treatment response ( 3 ). However, the reliable detection and molecular characterization of these rare cells in peripheral blood remain technically challenging due to their extreme scarcity ( 4 ) and heterogeneity ( 5 , 6 ). The rarity of CTCs, often present at concentrations as low as one CTC per billion blood cells, poses a significant challenge and bottleneck for their detection and subsequent application ( 4 , 7 – 9 ). This scarcity limits the sensitivity and reliability of current detection methods, making it difficult to capture and analyze sufficient numbers of CTCs for accurate diagnosis and monitoring ( 3 , 10 , 11 ). To overcome these limitations, researchers have begun to focus on tumor-derived materials (TDMs) in circulation ( 12 ), which include not only CTCs but also other tumor-related substances such as apoptotic CTC fragments ( 13 , 14 ), exosomes ( 15 ), cell vesicles ( 16 ), and circulating free proteins ( 17 , 18 ). These tumor cell-related materials (CTRMs) are more abundant in the bloodstream compared to CTCs, offering a potentially richer source of information for cancer detection and monitoring ( 12 , 15 , 19 ). Their higher relative abundance can enhance the sensitivity and reliability of assays, providing a more comprehensive picture of tumor dynamics and therapeutic response. Alkaline phosphatase (AP) ( 20 ), particularly its placental-like isoforms ALPP (placental alkaline phosphatase, PLAP) ( 21 , 22 ) and ALPG (germ cell alkaline phosphatase) ( 23 , 24 ), has garnered significant interest as a potential cancer biomarker. These isoforms are normally restricted to placental and germ cells but are frequently re-expressed in a wide range of epithelial malignancies ( 25 – 27 ). This tumor-specific re-expression pattern suggests that functional AP activity could serve as a universal, functional marker for detecting TDMs in circulation, circumventing the limitations of antigen-dependent capture methods. Moreover, the functional activity of enzymes like AP might reflect the biological state and metabolic activity of tumor cells, potentially offering insights into therapy resistance and disease progression. Despite its promise, the development of a robust, sensitive, and specific assay to detect CTCs based on AP activity has been hindered by several obstacles. These include significant background interference from tissue-nonspecific alkaline phosphatase (TNAP) expressed by white blood cells (WBCs) ( 28 , 29 ), non-specific binding of probes ( 30 , 31 ), and the need for a highly efficient capture system to isolate rare CTCs from a vast excess of hematological cells. To address these challenges, we developed and systematically optimized a novel capture and detection platform utilizing a biotinylated DNA aptamer (BG2) ( 32 ) conjugated to magnetic beads for the specific isolation of AP-expressing CTRMs. This platform leverages the higher abundance of CTRMs compared to CTCs to enhance detection sensitivity. Furthermore, we employed a chemiluminescent detection method, which offers high sensitivity and specificity, allowing for the precise quantification of AP activity even in the presence of background interference. This study presents a comprehensive analytical validation of this optimized AP activity-based assay and investigates its clinical utility for cancer diagnosis, for predicting resistance to tyrosine kinase inhibitor (TKI) therapy, and for longitudinal monitoring of treatment response in patients with lung, colorectal, and other cancers. Our findings position AP activity not only as a highly sensitive diagnostic biomarker but also as a powerful functional indicator of therapeutic resistance and tumor dynamics. Materials and methods 1. Reagents and cell lines BG2-biotin, BG2-PEG-biotin, and BG2-t-biotin were synthesized by Sangon Biotech (Shanghai, China) as detailed in Supplementary Tables 1 and 2. Thiol (t) and TEG groups were introduced to link biotin and BG2, thereby enhancing the stability of the resulting constructs and reducing steric hindrance in the BG2 derivatives. Biotin (Cat: HY-B0511), biocytin (Cat: HY-101884), and biotin-PEG-OH (Cat: HY-147205C) were purchased from MedChemExpress (USA) and used for blocking streptavidin beads after pre-conjugation with BG2. SBI-425 (Cat: SML2935; C 13 H 12 ClN 3 O 4 S; Cas: 1451272-71-1) and TNAP inhibitor (Cat: 613810; C 17 H 16 N 2 O 4 S; Cas: 496014-13-2) were obtained from Merck (Germany) and used to inhibit TNAP activity from WBCs. H-HoArg-OH (Cat: LEYH9B0C8E9B; Cas: 156-86-5) was also purchased from Merck (Germany). Salmon DNA (Cat: H1060) was purchased from Solarbio (Beijing, China), while salmon DNA (Cat: D9156) and herring DNA (Cat: D8050) were obtained from Merck (Germany). These DNAs were used to block non-specific binding of oligonucleotides. The following cancer cell lines were used in this study: Lovo, SW620, SW480, Caco2, NCI-H1975, HCC827, A549, MCF-7, MDA-MB-231, MKN7, 7721, and Bewo. All cell lines were purchased from the American Type Culture Collection (ATCC) and cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum (FBS, Gibco, Grand Island, NY, USA) at 37°C in a humidified atmosphere containing 5% CO₂. 2. Collection of clinical samples and storage Blood samples were collected using cfDNA storage tubes (Cat: CW2815M; CWBIO, Jiangsu, China), with volumes ranging from 8 to 10 mL, and were subsequently transported to Sanmed Biotech Ltd. for further analysis. Specifically, 1 mL of each blood sample was utilized for AP activity evaluation, while the residual blood was employed for CTCs detection. The clinical samples from lung cancer (LC) patients and other cancer patients were collected from The First Affiliated Hospital of Zhengzhou University, with ethics approval number 2024-KY-0100-002. Written informed consent was obtained from all enrolled patients. All methods were performed in accordance with the relevant guidelines and regulations. 3. AP activity accessed on circulating tumor relative materials (CTRMs) To enrich apoptotic CTCs fragment and vehicles, 1 mL of blood was collected and diluted with 5-fold by using 1x PBS. Streptavidin beads (Cat: 557812; BD Biosciences, USA) were pre-conjugated with biotin-labeled BG2 at 2–8°C for 1 hour and blocked with 1 mg/mL biotin-PEG-OH. The conjugated complex was added to diluted samples and incubated at 2–8°C for 1 hour. Captured CTRMs were resuspended in a 400 µL mixture of AMPPD (Cat: 224179; Shenzhen Maxchem Technology Co. Ltd., China) and TNAP inhibitor (C 17 H 16 N 2 O 4 S) at 5 IC50 (5 × 190 nM). AP activity was analyzed using Fluoroskan Ascent ™ FL (Cat: 5210463; Thermo Fisher, USA) on the supernatant after centrifugation at 1200 rpm for 2 min. 4. CTC isolation and detection by using LiquidBiopsy Blood samples were collected, with volumes ranging from 8 to 10 mL, and diluted to 20 mL using 1× PBS buffer for the separation of PBMCs based on Ficoll (density: 1.077 g/mL). Subsequently, the samples were labeled using capture cocktail kits (Zhuhai Sanmed Biotech Ltd., Zhuhai, China) and detection kits containing anti-Pan CK iFluor647, anti-CD45 iFluor488, and DAPI after fixation with 1% PFA for 1 hour at room temperature. The labeled CTCs were then isolated using the LiquidBiopsy rare cell isolation system (LIQUIDBIOPSY400A, Zhuhai Sanmed Biotech Ltd., China) under a magnetic field and enriched on a microfluidic chip with sheath buffer after incubation with streptavidin-coated magnetic beads for 1 hour at 2–8°C. Finally, the isolated CTCs were imaged and visualized using a fluorescence microscope (Leica DM6000B, Leica, Germany), and classified as CK+/DAPI+/CD45 − according to our previously published criteria ( 33 ). 5. Quantification of ALPL and ALPG mRNA expression levels in cell lines by qPCR Cells were collected at 1000 rpm for 3 min following digestion with 0.25% trypsin for 2–3 min and lysed using lysis buffer RL (Cat: DP430, TIANGEN, Beijing, China) supplemented with 1% β-mercaptoethanol (Cat: HY-Y0326; MedChemExpress, USA). RNA was extracted using the RNAprep Pure Cell/Bacteria kit (Cat: DP430, TIANGEN, Beijing, China) according to the manufacturer’s protocol (Supplementary protocol 1) and reverse transcribed into cDNA using the PrimerScript RT reagent kit with gDNA Eraser (Cat: RR092A; Takara, Japan) following the manufacturer’s protocol (Supplementary protocol 2). The mRNA expression levels of ALPL and ALPG were evaluated by qPCR on the Real-Time PCR System (SLAN-96P, HONGSHI MEDICAL, Shanghai, China) according to the protocol (Supplementary protocol 3) and analyzed using GraphPad 8.2.0. Cell line with a ct value > 30 was defined as AP negative. 6. Assessment of AP expression levels by flow cytometry The expression levels of AP were evaluated in AP-positive cell lines, including Lovo (human colorectal cancer cells), MDA-MB-231 (human breast cancer cell line), NCI-H1975 (human lung adenocarcinoma cancer cell line), and Caco-2 (human colon adenocarcinoma cell line), as well as in AP-negative cells (WBCs), using antibodies against PLAP H17E2-APP (Cat: B21499702-CHO; Biointron, Shanghai, China) and ALPG (Cat: 7A308201; Origene, USA). After labeling with secondary antibodies, either Goat anti-rabbit IgG FITC (Cat: ab150081, Abcam, UK) or Goat anti-mouse IgG FITC (Cat: SF-131; Solarbio, Beijing, China), the cells were analyzed using the flow cytometry (CytoFLEX S, Beckman Coulter, USA). 7. Statistical analysis In this study, the relationship between AP activity and disease characteristics was examined in clinical samples, as well as its association with CTCs. Statistical analysis using Student's t-test on GraphPad Prism 8.2.0 revealed significant differences in AP activity among various subgroups, including comparisons between healthy donors (HD) and patients with LC or other cancers, as well as between TKI resistance-negative (TKI R-) and resistance-positive (TKI R+) groups, with clinical significance indicated by p values less than 0.05. A receiver operating characteristic (ROC) curve was generated to establish the optimal cutoff value for differentiating HD from tumor patients and to assess the correlation of AP with TKI resistance and CTCs. The cutoff value was considered reliable when the area under the curve (AUC) exceeded 0.8 ( 34 ). Additionally, the linearity of the system was evaluated through linear regression analysis using GraphPad Prism 8.2.0, comparing expected versus actual results and correlating CTC quantities with relative light units (RLU) measured on the Fluoroskan AscentTM FL. Results 1. Optimization and analytical validation of the AP activity-based evaluation assay To establish a highly sensitive and specific assay for the detection of CTCs via AP activity, we systematically optimized key parameters of the capture platform (Fig. 1 ). First, we evaluated the performance of magnetic nanoparticle probes (MNPs) conjugated with different biotinylated BG2 variants (BG2-biotin, BG2-bibiotin, BG2-PEG-biotin, and BG2-t-biotin) for their ability to specifically capture target cells against a high background of WBCs. As shown in Fig. 2 a- 2 b, BG2-PEG-biotin yielding the highest capture recovery and the most favorable signal-to-noise (S/N) ratio and was selected for all subsequent experiments (Fig. 2 a- 2 b). Following capture, free streptavidin binding sites on the MNPs required blocking to minimize non-specific background signals. We tested three blocking agents: biotin, biocytin, and biotin-PEG-OH. The results demonstrated that treatment with biotin-PEG-OH provided the highest S/N ratio (Fig. 2 c), and it was therefore adopted as the optimal blocking reagent. Additionally, to further suppress non-specific binding of WBCs to the capture platform, we investigated the efficacy of two oligonucleotide inhibitors, Salmon DNA and Herring DNA. Our analysis indicated that Salmon DNA from Solarbio more effectively reduced background signals from WBCs (Fig. 2 d) and was chosen as the optimal non-specificity blocker. Moreover, A significant source of background AP activity originates from TNAP expressed by WBCs. To inhibit this activity, we assessed the potency of three TNAP inhibitors. Based on its superior potency, C17H18N2O4S, was selected for use in the assay (Fig. 2 e). Furthermore, having defined the optimal conditions, we then assessed the analytical performance of the optimized assay. The capture efficiency of the selected BG2 conjugate and the resulting RLU signal both demonstrated excellent linearity over a range of spiked Lovo cell quantities (Fig. 2 f- 2 g), confirming the assay's quantitative capability. Finally, the precision of the fully optimized assay was evaluated by testing ten replicate samples, each containing 1000 Lovo cells in 0.2 mL of blood. The assay showed high reproducibility, with a percentage coefficient of variation (%CV) of 5.6% (Fig. 2 h). 2. The widely expression of ALPP and ALPG suggests AP Activity as a Potential Pan-Cancer Biomarker In this study, we evaluated the RNA expression level of AP in various tumor cell lines using quantitative PCR (qPCR), and assessed its protein expression by flow cytometry. The results demonstrated that the Ct values of intestinal cancer cell lines (Lovo, SW620, SW480, Caco2), lung cancer cell lines (NCI-H1975, HCC827, A549), breast cancer cell line (MDA-MB-231), gastric cancer cell line (MKN7), liver cancer cell line (7721), and human placental choriocarcinoma cell line (Bewo) were all below 30, which was lower than that of white blood cells (Figure. 3a), indicating that the ALPP gene is expressed in most tumor cell lines. A similar pattern was observed for ALPG (Figure. 3b). To further investigate the protein expression of ALPP and ALPG, we selected several cell lines with positive RNA expression and performed flow cytometry analysis using anti-PLAP and anti-ALPG antibodies. The results confirmed that all tested tumor cells expressed both ALPP and ALPG proteins, whereas white blood cells showed no detectable expression. Together, these findings indicate that ALPP and ALPG are broadly expressed in tumor cells, suggesting that AP activity may serve as a valuable biomarker for cancer research. 3. Association of AP Activity with Disease Status and TKI Resistance To evaluate the clinical relevance of AP activity, we compared its levels across various patient groups and assessed its diagnostic and predictive performance. AP activity was significantly elevated in patients with lung cancer (LC) (n = 135; p = 0.0372; Fig. 4 a) compared to HD (n = 45), but not in CTC counts (p = 0.1491; Fig. 4 c). Although a trend toward increased activity was observed in other cancer patients, the difference from HD did not reach statistical significance (n = 39; p = 0.0576; Fig. 4 a). Notably, AP activity was substantially higher in patients exhibiting resistance to TKI therapy ( p = 0.0001) (Fig. 4 b), while no significance in CTC counts (p = 0.7951; Fig. 4 d). The diagnostic utility of AP activity was further quantified using ROC curve analysis. For distinguishing LC patients from HD, AP activity demonstrated excellent performance with an AUC of 0.9340 (95% CI: 0.8977–0.9703, p 13,640, the assay achieved a sensitivity of 88% and a specificity of 91%, which was higher than in CTC counts (Threshold of > 0, sensitivity 57.78%, specificity 100.0%; AUC = 0.7889, 95% CI 0.7253–0.8524, p < 0.0001; Fig. 4 h). Similarly, strong discriminatory power was observed for the cohort of other cancers versus HD (AUC = 0.7652, 95% CI 0.6645–0.8659, p 7283 (Fig. 4 f), also higher sensitivity than in CTC counts (Threshold of > 0, sensitivity 64.10%, specificity 100.0%; AUC = 0.8205, 95% CI 0.7223–0.9187, p < 0.0001; Fig. 4 i). Most strikingly, AP activity exhibited outstanding performance in identifying TKI-resistant status. The ROC analysis for this application yielded an AUC of 0.9198 (95% CI: 0.8756–0.9639, p 126,150 effectively discriminated resistant patients, with a high sensitivity of 96% and a specificity of 85% (Fig. 4 g), which was higher than in CTC counts (Threshold of > 1, sensitivity 60.32%, specificity 68.57%; AUC = 0.6493, 95% CI 0.5552–0.7434, p = 0.0030; Fig. 4 j). Overall, these results suggest that AP activity serves not only as a sensitive and specific diagnostic biomarker but also as a potential indicator for predicting TKI resistance, highlighting its significant clinical potential in cancer management and treatment monitoring. 4. Association of AP Activity with CTC Status and Burden To investigate the relationship between AP activity and the presence of CTCs, patients were stratified into CTC-positive (CTC+) and CTC-negative (CTC-) groups. As summarized in Table 1 , the direct comparison of AP activity levels between these two groups, analyzed by Student's t-test, did not yield statistically significant differences across any of the cohorts, including LC ( p = 0.2443; Fig. 5 a) and other cancers ( p = 0.2664; Fig. 5 b), and in the lung cancer patients with TKI-resistant population ( p = 0.8554; Fig. 5 d; Table 1 ), but significant difference in patients without TKI-resistant ( p = 0.0153; Fig. 5 c; Table 1 ). Despite the lack of significant difference in mean AP activity between the dichotomized groups, a more nuanced relationship was revealed upon correlation analysis with the quantitative CTC burden. Spearman's rank correlation analysis demonstrated a significant positive correlation between the absolute number of CTCs and AP activity levels in patients with LC (r = 0.485, p = 0.000 ) , other cancers (r = 0.681, p = 0.007 ) , and in the TKI-resistant subgroup (r = 0.369, p = 0.003), as detailed in Table 2 . Additionally, the potential relationship between T790M mutation status and both AP activity and CTC quantity was further investigated. As shown in Supplementary Fig. 1a, no significant difference in AP activity was observed between T790M-positive and T790M-negative patients ( p = 0.9258) (Supplementary Fig. 1a). Similarly, CTC quantity did not differ significantly between these two genetic subgroups ( p = 0.8384; Supplementary Fig. 1b). Table 1 t-test analysis of AP activity based on CTC+/CTC- CTC patients p value LC AP Negative 58 0.127 positive 77 Other cancers AP Negative 14 0.089 positive 25 TKI AP Negative 19 0.959 positive 44 * Significant relevance at p < 0.05. Table 2 Correlation analysis of AP activity to CTC quantity Variable 1 Variable 2 Spearman's rho p value LC CTC LC AP 0.485 0.000** Other cancers CTC Other cancers AP 0.681 0.007** TKI CTC TKI AP 0.369 0.003** * Significant relevance at p < 0.05. In summary, our findings delineate a distinct pattern of association for AP activity: it robustly correlates with the quantitative burden of CTCs rather than their mere presence, underscoring its utility as a continuous biomarker for dynamic monitoring of tumor dissemination. Furthermore, the strong predictive capacity of AP activity for TKI resistance operates independently of the T790M mutation, suggesting that its role in identifying resistance extends beyond this specific genetic mechanism and may reflect broader, yet to be elucidated, biological pathways of treatment failure. 5. Longitudinal monitoring of AP activity and CTC quantity during treatment response To assess the potential of AP activity and CTC quantity as dynamic biomarkers for monitoring disease progression, longitudinal blood samples were collected from 9 cancer patients at baseline (T0) and after 3 months of therapy (T1). Patients exhibiting progressive disease (PD) showed a marked increase in both AP activity (Fig. 6 a) and CTC counts (Fig. 6 b) at T1 compared to T0. In contrast, patients achieving partial response (PR) or maintaining stable disease (SD) demonstrated a substantial decrease in AP activity (Fig. 6 c) and CTC quantity (Fig. 6 d) following treatment. These results indicate that both AP activity and CTC burden exhibit dynamic changes reflective of treatment response, with increasing levels associated with disease progression and decreasing levels correlating with favorable therapeutic outcomes, supporting their utility as complementary longitudinal biomarkers for monitoring tumor dynamics. Discussion This study presents a comprehensive validation of AP activity as a functional biomarker for detecting CTRMs and monitoring therapeutic response in multiple cancer types. By systematically optimizing a capture assay based on AP activity, we established a highly sensitive and specific platform capable of isolating CTRMs against a high background of white blood cells. Furthermore, we demonstrated that AP activity not only correlates with CTC abundance but also strongly predicts TKI resistance, independent of canonical genetic mechanisms. Longitudinal analysis further confirmed the utility of AP activity in dynamically reflecting disease progression and treatment response. Our results demonstrated that ALPP and ALPG, two alkaline phosphatase isoforms, are widely expressed in various tumor cell lines at both the RNA and protein levels. In contrast, WBCs showed no detectable expression of these isoforms. This broad expression pattern suggests that AP activity could serve as a potential pan - cancer biomarker. Previous studies have also reported the overexpression of AP in different types of cancer tissues ( 35 ). For example, elevated AP levels have been associated with bone - metastatic castration - resistant prostate cancer ( 36 ), and increased AP activity has been observed in esophageal cancer patients ( 37 ). The consistent overexpression of AP in diverse cancer types indicates its potential as a general biomarker for cancer, which could be further explored for early cancer detection and disease monitoring. AP activity was significantly elevated in patients with lung cancer and other cancers compared to HD. Although the difference in other cancer patients did not reach statistical significance, there was still a trend towards increased activity. These findings suggest that AP activity can be used as a diagnostic biomarker for some cancer types. The ROC curve analysis further quantified the diagnostic utility of AP activity. For lung cancer, AP activity showed excellent discriminatory power between patients and HD, with high sensitivity and specificity at optimal thresholds. Notably, AP activity was substantially higher in patients with TKI resistance. The outstanding performance of AP activity in identifying TKI - resistant status, as demonstrated by the high AUC, sensitivity, and specificity in the ROC analysis, highlights its potential as a predictive biomarker for TKI resistance. This is an important finding, as the ability to predict TKI resistance can help clinicians make more informed treatment decisions. The underlying mechanism for the association between AP activity and TKI resistance may involve changes in cellular signaling pathways related to AP function. For example, AP may be involved in cell survival and proliferation pathways that are dysregulated in TKI - resistant cancer cells( 38 ). Our study revealed an interesting relationship between AP activity and CTCs. Although there was no significant difference in mean AP activity between CTC - positive and CTC - negative groups, a significant positive correlation was observed between the absolute number of CTCs and AP activity levels in patients with lung cancer, other cancers, and the TKI - resistant subgroup. This indicates that AP activity is more closely related to the quantitative burden of CTCs rather than their mere presence. CTCs are considered to be an important indicator of tumor dissemination and prognosis ( 8 , 39 ). The correlation between AP activity and CTC burden suggests that AP activity could be used as a biomarker for monitoring tumor dissemination. The mechanism behind this correlation may be that CTCs, as they circulate in the bloodstream, contribute to the overall AP activity detected in the blood. Tumor cells, including CTCs, may express and secrete AP, and the more CTCs present, the higher the overall AP activity. We also found that CTC counts were significantly higher in patients with TKI resistance compared to non - resistant patients, and CTC quantity had a certain predictive performance for resistance status. However, there was no significant difference in AP activity or CTC quantity between T790M - positive and T790M - negative patients. This suggests that the role of AP activity in predicting TKI resistance is independent of the T790M mutation and may reflect other biological pathways related to treatment failure. The longitudinal monitoring of AP activity and CTC quantity in cancer patients during treatment response provided valuable insights into their potential as dynamic biomarkers. Patients with PD showed an increase in both AP activity and CTC counts, while patients with PR or SD demonstrated a decrease. These results indicate that both AP activity and CTC burden can reflect treatment response. The dynamic changes in AP activity and CTC quantity support their utility as complementary longitudinal biomarkers for monitoring tumor dynamics. By tracking these biomarkers over time, clinicians can more effectively assess the effectiveness of treatment and make timely adjustments to treatment strategies ( 1 ). Several limitations of this study should be acknowledged. First, the sample size in the longitudinal analysis was relatively small; further validation in larger prospective cohorts is warranted to strengthen the generalizability of our findings. Second, the use of a limited blood volume (1 mL) may have contributed to the lack of a significant association between AP activity and other cancers, potentially due to insufficient capture of rare CTCs in this malignancy. Future studies should consider employing larger sample volumes to improve detection sensitivity. Furthermore, the biological mechanisms underlying elevated AP activity in CTCs and its specific role in mediating TKI resistance require deeper investigation. It remains an open question whether AP activity serves merely as a marker of aggressive tumor phenotypes or plays an active functional role in resistance. In conclusion, we have developed and validated an AP activity-based assay that effectively detects CTCs and predicts TKI resistance with high accuracy. Our results position AP activity as a promising functional biomarker that complements existing genetic and enumeration-based approaches. Future studies should explore the integration of AP activity into multimodal biomarker panels and investigate its potential as a therapeutic target in treatment-resistant cancers. Abbreviations ALPG: Germ cell alkaline phosphatase); ALPP: Placental alkaline phosphatase, PLAP; AP: Alkaline phosphatase; ATCC: American Type Culture Collection; AUC: Area under the curve; CD45: Leukocyte common antigen 45; CI: Confidence interval; CK: Cytokeratin; CTCs: Circulating tumor cells; CTRM: Circulating Tumor-Related Materials; CV: Coefficient of variation; DAPI: 4',6-diamidino-2-phenylindole; DMEM: Dulbecco’s modified Eagle’s medium; FBS: Fetal bovine serum; HD: Healthy donor; LC: Lung cancer; PD: Progression disease; PR: Partial response; RLU: Relative light units; ROC: Receiver operating characteristic; SD: Stable disease; S/N: Ratio of signal to noise; TDMs: Tumor-derived materials; TKI: Tyrosine kinase inhibitor; TNAP: Tissue-nonspecific alkaline phosphatase; WBC, Leukocyte, white blood cell. Declarations Data availability statement All data relevant to the study are included in the article. Consent for publication statement Written informed consent for publication was obtained from the patient or their legal guardian. Conflict of interest disclosure Authors declare no competing interests. Ethics statement The clinical samples used in this study are approved by the ethics committee of The First Affiliated Hospital of Zhengzhou University, with ethics approval number 2024-KY-0100-002, and all enrolled patients sign the informed consents. Authors’ contributions Conceptualization: Dongjiang Tang. Data curation: Lin Chen. Formal analysis: Lin Chen. Investigation: Lin Chen. Methodology: Lin Chen and Panpan Qi. Project administration: Dongjiang Tang. Supervision: Dongjiang Tang. Validation: Zhonglin Yang and Yue Lu. Visualization: Lin Chen. Writing: Lin Chen. Review and editing: All the authors. Acknowledgements The authors would like to thank The First Affiliated Hospital of Zhengzhou University for their valuable suggestions during the study. 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Detection and localization of surgically resectable cancers with a multi-analyte blood test. %J Science. Science. 2018;359(6378):926-30. Melo SA LL, Kahlert C, Fernandez AF, Gammon ST, Kaye J, LeBleu VS, Mittendorf EA, Weitz J, Rahbari N, Reissfelder C, Pilarsky C, Fraga MF, Piwnica-Worms D, Kalluri R. Glypican-1 identifies cancer exosomes and detects early pancreatic cancer. Nature. 2015;523(7559):177-82. Bettegowda C SM, Leary RJ, Kinde I, Wang Y, Agrawal N, Bartlett BR, Wang H, Luber B, Alani RM, Antonarakis ES, Azad NS, Bardelli A, Brem H, Cameron JL, Lee CC, Fecher LA, Gallia GL, Gibbs P, Le D, Giuntoli RL, Goggins M, Hogarty MD, Holdhoff M, Hong SM, Jiao Y, Juhl HH, Kim JJ, Siravegna G, Laheru DA, Lauricella C, Lim M, Lipson EJ, Marie SK, Netto GJ, Oliner KS, Olivi A, Olsson L, Riggins GJ, Sartore-Bianchi A, Schmidt K, Shih lM, Oba-Shinjo SM, Siena S, Theodorescu D, Tie J, Harkins TT, Veronese S, Wang TL, Weingart JD, Wolfgang CL, Wood LD, Xing D, Hruban RH, Wu J, Allen PJ, Schmidt CM, Choti MA, Velculescu VE, Kinzler KW, Vogelstein B, Papadopoulos N, Diaz LA Jr. Detection of Circulating Tumor DNA in Early- and Late-Stage Human Malignancies %J Science Translational Medicine. Sci Transl Med. 2014;6(224):224ra24. DW M. Perspectives in alkaline phosphatase research. Clin Chem. 1992;38(12):2486-92. Fishman WH IN, Stolbach LL, Krant MJ. Serum alkaline phosphatase isoenzyme of human neoplastic cell origin. Cancer Res. 1968;28(1):150-4. Plage H FK, Hofbauer S, Roßner F, Schallenberg S, Elezkurtaj S, Lennartz M, Marx A, Samtleben H, Fisch M, Rink M, Slojewski M, Kaczmarek K, Ecke T, Klatte T, Koch S, Adamini N, Minner S, Simon R, Sauter G, Weischenfeldt J, Schlomm T, Horst D, Zecha H, Kluth M, Weinberger S. PLAP expression is linked to invasive tumor growth in urothelial carcinoma of the bladder %J International Urology and Nephrology. Int Urol Nephrol. 2025;57(5):1381-8. Hustin J CJ, Franchimont P. Immunohistochemical demonstration of placental alkaline phosphatase in various states of testicular development and in germ cell tumours. Int J Androl. 1987;10(1):29-35. JL. M. Alkaline Phosphatases : Structure, substrate specificity and functional relatedness to other members of a large superfamily of enzymes. Purinergic Signal. 2009;2(2):335-41. WH F. Perspectives on alkaline phosphatase isoenzymes. Am J Med. 1974;56(5):617-50. Heinrich D BØ, Guise TA, Suzuki H, Sartor O. Alkaline phosphatase in metastatic castration-resistant prostate cancer: reassessment of an older biomarker. Future Oncol. 2018;14(24):2543-56. Jiang C HF, Xia X, Guo X. . Prognostic value of alkaline phosphatase and bone-specific alkaline phosphatase in breast cancer: A systematic review and meta-analysis. Int J Biol Markers. 2023;38(1):25-36. Bessueille L KL, Quillard T, Goettsch C, Briolay A, Taraconat N, Balayssac S, Gilard V, Mebarek S, Peyruchaud O, Duboeuf F, Bouillot C, Pinkerton A, Mechtouff L, Buchet R, Hamade E, Zibara K, Fonta C, Canet-Soulas E, Millan JL, Magne D. Inhibition of alkaline phosphatase impairs dyslipidemia and protects mice from atherosclerosis. Transl Res. 2023;251:2-13. Gómez-Cuadrado L TN, Ma R, Qian B, Brunton VG. . Mouse models of metastasis: progress and prospects. Dis Model Mech. 2017;10(9):1061-74. Bai Y, Koh CG, Boreman M, Juang YG, Tang IC, Lee LJ, et al. Surface modification for enhancing antibody binding on polymer-based microfluidic device for enzyme-linked immunosorbent assay. Langmuir. 2006;22(22):9458-67. Bergmann N FB, Schmidt MC, Tams V, Beining K, Schwitte H, Boettcher AA, Martin DL, Bockelmann AC, Reusch TB, Rauch G. A quantitative real-time polymerase chain reaction assay for the seagrass pathogen Labyrinthula zosterae. Mol Ecol Resour. 2011;11(6):1076-81. Bing T SL, Wang J, Wang L, Liu X, Zhang N, Xiao X, Shangguan D. Aptameric Probe Specifically Binding Protein Heterodimer Rather Than Monomers. Adv Sci. 2019;6(11):1900143. Chen W ZJ, Huang L, Chen L, Zhou Y, Tang D, Xie Y, Wang H, Huang C. Detection of HER2-positive Circulating Tumor Cells Using the LiquidBiopsy System in Breast Cancer. Clin Breast Cancer. 2019;19(1):e239-e46. Antonelli P CD, Cesana BM. Statistical methods for evidence-based medicine: the diagnostic test. Part II. Minerva Anestesiol. 2008;74(9):481-8. Liu ZX HL, Fang SQ, Tan GH, Huang PG, Zeng Z, Xia X, Wang XX. Overexpression of pyruvate kinase M2 predicts a poor prognosis for patients with osteosarcoma. Tumour Biology. 2016;37(11):14923-8. Mikah P KL, Eminaga O, Herrmann E, Papavassilis P, Hinkelammert R, Semjonow A, Schrader AJ, Boegemann M. . Dynamic changes of alkaline phosphatase are strongly associated with PSA-decline and predict best clinical benefit earlier than PSA-changes under therapy with abiraterone acetate in bone metastatic castration resistant prostate cancer. BMC Cancer. 2016;16:214. Wei XL ZD, He MM, Jin Y, Wang DS, Zhou YX, Bai L, Li ZZ, Luo HY, Wang FH, Xu RH. The predictive value of alkaline phosphatase and lactate dehydrogenase for overall survival in patients with esophageal squamous cell carcinoma. Tumour Biology. 2016;37(2):1879-87. Rao SR SA, Marino D, Cheng X, Lwin ST, Orriss IR, Hamdy FC, Edwards CM. Tumour-derived alkaline phosphatase regulates tumour growth, epithelial plasticity and disease-free survival in metastatic prostate cancer. Br J Cancer. 2017;116(2):227-36. Bidard FC MS, Riethdorf S, Mueller V, Esserman LJ, Lucci A, Naume B, Horiguchi J, Gisbert-Criado R, Sleijfer S, Toi M, Garcia-Saenz JA, Hartkopf A, Generali D, Rothé F, Smerage J, Muinelo-Romay L, Stebbing J, Viens P, Magbanua MJM, Hall CS, Engebraaten O, Takata D, Vidal-Martínez J, Onstenk W, Fujisawa N, Diaz-Rubio E, Taran FA, Cappelletti MR, Ignatiadis M, Proudhon C, Wolf DM, Bauldry JB, Borgen E, Nagaoka R, Carañana V, Kraan J, Maestro M, Brucker SY, Weber K, Reyal F, Amara D, Karhade MG, Mathiesen RR, Tokiniwa H, Llombart-Cussac A, Meddis A, Blanche P, d'Hollander K, Cottu P, Park JW, Loibl S, Latouche A, Pierga JY, Pantel K. Circulating Tumor Cells in Breast Cancer Patients Treated by Neoadjuvant Chemotherapy: A Meta-analysis %J Journal of the National Cancer Institute. J Natl Cancer Inst. 2018;110(6):560-7. Additional Declarations No competing interests reported. Supplementary Files Supplementaryfigures.doc Supplementaryprotocols.doc Supplementarytables.doc Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 22 Apr, 2026 Reviews received at journal 21 Apr, 2026 Reviews received at journal 17 Apr, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviewers invited by journal 14 Apr, 2026 Editor assigned by journal 13 Apr, 2026 Editor invited by journal 16 Feb, 2026 Submission checks completed at journal 14 Feb, 2026 First submitted to journal 14 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8846023","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":627192029,"identity":"ebf9cd21-3064-4ccb-a8cd-aa0ded616cf4","order_by":0,"name":"Lin Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYDACCRBRIMHAD+EyJ7ARp8VAgkGygUQtQHQAqoWgDv7ZzcceMBhY5BmfP/zs0Y0K6zw+9gbGDx9z8Fhy51i6AdBhxWY30syNc86kF7PxHGCWnLkNtxYDiRwzCSCZuO0Gg5l0btvhxDaJBDZmXrxa8r+BtWzuP/5NOvcfUVpy2MBaNjDkAG1pIEKLxI00M4kEoJYZN3LKpHOOgfxysBmvX/hnJD+T+FBRl9jff3ybdE6NdZ58e/PBDx/xaAGDBFQuYwMB9aNgFIyCUTAKCAEAJ21JTCLeHecAAAAASUVORK5CYII=","orcid":"","institution":"South China University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Lin","middleName":"","lastName":"Chen","suffix":""},{"id":627192031,"identity":"9c54d985-53a8-4b26-b9c2-ff08a1f80dc7","order_by":1,"name":"Yue Lu","email":"","orcid":"","institution":"Zhuhai Sanmed Biotech Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Lu","suffix":""},{"id":627192032,"identity":"d4e1ed37-8b7b-410c-abcb-c03a01149cdf","order_by":2,"name":"Panpan Qi","email":"","orcid":"","institution":"Zhuhai Sanmed Biotech Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Panpan","middleName":"","lastName":"Qi","suffix":""},{"id":627192034,"identity":"eb6106ba-8b75-4cee-a28b-ebd5b7a53c41","order_by":3,"name":"Zhonglin Yang","email":"","orcid":"","institution":"Zhuhai Sanmed Biotech Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Zhonglin","middleName":"","lastName":"Yang","suffix":""},{"id":627192036,"identity":"d0709c04-14ef-4ebb-85a8-3744c40211bc","order_by":4,"name":"Dongjiang Tang","email":"","orcid":"","institution":"Zhuhai Sanmed Biotech Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Dongjiang","middleName":"","lastName":"Tang","suffix":""}],"badges":[],"createdAt":"2026-02-11 01:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8846023/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8846023/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107616274,"identity":"56afebce-589e-410d-9cbc-31e6c22c9963","added_by":"auto","created_at":"2026-04-23 09:13:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":174262,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic of the AP activity evaluation assay for circulating tumor relative materials (CTRM).\u003c/strong\u003e CTRM, included apoptotic CTCs, CTCs fragment, and vehicles/exosome, were isolated and enriched by using pre-conjugated complex of BG2-biotin-streptavidin-beads (BG2-MNPs) and assessed AP (alkaline phosphatase) activity on Chemiluminescence Apparatus to analyze the correlation of AP activity to clinical disease visual.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8846023/v1/9197a62cbaa41bfd4520e609.png"},{"id":107616251,"identity":"0d48ca86-0fcd-434c-9c5d-22adbf877ee8","added_by":"auto","created_at":"2026-04-23 09:13:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":54178,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDevelopment of AP activity evaluation assay.\u003c/strong\u003e The study was explored to select optimal BG2 for pre-conjugating of BG2-MNPs, and optimal biotin for blocking free streptavidin, and optimal oligonucleotide inhibitors for blocking non-specificity of WBCs, and optimal TNAP inhibitors for inhibiting tissue non-specificity alkaline phosphatase. 2a-2b: \u003cstrong\u003eEvaluation of BG2 conjugates for cell capture.\u003c/strong\u003e Capture efficiency of Lovo cells (spiked at 1000 cells per sample) against a high background of WBCs (100 million cells) was tested using MNPs pre-conjugated with different BG2 variants: BG2-biotin, BG2-bibiotin, BG2-PEG-biotin, and BG2-t-biotin. The variant yielding the highest recovery rate and signal-to-noise (S/N) ratio, defined as the relative light unit (RLU) of spiked samples (Lovo cells + WBCs) divided by the RLU of negative control samples (WBCs only), was selected for subsequent experiments. 2c: \u003cstrong\u003eSelection of a blocking agent for free streptavidin.\u003c/strong\u003e After cell capture, free streptavidin binding sites were blocked with biotin, biocytin, or biotin-PEG-OH. The S/N ratio was assessed for samples containing 1000 Lovo cells spiked into 0.2 mL of blood to determine the optimal blocking reagent. 2d: \u003cstrong\u003eOptimization of non-specific binding inhibition.\u003c/strong\u003e To inhibit non-specific binding of WBCs, two types of oligonucleotide inhibitors, Salmon DNA and Herring DNA, were evaluated for their effectiveness in improving assay specificity. 2e: \u003cstrong\u003eInhibition of tissue-nonspecific alkaline phosphatase (TNAP).\u003c/strong\u003e Different TNAP inhibitors were evaluated for their potency to suppress background activity from WBCs (200,000 cells). The half-maximal inhibitory concentrations (IC50) were determined to be 190 nM for C17H16N2O4S, 160 nM for C13H12ClN3O4S, and 250 nM for H-HoArg-OH. 2f-2g: \u003cstrong\u003eAssessment of assay linearity.\u003c/strong\u003e The linear relationship between the number of spiked Lovo cells and both the cell capture efficiency (\u003cstrong\u003ef\u003c/strong\u003e) and the resulting RLU signal (\u003cstrong\u003eg\u003c/strong\u003e) was demonstrated. 2h: \u003cstrong\u003ePrecision of the assay.\u003c/strong\u003e The reproducibility of the assay was determined by analyzing ten replicate samples, each containing 1000 Lovo cells spiked into 0.2 mL of blood. The precision is expressed as the percentage coefficient of variation (%CV).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8846023/v1/af0a905b620a4d7a2b7ed232.png"},{"id":107616362,"identity":"a95bb7ad-8264-4cfe-b7b7-bb5d96c51b51","added_by":"auto","created_at":"2026-04-23 09:13:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":39600,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvaluation of ALPP and ALPG expression level on tumor cell lines.\u003c/strong\u003e 3a-3b: Assessment of mRNA expression level of ALPP and ALPG by qPCR, respectively. 3c-3d: Evaluation of ALPP and ALPG expression level by using anti PLAP H17E2-APP antibody and anti ALPG antibody on flow-cytometry.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8846023/v1/da3ead8f38869654a70086ee.png"},{"id":107616345,"identity":"6f85b89b-28fc-4cdb-8792-8848344e9b36","added_by":"auto","created_at":"2026-04-23 09:13:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":78336,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation of AP activity to disease characteristics.\u003c/strong\u003e Statistical analysis was performed using a two-tailed Student's t-test and Receiver Operating Characteristic (ROC) Curve. Significance levels are indicated as *p \u0026lt; 0.05, **p \u0026lt; 0.01, and ***p \u0026lt; 0.001. 4a: Difference in AP activity between healthy donors (HD, n=45) and Lung cancer (LC, n=135) patients (\u003cem\u003ep\u003c/em\u003e=0.0372) and other cancers (n=39, \u003cem\u003ep\u003c/em\u003e=0.0576). 4b: Comparison of AP activity across TKI resistance (\u003cem\u003ep\u003c/em\u003e=0.0001) (63 patients with TKI resistance and 72 patients with non). 4c: Difference in CTC number between healthy donors (HD, n=45) and Lung cancer (LC, n=135) patients (\u003cem\u003ep\u003c/em\u003e=0.1491) and other cancers (n=39, \u003cem\u003ep\u003c/em\u003e=0.0162); 4d: Comparison of CTC counts across TKI resistance (\u003cem\u003ep\u003c/em\u003e=0.7951) (63 patients with TKI resistance and 72 patients with non); 4e: ROC curve analysis of AP activity in HD and LC patients (Threshold of \u0026gt;13,640,sensitivity 88%, specificity 91%; AUC=0.9340，95% CI 0.8977-0.9703，\u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001); 4f: ROC curve analysis of AP activity in HD and other cancers (Threshold of \u0026gt;7283,sensitivity 71.79%, specificity 66.67%; AUC=0.7652，95% CI 0.6645-0.8659，\u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001); 4g: ROC curve analysis of AP activity across TKI resistance (Threshold of \u0026gt;126,150, sensitivity 81%, specificity 88%; AUC=0.9198，95% CI 0.8756-0.9639，\u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001). 4h: ROC curve analysis of CTC counts in HD and LC patients (Threshold of \u0026gt;0, sensitivity 57.78%, specificity 100.0%; AUC=0.7889，95% CI 0.7253-0.8524，\u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001); 4i: ROC curve analysis of CTC counts in HD and other cancers (Threshold of \u0026gt;0,sensitivity 64.10%, specificity 100.0%; AUC=0.8205，95% CI 0.7223-0.9187，\u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001); 4j: ROC curve analysis of CTC counts across TKI resistance (Threshold of \u0026gt;1, sensitivity 60.32%, specificity 68.57%; AUC=0.6493，95% CI 0.5552-0.7434，\u003cem\u003ep\u003c/em\u003e=0.0030).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8846023/v1/85ebc0f1906dab8aae9104dc.png"},{"id":107616252,"identity":"d079b37a-8543-4fb6-ab62-6207965796a7","added_by":"auto","created_at":"2026-04-23 09:13:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":46794,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of AP activity to CTC.\u003c/strong\u003ePatients were categorized into two groups: those with CTC-positive (CTC+) and CTC-negative (CTC-) results. Differences in AP activity among various subgroups were statistically analyzed on GraphPad Prism 8.2.0. 5a-5b: Difference in AP activity between CTC+ and CTC- on Lung cancer (\u003cem\u003ep\u003c/em\u003e=0.2443) and other cancers (\u003cem\u003ep\u003c/em\u003e=0.2664)respectively. 5c: Comparison of AP activity across CTC+ and CTC- on Lung cancer patients without TKI resistance (\u003cem\u003ep\u003c/em\u003e=0.0153). 5d: Comparison of AP activity across CTC+ and CTC- on Lung cancer patients with TKI resistance (\u003cem\u003ep\u003c/em\u003e=0.8554).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8846023/v1/dce013905b4b18a11d81bc03.png"},{"id":107616267,"identity":"37e962b1-06d0-45ce-8b37-ed34daafca89","added_by":"auto","created_at":"2026-04-23 09:13:02","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":41754,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDynamic changes in AP activity and CTC quantity during treatment and their association with therapeutic response. \u003c/strong\u003ePeripheral blood samples were collected from 9 patients (5 with lung cancer, 2 with breast cancer, 1 with kidney cancer, and 1 unspecified) at baseline (T0) and after 3 months of therapy (T1) to evaluate the potential of AP activity and CTC abundance in predicting treatment response. 6a-6b: Changes shown for patients with progressive disease (PD); 6c-6d: Changes shown for those achieving partial response (PR) or stable disease (SD).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8846023/v1/e934d3d891686b979dfa37bc.png"},{"id":107707172,"identity":"e0752fa9-fe3d-41e1-b9f8-7bbf6c2a87fb","added_by":"auto","created_at":"2026-04-24 09:19:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":647791,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8846023/v1/697e9cf8-5f15-46dc-b9e6-2609206d6ee0.pdf"},{"id":107616396,"identity":"da581d3e-3cb9-4b76-8d8f-2997824e0027","added_by":"auto","created_at":"2026-04-23 09:13:12","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":231936,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigures.doc","url":"https://assets-eu.researchsquare.com/files/rs-8846023/v1/053fddd99056c35271f3b379.doc"},{"id":107616347,"identity":"daaa137d-e6ca-454f-a29b-d090ad8a9602","added_by":"auto","created_at":"2026-04-23 09:13:03","extension":"doc","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":60416,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryprotocols.doc","url":"https://assets-eu.researchsquare.com/files/rs-8846023/v1/3f1a4ece49bfddfb54d45ea0.doc"},{"id":107616357,"identity":"bc530663-f2af-41f7-aa4d-b1b37e990b3c","added_by":"auto","created_at":"2026-04-23 09:13:08","extension":"doc","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":33792,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytables.doc","url":"https://assets-eu.researchsquare.com/files/rs-8846023/v1/81c8179a6d8929c1779c6984.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"APtamer-Enhanced AP Assay: Dynamic Functional Profiling of Circulating Tumor Materials for Predicting TKI Resistance and Real-Time Treatment Monitoring","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCirculating tumor cells (CTCs) have emerged as crucial biomarkers for cancer diagnosis (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), prognosis (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), and monitoring treatment response (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). However, the reliable detection and molecular characterization of these rare cells in peripheral blood remain technically challenging due to their extreme scarcity (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) and heterogeneity (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The rarity of CTCs, often present at concentrations as low as one CTC per billion blood cells, poses a significant challenge and bottleneck for their detection and subsequent application (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). This scarcity limits the sensitivity and reliability of current detection methods, making it difficult to capture and analyze sufficient numbers of CTCs for accurate diagnosis and monitoring (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo overcome these limitations, researchers have begun to focus on tumor-derived materials (TDMs) in circulation (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), which include not only CTCs but also other tumor-related substances such as apoptotic CTC fragments (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), exosomes (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), cell vesicles (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), and circulating free proteins (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). These tumor cell-related materials (CTRMs) are more abundant in the bloodstream compared to CTCs, offering a potentially richer source of information for cancer detection and monitoring (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Their higher relative abundance can enhance the sensitivity and reliability of assays, providing a more comprehensive picture of tumor dynamics and therapeutic response.\u003c/p\u003e \u003cp\u003eAlkaline phosphatase (AP) (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), particularly its placental-like isoforms ALPP (placental alkaline phosphatase, PLAP) (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) and ALPG (germ cell alkaline phosphatase) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), has garnered significant interest as a potential cancer biomarker. These isoforms are normally restricted to placental and germ cells but are frequently re-expressed in a wide range of epithelial malignancies (\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). This tumor-specific re-expression pattern suggests that functional AP activity could serve as a universal, functional marker for detecting TDMs in circulation, circumventing the limitations of antigen-dependent capture methods. Moreover, the functional activity of enzymes like AP might reflect the biological state and metabolic activity of tumor cells, potentially offering insights into therapy resistance and disease progression.\u003c/p\u003e \u003cp\u003eDespite its promise, the development of a robust, sensitive, and specific assay to detect CTCs based on AP activity has been hindered by several obstacles. These include significant background interference from tissue-nonspecific alkaline phosphatase (TNAP) expressed by white blood cells (WBCs) (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), non-specific binding of probes (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), and the need for a highly efficient capture system to isolate rare CTCs from a vast excess of hematological cells.\u003c/p\u003e \u003cp\u003eTo address these challenges, we developed and systematically optimized a novel capture and detection platform utilizing a biotinylated DNA aptamer (BG2) (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) conjugated to magnetic beads for the specific isolation of AP-expressing CTRMs. This platform leverages the higher abundance of CTRMs compared to CTCs to enhance detection sensitivity. Furthermore, we employed a chemiluminescent detection method, which offers high sensitivity and specificity, allowing for the precise quantification of AP activity even in the presence of background interference. This study presents a comprehensive analytical validation of this optimized AP activity-based assay and investigates its clinical utility for cancer diagnosis, for predicting resistance to tyrosine kinase inhibitor (TKI) therapy, and for longitudinal monitoring of treatment response in patients with lung, colorectal, and other cancers. Our findings position AP activity not only as a highly sensitive diagnostic biomarker but also as a powerful functional indicator of therapeutic resistance and tumor dynamics.\u003c/p\u003e"},{"header":"Materials and methods","content":"\n\u003ch3\u003e1. Reagents and cell lines\u003c/h3\u003e\n\u003cp\u003eBG2-biotin, BG2-PEG-biotin, and BG2-t-biotin were synthesized by Sangon Biotech (Shanghai, China) as detailed in Supplementary Tables\u0026nbsp;1 and 2. Thiol (t) and TEG groups were introduced to link biotin and BG2, thereby enhancing the stability of the resulting constructs and reducing steric hindrance in the BG2 derivatives. Biotin (Cat: HY-B0511), biocytin (Cat: HY-101884), and biotin-PEG-OH (Cat: HY-147205C) were purchased from MedChemExpress (USA) and used for blocking streptavidin beads after pre-conjugation with BG2. SBI-425 (Cat: SML2935; C\u003csub\u003e13\u003c/sub\u003eH\u003csub\u003e12\u003c/sub\u003eClN\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003eS; Cas: 1451272-71-1) and TNAP inhibitor (Cat: 613810; C\u003csub\u003e17\u003c/sub\u003eH\u003csub\u003e16\u003c/sub\u003eN\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003eS; Cas: 496014-13-2) were obtained from Merck (Germany) and used to inhibit TNAP activity from WBCs. H-HoArg-OH (Cat: LEYH9B0C8E9B; Cas: 156-86-5) was also purchased from Merck (Germany). Salmon DNA (Cat: H1060) was purchased from Solarbio (Beijing, China), while salmon DNA (Cat: D9156) and herring DNA (Cat: D8050) were obtained from Merck (Germany). These DNAs were used to block non-specific binding of oligonucleotides. The following cancer cell lines were used in this study: Lovo, SW620, SW480, Caco2, NCI-H1975, HCC827, A549, MCF-7, MDA-MB-231, MKN7, 7721, and Bewo. All cell lines were purchased from the American Type Culture Collection (ATCC) and cultured in Dulbecco\u0026rsquo;s modified Eagle\u0026rsquo;s medium (DMEM) supplemented with 10% fetal bovine serum (FBS, Gibco, Grand Island, NY, USA) at 37\u0026deg;C in a humidified atmosphere containing 5% CO₂.\u003c/p\u003e\n\u003ch3\u003e2. Collection of clinical samples and storage\u003c/h3\u003e\n\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eBlood samples were collected using cfDNA storage tubes (Cat: CW2815M; CWBIO, Jiangsu, China), with volumes ranging from 8 to 10 mL, and were subsequently transported to Sanmed Biotech Ltd. for further analysis. Specifically, 1 mL of each blood sample was utilized for AP activity evaluation, while the residual blood was employed for CTCs detection. The clinical samples from lung cancer (LC) patients and other cancer patients were collected from The First Affiliated Hospital of Zhengzhou University, with ethics approval number 2024-KY-0100-002. Written informed consent was obtained from all enrolled patients. All methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003e3. AP activity accessed on circulating tumor relative materials (CTRMs)\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo enrich apoptotic CTCs fragment and vehicles, 1 mL of blood was collected and diluted with 5-fold by using 1x PBS. Streptavidin beads (Cat: 557812; BD Biosciences, USA) were pre-conjugated with biotin-labeled BG2 at 2\u0026ndash;8\u0026deg;C for 1 hour and blocked with 1 mg/mL biotin-PEG-OH. The conjugated complex was added to diluted samples and incubated at 2\u0026ndash;8\u0026deg;C for 1 hour. Captured CTRMs were resuspended in a 400 \u0026micro;L mixture of AMPPD (Cat: 224179; Shenzhen Maxchem Technology Co. Ltd., China) and TNAP inhibitor (C\u003csub\u003e17\u003c/sub\u003eH\u003csub\u003e16\u003c/sub\u003eN\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003eS) at 5 IC50 (5 \u0026times; 190 nM). AP activity was analyzed using Fluoroskan Ascent\u003csup\u003e\u0026trade;\u003c/sup\u003e FL (Cat: 5210463; Thermo Fisher, USA) on the supernatant after centrifugation at 1200 rpm for 2 min.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003e4. CTC isolation and detection by using LiquidBiopsy\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eBlood samples were collected, with volumes ranging from 8 to 10 mL, and diluted to 20 mL using 1\u0026times; PBS buffer for the separation of PBMCs based on Ficoll (density: 1.077 g/mL). Subsequently, the samples were labeled using capture cocktail kits (Zhuhai Sanmed Biotech Ltd., Zhuhai, China) and detection kits containing anti-Pan CK iFluor647, anti-CD45 iFluor488, and DAPI after fixation with 1% PFA for 1 hour at room temperature. The labeled CTCs were then isolated using the LiquidBiopsy rare cell isolation system (LIQUIDBIOPSY400A, Zhuhai Sanmed Biotech Ltd., China) under a magnetic field and enriched on a microfluidic chip with sheath buffer after incubation with streptavidin-coated magnetic beads for 1 hour at 2\u0026ndash;8\u0026deg;C. Finally, the isolated CTCs were imaged and visualized using a fluorescence microscope (Leica DM6000B, Leica, Germany), and classified as CK+/DAPI+/CD45\u0026thinsp;\u0026minus;\u0026thinsp;according to our previously published criteria (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003e5. Quantification of ALPL and ALPG mRNA expression levels in cell lines by qPCR\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eCells were collected at 1000 rpm for 3 min following digestion with 0.25% trypsin for 2\u0026ndash;3 min and lysed using lysis buffer RL (Cat: DP430, TIANGEN, Beijing, China) supplemented with 1% β-mercaptoethanol (Cat: HY-Y0326; MedChemExpress, USA). RNA was extracted using the RNAprep Pure Cell/Bacteria kit (Cat: DP430, TIANGEN, Beijing, China) according to the manufacturer\u0026rsquo;s protocol (Supplementary protocol 1) and reverse transcribed into cDNA using the PrimerScript RT reagent kit with gDNA Eraser (Cat: RR092A; Takara, Japan) following the manufacturer\u0026rsquo;s protocol (Supplementary protocol 2). The mRNA expression levels of ALPL and ALPG were evaluated by qPCR on the Real-Time PCR System (SLAN-96P, HONGSHI MEDICAL, Shanghai, China) according to the protocol (Supplementary protocol 3) and analyzed using GraphPad 8.2.0. Cell line with a ct value\u0026thinsp;\u0026gt;\u0026thinsp;30 was defined as AP negative.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003e6. Assessment of AP expression levels by flow cytometry\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe expression levels of AP were evaluated in AP-positive cell lines, including Lovo (human colorectal cancer cells), MDA-MB-231 (human breast cancer cell line), NCI-H1975 (human lung adenocarcinoma cancer cell line), and Caco-2 (human colon adenocarcinoma cell line), as well as in AP-negative cells (WBCs), using antibodies against PLAP H17E2-APP (Cat: B21499702-CHO; Biointron, Shanghai, China) and ALPG (Cat: 7A308201; Origene, USA). After labeling with secondary antibodies, either Goat anti-rabbit IgG FITC (Cat: ab150081, Abcam, UK) or Goat anti-mouse IgG FITC (Cat: SF-131; Solarbio, Beijing, China), the cells were analyzed using the flow cytometry (CytoFLEX S, Beckman Coulter, USA).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003e7. Statistical analysis\u003c/h3\u003e\n\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn this study, the relationship between AP activity and disease characteristics was examined in clinical samples, as well as its association with CTCs. Statistical analysis using Student's t-test on GraphPad Prism 8.2.0 revealed significant differences in AP activity among various subgroups, including comparisons between healthy donors (HD) and patients with LC or other cancers, as well as between TKI resistance-negative (TKI R-) and resistance-positive (TKI R+) groups, with clinical significance indicated by p values less than 0.05. A receiver operating characteristic (ROC) curve was generated to establish the optimal cutoff value for differentiating HD from tumor patients and to assess the correlation of AP with TKI resistance and CTCs. The cutoff value was considered reliable when the area under the curve (AUC) exceeded 0.8 (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Additionally, the linearity of the system was evaluated through linear regression analysis using GraphPad Prism 8.2.0, comparing expected versus actual results and correlating CTC quantities with relative light units (RLU) measured on the Fluoroskan AscentTM FL.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Results","content":"\n\u003ch3\u003e1. Optimization and analytical validation of the AP activity-based evaluation assay\u003c/h3\u003e\n\u003cp\u003eTo establish a highly sensitive and specific assay for the detection of CTCs via AP activity, we systematically optimized key parameters of the capture platform (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). First, we evaluated the performance of magnetic nanoparticle probes (MNPs) conjugated with different biotinylated BG2 variants (BG2-biotin, BG2-bibiotin, BG2-PEG-biotin, and BG2-t-biotin) for their ability to specifically capture target cells against a high background of WBCs. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, BG2-PEG-biotin yielding the highest capture recovery and the most favorable signal-to-noise (S/N) ratio and was selected for all subsequent experiments (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Following capture, free streptavidin binding sites on the MNPs required blocking to minimize non-specific background signals. We tested three blocking agents: biotin, biocytin, and biotin-PEG-OH. The results demonstrated that treatment with biotin-PEG-OH provided the highest S/N ratio (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec), and it was therefore adopted as the optimal blocking reagent. Additionally, to further suppress non-specific binding of WBCs to the capture platform, we investigated the efficacy of two oligonucleotide inhibitors, Salmon DNA and Herring DNA. Our analysis indicated that Salmon DNA from Solarbio more effectively reduced background signals from WBCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed) and was chosen as the optimal non-specificity blocker. Moreover, A significant source of background AP activity originates from TNAP expressed by WBCs. To inhibit this activity, we assessed the potency of three TNAP inhibitors. Based on its superior potency, C17H18N2O4S, was selected for use in the assay (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). Furthermore, having defined the optimal conditions, we then assessed the analytical performance of the optimized assay. The capture efficiency of the selected BG2 conjugate and the resulting RLU signal both demonstrated excellent linearity over a range of spiked Lovo cell quantities (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef-\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg), confirming the assay's quantitative capability. Finally, the precision of the fully optimized assay was evaluated by testing ten replicate samples, each containing 1000 Lovo cells in 0.2 mL of blood. The assay showed high reproducibility, with a percentage coefficient of variation (%CV) of 5.6% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eh).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e2. The widely expression of ALPP and ALPG suggests AP Activity as a Potential Pan-Cancer Biomarker\u003c/h3\u003e\n\u003cp\u003eIn this study, we evaluated the RNA expression level of AP in various tumor cell lines using quantitative PCR (qPCR), and assessed its protein expression by flow cytometry. The results demonstrated that the Ct values of intestinal cancer cell lines (Lovo, SW620, SW480, Caco2), lung cancer cell lines (NCI-H1975, HCC827, A549), breast cancer cell line (MDA-MB-231), gastric cancer cell line (MKN7), liver cancer cell line (7721), and human placental choriocarcinoma cell line (Bewo) were all below 30, which was lower than that of white blood cells (Figure. 3a), indicating that the ALPP gene is expressed in most tumor cell lines. A similar pattern was observed for ALPG (Figure. 3b). To further investigate the protein expression of ALPP and ALPG, we selected several cell lines with positive RNA expression and performed flow cytometry analysis using anti-PLAP and anti-ALPG antibodies. The results confirmed that all tested tumor cells expressed both ALPP and ALPG proteins, whereas white blood cells showed no detectable expression. Together, these findings indicate that ALPP and ALPG are broadly expressed in tumor cells, suggesting that AP activity may serve as a valuable biomarker for cancer research.\u003c/p\u003e\n\u003ch3\u003e3. Association of AP Activity with Disease Status and TKI Resistance\u003c/h3\u003e\n\u003cp\u003eTo evaluate the clinical relevance of AP activity, we compared its levels across various patient groups and assessed its diagnostic and predictive performance. AP activity was significantly elevated in patients with lung cancer (LC) (n\u0026thinsp;=\u0026thinsp;135; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0372; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea) compared to HD (n\u0026thinsp;=\u0026thinsp;45), but not in CTC counts (p\u0026thinsp;=\u0026thinsp;0.1491; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Although a trend toward increased activity was observed in other cancer patients, the difference from HD did not reach statistical significance (n\u0026thinsp;=\u0026thinsp;39; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0576; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Notably, AP activity was substantially higher in patients exhibiting resistance to TKI therapy (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb), while no significance in CTC counts (p\u0026thinsp;=\u0026thinsp;0.7951; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe diagnostic utility of AP activity was further quantified using ROC curve analysis. For distinguishing LC patients from HD, AP activity demonstrated excellent performance with an AUC of 0.9340 (95% CI: 0.8977\u0026ndash;0.9703, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). At an optimal threshold of \u0026gt;\u0026thinsp;13,640, the assay achieved a sensitivity of 88% and a specificity of 91%, which was higher than in CTC counts (Threshold of \u0026gt;\u0026thinsp;0, sensitivity 57.78%, specificity 100.0%; AUC\u0026thinsp;=\u0026thinsp;0.7889, 95% CI 0.7253\u0026ndash;0.8524, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eh). Similarly, strong discriminatory power was observed for the cohort of other cancers versus HD (AUC\u0026thinsp;=\u0026thinsp;0.7652, 95% CI 0.6645\u0026ndash;0.8659, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), with a sensitivity of 71.79% and specificity of 66.67% at a cut-off value of \u0026gt;\u0026thinsp;7283 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef), also higher sensitivity than in CTC counts (Threshold of \u0026gt;\u0026thinsp;0, sensitivity 64.10%, specificity 100.0%; AUC\u0026thinsp;=\u0026thinsp;0.8205, 95% CI 0.7223\u0026ndash;0.9187, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ei).\u003c/p\u003e \u003cp\u003eMost strikingly, AP activity exhibited outstanding performance in identifying TKI-resistant status. The ROC analysis for this application yielded an AUC of 0.9198 (95% CI: 0.8756\u0026ndash;0.9639, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). A threshold of \u0026gt;\u0026thinsp;126,150 effectively discriminated resistant patients, with a high sensitivity of 96% and a specificity of 85% (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg), which was higher than in CTC counts (Threshold of \u0026gt;\u0026thinsp;1, sensitivity 60.32%, specificity 68.57%; AUC\u0026thinsp;=\u0026thinsp;0.6493, 95% CI 0.5552\u0026ndash;0.7434, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0030; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ej).\u003c/p\u003e \u003cp\u003eOverall, these results suggest that AP activity serves not only as a sensitive and specific diagnostic biomarker but also as a potential indicator for predicting TKI resistance, highlighting its significant clinical potential in cancer management and treatment monitoring.\u003c/p\u003e\n\u003ch3\u003e4. Association of AP Activity with CTC Status and Burden\u003c/h3\u003e\n\u003cp\u003eTo investigate the relationship between AP activity and the presence of CTCs, patients were stratified into CTC-positive (CTC+) and CTC-negative (CTC-) groups. As summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the direct comparison of AP activity levels between these two groups, analyzed by Student's t-test, did not yield statistically significant differences across any of the cohorts, including LC (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.2443; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea) and other cancers (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.2664; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb), and in the lung cancer patients with TKI-resistant population (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.8554; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), but significant difference in patients without TKI-resistant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0153; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Despite the lack of significant difference in mean AP activity between the dichotomized groups, a more nuanced relationship was revealed upon correlation analysis with the quantitative CTC burden. Spearman's rank correlation analysis demonstrated a significant positive correlation between the absolute number of CTCs and AP activity levels in patients with LC (r\u0026thinsp;=\u0026thinsp;0.485, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000\u003cb\u003e)\u003c/b\u003e, other cancers (r\u0026thinsp;=\u0026thinsp;0.681, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007\u003cb\u003e)\u003c/b\u003e, and in the TKI-resistant subgroup (r\u0026thinsp;=\u0026thinsp;0.369, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Additionally, the potential relationship between T790M mutation status and both AP activity and CTC quantity was further investigated. As shown in Supplementary Fig.\u0026nbsp;1a, no significant difference in AP activity was observed between T790M-positive and T790M-negative patients (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.9258) (Supplementary Fig.\u0026nbsp;1a). Similarly, CTC quantity did not differ significantly between these two genetic subgroups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.8384; Supplementary Fig.\u0026nbsp;1b).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003et-test analysis of AP activity based on CTC+/CTC-\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epatients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLC AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOther cancers AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTKI AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e* Significant relevance at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation analysis of AP activity to CTC quantity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpearman's rho\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLC CTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLC AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther cancers CTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther cancers\u003c/p\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTKI CTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTKI AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e* Significant relevance at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn summary, our findings delineate a distinct pattern of association for AP activity: it robustly correlates with the quantitative burden of CTCs rather than their mere presence, underscoring its utility as a continuous biomarker for dynamic monitoring of tumor dissemination. Furthermore, the strong predictive capacity of AP activity for TKI resistance operates independently of the T790M mutation, suggesting that its role in identifying resistance extends beyond this specific genetic mechanism and may reflect broader, yet to be elucidated, biological pathways of treatment failure.\u003c/p\u003e\n\u003ch3\u003e5. Longitudinal monitoring of AP activity and CTC quantity during treatment response\u003c/h3\u003e\n\u003cp\u003eTo assess the potential of AP activity and CTC quantity as dynamic biomarkers for monitoring disease progression, longitudinal blood samples were collected from 9 cancer patients at baseline (T0) and after 3 months of therapy (T1). Patients exhibiting progressive disease (PD) showed a marked increase in both AP activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea) and CTC counts (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb) at T1 compared to T0. In contrast, patients achieving partial response (PR) or maintaining stable disease (SD) demonstrated a substantial decrease in AP activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec) and CTC quantity (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed) following treatment. These results indicate that both AP activity and CTC burden exhibit dynamic changes reflective of treatment response, with increasing levels associated with disease progression and decreasing levels correlating with favorable therapeutic outcomes, supporting their utility as complementary longitudinal biomarkers for monitoring tumor dynamics.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study presents a comprehensive validation of AP activity as a functional biomarker for detecting CTRMs and monitoring therapeutic response in multiple cancer types. By systematically optimizing a capture assay based on AP activity, we established a highly sensitive and specific platform capable of isolating CTRMs against a high background of white blood cells. Furthermore, we demonstrated that AP activity not only correlates with CTC abundance but also strongly predicts TKI resistance, independent of canonical genetic mechanisms. Longitudinal analysis further confirmed the utility of AP activity in dynamically reflecting disease progression and treatment response.\u003c/p\u003e \u003cp\u003eOur results demonstrated that ALPP and ALPG, two alkaline phosphatase isoforms, are widely expressed in various tumor cell lines at both the RNA and protein levels. In contrast, WBCs showed no detectable expression of these isoforms. This broad expression pattern suggests that AP activity could serve as a potential pan - cancer biomarker. Previous studies have also reported the overexpression of AP in different types of cancer tissues (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). For example, elevated AP levels have been associated with bone - metastatic castration - resistant prostate cancer (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), and increased AP activity has been observed in esophageal cancer patients (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). The consistent overexpression of AP in diverse cancer types indicates its potential as a general biomarker for cancer, which could be further explored for early cancer detection and disease monitoring.\u003c/p\u003e \u003cp\u003eAP activity was significantly elevated in patients with lung cancer and other cancers compared to HD. Although the difference in other cancer patients did not reach statistical significance, there was still a trend towards increased activity. These findings suggest that AP activity can be used as a diagnostic biomarker for some cancer types. The ROC curve analysis further quantified the diagnostic utility of AP activity. For lung cancer, AP activity showed excellent discriminatory power between patients and HD, with high sensitivity and specificity at optimal thresholds.\u003c/p\u003e \u003cp\u003eNotably, AP activity was substantially higher in patients with TKI resistance. The outstanding performance of AP activity in identifying TKI - resistant status, as demonstrated by the high AUC, sensitivity, and specificity in the ROC analysis, highlights its potential as a predictive biomarker for TKI resistance. This is an important finding, as the ability to predict TKI resistance can help clinicians make more informed treatment decisions. The underlying mechanism for the association between AP activity and TKI resistance may involve changes in cellular signaling pathways related to AP function. For example, AP may be involved in cell survival and proliferation pathways that are dysregulated in TKI - resistant cancer cells(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study revealed an interesting relationship between AP activity and CTCs. Although there was no significant difference in mean AP activity between CTC - positive and CTC - negative groups, a significant positive correlation was observed between the absolute number of CTCs and AP activity levels in patients with lung cancer, other cancers, and the TKI - resistant subgroup. This indicates that AP activity is more closely related to the quantitative burden of CTCs rather than their mere presence. CTCs are considered to be an important indicator of tumor dissemination and prognosis (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). The correlation between AP activity and CTC burden suggests that AP activity could be used as a biomarker for monitoring tumor dissemination. The mechanism behind this correlation may be that CTCs, as they circulate in the bloodstream, contribute to the overall AP activity detected in the blood. Tumor cells, including CTCs, may express and secrete AP, and the more CTCs present, the higher the overall AP activity.\u003c/p\u003e \u003cp\u003eWe also found that CTC counts were significantly higher in patients with TKI resistance compared to non - resistant patients, and CTC quantity had a certain predictive performance for resistance status. However, there was no significant difference in AP activity or CTC quantity between T790M - positive and T790M - negative patients. This suggests that the role of AP activity in predicting TKI resistance is independent of the T790M mutation and may reflect other biological pathways related to treatment failure.\u003c/p\u003e \u003cp\u003eThe longitudinal monitoring of AP activity and CTC quantity in cancer patients during treatment response provided valuable insights into their potential as dynamic biomarkers. Patients with PD showed an increase in both AP activity and CTC counts, while patients with PR or SD demonstrated a decrease. These results indicate that both AP activity and CTC burden can reflect treatment response. The dynamic changes in AP activity and CTC quantity support their utility as complementary longitudinal biomarkers for monitoring tumor dynamics. By tracking these biomarkers over time, clinicians can more effectively assess the effectiveness of treatment and make timely adjustments to treatment strategies (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral limitations of this study should be acknowledged. First, the sample size in the longitudinal analysis was relatively small; further validation in larger prospective cohorts is warranted to strengthen the generalizability of our findings. Second, the use of a limited blood volume (1 mL) may have contributed to the lack of a significant association between AP activity and other cancers, potentially due to insufficient capture of rare CTCs in this malignancy. Future studies should consider employing larger sample volumes to improve detection sensitivity. Furthermore, the biological mechanisms underlying elevated AP activity in CTCs and its specific role in mediating TKI resistance require deeper investigation. It remains an open question whether AP activity serves merely as a marker of aggressive tumor phenotypes or plays an active functional role in resistance.\u003c/p\u003e \u003cp\u003eIn conclusion, we have developed and validated an AP activity-based assay that effectively detects CTCs and predicts TKI resistance with high accuracy. Our results position AP activity as a promising functional biomarker that complements existing genetic and enumeration-based approaches. Future studies should explore the integration of AP activity into multimodal biomarker panels and investigate its potential as a therapeutic target in treatment-resistant cancers.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eALPG: Germ cell alkaline phosphatase); ALPP: Placental alkaline phosphatase, PLAP; AP: Alkaline phosphatase; ATCC: American Type Culture Collection; AUC: Area under the curve; CD45: Leukocyte common antigen 45; CI: Confidence interval; CK: Cytokeratin; CTCs: Circulating tumor cells; CTRM: Circulating Tumor-Related Materials; CV: Coefficient of variation; DAPI: 4\u0026apos;,6-diamidino-2-phenylindole; DMEM: Dulbecco\u0026rsquo;s modified Eagle\u0026rsquo;s medium; FBS: Fetal bovine serum; HD: Healthy donor; LC: Lung cancer; PD: Progression disease; PR: Partial response; RLU: Relative light units; ROC: Receiver operating characteristic; SD: Stable disease; S/N: Ratio of signal to noise; TDMs: Tumor-derived materials; TKI: Tyrosine kinase inhibitor; TNAP: Tissue-nonspecific alkaline phosphatase; WBC, Leukocyte, white blood cell.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data relevant to the study are included in the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent for publication was obtained from the patient or their legal guardian.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest disclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe clinical samples used in this study are approved by the ethics committee of The First Affiliated Hospital of Zhengzhou University, with ethics approval number 2024-KY-0100-002, and all enrolled patients sign the informed consents.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptualization:\u0026nbsp;\u003c/strong\u003eDongjiang Tang. \u003cstrong\u003eData curation:\u0026nbsp;\u003c/strong\u003eLin Chen. \u003cstrong\u003eFormal analysis:\u003c/strong\u003e Lin Chen. \u003cstrong\u003eInvestigation:\u0026nbsp;\u003c/strong\u003eLin Chen. \u003cstrong\u003eMethodology:\u0026nbsp;\u003c/strong\u003eLin Chen and Panpan Qi. \u003cstrong\u003eProject administration:\u003c/strong\u003e Dongjiang Tang. \u003cstrong\u003eSupervision:\u003c/strong\u003e Dongjiang Tang. \u003cstrong\u003eValidation:\u003c/strong\u003e Zhonglin Yang and Yue Lu. \u003cstrong\u003eVisualization:\u003c/strong\u003e Lin Chen. \u003cstrong\u003eWriting:\u0026nbsp;\u003c/strong\u003eLin Chen.\u003cstrong\u003e\u0026nbsp;Review and editing:\u003c/strong\u003e All the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank The First Affiliated Hospital of Zhengzhou University for their valuable suggestions during the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCohen SJ PC, Iannotti N, Saidman BH, Sabbath KD, Gabrail NY, Picus J, Morse M, Mitchell E, Miller MC, Doyle GV, Tissing H, Terstappen LW, Meropol NJ. . 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J Natl Cancer Inst. 2018;110(6):560-7.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Alkaline Phosphatase Activity, Circulating Tumor-Related Materials, BG2 Aptamer, TKI Resistance, Tumor Burden","lastPublishedDoi":"10.21203/rs.3.rs-8846023/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8846023/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe detection of circulating tumor cells (CTCs) is crucial for cancer management but remains challenging due to their rarity and heterogeneity. Alkaline phosphatase (AP) activity, particularly from cancer-associated isoforms ALPP and ALPG, offers a promising functional biomarker alternative. This study developed and validated a highly sensitive assay for detecting AP-expressing circulating tumor-related materials (CTRMs). Magnetic nanoparticles were conjugated with an optimized BG2-PEG-biotin aptamer for specific CTRM capture, with systematic optimization of probe design, blocking reagents, and background suppression. The assay demonstrated excellent linearity and reproducibility (%CV\u0026thinsp;=\u0026thinsp;5.6%). Clinical validation across lung cancer (LC) and other cancers revealed significantly elevated AP activity in LC (p\u0026thinsp;=\u0026thinsp;0.0372) and other cancer patients (p\u0026thinsp;=\u0026thinsp;0.0576) versus healthy donors. Most notably, AP activity was substantially higher in tyrosine kinase inhibitor (TKI)-resistant patients (p\u0026thinsp;=\u0026thinsp;0.0001). ROC analysis showed exceptional performance for distinguishing LC from healthy donors (AUC\u0026thinsp;=\u0026thinsp;0.9340) and identifying TKI resistance (AUC\u0026thinsp;=\u0026thinsp;0.9198). While no significant difference was found between CTC-positive and negative groups, AP activity strongly correlated with quantitative CTC burden in LC (r\u0026thinsp;=\u0026thinsp;0.485, p\u0026thinsp;=\u0026thinsp;0.000) and TKI-resistant subgroups (r\u0026thinsp;=\u0026thinsp;0.369, p\u0026thinsp;=\u0026thinsp;0.003). Longitudinal monitoring demonstrated that dynamic AP activity changes reflected treatment response. This study establishes AP activity as a sensitive, specific functional biomarker for CTRM detection that effectively predicts TKI resistance and dynamically monitors tumor burden, highlighting its significant clinical potential for non-invasive cancer management.\u003c/p\u003e","manuscriptTitle":"APtamer-Enhanced AP Assay: Dynamic Functional Profiling of Circulating Tumor Materials for Predicting TKI Resistance and Real-Time Treatment Monitoring","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 09:12:27","doi":"10.21203/rs.3.rs-8846023/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-22T05:29:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-21T13:33:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-17T16:49:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"74075073468139762602128306618730176635","date":"2026-04-15T17:41:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"274987948868094247545599140491717393977","date":"2026-04-15T17:20:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"251092700633640911397655918842243801946","date":"2026-04-15T08:09:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-14T16:46:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-13T14:56:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-16T11:33:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-15T01:56:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-15T01:52:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9faf2825-780f-4ab9-87d5-ce5a7c09344d","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":66755301,"name":"Health sciences/Biomarkers"},{"id":66755302,"name":"Biological sciences/Cancer"},{"id":66755303,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-05-11T10:25:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 09:12:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8846023","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8846023","identity":"rs-8846023","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}