Evaluating cell free DNA quantification and integrity in breast cancer using fluorometric, electrophoretic and PCR based platforms

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However, different quantification and integrity assessment methods may yield variable results. This study aimed to compare cfDNA concentration and integrity between breast cancer (BC) patients and healthy controls using three complementary analytical techniques and to evaluate the concordance between these approaches. Methods Plasma cfDNA from BC patients and healthy controls was quantified using Qubit fluorometry, electrophoretic profiling by TapeStation (TS), and ALU-based quantitative PCR (qPCR) assays. cfDNA integrity was assessed using two indices: TS-derived DNA Integrity Index (DII) and ALU-qPCR–derived DII (ALU 247/115 ratio). Spearman correlation analyses were performed to compare results across methods. Results cfDNA concentration was significantly elevated in BC patients compared with controls across all three platforms, even after genomic DNA (gDNA) correction. Qubit, TS, and gDNA-corrected cfDNA concentrations showed strong correlations in both groups, indicating robust cross-method agreement. In contrast, cfDNA integrity showed method-dependent differences: ALU-qPCR–derived DII was significantly higher in tumor samples, whereas TS-derived DII did not differ between groups. Moreover, TS DII and ALU-qPCR DII were not correlated, suggesting that the two methods capture distinct aspects of cfDNA fragmentation. Conclusions cfDNA quantification is consistent across fluorometric, electrophoretic, and qPCR-based methods. However, cfDNA integrity assessments vary substantially by technique. ALU-qPCR appears more sensitive in distinguishing BC- associated fragmentation patterns. Combining cfDNA quantification with ALU-qPCR-based integrity analysis may enhance the diagnostic utility of cfDNA in BC. Breast cancer Cell-free DNA cfDNA integrity Liquid biopsy Figures Figure 1 Figure 2 Figure 3 Background Cell-free DNA (cfDNA) refers to short DNA fragments released into the bloodstream through apoptosis, necrosis, or active cellular secretion. In patients with malignancy, a fraction of this circulating DNA originates from tumor cells (circulating tumor DNA, ctDNA), reflecting tumor dynamics, burden, and genomic alterations 1 – 3 . ctDNA is identified within cfDNA by tumor-specific mutations, copy-number variations, gene rearrangements, and methylation signatures 4 . Its ability to detect cfDNA through a simple blood draw has made it a central analyte in liquid biopsy based cancer diagnostics 5 . In breast cancer (BC), elevated cfDNA levels and altered fragmentation profiles have been associated with advanced stage, poor prognosis, and minimal residual disease 6 – 8 , highlighting cfDNA as a potential non-invasive biomarker both for early detection and disease monitoring. Both the quantitative and qualitative characteristics of cfDNA are informative. The total cfDNA concentration reflects global DNA release and tumor cell turnover 9 , 10 , whereas the cfDNA integrity index (DII), defined as the ratio of long to short DNA fragments, captures the underlying mode of cell death 6 , 7 . High DII values are often linked to necrosis-driven DNA release from malignant cells, while low DII values are characteristic of the apoptotic fragmentation seen in healthy individuals 7 , 11 , 12 . Reliable measurement of cfDNA concentration and integrity is therefore essential for distinguishing tumor-derived cfDNA from background DNA of hematopoietic origin. However, cfDNA analysis is complicated by methodological diversity. Fluorometric assays such as Qubit quantify total double-stranded DNA rapidly but cannot discriminate cfDNA from contaminating gDNA. Electrophoretic instruments like the Agilent TS visualize cfDNA fragment size distribution, identifying mono- and di/tri-nucleosomal fragments, yet they may fail to detect highly degraded cfDNA 13 , 14 . In contrast, qPCR assays targeting repetitive elements such as ALU sequences enable sequence-specific quantification and DII calculation (e.g., ALU 247/ ALU 115), offering higher analytical sensitivity 15 . Reported discrepancies among these approaches raise concerns about assay comparability and interpretation of cfDNA-based biomarkers. Only a limited number of studies have directly compared cfDNA quantification and integrity across multiple analytical platforms, and virtually none have done so in a large, treatment-naïve Indian BC cohort. To address this gap, we performed a systematic comparison of cfDNA concentration and integrity in plasma from BC patients and healthy controls using three complementary techniques, Qubit fluorometry, TS electrophoretic profiling, and ALU-based qPCR. We further incorporated gDNA-correction of fluorometric data and assessed inter-method concordance. This work aims to identify the most reliable and sensitive approach for cfDNA assessment and to provide practical guidance for assay standardization in BC liquid-biopsy research. Materials and Methods Patients and Control Samples Peripheral blood samples were obtained from primary BC patients (n = 100) recruited prior to initiation of any treatment after voluntary consent from participants. Age- and gender-matched healthy individuals (n = 70) were included as normal controls. Study was approved by institutional ethics board. Peripheral blood samples were collected in EDTA tubes and processed within 2 hours of collection. Plasma was separated by centrifugation at 1300 × g for 10 min at 4°C and carefully aliquoted to avoid cellular contamination. Plasma aliquots were stored at − 80°C in the institute biorepository for prolonged periods until cfDNA extraction. Repeated freeze-thaw cycles were strictly avoided, as they are known to reduce cfDNA yield and promote fragmentation and degradation, as reported in previous studies. No consistent variation in cfDNA concentration attributable to storage duration was observed across samples. The study was conducted between February 2024 and November 2025. cfDNA Extraction cfDNA from BC patients and control samples was extracted from 500 µL of plasma using the BioChain Cfpure Cell Free DNA Extraction Kit (# K5011610) according to the manufacturer’s instructions. The extracted cfDNA was quantified using the Qubit High Sensitivity (HS) DNA Assay Kit (# Q32851) and stored at − 20°C. cfDNA Fragmentation Profiling and DII Calculation The fragmentation profile of cfDNA was analyzed using the TS4150 system (Agilent Technologies, Santa Clara, CA, USA). For each run, 2 µL of extracted cfDNA was mixed with 2 µL of HS D1000 sample buffer (# 5067–5603) in an 8-tube strip. Additionally, 2 µL of the HS D1000 ladder (# 5067–5587) prepared with the sample buffer was loaded into well A1 of the tube strip holder. Data were processed and analyzed using the TS Analysis Software version 5.1 (Agilent Technologies, Santa Clara, CA, USA). Mononucleosomal fragments (short fragments) were defined as cfDNA fragments ranging from 100 to 300 bp, while di- and tri-nucleosomal cfDNA fragments (long fragments) were defined as fragments between 301 and 1000 bp. The cfDNA Integrity Index (DII) was calculated using the following formula: DII=Area under the curve (AUC) of long fragments (301–1000 bp)/AUC of short fragments (100–300 bp) gDNA correction of the cfDNA total concentration To obtain a more accurate estimate of cfDNA levels, we corrected the Qubit derived total cfDNA concentration for gDNA contamination. Since the TS electrophoresis profiles provide fragment-size information, we used TS data to estimate the proportion of gDNA contamination in each sample. In the TS electropherogram, the gDNA contribution was quantified as the percentage area corresponding to high-molecular-weight fragments (> 1 Kb), which represents cellular gDNA. This gDNA percentage was then used to adjust the total cfDNA concentration measured by Qubit 16 using the formula: Corrected cfDNA concentration = Qubit total DNA × (1 – gDNA%) This correction allowed us to account for variability introduced by sample handling, leukocyte lysis, and differential gDNA release across samples. The corrected cfDNA values are likely to reflect cfDNA population more accurately, ensuring comparability between tumor and control samples. Estimation of ctDNA concentration and DII by qPCR The fraction of ctDNA within the total cfDNA was analyzed using ALU-based qPCR assays according to previous method 15 . Briefly, Two ALU repetitive elements, ALU115 (115 bp amplicon) and ALU247 (247 bp amplicon), were quantified using following primer sequences. ALU115 amplified both short and long DNA fragments and measured the total cfDNA content in plasma, whereas ALU247 targeted longer DNA fragments, predominantly released through necrotic processes, and was considered indicative of ctDNA. The primer pair used is as below: ALU 115 Forward: 5’-CCTGAGGTCAGGAGTTCGAG-3’ Reverse: 5’-CCCGAGTAGCTGGGATTACA-3’ ALU 247 Forward: 5’-GTGGCTCACGCCTGTAATC-3’ Reverse: 5’-CAGGCTGGAGTGCAGTGG-3’ For amplification, 1 µL of template DNA was added to a 10 µL reaction volume containing 3 pM primers. Quantification was performed using SYBR Green master mix (Takara) on the LightCycler 480 II platform (Roche Diagnostics) using the conditions standardized previously 15 . The DNA copy number and the DII, measured by the ratio of ALU247 to ALU115 (ALU247/ALU115), were calculated as described previously 15 . Statistical Analysis Statistical analyses were performed using R (version 4.5.1) software. Differences in cfDNA concentration and DII between control and tumor groups were assessed using the Mann-Whitney U test. Correlations of cfDNA concentration across different quantification methods, and the correlation of DII obtained by ALU-based qPCR and TS, were evaluated using Spearman’s rank correlation test. p value < 0.05 was considered statistically significant. Results cfDNA concentration is elevated in BC patients compared to normal controls The total cfDNA concentration was estimated using three complementary approaches: Qubit fluorometry, TS electrophoretic profiling, and ALU-based qPCR assays. cfDNA concentrations measured by both Qubit (mean concentration: 0.459 ng/µL in patients and 0.252 ng/µL in controls) and TS (mean concentration: 0.156 ng/µL in patients and 0.090 ng/µL in controls) showed a significant increase in BC patients compared with healthy individuals (p 50 years versus ≤ 50 years, early-stage versus late-stage disease, and molecular subtypes (detailed patient data and clinical characteristics are provided in Supplementary Table 1). Among molecular subtypes, cfDNA yield was highest in HER2-positive tumors (mean concentration: 0.516 ng/µL), followed by hormone receptor–positive tumors (HR+) (0.480 ng/µL) and triple-negative breast cancer (TNBC) (0.298 ng/µL) (Supplementary Fig. 1a). A trend toward higher cfDNA levels was observed in patients aged > 50 years (p = 0.059; Supplementary Fig. 1b). Late-stage disease also showed a trend toward higher cfDNA levels compared with early-stage disease (p = 0.093; Supplementary Fig. 1c). The fraction of ctDNA within the total cfDNA was further evaluated using ALU-based qPCR. The DII assessed using the ALU247/115 ratio, was significantly higher in patients relative to controls (p = 0.0002; Fig. 1 c). A higher DII, along with elevated cfDNA concentrations measured by Qubit and TS, is indicative of an increased ctDNA burden in BC patients. Together, these findings highlight the utility of multi-platform cfDNA assessment in distinguishing tumor-derived cfDNA signatures from those of healthy controls. TS profiles showed visible gDNA contamination in both groups We next evaluated cfDNA fragment distributions in the BC and control groups using TS electrophoretic profiling. The fragment analysis revealed detectable gDNA contamination in both tumor and control samples (Fig. 2 a-f). To assess the extent to which gDNA contamination contributed to the measured cfDNA concentrations and the DII values derived from ALU qPCR, we performed gDNA correction on the Qubit-estimated concentrations. After subtracting the gDNA fraction determined from the TS profiles, the corrected cfDNA concentrations remained significantly higher in patients (mean concentration: 0.219 ng/µL in patients and 0.153 ng/µL in controls, p < 0.0001, Fig. 2 g), indicating that the elevated cfDNA levels in patients were not attributable to gDNA contamination. We further examined the concordance between the different cfDNA quantification approaches. Pairwise correlation analysis demonstrated a strong positive association between cfDNA concentrations measured by Qubit and TS, even after gDNA correction, in both patients and controls (Table 1 ), indicating robust inter-method concordance in cfDNA quantification across independent analytical platforms. These findings validate the reliability of both fluorometric and electrophoretic quantification for assessing cfDNA burden in plasma samples. Table 1 Correlation between cfDNA estimation methods in BC patients and control groups by different platforms Comparison Spearman’s r s (Tumor) p-value Spearman’s r s (Control) p-value Qubit vs TS 0.609 < 0.0001 0.323 0.007 Qubit vs gDNA-corrected cfDNA 0.682 < 0.0001 0.834 < 0.0001 TS vs gDNA-corrected cfDNA 0.534 < 0.0001 0.549 < 0.0001 Measurement of cfDNA integrity is method dependent The fraction of ctDNA within the total cfDNA was evaluated by ALU-based RT-PCR (Fig. 1 c) and by TS fragmentation profile. ALU115 captured the total pool of circulating DNA by amplifying both short and long fragments, whereas ALU247 predominantly detected long DNA fragments, which are generally associated with necrosis and enriched in ctDNA. ALU247 levels were significantly higher in BC patients compared to control samples, as shown in Fig. 3 a (p < 0.0001). In contrast, cfDNA fragmentation profiles derived from TS electropherograms calculated as the ratio of di- and tri-nucleosomal (larger fragments) to mono-nucleosomal DNA (smaller fragment) (DII by L/S), did not differ significantly between tumor and control samples (Fig. 3 b, p = 0.180). Similarly, concentrations of mono- and di/tri-nucleosomal cfDNA were comparable between groups (Table 2 ), indicating that both approaches capture distinct biological and technical aspects of cfDNA. TS-based integrity indices are derived from electrophoretic size distribution and primarily reflect nucleosomal fragmentation patterns, which may be less sensitive to minor tumor-specific shifts, especially when tumor burden is low or masked by hematopoietic cfDNA. Correlation analysis between ALU-based DII and TS-derived DII further revealed no significant association (Fig. 3 c, r s = -0.075, p = 0.540 for controls; r s = -0.170, p = 0.091 for tumor samples). ALU247 amplifies sequence-specific fragments that may span sizes overlapping with TS-defined short fragments, whereas TS-based DII is derived purely from electrophoretic size distribution. Hence the correlation reflects fundamental methodological and biological differences, rather than a contradiction between the two approaches emphasizing that PCR-based DII is more sensitive and accurate for detecting subtle changes in cfDNA fragment composition and ctDNA enrichment. Table 2 Comparison of nucleosomal cfDNA features between patients and controls measured by tapestation (TS). Parameter Tumor (mean) Control (mean) p-value Mono-nucleosomal concentration (ng/µl) 0.125 ± 0.168 0.093 ± 0.045 0.212 Di-/tri-nucleosomal concentration (ng/µl) 0.048 ± 0.092 0.034 ± 0.026 0.067 Mono-nucleosomal fragment size (bp) 195.160 202.580 – Di-/tri-nucleosomal fragment size (bp) 562.820 545.565 – Discussion In this study, we systematically compared cfDNA quantification and integrity in BC patients and healthy controls using three complementary approaches: Qubit fluorometry, TS electrophoretic profiling, and ALU-based-qPCR. The total cfDNA concentration was consistently higher in BC patients compared to healthy controls by all three methods, remained significant even after correcting for gDNA contamination. This observation is consistent with previous reports linking elevated cfDNA levels to increased tumor cell turnover, apoptosis, and necrosis in cancer patients 11 , 17 – 19 . The pair-wise correlation analysis revealed that the cfDNA concentration measured by three quantification methods were strongly correlated, highlighting the robustness of concentration measurements across analytical platforms. However, when assessing DII, we observed a clear divergence between methods. The DII derived from ALU-based-qPCR was significantly higher in BC patients compared to controls, whereas TS-derived integrity indices did not differ between groups. Moreover, ALU-based DII and TS-DII did not correlate, highlighting the differences in capturing distinct biological and technical features of cfDNA by these two methods. ALU-based-qPCR relies on amplification of repetitive elements of different lengths, with the short fragment (ALU115) reflecting apoptotic cfDNA and the long fragment (ALU247) capturing necrosis-related or gDNA-derived fragments 7 , 20 – 22 . Thus, an increased ALU247/115 ratio in tumor patients likely reflects the contribution of necrotic tumor cell death, a hallmark of advanced tumor stage. TS integrity indices are derived from electrophoretic size distribution of cfDNA, focusing primarily on the relative abundance of mono- and di-nucleosomal fragments. This method may be less sensitive to subtle differences in cfDNA fragment origin, particularly if the tumor burden is modest or if the background of hematopoietic cfDNA masks tumor-specific alterations 23 , 24 . ALU-based-qPCR is a highly sensitive, sequence-specific method capable of detecting small shifts in cfDNA fragment composition. TS, while powerful for visualization and quantification of fragment profiles, may lack the resolution to detect these biological differences. Additionally, pre-analytical variables such as plasma processing, storage, and cfDNA extraction efficiency may disproportionately impact fragment size distributions measured by TS, but not qPCR amplification ratios 25 , 26 . Our results therefore highlight a critical methodological point: while cfDNA concentration measurements are reproducible across platforms, DII is method-dependent and requires cautious interpretation. The lack of agreement between ALU-based-qPCR and TS does not represent a contradiction but rather reflects their measurement of different biological aspects of cfDNA; apoptotic/necrotic origin versus physical fragment size distribution. In practical terms, our findings suggest that ALU-based-qPCR may be better suited for distinguishing ctDNA from background cfDNA in the circulation, whereas TS is more useful for assessing overall fragment profiles and detecting large-scale contamination with gDNA. Therefore, we emphasize the need to standardize cfDNA integrity assays before clinical implementation, as relying on a single platform could lead to conflicting conclusions. We further suggest that combining complementary methods, such as total cfDNA concentration with ALU-based-qPCR, may provide a more comprehensive understanding of cfDNA biology and enhance its utility as a biomarker in cancer research. Our data also underscore the importance of correcting for gDNA contamination, which significantly influences fluorometric quantification and may confound downstream analyses. Conclusion Taken together, our study demonstrates that cfDNA concentration is a robust marker of tumor presence, but cfDNA integrity requires careful methodological consideration. Future work with additional orthogonal assays, including gDNA correction using single-copy gene 26 , 27 and next-generation sequencing (NGS) of cfDNA fragmentation patterns, may help to reconcile these discrepancies and establish reliable cfDNA integrity metrics for clinical use. Abbreviations cfDNA Cell-free DNA ctDNA Circulating Tumor DNA DII DNA Integrity Index gDNA Genomic DNA HER2 Human Epidermal Growth Factor Receptor 2 HR+ Hormone Receptor-Positive NGS Next-Generation Sequencing TS TapeStation TNBC Triple-Negative Breast Cancer Declarations Ethics approval and consent to participate Ethical approval for this study was obtained from the Institutional Ethics Committee (IEC), St. John’s Medical College & Hospital, Bengaluru (IEC Study Ref. No.: 252/2023). Informed consent was obtained from all subjects involved in the study. Consent for publication Not applicable Competing interests The authors declare that they have no competing interests. Funding This work was supported by the Indian Council of Medical Research (ICMR) through the grant awarded to Jyothi S Prabhu (ICMR Project ID: IIRP-2023-1560). Author Contribution All authors contributed to the study conception and design. Experimental design, execution of experiments, data analysis was performed by G.R.A, V. P. N, S.N and J.S.P. Plasma processing, sample storage, and computational data entry were handled by M.S and A.C.E. G.B, B.G, A.A, N.M.A, J.D.N, and J assisted with recruitment of control participants and manuscript review. R.S.R provided clinical breast cancer samples. The first draft of the manuscript was prepared by G.R.A and J.S.P, and all authors contributed to revising the manuscript. All authors read and approved the final version of the manuscript. J.S.P acquired funding. J, G.B, B.G, and J.S.P supervised the study. All authors approved the final version of the manuscript. Acknowledgement The authors are grateful to Nadathur Estates and the Bagaria Education Trust for their support of all the breast cancer research activities at SJRI since 2008. Data Availability The data supporting the findings of this study are available with the corresponding author and will be shared upon reasonable request. References Qin Z, Ljubimov VA, Zhou C, Tong Y, Liang J. Cell-free circulating tumor DNA in cancer. Chin J Cancer. 2016;35(1):36. Tsui WA, Jiang P, Lo YD. Cell-free DNA fragmentomics in cancer. Cancer Cell. 2025. Landon BV, Annapragada AV, Niknafs N, Velculescu VE, Anagnostou V. Liquid biopsies across the cancer care continuum. Nat Med. 2025:1–16. Sobhani N, Generali D, Zanconati F, Bortul M, Scaggiante B. Cell-free DNA integrity for the monitoring of breast cancer: future perspectives? World J Clin Oncol. 2018;9(2):26. 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Ramesh","email":"","orcid":"","institution":"St.John's Medical College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Rakesh","middleName":"S.","lastName":"Ramesh","suffix":""},{"id":595447222,"identity":"31027f8c-fe89-460a-8583-4f4a8e21a21d","order_by":10,"name":"Jyothi D. N","email":"","orcid":"","institution":"St.John's Medical College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jyothi","middleName":"D.","lastName":"N","suffix":""},{"id":595447223,"identity":"d51fe0d1-1418-40ab-b962-c5960b529ffb","order_by":11,"name":"Jayakumari S","email":"","orcid":"","institution":"St.John's Medical College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jayakumari","middleName":"","lastName":"S","suffix":""},{"id":595447224,"identity":"0fe73eb4-8845-4540-8621-aa108de8c2a6","order_by":12,"name":"Jyothi S Prabhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACxgYGBmYGAxsgA8QEAjYitDA2MxikkaAFpKuZgeEwCQ5jnpF8/HFBwXl75tmHG5grKuoY+KQbCFgxIy2xeYbB7cTGvsQGxjNnDjOwyRwgpCXHsJnH4HYCYw/QN41tBxjYJBKI0nLOHqLlXx3RWg4wNoK1NDAToaXnWeJsHoPkRJCWgw3HDvMQ1GLYnnzgM88fO3vDHvaHDxtq6uTkZxDSMgGqwLCBgeEAkObBrx4I5PkPQBkElY6CUTAKRsGIBQCq9j/k+GpSqgAAAABJRU5ErkJggg==","orcid":"","institution":"St. John's Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Jyothi","middleName":"S","lastName":"Prabhu","suffix":""}],"badges":[],"createdAt":"2026-02-05 09:24:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8794932/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8794932/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103508184,"identity":"fa1db704-c838-4e73-8f7e-850bf45667d1","added_by":"auto","created_at":"2026-02-26 13:47:23","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":169815,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of cfDNA levels and DII between BC patients and healthy controls.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Total cfDNA concentration measured by Qubit fluorometry shows a significant increase in tumor samples compared to controls (p\u0026lt;0.0001). (b) cfDNA concentration estimated by TS electrophoretic profiling also demonstrates higher levels in tumor samples (p=0.0048). (c) DII calculated using ALU-based qPCR, is significantly increased in tumor samples, reflecting a higher proportion of longer DNA fragments (p=0.0002). Horizontal lines represent mean values; p-values from Mann-Whitney U test are shown.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8794932/v1/f4f3e9d2952061c0def684df.jpeg"},{"id":103439129,"identity":"097fb692-b2c5-42b5-bde3-de9d8aad06ac","added_by":"auto","created_at":"2026-02-25 16:58:42","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":71605,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTS fragment profiles and gDNA-corrected cfDNA concentration.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a-c) Representative TS electropherograms from tumor samples showing cfDNA fragment distributions with prominent mono- and di-nucleosomal peaks and varying degrees of gDNA contamination: no detectable gDNA (a), mild gDNA contamination (b), and moderate-to-high gDNA contamination (c). (d-f) Corresponding electropherograms from control samples showing no detectable gDNA (d), mild gDNA contamination (e), and moderate-to-high gDNA contamination (f). High-molecular-weight gDNA fragments are highlighted by red boxes. (g) gDNA-corrected cfDNA concentrations are significantly higher in tumor samples compared to controls (Mann-Whitney U test; p \u0026lt; 0.0001).\u003c/p\u003e","description":"","filename":"groupimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8794932/v1/98db5d68f6e45732fa7f8990.jpeg"},{"id":103439128,"identity":"be739fcf-d0af-470d-a7e0-399f80d96245","added_by":"auto","created_at":"2026-02-25 16:58:42","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":340273,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of qPCR- and TS-derived cfDNA integrity measurements.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Long-fragment cfDNA quantified by ALU247-qPCR is significantly elevated in tumor samples compared to controls (p \u0026lt; 0.0001). (b) TS-derived DII (ratio of large to small cfDNA fragments) shows no significant difference between groups (Mann-Whitney U test; p=0.18). (c) Correlation analysis demonstrates no association between ALU-based DII and TS-derived DII, indicating method dependency (r\u003csub\u003es \u003c/sub\u003e= -0.075, p = 0.54 for controls; r\u003csub\u003es\u003c/sub\u003e = -0.170, p = 0.091for tumor samples).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8794932/v1/26f98490702c3b7f94dd2186.jpeg"},{"id":105789254,"identity":"18568b73-fb65-41f4-bf45-39006c93e17b","added_by":"auto","created_at":"2026-03-31 07:13:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1421374,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8794932/v1/8dfc94bc-0b33-4e38-acb9-21d6d1d61caa.pdf"},{"id":103507577,"identity":"d843ef11-498c-401e-8575-af18f0769ee2","added_by":"auto","created_at":"2026-02-26 13:42:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":165451,"visible":true,"origin":"","legend":"","description":"","filename":"SupplimentaryFile.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8794932/v1/c974e7e63a3dcb239ebfb660.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluating cell free DNA quantification and integrity in breast cancer using fluorometric, electrophoretic and PCR based platforms","fulltext":[{"header":"Background","content":"\u003cp\u003eCell-free DNA (cfDNA) refers to short DNA fragments released into the bloodstream through apoptosis, necrosis, or active cellular secretion. In patients with malignancy, a fraction of this circulating DNA originates from tumor cells (circulating tumor DNA, ctDNA), reflecting tumor dynamics, burden, and genomic alterations\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. ctDNA is identified within cfDNA by tumor-specific mutations, copy-number variations, gene rearrangements, and methylation signatures\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Its ability to detect cfDNA through a simple blood draw has made it a central analyte in liquid biopsy based cancer diagnostics\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In breast cancer (BC), elevated cfDNA levels and altered fragmentation profiles have been associated with advanced stage, poor prognosis, and minimal residual disease\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, highlighting cfDNA as a potential non-invasive biomarker both for early detection and disease monitoring.\u003c/p\u003e \u003cp\u003eBoth the quantitative and qualitative characteristics of cfDNA are informative. The total cfDNA concentration reflects global DNA release and tumor cell turnover\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, whereas the cfDNA integrity index (DII), defined as the ratio of long to short DNA fragments, captures the underlying mode of cell death\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. High DII values are often linked to necrosis-driven DNA release from malignant cells, while low DII values are characteristic of the apoptotic fragmentation seen in healthy individuals\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Reliable measurement of cfDNA concentration and integrity is therefore essential for distinguishing tumor-derived cfDNA from background DNA of hematopoietic origin.\u003c/p\u003e \u003cp\u003eHowever, cfDNA analysis is complicated by methodological diversity. Fluorometric assays such as Qubit quantify total double-stranded DNA rapidly but cannot discriminate cfDNA from contaminating gDNA. Electrophoretic instruments like the Agilent TS visualize cfDNA fragment size distribution, identifying mono- and di/tri-nucleosomal fragments, yet they may fail to detect highly degraded cfDNA\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In contrast, qPCR assays targeting repetitive elements such as ALU sequences enable sequence-specific quantification and DII calculation (e.g., ALU 247/ ALU 115), offering higher analytical sensitivity\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Reported discrepancies among these approaches raise concerns about assay comparability and interpretation of cfDNA-based biomarkers.\u003c/p\u003e \u003cp\u003eOnly a limited number of studies have directly compared cfDNA quantification and integrity across multiple analytical platforms, and virtually none have done so in a large, treatment-na\u0026iuml;ve Indian BC cohort. To address this gap, we performed a systematic comparison of cfDNA concentration and integrity in plasma from BC patients and healthy controls using three complementary techniques, Qubit fluorometry, TS electrophoretic profiling, and ALU-based qPCR. We further incorporated gDNA-correction of fluorometric data and assessed inter-method concordance. This work aims to identify the most reliable and sensitive approach for cfDNA assessment and to provide practical guidance for assay standardization in BC liquid-biopsy research.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients and Control Samples\u003c/h2\u003e \u003cp\u003e Peripheral blood samples were obtained from primary BC patients (n\u0026thinsp;=\u0026thinsp;100) recruited prior to initiation of any treatment after voluntary consent from participants. Age- and gender-matched healthy individuals (n\u0026thinsp;=\u0026thinsp;70) were included as normal controls. Study was approved by institutional ethics board. Peripheral blood samples were collected in EDTA tubes and processed within 2 hours of collection. Plasma was separated by centrifugation at 1300 \u0026times; g for 10 min at 4\u0026deg;C and carefully aliquoted to avoid cellular contamination. Plasma aliquots were stored at \u0026minus;\u0026thinsp;80\u0026deg;C in the institute biorepository for prolonged periods until cfDNA extraction. Repeated freeze-thaw cycles were strictly avoided, as they are known to reduce cfDNA yield and promote fragmentation and degradation, as reported in previous studies. No consistent variation in cfDNA concentration attributable to storage duration was observed across samples. The study was conducted between February 2024 and November 2025.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ecfDNA Extraction\u003c/h3\u003e\n\u003cp\u003e cfDNA from BC patients and control samples was extracted from 500 \u0026micro;L of plasma using the BioChain Cfpure Cell Free DNA Extraction Kit (# K5011610) according to the manufacturer\u0026rsquo;s instructions. The extracted cfDNA was quantified using the Qubit High Sensitivity (HS) DNA Assay Kit (# Q32851) and stored at \u0026minus;\u0026thinsp;20\u0026deg;C.\u003c/p\u003e\n\u003ch3\u003ecfDNA Fragmentation Profiling and DII Calculation\u003c/h3\u003e\n\u003cp\u003eThe fragmentation profile of cfDNA was analyzed using the TS4150 system (Agilent Technologies, Santa Clara, CA, USA). For each run, 2 \u0026micro;L of extracted cfDNA was mixed with 2 \u0026micro;L of HS D1000 sample buffer (# 5067\u0026ndash;5603) in an 8-tube strip. Additionally, 2 \u0026micro;L of the HS D1000 ladder (# 5067\u0026ndash;5587) prepared with the sample buffer was loaded into well A1 of the tube strip holder.\u003c/p\u003e \u003cp\u003eData were processed and analyzed using the TS Analysis Software version 5.1 (Agilent Technologies, Santa Clara, CA, USA). Mononucleosomal fragments (short fragments) were defined as cfDNA fragments ranging from 100 to 300 bp, while di- and tri-nucleosomal cfDNA fragments (long fragments) were defined as fragments between 301 and 1000 bp.\u003c/p\u003e \u003cp\u003eThe cfDNA Integrity Index (DII) was calculated using the following formula:\u003c/p\u003e \u003cp\u003eDII=Area under the curve (AUC) of long fragments (301\u0026ndash;1000 bp)/AUC of short fragments (100\u0026ndash;300 bp)\u003c/p\u003e\n\u003ch3\u003egDNA correction of the cfDNA total concentration\u003c/h3\u003e\n\u003cp\u003eTo obtain a more accurate estimate of cfDNA levels, we corrected the Qubit derived total cfDNA concentration for gDNA contamination. Since the TS electrophoresis profiles provide fragment-size information, we used TS data to estimate the proportion of gDNA contamination in each sample. In the TS electropherogram, the gDNA contribution was quantified as the percentage area corresponding to high-molecular-weight fragments (\u0026gt;\u0026thinsp;1 Kb), which represents cellular gDNA. This gDNA percentage was then used to adjust the total cfDNA concentration measured by Qubit \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e using the formula:\u003c/p\u003e \u003cp\u003eCorrected cfDNA concentration\u0026thinsp;=\u0026thinsp;Qubit total DNA \u0026times; (1 \u0026ndash; gDNA%)\u003c/p\u003e \u003cp\u003eThis correction allowed us to account for variability introduced by sample handling, leukocyte lysis, and differential gDNA release across samples. The corrected cfDNA values are likely to reflect cfDNA population more accurately, ensuring comparability between tumor and control samples.\u003c/p\u003e\n\u003ch3\u003eEstimation of ctDNA concentration and DII by qPCR\u003c/h3\u003e\n\u003cp\u003eThe fraction of ctDNA within the total cfDNA was analyzed using ALU-based qPCR assays according to previous method\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Briefly, Two ALU repetitive elements, ALU115 (115 bp amplicon) and ALU247 (247 bp amplicon), were quantified using following primer sequences. ALU115 amplified both short and long DNA fragments and measured the total cfDNA content in plasma, whereas ALU247 targeted longer DNA fragments, predominantly released through necrotic processes, and was considered indicative of ctDNA. The primer pair used is as below:\u003c/p\u003e \u003cp\u003eALU 115 Forward: 5\u0026rsquo;-CCTGAGGTCAGGAGTTCGAG-3\u0026rsquo;\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eReverse: 5\u0026rsquo;-CCCGAGTAGCTGGGATTACA-3\u0026rsquo;\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eALU 247 Forward: 5\u0026rsquo;-GTGGCTCACGCCTGTAATC-3\u0026rsquo;\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eReverse: 5\u0026rsquo;-CAGGCTGGAGTGCAGTGG-3\u0026rsquo;\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFor amplification, 1 \u0026micro;L of template DNA was added to a 10 \u0026micro;L reaction volume containing 3 pM primers. Quantification was performed using SYBR Green master mix (Takara) on the LightCycler 480 II platform (Roche Diagnostics) using the conditions standardized previously\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe DNA copy number and the DII, measured by the ratio of ALU247 to ALU115 (ALU247/ALU115), were calculated as described previously\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using R (version 4.5.1) software. Differences in cfDNA concentration and DII between control and tumor groups were assessed using the Mann-Whitney U test. Correlations of cfDNA concentration across different quantification methods, and the correlation of DII obtained by ALU-based qPCR and TS, were evaluated using Spearman\u0026rsquo;s rank correlation test. p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ecfDNA concentration is elevated in BC patients compared to normal controls\u003c/h2\u003e \u003cp\u003eThe total cfDNA concentration was estimated using three complementary approaches: Qubit fluorometry, TS electrophoretic profiling, and ALU-based qPCR assays. cfDNA concentrations measured by both Qubit (mean concentration: 0.459 ng/\u0026micro;L in patients and 0.252 ng/\u0026micro;L in controls) and TS (mean concentration: 0.156 ng/\u0026micro;L in patients and 0.090 ng/\u0026micro;L in controls) showed a significant increase in BC patients compared with healthy individuals (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 and p\u0026thinsp;=\u0026thinsp;0.0048, respectively; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003ea\u0026ndash;b, Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eWe further evaluated cfDNA concentrations across clinically relevant subgroups, including patients aged\u0026thinsp;\u0026gt;\u0026thinsp;50 years versus \u0026le;\u0026thinsp;50 years, early-stage versus late-stage disease, and molecular subtypes (detailed patient data and clinical characteristics are provided in Supplementary Table\u0026nbsp;1). Among molecular subtypes, cfDNA yield was highest in HER2-positive tumors (mean concentration: 0.516 ng/\u0026micro;L), followed by hormone receptor\u0026ndash;positive tumors (HR+) (0.480 ng/\u0026micro;L) and triple-negative breast cancer (TNBC) (0.298 ng/\u0026micro;L) (Supplementary Fig.\u0026nbsp;1a). A trend toward higher cfDNA levels was observed in patients aged\u0026thinsp;\u0026gt;\u0026thinsp;50 years (p\u0026thinsp;=\u0026thinsp;0.059; Supplementary Fig.\u0026nbsp;1b). Late-stage disease also showed a trend toward higher cfDNA levels compared with early-stage disease (p\u0026thinsp;=\u0026thinsp;0.093; Supplementary Fig.\u0026nbsp;1c).\u003c/p\u003e \u003cp\u003eThe fraction of ctDNA within the total cfDNA was further evaluated using ALU-based qPCR. The DII assessed using the ALU247/115 ratio, was significantly higher in patients relative to controls (p\u0026thinsp;=\u0026thinsp;0.0002; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). A higher DII, along with elevated cfDNA concentrations measured by Qubit and TS, is indicative of an increased ctDNA burden in BC patients.\u003c/p\u003e \u003cp\u003eTogether, these findings highlight the utility of multi-platform cfDNA assessment in distinguishing tumor-derived cfDNA signatures from those of healthy controls.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eTS profiles showed visible gDNA contamination in both groups\u003c/h2\u003e \u003cp\u003eWe next evaluated cfDNA fragment distributions in the BC and control groups using TS electrophoretic profiling. The fragment analysis revealed detectable gDNA contamination in both tumor and control samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-f). To assess the extent to which gDNA contamination contributed to the measured cfDNA concentrations and the DII values derived from ALU qPCR, we performed gDNA correction on the Qubit-estimated concentrations. After subtracting the gDNA fraction determined from the TS profiles, the corrected cfDNA concentrations remained significantly higher in patients (mean concentration: 0.219 ng/\u0026micro;L in patients and 0.153 ng/\u0026micro;L in controls, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eg), indicating that the elevated cfDNA levels in patients were not attributable to gDNA contamination. We further examined the concordance between the different cfDNA quantification approaches. Pairwise correlation analysis demonstrated a strong positive association between cfDNA concentrations measured by Qubit and TS, even after gDNA correction, in both patients and controls (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), indicating robust inter-method concordance in cfDNA quantification across independent analytical platforms. These findings validate the reliability of both fluorometric and electrophoretic quantification for assessing cfDNA burden in plasma samples.\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\u003eCorrelation between cfDNA estimation methods in BC patients and control groups by different platforms\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpearman\u0026rsquo;s r\u003csub\u003es\u003c/sub\u003e (Tumor)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpearman\u0026rsquo;s r\u003csub\u003es\u003c/sub\u003e (Control)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQubit vs TS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQubit vs gDNA-corrected cfDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTS vs gDNA-corrected cfDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement of cfDNA integrity is method dependent\u003c/h2\u003e \u003cp\u003eThe fraction of ctDNA within the total cfDNA was evaluated by ALU-based RT-PCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003ec) and by TS fragmentation profile. ALU115 captured the total pool of circulating DNA by amplifying both short and long fragments, whereas ALU247 predominantly detected long DNA fragments, which are generally associated with necrosis and enriched in ctDNA. ALU247 levels were significantly higher in BC patients compared to control samples, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). In contrast, cfDNA fragmentation profiles derived from TS electropherograms calculated as the ratio of di- and tri-nucleosomal (larger fragments) to mono-nucleosomal DNA (smaller fragment) (DII by L/S), did not differ significantly between tumor and control samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, p\u0026thinsp;=\u0026thinsp;0.180). Similarly, concentrations of mono- and di/tri-nucleosomal cfDNA were comparable between groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), indicating that both approaches capture distinct biological and technical aspects of cfDNA. TS-based integrity indices are derived from electrophoretic size distribution and primarily reflect nucleosomal fragmentation patterns, which may be less sensitive to minor tumor-specific shifts, especially when tumor burden is low or masked by hematopoietic cfDNA. Correlation analysis between ALU-based DII and TS-derived DII further revealed no significant association (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, r\u003csub\u003es\u003c/sub\u003e = -0.075, p\u0026thinsp;=\u0026thinsp;0.540 for controls; r\u003csub\u003es\u003c/sub\u003e = -0.170, p\u0026thinsp;=\u0026thinsp;0.091 for tumor samples). ALU247 amplifies sequence-specific fragments that may span sizes overlapping with TS-defined short fragments, whereas TS-based DII is derived purely from electrophoretic size distribution. Hence the correlation reflects fundamental methodological and biological differences, rather than a contradiction between the two approaches emphasizing that PCR-based DII is more sensitive and accurate for detecting subtle changes in cfDNA fragment composition and ctDNA enrichment.\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\u003eComparison of nucleosomal cfDNA features between patients and controls measured by tapestation (TS).\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTumor (mean)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl (mean)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMono-nucleosomal concentration (ng/\u0026micro;l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.125\u0026thinsp;\u0026plusmn;\u0026thinsp;0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.093\u0026thinsp;\u0026plusmn;\u0026thinsp;0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDi-/tri-nucleosomal concentration (ng/\u0026micro;l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.048\u0026thinsp;\u0026plusmn;\u0026thinsp;0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.034\u0026thinsp;\u0026plusmn;\u0026thinsp;0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMono-nucleosomal fragment size (bp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e195.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e202.580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDi-/tri-nucleosomal fragment size (bp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e562.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e545.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we systematically compared cfDNA quantification and integrity in BC patients and healthy controls using three complementary approaches: Qubit fluorometry, TS electrophoretic profiling, and ALU-based-qPCR. The total cfDNA concentration was consistently higher in BC patients compared to healthy controls by all three methods, remained significant even after correcting for gDNA contamination. This observation is consistent with previous reports linking elevated cfDNA levels to increased tumor cell turnover, apoptosis, and necrosis in cancer patients\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The pair-wise correlation analysis revealed that the cfDNA concentration measured by three quantification methods were strongly correlated, highlighting the robustness of concentration measurements across analytical platforms. However, when assessing DII, we observed a clear divergence between methods. The DII derived from ALU-based-qPCR was significantly higher in BC patients compared to controls, whereas TS-derived integrity indices did not differ between groups. Moreover, ALU-based DII and TS-DII did not correlate, highlighting the differences in capturing distinct biological and technical features of cfDNA by these two methods. ALU-based-qPCR relies on amplification of repetitive elements of different lengths, with the short fragment (ALU115) reflecting apoptotic cfDNA and the long fragment (ALU247) capturing necrosis-related or gDNA-derived fragments\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Thus, an increased ALU247/115 ratio in tumor patients likely reflects the contribution of necrotic tumor cell death, a hallmark of advanced tumor stage. TS integrity indices are derived from electrophoretic size distribution of cfDNA, focusing primarily on the relative abundance of mono- and di-nucleosomal fragments. This method may be less sensitive to subtle differences in cfDNA fragment origin, particularly if the tumor burden is modest or if the background of hematopoietic cfDNA masks tumor-specific alterations\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. ALU-based-qPCR is a highly sensitive, sequence-specific method capable of detecting small shifts in cfDNA fragment composition. TS, while powerful for visualization and quantification of fragment profiles, may lack the resolution to detect these biological differences. Additionally, pre-analytical variables such as plasma processing, storage, and cfDNA extraction efficiency may disproportionately impact fragment size distributions measured by TS, but not qPCR amplification ratios\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Our results therefore highlight a critical methodological point: while cfDNA concentration measurements are reproducible across platforms, DII is method-dependent and requires cautious interpretation. The lack of agreement between ALU-based-qPCR and TS does not represent a contradiction but rather reflects their measurement of different biological aspects of cfDNA; apoptotic/necrotic origin versus physical fragment size distribution. In practical terms, our findings suggest that ALU-based-qPCR may be better suited for distinguishing ctDNA from background cfDNA in the circulation, whereas TS is more useful for assessing overall fragment profiles and detecting large-scale contamination with gDNA. Therefore, we emphasize the need to standardize cfDNA integrity assays before clinical implementation, as relying on a single platform could lead to conflicting conclusions. We further suggest that combining complementary methods, such as total cfDNA concentration with ALU-based-qPCR, may provide a more comprehensive understanding of cfDNA biology and enhance its utility as a biomarker in cancer research. Our data also underscore the importance of correcting for gDNA contamination, which significantly influences fluorometric quantification and may confound downstream analyses.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTaken together, our study demonstrates that cfDNA concentration is a robust marker of tumor presence, but cfDNA integrity requires careful methodological consideration. Future work with additional orthogonal assays, including gDNA correction using single-copy gene\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e and next-generation sequencing (NGS) of cfDNA fragmentation patterns, may help to reconcile these discrepancies and establish reliable cfDNA integrity metrics for clinical use.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ecfDNA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Cell-free DNA\u003c/p\u003e\n\u003cp\u003ectDNA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Circulating Tumor DNA\u003c/p\u003e\n\u003cp\u003eDII\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;DNA Integrity Index\u003c/p\u003e\n\u003cp\u003egDNA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Genomic DNA\u003c/p\u003e\n\u003cp\u003eHER2\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Human Epidermal Growth Factor Receptor 2\u003c/p\u003e\n\u003cp\u003eHR+\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Hormone Receptor-Positive\u003c/p\u003e\n\u003cp\u003eNGS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Next-Generation Sequencing\u003c/p\u003e\n\u003cp\u003eTS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;TapeStation\u003c/p\u003e\n\u003cp\u003eTNBC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Triple-Negative Breast Cancer\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eEthical approval for this study was obtained from the Institutional Ethics Committee (IEC), St. John\u0026rsquo;s Medical College \u0026amp; Hospital, Bengaluru (IEC Study Ref. No.: 252/2023). Informed consent was obtained from all subjects involved in the study.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the Indian Council of Medical Research (ICMR) through the grant awarded to Jyothi S Prabhu (ICMR Project ID: IIRP-2023-1560).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Experimental design, execution of experiments, data analysis was performed by G.R.A, V. P. N, S.N and J.S.P. Plasma processing, sample storage, and computational data entry were handled by M.S and A.C.E. G.B, B.G, A.A, N.M.A, J.D.N, and J assisted with recruitment of control participants and manuscript review. R.S.R provided clinical breast cancer samples. The first draft of the manuscript was prepared by G.R.A and J.S.P, and all authors contributed to revising the manuscript. All authors read and approved the final version of the manuscript. J.S.P acquired funding. J, G.B, B.G, and J.S.P supervised the study. All authors approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors are grateful to Nadathur Estates and the Bagaria Education Trust for their support of all the breast cancer research activities at SJRI since 2008.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data supporting the findings of this study are available with the corresponding author and will be shared upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eQin Z, Ljubimov VA, Zhou C, Tong Y, Liang J. Cell-free circulating tumor DNA in cancer. Chin J Cancer. 2016;35(1):36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsui WA, Jiang P, Lo YD. Cell-free DNA fragmentomics in cancer. Cancer Cell. 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLandon BV, Annapragada AV, Niknafs N, Velculescu VE, Anagnostou V. 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J Lab Med Qual Assur. 2019;41(4):214\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSinha SK, Brown H, Knopf K, Hall P, Shannon WD, Haack W. A novel cell-free DNA fragmentomic assay and its application for monitoring disease progression in real time for stage IV cancer patients. Cancers (Basel). 2025;17(21):3583.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNair MG, Ramesh RS, Naidu CM, Mavatkar AD, VP S, Ramamurthy V, et al. Estimation of ALU repetitive elements in plasma as a cost-effective liquid biopsy tool for disease prognosis in breast cancer. Cancers (Basel). 2023;15(4):1054.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlcaide M, Cheung M, Hillman J, Rassekh SR, Deyell RJ, Batist G, et al. Evaluating the quantity, quality and size distribution of cell-free DNA by multiplex droplet digital PCR. 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Anal Biochem. 2018;542:34\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoessler T, Paradiso V, Piscuoglio S, Nienhold R, Ho L, Christinat Y, et al. Reliability of liquid biopsy analysis: an inter-laboratory comparison of circulating tumor DNA extraction and sequencing with different platforms. Lab Invest. 2020;100(11):1475\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Breast cancer, Cell-free DNA, cfDNA integrity, Liquid biopsy","lastPublishedDoi":"10.21203/rs.3.rs-8794932/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8794932/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCell-free DNA (cfDNA) has emerged as a promising biomarker for cancer detection and monitoring. However, different quantification and integrity assessment methods may yield variable results. This study aimed to compare cfDNA concentration and integrity between breast cancer (BC) patients and healthy controls using three complementary analytical techniques and to evaluate the concordance between these approaches.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003ePlasma cfDNA from BC patients and healthy controls was quantified using Qubit fluorometry, electrophoretic profiling by TapeStation (TS), and ALU-based quantitative PCR (qPCR) assays. cfDNA integrity was assessed using two indices: TS-derived DNA Integrity Index (DII) and ALU-qPCR\u0026ndash;derived DII (ALU 247/115 ratio). Spearman correlation analyses were performed to compare results across methods.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ecfDNA concentration was significantly elevated in BC patients compared with controls across all three platforms, even after genomic DNA (gDNA) correction. Qubit, TS, and gDNA-corrected cfDNA concentrations showed strong correlations in both groups, indicating robust cross-method agreement. In contrast, cfDNA integrity showed method-dependent differences: ALU-qPCR\u0026ndash;derived DII was significantly higher in tumor samples, whereas TS-derived DII did not differ between groups. Moreover, TS DII and ALU-qPCR DII were not correlated, suggesting that the two methods capture distinct aspects of cfDNA fragmentation.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ecfDNA quantification is consistent across fluorometric, electrophoretic, and qPCR-based methods. However, cfDNA integrity assessments vary substantially by technique. ALU-qPCR appears more sensitive in distinguishing BC- associated fragmentation patterns. Combining cfDNA quantification with ALU-qPCR-based integrity analysis may enhance the diagnostic utility of cfDNA in BC.\u003c/p\u003e","manuscriptTitle":"Evaluating cell free DNA quantification and integrity in breast cancer using fluorometric, electrophoretic and PCR based platforms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-25 16:58:37","doi":"10.21203/rs.3.rs-8794932/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6daa43b6-5f86-4349-9db4-89287cdeb016","owner":[],"postedDate":"February 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-31T07:13:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-25 16:58:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8794932","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8794932","identity":"rs-8794932","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}